From f769764f0fd45d82fa32e766b37d12dcc303b37d Mon Sep 17 00:00:00 2001 From: alexbanwell1 <31886108+alexbanwell1@users.noreply.github.com> Date: Tue, 13 May 2025 09:46:04 +0100 Subject: [PATCH 01/70] [ENH] Add ETS/ARIMA Stuff (#2536) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * forecaster base and dummy * forecasting tests * forecasting tests * forecasting tests * forecasting tests * regression * notebook * regressor * regressor * regressor * tags * tags * requires_y * forecasting notebook * forecasting notebook * remove tags * fix forecasting testing (they still fail though) * _is_fitted -> is_fitted * _is_fitted -> is_fitted * _forecast * notebook * is_fitted * y_fitted * ETS forecaster * add y checks and conversion * add tag * tidy * _check_is_fitted() * _check_is_fitted() * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. (#2318) Co-authored-by: Alex Banwell * Add faster numba version of ETS forecaster * Seperate out predict code, and add test to test without creating a class - significantly faster! * Modify _verify_ets.py to allow easy switching between statsforecast versions. This confirms that my algorithms without class overheads is significantly faster than nixtla statsforecast, and with class overheads, it is faster than their current algorithm * Add basic gradient decent optimization algorithm for smoothing parameters * Ajb/forecasting (#2357) * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. * Add faster numba version of ETS forecaster * Seperate out predict code, and add test to test without creating a class - significantly faster! * Modify _verify_ets.py to allow easy switching between statsforecast versions. This confirms that my algorithms without class overheads is significantly faster than nixtla statsforecast, and with class overheads, it is faster than their current algorithm * Add basic gradient decent optimization algorithm for smoothing parameters --------- Co-authored-by: Alex Banwell * Add additional AutoETS algorithms, and comparison scripts * Add ARIMA model in * [MNT] Testing fixes (#2531) * adjust test for non numpy output * test list output * test dataframe output * change pickle test * equal nans * test scalar output * fix lists output * allow arrays of objects * allow arrays of objects * test for boolean elements (MERLIN) * switch to deep equals * switch to deep equals * switch to deep equals * message * testing fixes --------- Co-authored-by: Tony Bagnall * Automated `pre-commit` hook update (#2533) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [DOC] Improve type hint guide and add link to the page. (#2532) * type hints * bad change * text * Add new datasets to tsf_datasets.py * Add functions for writing out .tsf files, as well as functions for manipulating the train/test split and windowing * Fix issues causing tests to fail * [DOC] Add 'Raises' section to docstring (#1766) (#2484) * Fix line endings * Moved test_cboss.py to testing/tests directory * Updated docstring comments and made methods protected * Fix line endings * Moved test_cboss.py to testing/tests directory * Updated docstring comments and made methods protected * Updated * Updated * Removed test_cboss.py * Updated * Updated * Add files for generating the datasets, and the CSV for the chosen datasets * Add windowed series train/test files * Automated `pre-commit` hook update (#2541) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * fix test (#2528) * [BUG] add ExpSmoothingSeriesTransformer and MovingAverageSeriesTransformer to __init__ (#2550) * update docs to fix 2548 docs * update init to fix 2548 bug * Automated `pre-commit` hook update (#2567) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump ossf/scorecard-action in the github-actions group (#2569) Bumps the github-actions group with 1 update: [ossf/scorecard-action](https://github.com/ossf/scorecard-action). Updates `ossf/scorecard-action` from 2.4.0 to 2.4.1 - [Release notes](https://github.com/ossf/scorecard-action/releases) - [Changelog](https://github.com/ossf/scorecard-action/blob/main/RELEASE.md) - [Commits](https://github.com/ossf/scorecard-action/compare/v2.4.0...v2.4.1) --- updated-dependencies: - dependency-name: ossf/scorecard-action dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [ENH] Added class weights to feature based classifiers (#2512) * class weights added to classification/feature based * Automatic `pre-commit` fixes * Test function for Catch22Classifier added * Test function for SummaryClassifier added * Test for tsfreshClassifier added * Soft dependecy check added for tsfresh * Test signature test case added * added test_mlp.py (#2537) * test file for FCNNetwork added (#2559) * Documentation improvement of certain BaseClasses (#2516) Co-authored-by: Antoine Guillaume * [ENH] Test coverage for AEFCNNetwork Improved (#2558) * test file added for aefcn * Test file for aefcn added * Test file reforammted * soft dependency added * name issues resolved * [ENH] Test coverage for TimeCNNNetwork Improved (#2534) * Test coverage improved for cnn network * assertion changed for test_cnn * coverage improved along with naming * [ENH] Test coverage for Resnet Network (#2553) * Resnet pytest * Resnet pytest * Fixed tensorflow failing * Added Resnet in function name * πŸ“ Add shinymack as a contributor for code (#2577) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * πŸ“ Add kevinzb56 as a contributor for doc (#2588) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * [MNT] Raise version bound for `scikit-learn` 1.6 (#2486) * update ver and new tags * default tags * toml * Update _shapelets.py Fix linear estimator coefs issue * expected results * Change expected results * update * only linux * remove mixins just to see test * revert --------- Co-authored-by: Antoine Guillaume * [MNT] Bump the python-packages group across 1 directory with 2 updates (#2598) Updates the requirements on [scipy](https://github.com/scipy/scipy) and [sphinx](https://github.com/sphinx-doc/sphinx) to permit the latest version. Updates `scipy` to 1.15.2 - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.9.0...v1.15.2) Updates `sphinx` to 8.2.3 - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/master/CHANGES.rst) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v0.1.61611...v8.2.3) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production dependency-group: python-packages - dependency-name: sphinx dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Automated `pre-commit` hook update (#2581) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * Automated `pre-commit` hook update (#2603) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Adds support for distances that are asymmetric but supports unequal length (#2613) * Adds support for distances that are asymmetric but supports unequal length * Added name to contributors * create smoothing filters notebook (#2547) * Remove datasets added * Reorganise code for generating train/test cluster files, including adding sliding window and train/test transformers * Add NaiveForecaster * Fix Bug in NaiveForecaster * Fix dataset generate script stuff * [DOC] Notebook on Feature-based Clustering (#2579) * Feature-based clustering * Feature-based clustering update * Update clustering overview * formatting * Automated `CONTRIBUTORS.md` update (#2614) Co-authored-by: chrisholder <4674372+chrisholder@users.noreply.github.com> * Updated Interval Based Notebook (#2620) * [DOC] Added Docstring for regression forecasting (#2564) * Added Docstring for Regression * Added Docstring for Regression * exog fix * GSoC announcement (#2629) * Automated `pre-commit` hook update (#2632) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump tj-actions/changed-files from 45 to 46 in the github-actions group (#2637) * [MNT] Bump tj-actions/changed-files in the github-actions group Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 45 to 46 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v45...v46) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] * Update pr_precommit.yml --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthew Middlehurst * [MNT] Update numpy requirement in the python-packages group (#2643) Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version. Updates `numpy` to 2.2.4 - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.21.0...v2.2.4) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [MNT,DEP] _binary.py metrics deprecated (#2600) * functions deprecated * Empty-Commit * version changed * Support for unequal length timeseries in itakura parallelogram (#2647) * [ENH] Implement DTW with Global alignment (#2565) * Implements Dynamic Time Warping with Global Invariances * Adds Numba JIT compilation support * Adds docs and numba support for dtw_gi and test_distance fixed * Fixes doctests * Automatic `pre-commit` fixes * Minor changes * Minor changes * Remove dtw_gi function and combine with private method _dtw_gi * Adds parameter tests * Fixes doctests * Minor changes * [ENH] Adds kdtw kernel support for kernelkmeans (#2645) * Adds kdtw kernel support for kernelkmeans * Code refactor * Adds tests for kdtw clustering * minor changes * minor changes * [MNT] Skip some excected results tests when numba is disabled (#2639) * skip some numba tests * Empty commit for CI * Update testing_config.py --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Remove REDCOMETs from testing exclusion list (#2630) * remove excluded estimators * redcomets fix * Ensure ETS algorithms are behaving correctly, and do more testing on AutoETS, along with AutoETS forecaster class * Fix a couple of bugs in the forecasters, add Sktime and StatsForecast wrappers for their AutoETS implementations * [ENH] Replace `prts` metrics (#2400) * Pre-commit fixes * Position parameter in calculate_bias * Added recall metric * merged into into one file * test added * Changes in test and range_metrics * list of list running but error! * flattening lists, all cases passed * Empty-Commit * changes * Protected functions * Changes in documentation * Changed test cases into seperate functions * test cases added and added range recall * udf_gamma removed from precision * changes * more changes * recommended changes * changes * Added Parameters * removed udf_gamma from precision * Added binary to range * error fixing * test comparing prts and range_metrics * Beta parameter added in fscore * Added udf_gamma function * f-score failing when comparing against prts * fixed f-score output * alpha usage * Empty-Commit * added test case to use range-based input for metrics * soft dependency added * doc update --------- Co-authored-by: Matthew Middlehurst Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> * Clarify documentation regarding unequal length series limitation (#2589) Co-authored-by: Matthew Middlehurst * Automated `pre-commit` hook update (#2683) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump tj-actions/changed-files in the github-actions group (#2686) Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 46.0.1 to 46.0.3 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.1...v46.0.3) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [ENH] Set `outlier_norm` default to True for Catch22 estimators (#2659) * sets outlier_norm=True by deafault * Minor changes * Docs improvement * [MNT] Use MacOS for examples/ workflow (#2668) * update bash to 5.x for lastpipe support * added esig installation * install boost before esig * fixed examples path issue for excluded notebooks * switched to fixed version of macos * added signature_method.ipynb to excluded list * removed symlink for /bin/bash * Correct AutoETS algorithms to not use multiplicative error models for data which is not strictly positive. Add check to ets for this * Reject multiplicative components for data not strictly positive * Update dependencies.md (#2717) Correct typo in dependencies.md * Automated `pre-commit` hook update (#2708) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Test Coverage for Pairwise Distance (#2590) * Pairwise distance matrix test * Empty commit for CI --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * re-running notebook for fixing cell output error (#2597) * Docstring (#2609) * [DOC] Add 'Raises' section to docstring #1766 (#2617) * [DOC] Add 'Raises' section to docstring #1766 * Automatic `pre-commit` fixes * Update _base.py * Automatic `pre-commit` fixes --------- Co-authored-by: ayushsingh9720 <199482418+ayushsingh9720@users.noreply.github.com> * [DOC] Contributor docs update (#2554) * contributing docs update * contributing docs update 2 * typos * Update contributing.md new section * Update testing.md testing update * Update contributing.md dont steal code * Automatic `pre-commit` fixes * Update contributing.md if --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> Co-authored-by: Antoine Guillaume * prevent assignment on PRs (#2703) * Update run_examples.sh (#2701) * [BUG] SevenNumberSummary bugfix and input rename (#2555) * summary bugfix * maintainer * test * readme (#2556) * remove MutilROCKETRegressor from alias mapping (#2623) Co-authored-by: Matthew Middlehurst * Automated `pre-commit` hook update (#2731) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump the github-actions group with 2 updates (#2733) Bumps the github-actions group with 2 updates: [actions/create-github-app-token](https://github.com/actions/create-github-app-token) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `actions/create-github-app-token` from 1 to 2 - [Release notes](https://github.com/actions/create-github-app-token/releases) - [Commits](https://github.com/actions/create-github-app-token/compare/v1...v2) Updates `tj-actions/changed-files` from 46.0.3 to 46.0.4 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.3...v46.0.4) --- updated-dependencies: - dependency-name: actions/create-github-app-token dependency-version: '2' dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.4 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Fixed a few spelling/grammar mistakes on TSC docs examples (#2738) * Fix docstring inconsistencies in benchmarking module (resolves #809) (#2735) * issue#809 Fix docstrings for benchmarking functions * Fixed docstrings in results_loaders.py * Fix docstring inconsistencies in benchmarking module - resolves #809 * Fix docstring inconsistencies in benchmarking module - resolves #809 * [ENH] `best_on_top` addition in `plot_pairwise_scatter` (#2655) * Empty-Commit * best_on_top parameter added * changes * [ENH] Add dummy clusterer tags (#2551) * dummy clusterer tags * len * [ENH] Collection conversion cleanup and `df-list` fix (#2654) * collection conversion cleanup * notebook * fixes --------- Co-authored-by: Tony Bagnall * [MNT] Updated the release workflows (#2638) * edit release workflows to use trusted publishing * docs * [MNT,ENH] Update to allow Python 3.13 (#2608) * python 3.13 * tensorflow * esig * tensorflow * tensorflow * esig and matrix profile * signature notebook * remove prts * fix * remove annoying deps from all_extras * Update pyproject.toml * [ENH] Hard-Coded Tests for `test_metrics.py` (#2672) * Empty-Commit * hard-coded tests * changes * Changed single ticks to double (#2640) Co-authored-by: Matthew Middlehurst * πŸ“ Add HaroonAzamFiza as a contributor for doc (#2740) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * [ENH,MNT] Assign Bot (assigned issues>2) (#2702) * Empty-Commit * point 2 working * changes * changes in comment message * [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) (#2704) * added extra features * added comments * optimized code * optimized code * made changes requested by moderators * fixed conflicts * fixed conflicts * fixed conflicts --------- Co-authored-by: Ramana-Raja * πŸ“ Add Ramana-Raja as a contributor for code (#2741) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * Release v1.1.0 (#2696) * v1.1.0 draft * finish * Automated `pre-commit` hook update (#2743) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump the github-actions group with 2 updates (#2744) Bumps the github-actions group with 2 updates: [crs-k/stale-branches](https://github.com/crs-k/stale-branches) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `crs-k/stale-branches` from 7.0.0 to 7.0.1 - [Release notes](https://github.com/crs-k/stale-branches/releases) - [Commits](https://github.com/crs-k/stale-branches/compare/v7.0.0...v7.0.1) Updates `tj-actions/changed-files` from 46.0.4 to 46.0.5 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.4...v46.0.5) --- updated-dependencies: - dependency-name: crs-k/stale-branches dependency-version: 7.0.1 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.5 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [DOC] Add implementation references (#2748) * implementation references * better attribution * use gpu installs for periodic tests (#2747) * Use shape calculation in _fit to optimize QUANTTransformer (#2727) * [REF] Refactor Anomaly Detection Module into Submodules by Algorithm Family (#2694) * Refactor Anomaly Detection Module into Submodules by Algorithm Family * updated documentation and references * implemented suggested changes * minor changes * added headers for remaining algorithm family * removing tree-based header * Automated `pre-commit` hook update (#2756) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH]Type hints/forecasting (#2737) * Type hints for primitive data types in base module * Type hints for primitive data types and strings in forecating module * type hints for primitives in foreacasting module * Revert "type hints for primitives in foreacasting module" This reverts commit 575122d14b28742140ef1e16a3a351dd5db5072b. * type hints for primitives in forecasting module * Automated `pre-commit` hook update (#2766) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Implement `load_model` function for ensemble classifiers (#2631) * feat: implement `load_model` function for LITETimeClassifier Implement separate `load_model` function for LITETimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * feat: implement `load_model` function for InceptionTimeClassifier Implement separate `load_model` function for InceptionTimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * fix: typo in load model function * feat: convert load_model functions to classmethods * test: implement test for save load for LITETIME and Inception classification models * Automatic `pre-commit` fixes * refactor: move loading tests to separate files * Update _ae_abgru.py (#2771) * Automated `pre-commit` hook update (#2779) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [DOC] Fix Broken [Source] Link and Improve Documentation for suppress_output() (#2677) * Fix Broken [Source] Link and Improve Documentation for suppress_output() Function * modified docstring and added tests * modified docstring example * modifying docstring examples * modifying docstring examples * updating conf file * updated docstring * base transform tidy (#2773) * DOC: Add Raises section for invalid weights in KNeighborsTimeSeriesClassifier (#1766) (#2764) Document the ValueError raised during initialization when an unsupported value is passed to the 'weights' parameter. Clarifies expected exceptions for users and improves API documentation consistency. Co-authored-by: Matthew Middlehurst * [ENH] Fixes Issue Improve `_check_params` method in `kmeans.py` and `kmedoids.py` (#2682) * Improves _check_params * removes function and adds a var * minor changes * minor changes * minor changes * line endings to LF * use variable instead of duplicating strings * weird file change * weird file change --------- Co-authored-by: Matthew Middlehurst * [ENH] Add type hints for deep learning regression classes (#2644) * type hints for cnn for regrssion * editing import modules Model & Optim * type hints for disjoint_cnn for regrssion * FIX type hints _get_test_params * ENH Change linie of importing typing * type hints for _encoder for regrssion * type hints for _fcn for regrssion * type hints for _inception_time for regrssion * type hints for _lite_time for regrssion * type hints for _mlp for regrssion * type hints for _resnet for regrssion * type hints for _base for regrssion * FIX: mypy errors in _disjoint_cnn.py file * FIX: mypy typing errors * Fix: Delete variable types, back old-verbose * FIX: add model._save in save_last_model_to_file function * FIX: Put TYPE_CHECKING downside * Fix: Put Any at the top * [DOC] Add RotationForest Classifier Notebook for Time Series Classification (#2592) * Add RotationForest Classifier Notebook for Time Series Classification * Added references and modified doc * minor modifications to notebook description * Update rotation_forest.ipynb --------- Co-authored-by: Matthew Middlehurst * fix: Codeowners for benchmarking metrics AD (#2784) * [GOV] Supporting Developer role (#2775) * supporting dev role * pr req * Update governance.md * typo * Automatic `pre-commit` fixes * aeon --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT, ENH, DOC] Rework similarity search (#2473) * WIP remake module structure * Update _brute_force.py * Update test__commons.py * WIP mock and test * Add test for base subsequence * Fix subsequence_search tests * debug brute force mp * more debug of subsequence tests * more debug of subsequence tests * Add functional LSH neighbors * add notebook for sim search tasks * Updated series similarity search * Fix mistake addition in transformers and fix base classes * Fix registry and api reference * Update documentation and fix some leftover bugs * Update documentation and add default test params * Fix identifiers and test data shape for all_estimators tests * Fix missing params * Fix n_jobs params and tags, add some docs * Fix numba test bug and update testing data for sim search * Fix imports, testing data tests, and impose predict/_predict interface to all sim search estimators * Fix args * Fix extract test * update docs api and notebooks * remove notes * Patrick comments * Adress comments and clean index code * Fix Patrick comments * Fix variable suppression mistake * Divide base class into task specific * Fix typo in imports * Empty commit for CI * Fix typo again * Add check_inheritance exception for similarity search * Revert back to non per type base classes * Factor check index and typo in test --------- Co-authored-by: Patrick SchΓ€fer Co-authored-by: Matthew Middlehurst Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> * [ENH] Adapt the DCNN Networks to use Weight Norm Wrappers (#2628) * adapt the dcnn networks to use weight norm wrappers and remove l2 regularization * Automatic `pre-commit` fixes * add custom object * Automatic `pre-commit` fixes * fix trial --------- Co-authored-by: Matthew Middlehurst * [GOV] Remove inactive developers (#2776) * inactive devs * logo fix * Automated `pre-commit` hook update (#2792) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * Code to generate differenced datasets * Add AutoARIMA algorithm into Aeon * Add ArimaForecaster to forecasting list * Fix predict method to return the prediction in the correct format --------- Signed-off-by: dependabot[bot] Co-authored-by: Tony Bagnall Co-authored-by: Tony Bagnall Co-authored-by: MatthewMiddlehurst Co-authored-by: Alex Banwell Co-authored-by: Matthew Middlehurst Co-authored-by: aeon-actions-bot[bot] <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> Co-authored-by: Nikita Singh Co-authored-by: Ali El Hadi ISMAIL FAWAZ <54309336+hadifawaz1999@users.noreply.github.com> Co-authored-by: Cyril Meyer <69190238+Cyril-Meyer@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Co-authored-by: Akash Kawle <128881349+shinymack@users.noreply.github.com> Co-authored-by: Kevin Shah <161136814+kevinzb56@users.noreply.github.com> Co-authored-by: Antoine Guillaume Co-authored-by: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> Co-authored-by: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Co-authored-by: Divya Tiwari <108270861+itsdivya1309@users.noreply.github.com> Co-authored-by: chrisholder <4674372+chrisholder@users.noreply.github.com> Co-authored-by: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> Co-authored-by: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Co-authored-by: TinaJin0228 <60577222+TinaJin0228@users.noreply.github.com> Co-authored-by: Ayush Singh Co-authored-by: ayushsingh9720 <199482418+ayushsingh9720@users.noreply.github.com> Co-authored-by: HaroonAzamFiza Co-authored-by: adityagh006 <142653450+adityagh006@users.noreply.github.com> Co-authored-by: V_26@ Co-authored-by: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Co-authored-by: Ramana-Raja Co-authored-by: Ahmed Zahran <136983104+Ahmed-Zahran02@users.noreply.github.com> Co-authored-by: Adarsh Dubey Co-authored-by: Somto Onyekwelu <117727947+SomtoOnyekwelu@users.noreply.github.com> Co-authored-by: Saad Al-Tohamy <92796871+saadaltohamy@users.noreply.github.com> Co-authored-by: Patrick SchΓ€fer Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> Co-authored-by: Aadya Chinubhai <77720426+aadya940@users.noreply.github.com> --- .all-contributorsrc | 45 + .github/PULL_REQUEST_TEMPLATE.md | 2 +- .github/actions/cpu_all_extras/action.yml | 6 +- .../utilities/generate_developer_tables.py | 10 - .github/utilities/issue_assign.py | 60 +- .github/utilities/run_examples.sh | 10 +- .github/workflows/fast_release.yml | 13 +- .github/workflows/issue_assigned.yml | 6 +- .github/workflows/issue_comment_edited.yml | 6 +- .github/workflows/issue_comment_posted.yml | 6 +- .../workflows/periodic_github_maintenace.yml | 4 +- .github/workflows/periodic_tests.yml | 61 +- .github/workflows/pr_core_dep_import.yml | 4 +- .github/workflows/pr_examples.yml | 16 +- .github/workflows/pr_opened.yml | 6 +- .github/workflows/pr_precommit.yml | 8 +- .github/workflows/pr_pytest.yml | 15 +- .github/workflows/pr_typecheck.yml | 4 +- .github/workflows/precommit_autoupdate.yml | 7 +- .github/workflows/release.yml | 29 +- .github/workflows/scorecard.yml | 2 +- .github/workflows/update_contributors.yml | 2 +- .pre-commit-config.yaml | 6 +- CODEOWNERS | 3 +- CONTRIBUTORS.md | 111 +- README.md | 9 +- aeon/__init__.py | 2 +- aeon/anomaly_detection/__init__.py | 28 +- .../distance_based/__init__.py | 19 + .../{ => distance_based}/_cblof.py | 2 +- .../{ => distance_based}/_kmeans.py | 2 +- .../{ => distance_based}/_left_stampi.py | 2 +- .../{ => distance_based}/_lof.py | 2 +- .../{ => distance_based}/_merlin.py | 2 +- .../{ => distance_based}/_one_class_svm.py | 0 .../{ => distance_based}/_stomp.py | 2 +- .../distance_based/tests/__init__.py | 1 + .../{ => distance_based}/tests/test_cblof.py | 4 +- .../{ => distance_based}/tests/test_kmeans.py | 2 +- .../tests/test_left_stampi.py | 2 +- .../{ => distance_based}/tests/test_lof.py | 2 +- .../{ => distance_based}/tests/test_merlin.py | 2 +- .../tests/test_one_class_svm.py | 2 +- .../{ => distance_based}/tests/test_stomp.py | 2 +- .../distribution_based/__init__.py | 9 + .../{ => distribution_based}/_copod.py | 2 +- .../{ => distribution_based}/_dwt_mlead.py | 2 +- .../distribution_based/tests/__init__.py | 1 + .../tests/test_copod.py | 4 +- .../tests/test_dwt_mlead.py | 2 +- .../outlier_detection/__init__.py | 11 + .../{ => outlier_detection}/_iforest.py | 2 +- .../{ => outlier_detection}/_pyodadapter.py | 4 +- .../{ => outlier_detection}/_stray.py | 2 +- .../outlier_detection/tests/__init__.py | 1 + .../tests/test_iforest.py | 2 +- .../tests/test_pyod_adapter.py | 2 +- .../tests/test_stray.py | 2 +- .../whole_series/__init__.py | 7 + .../{ => whole_series}/_rockad.py | 0 .../whole_series/tests/__init__.py | 1 + .../{ => whole_series}/tests/test_rockad.py | 2 +- aeon/base/_base.py | 17 + aeon/base/_base_collection.py | 22 +- aeon/base/_base_series.py | 2 +- aeon/base/_compose.py | 4 +- .../metrics/anomaly_detection/__init__.py | 8 + .../metrics/anomaly_detection/_binary.py | 22 + .../anomaly_detection/range_metrics.py | 521 +++++ .../anomaly_detection/tests/test_metrics.py | 572 +++++ .../metrics/anomaly_detection/thresholding.py | 31 +- aeon/benchmarking/metrics/segmentation.py | 4 +- aeon/benchmarking/resampling.py | 32 +- aeon/benchmarking/results_loaders.py | 2 +- aeon/classification/base.py | 4 +- .../deep_learning/_inception_time.py | 40 + .../deep_learning/_lite_time.py | 40 + aeon/classification/deep_learning/base.py | 17 + .../tests/test_inception_time.py | 47 + .../deep_learning/tests/test_lite_time.py | 49 + .../classification/dictionary_based/_cboss.py | 8 +- .../dictionary_based/_redcomets.py | 9 +- .../distance_based/_time_series_neighbors.py | 6 + .../tests/test_probability_threshold.py | 2 +- .../early_classification/tests/test_teaser.py | 2 +- aeon/classification/feature_based/_catch22.py | 24 +- .../feature_based/_signature_classifier.py | 18 +- aeon/classification/feature_based/_summary.py | 16 +- aeon/classification/feature_based/_tsfresh.py | 15 +- .../feature_based/tests/test_catch22.py | 19 + .../feature_based/tests/test_signature.py | 21 + .../feature_based/tests/test_summary.py | 18 + .../feature_based/tests/test_tsfresh.py | 21 + aeon/clustering/_k_means.py | 16 +- aeon/clustering/_k_medoids.py | 24 +- aeon/clustering/_kernel_k_means.py | 104 + aeon/clustering/base.py | 25 +- aeon/clustering/deep_learning/_ae_dcnn.py | 3 +- aeon/clustering/deep_learning/_ae_fcn.py | 9 +- aeon/clustering/deep_learning/_ae_resnet.py | 9 +- aeon/clustering/dummy.py | 12 +- aeon/clustering/feature_based/_catch22.py | 11 +- aeon/clustering/tests/test_kernel_k_means.py | 24 + aeon/datasets/Final Dataset Selection.csv | 101 + aeon/datasets/__init__.py | 11 +- aeon/datasets/_data_writers.py | 301 ++- aeon/datasets/dataset_generation.py | 218 ++ aeon/datasets/tests/test_data_writers.py | 1 - .../tests/test_dataset_collections.py | 2 +- aeon/datasets/tsad_datasets.py | 2 +- aeon/datasets/tsf_datasets.py | 13 + aeon/distances/__init__.py | 8 + aeon/distances/_distance.py | 15 + aeon/distances/elastic/__init__.py | 10 + aeon/distances/elastic/_bounding_matrix.py | 89 +- aeon/distances/elastic/_dtw_gi.py | 551 +++++ aeon/distances/elastic/tests/test_bounding.py | 39 +- .../tests/test_distance_correctness.py | 6 + aeon/distances/tests/test_distances.py | 14 +- aeon/forecasting/__init__.py | 8 +- aeon/forecasting/_arima.py | 421 ++++ aeon/forecasting/_autoets.py | 457 ++++ aeon/forecasting/_autoets_gradient_params.py | 297 +++ aeon/forecasting/_compare_external_autoets.py | 207 ++ aeon/forecasting/_ets.py | 567 +++-- aeon/forecasting/_ets_fast.py | 476 ++++ aeon/forecasting/_naive.py | 94 + .../_plot_autoets_gradient_method.py | 66 + aeon/forecasting/_regression.py | 44 +- aeon/forecasting/_sktime_autoets.py | 78 + aeon/forecasting/_statsforecast_autoets.py | 78 + aeon/forecasting/_time_autoets.py | 37 + aeon/forecasting/_utils.py | 115 + aeon/forecasting/_verify_arima.py | 31 + aeon/forecasting/_verify_ets.py | 345 +++ aeon/forecasting/base.py | 2 +- aeon/forecasting/tests/test_ets.py | 113 +- aeon/networks/_ae_abgru.py | 1 + aeon/networks/_ae_dcnn.py | 29 +- aeon/networks/_dcnn.py | 30 +- aeon/networks/tests/test_ae_fcn.py | 288 +++ aeon/networks/tests/test_cnn.py | 22 - aeon/networks/tests/test_fcn.py | 196 ++ aeon/networks/tests/test_mlp.py | 179 ++ aeon/networks/tests/test_resnet.py | 109 + aeon/networks/tests/test_time_cnn.py | 274 +++ aeon/regression/_dummy.py | 8 +- aeon/regression/base.py | 12 +- aeon/regression/deep_learning/_cnn.py | 73 +- .../regression/deep_learning/_disjoint_cnn.py | 81 +- aeon/regression/deep_learning/_encoder.py | 70 +- aeon/regression/deep_learning/_fcn.py | 71 +- .../deep_learning/_inception_time.py | 158 +- aeon/regression/deep_learning/_lite_time.py | 116 +- aeon/regression/deep_learning/_mlp.py | 63 +- aeon/regression/deep_learning/_resnet.py | 73 +- aeon/regression/deep_learning/base.py | 23 +- aeon/regression/feature_based/_catch22.py | 13 +- .../tests/test_rotation_forest_regressor.py | 30 +- aeon/segmentation/_ggs.py | 1 + aeon/similarity_search/__init__.py | 6 +- aeon/similarity_search/_base.py | 81 + aeon/similarity_search/_commons.py | 504 ----- aeon/similarity_search/base.py | 232 -- aeon/similarity_search/collection/__init__.py | 11 + aeon/similarity_search/collection/_base.py | 112 + .../collection/motifs/__init__.py | 1 + .../collection/neighbors/__init__.py | 7 + .../collection/neighbors/_rp_cosine_lsh.py | 320 +++ .../collection/neighbors/tests/__init__.py | 1 + .../neighbors/tests/test_rp_cosine_lsh.py | 1 + .../collection/tests/__init__.py | 1 + .../collection/tests/test_base.py | 19 + .../distance_profiles/__init__.py | 18 - .../euclidean_distance_profile.py | 102 - .../squared_distance_profile.py | 319 --- .../distance_profiles/tests/__init__.py | 1 - .../tests/test_euclidean_distance.py | 208 -- .../tests/test_squared_distance.py | 200 -- .../matrix_profiles/__init__.py | 14 - .../matrix_profiles/stomp.py | 633 ------ .../matrix_profiles/tests/__init__.py | 1 - .../matrix_profiles/tests/test_stomp.py | 205 -- aeon/similarity_search/query_search.py | 428 ---- aeon/similarity_search/series/__init__.py | 15 + aeon/similarity_search/series/_base.py | 119 + aeon/similarity_search/series/_commons.py | 255 +++ .../series/motifs/__init__.py | 7 + .../similarity_search/series/motifs/_stomp.py | 528 +++++ .../series/motifs/tests/__init__.py | 1 + .../series/motifs/tests/test_stomp.py | 149 ++ .../series/neighbors/__init__.py | 9 + .../series/neighbors/_dummy.py | 207 ++ .../series/neighbors/_mass.py | 296 +++ .../series/neighbors/tests/__init__.py | 1 + .../series/neighbors/tests/test_dummy.py | 31 + .../series/neighbors/tests/test_mass.py | 44 + .../series/tests/__init__.py | 1 + .../series/tests/test_base.py | 19 + .../series/tests/test_commons.py | 171 ++ aeon/similarity_search/series_search.py | 436 ---- aeon/similarity_search/tests/test__commons.py | 49 - .../tests/test_query_search.py | 176 -- .../tests/test_series_search.py | 74 - aeon/testing/data_generation/_collection.py | 39 +- .../data_generation/tests/test_collection.py | 6 +- .../_yield_classification_checks.py | 2 +- .../_yield_clustering_checks.py | 27 +- .../_yield_estimator_checks.py | 22 +- .../_yield_regression_checks.py | 2 +- .../_yield_transformation_checks.py | 3 +- .../expected_classifier_outputs.py | 60 +- .../expected_distance_results.py | 7 + .../expected_regressor_outputs.py | 69 +- aeon/testing/mock_estimators/__init__.py | 8 +- .../_mock_similarity_search.py | 21 - .../_mock_similarity_searchers.py | 38 + aeon/testing/testing_config.py | 14 +- aeon/testing/testing_data.py | 176 +- aeon/testing/tests/test_all_estimators.py | 2 +- aeon/testing/tests/test_testing_data.py | 113 - aeon/testing/utils/deep_equals.py | 27 +- aeon/testing/utils/estimator_checks.py | 2 +- aeon/testing/utils/output_suppression.py | 58 +- .../utils/tests/test_output_supression.py | 51 +- aeon/transformations/base.py | 22 + aeon/transformations/collection/base.py | 120 +- .../convolution_based/_minirocket.py | 2 +- .../collection/feature_based/_catch22.py | 11 +- .../collection/feature_based/_summary.py | 12 +- .../feature_based/tests/test_catch22.py | 52 +- .../feature_based/tests/test_summary.py | 33 +- .../collection/interval_based/_quant.py | 19 +- aeon/transformations/format/__init__.py | 11 + .../transformations/format/_sliding_window.py | 92 + aeon/transformations/format/_train_test.py | 93 + aeon/transformations/format/base.py | 301 +++ aeon/transformations/series/__init__.py | 6 + aeon/transformations/series/_bkfilter.py | 3 +- aeon/transformations/series/_difference.py | 52 + aeon/transformations/series/base.py | 49 +- aeon/utils/base/_identifier.py | 2 + aeon/utils/base/_register.py | 16 +- aeon/utils/conversion/_convert_collection.py | 103 +- .../tests/test_convert_collection.py | 216 +- aeon/utils/data_types.py | 24 +- aeon/utils/networks/weight_norm.py | 1 + aeon/utils/numba/general.py | 93 +- aeon/utils/show_versions.py | 11 +- aeon/utils/tags/_tags.py | 6 +- aeon/utils/validation/collection.py | 365 ++-- .../utils/validation/tests/test_collection.py | 56 +- .../distances/_pairwise_distance_matrix.py | 19 +- .../visualisation/distances/tests/__init__.py | 1 + .../tests/test_pairwise_distance_matrix.py | 34 + aeon/visualisation/estimator/_shapelets.py | 2 + .../results/_critical_difference.py | 4 +- aeon/visualisation/results/_scatter.py | 11 +- .../results/tests/test_scatter.py | 13 + docs/_sphinxext/sphinx_remove_toctrees.py | 1 + docs/about.md | 20 +- docs/about/code_of_conduct_workgroup.md | 8 - docs/about/core_developers.md | 12 - docs/about/infrastructure_workgroup.md | 4 - docs/api_reference/anomaly_detection.rst | 66 +- docs/api_reference/similarity_search.rst | 66 +- docs/api_reference/transformations.rst | 2 + docs/api_reference/utils.rst | 8 +- docs/changelog.md | 1 + docs/changelogs/v1.0.md | 2 +- docs/changelogs/v1.1.md | 294 +++ docs/conf.py | 2 + docs/contributing.md | 34 +- docs/developer_guide.md | 22 + docs/developer_guide/adding_typehints.md | 69 +- docs/developer_guide/dependencies.md | 2 +- docs/developer_guide/documentation.md | 2 +- docs/developer_guide/release.md | 10 +- docs/developer_guide/testing.md | 3 +- docs/examples.md | 13 + docs/getting_started.md | 72 +- docs/governance.md | 9 + .../anomaly_detection/anomaly_detection.ipynb | 4 +- examples/classification/classification.ipynb | 386 ++-- .../classification/early_classification.ipynb | 691 ++++-- .../classification/img/rotation_forest.png | Bin 0 -> 182339 bytes examples/classification/interval_based.ipynb | 179 +- examples/classification/rotation_forest.ipynb | 203 ++ examples/clustering/clustering.ipynb | 69 +- .../clustering/feature_based_clustering.ipynb | 1489 +++++++++++++ examples/datasets/datasets.ipynb | 239 +- examples/similarity_search/code_speed.ipynb | 178 +- .../similarity_search/distance_profiles.ipynb | 6 +- .../similarity_search/similarity_search.ipynb | 571 +++-- examples/transformations/preprocessing.ipynb | 2 +- examples/transformations/sast.ipynb | 1929 ++++++++++++++++- .../transformations/smoothing_filters.ipynb | 326 +++ examples/visualisation/plotting_results.ipynb | 119 +- pyproject.toml | 41 +- 299 files changed, 18056 insertions(+), 6885 deletions(-) create mode 100644 aeon/anomaly_detection/distance_based/__init__.py rename aeon/anomaly_detection/{ => distance_based}/_cblof.py (98%) rename aeon/anomaly_detection/{ => distance_based}/_kmeans.py (99%) rename aeon/anomaly_detection/{ => distance_based}/_left_stampi.py (98%) rename aeon/anomaly_detection/{ => distance_based}/_lof.py (98%) rename aeon/anomaly_detection/{ => distance_based}/_merlin.py (99%) rename aeon/anomaly_detection/{ => distance_based}/_one_class_svm.py (100%) rename aeon/anomaly_detection/{ => distance_based}/_stomp.py (98%) create mode 100644 aeon/anomaly_detection/distance_based/tests/__init__.py rename aeon/anomaly_detection/{ => distance_based}/tests/test_cblof.py (96%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_kmeans.py (96%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_left_stampi.py (99%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_lof.py (99%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_merlin.py (96%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_one_class_svm.py (95%) rename aeon/anomaly_detection/{ => distance_based}/tests/test_stomp.py (95%) create mode 100644 aeon/anomaly_detection/distribution_based/__init__.py rename aeon/anomaly_detection/{ => distribution_based}/_copod.py (97%) rename aeon/anomaly_detection/{ => distribution_based}/_dwt_mlead.py (99%) create mode 100644 aeon/anomaly_detection/distribution_based/tests/__init__.py rename aeon/anomaly_detection/{ => distribution_based}/tests/test_copod.py (94%) rename aeon/anomaly_detection/{ => distribution_based}/tests/test_dwt_mlead.py (96%) create mode 100644 aeon/anomaly_detection/outlier_detection/__init__.py rename aeon/anomaly_detection/{ => outlier_detection}/_iforest.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/_pyodadapter.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/_stray.py (98%) create mode 100644 aeon/anomaly_detection/outlier_detection/tests/__init__.py rename aeon/anomaly_detection/{ => outlier_detection}/tests/test_iforest.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/tests/test_pyod_adapter.py (98%) rename aeon/anomaly_detection/{ => outlier_detection}/tests/test_stray.py (98%) create mode 100644 aeon/anomaly_detection/whole_series/__init__.py rename aeon/anomaly_detection/{ => whole_series}/_rockad.py (100%) create mode 100644 aeon/anomaly_detection/whole_series/tests/__init__.py rename aeon/anomaly_detection/{ => whole_series}/tests/test_rockad.py (97%) create mode 100644 aeon/benchmarking/metrics/anomaly_detection/range_metrics.py create mode 100644 aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py create mode 100644 aeon/classification/deep_learning/tests/test_inception_time.py create mode 100644 aeon/classification/deep_learning/tests/test_lite_time.py create mode 100644 aeon/datasets/Final Dataset Selection.csv create mode 100644 aeon/datasets/dataset_generation.py create mode 100644 aeon/distances/elastic/_dtw_gi.py create mode 100644 aeon/forecasting/_arima.py create mode 100644 aeon/forecasting/_autoets.py create mode 100644 aeon/forecasting/_autoets_gradient_params.py create mode 100644 aeon/forecasting/_compare_external_autoets.py create mode 100644 aeon/forecasting/_ets_fast.py create mode 100644 aeon/forecasting/_naive.py create mode 100644 aeon/forecasting/_plot_autoets_gradient_method.py create mode 100644 aeon/forecasting/_sktime_autoets.py create mode 100644 aeon/forecasting/_statsforecast_autoets.py create mode 100644 aeon/forecasting/_time_autoets.py create mode 100644 aeon/forecasting/_utils.py create mode 100644 aeon/forecasting/_verify_arima.py create mode 100644 aeon/forecasting/_verify_ets.py create mode 100644 aeon/networks/tests/test_ae_fcn.py delete mode 100644 aeon/networks/tests/test_cnn.py create mode 100644 aeon/networks/tests/test_fcn.py create mode 100644 aeon/networks/tests/test_mlp.py create mode 100644 aeon/networks/tests/test_resnet.py create mode 100644 aeon/networks/tests/test_time_cnn.py create mode 100644 aeon/similarity_search/_base.py delete mode 100644 aeon/similarity_search/_commons.py delete mode 100644 aeon/similarity_search/base.py create mode 100644 aeon/similarity_search/collection/__init__.py create mode 100644 aeon/similarity_search/collection/_base.py create mode 100644 aeon/similarity_search/collection/motifs/__init__.py create mode 100644 aeon/similarity_search/collection/neighbors/__init__.py create mode 100644 aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py create mode 100644 aeon/similarity_search/collection/neighbors/tests/__init__.py create mode 100644 aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py create mode 100644 aeon/similarity_search/collection/tests/__init__.py create mode 100644 aeon/similarity_search/collection/tests/test_base.py delete mode 100644 aeon/similarity_search/distance_profiles/__init__.py delete mode 100644 aeon/similarity_search/distance_profiles/euclidean_distance_profile.py delete mode 100644 aeon/similarity_search/distance_profiles/squared_distance_profile.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/__init__.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/test_squared_distance.py delete mode 100644 aeon/similarity_search/matrix_profiles/__init__.py delete mode 100644 aeon/similarity_search/matrix_profiles/stomp.py delete mode 100644 aeon/similarity_search/matrix_profiles/tests/__init__.py delete mode 100644 aeon/similarity_search/matrix_profiles/tests/test_stomp.py delete mode 100644 aeon/similarity_search/query_search.py create mode 100644 aeon/similarity_search/series/__init__.py create mode 100644 aeon/similarity_search/series/_base.py create mode 100644 aeon/similarity_search/series/_commons.py create mode 100644 aeon/similarity_search/series/motifs/__init__.py create mode 100644 aeon/similarity_search/series/motifs/_stomp.py create mode 100644 aeon/similarity_search/series/motifs/tests/__init__.py create mode 100644 aeon/similarity_search/series/motifs/tests/test_stomp.py create mode 100644 aeon/similarity_search/series/neighbors/__init__.py create mode 100644 aeon/similarity_search/series/neighbors/_dummy.py create mode 100644 aeon/similarity_search/series/neighbors/_mass.py create mode 100644 aeon/similarity_search/series/neighbors/tests/__init__.py create mode 100644 aeon/similarity_search/series/neighbors/tests/test_dummy.py create mode 100644 aeon/similarity_search/series/neighbors/tests/test_mass.py create mode 100644 aeon/similarity_search/series/tests/__init__.py create mode 100644 aeon/similarity_search/series/tests/test_base.py create mode 100644 aeon/similarity_search/series/tests/test_commons.py delete mode 100644 aeon/similarity_search/series_search.py delete mode 100644 aeon/similarity_search/tests/test__commons.py delete mode 100644 aeon/similarity_search/tests/test_query_search.py delete mode 100644 aeon/similarity_search/tests/test_series_search.py delete mode 100644 aeon/testing/mock_estimators/_mock_similarity_search.py create mode 100644 aeon/testing/mock_estimators/_mock_similarity_searchers.py create mode 100644 aeon/transformations/format/__init__.py create mode 100644 aeon/transformations/format/_sliding_window.py create mode 100644 aeon/transformations/format/_train_test.py create mode 100644 aeon/transformations/format/base.py create mode 100644 aeon/transformations/series/_difference.py create mode 100644 aeon/visualisation/distances/tests/__init__.py create mode 100644 aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py create mode 100644 docs/changelogs/v1.1.md create mode 100644 examples/classification/img/rotation_forest.png create mode 100644 examples/classification/rotation_forest.ipynb create mode 100644 examples/clustering/feature_based_clustering.ipynb create mode 100644 examples/transformations/smoothing_filters.ipynb diff --git a/.all-contributorsrc b/.all-contributorsrc index 859fb1e5e8..95453ca9e6 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2656,6 +2656,51 @@ "contributions": [ "doc" ] + }, + { + "login": "shinymack", + "name": "Akash Kawle", + "avatar_url": "https://avatars.githubusercontent.com/u/128881349?v=4", + "profile": "https://github.com/shinymack", + "contributions": [ + "code" + ] + }, + { + "login": "kevinzb56", + "name": "Kevin Shah", + "avatar_url": "https://avatars.githubusercontent.com/u/161136814?v=4", + "profile": "https://github.com/kevinzb56", + "contributions": [ + "doc" + ] + }, + { + "login": "tanishy7777", + "name": "Tanish Yelgoe", + "avatar_url": "https://avatars.githubusercontent.com/u/143334319?v=4", + "profile": "https://www.tanishyelgoe.tech/", + "contributions": [ + "code" + ] + }, + { + "login": "HaroonAzamFiza", + "name": "HaroonAzamFiza", + "avatar_url": "https://avatars.githubusercontent.com/u/183639840?v=4", + "profile": "https://github.com/HaroonAzamFiza", + "contributions": [ + "doc" + ] + }, + { + "login": "Ramana-Raja", + "name": "Ramana Raja", + "avatar_url": "https://avatars.githubusercontent.com/u/83065061?v=4", + "profile": "https://github.com/Ramana-Raja", + "contributions": [ + "code" + ] } ], "commitType": "docs" diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index c480942891..8953c4adb4 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -51,7 +51,7 @@ not applicable. To check a box, replace the space inside the square brackets wit --> ##### For all contributions -- [ ] I've added myself to the [list of contributors](https://github.com/aeon-toolkit/aeon/blob/main/.all-contributorsrc). Alternatively, you can use the [@all-contributors](https://allcontributors.org/docs/en/bot/usage) bot to do this for you after the PR has been merged. +- [ ] I've added myself to the [list of contributors](https://github.com/aeon-toolkit/aeon/blob/main/.all-contributorsrc). Alternatively, you can use the [@all-contributors](https://allcontributors.org/docs/en/bot/usage) bot to do this for you **after** the PR has been merged. - [ ] The PR title starts with either [ENH], [MNT], [DOC], [BUG], [REF], [DEP] or [GOV] indicating whether the PR topic is related to enhancement, maintenance, documentation, bugs, refactoring, deprecation or governance. ##### For new estimators and functions diff --git a/.github/actions/cpu_all_extras/action.yml b/.github/actions/cpu_all_extras/action.yml index ff75cd354f..da6a93c828 100644 --- a/.github/actions/cpu_all_extras/action.yml +++ b/.github/actions/cpu_all_extras/action.yml @@ -2,6 +2,10 @@ name: Pip install all_extras with CPU versions description: "For CI testing install the CPU version of dependencies with all extras if on ubuntu" inputs: + python_version: + description: "Python version used" + required: false + default: "3.11" additional_extras: description: "Comma-separated list of additional extras to install" required: false @@ -11,7 +15,7 @@ runs: using: "composite" steps: - name: Install CPU TensorFlow - if: runner.os == 'Linux' + if: ${{ runner.os == 'Linux' && inputs.python_version != '3.13' }} uses: nick-fields/retry@v3 with: timeout_minutes: 30 diff --git a/.github/utilities/generate_developer_tables.py b/.github/utilities/generate_developer_tables.py index afe39cf0d3..f03aa84831 100755 --- a/.github/utilities/generate_developer_tables.py +++ b/.github/utilities/generate_developer_tables.py @@ -72,9 +72,6 @@ def get_contributors(auth): iw = {c["login"] for c in iw} rmw = {c["login"] for c in rmw} - # add missing contributors with GitHub accounts - cocw |= {"KatieBuc"} - # get profiles from GitHub cocw = [get_profile(login, auth) for login in cocw] cw = [get_profile(login, auth) for login in cw] @@ -112,13 +109,6 @@ def get_profile(login, auth): if profile["name"] is None: profile["name"] = profile["login"] - # fix missing names - missing_names = { - "KatieBuc": "Katie Buchhorn", - } - if profile["name"] in missing_names: - profile["name"] = missing_names[profile["name"]] - return profile diff --git a/.github/utilities/issue_assign.py b/.github/utilities/issue_assign.py index 0acc002560..1696fd33fc 100755 --- a/.github/utilities/issue_assign.py +++ b/.github/utilities/issue_assign.py @@ -2,7 +2,11 @@ It checks if a comment on an issue or PR includes the trigger phrase (as defined) and a mentioned user. -If it does, it assigns the issue/PR to the mentioned user. +If it does, it assigns the issue to the mentioned user. +Users without write access can only have up to 2 open issues assigned. +Users with write access (or admin) are exempt from this limit. +If a non-write user already has 2 or more open issues, the bot +comments on the issue with links to the currently assigned open issues. """ import json @@ -19,13 +23,53 @@ issue_number = context_dict["event"]["issue"]["number"] issue = repo.get_issue(number=issue_number) comment_body = context_dict["event"]["comment"]["body"] +pr = context_dict["event"]["issue"].get("pull_request") +commenter = context_dict["event"]["comment"]["user"]["login"] -# Assign tagged used to the issue if the comment includes the trigger phrase body = comment_body.lower() -if "@aeon-actions-bot" in body and "assign" in body: - mentioned_users = re.findall(r"@[a-zA-Z0-9_-]+", comment_body) - mentioned_users = [user[1:] for user in mentioned_users] - mentioned_users.remove("aeon-actions-bot") +if "@aeon-actions-bot" in body and not pr: + # Assign commenter if comment includes "assign me" + if "assign me" in body: + issue.add_to_assignees(commenter) + # Assign tagged used to the issue if the comment includes the trigger phrase + elif "assign" in body: + mentioned_users = re.findall(r"@[a-zA-Z0-9_-]+", comment_body) + mentioned_users = [user[1:] for user in mentioned_users] + mentioned_users.remove("aeon-actions-bot") - for user in mentioned_users: - issue.add_to_assignees(user) + for user in mentioned_users: + user_obj = g.get_user(user) + permission = repo.get_collaborator_permission(user_obj) + + if permission in ["admin", "write"]: + issue.add_to_assignees(user) + else: + # First check if the user is already assigned to this issue + if user in [assignee.login for assignee in issue.assignees]: + continue + + # search for open issues only + query = f"repo:{repo.full_name} is:issue is:open assignee:{user}" + issues_assigned_to_user = g.search_issues(query) + assigned_count = issues_assigned_to_user.totalCount + + if assigned_count >= 2: + # link to issue + assigned_issues_list = [ + f"[#{assigned_issue.number}]({assigned_issue.html_url})" + for assigned_issue in issues_assigned_to_user + ] + + comment_message = ( + f"@{user}, you already have {assigned_count} " + f"open issues assigned." + "Users without write access are limited to self-assigning two" + "issues.\n\n" + "Here are the open issues assigned to you:\n" + + "\n".join( + f"- {issue_link}" for issue_link in assigned_issues_list + ) + ) + issue.create_comment(comment_message) + else: + issue.add_to_assignees(user) diff --git a/.github/utilities/run_examples.sh b/.github/utilities/run_examples.sh index fd7376c05b..860a6ecb5b 100755 --- a/.github/utilities/run_examples.sh +++ b/.github/utilities/run_examples.sh @@ -1,11 +1,14 @@ -#!/bin/bash +#!/opt/homebrew/bin/bash # Script to run all example notebooks. set -euxo pipefail CMD="jupyter nbconvert --to notebook --inplace --execute --ExecutePreprocessor.timeout=600" -excluded=() +excluded=( + # try removing when 3.9 is dropped + "examples/transformations/signature_method.ipynb" +) if [ "$1" = true ]; then excluded+=( "examples/datasets/load_data_from_web.ipynb" @@ -23,7 +26,6 @@ if [ "$1" = true ]; then "examples/classification/shapelet_based.ipynb" "examples/classification/convolution_based.ipynb" "examples/similarity_search/code_speed.ipynb" - ) fi @@ -32,7 +34,7 @@ notebooks=() runtimes=() # Loop over all notebooks in the examples directory. -find "examples/" -name "*.ipynb" -print0 | +find "examples" -name "*.ipynb" -print0 | while IFS= read -r -d "" notebook; do # Skip notebooks in the excluded list. if printf "%s\0" "${excluded[@]}" | grep -Fxqz -- "$notebook"; then diff --git a/.github/workflows/fast_release.yml b/.github/workflows/fast_release.yml index 8127170713..695589ee74 100644 --- a/.github/workflows/fast_release.yml +++ b/.github/workflows/fast_release.yml @@ -11,9 +11,10 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Build project run: | @@ -30,6 +31,12 @@ jobs: upload-wheels: runs-on: ubuntu-24.04 + environment: + name: release + url: https://pypi.org/p/aeon/ + permissions: + id-token: write + steps: - uses: actions/download-artifact@v4 with: @@ -38,5 +45,3 @@ jobs: - name: Publish package to PyPI uses: pypa/gh-action-pypi-publish@release/v1 - with: - password: ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/issue_assigned.yml b/.github/workflows/issue_assigned.yml index 589ea7ec98..343f468781 100644 --- a/.github/workflows/issue_assigned.yml +++ b/.github/workflows/issue_assigned.yml @@ -14,7 +14,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} @@ -25,10 +25,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/issue_comment_edited.yml b/.github/workflows/issue_comment_edited.yml index 1fe3283946..ddd9bf5520 100644 --- a/.github/workflows/issue_comment_edited.yml +++ b/.github/workflows/issue_comment_edited.yml @@ -15,7 +15,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} @@ -26,10 +26,10 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub diff --git a/.github/workflows/issue_comment_posted.yml b/.github/workflows/issue_comment_posted.yml index 752db0e385..80dfa25aab 100644 --- a/.github/workflows/issue_comment_posted.yml +++ b/.github/workflows/issue_comment_posted.yml @@ -14,16 +14,16 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/periodic_github_maintenace.yml b/.github/workflows/periodic_github_maintenace.yml index 99772f13d8..952150313b 100644 --- a/.github/workflows/periodic_github_maintenace.yml +++ b/.github/workflows/periodic_github_maintenace.yml @@ -16,14 +16,14 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} private-key: ${{ secrets.PR_APP_KEY }} - name: Stale Branches - uses: crs-k/stale-branches@v7.0.0 + uses: crs-k/stale-branches@v7.0.1 with: repo-token: ${{ steps.app-token.outputs.token }} days-before-stale: 140 diff --git a/.github/workflows/periodic_tests.yml b/.github/workflows/periodic_tests.yml index 64e297d68f..7a18e7e10f 100644 --- a/.github/workflows/periodic_tests.yml +++ b/.github/workflows/periodic_tests.yml @@ -18,10 +18,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Run check-manifest uses: pre-commit/action@v3.0.1 @@ -35,10 +35,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Run pre-commit uses: pre-commit/action@v3.0.1 @@ -46,28 +46,39 @@ jobs: extra_args: --all-files run-notebook-examples: - runs-on: ubuntu-24.04 + runs-on: macos-14 steps: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Install latest version of bash + run: | + brew install bash + /opt/homebrew/bin/bash --version + + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache with: cache_name: "run-notebook-examples" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" restore_cache: "false" - - uses: ./.github/actions/cpu_all_extras + - name: Install dependencies + uses: nick-fields/retry@v3 with: - additional_extras: "dev,binder" + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,binder,dev] + + - name: Show dependencies + run: python -m pip list - name: Run example notebooks run: .github/utilities/run_examples.sh false @@ -87,10 +98,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install aeon and dependencies uses: nick-fields/retry@v3 @@ -112,17 +123,17 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Use numba cache to set env variables but not restore cache uses: ./.github/actions/numba_cache with: cache_name: "test-no-soft-deps" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" restore_cache: "false" - name: Install aeon and dependencies @@ -152,7 +163,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12" ] + python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] steps: - name: Checkout @@ -177,9 +188,12 @@ jobs: python_version: ${{ matrix.python-version }} restore_cache: "false" - - uses: ./.github/actions/cpu_all_extras + - name: Install aeon and dependencies + uses: nick-fields/retry@v3 with: - additional_extras: "dev" + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,dev] - name: Show dependencies run: python -m pip list @@ -201,17 +215,20 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV - - uses: ./.github/actions/cpu_all_extras + - name: Install aeon and dependencies + uses: nick-fields/retry@v3 with: - additional_extras: "unstable_extras,dev" + timeout_minutes: 30 + max_attempts: 3 + command: python -m pip install .[all_extras,unstable_extras,dev] - name: Show dependencies run: python -m pip list diff --git a/.github/workflows/pr_core_dep_import.yml b/.github/workflows/pr_core_dep_import.yml index 1042610d1a..dc1965deb6 100644 --- a/.github/workflows/pr_core_dep_import.yml +++ b/.github/workflows/pr_core_dep_import.yml @@ -24,10 +24,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install aeon and dependencies uses: nick-fields/retry@v3 diff --git a/.github/workflows/pr_examples.yml b/.github/workflows/pr_examples.yml index adc266319d..cf32ccd3c1 100644 --- a/.github/workflows/pr_examples.yml +++ b/.github/workflows/pr_examples.yml @@ -19,16 +19,21 @@ concurrency: jobs: run-notebook-examples: - runs-on: ubuntu-24.04 + runs-on: macos-14 steps: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Install latest version of bash + run: | + brew install bash + /opt/homebrew/bin/bash --version + + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache @@ -36,12 +41,15 @@ jobs: with: cache_name: "run-notebook-examples" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" - uses: ./.github/actions/cpu_all_extras with: additional_extras: "dev,binder" + - name: Show dependencies + run: python -m pip list + - name: Run example notebooks run: .github/utilities/run_examples.sh ${{ github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full examples run') }} shell: bash diff --git a/.github/workflows/pr_opened.yml b/.github/workflows/pr_opened.yml index db957aa0e6..f6f6e88bef 100644 --- a/.github/workflows/pr_opened.yml +++ b/.github/workflows/pr_opened.yml @@ -20,16 +20,16 @@ jobs: with: sparse-checkout: .github/utilities - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install PyGithub run: pip install -Uq PyGithub - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/pr_precommit.yml b/.github/workflows/pr_precommit.yml index 2f63ef2ba7..547b4c6db6 100644 --- a/.github/workflows/pr_precommit.yml +++ b/.github/workflows/pr_precommit.yml @@ -18,7 +18,7 @@ jobs: steps: - name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} @@ -31,13 +31,13 @@ jobs: ref: ${{ github.head_ref }} token: ${{ steps.app-token.outputs.token }} - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Get changed files - uses: tj-actions/changed-files@v45 + uses: tj-actions/changed-files@v46.0.5 id: changed-files - name: List changed files diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml index ae1c792243..4b5679f76d 100644 --- a/.github/workflows/pr_pytest.yml +++ b/.github/workflows/pr_pytest.yml @@ -24,10 +24,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }} name: Restore numba cache @@ -35,7 +35,7 @@ jobs: with: cache_name: "test-no-soft-deps" runner_os: ${{ runner.os }} - python_version: "3.10" + python_version: "3.11" - name: Install aeon and dependencies uses: nick-fields/retry@v3 @@ -57,13 +57,15 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12" ] + python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] # skip python versions unless the PR has the 'full pytest actions' label pr-testing: - ${{ (github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full pytest actions')) }} exclude: - pr-testing: true python-version: "3.10" + - pr-testing: true + python-version: "3.12" steps: - name: Checkout @@ -90,6 +92,7 @@ jobs: - uses: ./.github/actions/cpu_all_extras with: + python_version: ${{ matrix.python-version }} additional_extras: "dev" - name: Show dependencies @@ -109,10 +112,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Disable Numba JIT run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV diff --git a/.github/workflows/pr_typecheck.yml b/.github/workflows/pr_typecheck.yml index 7f0f80a856..f6082ac585 100644 --- a/.github/workflows/pr_typecheck.yml +++ b/.github/workflows/pr_typecheck.yml @@ -24,10 +24,10 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python 3.10 + - name: Setup Python 3.11 uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install aeon, dependencies and mypy uses: nick-fields/retry@v3 diff --git a/.github/workflows/precommit_autoupdate.yml b/.github/workflows/precommit_autoupdate.yml index cc4e2896ab..a670feaf2f 100644 --- a/.github/workflows/precommit_autoupdate.yml +++ b/.github/workflows/precommit_autoupdate.yml @@ -13,15 +13,16 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - uses: browniebroke/pre-commit-autoupdate-action@v1.0.0 - if: always() name: Create app token - uses: actions/create-github-app-token@v1 + uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3b80dee509..58d937e67e 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -13,9 +13,10 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - uses: pre-commit/action@v3.0.1 with: @@ -28,9 +29,10 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python 3.11 + uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Build project run: | @@ -52,12 +54,14 @@ jobs: fail-fast: false matrix: os: [ ubuntu-24.04, macOS-14, windows-2022 ] - python-version: [ "3.9", "3.10", "3.11", "3.12" ] + python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ] steps: - - uses: actions/checkout@v4 + - name: Checkout + uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - name: Setup Python + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} @@ -94,6 +98,9 @@ jobs: max_attempts: 3 command: python -m pip install "${{ env.WHEELNAME }}[all_extras,dev]" + - name: Show dependencies + run: python -m pip list + - name: Tests run: python -m pytest -n logical @@ -101,6 +108,12 @@ jobs: needs: test-wheels runs-on: ubuntu-24.04 + environment: + name: release + url: https://pypi.org/p/aeon/ + permissions: + id-token: write + steps: - uses: actions/download-artifact@v4 with: @@ -109,5 +122,3 @@ jobs: - name: Publish package to PyPI uses: pypa/gh-action-pypi-publish@release/v1 - with: - password: ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/scorecard.yml b/.github/workflows/scorecard.yml index 95435746d4..3c57528fc5 100644 --- a/.github/workflows/scorecard.yml +++ b/.github/workflows/scorecard.yml @@ -27,7 +27,7 @@ jobs: persist-credentials: false - name: Run analysis - uses: ossf/scorecard-action@v2.4.0 + uses: ossf/scorecard-action@v2.4.1 with: results_file: results.sarif results_format: sarif diff --git a/.github/workflows/update_contributors.yml b/.github/workflows/update_contributors.yml index 2d80324ec7..5b69ccb12f 100644 --- a/.github/workflows/update_contributors.yml +++ b/.github/workflows/update_contributors.yml @@ -25,7 +25,7 @@ jobs: id: generate run: npx all-contributors generate - - uses: actions/create-github-app-token@v1 + - uses: actions/create-github-app-token@v2 id: app-token with: app-id: ${{ vars.PR_APP_ID }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b796f3572b..62dab7f167 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.4 + rev: v0.11.9 hooks: - id: ruff args: [ "--fix"] @@ -41,14 +41,14 @@ repos: args: [ "--py39-plus" ] - repo: https://github.com/pycqa/isort - rev: 6.0.0 + rev: 6.0.1 hooks: - id: isort name: isort args: [ "--profile=black", "--multi-line=3" ] - repo: https://github.com/pycqa/flake8 - rev: 7.1.1 + rev: 7.2.0 hooks: - id: flake8 additional_dependencies: [ flake8-bugbear, flake8-print, Flake8-pyproject ] diff --git a/CODEOWNERS b/CODEOWNERS index bc93e5d27a..a89833ba09 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -5,6 +5,7 @@ aeon/anomaly_detection/ @SebastianSchmidl @MatthewMiddlehurst aeon/benchmarking/ @TonyBagnall @MatthewMiddlehurst @hadifawaz1999 @dguijo +aeon/benchmarking/metrics/anomaly_detection/ @SebastianSchmidl @MatthewMiddlehurst aeon/classification/ @MatthewMiddlehurst @TonyBagnall aeon/classification/deep_learning/ @hadifawaz1999 @MatthewMiddlehurst @TonyBagnall @@ -17,8 +18,6 @@ aeon/distances/ @chrisholder @TonyBagnall aeon/networks/ @hadifawaz1999 -aeon/performance_metrics/anomaly_detection/ @SebastianSchmidl @MatthewMiddlehurst - aeon/regression/ @MatthewMiddlehurst @TonyBagnall @dguijo aeon/regression/deep_learning @hadifawaz1999 @MatthewMiddlehurst @TonyBagnall @dguijo diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 91c7468d42..18945d9902 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-259-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-264-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -28,12 +28,13 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Afzal Ansari
Afzal Ansari
πŸ’» πŸ“– Ahmed Bilal
Ahmed Bilal
πŸ“– AidenRushbrooke
AidenRushbrooke
πŸ’» ⚠️ + Akash Kawle
Akash Kawle
πŸ’» Akhil Jasson
Akhil Jasson
πŸ“– Akshat Nayak
Akshat Nayak
πŸ’» Akshat Rampuria
Akshat Rampuria
πŸ“– - Aleksandr Grekov
Aleksandr Grekov
πŸ“– + Aleksandr Grekov
Aleksandr Grekov
πŸ“– Alex Hawkins-Hooker
Alex Hawkins-Hooker
πŸ’» Alexandra Amidon
Alexandra Amidon
πŸ“ πŸ“– πŸ€” Ali Ismail-Fawaz
Ali Ismail-Fawaz
πŸ’» πŸ› πŸ“– ⚠️ 🚧 πŸ‘€ πŸ“’ βœ… πŸ§‘β€πŸ« πŸ’‘ @@ -41,9 +42,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ali Yazdizadeh
Ali Yazdizadeh
πŸ“– Alwin
Alwin
πŸ“– πŸ’» 🚧 An Hoang
An Hoang
πŸ› πŸ’» - Andreas Kanz
Andreas Kanz
βœ… + Andreas Kanz
Andreas Kanz
βœ… AndrΓ© Guarnier De Mitri
AndrΓ© Guarnier De Mitri
πŸ’» Angus Dempster
Angus Dempster
πŸ’» ⚠️ βœ… Antoine Guillaume
Antoine Guillaume
πŸ’» πŸ“– @@ -51,9 +52,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Aparna Sakshi
Aparna Sakshi
πŸ’» Arelo Tanoh
Arelo Tanoh
πŸ“– Arepalli Yashwanth Reddy
Arepalli Yashwanth Reddy
πŸ’» πŸ› πŸ“– - Arik Ermshaus
Arik Ermshaus
πŸ’» + Arik Ermshaus
Arik Ermshaus
πŸ’» Arnav
Arnav
πŸ’» Aryan Pola
Aryan Pola
πŸ’» πŸ“– Ayushmaan Seth
Ayushmaan Seth
πŸ’» πŸ‘€ ⚠️ πŸ“– πŸ“‹ βœ… @@ -61,9 +62,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Badr-Eddine Marani
Badr-Eddine Marani
πŸ’» Benedikt Heidrich
Benedikt Heidrich
πŸ’» Benjamin Bluhm
Benjamin Bluhm
πŸ’» πŸ“– πŸ’‘ - Bhaskar Dhariyal
Bhaskar Dhariyal
πŸ’» ⚠️ + Bhaskar Dhariyal
Bhaskar Dhariyal
πŸ’» ⚠️ Binay Kumar
Binay Kumar
πŸ’» πŸ“– ⚠️ Bohan Zhang
Bohan Zhang
πŸ’» Bouke Postma
Bouke Postma
πŸ’» πŸ› πŸ€” @@ -71,9 +72,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Carlos Borrajo
Carlos Borrajo
πŸ’» πŸ“– Carlos Ramos CarreΓ±o
Carlos Ramos CarreΓ±o
πŸ“– Chang Wei Tan
Chang Wei Tan
πŸ’» - Cheuk Ting Ho
Cheuk Ting Ho
πŸ’» + Cheuk Ting Ho
Cheuk Ting Ho
πŸ’» Christian Kastner
Christian Kastner
πŸ’» πŸ› Christopher Dahlin
Christopher Dahlin
πŸ’» Christopher Lo
Christopher Lo
πŸ’» πŸ€” @@ -81,9 +82,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ciaran Gilbert
Ciaran Gilbert
πŸ› πŸ’» πŸ“– ⚠️ πŸ€” ClaudiaSanches
ClaudiaSanches
πŸ’» ⚠️ Corvin Paul
Corvin Paul
πŸ“– - Cyril Meyer
Cyril Meyer
⚠️ πŸ“– πŸ’» + Cyril Meyer
Cyril Meyer
⚠️ πŸ“– πŸ’» Daniel Burkhardt Cerigo
Daniel Burkhardt Cerigo
πŸ’» Daniel L.
Daniel L.
πŸ“– Daniel MartΓ­n MartΓ­nez
Daniel MartΓ­n MartΓ­nez
πŸ“– πŸ› @@ -91,9 +92,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Daniele Carli
Daniele Carli
πŸ“– Dave Hirschfeld
Dave Hirschfeld
πŸš‡ David Buchaca Prats
David Buchaca Prats
πŸ’» - David Guijo-Rubio
David Guijo-Rubio
πŸ’» πŸ€” + David Guijo-Rubio
David Guijo-Rubio
πŸ’» πŸ€” Divya Tiwari
Divya Tiwari
πŸ’» πŸ”£ Dmitriy Valetov
Dmitriy Valetov
πŸ’» βœ… Doug Ollerenshaw
Doug Ollerenshaw
πŸ“– @@ -101,9 +102,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Dylan Sherry
Dylan Sherry
πŸš‡ Emilia Rose
Emilia Rose
πŸ’» ⚠️ Emmanuel Ferdman
Emmanuel Ferdman
πŸ“– - Er Jie Yong
Er Jie Yong
πŸ› πŸ’» + Er Jie Yong
Er Jie Yong
πŸ› πŸ’» Evan Miller
Evan Miller
βœ… Eyal Shafran
Eyal Shafran
πŸ’» Federico Garza
Federico Garza
πŸ’» πŸ’‘ @@ -111,9 +112,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ferdinand Rewicki
Ferdinand Rewicki
πŸ’» πŸ› Florian Stinner
Florian Stinner
πŸ’» ⚠️ Francesco Spinnato
Francesco Spinnato
πŸ’» - Franz Kiraly
Franz Kiraly
πŸ› πŸ’Ό πŸ’» πŸ“– 🎨 πŸ“‹ πŸ’‘ πŸ’΅ πŸ” πŸ€” 🚧 πŸ§‘β€πŸ« πŸ“† πŸ’¬ πŸ‘€ πŸ“’ ⚠️ βœ… πŸ“Ή + Franz Kiraly
Franz Kiraly
πŸ› πŸ’Ό πŸ’» πŸ“– 🎨 πŸ“‹ πŸ’‘ πŸ’΅ πŸ” πŸ€” 🚧 πŸ§‘β€πŸ« πŸ“† πŸ’¬ πŸ‘€ πŸ“’ ⚠️ βœ… πŸ“Ή Freddy A Boulton
Freddy A Boulton
πŸš‡ ⚠️ Futuer
Futuer
πŸ“– Gabriel Riegner
Gabriel Riegner
πŸ“– @@ -121,219 +122,223 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d George Langley
George Langley
πŸ“– George Oastler
George Oastler
πŸ’» ⚠️ πŸ“¦ πŸ’‘ πŸ“– Gilberto Barbosa
Gilberto Barbosa
πŸ’» - Grace Gao
Grace Gao
πŸ’» πŸ› + Grace Gao
Grace Gao
πŸ’» πŸ› Guilherme Arcencio
Guilherme Arcencio
πŸ’» ⚠️ Guzal Bulatova
Guzal Bulatova
πŸ› πŸ’» πŸ“‹ πŸ§‘β€πŸ« πŸ“† πŸ‘€ ⚠️ HYang1996
HYang1996
πŸ’» ⚠️ πŸ“– βœ… + HaroonAzamFiza
HaroonAzamFiza
πŸ“– Harshitha Sudhakar
Harshitha Sudhakar
πŸ“– πŸ’» Hedeer El Showk
Hedeer El Showk
πŸ› πŸ“– πŸ’» Huayi Wei
Huayi Wei
βœ… - Ifeanyi30
Ifeanyi30
πŸ’» - Ilja Maurer
Ilja Maurer
πŸ’» + Ifeanyi30
Ifeanyi30
πŸ’» + Ilja Maurer
Ilja Maurer
πŸ’» Ilyas Moutawwakil
Ilyas Moutawwakil
πŸ’» πŸ“– Ireoluwatomiwa
Ireoluwatomiwa
πŸ“– Ishan Nangia
Ishan Nangia
πŸ€” Ivan Knyazev
Ivan Knyazev
πŸ“– Jack Russon
Jack Russon
πŸ’» James Large
James Large
πŸ’» πŸ“– ⚠️ πŸš‡ 🚧 - James Morrill
James Morrill
πŸ’» - Jasmine Liaw
Jasmine Liaw
πŸ’» + James Morrill
James Morrill
πŸ’» + Jasmine Liaw
Jasmine Liaw
πŸ’» Jason Lines
Jason Lines
πŸ’» πŸ’Ό πŸ“– 🎨 πŸ“‹ πŸ” πŸ€” πŸ“† πŸ’¬ πŸ‘€ πŸ“’ πŸ’‘ Jason Mok
Jason Mok
πŸ“– Jason Pong
Jason Pong
πŸ’» ⚠️ Jaume Mateu
Jaume Mateu
πŸ’» JonathanBechtel
JonathanBechtel
πŸ’» πŸ€” ⚠️ Joren Hammudoglu
Joren Hammudoglu
πŸš‡ - Juan Orduz
Juan Orduz
βœ… πŸ“– - Julian Cooper
Julian Cooper
πŸ’» πŸ€” + Juan Orduz
Juan Orduz
βœ… πŸ“– + Julian Cooper
Julian Cooper
πŸ’» πŸ€” Juliana
Juliana
πŸ’» Justin Shenk
Justin Shenk
πŸ“– Kai Lion
Kai Lion
πŸ’» ⚠️ πŸ“– Kavin Anand
Kavin Anand
πŸ“– Kavya Rambhia
Kavya Rambhia
πŸ’» Kejsi Take
Kejsi Take
πŸ’» - Kevin Lam
Kevin Lam
πŸ’» πŸ’‘ ⚠️ - Kirstie Whitaker
Kirstie Whitaker
πŸ€” πŸ” + Kevin Lam
Kevin Lam
πŸ’» πŸ’‘ ⚠️ + Kevin Shah
Kevin Shah
πŸ“– + Kirstie Whitaker
Kirstie Whitaker
πŸ€” πŸ” Kishan Manani
Kishan Manani
πŸ’» πŸ“– ⚠️ πŸ› πŸ€” Krum Arnaudov
Krum Arnaudov
πŸ› πŸ’» Kutay Koralturk
Kutay Koralturk
πŸ’» πŸ› Leonidas Tsaprounis
Leonidas Tsaprounis
πŸ’» πŸ› πŸ§‘β€πŸ« πŸ‘€ Lielle Ravid
Lielle Ravid
πŸ’» πŸ“– + + Logan Duffy
Logan Duffy
πŸ’» πŸ“– ⚠️ πŸ› πŸ€” Lorena Pantano
Lorena Pantano
πŸ€” Lorenzo Toniazzi
Lorenzo Toniazzi
πŸ’» - - Lovkush
Lovkush
πŸ’» ⚠️ πŸ€” πŸ§‘β€πŸ« πŸ“† Luca Bennett
Luca Bennett
πŸ’» πŸ“– ⚠️ Luis Ventura
Luis Ventura
πŸ’» Luis Zugasti
Luis Zugasti
πŸ“– Lukasz Mentel
Lukasz Mentel
πŸ’» πŸ“– πŸš‡ ⚠️ πŸ› 🚧 πŸ§‘β€πŸ« + + Marcelo Trylesinski
Marcelo Trylesinski
πŸ“– Marco Gorelli
Marco Gorelli
πŸš‡ Margaret Gorlin
Margaret Gorlin
πŸ’» πŸ’‘ ⚠️ - - Mariam Jabara
Mariam Jabara
πŸ’» Marielle
Marielle
πŸ“– πŸ’» πŸ€” Markus LΓΆning
Markus LΓΆning
πŸ’» ⚠️ 🚧 πŸ“¦ πŸ‘€ πŸš‡ πŸ’‘ πŸ› βœ… πŸ’Ό πŸ“– 🎨 πŸ“‹ πŸ” πŸ€” πŸ“† πŸ’¬ πŸ“’ πŸ§‘β€πŸ« πŸ“Ή Martin Walter
Martin Walter
πŸ’» πŸ› πŸ“† πŸ” πŸ§‘β€πŸ« πŸ€” 🎨 πŸ‘€ πŸ“– πŸ“’ Martina G. Vilas
Martina G. Vilas
πŸ‘€ πŸ€” + + Matthew Middlehurst
Matthew Middlehurst
πŸ› πŸ’» πŸ”£ πŸ“– 🎨 πŸ’‘ πŸ€” πŸš‡ 🚧 πŸ§‘β€πŸ« πŸ“£ πŸ’¬ πŸ”¬ πŸ‘€ ⚠️ βœ… πŸ“’ Max Patzelt
Max Patzelt
πŸ’» Miao Cai
Miao Cai
πŸ› πŸ’» - - Michael F. Mbouopda
Michael F. Mbouopda
πŸ’» πŸ› πŸ“– Michael Feil
Michael Feil
πŸ’» ⚠️ πŸ€” Michal Chromcak
Michal Chromcak
πŸ’» πŸ“– ⚠️ βœ… Mirae Parker
Mirae Parker
πŸ’» ⚠️ Mohammed Saif Kazamel
Mohammed Saif Kazamel
πŸ› + + Morad :)
Morad :)
πŸ’» ⚠️ πŸ“– Multivin12
Multivin12
πŸ’» ⚠️ MΓ‘rcio A. Freitas Jr
MΓ‘rcio A. Freitas Jr
πŸ“– - - Niek van der Laan
Niek van der Laan
πŸ’» Nikhil Gupta
Nikhil Gupta
πŸ’» πŸ› πŸ“– Nikola Shahpazov
Nikola Shahpazov
πŸ“– Nilesh Kumar
Nilesh Kumar
πŸ’» Nima Nooshiri
Nima Nooshiri
πŸ“– + + Ninnart Fuengfusin
Ninnart Fuengfusin
πŸ’» Noa Ben Ami
Noa Ben Ami
πŸ’» ⚠️ πŸ“– Oleksandr Shchur
Oleksandr Shchur
πŸ› πŸ’» - - Oleksii Kachaiev
Oleksii Kachaiev
πŸ’» ⚠️ Oliver Matthews
Oliver Matthews
πŸ’» Patrick MΓΌller
Patrick MΓΌller
πŸ’» Patrick Rockenschaub
Patrick Rockenschaub
πŸ’» 🎨 πŸ€” ⚠️ Patrick SchΓ€fer
Patrick SchΓ€fer
πŸ’» βœ… + + Paul
Paul
πŸ“– Paul Rabich
Paul Rabich
πŸ’» Paul Yim
Paul Yim
πŸ’» πŸ’‘ ⚠️ - - Philip
Philip
πŸ“– Philipp Kortmann
Philipp Kortmann
πŸ’» πŸ“– Phillip Wenig
Phillip Wenig
πŸ’» Piyush Gade
Piyush Gade
πŸ’» πŸ‘€ Pulkit Verma
Pulkit Verma
πŸ“– + + Quaterion
Quaterion
πŸ› Rafael AyllΓ³n-GavilΓ‘n
Rafael AyllΓ³n-GavilΓ‘n
πŸ’» Rakshitha Godahewa
Rakshitha Godahewa
πŸ’» πŸ“– - - + Ramana Raja
Ramana Raja
πŸ’» RavenRudi
RavenRudi
πŸ’» Raya Chakravarty
Raya Chakravarty
πŸ“– Rick van Hattem
Rick van Hattem
πŸš‡ Rishabh Bali
Rishabh Bali
πŸ’» + + Rishav Kumar Sinha
Rishav Kumar Sinha
πŸ“– Rishi Kumar Ray
Rishi Kumar Ray
πŸš‡ Riya Elizabeth John
Riya Elizabeth John
πŸ’» ⚠️ πŸ“– Ronnie Llamado
Ronnie Llamado
πŸ“– - - Ryan Kuhns
Ryan Kuhns
πŸ’» πŸ“– βœ… πŸ’‘ πŸ€” πŸ‘€ ⚠️ Sagar Mishra
Sagar Mishra
⚠️ Sajaysurya Ganesh
Sajaysurya Ganesh
πŸ’» πŸ“– 🎨 πŸ’‘ πŸ€” ⚠️ βœ… Saransh Chopra
Saransh Chopra
πŸ“– πŸš‡ + + Satya Prakash Pattnaik
Satya Prakash Pattnaik
πŸ“– Saurabh Dasgupta
Saurabh Dasgupta
πŸ’» Sebastiaan Koel
Sebastiaan Koel
πŸ’» πŸ“– Sebastian Hagn
Sebastian Hagn
πŸ“– - - Sebastian Schmidl
Sebastian Schmidl
πŸ› πŸ’» πŸ“– πŸ”¬ ⚠️ πŸ‘€ πŸ”£ Sharathchenna
Sharathchenna
πŸ’» Shivansh Subramanian
Shivansh Subramanian
πŸ“– πŸ’» Solomon Botchway
Solomon Botchway
🚧 + + Stanislav Khrapov
Stanislav Khrapov
πŸ’» Stijn Rotman
Stijn Rotman
πŸ’» Svea Marie Meyer
Svea Marie Meyer
πŸ“– πŸ’» Sylvain Combettes
Sylvain Combettes
πŸ’» πŸ› - - TNTran92
TNTran92
πŸ’» Taiwo Owoseni
Taiwo Owoseni
πŸ’» + Tanish Yelgoe
Tanish Yelgoe
πŸ’» Thach Le Nguyen
Thach Le Nguyen
πŸ’» ⚠️ + + TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
πŸ“– Thomas Buckley-Houston
Thomas Buckley-Houston
πŸ› Tom Xu
Tom Xu
πŸ’» πŸ“– Tomasz Chodakowski
Tomasz Chodakowski
πŸ’» πŸ“– πŸ› Tony Bagnall
Tony Bagnall
πŸ’» πŸ’Ό πŸ“– 🎨 πŸ“‹ πŸ” πŸ€” πŸ“† πŸ’¬ πŸ‘€ πŸ“’ πŸ”£ - - Tvisha Vedant
Tvisha Vedant
πŸ’» Utkarsh Kumar
Utkarsh Kumar
πŸ’» πŸ“– Utsav Kumar Tiwari
Utsav Kumar Tiwari
πŸ’» πŸ“– + + Vedant
Vedant
πŸ“– Viktor Dremov
Viktor Dremov
πŸ’» ViktorKaz
ViktorKaz
πŸ’» πŸ“– 🎨 Vyomkesh Vyas
Vyomkesh Vyas
πŸ’» πŸ“– πŸ’‘ ⚠️ Wayne Adams
Wayne Adams
πŸ“– - - William Templier
William Templier
πŸ“– William Zeng
William Zeng
πŸ› William Zheng
William Zheng
πŸ’» ⚠️ + + Yair Beer
Yair Beer
πŸ’» Yash Lamba
Yash Lamba
πŸ’» Yi-Xuan Xu
Yi-Xuan Xu
πŸ’» ⚠️ 🚧 πŸ“– Ziyao Wei
Ziyao Wei
πŸ’» aa25desh
aa25desh
πŸ’» πŸ› - - abandus
abandus
πŸ€” πŸ’» adoherty21
adoherty21
πŸ› alexbanwell1
alexbanwell1
πŸ’» 🎨 πŸ“– + + bethrice44
bethrice44
πŸ› πŸ’» πŸ‘€ ⚠️ big-o
big-o
πŸ’» ⚠️ 🎨 πŸ€” πŸ‘€ βœ… πŸ§‘β€πŸ« bobbys
bobbys
πŸ’» brett koonce
brett koonce
πŸ“– btrtts
btrtts
πŸ“– - - chizzi25
chizzi25
πŸ“ chrisholder
chrisholder
πŸ’» ⚠️ πŸ“– 🎨 πŸ’‘ πŸ› danbartl
danbartl
πŸ› πŸ’» πŸ‘€ πŸ“’ ⚠️ βœ… πŸ“Ή + + hamzahiqb
hamzahiqb
πŸš‡ hiqbal2
hiqbal2
πŸ“– jesellier
jesellier
πŸ’» jschemm
jschemm
πŸ’» julu98
julu98
πŸ› - - kkoziara
kkoziara
πŸ’» πŸ› matteogales
matteogales
πŸ’» 🎨 πŸ€” neuron283
neuron283
πŸ’» + + nileenagp
nileenagp
πŸ’» oleskiewicz
oleskiewicz
πŸ’» πŸ“– ⚠️ pabworks
pabworks
πŸ’» ⚠️ patiently pending world peace
patiently pending world peace
πŸ’» raishubham1
raishubham1
πŸ“– - - simone-pignotti
simone-pignotti
πŸ’» πŸ› sophijka
sophijka
πŸ“– 🚧 sri1419
sri1419
πŸ’» + + tensorflow-as-tf
tensorflow-as-tf
πŸ’» vNtzYy
vNtzYy
πŸ› ved pawar
ved pawar
πŸ“– vedazeren
vedazeren
πŸ’» ⚠️ vincent-nich12
vincent-nich12
πŸ’» - - vollmersj
vollmersj
πŸ“– xiaobenbenecho
xiaobenbenecho
πŸ’» xiaopu222
xiaopu222
πŸ“– diff --git a/README.md b/README.md index e1475d6d85..3f6eb132f1 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ We strive to provide a broad library of time series algorithms including the latest advances, offer efficient implementations using numba, and interfaces with other time series packages to provide a single framework for algorithm comparison. -The latest `aeon` release is `v1.0.0`. You can view the full changelog +The latest `aeon` release is `v1.1.0`. You can view the full changelog [here](https://www.aeon-toolkit.org/en/stable/changelog.html). Our webpage and documentation is available at https://aeon-toolkit.org. @@ -29,7 +29,7 @@ does not apply: | Overview | | |-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **CI/CD** | [![github-actions-release](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/release.yml?logo=github&label=build%20%28release%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [![github-actions-main](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/pr_pytest.yml?logo=github&branch=main&label=build%20%28main%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [![github-actions-nightly](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/periodic_tests.yml?logo=github&label=build%20%28nightly%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/stable?logo=readthedocs&label=docs%20%28stable%29)](https://www.aeon-toolkit.org/en/stable/) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/latest?logo=readthedocs&label=docs%20%28latest%29)](https://www.aeon-toolkit.org/en/latest/) [![!codecov](https://img.shields.io/codecov/c/github/aeon-toolkit/aeon?label=codecov&logo=codecov)](https://codecov.io/gh/aeon-toolkit/aeon) [![openssf-scorecard](https://api.scorecard.dev/projects/github.com/aeon-toolkit/aeon/badge)](https://img.shields.io/ossf-scorecard/github.com/aeon-toolkit/aeon?label=openssf%20scorecard&style=flat) | +| **CI/CD** | [![github-actions-release](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/release.yml?logo=github&label=build%20%28release%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [![github-actions-main](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/pr_pytest.yml?logo=github&branch=main&label=build%20%28main%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [![github-actions-nightly](https://img.shields.io/github/actions/workflow/status/aeon-toolkit/aeon/periodic_tests.yml?logo=github&label=build%20%28nightly%29)](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/stable?logo=readthedocs&label=docs%20%28stable%29)](https://www.aeon-toolkit.org/en/stable/) [![docs-main](https://img.shields.io/readthedocs/aeon-toolkit/latest?logo=readthedocs&label=docs%20%28latest%29)](https://www.aeon-toolkit.org/en/latest/) [![!codecov](https://img.shields.io/codecov/c/github/aeon-toolkit/aeon?label=codecov&logo=codecov)](https://codecov.io/gh/aeon-toolkit/aeon) [![openssf-scorecard](https://api.scorecard.dev/projects/github.com/aeon-toolkit/aeon/badge)](https://scorecard.dev/viewer/?uri=github.com/aeon-toolkit/aeon) | | **Code** | [![!pypi](https://img.shields.io/pypi/v/aeon?logo=pypi&color=blue)](https://pypi.org/project/aeon/) [![!conda](https://img.shields.io/conda/vn/conda-forge/aeon?logo=anaconda&color=blue)](https://anaconda.org/conda-forge/aeon) [![!python-versions](https://img.shields.io/pypi/pyversions/aeon?logo=python)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![license](https://img.shields.io/badge/license-BSD%203--Clause-green?logo=style)](https://github.com/aeon-toolkit/aeon/blob/main/LICENSE) [![binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/aeon-toolkit/aeon/main?filepath=examples) | | **Community** | [![!slack](https://img.shields.io/static/v1?logo=slack&label=Slack&message=chat&color=lightgreen)](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) [![!linkedin](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/aeon-toolkit/) [![!x-twitter](https://img.shields.io/static/v1?logo=x&label=X/Twitter&message=news&color=lightblue)](https://twitter.com/aeon_toolkit) | | **Affiliation** | [![numfocus](https://img.shields.io/badge/NumFOCUS-Affiliated%20Project-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org/sponsored-projects/affiliated-projects) | @@ -161,7 +161,8 @@ If you use `aeon` we would appreciate a citation of the following [paper](https: If you let us know about your paper using `aeon`, we will happily list it [here](https://www.aeon-toolkit.org/en/stable/papers_using_aeon.html). -## πŸ’¬ Further information +## πŸ‘₯ Further information `aeon` was forked from `sktime` `v0.16.0` in 2022 by an initial group of eight core -developers. +developers. You can read more about the project's history and governance structure in +our [About Us page](https://www.aeon-toolkit.org/en/stable/about.html). diff --git a/aeon/__init__.py b/aeon/__init__.py index c77db1e335..f8c45d7805 100644 --- a/aeon/__init__.py +++ b/aeon/__init__.py @@ -1,3 +1,3 @@ """aeon toolkit.""" -__version__ = "1.0.0" +__version__ = "1.1.0" diff --git a/aeon/anomaly_detection/__init__.py b/aeon/anomaly_detection/__init__.py index c1f87846e7..65343cd774 100644 --- a/aeon/anomaly_detection/__init__.py +++ b/aeon/anomaly_detection/__init__.py @@ -1,31 +1,7 @@ """Time Series Anomaly Detection.""" __all__ = [ - "CBLOF", - "COPOD", - "DWT_MLEAD", - "IsolationForest", - "KMeansAD", - "LeftSTAMPi", - "LOF", - "MERLIN", - "OneClassSVM", - "ROCKAD", - "PyODAdapter", - "STOMP", - "STRAY", + "BaseAnomalyDetector", ] -from aeon.anomaly_detection._cblof import CBLOF -from aeon.anomaly_detection._copod import COPOD -from aeon.anomaly_detection._dwt_mlead import DWT_MLEAD -from aeon.anomaly_detection._iforest import IsolationForest -from aeon.anomaly_detection._kmeans import KMeansAD -from aeon.anomaly_detection._left_stampi import LeftSTAMPi -from aeon.anomaly_detection._lof import LOF -from aeon.anomaly_detection._merlin import MERLIN -from aeon.anomaly_detection._one_class_svm import OneClassSVM -from aeon.anomaly_detection._pyodadapter import PyODAdapter -from aeon.anomaly_detection._rockad import ROCKAD -from aeon.anomaly_detection._stomp import STOMP -from aeon.anomaly_detection._stray import STRAY +from aeon.anomaly_detection.base import BaseAnomalyDetector diff --git a/aeon/anomaly_detection/distance_based/__init__.py b/aeon/anomaly_detection/distance_based/__init__.py new file mode 100644 index 0000000000..5eb342b780 --- /dev/null +++ b/aeon/anomaly_detection/distance_based/__init__.py @@ -0,0 +1,19 @@ +"""Distance basedTime Series Anomaly Detection.""" + +__all__ = [ + "CBLOF", + "KMeansAD", + "LeftSTAMPi", + "LOF", + "MERLIN", + "OneClassSVM", + "STOMP", +] + +from aeon.anomaly_detection.distance_based._cblof import CBLOF +from aeon.anomaly_detection.distance_based._kmeans import KMeansAD +from aeon.anomaly_detection.distance_based._left_stampi import LeftSTAMPi +from aeon.anomaly_detection.distance_based._lof import LOF +from aeon.anomaly_detection.distance_based._merlin import MERLIN +from aeon.anomaly_detection.distance_based._one_class_svm import OneClassSVM +from aeon.anomaly_detection.distance_based._stomp import STOMP diff --git a/aeon/anomaly_detection/_cblof.py b/aeon/anomaly_detection/distance_based/_cblof.py similarity index 98% rename from aeon/anomaly_detection/_cblof.py rename to aeon/anomaly_detection/distance_based/_cblof.py index 506974f6ca..18bb044c14 100644 --- a/aeon/anomaly_detection/_cblof.py +++ b/aeon/anomaly_detection/distance_based/_cblof.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_kmeans.py b/aeon/anomaly_detection/distance_based/_kmeans.py similarity index 99% rename from aeon/anomaly_detection/_kmeans.py rename to aeon/anomaly_detection/distance_based/_kmeans.py index c114911c3b..bb8f188a1d 100644 --- a/aeon/anomaly_detection/_kmeans.py +++ b/aeon/anomaly_detection/distance_based/_kmeans.py @@ -65,7 +65,7 @@ class KMeansAD(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import KMeansAD + >>> from aeon.anomaly_detection.distance_based import KMeansAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = KMeansAD(n_clusters=3, window_size=4, stride=1, random_state=0) >>> detector.fit_predict(X) diff --git a/aeon/anomaly_detection/_left_stampi.py b/aeon/anomaly_detection/distance_based/_left_stampi.py similarity index 98% rename from aeon/anomaly_detection/_left_stampi.py rename to aeon/anomaly_detection/distance_based/_left_stampi.py index d71cc5bd26..43078ce021 100644 --- a/aeon/anomaly_detection/_left_stampi.py +++ b/aeon/anomaly_detection/distance_based/_left_stampi.py @@ -44,7 +44,7 @@ class LeftSTAMPi(BaseAnomalyDetector): Internally,this is applying the incremental approach outlined below. >>> import numpy as np # doctest: +SKIP - >>> from aeon.anomaly_detection import LeftSTAMPi # doctest: +SKIP + >>> from aeon.anomaly_detection.distance_based import LeftSTAMPi # doctest: +SKIP >>> X = np.random.default_rng(42).random((10)) # doctest: +SKIP >>> detector = LeftSTAMPi(window_size=3, n_init_train=3) # doctest: +SKIP >>> detector.fit_predict(X) # doctest: +SKIP diff --git a/aeon/anomaly_detection/_lof.py b/aeon/anomaly_detection/distance_based/_lof.py similarity index 98% rename from aeon/anomaly_detection/_lof.py rename to aeon/anomaly_detection/distance_based/_lof.py index 99ac068584..2c3615d906 100644 --- a/aeon/anomaly_detection/_lof.py +++ b/aeon/anomaly_detection/distance_based/_lof.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_merlin.py b/aeon/anomaly_detection/distance_based/_merlin.py similarity index 99% rename from aeon/anomaly_detection/_merlin.py rename to aeon/anomaly_detection/distance_based/_merlin.py index 5928d156d6..b63224acd5 100644 --- a/aeon/anomaly_detection/_merlin.py +++ b/aeon/anomaly_detection/distance_based/_merlin.py @@ -43,7 +43,7 @@ class MERLIN(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import MERLIN + >>> from aeon.anomaly_detection.distance_based import MERLIN >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 5, 1, 2, 3, 4]) >>> detector = MERLIN(min_length=4, max_length=5) >>> detector.fit_predict(X) diff --git a/aeon/anomaly_detection/_one_class_svm.py b/aeon/anomaly_detection/distance_based/_one_class_svm.py similarity index 100% rename from aeon/anomaly_detection/_one_class_svm.py rename to aeon/anomaly_detection/distance_based/_one_class_svm.py diff --git a/aeon/anomaly_detection/_stomp.py b/aeon/anomaly_detection/distance_based/_stomp.py similarity index 98% rename from aeon/anomaly_detection/_stomp.py rename to aeon/anomaly_detection/distance_based/_stomp.py index af39891149..3f8be36432 100644 --- a/aeon/anomaly_detection/_stomp.py +++ b/aeon/anomaly_detection/distance_based/_stomp.py @@ -38,7 +38,7 @@ class STOMP(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import STOMP # doctest: +SKIP + >>> from aeon.anomaly_detection.distance_based import STOMP # doctest: +SKIP >>> X = np.random.default_rng(42).random((10, 2), dtype=np.float64) >>> detector = STOMP(X, window_size=2) # doctest: +SKIP >>> detector.fit_predict(X, axis=0) # doctest: +SKIP diff --git a/aeon/anomaly_detection/distance_based/tests/__init__.py b/aeon/anomaly_detection/distance_based/tests/__init__.py new file mode 100644 index 0000000000..03b6c4a5e8 --- /dev/null +++ b/aeon/anomaly_detection/distance_based/tests/__init__.py @@ -0,0 +1 @@ +"""Distance based test code.""" diff --git a/aeon/anomaly_detection/tests/test_cblof.py b/aeon/anomaly_detection/distance_based/tests/test_cblof.py similarity index 96% rename from aeon/anomaly_detection/tests/test_cblof.py rename to aeon/anomaly_detection/distance_based/tests/test_cblof.py index c8d9f5d9c8..d1472af6a2 100644 --- a/aeon/anomaly_detection/tests/test_cblof.py +++ b/aeon/anomaly_detection/distance_based/tests/test_cblof.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection import CBLOF +from aeon.anomaly_detection.distance_based import CBLOF from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -21,7 +21,7 @@ def test_cblof_default(): pred = cblof.fit_predict(series, axis=0) assert pred.shape == (80,) - assert pred.dtype == np.float_ + assert np.issubdtype(pred.dtype, np.floating) assert 50 <= np.argmax(pred) <= 60 diff --git a/aeon/anomaly_detection/tests/test_kmeans.py b/aeon/anomaly_detection/distance_based/tests/test_kmeans.py similarity index 96% rename from aeon/anomaly_detection/tests/test_kmeans.py rename to aeon/anomaly_detection/distance_based/tests/test_kmeans.py index 9812d7696b..2647411b88 100644 --- a/aeon/anomaly_detection/tests/test_kmeans.py +++ b/aeon/anomaly_detection/distance_based/tests/test_kmeans.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import KMeansAD +from aeon.anomaly_detection.distance_based import KMeansAD def test_kmeansad_univariate(): diff --git a/aeon/anomaly_detection/tests/test_left_stampi.py b/aeon/anomaly_detection/distance_based/tests/test_left_stampi.py similarity index 99% rename from aeon/anomaly_detection/tests/test_left_stampi.py rename to aeon/anomaly_detection/distance_based/tests/test_left_stampi.py index 589d163f7b..6444bccdfe 100644 --- a/aeon/anomaly_detection/tests/test_left_stampi.py +++ b/aeon/anomaly_detection/distance_based/tests/test_left_stampi.py @@ -8,7 +8,7 @@ import numpy as np import pytest -from aeon.anomaly_detection._left_stampi import LeftSTAMPi +from aeon.anomaly_detection.distance_based._left_stampi import LeftSTAMPi from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_lof.py b/aeon/anomaly_detection/distance_based/tests/test_lof.py similarity index 99% rename from aeon/anomaly_detection/tests/test_lof.py rename to aeon/anomaly_detection/distance_based/tests/test_lof.py index 846aa78a5a..033d11295b 100644 --- a/aeon/anomaly_detection/tests/test_lof.py +++ b/aeon/anomaly_detection/distance_based/tests/test_lof.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection import LOF +from aeon.anomaly_detection.distance_based import LOF from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_merlin.py b/aeon/anomaly_detection/distance_based/tests/test_merlin.py similarity index 96% rename from aeon/anomaly_detection/tests/test_merlin.py rename to aeon/anomaly_detection/distance_based/tests/test_merlin.py index 20fe7c697e..ccf7e3300d 100644 --- a/aeon/anomaly_detection/tests/test_merlin.py +++ b/aeon/anomaly_detection/distance_based/tests/test_merlin.py @@ -4,7 +4,7 @@ import numpy as np -from aeon.anomaly_detection import MERLIN +from aeon.anomaly_detection.distance_based import MERLIN TEST_DATA = np.array( [ diff --git a/aeon/anomaly_detection/tests/test_one_class_svm.py b/aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py similarity index 95% rename from aeon/anomaly_detection/tests/test_one_class_svm.py rename to aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py index c99f0ff755..7a3aca2042 100644 --- a/aeon/anomaly_detection/tests/test_one_class_svm.py +++ b/aeon/anomaly_detection/distance_based/tests/test_one_class_svm.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import OneClassSVM +from aeon.anomaly_detection.distance_based import OneClassSVM def test_one_class_svm_univariate(): diff --git a/aeon/anomaly_detection/tests/test_stomp.py b/aeon/anomaly_detection/distance_based/tests/test_stomp.py similarity index 95% rename from aeon/anomaly_detection/tests/test_stomp.py rename to aeon/anomaly_detection/distance_based/tests/test_stomp.py index b1adfc1d12..b506c89ea0 100644 --- a/aeon/anomaly_detection/tests/test_stomp.py +++ b/aeon/anomaly_detection/distance_based/tests/test_stomp.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import STOMP +from aeon.anomaly_detection.distance_based import STOMP from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/distribution_based/__init__.py b/aeon/anomaly_detection/distribution_based/__init__.py new file mode 100644 index 0000000000..e52a7512ba --- /dev/null +++ b/aeon/anomaly_detection/distribution_based/__init__.py @@ -0,0 +1,9 @@ +"""Distribution based Time Series Anomaly Detection.""" + +__all__ = [ + "COPOD", + "DWT_MLEAD", +] + +from aeon.anomaly_detection.distribution_based._copod import COPOD +from aeon.anomaly_detection.distribution_based._dwt_mlead import DWT_MLEAD diff --git a/aeon/anomaly_detection/_copod.py b/aeon/anomaly_detection/distribution_based/_copod.py similarity index 97% rename from aeon/anomaly_detection/_copod.py rename to aeon/anomaly_detection/distribution_based/_copod.py index ee448b96b8..bd2af0e084 100644 --- a/aeon/anomaly_detection/_copod.py +++ b/aeon/anomaly_detection/distribution_based/_copod.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_dwt_mlead.py b/aeon/anomaly_detection/distribution_based/_dwt_mlead.py similarity index 99% rename from aeon/anomaly_detection/_dwt_mlead.py rename to aeon/anomaly_detection/distribution_based/_dwt_mlead.py index e78bb1d7d9..cb0de0c015 100644 --- a/aeon/anomaly_detection/_dwt_mlead.py +++ b/aeon/anomaly_detection/distribution_based/_dwt_mlead.py @@ -78,7 +78,7 @@ class DWT_MLEAD(BaseAnomalyDetector): Examples -------- >>> import numpy as np - >>> from aeon.anomaly_detection import DWT_MLEAD + >>> from aeon.anomaly_detection.distribution_based import DWT_MLEAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = DWT_MLEAD( ... start_level=1, quantile_boundary_type='percentile', quantile_epsilon=0.01 diff --git a/aeon/anomaly_detection/distribution_based/tests/__init__.py b/aeon/anomaly_detection/distribution_based/tests/__init__.py new file mode 100644 index 0000000000..2f368970c0 --- /dev/null +++ b/aeon/anomaly_detection/distribution_based/tests/__init__.py @@ -0,0 +1 @@ +"""Distribution based test code.""" diff --git a/aeon/anomaly_detection/tests/test_copod.py b/aeon/anomaly_detection/distribution_based/tests/test_copod.py similarity index 94% rename from aeon/anomaly_detection/tests/test_copod.py rename to aeon/anomaly_detection/distribution_based/tests/test_copod.py index b1cddaa4dc..40969da0e7 100644 --- a/aeon/anomaly_detection/tests/test_copod.py +++ b/aeon/anomaly_detection/distribution_based/tests/test_copod.py @@ -3,7 +3,7 @@ import numpy as np import pytest -from aeon.anomaly_detection import COPOD +from aeon.anomaly_detection.distribution_based import COPOD from aeon.testing.data_generation import make_example_1d_numpy from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -21,7 +21,7 @@ def test_copod_default(): pred = copod.fit_predict(series, axis=0) assert pred.shape == (80,) - assert pred.dtype == np.float_ + assert np.issubdtype(pred.dtype, np.floating) assert 50 <= np.argmax(pred) <= 60 diff --git a/aeon/anomaly_detection/tests/test_dwt_mlead.py b/aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py similarity index 96% rename from aeon/anomaly_detection/tests/test_dwt_mlead.py rename to aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py index c5d09bddc2..664d715122 100644 --- a/aeon/anomaly_detection/tests/test_dwt_mlead.py +++ b/aeon/anomaly_detection/distribution_based/tests/test_dwt_mlead.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import DWT_MLEAD +from aeon.anomaly_detection.distribution_based import DWT_MLEAD def test_dwt_mlead_output(): diff --git a/aeon/anomaly_detection/outlier_detection/__init__.py b/aeon/anomaly_detection/outlier_detection/__init__.py new file mode 100644 index 0000000000..ad9b7868e5 --- /dev/null +++ b/aeon/anomaly_detection/outlier_detection/__init__.py @@ -0,0 +1,11 @@ +"""Time Series Outlier Detection.""" + +__all__ = [ + "IsolationForest", + "PyODAdapter", + "STRAY", +] + +from aeon.anomaly_detection.outlier_detection._iforest import IsolationForest +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._stray import STRAY diff --git a/aeon/anomaly_detection/_iforest.py b/aeon/anomaly_detection/outlier_detection/_iforest.py similarity index 98% rename from aeon/anomaly_detection/_iforest.py rename to aeon/anomaly_detection/outlier_detection/_iforest.py index a410c3542d..f13152d0e7 100644 --- a/aeon/anomaly_detection/_iforest.py +++ b/aeon/anomaly_detection/outlier_detection/_iforest.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.anomaly_detection._pyodadapter import PyODAdapter +from aeon.anomaly_detection.outlier_detection._pyodadapter import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/_pyodadapter.py b/aeon/anomaly_detection/outlier_detection/_pyodadapter.py similarity index 98% rename from aeon/anomaly_detection/_pyodadapter.py rename to aeon/anomaly_detection/outlier_detection/_pyodadapter.py index c520cc6f19..5a068857c6 100644 --- a/aeon/anomaly_detection/_pyodadapter.py +++ b/aeon/anomaly_detection/outlier_detection/_pyodadapter.py @@ -59,7 +59,9 @@ class PyODAdapter(BaseAnomalyDetector): -------- >>> import numpy as np >>> from pyod.models.lof import LOF # doctest: +SKIP - >>> from aeon.anomaly_detection import PyODAdapter # doctest: +SKIP + >>> from aeon.anomaly_detection.outlier_detection import ( + ... PyODAdapter + ... ) # doctest: +SKIP >>> X = np.random.default_rng(42).random((10, 2), dtype=np.float64) >>> detector = PyODAdapter(LOF(), window_size=2) # doctest: +SKIP >>> detector.fit_predict(X, axis=0) # doctest: +SKIP diff --git a/aeon/anomaly_detection/_stray.py b/aeon/anomaly_detection/outlier_detection/_stray.py similarity index 98% rename from aeon/anomaly_detection/_stray.py rename to aeon/anomaly_detection/outlier_detection/_stray.py index 2c5d669033..e7512e2d24 100644 --- a/aeon/anomaly_detection/_stray.py +++ b/aeon/anomaly_detection/outlier_detection/_stray.py @@ -54,7 +54,7 @@ class STRAY(BaseAnomalyDetector): Examples -------- - >>> from aeon.anomaly_detection import STRAY + >>> from aeon.anomaly_detection.outlier_detection import STRAY >>> from aeon.datasets import load_airline >>> import numpy as np >>> X = load_airline() diff --git a/aeon/anomaly_detection/outlier_detection/tests/__init__.py b/aeon/anomaly_detection/outlier_detection/tests/__init__.py new file mode 100644 index 0000000000..7ac2efbbaa --- /dev/null +++ b/aeon/anomaly_detection/outlier_detection/tests/__init__.py @@ -0,0 +1 @@ +"""Outlier based test code.""" diff --git a/aeon/anomaly_detection/tests/test_iforest.py b/aeon/anomaly_detection/outlier_detection/tests/test_iforest.py similarity index 98% rename from aeon/anomaly_detection/tests/test_iforest.py rename to aeon/anomaly_detection/outlier_detection/tests/test_iforest.py index 59c1121022..a66d1003fb 100644 --- a/aeon/anomaly_detection/tests/test_iforest.py +++ b/aeon/anomaly_detection/outlier_detection/tests/test_iforest.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import IsolationForest +from aeon.anomaly_detection.outlier_detection import IsolationForest from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_pyod_adapter.py b/aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py similarity index 98% rename from aeon/anomaly_detection/tests/test_pyod_adapter.py rename to aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py index eff4d5b325..ee75078133 100644 --- a/aeon/anomaly_detection/tests/test_pyod_adapter.py +++ b/aeon/anomaly_detection/outlier_detection/tests/test_pyod_adapter.py @@ -6,7 +6,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import PyODAdapter +from aeon.anomaly_detection.outlier_detection import PyODAdapter from aeon.utils.validation._dependencies import _check_soft_dependencies diff --git a/aeon/anomaly_detection/tests/test_stray.py b/aeon/anomaly_detection/outlier_detection/tests/test_stray.py similarity index 98% rename from aeon/anomaly_detection/tests/test_stray.py rename to aeon/anomaly_detection/outlier_detection/tests/test_stray.py index cbf6caabb3..8429a8a3c5 100644 --- a/aeon/anomaly_detection/tests/test_stray.py +++ b/aeon/anomaly_detection/outlier_detection/tests/test_stray.py @@ -5,7 +5,7 @@ import numpy as np from sklearn.preprocessing import MinMaxScaler -from aeon.anomaly_detection import STRAY +from aeon.anomaly_detection.outlier_detection import STRAY def test_default_1D(): diff --git a/aeon/anomaly_detection/whole_series/__init__.py b/aeon/anomaly_detection/whole_series/__init__.py new file mode 100644 index 0000000000..7098b8cd08 --- /dev/null +++ b/aeon/anomaly_detection/whole_series/__init__.py @@ -0,0 +1,7 @@ +"""Whole Time Series Anomaly Detection.""" + +__all__ = [ + "ROCKAD", +] + +from aeon.anomaly_detection.whole_series._rockad import ROCKAD diff --git a/aeon/anomaly_detection/_rockad.py b/aeon/anomaly_detection/whole_series/_rockad.py similarity index 100% rename from aeon/anomaly_detection/_rockad.py rename to aeon/anomaly_detection/whole_series/_rockad.py diff --git a/aeon/anomaly_detection/whole_series/tests/__init__.py b/aeon/anomaly_detection/whole_series/tests/__init__.py new file mode 100644 index 0000000000..9292e8d9bd --- /dev/null +++ b/aeon/anomaly_detection/whole_series/tests/__init__.py @@ -0,0 +1 @@ +"""Whole series anomaly detection tests.""" diff --git a/aeon/anomaly_detection/tests/test_rockad.py b/aeon/anomaly_detection/whole_series/tests/test_rockad.py similarity index 97% rename from aeon/anomaly_detection/tests/test_rockad.py rename to aeon/anomaly_detection/whole_series/tests/test_rockad.py index d9d133b9a8..7d3694b2c8 100644 --- a/aeon/anomaly_detection/tests/test_rockad.py +++ b/aeon/anomaly_detection/whole_series/tests/test_rockad.py @@ -4,7 +4,7 @@ import pytest from sklearn.utils import check_random_state -from aeon.anomaly_detection import ROCKAD +from aeon.anomaly_detection.whole_series import ROCKAD def test_rockad_univariate(): diff --git a/aeon/base/_base.py b/aeon/base/_base.py index 41ac7010f3..5a336c7397 100644 --- a/aeon/base/_base.py +++ b/aeon/base/_base.py @@ -86,6 +86,11 @@ class and object methods, class attributes ------- self : object Reference to self. + + Raises + ------ + TypeError + If 'keep' is not a string or a list of strings. """ # retrieve parameters to copy them later params = self.get_params(deep=False) @@ -415,6 +420,18 @@ def __sklearn_is_fitted__(self): """Check fitted status and return a Boolean value.""" return self.is_fitted + def __sklearn_tags__(self): + """Return sklearn style tags for the estimator.""" + aeon_tags = self.get_tags() + sklearn_tags = super().__sklearn_tags__() + sklearn_tags.non_deterministic = aeon_tags.get("non_deterministic", False) + sklearn_tags.target_tags.one_d_labels = True + sklearn_tags.input_tags.three_d_array = True + sklearn_tags.input_tags.allow_nan = aeon_tags.get( + "capability:missing_values", False + ) + return sklearn_tags + def _validate_data(self, **kwargs): """Sklearn data validation.""" raise NotImplementedError( diff --git a/aeon/base/_base_collection.py b/aeon/base/_base_collection.py index ea3b21ed32..4d7f4b4564 100644 --- a/aeon/base/_base_collection.py +++ b/aeon/base/_base_collection.py @@ -1,4 +1,24 @@ -"""Base class for estimators that fit collections of time series.""" +""" +Base class for estimators that fit collections of time series. + + class name: BaseCollectionEstimator + +Defining methods: + preprocessing - _preprocess_collection(self, X, store_metadata=True) + input checking - _check_X(self, X) + input conversion - _convert_X(self, X) + shape checking - _check_shape(self, X) + +Inherited inspection methods: + hyper-parameter inspection - get_params() + fitted parameter inspection - get_fitted_params() + +State: + fitted model/strategy - by convention, any attributes ending in "_" + fitted state flag - is_fitted (property) + fitted state inspection - check_is_fitted() + +""" from abc import abstractmethod diff --git a/aeon/base/_base_series.py b/aeon/base/_base_series.py index 6c86940f5b..f46091142a 100644 --- a/aeon/base/_base_series.py +++ b/aeon/base/_base_series.py @@ -99,7 +99,7 @@ def _preprocess_series(self, X, axis, store_metadata): self.metadata_ = meta return self._convert_X(X, axis) - def _check_X(self, X, axis): + def _check_X(self, X, axis: int = 0): """Check input X is valid. Check if the input data is a compatible type, and that this estimator is diff --git a/aeon/base/_compose.py b/aeon/base/_compose.py index 0995e85de6..8661245806 100644 --- a/aeon/base/_compose.py +++ b/aeon/base/_compose.py @@ -12,8 +12,10 @@ class ComposableEstimatorMixin(ABC): """Handles parameter management for estimators composed of named estimators. - Parts (i.e. get_params and set_params) adapted or copied from the scikit-learn + Parts (i.e. get_params and set_params) adapted from the scikit-learn 1.5.0 ``_BaseComposition`` class in utils/metaestimators.py. + https://github.com/scikit-learn/scikit-learn/ + Copyright (c) 2007-2024 The scikit-learn developers, BSD-3 """ # Attribute name containing an iterable of processed (str, estimator) tuples diff --git a/aeon/benchmarking/metrics/anomaly_detection/__init__.py b/aeon/benchmarking/metrics/anomaly_detection/__init__.py index fdbf13cec9..cf6ccac42c 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/__init__.py +++ b/aeon/benchmarking/metrics/anomaly_detection/__init__.py @@ -14,6 +14,9 @@ "range_pr_auc_score", "range_pr_vus_score", "range_roc_vus_score", + "ts_precision", + "ts_recall", + "ts_fscore", ] from aeon.benchmarking.metrics.anomaly_detection._binary import ( @@ -35,3 +38,8 @@ range_roc_auc_score, range_roc_vus_score, ) +from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( + ts_fscore, + ts_precision, + ts_recall, +) diff --git a/aeon/benchmarking/metrics/anomaly_detection/_binary.py b/aeon/benchmarking/metrics/anomaly_detection/_binary.py index 85d54a5cb6..085d7c04f9 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_binary.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_binary.py @@ -6,11 +6,19 @@ import warnings import numpy as np +from deprecated.sphinx import deprecated from aeon.benchmarking.metrics.anomaly_detection._util import check_y from aeon.utils.validation._dependencies import _check_soft_dependencies +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_precision is deprecated and will be removed in v1.2.0. " + "Please use ts_precision from the range_metrics module instead.", + category=FutureWarning, +) def range_precision( y_true: np.ndarray, y_pred: np.ndarray, @@ -70,6 +78,13 @@ def range_precision( return ts_precision(y_true, y_pred, alpha=alpha, cardinality=cardinality, bias=bias) +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_recall is deprecated and will be removed in v1.2.0. " + "Please use ts_recall from the range_metrics module instead.", + category=FutureWarning, +) def range_recall( y_true: np.ndarray, y_pred: np.ndarray, @@ -131,6 +146,13 @@ def range_recall( return ts_recall(y_true, y_pred, alpha=alpha, cardinality=cardinality, bias=bias) +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_f_score is deprecated and will be removed in v1.2.0. " + "Please use ts_fscore from the range_metrics module instead.", + category=FutureWarning, +) def range_f_score( y_true: np.ndarray, y_pred: np.ndarray, diff --git a/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py new file mode 100644 index 0000000000..9084188f59 --- /dev/null +++ b/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py @@ -0,0 +1,521 @@ +"""Calculate Precision, Recall, and F1-Score for time series anomaly detection.""" + +__maintainer__ = [] +__all__ = ["ts_precision", "ts_recall", "ts_fscore"] + +import numpy as np + + +def _flatten_ranges(ranges): + """ + If the input is a list of lists, it flattens it into a single list. + + Parameters + ---------- + ranges : list of tuples or list of lists of tuples + The ranges to flatten. each tuple shoulod be in the format of (start, end). + + Returns + ------- + list of tuples + A flattened list of ranges. + + Examples + -------- + >>> _flatten_ranges([[(1, 5), (10, 15)], [(20, 25)]]) + [(1, 5), (10, 15), (20, 25)] + """ + if not ranges: + return [] + if isinstance(ranges[0], list): + flat = [] + for sublist in ranges: + for pred in sublist: + flat.append(pred) + return flat + return ranges + + +def udf_gamma_def(overlap_count): + """User-defined gamma function. Should return a gamma value > 1. + + Parameters + ---------- + overlap_count : int + The number of overlapping ranges. + + Returns + ------- + float + The user-defined gamma value (>1). + """ + return_val = 1 + 0.1 * overlap_count # modify this function as needed + + return return_val + + +def _calculate_bias(position, length, bias_type="flat"): + """Calculate bias value based on position and length. + + Parameters + ---------- + position : int + Current position in the range + length : int + Total length of the range + bias_type : str, default="flat" + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + """ + if bias_type == "flat": + return 1.0 + elif bias_type == "front": + return 1.0 - (position - 1) / length + elif bias_type == "middle": + if length / 2 == 0: + return 1.0 + if position <= length / 2: + return position / (length / 2) + else: + return (length - position + 1) / (length / 2) + elif bias_type == "back": + return position / length + else: + raise ValueError(f"Invalid bias type: {bias_type}") + + +def _gamma_select(cardinality, gamma): + """Select a gamma value based on the cardinality type. + + Parameters + ---------- + cardinality : int + The number of overlapping ranges. + gamma : str + Gamma to use. Should be one of ["one", "reciprocal", "udf_gamma"]. + + Returns + ------- + float + The selected gamma value. + + Raises + ------ + ValueError + If an invalid `gamma` type is provided or if `udf_gamma` is required + but not provided. + """ + if gamma == "one": + return 1.0 + elif gamma == "reciprocal": + return 1 / cardinality if cardinality > 1 else 1.0 + elif gamma == "udf_gamma": + if udf_gamma_def(cardinality) is not None: + return 1.0 / udf_gamma_def(cardinality) + else: + raise ValueError("udf_gamma must be provided for 'udf_gamma' gamma type.") + else: + raise ValueError( + "Invalid gamma type. Choose from ['one', 'reciprocal', 'udf_gamma']." + ) + + +def _calculate_overlap_reward_precision(pred_range, overlap_set, bias_type): + """Overlap Reward for y_pred. + + Parameters + ---------- + pred_range : tuple + The predicted range. + overlap_set : set + The set of overlapping positions. + bias_type : str + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + + Returns + ------- + float + The weighted value for overlapping positions only. + """ + start, end = pred_range + length = end - start + 1 + + max_value = 0 # Total possible weighted value for all positions. + my_value = 0 # Weighted value for overlapping positions only. + + for i in range(1, length + 1): + global_position = start + i - 1 + bias_value = _calculate_bias(i, length, bias_type) + max_value += bias_value + + if global_position in overlap_set: + my_value += bias_value + + return my_value / max_value if max_value > 0 else 0.0 + + +def _calculate_overlap_reward_recall(real_range, overlap_set, bias_type): + """Overlap Reward for y_real. + + Parameters + ---------- + real_range : tuple + The real range. + overlap_set : set + The set of overlapping positions. + bias_type : str + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + + Returns + ------- + float + The weighted value for overlapping positions only. + """ + start, end = real_range + length = end - start + 1 + + max_value = 0.0 # Total possible weighted value for all positions. + my_value = 0.0 # Weighted value for overlapping positions only. + + for i in range(1, length + 1): + global_position = start + i - 1 + bias_value = _calculate_bias(i, length, bias_type) + max_value += bias_value + + if global_position in overlap_set: + my_value += bias_value + + return my_value / max_value if max_value > 0 else 0.0 + + +def _binary_to_ranges(binary_sequence): + """ + Convert a binary sequence to a list of anomaly ranges. + + Parameters + ---------- + binary_sequence : list + Binary sequence where 1 indicates anomaly and 0 indicates normal. + + Returns + ------- + list of tuples + List of anomaly ranges as (start, end) tuples. + + """ + ranges = [] + start = None + + for i, val in enumerate(binary_sequence): + if val and start is None: + start = i + elif not val and start is not None: + ranges.append((start, i - 1)) + start = None + + if start is not None: + ranges.append((start, len(binary_sequence) - 1)) + + return ranges + + +def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): + """ + Calculate Precision for time series anomaly detection. + + Precision measures the proportion of correctly predicted anomaly positions + out of all all the predicted anomaly positions, aggregated across the entire time + series. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one"]. + + Returns + ------- + float + Precision + + Raises + ------ + ValueError + If an invalid `gamma` type is provided. + ValueError + If input sequence is binary and y_real and y_pred are of different lengths. + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), MontrΓ©al, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + # Check if inputs are binary or range-based + is_binary = False + if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( + y_pred[0], (int, np.integer) + ): + is_binary = True + elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, np.integer) + ): + is_binary = True + + if is_binary: + if not isinstance(y_pred, (list, tuple, np.ndarray)) or not isinstance( + y_real, (list, tuple, np.ndarray) + ): + raise ValueError( + "For binary inputs, y_pred and y_real should be list or tuple, " + "or numpy array of integers." + ) + if len(y_pred) != len(y_real): + raise ValueError( + "For binary inputs, y_pred and y_real must be of the same length." + ) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_real_ranges = _binary_to_ranges(y_real) + else: + y_pred_ranges = y_pred + y_real_ranges = y_real + + if gamma not in ["reciprocal", "one"]: + raise ValueError("Invalid gamma type for precision. Use 'reciprocal' or 'one'.") + + # Flattening y_pred and y_real to resolve nested lists + flat_y_pred = _flatten_ranges(y_pred_ranges) + flat_y_real = _flatten_ranges(y_real_ranges) + + total_overlap_reward = 0.0 + total_cardinality = 0 + + for pred_range in flat_y_pred: + overlap_set = set() + cardinality = 0 + + for real_start, real_end in flat_y_real: + overlap_start = max(pred_range[0], real_start) + overlap_end = min(pred_range[1], real_end) + + if overlap_start <= overlap_end: + overlap_set.update(range(overlap_start, overlap_end + 1)) + cardinality += 1 + + overlap_reward = _calculate_overlap_reward_precision( + pred_range, overlap_set, bias_type + ) + gamma_value = _gamma_select(cardinality, gamma) + total_overlap_reward += gamma_value * overlap_reward + total_cardinality += 1 + + precision = ( + total_overlap_reward / total_cardinality if total_cardinality > 0 else 0.0 + ) + return precision + + +def ts_recall(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): + """ + Calculate Recall for time series anomaly detection. + + Recall measures the proportion of correctly predicted anomaly positions + out of all the real/actual (ground truth) anomaly positions, aggregated across the + entire time series. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one", "udf_gamma"]. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + alpha : float, default: 0.0 + Weight for existence reward in recall calculation. + + Returns + ------- + float + Recall + + Raises + ------ + ValueError + If input sequence is binary and y_real and y_pred are of different lengths. + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), MontrΓ©al, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + is_binary = False + if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( + y_pred[0], (int, np.integer) + ): + is_binary = True + elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, np.integer) + ): + is_binary = True + + if is_binary: + if not isinstance(y_pred, (list, tuple, np.ndarray)) or not isinstance( + y_real, (list, tuple, np.ndarray) + ): + raise ValueError( + "For binary inputs, y_pred and y_real should be list or tuple, " + "or numpy array of integers." + ) + if len(y_pred) != len(y_real): + raise ValueError( + "For binary inputs, y_pred and y_real must be of the same length." + ) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_real_ranges = _binary_to_ranges(y_real) + else: + y_pred_ranges = y_pred + y_real_ranges = y_real + + # Flattening y_pred and y_real to resolve nested lists + flat_y_pred = _flatten_ranges(y_pred_ranges) + flat_y_real = _flatten_ranges(y_real_ranges) + + total_overlap_reward = 0.0 + + for real_range in flat_y_real: + overlap_set = set() + cardinality = 0 + + for pred_range in flat_y_pred: + overlap_start = max(real_range[0], pred_range[0]) + overlap_end = min(real_range[1], pred_range[1]) + + if overlap_start <= overlap_end: + overlap_set.update(range(overlap_start, overlap_end + 1)) + cardinality += 1 + + existence_reward = 1.0 if overlap_set else 0.0 + + if overlap_set: + overlap_reward = _calculate_overlap_reward_recall( + real_range, overlap_set, bias_type + ) + gamma_value = _gamma_select(cardinality, gamma) + overlap_reward *= gamma_value + else: + overlap_reward = 0.0 + + recall_score = alpha * existence_reward + (1 - alpha) * overlap_reward + total_overlap_reward += recall_score + + recall = total_overlap_reward / len(flat_y_real) if flat_y_real else 0.0 + return recall + + +def ts_fscore( + y_pred, + y_real, + gamma="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + beta=1.0, +): + """ + Calculate F1-Score for time series anomaly detection. + + F-1 Score is the harmonic mean of Precision and Recall, providing + a single metric to evaluate the performance of an anomaly detection model. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one", "udf_gamma"]. + p_bias : str, default="flat" + Type of bias to apply for precision. + Should be one of ["flat", "front", "middle", "back"]. + r_bias : str, default="flat" + Type of bias to apply for recall. + Should be one of ["flat", "front", "middle", "back"]. + p_alpha : float, default=0.0 + Weight for existence reward in Precision calculation. + r_alpha : float, default=0.0 + Weight for existence reward in Recall calculation. + beta : float, default=1.0 + F-score beta determines the weight of recall in the combined score. + beta < 1 lends more weight to precision, while beta > 1 favors recall. + + Returns + ------- + float + F1-Score + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), MontrΓ©al, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + precision = ts_precision(y_pred, y_real, gamma, p_bias) + recall = ts_recall(y_pred, y_real, gamma, r_bias, r_alpha) + + if precision + recall > 0: + fscore = ((1 + beta**2) * (precision * recall)) / (beta**2 * precision + recall) + else: + fscore = 0.0 + + return fscore diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py new file mode 100644 index 0000000000..0fbbe16fa3 --- /dev/null +++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py @@ -0,0 +1,572 @@ +"""Test cases for the range-based anomaly detection metrics.""" + +import numpy as np + +from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( + ts_fscore, + ts_precision, + ts_recall, +) + + +def test_single_overlapping_range(): + """Test for single overlapping range.""" + y_pred = np.array([0, 1, 1, 1, 1, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.600000 + expected_f1 = 0.666667 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for single overlapping range! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for single overlapping range! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for single overlapping range! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_non_overlapping_ranges(): + """Test for multiple non-overlapping ranges.""" + y_pred = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]) + y_real = np.array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]) + + expected_precision = 0.000000 + expected_recall = 0.000000 + expected_f1 = 0.000000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple non-overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple non-overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple non-overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_overlapping_ranges(): + """Test for multiple overlapping ranges.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + + expected_precision = 0.666667 + expected_recall = 0.400000 + expected_f1 = 0.500000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_nested_lists_of_predictions(): + """Test for nested lists of predictions.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0]) + + expected_precision = 0.555556 + expected_recall = 0.566667 + expected_f1 = 0.561056 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for nested lists of predictions! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for nested lists of predictions! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for nested lists of predictions! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_all_encompassing_range(): + """Test for all encompassing range.""" + y_pred = np.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + y_real = np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0]) + + expected_precision = 0.600000 + expected_recall = 1.000000 + expected_f1 = 0.750000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for all encompassing range! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for all encompassing range! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for all encompassing range! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_range_based_input(): + """Test with input being range-based or bianry-based.""" + y_pred_range = [(1, 2)] + y_true_range = [(1, 1)] + y_pred_binary = np.array([0, 1, 1, 0]) + y_true_binary = np.array([0, 1, 0, 0]) + + expected_precision = 0.5 + expected_recall = 1.000000 + expected_f1 = 0.666667 + + # for range-based input + precision_range = ts_precision( + y_pred_range, y_true_range, gamma="reciprocal", bias_type="flat" + ) + recall_range = ts_recall( + y_pred_range, + y_true_range, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score_range = ts_fscore( + y_pred_range, + y_true_range, + gamma="reciprocal", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision_range, + expected_precision, + decimal=6, + err_msg=( + f"Precision mismatch: " + f"ts_precision={precision_range} vs" + f"expected_precision_range={expected_precision}" + ), + ) + np.testing.assert_almost_equal( + recall_range, + expected_recall, + decimal=6, + err_msg=( + f"Recall mismatch: " + f"ts_recall={recall_range} vs expected_recall_range={expected_recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score_range, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score mismatch: " + f"ts_fscore={f1_score_range} vs expected_f_score_range={expected_f1}" + ), + ) + + # for binary input + precision_binary = ts_precision( + y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat" + ) + recall_binary = ts_recall( + y_pred_binary, + y_true_binary, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score_binary = ts_fscore( + y_pred_binary, + y_true_binary, + gamma="reciprocal", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision_binary, + expected_precision, + decimal=6, + err_msg=( + f"Precision mismatch: " + f"ts_precision={precision_range} vs " + f"expected_precision_binary={expected_precision}" + ), + ) + np.testing.assert_almost_equal( + recall_binary, + expected_recall, + decimal=6, + err_msg=( + f"Recall mismatch: " + f"ts_recall={recall_range} vs expected_recall_binary={expected_recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score_binary, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score mismatch: " + f"ts_fscore={f1_score_range} vs expected_f_score_binary={expected_f1}" + ), + ) + + +def test_multiple_overlapping_ranges_with_gamma_reciprocal(): + """Test for multiple overlapping ranges with gamma=reciprocal.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.666667 + expected_recall = 0.200000 + expected_f1 = 0.307692 + + precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="reciprocal", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_overlapping_ranges_with_bias_middle(): + """Test for multiple overlapping ranges with bias_type=middle.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.333333 + expected_f1 = 0.461538 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="middle") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="middle", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_overlapping_ranges_with_bias_middle_gamma_reciprocal(): + """Test for multiple overlapping ranges with bias_type=middle, gamma=reciprocal.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.166667 + expected_f1 = 0.272727 + + precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="middle") + recall = ts_recall( + y_pred, + y_real, + gamma="reciprocal", + bias_type="middle", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="reciprocal", + beta=1, + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) diff --git a/aeon/benchmarking/metrics/anomaly_detection/thresholding.py b/aeon/benchmarking/metrics/anomaly_detection/thresholding.py index 40dda9a9d3..f20b31f1ba 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/thresholding.py +++ b/aeon/benchmarking/metrics/anomaly_detection/thresholding.py @@ -38,8 +38,8 @@ def percentile_threshold(y_score: np.ndarray, percentile: int) -> float: def sigma_threshold(y_score: np.ndarray, factor: float = 2) -> float: r"""Calculate a threshold based on the standard deviation of the anomaly scores. - Computes a threshold :math:`\theta` based on the anomaly scoring's mean - :math:`\mu_s` and the standard deviation :math:`\sigma_s`, ignoring NaNs: + Computes a threshold :math:``\theta`` based on the anomaly scoring's mean + :math:``\mu_s`` and the standard deviation :math:``\sigma_s``, ignoring NaNs: .. math:: \theta = \mu_{s} + x \cdot \sigma_{s} @@ -49,7 +49,7 @@ def sigma_threshold(y_score: np.ndarray, factor: float = 2) -> float: y_score : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). factor : float - Number of standard deviations to use as threshold (:math:`x`). + Number of standard deviations to use as threshold (:math:``x``). Returns ------- @@ -62,14 +62,15 @@ def sigma_threshold(y_score: np.ndarray, factor: float = 2) -> float: def top_k_points_threshold( y_true: np.ndarray, y_score: np.ndarray, k: int | None = None ) -> float: - """Calculate a threshold such that at least `k` anomalous points are found. + """Calculate a threshold such that at least ``k`` anomalous points are found. The anomalies are single-point anomalies. Computes a threshold based on the number of expected anomalies (number of anomalies). This method iterates over all possible thresholds from high to low to - find the first threshold that yields `k` or more anomalous points. If `k` is `None`, - the ground truth data is used to calculate the real number of anomalies. + find the first threshold that yields ``k`` or more anomalous points. If ``k`` + is ``None``,the ground truth data is used to calculate the real number of + anomalies. Parameters ---------- @@ -78,13 +79,13 @@ def top_k_points_threshold( y_score : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). k : optional int - Number of expected anomalies. If `k` is `None`, the ground truth data is used - to calculate the real number of anomalies. + Number of expected anomalies. If ``k`` is ``None``, the ground truth data + is used to calculate the real number of anomalies. Returns ------- float - Threshold such that there are at least `k` anomalous points. + Threshold such that there are at least ``k`` anomalous points. """ if k is None: return np.nanpercentile(y_score, (1 - y_true.sum() / y_true.shape[0]) * 100) @@ -95,15 +96,15 @@ def top_k_points_threshold( def top_k_ranges_threshold( y_true: np.ndarray, y_score: np.ndarray, k: int | None = None ) -> float: - """Calculate a threshold such that at least `k` anomalies are found. + """Calculate a threshold such that at least ``k`` anomalies are found. The anomalies are either single-points anomalies or continuous anomalous ranges. Computes a threshold based on the number of expected anomalous subsequences / ranges (number of anomalies). This method iterates over all possible thresholds from high to low to find the first threshold that yields `k` or more continuous - anomalous ranges. If `k` is `None`, the ground truth data is used to calculate the - real number of anomalies (anomalous ranges). + anomalous ranges. If ``k`` is ``None``, the ground truth data is used to + calculate the real number of anomalies (anomalous ranges). Parameters ---------- @@ -112,13 +113,13 @@ def top_k_ranges_threshold( y_score : np.ndarray Anomaly scores for each point of the time series of shape (n_instances,). k : optional int - Number of expected anomalies. If `k` is `None`, the ground truth data is used - to calculate the real number of anomalies. + Number of expected anomalies. If ``k`` is ``None``, the ground truth data + is used to calculate the real number of anomalies. Returns ------- float - Threshold such that there are at least `k` anomalous ranges. + Threshold such that there are at least ``k`` anomalous ranges. """ if k is None: k = _count_anomaly_ranges(y_true) diff --git a/aeon/benchmarking/metrics/segmentation.py b/aeon/benchmarking/metrics/segmentation.py index 4733134279..5dfac8891d 100644 --- a/aeon/benchmarking/metrics/segmentation.py +++ b/aeon/benchmarking/metrics/segmentation.py @@ -47,7 +47,7 @@ def hausdorff_error( .. seealso:: - This function wraps :py:func:`scipy.spatial.distance.directed_hausdorff` + This function wraps :py:func:``scipy.spatial.distance.directed_hausdorff`` Parameters ---------- @@ -56,7 +56,7 @@ def hausdorff_error( pred_change_points: array_like Integer indexes (positions) of predicted change points symmetric: bool - If `True` symmetric Hausdorff distance will be used + If ``True`` symmetric Hausdorff distance will be used seed: int, default=0 Local numpy.random.RandomState seed. Default is 0, a random shuffling of u and v that guarantees reproducibility. diff --git a/aeon/benchmarking/resampling.py b/aeon/benchmarking/resampling.py index 8ce9381203..b0f96a70c8 100644 --- a/aeon/benchmarking/resampling.py +++ b/aeon/benchmarking/resampling.py @@ -32,10 +32,10 @@ def resample_data(X_train, y_train, X_test, y_test, random_state=None): y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- @@ -93,10 +93,10 @@ def resample_data_indices(y_train, y_test, random_state=None): y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- @@ -136,10 +136,10 @@ def stratified_resample_data(X_train, y_train, X_test, y_test, random_state=None y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- @@ -200,10 +200,10 @@ def stratified_resample_data_indices(y_train, y_test, random_state=None): y_test : np.ndarray Test data labels. random_state : int, RandomState instance or None, default=None - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used - by `np.random`. + If ``int``, random_state is the seed used by the random number generator; + If ``RandomState`` instance, random_state is the random number generator; + If ``None``, the random number generator is the ``RandomState`` instance + used by ``np.random``. Returns ------- diff --git a/aeon/benchmarking/results_loaders.py b/aeon/benchmarking/results_loaders.py index fae88b6919..b3ed1deaec 100644 --- a/aeon/benchmarking/results_loaders.py +++ b/aeon/benchmarking/results_loaders.py @@ -29,7 +29,7 @@ "Arsenal": ["TheArsenal", "AFC", "ArsenalClassifier"], "ROCKET": ["ROCKETClassifier", "ROCKETRegressor"], "MiniROCKET": ["MiniROCKETClassifier"], - "MR": ["MultiROCKET", "MultiROCKETClassifier", "MultiROCKETRegressor"], + "MR": ["MultiROCKET", "MultiROCKETClassifier"], "Hydra": ["hydraclassifier"], "MR-Hydra": [ "Hydra-MultiROCKET", diff --git a/aeon/classification/base.py b/aeon/classification/base.py index 03cbc356d6..92d3b304a8 100644 --- a/aeon/classification/base.py +++ b/aeon/classification/base.py @@ -26,6 +26,7 @@ class name: BaseClassifier import numpy as np import pandas as pd +from sklearn.base import ClassifierMixin from sklearn.metrics import get_scorer, get_scorer_names from sklearn.model_selection import cross_val_predict @@ -35,7 +36,7 @@ class name: BaseClassifier from aeon.utils.validation.labels import check_classification_y -class BaseClassifier(BaseCollectionEstimator): +class BaseClassifier(ClassifierMixin, BaseCollectionEstimator): """ Abstract base class for time series classifiers. @@ -66,7 +67,6 @@ def __init__(self): self.classes_ = [] # classes seen in y, unique labels self.n_classes_ = -1 # number of unique classes in y self._class_dictionary = {} - self._estimator_type = "classifier" super().__init__() diff --git a/aeon/classification/deep_learning/_inception_time.py b/aeon/classification/deep_learning/_inception_time.py index 00c4e479f9..eaacf44775 100644 --- a/aeon/classification/deep_learning/_inception_time.py +++ b/aeon/classification/deep_learning/_inception_time.py @@ -350,6 +350,46 @@ def _predict_proba(self, X) -> np.ndarray: return probs + @classmethod + def load_model(self, model_path, classes): + """Load pre-trained classifiers instead of fitting. + + When calling this function, all funcationalities can be used + such as predict, predict_proba, etc. with the loaded models. + + Parameters + ---------- + model_path : list of str (list of paths including the model names and extension) + The directory where the models will be saved including the model + names with a ".keras" extension. + classes : np.ndarray + The set of unique classes the pre-trained loaded model is trained + to predict during the classification task. + + Returns + ------- + None + """ + assert ( + type(model_path) is list + ), "model_path should be a list of paths to the models" + + classifier = self() + classifier.classifiers_ = [] + + for i in range(len(model_path)): + clf = IndividualInceptionClassifier() + clf.load_model(model_path[i], classes) + classifier.classifiers_.append(clf) + + classifier.n_classifiers = len(classifier.classifiers_) + + classifier.classes_ = classes + classifier.n_classes_ = len(classes) + classifier.is_fitted = True + + return classifier + @classmethod def _get_test_params(cls, parameter_set="default"): """Return testing parameter settings for the estimator. diff --git a/aeon/classification/deep_learning/_lite_time.py b/aeon/classification/deep_learning/_lite_time.py index f115d53122..bf3922f1d2 100644 --- a/aeon/classification/deep_learning/_lite_time.py +++ b/aeon/classification/deep_learning/_lite_time.py @@ -282,6 +282,46 @@ def _predict_proba(self, X) -> np.ndarray: return probs + @classmethod + def load_model(self, model_path, classes): + """Load pre-trained classifiers instead of fitting. + + When calling this function, all funcationalities can be used + such as predict, predict_proba, etc. with the loaded models. + + Parameters + ---------- + model_path : list of str (list of paths including the model names and extension) + The director where the models will be saved including the model + names with a ".keras" extension. + classes : np.ndarray + The set of unique classes the pre-trained loaded model is trained + to predict during the classification task. + + Returns + ------- + None + """ + assert ( + type(model_path) is list + ), "model_path should be a list of paths to the models" + + classifier = self() + classifier.classifiers_ = [] + + for i in range(len(model_path)): + clf = IndividualLITEClassifier() + clf.load_model(model_path=model_path[i], classes=classes) + classifier.classifiers_.append(clf) + + classifier.n_classifiers = len(classifier.classifiers_) + + classifier.classes_ = classes + classifier.n_classes_ = len(classes) + classifier.is_fitted = True + + return classifier + @classmethod def _get_test_params(cls, parameter_set="default"): """Return testing parameter settings for the estimator. diff --git a/aeon/classification/deep_learning/base.py b/aeon/classification/deep_learning/base.py index 2ed56bc0bc..61ddeb3a72 100644 --- a/aeon/classification/deep_learning/base.py +++ b/aeon/classification/deep_learning/base.py @@ -1,6 +1,23 @@ """ Abstract base class for the Keras neural network classifiers. + class name: BaseDeepClassifier + +Defining methods: + fitting - fit(self, X, y) + predicting - predict(self, X) + - predict_proba(self, X) + model building - build_model(self, input_shape, n_classes) (abstract method) + +Inherited inspection methods: + hyper-parameter inspection - get_params() + fitted parameter inspection - get_fitted_params() + +State: + fitted model/strategy - by convention, any attributes ending in "_" + fitted state flag - is_fitted (property) + fitted state inspection - check_is_fitted() + The reason for this class between BaseClassifier and deep_learning classifiers is because we can generalise tags, _predict and _predict_proba """ diff --git a/aeon/classification/deep_learning/tests/test_inception_time.py b/aeon/classification/deep_learning/tests/test_inception_time.py new file mode 100644 index 0000000000..5c80fdbee3 --- /dev/null +++ b/aeon/classification/deep_learning/tests/test_inception_time.py @@ -0,0 +1,47 @@ +"""Tests for save/load functionality of InceptionTimeClassifier.""" + +import glob +import os +import tempfile + +import numpy as np +import pytest + +from aeon.classification.deep_learning import InceptionTimeClassifier +from aeon.testing.data_generation import make_example_3d_numpy +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies("tensorflow", severity="none"), + reason="skip test if required soft dependency not available", +) +def test_save_load_inceptiontime(): + """Test saving and loading for InceptionTimeClassifier.""" + with tempfile.TemporaryDirectory() as temp: + temp_dir = os.path.join(temp, "") + + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True + ) + + model = InceptionTimeClassifier( + n_epochs=1, random_state=42, save_best_model=True, file_path=temp_dir + ) + model.fit(X, y) + + y_pred_orig = model.predict(X) + + model_file = glob.glob(os.path.join(temp_dir, f"{model.best_file_name}*.keras")) + + loaded_model = InceptionTimeClassifier.load_model( + model_path=model_file, classes=model.classes_ + ) + + assert isinstance(loaded_model, InceptionTimeClassifier) + + preds = loaded_model.predict(X) + assert isinstance(preds, np.ndarray) + + assert len(preds) == len(y) + np.testing.assert_array_equal(preds, y_pred_orig) diff --git a/aeon/classification/deep_learning/tests/test_lite_time.py b/aeon/classification/deep_learning/tests/test_lite_time.py new file mode 100644 index 0000000000..f6c3858eb7 --- /dev/null +++ b/aeon/classification/deep_learning/tests/test_lite_time.py @@ -0,0 +1,49 @@ +"""Tests for save/load functionality of LiteTimeClassifier.""" + +import glob +import os +import tempfile + +import numpy as np +import pytest + +from aeon.classification.deep_learning import LITETimeClassifier +from aeon.testing.data_generation import make_example_3d_numpy +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies("tensorflow", severity="none"), + reason="skip test if required soft dependency not available", +) +def test_save_load_litetim(): + """Test saving and loading for LiteTimeClassifier.""" + with tempfile.TemporaryDirectory() as temp: + temp_dir = os.path.join(temp, "") + + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True + ) + + model = LITETimeClassifier( + n_epochs=1, random_state=42, save_best_model=True, file_path=temp_dir + ) + model.fit(X, y) + + y_pred_orig = model.predict(X) + + model_files = glob.glob( + os.path.join(temp_dir, f"{model.best_file_name}*.keras") + ) + + loaded_model = LITETimeClassifier.load_model( + model_path=model_files, classes=model.classes_ + ) + + assert isinstance(loaded_model, LITETimeClassifier) + + preds = loaded_model.predict(X) + assert isinstance(preds, np.ndarray) + + assert len(preds) == len(y) + np.testing.assert_array_equal(preds, y_pred_orig) diff --git a/aeon/classification/dictionary_based/_cboss.py b/aeon/classification/dictionary_based/_cboss.py index 652b4a76ff..efb0ede145 100644 --- a/aeon/classification/dictionary_based/_cboss.py +++ b/aeon/classification/dictionary_based/_cboss.py @@ -93,6 +93,13 @@ class ContractableBOSS(BaseClassifier): weights_ : Weight of each classifier in the ensemble. + Raises + ------ + ValueError + Raised when ``min_window`` is greater than ``max_window + 1``. + This ensures that ``min_window`` does not exceed ``max_window``, + preventing invalid window size configurations. + See Also -------- BOSSEnsemble, IndividualBOSS @@ -305,7 +312,6 @@ def _predict(self, X) -> np.ndarray: ------- 1D np.ndarray Predicted class labels shape = (n_cases). - """ rng = check_random_state(self.random_state) return np.array( diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py index f286467416..c593c8922f 100644 --- a/aeon/classification/dictionary_based/_redcomets.py +++ b/aeon/classification/dictionary_based/_redcomets.py @@ -66,7 +66,8 @@ class REDCOMETS(BaseClassifier): Notes ----- - Adapted from the implementation at https://github.com/zy18811/RED-CoMETS + Adapted from the implementation at https://github.com/zy18811/RED-CoMETS by + the code owner. References ---------- @@ -255,8 +256,7 @@ def _build_univariate_ensemble(self, X, y): sfa_clfs = [] for sfa in sfa_transforms: sfa.fit(X_smote, y_smote) - sfa_dics = sfa.transform_words(X_smote) - X_sfa = sfa_dics[:, 0, :] + X_sfa = sfa.transform_words(X_smote)[0] rf = RandomForestClassifier( n_estimators=self.n_trees, @@ -417,8 +417,7 @@ def _predict_proba_unvivariate(self, X) -> np.ndarray: pred_mat = np.zeros((X.shape[0], self.n_classes_)) for sfa, (rf, weight) in zip(self.sfa_transforms, self.sfa_clfs): - sfa_dics = sfa.transform_words(X) - X_sfa = sfa_dics[:, 0, :] + X_sfa = sfa.transform_words(X)[0] rf_pred_mat = rf.predict_proba(X_sfa) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index f89b1be636..ded113b69e 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -52,6 +52,12 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier): ``-1`` means using all processors. for more details. Parameter for compatibility purposes, still unimplemented. + Raises + ------ + ValueError + If ``weights`` is not among the supported values. + See the ``weights`` parameter description for valid options. + Examples -------- >>> from aeon.datasets import load_unit_test diff --git a/aeon/classification/early_classification/tests/test_probability_threshold.py b/aeon/classification/early_classification/tests/test_probability_threshold.py index 559c0689d0..ad142d5449 100644 --- a/aeon/classification/early_classification/tests/test_probability_threshold.py +++ b/aeon/classification/early_classification/tests/test_probability_threshold.py @@ -32,7 +32,7 @@ def test_early_prob_threshold_near_classification_points(): X = X_test[:, :, :i] if i == 20: - with pytest.raises(ValueError): + with pytest.raises(IndexError): pt.update_predict_proba(X) else: _, decisions = pt.update_predict_proba(X) diff --git a/aeon/classification/early_classification/tests/test_teaser.py b/aeon/classification/early_classification/tests/test_teaser.py index e85ee8f1e1..2af3cf34bd 100644 --- a/aeon/classification/early_classification/tests/test_teaser.py +++ b/aeon/classification/early_classification/tests/test_teaser.py @@ -80,7 +80,7 @@ def test_teaser_near_classification_points(): X = X_test[:, :, :i] if i == 20: - with pytest.raises(ValueError): + with pytest.raises(IndexError): teaser.update_predict_proba(X) else: _, decisions = teaser.update_predict(X) diff --git a/aeon/classification/feature_based/_catch22.py b/aeon/classification/feature_based/_catch22.py index bfad28dd44..26a56d0a91 100644 --- a/aeon/classification/feature_based/_catch22.py +++ b/aeon/classification/feature_based/_catch22.py @@ -43,8 +43,11 @@ class Catch22Classifier(BaseClassifier): true. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used time + series that were already normalized. replace_nans : bool, default=True Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, default=False @@ -67,6 +70,17 @@ class Catch22Classifier(BaseClassifier): if None a 'prefer' value of "threads" is used by default. Valid options are "loky", "multiprocessing", "threading" or a custom backend. See the joblib Parallel documentation for more details. + class_weight{β€œbalanced”, β€œbalanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The β€œbalanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The β€œbalanced_subsample” mode is the same as β€œbalanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -125,13 +139,14 @@ def __init__( self, features="all", catch24=True, - outlier_norm=False, + outlier_norm=True, replace_nans=True, use_pycatch22=False, estimator=None, random_state=None, n_jobs=1, parallel_backend=None, + class_weight=None, ): self.features = features self.catch24 = catch24 @@ -142,6 +157,7 @@ def __init__( self.random_state = random_state self.n_jobs = n_jobs self.parallel_backend = parallel_backend + self.class_weight = class_weight super().__init__() @@ -175,7 +191,7 @@ def _fit(self, X, y): self.estimator_ = _clone_estimator( ( - RandomForestClassifier(n_estimators=200) + RandomForestClassifier(n_estimators=200, class_weight=self.class_weight) if self.estimator is None else self.estimator ), diff --git a/aeon/classification/feature_based/_signature_classifier.py b/aeon/classification/feature_based/_signature_classifier.py index 88308436f5..a3f659efcf 100644 --- a/aeon/classification/feature_based/_signature_classifier.py +++ b/aeon/classification/feature_based/_signature_classifier.py @@ -61,6 +61,17 @@ class SignatureClassifier(BaseClassifier): Signature truncation depth. random_state : int, default=None If `int`, random_state is the seed used by the random number generator; + class_weight{β€œbalanced”, β€œbalanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The β€œbalanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The β€œbalanced_subsample” mode is the same as β€œbalanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -105,6 +116,7 @@ def __init__( sig_tfm="signature", depth=4, random_state=None, + class_weight=None, ): self.estimator = estimator self.augmentation_list = augmentation_list @@ -116,7 +128,7 @@ def __init__( self.sig_tfm = sig_tfm self.depth = depth self.random_state = random_state - + self.class_weight = class_weight super().__init__() self.signature_method = SignatureTransformer( @@ -135,7 +147,9 @@ def _setup_classification_pipeline(self): """Set up the full signature method pipeline.""" # Use rf if no classifier is set if self.estimator is None: - classifier = RandomForestClassifier(random_state=self.random_state) + classifier = RandomForestClassifier( + random_state=self.random_state, class_weight=self.class_weight + ) else: classifier = _clone_estimator(self.estimator, self.random_state) diff --git a/aeon/classification/feature_based/_summary.py b/aeon/classification/feature_based/_summary.py index a4f34ff688..b6e0056392 100644 --- a/aeon/classification/feature_based/_summary.py +++ b/aeon/classification/feature_based/_summary.py @@ -43,6 +43,17 @@ class SummaryClassifier(BaseClassifier): If `RandomState` instance, random_state is the random number generator; If `None`, the random number generator is the `RandomState` instance used by `np.random`. + class_weight{β€œbalanced”, β€œbalanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The β€œbalanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The β€œbalanced_subsample” mode is the same as β€œbalanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -85,6 +96,7 @@ def __init__( estimator=None, n_jobs=1, random_state=None, + class_weight=None, ): self.summary_stats = summary_stats self.estimator = estimator @@ -92,6 +104,8 @@ def __init__( self.n_jobs = n_jobs self.random_state = random_state + self.class_weight = class_weight + super().__init__() def _fit(self, X, y): @@ -120,7 +134,7 @@ def _fit(self, X, y): self.estimator_ = _clone_estimator( ( - RandomForestClassifier(n_estimators=200) + RandomForestClassifier(n_estimators=200, class_weight=self.class_weight) if self.estimator is None else self.estimator ), diff --git a/aeon/classification/feature_based/_tsfresh.py b/aeon/classification/feature_based/_tsfresh.py index 28dc2dac11..00021da5d8 100644 --- a/aeon/classification/feature_based/_tsfresh.py +++ b/aeon/classification/feature_based/_tsfresh.py @@ -46,6 +46,17 @@ class TSFreshClassifier(BaseClassifier): If `RandomState` instance, random_state is the random number generator; If `None`, the random number generator is the `RandomState` instance used by `np.random`. + class_weight{β€œbalanced”, β€œbalanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The β€œbalanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The β€œbalanced_subsample” mode is the same as β€œbalanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -86,6 +97,7 @@ def __init__( n_jobs=1, chunksize=None, random_state=None, + class_weight=None, ): self.default_fc_parameters = default_fc_parameters self.relevant_feature_extractor = relevant_feature_extractor @@ -99,6 +111,7 @@ def __init__( self._transformer = None self._return_majority_class = False self._majority_class = 0 + self.class_weight = class_weight super().__init__() @@ -137,7 +150,7 @@ def _fit(self, X, y): ) self.estimator_ = _clone_estimator( ( - RandomForestClassifier(n_estimators=200) + RandomForestClassifier(n_estimators=200, class_weight=self.class_weight) if self.estimator is None else self.estimator ), diff --git a/aeon/classification/feature_based/tests/test_catch22.py b/aeon/classification/feature_based/tests/test_catch22.py index 5a709fe4ea..c8067ee57b 100644 --- a/aeon/classification/feature_based/tests/test_catch22.py +++ b/aeon/classification/feature_based/tests/test_catch22.py @@ -1,6 +1,7 @@ """Test catch 22 classifier.""" import numpy as np +import pytest from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import RidgeClassifier @@ -19,3 +20,21 @@ def test_catch22(): c22.fit(X, y) p = c22.predict_proba(X) assert np.all(np.isin(p, [0, 1])) + + +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_catch22_classifier_with_class_weight(class_weight): + """Test catch22 classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = Catch22Classifier( + estimator=RandomForestClassifier(n_estimators=5), + outlier_norm=True, + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) diff --git a/aeon/classification/feature_based/tests/test_signature.py b/aeon/classification/feature_based/tests/test_signature.py index b5c29df2d3..2d3d2972d0 100644 --- a/aeon/classification/feature_based/tests/test_signature.py +++ b/aeon/classification/feature_based/tests/test_signature.py @@ -18,3 +18,24 @@ def test_signature_classifier(): cls = SignatureClassifier(estimator=None) cls._fit(X, y) assert isinstance(cls.pipeline.named_steps["classifier"], RandomForestClassifier) + + +@pytest.mark.skipif( + not _check_soft_dependencies("esig", severity="none"), + reason="skip test if required soft dependency esig not available", +) +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_signature_classifier_with_class_weight(class_weight): + """Test signature classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = SignatureClassifier( + estimator=RandomForestClassifier(n_estimators=5), + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) diff --git a/aeon/classification/feature_based/tests/test_summary.py b/aeon/classification/feature_based/tests/test_summary.py index de698e61cc..0f53130ce0 100644 --- a/aeon/classification/feature_based/tests/test_summary.py +++ b/aeon/classification/feature_based/tests/test_summary.py @@ -1,6 +1,7 @@ """Test summary classifier.""" import numpy as np +import pytest from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import RidgeClassifier @@ -19,3 +20,20 @@ def test_summary_classifier(): cls.fit(X, y) p = cls.predict_proba(X) assert np.all(np.isin(p, [0, 1])) + + +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_summary_classifier_with_class_weight(class_weight): + """Test summary classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = SummaryClassifier( + estimator=RandomForestClassifier(n_estimators=5), + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) diff --git a/aeon/classification/feature_based/tests/test_tsfresh.py b/aeon/classification/feature_based/tests/test_tsfresh.py index 3ab965e6a3..92583e6662 100644 --- a/aeon/classification/feature_based/tests/test_tsfresh.py +++ b/aeon/classification/feature_based/tests/test_tsfresh.py @@ -37,3 +37,24 @@ def test_tsfresh_classifier(): assert cls._majority_class in [0, 1] cls.verbose = 1 cls.fit(X, y) + + +@pytest.mark.skipif( + not _check_soft_dependencies("tsfresh", severity="none"), + reason="skip test if required soft dependency tsfresh not available", +) +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_tsfresh_classifier_with_class_weight(class_weight): + """Test tsfresh classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = TSFreshClassifier( + estimator=RandomForestClassifier(n_estimators=5), + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) diff --git a/aeon/clustering/_k_means.py b/aeon/clustering/_k_means.py index e4e459a5cf..8b682d3426 100644 --- a/aeon/clustering/_k_means.py +++ b/aeon/clustering/_k_means.py @@ -287,6 +287,13 @@ def _predict(self, X: np.ndarray, y=None) -> np.ndarray: def _check_params(self, X: np.ndarray) -> None: self._random_state = check_random_state(self.random_state) + _incorrect_init_str = ( + f"The value provided for init: {self.init} is " + f"invalid. The following are a list of valid init algorithms " + f"strings: random, kmeans++, first. You can also pass a " + f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" + ) + if isinstance(self.init, str): if self.init == "random": self._init = self._random_center_initializer @@ -294,16 +301,13 @@ def _check_params(self, X: np.ndarray) -> None: self._init = self._kmeans_plus_plus_center_initializer elif self.init == "first": self._init = self._first_center_initializer + else: + raise ValueError(_incorrect_init_str) else: if isinstance(self.init, np.ndarray) and len(self.init) == self.n_clusters: self._init = self.init.copy() else: - raise ValueError( - f"The value provided for init: {self.init} is " - f"invalid. The following are a list of valid init algorithms " - f"strings: random, kmedoids++, first. You can also pass a" - f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" - ) + raise ValueError(_incorrect_init_str) if self.distance_params is None: self._distance_params = {} diff --git a/aeon/clustering/_k_medoids.py b/aeon/clustering/_k_medoids.py index 12d0f2819d..b2abe27aef 100644 --- a/aeon/clustering/_k_medoids.py +++ b/aeon/clustering/_k_medoids.py @@ -46,13 +46,17 @@ class TimeSeriesKMedoids(BaseClusterer): The number of clusters to form as well as the number of centroids to generate. init : str or np.ndarray, default='random' Method for initialising cluster centers. Any of the following are valid: - ['kmedoids++', 'random', 'first']. + ['kmedoids++', 'random', 'first', 'build']. Random is the default as it is very fast and it was found in [2] to perform about as well as the other methods. Kmedoids++ is a variant of kmeans++ [4] and is slower but often more accurate than random. It works by choosing centroids that are distant from one another. First is the fastest method and simply chooses the - first k time series as centroids. + first k time series as centroids. Build [1] greedily selects the k medoids + by first selecting the medoid that minimizes the sum of distances + to all other points(this point is the most centrally located) and then + iteratively selects the next k-1 medoids that maximizes the decrease in sum + of distances of all other points to their respective medoids selected so far. If a np.ndarray provided it must be of shape (n_clusters,) and contain the indexes of the time series to use as centroids. distance : str or Callable, default='msm' @@ -428,6 +432,13 @@ def _assign_clusters( def _check_params(self, X: np.ndarray) -> None: self._random_state = check_random_state(self.random_state) + _incorrect_init_str = ( + f"The value provided for init: {self.init} is " + f"invalid. The following are a list of valid init algorithms " + f"strings: random, kmedoids++, first, build. You can also pass a " + f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" + ) + if isinstance(self.init, str): if self.init == "random": self._init = self._random_center_initializer @@ -437,16 +448,13 @@ def _check_params(self, X: np.ndarray) -> None: self._init = self._first_center_initializer elif self.init == "build": self._init = self._pam_build_center_initializer + else: + raise ValueError(_incorrect_init_str) else: if isinstance(self.init, np.ndarray) and len(self.init) == self.n_clusters: self._init = self.init else: - raise ValueError( - f"The value provided for init: {self.init} is " - f"invalid. The following are a list of valid init algorithms " - f"strings: random, kmedoids++, first. You can also pass a" - f"np.ndarray of size (n_clusters, n_channels, n_timepoints)" - ) + raise ValueError(_incorrect_init_str) if self.distance_params is not None: self._distance_params = self.distance_params diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py index 6aab712def..062b06ebc8 100644 --- a/aeon/clustering/_kernel_k_means.py +++ b/aeon/clustering/_kernel_k_means.py @@ -3,9 +3,99 @@ from typing import Optional, Union import numpy as np +from numba import njit from numpy.random import RandomState from aeon.clustering.base import BaseClusterer +from aeon.distances.pointwise._squared import squared_pairwise_distance + + +@njit(cache=True, fastmath=True) +def _kdtw_lk(x, y, local_kernel): + channels = np.shape(x)[1] + padding_vector = np.zeros((1, channels)) + + x = np.concatenate((padding_vector, x), axis=0) + y = np.concatenate((padding_vector, y), axis=0) + + x_timepoints, _ = np.shape(x) + y_timepoints, _ = np.shape(y) + + cost_matrix = np.zeros((x_timepoints, y_timepoints)) + cumulative_dp_diag = np.zeros((x_timepoints, y_timepoints)) + diagonal_weights = np.zeros(max(x_timepoints, y_timepoints)) + + min_timepoints = min(x_timepoints, y_timepoints) + diagonal_weights[1] = 1.0 + for i in range(1, min_timepoints): + diagonal_weights[i] = local_kernel[i - 1, i - 1] + + cost_matrix[0, 0] = 1 + cumulative_dp_diag[0, 0] = 1 + + for i in range(1, x_timepoints): + cost_matrix[i, 1] = cost_matrix[i - 1, 1] * local_kernel[i - 1, 2] + cumulative_dp_diag[i, 1] = cumulative_dp_diag[i - 1, 1] * diagonal_weights[i] + + for j in range(1, y_timepoints): + cost_matrix[1, j] = cost_matrix[1, j - 1] * local_kernel[2, j - 1] + cumulative_dp_diag[1, j] = cumulative_dp_diag[1, j - 1] * diagonal_weights[j] + + for i in range(1, x_timepoints): + for j in range(1, y_timepoints): + local_cost = local_kernel[i - 1, j - 1] + cost_matrix[i, j] = ( + cost_matrix[i - 1, j] + + cost_matrix[i, j - 1] + + cost_matrix[i - 1, j - 1] + ) * local_cost + if i == j: + cumulative_dp_diag[i, j] = ( + cumulative_dp_diag[i - 1, j - 1] * local_cost + + cumulative_dp_diag[i - 1, j] * diagonal_weights[i] + + cumulative_dp_diag[i, j - 1] * diagonal_weights[j] + ) + else: + cumulative_dp_diag[i, j] = ( + cumulative_dp_diag[i - 1, j] * diagonal_weights[i] + + cumulative_dp_diag[i, j - 1] * diagonal_weights[j] + ) + cost_matrix = cost_matrix + cumulative_dp_diag + return cost_matrix[x_timepoints - 1, y_timepoints - 1] + + +def _kdtw(x, y, sigma=1.0, epsilon=1e-3): + """ + Callable kernel function for KernelKMeans. + + Parameters + ---------- + X: np.ndarray, of shape (n_timepoints, n_channels) + First time series sample. + y: np.ndarray, of shape (n_timepoints, n_channels) + Second time series sample. + sigma : float, default=1.0 + Parameter controlling the width of the exponential local kernel. Smaller sigma + values lead to a sharper decay of similarity with increasing distance. + epsilon : float, default=1e-3 + A small constant added for numerical stability to avoid zero values in the + local kernel matrix. + + Notes + ----- + Inspired by the original implementation + https://github.com/pfmarteau/KDTW/tree/master + Copyright (c) 2020 Pierre-FranΓ§ois Marteau, MIT License + + Returns + ------- + similarity : float + A scalar value representing the computed KDTW similarity between the two time + series. Higher values indicate greater similarity. + """ + distance = squared_pairwise_distance(x, y) + local_kernel = (np.exp(-distance / sigma) + epsilon) / (3 * (1 + epsilon)) + return _kdtw_lk(x, y, local_kernel) class TimeSeriesKernelKMeans(BaseClusterer): @@ -141,6 +231,20 @@ def _fit(self, X, y=None): if self.verbose is True: verbose = 1 + if self.kernel == "kdtw": + n_channels = X.shape[1] + + def kdtw_kernel(x, y, sigma=1.0, epsilon=1e-3): + if x.ndim == 1: + T = x.size // n_channels + x = x.reshape(T, n_channels) + if y.ndim == 1: + T = y.size // n_channels + y = y.reshape(T, n_channels) + return _kdtw(x, y, sigma=sigma, epsilon=epsilon) + + self.kernel = kdtw_kernel + self._tslearn_kernel_k_means = TsLearnKernelKMeans( n_clusters=self.n_clusters, kernel=self.kernel, diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py index 6c8b4344ae..6502b4c331 100644 --- a/aeon/clustering/base.py +++ b/aeon/clustering/base.py @@ -7,11 +7,12 @@ from typing import final import numpy as np +from sklearn.base import ClusterMixin from aeon.base import BaseCollectionEstimator -class BaseClusterer(BaseCollectionEstimator): +class BaseClusterer(ClusterMixin, BaseCollectionEstimator): """Abstract base class for time series clusterers. Parameters @@ -26,10 +27,6 @@ class BaseClusterer(BaseCollectionEstimator): @abstractmethod def __init__(self): - # required for compatibility with some sklearn interfaces e.g. - # CalibratedClassifierCV - self._estimator_type = "clusterer" - super().__init__() @final @@ -132,24 +129,6 @@ def fit_predict(self, X, y=None) -> np.ndarray: to return. y: ignored, exists for API consistency reasons. - Returns - ------- - np.ndarray (1d array of shape (n_cases,)) - Index of the cluster each time series in X belongs to. - """ - return self._fit_predict(X, y) - - def _fit_predict(self, X, y=None) -> np.ndarray: - """Fit predict using base methods. - - Parameters - ---------- - X : np.ndarray (2d or 3d array of shape (n_cases, n_timepoints) or shape - (n_cases, n_channels, n_timepoints)). - Time series instances to train clusterer and then have indexes each belong - to return. - y: ignored, exists for API consistency reasons. - Returns ------- np.ndarray (1d array of shape (n_cases,)) diff --git a/aeon/clustering/deep_learning/_ae_dcnn.py b/aeon/clustering/deep_learning/_ae_dcnn.py index 75f8eacfbe..19ac76d081 100644 --- a/aeon/clustering/deep_learning/_ae_dcnn.py +++ b/aeon/clustering/deep_learning/_ae_dcnn.py @@ -296,7 +296,8 @@ def _fit(self, X): try: self.model_ = tf.keras.models.load_model( - self.file_path + self.file_name_ + ".keras", compile=False + self.file_path + self.file_name_ + ".keras", + compile=False, ) if not self.save_best_model: os.remove(self.file_path + self.file_name_ + ".keras") diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py index a37a7d40a1..48c35f3dab 100644 --- a/aeon/clustering/deep_learning/_ae_fcn.py +++ b/aeon/clustering/deep_learning/_ae_fcn.py @@ -317,6 +317,7 @@ def _fit(self, X): outputs=X, batch_size=mini_batch_size, epochs=self.n_epochs, + verbose=self.verbose, ) try: @@ -345,6 +346,7 @@ def _fit_multi_rec_model( outputs, batch_size, epochs, + verbose, ): import tensorflow as tf @@ -451,9 +453,10 @@ def loss(y_true, y_pred): epoch_loss /= num_batches history["loss"].append(epoch_loss) - sys.stdout.write( - "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) - ) + if verbose: + sys.stdout.write( + "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) + ) for callback in self.callbacks_: callback.on_epoch_end(epoch, {"loss": float(epoch_loss)}) diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py index 868e47d846..bd38deb4c6 100644 --- a/aeon/clustering/deep_learning/_ae_resnet.py +++ b/aeon/clustering/deep_learning/_ae_resnet.py @@ -329,6 +329,7 @@ def _fit(self, X): outputs=X, batch_size=mini_batch_size, epochs=self.n_epochs, + verbose=self.verbose, ) try: @@ -359,6 +360,7 @@ def _fit_multi_rec_model( outputs, batch_size, epochs, + verbose, ): import tensorflow as tf @@ -463,9 +465,10 @@ def loss(y_true, y_pred): epoch_loss /= num_batches history["loss"].append(epoch_loss) - sys.stdout.write( - "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) - ) + if verbose: + sys.stdout.write( + "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) + ) for callback in self.callbacks_: callback.on_epoch_end(epoch, {"loss": float(epoch_loss)}) diff --git a/aeon/clustering/dummy.py b/aeon/clustering/dummy.py index 55dbbe92da..483d846a6f 100644 --- a/aeon/clustering/dummy.py +++ b/aeon/clustering/dummy.py @@ -54,6 +54,13 @@ class DummyClusterer(BaseClusterer): array([0, 1, 0]) """ + _tags = { + "X_inner_type": ["np-list", "numpy3D"], + "capability:missing_values": True, + "capability:multivariate": True, + "capability:unequal_length": True, + } + def __init__(self, strategy="uniform", n_clusters=3, random_state=None): self.strategy = strategy self.random_state = random_state @@ -78,8 +85,7 @@ def _fit(self, X, y=None): self : object Fitted estimator. """ - n_samples = X.shape[0] - + n_samples = len(X) if self.strategy == "random": rng = check_random_state(self.random_state) self.labels_ = rng.randint(self.n_clusters, size=n_samples) @@ -111,7 +117,7 @@ def _predict(self, X, y=None) -> np.ndarray: labels : ndarray of shape (n_samples,) Index of the cluster each sample belongs to. """ - n_samples = X.shape[0] + n_samples = len(X) if self.strategy == "random": rng = check_random_state(self.random_state) return rng.randint(self.n_clusters, size=n_samples) diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py index 33f0b79bc5..30fad7ff7e 100644 --- a/aeon/clustering/feature_based/_catch22.py +++ b/aeon/clustering/feature_based/_catch22.py @@ -42,9 +42,12 @@ class Catch22Clusterer(BaseClusterer): Extract the mean and standard deviation as well as the 22 Catch22 features if true. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. - outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + outlier_norm : bool, optional, default=False + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used + time series that were already normalized. replace_nans : bool, default=True Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, default=False @@ -103,7 +106,7 @@ def __init__( self, features="all", catch24=True, - outlier_norm=False, + outlier_norm=True, replace_nans=True, use_pycatch22=False, estimator=None, diff --git a/aeon/clustering/tests/test_kernel_k_means.py b/aeon/clustering/tests/test_kernel_k_means.py index f4af21f4f5..36a761a469 100644 --- a/aeon/clustering/tests/test_kernel_k_means.py +++ b/aeon/clustering/tests/test_kernel_k_means.py @@ -13,6 +13,12 @@ expected_results = [0, 0, 0, 0, 0] +expected_labels_kdtw = [0, 0, 0, 1, 2] + +expected_iters_kdtw = 2 + +expected_results_kdtw = [0, 2, 0, 0, 0] + @pytest.mark.skipif( not _check_estimator_deps(TimeSeriesKernelKMeans, severity="none"), @@ -37,3 +43,21 @@ def test_kernel_k_means(): for val in proba: assert np.count_nonzero(val == 1.0) == 1 + + kernel_kmeans_kdtw = TimeSeriesKernelKMeans( + kernel="kdtw", + random_state=1, + n_clusters=3, + kernel_params={"sigma": 2.0, "epsilon": 1e-4}, + ) + kernel_kmeans_kdtw.fit(X_train[0:max_train]) + kdtw_results = kernel_kmeans_kdtw.predict(X_test[0:max_train]) + kdtw_proba = kernel_kmeans_kdtw.predict_proba(X_test[0:max_train]) + + assert np.array_equal(kdtw_results, expected_results_kdtw) + assert kernel_kmeans_kdtw.n_iter_ == expected_iters_kdtw + assert np.array_equal(kernel_kmeans_kdtw.labels_, expected_labels_kdtw) + assert kdtw_proba.shape == (max_train, 3) + + for val in kdtw_proba: + assert np.count_nonzero(val == 1.0) == 1 diff --git a/aeon/datasets/Final Dataset Selection.csv b/aeon/datasets/Final Dataset Selection.csv new file mode 100644 index 0000000000..c336db5a22 --- /dev/null +++ b/aeon/datasets/Final Dataset Selection.csv @@ -0,0 +1,101 @@ +Dataset,Series,Category +weather_dataset,T1,Weather +weather_dataset,T2,Weather +weather_dataset,T3,Weather +weather_dataset,T4,Weather +weather_dataset,T5,Weather +solar_10_minutes_dataset,T1,Energy Production +solar_10_minutes_dataset,T2,Energy Production +solar_10_minutes_dataset,T3,Energy Production +solar_10_minutes_dataset,T4,Energy Production +solar_10_minutes_dataset,T5,Energy Production +sunspot_dataset_without_missing_values,T1,Other +wind_farms_minutely_dataset_without_missing_values,T1,Energy Production +wind_farms_minutely_dataset_without_missing_values,T2,Energy Production +wind_farms_minutely_dataset_without_missing_values,T3,Energy Production +wind_farms_minutely_dataset_without_missing_values,T4,Energy Production +wind_farms_minutely_dataset_without_missing_values,T5,Energy Production +elecdemand_dataset,T1,Energy Demand +us_births_dataset,T1,Demographic +saugeenday_dataset,T1,Weather +london_smart_meters_dataset_without_missing_values,T1,Energy Demand +london_smart_meters_dataset_without_missing_values,T2,Energy Demand +london_smart_meters_dataset_without_missing_values,T3,Energy Demand +traffic_hourly_dataset,T1,Transportation +traffic_hourly_dataset,T2,Transportation +traffic_hourly_dataset,T3,Transportation +traffic_hourly_dataset,T4,Transportation +traffic_hourly_dataset,T5,Transportation +electricity_hourly_dataset,T1,Energy Demand +electricity_hourly_dataset,T2,Energy Demand +electricity_hourly_dataset,T3,Energy Demand +pedestrian_counts_dataset,T1,Transportation +pedestrian_counts_dataset,T2,Transportation +pedestrian_counts_dataset,T3,Transportation +pedestrian_counts_dataset,T4,Transportation +pedestrian_counts_dataset,T5,Transportation +kdd_cup_2018_dataset_without_missing_values,T1,Other +australian_electricity_demand_dataset,T1,Energy Demand +australian_electricity_demand_dataset,T2,Energy Demand +australian_electricity_demand_dataset,T3,Energy Demand +oikolab_weather_dataset,T1,Weather +oikolab_weather_dataset,T2,Weather +oikolab_weather_dataset,T3,Weather +oikolab_weather_dataset,T4,Weather +m4_monthly_dataset,T122,Macro +m4_monthly_dataset,T145,Macro +m4_monthly_dataset,T180,Macro +m4_monthly_dataset,T186,Macro +m4_monthly_dataset,T17051,Micro +m4_monthly_dataset,T17088,Micro +m4_monthly_dataset,T17132,Micro +m4_monthly_dataset,T17146,Micro +m4_monthly_dataset,T26710,Demographic +m4_monthly_dataset,T27138,Industry +m4_monthly_dataset,T27170,Industry +m4_monthly_dataset,T27175,Industry +m4_monthly_dataset,T27186,Industry +m4_monthly_dataset,T37009,Finance +m4_monthly_dataset,T37070,Finance +m4_monthly_dataset,T37238,Finance +m4_monthly_dataset,T37248,Finance +m4_monthly_dataset,T47915,Other +m4_weekly_dataset,T1,Other +m4_weekly_dataset,T2,Other +m4_weekly_dataset,T19,Macro +m4_weekly_dataset,T20,Macro +m4_weekly_dataset,T21,Macro +m4_weekly_dataset,T55,Industry +m4_weekly_dataset,T56,Industry +m4_weekly_dataset,T60,Finance +m4_weekly_dataset,T61,Finance +m4_weekly_dataset,T62,Finance +m4_weekly_dataset,T224,Demographic +m4_weekly_dataset,T225,Demographic +m4_weekly_dataset,T226,Demographic +m4_weekly_dataset,T227,Demographic +m4_weekly_dataset,T248,Micro +m4_weekly_dataset,T249,Micro +m4_weekly_dataset,T250,Micro +m4_daily_dataset,T1,Macro +m4_daily_dataset,T2,Macro +m4_daily_dataset,T6,Macro +m4_daily_dataset,T130,Micro +m4_daily_dataset,T131,Micro +m4_daily_dataset,T145,Micro +m4_daily_dataset,T1604,Demographic +m4_daily_dataset,T1605,Demographic +m4_daily_dataset,T1606,Demographic +m4_daily_dataset,T1607,Demographic +m4_daily_dataset,T1614,Industry +m4_daily_dataset,T1615,Industry +m4_daily_dataset,T1634,Industry +m4_daily_dataset,T1650,Industry +m4_daily_dataset,T2036,Finance +m4_daily_dataset,T2037,Finance +m4_daily_dataset,T2041,Finance +m4_daily_dataset,T3595,Other +m4_daily_dataset,T3597,Other +m4_hourly_dataset,T170,Other +m4_hourly_dataset,T171,Other +m4_hourly_dataset,T172,Other diff --git a/aeon/datasets/__init__.py b/aeon/datasets/__init__.py index 4185769f6f..5ca365c171 100644 --- a/aeon/datasets/__init__.py +++ b/aeon/datasets/__init__.py @@ -16,7 +16,10 @@ "load_human_activity_segmentation_datasets", # Write functions "write_to_ts_file", + "write_to_tsf_file", "write_to_arff_file", + "write_regression_dataset", + "write_forecasting_dataset", # Single problem loaders "load_airline", "load_arrow_head", @@ -57,7 +60,13 @@ load_from_tsv_file, load_regression, ) -from aeon.datasets._data_writers import write_to_arff_file, write_to_ts_file +from aeon.datasets._data_writers import ( + write_forecasting_dataset, + write_regression_dataset, + write_to_arff_file, + write_to_ts_file, + write_to_tsf_file, +) from aeon.datasets._single_problem_loaders import ( load_acsf1, load_airline, diff --git a/aeon/datasets/_data_writers.py b/aeon/datasets/_data_writers.py index 29ec83e648..0f2ea35f90 100644 --- a/aeon/datasets/_data_writers.py +++ b/aeon/datasets/_data_writers.py @@ -1,9 +1,20 @@ +"""Dataset wrting functions.""" + import os import textwrap +from datetime import datetime import numpy as np +import pandas as pd + +from aeon.transformations.format import SlidingWindowTransformer, TrainTestTransformer +from aeon.transformations.series._difference import DifferencingSeriesTransformer -__all__ = ["write_to_ts_file", "write_to_arff_file"] +__all__ = [ + "write_to_ts_file", + "write_to_tsf_file", + "write_to_arff_file", +] def write_to_ts_file( @@ -83,7 +94,6 @@ def write_to_ts_file( class_labels=class_labels, comment=header, regression=regression, - extension=None, ) missing_values = "NaN" for i in range(n_cases): @@ -99,6 +109,186 @@ def write_to_ts_file( file.close() +def write_to_tsf_file( + df, + full_file_path, + metadata, + value_column_name="series_value", + attributes_types=None, + missing_val_symbol="?", +): + """ + Save a pandas DataFrame in TSF format. + + Parameters + ---------- + df : pandas.DataFrame + The DataFrame to be saved. It is assumed that one column contains the series + (by default, named "series_value") and all other columns are series attributes. + full_file_path : str + The full path (including file name) where the TSF file will be saved. + metadata : dict + A dictionary containing metadata for the forecasting problem. It must + include the following keys: + - "frequency" (str) + - "forecast_horizon" (int) + - "contain_missing_values" (bool) + - "contain_equal_length" (bool) + value_column_name : str, optional (default="series_value") + The name of the column that contains the time series values. + attributes_types : dict, optional + A dictionary mapping attribute column names to their TSF type + (one of "numeric", "string", "date"). + If not provided, the type is inferred from the DataFrame dtypes as follows: + - numeric dtypes -> "numeric" + - datetime dtypes -> "date" + - all others -> "string" + missing_val_symbol : str, optional (default="?") + The symbol to be used in the file to represent missing values in the series. + + Raises + ------ + Exception + If any required metadata or a series or attribute value is missing. + """ + # Validate metadata keys + required_meta = [ + "frequency", + "forecast_horizon", + "contain_missing_values", + "contain_equal_length", + ] + for key in required_meta: + if key not in metadata: + raise AttributeError(f"Missing metadata entry: {key}") + + # Determine attribute columns (all columns except the series column) + attribute_columns = [col for col in df.columns if col != value_column_name] + + # If no attributes are present, warn the user. + if not attribute_columns: + raise AttributeError( + "The DataFrame must contain at least one \ + attribute column besides the series column." + ) + + # Determine attribute types if not provided. + # For each attribute, assign a type: + # - numeric dtypes -> "numeric" + # - datetime dtypes -> "date" (and will be formatted as "%Y-%m-%d %H-%M-%S") + # - all others -> "string" + if attributes_types is None: + attributes_types = {} + for col in attribute_columns: + if pd.api.types.is_numeric_dtype(df[col]): + attributes_types[col] = "numeric" + elif pd.api.types.is_datetime64_any_dtype(df[col]): + attributes_types[col] = "date" + else: + attributes_types[col] = "string" + else: + # Ensure that a type is provided for each attribute column + for col in attribute_columns: + if col not in attributes_types: + raise ValueError( + f"Attribute type for column '{col}' is \ + missing in attributes_types." + ) + + # Build header lines for the TSF file. + header_lines = [] + # First, write the attribute lines (order matters!) + for col in attribute_columns: + att_type = attributes_types[col] + if att_type not in {"numeric", "string", "date"}: + raise ValueError( + f"Unsupported attribute type '{att_type}' for column '{col}'." + ) + header_lines.append(f"@attribute {col} {att_type}") + + # Now add the metadata lines. (The order here is flexible, + # but must appear before @data.) + header_lines.append(f"@frequency {metadata['frequency']}") + header_lines.append(f"@horizon {metadata['forecast_horizon']}") + header_lines.append( + f"@missing {'true' if metadata['contain_missing_values'] else 'false'}" + ) + header_lines.append( + f"@equallength {'true' if metadata['contain_equal_length'] else 'false'}" + ) + + # Add the data section tag. + header_lines.append("@data") + # Open file for writing using the same encoding as the loader. + with open(full_file_path, "w", encoding="cp1252") as f: + # Write header lines. + for line in header_lines: + f.write(line + "\n") + + # Process each row to write the data lines. + for idx, row in df.iterrows(): + parts = [] + # Process each attribute value. + for col in attribute_columns: + val = row[col] + col_type = attributes_types[col] + if pd.isna(val): + raise ValueError( + f"Missing value in attribute column '{col}' at row {idx}." + ) + if col_type == "numeric": + try: + val_str = str(int(val)) + except Exception as e: + raise ValueError( + f"Error converting value in column '{col}' \ + at row {idx} to integer: {e}" + ) from e + elif col_type == "date": + # Ensure val is a datetime; if not, attempt conversion. + if not isinstance(val, datetime): + try: + val = pd.to_datetime(val) + except Exception as e: + raise ValueError( + f"Error converting value in column '{col}' \ + at row {idx} to datetime: {e}" + ) from e + val_str = val.strftime("%Y-%m-%d %H-%M-%S") + elif col_type == "string": + val_str = str(val) + else: + # Should not get here because we validated types earlier. + raise ValueError( + f"Unsupported attribute type '{col_type}' for column '{col}'." + ) + parts.append(val_str) + + # Process the series data from value_column_name. + series_val = row[value_column_name] + if not hasattr(series_val, "__iter__"): + raise ValueError( + f"The series in column '{value_column_name}' \ + at row {idx} is not iterable." + ) + + series_str_parts = [] + for s in series_val: + # Check for missing values in the series. + if pd.isna(s): + series_str_parts.append(missing_val_symbol) + else: + series_str_parts.append(str(s).removesuffix(".0")) + # Join series values with commas. + series_str = ",".join(series_str_parts) + parts.append(series_str) + + # The data line consists of the attribute values and + # then the series, separated by colons. + line_data = ":".join(parts) + f.write(line_data + "\n") + + def _write_header( path, problem_name, @@ -108,25 +298,24 @@ def _write_header( comment=None, regression=False, class_labels=None, - extension=None, ): if class_labels is not None and regression: raise ValueError("Cannot have class_labels true for a regression problem") # create path if it does not exist - dir = os.path.join(path, "") + dir_path = os.path.join(path, "") try: - os.makedirs(dir, exist_ok=True) - except OSError: - raise ValueError(f"Error trying to access {dir} in _write_header") + os.makedirs(dir_path, exist_ok=True) + except OSError as exc: + raise ValueError(f"Error trying to access {dir_path} in _write_header") from exc # create ts file in the path - load_path = os.path.join(dir, problem_name) - file = open(load_path, "w") + load_path = os.path.join(dir_path, problem_name) + file = open(load_path, "w", encoding="utf-8") # write comment if any as a block at start of file if comment is not None: file.write("\n# ".join(textwrap.wrap("# " + comment))) file.write("\n") - """ Writes the header info for a ts file""" + # Writes the header info for a ts file file.write(f"@problemName {problem_name}\n") file.write("@timestamps false\n") file.write(f"@univariate {str(univariate).lower()}\n") @@ -175,7 +364,7 @@ def write_to_arff_file( ------- None """ - if not (isinstance(X, np.ndarray)): + if not isinstance(X, np.ndarray): raise TypeError( f" Wrong input data type {type(X)}. Convert to np.ndarray (n_cases, " f"n_channels, n_timepoints) if possible." @@ -187,31 +376,77 @@ def write_to_arff_file( f"received {X.shape}" ) - file = open(f"{path}/{problem_name}.arff", "w") + with open(f"{path}/{problem_name}.arff", "w", encoding="utf-8") as file: - # write comment if any as a block at start of file - if header is not None: - file.write("\n% ".join(textwrap.wrap("% " + header))) - file.write("\n") + # write comment if any as a block at start of file + if header is not None: + file.write("\n% ".join(textwrap.wrap("% " + header))) + file.write("\n") - # begin writing header information - file.write(f"@Relation {problem_name}\n") + # begin writing header information + file.write(f"@Relation {problem_name}\n") - # write each attribute - for i in range(X.shape[2]): - file.write(f"@attribute att{str(i)} numeric\n") + # write each attribute + for i in range(X.shape[2]): + file.write(f"@attribute att{str(i)} numeric\n") - # lass attribute if it exists - comma_separated_class_label = ",".join(str(label) for label in np.unique(y)) - file.write(f"@attribute target {{{comma_separated_class_label}}}\n") + # lass attribute if it exists + comma_separated_class_label = ",".join(str(label) for label in np.unique(y)) + file.write(f"@attribute target {{{comma_separated_class_label}}}\n") - # write data - file.write("@data\n") - for case, target in zip(X, y): - # turn attributes into comma-separated row - atts = ",".join([str(num) if not np.isnan(num) else "?" for num in case[0]]) - file.write(str(atts)) - file.write(f",{target}") - file.write("\n") # open a new line + # write data + file.write("@data\n") + for case, target in zip(X, y): + # turn attributes into comma-separated row + atts = ",".join([str(num) if not np.isnan(num) else "?" for num in case[0]]) + file.write(str(atts)) + file.write(f",{target}") + file.write("\n") # open a new line - file.close() + +def write_regression_dataset(series, full_file_path, dataset_name): + """Write a regression dataset to file.""" + train_series, test_series = TrainTestTransformer().fit_transform(series) + differenced_train_series = DifferencingSeriesTransformer().fit_transform( + train_series + ) + X_train, Y_train, train_indices = SlidingWindowTransformer().fit_transform( + differenced_train_series + ) + differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) + X_test, Y_test, test_indices = SlidingWindowTransformer().fit_transform( + differenced_test_series + ) + write_to_ts_file( + [[item] for item in X_train], + full_file_path, + Y_train, + f"{dataset_name}_TRAIN", + None, + True, + ) + write_to_ts_file( + [[item] for item in X_test], + full_file_path, + Y_test, + f"{dataset_name}_TEST", + None, + True, + ) + + +def write_forecasting_dataset(series, full_file_path, dataset_name): + """Write a regression dataset to file.""" + train_series, test_series = TrainTestTransformer().fit_transform(series) + differenced_train_series = DifferencingSeriesTransformer().fit_transform( + train_series + ) + differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) + train_df = pd.DataFrame(differenced_train_series) + train_df.to_csv( + f"{full_file_path}/{dataset_name}_TRAIN.csv", index=False, header=False + ) + test_df = pd.DataFrame(differenced_test_series) + test_df.to_csv( + f"{full_file_path}/{dataset_name}_TEST.csv", index=False, header=False + ) diff --git a/aeon/datasets/dataset_generation.py b/aeon/datasets/dataset_generation.py new file mode 100644 index 0000000000..674c7501f3 --- /dev/null +++ b/aeon/datasets/dataset_generation.py @@ -0,0 +1,218 @@ +"""Code to select datasets for regression-based forecasting experiments.""" + +import gc +import os +import tempfile +import time + +import pandas as pd + +from aeon.datasets import load_forecasting +from aeon.datasets._data_writers import ( + write_forecasting_dataset, + write_regression_dataset, +) + +filtered_datasets = [ + "nn5_daily_dataset_without_missing_values", + "nn5_weekly_dataset", + "m1_yearly_dataset", + "m1_quarterly_dataset", + "m1_monthly_dataset", + "m3_yearly_dataset", + "m3_quarterly_dataset", + "m3_monthly_dataset", + "m3_other_dataset", + "m4_yearly_dataset", + "m4_quarterly_dataset", + "m4_monthly_dataset", + "m4_weekly_dataset", + "m4_daily_dataset", + "m4_hourly_dataset", + "tourism_yearly_dataset", + "tourism_quarterly_dataset", + "tourism_monthly_dataset", + "car_parts_dataset_without_missing_values", + "hospital_dataset", + "weather_dataset", + "dominick_dataset", + "fred_md_dataset", + "solar_10_minutes_dataset", + "solar_weekly_dataset", + "solar_4_seconds_dataset", + "wind_4_seconds_dataset", + "sunspot_dataset_without_missing_values", + "wind_farms_minutely_dataset_without_missing_values", + "elecdemand_dataset", + "us_births_dataset", + "saugeenday_dataset", + "covid_deaths_dataset", + "cif_2016_dataset", + "london_smart_meters_dataset_without_missing_values", + "kaggle_web_traffic_dataset_without_missing_values", + "kaggle_web_traffic_weekly_dataset", + "traffic_hourly_dataset", + "traffic_weekly_dataset", + "electricity_hourly_dataset", + "electricity_weekly_dataset", + "pedestrian_counts_dataset", + "kdd_cup_2018_dataset_without_missing_values", + "australian_electricity_demand_dataset", + "covid_mobility_dataset_without_missing_values", + "rideshare_dataset_without_missing_values", + "vehicle_trips_dataset_without_missing_values", + "temperature_rain_dataset_without_missing_values", + "oikolab_weather_dataset", +] + + +def filter_datasets(): + """ + Filter datasets to identify and print time series with more than 1000 data points. + + This function iterates over a list of datasets, loads each dataset, + and checks each time series within it. If a series contains more than 1000 + data points, it is counted as a "hit." The function prints up to 10 matches + per dataset in the format: `,`. + + Returns + ------- + None + The function does not return anything but prints matching dataset + and series names to the console. + + Notes + ----- + - The function introduces a 1-second delay (`time.sleep(1)`) between processing + datasets to control HTTP request frequency. + - Uses `gc.collect()` to explicitly trigger garbage collection, to avoid + running out of memory + """ + num_hits = 0 + for dataset_name in filtered_datasets: + # print(f"{dataset_name}") + time.sleep(1) + dataset_counter = 0 + dataset = load_forecasting(dataset_name) + for index, row in enumerate(dataset["series_value"]): + if len(row) > 1000: + num_hits += 1 + dataset_counter += 1 + if dataset_counter <= 10: + print(f"{dataset_name},{dataset['series_name'][index]}") # noqa + # if dataset_counter > 0: + # print(f"{dataset_name}: Hits: {dataset_counter}") + del dataset + gc.collect() + # print(f"Num hits in datasets: {num_hits}") + + +# filter_datasets() + + +def filter_and_categorise_m4(frequency_type): + """ + Filter and categorize M4 dataset time series. + + Parameters + ---------- + frequency_type : str + The frequency type of the M4 dataset to process. + Accepted values: 'yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly'. + + Returns + ------- + None + The function does not return any values but prints categorized series + information. + + Notes + ----- + - The function constructs an appropriate prefix ('Y', 'Q', 'M', 'W', 'D', 'H') + based on the dataset type to match metadata identifiers. + - Limits printed results to 10 per category. + """ + metadata = pd.read_csv("C:/Users/alexb/Downloads/M4-info.csv") + m4daily = load_forecasting(f"m4_{frequency_type}_dataset") + categories = {} + prefix = "" + if frequency_type == "yearly": + prefix = "Y" + elif frequency_type == "quarterly": + prefix = "Q" + elif frequency_type == "monthly": + prefix = "M" + elif frequency_type == "weekly": + prefix = "W" + elif frequency_type == "daily": + prefix = "D" + elif frequency_type == "hourly": + prefix = "H" + for index, row in enumerate(m4daily["series_value"]): + if len(row) > 1000: + category = metadata.loc[ + metadata["M4id"] == f"{prefix}{m4daily['series_name'][index][1:]}", + "category", + ].values[0] + if category not in categories: + categories[category] = 1 + else: + categories[category] += 1 + if categories[category] <= 10: + print( # noqa + f"m4_{frequency_type}_dataset,\ + {m4daily['series_name'][index]},{category}" + ) + + +# filter_and_categorise_m4('monthly') +# filter_and_categorise_m4('weekly') +# filter_and_categorise_m4('daily') +# filter_and_categorise_m4('hourly') + + +def gen_datasets(problem_type, dataset_folder=None): + """ + Generate windowed train/test split of datasets. + + Returns + ------- + None + The function does not return anything but writes out the train and test + files to the specified directory. + + Notes + ----- + - Requires a CSV file containing a list of the series to process. + """ + final_series_selection = pd.read_csv("./aeon/datasets/Final Dataset Selection.csv") + current_dataset = "" + dataset = pd.DataFrame() + tmpdir = tempfile.mkdtemp() + folder = problem_type if dataset_folder is None else dataset_folder + location_of_datasets = f"./aeon/datasets/local_data/{folder}" + if not os.path.exists(location_of_datasets): + os.makedirs(location_of_datasets) + with open(f"{location_of_datasets}/windowed_series.txt", "w") as f: + for item in final_series_selection.to_records(index=False): + if current_dataset != item[0]: + dataset = load_forecasting(item[0], tmpdir) + current_dataset = item[0] + print(f"Current Dataset: {current_dataset}") # noqa + f.write(f"{item[0]}_{item[1]}\n") + series = ( + dataset[dataset["series_name"] == item[1]]["series_value"] + .iloc[0] + .to_numpy() + ) + dataset_name = f"{item[0]}_{item[1]}" + full_file_path = f"{location_of_datasets}/{dataset_name}" + if not os.path.exists(full_file_path): + os.makedirs(full_file_path) + if problem_type == "regression": + write_regression_dataset(series, full_file_path, dataset_name) + elif problem_type == "forecasting": + write_forecasting_dataset(series, full_file_path, dataset_name) + + +gen_datasets("forecasting", "differenced_forecasting") diff --git a/aeon/datasets/tests/test_data_writers.py b/aeon/datasets/tests/test_data_writers.py index d31700ac2b..e7428a39fc 100644 --- a/aeon/datasets/tests/test_data_writers.py +++ b/aeon/datasets/tests/test_data_writers.py @@ -128,7 +128,6 @@ def test_write_header(): _write_header( tmp, problem_name, - extension=".csv", comment="Hello", regression=True, ) diff --git a/aeon/datasets/tests/test_dataset_collections.py b/aeon/datasets/tests/test_dataset_collections.py index 624870ab5e..bb185fac14 100644 --- a/aeon/datasets/tests/test_dataset_collections.py +++ b/aeon/datasets/tests/test_dataset_collections.py @@ -69,7 +69,7 @@ def test_list_available_tser_datasets(): def test_list_available_tsf_datasets(): """Test recovering lists of available data sets.""" res = get_available_tsf_datasets() - assert len(res) == 53 + assert len(res) == 62 res = get_available_tsf_datasets("FOO") assert not res res = get_available_tsf_datasets("m1_monthly_dataset") diff --git a/aeon/datasets/tsad_datasets.py b/aeon/datasets/tsad_datasets.py index 4372772dc5..8f10af3eaf 100644 --- a/aeon/datasets/tsad_datasets.py +++ b/aeon/datasets/tsad_datasets.py @@ -67,7 +67,7 @@ def tsad_collections() -> dict[str, list[str]]: df = _load_indexfile() return ( df.groupby("collection_name") - .apply(lambda x: x["dataset_name"].to_list(), include_groups=False) + .apply(lambda x: x["dataset_name"].to_list()) .to_dict() ) diff --git a/aeon/datasets/tsf_datasets.py b/aeon/datasets/tsf_datasets.py index b5c008c3dd..562f9ad5ae 100644 --- a/aeon/datasets/tsf_datasets.py +++ b/aeon/datasets/tsf_datasets.py @@ -54,4 +54,17 @@ "australian_electricity_demand_dataset": 4659727, "covid_mobility_dataset_with_missing_values": 4663762, "covid_mobility_dataset_without_missing_values": 4663809, + "bitcoin_dataset_with_missing_values": 5121965, + "bitcoin_dataset_without_missing_values": 5122101, + "rideshare_dataset_with_missing_values": 5122114, + "rideshare_dataset_without_missing_values": 5122232, + "vehicle_trips_dataset_with_missing_values": 5122535, + "vehicle_trips_dataset_without_missing_values": 5122537, + "temperature_rain_dataset_with_missing_values": 5129073, + "temperature_rain_dataset_without_missing_values": 5129091, + "oikolab_weather_dataset": 5184708, + # These datasets generate HTTP Error 404: NOT FOUND errors + # "extended_wikipedia_web_traffic_daily_dataset_with_missing_values": 7370977, + # "extended_wikipedia_web_traffic_daily_dataset_without_missing_values": 7371038, + # "residential_power_and_battery_data": 8219786, } diff --git a/aeon/distances/__init__.py b/aeon/distances/__init__.py index e1d3205ef2..d6ff3f776a 100644 --- a/aeon/distances/__init__.py +++ b/aeon/distances/__init__.py @@ -18,6 +18,10 @@ "dtw_pairwise_distance", "dtw_cost_matrix", "dtw_alignment_path", + "dtw_gi_distance", + "dtw_gi_pairwise_distance", + "dtw_gi_cost_matrix", + "dtw_gi_alignment_path", "ddtw_distance", "ddtw_pairwise_distance", "ddtw_alignment_path", @@ -111,6 +115,10 @@ dtw_alignment_path, dtw_cost_matrix, dtw_distance, + dtw_gi_alignment_path, + dtw_gi_cost_matrix, + dtw_gi_distance, + dtw_gi_pairwise_distance, dtw_pairwise_distance, edr_alignment_path, edr_cost_matrix, diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py index 1263e11cb4..33f9141440 100644 --- a/aeon/distances/_distance.py +++ b/aeon/distances/_distance.py @@ -24,6 +24,10 @@ dtw_alignment_path, dtw_cost_matrix, dtw_distance, + dtw_gi_alignment_path, + dtw_gi_cost_matrix, + dtw_gi_distance, + dtw_gi_pairwise_distance, dtw_pairwise_distance, edr_alignment_path, edr_cost_matrix, @@ -447,6 +451,7 @@ def get_distance_function(method: Union[str, DistanceFunction]) -> DistanceFunct method Distance Function =============== ======================================== 'dtw' distances.dtw_distance + 'dtw_gi' distances.dtw_gi_distance 'shape_dtw' distances.shape_dtw_distance 'ddtw' distances.ddtw_distance 'wdtw' distances.wdtw_distance @@ -728,6 +733,16 @@ class DistanceType(Enum): "symmetric": True, "unequal_support": True, }, + { + "name": "dtw_gi", + "distance": dtw_gi_distance, + "pairwise_distance": dtw_gi_pairwise_distance, + "cost_matrix": dtw_gi_cost_matrix, + "alignment_path": dtw_gi_alignment_path, + "type": DistanceType.ELASTIC, + "symmetric": False, + "unequal_support": True, + }, { "name": "ddtw", "distance": ddtw_distance, diff --git a/aeon/distances/elastic/__init__.py b/aeon/distances/elastic/__init__.py index 0c386245ba..8e5d1aa9dd 100644 --- a/aeon/distances/elastic/__init__.py +++ b/aeon/distances/elastic/__init__.py @@ -10,6 +10,10 @@ "dtw_pairwise_distance", "dtw_cost_matrix", "dtw_alignment_path", + "dtw_gi_distance", + "dtw_gi_pairwise_distance", + "dtw_gi_cost_matrix", + "dtw_gi_alignment_path", "ddtw_distance", "ddtw_pairwise_distance", "ddtw_alignment_path", @@ -71,6 +75,12 @@ dtw_distance, dtw_pairwise_distance, ) +from aeon.distances.elastic._dtw_gi import ( + dtw_gi_alignment_path, + dtw_gi_cost_matrix, + dtw_gi_distance, + dtw_gi_pairwise_distance, +) from aeon.distances.elastic._edr import ( edr_alignment_path, edr_cost_matrix, diff --git a/aeon/distances/elastic/_bounding_matrix.py b/aeon/distances/elastic/_bounding_matrix.py index 2b710b9728..3b4d76b4a2 100644 --- a/aeon/distances/elastic/_bounding_matrix.py +++ b/aeon/distances/elastic/_bounding_matrix.py @@ -63,44 +63,67 @@ def create_bounding_matrix( def _itakura_parallelogram(x_size: int, y_size: int, max_slope_percent: float): """Itakura parallelogram bounding matrix. - This code was adapted from tslearn. This link to the original code line 974: + This code was adapted from the tslearn and pyts functions. + + pyts code: + https://pyts.readthedocs.io/en/latest/_modules/pyts/metrics/dtw.html#itakura_parallelogram + Copyright (c) 2018, Johann Faouzi and pyts contributors, BSD-3 + tslearn code (line 974): https://github.com/tslearn-team/tslearn/blob/main/tslearn/metrics/dtw_variants.py + Copyright (c) 2017, Romain Tavenard, BSD-2 """ - if x_size != y_size: - raise ValueError( - """Itakura parallelogram does not support unequal length time series. -Please consider using a full bounding matrix or a sakoe chiba bounding matrix -instead.""" - ) one_percent = min(x_size, y_size) / 100 max_slope = math.floor((max_slope_percent * one_percent) * 100) min_slope = 1 / float(max_slope) - max_slope *= float(x_size) / float(y_size) - min_slope *= float(x_size) / float(y_size) - - lower_bound = np.empty((2, y_size)) - lower_bound[0] = min_slope * np.arange(y_size) - lower_bound[1] = ( - (x_size - 1) - max_slope * (y_size - 1) + max_slope * np.arange(y_size) - ) - lower_bound_ = np.empty(y_size) - for i in range(y_size): - lower_bound_[i] = max(round(lower_bound[0, i], 2), round(lower_bound[1, i], 2)) - lower_bound_ = np.ceil(lower_bound_) - - upper_bound = np.empty((2, y_size)) - upper_bound[0] = max_slope * np.arange(y_size) - upper_bound[1] = ( - (x_size - 1) - min_slope * (y_size - 1) + min_slope * np.arange(y_size) - ) - upper_bound_ = np.empty(y_size) - for i in range(y_size): - upper_bound_[i] = min(round(upper_bound[0, i], 2), round(upper_bound[1, i], 2)) - upper_bound_ = np.floor(upper_bound_ + 1) - - bounding_matrix = np.full((x_size, y_size), False) - for i in range(y_size): - bounding_matrix[int(lower_bound_[i]) : int(upper_bound_[i]), i] = True + max_slope *= float(y_size - 1) / float(x_size - 2) + max_slope = max(max_slope, 1.0) + + min_slope *= float(y_size - 2) / float(x_size - 1) + min_slope = min(min_slope, 1.0) + + centered_scale = np.arange(x_size) - x_size + 1 + + lower_bound = np.empty(x_size, dtype=np.float64) + upper_bound = np.empty(x_size, dtype=np.float64) + + for i in range(x_size): + lb0 = min_slope * i + lb1 = max_slope * centered_scale[i] + y_size - 1 + lower_bound[i] = math.ceil(max(round(lb0, 2), round(lb1, 2))) + + ub0 = max_slope * i + 1 + ub1 = min_slope * centered_scale[i] + y_size + upper_bound[i] = math.floor(min(round(ub0, 2), round(ub1, 2))) + + if max_slope == 1.0: + if y_size > x_size: + for i in range(x_size - 1): + upper_bound[i] = lower_bound[i + 1] + else: + for i in range(x_size): + upper_bound[i] = lower_bound[i] + 1 + + for i in range(x_size): + if lower_bound[i] < 0: + lower_bound[i] = 0 + if lower_bound[i] > y_size: + lower_bound[i] = y_size + if upper_bound[i] < 0: + upper_bound[i] = 0 + if upper_bound[i] > y_size: + upper_bound[i] = y_size + + bounding_matrix = np.empty((x_size, y_size), dtype=np.bool_) + for i in range(x_size): + for j in range(y_size): + bounding_matrix[i, j] = False + + for i in range(x_size): + start = int(lower_bound[i]) + end = int(upper_bound[i]) + for j in range(start, end): + bounding_matrix[i, j] = True + return bounding_matrix diff --git a/aeon/distances/elastic/_dtw_gi.py b/aeon/distances/elastic/_dtw_gi.py new file mode 100644 index 0000000000..bff33e343e --- /dev/null +++ b/aeon/distances/elastic/_dtw_gi.py @@ -0,0 +1,551 @@ +r"""Dynamic time warping with Global Invariances (DTW-GI) between two time series.""" + +__maintainer__ = [] + +from typing import Optional, Union + +import numpy as np +from numba import njit +from numba.typed import List as NumbaList + +from aeon.distances.elastic._dtw import dtw_alignment_path, dtw_cost_matrix +from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list +from aeon.utils.validation.collection import _is_numpy_list_multivariate + + +@njit(cache=True, fastmath=True) +def _path2mat( + path: list[tuple[int, int]], + x_timepoints: int, + y_timepoints: int, +): + r"""Convert a warping alignment path to a binary warping matrix.""" + w = np.zeros((x_timepoints, y_timepoints)) + for i, j in path: + w[i, j] = 1 + return w + + +@njit(cache=True, fastmath=True) +def _dtw_gi( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +): + r""" + Compute Dynamic Time Warping with Global Invariance between the two time series. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float or None, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1`` then + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + - w_pi: binary warping matrix of shape (n0, n1) + - p: the final linear (Stiefel) matrix of shape (x_channels, y_channels) + - cost: final DTW cost considering global invariances + + If use_bias is True, also returns: + - bias + + """ + if x.ndim == 1 and y.ndim == 1: + x_ = x.reshape((1, x.shape[0])) + y_ = y.reshape((1, y.shape[0])) + elif x.ndim == 2 and y.ndim == 2: + x_ = x + y_ = y + else: + raise ValueError("x and y must be 1D or 2D") + + x_channels, x_timepoints = x_.shape + y_channels, y_timepoints = y_.shape + + x_m = np.sum(x_, axis=1) / x_.shape[1] + x_m = x_m.reshape((-1, 1)) + y_m = np.sum(y_, axis=1) / y_.shape[1] + y_m = y_m.reshape((-1, 1)) + + w_pi = np.zeros((x_timepoints, y_timepoints)) + if init_p is None: + p = np.eye(x_channels, y_channels, dtype=np.float64) + else: + p = init_p + + y_ = y_.astype(np.float64) + x_ = x_.astype(np.float64) + + bias = np.zeros((x_channels, 1)) + + for _ in range(max_iter): + w_pi_old = w_pi.copy() + y_transformed = p.dot(y_) + bias + + path, cost = dtw_alignment_path(x_, y_transformed, window, itakura_max_slope) + w_pi = _path2mat(path, x_timepoints, y_timepoints) + + if np.allclose(w_pi, w_pi_old): + break + + if use_bias: + m = (x_ - x_m).dot(w_pi).dot((y_ - y_m).T) + else: + m = x_.dot(w_pi).dot(y_.T) + + u, sigma, vt = np.linalg.svd(m, full_matrices=False) + p = u.dot(vt) + if use_bias: + bias = x_m - p.dot(y_m) + + y_trans = p.dot(y_) + bias + path, cost = dtw_alignment_path(x_, y_trans, window, itakura_max_slope) + + if use_bias: + return w_pi, p, bias, cost, x_, y_trans + else: + dummy_bias = np.zeros((x_channels, 1), dtype=np.float64) + return w_pi, p, dummy_bias, cost, x_, y_trans + + +@njit(cache=True, fastmath=True) +def dtw_gi_distance( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> float: + r"""Compute the DTW_GI distance between two time series x and y. + + The DTW_gi distance between 2 timeseries x and y is the similarity + measure that estimates both temporal alignment and does feature space + transformation between time series simultaneously. This means that the + time series do not have to lie in the same ambient space. + A good background into DTW with global invariances can be found in [1]_. + This implementation is inspired by [2]_. + + For example, if we have two time series x and y of lengths n and m + respectively, and we assume that the time series do not lie in the + same ambient space. Lets assume that features of x lie in :math:`\mathbb{R}^p` + and features of y lie in :math:`\mathbb{R}^q`. To compare the two time series, + we need to find an optimum mapping from the feature space of y to the feature space + where features of x lie. So think of it as optimizing on a family of functions F + that map features from y onto the feature space in which features of x + lie. (This is just one way to do it, the mapping can be + in the opposite direction as well. But this function assumes the former way). + + More formally, we define Dynamic Time Warping with Global Invariances (DTW-GI) + as the solution of the following joint optimization problem: + + :math:`\text{DTW-GI}(\mathbf{x}, \mathbf{y}) = + \min_{f \in \mathcal{F}, \pi \in \mathcal{A}(\mathbf{x}, \mathbf{y})} + \sqrt{\sum_{(i,j) \in \pi} d(x_i, f(y_j))^2},` + + This similarity measure estimates temporal alignment + with feature space transformation between time series + allowing the alignment of time series that do not exist in the + same ambient space. + + Note: The optimal warping path :math:`P^*` for a given cost matrix can be found + exactly through a dynamic programming formulation. This can be a time consuming + operation, and it is common to put a restriction on the amount of warping allowed. + This is implemented through the bounding_matrix structure, that supplies a mask for + allowable warpings. The most common bounding strategies include the + Sakoe-Chiba band [3]_. The width of the allowed warping is controlled through the + ``window`` parameter which sets the maximum proportion of warping allowed. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1``, + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + float + DTW_GI distance between x and y, minimum value 0. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + References + ---------- + .. [1] T. Vayer, R. Tavenard, L. Chapel, N. Courty, R. Flamary, and Y. Soullard, + β€œTime Series Alignment with Global Invariances,” arXiv.org, 2020. + https://arxiv.org/abs/2002.03848 + + .. [2] Romain Tavenard, β€œDTW with Global Invariances,” Github.io, Dec. 17, 2020. + https://rtavenar.github.io/hdr/parts/01/dtw/dtw_gi.html + + .. [3] Sakoe H. and Chiba S.: Dynamic programming algorithm optimization for + spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal + Processing 26(1):43-49, 1978. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_distance + >>> x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + >>> y = np.array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20]) + >>> dtw_gi_distance(x, y) # 1D series + 768.0 + >>> x = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [0, 1, 0, 2, 0]]) + >>> y = np.array([[11, 12, 13, 14],[7, 8, 9, 20],[1, 3, 4, 5]] ) + >>> round(dtw_gi_distance(x, y), 1) # 2D series with 3 channels, unequal length + 359.2 + """ + if x.ndim == 1 and y.ndim == 1: + _x = x.reshape((1, x.shape[0])) + _y = y.reshape((1, y.shape[0])) + return _dtw_gi(_x, _y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] + if x.ndim == 2 and y.ndim == 2: + return _dtw_gi(x, y, window, itakura_max_slope, init_p, max_iter, use_bias)[3] + raise ValueError("x and y must be 1D or 2D") + + +@njit(cache=True, fastmath=True) +def dtw_gi_cost_matrix( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + r"""Compute the DTW_GI cost matrix between two time series. + + The cost matrix is the pairwise Euclidean distance between all points + :math:`M_{i,j}=(x_i-y_{\text{trans},j})^2`. Where `y_trans` is the time + series we get by finding the optimal mapping from feature space of y + to feature space where features of x lie. It is used in the DTW GI + path calculations. + + Parameters + ---------- + x : np.ndarray + First time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + y : np.ndarray + Second time series, either univariate, shape ``(n_timepoints,)``, or + multivariate, shape ``(n_channels, n_timepoints)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. window is a percentage deviation, so if ``window = 0.1``, + 10% of the series length is the max warping allowed. + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + np.ndarray (n_timepoints, m_timepoints) + dtw gi cost matrix between x and y. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_cost_matrix + >>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + >>> y = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]) + >>> dtw_gi_cost_matrix(x, y) + array([[ 0., 1., 5., 14., 30., 55., 91., 140., 204., 285.], + [ 1., 0., 1., 5., 14., 30., 55., 91., 140., 204.], + [ 5., 1., 0., 1., 5., 14., 30., 55., 91., 140.], + [ 14., 5., 1., 0., 1., 5., 14., 30., 55., 91.], + [ 30., 14., 5., 1., 0., 1., 5., 14., 30., 55.], + [ 55., 30., 14., 5., 1., 0., 1., 5., 14., 30.], + [ 91., 55., 30., 14., 5., 1., 0., 1., 5., 14.], + [140., 91., 55., 30., 14., 5., 1., 0., 1., 5.], + [204., 140., 91., 55., 30., 14., 5., 1., 0., 1.], + [285., 204., 140., 91., 55., 30., 14., 5., 1., 0.]]) + """ + _, _, _, _, xnew, y_trans = _dtw_gi( + x, y, window, itakura_max_slope, init_p, max_iter, use_bias + ) + + return dtw_cost_matrix(xnew, y_trans, window, itakura_max_slope) + + +def dtw_gi_pairwise_distance( + X: Union[np.ndarray, list[np.ndarray]], + y: Optional[Union[np.ndarray, list[np.ndarray]]] = None, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + r"""Compute the DTW_GI pairwise distance between a set of time series. + + By default, this takes a collection of :math:`n` time series :math:`X` and returns a + matrix + :math:`D` where :math:`D_{i,j}` is the DTW_GI distance between the :math:`i^{th}` + and the :math:`j^{th}` series in :math:`X`. If :math:`X` is 2 dimensional, + it is assumed to be a collection of univariate series with shape ``(n_cases, + n_timepoints)``. If it is 3 dimensional, it is assumed to be shape ``(n_cases, + n_channels, n_timepoints)``. + + This function has an optional argument, :math:`y`, to allow calculation of the + distance matrix between :math:`X` and one or more series stored in :math:`y`. If + :math:`y` is 1 dimensional, we assume it is a single univariate series and the + distance matrix returned is shape ``(n_cases,1)``. If it is 2D, we assume it + is a collection of univariate series with shape ``(m_cases, m_timepoints)`` + and the distance ``(n_cases,m_cases)``. If it is 3 dimensional, + it is assumed to be shape ``(m_cases, m_channels, m_timepoints)``. + + Parameters + ---------- + X : np.ndarray or List of np.ndarray + A collection of time series instances of shape ``(n_cases, n_timepoints)`` + or ``(n_cases, n_channels, n_timepoints)``. + y : np.ndarray or List of np.ndarray or None, default=None + A single series or a collection of time series of shape ``(m_timepoints,)`` or + ``(m_cases, m_timepoints)`` or ``(m_cases, m_channels, m_timepoints)``. + If None, then the dtw gi pairwise distance between the instances of X is + calculated. + window : float or None, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + np.ndarray + DTW_GI pairwise matrix between the instances of X of shape + ``(n_cases, n_cases)`` or between X and y of shape ``(n_cases, + n_cases)``. + + Raises + ------ + ValueError + If X is not 2D or 3D array and if y is not 1D, 2D or 3D arrays when passing y. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_pairwise_distance + >>> # Distance between each time series in a collection of time series + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> dtw_gi_pairwise_distance(X) + array([[ 0., 26., 108.], + [ 26., 0., 26.], + [108., 26., 0.]]) + + >>> # Distance between two collections of time series + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> y = np.array([[[11, 12, 13]],[[14, 15, 16]], [[17, 18, 19]]]) + >>> dtw_gi_pairwise_distance(X, y) + array([[300., 507., 768.], + [147., 300., 507.], + [ 48., 147., 300.]]) + + >>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) + >>> y_univariate = np.array([11, 12, 13]) + >>> dtw_gi_pairwise_distance(X, y_univariate) + array([[300.], + [147.], + [ 48.]]) + + >>> # Distance between each TS in a collection of unequal-length time series + >>> X = [np.array([1, 2, 3]), np.array([4, 5, 6, 7]), np.array([8, 9, 10, 11, 12])] + >>> dtw_gi_pairwise_distance(X) + array([[ 0., 42., 292.], + [ 42., 0., 83.], + [292., 83., 0.]]) + """ + multivariate_conversion = _is_numpy_list_multivariate(X, y) + _X, unequal_length = _convert_collection_to_numba_list( + X, "X", multivariate_conversion + ) + + if y is None: + # To self + return _dtw_gi_pairwise_distance( + _X, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias + ) + _y, unequal_length = _convert_collection_to_numba_list( + y, "y", multivariate_conversion + ) + return _dtw_gi_from_multiple_to_multiple_distance( + _X, _y, window, itakura_max_slope, unequal_length, init_p, max_iter, use_bias + ) + + +@njit(cache=True, fastmath=True) +def _dtw_gi_from_multiple_to_multiple_distance( + x: NumbaList[np.ndarray], + y: NumbaList[np.ndarray], + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + n_cases = len(x) + m_cases = len(y) + distances = np.zeros((n_cases, m_cases)) + + for i in range(n_cases): + for j in range(m_cases): + x1, y1 = x[i], y[j] + distances[i, j] = dtw_gi_distance( + x1, y1, window, itakura_max_slope, init_p, max_iter, use_bias + ) + return distances + + +@njit(cache=True, fastmath=True) +def _dtw_gi_pairwise_distance( + X: NumbaList[np.ndarray], + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + unequal_length: bool = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> np.ndarray: + n_cases = len(X) + distances = np.zeros((n_cases, n_cases)) + + for i in range(n_cases): + for j in range(i + 1, n_cases): + x1, x2 = X[i], X[j] + distances[i, j] = dtw_gi_distance( + x1, x2, window, itakura_max_slope, init_p, max_iter, use_bias + ) + distances[j, i] = distances[i, j] + + return distances + + +@njit(cache=True, fastmath=True) +def dtw_gi_alignment_path( + x: np.ndarray, + y: np.ndarray, + window: Optional[float] = None, + itakura_max_slope: Optional[float] = None, + init_p: np.ndarray = None, + max_iter: int = 20, + use_bias: bool = False, +) -> tuple[list[tuple[int, int]], float]: + """Compute the DTW_GI alignment path between two time series. + + Parameters + ---------- + x : np.ndarray + First time series, shape ``(n_channels, n_timepoints)`` or ``(n_timepoints,)``. + y : np.ndarray + Second time series, shape ``(m_channels, m_timepoints)`` or ``(m_timepoints,)``. + window : float, default=None + The window to use for the bounding matrix. If None, no bounding matrix + is used. + itakura_max_slope : float, default=None + Maximum slope as a proportion of the number of time points used to create + Itakura parallelogram on the bounding matrix. Must be between 0. and 1. + init_p : array-like of shape (x_channels, y_channels), default=None + Initial linear transformation. If None, the identity matrix is used. + max_iter : int, default=20 + Maximum number of iterations for the iterative optimization. + use_bias : bool, default=False + If True, the feature space map is affine (with a bias term). + + Returns + ------- + List[Tuple[int, int]] + The alignment path between the two time series where each element is a tuple + of the index in x and the index in y that have the best alignment according + to the cost matrix. + float + The DTW_GI distance betweeen the two time series. + + Raises + ------ + ValueError + If x and y are not 1D or 2D arrays. + + Examples + -------- + >>> import numpy as np + >>> from aeon.distances import dtw_gi_alignment_path + >>> x = np.array([[1, 2, 3, 6]]) + >>> y = np.array([[1, 2, 3, 4]]) + >>> dtw_gi_alignment_path(x, y) + ([(0, 0), (1, 1), (2, 2), (3, 3)], 4.0) + """ + w_pi, _, _, cost, _, _ = _dtw_gi( + x, y, window, itakura_max_slope, init_p, max_iter, use_bias + ) + min_alignment_path = [] + for i in range(len(w_pi)): + for j in range(len(w_pi[0])): + if w_pi[i, j] == 1: + min_alignment_path.append((i, j)) + + return min_alignment_path, cost diff --git a/aeon/distances/elastic/tests/test_bounding.py b/aeon/distances/elastic/tests/test_bounding.py index 32ac1987b0..f0c2d83737 100644 --- a/aeon/distances/elastic/tests/test_bounding.py +++ b/aeon/distances/elastic/tests/test_bounding.py @@ -1,7 +1,6 @@ """Test for bounding matrix.""" import numpy as np -import pytest from aeon.distances import create_bounding_matrix @@ -37,10 +36,34 @@ def test_itakura_parallelogram(): matrix = create_bounding_matrix(10, 10, itakura_max_slope=0.2) assert isinstance(matrix, np.ndarray) - with pytest.raises( - ValueError, - match="""Itakura parallelogram does not support unequal length time series. -Please consider using a full bounding matrix or a sakoe chiba bounding matrix -instead.""", - ): - create_bounding_matrix(5, 10, itakura_max_slope=0.2) + expected_result_5_7 = np.array( + [ + [True, False, False, False, False, False, False], + [False, True, True, True, True, False, False], + [False, False, True, True, True, False, False], + [False, False, True, True, True, True, False], + [False, False, False, False, False, False, True], + ] + ) + + expected_result_7_5 = np.array( + [ + [True, False, False, False, False], + [False, True, False, False, False], + [False, True, True, True, False], + [False, True, True, True, False], + [False, True, True, True, False], + [False, False, False, True, False], + [False, False, False, False, True], + ] + ) + + matrix = create_bounding_matrix(5, 7, itakura_max_slope=0.5) + assert isinstance(matrix, np.ndarray) + assert matrix.shape == (5, 7) + assert np.array_equal(matrix, expected_result_5_7) + + matrix = create_bounding_matrix(7, 5, itakura_max_slope=0.5) + assert isinstance(matrix, np.ndarray) + assert matrix.shape == (7, 5) + assert np.array_equal(matrix, expected_result_7_5) diff --git a/aeon/distances/elastic/tests/test_distance_correctness.py b/aeon/distances/elastic/tests/test_distance_correctness.py index f33ca088c3..2a0c203b81 100644 --- a/aeon/distances/elastic/tests/test_distance_correctness.py +++ b/aeon/distances/elastic/tests/test_distance_correctness.py @@ -10,6 +10,7 @@ from aeon.distances import ( ddtw_distance, dtw_distance, + dtw_gi_distance, edr_distance, erp_distance, euclidean_distance, @@ -23,6 +24,7 @@ distances = [ "dtw", + "dtw_gi", "wdtw", "lcss", "msm", @@ -36,6 +38,7 @@ distance_parameters = { "dtw": [0.0, 0.1, 1.0], # window + "dtw_gi": [0.0, 0.1, 1.0], # window "wdtw": [0.0, 0.1, 1.0], # parameter g "wddtw": [0.0, 0.1, 1.0], # parameter g "erp": [0.0, 0.1, 1.0], # window @@ -63,6 +66,7 @@ "euclidean": 27.51835240, "squared": 757.25971908652, "dtw": [757.259719, 330.834497, 330.834497], + "dtw_gi": [259.5333502342899, 310.10738471013804, 310.10738471013804], "wdtw": [165.41724, 3.308425, 0], "msm": [70.014828, 89.814828, 268.014828], "erp": [169.3715, 102.0979, 102.097904], @@ -90,6 +94,8 @@ def test_multivariate_correctness(): for j in range(0, 3): d = dtw_distance(case1, case2, window=distance_parameters["dtw"][j]) assert_almost_equal(d, basic_motions_distances["dtw"][j], 4) + d = dtw_gi_distance(case1, case2, window=distance_parameters["dtw_gi"][j]) + assert_almost_equal(d, basic_motions_distances["dtw_gi"][j], 4) d = wdtw_distance(case1, case2, g=distance_parameters["wdtw"][j]) assert_almost_equal(d, basic_motions_distances["wdtw"][j], 4) d = lcss_distance(case1, case2, epsilon=distance_parameters["lcss"][j] / 50.0) diff --git a/aeon/distances/tests/test_distances.py b/aeon/distances/tests/test_distances.py index c2dcefe436..4efe396ab2 100644 --- a/aeon/distances/tests/test_distances.py +++ b/aeon/distances/tests/test_distances.py @@ -28,7 +28,7 @@ def _validate_distance_result( - x, y, name, distance, expected_result=10, check_xy_permuted=True + x, y, name, distance, symmetric, expected_result=10, check_xy_permuted=True ): """ Validate the distance result by comparing it with the expected result. @@ -57,12 +57,14 @@ def _validate_distance_result( assert isinstance(dist_result_to_self, float) # If unequal length swap where x and y are to ensure it works both ways around - if original_x.shape[-1] != original_y.shape[-1] and check_xy_permuted: + + if symmetric and original_x.shape[-1] != original_y.shape[-1] and check_xy_permuted: _validate_distance_result( original_y, original_x, name, distance, + symmetric, expected_result, check_xy_permuted=False, ) @@ -82,6 +84,7 @@ def test_distances(dist): make_example_1d_numpy(10, random_state=2), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][0], ) @@ -91,6 +94,7 @@ def test_distances(dist): make_example_2d_numpy_series(10, 1, random_state=2), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][0], ) @@ -100,6 +104,7 @@ def test_distances(dist): make_example_2d_numpy_series(10, 1, random_state=2), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][1], ) @@ -111,6 +116,7 @@ def test_distances(dist): make_example_1d_numpy(10, random_state=2), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][2], ) @@ -120,6 +126,7 @@ def test_distances(dist): make_example_2d_numpy_series(10, 1, random_state=2), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][2], ) @@ -129,6 +136,7 @@ def test_distances(dist): make_example_2d_numpy_series(10, 10, random_state=2), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][3], ) @@ -140,6 +148,7 @@ def test_distances(dist): np.array([15.0]), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][4], ) @@ -149,6 +158,7 @@ def test_distances(dist): np.array([[15.0]]), dist["name"], dist["distance"], + dist["symmetric"], _expected_distance_results[dist["name"]][4], ) diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index de203a0bcd..7d39be08e3 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -1,13 +1,19 @@ """Forecasters.""" __all__ = [ + "ARIMAForecaster", "DummyForecaster", "BaseForecaster", "RegressionForecaster", "ETSForecaster", + "AutoETSForecaster", + "NaiveForecaster", ] +from aeon.forecasting._arima import ARIMAForecaster +from aeon.forecasting._autoets import AutoETSForecaster from aeon.forecasting._dummy import DummyForecaster -from aeon.forecasting._ets import ETSForecaster +from aeon.forecasting._ets_fast import ETSForecaster +from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster from aeon.forecasting.base import BaseForecaster diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py new file mode 100644 index 0000000000..4de0fee3d3 --- /dev/null +++ b/aeon/forecasting/_arima.py @@ -0,0 +1,421 @@ +"""ARIMAForecaster class. + +An implementation of the arima statistics forecasting algorithm. + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["ARIMAForecaster"] + +from math import comb + +import numpy as np + +from aeon.forecasting._utils import calc_seasonal_period, kpss_test +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + + +class ARIMAForecaster(BaseForecaster): + """ARIMA forecaster. + + An implementation of the Hyndman-Khandakar Auto ARIMA forecasting algorithm[1]_. + Adjusted to add basic seasonal ARIMA. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + """ + + def __init__(self, horizon=1): + super().__init__(horizon=horizon, axis=1) + self.data_ = [] + self.differenced_data_ = [] + self.residuals_ = [] + self.aic_ = 0 + self.p_ = 0 + self.d_ = 0 + self.q_ = 0 + self.ps_ = 0 + self.ds_ = 0 + self.qs_ = 0 + self.seasonal_period_ = 0 + self.constant_term_ = 0 + self.c_ = 0 + self.phi_ = 0 + self.phi_s_ = 0 + self.theta_ = 0 + self.theta_s_ = 0 + + def _fit(self, y, exog=None): + """Fit AutoARIMA forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ARIMAForecaster. + """ + self.data_ = np.array(y.squeeze(), dtype=np.float64) + ( + self.differenced_data_, + self.aic_, + self.p_, + self.d_, + self.q_, + self.ps_, + self.ds_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + parameters, + ) = auto_arima(self.data_) + (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = extract_params( + parameters, self.p_, self.q_, self.ps_, self.qs_, self.constant_term_ + ) + ( + self.aic_, + self.residuals_, + ) = arima_log_likelihood( + parameters, + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + y = np.array(y, dtype=np.float64) + value = calc_arima( + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + len(self.differenced_data_), + self.c_, + self.phi_, + self.phi_s_, + self.theta_, + self.theta_s_, + self.residuals_, + ) + history = self.data_[::-1] + differenced_history = np.diff(self.data_, n=self.d_)[::-1] + # Step 1: undo seasonal differencing on y^(d) + for k in range(1, self.ds_ + 1): + lag = k * self.seasonal_period_ + value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] + + # Step 2: undo ordinary differencing + for k in range(1, self.d_ + 1): + value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] + + if y is None: + return np.array([value]) + else: + return np.insert(y, 0, value)[:-1] + + +# Define the ARIMA(p, d, q) likelihood function +def arima_log_likelihood( + params, data, p, q, ps, qs, seasonal_period, include_constant_term +): + """Calculate the log-likelihood of an ARIMA model given the parameters.""" + c, phi, phi_s, theta, theta_s = extract_params( + params, p, q, ps, qs, include_constant_term + ) # Extract parameters + + # Initialize residuals + n = len(data) + residuals = np.zeros(n) + for t in range(n): + y_hat = calc_arima( + data, + p, + q, + ps, + qs, + seasonal_period, + t, + c, + phi, + phi_s, + theta, + theta_s, + residuals, + ) + residuals[t] = data[t] - y_hat + # Calculate the log-likelihood + variance = np.mean(residuals**2) + liklihood = n * (np.log(2 * np.pi) + np.log(variance) + 1) + k = len(params) + aic = liklihood + 2 * k + return ( + aic, + residuals, + ) # Return negative log-likelihood for minimization + + +def extract_params(params, p, q, ps, qs, include_constant_term): + """Extract ARIMA parameters from the parameter vector.""" + # Extract parameters + c = params[0] if include_constant_term else 0 # Constant term + # AR coefficients + phi = params[include_constant_term : p + include_constant_term] + # Seasonal AR coefficients + phi_s = params[include_constant_term + p : p + ps + include_constant_term] + # MA coefficients + theta = params[include_constant_term + p + ps : p + ps + q + include_constant_term] + # Seasonal MA coefficents + theta_s = params[ + include_constant_term + p + ps + q : include_constant_term + p + ps + q + qs + ] + return c, phi, phi_s, theta, theta_s + + +def calc_arima( + data, p, q, ps, qs, seasonal_period, t, c, phi, phi_s, theta, theta_s, residuals +): + """Calculate the ARIMA forecast for time t.""" + # AR part + ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) + # Seasonal AR part + ars_term = ( + 0 + if (t - seasonal_period * ps) < 0 + else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) + ) + # MA part + ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) + # Seasonal MA part + mas_term = ( + 0 + if (t - seasonal_period * qs) < 0 + else np.dot( + theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] + ) + ) + y_hat = c + ar_term + ma_term + ars_term + mas_term + return y_hat + + +def nelder_mead( + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + tol=1e-6, + max_iter=500, +): + """Implement the nelder-mead optimisation algorithm.""" + num_params = include_constant_term + p + ps + q + qs + points = np.full((num_params + 1, num_params), 0.5) + for i in range(num_params): + points[i + 1][i] = 0.6 + values = np.array( + [ + arima_log_likelihood( + v, data, p, q, ps, qs, seasonal_period, include_constant_term + )[0] + for v in points + ] + ) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = arima_log_likelihood( + reflected_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if between best and second best, use reflected value + if len(values) > 1 and values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = arima_log_likelihood( + expanded_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = arima_log_likelihood( + contracted_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = arima_log_likelihood( + points[i], + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] + + +# def calc_moving_variance(data, window): +# X = np.lib.stride_tricks.sliding_window_view(data, window_shape=window) +# return X.var() + + +def auto_arima(data): + """ + Implement the Hyndman-Khandakar algorithm. + + For automatic ARIMA model selection. + """ + seasonal_period = calc_seasonal_period(data) + difference = 0 + while not kpss_test(data)[1]: + data = np.diff(data, n=1) + difference += 1 + seasonal_difference = 1 if seasonal_period > 1 else 0 + if seasonal_difference: + data = data[seasonal_period:] - data[:-seasonal_period] + include_c = 1 if difference == 0 else 0 + model_parameters = [ + [2, 2, 0, 0, include_c], + [0, 0, 0, 0, include_c], + [1, 0, 0, 0, include_c], + [0, 1, 0, 0, include_c], + ] + model_points = [] + for p in model_parameters: + points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) + p.append(aic) + model_points.append(points) + current_model = max(model_parameters, key=lambda item: item[5]) + current_points = model_points[model_parameters.index(current_model)] + while True: + better_model = False + for param_no in range(4): + for adjustment in [-1, 1]: + if (current_model[param_no] + adjustment) < 0: + continue + model = current_model.copy() + model[param_no] += adjustment + for constant_term in [0, 1]: + points, aic = nelder_mead( + data, + model[0], + model[1], + model[2], + model[3], + seasonal_period, + constant_term, + ) + if aic < current_model[5]: + current_model = model + current_points = points + current_model[5] = aic + current_model[4] = constant_term + better_model = True + if not better_model: + break + return ( + data, + current_model[5], + current_model[0], + difference, + current_model[1], + current_model[2], + seasonal_difference, + current_model[3], + seasonal_period, + current_model[4], + current_points, + ) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py new file mode 100644 index 0000000000..7501bee0e2 --- /dev/null +++ b/aeon/forecasting/_autoets.py @@ -0,0 +1,457 @@ +"""AutoETS class. + +Extends the ETSForecaster to automatically calculate the smoothing parameters + +""" + +__maintainer__ = [] +__all__ = ["AutoETSForecaster"] +import numpy as np +from numba import njit +from scipy.optimize import minimize + +from aeon.forecasting._autoets_gradient_params import _calc_model_liklihood +from aeon.forecasting._ets_fast import _fit, _predict +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + + +class AutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster. + + An implementation of the exponential smoothing statistics forecasting algorithm. + Chooses betweek additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + + Examples + -------- + >>> from aeon.forecasting import AutoETSForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = AutoETSForecaster() + >>> forecaster.fit(y) + AutoETSForecaster() + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + method="internal_nelder_mead", + horizon=1, + ): + self.method = method + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self.alpha_ = 0 + self.beta_ = 0 + self.gamma_ = 0 + self.phi_ = 0 + self.error_type_ = 0 + self.trend_type_ = 0 + self.seasonality_type_ = 0 + self.seasonal_period_ = 0 + self.n_timepoints_ = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 + self.residuals_ = [] + self.fitted_values_ = [] + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + ( + self.error_type_, + self.trend_type_, + self.seasonality_type_, + self.seasonal_period_, + self.alpha_, + self.beta_, + self.gamma_, + self.phi_, + ) = auto_ets(data, self.method) + ( + self.level_, + self.trend_, + self.seasonality_, + self.n_timepoints_, + self.residuals_, + self.fitted_values_, + self.avg_mean_sq_err_, + self.liklihood_, + self.k_, + self.aic_, + ) = _fit( + data, + self.error_type_, + self.trend_type_, + self.seasonality_type_, + self.seasonal_period_, + self.alpha_, + self.beta_, + self.gamma_, + self.phi_, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = _predict( + self.trend_type_, + self.seasonality_type_, + self.level_, + self.trend_, + self.seasonality_, + self.phi_, + self.horizon, + self.n_timepoints_, + self.seasonal_period_, + ) + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + +def auto_ets(data, method="internal_nelder_mead"): + """Return the best ETS model based on the supplied data, and optimisation method.""" + if method == "internal_nelder_mead": + return auto_ets_nelder_mead(data) + elif method == "internal_gradient": + return auto_ets_gradient(data) + else: + return auto_ets_scipy(data, method) + + +def auto_ets_scipy(data, method): + """Calculate ETS model parameters based on scipy optimisation functions.""" + seasonal_period = calc_seasonal_period(data) + lowest_liklihood = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + optimise_result = optimise_params_scipy( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + method, + ) + alpha, beta, gamma = optimise_result.x + liklihood_ = optimise_result.fun + phi = 0.98 + if lowest_liklihood == -1 or lowest_liklihood > liklihood_: + lowest_liklihood = liklihood_ + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +def auto_ets_gradient(data): + """ + Calc model params using pytorch. + + Calculate ETS model parameters based on the + internal gradient-based approach using pytorch. + """ + seasonal_period = calc_seasonal_period(data) + lowest_liklihood = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + (alpha, beta, gamma, phi, _residuals, liklihood_) = ( + _calc_model_liklihood( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + ) + if lowest_liklihood == -1 or lowest_liklihood > liklihood_: + lowest_liklihood = liklihood_ + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +@njit(nogil=NOGIL, cache=CACHE) +def auto_ets_nelder_mead(data): + """Calculate model parameters based on the internal nelder-mead implementation.""" + seasonal_period = calc_seasonal_period(data) + lowest_aic = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + ([alpha, beta, gamma, phi], aic) = nelder_mead( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + if trend_type == 0: + phi = 1 + if lowest_aic == -1 or lowest_aic > aic: + lowest_aic = aic + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +def optimise_params_scipy( + data, error_type, trend_type, seasonality_type, seasonal_period, method +): + """Optimise the ETS model parameters using the scipy algorithms.""" + + def run_ets_scipy(parameters): + alpha, beta, gamma, phi = parameters + if not ( + 0 <= alpha <= 1 and 0 <= beta <= 1 and 0 <= gamma <= 1 and 0 <= phi <= 1 + ): + return float("inf") + ( + _level, + _trend, + _seasonality, + _n_timepoints, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + _k, + aic_, + ) = _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return aic_ + + initial_points = [0.5, 0.5, 0.5, 0.5] + return minimize( + run_ets_scipy, initial_points, bounds=[[0, 1] for i in range(3)], method=method + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def run_ets( + parameters, data, error_type, trend_type, seasonality_type, seasonal_period +): + """Create and fit an ETS model and return the liklihood.""" + alpha, beta, gamma, phi = parameters + if not ( + 0 <= alpha <= 1 + and 0 <= beta <= 1 + and 0 <= gamma <= 1 + and 0.8 <= phi <= 1 + and ( + data.min() > 0 + or (error_type != 2 and trend_type != 2 and seasonality_type != 2) + ) + ): + return np.finfo(np.float64).max + ( + _level, + _trend, + _seasonality, + _n_timepoints, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + _k, + aic_, + ) = _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return aic_ + + +@njit(nogil=NOGIL, cache=CACHE) +def nelder_mead( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + tol=1e-6, + max_iter=500, +): + """Implement the nelder-mead optimisation algorithm.""" + points = np.array( + [ + [0.5, 0.5, 0.5, 0.9], + [0.6, 0.5, 0.5, 0.9], + [0.5, 0.6, 0.5, 0.9], + [0.5, 0.5, 0.6, 0.9], + [0.5, 0.5, 0.5, 0.95], + ] + ) + values = np.array( + [ + run_ets(v, data, error_type, trend_type, seasonality_type, seasonal_period) + for v in points + ] + ) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = run_ets( + reflected_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # if between best and second best, use reflected value + if values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = run_ets( + expanded_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = run_ets( + contracted_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = run_ets( + points[i], + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] diff --git a/aeon/forecasting/_autoets_gradient_params.py b/aeon/forecasting/_autoets_gradient_params.py new file mode 100644 index 0000000000..119211a29a --- /dev/null +++ b/aeon/forecasting/_autoets_gradient_params.py @@ -0,0 +1,297 @@ +"""AutoETSForecaster class. + +Extends the ETSForecaster to automatically calculate the smoothing parameters + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = [] + +import torch + +from aeon.forecasting._ets_fast import ADDITIVE, MULTIPLICATIVE, NONE, ETSForecaster + + +def _calc_model_liklihood( + data, error_type, trend_type, seasonality_type, seasonal_period +): + alpha, beta, gamma, phi = _optimise_parameters( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + forecaster = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + 1, + ) + forecaster.fit(data) + return alpha, beta, gamma, phi, forecaster.residuals_, forecaster.liklihood_ + + +def _optimise_parameters( + data, error_type, trend_type, seasonality_type, seasonal_period +): + torch.autograd.set_detect_anomaly(True) + data = torch.tensor(data) + n_timepoints = len(data) + if seasonality_type == 0: + seasonal_period = 1 + level, trend, seasonality = _initialise( + trend_type, seasonality_type, seasonal_period, data + ) + alpha = torch.tensor(0.1, requires_grad=True) # Level smoothing + parameters = [alpha] + if trend_type == NONE: + beta = torch.tensor(0) # Trend smoothing + else: + beta = torch.tensor(0.05, requires_grad=True) # Trend smoothing + parameters.append(beta) + if seasonality_type == NONE: + gamma = torch.tensor(0) # Trend smoothing + else: + gamma = torch.tensor(0.05, requires_grad=True) # Seasonality smoothing + parameters.append(gamma) + phi = torch.tensor(0.98, requires_grad=True) # Damping factor + batch_size = len(data) # seasonal_period * 2 + num_batches = len(data) // batch_size + # residuals_ = torch.zeros(n_timepoints) # 1 Less residual than data points + optimizer = torch.optim.SGD([alpha, beta, gamma, phi], lr=0.01) + for _epoch in range(10): # number of epochs + for i in range(0, num_batches): + batch_of_data = data[i * batch_size : (i + 1) * batch_size] + liklihood_ = torch.tensor(0, dtype=torch.float64) + mul_liklihood_pt2 = torch.tensor(0, dtype=torch.float64) + for t, data_item in enumerate(batch_of_data): + # Calculate level, trend, and seasonal components + fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( + _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[t % seasonal_period], + data_item, + alpha, + beta, + gamma, + phi, + ) + ) + liklihood_ += error * error + mul_liklihood_pt2 += torch.log(torch.abs(fitted_value)) + liklihood_ = (n_timepoints - seasonal_period) * torch.log(liklihood_) + if error_type == MULTIPLICATIVE: + liklihood_ += 2 * mul_liklihood_pt2 + liklihood_.backward() + optimizer.step() + optimizer.zero_grad() + # Impose sensible parameter limits + alpha = alpha.clone().detach().requires_grad_().clamp(0, 1) + if trend_type != NONE: + # Impose sensible parameter limits + beta = beta.clone().detach().requires_grad_().clamp(0, 1) + if seasonality_type != NONE: + # Impose sensible parameter limits + gamma = gamma.clone().detach().requires_grad_().clamp(0, 1) + # Impose sensible parameter limits + phi = phi.clone().detach().requires_grad_().clamp(0.1, 0.98) + level = level.clone().detach() + trend = trend.clone().detach() + seasonality = seasonality.clone().detach() + return alpha.item(), beta.item(), gamma.item(), phi.item() + + +def _predict( + trend_type, + seasonality_type, + level, + trend, + seasonality, + phi, + horizon, + n_timepoints, + seasonal_period, +): + # Generate forecasts based on the final values of level, trend, and seasonals + if phi == 1: # No damping case + phi_h = float(horizon) + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = phi * (1 - phi**horizon) / (1 - phi) + seasonal_index = (n_timepoints + horizon) % seasonal_period + return _predict_value( + trend_type, seasonality_type, level, trend, seasonality[seasonal_index], phi_h + )[0] + + +def _initialise(trend_type, seasonality_type, seasonal_period, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + level = torch.mean(data[:seasonal_period]) + # Initial Trend + if trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + trend = torch.mean( + data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] + ) + elif trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + trend = torch.mean( + data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] + ) + else: + # No trend + trend = torch.tensor(0) + # Initial Seasonality + if seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + seasonality = data[:seasonal_period] - level + elif seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + seasonality = data[:seasonal_period] / level + else: + # No seasonality + seasonality = torch.zeros(1) + return level, trend, seasonality + + +def _update_states( + error_type, + trend_type, + seasonality_type, + curr_level, + curr_trend, + curr_seasonality, + data_item: int, + alpha, + beta, + gamma, + phi, +): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + fitted_value, damped_trend, trend_level_combination = _predict_value( + trend_type, seasonality_type, curr_level, curr_trend, curr_seasonality, phi + ) + # Calculate the error term (observed value - fitted value) + if error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if error_type == MULTIPLICATIVE: + level = trend_level_combination.clone() * (1 + alpha.clone() * error.clone()) + trend = damped_trend.clone() * (1 + beta.clone() * error.clone()) + seasonality = curr_seasonality.clone() * (1 + gamma.clone() * error.clone()) + if seasonality_type == ADDITIVE: + # Add seasonality correction + level += alpha.clone() * error.clone() * curr_seasonality.clone() + seasonality += ( + gamma.clone() * error.clone() * trend_level_combination.clone() + ) + if trend_type == ADDITIVE: + trend += ( + (curr_level.clone() + curr_seasonality.clone()) + * beta.clone() + * error.clone() + ) + else: + trend += ( + (curr_seasonality.clone() / curr_level.clone()) + * beta.clone() + * error.clone() + ) + elif trend_type == ADDITIVE: + trend += curr_level.clone() * beta.clone() * error.clone() + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= curr_seasonality.clone() + trend_correction *= curr_seasonality.clone() + seasonality_correction *= trend_level_combination.clone() + if trend_type == MULTIPLICATIVE: + trend_correction *= curr_level.clone() + level = ( + trend_level_combination.clone() + + alpha.clone() * error.clone() / level_correction + ) + trend = damped_trend.clone() + beta.clone() * error.clone() / trend_correction + seasonality = ( + curr_seasonality.clone() + + gamma.clone() * error.clone() / seasonality_correction + ) + return (fitted_value, error, level, trend, seasonality) + + +def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): + """ + + Generate various useful values, including the next fitted value. + + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model + + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if trend_type == MULTIPLICATIVE: + damped_trend = trend.clone() ** phi.clone() + trend_level_combination = level.clone() * damped_trend.clone() + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend.clone() * phi.clone() + trend_level_combination = level.clone() + damped_trend.clone() + # Calculate forecast (fitted value) based on the current components + if seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination.clone() * seasonality.clone() + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination.clone() + seasonality.clone() + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_compare_external_autoets.py b/aeon/forecasting/_compare_external_autoets.py new file mode 100644 index 0000000000..b57f67a874 --- /dev/null +++ b/aeon/forecasting/_compare_external_autoets.py @@ -0,0 +1,207 @@ +"""Test Other Packages AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import math +import time + +import matplotlib.pyplot as plt +from sktime.forecasting.ets import AutoETS as sktime_AutoETS +from statsforecast.models import AutoETS as sf_AutoETS +from statsforecast.utils import AirPassengers as ap +from statsforecast.utils import AirPassengersDF +from statsmodels.tsa.exponential_smoothing.ets import ETSModel + +from aeon.forecasting._autoets import auto_ets +from aeon.forecasting._ets_fast import ETSForecaster + +plt.rcParams["figure.figsize"] = (12, 8) + + +def test_other_forecasters(): + """TestOtherForecasters.""" + plt.plot(AirPassengersDF.ds, AirPassengersDF.y, label="Actual Values", color="blue") + # Statsmodels + start = time.perf_counter() + statsmodels_model = ETSModel( + ap, + error="mul", + trend=None, + damped_trend=False, + seasonal="mul", + seasonal_periods=12, + ) + statsmodels_fit = statsmodels_model.fit(maxiter=10000) + end = time.perf_counter() + statsmodels_time = end - start + print( # noqa + f"Statsmodels: Alpha: {statsmodels_fit.alpha}, \ + Beta: statsmodels_fit.beta, gamma: {statsmodels_fit.gamma}, \ + phi: statsmodels_fit.phi" + ) + print(f"Statsmodels AIC: {statsmodels_fit.aic}") # noqa + sm_internal_model = ETSForecaster( + 2, 0, 2, 12, statsmodels_fit.alpha, 0, statsmodels_fit.gamma, 1 + ) + sm_internal_model.fit(ap) + print(f"Statsmodels AIC: {sm_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + statsmodels_fit.fittedvalues, + label="statsmodels fit", + color="green", + ) + # Sktime + start = time.perf_counter() + sktime_model = sktime_AutoETS(auto=True, sp=12) + sktime_model.fit(ap) + end = time.perf_counter() + sktime_time = end - start + # pylint: disable=W0212 + print( # noqa + f"Sktime: Alpha: {sktime_model._fitted_forecaster.alpha}, \ + Beta: {sktime_model._fitted_forecaster.beta}, \ + gamma: {sktime_model._fitted_forecaster.gamma}, \ + phi: sktime_model._fitted_forecaster.phi" + ) + + if sktime_model._fitted_forecaster.error == "add": + sk_error = 1 + elif sktime_model._fitted_forecaster.error == "mul": + sk_error = 2 + else: + sk_error = 0 + if sktime_model._fitted_forecaster.trend == "add": + sk_trend = 1 + elif sktime_model._fitted_forecaster.trend == "mul": + sk_trend = 2 + else: + sk_trend = 0 + if sktime_model._fitted_forecaster.seasonal == "add": + sk_seasonal = 1 + elif sktime_model._fitted_forecaster.seasonal == "mul": + sk_seasonal = 2 + else: + sk_seasonal = 0 + print( # noqa + f"Error Type: {sk_error}, Trend Type: {sk_trend}, \ + Seasonality Type: {sk_seasonal}, Seasonal Period: {12}" + ) + print(f"Sktime AIC: {sktime_model._fitted_forecaster.aic}") # noqa + sk_internal_model = ETSForecaster( + sk_error, + sk_trend, + sk_seasonal, + 12, + sktime_model._fitted_forecaster.alpha, + sktime_model._fitted_forecaster.beta, + sktime_model._fitted_forecaster.gamma, + 1, + ) + sk_internal_model.fit(ap) + print(f"Sktime AIC: {sk_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + sktime_model._fitted_forecaster.fittedvalues, + label="sktime fitted values", + color="red", + ) + # pylint: enable=W0212 + # internal + start = time.perf_counter() + ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) = auto_ets(ap) + internal_model = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + internal_model.fit(ap) + end = time.perf_counter() + internal_time = end - start + print( # noqa + f"Internal: Alpha: {internal_model.alpha}, Beta: {internal_model.beta}, \ + gamma: {internal_model.gamma}, phi: {internal_model.phi}" + ) + print( # noqa + f"Error Type: {internal_model.error_type}, \ + Trend Type: {internal_model.trend_type}, \ + Seasonality Type: {internal_model.seasonality_type}, \ + Seasonal Period: {internal_model.seasonal_period}" + ) + print(f"Internal AIC: {internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds[seasonal_period:], + internal_model.fitted_values_, + label="Internal fitted values", + color="black", + ) + # statsforecast + start = time.perf_counter() + sf_model = sf_AutoETS(season_length=12) + sf_model.fit(ap) + end = time.perf_counter() + statsforecast_time = end - start + print( # noqa + f"Statsforecast: Alpha: {sf_model.model_['par'][0]}, \ + Beta: {sf_model.model_['par'][1]}, gamma: {sf_model.model_['par'][2]}, \ + phi: {sf_model.model_['par'][3]}" + ) + print( # noqa + f"Statsforecast Model Type: {sf_model.model_['method']}, \ + AIC: {sf_model.model_['aic']}" + ) + sf_internal_model = ETSForecaster( + 2 if sf_model.model_["components"][0] == "M" else 1, + ( + 2 + if sf_model.model_["components"][1] == "M" + else 1 if sf_model.model_["components"][1] == "A" else 0 + ), + ( + 2 + if sf_model.model_["components"][2] == "M" + else 1 if sf_model.model_["components"][2] == "A" else 0 + ), + 12, + 0 if math.isnan(sf_model.model_["par"][0]) else sf_model.model_["par"][0], + 0 if math.isnan(sf_model.model_["par"][1]) else sf_model.model_["par"][1], + 0 if math.isnan(sf_model.model_["par"][2]) else sf_model.model_["par"][2], + 0 if math.isnan(sf_model.model_["par"][3]) else sf_model.model_["par"][3], + ) + sf_internal_model.fit(ap) + print(f"Statsforecast AIC: {sf_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + sf_model.model_["fitted"], + label="statsforecast fitted values", + color="orange", + ) + print( # noqa + f"Statsmodels Time: {statsmodels_time}\ + Sktime Time: {sktime_time}\ + Internal Time: {internal_time}\ + Statsforecast Time: {statsforecast_time}" + ) # noqa + plt.ylabel("Air Passenger Numbers") + plt.grid() + plt.legend() + plt.show() + + +if __name__ == "__main__": + test_other_forecasters() diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index 2635b83457..fb29ce4e47 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -3,20 +3,20 @@ An implementation of the exponential smoothing statistics forecasting algorithm. Implements additive and multiplicative error models, None, additive and multiplicative (including damped) trend and -None, additive and multiplicative seasonality +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + """ __maintainer__ = [] -__all__ = ["ETSForecaster", "NONE", "ADDITIVE", "MULTIPLICATIVE"] +__all__ = ["ETSForecaster"] import numpy as np -from numba import njit from aeon.forecasting.base import BaseForecaster -NOGIL = False -CACHE = True - NONE = 0 ADDITIVE = 1 MULTIPLICATIVE = 2 @@ -25,44 +25,31 @@ class ETSForecaster(BaseForecaster): """Exponential Smoothing forecaster. - An implementation of the exponential smoothing forecasting algorithm. - Implements additive and multiplicative error models, None, additive and - multiplicative (including damped) trend and None, additive and mutliplicative - seasonality. See [1]_ for a description. + An implementation of the exponential smoothing statistics forecasting algorithm. + Implements additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. Parameters ---------- - error_type : int, default = 1 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - trend_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - seasonality_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - seasonal_period : int, default=1 - Length of seasonality period. If seasonality_type is NONE, this is assumed to - be 1 alpha : float, default = 0.1 Level smoothing parameter. beta : float, default = 0.01 - Trend smoothing parameter. If trend_type is NONE, this is assumed to be 0.0. + Trend smoothing parameter. gamma : float, default = 0.01 - Seasonal smoothing parameter. If seasonality is NONE, this is assumed to be - 0.0. + Seasonal smoothing parameter. phi : float, default = 0.99 Trend damping smoothing parameters horizon : int, default = 1 The horizon to forecast to. - - Attributes - ---------- - mean_sq_err_ : float - Mean squared error. - likelihood_ : float - Likelihood of the fitted model based on residuals. - residuals_ : arraylike - List of train set differences between fitted and actual values. - n_timpoints_ : int - Length of the series passed to fit. + error_type : int + The type of error model; either Additive(1) or Multiplicative(2) + trend_type : int + The type of trend model; one of None(0), additive(1) or multiplicative(2). + seasonality_type : int + The type of seasonality model; one of None(0), additive(1) or multiplicative(2). + seasonal_period : int + The period of the seasonality (m) (e.g., for quaterly data seasonal_period = 4). References ---------- @@ -74,37 +61,48 @@ class ETSForecaster(BaseForecaster): >>> from aeon.forecasting import ETSForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1) + >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8) + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + seasonality_type=2, trend_type=2) >>> forecaster.predict() - 449.9435566831507 + 366.90200486015596 """ def __init__( self, - error_type=ADDITIVE, - trend_type=NONE, - seasonality_type=NONE, - seasonal_period=1, - alpha=0.1, - beta=0.01, - gamma=0.01, - phi=0.99, - horizon=1, + error_type: int = ADDITIVE, + trend_type: int = NONE, + seasonality_type: int = NONE, + seasonal_period: int = 1, + alpha: float = 0.1, + beta: float = 0.01, + gamma: float = 0.01, + phi: float = 0.99, + horizon: int = 1, ): - self.error_type = error_type - self.trend_type = trend_type - self.seasonality_type = seasonality_type - self.seasonal_period = seasonal_period self.alpha = alpha self.beta = beta self.gamma = gamma self.phi = phi - self.mean_sq_err_ = 0 - self.likelihood_ = 0 + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self._beta = beta + self._gamma = gamma + self.error_type = error_type + self.trend_type = trend_type + self.seasonality_type = seasonality_type + self.seasonal_period = seasonal_period + self._seasonal_period = seasonal_period + self.n_timepoints = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 self.residuals_ = [] - self.n_timpoints_ = 0 super().__init__(horizon=horizon, axis=1) def _fit(self, y, exog=None): @@ -124,39 +122,153 @@ def _fit(self, y, exog=None): self Fitted BaseForecaster. """ - self.n_timepoints_ = len(y) - if self.error_type != MULTIPLICATIVE and self.error_type != ADDITIVE: - raise ValueError("Error must be either additive or multiplicative") - self._seasonal_period = self.seasonal_period - if self.seasonal_period < 1 or self.seasonality_type == NONE: + assert ( + self.error_type != NONE + ), "Error must be either additive or multiplicative" + if self._seasonal_period < 1 or self.seasonality_type == NONE: self._seasonal_period = 1 - self._beta = self.beta - if self.trend_type == NONE or self.trend_type is None: - self._beta = 0 - self._gamma = self.gamma - if self.seasonality_type == NONE or self.trend_type is None: - self._gamma = 0 - data = np.array(y.squeeze(), dtype=np.float64) - ( - self._level, - self._trend, - self._seasonality, - self.residuals_, - self.mean_sq_err_, - self.likelihood_, - ) = _fit_numba( - data, - self.error_type, - self.trend_type, - self.seasonality_type, - self._seasonal_period, - self.alpha, - self._beta, - self._gamma, - self.phi, + if self.trend_type == NONE: + self._beta = ( + 0 # Required for the equations in _update_states to work correctly + ) + if self.seasonality_type == NONE: + self._gamma = ( + 0 # Required for the equations in _update_states to work correctly + ) + data = y.squeeze() + self.n_timepoints = len(data) + self._initialise(data) + num_vals = self.n_timepoints - self._seasonal_period + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + # 1 Less residual than data points + self.residuals_ = np.zeros(num_vals) + for t, data_item in enumerate(data[self._seasonal_period :]): + # Calculate level, trend, and seasonal components + fitted_value, error = self._update_states( + data_item, t % self._seasonal_period + ) + self.residuals_[t] = error + self.avg_mean_sq_err_ += (data_item - fitted_value) ** 2 + liklihood_error = error + if self.error_type == MULTIPLICATIVE: + liklihood_error *= fitted_value + self.liklihood_ += liklihood_error**2 + self.avg_mean_sq_err_ /= num_vals + self.liklihood_ = num_vals * np.log(self.liklihood_) + self.k_ = ( + self.seasonal_period * (self.seasonality_type != 0) + + 2 * (self.trend_type != 0) + + 2 + + 1 * (self.phi != 1) ) + self.aic_ = self.liklihood_ + 2 * self.k_ - num_vals * np.log(num_vals) return self + def _update_states(self, data_item, seasonal_index): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + level = self.level_ + trend = self.trend_ + seasonality = self.seasonality_[seasonal_index] + fitted_value, damped_trend, trend_level_combination = self._predict_value( + level, trend, seasonality, self.phi + ) + # Calculate the error term (observed value - fitted value) + if self.error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if self.error_type == MULTIPLICATIVE: + self.level_ = trend_level_combination * (1 + self.alpha * error) + self.trend_ = damped_trend * (1 + self._beta * error) + self.seasonality_[seasonal_index] = seasonality * (1 + self._gamma * error) + if self.seasonality_type == ADDITIVE: + self.level_ += ( + self.alpha * error * seasonality + ) # Add seasonality correction + self.seasonality_[seasonal_index] += ( + self._gamma * error * trend_level_combination + ) + if self.trend_type == ADDITIVE: + self.trend_ += (level + seasonality) * self._beta * error + else: + self.trend_ += seasonality / level * self._beta * error + elif self.trend_type == ADDITIVE: + self.trend_ += level * self._beta * error + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if self.seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= seasonality + trend_correction *= seasonality + seasonality_correction *= trend_level_combination + if self.trend_type == MULTIPLICATIVE: + trend_correction *= level + self.level_ = ( + trend_level_combination + self.alpha * error / level_correction + ) + self.trend_ = damped_trend + self._beta * error / trend_correction + self.seasonality_[seasonal_index] = ( + seasonality + self._gamma * error / seasonality_correction + ) + return (fitted_value, error) + + def _initialise(self, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + self.level_ = np.mean(data[: self._seasonal_period]) + # Initial Trend + if self.trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + self.trend_ = np.mean( + data[self._seasonal_period : 2 * self._seasonal_period] + - data[: self._seasonal_period] + ) + elif self.trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + self.trend_ = np.mean( + data[self._seasonal_period : 2 * self._seasonal_period] + / data[: self._seasonal_period] + ) + else: + # No trend + self.trend_ = 0 + # Initial Seasonality + if self.seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + self.seasonality_ = data[: self._seasonal_period] - self.level_ + elif self.seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + self.seasonality_ = data[: self._seasonal_period] / self.level_ + else: + # No seasonality + self.seasonality_ = [0] + def _predict(self, y=None, exog=None): """ Predict the next horizon steps ahead. @@ -166,7 +278,7 @@ def _predict(self, y=None, exog=None): y : np.ndarray, default = None A time series to predict the next horizon value for. If None, predict the next horizon value after series seen in fit. - exog : np.ndarray, default = None + exog : np.ndarray, default =None Optional exogenous time series data assumed to be aligned with y Returns @@ -174,243 +286,60 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - return _predict_numba( - self.trend_type, - self.seasonality_type, - self._level, - self._trend, - self._seasonality, - self.phi, - self.horizon, - self.n_timepoints_, - self.seasonal_period, - ) - - -@njit(nogil=NOGIL, cache=CACHE) -def _fit_numba( - data, - error_type, - trend_type, - seasonality_type, - seasonal_period, - alpha, - beta, - gamma, - phi, -): - n_timepoints = len(data) - level, trend, seasonality = _initialise( - trend_type, seasonality_type, seasonal_period, data - ) - mse = 0 - lhood = 0 - mul_likelihood_pt2 = 0 - res = np.zeros(n_timepoints) # 1 Less residual than data points - for t, data_item in enumerate(data[seasonal_period:]): - # Calculate level, trend, and seasonal components - fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( - _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality[t % seasonal_period], - data_item, - alpha, - beta, - gamma, - phi, - ) - ) - res[t] = error - mse += (data_item - fitted_value) ** 2 - lhood += error * error - mul_likelihood_pt2 += np.log(np.fabs(fitted_value)) - mse /= n_timepoints - seasonal_period - lhood = (n_timepoints - seasonal_period) * np.log(lhood) - if error_type == MULTIPLICATIVE: - lhood += 2 * mul_likelihood_pt2 - return level, trend, seasonality, res, mse, lhood - - -def _predict_numba( - trend_type, - seasonality_type, - level, - trend, - seasonality, - phi, - horizon, - n_timepoints, - seasonal_period, -): - # Generate forecasts based on the final values of level, trend, and seasonals - if phi == 1: # No damping case - phi_h = float(horizon) - else: - # Geometric series formula for calculating phi + phi^2 + ... + phi^h - phi_h = phi * (1 - phi**horizon) / (1 - phi) - seasonal_index = (n_timepoints + horizon) % seasonal_period - return _predict_value( - trend_type, - seasonality_type, - level, - trend, - seasonality[seasonal_index], - phi_h, - )[0] - - -@njit(nogil=NOGIL, cache=CACHE) -def _initialise(trend_type, seasonality_type, seasonal_period, data): - """ - Initialize level, trend, and seasonality values for the ETS model. - - Parameters - ---------- - data : array-like - The time series data - (should contain at least two full seasons if seasonality is specified) - """ - # Initial Level: Mean of the first season - level = np.mean(data[:seasonal_period]) - # Initial Trend - if trend_type == ADDITIVE: - # Average difference between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] - ) - elif trend_type == MULTIPLICATIVE: - # Average ratio between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] - ) - else: - # No trend - trend = 0 - # Initial Seasonality - if seasonality_type == ADDITIVE: - # Seasonal component is the difference - # from the initial level for each point in the first season - seasonality = data[:seasonal_period] - level - elif seasonality_type == MULTIPLICATIVE: - # Seasonal component is the ratio of each point in the first season - # to the initial level - seasonality = data[:seasonal_period] / level - else: - # No seasonality - seasonality = np.zeros(1) - return level, trend, seasonality - - -@njit(nogil=NOGIL, cache=CACHE) -def _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality, - data_item: int, - alpha, - beta, - gamma, - phi, -): - """ - Update level, trend, and seasonality components. - - Using state space equations for an ETS model. - - Parameters - ---------- - data_item: float - The current value of the time series. - seasonal_index: int - The index to update the seasonal component. - """ - # Retrieve the current state values - curr_level = level - curr_seasonality = seasonality - fitted_value, damped_trend, trend_level_combination = _predict_value( - trend_type, seasonality_type, level, trend, seasonality, phi - ) - # Calculate the error term (observed value - fitted value) - if error_type == MULTIPLICATIVE: - error = data_item / fitted_value - 1 # Multiplicative error - else: - error = data_item - fitted_value # Additive error - # Update level - if error_type == MULTIPLICATIVE: - level = trend_level_combination * (1 + alpha * error) - trend = damped_trend * (1 + beta * error) - seasonality = curr_seasonality * (1 + gamma * error) - if seasonality_type == ADDITIVE: - level += alpha * error * curr_seasonality # Add seasonality correction - seasonality += gamma * error * trend_level_combination - if trend_type == ADDITIVE: - trend += (curr_level + curr_seasonality) * beta * error - else: - trend += curr_seasonality / curr_level * beta * error - elif trend_type == ADDITIVE: - trend += curr_level * beta * error - else: - level_correction = 1 - trend_correction = 1 - seasonality_correction = 1 - if seasonality_type == MULTIPLICATIVE: - # Add seasonality correction - level_correction *= curr_seasonality - trend_correction *= curr_seasonality - seasonality_correction *= trend_level_combination - if trend_type == MULTIPLICATIVE: - trend_correction *= curr_level - level = trend_level_combination + alpha * error / level_correction - trend = damped_trend + beta * error / trend_correction - seasonality = curr_seasonality + gamma * error / seasonality_correction - return (fitted_value, error, level, trend, seasonality) - - -@njit(nogil=NOGIL, cache=CACHE) -def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): - """ - - Generate various useful values, including the next fitted value. + # Generate forecasts based on the final values of level, trend, and seasonals + if self.phi == 1: # No damping case + phi_h = 1 + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = self.phi * (1 - self.phi**self.horizon) / (1 - self.phi) + seasonality = self.seasonality_[ + (self.n_timepoints + self.horizon) % self._seasonal_period + ] + fitted_value = self._predict_value( + self.level_, self.trend_, seasonality, phi_h + )[0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + def _predict_value(self, level, trend, seasonality, phi): + """ - Parameters - ---------- - trend : float - The current trend value for the model - level : float - The current level value for the model - seasonality : float - The current seasonality value for the model - phi : float - The damping parameter for the model + Generate various useful values, including the next fitted value. - Returns - ------- - fitted_value : float - single prediction based on the current state variables. - damped_trend : float - The damping parameter combined with the trend dependant on the model type - trend_level_combination : float - Combination of the trend and level based on the model type. - """ - # Apply damping parameter and - # calculate commonly used combination of trend and level components - if trend_type == MULTIPLICATIVE: - damped_trend = trend**phi - trend_level_combination = level * damped_trend - else: # Additive trend, if no trend, then trend = 0 - damped_trend = trend * phi - trend_level_combination = level + damped_trend + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model - # Calculate forecast (fitted value) based on the current components - if seasonality_type == MULTIPLICATIVE: - fitted_value = trend_level_combination * seasonality - else: # Additive seasonality, if no seasonality, then seasonality = 0 - fitted_value = trend_level_combination + seasonality - return fitted_value, damped_trend, trend_level_combination + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if self.trend_type == MULTIPLICATIVE: + damped_trend = trend**phi + trend_level_combination = level * damped_trend + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend * phi + trend_level_combination = level + damped_trend + + # Calculate forecast (fitted value) based on the current components + if self.seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination * seasonality + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination + seasonality + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_ets_fast.py b/aeon/forecasting/_ets_fast.py new file mode 100644 index 0000000000..fdbd9c005a --- /dev/null +++ b/aeon/forecasting/_ets_fast.py @@ -0,0 +1,476 @@ +"""ETSForecaster class. + +An implementation of the exponential smoothing statistics forecasting algorithm. +Implements additive and multiplicative error models, +None, additive and multiplicative (including damped) trend and +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["ETSForecaster"] + +import numpy as np +from numba import njit + +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + +NONE = 0 +ADDITIVE = 1 +MULTIPLICATIVE = 2 + + +class ETSForecaster(BaseForecaster): + """Exponential Smoothing forecaster. + + An implementation of the exponential smoothing statistics forecasting algorithm. + Implements additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. + + Parameters + ---------- + alpha : float, default = 0.1 + Level smoothing parameter. + beta : float, default = 0.01 + Trend smoothing parameter. + gamma : float, default = 0.01 + Seasonal smoothing parameter. + phi : float, default = 0.99 + Trend damping smoothing parameters + horizon : int, default = 1 + The horizon to forecast to. + error_type : int + The type of error model; either Additive(1) or Multiplicative(2) + trend_type : int + The type of trend model; one of None(0), additive(1) or multiplicative(2). + seasonality_type : int + The type of seasonality model; one of None(0), additive(1) or multiplicative(2). + seasonal_period : int + The period of the seasonality (m) (e.g., for quaterly data seasonal_period = 4). + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + + Examples + -------- + >>> from aeon.forecasting import ETSForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) + >>> forecaster.fit(y) + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + seasonality_type=2, trend_type=2) + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + error_type=ADDITIVE, + trend_type=NONE, + seasonality_type=NONE, + seasonal_period=1, + alpha=0.1, + beta=0.01, + gamma=0.01, + phi=0.99, + horizon=1, + ): + self.alpha = alpha + self.beta = beta + self.gamma = gamma + self.phi = phi + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self._beta = beta + self._gamma = gamma + self.error_type = error_type + self.trend_type = trend_type + self.seasonality_type = seasonality_type + self.seasonal_period = seasonal_period + self._seasonal_period = seasonal_period + self.n_timepoints_ = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 + self.residuals_ = [] + self.fitted_values_ = [] + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ETSForecaster. + """ + assert ( + self.error_type != NONE + ), "Error must be either additive or multiplicative" + if self._seasonal_period < 1 or self.seasonality_type == NONE: + self._seasonal_period = 1 + + if self.trend_type == NONE: + # Required for the equations in _update_states to work correctly + self._beta = 0 + if self.seasonality_type == NONE: + # Required for the equations in _update_states to work correctly + self._gamma = 0 + data = y.squeeze() + ( + self.level_, + self.trend_, + self.seasonality_, + self.n_timepoints_, + self.residuals_, + self.fitted_values_, + self.avg_mean_sq_err_, + self.liklihood_, + self.k_, + self.aic_, + ) = _fit( + data, + self.error_type, + self.trend_type, + self.seasonality_type, + self._seasonal_period, + self.alpha, + self._beta, + self._gamma, + self.phi, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = _predict( + self.trend_type, + self.seasonality_type, + self.level_, + self.trend_, + self.seasonality_, + self.phi, + self.horizon, + self.n_timepoints_, + self._seasonal_period, + ) + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + def _initialise(self, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + self.level_, self.trend_, self.seasonality_ = _initialise( + self.trend_type, self.seasonality_type, self._seasonal_period, data + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, +): + assert error_type != NONE, "Error must be either additive or multiplicative" + assert ( + error_type != MULTIPLICATIVE + and trend_type != MULTIPLICATIVE + and seasonality_type != MULTIPLICATIVE + or data.min() > 0 + ), "Data must be positive with multiplicative components" + if seasonal_period < 1 or seasonality_type == NONE: + seasonal_period = 1 + if trend_type == NONE: + # Required for the equations in _update_states to work correctly + beta = 0 + if seasonality_type == NONE: + # Required for the equations in _update_states to work correctly + gamma = 0 + n_timepoints = len(data) - seasonal_period + level, trend, seasonality = _initialise( + trend_type, seasonality_type, seasonal_period, data + ) + avg_mean_sq_err_ = 0 + liklihood_ = 0 + residuals_ = np.zeros(n_timepoints) # 1 Less residual than data points + fitted_values_ = np.zeros(n_timepoints) + for t, data_item in enumerate(data[seasonal_period:]): + # Calculate level, trend, and seasonal components + fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( + _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[t % seasonal_period], + data_item, + alpha, + beta, + gamma, + phi, + ) + ) + residuals_[t] = error + fitted_values_[t] = fitted_value + avg_mean_sq_err_ += (data_item - fitted_value) ** 2 + liklihood_error = error + if error_type == MULTIPLICATIVE: + liklihood_error *= fitted_value + liklihood_ += liklihood_error**2 + avg_mean_sq_err_ /= n_timepoints + liklihood_ = n_timepoints * np.log(liklihood_) + k_ = ( + seasonal_period * (seasonality_type != 0) + + 2 * (trend_type != 0) + + 2 + + 1 * (phi != 1) + ) + aic_ = liklihood_ + 2 * k_ - n_timepoints * np.log(n_timepoints) + return ( + level, + trend, + seasonality, + n_timepoints, + residuals_, + fitted_values_, + avg_mean_sq_err_, + liklihood_, + k_, + aic_, + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def _predict( + trend_type, + seasonality_type, + level, + trend, + seasonality, + phi, + horizon, + n_timepoints, + seasonal_period, +): + # Generate forecasts based on the final values of level, trend, and seasonals + if phi == 1: # No damping case + phi_h = 1 + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = phi * (1 - phi**horizon) / (1 - phi) + seasonal_index = (n_timepoints + horizon) % seasonal_period + return _predict_value( + trend_type, + seasonality_type, + level, + trend, + seasonality[seasonal_index], + phi_h, + )[0] + + +@njit(nogil=NOGIL, cache=CACHE) +def _initialise(trend_type, seasonality_type, seasonal_period, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + level = np.mean(data[:seasonal_period]) + # Initial Trend + if trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + trend = np.mean( + data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] + ) + elif trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + trend = np.mean( + data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] + ) + else: + # No trend + trend = 0 + # Initial Seasonality + if seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + seasonality = data[:seasonal_period] - level + elif seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + seasonality = data[:seasonal_period] / level + else: + # No seasonality + seasonality = np.zeros(1, dtype=np.float64) + return level, trend, seasonality + + +@njit(nogil=NOGIL, cache=CACHE) +def _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality, + data_item: int, + alpha, + beta, + gamma, + phi, +): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + curr_level = level + curr_seasonality = seasonality + fitted_value, damped_trend, trend_level_combination = _predict_value( + trend_type, seasonality_type, level, trend, seasonality, phi + ) + # Calculate the error term (observed value - fitted value) + if error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if error_type == MULTIPLICATIVE: + level = trend_level_combination * (1 + alpha * error) + trend = damped_trend * (1 + beta * error) + seasonality = curr_seasonality * (1 + gamma * error) + if seasonality_type == ADDITIVE: + level += alpha * error * curr_seasonality # Add seasonality correction + seasonality += gamma * error * trend_level_combination + if trend_type == ADDITIVE: + trend += (curr_level + curr_seasonality) * beta * error + else: + trend += curr_seasonality / curr_level * beta * error + elif trend_type == ADDITIVE: + trend += curr_level * beta * error + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= curr_seasonality + trend_correction *= curr_seasonality + seasonality_correction *= trend_level_combination + if trend_type == MULTIPLICATIVE: + trend_correction *= curr_level + level = trend_level_combination + alpha * error / level_correction + trend = damped_trend + beta * error / trend_correction + seasonality = curr_seasonality + gamma * error / seasonality_correction + return (fitted_value, error, level, trend, seasonality) + + +@njit(nogil=NOGIL, cache=CACHE) +def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): + """ + + Generate various useful values, including the next fitted value. + + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model + + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if trend_type == MULTIPLICATIVE: + damped_trend = trend**phi + trend_level_combination = level * damped_trend + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend * phi + trend_level_combination = level + damped_trend + + # Calculate forecast (fitted value) based on the current components + if seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination * seasonality + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination + seasonality + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py new file mode 100644 index 0000000000..9bdfa82fb9 --- /dev/null +++ b/aeon/forecasting/_naive.py @@ -0,0 +1,94 @@ +"""ETSForecaster class. + +An implementation of the exponential smoothing statistics forecasting algorithm. +Implements additive and multiplicative error models, +None, additive and multiplicative (including damped) trend and +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["NaiveForecaster"] + +import numpy as np + +from aeon.forecasting.base import BaseForecaster + +NONE = 0 +ADDITIVE = 1 +MULTIPLICATIVE = 2 + + +class NaiveForecaster(BaseForecaster): + """Naive forecaster. + + Forecasts future values as the last observed value. + + Parameters + ---------- + horizon : int, default = 1 + The number of steps ahead to forecast. + + Examples + -------- + >>> from aeon.forecasting import NaiveForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = NaiveForecaster() + >>> forecaster.fit(y) + NaiveForecaster() + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + horizon=1, + ): + self.last_value_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Naive forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted NaiveForecaster. + """ + self.last_value_ = y[0][-1] + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + if y is None: + return np.array([self.last_value_]) + else: + return np.insert(y, 0, self.last_value_)[:-1] diff --git a/aeon/forecasting/_plot_autoets_gradient_method.py b/aeon/forecasting/_plot_autoets_gradient_method.py new file mode 100644 index 0000000000..a84a41baa1 --- /dev/null +++ b/aeon/forecasting/_plot_autoets_gradient_method.py @@ -0,0 +1,66 @@ +"""Test AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import matplotlib.pyplot as plt +from statsforecast.utils import AirPassengers as ap +from statsforecast.utils import AirPassengersDF + +from aeon.forecasting._autoets import auto_ets +from aeon.forecasting._ets_fast import ETSForecaster + +plt.rcParams["figure.figsize"] = (12, 8) + + +def test_autoets_forecaster(): + """TestETSForecaster.""" + ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) = auto_ets(ap, method="internal_gradient") + print( # noqa + f"Error Type: {error_type}, Trend Type: {trend_type}, \ + Seasonality Type: {seasonality_type}, Seasonal Period: {seasonal_period}, \ + Alpha: {alpha}, Beta: {beta}, Gamma: {gamma}, Phi: {phi}" + ) # noqa + etsforecaster = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + 1, + ) + etsforecaster.fit(ap) + print(f"liklihood: {etsforecaster.liklihood_}") # noqa + + # assert np.allclose([parameter.item() for parameter in parameters], + # [0.1,0.05,0.05,0.98]) + plt.plot(AirPassengersDF.ds, AirPassengersDF.y, label="Actual Values", color="blue") + plt.plot( + AirPassengersDF.ds, + etsforecaster.fitted_values_, + label="Predicted Values", + color="green", + ) + plt.plot( + AirPassengersDF.ds, etsforecaster.residuals_, label="Residuals", color="red" + ) + plt.ylabel("Air Passenger Numbers") + plt.grid() + plt.legend() + plt.show() + + +if __name__ == "__main__": + test_autoets_forecaster() diff --git a/aeon/forecasting/_regression.py b/aeon/forecasting/_regression.py index 79393160b1..2330073afc 100644 --- a/aeon/forecasting/_regression.py +++ b/aeon/forecasting/_regression.py @@ -37,7 +37,7 @@ class RegressionForecaster(BaseForecaster): with sklearn regressors. """ - def __init__(self, window, horizon=1, regressor=None): + def __init__(self, window: int, horizon: int = 1, regressor=None): self.window = window self.regressor = regressor super().__init__(horizon=horizon, axis=1) @@ -50,7 +50,11 @@ def _fit(self, y, exog=None): Parameters ---------- - X : Time series on which to learn a forecaster + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead. + exog : np.ndarray, default=None + Optional exogenous time series data. Included for interface + compatibility but ignored in this estimator. Returns ------- @@ -74,14 +78,44 @@ def _fit(self, y, exog=None): return self def _predict(self, y=None, exog=None): - """Predict values for time series X.""" + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default=None + Optional exogenous time series data. Included for interface + compatibility but ignored in this estimator. + + Returns + ------- + np.ndarray + single prediction self.horizon steps ahead of y. + """ if y is None: return self.regressor_.predict(self.last_) last = y[:, -self.window :] return self.regressor_.predict(last) def _forecast(self, y, exog=None): - """Forecast values for time series X. + """ + Forecast the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray + A time series to predict the next horizon value for. + exog : np.ndarray, default=None + Optional exogenous time series data. Included for interface + compatibility but ignored in this estimator. + + Returns + ------- + np.ndarray + single prediction self.horizon steps ahead of y. NOTE: deal with horizons """ @@ -89,7 +123,7 @@ def _forecast(self, y, exog=None): return self.predict() @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params(cls, parameter_set: str = "default"): """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/forecasting/_sktime_autoets.py b/aeon/forecasting/_sktime_autoets.py new file mode 100644 index 0000000000..127d93040b --- /dev/null +++ b/aeon/forecasting/_sktime_autoets.py @@ -0,0 +1,78 @@ +"""SktimeAutoETS class. + +Wraps sktime AutoETS model for forecasting. + +""" + +__maintainer__ = [] +__all__ = ["SktimeAutoETSForecaster"] + + +import numpy as np +from sktime.forecasting.ets import AutoETS + +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + + +class SktimeAutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster from sktime. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + """ + + def __init__( + self, + horizon=1, + ): + self.model_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + season_length = calc_seasonal_period(data) + self.model_ = AutoETS(auto=True, sp=season_length) + self.model_.fit(data) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = self.model_.predict(self.horizon, exog)[0][0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] diff --git a/aeon/forecasting/_statsforecast_autoets.py b/aeon/forecasting/_statsforecast_autoets.py new file mode 100644 index 0000000000..8ce77d257d --- /dev/null +++ b/aeon/forecasting/_statsforecast_autoets.py @@ -0,0 +1,78 @@ +"""StatsforecastAutoETS class. + +Wraps statsforecast AutoETS model for forecasting. + +""" + +__maintainer__ = [] +__all__ = ["StatsForecastAutoETSForecaster"] + + +import numpy as np +from statsforecast.models import AutoETS + +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + + +class StatsForecastAutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster from statsforecast. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + """ + + def __init__( + self, + horizon=1, + ): + self.model_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + season_length = calc_seasonal_period(data) + self.model_ = AutoETS(season_length=season_length) + self.model_.fit(data) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = self.model_.predict(self.horizon, exog)["mean"][0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] diff --git a/aeon/forecasting/_time_autoets.py b/aeon/forecasting/_time_autoets.py new file mode 100644 index 0000000000..3d9e263e15 --- /dev/null +++ b/aeon/forecasting/_time_autoets.py @@ -0,0 +1,37 @@ +"""Test AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import timeit + +from statsforecast.utils import AirPassengers as ap + +from aeon.forecasting._autoets import nelder_mead, optimise_params_scipy + + +def test_optimise_params(): + nelder_mead(ap, 2, 2, 2, 12) + + +def test_optimise_params_scipy(): + optimise_params_scipy(ap, 2, 2, 2, 12, method="L-BFGS-B") + + +def test_autoets_forecaster(): + """TestETSForecaster.""" + for _i in range(20): + test_optimise_params() + test_optimise_params_scipy() + optim_ets_time = timeit.timeit(test_optimise_params, globals={}, number=1000) + print(f"Execution time Optimise params: {optim_ets_time} seconds") # noqa + optim_ets_scipy_time = timeit.timeit( + test_optimise_params_scipy, globals={}, number=1000 + ) + print( # noqa + f"Execution time Optimise params Scipy: {optim_ets_scipy_time} seconds" + ) + + +if __name__ == "__main__": + test_autoets_forecaster() diff --git a/aeon/forecasting/_utils.py b/aeon/forecasting/_utils.py new file mode 100644 index 0000000000..aeee0db3ae --- /dev/null +++ b/aeon/forecasting/_utils.py @@ -0,0 +1,115 @@ +""" +Forecasting utilities class. + +Contains useful utility methods for forecasting time series data. + +""" + +import numpy as np +from numba import njit + + +@njit(cache=True, fastmath=True) +def calc_seasonal_period(data): + """Estimate the seasonal period based on the autocorrelation of the series.""" + lags = _acf(data, 24) + lags = np.concatenate((np.array([1.0]), lags)) + peaks = [] + mean_lags = np.mean(lags) + for i in range(1, len(lags) - 1): # Skip the first (lag 0) and last elements + if lags[i] >= lags[i - 1] and lags[i] >= lags[i + 1] and lags[i] > mean_lags: + peaks.append(i) + if not peaks: + return 1 + else: + return peaks[0] + + +@njit(cache=True, fastmath=True) +def _acf(X, max_lag): + length = len(X) + X_t = np.zeros(max_lag, dtype=float) + for lag in range(1, max_lag + 1): + lag_length = length - lag + x1 = X[:-lag] + x2 = X[lag:] + s1 = np.sum(x1) + s2 = np.sum(x2) + m1 = s1 / lag_length + m2 = s2 / lag_length + ss1 = np.sum(x1 * x1) + ss2 = np.sum(x2 * x2) + v1 = ss1 - s1 * m1 + v2 = ss2 - s2 * m2 + v1_is_zero, v2_is_zero = v1 <= 1e-9, v2 <= 1e-9 + if v1_is_zero and v2_is_zero: # Both zero variance, + # so must be 100% correlated + X_t[lag - 1] = 1 + elif v1_is_zero or v2_is_zero: # One zero variance + # the other not + X_t[lag - 1] = 0 + else: + X_t[lag - 1] = np.sum((x1 - m1) * (x2 - m2)) / np.sqrt(v1 * v2) + return X_t + + +def kpss_test(y, regression="c", lags=None): # Test if time series is stationary + """ + Implement the KPSS test for stationarity. + + Parameters + ---------- + y (array-like): Time series data + regression (str): 'c' for constant, 'ct' for constant + trend + lags (int): Number of lags for HAC variance estimation (default: sqrt(n)) + + Returns + ------- + kpss_stat (float): KPSS test statistic + stationary (bool): Whether the series is stationary according to the test + """ + y = np.asarray(y) + n = len(y) + + # Step 1: Fit regression model to estimate residuals + if regression == "c": # Constant + X = np.ones((n, 1)) + elif regression == "ct": # Constant + Trend + X = np.column_stack((np.ones(n), np.arange(1, n + 1))) + else: + raise ValueError("regression must be 'c' or 'ct'") + + beta = np.linalg.lstsq(X, y, rcond=None)[0] # Estimate regression coefficients + residuals = y - X @ beta # Get residuals (u_t) + + # Step 2: Compute cumulative sum of residuals (S_t) + S_t = np.cumsum(residuals) + + # Step 3: Estimate long-run variance (HAC variance) + if lags is None: + # lags = int(12 * (n / 100)**(1/4)) # Default statsmodels lag length + lags = int(np.sqrt(n)) # Default lag length + + gamma_0 = np.sum(residuals**2) / (n - X.shape[1]) # Lag-0 autocovariance + gamma = [np.sum(residuals[k:] * residuals[:-k]) / n for k in range(1, lags + 1)] + + # Bartlett weights + weights = [1 - (k / (lags + 1)) for k in range(1, lags + 1)] + + # Long-run variance + sigma_squared = gamma_0 + 2 * np.sum([w * g for w, g in zip(weights, gamma)]) + + # Step 4: Calculate the KPSS statistic + kpss_stat = np.sum(S_t**2) / (n**2 * sigma_squared) + + if regression == "ct": + # p. 162 Kwiatkowski et al. (1992): y_t = beta * t + r_t + e_t, + # where beta is the trend, r_t a random walk and e_t a stationary + # error term. + crit = 0.146 + else: # hypo == "c" + # special case of the model above, where beta = 0 (so the null + # hypothesis is that the data is stationary around r_0). + crit = 0.463 + + return kpss_stat, kpss_stat < crit diff --git a/aeon/forecasting/_verify_arima.py b/aeon/forecasting/_verify_arima.py new file mode 100644 index 0000000000..34758eb6eb --- /dev/null +++ b/aeon/forecasting/_verify_arima.py @@ -0,0 +1,31 @@ +from pmdarima import auto_arima as pmd_auto_arima +from statsforecast.utils import AirPassengers as ap +from statsmodels.tsa.stattools import kpss + +from aeon.forecasting._arima import ARIMAForecaster, auto_arima, nelder_mead +from aeon.forecasting._utils import kpss_test + + +def test_arima(): + model = pmd_auto_arima( + ap, + seasonal=True, + m=12, + trace=True, + error_action="ignore", + suppress_warnings=True, + ) + print(model.summary()) # noqa + print(f"Optimal Model: {nelder_mead(ap, 2, 1, 1, 0, 1, 0, 12, True)}") # noqa + print(model.predict(n_periods=1)) # noqa + print(kpss_test(ap)) # noqa + print(kpss(ap, regression="c", nlags=12)) # noqa + print(auto_arima(ap)) # noqa + forecaster = ARIMAForecaster() + forecaster.fit(ap) + print(forecaster.predict()) # noqa + + +if __name__ == "__main__": + test_arima() +# Fit Auto-ARIMA model diff --git a/aeon/forecasting/_verify_ets.py b/aeon/forecasting/_verify_ets.py new file mode 100644 index 0000000000..65d3ca0faf --- /dev/null +++ b/aeon/forecasting/_verify_ets.py @@ -0,0 +1,345 @@ +"""Script to test ETS implementation against ETS implementations from other modules.""" + +import random +import time +import timeit + +import numpy as np +from statsforecast.utils import AirPassengers as ap + +import aeon.forecasting._ets as ets +import aeon.forecasting._ets_fast as etsfast +from aeon.forecasting import ETSForecaster + +NA = -99999.0 +MAX_NMSE = 30 +MAX_SEASONAL_PERIOD = 24 + + +def setup(): + """Generate parameters required for ETS algorithms.""" + y = ap + m = random.randint(2, 24) + error = random.randint(1, 2) + trend = random.randint(0, 2) + season = random.randint(0, 2) + alpha = round(random.random(), 4) + if alpha == 0: + alpha = round(random.random(), 4) + beta = round(random.random() * alpha, 4) # 0 < beta < alpha + if beta == 0: + beta = round(random.random() * alpha, 4) + gamma = round(random.random() * (1 - alpha), 4) # 0 < beta < alpha + if gamma == 0: + gamma = round(random.random() * (1 - alpha), 4) + phi = round( + random.random() * 0.18 + 0.8, 4 + ) # Common constraint for phi is 0.8 < phi < 0.98 + return (y, m, error, trend, season, alpha, beta, gamma, phi) + + +def statsmodels_version( + y: np.ndarray, + m: int, + f1: ETSForecaster, + errortype: int, + trendtype: int, + seasontype: int, + alpha: float, + beta: float, + gamma: float, + phi: float, +): + """Hide the differences between different statsforecast versions.""" + from statsmodels.tsa.holtwinters import ExponentialSmoothing + + ets_model = ExponentialSmoothing( + y[m:], + trend="add" if trendtype == 1 else "mul" if trendtype == 2 else None, + damped_trend=(phi != 1 and trendtype != 0), + seasonal="add" if seasontype == 1 else "mul" if seasontype == 2 else None, + seasonal_periods=m if seasontype != 0 else None, + initialization_method="known", + initial_level=f1.level_, + initial_trend=f1.trend_ if trendtype != 0 else None, + initial_seasonal=f1.seasonality_ if seasontype != 0 else None, + ) + results = ets_model.fit( + smoothing_level=alpha, + smoothing_trend=( + beta / alpha if trendtype != 0 else None + ), # statsmodels uses beta*=beta/alpha + smoothing_seasonal=gamma if seasontype != 0 else None, + damping_trend=phi if trendtype != 0 else None, + optimized=False, + ) + avg_mean_sq_err = results.sse / (len(y) - m) + # Back-calculate our log-likelihood proxy from AIC + if errortype == 1: + residuals = y[m:] - results.fittedvalues + assert np.allclose(residuals, results.resid) + else: + residuals = y[m:] / results.fittedvalues - 1 + return ( + (np.array([avg_mean_sq_err])), + residuals, + (results.aic - 2 * results.k + (len(y) - m) * np.log(len(y) - m)), + ) + + +def obscure_statsforecast_version( + y: np.ndarray, + m: int, + f1: ETSForecaster, + errortype: int, + trendtype: int, + seasontype: int, + alpha: float, + beta: float, + gamma: float, + phi: float, +): + """Hide the differences between different statsforecast versions.""" + init_state = np.zeros(len(y) * (1 + (trendtype > 0) + m * (seasontype > 0) + 1)) + init_state[0] = f1.level_ + init_state[1] = f1.trend_ + init_state[1 + (trendtype != 0) : m + 1 + (trendtype != 0)] = f1.seasonality_[::-1] + # from statsforecast.ets import pegelsresid_C + # amse, e, _x, lik = pegelsresid_C( + # y[m:], + # m, + # init_state, + # "A" if errortype == 1 else "M", + # "A" if trendtype == 1 else "M" if trendtype == 2 else "N", + # "A" if seasontype == 1 else "M" if seasontype == 2 else "N", + # phi != 1, + # alpha, + # beta, + # gamma, + # phi, + # nmse, + # ) + from statsforecast.ets import etscalc + + e = np.zeros(len(y)) + amse = np.zeros(MAX_NMSE) + lik = etscalc( + y[m:], + len(y) - m, + init_state, + m, + errortype, + trendtype, + seasontype, + alpha, + beta, + gamma, + phi, + e, + amse, + 1, + ) + return amse, e[:-m], lik + + +def test_ets_comparison(setup_func, random_seed, catch_errors): + """Run both our statsforecast and our implementation and crosschecks.""" + random.seed(random_seed) + ( + y, + m, + error, + trend, + season, + alpha, + beta, + gamma, + phi, + ) = setup_func() + # tsml-eval implementation + start = time.perf_counter() + f1 = ETSForecaster( + error, + trend, + season, + m, + alpha, + beta, + gamma, + phi, + 1, + ) + f1.fit(y) + end = time.perf_counter() + time_fitets = end - start + e_fitets = f1.residuals_ + amse_fitets = f1.avg_mean_sq_err_ + lik_fitets = f1.liklihood_ + f1 = ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + # pylint: disable=W0212 + f1._fit(y)._initialise(y) + # pylint: enable=W0212 + if season == 0: + m = 1 + # Nixtla/statsforcast implementation + start = time.perf_counter() + amse_etscalc, e_etscalc, lik_etscalc = statsmodels_version( + y, m, f1, error, trend, season, alpha, beta, gamma, phi + ) + end = time.perf_counter() + time_etscalc = end - start + amse_etscalc = amse_etscalc[0] + + if catch_errors: + try: + # Comparing outputs and runtime + assert np.allclose(e_fitets, e_etscalc), "Residuals Compare failed" + assert np.allclose(amse_fitets, amse_etscalc), "AMSE Compare failed" + assert np.isclose(lik_fitets, lik_etscalc), "Liklihood Compare failed" + return True + except AssertionError as e: + print(e) # noqa + print( # noqa + f"Seed: {random_seed}, Model: Error={error}, Trend={trend},\ + Seasonality={season}, seasonal period={m},\ + alpha={alpha}, beta={beta}, gamma={gamma}, phi={phi}" + ) + return False + else: + print( # noqa + f"Seed: {random_seed}, Model: Error={error}, Trend={trend},\ + Seasonality={season}, seasonal period={m}, alpha={alpha},\ + beta={beta}, gamma={gamma}, phi={phi}" + ) + diff_indices = np.where( + np.abs(e_fitets - e_etscalc) > 1e-3 * np.abs(e_etscalc) + 1e-2 + )[0] + for index in diff_indices: + print( # noqa + f"Index {index}: e_fitets = {e_fitets[index]},\ + e_etscalc = {e_etscalc[index]}" + ) + print(amse_fitets) # noqa + print(amse_etscalc) # noqa + print(lik_fitets) # noqa + print(lik_etscalc) # noqa + assert np.allclose(e_fitets, e_etscalc) + assert np.allclose(amse_fitets, amse_etscalc) + # assert np.isclose(lik_fitets, lik_etscalc) + print(f"Time for ETS: {time_fitets:0.20f}") # noqa + print(f"Time for statsforecast ETS: {time_etscalc}") # noqa + return True + + +def time_etsfast(): + """Test function for optimised numba ets algorithm.""" + etsfast.ETSForecaster(2, 2, 2, 4).fit(ap).predict() + + +def time_etsnoopt(): + """Test function for non-optimised ets algorithm.""" + ets.ETSForecaster(2, 2, 2, 4).fit(ap).predict() + + +def time_etsfast_noclass(): + """Test function for optimised ets algorithm without the class based structure.""" + data = np.array(ap.squeeze(), dtype=np.float64) + # pylint: disable=W0212 + ( + level, + trend, + seasonality, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + ) = etsfast._fit(data, 2, 2, 2, 4, 0.1, 0.01, 0.01, 0.99) + etsfast._predict(2, 2, level, trend, seasonality, 0.99, 1, 144, 4) + # pylint: enable=W0212 + + +def time_sf(): + """Test function for statsforecast ets algorithm.""" + x = np.zeros(144 * 7) + x[0:6] = [122.75, 1.123230970596215, 0.91242363, 0.96130346, 1.07535642, 1.0509165] + obscure_statsforecast_version( + ap[4:], + 4, + x, + 2, + 2, + 2, + 0.1, + 0.01, + 0.01, + 0.99, + ) + + +def time_compare(random_seed): + """Compare timings of different ets algorithms.""" + random.seed(random_seed) + (y, m, error, trend, season, alpha, beta, gamma, phi) = setup() + # etsnoopt_time = timeit.timeit(time_etsnoopt, globals={}, number=10000) + # print (f"Execution time ETS No-opt: {etsnoopt_time} seconds") + # Do a few iterations to remove background/overheads. Makes comparison more reliable + for _i in range(10): + time_etsfast() + time_sf() + time_etsfast_noclass() + etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) + print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa + etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) + print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) + print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa + etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) + print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa + etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) + print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) + print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa + # _ets_fast_nostruct implementation + start = time.perf_counter() + f3 = etsfast.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + f3.fit(y) + end = time.perf_counter() + etsfast_time = end - start + # _ets_fast implementation + # _ets implementation + start = time.perf_counter() + f1 = ets.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + f1.fit(y) + end = time.perf_counter() + etsnoopt_time = end - start + assert np.allclose(f1.residuals_, f3.residuals_) + assert np.allclose(f1.avg_mean_sq_err_, f3.avg_mean_sq_err_) + assert np.isclose(f1.liklihood_, f3.liklihood_) + print( # noqa + f"ETS No-optimisation Time: {etsnoopt_time},\ + Fast time: {etsfast_time}" + ) + return etsnoopt_time, etsfast_time + + +if __name__ == "__main__": + np.set_printoptions(threshold=np.inf) + test_ets_comparison(setup, 300, False) + SUCCESSES = True + for i in range(0, 300): + SUCCESSES &= test_ets_comparison(setup, i, True) + if SUCCESSES: + print("Test Completed Successfully with no errors") # noqa + # time_compare(300) + # avg_ets = 0 + # avg_etsfast = 0 + # avg_etsfast_ns = 0 + # iterations = 100 + # for i in range (iterations): + # time_ets, etsfast_time = time_compare(300) + # avg_ets += time_ets + # avg_etsfast += etsfast_time + # avg_ets/= iterations + # avg_etsfast/= iterations + # avg_etsfast_ns /= iterations + # print(f"Avg ETS Time: {avg_ets}, Avg Fast ETS time: {avg_etsfast},\ diff --git a/aeon/forecasting/base.py b/aeon/forecasting/base.py index e67712c58a..cf2db8d80c 100644 --- a/aeon/forecasting/base.py +++ b/aeon/forecasting/base.py @@ -36,7 +36,7 @@ class BaseForecaster(BaseSeriesEstimator): "y_inner_type": "np.ndarray", } - def __init__(self, horizon, axis): + def __init__(self, horizon: int, axis: int): self.horizon = horizon self.meta_ = None # Meta data related to y on the last fit super().__init__(axis) diff --git a/aeon/forecasting/tests/test_ets.py b/aeon/forecasting/tests/test_ets.py index ce7513a965..c5c5118b60 100644 --- a/aeon/forecasting/tests/test_ets.py +++ b/aeon/forecasting/tests/test_ets.py @@ -1,27 +1,92 @@ -"""Test ETS forecaster.""" +"""Test ETS.""" -import pytest +__maintainer__ = [] +__all__ = [] + +import numpy as np from aeon.forecasting import ETSForecaster -from aeon.testing.data_generation import make_example_1d_numpy - - -def test_ets_params(): - """Test ETS forecaster.""" - y = make_example_1d_numpy(n_timepoints=100) - forecaster = ETSForecaster(error_type=3) - with pytest.raises( - ValueError, match="Error must be either additive or " "multiplicative" - ): - forecaster.fit(y) - forecaster = ETSForecaster(seasonality_type=-3) - forecaster.fit(y) - assert forecaster._seasonal_period == 1 - forecaster = ETSForecaster(trend_type=None, seasonality_type=0, beta=1.0, gamma=1.0) - forecaster.fit(y) - assert forecaster._beta == 0 - assert forecaster._gamma == 0 - - forecaster = ETSForecaster(error_type=2, phi=1.0) - pred = forecaster.forecast(y) - assert isinstance(pred, float) + + +def test_ets_forecaster_additive(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.5, + beta=0.3, + gamma=0.4, + phi=1, + horizon=1, + error_type=1, + trend_type=1, + seasonality_type=1, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 9.191190608800001) + + +def test_ets_forecaster_mult_error(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.6, + gamma=0.1, + phi=0.97, + horizon=1, + error_type=2, + trend_type=1, + seasonality_type=1, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.20176819429869) + + +def test_ets_forecaster_mult_compnents(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.4, + beta=0.2, + gamma=0.5, + phi=0.8, + horizon=1, + error_type=1, + trend_type=2, + seasonality_type=2, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 12.301259229712382) + + +def test_ets_forecaster_multiplicative(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.5, + gamma=0.2, + phi=0.85, + horizon=1, + error_type=2, + trend_type=2, + seasonality_type=2, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.811888294476528) diff --git a/aeon/networks/_ae_abgru.py b/aeon/networks/_ae_abgru.py index aac0e67d1b..ca9f0494ad 100644 --- a/aeon/networks/_ae_abgru.py +++ b/aeon/networks/_ae_abgru.py @@ -161,6 +161,7 @@ def build_network(self, input_shape, **kwargs): x = tf.keras.layers.Flatten()(x) x = tf.keras.layers.Dense(self.latent_space_dim)(x) elif self.temporal_latent_space: + shape_before_flatten = x.shape[1:] x = tf.keras.layers.Conv1D(filters=self.latent_space_dim, kernel_size=1)(x) encoder = tf.keras.models.Model(inputs=input_layer, outputs=x, name="encoder") diff --git a/aeon/networks/_ae_dcnn.py b/aeon/networks/_ae_dcnn.py index da953ec717..ea475d0161 100644 --- a/aeon/networks/_ae_dcnn.py +++ b/aeon/networks/_ae_dcnn.py @@ -241,20 +241,25 @@ def _dcnn_layer( ): import tensorflow as tf + from aeon.utils.networks.weight_norm import _WeightNormalization + _add = tf.keras.layers.Conv1D(_num_filters, kernel_size=1)(_inputs) - x = tf.keras.layers.Conv1D( - _num_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding=_padding_encoder, - kernel_regularizer="l2", + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _num_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding_encoder, + ) )(_inputs) - x = tf.keras.layers.Conv1D( - _num_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding=_padding_encoder, - kernel_regularizer="l2", + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _num_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding_encoder, + activation=_activation, + ) )(x) output = tf.keras.layers.Add()([x, _add]) output = tf.keras.layers.Activation(_activation)(output) diff --git a/aeon/networks/_dcnn.py b/aeon/networks/_dcnn.py index 243340c30e..051ce7d07e 100644 --- a/aeon/networks/_dcnn.py +++ b/aeon/networks/_dcnn.py @@ -146,21 +146,25 @@ def _dcnn_layer( ): import tensorflow as tf + from aeon.utils.networks.weight_norm import _WeightNormalization + _add = tf.keras.layers.Conv1D(_n_filters, kernel_size=1)(_inputs) - x = tf.keras.layers.Conv1D( - _n_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding=_padding, - kernel_regularizer="l2", + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _n_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding, + ) )(_inputs) - x = tf.keras.layers.Conv1D( - _n_filters, - kernel_size=_kernel_size, - dilation_rate=_dilation_rate, - padding="causal", - kernel_regularizer="l2", - activation=_activation, + x = _WeightNormalization( + tf.keras.layers.Conv1D( + _n_filters, + kernel_size=_kernel_size, + dilation_rate=_dilation_rate, + padding=_padding, + activation=_activation, + ) )(x) output = tf.keras.layers.Add()([x, _add]) output = tf.keras.layers.Activation(_activation)(output) diff --git a/aeon/networks/tests/test_ae_fcn.py b/aeon/networks/tests/test_ae_fcn.py new file mode 100644 index 0000000000..6f19820d02 --- /dev/null +++ b/aeon/networks/tests/test_ae_fcn.py @@ -0,0 +1,288 @@ +"""Test for the AEFCNNetwork class.""" + +import pytest + +from aeon.networks import AEFCNNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_aefcn_default(): + """Default testing for aefcn.""" + model = AEFCNNetwork() + assert model.latent_space_dim == 128 + assert model.temporal_latent_space is False + assert model.n_layers == 3 + assert model.n_filters is None + assert model.kernel_size is None + assert model.activation == "relu" + assert model.padding == "same" + assert model.strides == 1 + assert model.dilation_rate == 1 + assert model.use_bias is True + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("latent_space_dim", [64, 128, 256]) +def test_aefcn_latent_space(latent_space_dim): + """Test AEFCNNetwork with different latent space dimensions.""" + import tensorflow as tf + + aefcn = AEFCNNetwork(latent_space_dim=latent_space_dim) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "kernel_size, should_raise", + [ + ([8, 5, 3], False), + (3, False), + ([5, 5], True), + ([3, 3, 3, 3], True), + ], +) +def test_aefcnnetwork_kernel_size(kernel_size, should_raise): + """Test AEFCNNetwork with different kernel sizes.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of kernels .* should be the same as number of layers", + ): + AEFCNNetwork(kernel_size=kernel_size, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(kernel_size=kernel_size, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_filters, should_raise", + [ + ([128, 256, 128], False), + (32, False), + ([32, 64], True), + ([16, 32, 64, 128], True), + ], +) +def test_aefcnnetwork_n_filters(n_filters, should_raise): + """Test AEFCNNetwork with different number of filters.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of filters .* should be the same as number of layers", + ): + AEFCNNetwork(n_filters=n_filters, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(n_filters=n_filters, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dilation_rate, should_raise", + [ + ([1, 2, 1], False), + (2, False), + ([1, 2], True), + ([1, 2, 2, 1], True), + ], +) +def test_aefcnnetwork_dilation_rate(dilation_rate, should_raise): + """Test AEFCNNetwork with different dilation rates.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of dilations .* should be the same as number of layers", + ): + AEFCNNetwork(dilation_rate=dilation_rate, n_layers=3).build_network( + (1000, 5) + ) + else: + aefcn = AEFCNNetwork(dilation_rate=dilation_rate, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides, should_raise", + [ + ([1, 2, 1], False), + (2, False), + ([1, 2], True), + ([1, 2, 2, 1], True), + ], +) +def test_aefcnnetwork_strides(strides, should_raise): + """Test AEFCNNetwork with different strides.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of strides .* should be the same as number of layers", + ): + AEFCNNetwork(strides=strides, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(strides=strides, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "padding, should_raise", + [ + (["same", "valid", "same"], False), + ("same", False), + (["same", "valid"], True), + ( + ["same", "valid", "same", "valid"], + True, + ), + ], +) +def test_aefcnnetwork_padding(padding, should_raise): + """Test AEFCNNetwork with different paddings.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of paddings .* should be the same as number of layers", + ): + AEFCNNetwork(padding=padding, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(padding=padding, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation, should_raise", + [ + (["relu", "sigmoid", "tanh"], False), + ("sigmoid", False), + (["relu", "sigmoid"], True), + ( + ["relu", "sigmoid", "tanh", "softmax"], + True, + ), + ], +) +def test_aefcnnetwork_activation(activation, should_raise): + """Test AEFCNNetwork with different activations.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of activations .* should be the same as number of layers", + ): + AEFCNNetwork(activation=activation, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(activation=activation, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "use_bias, should_raise", + [ + ([True, False, True], False), + (True, False), + ([True, False], True), + ([True, False, True, False], True), + ], +) +def test_aefcnnetwork_use_bias(use_bias, should_raise): + """Test AEFCNNetwork with different use_bias values.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of biases .* should be the same as number of layers", + ): + AEFCNNetwork(use_bias=use_bias, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(use_bias=use_bias, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("temporal_latent_space", [True, False]) +def test_aefcnnetwork_temporal_latent_space(temporal_latent_space): + """Test for temporal latent space.""" + import tensorflow as tf + + input_shape = (1000, 5) + + aefcn = AEFCNNetwork( + latent_space_dim=128, temporal_latent_space=temporal_latent_space + ) + + encoder, decoder = aefcn.build_network(input_shape) + + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + if temporal_latent_space: + assert any( + isinstance(layer, tf.keras.layers.Conv1D) for layer in encoder.layers + ), "Expected Conv1D layer in encoder but not found." + else: + assert any( + isinstance(layer, tf.keras.layers.Dense) for layer in decoder.layers + ), "Expected Dense layer in decoder but not found." diff --git a/aeon/networks/tests/test_cnn.py b/aeon/networks/tests/test_cnn.py deleted file mode 100644 index c859397b34..0000000000 --- a/aeon/networks/tests/test_cnn.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Tests for the CNN Model.""" - -import pytest - -from aeon.networks import TimeCNNNetwork -from aeon.utils.validation._dependencies import _check_soft_dependencies - -__maintainer__ = [] - - -@pytest.mark.skipif( - not _check_soft_dependencies(["tensorflow"], severity="none"), - reason="Tensorflow soft dependency unavailable.", -) -def test_cnn_input_shape_padding(): - """Test of CNN network with input_shape < 60.""" - input_shape = (40, 2) - network = TimeCNNNetwork() - input_layer, output_layer = network.build_network(input_shape=input_shape) - - assert input_layer is not None - assert output_layer is not None diff --git a/aeon/networks/tests/test_fcn.py b/aeon/networks/tests/test_fcn.py new file mode 100644 index 0000000000..60c1cd42f5 --- /dev/null +++ b/aeon/networks/tests/test_fcn.py @@ -0,0 +1,196 @@ +"""Test for the FCNNetwork class.""" + +import pytest + +from aeon.networks import FCNNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_fcnnetwork_valid(): + """Test FCNNetwork with valid configurations.""" + input_shape = (100, 5) + model = FCNNetwork(n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation, should_raise", + [ + (["relu", "sigmoid", "tanh"], False), + (["relu", "sigmoid"], True), + ( + ["relu", "sigmoid", "tanh", "softmax"], + True, + ), + ("relu", False), + ("sigmoid", False), + ("tanh", False), + ("softmax", False), + ], +) +def test_fcnnetwork_activation(activation, should_raise): + """Test FCNNetwork with valid and invalid activation configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(activation=activation) + model.build_network(input_shape) + else: + model = FCNNetwork(activation=activation) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "kernel_size, should_raise", + [ + ([3, 1, 2], False), + ([1, 3], True), + ([3, 1, 1, 3], True), + (3, False), + ], +) +def test_fcnnetwork_kernel_size(kernel_size, should_raise): + """Test FCNNetwork with valid and invalid kernel_size configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(kernel_size=kernel_size, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(kernel_size=kernel_size, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dilation_rate, should_raise", + [ + ([1, 2, 1], False), + ([1, 4], True), + ([1, 2, 4, 1], True), + (1, False), + ], +) +def test_fcnnetwork_dilation_rate(dilation_rate, should_raise): + """Test FCNNetwork with valid and invalid dilation_rate configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(dilation_rate=dilation_rate, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(dilation_rate=dilation_rate, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides, should_raise", + [ + ([1, 2, 3], False), + ([1, 1], True), + ([1, 2, 2, 1], True), + (1, False), + ], +) +def test_fcnnetwork_strides(strides, should_raise): + """Test FCNNetwork with valid and invalid strides configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(strides=strides, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(strides=strides, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "padding, should_raise", + [ + (["same", "same", "valid"], False), + (["valid", "same"], True), + (["same", "valid", "same", "valid"], True), + ("same", False), + ("valid", False), + ], +) +def test_fcnnetwork_padding(padding, should_raise): + """Test FCNNetwork with valid and invalid padding configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(padding=padding, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(padding=padding, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_filters, should_raise", + [ + ([32, 64, 128], False), # Valid case with a list of filters + ([32, 64], True), # Invalid case with fewer filters than layers + ([32, 64, 128, 256], True), # Invalid case with more filters than layers + (32, False), # Valid case with a single filter value + ], +) +def test_fcnnetwork_n_filters(n_filters, should_raise): + """Test FCNNetwork with valid and invalid n_filters configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(n_filters=n_filters, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(n_filters=n_filters, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") diff --git a/aeon/networks/tests/test_mlp.py b/aeon/networks/tests/test_mlp.py new file mode 100644 index 0000000000..421a4f2841 --- /dev/null +++ b/aeon/networks/tests/test_mlp.py @@ -0,0 +1,179 @@ +"""Tests for the MLPNetwork Model.""" + +import pytest + +from aeon.networks import MLPNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_layers, n_units, activation", + [ + (3, 500, "relu"), + (5, [256, 128, 128, 64, 32], "sigmoid"), + (2, 128, ["tanh", "relu"]), + ], +) +def test_mlp_initialization(n_layers, n_units, activation): + """Test whether MLPNetwork initializes correctly with different configurations.""" + from tensorflow.keras.layers import Dense, Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(n_layers=n_layers, n_units=n_units, activation=activation) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + + # Check dropout and dense layers ordering + for i in range(n_layers): + dropout_layer = layers[2 + 2 * i] # Dropout before Dense + dense_layer = layers[3 + 2 * i] # Dense comes after Dropout + + assert isinstance( + dropout_layer, Dropout + ), f"Expected Dropout at index {2 + 2 * i}" + assert isinstance(dense_layer, Dense), f"Expected Dense at index {3 + 2 * i}" + + # Assert activation function + expected_activation = ( + activation[i] if isinstance(activation, list) else activation + ) + assert dense_layer.activation.__name__ == expected_activation, ( + f"Expected activation {expected_activation}, " + f"got {dense_layer.activation.__name__}" + ) + + # Assert number of units + expected_units = n_units[i] if isinstance(n_units, list) else n_units + assert ( + dense_layer.units == expected_units + ), f"Expected {expected_units} units, got {dense_layer.units}" + + # Check last layer is Dropout + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + # Assert model parameters (Just for show) + assert mlp.n_layers == n_layers + assert mlp.n_units == n_units + assert mlp.activation == activation + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dropout_rate, n_layers", + [ + (0.2, 3), + ([0.1, 0.2, 0.3], 3), + pytest.param([0.1, 0.2], 3, marks=pytest.mark.xfail(raises=AssertionError)), + ], +) +def test_mlp_dropout_rate(dropout_rate, n_layers): + """Test MLPNetwork dropout_rate configurations.""" + from tensorflow.keras.layers import Dense, Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(n_layers=n_layers, dropout_rate=dropout_rate) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + # Check first two layers + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + + # Check dropout and dense layers ordering + for i in range(n_layers): + dropout_layer = layers[2 + 2 * i] + dense_layer = layers[3 + 2 * i] + + assert isinstance( + dropout_layer, Dropout + ), f"Expected Dropout at index {2 + 2 * i}" + assert isinstance(dense_layer, Dense), f"Expected Dense at index {3 + 2 * i}" + + # Assert dropout rates match expected values + expected_dropout = ( + dropout_rate[i] if isinstance(dropout_rate, list) else dropout_rate + ) + assert ( + dropout_layer.rate == expected_dropout + ), f"Expected {expected_dropout},got {dropout_layer.rate}" + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dropout_last", + [0.3, 0.5, pytest.param(1.2, marks=pytest.mark.xfail(raises=AssertionError))], +) +def test_mlp_dropout_last(dropout_last): + """Test MLPNetwork dropout_last configurations.""" + from tensorflow.keras.layers import Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(dropout_last=dropout_last) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + assert ( + layers[-1].rate == dropout_last + ), f"Expected {dropout_last}, got {layers[-1].rate}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("use_bias", [True, False]) +def test_mlp_use_bias(use_bias): + """Test MLPNetwork use_bias configurations.""" + from tensorflow.keras.layers import Dense, Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(use_bias=use_bias) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + # Find the last Dense layer before the final Dropout layer + last_dense_layer = next( + (layer for layer in reversed(layers) if isinstance(layer, Dense)), None + ) + + assert last_dense_layer is not None, "No Dense layer found before final Dropout" + assert isinstance(last_dense_layer, Dense), "Expected last layer to be Dense" + + assert ( + last_dense_layer.use_bias == use_bias + ), f"Expected use_bias {use_bias}, got {last_dense_layer.use_bias}" diff --git a/aeon/networks/tests/test_resnet.py b/aeon/networks/tests/test_resnet.py new file mode 100644 index 0000000000..4d5c58c5b9 --- /dev/null +++ b/aeon/networks/tests/test_resnet.py @@ -0,0 +1,109 @@ +"""Tests for the ResNet Model.""" + +import pytest + +from aeon.networks import ResNetNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_default_initialization(): + """Test if the network initializes with proper attributes.""" + model = ResNetNetwork() + assert isinstance( + model, ResNetNetwork + ), "Model initialization failed: Incorrect type" + assert model.n_residual_blocks == 3, "Default residual blocks count mismatch" + assert ( + model.n_conv_per_residual_block == 3 + ), "Default convolution blocks count mismatch" + assert model.n_filters is None, "Default n_filters should be None" + assert model.kernel_size is None, "Default kernel_size should be None" + assert model.strides == 1, "Default strides value mismatch" + assert model.dilation_rate == 1, "Default dilation rate mismatch" + assert model.activation == "relu", "Default activation mismatch" + assert model.use_bias is True, "Default use_bias mismatch" + assert model.padding == "same", "Default padding mismatch" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_custom_initialization(): + """Test whether custom kwargs are correctly set.""" + model = ResNetNetwork( + n_residual_blocks=3, + n_conv_per_residual_block=3, + n_filters=[64, 128, 128], + kernel_size=[8, 5, 3], + activation="relu", + strides=1, + padding="same", + ) + model.build_network((128, 1)) + assert isinstance( + model, ResNetNetwork + ), "Custom initialization failed: Incorrect type" + assert model._n_filters == [64, 128, 128], "n_filters list mismatch" + assert model._kernel_size == [8, 5, 3], "kernel_size list mismatch" + assert model._activation == ["relu", "relu", "relu"], "activation list mismatch" + assert model._strides == [1, 1, 1], "strides list mismatch" + assert model._padding == ["same", "same", "same"], "padding list mismatch" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_invalid_initialization(): + """Test if the network raises valid exceptions for invalid configurations.""" + with pytest.raises(ValueError, match=".*same as number of residual blocks.*"): + ResNetNetwork(n_filters=[64, 128], n_residual_blocks=3).build_network((128, 1)) + + with pytest.raises(ValueError, match=".*same as number of convolution layers.*"): + ResNetNetwork(kernel_size=[8, 5], n_conv_per_residual_block=3).build_network( + (128, 1) + ) + + with pytest.raises(ValueError, match=".*same as number of convolution layers.*"): + ResNetNetwork(strides=[1, 2], n_conv_per_residual_block=3).build_network( + (128, 1) + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_build_network(): + """Test network building with various input shapes.""" + model = ResNetNetwork() + + input_shapes = [(128, 1), (256, 3), (512, 1)] + for shape in input_shapes: + input_layer, output_layer = model.build_network(shape) + assert hasattr(input_layer, "shape"), "Input layer type mismatch" + assert hasattr(output_layer, "shape"), "Output layer type mismatch" + assert input_layer.shape[1:] == shape, "Input shape mismatch" + assert output_layer.shape[-1] == 128, "Output layer mismatch" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_shortcut_layer(): + """Test the shortcut layer functionality.""" + model = ResNetNetwork() + + input_shape = (128, 64) + input_layer, output_layer = model.build_network(input_shape) + + shortcut = model._shortcut_layer(input_layer, output_layer) + + assert hasattr(shortcut, "shape"), "Shortcut layer output type mismatch" + assert shortcut.shape[-1] == 128, "Shortcut output shape mismatch" diff --git a/aeon/networks/tests/test_time_cnn.py b/aeon/networks/tests/test_time_cnn.py new file mode 100644 index 0000000000..3f31f1db10 --- /dev/null +++ b/aeon/networks/tests/test_time_cnn.py @@ -0,0 +1,274 @@ +"""Tests for the TimeCNNNetwork Model.""" + +import pytest + +from aeon.networks import TimeCNNNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_time_cnn_input_shape_padding(): + """Test of CNN network with input_shape < 60.""" + input_shape = (40, 2) + network = TimeCNNNetwork() + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation, n_layers, should_raise", + [ + ("relu", 2, False), + ("sigmoid", 2, False), + ("tanh", 2, False), + (["relu", "sigmoid", "tanh"], 2, True), + (["relu"], 2, True), + ], +) +def test_time_cnn_activation(activation, n_layers, should_raise): + """Test activation configuration handling.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(activation=activation, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(activation=activation, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "kernel_size, n_layers, should_raise", + [ + (7, 2, False), + ([5, 3], 2, False), + ([5, 3, 2], 2, True), + ([5], 2, True), + ], +) +def test_time_cnn_kernel_size(kernel_size, n_layers, should_raise): + """Test kernel size configuration with different layer counts.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(n_layers=n_layers, kernel_size=kernel_size) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(n_layers=n_layers, kernel_size=kernel_size) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_layers,n_filters,should_raise", + [ + (2, [8, 16], False), + (1, [12, 10, 4], True), + (2, 8, False), + (3, [8], True), + ], +) +def test_time_cnn_n_filters(n_layers, n_filters, should_raise): + """Test filter configuration handling.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(n_layers=n_layers, n_filters=n_filters) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(n_layers=n_layers, n_filters=n_filters) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "avg_pool_size, n_layers, should_raise", + [ + (3, 2, False), + ([2, 3], 2, False), + ([2, 3, 4], 2, True), + ([2], 2, True), + ], +) +def test_time_cnn_avg_pool_size(avg_pool_size, n_layers, should_raise): + """Test average pool size configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(avg_pool_size=avg_pool_size, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(avg_pool_size=avg_pool_size, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides_pooling, n_layers, should_raise", + [ + (None, 2, False), + (2, 2, False), + ([2, 3], 2, False), + ([2, 3, 4], 2, True), + ([2], 2, True), + ], +) +def test_time_cnn_strides_pooling(strides_pooling, n_layers, should_raise): + """Test strides pooling configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(strides_pooling=strides_pooling, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(strides_pooling=strides_pooling, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "padding, n_layers, should_raise", + [ + ("valid", 2, False), + ("same", 2, False), + (["same", "valid"], 2, False), + (["same", "valid", "same"], 2, True), + (["same"], 2, True), + ], +) +def test_time_cnn_padding(padding, n_layers, should_raise): + """Test padding override behavior for different inputs.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(padding=padding, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(padding=padding, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dilation, n_layers, should_raise", + [ + (2, 2, False), + ([1, 2], 2, False), + ([1, 2, 3], 2, True), + ([1], 2, True), + ], +) +def test_time_cnn_dilation_rate(dilation, n_layers, should_raise): + """Test dilation rate configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(dilation_rate=dilation, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(dilation_rate=dilation, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides, n_layers, should_raise", + [ + (1, 2, False), + ([1, 2], 2, False), + ([1, 2, 3], 2, True), + ([1], 2, True), + ], +) +def test_time_cnn_strides(strides, n_layers, should_raise): + """Test strides configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(strides=strides, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(strides=strides, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "use_bias, n_layers, should_raise", + [ + (True, 2, False), + ([True, False], 2, False), + ([True, False, True], 2, True), + ([True], 2, True), + ], +) +def test_time_cnn_use_bias(use_bias, n_layers, should_raise): + """Test bias usage configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(use_bias=use_bias, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(use_bias=use_bias, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") diff --git a/aeon/regression/_dummy.py b/aeon/regression/_dummy.py index b767de8d78..322dcbf01d 100644 --- a/aeon/regression/_dummy.py +++ b/aeon/regression/_dummy.py @@ -15,13 +15,13 @@ class DummyRegressor(BaseRegressor): This regressor is a wrapper for the scikit-learn DummyClassifier that serves as a simple baseline to compare against other more complex regressors. - The specific behaviour of the baseline is selected with the `strategy` parameter. + The specific behaviour of the baseline is selected with the ``strategy`` parameter. All strategies make predictions that ignore the input feature values passed - as the `X` argument to `fit` and `predict`. The predictions, however, - typically depend on values observed in the `y` parameter passed to `fit`. + as the ``X`` argument to ``fit`` and ``predict``. The predictions, however, + typically depend on values observed in the ``y`` parameter passed to ``fit``. - Function-identical to `sklearn.dummy.DummyRegressor`, which is called inside. + Function-identical to ``sklearn.dummy.DummyRegressor``, which is called inside. Parameters ---------- diff --git a/aeon/regression/base.py b/aeon/regression/base.py index 5aed9c80b9..dbe40732bb 100644 --- a/aeon/regression/base.py +++ b/aeon/regression/base.py @@ -25,6 +25,7 @@ class name: BaseRegressor import numpy as np import pandas as pd +from sklearn.base import RegressorMixin from sklearn.metrics import get_scorer, get_scorer_names from sklearn.model_selection import cross_val_predict from sklearn.utils.multiclass import type_of_target @@ -33,7 +34,7 @@ class name: BaseRegressor from aeon.base._base import _clone_estimator -class BaseRegressor(BaseCollectionEstimator): +class BaseRegressor(RegressorMixin, BaseCollectionEstimator): """Abstract base class for time series regressors. The base regressor specifies the methods and method signatures that all @@ -54,9 +55,6 @@ class BaseRegressor(BaseCollectionEstimator): @abstractmethod def __init__(self): - # required for compatibility with some sklearn interfaces - self._estimator_type = "regressor" - super().__init__() @final @@ -169,7 +167,7 @@ def fit_predict(self, X, y) -> np.ndarray: allowed and converted into one of the above. Different estimators have different capabilities to handle different - types of input. If `self.get_tag("capability:multivariate")`` is False, + types of input. If ``self.get_tag("capability:multivariate")`` is False, they cannot handle multivariate series, so either ``n_channels == 1`` is true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag( "capability:unequal_length")`` is False, they cannot handle unequal @@ -210,7 +208,7 @@ def score(self, X, y, metric="r2", metric_params=None) -> float: allowed and converted into one of the above. Different estimators have different capabilities to handle different - types of input. If `self.get_tag("capability:multivariate")`` is False, + types of input. If ``self.get_tag("capability:multivariate")`` is False, they cannot handle multivariate series, so either ``n_channels == 1`` is true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag( "capability:unequal_length")`` is False, they cannot handle unequal @@ -222,7 +220,7 @@ def score(self, X, y, metric="r2", metric_params=None) -> float: (ground truth) for fitting indices corresponding to instance indices in X. metric : Union[str, callable], default="r2", Defines the scoring metric to test the fit of the model. For supported - strings arguments, check `sklearn.metrics.get_scorer_names`. + strings arguments, check ``sklearn.metrics.get_scorer_names``. metric_params : dict, default=None, Contains parameters to be passed to the scoring function. If None, no parameters are passed. diff --git a/aeon/regression/deep_learning/_cnn.py b/aeon/regression/deep_learning/_cnn.py index 351e3964d3..e2f6635fa2 100644 --- a/aeon/regression/deep_learning/_cnn.py +++ b/aeon/regression/deep_learning/_cnn.py @@ -1,5 +1,7 @@ """Time Convolutional Neural Network (TimeCNN) regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["TimeCNNRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import TimeCNNNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class TimeCNNRegressor(BaseDeepRegressor): """Time Series Convolutional Neural Network (CNN). @@ -120,39 +128,39 @@ class TimeCNNRegressor(BaseDeepRegressor): >>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12, ... return_y=True, regression_target=True, ... random_state=0) - >>> rgs = TimeCNNRegressor(n_epochs=20, bacth_size=4) # doctest: +SKIP + >>> rgs = TimeCNNRegressor(n_epochs=20, batch_size=4) # doctest: +SKIP >>> rgs.fit(X, y) # doctest: +SKIP TimeCNNRegressor(...) """ def __init__( self, - n_layers=2, - kernel_size=7, - n_filters=None, - avg_pool_size=3, - activation="sigmoid", - padding="valid", - strides=1, - dilation_rate=1, - n_epochs=2000, - batch_size=16, - callbacks=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - verbose=False, - loss="mean_squared_error", - output_activation="linear", - metrics="mean_squared_error", - random_state=None, - use_bias=True, - optimizer=None, - ): + n_layers: int = 2, + kernel_size: int | list[int] = 7, + n_filters: int | list[int] | None = None, + avg_pool_size: int | list[int] = 3, + activation: str | list[str] = "sigmoid", + padding: str | list[str] = "valid", + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + n_epochs: int = 2000, + batch_size: int = 16, + callbacks: Callback | list[Callback] | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + verbose: bool = False, + loss: str = "mean_squared_error", + output_activation: str = "linear", + metrics: str | list[str] = "mean_squared_error", + random_state: int | np.random.RandomState | None = None, + use_bias: bool | list[bool] = True, + optimizer: tf.keras.optimizers.Optimizer | None = None, + ) -> None: self.n_layers = n_layers self.avg_pool_size = avg_pool_size self.padding = padding @@ -196,7 +204,9 @@ def __init__( use_bias=self.use_bias, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -213,7 +223,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf from tensorflow import keras @@ -239,7 +248,7 @@ def build_model(self, input_shape, **kwargs): ) return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> TimeCNNRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -316,7 +325,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_disjoint_cnn.py b/aeon/regression/deep_learning/_disjoint_cnn.py index cc2b0cb321..ac5e61d202 100644 --- a/aeon/regression/deep_learning/_disjoint_cnn.py +++ b/aeon/regression/deep_learning/_disjoint_cnn.py @@ -1,5 +1,7 @@ """DisjointCNN regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["DisjointCNNRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import DisjointCNNNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class DisjointCNNRegressor(BaseDeepRegressor): """Disjoint Convolutional Neural Netowkr regressor. @@ -159,37 +167,37 @@ class DisjointCNNRegressor(BaseDeepRegressor): def __init__( self, - n_layers=4, - n_filters=64, - kernel_size=None, - dilation_rate=1, - strides=1, - padding="same", - activation="elu", - use_bias=True, - kernel_initializer="he_uniform", - pool_size=5, - pool_strides=None, - pool_padding="valid", - hidden_fc_units=128, - activation_fc="relu", - n_epochs=2000, - batch_size=16, - use_mini_batch_size=False, - random_state=None, - verbose=False, - output_activation="linear", - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - callbacks=None, + n_layers: int = 4, + n_filters: int | list[int] = 64, + kernel_size: int | list[int] | None = None, + dilation_rate: int | list[int] = 1, + strides: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "elu", + use_bias: bool | list[bool] = True, + kernel_initializer: str | list[str] = "he_uniform", + pool_size: int = 5, + pool_strides: int | None = None, + pool_padding: str = "valid", + hidden_fc_units: int = 128, + activation_fc: str = "relu", + n_epochs: int = 2000, + batch_size: int = 16, + use_mini_batch_size: bool = False, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + output_activation: str = "linear", + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + callbacks: Callback | list[Callback] | None = None, ): self.n_layers = n_layers self.n_filters = n_filters @@ -247,7 +255,9 @@ def __init__( activation_fc=self.activation_fc, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -266,7 +276,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -291,7 +300,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> DisjointCNNRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -376,7 +385,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_encoder.py b/aeon/regression/deep_learning/_encoder.py index fd3bf855cb..7388ce0928 100644 --- a/aeon/regression/deep_learning/_encoder.py +++ b/aeon/regression/deep_learning/_encoder.py @@ -1,18 +1,27 @@ """Encoder Regressor.""" +from __future__ import annotations + __author__ = ["AnonymousCodes911", "hadifawaz1999"] __all__ = ["EncoderRegressor"] + import gc import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import EncoderNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class EncoderRegressor(BaseDeepRegressor): """ @@ -111,31 +120,31 @@ class EncoderRegressor(BaseDeepRegressor): def __init__( self, - n_epochs=100, - batch_size=12, - kernel_size=None, - n_filters=None, - dropout_proba=0.2, - activation="sigmoid", - output_activation="linear", - max_pool_size=2, - padding="same", - strides=1, - fc_units=256, - callbacks=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - use_bias=True, - optimizer=None, - random_state=None, + n_epochs: int = 100, + batch_size: int = 12, + kernel_size: list[int] | None = None, + n_filters: list[int] | None = None, + dropout_proba: float = 0.2, + activation: str = "sigmoid", + output_activation: str = "linear", + max_pool_size: int = 2, + padding: str = "same", + strides: int = 1, + fc_units: int = 256, + callbacks: Callback | list[Callback] | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + use_bias: bool = True, + optimizer: tf.keras.optimizers.Optimizer | None = None, + random_state: int | np.random.RandomState | None = None, ): self.n_filters = n_filters self.max_pool_size = max_pool_size @@ -179,7 +188,9 @@ def __init__( activation=self.activation, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d, m), where d @@ -195,7 +206,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -222,7 +232,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> EncoderRegressor: """Fit the classifier on the training set (X, y). Parameters @@ -299,7 +309,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_fcn.py b/aeon/regression/deep_learning/_fcn.py index a6905580ac..082b8a7038 100644 --- a/aeon/regression/deep_learning/_fcn.py +++ b/aeon/regression/deep_learning/_fcn.py @@ -1,5 +1,7 @@ """Fully Convolutional Network (FCN) regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["FCNRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import FCNNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class FCNRegressor(BaseDeepRegressor): """Fully Convolutional Network (FCN). @@ -117,32 +125,32 @@ class FCNRegressor(BaseDeepRegressor): def __init__( self, - n_layers=3, - n_filters=None, - kernel_size=None, - dilation_rate=1, - strides=1, - padding="same", - activation="relu", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - n_epochs=2000, - batch_size=16, - use_mini_batch_size=False, - callbacks=None, - verbose=False, - output_activation="linear", - loss="mean_squared_error", - metrics="mean_squared_error", - random_state=None, - use_bias=True, - optimizer=None, - ): + n_layers: int = 3, + n_filters: int | list[int] | None = None, + kernel_size: int | list[int] | None = None, + dilation_rate: int | list[int] = 1, + strides: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + n_epochs: int = 2000, + batch_size: int = 16, + use_mini_batch_size: bool = False, + callbacks: Callback | list[Callback] | None = None, + verbose: bool = False, + output_activation: str = "linear", + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + random_state: int | np.random.RandomState | None = None, + use_bias: bool = True, + optimizer: tf.keras.optimizers.Optimizer | None = None, + ) -> None: self.n_layers = n_layers self.kernel_size = kernel_size self.n_filters = n_filters @@ -182,7 +190,9 @@ def __init__( use_bias=self.use_bias, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -199,7 +209,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -225,7 +234,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> FCNRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -310,7 +319,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_inception_time.py b/aeon/regression/deep_learning/_inception_time.py index 96e8a38362..e0d46f8089 100644 --- a/aeon/regression/deep_learning/_inception_time.py +++ b/aeon/regression/deep_learning/_inception_time.py @@ -1,5 +1,7 @@ """InceptionTime and Inception regressors.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["InceptionTimeRegressor"] @@ -7,6 +9,7 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any import numpy as np from sklearn.utils import check_random_state @@ -15,6 +18,10 @@ from aeon.regression.base import BaseRegressor from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class InceptionTimeRegressor(BaseRegressor): """InceptionTime ensemble regressor. @@ -179,39 +186,39 @@ class InceptionTimeRegressor(BaseRegressor): def __init__( self, - n_regressors=5, - n_filters=32, - n_conv_per_layer=3, - kernel_size=40, - use_max_pooling=True, - max_pool_size=3, - strides=1, - dilation_rate=1, - padding="same", - activation="relu", - use_bias=False, - use_residual=True, - use_bottleneck=True, - bottleneck_size=32, - depth=6, - use_custom_filters=False, - output_activation="linear", - file_path="./", - save_last_model=False, - save_best_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + n_regressors: int = 5, + n_filters: int | list[int] = 32, + n_conv_per_layer: int | list[int] = 3, + kernel_size: int | list[int] = 40, + use_max_pooling: bool | list[bool] = True, + max_pool_size: int | list[int] = 3, + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + use_bias: bool | list[bool] = False, + use_residual: bool = True, + use_bottleneck: bool = True, + bottleneck_size: int = 32, + depth: int = 6, + use_custom_filters: bool = False, + output_activation: str = "linear", + file_path: str = "./", + save_last_model: bool = False, + save_best_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_regressors = n_regressors @@ -251,11 +258,11 @@ def __init__( self.metrics = metrics self.optimizer = optimizer - self.regressors_ = [] + self.regressors_: list[IndividualInceptionRegressor] = [] super().__init__() - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> InceptionTimeRegressor: """Fit each of the Individual Inception models. Parameters @@ -313,7 +320,7 @@ def _fit(self, X, y): return self - def _predict(self, X) -> np.ndarray: + def _predict(self, X: np.ndarray) -> np.ndarray: """Predict the values of the test set using InceptionTime. Parameters @@ -337,7 +344,9 @@ def _predict(self, X) -> np.ndarray: return ypreds @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters @@ -507,38 +516,38 @@ class IndividualInceptionRegressor(BaseDeepRegressor): def __init__( self, - n_filters=32, - n_conv_per_layer=3, - kernel_size=40, - use_max_pooling=True, - max_pool_size=3, - strides=1, - dilation_rate=1, - padding="same", - activation="relu", - use_bias=False, - use_residual=True, - use_bottleneck=True, - bottleneck_size=32, - depth=6, - use_custom_filters=False, - output_activation="linear", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + n_filters: int | list[int] = 32, + n_conv_per_layer: int | list[int] = 3, + kernel_size: int | list[int] = 40, + use_max_pooling: bool | list[bool] = True, + max_pool_size: int | list[int] = 3, + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + use_bias: bool | list[bool] = False, + use_residual: bool = True, + use_bottleneck: bool = True, + bottleneck_size: int = 32, + depth: int = 6, + use_custom_filters: bool = False, + output_activation: str = "linear", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): # predefined self.n_filters = n_filters @@ -595,7 +604,9 @@ def __init__( use_custom_filters=self.use_custom_filters, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """ Construct a compiled, un-trained, keras model that is ready for training. @@ -609,7 +620,6 @@ def build_model(self, input_shape, **kwargs): tf.keras.models.Model A compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -631,7 +641,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> IndividualInceptionRegressor: """ Fit the regressor on the training set (X, y). @@ -721,7 +731,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py index ffd050f176..d21a0b391b 100644 --- a/aeon/regression/deep_learning/_lite_time.py +++ b/aeon/regression/deep_learning/_lite_time.py @@ -1,5 +1,7 @@ """LITETime and LITE regressors.""" +from __future__ import annotations + __author__ = ["aadya940", "hadifawaz1999"] __all__ = ["IndividualLITERegressor", "LITETimeRegressor"] @@ -7,6 +9,7 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any import numpy as np from sklearn.utils import check_random_state @@ -14,6 +17,10 @@ from aeon.networks import LITENetwork from aeon.regression.deep_learning.base import BaseDeepRegressor, BaseRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class LITETimeRegressor(BaseRegressor): """LITETime or LITEMVTime ensemble Regressor. @@ -105,6 +112,7 @@ class LITETimeRegressor(BaseRegressor): ----- Adapted from the implementation from Ismail-Fawaz et. al https://github.com/MSD-IRIMAS/LITE + by the code owner. References ---------- @@ -136,29 +144,29 @@ class LITETimeRegressor(BaseRegressor): def __init__( self, - n_regressors=5, - use_litemv=False, - n_filters=32, - kernel_size=40, - strides=1, - activation="relu", - output_activation="linear", - file_path="./", - save_last_model=False, - save_best_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + n_regressors: int = 5, + use_litemv: bool = False, + n_filters: int = 32, + kernel_size: int = 40, + strides: int | list[int] = 1, + activation: str | list[str] = "relu", + output_activation: str = "linear", + file_path: str = "./", + save_last_model: bool = False, + save_best_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_regressors = n_regressors @@ -190,11 +198,11 @@ def __init__( self.metrics = metrics self.optimizer = optimizer - self.regressors_ = [] + self.regressors_: list[IndividualLITERegressor] = [] super().__init__() - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> LITETimeRegressor: """Fit the ensemble of IndividualLITERegressor models. Parameters @@ -239,7 +247,7 @@ def _fit(self, X, y): return self - def _predict(self, X) -> np.ndarray: + def _predict(self, X: np.ndarray) -> np.ndarray: """Predict the values of the test set using LITETime. Parameters @@ -262,7 +270,7 @@ def _predict(self, X) -> np.ndarray: return vals @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params(cls, parameter_set: str = "default") -> dict | list[dict]: """Return testing parameter settings for the estimator. Parameters @@ -388,6 +396,7 @@ class IndividualLITERegressor(BaseDeepRegressor): ----- Adapted from the implementation from Ismail-Fawaz et. al https://github.com/MSD-IRIMAS/LITE + by the code owner. References ---------- @@ -411,28 +420,28 @@ class IndividualLITERegressor(BaseDeepRegressor): def __init__( self, - use_litemv=False, - n_filters=32, - kernel_size=40, - strides=1, - activation="relu", - output_activation="linear", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - batch_size=64, - use_mini_batch_size=False, - n_epochs=1500, - callbacks=None, - random_state=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - optimizer=None, + use_litemv: bool = False, + n_filters: int = 32, + kernel_size: int = 40, + strides: int | list[int] = 1, + activation: str | list[str] = "relu", + output_activation: str = "linear", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + batch_size: int = 64, + use_mini_batch_size: bool = False, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + random_state: int | np.random.RandomState | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.use_litemv = use_litemv self.n_filters = n_filters @@ -472,7 +481,9 @@ def __init__( activation=self.activation, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """ Construct a compiled, un-trained, keras model that is ready for training. @@ -485,7 +496,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf rng = check_random_state(self.random_state) @@ -511,7 +521,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> IndividualLITERegressor: """ Fit the Regressor on the training set (X, y). @@ -600,7 +610,7 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params(cls, parameter_set: str = "default") -> dict | list[dict]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_mlp.py b/aeon/regression/deep_learning/_mlp.py index 7de083e72f..fe1b28754f 100644 --- a/aeon/regression/deep_learning/_mlp.py +++ b/aeon/regression/deep_learning/_mlp.py @@ -1,5 +1,7 @@ """Multi Layer Perceptron Network (MLP) regressor.""" +from __future__ import annotations + __author__ = ["Aadya-Chinubhai", "hadifawaz1999"] __all__ = ["MLPRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import MLPNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class MLPRegressor(BaseDeepRegressor): """Multi Layer Perceptron Network (MLP). @@ -108,28 +116,28 @@ class MLPRegressor(BaseDeepRegressor): def __init__( self, - n_layers=3, - n_units=500, - activation="relu", - dropout_rate=None, - dropout_last=None, - use_bias=True, - n_epochs=2000, - batch_size=16, - callbacks=None, - verbose=False, - loss="mean_squared_error", - metrics="mean_squared_error", - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - random_state=None, - output_activation="linear", - optimizer=None, + n_layers: int = 3, + n_units: int | list[int] = 500, + activation: str | list[str] = "relu", + dropout_rate: float | list[float] | None = None, + dropout_last: float = 0.3, + use_bias: bool = True, + n_epochs: int = 2000, + batch_size: int = 16, + callbacks: Callback | list[Callback] | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + metrics: str | list[str] = "mean_squared_error", + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + random_state: int | np.random.RandomState | None = None, + output_activation: str = "linear", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_layers = n_layers self.n_units = n_units @@ -168,7 +176,9 @@ def __init__( use_bias=self.use_bias, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -185,7 +195,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf from tensorflow import keras @@ -211,7 +220,7 @@ def build_model(self, input_shape, **kwargs): ) return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> MLPRegressor: """Fit the Regressor on the training set (X, y). Parameters @@ -292,7 +301,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/_resnet.py b/aeon/regression/deep_learning/_resnet.py index 7f89a18ade..e123427517 100644 --- a/aeon/regression/deep_learning/_resnet.py +++ b/aeon/regression/deep_learning/_resnet.py @@ -1,5 +1,7 @@ """Residual Network (ResNet) regressor.""" +from __future__ import annotations + __maintainer__ = ["hadifawaz1999"] __all__ = ["ResNetRegressor"] @@ -7,12 +9,18 @@ import os import time from copy import deepcopy +from typing import TYPE_CHECKING, Any +import numpy as np from sklearn.utils import check_random_state from aeon.networks import ResNetNetwork from aeon.regression.deep_learning.base import BaseDeepRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class ResNetRegressor(BaseDeepRegressor): """ @@ -126,39 +134,39 @@ class ResNetRegressor(BaseDeepRegressor): >>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12, ... return_y=True, regression_target=True, ... random_state=0) - >>> rgs = ResNetRegressor(n_epochs=20, bacth_size=4) # doctest: +SKIP + >>> rgs = ResNetRegressor(n_epochs=20, batch_size=4) # doctest: +SKIP >>> rgs.fit(X, y) # doctest: +SKIP ResNetRegressor(...) """ def __init__( self, - n_residual_blocks=3, - n_conv_per_residual_block=3, - n_filters=None, - kernel_size=None, - strides=1, - dilation_rate=1, - padding="same", - activation="relu", - use_bias=True, - n_epochs=1500, - callbacks=None, - verbose=False, - loss="mean_squared_error", - output_activation="linear", - metrics="mean_squared_error", - batch_size=64, - use_mini_batch_size=False, - random_state=None, - file_path="./", - save_best_model=False, - save_last_model=False, - save_init_model=False, - best_file_name="best_model", - last_file_name="last_model", - init_file_name="init_model", - optimizer=None, + n_residual_blocks: int = 3, + n_conv_per_residual_block: int = 3, + n_filters: int | list[int] | None = None, + kernel_size: int | list[int] | None = None, + strides: int | list[int] = 1, + dilation_rate: int | list[int] = 1, + padding: str | list[str] = "same", + activation: str | list[str] = "relu", + use_bias: bool | list[bool] = True, + n_epochs: int = 1500, + callbacks: Callback | list[Callback] | None = None, + verbose: bool = False, + loss: str = "mean_squared_error", + output_activation: str = "linear", + metrics: str | list[str] = "mean_squared_error", + batch_size: int = 64, + use_mini_batch_size: bool = False, + random_state: int | np.random.RandomState | None = None, + file_path: str = "./", + save_best_model: bool = False, + save_last_model: bool = False, + save_init_model: bool = False, + best_file_name: str = "best_model", + last_file_name: str = "last_model", + init_file_name: str = "init_model", + optimizer: tf.keras.optimizers.Optimizer | None = None, ): self.n_residual_blocks = n_residual_blocks self.n_conv_per_residual_block = n_conv_per_residual_block @@ -201,7 +209,9 @@ def __init__( padding=self.padding, ) - def build_model(self, input_shape, **kwargs): + def build_model( + self, input_shape: tuple[int, ...], **kwargs: Any + ) -> tf.keras.Model: """Construct a compiled, un-trained, keras model that is ready for training. In aeon, time series are stored in numpy arrays of shape (d,m), where d @@ -218,7 +228,6 @@ def build_model(self, input_shape, **kwargs): ------- output : a compiled Keras Model """ - import numpy as np import tensorflow as tf self.optimizer_ = ( @@ -246,7 +255,7 @@ def build_model(self, input_shape, **kwargs): return model - def _fit(self, X, y): + def _fit(self, X: np.ndarray, y: np.ndarray) -> ResNetRegressor: """Fit the regressor on the training set (X, y). Parameters @@ -331,7 +340,9 @@ def _fit(self, X, y): return self @classmethod - def _get_test_params(cls, parameter_set="default"): + def _get_test_params( + cls, parameter_set: str = "default" + ) -> dict[str, Any] | list[dict[str, Any]]: """Return testing parameter settings for the estimator. Parameters diff --git a/aeon/regression/deep_learning/base.py b/aeon/regression/deep_learning/base.py index b48e3b2792..52b6f38c1d 100644 --- a/aeon/regression/deep_learning/base.py +++ b/aeon/regression/deep_learning/base.py @@ -5,15 +5,22 @@ because we can generalise tags and _predict """ +from __future__ import annotations + __maintainer__ = [] __all__ = ["BaseDeepRegressor"] from abc import abstractmethod +from typing import TYPE_CHECKING, Any import numpy as np from aeon.regression.base import BaseRegressor +if TYPE_CHECKING: + import tensorflow as tf + from tensorflow.keras.callbacks import Callback + class BaseDeepRegressor(BaseRegressor): """Abstract base class for deep learning time series regression. @@ -41,7 +48,7 @@ class BaseDeepRegressor(BaseRegressor): } @abstractmethod - def __init__(self, batch_size=40, last_file_name="last_model"): + def __init__(self, batch_size: int = 40, last_file_name: str = "last_model"): self.batch_size = batch_size self.last_file_name = last_file_name @@ -50,7 +57,7 @@ def __init__(self, batch_size=40, last_file_name="last_model"): super().__init__() @abstractmethod - def build_model(self, input_shape): + def build_model(self, input_shape: tuple[int, ...]) -> tf.keras.Model: """ Construct a compiled, un-trained, keras model that is ready for training. @@ -65,7 +72,7 @@ def build_model(self, input_shape): """ ... - def summary(self): + def summary(self) -> dict[str, Any] | None: """ Summary function to return the losses/metrics for model fit. @@ -77,7 +84,7 @@ def summary(self): """ return self.history.history if self.history is not None else None - def _predict(self, X): + def _predict(self, X: np.ndarray) -> np.ndarray: """ Find regression estimate for all cases in X. @@ -96,7 +103,7 @@ def _predict(self, X): y_pred = np.squeeze(y_pred, axis=-1) return y_pred - def save_last_model_to_file(self, file_path="./"): + def save_last_model_to_file(self, file_path: str = "./") -> None: """Save the last epoch of the trained deep learning model. Parameters @@ -110,7 +117,7 @@ def save_last_model_to_file(self, file_path="./"): """ self.model_.save(file_path + self.last_file_name + ".keras") - def load_model(self, model_path): + def load_model(self, model_path: str) -> None: """Load a pre-trained keras model instead of fitting. When calling this function, all functionalities can be used @@ -132,7 +139,9 @@ def load_model(self, model_path): self.model_ = tf.keras.models.load_model(model_path) self.is_fitted = True - def _get_model_checkpoint_callback(self, callbacks, file_path, file_name): + def _get_model_checkpoint_callback( + self, callbacks: Callback | list[Callback], file_path: str, file_name: str + ) -> list[Callback]: import tensorflow as tf model_checkpoint_ = tf.keras.callbacks.ModelCheckpoint( diff --git a/aeon/regression/feature_based/_catch22.py b/aeon/regression/feature_based/_catch22.py index 87e158ca9b..1ab04ee6e1 100644 --- a/aeon/regression/feature_based/_catch22.py +++ b/aeon/regression/feature_based/_catch22.py @@ -43,8 +43,11 @@ class Catch22Regressor(BaseRegressor): True. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used time + series that were already normalized. replace_nans : bool, optional, default=True Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, optional, default=False @@ -94,8 +97,8 @@ class Catch22Regressor(BaseRegressor): >>> reg.fit(X, y) Catch22Regressor(...) >>> reg.predict(X) - array([0.63821896, 1.0906666 , 0.58323551, 1.57550709, 0.48413489, - 0.70976176, 1.33206165, 1.09927538, 1.51673405, 0.31683308]) + array([0.63821896, 1.0906666 , 0.64351536, 1.57550709, 0.46036267, + 0.79297397, 1.32882497, 1.12603087, 1.51673405, 0.31683308]) """ _tags = { @@ -110,7 +113,7 @@ def __init__( self, features="all", catch24=True, - outlier_norm=False, + outlier_norm=True, replace_nans=True, use_pycatch22=False, estimator=None, diff --git a/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py b/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py index ef26b76099..9a89bbcbed 100644 --- a/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py +++ b/aeon/regression/sklearn/tests/test_rotation_forest_regressor.py @@ -24,21 +24,21 @@ def test_rotf_output(): rotf.fit(X_train, y_train) expected = [ - 0.02694297, - 0.02694297, - 0.01997832, - 0.04276962, - 0.09027588, - 0.02706564, - 0.02553648, - 0.04075808, - 0.02900289, - 0.04248546, - 0.02694297, - 0.03667328, - 0.0235855, - 0.03444119, - 0.0235855, + 0.026, + 0.0245, + 0.0224, + 0.0453, + 0.0892, + 0.0314, + 0.026, + 0.0451, + 0.0287, + 0.04, + 0.026, + 0.0378, + 0.0265, + 0.0356, + 0.0281, ] np.testing.assert_array_almost_equal(expected, rotf.predict(X_test[:15]), decimal=4) diff --git a/aeon/segmentation/_ggs.py b/aeon/segmentation/_ggs.py index d8bdd21d71..0a1bb615af 100644 --- a/aeon/segmentation/_ggs.py +++ b/aeon/segmentation/_ggs.py @@ -23,6 +23,7 @@ Based on the work from [1]_. - source code adapted based on: https://github.com/cvxgrp/GGS + Copyright (c) 2018, Stanford University Convex Optimization Group, BSD-2 - paper available at: https://stanford.edu/~boyd/papers/pdf/ggs.pdf References diff --git a/aeon/similarity_search/__init__.py b/aeon/similarity_search/__init__.py index f576c41f03..26b79c7da2 100644 --- a/aeon/similarity_search/__init__.py +++ b/aeon/similarity_search/__init__.py @@ -1,7 +1,5 @@ """Similarity search module.""" -__all__ = ["BaseSimilaritySearch", "QuerySearch", "SeriesSearch"] +__all__ = ["BaseSimilaritySearch"] -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.query_search import QuerySearch -from aeon.similarity_search.series_search import SeriesSearch +from aeon.similarity_search._base import BaseSimilaritySearch diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py new file mode 100644 index 0000000000..a2345ee558 --- /dev/null +++ b/aeon/similarity_search/_base.py @@ -0,0 +1,81 @@ +"""Base class for similarity search.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "BaseSimilaritySearch", +] + + +from abc import abstractmethod +from typing import Union + +import numpy as np +from numba.typed import List + +from aeon.base import BaseAeonEstimator + + +class BaseSimilaritySearch(BaseAeonEstimator): + """Base class for similarity search applications.""" + + _tags = { + "requires_y": False, + "fit_is_empty": False, + } + + @abstractmethod + def __init__(self): + super().__init__() + + @abstractmethod + def fit( + self, + X: Union[np.ndarray, List], + y=None, + ): + """ + Fit estimator to X. + + State change: + Changes state to "fitted". + + Writes to self: + _is_fitted : flag is set to True. + + Parameters + ---------- + X : Series or Collection, any supported type + Data to fit transform to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i) + y: ignored, exists for API consistency reasons. + + Returns + ------- + self : a fitted instance of the estimator + """ + ... + + @abstractmethod + def predict( + self, + X: Union[np.ndarray, None] = None, + ): + """ + Predict method. + + Can either work with new series or with None (for case when predict can be made + using the data given in fit against itself) depending on the estimator. + + Parameters + ---------- + X : Series or Collection, any supported type + Data to fit transform to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i + None : If None type is accepted, it means that the predict function will + work only with the data given in fit. (e.g. self matrix profile instead) + """ + ... diff --git a/aeon/similarity_search/_commons.py b/aeon/similarity_search/_commons.py deleted file mode 100644 index 1d20a6a5b0..0000000000 --- a/aeon/similarity_search/_commons.py +++ /dev/null @@ -1,504 +0,0 @@ -"""Helper and common function for similarity search estimators and functions.""" - -__maintainer__ = ["baraline"] - -import warnings - -import numpy as np -from numba import njit, prange -from numba.typed import List -from scipy.signal import convolve - -from aeon.utils.numba.general import ( - get_all_subsequences, - normalise_subsequences, - sliding_mean_std_one_series, - z_normalise_series_2d, -) - - -@njit(cache=True, fastmath=True) -def _compute_dist_profile(X_subs, q): - """ - Compute the distance profile between subsequences and a query. - - Parameters - ---------- - X_subs : array, shape=(n_samples, n_channels, query_length) - Input subsequences extracted from a time series. - q : array, shape=(n_channels, query_length) - Query used for the distance computation - - Returns - ------- - dist_profile : np.ndarray, 1D array of shape (n_samples) - The distance between the query all subsequences. - - """ - n_candidates, n_channels, q_length = X_subs.shape - dist_profile = np.zeros(n_candidates) - for i in range(n_candidates): - for j in range(n_channels): - for k in range(q_length): - dist_profile[i] += (X_subs[i, j, k] - q[j, k]) ** 2 - return dist_profile - - -@njit(cache=True, fastmath=True) -def naive_squared_distance_profile( - X, - q, - mask, - normalise=False, - X_means=None, - X_stds=None, -): - """ - Compute a squared euclidean distance profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints) - Input time series dataset to search in. - q : array, shape=(n_channels, query_length) - Query used during the search. - mask : array, shape=(n_samples, n_timepoints - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - X_means : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Mean of each candidate (subsequence) of length query_length in X. The - default is None, meaning that these values will be computed if normalise - is True. If provided, the computations will be skipped. - X_stds : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Standard deviation of each candidate (subsequence) of length query_length - in X. The default is None, meaning that these values will be computed if - normalise is True. If provided, the computations will be skipped. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) - The distance between the query and all candidates in X. - - """ - query_length = q.shape[1] - dist_profiles = List() - # Init distance profile array with unequal length support - for i in range(len(X)): - dist_profiles.append(np.zeros(X[i].shape[1] - query_length + 1)) - if normalise: - q = z_normalise_series_2d(q) - else: - q = q.astype(np.float64) - for i in range(len(X)): - # Numba don't support strides with integers ? - - X_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - if X_means is None and X_stds is None: - _X_means, _X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - else: - _X_means, _X_stds = X_means[i], X_stds[i] - X_subs = normalise_subsequences(X_subs, _X_means, _X_stds) - dist_profile = _compute_dist_profile(X_subs, q) - dist_profile[~mask[i]] = np.inf - dist_profiles[i] = dist_profile - return dist_profiles - - -@njit(cache=True, fastmath=True) -def naive_squared_matrix_profile(X, T, query_length, mask, normalise=False): - """ - Compute a squared euclidean matrix profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints_x) - Input time series dataset to search in. - T : array, shape=(n_channels, n_timepoints_t) - Time series from which queries are extracted. - query_length : int - Length of the queries to extract from T. - mask : array, shape=(n_samples, n_timepoints_x - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_timepoints_t - query_length + 1) - The minimum distance between each query in T and all candidates in X. - """ - X_subs = List() - for i in range(len(X)): - i_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - X_means, X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - i_subs = normalise_subsequences(i_subs, X_means, X_stds) - X_subs.append(i_subs) - - n_candidates = T.shape[1] - query_length + 1 - mp = np.full(n_candidates, np.inf) - - for i in range(n_candidates): - q = T[:, i : i + query_length] - if normalise: - q = z_normalise_series_2d(q) - for id_sample in range(len(X)): - dist_profile = _compute_dist_profile(X_subs[id_sample], q) - dist_profile[~mask[id_sample]] = np.inf - mp[i] = min(mp[i], dist_profile.min()) - return mp - - -def fft_sliding_dot_product(X, q): - """ - Use FFT convolution to calculate the sliding window dot product. - - This function applies the Fast Fourier Transform (FFT) to efficiently compute - the sliding dot product between the input time series `X` and the query `q`. - The dot product is computed for each channel individually. The sliding window - approach ensures that the dot product is calculated for every possible subsequence - of `X` that matches the length of `q` - - Parameters - ---------- - X : array, shape=(n_channels, n_timepoints) - Input time series - q : array, shape=(n_channels, query_length) - Input query - - Returns - ------- - out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - Sliding dot product between q and X. - """ - n_channels, n_timepoints = X.shape - query_length = q.shape[1] - out = np.zeros((n_channels, n_timepoints - query_length + 1)) - for i in range(n_channels): - out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real - return out - - -def get_ith_products(X, T, L, ith): - """ - Compute dot products between X and the i-th subsequence of size L in T. - - Parameters - ---------- - X : array, shape = (n_channels, n_timepoints_X) - Input data. - T : array, shape = (n_channels, n_timepoints_T) - Data containing the query. - L : int - Overall query length. - ith : int - Query starting index in T. - - Returns - ------- - np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - return fft_sliding_dot_product(X, T[:, ith : ith + L]) - - -@njit(cache=True) -def numba_roll_1D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_columns) - Array to roll. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - length = array.shape[0] - _a1 = array[: length - shift] - array[shift:] = _a1 - array[:shift] = warparound_value - return array - - -@njit(cache=True) -def numba_roll_2D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_rows, n_columns) - Array to roll. Can also be a TypedList in the case where n_columns changes - between rows. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - for i in prange(len(array)): - length = len(array[i]) - _a1 = array[i][: length - shift] - array[i][shift:] = _a1 - array[i][:shift] = warparound_value - return array - - -@njit(cache=True) -def extract_top_k_and_threshold_from_distance_profiles_one_series( - distance_profiles, - id_x, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the top-k smallest values from distance profiles and apply threshold. - - This function processes a distance profile and extracts the top-k smallest - distance values, optionally applying a threshold to exclude distances above - a given value. It also optionally handles exclusion zones to avoid selecting - neighboring timestamps. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - id_x : int - Identifier of the series or subsequence from which the distance profile - is computed. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int or None, optional, default=None - Size of the exclusion zone around the current subsequence. This prevents - selecting neighboring subsequences within the specified range, useful for - avoiding trivial matches in time series data. If set to `None`, no - exclusion zone is applied. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - top_k_dist : np.ndarray - Array of the top-k smallest distance values, potentially excluding values above - the threshold or those within the exclusion zone. - top_k : np.ndarray - Array of shape (k, 2) where each row contains the `id_x` identifier and the - index of the corresponding subsequence (or timestamp) with the top-k smallest - distances. - """ - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort = np.argsort(distance_profiles) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - else: - _k = k - - if exclusion_size is None: - indexes = np.zeros((_k, 2), dtype=np.int_) - for i in range(_k): - indexes[i, 0] = id_x - indexes[i, 1] = _argsort[i] - return distance_profiles[_argsort[:_k]], indexes - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=np.int_) - exclusion_size - top_k_dist = np.zeros((_k), dtype=np.float64) - - top_k[0, 0] = id_x - top_k[0, 1] = _argsort[0] - - top_k_dist[0] = distance_profiles[_argsort[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_timestamp = _argsort[i_current] - - insert = True - LB = candidate_timestamp >= (top_k[:, 1] - exclusion_size) - UB = candidate_timestamp <= (top_k[:, 1] + exclusion_size) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted, 0] = id_x - top_k[n_inserted, 1] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] - - -def extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the best matches from a distance profile given k and threshold parameters. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - # This whole function could be optimized and maybe made in numba to avoid stepping - # out of numba mode during distance computations - - n_cases_ = len(distance_profiles) - - id_timestamps = np.concatenate( - [np.arange(distance_profiles[i].shape[0]) for i in range(n_cases_)] - ) - id_samples = np.concatenate( - [[i] * distance_profiles[i].shape[0] for i in range(n_cases_)] - ) - - distance_profiles = np.concatenate(distance_profiles) - - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort_1d = np.argsort(distance_profiles) - _argsort = np.asarray( - [ - [id_samples[_argsort_1d[i]], id_timestamps[_argsort_1d[i]]] - for i in range(len(_argsort_1d)) - ], - dtype=int, - ) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - warnings.warn( - f"Only {_k} matches are bellow the threshold of {threshold}, while" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - warnings.warn( - f"The number of possible match is {_argsort.shape[0]}, but got" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - else: - _k = k - - if exclusion_size is None: - return distance_profiles[_argsort_1d[:_k]], _argsort[:_k] - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=int) - top_k_dist = np.zeros((_k), dtype=float) - - top_k[0] = _argsort[0, :] - top_k_dist[0] = distance_profiles[_argsort_1d[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_sample, candidate_timestamp = _argsort[i_current] - - insert = True - is_from_same_sample = top_k[:, 0] == candidate_sample - if np.any(is_from_same_sample): - LB = candidate_timestamp >= ( - top_k[is_from_same_sample, 1] - exclusion_size - ) - UB = candidate_timestamp <= ( - top_k[is_from_same_sample, 1] + exclusion_size - ) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort_1d[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] diff --git a/aeon/similarity_search/base.py b/aeon/similarity_search/base.py deleted file mode 100644 index 5b0ce8c555..0000000000 --- a/aeon/similarity_search/base.py +++ /dev/null @@ -1,232 +0,0 @@ -"""Base class for similarity search.""" - -__maintainer__ = ["baraline"] - -from abc import abstractmethod -from collections.abc import Iterable -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads -from numba.typed import List - -from aeon.base import BaseCollectionEstimator -from aeon.utils.numba.general import sliding_mean_std_one_series - - -class BaseSimilaritySearch(BaseCollectionEstimator): - """ - Base class for similarity search applications. - - Parameters - ---------- - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function of the child classes. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - _tags = { - "capability:multivariate": True, - "capability:unequal_length": True, - "capability:multithreading": True, - "fit_is_empty": False, - "X_inner_type": ["np-list", "numpy3D"], - } - - @abstractmethod - def __init__( - self, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - ): - self.distance = distance - self.distance_args = distance_args - self.inverse_distance = inverse_distance - self.normalise = normalise - self.n_jobs = n_jobs - self.speed_up = speed_up - super().__init__() - - @final - def fit(self, X: np.ndarray, y=None): - """ - Fit method: data preprocessing and storage. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to be used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - """ - prev_threads = get_num_threads() - X = self._preprocess_collection(X) - # Store minimum number of n_timepoints for unequal length collections - self.min_timepoints_ = min([X[i].shape[-1] for i in range(len(X))]) - self.n_channels_ = X[0].shape[0] - self.n_cases_ = len(X) - if self.metadata_["unequal_length"]: - X = List(X) - set_num_threads(self._n_jobs) - self._fit(X, y) - set_num_threads(prev_threads) - self.is_fitted = True - return self - - def _store_mean_std_from_inputs(self, query_length: int) -> None: - """ - Store the mean and std of each subsequence of size query_length in X_. - - Parameters - ---------- - query_length : int - Length of the query. - - Returns - ------- - None - - """ - means = [] - stds = [] - - for i in range(len(self.X_)): - _mean, _std = sliding_mean_std_one_series(self.X_[i], query_length, 1) - - stds.append(_std) - means.append(_mean) - - self.X_means_ = List(means) - self.X_stds_ = List(stds) - - def _init_X_index_mask( - self, - X_index: Optional[Iterable[int]], - query_length: int, - exclusion_factor: Optional[float] = 2.0, - ) -> np.ndarray: - """ - Initiliaze the mask indicating the candidates to be evaluated in the search. - - Parameters - ---------- - X_index : Iterable - Any Iterable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_ (the training data). - query_length : int - Length of the queries. - exclusion_factor : float, optional - The exclusion factor is used to prevent candidates close or equal to the - query sample point to be returned as best matches. It is used to define a - region between :math:`id_timestamp - query_length//exclusion_factor` and - :math:`id_timestamp + query_length//exclusion_factor` which cannot be used - in the search. The default is 2.0. - - Raises - ------ - ValueError - If the length of the q_index iterable is not two, will raise a ValueError. - TypeError - If q_index is not an iterable, will raise a TypeError. - - Returns - ------- - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in the - similarity search. - - """ - if self.metadata_["unequal_length"]: - mask = List( - [ - np.ones(self.X_[i].shape[1] - query_length + 1, dtype=bool) - for i in range(self.n_cases_) - ] - ) - else: - mask = np.ones( - (self.n_cases_, self.min_timepoints_ - query_length + 1), - dtype=bool, - ) - if X_index is not None: - if isinstance(X_index, Iterable): - if len(X_index) != 2: - raise ValueError( - "The X_index should contain an interable of size 2 such as " - "(id_sample, id_timestamp), but got an iterable of " - "size {}".format(len(X_index)) - ) - else: - raise TypeError( - "If not None, the X_index parameter should be an iterable, here " - "X_index is of type {}".format(type(X_index)) - ) - - if exclusion_factor <= 0: - raise ValueError( - "The value of exclusion_factor should be superior to 0, but got " - "{}".format(len(exclusion_factor)) - ) - - i_instance, i_timestamp = X_index - profile_length = self.X_[i_instance].shape[1] - query_length + 1 - exclusion_LB = max(0, int(i_timestamp - query_length // exclusion_factor)) - exclusion_UB = min( - profile_length, - int(i_timestamp + query_length // exclusion_factor), - ) - mask[i_instance][exclusion_LB:exclusion_UB] = False - - return mask - - @abstractmethod - def _fit(self, X, y=None): ... - - @abstractmethod - def get_speedup_function_names(self): - """Return a dictionnary containing the name of the speedup functions.""" - ... diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py new file mode 100644 index 0000000000..dea25853be --- /dev/null +++ b/aeon/similarity_search/collection/__init__.py @@ -0,0 +1,11 @@ +"""Similarity search for time series collection.""" + +__all__ = [ + "BaseCollectionSimilaritySearch", + "RandomProjectionIndexANN", +] + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py new file mode 100644 index 0000000000..9bd6f7cb31 --- /dev/null +++ b/aeon/similarity_search/collection/_base.py @@ -0,0 +1,112 @@ +"""Base similiarity search for collections.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "BaseCollectionSimilaritySearch", +] + +from abc import abstractmethod +from typing import final + +import numpy as np + +from aeon.base import BaseCollectionEstimator +from aeon.similarity_search._base import BaseSimilaritySearch + + +class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySearch): + """ + Similarity search base class for collections. + + Such estimators include nearest neighbors on whole series or subsequences with + indexing or concenssus motifs search over a collection. + """ + + # tag values specific to CollectionTransformers + _tags = { + "input_data_type": "Collection", + "capability:multivariate": True, + "X_inner_type": ["numpy3D"], + } + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Input array to be used as database for the similarity search. If it is an + unequal length collection, it should be a list of 2d numpy arrays. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 3D raise an error. + + Returns + ------- + self + """ + self.reset() + X = self._preprocess_collection(X) + self.n_channels_ = self.metadata_["n_channels"] + self.n_cases_ = self.metadata_["n_cases"] + self._fit(X, y=y) + self.is_fitted = True + return self + + @abstractmethod + def _fit(self, X: np.ndarray, y=None): ... + + @final + def predict(self, X, **kwargs): + """ + Predict function. + + Parameters + ---------- + X : np.ndarray, 3D array of shape = (n_cases, n_channels, n_timepoints) + Collections of series to predict on. + kwargs : dict, optional + Additional keyword arguments to be passed to the _predict function of the + estimator. + + Returns + ------- + indexes : np.ndarray, shape = (n_cases, k) + Indexes of series in the that are similar to X. + distances : np.ndarray, shape = (n_cases, k) + Distance of the matches to each series + + """ + self._check_is_fitted() + X = self._preprocess_collection(X, store_metadata=False) + self._check_predict_series_format(X) + indexes, distances = self._predict(X, **kwargs) + return indexes, distances + + def _check_predict_series_format(self, X): + """ + Check whether a series X in predict is correctly formated. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + """ + if self.n_channels_ != X[0].shape[0]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X[0].shape[0]} channels." + ) + + @abstractmethod + def _predict(self, X, **kwargs): ... diff --git a/aeon/similarity_search/collection/motifs/__init__.py b/aeon/similarity_search/collection/motifs/__init__.py new file mode 100644 index 0000000000..b7169f1ade --- /dev/null +++ b/aeon/similarity_search/collection/motifs/__init__.py @@ -0,0 +1 @@ +"""Motif discovery for time series collection.""" diff --git a/aeon/similarity_search/collection/neighbors/__init__.py b/aeon/similarity_search/collection/neighbors/__init__.py new file mode 100644 index 0000000000..f5cf0d925b --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/__init__.py @@ -0,0 +1,7 @@ +"""Neighbors search for time series collection.""" + +__all__ = ["RandomProjectionIndexANN"] + +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py new file mode 100644 index 0000000000..167ec538c6 --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -0,0 +1,320 @@ +"""Random projection LSH index.""" + +import numpy as np +from numba import get_num_threads, njit, prange, set_num_threads + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD, z_normalise_series_3d + + +@njit(cache=True) +def _bool_hamming_dist(X, Y): + """ + Compute a hamming distance on boolean arrays. + + Parameters + ---------- + X : np.ndarray of shape (n_timepoints) + A boolean array + + Y : np.ndarray of shape (n_timepoints) + A boolean array + + Returns + ------- + d : int + The hamming distance between X and Y. + + """ + d = np.uint64(0) + for i in range(X.shape[0]): + d += X[i] ^ Y[i] + return d + + +@njit(cache=True, parallel=True) +def _bool_hamming_dist_matrix(X_bool, collection_bool): + """ + Compute the distances between X_bool and each boolean array of collection_bool. + + Each array of collection_bool represent the hash value of a bucket in the index. + + Parameters + ---------- + X_bool : np.ndarray of shape (n_timepoints) + A 1D boolean array + collection_bool : np.ndarray of shape (n_cases, n_timepoints) + A 2D boolean array + + Returns + ------- + res : np.ndarray of shape (n_cases) + The distance of X_bool to all buckets in the index + + """ + n_buckets = collection_bool.shape[0] + res = np.zeros(n_buckets, dtype=np.uint64) + for i in prange(n_buckets): + res[i] = _bool_hamming_dist(collection_bool[i], X_bool) + return res + + +@njit(cache=True, fastmath=True) +def _nb_flat_dot(X, Y): + n_channels, n_timepoints = X.shape + out = 0 + for i in prange(n_channels): + for j in prange(n_timepoints): + out += X[i, j] * Y[i, j] + return out >= 0 + + +@njit(cache=True, parallel=True) +def _collection_to_bool(X, hash_funcs, start_points, length): + """ + Transform a collection of time series X to their boolean hash representation. + + Parameters + ---------- + X : np.ndarray of shape (n_cases, n_channels, n_timepoints) + Time series collection to transform. + hash_funcs : np.ndarray of shape (n_hash, n_channels, length) + The random projection vectors used to compute the boolean hash + start_points : np.ndarray of shape (n_hash) + The starting index where the random vector should be applied when computing + the distance to the input series. + length : int + Length of the random vectors. + + Returns + ------- + res : np.ndarray of shape (n_cases, n_hash) + The boolean representation of all series in X. + + """ + n_hash_funcs = hash_funcs.shape[0] + n_samples = X.shape[0] + res = np.empty((n_samples, n_hash_funcs), dtype=np.bool_) + for j in prange(n_hash_funcs): + for i in range(n_samples): + res[i, j] = _nb_flat_dot( + X[i, :, start_points[j] : start_points[j] + length], hash_funcs[j] + ) + return res + + +class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): + """ + Random Projection Locality Sensitive Hashing index with cosine similarity. + + In this method based on SimHash, we define a hash function as a boolean operation + such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time series + ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we compute + ``X.V > 0`` to obtain the boolean result. + In the case where ``L k - current_k: + candidates = candidates[: k - current_k] + top_k[current_k : current_k + len(candidates)] = candidates + top_k_dist[current_k : current_k + len(candidates)] = dists[ + ids[_i_bucket] + ] + current_k += len(candidates) + _i_bucket += 1 + + return top_k[:current_k], top_k_dist[:current_k] + + def _collection_to_hashes(self, X): + return _collection_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) diff --git a/aeon/similarity_search/collection/neighbors/tests/__init__.py b/aeon/similarity_search/collection/neighbors/tests/__init__.py new file mode 100644 index 0000000000..89bc3412fb --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection neighbors module.""" diff --git a/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py new file mode 100644 index 0000000000..82c1d102f3 --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py @@ -0,0 +1 @@ +"""Tests for RandomProjectionIndexANN.""" diff --git a/aeon/similarity_search/collection/tests/__init__.py b/aeon/similarity_search/collection/tests/__init__.py new file mode 100644 index 0000000000..d136a8571e --- /dev/null +++ b/aeon/similarity_search/collection/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection base class and commons.""" diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py new file mode 100644 index 0000000000..7f538cdd59 --- /dev/null +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -0,0 +1,19 @@ +"""Test for collection similarity search base class.""" + +__maintainer__ = ["baraline"] + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockCollectionSimilaritySearch, +) +from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator + + +def test_input_shape_fit_predict_collection(): + """Test input shapes.""" + estimator = MockCollectionSimilaritySearch() + datatypes = _get_datatypes_for_estimator(estimator) + # dummy data to pass to fit when testing predict/predict_proba + for datatype in datatypes: + X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] + X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] + estimator.fit(X_train, y_train).predict(X_test) diff --git a/aeon/similarity_search/distance_profiles/__init__.py b/aeon/similarity_search/distance_profiles/__init__.py deleted file mode 100644 index 4be73f9d8e..0000000000 --- a/aeon/similarity_search/distance_profiles/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "euclidean_distance_profile", - "normalised_euclidean_distance_profile", - "squared_distance_profile", - "normalised_squared_distance_profile", -] - - -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) diff --git a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py b/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py deleted file mode 100644 index 1dd781e467..0000000000 --- a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba.typed import List - -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -def euclidean_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = squared_distance_profile(X, q, mask) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles - - -def normalised_euclidean_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles diff --git a/aeon/similarity_search/distance_profiles/squared_distance_profile.py b/aeon/similarity_search/distance_profiles/squared_distance_profile.py deleted file mode 100644 index a42beeac2f..0000000000 --- a/aeon/similarity_search/distance_profiles/squared_distance_profile.py +++ /dev/null @@ -1,319 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit, prange -from numba.typed import List - -from aeon.similarity_search._commons import fft_sliding_dot_product -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def squared_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profile : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - distance_profiles = _squared_distance_profile(QX, X, q, mask) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -def normalised_squared_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: np.ndarray, - X_stds: np.ndarray, - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - query_length = q.shape[1] - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - - distance_profiles = _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length - ) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -@njit(cache=True, fastmath=True, parallel=True) -def _squared_distance_profile(QX, X, q, mask): - """ - Compute squared distance profiles between query subsequence and time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each - element corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = List() - query_length = q.shape[1] - - # Init distance profile array with unequal length support - for i_instance in range(len(X)): - profile_length = X[i_instance].shape[1] - query_length + 1 - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _squared_dist_profile_one_series(QX[i_instance], X[i_instance], q)[ - mask[i_instance] - ] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _squared_dist_profile_one_series(QT, T, Q): - """ - Compute squared distance profile between query subsequence and a single time series. - - This function calculates the squared distance profile for a single time series by - leveraging the dot product of the query and time series as well as precomputed sums - of squares to efficiently compute the squared distances. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - Q : np.ndarray - 2D array of shape (n_channels, query_length) representing query subsequence. - - Returns - ------- - distance_profile : np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) - The squared distance profile between the query and the input time series. - """ - n_channels, profile_length = QT.shape - query_length = Q.shape[1] - _QT = -2 * QT - distance_profile = np.zeros(profile_length) - for k in prange(n_channels): - _sum = 0 - _qsum = 0 - for j in prange(query_length): - _sum += T[k, j] ** 2 - _qsum += Q[k, j] ** 2 - - distance_profile += _qsum + _QT[k] - distance_profile[0] += _sum - for i in prange(1, profile_length): - _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 - distance_profile[i] += _sum - return distance_profile - - -@njit(cache=True, fastmath=True, parallel=True) -def _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length -): - """ - Compute the normalised squared distance profiles between query subsequence and input time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each element - corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - - Returns - ------- - List of np.ndarray - List of 2D arrays, each of shape (n_channels, n_timepoints - query_length + 1). - Each array contains the normalised squared distance profile between the query subsequence and the corresponding time series. - Entries in the array are set to infinity where the mask is False. - """ - distance_profiles = List() - Q_is_constant = q_stds <= AEON_NUMBA_STD_THRESHOLD - # Init distance profile array with unequal length support - for i_instance in range(len(QX)): - profile_length = QX[i_instance].shape[1] - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _normalised_squared_dist_profile_one_series( - QX[i_instance], - X_means[i_instance], - X_stds[i_instance], - q_means, - q_stds, - query_length, - Q_is_constant, - )[mask[i_instance]] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _normalised_squared_dist_profile_one_series( - QT, T_means, T_stds, Q_means, Q_stds, query_length, Q_is_constant -): - """ - Compute the z-normalised squared Euclidean distance profile for one time series. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T_means : np.ndarray, 1D array of length n_channels - The mean values of the time series for each channel. - - T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) - The standard deviations of the time series for each channel and position. - Q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - Q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - Q_is_constant : np.ndarray - 1D array of shape (n_channels,) where each element is a Boolean indicating - whether the query standard deviation for that channel is less than or equal - to a specified threshold. - - Returns - ------- - np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the - z-normalised squared distance profile between the query subsequence and the time - series. Entries are computed based on the z-normalised values, with special - handling for constant values. - """ - n_channels, profile_length = QT.shape - distance_profile = np.zeros(profile_length) - - for i in prange(profile_length): - Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD - for k in prange(n_channels): - # Two Constant case - if Q_is_constant[k] and Sub_is_constant[k]: - _val = 0 - # One Constant case - elif Q_is_constant[k] or Sub_is_constant[k]: - _val = query_length - else: - denom = query_length * Q_stds[k] * T_stds[k, i] - - p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom - p = min(p, 1.0) - - _val = abs(2 * query_length * (1.0 - p)) - distance_profile[i] += _val - - return distance_profile diff --git a/aeon/similarity_search/distance_profiles/tests/__init__.py b/aeon/similarity_search/distance_profiles/tests/__init__.py deleted file mode 100644 index 566dda7367..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for distance profiles.""" diff --git a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py b/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py deleted file mode 100644 index 2eafff78bb..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py +++ /dev/null @@ -1,208 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = euclidean_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 - for T in naive_squared_distance_profile( - X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds - ) - ] - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py b/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py deleted file mode 100644 index cdb7b35cbc..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = squared_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/matrix_profiles/__init__.py b/aeon/similarity_search/matrix_profiles/__init__.py deleted file mode 100644 index d04f1cbfd3..0000000000 --- a/aeon/similarity_search/matrix_profiles/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "stomp_normalised_euclidean_matrix_profile", - "stomp_euclidean_matrix_profile", - "stomp_normalised_squared_matrix_profile", - "stomp_squared_matrix_profile", -] -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) diff --git a/aeon/similarity_search/matrix_profiles/stomp.py b/aeon/similarity_search/matrix_profiles/stomp.py deleted file mode 100644 index 509e68ad49..0000000000 --- a/aeon/similarity_search/matrix_profiles/stomp.py +++ /dev/null @@ -1,633 +0,0 @@ -"""Implementation of stomp for euclidean and squared euclidean distance profile.""" - -from typing import Optional - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit -from numba.typed import List - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles_one_series, - get_ith_products, - numba_roll_1D_no_warparound, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - _normalised_squared_dist_profile_one_series, - _squared_dist_profile_one_series, -) -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def stomp_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_squared_matrix_profile( - X, - T, - L, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def stomp_normalised_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_normalised_squared_matrix_profile( - X, - T, - L, - X_means, - X_stds, - T_means, - T_stds, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_normalised_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - """ - Compute the Matrix Profile using the STOMP algorithm with normalised distances. - - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - Length of the subsequences used for the distance computation. - XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Precomputed dot products between each time series in X and the query series T. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - tuple of np.ndarray - - MP : array of shape (n_queries,) - Matrix profile distances for each query subsequence. - - IP : array of shape (n_queries,) - Indexes of the top matches for each query subsequence. - """ - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - dist_profiles = _normalised_squared_dist_profile_one_series( - XdotT[i_x], - X_means[i_x], - X_stds[i_x], - T_means[:, i], - T_stds[:, i], - L, - T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD, - ) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - Q = T[:, i : i + L] - dist_profiles = _squared_dist_profile_one_series(XdotT[i_x], X[i_x], Q) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _sort_out_tops(top_dists, prev_top_dists, top_indexes, prev_to_indexes, k): - """ - Sort and combine top distance results from previous and current computations. - - Parameters - ---------- - top_dists : np.ndarray - Array of distances from the current computation. Shape should be (n,). - prev_top_dists : np.ndarray - Array of distances from previous computations. Shape should be (n,). - top_indexes : np.ndarray - Array of indexes corresponding to the top distances from current computation. - Shape should be (n,). - prev_to_indexes : np.ndarray - Array of indexes corresponding to the top distances from previous computations. - Shape should be (n,). - k : int, default=1 - The number of best matches to return during predict for each subsequence. - - Returns - ------- - tuple - A tuple containing two elements: - - A 1D numpy array of sorted distances, of length min(k, - total number of distances). - - A 1D numpy array of indexes corresponding to the sorted distances, - of length min(k, total number of distances). - """ - all_dists = np.concatenate((prev_top_dists, top_dists)) - all_indexes = np.concatenate((prev_to_indexes, top_indexes)) - if k == np.inf: - return all_dists, all_indexes - else: - idx = np.argsort(all_dists)[:k] - return all_dists[idx], all_indexes[idx] - - -@njit(cache=True, fastmath=True) -def _update_dot_products_one_series( - X, - T, - XT_products, - L, - i_query, -): - """ - Update dot products of the i-th query of size L in T from the dot products of i-1. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input time series on which the sliding dot product is computed. - T: np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - i_query : int - Query starting index in T. - - Returns - ------- - XT_products : np.ndarray of shape (n_cases, n_channels, n_timepoints - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - n_channels = T.shape[0] - Q = T[:, i_query : i_query + L] - n_candidates = X.shape[1] - L + 1 - - for i_ft in range(n_channels): - # first element of all 0 to n-1 candidates * first element of previous query - _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] - # last element of all 1 to n candidates * last element of current query - _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] - - XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 - - # Compute first dot product - XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) - return XT_products diff --git a/aeon/similarity_search/matrix_profiles/tests/__init__.py b/aeon/similarity_search/matrix_profiles/tests/__init__.py deleted file mode 100644 index 3feb8d4ca5..0000000000 --- a/aeon/similarity_search/matrix_profiles/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for series methods.""" diff --git a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py b/aeon/similarity_search/matrix_profiles/tests/test_stomp.py deleted file mode 100644 index ffcf7d0b6a..0000000000 --- a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py +++ /dev/null @@ -1,205 +0,0 @@ -"""Tests for stomp algorithm.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_equal - -from aeon.distances import get_distance_function -from aeon.similarity_search._commons import get_ith_products -from aeon.similarity_search.matrix_profiles.stomp import ( - _update_dot_products_one_series, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["int64", "float64"] -K_VALUES = [1] - - -def test__update_dot_products_one_series(): - """Test the _update_dot_product function.""" - X = np.random.rand(1, 50) - T = np.random.rand(1, 25) - L = 10 - current_product = get_ith_products(X, T, L, 0) - for i_query in range(1, T.shape[1] - L + 1): - new_product = get_ith_products( - X, - T, - L, - i_query, - ) - current_product = _update_dot_products_one_series( - X, - T, - current_product, - L, - i_query, - ) - assert_array_almost_equal(new_product, current_product) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask, k=k) - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [distance(q, X[j, :, _i : _i + L]) for _i in range(X.shape[-1] - L + 1)] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index( - np.argsort(expected.ravel(), kind="stable"), expected.shape - ) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_normalised_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - X_means = [] - X_stds = [] - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], L, 1) - - X_stds.append(_std) - X_means.append(_mean) - X_means = np.asarray(X_means) - X_stds = np.asarray(X_stds) - - S_means, S_stds = sliding_mean_std_one_series(S, L, 1) - - mp, ip = stomp_normalised_squared_matrix_profile( - X, S, L, X_means, X_stds, S_means, S_stds, mask, k=k - ) - - for i in range(S.shape[-1] - L + 1): - q = (S[:, i : i + L] - S_means[:, i]) / S_stds[:, i] - - expected = np.array( - [ - [ - distance( - q, - (X[j, :, _i : _i + L] - X_means[j, :, _i]) / X_stds[j, :, _i], - ) - for _i in range(X.shape[-1] - L + 1) - ] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_stomp_squared_matrix_profile_unequal_length(dtype): - """Test stomp with unequal length.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - L = 3 - mask = List( - [ - np.ones(X[0].shape[1] - L + 1, dtype=bool), - np.ones(X[1].shape[1] - L + 1, dtype=bool), - ] - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = [ - [ - distance(q, X[j][:, _i : _i + q.shape[-1]]) - for _i in range(X[j].shape[-1] - q.shape[-1] + 1) - ] - for j in range(len(X)) - ] - assert_almost_equal(mp[i][0], np.concatenate(expected).min()) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile_inverse(dtype, k): - """Test stomp series search for inverse distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile( - X, - S, - L, - mask, - k=k, - inverse_distance=True, - ) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [ - distance(q, X[j, :, _i : _i + q.shape[-1]]) - for _i in range(X.shape[-1] - q.shape[-1] + 1) - ] - for j in range(X.shape[0]) - ] - ) - expected += 1e-8 - expected = 1 / expected - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) diff --git a/aeon/similarity_search/query_search.py b/aeon/similarity_search/query_search.py deleted file mode 100644 index 393439148d..0000000000 --- a/aeon/similarity_search/query_search.py +++ /dev/null @@ -1,428 +0,0 @@ -"""Base class for query search.""" - -__maintainer__ = ["baraline"] - -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles, -) -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -class QuerySearch(BaseSimilaritySearch): - """ - Query search estimator. - - The query search estimator will return a set of matches of a query in a search space - , which is defined by a time series dataset given during fit. Depending on the `k` - and/or `threshold` parameters, which condition what is considered a valid match - during the search, the number of matches will vary. If `k` is used, at most `k` - matches (the `k` best) will be returned, if `threshold` is used and `k` is set to - `np.inf`, all the candidates which distance to the query is inferior or equal to - `threshold` will be returned. If both are used, the `k` best matches to the query - with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for a given query. - threshold : float, default=np.inf - The number of best matches to return during predict for a given query. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - store_distance_profiles : bool, default=False. - Whether to store the computed distance profiles in the attribute - "distance_profiles_" after calling the predict method. It will store the raw - distance profile, meaning without potential inversion or thresholding applied. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - store_distance_profiles: bool = False, - ): - self.k = k - self.threshold = threshold - self.store_distance_profiles = store_distance_profiles - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X: np.ndarray, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.distance_profile_function_ = self._get_distance_profile_function() - return self - - @final - def predict( - self, - X: np.ndarray, - axis=1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ) -> np.ndarray: - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : Iterable - An Interable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - :math:`id_timestamp - query_length//exclusion_factor` to - :math:`id_timestamp + query_length//exclusion_factor`. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- :math:`query_length//exclusion_factor`. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at :math:`id_timestamp`, the second best match will be located at - :math:`id_timestamp` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - - query_dim, query_length = self._check_query_format(X, axis) - - mask = self._init_X_index_mask( - X_index, - query_length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - self.query_means_ = np.mean(X, axis=-1) - self.query_stds_ = np.std(X, axis=-1) - if self._previous_query_length != query_length: - self._store_mean_std_from_inputs(query_length) - - if apply_exclusion_to_result: - exclusion_size = query_length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = query_length - - X_preds = self._predict( - self._call_distance_profile(X, mask), - exclusion_size=exclusion_size, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, distance_profiles: np.ndarray, exclusion_size: Optional[int] = None - ) -> np.ndarray: - """ - Private predict method for QuerySearch. - - It takes the distance profiles and apply the `k` and `threshold` conditions to - return the set of best matches. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - Precomputed distance profile. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - - """ - if self.store_distance_profiles: - self.distance_profiles_ = distance_profiles - # Define id sample and timestamp to not "loose" them due to concatenation - return extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=self.k, - threshold=self.threshold, - exclusion_size=exclusion_size, - inverse_distance=self.inverse_distance, - ) - - def _check_query_format(self, X, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the query X is univariate " - "do X = X[np.newaxis, :]." - ) - - query_dim, query_length = X.shape - if query_length >= self.min_timepoints_: - raise ValueError( - "The length of the query should be inferior or equal to the length of " - "data (X_) provided during fit, but got {} for X and {} for X_".format( - query_length, self.min_timepoints_ - ) - ) - - if query_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the query X and the data " - "(X_) provided during fit, but got {} for X and {} for X_".format( - query_dim, self.n_channels_ - ) - ) - return query_dim, query_length - - def _get_distance_profile_function(self): - """ - Given distance and speed_up parameters, return the distance profile function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The distance profile function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _QUERY_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_profile = distance_dict.get(self.normalise).get(self.speed_up) - - if speed_up_profile is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_profile - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - def _call_distance_profile(self, X: np.ndarray, mask: np.ndarray) -> np.ndarray: - """ - Obtain the distance profile function and call it with the query and the mask. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in - the similarity search. - - Returns - ------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - The distance profiles between the input time series and the query. - - """ - if self.normalise: - distance_profiles = self.distance_profile_function_( - self.X_, - X, - mask, - self.X_means_, - self.X_stds_, - self.query_means_, - self.query_stds_, - ) - else: - distance_profiles = self.distance_profile_function_(self.X_, X, mask) - - return distance_profiles - - @classmethod - def get_speedup_function_names(self) -> dict: - """ - Get available speedup for query search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _QUERY_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _QUERY_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _QUERY_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_QUERY_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": normalised_euclidean_distance_profile, - "Mueen": normalised_euclidean_distance_profile, - }, - False: { - "fastest": euclidean_distance_profile, - "Mueen": euclidean_distance_profile, - }, - }, - "squared": { - True: { - "fastest": normalised_squared_distance_profile, - "Mueen": normalised_squared_distance_profile, - }, - False: { - "fastest": squared_distance_profile, - "Mueen": squared_distance_profile, - }, - }, -} diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py new file mode 100644 index 0000000000..1ecc20614a --- /dev/null +++ b/aeon/similarity_search/series/__init__.py @@ -0,0 +1,15 @@ +"""Similarity search for series.""" + +__all__ = [ + "BaseSeriesSimilaritySearch", + "MassSNN", + "StompMotif", + "DummySNN", +] + +from aeon.similarity_search.series._base import ( + BaseSeriesSimilaritySearch, +) +from aeon.similarity_search.series.motifs._stomp import StompMotif +from aeon.similarity_search.series.neighbors._dummy import DummySNN +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py new file mode 100644 index 0000000000..6139835e77 --- /dev/null +++ b/aeon/similarity_search/series/_base.py @@ -0,0 +1,119 @@ +"""Base similiarity search for series.""" + +__maintainer__ = ["baraline"] +__all__ = ["BaseSeriesSimilaritySearch"] + +from abc import abstractmethod +from typing import final + +import numpy as np + +from aeon.base import BaseSeriesEstimator +from aeon.similarity_search._base import BaseSimilaritySearch + + +class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): + """ + Base class for similarity search applications on single series. + + Such estimators include nearest neighbors on subsequences extracted from a series + or motif discovery on single series. + """ + + _tags = { + "input_data_type": "Series", + "capability:multivariate": True, + } + + @abstractmethod + def __init__(self, axis=1): + super().__init__(axis=axis) + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input series to be used for the similarity search operations. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 2D raise an error. + + Returns + ------- + self + """ + self.reset() + X = self._preprocess_series(X, self.axis, True) + self.n_channels_ = self.metadata_["n_channels"] + timepoint_idx = 1 if self.axis == 1 else 0 + self.n_timepoints_ = X.shape[timepoint_idx] + self.X_ = X + self._fit(X, y=y) + self.is_fitted = True + return self + + @abstractmethod + def _fit( + self, + X: np.ndarray, + y=None, + ): ... + + @final + def predict(self, X, **kwargs): + """ + Predict function. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series to predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to the estimator. + + Returns + ------- + indexes : np.ndarray, shape = (k) + Indexes of series in the that are similar to X. + distances : np.ndarray, shape = (k) + Distance of the matches to each series + + """ + self._check_is_fitted() + X = self._preprocess_series(X, self.axis, False) + self._check_predict_series_format(X) + indexes, distances = self._predict(X, **kwargs) + return indexes, distances + + @abstractmethod + def _predict(self, X, **kwargs): ... + + def _check_predict_series_format(self, X): + """ + Check wheter a series X is correctly formated regarding series given in fit. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + + """ + channel_idx = 0 if self.axis == 1 else 1 + if self.n_channels_ != X.shape[channel_idx]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X.shape[channel_idx]} channels." + ) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py new file mode 100644 index 0000000000..646c38e5ff --- /dev/null +++ b/aeon/similarity_search/series/_commons.py @@ -0,0 +1,255 @@ +"""Helper and common function for similarity search series estimators.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numba import njit +from scipy.signal import convolve + +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD + + +def _check_X_index(X_index: int, n_timepoints: int, length: int): + """ + Check wheter a X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : int + Index of a timestamp in X_. + n_timepoints: int + Number of timepoints in the serie X_ + length: int + Length parameter of the estimator + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + max_timepoints = n_timepoints - length + if X_index >= max_timepoints or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of timepoint in series given during fit. Expected a value " + f"between [0, {max_timepoints - 1}] but got {X_index}" + ) + + +def fft_sliding_dot_product(X, q): + """ + Use FFT convolution to calculate the sliding window dot product. + + This function applies the Fast Fourier Transform (FFT) to efficiently compute + the sliding dot product between the input time series `X` and the query `q`. + The dot product is computed for each channel individually. The sliding window + approach ensures that the dot product is calculated for every possible subsequence + of `X` that matches the length of `q` + + Parameters + ---------- + X : array, shape=(n_channels, n_timepoints) + Input time series + q : array, shape=(n_channels, query_length) + Input query + + Returns + ------- + out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + Sliding dot product between q and X. + """ + n_channels, n_timepoints = X.shape + query_length = q.shape[1] + out = np.zeros((n_channels, n_timepoints - query_length + 1)) + for i in range(n_channels): + out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real + return out + + +def get_ith_products(X, T, L, ith): + """ + Compute dot products between X and the i-th subsequence of size L in T. + + Parameters + ---------- + X : array, shape = (n_channels, n_timepoints_X) + Input data. + T : array, shape = (n_channels, n_timepoints_T) + Data containing the query. + L : int + Overall query length. + ith : int + Query starting index in T. + + Returns + ------- + np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + return fft_sliding_dot_product(X, T[:, ith : ith + L]) + + +@njit(cache=True, fastmath=True) +def _inverse_distance_profile(dist_profile): + return 1 / (dist_profile + AEON_NUMBA_STD_THRESHOLD) + + +@njit(cache=True) +def _extract_top_k_from_dist_profile( + dist_profile, + k, + threshold, + allow_trivial_matches, + exclusion_size, +): + """ + Given a distance profile, extract the top k lowest distances. + + Parameters + ---------- + dist_profile : np.ndarray, shape = (n_timepoints - length + 1) + A distance profile of length ``n_timepoints - length + 1``, with + ``length`` the size of the query used to compute the distance profiles. + k : int + Number of best matches to return + threshold : float + A threshold on the distances of the best matches. To be returned, a candidate + must have a distance below this threshold. This can reduce the number of + returned matches to be below ``k`` + allow_trivial_matches : bool + Whether to allow returning matches that are in the same neighborhood by + ignoring the exclusion zone defined by the ``exclusion_size`` parameter. + If False, the exclusion zone is applied. + exclusion_size : int + The size of the exlusion size to apply when ``allow_trivial_matches`` is + False. It is applied on both side of existing matches (+/- their indexes). + + Returns + ------- + top_k_indexes : np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + top_k_distances : np.ndarray, shape = (k) + The distances of the best matches. + + """ + top_k_indexes = np.zeros(k, dtype=np.int64) - 1 + top_k_distances = np.full(k, np.inf, dtype=np.float64) + ub = np.full(k, np.inf) + lb = np.full(k, -1.0) + # Could be optimized by using argpartition + sorted_indexes = np.argsort(dist_profile) + _current_k = 0 + if not allow_trivial_matches: + _current_j = 0 + # Until we extract k value or explore all the array or until dist is > threshold + while _current_k < k and _current_j < len(sorted_indexes): + # if we didn't insert anything or there is a conflict in lb/ub + if _current_k > 0 and np.any( + (sorted_indexes[_current_j] >= lb[:_current_k]) + & (sorted_indexes[_current_j] <= ub[:_current_k]) + ): + pass + else: + _idx = sorted_indexes[_current_j] + if dist_profile[_idx] <= threshold: + top_k_indexes[_current_k] = _idx + top_k_distances[_current_k] = dist_profile[_idx] + ub[_current_k] = min( + top_k_indexes[_current_k] + exclusion_size, + len(dist_profile), + ) + lb[_current_k] = max(top_k_indexes[_current_k] - exclusion_size, 0) + _current_k += 1 + else: + break + _current_j += 1 + else: + _current_k += min(k, len(dist_profile)) + dist_profile = dist_profile[sorted_indexes[:_current_k]] + dist_profile = dist_profile[dist_profile <= threshold] + _current_k = len(dist_profile) + + top_k_indexes[:_current_k] = sorted_indexes[:_current_k] + top_k_distances[:_current_k] = dist_profile[:_current_k] + + return top_k_indexes[:_current_k], top_k_distances[:_current_k] + + +# Could add aggregation function as parameter instead of just max +def _extract_top_k_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): + criterion = np.zeros(len(MP)) + + for i in range(len(MP)): + if len(MP[i]) > 0: + criterion[i] = max(MP[i]) + else: + criterion[i] = np.inf + idx, _ = _extract_top_k_from_dist_profile( + criterion, k, np.inf, allow_trivial_matches, exclusion_size + ) + return ( + [IP[i] for i in idx], + [MP[i] for i in idx], + ) + + +def _extract_top_r_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): + criterion = np.zeros(len(MP)) + for i in range(len(MP)): + criterion[i] = len(MP[i]) + idx, _ = _extract_top_k_from_dist_profile( + _inverse_distance_profile(criterion), + k, + np.inf, + allow_trivial_matches, + exclusion_size, + ) + return [IP[i] for i in idx], [MP[i] for i in idx] + + +@njit(cache=True, fastmath=True) +def _update_dot_products( + X, + T, + XT_products, + L, + i_query, +): + """ + Update dot products of the i-th query of size L in T from the dot products of i-1. + + Parameters + ---------- + X: np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input time series on which the sliding dot product is computed. + T: np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + L : int + The length of the subsequences considered during the search. This parameter + cannot be larger than n_timepoints and series_length. + i_query : int + Query starting index in T. + + Returns + ------- + XT_products : np.ndarray of shape (n_channels, n_timepoints - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + n_channels = T.shape[0] + Q = T[:, i_query : i_query + L] + n_candidates = X.shape[1] - L + 1 + + for i_ft in range(n_channels): + # first element of all 0 to n-1 candidates * first element of previous query + _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] + # last element of all 1 to n candidates * last element of current query + _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] + + XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 + + # Compute first dot product + XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) + return XT_products diff --git a/aeon/similarity_search/series/motifs/__init__.py b/aeon/similarity_search/series/motifs/__init__.py new file mode 100644 index 0000000000..56e3bc276f --- /dev/null +++ b/aeon/similarity_search/series/motifs/__init__.py @@ -0,0 +1,7 @@ +"""Motif discovery for single series.""" + +__all__ = [ + "StompMotif", +] + +from aeon.similarity_search.series.motifs._stomp import StompMotif diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py new file mode 100644 index 0000000000..0f43bbf487 --- /dev/null +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -0,0 +1,528 @@ +"""Implementation of STOMP with squared euclidean distance.""" + +__maintainer__ = ["baraline"] +__all__ = ["StompMotif"] + +from typing import Optional + +import numpy as np +from numba import njit +from numba.typed import List + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + get_ith_products, +) +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.utils.numba.general import sliding_mean_std_one_series + + +class StompMotif(BaseSeriesSimilaritySearch): + """ + Estimator to extract top k motifs using STOMP, descibed in [1]_. + + This estimators allows to perform multiple type of motif search operations by using + different parameterization. We base oursleves on Figure 3 of [2]_ to establish the + following list, but modify the confusing naming for some of them. We do not yet + support "Learning" and "Valmod" motifs : + + - for "Pair Motifs" : This is the default configuration: { + "motif_size": 1, + } + + - for "k-motifs" : the extension of pair motifs: { + "motif_size": k, + } + + - for "r-motifs" (originaly named k-motifs, which was confusing as it is a range + based motif): { + "motif_size":np.inf, + "dist_threshold":r, + "motif_extraction_method":"r_motifs" + } + + Parameters + ---------- + length : int + The length of the motifs to extract. This is the length of the subsequence + that will be used in the computations. + normalize : bool + Wheter the computations between subsequences should use a z-normalied distance. + + Notes + ----- + This estimator only provides an exact computation method, faster approximate methods + also exist in the litterature. We use a squared euclidean distance instead of the + euclidean distance, if you want euclidean distance results, you should square root + the obtained results. + + References + ---------- + .. [1] Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael + Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, and Eamonn Keogh. 2016. + Matrix profile II: Exploiting a novel algorithm and GPUs to break the one hundred + million barrier for time series motifs and joins. In 2016 IEEE 16th international + conference on data mining (ICDM). IEEE, 739–748. + .. [2] Patrick SchΓ€fer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection + of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737. + https://doi.org/10.14778/3574245.3574257 + """ + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.normalize = normalize + self.length = length + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def fit_predict(self, X, **kwargs): + """ + Fit and predict on a single series X in order to compute self-motifs. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series to fit and predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to the estimator during predict. + + Returns + ------- + indexes : np.ndarray + Indexes of series in the that are similar to X. + distances : np.ndarray + Distance of the matches to each series + """ + self.fit(X) + return self.predict(X, is_self_computation=True, **kwargs) + + def _predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + motif_extraction_method: Optional[str] = "k_motifs", + is_self_computation: Optional[bool] = False, + ): + """ + Exctract the motifs of X_ relative to a series X using STOMP matrix prfoile. + + To compute self-motifs, X is set to None. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, n_timepoint) + Series to use to compute the matrix profile against X_. Motifs will then be + extracted from the matrix profile. + k : int + The number of motifs to return. The default is 1, meaning we return only + the motif set with the minimal sum of distances to its query. + motif_size : int + The number of subsequences in a motif excluding the motif candidate. This + means that the number of subsequences in the returned motifs will be + ``motif_size + 1``. For example, with the default is 1, this means that we + extract motif pairs (the motif candidate from X and its best match in X_) + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Whether a neighbor of a match to a query can also be considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + exclusion_factor : float, default=0.5. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length*exclusion_factor)` and end at + :math:`id_timestamp + floor(length*exclusion_factor)`. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + motif_extraction_method : str + A string indicating the methodology to use to extract the top k motifs from + the matrix profile. Available methods are "r_motifs" and "k_motifs": + - "r_motifs" means we rank motif set by their cardinality (number of matches + with a distance at most dist_threshold to the candidate motif), with higher + is better. + - "k_motifs" means rank motifs by their maximum distance to their matches. + For example, if a 3-motif has distances to its matches equal to + ``[0.1,0.2,0.5]`` will have a score of ``max([0.1,0.2,0.5])=0.5``. + is_self_computation : bool + Wheter X is equal to the series X_ given during fit. + + Returns + ------- + np.ndarray, shape = (k, motif_size) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k, motif_size) + The distances of the best matches. + + """ + if motif_extraction_method not in ["k_motifs", "r_motifs"]: + raise ValueError( + "Expected motif_extraction_method to be either 'k_motifs' or 'r_motifs'" + f"but got {motif_extraction_method}" + ) + + MP, IP = self.compute_matrix_profile( + X, + motif_size=motif_size, + dist_threshold=dist_threshold, + allow_trivial_matches=allow_trivial_matches, + exclusion_factor=exclusion_factor, + inverse_distance=inverse_distance, + is_self_computation=is_self_computation, + ) + if motif_extraction_method == "k_motifs": + return _extract_top_k_motifs( + MP, IP, k, allow_trivial_matches, int(self.length * exclusion_factor) + ) + elif motif_extraction_method == "r_motifs": + return _extract_top_r_motifs( + MP, IP, k, allow_trivial_matches, int(self.length * exclusion_factor) + ) + + def compute_matrix_profile( + self, + X: np.ndarray, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + is_self_computation: Optional[bool] = False, + ): + """ + Compute matrix profile. + + The matrix profile is computed on the series given in fit (X_). If X is + not given, computes the self matrix profile of X_. Otherwise, compute the matrix + profile of X_ relative to X. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A 2D array time series against which the matrix profile of X_ will be + computed. + motif_size : int + The number of subsequences in a motif. Default is 1, meaning we extract + motif pairs (the query and its best match). + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the worst matches to the query will be returned instead of the best ones. + exclusion_factor : float, default=0.5 + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. + is_self_computation : bool + Wheter X is equal to the series X_ given during fit. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + if is_self_computation and self.normalize: + X_means, X_stds = self.X_means_, self.X_stds_ + elif not is_self_computation and self.normalize: + X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) + + X_dotX = get_ith_products(X, self.X_, self.length, 0) + exclusion_size = int(self.length * exclusion_factor) + + if np.isinf(motif_size): + # convert infs here as numba seem to not be able to do == np.inf ? + motif_size = X.shape[1] - self.length + 1 + + if self.normalize: + MP, IP = _stomp_normalized( + self.X_, + X, + X_dotX, + self.X_means_, + self.X_stds_, + X_means, + X_stds, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_computation, + ) + else: + MP, IP = _stomp( + self.X_, + X, + X_dotX, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_computation, + ) + return MP, IP + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True) +def _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + X_A_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Means of each subsequences of X_A of size L. + X_A_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Stds of each subsequences of X of size L. + X_B_means : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Means of each subsequences of X_B of size L. + X_B_stds : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Stds of each subsequences of X_B of size L. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Whether the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Whether X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + 1 + MP = List() + IP = List() + + for i_q in range(n_queries): + # size T.shape[1] - L + 1 + dist_profile = _normalized_squared_distance_profile( + AdotB, X_B_means, X_B_stds, X_A_means[:, i_q], X_A_stds[:, i_q], L + ) + + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp + 1) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + _top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP + + +@njit(cache=True, fastmath=True) +def _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with non-normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Wheter X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + 1 + MP = List() + IP = List() + + # For each query of size L in X_A + for i_q in range(n_queries): + Q = X_A[:, i_q : i_q + L] + dist_profile = _squared_distance_profile(AdotB, X_B, Q) + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp + 1) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + _top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP diff --git a/aeon/similarity_search/series/motifs/tests/__init__.py b/aeon/similarity_search/series/motifs/tests/__init__.py new file mode 100644 index 0000000000..d0d8f2c42c --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series motif search methods.""" diff --git a/aeon/similarity_search/series/motifs/tests/test_stomp.py b/aeon/similarity_search/series/motifs/tests/test_stomp.py new file mode 100644 index 0000000000..67ff930de1 --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/test_stomp.py @@ -0,0 +1,149 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numpy.testing import assert_almost_equal, assert_array_almost_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + get_ith_products, +) +from aeon.similarity_search.series.motifs._stomp import _stomp, _stomp_normalized +from aeon.similarity_search.series.neighbors._dummy import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import ( + get_all_subsequences, + sliding_mean_std_one_series, + z_normalise_series_3d, +) + +MOTIFS_SIZE_VALUES = [1, 3] +THRESHOLD = [np.inf, 0.75] +THRESHOLD_NORM = [np.inf, 4.5] +NN_MATCHES = [True, False] +INVERSE = [True, False] + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp(motif_size, threshold, allow_trivial_matches, inverse_distance): + """Test STOMP method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = get_all_subsequences(X_B, L, 1) + X_A_subs = get_all_subsequences(X_A, L, 1) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD_NORM) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp_normalised( + motif_size, threshold, allow_trivial_matches, inverse_distance +): + """Test STOMP normalised method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + X_A_means, X_A_stds = sliding_mean_std_one_series(X_A, L, 1) + X_B_means, X_B_stds = sliding_mean_std_one_series(X_B, L, 1) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = z_normalise_series_3d(get_all_subsequences(X_B, L, 1)) + X_A_subs = z_normalise_series_3d(get_all_subsequences(X_A, L, 1)) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) diff --git a/aeon/similarity_search/series/neighbors/__init__.py b/aeon/similarity_search/series/neighbors/__init__.py new file mode 100644 index 0000000000..047bfbe9c4 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/__init__.py @@ -0,0 +1,9 @@ +"""Subsequence Neighbor search for series.""" + +__all__ = [ + "DummySNN", + "MassSNN", +] + +from aeon.similarity_search.series.neighbors._dummy import DummySNN +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py new file mode 100644 index 0000000000..399297b5c9 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -0,0 +1,207 @@ +"""Implementation of NN with brute force.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["DummySNN"] + +import numpy as np +from numba import get_num_threads, njit, prange, set_num_threads + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _check_X_index, + _extract_top_k_from_dist_profile, + _inverse_distance_profile, +) +from aeon.utils.numba.general import ( + get_all_subsequences, + z_normalise_series_2d, + z_normalise_series_3d, +) +from aeon.utils.validation import check_n_jobs + + +class DummySNN(BaseSeriesSimilaritySearch): + """Estimator to compute the on profile and distance profile using brute force.""" + + _tags = {"capability:multithreading": True} + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + n_jobs: Optional[int] = 1, + ): + self.normalize = normalize + self.n_jobs = n_jobs + self.length = length + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + prev_threads = get_num_threads() + + set_num_threads(check_n_jobs(self.n_jobs)) + + self.X_subs = get_all_subsequences(self.X_, self.length, 1) + if self.normalize: + self.X_subs = z_normalise_series_3d(self.X_subs) + set_num_threads(prev_threads) + return self + + def _predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + dist_threshold : float + The maximum distance of neighbors to X. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=0.5 + A factor of the query length used to define the exclusion zone when + ``allow_neighboring_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. + X_index : int, optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + if X.shape[1] != self.length: + raise ValueError( + f"Expected X to have {self.length} timepoints but" + f" got {X.shape[1]} timepoints." + ) + + X_index = _check_X_index(X_index, self.n_timepoints_, self.length) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + exclusion_size = int(self.length * exclusion_factor) + if X_index is not None: + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + if k == np.inf: + k = len(dist_profile) + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + dist_threshold, + allow_neighboring_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profile : np.ndarray, 1D array of shape (n_candidates) + The distance profile of X to X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``, with ``n_timepoints`` the + length of X_. + + """ + prev_threads = get_num_threads() + set_num_threads(check_n_jobs(self.n_jobs)) + if self.normalize: + X = z_normalise_series_2d(X) + distance_profile = _naive_squared_distance_profile(self.X_subs, X) + set_num_threads(prev_threads) + return distance_profile + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 20} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True, parallel=True) +def _naive_squared_distance_profile( + X_subs, + Q, +): + """ + Compute a squared euclidean distance profile. + + Parameters + ---------- + X_subs : array, shape=(n_subsequences, n_channels, length) + Subsequences of size length of the input time series to search in. + Q : array, shape=(n_channels, query_length) + Query used during the search. + + Returns + ------- + out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) + The distance between the query and all candidates in X. + + """ + n_subs, n_channels, length = X_subs.shape + dist_profile = np.zeros(n_subs) + for i in prange(n_subs): + for j in range(n_channels): + for k in range(length): + dist_profile[i] += (X_subs[i, j, k] - Q[j, k]) ** 2 + return dist_profile diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py new file mode 100644 index 0000000000..695dce8844 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -0,0 +1,296 @@ +"""Implementation of NN with MASS.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["MassSNN"] + +import numpy as np +from numba import njit + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _check_X_index, + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + fft_sliding_dot_product, +) +from aeon.utils.numba.general import ( + AEON_NUMBA_STD_THRESHOLD, + sliding_mean_std_one_series, +) + + +class MassSNN(BaseSeriesSimilaritySearch): + """ + Estimator to compute the subsequences nearest neighbors using MASS _[1]. + + Parameters + ---------- + length : int + The length of the subsequences to use for the search. + normalize : bool + Whether the subsequences should be z-normalized. + + References + ---------- + .. [1] Abdullah Mueen, Yan Zhu, Michael Yeh, Kaveh Kamgar, Krishnamurthy + Viswanathan, Chetan Kumar Gupta and Eamonn Keogh (2015), The Fastest Similarity + Search Algorithm for Time Series Subsequences under Euclidean Distance. + """ + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.normalize = normalize + self.length = length + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def _predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 0.5, + inverse_distance: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X_ to X + for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Whether a neighbors of a match to a query can be also considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. + X_index : int, optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + if X.shape[1] != self.length: + raise ValueError( + f"Expected X to have {self.length} timepoints but" + f" got {X.shape[1]} timepoints." + ) + X_index = _check_X_index(X_index, self.n_timepoints_, self.length) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + exclusion_size = int(self.length * exclusion_factor) + if X_index is not None: + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + if k == np.inf: + k = len(dist_profile) + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) + The distance profile of X to all samples in X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``. If X_ is an unequal length + collection, returns a numba typed list instead of an ndarray. + + """ + QT = fft_sliding_dot_product(self.X_, X) + + if self.normalize: + distance_profile = _normalized_squared_distance_profile( + QT, + self.X_means_, + self.X_stds_, + X.mean(axis=1), + X.std(axis=1), + self.length, + ) + else: + distance_profile = _squared_distance_profile( + QT, + self.X_, # T + X, # Q + ) + + return distance_profile + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 20} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True) +def _squared_distance_profile(QT, T, Q): + """ + Compute squared Euclidean distance profile between query and a time series. + + This function calculates the squared distance profile for a single time series by + leveraging the dot product of the query and time series as well as precomputed sums + of squares to efficiently compute the squared distances. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + Q : np.ndarray + 2D array of shape (n_channels, query_length) representing query subsequence. + + Returns + ------- + distance_profile : np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) + The squared distance profile between the query and the input time series. + """ + n_channels, profile_length = QT.shape + query_length = Q.shape[1] + _QT = -2 * QT + distance_profile = np.zeros(profile_length) + for k in range(n_channels): + _sum = 0 + _qsum = 0 + for j in range(query_length): + _sum += T[k, j] ** 2 + _qsum += Q[k, j] ** 2 + + distance_profile += _qsum + _QT[k] + distance_profile[0] += _sum + for i in range(1, profile_length): + _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 + distance_profile[i] += _sum + return distance_profile + + +@njit(cache=True, fastmath=True) +def _normalized_squared_distance_profile( + QT, T_means, T_stds, Q_means, Q_stds, query_length +): + """ + Compute the z-normalized squared Euclidean distance profile for one time series. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T_means : np.ndarray, 1D array of length n_channels + The mean values of the time series for each channel. + T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) + The standard deviations of the time series for each channel and position. + Q_means : np.ndarray, 1D array of shape (n_channels) + Means of the query q + Q_stds : np.ndarray, 1D array of shape (n_channels) + Stds of the query q + query_length : int + The length of the query subsequence used for the distance profile computation. + + + Returns + ------- + np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the + z-normalized squared distance profile between the query subsequence and the time + series. Entries are computed based on the z-normalized values, with special + handling for constant values. + """ + n_channels, profile_length = QT.shape + distance_profile = np.zeros(profile_length) + Q_is_constant = Q_stds <= AEON_NUMBA_STD_THRESHOLD + for i in range(profile_length): + Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD + for k in range(n_channels): + # Two Constant case + if Q_is_constant[k] and Sub_is_constant[k]: + _val = 0 + # One Constant case + elif Q_is_constant[k] or Sub_is_constant[k]: + _val = query_length + else: + denom = query_length * Q_stds[k] * T_stds[k, i] + + p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom + p = min(p, 1.0) + + _val = abs(2 * query_length * (1.0 - p)) + distance_profile[i] += _val + + return distance_profile diff --git a/aeon/similarity_search/series/neighbors/tests/__init__.py b/aeon/similarity_search/series/neighbors/tests/__init__.py new file mode 100644 index 0000000000..00ef2e73ec --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series neighbors search methods.""" diff --git a/aeon/similarity_search/series/neighbors/tests/test_dummy.py b/aeon/similarity_search/series/neighbors/tests/test_dummy.py new file mode 100644 index 0000000000..e064b39fbf --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_dummy.py @@ -0,0 +1,31 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series.neighbors._dummy import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import get_all_subsequences + + +def test__naive_squared_distance_profile(): + """Test Euclidean distance with brute force.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + + dist_profile = _naive_squared_distance_profile(get_all_subsequences(X, L, 1), Q) + + for i_t in range(X.shape[1] - L + 1): + S = X[:, i_t : i_t + L] + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/neighbors/tests/test_mass.py b/aeon/similarity_search/series/neighbors/tests/test_mass.py new file mode 100644 index 0000000000..b6bf1953ea --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_mass.py @@ -0,0 +1,44 @@ +"""Tests for MASS algorithm.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series._commons import fft_sliding_dot_product +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import sliding_mean_std_one_series, z_normalise_series_2d + + +def test__squared_distance_profile(): + """Test squared distance profile.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + dist_profile = _squared_distance_profile(QX, X, Q) + for i_t in range(X.shape[1] - L + 1): + assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) + + +def test__normalized_squared_distance_profile(): + """Test Euclidean distance.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + X_mean, X_std = sliding_mean_std_one_series(X, L, 1) + Q_mean = Q.mean(axis=1) + Q_std = Q.std(axis=1) + + dist_profile = _normalized_squared_distance_profile( + QX, X_mean, X_std, Q_mean, Q_std, L + ) + Q = z_normalise_series_2d(Q) + for i_t in range(X.shape[1] - L + 1): + S = z_normalise_series_2d(X[:, i_t : i_t + L]) + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/tests/__init__.py b/aeon/similarity_search/series/tests/__init__.py new file mode 100644 index 0000000000..4762fe16ce --- /dev/null +++ b/aeon/similarity_search/series/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for base class and commons functions.""" diff --git a/aeon/similarity_search/series/tests/test_base.py b/aeon/similarity_search/series/tests/test_base.py new file mode 100644 index 0000000000..33b78082c3 --- /dev/null +++ b/aeon/similarity_search/series/tests/test_base.py @@ -0,0 +1,19 @@ +"""Test for series similarity search base class.""" + +__maintainer__ = ["baraline"] + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockSeriesSimilaritySearch, +) +from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator + + +def test_input_shape_fit_predict_collection_motifs(): + """Test input shapes.""" + estimator = MockSeriesSimilaritySearch() + datatypes = _get_datatypes_for_estimator(estimator) + # dummy data to pass to fit when testing predict/predict_proba + for datatype in datatypes: + X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] + X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] + estimator.fit(X_train, y_train).predict(X_test) diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py new file mode 100644 index 0000000000..abed374318 --- /dev/null +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -0,0 +1,171 @@ +"""Test _commons.py functions.""" + +__maintainer__ = ["baraline"] +import numpy as np +import pytest +from numba.typed import List +from numpy.testing import assert_, assert_array_almost_equal, assert_array_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + fft_sliding_dot_product, + get_ith_products, +) +from aeon.testing.data_generation import ( + make_example_1d_numpy, + make_example_2d_numpy_series, +) + +K_VALUES = [1, 3, 5] +THRESHOLDS = [np.inf, 1.5] +NN_MATCHES = [False, True] +EXCLUSION_SIZE = [3, 5] + + +def test_fft_sliding_dot_product(): + """Test the fft_sliding_dot_product function.""" + L = 4 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + + values = fft_sliding_dot_product(X, Q) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__update_dot_products(): + """Test the _update_dot_product function.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=20) + T = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 7 + current_product = get_ith_products(X, T, L, 0) + for i_query in range(1, T.shape[1] - L + 1): + new_product = get_ith_products( + X, + T, + L, + i_query, + ) + current_product = _update_dot_products( + X, + T, + current_product, + L, + i_query, + ) + assert_array_almost_equal(new_product, current_product) + + +def test_get_ith_products(): + """Test i-th dot product of a subsequence of size L.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 5 + + values = get_ith_products(X, Q, L, 0) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 0:L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + values = get_ith_products(X, Q, L, 4) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 4 : 4 + L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__inverse_distance_profile(): + """Test method to inverse a TypedList of distance profiles.""" + X = make_example_1d_numpy() + X_inv = _inverse_distance_profile(X) + assert_array_almost_equal(1 / (X + 1e-8), X_inv) + + +def test__extract_top_k_motifs(): + """Test motif extraction based on max distance.""" + MP = np.array( + [ + [1.0, 2.0], + [1.0, 4.0], + [0.5, 0.9], + [0.6, 0.7], + ] + ) + + IP = np.array( + [ + [1, 2], + [1, 4], + [0, 3], + [0, 7], + ] + ) + IP_k, MP_k = _extract_top_k_motifs(MP, IP, 2, True, 0) + assert_(len(MP_k) == 2) + assert_array_equal(MP_k[0], [0.6, 0.7]) + assert_array_equal(IP_k[0], [0, 7]) + assert_array_equal(MP_k[1], [0.5, 0.9]) + assert_array_equal(IP_k[1], [0, 3]) + + +def test__extract_top_r_motifs(): + """Test motif extraction based on motif set cardinality.""" + MP = List() + MP.append(List([1.0, 1.5, 2.0, 1.5])) + MP.append(List([1.0, 4.0])) + MP.append(List([0.5, 0.9, 1.0])) + MP.append(List([0.6, 0.7])) + + IP = List() + IP.append(List([1, 2, 3, 4])) + IP.append(List([1, 4])) + IP.append(List([0, 3, 6])) + IP.append(List([0, 7])) + + IP_k, MP_k = _extract_top_r_motifs(MP, IP, 2, True, 0) + assert_(len(MP_k) == 2) + assert_array_equal(MP_k[0], [1.0, 1.5, 2.0, 1.5]) + assert_array_equal(IP_k[0], [1, 2, 3, 4]) + assert_array_equal(MP_k[1], [0.5, 0.9, 1.0]) + assert_array_equal(IP_k[1], [0, 3, 6]) + + +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLDS) +@pytest.mark.parametrize("allow_nn_matches", NN_MATCHES) +@pytest.mark.parametrize("exclusion_size", EXCLUSION_SIZE) +def test__extract_top_k_from_dist_profile( + k, threshold, allow_nn_matches, exclusion_size +): + """Test method to esxtract the top k candidates from a list of distance profiles.""" + X = make_example_1d_numpy(n_timepoints=30) + X_sort = np.argsort(X) + exclusion_size = 3 + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, k, threshold, allow_nn_matches, exclusion_size + ) + + if len(top_k_indexes) == 0 or len(top_k_distances) == 0: + raise AssertionError("_extract_top_k_from_dist_profile returned empty list") + for i, index in enumerate(top_k_indexes): + assert_(X[index] == top_k_distances[i]) + + assert_(np.all(top_k_distances <= threshold)) + + if allow_nn_matches: + assert_(np.all(top_k_distances <= X[X_sort[len(top_k_indexes) - 1]])) + + if not allow_nn_matches: + same_X = np.sort(top_k_indexes) + if len(same_X) > 1: + assert_(np.all(np.diff(same_X) >= exclusion_size)) diff --git a/aeon/similarity_search/series_search.py b/aeon/similarity_search/series_search.py deleted file mode 100644 index 3c36cf9c4a..0000000000 --- a/aeon/similarity_search/series_search.py +++ /dev/null @@ -1,436 +0,0 @@ -"""Base class for series search.""" - -__maintainer__ = ["baraline"] - -from typing import Union, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - - -class SeriesSearch(BaseSimilaritySearch): - """ - Series search estimator. - - The series search estimator will return a set of matches for each subsequence of - size L in a time series given during predict. The matching of each subsequence will - be made against all subsequence of size L inside the time series given during fit, - which will represent the search space. - - Depending on the `k` and/or `threshold` parameters, which condition what is - considered a valid match during the search, the number of matches will vary. If `k` - is used, at most `k` matches (the `k` best) will be returned, if `threshold` is used - and `k` is set to `np.inf`, all the candidates which distance to the query is - inferior or equal to `threshold` will be returned. If both are used, the `k` best - matches to the query with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : array, shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Union[None, dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - ): - self.k = k - self.threshold = threshold - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : array, shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.matrix_profile_function_ = self._get_series_method_function() - return self - - @final - def predict( - self, - X: np.ndarray, - length: int, - axis: int = 1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ): - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : int - An integer indicating if X was extracted is part of the dataset that was - given during the fit method. If so, this integer should be the sample id. - The search will define an exclusion zone for the queries extarcted from X - in order to avoid matching with themself. If None, it is considered that - the query is not extracted from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - ``id_timestamp - query_length//exclusion_factor`` to - ``id_timestamp + query_length//exclusion_factor``. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- ``query_length//exclusion_factor``. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at ``id_timestamp``, the second best match will be located at - ``id_timestamp`` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - series_dim, series_length = self._check_series_format(X, length, axis) - - mask = self._init_X_index_mask( - None if X_index is None else [X_index, 0], - length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - _mean, _std = sliding_mean_std_one_series(X, length, 1) - self.T_means_ = _mean - self.T_stds_ = _std - if self._previous_query_length != length: - self._store_mean_std_from_inputs(length) - - if apply_exclusion_to_result: - exclusion_size = length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = length - - X_preds = self._predict( - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ): - """ - Private predict method for SeriesSearch. - - This method calculates the matrix profile for a given time series dataset by - comparing all possible subsequences of a specified length against a reference - time series. It handles exclusion zones to prevent nearby matches from being - selected and supports normalization. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for - the first query of size L in T. This mask will be updated during the - algorithm. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - if self.normalise: - return self.matrix_profile_function_( - self.X_, - X, - length, - self.X_means_, - self.X_stds_, - self.T_means_, - self.T_stds_, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - else: - return self.matrix_profile_function_( - self.X_, - X, - length, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - - def _check_series_format(self, X, length, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the series X is univariate " - "do X = X[np.newaxis, :]." - ) - - series_dim, series_length = X.shape - if series_length < length: - raise ValueError( - "The length of the series should be superior or equal to the length " - "parameter given during predict, but got {} < {}".format( - series_length, length - ) - ) - - if series_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the series X and the data" - " (X_) provided during fit, but got {} for X and {} for X_".format( - series_dim, self.n_channels_ - ) - ) - return series_dim, series_length - - def _get_series_method_function(self): - """ - Given distance and speed_up parameters, return the series method function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The series method function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _SERIES_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_series_method = distance_dict.get(self.normalise).get( - self.speed_up - ) - - if speed_up_series_method is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_series_method - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - @classmethod - def get_speedup_function_names(self): - """ - Get available speedup for series search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _SERIES_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _SERIES_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _SERIES_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_SERIES_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": stomp_normalised_euclidean_matrix_profile, - "STOMP": stomp_normalised_euclidean_matrix_profile, - }, - False: { - "fastest": stomp_euclidean_matrix_profile, - "STOMP": stomp_euclidean_matrix_profile, - }, - }, - "squared": { - True: { - "fastest": stomp_normalised_squared_matrix_profile, - "STOMP": stomp_normalised_squared_matrix_profile, - }, - False: { - "fastest": stomp_squared_matrix_profile, - "STOMP": stomp_squared_matrix_profile, - }, - }, -} diff --git a/aeon/similarity_search/tests/test__commons.py b/aeon/similarity_search/tests/test__commons.py deleted file mode 100644 index a97519ad31..0000000000 --- a/aeon/similarity_search/tests/test__commons.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Test _commons.py functions.""" - -__maintainer__ = ["baraline"] - -import numpy as np -from numpy.testing import assert_array_almost_equal - -from aeon.similarity_search._commons import ( - fft_sliding_dot_product, - naive_squared_distance_profile, - naive_squared_matrix_profile, -) - - -def test_fft_sliding_dot_product(): - """Test the fft_sliding_dot_product function.""" - X = np.random.rand(1, 10) - q = np.random.rand(1, 5) - - values = fft_sliding_dot_product(X, q) - - assert_array_almost_equal( - values[0], - [np.dot(q[0], X[0, i : i + 5]) for i in range(X.shape[1] - 5 + 1)], - ) - - -def test_naive_squared_distance_profile(): - """Test naive squared distance profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.array([[1, 2, 3]]) - query_length = Q.shape[1] - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - dist_profile = naive_squared_distance_profile(X, Q, mask) - assert_array_almost_equal(dist_profile[0], np.array([3.0, 0.0, 3.0, 12.0])) - - -def test_naive_squared_matrix_profile(): - """Test naive squared matrix profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.zeros((1, 6)) - - Q[0] = np.arange(6, 12) - query_length = 3 - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - matrix_profile = naive_squared_matrix_profile(X, Q, query_length, mask) - assert_array_almost_equal(matrix_profile, np.array([27.0, 48.0, 75.0, 108.0])) diff --git a/aeon/similarity_search/tests/test_query_search.py b/aeon/similarity_search/tests/test_query_search.py deleted file mode 100644 index f97f6a50bf..0000000000 --- a/aeon/similarity_search/tests/test_query_search.py +++ /dev/null @@ -1,176 +0,0 @@ -"""Tests for QuerySearch.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numpy.testing import assert_almost_equal, assert_array_equal - -from aeon.similarity_search.query_search import QuerySearch - -DATATYPES = ["int64", "float64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_equal_length(dtype): - """Test the mean and std computation of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i, :, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_unequal_length(dtype): - """Test the mean and std computation of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i][:, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_threshold_and_k(dtype): - """Test the k and threshold combination of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=3, threshold=1) - search.fit(X) - dist, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_inverse_distance(dtype): - """Test the inverse distance parameter of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, inverse_distance=True) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 5)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean(dtype): - """Test the functionality of QuerySearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean_unequal_length(dtype): - """Test the functionality of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_speedup(dtype): - """Test the speedup functionality of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, speed_up="fastest") - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch( - k=1, - distance="euclidean", - speed_up="fastest", - normalise=True, - ) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) diff --git a/aeon/similarity_search/tests/test_series_search.py b/aeon/similarity_search/tests/test_series_search.py deleted file mode 100644 index a10109359c..0000000000 --- a/aeon/similarity_search/tests/test_series_search.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Tests for SeriesSearch similarity search algorithm.""" - -__maintainer__ = ["baraline"] - - -import numpy as np -import pytest - -from aeon.similarity_search.series_search import SeriesSearch - -DATATYPES = ["int64", "float64"] -K_VALUES = [1, 3] -normalise = [True, False] - -# See #2236 -# @pytest.mark.parametrize("k", K_VALUES) -# @pytest.mark.parametrize("normalise", normalise) -# def test_SeriesSearch_k(k, normalise): -# """Test the k and threshold combination of SeriesSearch.""" -# X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) -# S = np.asarray([[3, 4, 5, 4, 3, 4]]) -# L = 3 -# -# search = SeriesSearch(k=k, normalise=normalise) -# search.fit(X) -# mp, ip = search.predict(S, L) -# -# assert mp[0].shape[0] == ip[0].shape[0] == k -# assert len(mp) == len(ip) == S.shape[1] - L + 1 -# assert ip[0].shape[1] == 2 - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_error_predict(dtype): - """Test the functionality of SeriesSearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5]], dtype=dtype) - L = 100 - - search = SeriesSearch() - search.fit(X) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - L = 3 - S = np.asarray( - [ - [3, 4, 5, 4, 3, 4], - [6, 5, 3, 2, 4, 5], - ], - dtype=dtype, - ) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - - S = [6, 5, 3, 2, 4, 5] - with pytest.raises(TypeError): - mp, ip = search.predict(S, L) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_process_unequal_length(dtype): - """Test the functionality of SeriesSearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - S = np.asarray([[3, 4, 5, 4, 3, 4]], dtype=dtype) - L = 3 - - search = SeriesSearch() - search.fit(X) - mp, ip = search.predict(S, L) diff --git a/aeon/testing/data_generation/_collection.py b/aeon/testing/data_generation/_collection.py index b2fbcf3ec4..b471acbc18 100644 --- a/aeon/testing/data_generation/_collection.py +++ b/aeon/testing/data_generation/_collection.py @@ -408,15 +408,11 @@ def make_example_dataframe_list( ... random_state=0, ... ) >>> print(data) - [ 0 1 - 0 0.000000 1.688531 - 1 1.715891 1.694503 - 2 1.247127 0.768763 - 3 0.595069 0.113426, 0 1 - 0 2.000000 3.166900 - 1 2.115580 2.272178 - 2 3.702387 0.284144 - 3 0.348517 0.080874] + [ 0 1 2 3 + 0 0.000000 1.688531 1.715891 1.694503 + 1 1.247127 0.768763 0.595069 0.113426, 0 1 2 3 + 0 2.000000 3.166900 2.115580 2.272178 + 1 3.702387 0.284144 0.348517 0.080874] >>> print(labels) [0 1] >>> get_type(data) @@ -428,14 +424,14 @@ def make_example_dataframe_list( for i in range(n_cases): n_timepoints = rng.randint(min_n_timepoints, max_n_timepoints + 1) - x = n_labels * rng.uniform(size=(n_timepoints, n_channels)) + x = n_labels * rng.uniform(size=(n_channels, n_timepoints)) label = x[0, 0].astype(int) if i < n_labels and n_cases > i: x[0, 0] = i label = i x = x * (label + 1) - X.append(pd.DataFrame(x, index=range(n_timepoints), columns=range(n_channels))) + X.append(pd.DataFrame(x, index=range(n_channels), columns=range(n_timepoints))) y[i] = label if regression_target: @@ -574,16 +570,16 @@ def make_example_multi_index_dataframe( ... random_state=0, ... ) >>> print(data) # doctest: +NORMALIZE_WHITESPACE - channel_0 channel_1 + channel 0 1 case timepoint - 0 0 0.000000 1.247127 - 1 1.688531 0.768763 - 2 1.715891 0.595069 - 3 1.694503 0.113426 - 1 0 2.000000 3.702387 - 1 3.166900 0.284144 - 2 2.115580 0.348517 - 3 2.272178 0.080874 + 0 0 0.000000 1.247127 + 1 1.688531 0.768763 + 2 1.715891 0.595069 + 3 1.694503 0.113426 + 1 0 2.000000 3.702387 + 1 3.166900 0.284144 + 2 2.115580 0.348517 + 3 2.272178 0.080874 >>> print(labels) [0 1] >>> get_type(data) @@ -616,8 +612,7 @@ def make_example_multi_index_dataframe( y[i] = label X = X.reset_index(drop=True) - X = X.set_index(["case", "timepoint"]).pivot(columns="channel") - X.columns = [f"channel_{i}" for i in range(n_channels)] + X = X.pivot(index=["case", "timepoint"], columns=["channel"], values="value") if regression_target: y = y.astype(np.float32) diff --git a/aeon/testing/data_generation/tests/test_collection.py b/aeon/testing/data_generation/tests/test_collection.py index 58a781656c..6fa3566983 100644 --- a/aeon/testing/data_generation/tests/test_collection.py +++ b/aeon/testing/data_generation/tests/test_collection.py @@ -178,13 +178,13 @@ def test_make_example_dataframe_list( assert all(isinstance(x, pd.DataFrame) for x in X) assert isinstance(y, np.ndarray) assert len(X) == n_cases - assert all([x.shape[1] == n_channels for x in X]) + assert all([x.shape[0] == n_channels for x in X]) if min_n_timepoints == max_n_timepoints: - assert all([x.shape[0] == min_n_timepoints for x in X]) + assert all([x.shape[1] == min_n_timepoints for x in X]) else: assert all( [ - x.shape[0] >= min_n_timepoints and x.shape[0] <= max_n_timepoints + x.shape[1] >= min_n_timepoints and x.shape[1] <= max_n_timepoints for x in X ] ) diff --git a/aeon/testing/estimator_checking/_yield_classification_checks.py b/aeon/testing/estimator_checking/_yield_classification_checks.py index 09f15877be..1ab7b4842a 100644 --- a/aeon/testing/estimator_checking/_yield_classification_checks.py +++ b/aeon/testing/estimator_checking/_yield_classification_checks.py @@ -31,7 +31,7 @@ def _yield_classification_checks(estimator_class, estimator_instances, datatypes): """Yield all classification checks for an aeon classifier.""" # only class required - if sys.platform != "darwin": # We cannot guarantee same results on ARM macOS + if sys.platform == "linux": # We cannot guarantee same results on ARM macOS # Compare against results for both UnitTest and BasicMotions if available yield partial( check_classifier_against_expected_results, diff --git a/aeon/testing/estimator_checking/_yield_clustering_checks.py b/aeon/testing/estimator_checking/_yield_clustering_checks.py index 5205316f94..4e3940c489 100644 --- a/aeon/testing/estimator_checking/_yield_clustering_checks.py +++ b/aeon/testing/estimator_checking/_yield_clustering_checks.py @@ -77,18 +77,33 @@ def check_clustering_random_state_deep_learning(estimator, datatype): deep_clr1 = _clone_estimator(estimator, random_state=random_state) deep_clr1.fit(FULL_TEST_DATA_DICT[datatype]["train"][0]) - layers1 = deep_clr1.training_model_.layers[1:] + encoder_layers1 = deep_clr1.training_model_.layers[1].layers[1:] + decoder_layers1 = deep_clr1.training_model_.layers[2].layers[1:] deep_clr2 = _clone_estimator(estimator, random_state=random_state) deep_clr2.fit(FULL_TEST_DATA_DICT[datatype]["train"][0]) - layers2 = deep_clr2.training_model_.layers[1:] + encoder_layers2 = deep_clr2.training_model_.layers[1].layers[1:] + decoder_layers2 = deep_clr2.training_model_.layers[2].layers[1:] - assert len(layers1) == len(layers2) + assert len(encoder_layers1) == len(encoder_layers2) + assert len(decoder_layers1) == len(decoder_layers2) - for i in range(len(layers1)): - weights1 = layers1[i].get_weights() - weights2 = layers2[i].get_weights() + for i in range(len(encoder_layers1)): + weights1 = encoder_layers1[i].get_weights() + weights2 = encoder_layers2[i].get_weights() + + assert len(weights1) == len(weights2) + + for j in range(len(weights1)): + _weight1 = np.asarray(weights1[j]) + _weight2 = np.asarray(weights2[j]) + + np.testing.assert_almost_equal(_weight1, _weight2, 4) + + for i in range(len(decoder_layers1)): + weights1 = decoder_layers1[i].get_weights() + weights2 = decoder_layers2[i].get_weights() assert len(weights1) == len(weights2) diff --git a/aeon/testing/estimator_checking/_yield_estimator_checks.py b/aeon/testing/estimator_checking/_yield_estimator_checks.py index a35fb89667..6cf4ee7948 100644 --- a/aeon/testing/estimator_checking/_yield_estimator_checks.py +++ b/aeon/testing/estimator_checking/_yield_estimator_checks.py @@ -22,6 +22,7 @@ from aeon.regression import BaseRegressor from aeon.regression.deep_learning.base import BaseDeepRegressor from aeon.segmentation import BaseSegmenter +from aeon.similarity_search import BaseSimilaritySearch from aeon.testing.estimator_checking._yield_anomaly_detection_checks import ( _yield_anomaly_detection_checks, ) @@ -231,9 +232,10 @@ def check_inheritance(estimator_class): # Only transformers can inherit from multiple base types currently if n_base_types > 1: - assert issubclass( - estimator_class, BaseTransformer - ), "Only transformers can inherit from multiple base types." + assert issubclass(estimator_class, BaseTransformer) or issubclass( + estimator_class, BaseSimilaritySearch + ), "Only transformers or similarity search estimators can inherit from multiple" + "base types." def check_has_common_interface(estimator_class): @@ -627,10 +629,9 @@ def check_persistence_via_pickle(estimator, datatype): same, msg = deep_equals(output, results[i], return_msg=True) if not same: raise ValueError( - f"Running {method} after serialisation parameters gives " - f"different results. " - f"{type(estimator)} returns data as {type(output)}: test " - f"equivalence message: {msg}" + f"Running {type(estimator)} {method} with test parameters after " + f"serialisation gives different results. " + f"Check equivalence message: {msg}" ) i += 1 @@ -657,9 +658,8 @@ def check_fit_deterministic(estimator, datatype): same, msg = deep_equals(output, results[i], return_msg=True) if not same: raise ValueError( - f"Running {method} with test parameters after two calls to fit " - f"gives different results." - f"{type(estimator)} returns data as {type(output)}: test " - f"equivalence message: {msg}" + f"Running {type(estimator)} {method} with test parameters after " + f"two calls to fit gives different results." + f"Check equivalence message: {msg}" ) i += 1 diff --git a/aeon/testing/estimator_checking/_yield_regression_checks.py b/aeon/testing/estimator_checking/_yield_regression_checks.py index 52933e81f7..73bba3afaf 100644 --- a/aeon/testing/estimator_checking/_yield_regression_checks.py +++ b/aeon/testing/estimator_checking/_yield_regression_checks.py @@ -26,7 +26,7 @@ def _yield_regression_checks(estimator_class, estimator_instances, datatypes): """Yield all regression checks for an aeon regressor.""" # only class required - if sys.platform != "darwin": # We cannot guarantee same results on ARM macOS + if sys.platform == "linux": # We cannot guarantee same results on ARM macOS # Compare against results for both Covid3Month and CardanoSentiment if available yield partial( check_regressor_against_expected_results, diff --git a/aeon/testing/estimator_checking/_yield_transformation_checks.py b/aeon/testing/estimator_checking/_yield_transformation_checks.py index 4a8c51f795..63538ba2dd 100644 --- a/aeon/testing/estimator_checking/_yield_transformation_checks.py +++ b/aeon/testing/estimator_checking/_yield_transformation_checks.py @@ -26,7 +26,8 @@ def _yield_transformation_checks(estimator_class, estimator_instances, datatypes): """Yield all transformation checks for an aeon transformer.""" # only class required - if sys.platform != "darwin": + if sys.platform == "linux": # We cannot guarantee same results on ARM macOS + # Compare against results for both UnitTest and BasicMotions if available yield partial( check_transformer_against_expected_results, estimator_class=estimator_class, diff --git a/aeon/testing/expected_results/expected_classifier_outputs.py b/aeon/testing/expected_results/expected_classifier_outputs.py index 25ace24642..771c47e7c6 100644 --- a/aeon/testing/expected_results/expected_classifier_outputs.py +++ b/aeon/testing/expected_results/expected_classifier_outputs.py @@ -67,16 +67,16 @@ ) unit_test_proba["TemporalDictionaryEnsemble"] = np.array( [ - [0.2778, 0.7222], - [0.7222, 0.2778], + [0.3307, 0.6693], + [0.6693, 0.3307], [0.0, 1.0], - [0.6251, 0.3749], - [0.3749, 0.6251], + [0.5538, 0.4462], + [0.6693, 0.3307], [1.0, 0.0], - [0.3749, 0.6251], + [0.4462, 0.5538], [0.0, 1.0], - [0.4653, 0.5347], - [0.3749, 0.6251], + [0.5538, 0.4462], + [0.4462, 0.5538], ] ) unit_test_proba["WEASEL"] = np.array( @@ -263,16 +263,16 @@ ) unit_test_proba["HIVECOTEV2"] = np.array( [ - [0.0613, 0.9387], - [0.5531, 0.4479], - [0.0431, 0.9569], + [0.2239, 0.7761], + [0.6732, 0.3268], + [0.1211, 0.8789], [1.0, 0.0], - [0.9751, 0.0249], + [0.9818, 0.0182], [1.0, 0.0], - [0.7398, 0.2602], - [0.0365, 0.9635], - [0.7829, 0.2171], - [0.9236, 0.0764], + [0.7201, 0.2799], + [0.2058, 0.7942], + [0.8412, 0.1588], + [0.9441, 0.0559], ] ) unit_test_proba["CanonicalIntervalForestClassifier"] = np.array( @@ -293,12 +293,12 @@ [ [0.1, 0.9], [0.8, 0.2], - [0.0, 1.0], + [0.1, 0.9], [1.0, 0.0], [0.7, 0.3], [0.9, 0.1], [0.8, 0.2], - [0.4, 0.6], + [0.5, 0.5], [0.9, 0.1], [1.0, 0.0], ] @@ -379,11 +379,11 @@ [0.3505, 0.6495], [0.1753, 0.8247], [0.8247, 0.1753], - [0.3505, 0.6495], + [0.6495, 0.3505], [0.701, 0.299], [0.6495, 0.3505], [0.1753, 0.8247], - [0.5258, 0.4742], + [0.8247, 0.1753], [1.0, 0.0], ] ) @@ -656,12 +656,12 @@ ) basic_motions_proba["FreshPRINCEClassifier"] = np.array( [ - [0.0, 0.0, 0.1, 0.9], + [0.0, 0.0, 0.2, 0.8], [0.9, 0.1, 0.0, 0.0], [0.0, 0.0, 0.8, 0.2], [0.1, 0.9, 0.0, 0.0], - [0.1, 0.0, 0.0, 0.9], - [0.0, 0.0, 0.1, 0.9], + [0.1, 0.0, 0.1, 0.8], + [0.0, 0.0, 0.2, 0.8], [0.7, 0.3, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0], @@ -782,15 +782,15 @@ ) basic_motions_proba["DrCIFClassifier"] = np.array( [ + [0.1, 0.0, 0.2, 0.7], + [0.5, 0.4, 0.0, 0.1], + [0.0, 0.0, 0.8, 0.2], + [0.1, 0.9, 0.0, 0.0], + [0.1, 0.0, 0.3, 0.6], [0.0, 0.0, 0.2, 0.8], - [0.4, 0.5, 0.1, 0.0], - [0.0, 0.0, 0.7, 0.3], - [0.2, 0.8, 0.0, 0.0], - [0.0, 0.0, 0.3, 0.7], - [0.0, 0.0, 0.3, 0.7], - [0.7, 0.2, 0.1, 0.0], - [0.0, 0.0, 0.7, 0.3], - [0.1, 0.7, 0.1, 0.1], + [0.5, 0.3, 0.0, 0.2], + [0.0, 0.0, 0.8, 0.2], + [0.2, 0.7, 0.0, 0.1], [0.0, 0.9, 0.0, 0.1], ] ) diff --git a/aeon/testing/expected_results/expected_distance_results.py b/aeon/testing/expected_results/expected_distance_results.py index 8e7c6873c5..7126c5c624 100644 --- a/aeon/testing/expected_results/expected_distance_results.py +++ b/aeon/testing/expected_results/expected_distance_results.py @@ -41,6 +41,13 @@ 4.0997661869195205, 25.0, ], + "dtw_gi": [ + 0.344520787316184, + 0.344520787316184, + 0.2998607605839068, + 5.893210968537887, + 25.0, + ], "ddtw": [ 0.2963709096971962, 0.2963709096971962, diff --git a/aeon/testing/expected_results/expected_regressor_outputs.py b/aeon/testing/expected_results/expected_regressor_outputs.py index 3840711d1f..f2d2ac5696 100644 --- a/aeon/testing/expected_results/expected_regressor_outputs.py +++ b/aeon/testing/expected_results/expected_regressor_outputs.py @@ -188,32 +188,21 @@ ) cardano_sentiment_preds["FreshPRINCERegressor"] = np.array( - [ - 0.3484, - 0.1438, - 0.3705, - 0.0842, - 0.3892, - 0.3705, - 0.1342, - 0.3476, - 0.0959, - 0.409, - ] + [0.36, 0.14, 0.36, 0.08, 0.45, 0.35, 0.15, 0.28, 0.09, 0.37] ) cardano_sentiment_preds["Catch22Regressor"] = np.array( [ - 0.2537, - 0.1417, - 0.2980, + 0.2715, + 0.175, + 0.3152, 0.1324, - 0.3519, + 0.3341, 0.1919, - 0.1790, + 0.179, 0.1295, - 0.1644, - 0.3836, + 0.1744, + 0.3658, ] ) @@ -293,36 +282,36 @@ ) cardano_sentiment_preds["RISTRegressor"] = np.array( - [0.0825, 0.1924, 0.7180, 0.0413, 0.4840, 0.0825, 0.2336, 0.0000, 0.0413, 0.2814] -) - -cardano_sentiment_preds["CanonicalIntervalForestRegressor"] = np.array( [ + 0.0745, + 0.0745, + 0.448, + 0.0413, + 0.484, + 0.0825, + 0.0413, + 0.1419, + -0.101, 0.2814, - 0.1796, - 0.3305, - 0.2093, - 0.2403, - 0.2543, - 0.1683, - 0.2636, - 0.1321, - 0.2302, ] ) +cardano_sentiment_preds["CanonicalIntervalForestRegressor"] = np.array( + [0.28, 0.15, 0.33, 0.14, 0.19, 0.22, 0.15, 0.23, 0.12, 0.2] +) + cardano_sentiment_preds["DrCIFRegressor"] = np.array( [ - 0.2621, - 0.2652, - 0.2569, + 0.252, + 0.21, + 0.2664, 0.1791, - 0.1364, + 0.1999, 0.1513, - 0.1549, - 0.1407, - 0.1197, - 0.1924, + 0.1448, + 0.0956, + 0.1547, + 0.111, ] ) diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py index 219fc3e987..e9e83aa263 100644 --- a/aeon/testing/mock_estimators/__init__.py +++ b/aeon/testing/mock_estimators/__init__.py @@ -30,7 +30,8 @@ "MockMultivariateSeriesTransformer", "MockSeriesTransformerNoFit", # similarity search - "MockSimilaritySearch", + "MockSeriesSimilaritySearch", + "MockCollectionSimilaritySearch", ] from aeon.testing.mock_estimators._mock_anomaly_detectors import ( @@ -64,4 +65,7 @@ MockSeriesTransformerNoFit, MockUnivariateSeriesTransformer, ) -from aeon.testing.mock_estimators._mock_similarity_search import MockSimilaritySearch +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockCollectionSimilaritySearch, + MockSeriesSimilaritySearch, +) diff --git a/aeon/testing/mock_estimators/_mock_similarity_search.py b/aeon/testing/mock_estimators/_mock_similarity_search.py deleted file mode 100644 index 55c9c435c7..0000000000 --- a/aeon/testing/mock_estimators/_mock_similarity_search.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Mock similarity searchers useful for testing and debugging.""" - -__maintainer__ = ["baraline"] -__all__ = [ - "MockSimilaritySearch", -] - -from aeon.similarity_search.base import BaseSimilaritySearch - - -class MockSimilaritySearch(BaseSimilaritySearch): - """Mock similarity search for testing base class predict.""" - - def _fit(self, X, y=None): - """_fit dummy.""" - self.X_ = X - return self - - def predict(self, X): - """Predict dummy.""" - return [(0, 0)] diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py new file mode 100644 index 0000000000..ddf001daf3 --- /dev/null +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -0,0 +1,38 @@ +"""Mock series transformers useful for testing and debugging.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "MockSeriesSimilaritySearch", + "MockCollectionSimilaritySearch", +] + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch + + +class MockSeriesSimilaritySearch(BaseSeriesSimilaritySearch): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def _predict(self, X): + """top-1 motif start timestamp index in X, and distances to the match in X_.""" + return [0], [0.1] + + +class MockCollectionSimilaritySearch(BaseCollectionSimilaritySearch): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def _predict(self, X): + """top-1 motif start timestamp index in X, and distances to the match in X_.""" + return [0, 0], [0.1] diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index deb71adb00..b17b9626d1 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -23,8 +23,9 @@ NUMBA_DISABLED = os.environ.get("NUMBA_DISABLE_JIT") == "1" # exclude estimators here for short term fixes -# Hydra excluded because it returns a pytorch Tensor -EXCLUDE_ESTIMATORS = ["REDCOMETS", "HydraTransformer"] +EXCLUDE_ESTIMATORS = [ + "HydraTransformer", # returns a pytorch Tensor +] # Exclude specific tests for estimators here EXCLUDED_TESTS = { @@ -50,6 +51,7 @@ "RSASTClassifier": ["check_fit_deterministic"], "SAST": ["check_fit_deterministic"], "RSAST": ["check_fit_deterministic"], + "MatrixProfile": ["check_fit_deterministic", "check_persistence_via_pickle"], # missed in legacy testing, changes state in predict/transform "FLUSSSegmenter": ["check_non_state_changing_method"], "InformationGainSegmenter": ["check_non_state_changing_method"], @@ -57,10 +59,6 @@ "ClaSPSegmenter": ["check_non_state_changing_method"], "HMMSegmenter": ["check_non_state_changing_method"], "RSTSF": ["check_non_state_changing_method"], - # Keeps length during predict to avoid recomputing means and std of data in fit - # if the next predict calls uses the same query length parameter. - "QuerySearch": ["check_non_state_changing_method"], - "SeriesSearch": ["check_non_state_changing_method"], # Unknown issue not producing the same results "RDSTRegressor": ["check_regressor_against_expected_results"], "RISTRegressor": ["check_regressor_against_expected_results"], @@ -70,6 +68,10 @@ EXCLUDED_TESTS_NO_NUMBA = { # See issue #622 "HIVECOTEV2": ["check_classifier_against_expected_results"], + # Other failures + "TemporalDictionaryEnsemble": ["check_classifier_against_expected_results"], + "OrdinalTDE": ["check_classifier_against_expected_results"], + "CanonicalIntervalForestRegressor": ["check_regressor_against_expected_results"], } diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index eb134cddda..3337f83b0c 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -10,7 +10,8 @@ from aeon.forecasting import BaseForecaster from aeon.regression import BaseRegressor from aeon.segmentation import BaseSegmenter -from aeon.similarity_search import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.testing.data_generation import ( make_example_1d_numpy, make_example_2d_dataframe_collection, @@ -219,50 +220,6 @@ }, } -EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { - "numpy3D": { - "train": ( - make_example_3d_numpy( - n_cases=10, - n_channels=1, - n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=1, - min_n_timepoints=20, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION = { "numpy3D": { "train": make_example_3d_numpy( @@ -401,50 +358,6 @@ }, } -EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { - "numpy3D": { - "train": ( - make_example_3d_numpy( - n_cases=10, - n_channels=2, - n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=2, - min_n_timepoints=20, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION = { "np-list": { "train": make_example_3d_numpy_list( @@ -553,30 +466,6 @@ }, } -UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=1, - min_n_timepoints=10, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - UNEQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION = { "np-list": { "train": make_example_3d_numpy_list( @@ -685,30 +574,6 @@ }, } -UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=2, - min_n_timepoints=10, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - X_classification_missing_train, y_classification_missing_train = make_example_3d_numpy( n_cases=10, n_channels=1, @@ -825,12 +690,6 @@ for k, v in EQUAL_LENGTH_UNIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"EqualLengthUnivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"EqualLengthMultivariate-Classification-{k}": v @@ -843,12 +702,6 @@ for k, v in EQUAL_LENGTH_MULTIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"EqualLengthMultivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"UnequalLengthUnivariate-Classification-{k}": v @@ -861,12 +714,6 @@ for k, v in UNEQUAL_LENGTH_UNIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"UnequalLengthUnivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"UnequalLengthMultivariate-Classification-{k}": v @@ -879,12 +726,6 @@ for k, v in UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"UnequalLengthMultivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"MissingValues-Classification-{k}": v @@ -916,9 +757,12 @@ def _get_datatypes_for_estimator(estimator): FULL_TEST_DATA_DICT. Each tuple is formatted (data_key, label_key). """ datatypes = [] - univariate, multivariate, unequal_length, missing_values = ( - _get_capabilities_for_estimator(estimator) - ) + ( + univariate, + multivariate, + unequal_length, + missing_values, + ) = _get_capabilities_for_estimator(estimator) task = _get_task_for_estimator(estimator) inner_types = estimator.get_tag("X_inner_type") @@ -1012,19 +856,19 @@ def _get_task_for_estimator(estimator): or isinstance(estimator, BaseEarlyClassifier) or isinstance(estimator, BaseClusterer) or isinstance(estimator, BaseCollectionTransformer) + or isinstance(estimator, BaseCollectionSimilaritySearch) ): data_label = "Classification" # collection data with continuous target labels elif isinstance(estimator, BaseRegressor): data_label = "Regression" - elif isinstance(estimator, BaseSimilaritySearch): - data_label = "SimilaritySearch" # series data with no secondary input elif ( isinstance(estimator, BaseAnomalyDetector) or isinstance(estimator, BaseSegmenter) or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) + or isinstance(estimator, BaseSeriesSimilaritySearch) ): data_label = "None" else: diff --git a/aeon/testing/tests/test_all_estimators.py b/aeon/testing/tests/test_all_estimators.py index 2716021bba..192d63b1d6 100644 --- a/aeon/testing/tests/test_all_estimators.py +++ b/aeon/testing/tests/test_all_estimators.py @@ -22,7 +22,7 @@ i = 0 elif i == 11: i = 1 - elif i == 12: + elif i == 13: i = 2 os_str = platform.system() diff --git a/aeon/testing/tests/test_testing_data.py b/aeon/testing/tests/test_testing_data.py index f9afe264dd..891bd5851a 100644 --- a/aeon/testing/tests/test_testing_data.py +++ b/aeon/testing/tests/test_testing_data.py @@ -6,19 +6,15 @@ from aeon.testing.testing_data import ( EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, EQUAL_LENGTH_MULTIVARIATE_REGRESSION, - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH, EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, EQUAL_LENGTH_UNIVARIATE_REGRESSION, - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH, FULL_TEST_DATA_DICT, MISSING_VALUES_CLASSIFICATION, MISSING_VALUES_REGRESSION, UNEQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION, - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH, UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_UNIVARIATE_REGRESSION, - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH, ) from aeon.utils.data_types import COLLECTIONS_DATA_TYPES from aeon.utils.validation import ( @@ -108,31 +104,6 @@ def test_equal_length_univariate_collection(): EQUAL_LENGTH_UNIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0], include_2d=True - ) - assert is_univariate(EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0]) - assert is_equal_length( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert is_univariate( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_unequal_length_univariate_collection(): """Test the contents of the unequal length univariate data dictionary.""" @@ -182,34 +153,6 @@ def test_unequal_length_univariate_collection(): UNEQUAL_LENGTH_UNIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert is_univariate( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert is_univariate( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_equal_length_multivariate_collection(): """Test the contents of the equal length multivariate data dictionary.""" @@ -259,34 +202,6 @@ def test_equal_length_multivariate_collection(): EQUAL_LENGTH_MULTIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert is_equal_length( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_unequal_length_multivariate_collection(): """Test the contents of the unequal length multivariate data dictionary.""" @@ -348,34 +263,6 @@ def test_unequal_length_multivariate_collection(): UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert not is_univariate( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_equal_length( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not is_univariate( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_missing_values_collection(): """Test the contents of the missing value data dictionary.""" diff --git a/aeon/testing/utils/deep_equals.py b/aeon/testing/utils/deep_equals.py index 5183ee3f2c..4a39014d7b 100644 --- a/aeon/testing/utils/deep_equals.py +++ b/aeon/testing/utils/deep_equals.py @@ -56,7 +56,7 @@ def _deep_equals(x, y, depth, ignore_index): elif isinstance(x, pd.DataFrame): return _dataframe_equals(x, y, depth, ignore_index) elif isinstance(x, np.ndarray): - return _numpy_equals(x, y, depth) + return _numpy_equals(x, y, depth, ignore_index) elif isinstance(x, (list, tuple)): return _list_equals(x, y, depth, ignore_index) elif isinstance(x, dict): @@ -84,6 +84,8 @@ def _deep_equals(x, y, depth, ignore_index): def _series_equals(x, y, depth, ignore_index): if x.dtype != y.dtype: return False, f"x.dtype ({x.dtype}) != y.dtype ({y.dtype}), depth={depth}" + if x.shape != y.shape: + return False, f"x.shape ({x.shape}) != y.shape ({y.shape}), depth={depth}" # if columns are object, recurse over entries and index if x.dtype == "object": @@ -108,7 +110,12 @@ def _series_equals(x, y, depth, ignore_index): def _dataframe_equals(x, y, depth, ignore_index): if not x.columns.equals(y.columns): - return False, f"x.columns ({x.columns}) != y.columns ({y.columns})" + return ( + False, + f"x.columns ({x.columns}) != y.columns ({y.columns}), depth={depth}", + ) + if x.shape != y.shape: + return False, f"x.shape ({x.shape}) != y.shape ({y.shape}), depth={depth}" # if columns are equal and at least one is object, recurse over Series if sum(x.dtypes == "object") > 0: @@ -128,15 +135,23 @@ def _dataframe_equals(x, y, depth, ignore_index): return eq, msg -def _numpy_equals(x, y, depth): +def _numpy_equals(x, y, depth, ignore_index): if x.dtype != y.dtype: - return False, f"x.dtype ({x.dtype}) != y.dtype ({y.dtype})" + return False, f"x.dtype ({x.dtype}) != y.dtype ({y.dtype}), depth={depth}" + if x.shape != y.shape: + return False, f"x.shape ({x.shape}) != y.shape ({y.shape}), depth={depth}" + if x.dtype == "object": - eq, msg = _deep_equals(x.tolist(), y.tolist(), depth, ignore_index=True) + for i in range(len(x)): + eq, msg = _deep_equals(x[i], y[i], depth + 1, ignore_index) + + if not eq: + return False, msg + f", idx={i}" else: eq = np.allclose(x, y, equal_nan=True) msg = "" if eq else f"x ({x}) != y ({y}), depth={depth}" - return eq, msg + return eq, msg + return True, "" def _csrmatrix_equals(x, y, depth): diff --git a/aeon/testing/utils/estimator_checks.py b/aeon/testing/utils/estimator_checks.py index b2e0973dbf..d556ff0249 100644 --- a/aeon/testing/utils/estimator_checks.py +++ b/aeon/testing/utils/estimator_checks.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search import BaseSimilaritySearch from aeon.testing.testing_data import FULL_TEST_DATA_DICT from aeon.utils.validation import get_n_cases diff --git a/aeon/testing/utils/output_suppression.py b/aeon/testing/utils/output_suppression.py index dd640e8137..80ae99eb81 100644 --- a/aeon/testing/utils/output_suppression.py +++ b/aeon/testing/utils/output_suppression.py @@ -11,7 +11,63 @@ @contextmanager def suppress_output(suppress_stdout=True, suppress_stderr=True): - """Redirects stdout and/or stderr to devnull.""" + """ + Context manager to suppress stdout and/or stderr output. + + This function redirects standard output (stdout) and standard error (stderr) + to `devnull`, effectively silencing any print statements or error messages + within its context. + + Parameters + ---------- + suppress_stdout : bool, optional, default=True + If True, redirects stdout to null, suppressing print statements. + suppress_stderr : bool, optional, default=True + If True, redirects stderr to null, suppressing error messages. + + Examples + -------- + Suppressing both stdout and stderr: + + >>> import sys + >>> with suppress_output(): + ... print("This will not be displayed") + ... print("Error messages will be hidden", file=sys.stderr) + + Suppressing only stdout: + + >>> sys.stderr = sys.stdout # Needed so doctest can capture stderr + >>> with suppress_output(suppress_stdout=True, suppress_stderr=False): + ... print("This will not be shown") + ... print("Error messages will still be visible", file=sys.stderr) + Error messages will still be visible + + Suppressing only stderr: + + >>> with suppress_output(suppress_stdout=False, suppress_stderr=True): + ... print("This will be shown") + ... print("Error messages will be hidden", file=sys.stderr) + This will be shown + + Using as a function wrapper: + + Suppressing both stdout and stderr: + + >>> @suppress_output() + ... def noisy_function(): + ... print("Noisy output") + ... print("Noisy error", file=sys.stderr) + >>> noisy_function() + + Suppressing only stdout: + + >>> @suppress_output(suppress_stderr=False) + ... def noisy_function(): + ... print("Noisy output") + ... print("Noisy error", file=sys.stderr) + >>> noisy_function() + Noisy error + """ with open(devnull, "w") as null: stdout = sys.stdout stderr = sys.stderr diff --git a/aeon/testing/utils/tests/test_output_supression.py b/aeon/testing/utils/tests/test_output_supression.py index 56f7b18ec5..e1d666fc3c 100644 --- a/aeon/testing/utils/tests/test_output_supression.py +++ b/aeon/testing/utils/tests/test_output_supression.py @@ -1,22 +1,55 @@ """Test output suppression decorator.""" +import io import sys from aeon.testing.utils.output_suppression import suppress_output -@suppress_output() def test_suppress_output(): """Test suppress_output method with True inputs.""" - print( # noqa: T201 - "Hello world! If this is visible suppress_output is not working!" - ) - print( # noqa: T201 - "Error! If this is visible suppress_output is not working!", file=sys.stderr - ) + + @suppress_output() + def inner_test(): + + print( # noqa: T201 + "Hello world! If this is visible suppress_output is not working!" + ) + print( # noqa: T201 + "Error! If this is visible suppress_output is not working!", file=sys.stderr + ) + + stdout_capture = io.StringIO() + stderr_capture = io.StringIO() + sys.stdout = stdout_capture + sys.stderr = stderr_capture + + inner_test() + + assert stdout_capture.getvalue() == "", "stdout was not suppressed!" + assert stderr_capture.getvalue() == "", "stderr was not suppressed!" -@suppress_output(suppress_stdout=False, suppress_stderr=False) def test_suppress_output_false(): """Test suppress_output method with False inputs.""" - pass + + @suppress_output(suppress_stdout=False, suppress_stderr=False) + def inner_test(): + print("This should be visible.") # noqa: T201 + print( # noqa: T201 + "This error message should also be visible.", file=sys.stderr + ) + + stdout_capture = io.StringIO() + stderr_capture = io.StringIO() + sys.stdout = stdout_capture + sys.stderr = stderr_capture + + inner_test() + + assert ( # noqa: T201 + "This should be visible." in stdout_capture.getvalue() + ), "stdout was incorrectly suppressed!" + assert ( # noqa: T201 + "This error message should also be visible." in stderr_capture.getvalue() + ), "stderr was incorrectly suppressed!" diff --git a/aeon/transformations/base.py b/aeon/transformations/base.py index 7e4998a910..f0ae37d008 100644 --- a/aeon/transformations/base.py +++ b/aeon/transformations/base.py @@ -5,6 +5,9 @@ from abc import abstractmethod +import numpy as np +import pandas as pd + from aeon.base import BaseAeonEstimator @@ -90,3 +93,22 @@ def fit_transform(self, X, y=None): Additional data, e.g., labels for transformation. """ ... + + def _check_y(self, y, n_cases=None): + # Check y valid input for supervised transform + if not isinstance(y, (pd.Series, np.ndarray)): + raise TypeError( + f"y must be a np.array or a pd.Series, but found type: {type(y)}" + ) + + if isinstance(y, np.ndarray) and y.ndim > 1: + raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") + + if n_cases is not None: + # Check matching number of labels + n_labels = y.shape[0] + if n_cases != n_labels: + raise ValueError( + f"Mismatch in number of cases. Number in X = {n_cases} nos in y = " + f"{n_labels}" + ) diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py index 013001d80e..0972341771 100644 --- a/aeon/transformations/collection/base.py +++ b/aeon/transformations/collection/base.py @@ -19,7 +19,7 @@ class name: BaseCollectionTransformer fitted state inspection - check_is_fitted() """ -__maintainer__ = [] +__maintainer__ = ["MatthewMiddlehurst"] __all__ = [ "BaseCollectionTransformer", ] @@ -27,17 +27,15 @@ class name: BaseCollectionTransformer from abc import abstractmethod from typing import final -import numpy as np -import pandas as pd - from aeon.base import BaseCollectionEstimator from aeon.transformations.base import BaseTransformer +from aeon.utils.validation import get_n_cases class BaseCollectionTransformer(BaseCollectionEstimator, BaseTransformer): """Transformer base class for collections.""" - # tag values specific to CollectionTransformers + # default tag values for collection transformers _tags = { "input_data_type": "Collection", "output_data_type": "Collection", @@ -64,8 +62,8 @@ def fit(self, X, y=None): X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ``( - n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number - of channels, unequal length series) of shape ``[n_cases]``, 2D np.array + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. @@ -84,22 +82,25 @@ def fit(self, X, y=None): ------- self : a fitted instance of the estimator """ - if self.get_tag("requires_y"): - if y is None: - raise ValueError("Tag requires_y is true, but fit called with y=None") - # skip the rest if fit_is_empty is True if self.get_tag("fit_is_empty"): self.is_fitted = True return self + + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit self.reset() # input checks and datatype conversion - X_inner = self._preprocess_collection(X) - y_inner = y - self._fit(X=X_inner, y=y_inner) + X = self._preprocess_collection(X, store_metadata=True) + if y is not None: + self._check_y(y, n_cases=self.metadata_["n_cases"]) - self.is_fitted = True + self._fit(X=X, y=y) + self.is_fitted = True return self @final @@ -118,8 +119,8 @@ def transform(self, X, y=None): X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ``( - n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number - of channels, unequal length series) of shape ``[n_cases]``, 2D np.array + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. @@ -139,18 +140,19 @@ def transform(self, X, y=None): ------- transformed version of X """ - # check whether is fitted - self._check_is_fitted() + fit_empty = self.get_tag("fit_is_empty") + if not fit_empty: + self._check_is_fitted() - # input check and conversion for X/y - X_inner = self._preprocess_collection(X, store_metadata=False) - y_inner = y + # input checks and datatype conversion + X = self._preprocess_collection(X, store_metadata=False) + if y is not None: + self._check_y(y, n_cases=get_n_cases(X)) - if not self.get_tag("fit_is_empty"): + if not fit_empty: self._check_shape(X) - Xt = self._transform(X=X_inner, y=y_inner) - + Xt = self._transform(X, y) return Xt @final @@ -171,10 +173,10 @@ def fit_transform(self, X, y=None): ---------- X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, - any number of channels, equal length series of shape ``(n_cases, - n_channels, n_timepoints)`` or list of numpy arrays (any number of - channels, unequal length series) of shape ``[n_cases]``, 2D np.array ``( - n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of + any number of channels, equal length series of shape ``( + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array + ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. Different estimators have different capabilities to handle different @@ -192,14 +194,21 @@ def fit_transform(self, X, y=None): ------- transformed version of X """ - # input checks and datatype conversion + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit self.reset() - X_inner = self._preprocess_collection(X) - y_inner = y - Xt = self._fit_transform(X=X_inner, y=y_inner) - self.is_fitted = True + # input checks and datatype conversion + X = self._preprocess_collection(X, store_metadata=True) + if y is not None: + self._check_y(y, n_cases=self.metadata_["n_cases"]) + + Xt = self._fit_transform(X=X, y=y) + self.is_fitted = True return Xt @final @@ -222,8 +231,8 @@ def inverse_transform(self, X, y=None): X : np.ndarray or list Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ``( - n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number - of channels, unequal length series) of shape ``[n_cases]``, 2D np.array + n_cases, n_channels, n_timepoints)`` or list of numpy arrays (number + of channels, series length) of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of series ``i``. Other types are allowed and converted into one of the above. @@ -297,6 +306,7 @@ def _transform(self, X, y=None): ------- transformed version of X """ + ... def _fit_transform(self, X, y=None): """Fit to data, then transform it. @@ -341,41 +351,3 @@ def _inverse_transform(self, X, y=None): raise NotImplementedError( f"{self.__class__.__name__} does not support inverse_transform" ) - - def _update(self, X, y=None): - """Update transformer with X and y. - - private _update containing the core logic, called from update - - Parameters - ---------- - X : Input data - Data to fit transform to, of valid collection type. - y : Target variable, default=None - Additional data, e.g., labels for transformation - - Returns - ------- - self: a fitted instance of the estimator. - """ - # standard behaviour: no update takes place, new data is ignored - return self - - -def _check_y(self, y, n_cases): - if y is None: - return None - # Check y valid input for collection transformations - if not isinstance(y, (pd.Series, np.ndarray)): - raise TypeError( - f"y must be a np.array or a pd.Series, but found type: {type(y)}" - ) - if isinstance(y, np.ndarray) and y.ndim > 1: - raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") - # Check matching number of labels - n_labels = y.shape[0] - if n_cases != n_labels: - raise ValueError( - f"Mismatch in number of cases. Number in X = {n_cases} nos in y = " - f"{n_labels}" - ) diff --git a/aeon/transformations/collection/convolution_based/_minirocket.py b/aeon/transformations/collection/convolution_based/_minirocket.py index 603c381fb7..cdc62d42b0 100644 --- a/aeon/transformations/collection/convolution_based/_minirocket.py +++ b/aeon/transformations/collection/convolution_based/_minirocket.py @@ -55,7 +55,7 @@ class MiniRocket(BaseCollectionTransformer): Notes ----- Directly adapted from the original implementation - https://github.com/angus924/minirocket. + https://github.com/angus924/minirocket with owner permission. Examples -------- diff --git a/aeon/transformations/collection/feature_based/_catch22.py b/aeon/transformations/collection/feature_based/_catch22.py index daae46f583..0431da8df1 100644 --- a/aeon/transformations/collection/feature_based/_catch22.py +++ b/aeon/transformations/collection/feature_based/_catch22.py @@ -111,8 +111,11 @@ class Catch22(BaseCollectionTransformer): true. If a List of specific features to extract is provided, "Mean" and/or "StandardDeviation" must be added to the List to extract these features. outlier_norm : bool, optional, default=False - Normalise each series during the two outlier Catch22 features, which can take a - while to process for large values. + If True, each time series is normalized during the computation of the two + outlier Catch22 features, which can take a while to process for large values + as it depends on the max value in the timseries. Note that this parameter + did not exist in the original publication/implementation as they used time + series that were already normalized. replace_nans : bool, default=False Replace NaN or inf values from the Catch22 transform with 0. use_pycatch22 : bool, optional, default=False @@ -163,7 +166,7 @@ class Catch22(BaseCollectionTransformer): [1.15639531e+00 1.31700577e+00 5.66227710e-01 2.00000000e+00 3.89048349e-01 2.33853577e-01 1.00000000e+00 3.00000000e+00 8.23045267e-03 0.00000000e+00 1.70859420e-01 2.00000000e+00 - 1.00000000e+00 2.00000000e-01 0.00000000e+00 1.10933565e-32 + 1.00000000e+00 7.00000000e-01 2.00000000e-01 1.10933565e-32 4.00000000e+00 2.04319187e+00 0.00000000e+00 0.00000000e+00 1.96349541e+00 5.51667002e-01] """ @@ -181,7 +184,7 @@ def __init__( self, features="all", catch24=False, - outlier_norm=False, + outlier_norm=True, replace_nans=False, use_pycatch22=False, n_jobs=1, diff --git a/aeon/transformations/collection/feature_based/_summary.py b/aeon/transformations/collection/feature_based/_summary.py index 12dba4e756..ed9a90fbd9 100644 --- a/aeon/transformations/collection/feature_based/_summary.py +++ b/aeon/transformations/collection/feature_based/_summary.py @@ -1,6 +1,6 @@ """Summary feature transformer.""" -__maintainer__ = [] +__maintainer__ = ["MatthewMiddlehurst"] __all__ = ["SevenNumberSummary"] import numpy as np @@ -22,12 +22,12 @@ class SevenNumberSummary(BaseCollectionTransformer): Parameters ---------- - summary_stats : ["default", "percentiles", "bowley", "tukey"], default="default" + summary_stats : ["default", "quantiles", "bowley", "tukey"], default="default" The summary statistics to compute. The options are as follows, with float denoting the percentile value extracted from the series: - "default": mean, std, min, max, 0.25, 0.5, 0.75 - - "percentiles": 0.215, 0.887, 0.25, 0.5, 0.75, 0.9113, 0.9785 + - "quantiles": 0.0215, 0.0887, 0.25, 0.5, 0.75, 0.9113, 0.9785 - "bowley": min, max, 0.1, 0.25, 0.5, 0.75, 0.9 - "tukey": min, max, 0.125, 0.25, 0.5, 0.75, 0.875 @@ -89,10 +89,10 @@ def _get_functions(self): 0.5, 0.75, ] - elif self.summary_stats == "percentiles": + elif self.summary_stats == "quantiles": return [ - 0.215, - 0.887, + 0.0215, + 0.0887, 0.25, 0.5, 0.75, diff --git a/aeon/transformations/collection/feature_based/tests/test_catch22.py b/aeon/transformations/collection/feature_based/tests/test_catch22.py index 5b5bc28925..9ebc67866a 100644 --- a/aeon/transformations/collection/feature_based/tests/test_catch22.py +++ b/aeon/transformations/collection/feature_based/tests/test_catch22.py @@ -120,7 +120,7 @@ def test_catch22_wrapper_on_basic_motions(): 0.0616, 1.0, 0.5, - -0.2, + -0.2799, 0.04, 0.4158, 4.0, @@ -231,7 +231,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 1.0, -0.11, - -0.72, + -0.81, 1.7181, 8.0, 1.8142, @@ -255,7 +255,7 @@ def test_catch22_wrapper_on_basic_motions(): 4.0, 0.3333, -0.15, - 0.03, + 0.01, 32.285, 8.0, 1.9501, @@ -298,8 +298,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.1303, 3.0, 0.3333, - -0.23, - -0.04, + -0.2299, + 0.06, 14.3938, 5.0, 2.0059, @@ -320,8 +320,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.1047, 2.0, 0.3333, - 0.15, - -0.18, + 0.06, + -0.235, 7.1407, 6.0, 2.0097, @@ -342,7 +342,7 @@ def test_catch22_wrapper_on_basic_motions(): 0.064, 1.0, 0.3333, - 0.18, + 0.20, 0.3, 1.6007, 5.0, @@ -365,7 +365,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 0.3333, -0.14, - 0.1, + -0.0399, 7.3076, 5.0, 1.9736, @@ -389,7 +389,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 0.3333, -0.13, - 0.02, + -0.3399, 0.0081, 5.0, 2.133, @@ -410,8 +410,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.5715, 2.0, 1.0, - -0.12, - -0.02, + -0.1399, + -0.0099, 0.1288, 7.0, 1.9505, @@ -476,8 +476,8 @@ def test_catch22_wrapper_on_basic_motions(): 6.8497, 2.0, 0.3333, - -0.06, - 0.05, + -0.0799, + 0.03, 0.0013, 7.0, 2.039, @@ -498,8 +498,8 @@ def test_catch22_wrapper_on_basic_motions(): 3.1416, 2.0, 1.0, - -0.155, - 0.125, + -0.1999, + 0.1200, 0.0212, 7.0, 1.8706, @@ -522,7 +522,7 @@ def test_catch22_wrapper_on_basic_motions(): 0.1723, 1.0, 1.0, - -0.01, + -0.0799, -0.17, 8.3186, 5.0, @@ -544,8 +544,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.1222, 1.0, 1.0, - 0.09, - 0.01, + 0.08, + -0.0099, 5.3016, 4.0, 2.0075, @@ -566,8 +566,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.0841, 2.0, 0.5, - 0.13, - -0.08, + -0.0199, + -0.1199, 1.7627, 5.0, 2.1476, @@ -611,7 +611,7 @@ def test_catch22_wrapper_on_basic_motions(): 1.0, 0.5, -0.05, - -0.11, + -0.0999, 0.2086, 6.0, 2.0597, @@ -656,8 +656,8 @@ def test_catch22_wrapper_on_basic_motions(): 0.0718, 1.0, 0.3333, - 0.03, - 0.13, + 0.025, + -0.1399, 0.501, 6.0, 2.0492, @@ -701,7 +701,7 @@ def test_catch22_wrapper_on_basic_motions(): 2.0, 0.5, -0.13, - 0.19, + 0.29, 0.3096, 6.0, 1.8881, @@ -745,7 +745,7 @@ def test_catch22_wrapper_on_basic_motions(): 3.0, 1.0, 0.11, - 0.35, + 0.285, 0.2719, 7.0, 1.7647, diff --git a/aeon/transformations/collection/feature_based/tests/test_summary.py b/aeon/transformations/collection/feature_based/tests/test_summary.py index d35e54f9ac..faf1315573 100644 --- a/aeon/transformations/collection/feature_based/tests/test_summary.py +++ b/aeon/transformations/collection/feature_based/tests/test_summary.py @@ -1,28 +1,21 @@ """Test summary features transformer.""" +import numpy as np import pytest from aeon.transformations.collection.feature_based import SevenNumberSummary -def test_summary_features(): - """Test get functions.""" - x = SevenNumberSummary() - f = x._get_functions() - assert len(f) == 7 - assert callable(f[0]) - x = SevenNumberSummary(summary_stats="percentiles") - f = x._get_functions() - assert len(f) == 7 - assert isinstance(f[0], float) - assert f[1] == 0.887 - x = SevenNumberSummary(summary_stats="bowley") - f = x._get_functions() - assert len(f) == 7 - assert callable(f[0]) - assert f[6] == 0.9 - x = SevenNumberSummary(summary_stats="tukey") - assert len(x._get_functions()) == 7 +@pytest.mark.parametrize("summary_stats", ["default", "quantiles", "bowley", "tukey"]) +def test_summary_features(summary_stats): + """Test different summary_stats options.""" + sns = SevenNumberSummary() + t = sns.fit_transform(np.ones((10, 2, 5))) + assert t.shape == (10, 14) + + +def test_summary_features_invalid(): + """Test invalid summary_stats option.""" with pytest.raises(ValueError, match="Summary function input invalid"): - x = SevenNumberSummary(summary_stats="invalid") - x._get_functions() + sns = SevenNumberSummary(summary_stats="invalid") + sns.fit_transform(np.ones((10, 2, 5))) diff --git a/aeon/transformations/collection/interval_based/_quant.py b/aeon/transformations/collection/interval_based/_quant.py index 257bbd85d4..cdb31cb845 100644 --- a/aeon/transformations/collection/interval_based/_quant.py +++ b/aeon/transformations/collection/interval_based/_quant.py @@ -76,7 +76,6 @@ def __init__(self, interval_depth=6, quantile_divisor=4): def _fit(self, X, y=None): import torch - import torch.nn.functional as F X = torch.tensor(X).float() @@ -85,17 +84,19 @@ def _fit(self, X, y=None): if self.interval_depth < 1: raise ValueError("interval_depth must be >= 1") + in_length = X.shape[-1] + representation_functions = ( - lambda X: X, - lambda X: F.avg_pool1d(F.pad(X.diff(), (2, 2), "replicate"), 5, 1), - lambda X: X.diff(n=2), - lambda X: torch.fft.rfft(X).abs(), + in_length, # lambda X: X + in_length + - 1, # lambda X: F.avg_pool1d(F.pad(X.diff(), (2, 2), "replicate"), 5, 1) + in_length - 2, # lambda X: X.diff(n=2) + in_length // 2 + 1, # lambda X: torch.fft.rfft(X).abs() ) - self.intervals_ = [] - for function in representation_functions: - Z = function(X) - self.intervals_.append(self._make_intervals(input_length=Z.shape[-1])) + + for length in representation_functions: + self.intervals_.append(self._make_intervals(input_length=length)) return self diff --git a/aeon/transformations/format/__init__.py b/aeon/transformations/format/__init__.py new file mode 100644 index 0000000000..9409e0c3a4 --- /dev/null +++ b/aeon/transformations/format/__init__.py @@ -0,0 +1,11 @@ +"""Format transformations.""" + +__all__ = [ + "SlidingWindowTransformer", + "TrainTestTransformer", + "BaseFormatTransformer", +] + +from aeon.transformations.format._sliding_window import SlidingWindowTransformer +from aeon.transformations.format._train_test import TrainTestTransformer +from aeon.transformations.format.base import BaseFormatTransformer diff --git a/aeon/transformations/format/_sliding_window.py b/aeon/transformations/format/_sliding_window.py new file mode 100644 index 0000000000..899eaaf44a --- /dev/null +++ b/aeon/transformations/format/_sliding_window.py @@ -0,0 +1,92 @@ +"""Sliding Window transformation.""" + +__maintainer__ = [] +__all__ = ["SlidingWindowTransformer"] + +import numpy as np + +from aeon.transformations.format.base import BaseFormatTransformer + + +class SlidingWindowTransformer(BaseFormatTransformer): + """ + Create windowed views of a series by extracting fixed-length overlapping segments. + + This transformer generates multiple subsequences (windows) of a specified width from + the input time series. Each window represents a shifted view of the series, moving + forward by one time step. + + Parameters + ---------- + window_size : int, optional (default=100) + The number of consecutive time points in each window. + + Notes + ----- + - The function assumes that `window_width` is smaller than the length of `series`. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.format import SlidingWindowTransformer + >>> X = np.array([1, 2, 3, 4, 5, 6]) + >>> transformer = SlidingWindowTransformer(3) + >>> Xt = transformer.fit_transform(X) + >>> print(Xt) + ([[1, 2], [2, 3], [3, 4], [4, 5]], [3, 4, 5, 6], [0, 1, 2, 3]) + + + Returns + ------- + X : np.ndarray (2D) + A numpy array where each element is a window (subsequence) of length + `window_width - 1` from the original series. + Y : np.ndarray (1D) + A numpy array containing the next value in the series for each window. + indices : list of int + A list of starting indices corresponding to each extracted window. + + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + "output_data_type": "Tuple", + } + + def __init__(self, window_size: int = 100): + super().__init__(axis=1) + if window_size <= 1: + raise ValueError(f"window_size must be > 1, got {window_size}") + self.window_size = window_size + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + The input time series from which windows will be created. + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + X = X[0] + # Generate windowed versions of train and test sets + X_t = np.zeros((len(X) - self.window_size + 1, self.window_size - 1)) + Y_t = np.zeros(len(X) - self.window_size + 1) + indices = np.zeros(len(X) - self.window_size + 1) + for i in range(len(X) - self.window_size + 1): + X_t[i] = X[ + i : i + self.window_size - 1 + ] # Create a view from current index onward + Y_t[i] = X[i + self.window_size - 1] # Next value + indices[i] = i + return X_t, Y_t, indices diff --git a/aeon/transformations/format/_train_test.py b/aeon/transformations/format/_train_test.py new file mode 100644 index 0000000000..0d31d48aa9 --- /dev/null +++ b/aeon/transformations/format/_train_test.py @@ -0,0 +1,93 @@ +"""Sliding Window transformation.""" + +__maintainer__ = [] +__all__ = ["TrainTestTransformer"] + +import math + +from aeon.transformations.format.base import BaseFormatTransformer + + +class TrainTestTransformer(BaseFormatTransformer): + """ + Convert a single time series into train/test sets. + + This function assumes that the input DataFrame contains only one time series. + It splits the series into training and testing sets based on + the specified proportion. + + Parameters + ---------- + train_proportion : float, optional (default=0.7) + The proportion of the time series to use for training, + with the remaining used for test. + max_series_length : int, optional (default=10000) + The maximum length of the series to consider. If the series is longer + than this value, it will be truncated. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.format import TrainTestTransformer + >>> X = np.array([-3, -2, -1, 0, 1, 2, 3, 4]) + >>> transformer = TrainTestTransformer(0.75) + >>> Xt = transformer.fit_transform(X) + >>> print(Xt) + (array([-3, -2, -1, 0, 1, 2]), array([3, 4])) + + Returns + ------- + None + A tuple containing the training and testing sets. + + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + "output_data_type": "Tuple", + } + + def __init__( + self, train_proportion: float = 0.7, max_series_length: int = 10000 + ) -> None: + super().__init__(axis=1) + if train_proportion <= 0 or train_proportion >= 1: + raise ValueError( + f"train_proportion must be between 0 and 1, got {train_proportion}" + ) + self.train_proportion = train_proportion + self.max_series_length = max_series_length + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + X = X[0] + # Compute split index + if len(X) < self.max_series_length or self.max_series_length == -1: + end_location = len(X) + else: + end_location = self.max_series_length + train_test_split_location = math.ceil(end_location * self.train_proportion) + + # Split into train and test sets + train_series = X[:train_test_split_location] + test_series = X[train_test_split_location:end_location] + + # Generate windowed versions of train and test sets + return train_series, test_series diff --git a/aeon/transformations/format/base.py b/aeon/transformations/format/base.py new file mode 100644 index 0000000000..9047c667e1 --- /dev/null +++ b/aeon/transformations/format/base.py @@ -0,0 +1,301 @@ +"""Base class for Series transformers. + +class name: BaseSeriesTransformer + +Defining methods: +fitting - fit(self, X, y=None) +transform - transform(self, X, y=None) +fit & transform - fit_transform(self, X, y=None) +""" + +from abc import abstractmethod +from typing import final + +import numpy as np +import pandas as pd + +from aeon.base import BaseSeriesEstimator +from aeon.transformations.base import BaseTransformer + + +class BaseFormatTransformer(BaseSeriesEstimator, BaseTransformer): + """Transformer base class for collections.""" + + # tag values specific to SeriesTransformers + _tags = { + "input_data_type": "Series", + "output_data_type": "Tuple", + } + + @abstractmethod + def __init__(self, axis): + super().__init__(axis=axis) + + @final + def fit(self, X, y=None, axis=1): + """Fit transformer to X, optionally using y if supervised. + + State change: + Changes state to "fitted". + + Parameters + ---------- + X : Input data + Time series to fit transform to, of type ``np.ndarray``, ``pd.Series`` + ``pd.DataFrame``. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + self : a fitted instance of the estimator + """ + # skip the rest if fit_is_empty is True + if self.get_tag("fit_is_empty"): + self.is_fitted = True + return self + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + # reset estimator at the start of fit + self.reset() + X = self._preprocess_series(X, axis=axis, store_metadata=True) + if y is not None: + self._check_y(y) + self._fit(X=X, y=y) + self.is_fitted = True + return self + + @final + def transform(self, X, y=None, axis=1): + """Transform X and return a transformed version. + + State required: + Requires state to be "fitted". + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + transformed version of X with the same axis as passed by the user, if axis + not None. + """ + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + Xt = self._transform(X, y) + return Xt + + @final + def fit_transform(self, X, y=None, axis=1): + """ + Fit to data, then transform it. + + Fits the transformer to X and y and returns a transformed version of X. + + Changes state to "fitted". Model attributes (ending in "_") : dependent on + estimator. + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + transformed version of X with the same axis as passed by the user, if axis + not None. + """ + # input checks and datatype conversion, to avoid doing in both fit and transform + self.reset() + X = self._preprocess_series(X, axis=axis, store_metadata=True) + Xt = self._fit_transform(X=X, y=y) + self.is_fitted = True + return Xt + + @final + def inverse_transform(self, X, y=None, axis=1): + """Inverse transform X and return an inverse transformed version. + + State required: + Requires state to be "fitted". + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + inverse transformed version of X + of the same type as X + """ + if not self.get_tag("capability:inverse_transform"): + raise NotImplementedError( + f"{type(self)} does not implement inverse_transform" + ) + + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + Xt = self._inverse_transform(X=X, y=y) + return Xt + + @final + def update(self, X, y=None, update_params=True, axis=1): + """Update transformer with X, optionally y. + + Parameters + ---------- + X : data to update of valid series type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + update_params : bool, default=True + whether the model is updated. Yes if true, if false, simply skips call. + argument exists for compatibility with forecasting module. + axis : int, default=None + axis along which to update. If None, uses self.axis. + + Returns + ------- + self : a fitted instance of the estimator + """ + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis, False) + return self._update(X=X, y=y, update_params=update_params) + + def _fit(self, X, y=None): + """Fit transformer to X and y. + + private _fit containing the core logic, called from fit + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + self: a fitted instance of the estimator + """ + # default fit is "no fitting happens" + return self + + @abstractmethod + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing the core logic, called from transform + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + transformed version of X + """ + + def _fit_transform(self, X, y=None): + """Fit to data, then transform it. + + Fits the transformer to X and y and returns a transformed version of X. + + private _fit_transform containing the core logic, called from fit_transform. + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation. + + Returns + ------- + transformed version of X. + """ + # Non-optimized default implementation; override when a better + # method is possible for a given algorithm. + self._fit(X, y) + return self._transform(X, y) + + def _inverse_transform(self, X, y=None): + """Inverse transform X and return an inverse transformed version. + + private _inverse_transform containing core logic, called from inverse_transform. + + Parameters + ---------- + X : Input data + Time series to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + inverse transformed version of X + of the same type as X. + """ + raise NotImplementedError( + f"{self.__class__.__name__} does not support inverse_transform" + ) + + def _update(self, X, y=None, update_params=True): + # standard behaviour: no update takes place, new data is ignored + return self + + def _check_y(self, y): + # Check y valid input for supervised transform + if not isinstance(y, (pd.Series, np.ndarray)): + raise TypeError( + f"y must be a np.array or a pd.Series, but found type: {type(y)}" + ) + if isinstance(y, np.ndarray) and y.ndim > 1: + raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py index 031073b2e6..677f48db01 100644 --- a/aeon/transformations/series/__init__.py +++ b/aeon/transformations/series/__init__.py @@ -5,9 +5,12 @@ "BaseSeriesTransformer", "ClaSPTransformer", "DFTSeriesTransformer", + "DifferencingSeriesTransformer", "Dobin", + "ExpSmoothingSeriesTransformer", "GaussSeriesTransformer", "MatrixProfileSeriesTransformer", + "MovingAverageSeriesTransformer", "PLASeriesTransformer", "SGSeriesTransformer", "StatsModelsACF", @@ -30,9 +33,12 @@ from aeon.transformations.series._boxcox import BoxCoxTransformer from aeon.transformations.series._clasp import ClaSPTransformer from aeon.transformations.series._dft import DFTSeriesTransformer +from aeon.transformations.series._difference import DifferencingSeriesTransformer from aeon.transformations.series._dobin import Dobin +from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer from aeon.transformations.series._gauss import GaussSeriesTransformer from aeon.transformations.series._matrix_profile import MatrixProfileSeriesTransformer +from aeon.transformations.series._moving_average import MovingAverageSeriesTransformer from aeon.transformations.series._pca import PCASeriesTransformer from aeon.transformations.series._pla import PLASeriesTransformer from aeon.transformations.series._scaled_logit import ScaledLogitSeriesTransformer diff --git a/aeon/transformations/series/_bkfilter.py b/aeon/transformations/series/_bkfilter.py index 62440d1a2c..65f684b2bc 100644 --- a/aeon/transformations/series/_bkfilter.py +++ b/aeon/transformations/series/_bkfilter.py @@ -34,8 +34,9 @@ class BKFilter(BaseSeriesTransformer): Notes ----- - Adapted from statsmodels implementation + Adapted from statsmodels 0.14.4 implementation https://github.com/statsmodels/statsmodels/blob/main/statsmodels/tsa/filters/bk_filter.py + Copyright (c) 2009-2018 statsmodels Developers, BSD-3 References ---------- diff --git a/aeon/transformations/series/_difference.py b/aeon/transformations/series/_difference.py new file mode 100644 index 0000000000..42addd377b --- /dev/null +++ b/aeon/transformations/series/_difference.py @@ -0,0 +1,52 @@ +"""Differencing transformations.""" + +__maintainer__ = ["TonyBagnall"] +__all__ = ["DifferencingSeriesTransformer"] + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class DifferencingSeriesTransformer(BaseSeriesTransformer): + """Differencing transformations. + + This transformer returns the differenced series of the input time series. + The differenced series is obtained by subtracting the previous value + from the current value. + + Examples + -------- + >>> from aeon.transformations.series import DifferencingSeriesTransformer + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> transformer = DifferencingSeriesTransformer() + >>> y_hat = transformer.fit_transform(y) + """ + + _tags = { + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__( + self, + ): + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing the core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed, shape (n_channels, n_timepoints) + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + transformed version of X + """ + X = X[0] + return X[1:] - X[:-1] diff --git a/aeon/transformations/series/base.py b/aeon/transformations/series/base.py index cdbd7e50c9..3afa1011bc 100644 --- a/aeon/transformations/series/base.py +++ b/aeon/transformations/series/base.py @@ -11,9 +11,6 @@ class name: BaseSeriesTransformer from abc import abstractmethod from typing import final -import numpy as np -import pandas as pd - from aeon.base import BaseSeriesEstimator from aeon.transformations.base import BaseTransformer @@ -21,7 +18,7 @@ class name: BaseSeriesTransformer class BaseSeriesTransformer(BaseSeriesEstimator, BaseTransformer): """Transformer base class for collections.""" - # tag values specific to SeriesTransformers + # default tag values for series transformers _tags = { "input_data_type": "Series", "output_data_type": "Series", @@ -58,19 +55,24 @@ def fit(self, X, y=None, axis=1): ------- self : a fitted instance of the estimator """ - # skip the rest if fit_is_empty is True if self.get_tag("fit_is_empty"): self.is_fitted = True return self + if self.get_tag("requires_y"): if y is None: raise ValueError("Tag requires_y is true, but fit called with y=None") + # reset estimator at the start of fit self.reset() + + # input checks and datatype conversion X = self._preprocess_series(X, axis=axis, store_metadata=True) if y is not None: self._check_y(y) + self._fit(X=X, y=y) + self.is_fitted = True return self @@ -101,9 +103,18 @@ def transform(self, X, y=None, axis=1): transformed version of X with the same axis as passed by the user, if axis not None. """ - # check whether is fitted - self._check_is_fitted() + fit_empty = self.get_tag("fit_is_empty") + if not fit_empty: + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + if y is not None: + self._check_y(y) + + # #2768 + # if not fit_empty: + # self._check_shape(X) + Xt = self._transform(X, y) return self._postprocess_series(Xt, axis=axis) @@ -137,10 +148,20 @@ def fit_transform(self, X, y=None, axis=1): transformed version of X with the same axis as passed by the user, if axis not None. """ - # input checks and datatype conversion, to avoid doing in both fit and transform + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + + # reset estimator at the start of fit self.reset() + + # input checks and datatype conversion X = self._preprocess_series(X, axis=axis, store_metadata=True) + if y is not None: + self._check_y(y) + Xt = self._fit_transform(X=X, y=y) + self.is_fitted = True return self._postprocess_series(Xt, axis=axis) @@ -263,7 +284,8 @@ def _fit_transform(self, X, y=None): """ # Non-optimized default implementation; override when a better # method is possible for a given algorithm. - return self._fit(X, y)._transform(X, y) + self._fit(X, y) + return self._transform(X, y) def _inverse_transform(self, X, y=None): """Inverse transform X and return an inverse transformed version. @@ -325,12 +347,3 @@ def _postprocess_series(self, Xt, axis): return Xt else: return Xt.T - - def _check_y(self, y): - # Check y valid input for supervised transform - if not isinstance(y, (pd.Series, np.ndarray)): - raise TypeError( - f"y must be a np.array or a pd.Series, but found type: {type(y)}" - ) - if isinstance(y, np.ndarray) and y.ndim > 1: - raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") diff --git a/aeon/utils/base/_identifier.py b/aeon/utils/base/_identifier.py index cf2722cfcb..03e8d8beaf 100644 --- a/aeon/utils/base/_identifier.py +++ b/aeon/utils/base/_identifier.py @@ -55,6 +55,8 @@ def get_identifier(estimator): identifiers.remove("collection-estimator") if len(identifiers) > 1 and "transformer" in identifiers: identifiers.remove("transformer") + if len(identifiers) > 1 and "similarity-search" in identifiers: + identifiers.remove("similarity-search") if len(identifiers) > 1: TypeError( diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 1d81c2512c..5e81e29b33 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -24,7 +24,9 @@ from aeon.forecasting.base import BaseForecaster from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer @@ -36,6 +38,7 @@ "estimator": BaseAeonEstimator, "series-estimator": BaseSeriesEstimator, "transformer": BaseTransformer, + "similarity-search": BaseSimilaritySearch, # estimator types "anomaly-detector": BaseAnomalyDetector, "collection-transformer": BaseCollectionTransformer, @@ -44,14 +47,21 @@ "early_classifier": BaseEarlyClassifier, "regressor": BaseRegressor, "segmenter": BaseSegmenter, - "similarity_searcher": BaseSimilaritySearch, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, + "series-similarity-search": BaseSeriesSimilaritySearch, + "collection-similarity-search": BaseCollectionSimilaritySearch, } # base classes which are valid for estimator to directly inherit from VALID_ESTIMATOR_BASES = { k: BASE_CLASS_REGISTER[k] for k in BASE_CLASS_REGISTER.keys() - - {"estimator", "collection-estimator", "series-estimator", "transformer"} + - { + "estimator", + "collection-estimator", + "series-estimator", + "transformer", + "similarity-search", + } } diff --git a/aeon/utils/conversion/_convert_collection.py b/aeon/utils/conversion/_convert_collection.py index 0e3e28f1af..696b511a08 100644 --- a/aeon/utils/conversion/_convert_collection.py +++ b/aeon/utils/conversion/_convert_collection.py @@ -3,33 +3,31 @@ This contains all functions to convert supported collection data types. String identifier meanings (from aeon.utils.conversion import COLLECTIONS_DATA_TYPES) : + numpy3D : 3D numpy array of time series shape (n_cases, n_channels, n_timepoints) np-list : list of 2D numpy arrays shape (n_channels, n_timepoints_i) df-list : list of 2D pandas dataframes shape (n_channels, n_timepoints_i) numpy2D : 2D numpy array of univariate time series shape (n_cases, n_timepoints) pd-wide : pd.DataFrame of univariate time series shape (n_cases, n_timepoints) -pd-multiindex : pd.DataFrame with multi-index, +pd-multiindex : pd.DataFrame with MultiIndex, index [case, timepoint], columns [channel] -For the seven supported, this gives 42 different converters. +For the six supported, this gives 30 different converters. Rather than using them directly, we recommend using the conversion function convert_collection. """ +__maintainer__ = ["TonyBagnall", "MatthewMiddlehurst"] + from collections.abc import Sequence +from copy import deepcopy from typing import Union import numpy as np import pandas as pd from numba.typed import List as NumbaList -from aeon.utils.data_types import COLLECTIONS_DATA_TYPES -from aeon.utils.validation.collection import _equal_length, get_type - - -def convert_identity(X): - """Convert identity.""" - return X - +from aeon.utils.data_types import COLLECTIONS_DATA_TYPES, COLLECTIONS_UNEQUAL_DATA_TYPES +from aeon.utils.validation.collection import get_type, is_equal_length NUMPY3D_ERROR = ( "Input should be 3-dimensional NumPy array with shape (" @@ -61,8 +59,7 @@ def _from_numpy3d_to_np_list(X): """ if X.ndim != 3: raise TypeError(NUMPY3D_ERROR) - np_list = [x for x in X] - return np_list + return [x for x in X] def _from_numpy3d_to_df_list(X): @@ -86,7 +83,7 @@ def _from_numpy3d_to_df_list(X): """ if X.ndim != 3: raise TypeError(NUMPY3D_ERROR) - df_list = [pd.DataFrame(np.transpose(x)) for x in X] + df_list = [pd.DataFrame(x) for x in X] return df_list @@ -135,12 +132,12 @@ def _from_numpy3d_to_pd_multiindex(X): n_cases, n_channels, n_timepoints = X.shape multi_index = pd.MultiIndex.from_product( [range(n_cases), range(n_channels), range(n_timepoints)], - names=["instances", "columns", "timepoints"], + names=["case", "channel", "timepoint"], ) X_mi = pd.DataFrame({"X": X.flatten()}, index=multi_index) - X_mi = X_mi.unstack(level="columns") - X_mi.columns = [f"var_{i}" for i in range(n_channels)] + X_mi = X_mi.unstack(level=["channel"]) + X_mi.columns = X_mi.columns.droplevel() return X_mi @@ -161,7 +158,7 @@ def _from_np_list_to_df_list(X): n_cases = len(X) df_list = [] for i in range(n_cases): - df_list.append(pd.DataFrame(np.transpose(X[i]))) + df_list.append(pd.DataFrame(X[i])) return df_list @@ -185,9 +182,7 @@ def _from_np_list_to_pd_multiindex(X): def _from_df_list_to_np_list(X): - n_cases = len(X) - list = [np.transpose(np.array(X[i])) for i in range(n_cases)] - return list + return [x.to_numpy() for x in X] def _from_df_list_to_numpy3d(X): @@ -197,12 +192,12 @@ def _from_df_list_to_numpy3d(X): for i in range(len(X)): if not n == len(X[i]) or not set(X[i].columns) == cols: raise TypeError("Cannot convert unequal length series to numpy3D") - nump3D = np.array([x.to_numpy().transpose() for x in X]) + nump3D = np.array([x.to_numpy() for x in X]) return nump3D def _from_df_list_to_numpy2d(X): - if not _equal_length(X, "df-list"): + if not is_equal_length(X): raise TypeError( f"{type(X)} does not store equal length series." f"Cannot convert unequal length to numpy flat" @@ -212,7 +207,7 @@ def _from_df_list_to_numpy2d(X): def _from_df_list_to_pd_wide(X): - if not _equal_length(X, "df-list"): + if not is_equal_length(X): raise TypeError( f"{type(X)} does not store equal length series, " f"Cannot convert unequal length pd wide" @@ -222,9 +217,26 @@ def _from_df_list_to_pd_wide(X): def _from_df_list_to_pd_multiindex(X): - n = len(X) - mi = pd.concat(X, axis=0, keys=range(n), names=["instances", "timepoints"]) - return mi + df = pd.concat( + [x.melt(ignore_index=False).reset_index() for x in X], + axis=0, + keys=range(len(X)), + ).reset_index(level=0) + df.rename( + columns={ + df.columns[0]: "case", + df.columns[1]: "channel", + df.columns[2]: "timepoint", + }, + inplace=True, + ) + df = df.sort_values([df.columns[0], df.columns[1], df.columns[2]]) + df = df.pivot( + index=[df.columns[0], df.columns[2]], + columns=df.columns[1], + values=df.columns[3], + ) + return df def _from_numpy2d_to_numpy3d(X): @@ -246,7 +258,7 @@ def _from_numpy2d_to_df_list(X): if not isinstance(X, np.ndarray) or X.ndim != 2: raise TypeError(NUMPY2D_INPUT_ERROR) X_3d = X.reshape(X.shape[0], 1, X.shape[1]) - X_list = [pd.DataFrame(np.transpose(x)) for x in X_3d] + X_list = [pd.DataFrame(x) for x in X_3d] return X_list @@ -285,10 +297,9 @@ def _pd_wide_to_pd_multiindex(X): return _from_numpy3d_to_pd_multiindex(X_3d) -def _from_pd_multiindex_to_df_list(X): - instance_index = X.index.levels[0] - Xlist = [X.loc[i].rename_axis(None) for i in instance_index] - return Xlist +def _from_pd_multiindex_to_numpy3d(X): + df_list = _from_pd_multiindex_to_df_list(X) + return _from_df_list_to_numpy3d(df_list) def _from_pd_multiindex_to_np_list(X): @@ -296,9 +307,14 @@ def _from_pd_multiindex_to_np_list(X): return _from_df_list_to_np_list(df_list) -def _from_pd_multiindex_to_numpy3d(X): - df_list = _from_pd_multiindex_to_df_list(X) - return _from_df_list_to_numpy3d(df_list) +def _from_pd_multiindex_to_df_list(X): + df_list = [ + X.loc[i].melt(ignore_index=False).reset_index() for i in X.index.levels[0] + ] + return [ + x.pivot(index=x.columns[1], columns=x.columns[0], values=x.columns[2]) + for x in df_list + ] def _from_pd_multiindex_to_numpy2d(X): @@ -311,10 +327,15 @@ def _from_pd_multiindex_to_pd_wide(X): return _from_df_list_to_pd_wide(df_list) +def _copy_data(X): + return deepcopy(X) + + convert_dictionary = dict() -# assign identity function to type conversion to self +# assign copy function to type conversion to self for x in COLLECTIONS_DATA_TYPES: - convert_dictionary[(x, x)] = convert_identity + convert_dictionary[(x, x)] = _copy_data + # numpy3D -> * convert_dictionary[("numpy3D", "np-list")] = _from_numpy3d_to_np_list convert_dictionary[("numpy3D", "df-list")] = _from_numpy3d_to_df_list @@ -356,7 +377,7 @@ def _from_pd_multiindex_to_pd_wide(X): def convert_collection(X, output_type): """Convert from one of collections compatible data structure to another. - See :obj:`aeon.utils.conversion.COLLECTIONS_DATA_TYPE` for the list. + See :obj:`aeon.utils.data_types.COLLECTIONS_DATA_TYPE` for the list. Parameters ---------- @@ -392,7 +413,7 @@ def convert_collection(X, output_type): raise TypeError( f"Attempting to convert from {input_type} to {output_type} " f"but this is not a valid conversion. See " - f"aeon.utils.conversion.COLLECTIONS_DATA_TYPE " + f"aeon.utils.data_types.COLLECTIONS_DATA_TYPE " f"for the list of valid collections" ) return convert_dictionary[(input_type, output_type)](X) @@ -412,12 +433,12 @@ def resolve_equal_length_inner_type(inner_types: Sequence[str]) -> str: return "np-list" if "numpy2D" in inner_types: return "numpy2D" - if "pd-multiindex" in inner_types: - return "pd-multiindex" if "df-list" in inner_types: return "df-list" if "pd-wide" in inner_types: return "pd-wide" + if "pd-multiindex" in inner_types: + return "pd-multiindex" raise ValueError( f"Error, no valid inner types in {inner_types} must be one of " f"{COLLECTIONS_DATA_TYPES}" @@ -440,7 +461,7 @@ def resolve_unequal_length_inner_type(inner_types: Sequence[str]) -> str: return "pd-multiindex" raise ValueError( f"Error, no valid inner types for unequal series in {inner_types} " - f"must be np-list, df-list or pd-multiindex" + f"must be one of {COLLECTIONS_UNEQUAL_DATA_TYPES}" ) diff --git a/aeon/utils/conversion/tests/test_convert_collection.py b/aeon/utils/conversion/tests/test_convert_collection.py index 3776dc7f4f..5c89fc7f9a 100644 --- a/aeon/utils/conversion/tests/test_convert_collection.py +++ b/aeon/utils/conversion/tests/test_convert_collection.py @@ -1,5 +1,7 @@ """Unit tests for check/convert functions.""" +from copy import deepcopy + import numpy as np import pytest @@ -8,6 +10,7 @@ EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, ) +from aeon.testing.utils.deep_equals import deep_equals from aeon.utils.conversion._convert_collection import ( _from_numpy2d_to_df_list, _from_numpy2d_to_np_list, @@ -23,15 +26,12 @@ resolve_equal_length_inner_type, resolve_unequal_length_inner_type, ) -from aeon.utils.data_types import COLLECTIONS_DATA_TYPES -from aeon.utils.validation.collection import ( - _equal_length, - get_n_cases, - get_type, - has_missing, - is_equal_length, - is_univariate, +from aeon.utils.data_types import ( + COLLECTIONS_DATA_TYPES, + COLLECTIONS_MULTIVARIATE_DATA_TYPES, + COLLECTIONS_UNEQUAL_DATA_TYPES, ) +from aeon.utils.validation import get_type @pytest.mark.parametrize("input_data", COLLECTIONS_DATA_TYPES) @@ -39,59 +39,138 @@ def test_convert_collection(input_data, output_data): """Test all valid and invalid conversions.""" # All should work with univariate equal length - X = convert_collection( - EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], output_data - ) - assert get_type(X) == output_data + X = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0] + Xc = convert_collection(X, output_data) + assert get_type(Xc) == output_data + assert _conversion_shape_3d(X, input_data) == _conversion_shape_3d(Xc, output_data) + # Test with multivariate if input_data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION: if output_data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION: - X = convert_collection( - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], - output_data, + X = EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0] + Xc = convert_collection(X, output_data) + assert get_type(Xc) == output_data + assert _conversion_shape_3d(X, input_data) == _conversion_shape_3d( + Xc, output_data ) - assert get_type(X) == output_data else: with pytest.raises(TypeError, match="Cannot convert multivariate"): - X = convert_collection( + convert_collection( EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], output_data, ) + # Test with unequal length if input_data in UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION: - if ( - output_data in UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION - or output_data == "pd-multiindex" - ): - X = convert_collection( - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], + if output_data in UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION: + X = UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0] + Xc = convert_collection( + X, output_data, ) - assert get_type(X) == output_data + assert get_type(Xc) == output_data + assert _conversion_shape_3d(X, input_data) == _conversion_shape_3d( + Xc, output_data + ) else: with pytest.raises(TypeError, match="Cannot convert unequal"): - X = convert_collection( + convert_collection( UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], output_data, ) +def _conversion_shape_3d(X, input_data): + if input_data == "numpy3D": + return X.shape + elif input_data == "numpy2D" or input_data == "pd-wide": + return X.shape[0], 1, X.shape[1] + elif input_data == "pd-multiindex": + return ( + len(X.index.get_level_values(0).unique()), + X.columns.nunique(), + X.loc[X.index.get_level_values(0).unique()[-1]].index.nunique(), + ) + elif input_data == "df-list" or input_data == "np-list": + return len(X), X[-1].shape[0], X[-1].shape[1] + else: + raise TypeError(f"Unknown data type: {input_data}") + + @pytest.mark.parametrize("input_data", COLLECTIONS_DATA_TYPES) -def test_convert_df_list(input_data): - """Test that df list is correctly transposed.""" - X = convert_collection( - EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], "df-list" +def test_self_conversion(input_data): + """Test that data is correctly copied when converting to same data type.""" + X = deepcopy(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0]) + Xc = convert_collection( + EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], input_data ) - assert X[0].shape == (20, 1) - if input_data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION: - X = convert_collection( - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], "df-list" - ) - assert X[0].shape == (20, 2) + assert X is not Xc + assert deep_equals(X, Xc) + + +@pytest.mark.parametrize("input_data", COLLECTIONS_DATA_TYPES) +def test_conversion_loop_returns_same_data(input_data): + """Test that chaining conversions ending at the start gives the same data.""" + dtypes = COLLECTIONS_DATA_TYPES.copy() + np.random.shuffle(dtypes) + Xc = deepcopy(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0]) + for i in dtypes: + Xc = convert_collection(Xc, i) + Xc = convert_collection(Xc, input_data) + + eq, msg = deep_equals( + EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], + Xc, + ignore_index=True, + return_msg=True, + ) + assert eq, msg + + +@pytest.mark.parametrize("input_data", COLLECTIONS_MULTIVARIATE_DATA_TYPES) +def test_conversion_loop_returns_same_data_multivariate(input_data): + """Test that chaining conversions ending at the start gives the same data.""" + dtypes = COLLECTIONS_MULTIVARIATE_DATA_TYPES.copy() + np.random.shuffle(dtypes) + Xc = deepcopy(EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0]) + for i in dtypes: + Xc = convert_collection(Xc, i) + Xc = convert_collection(Xc, input_data) + + eq, msg = deep_equals( + EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[input_data]["train"][0], + Xc, + ignore_index=True, + return_msg=True, + ) + assert eq, msg + + +@pytest.mark.parametrize("input_data", COLLECTIONS_UNEQUAL_DATA_TYPES) +def test_conversion_loop_returns_same_data_unequal(input_data): + """Test that chaining conversions ending at the start gives the same data.""" + dtypes = COLLECTIONS_UNEQUAL_DATA_TYPES.copy() + np.random.shuffle(dtypes) + Xc = deepcopy(UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0]) + for i in dtypes: + Xc = convert_collection(Xc, i) + Xc = convert_collection(Xc, input_data) + + eq, msg = deep_equals( + UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[input_data]["train"][0], + Xc, + ignore_index=True, + return_msg=True, + ) + assert eq, msg def test_resolve_equal_length_inner_type(): """Test the resolution of inner type for equal length collections.""" + for input in COLLECTIONS_DATA_TYPES: + X = resolve_equal_length_inner_type([input]) + assert X == input + test = ["numpy3D"] X = resolve_equal_length_inner_type(test) assert X == "numpy3D" @@ -102,9 +181,16 @@ def test_resolve_equal_length_inner_type(): X = resolve_equal_length_inner_type(test) assert X == "np-list" + with pytest.raises(ValueError, match="no valid inner types"): + resolve_equal_length_inner_type(["invalid"]) + def test_resolve_unequal_length_inner_type(): """Test the resolution of inner type for unequal length collections.""" + for input in COLLECTIONS_UNEQUAL_DATA_TYPES: + X = resolve_unequal_length_inner_type([input]) + assert X == input + test = ["np-list"] X = resolve_unequal_length_inner_type(test) assert X == "np-list" @@ -112,64 +198,8 @@ def test_resolve_unequal_length_inner_type(): X = resolve_unequal_length_inner_type(test) assert X == "np-list" - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_get_n_cases(data): - """Test getting the number of cases.""" - assert get_n_cases(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == 10 - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_get_type(data): - """Test getting the type.""" - assert get_type(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == data - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_equal_length(data): - """Test if equal length series correctly identified.""" - assert _equal_length(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0], data) - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_is_equal_length(data): - """Test if equal length series correctly identified.""" - assert is_equal_length(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) - - -@pytest.mark.parametrize("data", ["df-list", "np-list"]) -def test_unequal_length(data): - """Test if unequal length series correctly identified.""" - assert not _equal_length( - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0], data - ) - - -@pytest.mark.parametrize("data", ["df-list", "np-list"]) -def test_is_unequal_length(data): - """Test if unequal length series correctly identified.""" - assert not is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0] - ) - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_has_missing(data): - """Test if missing values are correctly identified.""" - assert not has_missing(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) - X = np.random.random(size=(10, 2, 20)) - X[5][1][12] = np.nan - assert has_missing(X) - - -@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) -def test_is_univariate(data): - """Test if univariate series are correctly identified.""" - assert is_univariate(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) - if data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION.keys(): - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[data]["train"][0] - ) + with pytest.raises(ValueError, match="no valid inner types"): + resolve_unequal_length_inner_type(["numpy3D"]) NUMPY3D = [ diff --git a/aeon/utils/data_types.py b/aeon/utils/data_types.py index 0202679fb3..aa6b14ba49 100644 --- a/aeon/utils/data_types.py +++ b/aeon/utils/data_types.py @@ -27,7 +27,29 @@ # of shape (n_channels, n_timepoints_i) "numpy2D", # 2D np.ndarray of shape (n_cases, n_timepoints) "pd-wide", # 2D pd.DataFrame of shape (n_cases, n_timepoints) - "pd-multiindex", # pd.DataFrame with multi-index, + "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], + # columns [channel] +] + +# subset of collections capable of handling multivariate time series +COLLECTIONS_MULTIVARIATE_DATA_TYPES = [ + "numpy3D", # 3D np.ndarray of format (n_cases, n_channels, n_timepoints) + "np-list", # python list of 2D np.ndarray of length [n_cases], + # each of shape (n_channels, n_timepoints_i) + "df-list", # python list of 2D pd.DataFrames of length [n_cases], each + # of shape (n_channels, n_timepoints_i) + "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], + # columns [channel] +] + +# subset of collections capable of handling unequal length time series +COLLECTIONS_UNEQUAL_DATA_TYPES = [ + "np-list", # python list of 2D np.ndarray of length [n_cases], + # each of shape (n_channels, n_timepoints_i) + "df-list", # python list of 2D pd.DataFrames of length [n_cases], each + # of shape (n_channels, n_timepoints_i) + "pd-multiindex", # pd.DataFrame with MultiIndex, index [case, timepoint], + # columns [channel] ] HIERARCHICAL_DATA_TYPES = ["pd_multiindex_hier"] # pd.DataFrame diff --git a/aeon/utils/networks/weight_norm.py b/aeon/utils/networks/weight_norm.py index 459cfd7104..c4825c1c91 100644 --- a/aeon/utils/networks/weight_norm.py +++ b/aeon/utils/networks/weight_norm.py @@ -5,6 +5,7 @@ if _check_soft_dependencies(["tensorflow"], severity="none"): import tensorflow as tf + @tf.keras.utils.register_keras_serializable(package="aeon") class _WeightNormalization(tf.keras.layers.Wrapper): """Apply weight normalization to a Keras layer.""" diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 10e96abde6..58ab9d15e9 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -8,7 +8,9 @@ "first_order_differences_3d", "z_normalise_series_with_mean", "z_normalise_series", + "z_normalise_series_with_mean_std", "z_normalise_series_2d", + "z_normalise_series_2d_with_mean_std", "z_normalise_series_3d", "set_numba_random_seed", "choice_log", @@ -20,6 +22,8 @@ "slope_derivative_2d", "slope_derivative_3d", "generate_combinations", + "get_all_subsequences", + "compute_mean_stds_collection_parallel", ] @@ -273,7 +277,7 @@ def z_normalise_series_2d_with_mean_std( Parameters ---------- - X : array, shape = (n_channels, n_timestamps) + X : array, shape = (n_channels, n_timepoints) Input array to normalise. mean : array, shape = (n_channels) Mean of each channel of X. @@ -282,7 +286,7 @@ def z_normalise_series_2d_with_mean_std( Returns ------- - arr : array, shape = (n_channels, n_timestamps) + arr : array, shape = (n_channels, n_timepoints) The normalised array """ arr = np.zeros(X.shape) @@ -376,10 +380,10 @@ def get_subsequence( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -408,10 +412,10 @@ def get_subsequence_with_mean_std( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -451,15 +455,56 @@ def get_subsequence_with_mean_std( return values, means, stds +@njit(cache=True, fastmath=True, parallel=True) +def compute_mean_stds_collection_parallel(X): + """ + Return the mean and standard deviation for each channel of all series in X. + + Parameters + ---------- + X : array, shape (n_cases, n_channels, n_timepoints) + A time series collection + + Returns + ------- + means : array, shape (n_cases, n_channels) + The mean of each channel of each time series in X. + stds : array, shape (n_cases, n_channels) + The std of each channel of each time series in X. + + """ + n_channels = X[0].shape[0] + n_cases = len(X) + means = np.zeros((n_cases, n_channels)) + stds = np.zeros((n_cases, n_channels)) + for i_x in prange(n_cases): + n_timepoints = X[i_x].shape[1] + _s = np.zeros(n_channels) + _s2 = np.zeros(n_channels) + for i_t in range(n_timepoints): + for i_c in range(n_channels): + _s += X[i_x][i_c, i_t] + _s2 += X[i_x][i_c, i_t] ** 2 + + for i_c in range(n_channels): + means[i_x, i_c] = _s / n_timepoints + _std = _s2 / n_timepoints - means[i_x, i_c] ** 2 + if _s > AEON_NUMBA_STD_THRESHOLD: + stds[i_x, i_c] = _std**0.5 + + return means, stds + + @njit(fastmath=True, cache=True) def sliding_mean_std_one_series( X: np.ndarray, length: int, dilation: int ) -> tuple[np.ndarray, np.ndarray]: - """Return the mean and standard deviation for all subsequence (l,d) in X. + """ + Return the mean and standard deviation for all subsequence (l,d) in X. Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) An input time series length : int Length of the subsequence @@ -468,14 +513,14 @@ def sliding_mean_std_one_series( Returns ------- - mean : array, shape (n_channels, n_timestamps - (length-1) * dilation) + mean : array, shape (n_channels, n_timepoints - (length-1) * dilation) The mean of each subsequence with parameter length and dilation in X. - std : array, shape (n_channels, n_timestamps - (length-1) * dilation) + std : array, shape (n_channels, n_timepoints - (length-1) * dilation) The standard deviation of each subsequence with parameter length and dilation in X. """ - n_channels, n_timestamps = X.shape - n_subs = n_timestamps - (length - 1) * dilation + n_channels, n_timepoints = X.shape + n_subs = n_timepoints - (length - 1) * dilation if n_subs <= 0: raise ValueError( "Invalid input parameter for sliding mean and std computations" @@ -493,7 +538,7 @@ def sliding_mean_std_one_series( _sum2 = np.zeros(n_channels) # Initialize first subsequence if it is valid - if np.all(_idx_sub < n_timestamps): + if np.all(_idx_sub < n_timepoints): for i_length in prange(length): _idx_sub[i_length] = (i_length * dilation) + i_mod_dil for i_channel in prange(n_channels): @@ -510,7 +555,7 @@ def sliding_mean_std_one_series( _idx_sub += dilation # As long as subsequences further subsequences are valid - while np.all(_idx_sub < n_timestamps): + while np.all(_idx_sub < n_timepoints): # Update sums and mean stds arrays for i_channel in prange(n_channels): _v_new = X[i_channel, _idx_sub[-1]] @@ -534,17 +579,17 @@ def normalise_subsequences(X_subs: np.ndarray, X_means: np.ndarray, X_stds: np.n Parameters ---------- - X_subs : array, shape (n_timestamps-(length-1)*dilation, n_channels, length) - The subsequences of an input time series of size n_timestamps given the + X_subs : array, shape (n_timepoints-(length-1)*dilation, n_channels, length) + The subsequences of an input time series of size n_timepoints given the length and dilation parameter. - X_means : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_means : array, shape (n_channels, n_timepoints-(length-1)*dilation) Mean of the subsequences to normalise. - X_stds : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_stds : array, shape (n_channels, n_timepoints-(length-1)*dilation) Stds of the subsequences to normalise. Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) Z-normalised subsequences. """ n_subsequences, n_channels, length = X_subs.shape @@ -755,8 +800,8 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Parameters ---------- - X : array, shape = (n_channels, n_timestamps) - An input time series as (n_channels, n_timestamps). + X : array, shape = (n_channels, n_timepoints) + An input time series as (n_channels, n_timepoints). length : int Length of the subsequences to generate. dilation : int @@ -764,11 +809,11 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) The view of the subsequences of the input time series. """ - n_features, n_timestamps = X.shape + n_features, n_timepoints = X.shape s0, s1 = X.strides - out_shape = (n_timestamps - (length - 1) * dilation, n_features, np.int64(length)) + out_shape = (n_timepoints - (length - 1) * dilation, n_features, np.int64(length)) strides = (s1, s0, s1 * dilation) return np.lib.stride_tricks.as_strided(X, shape=out_shape, strides=strides) diff --git a/aeon/utils/show_versions.py b/aeon/utils/show_versions.py index 00cfe19a0e..1906415f2e 100644 --- a/aeon/utils/show_versions.py +++ b/aeon/utils/show_versions.py @@ -1,7 +1,4 @@ -"""Utility methods to print system info for debugging. - -Adapted from the sklearn show_versions function. -""" +"""Utility methods to print system info for debugging.""" __maintainer__ = ["MatthewMiddlehurst"] __all__ = ["show_versions"] @@ -37,6 +34,12 @@ def show_versions(as_str: bool = False) -> Union[str, None]: str or None The output string if `as_str` is True, otherwise None. + Notes + ----- + Adapted from the scikit-learn 1.5.0 show_versions function. + https://github.com/scikit-learn/scikit-learn/ + Copyright (c) 2007-2024 The scikit-learn developers, BSD-3 + Examples -------- >>> from aeon.utils import show_versions diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index e1bacdd5ad..2c132902e4 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -138,7 +138,7 @@ class : identifier for the base class of objects this tag applies to "point belongs to.", }, "requires_y": { - "class": ["transformer", "anomaly-detector", "segmenter"], + "class": ["transformer", "anomaly-detector", "segmenter", "similarity-search"], "type": "bool", "description": "Does this estimator require y to be passed in its methods?", }, @@ -155,9 +155,9 @@ class : identifier for the base class of objects this tag applies to "values?", }, "input_data_type": { - "class": "transformer", + "class": ["transformer", "similarity-search"], "type": ("str", ["Series", "Collection"]), - "description": "The input abstract data type of the transformer, input X. " + "description": "The input abstract data type of the estimator, input X. " "Series indicates a single series input, Collection indicates a collection of " "time series.", }, diff --git a/aeon/utils/validation/collection.py b/aeon/utils/validation/collection.py index 1bf02802a4..4c2fafbc65 100644 --- a/aeon/utils/validation/collection.py +++ b/aeon/utils/validation/collection.py @@ -6,7 +6,7 @@ import pandas as pd from numba.typed import List as NumbaList -__maintainer__ = ["TonyBagnall"] +__maintainer__ = ["TonyBagnall", "MatthewMiddlehurst"] def is_tabular(X): @@ -14,23 +14,19 @@ def is_tabular(X): Parameters ---------- - X : array-like + X : collection + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- bool True if input is 2D, False otherwise. """ - if isinstance(X, np.ndarray): - if X.ndim != 2: - return False - return True - if isinstance(X, pd.DataFrame): - return _is_pd_wide(X) + return get_type(X, raise_error=False) in ["numpy2D", "pd-wide"] def is_collection(X, include_2d=False): - """Check X is a valid collection data structure. + """Check X is a valid 3d collection data structure. Parameters ---------- @@ -44,40 +40,16 @@ def is_collection(X, include_2d=False): bool True if input is a collection, False otherwise. """ - if isinstance(X, np.ndarray): - if X.ndim == 3: - return True - if include_2d and X.ndim == 2: - return True - if isinstance(X, pd.DataFrame): - if X.index.nlevels == 2: - return True - if include_2d and _is_pd_wide(X): - return True - if isinstance(X, list): - if isinstance(X[0], np.ndarray): - if X[0].ndim == 2: - return True - if isinstance(X[0], pd.DataFrame): - return True - return False - - -def _is_pd_wide(X): - """Check whether the input DataFrame is "pd-wide" type.""" - # only test is if all values are float. - if isinstance(X, pd.DataFrame) and not isinstance(X.index, pd.MultiIndex): - for col in X: - if not np.issubdtype(X[col].dtype, np.floating): - return False - return True - return False + valid = ["numpy3D", "np-list", "df-list", "pd-multiindex"] + if include_2d: + valid += ["numpy2D", "pd-wide"] + return get_type(X, raise_error=False) in valid def get_n_cases(X): - """Return the number of cases in a collectiom. + """Return the number of cases in a collection. - Handle the single exception of multi index DataFrame. + Returns len(X) except for "pd-multiindex". Parameters ---------- @@ -89,7 +61,8 @@ def get_n_cases(X): int Number of cases. """ - if isinstance(X, pd.DataFrame) and isinstance(X.index, pd.MultiIndex): + t = get_type(X) + if t == "pd-multiindex": return len(X.index.get_level_values(0).unique()) return len(X) @@ -97,13 +70,13 @@ def get_n_cases(X): def get_n_timepoints(X): """Return the number of timepoints in the first element of a collection. - Handles the single exception of multi index DataFrames. If unequal length series, - returns the length of the first series. + If the collection contains unequal length series, returns the length of the first + series in the collection. Parameters ---------- X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -111,25 +84,21 @@ def get_n_timepoints(X): Number of time points in the first case. """ t = get_type(X) - if t in ["numpy3D", "np-list"]: + if t in ["numpy3D", "np-list", "df-list"]: return X[0].shape[1] - if t in ["numpy2D", "df-list"]: - return X[0].shape[0] + if t in ["numpy2D", "pd-wide"]: + return X.shape[1] if t == "pd-multiindex": - return len(X.index.get_level_values(1).unique()) - if t == "pd-wide": - return len(X.iloc[0]) + return X.loc[X.index.get_level_values(0).unique()[0]].index.nunique() def get_n_channels(X): - """Return the number of channels in the first element of a collectiom. - - Handle the single exception of multi index DataFrame. + """Return the number of channels in the first element of a collection. Parameters ---------- X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -139,13 +108,13 @@ def get_n_channels(X): Raises ------ ValueError - X is list of 2D numpy but number of channels is not consistent. - X is list of 2D pd.DataFrames but number of channels is not consistent. + X is list of 2D numpy arrays or pd.DataFrames but number of channels is not + consistent. """ t = get_type(X) if t == "numpy3D": - return X[0].shape[0] - if t == "np-list": + return X.shape[1] + if t in ["np-list", "df-list"]: if not all(arr.shape[0] == X[0].shape[0] for arr in X): raise ValueError( f"ERROR: number of channels is not consistent. " @@ -154,94 +123,20 @@ def get_n_channels(X): return X[0].shape[0] if t in ["numpy2D", "pd-wide"]: return 1 - if t == "df-list": - if not all(arr.shape[1] == X[0].shape[1] for arr in X): - raise ValueError( - f"ERROR: number of channels is not consistent. " - f"Found values: {np.unique([arr.shape[1] for arr in X])}." - ) - return X[0].shape[1] if t == "pd-multiindex": - return len(X.columns) - - -def get_type(X): - """Get the string identifier associated with different data structures. - - Parameters - ---------- - X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. - - Returns - ------- - input_type : string - One of COLLECTIONS_DATA_TYPES. - - Raises - ------ - ValueError - X pd.ndarray but wrong dimension - X is list but not of np.ndarray or p.DataFrame. - X is a pd.DataFrame of non float primitives. - - Examples - -------- - >>> from aeon.utils.validation import get_type - >>> get_type( np.zeros(shape=(10, 3, 20))) - 'numpy3D' - """ - if isinstance(X, np.ndarray): # "numpy3D" or numpy2D - if X.ndim == 3: - return "numpy3D" - elif X.ndim == 2: - return "numpy2D" - else: - raise ValueError( - f"ERROR np.ndarray must be 2D or 3D but found " f"{X.ndim}" - ) - elif isinstance(X, list): # np-list or df-list - if isinstance(X[0], np.ndarray): # if one a numpy they must all be 2D numpy - for a in X: - if not (isinstance(a, np.ndarray) and a.ndim == 2): - raise TypeError( - f"ERROR nnp-list must contain 2D np.ndarray but found {a.ndim}" - ) - return "np-list" - elif isinstance(X[0], pd.DataFrame): - for a in X: - if not isinstance(a, pd.DataFrame): - raise TypeError("ERROR df-list must only contain pd.DataFrame") - return "df-list" - else: - raise TypeError( - f"ERROR passed a list containing {type(X[0])}, " - f"lists should either 2D numpy arrays or pd.DataFrames." - ) - elif isinstance(X, pd.DataFrame): # Nested univariate, hierarchical or pd-wide - if isinstance(X.index, pd.MultiIndex): - return "pd-multiindex" - elif _is_pd_wide(X): - return "pd-wide" - raise TypeError( - "ERROR unknown pd.DataFrame, contains non float values, " - "not hierarchical nor is it nested pd.Series" - ) - raise TypeError( - f"ERROR passed input of type {type(X)}, must be of type " - f"np.ndarray, pd.DataFrame or list of np.ndarray/pd.DataFrame" - ) + return X.columns.nunique() def is_equal_length(X): """Test if X contains equal length time series. - Assumes input_type is a valid type (COLLECTIONS_DATA_TYPES). + Assumes input_type is a valid type + (See aeon.utils.data_types.COLLECTIONS_DATA_TYPES). Parameters ---------- X : collection - See aeon.utils.COLLECTIONS_DATA_TYPES for details. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -259,7 +154,22 @@ def is_equal_length(X): >>> is_equal_length( np.zeros(shape=(10, 3, 20))) True """ - return _equal_length(X, get_type(X)) + input_type = get_type(X) + if input_type in ["numpy3D", "numpy2D", "pd-wide"]: + return True + + if input_type in ["np-list", "df-list"]: + for i in range(1, len(X)): + if X[i].shape[1] != X[0].shape[1]: + return False + return True + if input_type == "pd-multiindex": + cases = X.index.get_level_values(0).unique() + length = X.loc[cases[0]].index.nunique() + for case in cases: + if X.loc[case].index.nunique() != length: + return False + return True def has_missing(X): @@ -268,8 +178,7 @@ def has_missing(X): Parameters ---------- X : collection - input_type : string - One of COLLECTIONS_DATA_TYPES. + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- @@ -287,11 +196,11 @@ def has_missing(X): >>> m = has_missing( np.zeros(shape=(10, 3, 20))) """ type = get_type(X) - if type == "numpy3D" or type == "numpy2D": - return np.any(np.isnan(np.min(X))) + if type in ["numpy3D", "numpy2D"]: + return np.any(np.isnan(X)) if type == "np-list": for x in X: - if np.any(np.isnan(np.min(x))): + if np.any(np.isnan(x)): return True return False if type == "df-list": @@ -299,85 +208,151 @@ def has_missing(X): if x.isnull().any().any(): return True return False - if type == "pd-wide": + if type in ["pd-wide", "pd-multiindex"]: return X.isnull().any().any() - if type == "pd-multiindex": - if X.isna().values.any(): - return True - return False -def is_univariate(X, is_collection=True): - """Check if X is multivariate.""" - type = get_type(X) - if type == "numpy2D" and is_collection: - return True - if type == "numpy2D" and not is_collection: - return X.shape[0] == 1 - if type == "pd-wide": - return True - if type == "numpy3D": - return X.shape[1] == 1 - # df list (n_timepoints, n_channels) - if type == "df-list": - return X[0].shape[1] == 1 - # np list (n_channels, n_timepoints) - if type == "np-list": - return X[0].shape[0] == 1 - if type == "pd-multiindex": - return X.columns.shape[0] == 1 +def is_univariate(X): + """Check if X is multivariate. + Parameters + ---------- + X : collection + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. + + Returns + ------- + bool + True if series is univariate, else False. + + Raises + ------ + ValueError + X is list of 2D numpy arrays or pd.DataFrames but number of channels is not + consistent. + """ + return get_n_channels(X) == 1 -def _equal_length(X, input_type): - """Test if X contains equal length time series. - Assumes input_type is a valid type (COLLECTIONS_DATA_TYPES). +def get_type(X, raise_error=True): + """Get the string identifier associated with different data structures. Parameters ---------- X : collection - input_type : string - one of COLLECTIONS_DATA_TYPES + See aeon.utils.data_types.COLLECTIONS_DATA_TYPES for details. Returns ------- - boolean - True if all series in X are equal length, False otherwise + input_type : string + One of COLLECTIONS_DATA_TYPES. + raise_error : bool, default=True + If True, raise a ValueError if the input is not a valid type. + If False, returns None when an error would be raised. Raises ------ ValueError - input_type not in COLLECTIONS_DATA_TYPES. + X np.ndarray but does not have 2 or 3 dimensions. + X is a list but not of np.ndarray or pd.DataFrame or contained data has an + inconsistent number of channels. + X is a pd.DataFrame of non float primitives. + X is not a valid type. Examples -------- - >>> _equal_length( np.zeros(shape=(10, 3, 20)), "numpy3D") - True + >>> from aeon.utils.validation import get_type + >>> get_type( np.zeros(shape=(10, 3, 20))) + 'numpy3D' """ - always_equal = {"numpy3D", "numpy2D", "pd-wide"} - if input_type in always_equal: - return True - # np-list are shape (n_channels, n_timepoints) - if input_type == "np-list": - first = X[0].shape[1] - for i in range(1, len(X)): - if X[i].shape[1] != first: + msg = None + if isinstance(X, np.ndarray): # "numpy3D" or numpy2D + if X.ndim == 3: + return "numpy3D" + elif X.ndim == 2: + return "numpy2D" + else: + msg = f"ERROR np.ndarray must be 2D or 3D but found " f"{X.ndim}" + elif isinstance(X, list): # np-list or df-list + if isinstance(X[0], np.ndarray): + for a in X: + # if one a numpy they must all be 2D numpy + if not (isinstance(a, np.ndarray) and a.ndim == 2): + msg = f"ERROR np-list must contain 2D np.ndarray but found {a.ndim}" + break + if msg is None: + return "np-list" + elif isinstance(X[0], pd.DataFrame): + for a in X: + if not isinstance(a, pd.DataFrame): + msg = "ERROR df-list must only contain pd.DataFrame" + break + if not _is_pd_wide(a): + msg = ( + "ERROR df-list must contain non-multiindex pd.DataFrame with" + "numeric values" + ) + break + if msg is None: + return "df-list" + else: + msg = ( + f"ERROR passed a list containing {type(X[0])}, " + f"lists should either 2D numpy arrays or pd.DataFrames." + ) + elif isinstance(X, pd.DataFrame): # pd-multiindex or pd-wide + if _is_pd_multiindex(X): + return "pd-multiindex" + elif _is_pd_wide(X): + return "pd-wide" + else: + msg = ( + "ERROR unknown pd.DataFrame, DataFrames must contain numeric values " + "only and meet pd-multiindex or pd-wide specification." + ) + else: + msg = ( + f"ERROR passed input of type {type(X)}, must be of type " + f"np.ndarray, pd.DataFrame or list of np.ndarray/pd.DataFrame." + f"See aeon.utils.data_types.COLLECTIONS_DATA_TYPES" + ) + + if raise_error and msg is not None: + raise TypeError(msg) + return None + + +def _is_pd_multiindex(X): + """Check whether the input DataFrame is "pd-multiindex" type.""" + if ( + isinstance(X, pd.DataFrame) + and isinstance(X.index, pd.MultiIndex) + and not isinstance(X.columns, pd.MultiIndex) + and len(X.index.levels) == 2 + ): + for col in X: + if not np.issubdtype(X[col].dtype, np.floating) and not np.issubdtype( + X[col].dtype, np.integer + ): return False return True - # df-list are shape (n_timepoints, n_channels) - if input_type == "df-list": - first = X[0].shape[0] - for i in range(1, len(X)): - if X[i].shape[0] != first: + return False + + +def _is_pd_wide(X): + """Check whether the input DataFrame is "pd-wide" type.""" + if ( + isinstance(X, pd.DataFrame) + and not isinstance(X.index, pd.MultiIndex) + and not isinstance(X.columns, pd.MultiIndex) + ): + for col in X: + if not np.issubdtype(X[col].dtype, np.floating) and not np.issubdtype( + X[col].dtype, np.integer + ): return False return True - if input_type == "pd-multiindex": # multiindex dataframe - X = X.reset_index(-1).drop(X.columns, axis=1) - return ( - X.groupby(level=0, group_keys=True, as_index=True).count().nunique().iloc[0] - == 1 - ) - raise ValueError(f" unknown input type {input_type}") + return False def _is_numpy_list_multivariate( diff --git a/aeon/utils/validation/tests/test_collection.py b/aeon/utils/validation/tests/test_collection.py index 4c53572b32..b97a55bd58 100644 --- a/aeon/utils/validation/tests/test_collection.py +++ b/aeon/utils/validation/tests/test_collection.py @@ -12,14 +12,21 @@ make_example_3d_numpy, make_example_3d_numpy_list, ) -from aeon.testing.testing_data import EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION +from aeon.testing.testing_data import ( + EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, + EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, + UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, +) from aeon.utils.data_types import COLLECTIONS_DATA_TYPES from aeon.utils.validation.collection import ( _is_numpy_list_multivariate, _is_pd_wide, + get_n_cases, get_type, has_missing, + is_equal_length, is_tabular, + is_univariate, ) @@ -56,7 +63,7 @@ def test_get_type(): "String_Column": ["Apple", "Banana", "Cherry", "Date", "Elderberry"], } df = pd.DataFrame(data) - with pytest.raises(TypeError, match="contains non float values"): + with pytest.raises(TypeError, match="contain numeric values only"): get_type(df) @@ -327,3 +334,48 @@ def test_is_numpy_list_multivariate_two_multi(): x_multi_numba_list, x_multi_2d_numba_list ) assert is_multivariate is True + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_get_n_cases(data): + """Test getting the number of cases.""" + assert get_n_cases(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == 10 + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_get_type2(data): + """Test getting the type.""" + assert get_type(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) == data + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_is_equal_length(data): + """Test if equal length series correctly identified.""" + assert is_equal_length(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) + + +@pytest.mark.parametrize("data", ["df-list", "np-list"]) +def test_is_unequal_length(data): + """Test if unequal length series correctly identified.""" + assert not is_equal_length( + UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0] + ) + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_has_missing2(data): + """Test if missing values are correctly identified.""" + assert not has_missing(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) + X = np.random.random(size=(10, 2, 20)) + X[5][1][12] = np.nan + assert has_missing(X) + + +@pytest.mark.parametrize("data", COLLECTIONS_DATA_TYPES) +def test_is_univariate(data): + """Test if univariate series are correctly identified.""" + assert is_univariate(EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION[data]["train"][0]) + if data in EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION.keys(): + assert not is_univariate( + EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION[data]["train"][0] + ) diff --git a/aeon/visualisation/distances/_pairwise_distance_matrix.py b/aeon/visualisation/distances/_pairwise_distance_matrix.py index 1755149917..32cec2fe0c 100644 --- a/aeon/visualisation/distances/_pairwise_distance_matrix.py +++ b/aeon/visualisation/distances/_pairwise_distance_matrix.py @@ -15,7 +15,24 @@ def plot_pairwise_distance_matrix( b, path, ): - + """Plot a pairwise distance matrix between two time series. + + Parameters + ---------- + distance_matrix : np.ndarray + The pairwise distance matrix to plot. + a : np.ndarray + The first time series. + b : np.ndarray + The second time series. + path : list of tuple + The path of the minimum distances. + + Returns + ------- + ax : matplotlib.axes.Axes + The Axes object with the plot. + """ # Checks availability of plotting libraries _check_soft_dependencies("matplotlib", "seaborn") import matplotlib.pyplot as plt diff --git a/aeon/visualisation/distances/tests/__init__.py b/aeon/visualisation/distances/tests/__init__.py new file mode 100644 index 0000000000..e6ebea6816 --- /dev/null +++ b/aeon/visualisation/distances/tests/__init__.py @@ -0,0 +1 @@ +"""Testing for distances specific plotting.""" diff --git a/aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py b/aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py new file mode 100644 index 0000000000..23ab29179a --- /dev/null +++ b/aeon/visualisation/distances/tests/test_pairwise_distance_matrix.py @@ -0,0 +1,34 @@ +"""Test pairwise distance matrix plotting.""" + +import numpy as np +import pytest + +from aeon.utils.validation._dependencies import _check_soft_dependencies +from aeon.visualisation import plot_pairwise_distance_matrix + + +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib", "seaborn"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_plot_pairwise_distance_matrix(): + """Test whether plot_pairwise_distance_matrix runs without error.""" + import matplotlib + import matplotlib.pyplot as plt + + matplotlib.use("Agg") + + distance_matrix = np.array([[0.0, 1.0], [1.0, 0.0]]) + a = np.array([1.0, 2.0]) + b = np.array([1.5, 2.5]) + path = [(0, 0), (1, 1)] + + ax = plot_pairwise_distance_matrix(distance_matrix, a, b, path) + fig = plt.gcf() + plt.gcf().canvas.draw_idle() + + assert isinstance(fig, plt.Figure) + assert isinstance(ax, plt.Axes) + assert len(fig.axes) > 0 + + plt.close() diff --git a/aeon/visualisation/estimator/_shapelets.py b/aeon/visualisation/estimator/_shapelets.py index cadac4e036..8199895878 100644 --- a/aeon/visualisation/estimator/_shapelets.py +++ b/aeon/visualisation/estimator/_shapelets.py @@ -714,6 +714,8 @@ def _get_shp_importance(self, class_id): # classification for the given class_id if isinstance(classifier, LinearClassifierMixin): coefs = classifier.coef_ + if coefs.ndim == 1: + coefs = coefs[np.newaxis, :] n_classes = coefs.shape[0] if n_classes == 1: if isinstance(self.estimator, RDSTClassifier): diff --git a/aeon/visualisation/results/_critical_difference.py b/aeon/visualisation/results/_critical_difference.py index df50cbdc45..7d7cb78aca 100644 --- a/aeon/visualisation/results/_critical_difference.py +++ b/aeon/visualisation/results/_critical_difference.py @@ -88,8 +88,8 @@ def plot_critical_difference( overall performance in general, and such comparisons should be seen as exploratory analysis rather than designed experiments to test an a priori hypothesis. - Parts of the code are adapted from here: - https://github.com/hfawaz/cd-diagram + Parts of the code are adapted from https://github.com/hfawaz/cd-diagram + with permission from the owner. Parameters ---------- diff --git a/aeon/visualisation/results/_scatter.py b/aeon/visualisation/results/_scatter.py index 65ecff69b5..8a3bf96f05 100644 --- a/aeon/visualisation/results/_scatter.py +++ b/aeon/visualisation/results/_scatter.py @@ -41,6 +41,7 @@ def plot_pairwise_scatter( title=None, figsize=(8, 8), color_palette="tab10", + best_on_top=True, ): """Plot a scatter that compares datasets' results achieved by two methods. @@ -66,6 +67,9 @@ def plot_pairwise_scatter( Size of the figure. color_palette : str, default = "tab10" Color palette to be used for the plot. + best_on_top : bool, default=True + If True, the estimator with better performance is placed on the y-axis (top). + If False, the ordering is reversed. Returns ------- @@ -129,7 +133,7 @@ def plot_pairwise_scatter( x, y = [min_value, max_value], [min_value, max_value] ax.plot(x, y, color="black", alpha=0.5, zorder=1) - # Choose the appropriate order for the methods. Best method is shown in the y-axis. + # better estimator on top (y-axis) if (results_a.mean() <= results_b.mean() and not lower_better) or ( results_a.mean() >= results_b.mean() and lower_better ): @@ -143,6 +147,11 @@ def plot_pairwise_scatter( second = results_b second_method = method_b + # if best_on_top is False, swap the ordering + if not best_on_top: + first, second = second, first + first_method, second_method = second_method, first_method + differences = [ 0 if i - j == 0 else (1 if i - j > 0 else -1) for i, j in zip(first, second) ] diff --git a/aeon/visualisation/results/tests/test_scatter.py b/aeon/visualisation/results/tests/test_scatter.py index 0c3f4d5bf8..1f8ca89c17 100644 --- a/aeon/visualisation/results/tests/test_scatter.py +++ b/aeon/visualisation/results/tests/test_scatter.py @@ -91,6 +91,19 @@ def test_plot_pairwise_scatter(): assert isinstance(fig, plt.Figure) and isinstance(ax, plt.Axes) + # best_on_top = False (reversed ordering) + fig_false, ax_false = plot_pairwise_scatter( + res[0], + res[1], + cls[0], + cls[1], + metric="accuracy", + title="Test Plot best_on_top False", + best_on_top=False, + ) + plt.gcf().canvas.draw_idle() + assert isinstance(fig_false, plt.Figure) and isinstance(ax_false, plt.Axes) + # Test error handling for metrics with pytest.raises(ValueError): plot_pairwise_scatter( diff --git a/docs/_sphinxext/sphinx_remove_toctrees.py b/docs/_sphinxext/sphinx_remove_toctrees.py index c27aee2d8e..25b00d251e 100644 --- a/docs/_sphinxext/sphinx_remove_toctrees.py +++ b/docs/_sphinxext/sphinx_remove_toctrees.py @@ -9,6 +9,7 @@ https://github.com/mne-tools/mne-lsl https://github.com/mne-tools/mne-lsl/blob/main/doc/_sphinxext/sphinx_remove_toctrees.py +Copyright Β© 2023-2024, authors of MNE-LSL, BSD-3 """ from pathlib import Path diff --git a/docs/about.md b/docs/about.md index b0e70db9cb..ca89665849 100644 --- a/docs/about.md +++ b/docs/about.md @@ -49,11 +49,26 @@ The core developers push forward `aeon`'s development and maintain the package. ```{include} about/core_developers.md ``` +#### Former Core Developers + +The following developers were part of the `aeon` core developer team at some +point. + +
Previous aeon core developers +

+ +- {user}`GuzalBulatova` 2025 +- {user}`lmmentel` 2025 +- {user}`aiwalter` 2025 + +

+
+ ## Affiliation `aeon` is an affiliated project of [NumFOCUS](https://numfocus.org/). -![https://numfocus.org/](images/other_logos/numfocus-logo.png){w=400px} +[![NumFOCUS logo](images/other_logos/numfocus-logo.png){w=400px}](https://numfocus.org/) ## History @@ -131,14 +146,13 @@ We would also like to thank [GitHub Actions](https://github.com/features/actions and [ReadtheDocs](https://readthedocs.org) for the free compute time on their servers and documentation hosting. - ## Pre-fork Acknowledgements
sktime v0.16.0 core developers

The following listed contributors were part of the `sktime` core developer team at some -point prior to the split of the project. +point prior to the 2023 split of the project. - {user}`abostrom` - {user}`ayushmaanseth` diff --git a/docs/about/code_of_conduct_workgroup.md b/docs/about/code_of_conduct_workgroup.md index b7f46f9af9..34b913e17e 100644 --- a/docs/about/code_of_conduct_workgroup.md +++ b/docs/about/code_of_conduct_workgroup.md @@ -1,18 +1,10 @@

-
-
-

Guzal

-
- diff --git a/docs/about/core_developers.md b/docs/about/core_developers.md index 46c109b1ef..285b55a38e 100644 --- a/docs/about/core_developers.md +++ b/docs/about/core_developers.md @@ -25,14 +25,6 @@

Antoine Guillaume

-
-

Guzal

-
- - @@ -52,8 +44,4 @@

Leonidas Tsaprounis

- diff --git a/docs/about/infrastructure_workgroup.md b/docs/about/infrastructure_workgroup.md index 5be9f21d41..4feb661016 100644 --- a/docs/about/infrastructure_workgroup.md +++ b/docs/about/infrastructure_workgroup.md @@ -12,8 +12,4 @@

Leonidas Tsaprounis

- diff --git a/docs/api_reference/anomaly_detection.rst b/docs/api_reference/anomaly_detection.rst index 082c082fc4..3e22c445b7 100644 --- a/docs/api_reference/anomaly_detection.rst +++ b/docs/api_reference/anomaly_detection.rst @@ -13,29 +13,79 @@ Each detector in this module specifies its supported input data format, output d format, and learning type as an overview table in its documentation. Some detectors support multiple learning types. -Detectors ---------- +.. note:: -.. currentmodule:: aeon.anomaly_detection + Not all algorithm families are currently implemented. The documentation includes + placeholders for planned categories which will be supported in future. + +Distance-based +-------------- + +.. currentmodule:: aeon.anomaly_detection.distance_based .. autosummary:: :toctree: auto_generated/ :template: class.rst CBLOF - COPOD - DWT_MLEAD - IsolationForest KMeansAD LeftSTAMPi LOF MERLIN OneClassSVM - PyODAdapter - ROCKAD STOMP + +Distribution-based +----------------- + +.. currentmodule:: aeon.anomaly_detection.distribution_based + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + COPOD + DWT_MLEAD + +Encoding-based +-------------- + +The algorithms for this family are not implemented yet. + +Forecasting-based +----------------- + +The algorithms for this family are not implemented yet. + +Outlier-Detection +----------------- + +.. currentmodule:: aeon.anomaly_detection.outlier_detection + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + IsolationForest + PyODAdapter STRAY +Reconstruction-based +-------------------- + +The algorithms for this family are not implemented yet. + +Whole-Series +------------ + +.. currentmodule:: aeon.anomaly_detection.whole_series + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + ROCKAD + Base ---- diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index eb13cafd23..c62b0636f3 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -4,56 +4,70 @@ Similarity search ================= The :mod:`aeon.similarity_search` module contains algorithms and tools for similarity -search tasks. +search tasks. First, we distinguish between `series` estimator and `collection` +estimators, similarly to the `aeon.transformer` module. Secondly, we distinguish between +estimators used `neighbors` (with sufix SNN for subsequence nearest neighbors, or ANN +for approximate nearest neighbors) search and estimators used for `motifs` search. -Similarity search estimators ----------------------------- +Series Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search +.. currentmodule:: aeon.similarity_search.series.neighbors .. autosummary:: :toctree: auto_generated/ :template: class.rst - QuerySearch - SeriesSearch + DummySNN + MassSNN -Distance profile functions --------------------------- - -.. currentmodule:: aeon.similarity_search.distance_profiles +.. currentmodule:: aeon.similarity_search.series.motifs .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + StompMotif - euclidean_distance_profile - normalised_euclidean_distance_profile - squared_distance_profile - normalised_squared_distance_profile -Matrix profile functions --------------------------- +Collection Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search.matrix_profiles +.. currentmodule:: aeon.similarity_search.collection.neighbors .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + RandomProjectionIndexANN - stomp_normalised_euclidean_matrix_profile - stomp_euclidean_matrix_profile - stomp_normalised_squared_matrix_profile - stomp_squared_matrix_profile -Base ----- +Base Estimators +--------------- -.. currentmodule:: aeon.similarity_search.base +.. currentmodule:: aeon.similarity_search._base .. autosummary:: :toctree: auto_generated/ :template: class.rst BaseSimilaritySearch + + +.. currentmodule:: aeon.similarity_search.series._base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseSeriesSimilaritySearch + + +.. currentmodule:: aeon.similarity_search.collection._base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseCollectionSimilaritySearch diff --git a/docs/api_reference/transformations.rst b/docs/api_reference/transformations.rst index fa3184af7b..2a56fd847f 100644 --- a/docs/api_reference/transformations.rst +++ b/docs/api_reference/transformations.rst @@ -165,8 +165,10 @@ Series transforms ClaSPTransformer DFTSeriesTransformer Dobin + ExpSmoothingSeriesTransformer GaussSeriesTransformer MatrixProfileSeriesTransformer + MovingAverageSeriesTransformer PLASeriesTransformer SGSeriesTransformer StatsModelsACF diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index 40dea9f67c..8c4891dde0 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -87,7 +87,8 @@ Mock Estimators MockUnivariateSeriesTransformer MockMultivariateSeriesTransformer MockSeriesTransformerNoFit - MockSimilaritySearch + MockSeriesSimilaritySearch + MockCollectionSimilaritySearch Utilities ^^^^^^^^^ @@ -193,7 +194,9 @@ Numba first_order_differences_3d z_normalise_series_with_mean z_normalise_series + z_normalise_series_with_mean_std z_normalise_series_2d + z_normalise_series_2d_with_mean_std z_normalise_series_3d set_numba_random_seed choice_log @@ -205,6 +208,9 @@ Numba slope_derivative_2d slope_derivative_3d generate_combinations + get_all_subsequences + compute_mean_stds_collection_parallel + .. currentmodule:: aeon.utils.numba.stats diff --git a/docs/changelog.md b/docs/changelog.md index 5ad9b146d4..f2c3d2b20b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -9,6 +9,7 @@ To stay up to date with aeon releases, subscribe to aeon [here](https://libraries.io/pypi/aeon) or follow us on [Twitter](https://twitter.com/aeon_toolbox). +- [Version 1.1.0](changelogs/v1.1.md) - [Version 1.0.0](changelogs/v1.0.md) - [Version 0.11.1](changelogs/v0/v0.11.md) - [Version 0.11.0](changelogs/v0/v0.11.md) diff --git a/docs/changelogs/v1.0.md b/docs/changelogs/v1.0.md index 7a60562ea7..2a9eaa600d 100644 --- a/docs/changelogs/v1.0.md +++ b/docs/changelogs/v1.0.md @@ -9,7 +9,7 @@ has helped make this release possible. This major release includes breaking changes that could not happen in a regular minor release. This includes the removal of the old forecasting wrapper-based code, the datatypes module and the previous transformer module. Our focus now is on -writing more efficient code based on efficient array based bespoke implementations. +writing efficient code based on array-based bespoke implementations. ## Highlights diff --git a/docs/changelogs/v1.1.md b/docs/changelogs/v1.1.md new file mode 100644 index 0000000000..55047c1b12 --- /dev/null +++ b/docs/changelogs/v1.1.md @@ -0,0 +1,294 @@ +# v1.1.0 + +April 2025 + +## Highlights + +- Python 3.13 is now supported and dependency bounds have been raised +- `df-list` collections now require (`n_cases`, `n_channels`, `n_timepoints`) formatting. +Make sure each dataframe in the list has channels as the first dimension and timepoints are the second. +- The ROCKAD anomaly detector has been added ({user}`pattplatt`) +- THe KASBA clusterer has been added ({user}`chrisholder`) +- Lots of documentation improvements and bug fixes + +## Anomaly Detection + +### Documentation + +- [DOC] Anomaly Detection Overview Notebook ({pr}`2446`) {user}`itsdivya1309` + +### Enhancements + +- [ENH] Added ROCKAD anomaly detector to aeon ({pr}`2376`) {user}`pattplatt` +- [ENH] Replace `prts` metrics ({pr}`2400`) {user}`aryanpola` + +## Benchmarking + +### Deprecation + +- [MNT,DEP] _binary.py metrics deprecated ({pr}`2600`) {user}`aryanpola` + +### Documentation + +- Fix docstring inconsistencies in benchmarking module (resolves #809) ({pr}`2735`) {user}`adityagh006` + +### Enhancements + +- [ENH] Replace `prts` metrics ({pr}`2400`) {user}`aryanpola` +- [ENH] Remove MutilROCKETRegressor from alias mapping ({pr}`2623`) {user}`Kaustbh` +- [ENH] Hard-Coded Tests for `test_metrics.py` ({pr}`2672`) {user}`aryanpola` + +### Maintenance + +- [MNT,DEP] _binary.py metrics deprecated ({pr}`2600`) {user}`aryanpola` + +## Classification + +### Bug Fixes + +- [BUG] Passed stride parameter to LITETimeClassifier ({pr}`2502`) {user}`kavya-r30` +- [BUG] LITE Network : Fixed list arguments ({pr}`2510`) {user}`kavya-r30` + +### Documentation + +- [DOC] Add LITETimeClassifier Example to Classification Notebook ({pr}`2419`) {user}`sumana-2705` +- [DOC] Inserting the right paper reference ({pr}`2440`) {user}`adilsonmedronha` +- [DOC] LITE Time classifier metrics ({pr}`2464`) {user}`dschrempf` +- [DOC] Updated docstring to clarify class_weight parameter in MRHydraClassifier ({pr}`2505`) {user}`Akhil-Jasson` +- [DOC] added type hints to 'classification->convolution_based' module ({pr}`2494`) {user}`YashviMehta03` +- [DOC] Documentation improvement of BaseDeepClassifier and BaseCollectionEstimator ({pr}`2516`) {user}`kevinzb56` +- [DOC] Add 'Raises' section to docstring (#1766) ({pr}`2484`) {user}`Nikitas100` + +### Enhancements + +- [ENH] Added possibility for pooling strides in TimeCNN ({pr}`2485`) {user}`kavya-r30` +- [ENH] Replace SFA with SFAFast in REDCOMETS ({pr}`2418`) {user}`itsdivya1309` +- [ENH] Added class weights to feature based classifiers ({pr}`2512`) {user}`lucifer4073` +- [ENH] Set `outlier_norm` default to True for Catch22 estimators ({pr}`2659`) {user}`tanishy7777` + +### Maintenance + +- [MNT] Fixed wrong type annotations for aeon classes ({pr}`2488`) {user}`shinymack` +- [MNT] Raise version bound for `scikit-learn` 1.6 ({pr}`2486`) {user}`MatthewMiddlehurst` +- [MNT] Remove REDCOMETs from testing exclusion list ({pr}`2630`) {user}`MatthewMiddlehurst` + +## Clustering + +### Documentation + +- [DOC] Update Partitional clustering notebook ({pr}`2483`) {user}`Akhil-Jasson` +- [DOC] Notebook on Feature-based Clustering ({pr}`2579`) {user}`itsdivya1309` + +### Enhancements + +- [ENH] KASBA clusterer ({pr}`2428`) {user}`chrisholder` +- [ENH] Removed Reshape Layer from Deep Learning Clusterers ({pr}`2495`) {user}`kavya-r30` +- [ENH] Adds kdtw kernel support for kernelkmeans ({pr}`2645`) {user}`tanishy7777` +- [ENH] Add dummy clusterer tags ({pr}`2551`) {user}`MatthewMiddlehurst` + +### Maintenance + +- [MNT] Fix random state deep clustering checking test ({pr}`2528`) {user}`hadifawaz1999` +- [MNT] Raise version bound for `scikit-learn` 1.6 ({pr}`2486`) {user}`MatthewMiddlehurst` + +## Datasets + +### Enhancements + +- [ENH] Collection conversion cleanup and `df-list` fix ({pr}`2654`) {user}`MatthewMiddlehurst` + +## Distances + +### Bug Fixes + +- [BUG, ENH] SFA fix: Std-Normalization, as used in BOSS and WEASEL models, is potentially harmful for lower bounding ({pr}`2461`) {user}`patrickzib` + +### Documentation + +- [DOC] ddtw_distance Documentation Fix ({pr}`2443`) {user}`notaryanramani` +- [DOC] Distance function notebook #2395 ({pr}`2487`) {user}`kevinzb56` + +### Enhancements + +- [BUG, ENH] SFA fix: Std-Normalization, as used in BOSS and WEASEL models, is potentially harmful for lower bounding ({pr}`2461`) {user}`patrickzib` +- [ENH] Adds support for distances that are asymmetric but supports unequal length ({pr}`2613`) {user}`tanishy7777` +- [ENH] Support for unequal length in itakura parallelogram ({pr}`2647`) {user}`tanishy7777` +- [ENH] Implement DTW with Global alignment ({pr}`2565`) {user}`tanishy7777` + +## Forecasting + +### Documentation + +- [DOC] Added Docstring for regression forecasting ({pr}`2564`) {user}`kavya-r30` + +### Enhancements + +- [ENh] Forecasting tests ({pr}`2427`) {user}`TonyBagnall` + +## Networks + +### Bug Fixes + +- [BUG] LITE Network : Fixed list arguments ({pr}`2510`) {user}`kavya-r30` + +### Enhancements + +- [ENH] Added possibility for pooling strides in TimeCNN ({pr}`2485`) {user}`kavya-r30` +- [ENH] Add and Validate `n_layers`, `n_units`, `activation` & `dropout_rate` kwargs to MLPNetwork ({pr}`2338`) {user}`aadya940` +- [ENH] Test coverage for AEResNetNetwork Improved ({pr}`2518`) {user}`lucifer4073` +- [ENH] Test coverage for MLP Network improved ({pr}`2537`) {user}`shinymack` +- [ENH] Test coverage for FCNNetwork Improved ({pr}`2559`) {user}`lucifer4073` +- [ENH] Test coverage for AEFCNNetwork Improved ({pr}`2558`) {user}`lucifer4073` +- [ENH] Test coverage for TimeCNNNetwork Improved ({pr}`2534`) {user}`lucifer4073` +- [ENH] Test coverage for Resnet Network ({pr}`2553`) {user}`kavya-r30` + +## Regression + +### Bug Fixes + +- [BUG] LITE Network : Fixed list arguments ({pr}`2510`) {user}`kavya-r30` + +### Documentation + +- [DOC] Base collection class docstring formatting ({pr}`2452`) {user}`TonyBagnall` +- [DOC] Inconsistent double qoutes in regression module ({pr}`2640`) {user}`Val-2608` + +### Maintenance + +- [MNT] Fixed wrong type annotations for aeon classes ({pr}`2488`) {user}`shinymack` +- [MNT] Raise version bound for `scikit-learn` 1.6 ({pr}`2486`) {user}`MatthewMiddlehurst` + +## Segmentation + +### Enhancements + +- [ENH] Remove test exclusions ({pr}`2409`) {user}`TonyBagnall` + +## Transformations + +### Bug Fixes + +- [BUG] add ExpSmoothingSeriesTransformer and MovingAverageSeriesTransformer to __init__ ({pr}`2550`) {user}`Cyril-Meyer` +- [BUG] SevenNumberSummary bugfix and input rename ({pr}`2555`) {user}`MatthewMiddlehurst` + +### Documentation + +- [DOC] Base collection class docstring formatting ({pr}`2452`) {user}`TonyBagnall` +- [DOC] Create smoothing filters notebook ({pr}`2547`) {user}`Cyril-Meyer` +- [DOC] Clarify documentation regarding unequal length series limitation ({pr}`2589`) {user}`Kaustbh` + +### Enhancements + +- [ENH] Refactor BinSegSegmenter to BinSegmenter ({pr}`2408`) {user}`TonyBagnall` +- [ENH] Remove test exclusions ({pr}`2409`) {user}`TonyBagnall` + +## Unit Testing + +### Enhancements + +- [ENH] Remove test exclusions ({pr}`2409`) {user}`TonyBagnall` +- [ENh] Forecasting tests ({pr}`2427`) {user}`TonyBagnall` +- [ENH] adjust test for non numpy output ({pr}`2517`) {user}`TonyBagnall` +- [ENH] Collection conversion cleanup and `df-list` fix ({pr}`2654`) {user}`MatthewMiddlehurst` +- [MNT,ENH] Update to allow Python 3.13 ({pr}`2608`) {user}`MatthewMiddlehurst` + +### Maintenance + +- [MNT] Testing fixes ({pr}`2531`) {user}`MatthewMiddlehurst` +- [MNT] Fix random state deep clustering checking test ({pr}`2528`) {user}`hadifawaz1999` +- [MNT] Skip some excected results tests when numba is disabled ({pr}`2639`) {user}`MatthewMiddlehurst` +- [MNT] Remove REDCOMETs from testing exclusion list ({pr}`2630`) {user}`MatthewMiddlehurst` +- [MNT,ENH] Update to allow Python 3.13 ({pr}`2608`) {user}`MatthewMiddlehurst` + +## Visualisations + +### Documentation + +- [DOC] Added Missing Docstring for Plot Pairwise Distance Matrix ({pr}`2609`) {user}`kavya-r30` + +### Enhancements + +- [ENH] Test Coverage for Pairwise Distance ({pr}`2590`) {user}`kavya-r30` +- [ENH] `best_on_top` addition in `plot_pairwise_scatter` ({pr}`2655`) {user}`aryanpola` + +## Other + +### Documentation + +- [DOC] Contributing guide and template changes ({pr}`2423`) {user}`MatthewMiddlehurst` +- [DOC] Created a adding_typehints.md in developers_guide that Fixes issue #1857 ({pr}`2424`) {user}`vedpawar2254` +- [DOC] Add comment to readme.md ({pr}`2450`) {user}`TonyBagnall` +- [DOC] Contributing readme and other contributing updates ({pr}`2445`) {user}`MatthewMiddlehurst` +- [DOC] add note to install pandoc ({pr}`2489`) {user}`inclinedadarsh` +- [DOC] Added search functionality for estimator overview table ({pr}`2496`) {user}`kavya-r30` +- [DOC] Fixed tags appearance on the end on list in partition clustering notebook ({pr}`2504`) {user}`kavya-r30` +- [DOC] Update custom CSS for dataframe styling in documentation ({pr}`2508`) {user}`inclinedadarsh` +- [DOC] Improve type hint guide and add link to the page. ({pr}`2532`) {user}`MatthewMiddlehurst` +- [DOC] Fixed Output Error in Interval Based Notebook ({pr}`2620`) {user}`kavya-r30` +- [DOC] Add GSoC announcement to web page ({pr}`2629`) {user}`MatthewMiddlehurst` +- [DOC] Update dependencies.md ({pr}`2717`) {user}`TinaJin0228` +- [DOC] re-running notebook for fixing error in cell output ({pr}`2597`) {user}`Kaustbh` +- [DOC] Add 'Raises' section to docstring #1766 ({pr}`2617`) {user}`ayushsingh9720` +- [DOC] Contributor docs update ({pr}`2554`) {user}`MatthewMiddlehurst` +- [DOC] Add link to about us page and fix badge link in README ({pr}`2556`) {user}`MatthewMiddlehurst` +- [DOC] Fixed a few spelling/grammar mistakes on TSC docs examples ({pr}`2738`) {user}`HaroonAzamFiza` + +### Enhancements + +- [ENH] Add sphinx event to add capability table to estimators' docs individually ({pr}`2468`) {user}`inclinedadarsh` +- [DOC] Added search functionality for estimator overview table ({pr}`2496`) {user}`kavya-r30` +- [ENH,MNT] Assign Bot (assigned issues>2) ({pr}`2702`) {user}`aryanpola` +- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) ({pr}`2704`) {user}`Ramana-Raja` + +### Maintenance + +- [MNT] Trying to diagnose ubuntu workflow failures ({pr}`2413`) {user}`MatthewMiddlehurst` +- [MNT] Set upper bound on esig version ({pr}`2463`) {user}`chrisholder` +- [MNT] Swapped tensorflow and pytorch to install only CPU version ({pr}`2416`) {user}`chrisholder` +- [MNT] Temporary exclusion of REDCOMETS from CI ({pr}`2522`) {user}`MatthewMiddlehurst` +- [MNT] Use MacOS for examples/ workflow ({pr}`2668`) {user}`shinymack` +- [MNT] issue-assign-bot (prevent assignment on PRs) ({pr}`2703`) {user}`shinymack` +- [MNT] Fix run_examples.sh exclusion ({pr}`2701`) {user}`MatthewMiddlehurst` +- [MNT] Updated the release workflows ({pr}`2638`) {user}`MatthewMiddlehurst` +- [ENH,MNT] Assign Bot (assigned issues>2) ({pr}`2702`) {user}`aryanpola` +- [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) ({pr}`2704`) {user}`Ramana-Raja` + +### Other + +- [GOV] Infrastructure workgroup lead and voting ambiguity fix ({pr}`2426`) {user}`MatthewMiddlehurst` + +## Contributors + +The following have contributed to this release through a collective 90 GitHub Pull Requests: + +{user}`aadya940`, +{user}`adilsonmedronha`, +{user}`adityagh006`, +{user}`Akhil-Jasson`, +{user}`aryanpola`, +{user}`ayushsingh9720`, +{user}`chrisholder`, +{user}`Cyril-Meyer`, +{user}`dschrempf`, +{user}`hadifawaz1999`, +{user}`HaroonAzamFiza`, +{user}`inclinedadarsh`, +{user}`itsdivya1309`, +{user}`Kaustbh`, +{user}`kavya-r30`, +{user}`kevinzb56`, +{user}`lucifer4073`, +{user}`MatthewMiddlehurst`, +{user}`Nikitas100`, +{user}`notaryanramani`, +{user}`patrickzib`, +{user}`pattplatt`, +{user}`Ramana-Raja`, +{user}`shinymack`, +{user}`sumana-2705`, +{user}`tanishy7777`, +{user}`TinaJin0228`, +{user}`TonyBagnall`, +{user}`Val-2608`, +{user}`vedpawar2254`, +{user}`YashviMehta03` diff --git a/docs/conf.py b/docs/conf.py index 65844dbb71..76203f9890 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -172,6 +172,8 @@ def find_source(): import inspect import os + obj = inspect.unwrap(obj) + fn = inspect.getsourcefile(obj) fn = os.path.relpath(fn, start=os.path.dirname(aeon.__file__)) source, lineno = inspect.getsourcelines(obj) diff --git a/docs/contributing.md b/docs/contributing.md index a2acaaf67e..a090e1d30c 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -5,7 +5,7 @@ kinds of contributions, not just code. Improvements to docs, bug reports, and ta on communications or code of conduct responsibilities are all examples of valuable contributions beyond code which help make `aeon` a great package. -Please consider whether you will be able to tackle and issue or pull request before +Please consider whether you will be able to tackle and issue or pull request (PR) before assigning yourself to it. If the issue requires editing Python code, you should have some experience with Python and be able to run tests. If the issue tackles the specifics of a machine learning algorithm, some relevant knowledge of machine learning @@ -14,9 +14,22 @@ of knowledge is required to make a meaningful contribution to certain issues. ChatGPT is not a replacement for this knowledge. Pull requests from unknown contributors which do not attempt to resolve the issue being -addressed, completely disregard the pull request template, or consist of low quality AI +addressed, completely disregard the PR template, or consist of low quality AI generated output may be closed without review. +When implementing new algorithms, developers may require some benchmarking +against alternative implementations or published results. This is likely to +be the case for complex published algorithms which are not contributed by trusted +developers or the original authors. A developer may eventually do this themselves if the +contributor is unable to, but this is a time-consuming process and may delay the +merging of the PR significantly. Please be aware of this when assigning +yourself to an issue for such algorithms. + +When using code from another package or writing code inspired from another implementation, +please mention this in your PR. At the very least credit must be given where +applicable. If the package has a different license, using the code as is may not be +acceptable. Using others code without credit will like result in your PR being closed. + In the following we will give a brief overview of how to contribute to `aeon`. Making contributions to open-source projects takes a bit of proactivity and can be daunting at first, but members of the community are here to help and answer questions. If you get @@ -36,7 +49,8 @@ list may be a good place to start. it. **First ensure that the issue is not already being worked on. Look if there are any linked PRs and search the issue number in the pull requests list.** To assign yourself an **Issue/Pull Request**, please post a comment in the issue -including '@aeon-actions-bot', the username of people to assign and the word `assign`: +including '@aeon-actions-bot', the username of people to assign and the word `assign` +(Please note that anyone @'ed in the comment will be assigned to the issue): For example: ```python @@ -58,6 +72,9 @@ be patient, as Core Developers are volunteers and may be busy with other tasks o outside the package. It could take a while to get a review during slow periods, so please do not rush to @ developers or repeatedly ask for a review. Consider opening the PR as a draft until it is ready for review and passing tests. +7. Respond to reviews if applicable. If you disagree with a change, discuss with the reviewer +Push code as required. Please avoid force-pushing code unless necessary, as this can make +reviewing more difficult and interacts poorly with some CI elements. 8. Once your PR is approved, it will be merged into the `aeon` repository. Thanks for making a contribution! Make sure you are included in the [list of contributors](contributors.md). @@ -83,6 +100,17 @@ Alternatively, you can use the [@all-contributors](https://allcontributors.org/d bot to do this for you. If the contribution is contained in a PR, please only @ the bot when the PR has been merged. A list of relevant tags can be found [here](https://allcontributors.org/docs/en/emoji-key). +## Joining `aeon` as a Core Developer + +`aeon` Core Developers have write access to the repository and the ability to vote on +community decisions. For more details on this role, please refer to the +[about](about.md) and [governance](governance.md) pages. + +If you would like to become a Core Developer, the best way is to reach out and express +your interest. We are particularly open to dedicated contributors who have made +high-quality contributions to the project, as well as time series researchers and +industry professionals. + ## Further Reading For further information on contributing to `aeon`, please see the following pages. diff --git a/docs/developer_guide.md b/docs/developer_guide.md index b74a075c72..b06332f426 100644 --- a/docs/developer_guide.md +++ b/docs/developer_guide.md @@ -20,6 +20,27 @@ their [developer's guide](https://scikit-learn.org/stable/developers/index.html) :::{grid-item-card} :text-align: center +Type Hints + +^^^ + +Adding type hints to `aeon` code. + ++++ + +```{button-ref} developer_guide/adding_typehints +:color: primary +:click-parent: +:expand: + +Type Hints +``` + +::: + +:::{grid-item-card} +:text-align: center + AEP's ^^^ @@ -190,6 +211,7 @@ Testing ```{toctree} :hidden: +developer_guide/adding_typehints.md developer_guide/aep.md developer_guide/coding_standards.md developer_guide/dependencies.md diff --git a/docs/developer_guide/adding_typehints.md b/docs/developer_guide/adding_typehints.md index 7c034f5b58..5f77ce119b 100644 --- a/docs/developer_guide/adding_typehints.md +++ b/docs/developer_guide/adding_typehints.md @@ -1,53 +1,54 @@ # Adding Type Hints -## Introduction to Type Hints - -Type hints are a way to indicate the expected data types of variables, function parameters, and return values in Python. They enhance code readability and help with static type checking, making it easier to catch errors before runtime. - - -Type hints act as a form of documentation that helps developers understand the types of arguments a function expects and what it returns. - - -## Basic Syntax - -For example, here is a simple function whose argument and return type are declared in the annotations: +Type hints are a way to indicate the expected data types of variables, function +parameters, and return values. They enhance code readability and help with static +type checking, making it easier to catch errors. +For example, here is a simple function whose argument and return type are declared +in the annotations: ```python -def greeting(name: str) -> str: - return 'Hello ' + name -``` - - -Learn more about type hints in [python docs](https://docs.python.org/3/library/typing.html) and [PEP 484](https://peps.python.org/pep-0484/) - - -# Dealing with Soft Dependency Type Hints +from typing import List +def sum_ints_return_str(int_list: List[int]) -> str: + return str(sum(int_list)) +``` +Type hints are not currently mandatory in `aeon`, but we aim to progressively integrate +them into the code base. Learn more about type hints in the +[Python documentation](https://docs.python.org/3/library/typing.html) +and [PEP 484](https://peps.python.org/pep-0484/). -When working with models that have soft dependencies, additional considerations are required to ensure that your code remains robust and maintainable. Soft dependencies are optional packages or modules that your application does not require at runtime but may be used in specific situations, such as during type-checking or when certain features are enabled. +## Soft Dependency Type Hints - The typing.TYPE_CHECKING constant ensures that imports for type hints are only evaluated when type-checking is done and NOT in the runtime. This prevents errors when the soft dependancies are not available. Here is an example that of [PyODAdapter](https://github.com/aeon-toolkit/aeon/blob/main/aeon/anomaly_detection/_pyodadapter.py): +When working with modules that use soft dependencies, additional considerations are +required to ensure that your code can still run even without these dependencies +installed. +Here is an example snippet taken from [PyODAdapter](https://www.aeon-toolkit.org/en/stable/api_reference/auto_generated/aeon.anomaly_detection.outlier_detection.PyODAdapter.html). +It uses the `pyod` library, which is a soft dependency. The `TYPE_CHECKING` constant +is used to ensure that the `pyod` library is only imported at the top level while type +checking is performed. `from __future__ import annotations` is used to allow forward +references in type hints. See [PEP 563](https://peps.python.org/pep-0563/) for more +information. The `pyod` `BaseDetector` class can now be used in type hints with +these additions. ```python -from aeon.anomaly_detection.base import BaseAnomalyDetector -from aeon.utils.validation._dependencies import _check_soft_dependencies -from typing import TYPE_CHECKING, Any +from __future__ import annotations +from aeon.anomaly_detection.base import BaseAnomalyDetector +from typing import TYPE_CHECKING if TYPE_CHECKING: from pyod.models.base import BaseDetector - class PyODAdapter(BaseAnomalyDetector): - ... - - def _is_pyod_model(model: Any) -> bool: - """Check if the provided model is a PyOD model.""" - from pyod.models.base import BaseDetector - - return isinstance(model, BaseDetector) - ... + def __init__( + self, pyod_model: BaseDetector, window_size: int = 10, stride: int = 1 + ): + self.pyod_model = pyod_model + self.window_size = window_size + self.stride = stride + + super().__init__(axis=0) ``` diff --git a/docs/developer_guide/dependencies.md b/docs/developer_guide/dependencies.md index 53c0f326fd..9fb649c0bf 100644 --- a/docs/developer_guide/dependencies.md +++ b/docs/developer_guide/dependencies.md @@ -15,7 +15,7 @@ We are unlikely to add new core dependencies, without a strong reason. Soft depe should be the first choice for new dependencies, but ideally the code should be written in `aeon` itself if possible. -Al dependencies are managed in the [`pyproject.toml`](https://github.com/aeon-toolkit/aeon/blob/main/pyproject.toml) +All dependencies are managed in the [`pyproject.toml`](https://github.com/aeon-toolkit/aeon/blob/main/pyproject.toml) file following the [PEP 621](https://www.python.org/dev/peps/pep-0621/) convention. Core dependencies are listed in the `dependencies` dependency set and developer dependencies are listed in the `dev` and `docs` dependency sets. diff --git a/docs/developer_guide/documentation.md b/docs/developer_guide/documentation.md index 20a4be04fd..d4c18ab384 100644 --- a/docs/developer_guide/documentation.md +++ b/docs/developer_guide/documentation.md @@ -154,7 +154,7 @@ Here are a few examples of `aeon` code with good documentation. ### Estimators -[BOSSEnsemble](https://www.aeon-toolkit.org/en/latest/api_reference/auto_generated/aeon.classification.dictionary_based.BOSSEnsemble.html#aeon.classification.dictionary_based.BOSSEnsemble) +[BOSSEnsemble](https://www.aeon-toolkit.org/en/stable/api_reference/auto_generated/aeon.classification.dictionary_based.BOSSEnsemble.html#aeon.classification.dictionary_based.BOSSEnsemble) ### Functions diff --git a/docs/developer_guide/release.md b/docs/developer_guide/release.md index 13f0e41577..45c0d6dcf9 100644 --- a/docs/developer_guide/release.md +++ b/docs/developer_guide/release.md @@ -41,7 +41,11 @@ The release process is as follows, on high-level: Creation of the GitHub release trigger the `pypi` release workflow. -5. **Wait for the ``pypi`` release CI/CD to finish.** +5. **Approve the release workflow.** + The release workflow will be automatically created in the GitHub Actions tab. This + must be approved by a member of the release management workgroup before it will run. + +6. **Wait for the ``pypi`` release CI/CD to finish.** If tests fail due to sporadic unrelated failure, restart. If tests fail genuinely, something went wrong in the above steps, investigate, fix, and repeat. If the bug is known and sporadic (i.e. failure to read data from an external source), the release @@ -49,7 +53,7 @@ Creation of the GitHub release trigger the `pypi` release workflow. the workflow can be manually run from the GitHub Actions tab if more PRs are required. -6. **Release workflow completion tasks.** +7. **Release workflow completion tasks.** Once the release workflow has passed, check `aeon` version on `pypi`, this should be the new version. A validatory installation of `aeon` in a new Python environment should be carried out according to the installation instructions. If the installation @@ -58,7 +62,7 @@ Creation of the GitHub release trigger the `pypi` release workflow. ## `conda-forge` release and release validation -7. **Merge the ``conda-forge`` release PR.** +8. **Merge the ``conda-forge`` release PR.** After some time a PR will be automatically created in the [aeon conda-forge feedstock](https://github.com/conda-forge/aeon-feedstock). Follow the instructions in the PR to merge it, making sure to update any dependencies that have changed and dependency version bounds. diff --git a/docs/developer_guide/testing.md b/docs/developer_guide/testing.md index 94ccf07f89..83fc37628a 100644 --- a/docs/developer_guide/testing.md +++ b/docs/developer_guide/testing.md @@ -166,7 +166,8 @@ The `aeon` PR testing workflow runs on every PR to the main branch. By default, will run a constrained set of tests excluding some tests such as those which are noticeably expensive or prone to failure (i.e. I/O from external sources). The estimators run will also be split into smaller subsets to spread them over -different Python version and operating system combinations. This is controlled by the +different Python version and operating system combinations. This can result in failures +in some runs (likely 3), while others pass without issue. This is controlled by the `PR_TESTING` flag in [`testing/testing_config.py`](https://github.com/aeon-toolkit/aeon/blob/main/aeon/testing/testing_config.py). A large portion of testing time is spent compiling `numba` functions. By default, diff --git a/docs/examples.md b/docs/examples.md index 7817ea24be..025b43ff2e 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -118,6 +118,19 @@ Early TSC ::: +::: + +:::{grid-item-card} +:img-top: examples/classification/img/rotation_forest.png +:class-img-top: aeon-card-image-m +:link: /examples/classification/rotation_forest.ipynb +:link-type: ref +:text-align: center + +Rotation Forest Classifier + +::: + :::: ## Regression diff --git a/docs/getting_started.md b/docs/getting_started.md index 36f18583cb..ccf29cee33 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -21,8 +21,9 @@ classical techniques for the following learning tasks: - [**Clustering**](api_reference/clustering), where a collection of time series without any labels are used to train a model to label cases ([more details](examples/clustering/clustering.ipynb)). -- [**Similarity search**](api_reference/similarity_search), where the goal is to evaluate - the similarity between a query time series and a collection of other longer time series +- [**Similarity search**](api_reference/similarity_search), where the goal is to find + time series motifs or nearest neighbors in an efficient way for either single series + or collections. ([more details](examples/similarity_search/similarity_search.ipynb)). - [**Anomaly detection**](api_reference/anomaly_detection), where the goal is to find values or areas of a single time series that are not representative of the whole series. @@ -114,7 +115,7 @@ written the notebook. ```{code-block} python >>> from aeon.datasets import load_airline ->>> from aeon.anomaly_detection import STOMP +>>> from aeon.anomaly_detection.distance_based import STOMP >>> stomp = STOMP(window_size=200) >>> scores = est.fit_predict(X) # Get the anomaly scores ``` @@ -309,45 +310,38 @@ new data. ### Similarity Search -The similarity search module in `aeon` offers a set of functions and estimators to solve -tasks related to time series similarity search. The estimators can be used standalone -or as parts of pipelines, while the functions give you the tools to build your own -estimators that would rely on similarity search at some point. - -The estimators are inheriting from the [BaseSimiliaritySearch](similarity_search.base.BaseSimiliaritySearch) -class accepts as inputs 3D time series (n_cases, n_channels, n_timepoints) for the -fit method. Univariate and single series can still be used, but will need to be reshaped -to this format. - -This collection, asked for the fit method, is stored as a database. It will be used in -the predict method, which expects a single 2D time series as input -(n_channels, query_length). This 2D time series will be used as a query to search for in -the 3D database. - -The result of the predict method will then depends on wheter you use the [QuerySearch](similarity_search.query_search.QuerySearch) -and the [SeriesSearch](similarity_search.series_search.SeriesSearch) estimator. In [QuerySearch](similarity_search.query_search.QuerySearch), the 2D series is a subsequence -for which we want to indentify the best (or worst !) matches in the 3D database. -For [SeriesSearch](similarity_search.series_search.SeriesSearch), we require a `length` parmater, and we will search for the best -matches of all subsequences of size `length` in the 2D series inside the 3D database. -By default, these estimators will use the Euclidean (or squared Euclidean) distance, -but more distance will be added in the future. - +The similarity search module in `aeon` offers a set of estimators to solve tasks +related to time series similarity search. The estimators can be used standalone for +data analysis purposes or as parts of pipelines, to perform other tasks such as +classification or clustering. + +Similarly to the transformation module, similarity search estimators are either defined +for single series or for collection of series. The estimators are inheriting from the +[BaseSimiliaritySearch](similarity_search._base.BaseSimiliaritySearch) class, which +both [BaseSeriesSimiliaritySearch](similarity_search.series._base.BaseSeriesSimiliaritySearch) +and [BaseCollectionSimiliaritySearch](similarity_search.collection._base.BaseCollectionSimiliaritySearch) +inherit from. + +All estimators use a `fit` `predict` interface, where `predict` outputs both the +indexes of the neighbors or motifs and a distance or similarity measure linked to them. +For example, using `StompMotif` to compute the matrix profile between two series : ```{code-block} python >>> import numpy as np ->>> from aeon.similarity_search import QuerySearch ->>> X = [[[1, 2, 3, 4, 5, 6, 7]], # 3D array example (univariate) -... [[4, 4, 4, 5, 6, 7, 3]]] # Two samples, one channel, seven series length ->>> X = np.array(X) # X is of shape (2, 1, 7) : (n_cases, n_channels, n_timepoints) ->>> top_k = QuerySearch(k=2) ->>> top_k.fit(X) # fit the estimator on train data -... ->>> q = np.array([[4, 5, 6]]) # q is of shape (1,3) : ->>> top_k.predict(q) # Identify the two (k=2) most similar subsequences of length 3 in X -[(0, 3), (1, 2)] +>>> from aeon.similarity_search.series import StompMotif +>>> X1 = np.array([1, 1, 2, 4, 6, 6, 7]) # single series (univariate) +>>> X2 = np.array([0, 1, 2, 2, 4, 5, 7, 9, 4, 6]) # single series (univariate) +>>> top_k = StompMotif(4).fit(X1) # 4 is length of the motif to search +>>> distances, indexes = top_k.predict(X2, k=1) ``` -The output of predict gives a list of size `k`, where each element is a set indicating -the location of the best matches in X as `(id_sample, id_timestamp)`. This is equivalent -to the subsequence `X[id_sample, :, id_timestamps:id_timestamp + q.shape[0]]`. +Some things to note on this example : + +- We defined `1D` series of shape `(n_timepoints)`, but internally, series estimator +will use a `2D` representation as `(n_channels, n_timepoints)`. +- The output of predict gives a two lists of size `k` (the number of motifs to extract) +which can be read as follows : `distances[i] = d(X1[:, indexes[i][0]],X2[:, indexes[i][1]])` + +For more examples and use cases you can check the example section of the module, +starting with the general [similarity search notebook](examples/similarity_search/similarity_search.ipynb) ## Transformers diff --git a/docs/governance.md b/docs/governance.md index c3f5440372..5f65142a3c 100644 --- a/docs/governance.md +++ b/docs/governance.md @@ -23,6 +23,15 @@ as detailed in the contributing guide. Contributors play a crucial role in shapi project through participating in discussions and influencing the decision-making process. +### Supporting Developers + +Supporting developers are contributors who have been nominated by a core developer +and granted write access to the `aeon` repository. No vote is required for this role, +but the nominator must notify the Core Developers and create a Pull Request. +Supporting developers can have their access revoked at any time by a core developer +if it is determined that they are abusing this access. Access will also be removed +after 6 months of inactivity. + ### Core Developers Core developers are community members that have made significant contributions and are diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index 9f393437e9..7afd00aff8 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -185,7 +185,7 @@ "metadata": {}, "outputs": [], "source": [ - "from aeon.anomaly_detection import STOMP\n", + "from aeon.anomaly_detection.distance_based import STOMP\n", "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", "detector = STOMP(window_size=200)\n", @@ -211,7 +211,7 @@ "source": [ "from pyod.models.ocsvm import OCSVM\n", "\n", - "from aeon.anomaly_detection import PyODAdapter\n", + "from aeon.anomaly_detection.outlier_detection import PyODAdapter\n", "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", "detector = PyODAdapter(OCSVM(), window_size=3)\n", diff --git a/examples/classification/classification.ipynb b/examples/classification/classification.ipynb index 8ec2f4563b..6971838705 100644 --- a/examples/classification/classification.ipynb +++ b/examples/classification/classification.ipynb @@ -2,6 +2,10 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "_pBlXBeTh5IG" + }, "source": [ "# Time Series Classification\n", "\n", @@ -14,18 +18,18 @@ " be easy, because the basic usage is identical.\n", "\n", "\"time" - ], - "metadata": { - "collapsed": false, - "id": "_pBlXBeTh5IG" - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "weha73tPh5IH" + }, "source": [ "## Classification Notebooks\n", "\n", - "This note book gives an overview of TSC. More specific notebooks on TSC are base on\n", + "This notebook gives an overview of TSC. More specific notebooks on TSC are based on\n", "the type of representation or transformation they use:\n", "\n", "- [Convolution based](convolution_based.ipynb)\n", @@ -37,14 +41,14 @@ "- [Shapelet based](shapelet_based.ipynb)\n", "- [Hybrid](hybrid.ipynb)\n", "- [Early classification](early_classification.ipynb)\n" - ], - "metadata": { - "collapsed": false, - "id": "weha73tPh5IH" - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "EyjESzTQh5II" + }, "source": [ "## Data Storage and Problem Types\n", "\n", @@ -54,23 +58,26 @@ "multivariate, with at least three dimensions (x,y,z co-ordinates). The image above is\n", " a univariate problem: each series has its own label. The dimension of the time\n", " series instance is also often called the channel. We recommend storing time series\n", - " in 3D numpy array of shape `(n_cases, n_channels, n_timepoints)` and\n", - " where possible our single problem loaders will return a\n", + " in 3D numpy array of shape `(n_cases, n_channels, n_timepoints)` and,\n", + " where possible, our single problem loaders will return a\n", " 3D numpy. Unequal length classification problems are stored in a list of 2D numpy\n", " arrays. More details on data storage can be found in the [data storage](../datasets/datasets.ipynb) notebook." - ], - "metadata": { - "collapsed": false, - "id": "EyjESzTQh5II" - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bjW-qRxOh5II", + "outputId": "a17f6f06-04b2-4fed-877e-92ef9680cdef" + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "ArrowHead series of type and shape (36, 1, 251)\n", "Motions type of shape (40,)\n" @@ -87,17 +94,14 @@ "motions, motions_labels = load_basic_motions(split=\"train\")\n", "print(f\"ArrowHead series of type {type(arrow)} and shape {arrow.shape}\")\n", "print(f\"Motions type {type(motions)} of shape {motions_labels.shape}\")" - ], - "metadata": { - "id": "bjW-qRxOh5II", - "outputId": "a17f6f06-04b2-4fed-877e-92ef9680cdef", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "pPrsdjsOh5IJ" + }, "source": [ "We use 3D numpy even if the data is univariate: even though classifiers\n", "can work using a 2D array of shape `(n_cases, n_timepoints)`, this 2D shape can get\n", @@ -135,43 +139,39 @@ " involved a subject performing one of four tasks (walking, resting, running and\n", " badminton) for ten seconds. Time series in this data set have six dimensions or\n", " channels." - ], - "metadata": { - "collapsed": false, - "id": "pPrsdjsOh5IJ" - } + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "9T5zoVT9h5IJ", - "outputId": "2aa3e84a-9fdd-4cd7-fcff-4f6f8172c5ce", "colab": { "base_uri": "https://localhost:8080/", "height": 469 - } + }, + "id": "9T5zoVT9h5IJ", + "outputId": "2aa3e84a-9fdd-4cd7-fcff-4f6f8172c5ce" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 7, "metadata": {}, - "execution_count": 7 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -187,33 +187,33 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "TtIuima2h5IK", - "outputId": "17310dc6-8ba5-45bb-8e2b-a07f80402bef", "colab": { "base_uri": "https://localhost:8080/", "height": 469 - } + }, + "id": "TtIuima2h5IK", + "outputId": "17310dc6-8ba5-45bb-8e2b-a07f80402bef" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 8, "metadata": {}, - "execution_count": 8 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -240,16 +240,23 @@ { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qG96PKaCh5IK", + "outputId": "07ae1abe-a9d2-4e19-f515-9ca20e017177" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.72" ] }, + "execution_count": 9, "metadata": {}, - "execution_count": 9 + "output_type": "execute_result" } ], "source": [ @@ -262,14 +269,7 @@ "rand_forest.fit(arrow2d, arrow_labels)\n", "y_pred = rand_forest.predict(arrow_test)\n", "accuracy_score(arrow_test_labels, y_pred)" - ], - "metadata": { - "id": "qG96PKaCh5IK", - "outputId": "07ae1abe-a9d2-4e19-f515-9ca20e017177", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", @@ -290,44 +290,44 @@ "We show the simplest use cases for classifiers and demonstrate how to build bespoke\n", "pipelines for time series classification. An accurate and relatively\n", "fast classifier is the [ROCKET](https://link.springer.com/article/10.1007/s10618-020-00701-z) classifier. ROCKET is a convolution based algorithm\n", - "described in detail in the [convolution based](convolution_based.ipynb) note book." + "described in detail in the [convolution based](convolution_based.ipynb) notebook." ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-11-16T19:16:46.486243Z", "start_time": "2024-11-16T19:15:42.973051Z" }, - "id": "2xIrRErYh5IL", - "outputId": "372654b5-3fae-42e8-a315-da9e33ad8e38", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "2xIrRErYh5IL", + "outputId": "372654b5-3fae-42e8-a315-da9e33ad8e38" }, - "source": [ - "from aeon.classification.convolution_based import RocketClassifier\n", - "\n", - "rocket = RocketClassifier(n_kernels=2000)\n", - "rocket.fit(arrow, arrow_labels)\n", - "y_pred = rocket.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" - ], "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.76" ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 10 + "output_type": "execute_result" } ], - "execution_count": null + "source": [ + "from aeon.classification.convolution_based import RocketClassifier\n", + "\n", + "rocket = RocketClassifier(n_kernels=2000)\n", + "rocket.fit(arrow, arrow_labels)\n", + "y_pred = rocket.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" + ] }, { "cell_type": "markdown", @@ -350,22 +350,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "u0rqqET8h5IL", - "outputId": "b1347f40-c82b-4ecf-ec72-500b1f7f8a12", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "u0rqqET8h5IL", + "outputId": "b1347f40-c82b-4ecf-ec72-500b1f7f8a12" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.8685714285714285" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], "source": [ @@ -380,36 +380,27 @@ }, { "cell_type": "markdown", - "source": [ - "The LITETime Classifier is an efficient deep learning-based model for time series classification. It is designed to handle both univariate and multivariate time series data effectively, offering lightweight architecture and competitive performance. For simplicity, this notebook uses 10 epochs to demonstrate the classifier's functionality. To observe the full performance of deep learning models in aeon, it’s recommended to use the library's default epochs. The reduced epochs here simplify the demonstration and reduce runtime. Deep learning approaches for time series classification, are further described in the [deep learning notebook](./deep_learning.ipynb).\n" - ], "metadata": { "id": "gTQRU2rkuPvw" - } + }, + "source": [ + "The LITETime Classifier is an efficient deep learning-based model for time series classification. It is designed to handle both univariate and multivariate time series data effectively, offering lightweight architecture and competitive performance. For simplicity, this notebook uses 10 epochs to demonstrate the classifier's functionality. To observe the full performance of deep learning models in aeon, it’s recommended to use the library's default epochs. The reduced epochs here simplify the demonstration and reduce runtime. Deep learning approaches for time series classification, are further described in the [deep learning notebook](./deep_learning.ipynb).\n" + ] }, { "cell_type": "code", - "source": [ - "from aeon.classification.deep_learning import LITETimeClassifier\n", - "\n", - "lite_time = LITETimeClassifier(n_epochs=10, batch_size=32, random_state=42)\n", - "lite_time.fit(arrow, arrow_labels)\n", - "y_pred = lite_time.predict(arrow_test)\n", - "\n", - "accuracy_score(arrow_test_labels, y_pred)" - ], + "execution_count": null, "metadata": { - "id": "-nnwMXqtSzzc", - "outputId": "5ca88c72-3d6d-4d0b-90e7-b76da94aa62f", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "-nnwMXqtSzzc", + "outputId": "5ca88c72-3d6d-4d0b-90e7-b76da94aa62f" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 122ms/step\n", "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 126ms/step\n", @@ -419,51 +410,67 @@ ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "0.3942857142857143" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" } + ], + "source": [ + "from aeon.classification.deep_learning import LITETimeClassifier\n", + "\n", + "lite_time = LITETimeClassifier(n_epochs=10, batch_size=32, random_state=42)\n", + "lite_time.fit(arrow, arrow_labels)\n", + "y_pred = lite_time.predict(arrow_test)\n", + "\n", + "accuracy_score(arrow_test_labels, y_pred)" ] }, { "cell_type": "markdown", - "source": [], "metadata": { "collapsed": false, "id": "3y4vwmA1h5IL" - } + }, + "source": [] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "OaBVEJmnh5IM" + }, "source": [ "## Multivariate Classification\n", "To use ``sklearn`` classifiers directly on multivariate data, one option is to flatten\n", "the data so that the 3D array `(n_cases, n_channels, n_timepoints)` becomes a 2D array\n", "of shape `(n_cases, n_channels*n_timepoints)`." - ], - "metadata": { - "collapsed": false, - "id": "OaBVEJmnh5IM" - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1mfxhLaZh5IM", + "outputId": "c0a7278f-7feb-45dc-a337-e0da2bcbbf60" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.925" ] }, + "execution_count": 14, "metadata": {}, - "execution_count": 14 + "output_type": "execute_result" } ], "source": [ @@ -475,71 +482,70 @@ "rand_forest.fit(motions2d, motions_labels)\n", "y_pred = rand_forest.predict(motions2d_test)\n", "accuracy_score(motions_test_labels, y_pred)" - ], - "metadata": { - "id": "1mfxhLaZh5IM", - "outputId": "c0a7278f-7feb-45dc-a337-e0da2bcbbf60", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "However, many ``aeon`` classifiers, including ROCKET and HC2, are configured to\n", - "work with multivariate input. This works exactly like univariate classification. For example:" - ], "metadata": { "collapsed": false, "id": "Hc2DrT2Fh5IM" - } + }, + "source": [ + "However, many ``aeon`` classifiers, including ROCKET and HC2, are configured to\n", + "work with multivariate input. This works exactly like univariate classification. For example:" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yXZW8cAch5IM", + "outputId": "f3b7b3b7-8204-4e30-cca8-1f07b4d53d90" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "1.0" ] }, + "execution_count": 15, "metadata": {}, - "execution_count": 15 + "output_type": "execute_result" } ], "source": [ "rocket.fit(motions, motions_labels)\n", "y_pred = rocket.predict(motions_test)\n", "accuracy_score(motions_test_labels, y_pred)" - ], - "metadata": { - "id": "yXZW8cAch5IM", - "outputId": "f3b7b3b7-8204-4e30-cca8-1f07b4d53d90", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "A list of classifiers capable of handling multivariate classification can be obtained\n", - " with this code" - ], "metadata": { "collapsed": false, "id": "vW1usODIh5IM" - } + }, + "source": [ + "A list of classifiers capable of handling multivariate classification can be obtained\n", + " with this code" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-efZQXWCh5IN", + "outputId": "778d4b99-7f28-4722-bbc1-c0937d8bdfb9" + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n", @@ -625,8 +631,9 @@ " aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" ] }, + "execution_count": 16, "metadata": {}, - "execution_count": 16 + "output_type": "execute_result" } ], "source": [ @@ -636,14 +643,7 @@ " tag_filter={\"capability:multivariate\": True},\n", " type_filter=\"classifier\",\n", ")" - ], - "metadata": { - "id": "-efZQXWCh5IN", - "outputId": "778d4b99-7f28-4722-bbc1-c0937d8bdfb9", - "colab": { - "base_uri": "https://localhost:8080/" - } - } + ] }, { "cell_type": "markdown", @@ -664,22 +664,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xtlozU2Hh5IN", - "outputId": "9c5478f1-0184-4afa-87e3-1526988796fe", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "xtlozU2Hh5IN", + "outputId": "9c5478f1-0184-4afa-87e3-1526988796fe" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.9" ] }, + "execution_count": 17, "metadata": {}, - "execution_count": 17 + "output_type": "execute_result" } ], "source": [ @@ -702,38 +702,38 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "-7NDHcmzh5IN" + }, "source": [ "## sklearn Compatibility\n", "\n", "`aeon` classifiers are compatible with `sklearn` model selection and\n", "composition tools using `aeon` data formats. For example, cross-validation can\n", "be performed using the `sklearn` `cross_val_score` and `KFold` functionality:" - ], - "metadata": { - "collapsed": false, - "id": "-7NDHcmzh5IN" - } + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "Pw_ZNfJvh5IN", - "outputId": "7963c9b6-673f-4d66-95df-e7aa418e39ce", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "Pw_ZNfJvh5IN", + "outputId": "7963c9b6-673f-4d66-95df-e7aa418e39ce" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([0.88888889, 0.66666667, 0.77777778, 0.77777778])" ] }, + "execution_count": 18, "metadata": {}, - "execution_count": 18 + "output_type": "execute_result" } ], "source": [ @@ -744,35 +744,35 @@ }, { "cell_type": "markdown", - "source": [ - "Parameter tuning can be done using `sklearn` `GridSearchCV`. For example, we can tune\n", - " the _k_ and distance measure for a K-NN classifier:" - ], "metadata": { "collapsed": false, "id": "aJNXKkYHh5IO" - } + }, + "source": [ + "Parameter tuning can be done using `sklearn` `GridSearchCV`. For example, we can tune\n", + " the _k_ and distance measure for a K-NN classifier:" + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "K67ps0Bnh5IO", - "outputId": "460d0d39-ae25-4cfa-ea50-02dde57654da", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "K67ps0Bnh5IO", + "outputId": "460d0d39-ae25-4cfa-ea50-02dde57654da" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.8" ] }, + "execution_count": 19, "metadata": {}, - "execution_count": 19 + "output_type": "execute_result" } ], "source": [ @@ -792,34 +792,34 @@ }, { "cell_type": "markdown", - "source": [ - "Probability calibration is possible with the `sklearn` `CalibratedClassifierCV`:" - ], "metadata": { "collapsed": false, "id": "FtiuhfARh5IO" - } + }, + "source": [ + "Probability calibration is possible with the `sklearn` `CalibratedClassifierCV`:" + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "oyywFEuhh5IO", - "outputId": "719c1f06-7eff-429b-dd26-e7da6be20972", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "oyywFEuhh5IO", + "outputId": "719c1f06-7eff-429b-dd26-e7da6be20972" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.7485714285714286" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } ], "source": [ @@ -892,21 +892,26 @@ }, { "cell_type": "code", - "source": [], + "execution_count": null, "metadata": { "id": "ms0mSnWEU11v" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] } ], "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, "interpreter": { "hash": "9d800c14abb2bd109b7479fe8830174a66f0a4a77373f77c2c7334932e1a4922" }, "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "display_name": "Python 3", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -919,12 +924,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" - }, - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "accelerator": "GPU" + } }, "nbformat": 4, "nbformat_minor": 0 diff --git a/examples/classification/early_classification.ipynb b/examples/classification/early_classification.ipynb index 97cd1a23ac..b4649a46a6 100644 --- a/examples/classification/early_classification.ipynb +++ b/examples/classification/early_classification.ipynb @@ -2,6 +2,9 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "# Early time series classification with aeon\n", "\n", @@ -9,23 +12,28 @@ "\n", "This notebook gives a quick guide to get you started with running eTSC algorithms in aeon.\n", "\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Data sets and problem types\n", "The UCR/UEA [time series classification archive](https://timeseriesclassification.com/) contains a large number of example TSC problems that have been used thousands of times in the literature to assess TSC algorithms. Read the data loading documentation and notebooks for details on the aeon data formats and loading data for aeon." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + } + }, + "outputs": [], "source": [ "# Imports used in this notebook\n", "import numpy as np\n", @@ -33,23 +41,20 @@ "from aeon.classification.early_classification._teaser import TEASER\n", "from aeon.classification.interval_based import TimeSeriesForestClassifier\n", "from aeon.datasets import load_arrow_head" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - } - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "text/plain": "(175, 1, 251)" + "text/plain": [ + "(175, 1, 251)" + ] }, "execution_count": 2, "metadata": {}, @@ -62,30 +67,444 @@ "arrow_test_X, arrow_test_y = load_arrow_head(split=\"test\")\n", "\n", "arrow_test_X.shape" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Building the TEASER classifier\n", "\n", "TEASER \\[1\\] is a two-tier model using a base classifier to make predictions and a decision making estimator to decide whether these predictions are safe. As a first tier, TEASER requires a TSC algorithm, such as WEASEL, which produces class probabilities as output. As a second tier an anomaly detector is required, such as a one-class SVM." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 3, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "text/plain": "TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n random_state=0)", - "text/html": "
TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n       estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n       random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "text/html": [ + "
TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n",
+       "       estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n",
+       "       random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "TEASER(classification_points=[25, 50, 75, 100, 125, 150, 175, 200, 251],\n", + " estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n", + " random_state=0)" + ] }, "execution_count": 3, "metadata": {}, @@ -99,42 +518,33 @@ " estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),\n", ")\n", "teaser.fit(arrow_train_X, arrow_train_y)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Determine the accuracy and earliness on the test data\n", "\n", - "Commonly accuracy is used to determine the correctness of the predictions, while earliness is used to determine how much of the series is required on average to obtain said accuracy. I.e. for the below values, using just 43% of the full test data, we were able to get an accuracy of 69%." - ], - "metadata": { - "collapsed": false - } + "Commonly accuracy is used to determine the correctness of the predictions, while earliness is used to determine how much of the series is required on average to obtain said accuracy. I.e. for the below values, using just 34% of the full test data, we were able to get an accuracy of 65%." + ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "collapsed": false + }, "outputs": [ { - "ename": "ValueError", - "evalue": "zero-size array to reduction operation minimum which has no identity", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[10], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m hm, acc, earl \u001B[38;5;241m=\u001B[39m \u001B[43mteaser\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mscore\u001B[49m\u001B[43m(\u001B[49m\u001B[43marrow_test_X\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43marrow_test_y\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mEarliness on Test Data \u001B[39m\u001B[38;5;132;01m%2.2f\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m earl)\n\u001B[0;32m 3\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAccuracy on Test Data \u001B[39m\u001B[38;5;132;01m%2.2f\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m acc)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\early_classification\\base.py:314\u001B[0m, in \u001B[0;36mBaseEarlyClassifier.score\u001B[1;34m(self, X, y)\u001B[0m\n\u001B[0;32m 311\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcheck_is_fitted()\n\u001B[0;32m 313\u001B[0m \u001B[38;5;66;03m# boilerplate input checks for predict-like methods\u001B[39;00m\n\u001B[1;32m--> 314\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_X\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 315\u001B[0m X \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_convert_X(X)\n\u001B[0;32m 317\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_score(X, y)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\early_classification\\base.py:631\u001B[0m, in \u001B[0;36mBaseEarlyClassifier._check_X\u001B[1;34m(self, X)\u001B[0m\n\u001B[0;32m 629\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_check_X\u001B[39m(\u001B[38;5;28mself\u001B[39m, X):\n\u001B[0;32m 630\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"To follow.\"\"\"\u001B[39;00m\n\u001B[1;32m--> 631\u001B[0m metadata \u001B[38;5;241m=\u001B[39m \u001B[43m_get_metadata\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 632\u001B[0m \u001B[38;5;66;03m# Check classifier capabilities for X\u001B[39;00m\n\u001B[0;32m 633\u001B[0m allow_multivariate \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_tag(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcapability:multivariate\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\early_classification\\base.py:707\u001B[0m, in \u001B[0;36m_get_metadata\u001B[1;34m(X)\u001B[0m\n\u001B[0;32m 705\u001B[0m metadata \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m 706\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmultivariate\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m is_univariate(X)\n\u001B[1;32m--> 707\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmissing_values\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mhas_missing\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 708\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124munequal_length\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m is_equal_length(X)\n\u001B[0;32m 709\u001B[0m metadata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mn_cases\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m get_n_cases(X)\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\utils\\validation\\collection.py:305\u001B[0m, in \u001B[0;36mhas_missing\u001B[1;34m(X)\u001B[0m\n\u001B[0;32m 303\u001B[0m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m=\u001B[39m get_type(X)\n\u001B[0;32m 304\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumpy3D\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumpy2D\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 305\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m np\u001B[38;5;241m.\u001B[39many(np\u001B[38;5;241m.\u001B[39misnan(\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmin\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m))\n\u001B[0;32m 306\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnp-list\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m 307\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m X:\n", - "File \u001B[1;32m<__array_function__ internals>:180\u001B[0m, in \u001B[0;36mamin\u001B[1;34m(*args, **kwargs)\u001B[0m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2918\u001B[0m, in \u001B[0;36mamin\u001B[1;34m(a, axis, out, keepdims, initial, where)\u001B[0m\n\u001B[0;32m 2802\u001B[0m \u001B[38;5;129m@array_function_dispatch\u001B[39m(_amin_dispatcher)\n\u001B[0;32m 2803\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mamin\u001B[39m(a, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, out\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, keepdims\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39m_NoValue, initial\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39m_NoValue,\n\u001B[0;32m 2804\u001B[0m where\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39m_NoValue):\n\u001B[0;32m 2805\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 2806\u001B[0m \u001B[38;5;124;03m Return the minimum of an array or minimum along an axis.\u001B[39;00m\n\u001B[0;32m 2807\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 2916\u001B[0m \u001B[38;5;124;03m 6\u001B[39;00m\n\u001B[0;32m 2917\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m-> 2918\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_wrapreduction\u001B[49m\u001B[43m(\u001B[49m\u001B[43ma\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mminimum\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mmin\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 2919\u001B[0m \u001B[43m \u001B[49m\u001B[43mkeepdims\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkeepdims\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minitial\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwhere\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mwhere\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86\u001B[0m, in \u001B[0;36m_wrapreduction\u001B[1;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001B[0m\n\u001B[0;32m 83\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 84\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m reduction(axis\u001B[38;5;241m=\u001B[39maxis, out\u001B[38;5;241m=\u001B[39mout, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mpasskwargs)\n\u001B[1;32m---> 86\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m ufunc\u001B[38;5;241m.\u001B[39mreduce(obj, axis, dtype, out, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mpasskwargs)\n", - "\u001B[1;31mValueError\u001B[0m: zero-size array to reduction operation minimum which has no identity" + "name": "stdout", + "output_type": "stream", + "text": [ + "Earliness on Test Data 0.34\n", + "Accuracy on Test Data 0.65\n", + "Harmonic Mean on Test Data 0.65\n" ] } ], @@ -143,55 +553,55 @@ "print(\"Earliness on Test Data %2.2f\" % earl)\n", "print(\"Accuracy on Test Data %2.2f\" % acc)\n", "print(\"Harmonic Mean on Test Data %2.2f\" % hm)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "### Determine the accuracy and earliness on the train data" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "### Determine the accuracy and earliness on the train data" + ] }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Earliness on Train Data 0.31\n", - "Accuracy on Train Data 0.69\n" + "Earliness on Train Data 0.28\n", + "Accuracy on Train Data 0.72\n" ] } ], "source": [ "print(\"Earliness on Train Data %2.2f\" % teaser._train_earliness)\n", "print(\"Accuracy on Train Data %2.2f\" % teaser._train_accuracy)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### Comparison to Classification on full Test Data\n", "\n", - "With the full test data, we would obtain 68% accuracy with the same classifier." - ], - "metadata": { - "collapsed": false - } + "With the full test data, we would obtain 67% accuracy with the same classifier." + ] }, { "cell_type": "code", "execution_count": 6, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -208,45 +618,45 @@ " .score(arrow_test_X, arrow_test_y)\n", ")\n", "print(\"Accuracy on the full Test Data %2.2f\" % accuracy)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Classifying with incomplete time series\n", "\n", "The main draw of eTSC is the capabilility to make classifications with incomplete time series. aeon eTSC algorithms accept inputs with less time points than the full series length, and output two items: The prediction made and whether the algorithm thinks the prediction is safe. Information about the decision such as the time stamp it was made at can be obtained from the state_info attribute.\n", "\n", "### First test with only 50 datapoints (out of 251)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 7, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 10 Finished prediction\n", - " [ 0 1 4 5 9 11 24 30 32 35]\n", + " [ 0 4 5 8 9 16 18 21 22 30]\n", "First 10 Probabilities of finished predictions\n", - " [[0.9 0. 0.1]\n", - " [0.3 0.1 0.6]\n", - " [0.8 0.1 0.1]\n", - " [0.7 0.3 0. ]\n", - " [0.5 0.2 0.3]\n", - " [0.6 0.2 0.2]\n", - " [0.1 0.2 0.7]\n", + " [[0.8 0. 0.2]\n", " [0.8 0. 0.2]\n", + " [0.6 0.1 0.3]\n", + " [0.2 0.2 0.6]\n", + " [0.6 0.3 0.1]\n", + " [0. 0.3 0.7]\n", " [0.3 0.1 0.6]\n", - " [0.9 0. 0.1]]\n" + " [0.1 0.3 0.6]\n", + " [0.8 0.1 0.1]\n", + " [0.8 0. 0.2]]\n" ] } ], @@ -256,96 +666,96 @@ "idx = (probas >= 0).all(axis=1)\n", "print(\"First 10 Finished prediction\\n\", np.argwhere(idx).flatten()[:10])\n", "print(\"First 10 Probabilities of finished predictions\\n\", probas[idx][:10])" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 8, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy with 50 points on Test Data 0.57\n" + "Accuracy with 50 points on Test Data 0.65\n" ] } ], "source": [ "_, acc, _ = teaser.score(X, arrow_test_y)\n", "print(\"Accuracy with 50 points on Test Data %2.2f\" % acc)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### We may also do predictions in a streaming scenario where more data becomes available from time to time\n", "\n", "The rationale is to keep the state info from the previous predictions in the TEASER object and use it whenever new data is available." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 9, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Earliness on length 25 is 0.10\n", - "Accuracy on length 25 is 0.50\n", - "Harmonic Mean on length 25 is 0.64\n", + "Accuracy on length 25 is 0.43\n", + "Harmonic Mean on length 25 is 0.59\n", "...........\n", "Earliness on length 50 is 0.20\n", - "Accuracy on length 50 is 0.57\n", - "Harmonic Mean on length 50 is 0.67\n", + "Accuracy on length 50 is 0.67\n", + "Harmonic Mean on length 50 is 0.73\n", "...........\n", - "Earliness on length 75 is 0.27\n", - "Accuracy on length 75 is 0.72\n", - "Harmonic Mean on length 75 is 0.72\n", + "Earliness on length 75 is 0.26\n", + "Accuracy on length 75 is 0.62\n", + "Harmonic Mean on length 75 is 0.67\n", "...........\n", - "Earliness on length 100 is 0.32\n", - "Accuracy on length 100 is 0.62\n", - "Harmonic Mean on length 100 is 0.65\n", + "Earliness on length 100 is 0.29\n", + "Accuracy on length 100 is 0.64\n", + "Harmonic Mean on length 100 is 0.67\n", "...........\n", - "Earliness on length 125 is 0.35\n", - "Accuracy on length 125 is 0.69\n", + "Earliness on length 125 is 0.30\n", + "Accuracy on length 125 is 0.64\n", "Harmonic Mean on length 125 is 0.67\n", "...........\n", - "Earliness on length 150 is 0.37\n", - "Accuracy on length 150 is 0.69\n", + "Earliness on length 150 is 0.32\n", + "Accuracy on length 150 is 0.64\n", "Harmonic Mean on length 150 is 0.66\n", "...........\n", - "Earliness on length 175 is 0.39\n", - "Accuracy on length 175 is 0.68\n", - "Harmonic Mean on length 175 is 0.64\n", + "Earliness on length 175 is 0.33\n", + "Accuracy on length 175 is 0.63\n", + "Harmonic Mean on length 175 is 0.65\n", "...........\n", - "Earliness on length 200 is 0.40\n", - "Accuracy on length 200 is 0.69\n", + "Earliness on length 200 is 0.34\n", + "Accuracy on length 200 is 0.64\n", "Harmonic Mean on length 200 is 0.65\n", "...........\n", - "Earliness on length 251 is 0.40\n", - "Accuracy on length 251 is 0.67\n", - "Harmonic Mean on length 251 is 0.63\n", + "Earliness on length 251 is 0.34\n", + "Accuracy on length 251 is 0.66\n", + "Harmonic Mean on length 251 is 0.66\n", "...........\n", - "Time Stamp of final decisions [ 50 50 251 75 50 50 175 200 175 50 75 50 75 75 100 251 100 100\n", - " 125 75 100 100 75 100 50 125 75 100 75 75 50 75 50 125 175 50\n", - " 50 75 75 125 50 75 75 50 175 100 150 125 75 100 75 75 75 75\n", - " 50 100 50 175 75 50 200 50 50 50 75 200 75 125 75 125 150 175\n", - " 125 50 150 50 75 75 50 100 75 251 251 75 50 100 50 150 100 50\n", - " 75 100 251 50 50 50 200 100 75 50 200 100 50 50 50 50 251 100\n", - " 75 75 125 50 125 100 100 50 75 175 175 50 50 100 175 150 100 100\n", - " 50 100 100 100 175 50 50 100 100 175 251 125 125 100 100 125 100 125\n", - " 100 125 50 175 75 125 100 100 125 50 50 100 125 100 100 100 251 150\n", - " 50 75 175 125 50 50 125 75 50 100 175 50 100]\n" + "Time Stamp of final decisions [ 50 150 75 75 50 50 251 125 50 50 75 75 75 75 75 150 50 75\n", + " 50 75 125 50 50 251 75 75 75 75 75 75 50 175 251 50 175 50\n", + " 50 75 75 75 75 50 75 251 75 75 50 50 75 175 75 75 125 50\n", + " 75 50 50 100 175 75 150 50 75 50 75 75 75 75 50 75 50 75\n", + " 150 50 125 50 100 75 50 50 175 75 251 75 50 75 75 175 50 50\n", + " 75 50 50 50 75 50 75 100 75 100 50 50 50 50 50 50 50 150\n", + " 75 50 50 50 251 100 125 75 125 100 75 50 75 50 50 75 200 50\n", + " 50 100 50 50 75 75 50 150 50 75 50 75 200 50 75 75 200 200\n", + " 75 50 75 200 75 75 50 200 75 75 75 100 200 75 75 50 100 50\n", + " 50 75 50 75 75 75 75 50 75 75 50 100 75]\n" ] } ], @@ -378,22 +788,19 @@ " print(\"...........\")\n", "\n", "print(\"Time Stamp of final decisions\", final_decisions)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "\n", "#### References:\n", "\n", "\\[1\\] SchΓ€fer, P., & Leser, U. (2020). TEASER: early and accurate time series classification. Data mining and knowledge discovery, 34(5), 1336-1362" - ], - "metadata": { - "collapsed": false - } + ] } ], "metadata": { diff --git a/examples/classification/img/rotation_forest.png b/examples/classification/img/rotation_forest.png new file mode 100644 index 0000000000000000000000000000000000000000..c25b73ee51d0c4dc8295034dc9f4b01b7b28ad81 GIT binary patch literal 182339 zcmeFZcT`l__CHv*Piax44TvN$0FsfcL=&MvauqoUisVdzWFxd7AfZ4&K*?1_keoq9 zK(gdaf#jTXntgr0nSO7rnOXDS%v$rUUae3P?mhRMvp;E{H>%2VRFw3TC=`kcEq`Af zg*s7;LLI&E=W%$a>n@!S{3YfrqwTC=_r%%N*wGZFWbABjZRc!lVRG5U)X~Yp&Q_3@ z{{}BV_hoZuXL~0xK0cfO<12XW9L@Ng><-4^r<|~t*KtCjsEv{TkEBVbTA+@gQ0V)2 zFi$_s4dO6Z9fsOv=F69_{QZL6fAG0vbI;&lAp8ff9{*cGgO7s69*LR+yf(SKd@wT0 zw1_G&>{v9*D7`TG!E3|2S!Yqac5-NP&gKHV*x>j5$1CBolLp0qd=4Mpe$4K8^uOM^ zuXDZ_F6cjA3Echz|M4k&sCGfd_P^eF#vX80^uOM@9WE2X@L%s-u21+u^CC)jyxfVq>L!h92`DK|Qo2R|)QQ5HyJD$^M~iN}PIn zdbh;Ix$K6@bPH``>rDPYed(z1Gu!f^q8E~X^5ls^yi_^i-VxN7$H=?QvEtVPXTLl@ z$`)|)OJzco7z)+);@HWz1(PlDSv-0LulnQJl%nnEBF^~u_^dCIjZU0n!_>T`OYDgJ z7Yg;hesk#*vxxQkjrsmmPAu_9Rn@&hJ95|eH|OxIiWit}+CSs#J%W->UL2uYn4KkE zSBd-4-mYP5YwNx-7p0l0PslNCu)om3TwB;7iz{GB;BzE7HPXsiM2Vv#wMXW>%Fr{jRPqM^{&^$>!MgmGK7qeQy0C zRh`@?_`twGlWPCunNCteOUq*UC_HN}xILr25)^8yHEM@0PS8v_@efh7LdezEZOMwM zf@aO0Q33x#4O2XxF?)KSo11%VVIdQKu~&2Q5!BMX0M3@B!BSVNYJVDfahJKs5+M}I z+gxp&ju)*GC!uCa=E zH7i=kB0i z%eTsR7?s76H0Ngx;8LYg8q!tw?%wSv!C47gcF5BSJl-myJ&F24{k@~b9!)f=tca8H z;)H23UKb{JP4=y7fpu@ikj&Aohp;J-L;4HttY$h0Fart3pW%@%JY`mJl(?l zd z6@4YR*~rS9@aDr5PhUM99fEqY0;f(+(udes+|HUsAmc4{m|d+fyoAOmL7&)w<4a$) zGtX}YY3u2cy!UrxcO&?WGT{lZ<7PTmHaA5*DKBudqx-TS`LS4Z60ww2R9N^cu&+9h zL5o8@=|&!GlSYOZci(tP4^3F<9d?!bY$_@(d`{2d!au@=ml>40^yQcaOf|t&K;wTyGKx4U*E6Lc^@8ltuGAfmAkKp1_U&87vF}d+2(o9!f#bdSMb4N_bXfeXw6~{@jt<>Cvhtcg+&X8Gb-L_|awiD<(P@E1021a=lp7?!cAsWcic%^%;s{d(DQ3^iPxP_#38aZ7?&kI^5T$LN?}luZfBl1{eJS1EQ=pLU&+<=hD)INq~c8 zk*t893EM%ThULtNW?QdM(h7O)I0rI`Yl_+p3C;N||9TBuR5I)=^P(6M6bbJTJGrpHsVH_j5bp&E>I^2CtN#j1rFi;oDs-dm@ zK9mKW5YD0DUG?V?)b0BfMfOpdg~OGmDi zJY#~HTx7R=s?uT9_wK!WoheET`4;VL1IsMv&@`L=0%Vt{^`%CAQEBLK+Zu0(q>*%Y zW(z!$fI>>~@G1tDxv1^H&$o1f-FdXyCnk1PE3>(@K$v#IcC6J3mrjZ0vS zh`ymkp~^Ng;zq7qxkA*;h~8KvXTi^$EhKyII?_UoQkl||tRiz4t`ulx8Co3dZRccX ze==u6Jqupri5r=2PmK%?Rw+_}4I})9@!jpMtu*5*U!#q0upNH&B1!BGm3xeAAVXu- zorz2fQ(I*>s)v@1W5+!wVY(!mHbf9$V?|bp<9L1YyLW%Z!fI%U;DKiq6V9V2!m1eY z;3&muOrhIdE@AHn1Iu8qoGMK z%Nkl+mq|u^FvR|L>!&tXR@z_(L#C$W)1})dX?caj3`Dkjda;S`f3K4vYtusa9mwnG@+_g&HD&C>D{kIp}v&D&6dFg zhT&0zg+y~*nK4$%OSe5$B{Cx;!}m1P4pr90-`J?aX<}kRx7qi z3eiG@z5*L7AsQGd1H6E#LLi->?EcPr*qS@la)L|%vd=FvS&jV&Sy^8fw$9ScS0HI- zgs$ySI3j3j%bq+#=e@V(fX)hN@6OPpUAT!rD$fsa-&ycvpEN}$b7-VY9qjM=pSk9X z6jOLoUaF_5QK+|G4U28?up?JtCcxv5EG#U9^_KDM8LGIbC=FKZOnYi@=yjC?Y}T1C ze*7nQ7bOD!!C1&g_s?+CpZc%2{x6W%e|^pW`#|u2F6Z|M{LjPsXCnMRCK3O93w-oH zQ~96a`2Ql~`{(NZfAz3r1abe|`PMB)Kx7oumpTGy`5rLUyRt&btPi>7=Q#Gmct#cZ z!myA3L8*-ffx-aR=aC7xpycA>l5%>o^z6lpX~V-0d35vS;a`MtfS6HVM9mO!;K|Ho z$g%G zl>qqs?=Np_sP^ji_Md0my0CfXt$9w}yyML=MoqeT9Y0zVrPG2b@ti)YrLIkjpFLVG zVY74;=f$4V&Bx!eDsY@^IvlEyc8=-<>Xqigq|!;yQz_k?;}`47kW&%18M=!nX}0HE zb+e!iW5u|zea;AMV6j*z+vZdw=0)e56XwNT*md*FiIX~7aJ_(-yC|xWOWTuws`QV# zV1zX{Ph+8L8rCdUm&v#4mkmWOGs7@f6EJ#xELS9_CVSTNmwrldT(+~&eyd(JqQ^VZTCJcouZo>TMb;(E{}al=Qo zZ`Fa^VRK!W#+$-w?X7=&d>+aY-)WWSRjb^&nod%?<~#d!pTbuJ@JLR6KK5tBWmbjv zKc|MuizDG~zqGvi4+X~4iAyuHt=DN1Q>Lxf6fG%-uwfRa{aHS2x~d_dNB9YyE8|=p z8H#4f%6J7Yk4^u)N+a)W83S=w_3j19#}*bG9v&W`ni#_PgzzrXF*2rSk6t(bR+QaW zxY%M}+Yru`^{*3W(~S3bEPE4*jSoLwxrbXElB{y^U7POwe8;P5<0ZxB_}pHJIWG$f zi+<&iBS&-#Jd0D5q8N?!^Q}5DgJQrRyLfO@J?MaPT3PqO-?#DXziz{>WlY3o=M-+S zuUhqOfg>JZjbe%~)r?g`L`Eo!prBw?F^orO5|G}iiuXZ5p@y{h&NNLWxL-HBQ=p%y z-@g4!%w-|LM>#IMWk#5&TJd4zX(6vcUR$l4pjk^7o}`5>!JvD4df0E?)P@P<>v9-t zz@wSo1BknVk%`F^z_;@p0Aow?r$pJ)%xn}Cet(X-J9z=sXlsb%BYPlA;Y)DPe_Z$b za4o+L%Yme*4+bCb+!pG1cEbk-CAjV?IqLPP4hGrWg3*G z2oW?G);mi~PvT}<5z&Y8^y#kmYS*Z054Ici!kkNkm6 z!^mW2ItV@d`4G!)q+IQPq4S+_RsP}d5qKavk|ns-E}MSb+?Io+=ay=QUjE)d|4Fz} z&&|ef@Or=ma@PNPdFyLs>Z`RSq4Q1KWobCIe|C&`Mi$tRGp|-FOo1Mw5X$m~P=#oU z6&(Nf0^}EJV_4X_h6- z#`iPi!eGboy3~3+=gd;bHRRKcgadb2>oW5)~-qM^(f?X(}OMe3b$UtgIt)vt%-7VwLWKI6)3#B{JcJr zFDr;iyuPlkuH(uT|HL0uTS05{{aX9Gn_3&zy_h8XD>{05F=8y9$AGEB{OKJ2`ZE9N zvdMf;_B~!+-jvhGLq>T&g_*4dV-U`QHlg9g$Zu?Hz~0aH7wiH+l1QK}?u-UY=Q)lm z=;-Q3i*ci#C7lRkYiMgzhp!a#IVgMa;>BY#Gt$OK=K1UaBH1;-V5Nl?@0pxB6}KC-(HH|spL zbSB?=Xu#Y$OF5S2>{*kZ=19>j&TziXI}R~1m4@0JY6-7aR#$f&MgN6*#{B(9$hA&_ z#`W>KSUnrv!qD>ASi-$;qJJ@-g&SzG+}m0P88xiHj?7CzL80OIn*A-J3^=S#C{bO! zaa+>ES?jRE>jBg0isvxMoV!`KgmE*N^_k9wjuB*lzEJZB31L9{(uitH&hwl;s{{ML z7|Z*f9V!M)fu{^;1?XhOND-vqq7k;PmJtmk&ZNg|^N{%~4-XFrm5Nmx4E#)w=xt)6 zqUa91b9L)U!w=Ew_38f{~ABZB<}KH z&|{-*UazpTj`Y&FZsx_Y6~9?mxZ1_HHj|kK^nxxQ7RWxA&CJX?GqD=NR^6=$uTNsi zb%l}Y-||Pi#rLI8@lG%;w?^g$iZwuZ(TdUJ;jrIDo7RV`xvh@1`cg5lJ64HiTq0$D;f}o8t5(0o+C8i7i!?dTeI0aG-yEPoesknnLLJH&loHpvfD8Loi27UDZajK`AEPTrJ;YwtD5*$#UFgvjGj4 z?pop_505?33q1Ol(L~+k9HJ&+uhk2rSivIK#ki#I%79;!cy_g`v68OM9mbda1w{ei zNj-qc(%4jWXe5GO3C{@}NxR7Zh)xPdelI;cltqP*(O=-D$;_On6A&T0ELRwCg(9_w zY`u3V=DqhSXMcq>$7sUY&sRCKwR|q5z-wvvIj3gkRS&uAtjZC011|9KGcrgxzi-9$ z*4T%?#yW5B?ka{NfJy!*u%-yV3e5V?e{h{-t z+PQMiZGpCe{sKF#=ry{>lYeO(J4uTN33#MucV&UCDVhscvX=ANKeKps;;_&PJvNS7 zy4DcS7q9j_hNY8Wpy>3ZMM&*FptcWJ8){FU=S-M#;eWfb?6ykl*`*C)7QQ$+AGGNm zoqf;rpFdTpE(&SC31!oI`#w9o`C1}qIIBvJp6Wrdx9lMDz;HP_sU)dAswvCehs_(4 zfU)Qm7vq4@UNKx-Jb4V{RUyyG;W>I0Rt5ZNR~za}5~?qK18KyS^{d~`Ck(i)`Y}{} z`}SgwVG(SsrK8%V?)W$f_lH|W#l=xVal^yI!Isjv6_&&;D-0R8gF?PbQ!s&owu@{yeu_-@uE6jF^L; zor<*142A>ga>{r~Wl6%`$vOasw#)uS@is-W*g?5(B>2#JuIe6oQ_kO{bh;gZ@ydNYpV!SKrBY0vI z@I0w7JF2$vM2v=W%v8zKg|`dja;A~c)h~X)mA39kdhcmdF^J-0Jg&0Jhu@d<*i-|v zie(z86DKC={Re~5I@Zh>%kE_%mKB`j>?{b7oO%@rCg0yCk6e?pgbmA4VIf&; zvESzz5ktxp!_C(5N90a4#~d~}h!4 zWuY$_c29(ZfNy2lUV;Za1t>r0??(pXiNGIz3=J8AqoUs1mYn~kUtGT*G+-X>B30b% zo`=Ey&Ke@zo7&oDGT!o5iyJ7;I~;!A;t!Sz>~S`pDsHYs450(?cd}xP$Urt!ujz>< zJxg+C1|UGp=i5id>$hVrJmuRN_HY>fatGODm(Q_v)rF->ZI%7qX!Kc?YNlJG#MuHs zNK%|+iOf#Q0cU)tLZnCr5tErT)tX4MTQn3~-XtSBYtrWAAdjAq)~6T8K-pev@ux#A z&2#INJb*FArt%u#CYzaT_C~33^F#OGtxk|D^MOO*d5rGFmN;W!0tuP_P{k6}5yztg z*<*8@=>p?#s5R*o2<(jDV7x86zbf+R=65AjD>`q?>0n7}8VHuI@bdA-yuIfa9RBj< z%f_ZA{N+2IAb*b^(SgxI8r-y6>+{mLfgjBbC(h9isV@#>k5U8{KA9K85;1qE_ZNwpP4tV5 zhX$IND^^X60ISOcEuL8#jjuDsROa@yYZ%FNJw3{r+l)~5)tzgr#-Nv@Q->%iQx9LUpoHF@B)k_7ulp3A;_waR<# zI`tIw$|FYuDHpCe0_R5-Y_HQ^%3Ya&Y4AvNi>(PI&O4hPuUy9mP3!T&pUF;>&0OGz zf&(Qu*AUL1s4Tl8`GtJ=0rTQt%iwl#S5>@?lDN7*^sswj(4S5)#rVSBKE-KH95MXg zG@!;rjFU?bx`#YriNUrPC>-0m5^oIr$eA*wy z@~s$XqV~12q`ZW^Y91isNE~kVhsRgX>c7ui(=R<&8|1x)lb|{ko|u@}DkVoDC+T+9 z+h8O0xytzPFf66XCZn`^8m{`GA*pW;F@@&IZ!ggSaBK|@qWir?j&u48aQ7$x?Xd zq|csEiSulxq1O(50rL*Iv%{htVE_V8JojIOn+=N5^IL|y%nxORvMQuKesk7$qc_(~ zqneTiG&Wm2r_M^)=nt4Emfb`SG}v^ldDDW@8Xh#{81RWNmlr32o5}VJb$4rlklFTQr0Uf2&i1xN zLzhjogu{yzm6!yBQdg4xYXyZ6&n$t*@`tUhIp{6R!2c>wi z@z{C9fCdQ6@ko~1?eUMljGy_fG0sjV#ht*xtDZ&pp2Z_#ZC5KFxvT?dozXsV;5C)Xnd z@Fi6_Uh~D)GQRcN%eExteHo|T?&6I~8B06(R!1L?-9H_-o;|a-sVhA)T+0uO#GvS@ z8dO35#R)cQYHBJ*iTj8(zxQL07m2KF#+5tocG)21NhfzwKD10M&quQK?||G;Rwbg4 zg}T+?4i)98Q>U~pOWHP0xdyc+Yg`w$8d3+=Y}voF?tc{&aCLBB1O2Cfckfa$2xw>M z7ToA_?d;ZqHKPjScxo5q2ItWlC3IVj^uf<(&tKvWz8wSNJPR|LxEz&BU{eVHxwHvk znl;EfjWl(Q!N_pN@zJCS`0OKG+QR1@I z*K&LMl>Oe$+L;SQUYs7{9+F-=7~tc+jN8kjfBR5JvcNPtCc)oa&P*Y3y88eeZBG64 zBHaS>%;W2`bQ}GBeVlh*{L8W{?+pvV{?lDf%;#q!wmT3<@DzgZ1oOL6k3T?|nT2im zjMP%e<{S@boBLjKh?|?-Y1Ng6>)-GlJ>0M8ZthZUiQC_;(SeXvk#IHnAau)XRPM?1 z&8y?}Q5xtlHZQZ%OR$bPuZeHnT<+BeK<*4K*Ms zO0g0g0|(C2ZI)|24zkqim4~+o7n-V6_+kW1DUl(BoDamKs%HzXAE@;ma(n&Cb^o2J z+S~qCd5oNDL*7tuR{S3N(R+2?1vWHD7tjs{_1W+ugy9Y@|ZH0M7;zbK#$U%x%3nrx4F; zP*jjLs=_B-IqMD*fN@>hx!Oa`lEOZtN^gT>0q3#vy7?XDR+0AIhve}=BO{-rPe(%# z^mhc*P8FGe^ATF9&GbLBvgYRJaXUVBt^sL%-%|{k)rt}3n?dtuymhEi zTP_ft!OivXLssNV$0tu(WaU8g%P+vbavMWrE`OuC8sDa>JRYQa+GBEI~+%PE9HulecP0-b2OI_OQu~MAaoL4*4j4Z*Y z=)%QtmgvQn(w8q@oTsNJ0=a`gm;#?+**iQQ`%2m!WO5%EnSdpk$H3^kEMUtJbDd8~ z*U4drFcQKQTo!v1jdIK)L7#@uCI?`>oV>h_xN?vD`dYd1`tT4IAVNO|2J|C&4MezI zqhTNC)h|lh-=$^;az7(&|5MxvfU%RX#eh7xYE~=XPxv!SSAcYu3Vax`4o3Q}D@9|F zJOA?ap0L}mr%2IiH*3U{ir9Q}J%|`6cH)50ExokM+pY}q$JawcD>EHBk7NTeRmay^ zr^8lkA!>;wsmqk_ZglPyJB`hHDCItBXNx=bgD%!{Qd z7B0-RmXpL>raq$2)sP(8Cn!&xP)iMB3cvidH1f7v_tu%-%EM<+AU<*8xx6}X(ICef z4VBe{=FIk%nFL`Z?~d(5Q&Uscc$$XJA`3ypv$0T5jcD;7_q7s7liHw+x^vgr4N82>97`cFRCe*u6)I!85h)- z?)BlXM`?KVwV{d%;@HV}n9WwRT^YWjg&>CZ0@W`Bfh{y;%eDA?|HUAMaQ2W)eF;9a z?(lpDRNARdl2(0~c%+Ek-k>UKQwnles_=9WYtEqTsoKWI89uv}PyV!ADawXSn9uTR zNNN<%iBYYTDyOQ2hn-|=P?81=4-}eKe^W(7>FHcfPi3s!OQ;??dCG{A&nV)toYPgDcRvqMH)yEw;meXiF@*t|nqE7K4UIMeZ9gXCQoXMjY)UO7BM zbVEsxjR!K6v=GLoq1l^RS2Pk45t-@C_4AKlCw%;Pxha|-WY+NRFGuz+nU$wz@GxmSUg!AetYj(Ac zkB&ZqWWaE#D{Sbju!QwQY%rkBRLaNGaAHNc6?a&Z(eZcA)dXTk?!N8-u@3g%X|Rx@ zrCo#4GcwlKrZvX-w6($i&;aW3E!Jn`+p$1;LCrgSFVDB|#2u%a$^uDt>dV)cC0@G) z$ntqhC)rV=ByQR`Wt{hFrqsH>!a&9!{}c9EJY*pGym~=m{|+3u^Rw~2W0juGjeXMD zHA*qFV+0K``{8GVdw0&)JV3e$wg~(Cn`E%@H)9notKO4HB#GIs5HJ8!P9H-i)Y41K z-jYCfm&FuF{u9*kMBN*9na5$BLCT!nWv*wYD>?Vme2hsUL_saVc}G+r_s#iKvzEAN zP`BvC-N)pbM54v8oHV>^?gq<*u)re|eK|74j^p8}W=!Cd{7PIzni-Y^6HZ-#zoqkGvVw^$ABv!x#~d1j z+n=ry4Qf|kHv51Q)MeMR)a#J0Q>uwDXV6KdOS1|QV-@6QP0*NdYg0zDwX#8%GCmZq za@rld5W~&nu@Gc4`s!mUKh}fQ4*uA^eh!UP!hM2IDi`2Ytg7ly+CUm)WS)sycBd0w z2V9u$AhavQisgeG1`gR(*n@eD4i1jh*NJ!tJ zvoJzF4SUjr=yla^&dHIw(_8s^a~rCA$IJOKAkX#Ym7`7PSS+T7@AQ>Bl>#?UO>T%_FgcvoWFo+eVQZ`7sx&I<$Dxf;L}Oyb()Zkj;H0& z*XD}kYmbxk0PBwnt)6vhFyA@5Q1&h3y3QsNF4>~X)`z$>>aRC3qIN{JuoR$l1bUIQ zEcZBEr`?y^V5@2XZqv=SelyW*^bt2JItA?#GhO)srx_)ZGGoP&fC;BzUfMxVnU1%2 zHzX(wsOW^`ZTj-N0ikrHx~kCdsDHnqnXa9E{;$85nxDe8q`V#q$ODH4p+c2=q5I|C z_G3TP@+~?QU?q!s5)Nb&?!RF{Ll_)n$CHJ8Fc6`6E`wsgs`cAbTDF#Aes=+VF@qf~ z%w@hW8HCb$ux|e&qLAp4tPp{L0XbD|i$nuvgl9XlooJ68KTb5;v_;SY3x`EpQuIyl z0g6B$kbu^iZ_0>^F|0A(_z`%($Z`D>)l%R8i3k~TJ6&`~M zec;9FDG#C`XhPtBR6D(b=W^eh%BB}e+dQ^_D0@pzpMGbXo;JLV5`;|k6Of6JSTVxH z@gg?#c-Zk^b*6=BQLImQYBWS-{e*k~Cd-xyi2-X|S7`$^`RbgYRYap@Ygv?KS7u^z zKslmx)MH6>G{xl&(8wX|K6evwD20hy-8EFR2vb4~f^jZQ5lg+C#}2ra6#;9C>-312*_t+_ff;BVMus#3;763=AXLFj=U4$VCqAs>5t(*kdr6`nLl6 z3hlZ#az~M3>ivXUi*h)Z6&IJ{)7-J;zV)BBqi zIXRPCO0DxIX$tu>i#E)E{q>PP-?9N*&4Gb)79H>T$(I#|2e*EqFDDl}W07pJyM%c>dCHoN+Sr-y4!jJmS&-WC__%KPa z0Dj=ntGHY0YO9qRWJr1Pi`_jD|jpMeBi= zht$m=WXoIny6=C1CmPwil?_T}AVB24x0OwKp3`KF4wSFfV({iW0kf}9wO*46cp?$9 z3PmdK@w7VCRCo7hyXFBm|8v)a0G@5^h&6v`g#-i^Tuji6-(PU&+gW1x6NP&4LV^*s zlo%s!SD~4uv)Z|1RN?%Fn}=syC+j(aMP!wtr$JOBsuEUqRlF%6Ool13z7@zMY6u|> zsE6U4TEwl_39yf)EJ==yc))ca+`PScRx`p2R zT0v)@ib8^2KGb%oi~viCL%D2AA^K`EusHLZU4@4z#Gr{=r~iRC%>W{=$ptBd&bW7V zgQSHdBM@f=5Pt`tQ&U?D1gNsBwT&U(8z^DI_Dg>uCN_wBV{j9fq`kS(z8eVf*n9P6 zd?HT%np|)M3$nmnPV?iSpXQ!~&pz&f!>{UYsQMK>>xV0fE{L5BItVzPSSb>o*G3mh z&_~10s6`$scmB0li32Ny?*cXIvgH&&20#Fquis>_5V=6BgLQ`aY_DbmASU#@sI9(1 zDjv92QHqk;62vdQ(~8k0dxtZ1NyVJu+5s&r>=6YN`mArvaK z1AgNCP`TVON}AReuZNHy1LeC7HYdEW;uAX&xe4tW?D&w$m7qS?Lv}BSW>s@WaeO=?Y{ebU%6iX>{B8ri(-&FqX?$Ga3RIG zO3u7_E(-B^A)CiyWo1>hT&Iyj1d3$YSN6NPru7j}+n5660wEy>jzBS>fG$X)z1_)x zK%JO7ZflPLns=mn=KBk=iJCUmfs4GG-_f1rW_(qfo8qqXp^EyVN>O4;C(g2lz=!{? z7A?}(JIhSDn8dyH`Aurn!OM4sb|%DAgAMq%s?t!6_1dLY1*MZ-#FqH<#97We2`pd; zHhlh8AH(Q5k=~2AX!^fO>RnSL-n3srbP5Ds}0(6Y9J7ZkUlmy zw>MPK5+~UnYTD3IX=iln=1ul{zH62ppPx@O#%{_y`a?=HZ&UQhQX(K1Z0`yUw!oT> zN4My9cb1V>$S>A#POJiCJ}^izbPmb^DG>IK}Jm61s~Pdk2ieF->h`v zYO5HkP=-=H{i90ywA)S8!*5A-GkwXqZ7E9Erq>_Xh9cD+f(Q-0y*f^lOqsS&?OL}85kU-63o@p z*1nie1soj$eq+!YJ6fwU)i&Nx|A8Y+;XGdo)DVyk7&vtjK2g$gZgnm|PWfpiNbY!& zD*71ZST{akk_L(eK!p10UY+^=d^`*I)$+7_25m6#&@xE3A0ZxGkLO4PLL+3_c3&x4 zLdK7JL3GBoYLP+MtP7%P0%$&?3QU8hjq~UPRqW1g5z=_(NW)10or+%M@#m2b;1EwO zK9XBs8o5Wqqo)4mEE^`KD+AM#n|Xm-KTTKINEt}qayf?INz-6;HDF2m zuB&V8O_#OY#eR@R0lX)yO}Eo+m_jLT&`RH$t9ah>6<89I8y;NU)LM*_d~9l(oP-X8 z&DvMD#JTvB(t`diY|i}?5JXE;&d@~+`o;M|mBlz~WD1k{#OSlB)M z{Ak|eUV%g%TaZewfL3Yx+AMh#c$RxEw!=~eDBg})H@a@r(FBpnxOcP5lt}! zCpkVup8?j|4p`iIJ~}?S&^oy<-;$^kR)Yu@5J;kL2F9xau2buEZth0=f$C}*WZ^+> zJE^;+O4Ns3>0<;gRqMwi`;eVxJ0^9qXHR@P!$FS`pWjCuOVHPm3BiV$@kwJ z?3;+ICEQ1X!XSiPwT@>uz8fpyrXGsPw5Aib>s0_+*IK+f36JWdk$2s}8?D`Kkb8ki zJj~+*#|(s+8%XL3#%l3|+g9xrY6?`IZSUQ3_f71;QwKcXVr6a&EvNSFYqEh6rKP3m z>FH~*YYSU7Ga_MF@Xf*cZ-r2gGx@M0G*aowO+E<70xQr@8U@e=8vJYn-QlS!WP`?amx_5ndqB%sl|TIW z%;%7Bzr<-W$QZ4ThS>bz)EAF+#jZJl#>{-EE+bUE6}G($k)~OME}~eGV%r27XnCWi1Lw!9#eY`>A zCY#5%)ORa7^K5wU>(BIjddUKn$5VEmP36O1#_=FVpX>&a!&CCa;2;g4qHTlnJ@jOy zWLR(iP;(`8jeraa4Y*!3;x5+E&o~Qkhy4uxAkdb86qJboSA{wJW zc25DB|31CO&fBQBEBl443gN2PRTvM4X9FG(F&vP%{{(_N2bAW}q|xij>|Q0#i?_~g zl|cXYGw*HIQj?zApD=CK_qLs&=1+~)X^44l{h)|lq_4$B@)^0A#Xv~HVs2}>#Btmo zvG6XHyOW^TLe}mhHKWjd1OY&fL)dl56HD4z6dT1^b$69*ujWIzq0>$7)9s9C;kB{W zm-5mfUfGwRUIBMPrY}%{cHrcYNgI!J&zA zbG0HbyNH|H(J&PG9+NJte=|!DWZ&~U%>~P&FW1Ts@cOxFL1^amWEvY88vz{&Ix_Gq zgO!J0zK3{L4h_JLEPhT7vP+v+JT|QTX+<-4)@FatNyr_;O?DN`_e)QCYIWZ%ahr_* zN9K$L18kqzEM2Um`+6IM+HnY$V!pvX(6D&)I2F}^NGHXzyN9;tvxa` zkPK-zBAtapEK96}gG3wSp20C&qz^*p7kO+fWPAj=U5T2>{vf3x=TRV|jS%0N?$x(> zKErws*+fwgFM?Qs)J#ut0HmOj;p#ZC_2KkCD;FtZ$@z-c!Dqe$+5)&i2H0YgYI_^{ zj;z#}LR$`_uip^P=ug9uusm9GF~2TGG*~@JnFMv)4QHMT{?f%Dh{b3?xV=8~Rxa2| zYvcv#{sl=71f^Gjg(o-O5TgaeD`c(YYK2}TFAFCo#)W%U5Zh zz4?Og(m6o<9^kmoh*~G@ZZfCYkKCPU%^)d+b7p`5V!1fvMbGb)YKlVEOw8AbpQAp*+8gp3u|?M_6IVE>&}ZiWBN|fA;Ce!S~H3tlC@EYV80UF zsDr4!NcI|ne4_rj%JaJPV%9|wJSPr+?M2=IMh3I!=jVO0MA_qMf%;hSLL?P}j=tfY z9xY^*xpm?koI9e-RNe<^@-`z@k>Pt$P#a!HiT#OM6#XyR+h_n*5nf1!^Oz*HkbB}@ zB_$0<3VkPu0ECl#L#!PMGnLWubir3CF;SxSJ_Gc_R>qkXy;}9EdCQavRy`4tBE(r8 zqeQttRlKFJkkbwZPA{nk^pD_lP*b~rDY2qYgt4?&JDII@8`b7HZ#zO#U7bnHMLnMr zD}?KFM$&7n{CJ>%0)%P^>r?O%vhV}g`$ilqkG+75nj3hQ6MO`MsaR-SC5!@V= zq(~a+7g4G{EU*!0I`=x}ncpVh2WE@XX~8F!5MS&o^_VO?$xCSgj7&LBEEziQ-;15U zZw&qV!=?6s758y-?=`b@e|HPMV~MS_zk~FJ{T-5ss1AcAIK8eeO}Nr17Q_P>X-0>M z;i1GkL$V@D&@`0Av?;V&HGKj$Hb6w@r7L29kf%eA%8DHC0}|{#WcjEW0Snq1>tSMI zTpOD{2W30S$=P{ttO99@dV>fQ!bRYp93JdLi=n@>TRPv!*77Lysl5hEWQwyakkhtv@qb>WqN07}uc1VG2I)CX9(tkYly{M`nJ1pPJMNg}I~ zx-&a_S`(2760$U&v2eJpJSJkYUjPY6g3}4WFKU1eAp5;0hH1M+kJSshzwW=u^Bh+| zx3yLtAoGC*efU9w0F7)@HGwgX&dwOT0=@$RcxTxjn0ExPjR+RYuy_<$>y~<#BUahD z>&j^=S#iuha2m)L>Hy^S^1Yf~WI#NJ0L^U3(HC%A9Eyq!Spv-rVC=TRktiK-Dg=|5 z^HgkZ5|HG*Zs7Guf?46bk7SJ4vmg6Uoh1L*T0q(rK4lroM(?%63MvLKkToP*O2{__ zP^hcjc@B?|DBElQ^BM#J(`#NwCE?T(2yGL=*L~Qt3G;7l%i-Ay4SP~;@KGeV5Sm05 zJ@^FD83}+X5{PEK&Q4ekDAypCHzsia)$ibnIXMN|Vq2CFZ!sN5i4155?>! zeA{nA1X>E1Wn3y1e}F9}WcLC>cVP%Z-1`B?Dx7BYHSk zMvED#Y(_(4!y!Ca-BcGINv}QChDhF!*RQKZgHCT%EQuj~q)R6Ni{?8oUrqF45KX7v z+?)!zc2zvfdRgDOXo%y!!!0o@0YU3ZbPU- zp)S{_VN(&QGO!SQ)h}9ifW2&myn>^8;P)dvRe<3(n`#%ym6gyh(E&%90O<5yU4~Oa zIJ#fY)26t48$rqt6UTrk{f=e6w^R${4S}8adb(EQoXqFs zPNBBqnxZ5H2e@s)uOx*3~08Jg8pYHh~b`_^;+TWmNCfr>9HQSRtYUKF+u}{|btOBPqQYxT#179Zu0gwn=BJ1Gu2q7|B~gN8ttIe!v4n$7+Az z*t-e*a8Jpn=*0A4rnhcn#H=^)_2PxCE+B$3Koqt5^BjOPWHtQ$gz&DP#K1*ROola} zR1zTWj5Opz!1MuAkZcem2RL{6*1hTwFoX6j6iS}{mV4e=HZ&VFwkSjh;3K#V*w6^L z^*_(W3!!s=rt6uIhE3Niw54n2f(L`dcwf8@s2^%Y7q5)9gIwJ~QZXw2j??(YQioVU z_jY%v=m`*e;MDr`ySX8~*J*01weo79Rr^E}5_!LMt5yy4wS1V-RoT)aHlcDU+>ZAC z86d(DH6k;O)}S9|O&;RRt?6{_kdzic8rC4`4lR~JpZCGW#N;cOhBdxFS#}3{$AFzG zhq0-&e%jwXzqC|w(DT_4AWJ9jij$N+QhByD^4quNer6NO+K z`oPtefF70Q)XENpq|A2n(?cu3c8-og=cca9ti5i7*mn5G4m1*$s z;YZDMT_93cm4~uNQQqu`x*J4xB&dd%^p)7eczSu^D<2w`Y(?qjI>n~Oe@qrFnoqTWK`>zCTg)YQR9)gd6qR{_z4li<*f zj*bGjoA75F?7#U>n&>mMv%evIic9U{t_vS_*5HvF(*qh6R;IolJuK7+M4EEEDIDdxz7~}U;1$PqJW;$9G zk5DhpLdyYBKilM0w<~l{x=xHBod8Q^NP9M{gsx1ZEC7^TI`xVseM8e$; zn6zv!@G5d4d1^NF-YNhgbGM=&_WZBbePuF`k?JVmiNT~jyn~!9)?aAb1)fo!$@r{U ze?#P*nV*>%pu(qv9A>%7%z%eenvfHgLXnH;EA}EmZ*K~;DKP9@f*mP>ccZI$UQgI! zpiZ11hMpsO6fCc(9O_)3?WUR!xWJKaA`^f((M;lHyzl#qt+a}YQkvd>0@D-; zUkQkcYC&*2oNLlfH+N&b(H>5uDDfQk-`n2@Seym@P;ZuY0Jv>6OL#)2?M&R{q^kiO zJ0P~*!VxR&q!1%|kO@(>Xb~^jq5`OsTp*M0%oiF-4+kAJq6X9*4%imq3j6;XTi+em z^BTS%8A(G4?Px1$kdlVcmQ)%vC8Uyyh&It4Dj`am8d`@I8VV_j(xNSCYiMcyuIJ;N z@Avn6y?*D9^Ew>*yg%>fdG7nVulu^MXO8hshRMptR0FhdI_~fj8wAWT7`g0;|}nudY~G{W~YDV7ajyZ z$<_LmMIg9XVw8d2J9%hmtomn^mS~zUEZtb0e(dF*U0uiysbEDPlsd)_%A89=V`~u7 zJQApBH}6chsywP50igpXvzd!tywe3?BA}6#mE{VGD&j}?_?&(2bDJ;KpGG_RXWNQ? zr|V_<25uHlgJwtshIpEfpA~D^QQ_9N7=ypK5OXOUr zsgHH&>!z@qHJI9ewDtOhHkvqOsyW+Q@tv(yo9zB58A`u_($yIj^60xRu75K{0RIym`8+M;;oT>(`eRe=II6pgp=d%k0Lg zbq&QPMu{T7%e>Xnb&J34>sicxeaLUS>c`{Zp1eIMmg=_rWb(N7tFP|~T6`H7MvQsf z7g5pqVX`wt`%zNL5q7(Y1{>oS?nQ&m8BZXY<5(Qy443&-FS9F)co(c=ej~#Pqr)h7 z!=24URO8jjjrm%=*jKN96rc1g?5*yQiF^p&4ZU8XOIgHl4)dayO)jzT#355vaz!oV z@E^pmvhR3x;`h&*FBpj@XZ4uh#?Xfk9|C8jq1Rvw_cj7DyyJ}X(hZ)Y9&VrfZ2$pD zL(&PjML@le+0`T~HHLw4YA^faFMmF(#5~2W1uodjmJT(;cWqW4bvgvndD7Jtaf+U) z7**3iAFk?Y)Q5Wm5VYI2rzev>2J)(Kt-@bt<2&4C7I==QI`!6I5t;dvg*m^ zB3Z34#CJKQEr=uI36+E|6v*AK`txV@^6xRQPbdS(gd}8LjST}|l-yn^F3yP}cOp+W z&e4}5gLp59E5P}xy^6CU=`)UoJ*n z`h1JdYFZW@c(_i2yP>c`M+e%9E)jqNE^r%~xM+k_fhU3{XJ8}9^(u%pNTltGKZI8% zW>SDbmQ2z&=+CvnR!?!i_S@I4IZ2m~xW&MLhfKv1oi}FpiDC)0dp*il*V(anPyw)% z+zNH{p*DxZ_a(ab{T_psPKDJPP{aU)G6q2OiJTeY)Ra&6)1d9=$a}cU;SuT66*~2J zP8Y9GG};aO!?H>95<0ztUkaCh4xfOJgh-!5sV&BrBa)aZ{L*cHZzgkVQr6W;=?3ZN zDnn!A791}dEk73w~JnZ3&l%ugvG4?o6{x*o%`k@^bC3EzV%e(pH z-KiEwOPaF=noYCUuM6BFmhNPw#*ud-$1&cL>oS~ko|wTRr8ys_Bbe@Dw|8jEF^_vi-6Yk4y$`+eYCyU4NZLep!&ndVfA1GS5yj5Fg2JgY4r}S5!hjJ;udSt ztg{v6Jbnh_1>X_4boNN}?gRBhqeBWqGWr-bB_w&x!##~W5BGRAT?MJ|`He=TLGDF^ zWc5d;8Ae&RiT&mk#NMWbqGyeHCXBEg(1wNMF--UD_8cDa65nC|k1__227uW#V}!$j z9ty$+EmnY06jvjd+eGi@ay?ghjS^CV`eoM;jzKSRhFR{zJe z!oU|aoj=A}@1BB>a_2MXjM5Re$>8C5Po>xLy3Q37W1ozWKu(-cgL7pV@nK%*grn>o z`1aECD4L>)wEMCw&*?Y*OoR2wLBtP}b8pF~Q5>LIV`AgozR(9QtakYu=)b|1=an7cnRP%l6IW< zClx3QrdmFE4xQ$B(YlGMA?O919(2T?^B!eJa{2Ec{|WMnF!X5C^z;Og0i{*^If=#i z0txr!pC8%ix#SxD8i{lYpf-2kg&vDl01GIk5ttRD5!7om@Dc^b#M9>e5e<26jHHTy z@U{V6PI~(kt=JUiWKFeim0q9L)6wZeKpK_3f19*zy;j6zWy%rF2>E0NPT5(5EU;AW z`^Y}zsY1tN_qoqoFaK&g!M5uxd23>zq6gcGo~>cvOoi;~p&TbFhD{%%6(W;0uV?%J z6?X1BdFD*owM*8ABi2j~WniGk7K5!16ffQ7-;PFv7uUX}TE!)|N1!OKjI^Yf?aSAQ z(2i3~#Br#4b|E&~RgCgV+=D^J5yxTZatxsS)8eWX8ft%8KxX>EB|)nB=HJ(`39sYT z7i4;Z6w)drT2QF(c$y%J>^f)lMevy4Z*556jzKGO+H#W_MGYqlQD_6C0f&8_9D6U5l#L)u~<-Qyp-_N^E|6KC_(`Xp_ryi9OA7uNGs-GRy z8S{ZvPnk<40w zVt}qS1QYi+lr;#01Gc82E1pR_=!HimL$rx;hE?BA9{DCX0*G8{8V>CJo_>61mWc7I zo7%B+F4v}r3Je}0F)=;wVeKGdkd{7#!S+ybGz?0!EjVXUot$yz4t+CL-=w-vmJ{LNBpj|pI z3@2$s)LIn6|JyGe;_qVoRCK8;&evn@?~X8DQH#c*SUd^0KDLA-9Y**`+#?GBUdcTOs3vghFuO@&V#R|D(@EAptUxPWb=t)2K&eFvuDnV*7%9 zAK$*OAmf;tc%pbR7i6n_%PI~}v`al(Nqb?ixuD?6`PKwJKL%kqQTtBw?29x(lHdo# zI#8F`+CLe+%lPGmV$xSk^y2z*a8!lpDACiKto_|_zO*+kbMVxt59d=O z|MB*sQxqvzk>4Q23CPJVSzLSbLtl+p=H-bW+wt|SM*m-f3Y3#Gz2YJ(;fN+V#{e53 z(=8oyCHo@?A&W^G(!J$rEPy;2!2ZnHv(w9_m?aap&?Od%+cCz9A#9Jnma>BVn0DTn zYMvKJChxl~xu6$W**D$y7}mN#&mpaQ{h&$%P{ZYc zAIYwdF6Y^Q5VQwYJn`(b;fS$x-smSKX^d{TJaQTTEm2LB|*+Y>GgO>xrx09aZ|bxQdZlZMG$bU7{fH6YzT_ddr`&T;Iq<%xm< zqJ7(yRec<7DiVG{#Xe?xc2@Lqy1uVfkC0Y_c-3TIF*cZC{Ngye=e-?747UTtm)dE*Qpg*3LT;eqXuJY5RN;pvw5!*-Xd- ze^=$g-Rl`2qYw$ICHeNP)gM1(NCF;Y`}U%!h!_MSlYCbT+lXQeVX(g|Zl3Phga36pICq@> zr#%=)&u=O!r=VwJmv{6A8<^E-*IbR-E`rRGrqr06`g^-SA7DqEju zgkN0yog%39An5@0oY$33+@GKW?Pnug039nqS{Ni;b+Z5Z!oaRMbAA{`QM1`9>$fN$ zm)#P*C(~rRW%*4W1#Lhe=iz?K#5d%!2oO&c>dB8Q`y(U?S zDJy)E?(DPhJjC!w>WCbf#7Ec9V$!waEmjxX*D#oMdaynd4l30RHqeimVJK#Ccxhy0 zr180i6-FGDJv}|ULqTMiOerWB=i&?Zrl)mr zZdWu&M}i3G?K)zx8UAqv++u(NE+Y}M=w*NB*eLW)dvjx+N&T<#PF zi#^+4B5w9#p{f!U669oSC&siQC@cd}144gj6u>N?4+~*{*+xVtVy90q8QSy$6hX== z$WNXe{Bx1KJRCDJ!?-(Vxm4Ud?_8rXq@{H?u3aKCHjt^I zp7v8*^r0RBY0o5V7mEsINfYneY$eaPS7+>!7RkU%(A^z`@>ptlt^u8oB&S_lPWRAu zV^~kXSqPKmdEGOe5Nt-$%*$Q-UWne;hVDMAlH(S=&y3M^I)=C3BWy;u^YqF%v_{AA_ioi~&0S!np^*-oWB8Tb*Hl9cDyJV~iR+m6R0zg6r z!baL7#d3qSu`fl;(v!MdpvBt+fdzOs!UVDJyWoyjR(nTdeyJ@RbI|=5F&S*1={&Xf ze@DuP-i2^Pn1A|}Uu}IkX;I~_kJ7ctu0+~4R1bM*m8@L3l7uVBRcyZ}Rf*^&`SaV< zK8==J{Iu@SPV;;%o5svzCZc!6{>Uu3{P>O((~q}<8(A8AD#IE(G7=#hh=Lo622o9t zHYld~$mBrYP**ZsEp@wm9pJ{>C8U?=dfC1`-%Q$8f7fsj6QA$95VlNPY1{30gEBUY zo2UPChSbo2?xgFBekTg_!}=wrCn|EG%dwqnsIqO(kA=)sL}l>#0~xQ^M#bp5Vr*OU z&Q3mbVkh3m7(kh3oOddkl!(SeuHa4_gCTlHBG8OjfZ`J`4%QBeG ziAx=V60wMiFgMDX8B)?AZZ<;Z?!-vx@aAixhwpy0SbTt+pzdLagIFq&0sx*nnAFpA z&n@Zu6q~5INyI&O2`$0We@8(;h$=jgIul5k2S}5nGfsSK3j9t>yt|^PO%Hhpzg1b5bpNAb>y*~YswOQ}`XcL=yH)2er zTIt8YO%j%Y66!aW&$C0HZhhLIU;dlw{3A@zQ$K92EjCHV2Atex!2ah`r zpzc>39D{#OYd~WA6f&uYaxua^)*mXCGyX?C7Wav91J#%|5uHLPLL|2%U!V0Q-YZ<{ zfEZR7eHKE}!ZITZR(&w~#fk^ugl6FS5L(Yaraymv{KBuXXPSYuJAtEM?r`(dFF22` zmV?N{sOqgi8eG^jTXPQJ${a9pe|`;QOV6QLes10#0|#}#`^4#536+`3R6*bBGTIK8>-p?YY^YJY%t*TGtoB|(O)i+h z6*ddbM1Ta%Fyt>3H*HzR$7>iCB?uyGZQF9<2d_4Lxs&!qAA_$3Y5E!wWZsEOVeX#h zJuxBZ@ig=xM7<2pNZhGp^ZM1^?)=W_lIXVTyW10C(36P%2La$@nhG8$9^tthGWJ)IH0nF}$mUcJ)d zV#XicAOM?>viDG=N!v|xj(p9lfVInAXjis--k4=?DUG90!uv5>`uv@BR9y*Ws2_py z%yT88U~jGPrCWNMiy_M_bA=aP+JIqE>SuoVjR zG8fVsN6n(|s}+4_w4Wbya0Yp<^owZ3N51)zbmCI7_C52>hIK@L9{GmyI?9?4lBcOh z9&1R_*a%+3wyW%V3WHEJ8@3Ici^$DM!WfF7kfa-r19U*vLr74PGzgajrJ01r4&-sY zBHB%a2m~!Qgl3sw0JRbs*L1%Jfyd+VjY76CUM8cjwXuq6DD`bC-h~JT2w{*z5I!Y4 zqIW&SU4LayAX=_N+SlTG=h6p%IrLK`iHmX<2a*4|+W^ot0b)qQpBil@WE$a0bmYJx zH@uP78&OBY5nW_y*uy&NWs+- zHkf|EMTZoUxa;KsZf&v!n#l}MZ?C}7qnP_QMvp-G1mr9RAOi8b$q>K`q6zD*jXH)QNQMHh)@d%H@T}W1G|CJ>>iX!B@r$eBxYZ8# z3~VF=1d5_Y{4xYR8OpsVT7MNJxU%bxf8^bluTzr>3b&ptziy7@ zcVf10eWih}TQiWMe!SkMGanupz=*c_eVrA7>JZt1B32ILHp4^`=(~V?`>zz?D6Soy zm@1xrZMCwK9WL`n`{B$%CQ_h6f^nQWvo>bvgBzyyLp3mS9<`I$R(12Tg0;fSUjU7$$S{Z;V{!$$e6@6eqp}y zF+_fs4U}AstKbgb+i3u64lOS2J6iz7j@*|xfGp9B#ngv@ie&ozcdkH)Z}!SrV}5!# z0;YrfY5SKJnDT4jB-K5Nk%lzSVpn22f`#0PEL(ha{2+u?;i#0+9XoZ-4i-}wAS2rj z%Y%8oy?h~k@yqRDC2PEv=b^T|o1EOcHttwaJ@ksg5VT#yJqrNCWU?8SQuRJ-87~ji zpTT%%$5`>g0K!|!xz>R02T?$X_jK@u8dFKpFFN+4QH}$_U z9P6~4gZ*Gz{?JMBQ^h6GKOvE(D>)}>=I7Z10yO&DGlb4A>|XToq$v%GOyoQDNU>&S zdSGCHb$;Y3r~KqTW@ht}eewbVt7&ho#Nldrag7gluKw43{2t>{g8MSod!S+S{`zd? zSUaOqg7%`Xu>SiMJ@10AfAU_kEn-wwPBIG)4#uP$11<)E7oC{``1CYigdMIm<%L&~P(E zb2?9OyQ}!x9?#iY#$z6(hklH-o@-AR`1vW;2Rilx`DZt!EY5CBZ%(JS?kl$)&DPcq z^$!X<1Ouv)my56&%W+3HqUpQ%R zA7U22Pr~cup#nrsueytCea~fF9OZ~$zVHJ7MBGaYXktKPjm6!U& z%JOR-j(@+lgZcZT*KoIWVQa!2ke0vS(ffN$&}lzXR=Bue3F-;GL(CT zciVhj`kBL=aydLlxm6S0|4gvTJELj%_U&pr!_2atwYtau{Qkr$X}cS)&5_xcn3o9h~xBbF0=lefnIeThTUu%F4=T@3XVKQs38vaw!CVj@^f) z`kZj>(s1X?wVzIL#1T_qy@z>c}T^ykcVXkg~~Mn`p3W zO-@QGd-G;hZSAdX_dZCrd##CxjJ)K~35z<=Nep~^e6q4F)K`m`Yk4d!3tgw!S!h?o zE%~JFskx>P(Ic(DIrNwz0;lXuOeMAV#fpa&lUI z#hjOy*LByl#l^S#dR&g3d}O8RBq$ttvAlI zZB)h$9%D5vPU@PPH%xa{;!8WU?GX^zbc>S{(uP|o)edZazGo&MVhuj|h22o{TiV#H zJAC*sD+dQP6?U&bh?N7p;bpHoRxk(f&*V4Pj^AZi%HqCYZS6<3949gOP;&P2JVR1a z(*894?+s~=lLM{Gc_UxyaYtwEt=Mhh>Km9}Z1j308|^j^zZWJpwspyR>p-;Kzkk21 zrG+&&Hy0v@Eqcd}S=!ptns7WU=wPIJ|M8>p*|UP)Uaosz%5LH(>%G;=%4)cS?FPb9 z!s&s397PS-TRj)2@?i6MJ^&ZMB)BJ63mc?xqD<)i`SY&ZJhX2*MT31gmmS~Scj`R# zA%#BY`SUklp5*p_OI$n5wLY=l+Muq`iEk1&E^X{Qyjkpxzh#BQ#Yq`q{X&}RyV?Fw zFUDiQJ==cY*-7Ekr%#uEO=caGK7SJ*_Ol~JUnJMDj+w7p)u6lXy#K`D;gy_!@+#BD9O>Bha2n=M%i=O|+u~0+a&DW& zj8{>yqE&l6&JE`1I%)dHUtM*&9`_KZcj6SIHKP!(lrstT@>eBL9tI!qSrZVN_-tvB zHRZ{ZAj+|gLiEmYv!-EYX6`yM?j`))m~)qPjb~53=)L9O6PJFLH@#-+c^9TOb79$3 zTVbyN8+y^o-3eL3zszZEY;5kcjZGXeg?{ot)F-<#{8#B9rd@yXM96ctd+mHA2Z5{_N_0jX5_AC^tlfIrhs*4(6?tcO@b z3GT|u`dejxs3!4Yvpm)jR<3-!GDSzYaECZkI0ItErih3L38(MMb-Lu=`~m`&@B>Up z+`zcAmdMUyt>xkS|E;IyGrE&IAqk} zR{M{!`?es`CiIXG&opWu`k85RBl31Wjz-}%QD!t4Hj>2?YJYWYBWj*X+yET}U9*Jo5^pBEiE0y;|v@0y|^r@=g7>wT}n!?Kgvm625(ng?YVvHW>rj&96H&jW-kq$S+JOy z@QYq##Mtuvy><-9t%$o~Th|*0!5YMQut9_Tnb<^q5lJY|3SYjog2o}Cr#}N_F=zdFOpqn}wLlvxe8zubZ{DZz?~NmmiYZzAlR&$Z-i+hhNY zUrrk~08{0lGn`;@XwK`4_b-Nu_;bF!$^8cp5@4Hvgmqn+mx(Kg_C8WY-cU0WlhZ~# ziNR!w?FivDX^wIluIpWvslihK5f7i|4+wo&M@I6= zX_sL8bpxBE>Lt^H-J)Gs=&OBnE4G-c`d+q9SKksVbHJ)w>$j-RPmgqujL@TSs0;V^ z7mh=ZYipFI6R1&`$enPGkw+cl`$=)04hHS45+}X;WpP%FMWv*e0q)8D?&{15d_v){BjUqXJ8Xk`43kBeGqph&f=s{c$f?JXp@-xCFjm zc8%p&*XtH+F$mPIgQTDwwn<&Eq1Q3l(F0+!TyrJ*P2*s2$ms`AXE# z+s7_>(IUN(98Nx|)N>J^H2QX%3~znm3hFgoU<(Uj-f>%r4wq4FR9c+ec~Th8v@ktXH>lYsF?d$Wy8@EI=of0y`*W~!& z?Fi$(_ge?!%iwpQq9%D9_3|woEv%=C8u?IGd??7PFi;jWH3yP3ILVhT@?3=F-CpW6 zeGxfYpjA?@3sYw&Yk+Lm#_Uj)93Oyu*r7NaQ%>bHl4nJ6^e> z3R+QF?Rr|;_PObiAe2yBMMV!gbK>XnLG4+FL+5;IQ-Kkhdt)H5yK#Tw-|x$wOTI77 z_U+qQVf9|JFmZTtsQIWfr%!XKmls^CRvz-DJ78;@PC0?DM>yPO(d+qIq2b_!M7Nt4 z(3)e<`TbadlK_5lxYDw+axJ<%WjMgfvI5T8|UsG65S-Czu#vKeW9`_6H{6Z)E16|4clk`{26HAnGj%Jhwtt<@k4_X z*9DRn!>dP4Ksoa^d3jDC37^tZCCmybSH_n{?KER2r}gemv%s-2$2upRhsk<$%9(h0 zK5=X7d-IFzY~QcU{uvYU{DOkEX4lQ(e~_fN9TMI!OxOmJXeyV5dl^N4=H|YSbyvuG%sr

f}1gD%|w;rfMtk-~b}vbo_Gg-Me=* zbacw6PxGIxjoD7wbLQ8^x>hi7$t5)^xZ@OI|D)AkDYBzv| zAG~Y2KZuw9eTvVhxpa$NFQ0G8S%Lc>^7>`tPFMKS6BI+js%>LBC7vnsP=XKbgM;y8 zH{Fhj2?l*KA$8vvMAv$3QlH{t1-KSf8O&f`D2DHx8pF#^IP>8y)-;Wt1i(CU&FF?r zo06jrKP!Qq9hZK({yMx^a^%88X=!Noqq;!7$5^MUcmfS@;PvbHhLphD&qpGV?g5h* z&f9ArDFi|H#!xsw1(m|8HEX`ZUgLQF`Dn_*$&G?Wmitn1Mk*Sp+Lq)UJ<(__U*z9J z&&9cf#8*+uyw?%`aA2@=#mOQL+(-5}i6DAk{+g5)GDzj0(}IG6hs;S8!Th@}T@#XLdK)a9I+H@-)=d_p7VZ z;3V)f17(ah0288UKHpZTOBT9b5_JKt|9V?j;9yaw+mM1g9|$m_>@dJKCJ!odB1P0i zqG<6-eIp|`8c{bNz+bwF|AazdWt64>Wh-jKfM+%4Y`<0ntm?kafzzIHc)dw!FHw3xj{DM)yubQd^06uMoX7NfeHQ=ZIL)HZz57>8?}^eC;s%sB?7xs>JkBq z|B4nYQ>sD6U9wC`)N2^IgK;dkiHOhwLL~I08o0X3A=G@vMXyhd^ECfH!&>qTu8|N& z^MVfqNFj*=LQI+6c1?7e$;_&%JO$GS&Y#~QV))b_h0#Un4P4DHT^xoxOL_3>aG7{N zM}bXa`X>BjDk|799JochX`-zIA3`o5{h}M-@BchdGr61uA@p)8TcNjH0U7XFMj>W@fYzkVQqgp%$j-ID$m9;K9V&)Wd~EJ18hfN_E(VuVjf2>s96SW!}3Fcq(LlVxJ+HTpBr zD``{Dug)LQeBt3PYX+F%uY&z9zC&7i6g$DF8tvqE+T4HkY!~?@E-V-L z^!Z!qnbW6F0@Ja|IScbMuLIesQ|M@k8fA~mLxgvfHqY&vce>mv{qf_ptgP%-%;-WV zy*>3M@R9yjvIl#x^F96v4yGfNE$&M83kble<2haCy+U1E`yINPlpG*N_$_I>rjuBk zoV>hETxwRJ&c?_RU=GtiBxDl>Xm;Enm`m}dj}P3M+S)iT7I9MDzI%5K6*$FrFnN%` z?Z7fwW8ru1+{7VC1pFf!s_SP2YY=I-phhC^Yuoni19`2>#SyKrG?75q(29{|0=Ot@ zg@$VlE(0jX!In7RA|cE|i@wI1FDXY3IXX(fqJ(y0C*G}%zN+e)Jq{foxwZLOkzD#C z;YCgjq(3%3&cxO0LsB!gR}XV0>J@Di)80Tln8gv03w(!*pJ`K4QjQlo#uJu7d2%yJ zE0dCN*18=LabxxR&0SatDsO1i$*W)muL@e`)TBkC*TR=h;LqSS+=3znqCsU!NXdN5 zt)0Y0&bUVe=r>-Je0pj6&UeC^iY{4`9Yt~_3Cjl$-`#N?XqSR-w@dtseF0=B2rgmy zBn7104iCQ$aGC%bi*?WW`~9*>+mH&>5T!_+45IBX6CH(}`Ft&9=~k^?{kpc60cy;I zMEBhwCntZkb0En2nC|?0(A4$xm?4b4vF(s=8O&ClV^$u6%>f;^JS#G;>cxxy`~@f! zWURlzb75Rf{^}SN5~+mL_rn@<)$iZa;1~X(q3^H;f5}r#RE#+V1t8z*SDqJCo~-+E zaWC*m9U|rN6DI;fe`_E+p$yCMTJ|LQRy}BkMF}Ti>6AQQ^$V}QfV-v;2r7|8DTgPa z^lv6CbnISZ?y z)OjeSNlI1k#fB_14s?vbNcn(n=~vgM0s*Z1?CcRR0^d=SOGrvGaUtHTi{h`?Y{~ke zs?2i)H4z|As(JB?eqq~fQJT5dpSa~=!baHwD{&9xfrAHEB1;O(1xjGwmtzTX;L1S( zB{bwDC0&CQxe_zS+ajxEyUy;1FkowB6-gZ^R`&nha!3S8W+1DSc?3|vVz7tfKQLfh zht~q~_#`$A1qYqPqu_VZhdd%;8YUi$f8^}_4(j--|cS8yXO z$sgF8DmectmGLVLA@~KZT%)uwMtz~PM2rFq9Pyyv{1E0rW}I)KLUoQkXX4C~w_QJV z!>i(AJbIbv-i=Ib`N(N+-n==9^OcZDBXg#j1;5&gn=vVf^xscS`pW^4;vc*e_Ux)? zMGrZMG9<5#om@JPs?DboU)Y24g|(9P4&WW55Os8RX5muH#5d>V<8wmELD^^klAIqO zNUujv6gO7&s#G#%p}p4sEl>1p&4XL`pOg(m$4ruojq%BoTcMNpMXs#C-OJ9L%x}JZ zV+ZZ83P)iGdnucp*;gS(eIF^r6pvrMDhq4#b)@(V{04~&3)rpORi`O$dMij1RcGhj z=;`DCHjPb9sp#voTDVw0i`OFE1w_<7|* zn+TqVrHh)&ixTf@>*U(_u&nI5eZreEAu_(uPCdkmZR=*X36XU6u~$jnn7IT zjWDsX5VTdFghOcs3_&6+vNyjRgG~U|#1hwrv=m(uT4vauTPaQf5wqa;Bar)zL95D70ZG%?CJ z|9n78%W=~e&g+;1{sw5DGRoeb`2Kdi{?p@3u$lsT_D5hnqc5@vMbZY;Zh-4$7?g14 z=f#(@R9BE!ROE)-jEU=(aOdQu;XD4jRk7&++FTc@xu~$^KtcFmQZR@_mDQ(FT%cPf zg3?!6ttAb1!9ScT3sP3Lj-X)sc0>7ghZ$S;&Dg!xrS6G3?lPm z?QPo%CE53!r-7}D5JbPER{cvT zbCR7$`QV4(uu&1nT^UBOdWSd?51N|rBM5_(lR-a^pXmhqudMHP8S$;bJ?&U7ME6^t zic;)uL{Tk)GwhC(yk>mmPLBs9vmu$1UJd>qgQ?vDz|%yhAFBeT~|#`ksQ@bQdtk znyK0nLS#Z1X_^q_ul@-a`*3@|A6-$!n5-9oJVRDe7B?kPGPr91rI)SVPSO+-Ah?Vkyu`n0%Guv^ zr&_GsWiZG-NH7(UZjjy1nijTN?N82OlFQ$ZZV*Eg7>D9M?%D!5558<5 z74Hh8L+fS-p2Q(AZKGtdZ#jN06`&Bovki@3Oa{_L0f-{#5P%`w71wzbSEN6{z@r!p zOhTzjZ7=?rID;7&dPo8Lo3rEkW#w?sGb{BPx-w*J(ov!W+sTz~vIw`~FSfJL?zU~9 z86O`nbpIm-^9E%#Uaq@}J+^$>Ve(n8fDN4!9gLtP^_n{JAm~2je@p@a&YYHwjmeaz{`Oe^&);LxM|0)#L;__O=(7DJ2+GsH=`0I|k*%T@(xW)lD4F)li)> zle$M;Jjd@D$d0sQBtvRN-#`VPfT0M4p-1Nsu|PZAK*a#UbS)(ec`#3p-BWOf(iJs7 zj+8jgfvYIE$T|}NK|v_5xKOH4`Y~#$=%pNt*ExwIhmwccKcWqQ(z73?jKJ~^sM8#s zoL)aY@r(kr>#q%WQ?T}&Gh!#Gt6M0=U{4VwadGP*3et!NOzvU2x z80QrPZ)c)z3I>j#5V$PekWL07COZZgf`uio4m1qYMnSEvGsX?1gMt4ngNO|H@?TEH zigB4#7IjZrpWAE%)9j7Ai^^b<>&#id3s2h|gDx8a@Ta#`0L_`t_lZQJx%!5F`{e8ff*+q>X_XR*4Wm*iAHXSHi*O@A}+E zb%y3oh{A$Hw(BgZ(I}AH-@F23X@AI0vlllZM8swZ3{8+m|MV7M{J7Z<(r|=S?Kqg+ zccEM#R>(+0g!vAen~8O$(^70GMoH_gPWQQQK)|}Wnb9!x1h(<<9U_2fj&#F) zqM|mzQ+}IVU`hP`ClDC?+nyNmI}# zq~lBqOT2GNQ7_zy=bcISgGoU&o`RV?g*5VI3sxATf@?Q!+*s%IJTNSb5l!R`xO|P$ zJ$(%A2w~${6Fv!ZjOI#q?Og)y4{!;g)T3v~_j_IA^b;r&mGg0q_WnA_qm% z0UVejkoahC7dmbO8!qWOd9F@(vPq`Xof^atzO)VnrpK0ZFI^!imFFyQJgyclL-g7j zu8xnRhJqyrMBbaqN-Ki#vE!JEaVvu`qz7LsI=%5W*eGuUY!HZp8&94*LBiG$iTWO< zv{Z(vM3S%*+%@qt!ORDP@uH@) z5Z>6ybx!ZgNI{SEP=<<nxH_-v6>vn8x$X~>U1hWxq5k}Tx5w^LK&=Vl@t;NXz^G%1d zn5gseian7cVD10DiaKx)j+6bm(_=0Gf%b$vuC@ZJ4}wSqH7G%gAS+=YzXjDZBv}Mj zCCsR6XuQVxBQyu@3ed;v===Wt1{eWLY(D%?>_Z&cp*BX+jZiydoaMyKL zk!dJGAdWmWbp75~kbZUL_tn)0!3f|RhoIqTDy%f}&_*|ukoA&DHWq)$MaXmGPiNMwycXOXne{_Qz1*@*r3@y`&Gda_HG z4pPvMx*ebxA7C90btOdD6!2XAmhuLZLdfZ)^2YzNLVNVKW-_Ha*MAcWS^)(Z`hFWo zto{spI%qc2nBz@gP8W59tS(wxgc~KSbrLR|_dJCUH=tq&ZPg zAR}UsjuPiJs$6EU43d|AYQ68hNI_iB*SO7AQdZsu$4eISQNwI=dNj!hsD1zL9fcG{ zKtTnxgKS4OiRrO0yr*wBQRv>pkM9C9#mi4f?2iFa5RYcVzjb|GDF!}QLly1^&$52u zHJ`waW1%Yk7OSX6*zsjZP@o}Y1<~648A8Z3kCD5qNurET zieB0n`L^iH%DT_y^vh0)6P6foko0OPI$b={O-n{2(rA%V1~U&wQf5r02cQ*{YZ9)F z8jJ8EOLIfwu!p0Dup)sEWX$!Gl)qBH#oLrvtGIiQNZqrCXL;a-7)D#si9x2E?=)=TRe9Ss9=NJM~1==tom z8%z@d7ipds-rtSgD>CgkWr;X<=yxTL*H%Ops9O(UJRd1}PM9Df)HzMwKnE3dDWOXV2&2Q~hf=yxHZIWwW{_^*<6`o0Qap=)U9gF)N#mW;+WQV$Vok-!OB zEcYBjT==%xq3lu#h{_knDi~25A^oe1;nspMlx^IEy~U}cyd;IU+*zL4u2(Tx(Jy@Z zF+b#L0&=UEHoZc{uoY7T6iK5>_EYm>cGOoDsQM}DKYSRS>cc(uV=yj2pX=7BZSzH( znK+MWHeARMK-qrKKIorpsgU+T=5>MeJnmEc4<((moI4RVk_vRp-13_epEAAkEB?i4I4IiqpOtwA~7LRM-i9a zauKjf8a1Tdv>N*kKP^Dr0_4DYrKMTElErCK2oY!>LTDfaazAiP+n{D7Kd_!}xcdP% z9xo~J*ARu!moFP}-N9Q_Wx(8rNeNDL!-;BP--E);+g5hPx_+1Jh%8Tk#NI;WGx-AUtiNf(KO_BT4{tg?}!&p-x!+ zKn47#R|bUl+(QV6pP zq`4E7gD-?4>Am$E5p{qN70BbDF)7V2z(fjd&xNbFj(`D^l2?bWklr*t9LML2Ov&es zV5k8}Y^6k?qFFcXxJXEexsw>dZh2aFgiCN%xDQrzBCH-aPX$ z7nlSn3KId_isB9*Ia1S}g|-D6kM6%p@sa+xbfp|mkII|KP=0%8PmkS5%))>zh+pO0 zQ*TcqCgF%B-Af;380FwtbHED@}3!!5L74%dz=p#YhLJNkGplfjT{@DIu zwJOSYEFgA97n}gqx?C$YJU4;m7&3@+ZbR>{tfPY)szcplddz3QD&#zSRuQxJQb$oc zDrlz7o5|1g~M+?q{2#qB+^w zdXd;Q-FN{=m{&*hAqi1MktvB}Njf(KrrH~+qz1k>?k59{vyjx|{-^gI zftdIlQW4^vy&9*thUBsw-@yqTP{M$~6u{BNWi8bXlYDp-o-ad7VGLGA*!~;L&CTN$ zTv=?kAc(Cyd;0WELcF7eFlrWnxNnJF2GzngxUg8-*##lH{j}?ifbOUZhY(0?Fi?B6 z4od=eAotIcu2_KXt}d#zYuA2!D8mSU()~y{mZwhLz=^PYe|wuOW=%O}q=B_DpiAUy zXlW7FjmjJKsTKY}>aiybU{&g}EtF9Hm0n)?$X`7YN9Ptg3YHMUKpG?MeNQxb)gSF$_mSU?Mi%iPP_q}!f1{0* zfYSL~(^ED=R-;b8>gJY+(+Hl-SMWOuE{0Q#%@!omegNwh1ojedk2H|t?~0lD;+LpT zv`HA#i^Sg|1AG=nXGk6ac|zp(NCnT&e`QANj8KgoFU8%izMj9&Z4NDV8+S*Eha1d{ z!x-VbL#C8~8Brp4Bm8HN*quG=i%tW9-Z0Wb0sp?ZZ2<9s;9H#03e2MYD~TpIMnBqj z*!Vi&;SHEBR+N|XTC^&9EfLiKs1?;;GE3M6JwO$s?M6sOxM$!T{P3QrrnpNl_}{1~UiLv|YHO}=(11W>HF)$1Fp9oo<0_NsH=o^L0BO3Wm`3p*bvtP(NqV!__4 zV_U)U-^7neKwAz<(3_|~(0azp_|p^@kHMAWEfrH+K=KR_L!^@ciT9Pf6v1u+e=t`! zHf{owSpncq+AkECOHXd>8nsVMNVq88zBI@OTxa3&e@J`pc&y*|ef*ZFls!VWhEUNk zGP9F1QYuBHq>z<88f32`4cUsMsD$h-dS$dIGZo4vl=VHXyVv_O9-rTTzdzoO*W1W_ z-_Ps1&g(pn^Ei$(M5(xEp(xPW%IfR+MAEmJ9z(2C1!fM+4vi{Fgti_4ZupO^>-e_P z*<}dIRiF~}3=M(h<@wduPD4{O85wh-xhzIwK(2s1sK_c9Rz}g17fCPnFLIkIneLbM)YCjbBmUovX?lpWKUzic&OJ;Njr`R{Tx7TeejhpkM2Du)S+=X_H7w zv_+_!3iI(xq{+^$;3|nUfS%$GW{y|)KhIINlYp(Ptb8Zzzy*lEg7L(Bn%T0KZeG7m z^f+y8Z9Pbm@GNWAH*9}_{83JOK3vSP+(=0I{wHw2mhr7G>E&k z%()2pY6Z9&NI=yQOD8!xvEfePwYbslAaWa&5SBp6$pr{RD*%Y(c|sSXMnb}LN;?n6 z?KmzD6kmVx_SlN3Zvd2y#Uz3zKpg~4i{|CrdYqe-)7Wz0z2=}e<->!TnMRw6;CaBq z0@adL(ZYwPQUu&B$|ehjdcS0(2Exm|0wq2A+Jpyk;J}-8`ATjhd}zO+#%4q=gw(nc z@LqYNY_`u#r(0p1{C-{CN(4=yz$zmV(T73)h`u>KHC2s$8;K~9^jEHAk?PbWk1Mo=u3Tr5Ir(TB?xar;C7bE=&+(!bk zGtw;V{bZ!`ZF%R{cI%{AuYZ`IJ+7_cDGYk22>m zkKIkc4brE>&rmn;VxR>(@_ycKza=FqsR+1bi?#*xT7ovZ>_M83k3@R&{(*tGNi|UO zXNyAZly83m$NLKo1i~OYVW>T4@hWgfn+~0wtBJmU&|qmj1>{SoDf>hb7aS2lkIDkH z*@y#$i=i52LHFENTHxK!040#-mr-U6t-UAY)|LolxYt&LvWmX?|9n3cfT4+g`LI#s*oIGjC5dfK{usP|LfA!Zeqfv5pYM077S5>av;Xcm5c?mYuifWJsN{r+^L zWRO2H43d{YMr}ggzKbq6v$}Cx!$E$c3Xat2hzbXXz7DAyG=Ef5fnjNG;CTW@wrUZ4 z3mea2ao&UI^^or1;$O=js`C@J$th@wih(Rg1ey51q$nf8#AvRQ%pV1S7=gN$3vN}5 zqmDl5Oek$cXCl*R1Nu$cE*w|?b|!K`Tvqg3q{?jWv0=-ThT_E6SAb1uZbNE-czz8E zO=?e5d8JpuG~prHxgb^GlXIYe-c$kP2N|3AZno$H*eX@{((%uf4Ssq_qMC)(aOirAhI2FJ5-xRy18N#+3ZR}y!{f;Xs0|J` zJAWWU5Fu<_{CN$Rx9Fx#+GG(@ye*hNDw7j!2a|@Y09liP=W?SB3j(gDwn*zL)Q#No zPU{)gZVkf&Cb$R{wk1zT8T%N}&GJpcJ|y{2%7csx9b!~#crt%X5qerc#s_vXrrV;S z@%tHmOz5mgdx*le4Nq{qr-HUr3^b+PSCx*>5ftkMQI+9_g3OjxK!EV}A((Wz0-_`$ z4g~Y`VB4TwMm6a;1}GK(=*IoM#`6s#tqN#gV4nrxQ$BIaK8^kvB;KZkcXM1OE@(gr z#8@IFASz2>>7YB1ad)*~{5VX0-;YA?Z^9%AkWXyhfA$q(UA>kgCz z4i_)hV84#P)mH(Ab^-i~0Gu#&PzKS>Ab-Xt9hP~JLiHkKoNZ3JJRrBe8I#|dGYmyB zzcU5<;qQw9oty)1Hn@2Tp_`cu98r^MJ)J*mZzup z0XFZ$TO*Ve8-AoU1eO5x#BY8)5S$CTJ2G}5cw+GiZV}r)Ar<+6=go*C^HMX@7$qVr zQEJ$R6jar}1CJ{^{q-xYAJ10&oBX_aqfyDHm9F(Ta1~T;}ID?XYDA9UtV%K$G8vkT-oFXdFU#1yTqN?PNCsV0_`6xw`z7A90l8zNJJRcW@7_VM? zyogHMiatyTj(Yp|w^!wL5NHDp&}j!FiN%|H)3gZ@cz)KAs<9bR0*JF*TU#6acyd>y z9m1hOLy}yN5c}xi4rD_!iE!djjWU;Nqc!(@l~=(CqGan97@pHp;k?T~LFN^HhGUJHHYgoFS z#ip`vlYVpMGZsm`aEBjeBD){I4BD$8n){_G@b@Rb^4jyiKaHGdYJWS!E^YOb(YtJh zi6XRXU&MtM-PP6iqPd?fZ2TJHwzOY(hxY9!YnEPo|0=52esNv5ZCUV?t!BdKGXgJH zba&gIYJH=FplS70Z0zmjqOpZxnHT0^|8$PjXS=^k@ZzPMv^eL_l@KsCFgkiwLnFon zB{YGQrycl-oQgx-7~;Km4H_;K5u-$0F~H=KkhG_yHhU4$A-W^A*jnV?1yw!-ddf6T zLbJx1>jePYx8bl@95i!Jo@^s{6-sn!p9_6CRvHBO4P=*pd(RSDJ&-svsPOT}CAMsO z8h80MTyDJ-;}E{K*etq!UOU!>&z54&+bz!Z1*QBdi575PVVFE=@Vr!Z#Qt7HL-!+1 z%j(T($^}g8W|n#@N)vkxO1Fl{3|tAEyGbj~8J0cN)4NN0Ccf#-kXlW$3N4#x$E=6c zP+8_Z*#~M7Yd=PcE`AZ_zI`j%c64yGKE4GM;aGPp=~Acj=`7(|}R7*v+NiEaDVbuG(8|m7+88 z7nS%7Ix3c(KTyIq`&?SK;>*@;CK12woA|5OTUTU9X(n`x+qAd&{F2|Qq`Rk);lNpw zcKPlZdDF+$zgN6E^8Ojy^l`6<1=%k>C$hyTJ&u+eTc5_>YDwlhJpY@;I`xL*ue}W0 zonxeAt|#3p@fi8tq_Lsb@3Bl_-7cByW54yNx}cXpah4%|qE(sqMHgX3ZEM^qz-R-V zMX~^bp)c?T@a8@}#Dk6;k$pvffZ|6R&}<9(-7SVXjn6N13y|v-K5*3b4~WGVS;0`Y z_wpT))u7i`>oA`iA1l|rb)>!G-G^0~h9)!=@$_X{XKuRty2`%h53Oj(lhnN^z}%Q3 za6vKQ#9&izmoJCFlECud+*MenC{`~)T@Tk0nUP`4IKMy2lIJYIan#0R+K=r8tI1k# zM$I$g4O8>NZso%@d8Z1V4bCYFh75&uEIc(+e8qq6nS*%5BmY|+pNEuwCN<1%5&L+7 zD@E=5GD^d*d%+XRlf4GX0+y9B6>Y6t6o-oz=43Q6RT&#cR_cu!)I)HpMGulNZ;}2aal-VV5&ihKR=gSW|TtDi`mGt2owiK z>b_4(U)U|l*6Q4Ic42~Uj*aQN`}LSuFH=3I!y`hbt5&R5+}lt&Y(03^_&&@1yv+l$RCSb!Tr8^p=1W(|L^0^Tt}{9cH}BmsJRMvW-(<#ae%fvvj1zS4N1>={qcXX zYn0!ySp_ZU&9c8EUdzTS`|wgz71Skb_z-WgIa=+O%w7$@5?t5QWwA3%?=^+e)HED$ zI@-v=u66aQj}c`Zz2+qo??W6y9!+&L-9FMz>n_h{l4ft+cJtxHCwV)ihm{{v#h>2j zL*TuT@HzgW&nnF`yljuJ2}_Ky#-Dfd*X(7xq)QiTwD@7pV&ur0S2J4?P{z)f)&I(i zul;_QGV%C~^|{fociOBvJ`Rj$rZo+_td%Vz?9}GdyDiL1-zW$wh_Eg~Z+GpPzSPTL zh1Ys!zDlDd(|qaik}X;HB_g`_Vw%I;wmn*YDt=2ozxBOBivQ*f&Lmz=G#;MEnYZl5)*V7+<2HcH}<$5y*P-J0*e^9QRr z-RJYo*6q{uWWwS5qM~j`={Yyk)cmy)@vs?MroS>5GccGoV3vqsaN3Sk(3({=M^Ak)FpfOKNaL^ z#~KEgFZs(xGc$+R^-d5v-0c|xc}lnj+hZ!#GyOAT{3?4Z*K_c z>o23U6&$MU%vPz)8~m8F`8Q9B+cvEap>;VOJ@+a=uO`<+R%_k-t3iqP;YJ4FS4hMDQqp4p=Z<$OO(Ww#GEysv%s zR>9GFl3D6S&gFoEj*7}`Yu4q5)E|h@u6(bORIjVvLVtpW;`T!Cs`#eW?XSkgK6~xf zdo&#FadLzGlf)~Js*l9{WKW6+jL}KGCgV~y+pHsZ^lnO8D%~BA-IYr{O6yP8N5_mi zi9Nrb#pv7Nx7PflQ;l_vZ4Qo-;(-P=dFw32x`|8^KAIJyRW)zcFr+i6yqL3g-}1#q z{3gF&&|UqvEbc#8i?cXuKK^{1d~w4j@BDryt(~q{i~3X~mfnfmL>}8PENqg&;yOpS z803BV#gcPv{y+OlCKtn&J`U$~4ENyojdpxnIdFu@Br&b0sVJDctDaHjOzo`2D8JRK zSnk%Ogty~f2E3ej&u zgM%-FYedActfE?Iq6rFZ-nDiH@Q7LG%NK)uu=sqs;FI3WliyZyp=WVFBnjt19 zof^buRCQD)QNak3^j1dDe=zk`ZQ=CCljr)xpg?Kd>vQ>_sDvn3;;41-2iC-t+}l#S zQ}RkOV<#w&9*B3;WEaa;6@Pp-Y2r@Mn1 zC*{+PCbvZ;mL>)nmT8@weX+8`_vX$17yJ7yn?3$X?>O$5+0Om+7ybRAm*MGk0$)c| z{-%Htwa?gC2Nm#XL5TTLX~G)p(t%Qkg@-2?w9-ql=Z_xYa8<|coD|caG#h6m_H6su$?hhH zM-z0di5+_im1zocy~V^C&4Y*1tlh^xTgLoU*wNjNo7rEt>ZW=^g1=YU8x4;{kHY*y znPJA@ag}XN>opTJlN^*zz1&ikgmm2?O9o(vFCzw}oxaE}J zIT-owcN7?0Fc$f{TA<6j#DfJ0umLq6Ol^GD2Wu=tTi3R`lpm68Ql)ZcpzUM{oSR5m zUjIXCEq)7o`v@$uB}y+M3?U$*6$n8WL2`;-bf5QPSzo(RD%Vw2Ysg3JTf5Zue6`@i zS9@DCR1dBxckvK`n3`9=8kBn9d4)Z`z(z4sMN+^}1o2hbs@JDZLC3$x}#ikyX(cjek#j*SEuMZSLly?swwTYqgP;{C$X-M2zNg#K~zp zK>$H^DX$OGw>|+Htcum?)1-k*N_Wl-A{hbFkMBa5>r!r#^z7%oxNjd-P4}L;#2Ee9NbJzS@${9ovfBbUJ0&V!SFb#Lrj|lEC@N~z z7%O%CYiHdXkx|Wui@!Lo&5!n5yy=eHF>KJzgB-M~%3so*EBDQ_#N(M)j*mA58pY1A zWz=e2I-Xn^I>pEF)N>{vS8{*2bhh2m`Q{gE9=#4TY;6(gj-&yLe4SUpBI@ajJ>PVL zUD}ZT5yL+iLEzaWW&lk-`}xHd#3Z;_iBn?|>A8Xl-;07AJrbawb&!)=pnXiPs4=ja z3ARLq0!gJPh)VHK@@$-P!P0rkYrf-2M&br9K1M#VJJol1RZG!Y_1iaY_fKy1Gv_tl z(4-Z7^J>!TS8C_p9qRb3%-$ZH>u`Pt9~($ca-4zcHP@c-=i81ZcHJFDIiC^3P~gVR zNGZs*5c!=a4G`d>F7edsaB6n;Gv-}B*&7_xpfORF_; z*E&^t4Mkeblz)YY_VoA^wB6p5Y}{MtdfNQ+{p+EOT!EURJ2s69*)-kRdMjrKTe}k{ z{rhc~B@5Yb%UdFl1nqxSJ9f=pYWwx1f)YC3n~v_o1uA)d3&%a4zTXlto}8a#kV!I7 z%E`7D>yEUPuuYqJMVN0>47eT4`Z|0=hWSgDjiV9%%h`5H4pTOr-=U$nzvgKlv*V-< z?=pR*%JM(+MRcq!b$_2;8WUUW50wl1g+%TVyC}3ZEV@g5Qc|OMVP?WkfJzWg{-bK! zX%8lO8$3Y)(tysQ;y*#_< z0pVX27h}%u5}R?(-IOodTf54b`+0`ycJD{=@7Bugdb%A-d_b4h5Ok=ve{Q$bs}kY< z)V#m`xBlOPcg?x6CaL!6&|Eo3S(?z7#w^zdPh~5PvxFW{R5x-8Ar&HM$v@XlJNz*| z-W08)O?Z4j#nB7{`1>vmUlJzIzpg91YyaD^gU=x~@(hSS7fOHA6Oq2HE!3O9E&9=a zXGVWv;jVhk9}oHtO48Y@TaT&0>TcWnZNpL4<&2b<-e$%yeS%aB`|%GrV9J~%a>O8} z!-@nlY@yu@nZf^Bh5=y|`PVWm{8-LO8UR_+@MS@%TStsFHg4SbA7hRGdg*U<|Li&? zq`%MTr*vY%EM4@^jeVcK(CEDM;i9krPKvHzckJsIHuyf~#TNZCtlgJ=FOO56z+~KN zeQ6Jq_4KyI*PgX3dHWq<;|ix)_ayVzaSe@YVM@oBasuBz*v`s-DM^kgn#j$&({-$#a#p><2!q% zJodH4W!)PM+sar}k!~TIrg36VW!|ZrZ6@my7OZJ0Besh)@|(vI67NsfUDH`RR6Mb! zZghQlMeFh^-ooA=eH{MU8-$g&tmxi(v97OL@)7pa>3i8x8%$Dy!g(Gm?yVWqvai1` zF+MrIVz%-vt6RbY(IoZ4j=qGKaY3}Kj`h)523axu$`WTnJpxmE4%aW+BC$K!b?=*vd_^}decCY0>Z`(9!!PMD&bm!nS0jz-FSSaUj+E1TaK~$b`V@EO zz54&~SR2gSu@*>~SXO7!_nLJEUCdczFofAk8sdbW)7i2bw;D9sD4Xs=yUTgz4-D}IZ6Z_bP- zA0_FWqbcSgtV#K@Ln_J4QO{=Z@`9{c*Wqk`e)W$t=&x| z9#&{y3#!k_3H-SY5rKnnAohS&1@u7^CLo}ZGol8Dut191b`lmbDndMi)Nna*aF{9jfn6LW4*LH<$2xU0GqAqZm;mf%hvEwC-97hK! z6!v`zluOaUJbI6YPFh)FU&~NbGu=Ios>h0KYH~cpAm5=7rj_}o&+vXfvxAuV+VtX$ zLW9%t8pFq;+)UHwRRkM8I{WV8iq(2lS>@A6L%I28WQ5*H=R?l4=H zr*Y+kir^P-7dE4(UuS(s`H?`Y&n{@}QV~Z)LE!}tm$!9a$W8wmOm+t}Rl%(|2H9p5 zNMn|`Jc#NOED_Q;jy|^Ke;UH1S%V@PiDKm>j3{sIw7E_VzA!HQMbcSc4}ftz`lEX` zG+p)(UVEg-r1ENKXjCls>Sx}MqHgkDRu9(H~W;OTC&EJub;RF=bUCGZH-X)m>vrN$f$rp8EKATIG>9?1PuX ztrL69yy=}d#Bq zB}SK1+;2q%dkQcKy7dPKt)zGr2%ltr5V^rHvukXc7G}@HvKItR7It=8P_2*}sS_>- zvEBn^;a?w9oh|=I+dI_^1_mn|+m(zAF{(8WFdWY-N*z*2N^Ro)w#bqfpPrY>bCk@P z+^7|=_T%bLv6gcNm+u|qcQP{Xc~{>6-_$*5(cl)KJ$=DCRGKmL!K>(v?bbkx6D+k1^RiMP5Ed}o1(HB+N^kFCvTqY5 zVF@5DEqx0E_wWn-MDBj;7B?u*RF36;{<6-}2~$N?(K;oCLRQpdD<~ZZux}2WTj_DE z)il4X%!f~730v#m=Of??yU4|nz4W&Ba)8&0TN_Jh3(=lOebKfso zXZL(ek_toWec}lYRuGl8YGedXH2ZVU_Km|~)j{SWEF6oo;d6EAXHE@8m<~MuV>#0A zdORe+t!G2{*9{`AZs_L11et`zQ`mXL41*uE@73s*s!{3jK#b^;!0v;)7Sg~}l5HiT z6}kd^9X06u31J4?vguHU0SywZ8t_4)iCr;RB7-o@t%3$4t>|CyjZ*$A-AL?dtD|3? zZAa%HQ2--%tUsGn+23vw`~nz0*+|^MZ%(qKeV*2y&S$menNKV3ea7X+u<%5hl0*s} zlNa9=@@EFN^CT>ssVMb6kyh6gcrcGkY;=bpXW(L3LD@77CFH}*wcyy!72kwfPakyc z*0P;wc6jM@Cq%Qt2ZZWJ^)w{Qw6I&Vn&!Oajr&YC8F(M(29uYU{Ue|PBSoKq%DPYwV z!PP7OtM4(RFtfkFm2Xggw6Yp{J5lyDrNVn&rnQqR{x&c&nMIX1XDKT26xsq=01yWd?LH| zmUkAov>m%uUafQGbJG%!vCo(YEgs*Ee$g*BKTIQk^=Q&>^w;`w^qdTXX&K+K&lkV( zl$yD5A~nkFf5QeBhupw*fam@gkV}~cEiv57{JULLskd(Ovh{y$s^eAs%+Oj-iA*sN zBf20o5mP|R0gK{t#<4u{|FSp|oMy5|+IdN-BZ}I}pL@D1+Iv(a0qL^lI#xz43xa?*>2(o)1#nUb>({e|Cq>o&F?+SNJiIBv1sw{Vu@MAq7xXwz}A zZ&txTnNHeYciSMCT4pl6x5>AChEtdGT)}4x?@6Ouov+y6>&`!8NMS$$-7q=+*gB^u zb;EbpX^qn+>*9ZVeS7ibAxFhcnnPnh1CHc%S;IFoRH?%OS8HI;u|kdA#9XfcULu&2 z1#LfP4hY<2q9??v|FOapv;`?LC@id+2%NNqX^ATny0OHx1ILnTSm3e-0hNHaCuBxq zquqZ(*WCaBC&cUXys*Ch6-A}d4fOn3FUm{I&rtlBi{yPja5%1I>KXYUm|WkW`YNKk zyf{jNQvOtDFHrQT(KXD|lA05plLbW=c{>!mX*Cb;-kzwU+WRA$G~?LZWPZ~`sV3aK z)T-pGs@JwrQQ_I@>4b|>XxXOkjx=eaRZBZ1Ui7Kw-GWBUW?Q<0qO0bayVQ-Y@$KcA zQ2d-HHX6jUVzgplv_1d+H$F!p5hj(9vd}PFx>B2CY>%h61)A>LeM}_jRN+#>kMJ*V zpXTKGrNjMVwb2+a<>&DG#qqNgW!_uSdixe*1DA|k%$Jbg+m zLd3d>)Ape(!u{Y=bT@F}+pY?xw+2i^p&#Ap&6pg#w;%>Qt){3GNwJyZb!!(YWKj~K}OAVAXLyVRT5Wu=!K zPFPD|6!%@B-aq5AU56hX?T+Mi2vy3X{T}f+krx&V6$lEz?V0%{2WVR)A(#OFju?5PBvu2r5_vf7dpP(L zjR$;vdX{|vC&7HEw;DIfT*6`@qod#O#{qe^3)lL-|7ekVCJ|-CnYqz2;%r~6w|P}Ryw3?E4BY0~iNv|TSowH+lszsC@fDmWInRCv3v4F zx>0XM*Xdc?A$k;iz`rIs7H4&&o_-PBlaLVh)V!&@uWC=dw*aFkyK~IQ_fT+CEG%tS zJ3sg$*5Y(>#5cX@03D@zAx|(^_CRI2q1!IS-C(Ap*MDZgS1=&^AA_>z>LdLZRP^g2 zLURFir`i;%WenO!ZY~n-MWuUD_fyq}%H`{tYRb_BQ+imGbe#34_dR`xZQ65Jv^CG{ z;O5zzWbobR)bX*wI`^1c0bljx(E$xj8mkNPjb7!1O+0YUW!$W`&geiM&^Ev_)NAX+ByjFH@(BpwCOt--plf zOXXdxMq{VqrMrh}y8prVTsbq3!mS^;RlZIZ3;8g5-MV}~9&ARlhsPVk026`X|CTNGwdC{r+%xT)5Shv=ofl-P{+I(q3SLQZ8jiFTd?X$Cs^_JN)E5 zD1Bv1Gn@fGzMa+++-UX1Q%^j_%qrz+79(kORv)?Y;8pusv$w0l8+M=lP6N@q5R!HV zFS&0kwwdUr)!@NmmI5={X27VV{*Sxpz&*lz2Zg5!6h*e+uL}w+Hc{aAKq*fcKPpS} z2dT7Mr@wzRdc@=4lDCTkV~Ff1bN&m_q-0@%Fj$qiqig)z$vItQrp(#M937|)?k>02tea^d^_^`Z z9o~a}Md5SfJV)MUu}S_D z*7I!e*V5V2grzW{-bK8qEac{C58|7v19WcX6w6^BI9G0AK}^@Ta*}_r_}IkmHW@% zAQ|XB=ecHj`P4~U8`@a4i0w)bN;*C^2#9Mk35=Luem9WiFps%y9Yt&8GgnE;?nTRY`51EyO{(L{&DgOLrE?_X)_=rmnU&D)H|{h>)wO^q}l*@I%42uhIgYvZcIHMNB}-xm}XT0r4I+CwEodO(P{ww*6IVaNh= zQ?ZZ#vK9%C7Uq>ymoX6Z85&EN1eUZ`rc0QeIj#DP^V|JlPQ~rETrZ%^vQM5I*d{ z);adW(eqaUYw*eQr^4I5+O|kfso6To*xEhnE)$0DK59o<3+4xIz2JKt6N5c1(a%6J zKqf|^JPd`MS~-MkM7a->IGBOL8VKCxJs=i9Y!U_x8a$J!J5V$e+fl-Lwode+Y7J~y z@pjgcxAUd1&+yme51j^St?1ek3#)TmcVGWt^xdkreSCk8?4h2Cn$-?YPHg@u zGzVs!YitboYKFEz@PAbQ`u5x(Ur#|iN(NF8GK0=3tg;>cM?7`Y0-$PIfxL%^`6D8Z zMT~7 zEGztD$OuG-9U{w|3X4^nw!}3i8;e_HTAV?d_i`i_XiETofd0wP&Ca!_=0%S_!e$ zc^;;lu0{seWFOmAI;(rZ1@uywobW-ir2RLg5?Mwe-hBYws=*_dhO7nrkFIk7;0P^< z*xbT{fKWnoR%1I6o;(j%;c+I&D9J!ESmwN*@rBv~4ngXAdSPS2JR<6~{Zg#a)7Sws z*a7*{Y-lb_FfcRMfoDs#FBRVM9xe&)?(VRHB%ItBAUsFG8OI~3b=E`ssRv6Y^e0<^=?Vgz;hCDwd6)X03ETpB2!(&#i=s= z)@8W1h*06$X{4)=9g}0Oe71~mwK4bz3esjsq#Kx(1A~K}1F0nM_3zV1YRdqj$TjkT z&^OliEJD{)6mlHcTA=U`T1mwFsi~sC@Ye5UFf1i*+|Zm-y)jYdujj@E4Bcn61xWFL z-!7wISkLxq#jpgLju2b}w}W#%*$D68qxSG9UK{_s5B4(W4rpooPLAjw!ycIB+bGg1H0{s;oZXWh>T4TT z5CM^URd#ls002w_fDCQ88nC}nk&4jDkb#_N>_QuhDCrZ09ug6Y!a8Lw!TXluGcXg6 z>m_KcFT)8f1yMmT&ku7#3;@Ub`}=3e^RRvke3?*HY_*v+8crl{1A&$zN=RkFSQb^y1l!XeQgR&6-kn3LC{{rmgf3-Hy;fdf7X z&pMdX@yCGlxQC$|AjWkty;2#J>+0$P<3j)*D=GJ_G-3pQ_%fN7dLP~Y3N1+Fw&b5o znG;-P8C=1L{Q_Q=7L!d7eFH&_CtiK%>EXdgU&bhra!3iKyk{NI{3U_c5Hp|1mqg0< zug9k$4b~%)*xZ+)KWmU-APhgEsT&b*ahAdS9&-xRpwsJYvM_VK6{JHF>{Gac$_U!E znv9Hq+Pbgx(NqPTQ2e2fcZ5@>xC0nnP(?C`=!S@?W)#%c*;F9A=#3M6oZhL*AnX<;J7BRWgi=jBl30ZTA$9EY*> zDtL&PjYMT!qQ<*U0Ttgcuuib|wSRV%D0s=y^guG|fR}-tw`!z-5{?@GX*eA~?@2rd z|H^Bk6B_I5%ZoiGiSPrN3f4T0Yh+}EC{%Fq1;RX%8uNiO4jv?6T4Gcb3r1uxAxfT; zPe@PS0BeRu8lIG8l_c1^BG~Jb7r}?bZ{y<8Fhay)U{FH_WMOR$kGVlurG%jS08pN| z;I}%HVFH-g1ph01V52NV9HQ#~*KRGE9*o78gfSCIlwUx*#-^ta|8y)VDIvb581JH+ zUAG~n`0w(AHMn)CcR2z5GvJESQRbJp?^)|6s7M7c(z$%?_h6mWeY1FMR~oeHpC8h%X4LQxvBWH+0sc2l3wywot3V*jf?p$_MZ3 z0Z`Eqy?@|d2m~~i)M^%C_ZpOCe?L9KMiQ9sFkdmX zw7BJ{{((^fWZ)7K2kLY~2G&7g7$RcoQzfoW^g@ty>WJb%5;`QY%XKQGstAoRLf1%^ zl*en?*{?#bQPJ4APqc1kdb$>N8E8GFM)$Gv?B1^uSW-+~fj`S(xrRl6LX40s_x$y$ zji4rRYMv?Z{DZX!EiPkBYD8>JvwmUU83Mgu1=TQKBz)r6uNtJqxiGw=M!bIrS(#KV z<{*y{5-6f~IA0kmA9T2C0fOyv@)l8lFxc-vVuF=bHTGbO_9x(3$iDDz?T8&OMAXj$ z*GUD3EH$yO`dMlsTg1hiY?Mi~P~rEk z;$Oc1>(Yw`R^BI=*q~Y~7qCy74PlI?{A*wP_pVZU`|ywinS$`w2(DEWwE%#_9&vjs zDKb^9ua>d&;9Xdm5%nQ?E05uG0X_N}TnM~Mkw_61XZx5D-p@}dYFxXYlu(5qKmgz8 zd<)Y=Prwo%R+!{noQLxWjDFgy7&&lYm*eEYAbAhAI?kigDOj8!S2+b*Pc5HWKFAz^nL=0mgNC*RwTmhs@+?T*kwd!dlZvUNmUNiPcO}u}-H{n&%e{kSl zlCe0k(6ssco)Sr~?!$lwntF_I!(;F&uz=URAq@#iXbm?Lvy@x6um+y>5Z8kKh@%Fc zuSVJmtZE<;{Wmq~yKtl|BO?Xj189lNkrZ0g8E^Yx0NJ`9GD~9HjcC5pQvliO&a=&z zx;H0ID(e+l$o5C%bWsl>spxWpYKU7y&88}fy-?~M7by{+D^kXqk z2iC#gFbQYsm09+e4@rAy|Bil1Sw?c%cTda{vkKAS!e&u{EY(6nJl6-&K%skV7ZK7lrCS7dmz8uX6}QhD1j*n1lL?7EVOhAhM!P z3Dcm{6^SO3oqBtFu_ve;Ze;^t)NrW4jYCIdSnR^;uHd2_gk zuiS_({oj_m{NLdOgUytRn;Yx6>OVezkO5IsLFMKw0bl$}MI~<(h~(dstY8bt3bK#o z^^A;|3H3`HeYzgnlUI%WY?;9s{ZiY4F^CjXN?plV3*vWyHZ!Ru32}*fEsDaI&;Azm zabmg(aX!I4S`L#?MB-WqgsGOeyn~ww+V$i8YTT$)MJP)&({7K6_2iJj4bhQPcKZ?Z ze#zVexFsSlq#AttT+09DKqjuX=;)JO5HU67g5hCh<%chM)*x?yW=0)$T$dB#bh@ec z_&q=?*sCO5U>usr&Yc{vB+@{5)71X&_o+&LouuV3%-wiNNQlc| zd0L!-x{$DN$;{B1#s$%wob}1{GT*!t`e!;iZ|~SKI`S*W!@A3v2?kqt)74p*CkuG? zh0DTrhYr4q`7UBtP8woLSQ3nDipOi$-DJ{aS-z)WuL$$n5t-<~)oKewGGch_nCHjU zRw%Z_HgC3oBHr!S%PY;zN1igr-Mksn?Xic&|2%r|J@9?MUvUOMKLz`dn@s!8mTL%f z%?63c?kU)ZODsJ-9h}=-NV&(aZtWn^nd}#s5x2G`ue@T=gtki*lCen`_=&`ZsH_dB zg^$1lzIqB<3wSMB4LU5xOWhTkaOkyX_b3p0$Bh0B+yq&;xLi%1tmER+f{jJBQ{WYc zyON!OHBYOq2(8=&OXQfixPwMUmGF_Oh!R$pubddLGTsEQTdwg=GepXBN$jMkcKMM z)9}xGhWBW&2`0J;ev2MpLCO2h7o=CQ;$hiG)mPE&V2; z4u4Yy+0U`;7vzV(RYPbs?i=YG^_w^8zU|maBY!e2K9Vo zak1jQz>O=__&G6^6jm%v&zh7&jWVB;2loK0kl-_Yl=K8K`r|xxmtj$_K5VQ)g(WfJ zuqQ)w%2NsZS`JTOXthzH*(*aP6n0^)GE zdxe#kt1AEg%1%*KQrg0ITWICQ_o#CKVf0693*_ST8f;RUs244Ur6&rWAn<%mck}IG z;T215sfQxuNWMM4jg5^6^My2YH}Ry%2Y}9IU!Z755-Ji>B9c?ipLeQ`2^n^9NXXN( zd>|?mPrgx@NBawbvoR%irJ9e(pB$l;Zo2TtfT;sK`2ymPORJ{m8r6S8Qax5q%iN3! zozy>z&wgX@?Sp4<*N5XV*x__S6Jyu(EO+vT-(*tDC^GZ;b;!(Xc$cy3fg@frG?}kk zg;#N%Y>qLy<-(_}z3Ih;xlkABa?V`_F4hN}&{eZY*A<9=~*JmvzSij(9| zz{Ajkr^x<0JBgt>+;N&(dvP*Vxq2ImpkQQKSs6J8$(N;h!|@A|%*wNrysHw2 z1@`EqS+XtG)+cRF?U{G9wY8n~`FR0)q#C>#Syi$b^Fd}JBXykMs*uJt@3V`04ZNb* zIXPr?&S6O7Hi@T3KN-vf=W{(wFRzEn6{FXUd28_ zG5osm9kJPg5y!nt(fYl9OL}Qe0qN>&k~%ly-UQb$_nun4DWc}sZoXc6o^Y)$@5%k& zn-VX4x(U1c(9lr2RJP^GTkoK2hRjw8IX4)|(p#&J^RrWa%Y(Vke#k(RQLgYS>!Zfjy~>GaTLKF!!>NIH|HW*v^v4`Ou``6J zJEMVJU$LU$Ntx)E+ljfq&Y!^{TD^mJO}zP%a{tG&CKwP@S8w}O50v5-1WR!Zi46@6 z23ha9B6JJbtIf^S!pWF9*Xi{q2lxzoHYPbuR9~3A5wGYQhUA!RE1_VGO8C0ggQINk zIf{7Hn}H*G9z;2^!=PH{q(j`~$53Dpp5t&1;M!_WPV(842#C3pOcmDU#^f(QcM}%+ zA#b#8y>m}F4fQXR1JL;#O#qbhAY43m5u;!jmJ_V0F~Q>kiCtzHSw8Man8@wu|9R@5 z%NqMmth)*j^%px_u_EvJPUu*qgt%v)@gz59CUgQM@lTugsjl8eKG!FM%sGQ-{`Uy42jbSrNf{CJxC`RRg- z#fi;b_$CmCWp9wX4pJ%FpAA z?u4Z9Mfl80rQ29b^G@Mmw^|MDlk<0dDUoPM@~yeQSwhW8sh)uW9UGg?aC5??#`2@M zwbck&IIarv2+^`4$9pgb+q#@rU7)nDh2_}{y`6m5%mERXFYDYklxB6F6)@xWyX2pD z;b1&>sD^k=vW=|T#^Q*Ema0eh_B{AjzHgy9Hm>>C)y931-|x-FIjhVIp5_S_8;Tz< z@}uu6+0-%Kv>aro^WCW!-M zb8mnlM}pGHJM*+cE?@4tckTFdv*~C;?CXRF3eHUrtfMmR&WXNW=k#k~d3wpZd)@6m zA0PXPK4rH3KE(Va^&kDZtp)u19~4K%-u26~yCLhRR{JJ#bJGE>Gp7T+;#ik!Dg{n` zq2#_UDT{h~QNCtxUG6kf>Oz+3>j;%RZ>HNz*}E9UXwNQvx`11y&{@!c#sAobkPd%S zHfihkCyH2oMZ-MrwF~lo6peo6_*0H!)N7ODe5&@tp(dqi*?wQotKTDiLQXiCKBwc! z=ZG^iy=M4C+y1oIzB<~kwf0#0^yj|Qn_?ma_?FHx1fTET;64?4=+j}2Zw}tCjPEGV zZ-wOh2Fs+(CgnxJVY9B4s&%sYcR%rlYfUS6(&Zmer-{n66<<0#wvs;BKmMwz+za>Z zoyxmC4|Zo8?6`M88$*tdWq)W*-pLER6Bl&u$V;K)>Zt1u95_%s(I64VyNh^p?+aYN z@}e0Wte%wq5yu{>uElXyUqQaDCfeLQIVO-;bJy%=+q?NG$8pKcVqv(s76Nv!&0+6%Vp=xNaS2*r)e-%}a)eZMW6N27bH$N>DYdWQ{1^ z#W2_NqU}$9!ME9f#wpK|`l`{V`#cJC4=wcQ?aA>U7&yzv$A^0vp+?$yv}EQ>Pmd}( zNMcc}{^v7(`k=-khxF{FJ7GRNiN1%NP4>NPSDe58UEllKzPpS0Zgy8MhkQ96GyORK zVnSBz{TkN#4_gtE>b&jt|Rw|4~u9k^AJ`kA4<$P7V%IW<_5$y`;C1*oo;knF_L) zng=y-CVv>vT`fP?ne|m*VfA_=MXxYnhpz{9(zZ8jXAhp6unDHSS?vN~_n=(-bcrkduP$6p6U$^}hO;D<{=zGg?=~Az=-0#7#a~B?4^S6bC>)#sOquNvdplz4vmDYDV5IpYW zZIN3LYI`~vgMTCJ@26BrCXG5j&>Rl}=1 z7rF3=yx2R*>VdgdgGL=49jG{j-fbo$RepJ5C83Tk5_4epUkBH{YM`pz%HHTD$Xi); ze7dv}dF|)-Q4?blr-zp~(@P|nEM%4MzD%ERh&w!Ur(|YiPD-0g^R}Htqppav+s=wU zvkfDS5B7h)xZ(FeW60P57hJ|FlnWSTx{_n=*9ME^e>O&z?%bqQ_t89?&7**>?TD$g zZJUGgv8T&TOdR$1<`kp1jr!{vNzuj5Aa@0JxMBD1I~W}Eo|@$y`Z(vHr?=)w$@CqP z;q`8P)yBEM^~t`LulFtb*mdixWph`bU8VZxO`^`?MIJ2SSD6U+8IP4q!aHc>rfQ`q zuK(CGs?NS+Zi!oaW1>Iq4BZKGT?|yqI@r8TBnunwvL@i(>h?L=-cGmeSo-S)BT7#f zO#>enbJvZH_$xnWNiS*S|6ROcv!m6}eDLcf%P~BM#hIpz_mkE`Ar+aL`q)RbEOHYz zuK(EXb!;6!H~B^4$rGpP+SEBQC#JMYzLf zJ5^}w?w@Fm<#e_jG-mr)qFsM=N&A_0oB>OIo$c+I`5Q%#y-MQM%Ub5gn^q=Hv(n$Y zb|X>QaPrrMD=kS6#CpZH+R^9q-?rx#-&yctozB>;xTww9=M)dzNZeEVIPq26I4%7N zI)P5@mXW+}y|Z~*Y@InGiFfwMS#ym0vmDWG*#;QXk;6_Lam>EfTe zo@8!$E$rNMY`g1X*hX2#nKxBul}~M$uCUXyTlA4EuI=Nm+}+|d^5dtk%%$tAH|3NZ zS-(;!|7lHw625$J;p~`6Mdnh@bm{-2=`5h4-oCewA|N23AR(!wfPi!}2+|$WCEeYr z0wUcY-6bvECEeZ4&_j2=XYTL+-sM_)XSwEHX3qDVy`Sgv>{I9CGwyOH2-dVbx7(W6 zp&|GPw{yer2S9Uzyv!MaBZhS>q)S%+Y$gMb`$}6igVX*)lV8pwX^vcJDlxw^F@8L+ z(w(^(SnIV^-2(2<0xLmSsr#tJPsTfGAIySiboavM_<;2lefZlV zz8?tkbjskxp=)sYjmnD)nYr~j@0SQSf7dK@9*s@W|9;%1@0oj~x6)AyR;J#X3BZaz z>kj~5dWq!iNiuq}`u$UXlZ(~42LU0PmQZ1}Ejmv45A85alV^S7u-h*cp{ea{OV@Cv zp{t?@-K=kJeCD5W4N(D~?HROl6ULTf2)nJCgShg1xWe(RPTu!TV&49oa}JY~koY?2 zvV6LE|4Djx;wTTLIM_Xa%U-UGUpS5oTqp6c0>tGSNAVx>CYJNngJbWW3pE+r*bIM1 zL@8O8fB7r}k8OkRP7*qc-vg;iDid2Otdk+>>nK%D_WW_$;wcE$Mq;%Q*-Xf2dUw@* zx;Z-PB2V~Ru>JnhJ!L`oip3xVTXPs^Mvn)h{r_$@9IAhOAJLm4LTZf~>sE^>%(S@* z6p3m_aUlvk&^=UDu>%pc(c1tWKJO1+Vf_1dwBYdNkRM&hKy~`|2xw#?WO#nay9?py z;=jD-&mL0cWFxDs*u8y8IdOH}mRJ)vWXPg@%sCm0Z`a1^OADI3Y>BjZUr;|zFoI>< z0Ohj0*IXGbwy(A=7z>)PDm1(7qmIFhrN>7S73e=uXBf4RER?oSaajVhezZH z&3RR~YVDK>*s{E>F&XU9W)DNR)V^RWK4P9r(bgD36DK2<`xorQ`AK2g`S59)&i6F| zU)DQzmM410R@ux4`K`7|Hw5FcFkb$+d{dbGsv~ z@E$2daeOGL?vW{_&0>SH(U~w=-f4ik!F_RblT$di`je`T zgt;k8&5Q4YJyyha`5B73f0Wmkj3{qY4+D#R(95O!tl*PR5QH_^1|)ZGGDE`|nun(D zzz|mOJi)~&&_cx%+LLxgt8K_HJdi(B%VVomNCzykY-vkE{Vm+Nv|d`Ix<_ zY+N(^Ch@4LY{E0yPhRW$uT*+5RT?P#TM6WP41jPWKUmFLS%>IWgJ)G8}C? zx=Oh@npY?BY8U^U5($u-t&8Q>Y7E}uCCtr%H3N>$FCauz6{5{d+0`ghnAgI|Kiu-l z`FpW+DM^xZmrJWM%jE-k(1LXbfMVt84bM>RKy~tTWj#GJw3Pqx?lkTB4L| z(|x+-4H}V{@{o@4))paWzg45~&e@R9M%tW6R~VVN)-7Y04Db5r{S)vUiT%mfB4mC% zBh_E_DeF3|^gPiV54Ej0z4cRwa(v43rDRek+3~4=lV(*`cd=Ju)bU|>?xnj7tx-bY z%++tX@xS;91shNizKaj`K}71uzQ3CK^+5mJNmCf?Mlt25CU*xuYl&81Jz>UH43oHdym->F z`FoLoNeUw7=FAeXOpN+XLHBz}WrTKx#`r-_XiRo;*BmRsR$SAQvJ14fx-Yu+29MYx z)-|h&qz6C`2#yk`&k6D zTLoe}kcl%Q<+*QHcx`!fn*MAi{6xc`iIb(WJmIZ%A`DV4Dbk`=E-kfuTYMv}l3QI@ zz^C zrFoKY6^8tV4aFz(lTUvRk?U=LIJLOpC>>~F8jFvYPjw?DTFzxpg&3&5Flq5fh?DaQ zDAqkCoL_tynB8?KT+0kT>6t&cJw{cWSMs&|VdiGOL!F(Aq!}vt#Qe5F+r6`gx)dKz z-Wbvm^;JZVY~-#IEa7^&p|a5PZcJU6o{Whv-387bqj_-l{f(++`<_!}^6_Voy*=jR zccsrvQ5@cWpYocZd%A>R7#$#_?+jKZGC7XSA3 zm=;$KBc-&udO7SNsyaVawfJ`E{)k8Y*1I#uNq4B!hm2@>6W*%Knc6G$Lh6FcBy18$ zGzokVvOWcXE;|I7pSGnmJM$xY!%DTPu4pY%6<2{EZVH5}hqvR$&g{SAA?*!JR>IK6 zYY%J-Oyj2?n^Sn1&Mr&KUpl}>Kj1w?ezq`J+N{66v#6dFJx z8P>FE3DwC1zQ}o;R>n#~UYaowsH%86g5ECtlDfZw)hr9;jcf8GyNhK+oSuyj|F@Er zh5I|6pbhN{N5}uv?9Q0JzjLC(n%>B<+bBjk+PVC=bSYq1J>12a%ykCGLP>l1@rm(?!rBtWXWD1`Z&a303F!g-Njn!#qP9SiVRf4uAM$#xH zP38!k`eFsja4P`ZT5R>7o|13l1q8aE;8qtkxQKu7_RxcDZ2=3t48zjD*ykCt)tl?Q z#}3d(VJBb<`{Vf+vtlRwxEuHAS!zLzua50!px@ULB+vQ4I?7h}D^%B^j|KbvujO!U zJ^G>2$CX64TA&6MUVa|(zNE{D#>=oj5z_oE{Aq!7fk*ki<}csYQ|K)145t~oc+Z^; z15%#LxZyti^j$LM*A%t&6#7Oy}cH?%RP8^zv(sxzzPX_gL(s6ng;R)U95TOG8i zXMzLBMuL4)xX(ldl<3oPm>b3~!+f}6djB--SbA4Zo)b;m;kqL-*~l7tS#n?p`q{x2 z+>QzBTovlVOgNI)himyPV^~OGn}d(_%S&`<=P6(xHkY}o4ClHWPulm$;Ji7}FIu=e zCeq@*;nDjSoqXeo_b4#+n*LIduRe6GeF~aua33)67PJA$w_l$;=`ddB+-4S{VWb3g z)JMXzD-3Vq*yw2n-AD7pee`{|n-fzQ$+)jV-PIVhsVVjtGby-)E4*oV$yYg}y=i=e zE*8ojsX4ngX~Cz{)TI;``Az!EsXaAkd)*hZJ7maEWR2+8k+}7_uOGitFokQ)T?*OU z1q`%jX;Mmh;A;%14JDN7ZcJ>kmZMusopuk985-^fbqQqis~mhcp2y%}I9_i4>^9{_ z$$|uqv(E$fgt5_ER-H_0sj@ajr<144`@@F)1zO%dMY!0gA(~#;5E@)8r=pwX<(&b= z)J5B{Ni2@6oOq03ORj$O7V4v3#IKXd>MyMq!SEM1F!uPE=#C2%(7}?unqS!zT`v&8 zI2CH(O#x+ndh8Wy=s=G5ohue(?slHS{Wz5MDM}QY$Rn;FPsuyzD#9CHpZ2PIy$zLV zowyD2xPgxG2OVMXKMU_j*Xl2&t=v8F!aq}s?QC1m<(An!3!JDN?BkEfRa&9}wbRIk zmY}gwxY%^0>p8KH<}rF4&40o$a0lQ8DY#iV4ilOPuBh2rZh^o{2vFFR$S>cN!B5_w z!&Qr3K%s>zTqpa6hLZIA!aRCp3&?e`3>i4fD`$Om+f_zfRoj6U0k`zQ#Rv&wK|pNb zz8O*j`d6Z?8(9seFKUQ;+&Y;oa^UNhPt(I4zGSZ7vqPo08b1aB0Sd@vwUrH}bL5;} zZnVeCjpFLYC54BY(fL(^lAq*JbSBL1P-{Z9EcG@W6f?%@+>?;O2ft`daO zda}awl$9>c3UXX$=F?R|0CwunKqo|uI65UigNhLAyf!k=0>uq%c;=clPgSQm-0i$6 zU;@$%)}cvjz8G{r^f4kKx1B%3yAaEbt2MwU&lYr^KE~A|S*#zO&rus;v3z4WH_Z?q zC2}Y(GUkBAu>VsGgJzyjWrc}PWD0ey(wQR&LiNEIad+VQ>xt>3GHjIqsb7dBqgQIH zr49yX5&)q47wnwvfMrbJaiYW5f!PnzU7QIqFj+~iw4D0Syf>2G+wF`Ah8Q1ONl`Nv zzKGRokBhFm;>uU!WAU33s2&Ir=$MhyI=3C@)CK;)Inik-y zp6l%2;%sl{81}~v0?)_P5P8vsY|hWileJ*?FwoY3+7maJC7&Xx#u9G9j=VqHT?|kn zq)EKDNdS)d?(?SH7#}i>ufRbBx4{gJtxllpdLAReVL`Vmr-e{H&y_8jZSK2IAh+$S_8JET zxNizNI$_`r^wshBwNWrOV)eOG&!|7en=#rDFdaVS-#HxW@VvSpB2Jy&OoXunS2q5o ziquB+?=iv_t#V%95%30oa;AFy@xGfY^`_ZFi<2d%g2ut)){Z61?-mlCBV_Zr zIDa6(W!Sn~^>&OqPUS~%=nokh``;72(cKLmr@L{VANZofrJr8b*sl6}Kgq{cR3d#C zD$~Pz^!h%AhKN<^Z1C_6!B7zuQGmPsvEBn`wr}wDX#})9#Aa*g>Z0oM(ksM7WX~8FA%>awGpHgO=WfA>4WM#6t}d zPuhOHTzX&)#OI_cv(u1@`|SUhv!Dm)WMBazIHxgf9KNxN5MSx;P``8JicX`hX`K{p z+pjuqys~&ImC49t)Igu{&Bvq)>Z!w>rJ9mnm&eYZ#5Kk+l8Vty_sE5CbuG@(L7`RH zRNj1HipO-9D8FuUzg|9d#r5ZpWqALGth&6a9WMttqBRz&LVhtVV_K5Ced$BQl*#vo z*hA2m(Z1VFV^6@<(0-s3{i=E$LI1eHDC=`fNoPW)ESt)CwxX`8DA<1B%!YE9fcyr$<#T6Gg=$2sE#@B2QfMgfHTbK#9E;kA{M$1t zH3Ai0T8r8#ywl-`#R_mq>EG)8Jq~`Zqe|6sOUH^mQeF96gTw2mHvtU`D#WPVMtpA^ z2vLu2B3^2^PCgt?j>eF0+JtngP_3^s5877NH+s$>(ueESa#in~+6~&3E1WE)1@3bt ziG@?k4(t($%Ss^0-MM0i4z=ztJ)B_{OaIlje4UMpJLTR8D3Tv~a9GK$cH{`$iGW$4 z+z0X?A4vKz-f&nFfcs7WV065l6%Kd3%W7}t0;WrKnc#-?kGERDZ96$~$pc+}tB=FH zV$mq8+aRr05mfq6rS8o5YhBpC1$*Z~v+jf)6|BLPy?lL1 ze5S5Sr>(6H^<46rt9=$(qZpP-k@I2X8!u14n5QyTx1PkxcgL=inN@czBxe2^N^OLK z=uAXwfBQZXhJHcbO&O#Wvth$Nu(q1=?l_)H`%*3dXNKNB$8+YY`kg$6ws~=Z__kGj z@XDFAD14A(QR{HqMsE2d4ML_yL-|_E9)9q^9xR~hul;AIfzc1SPUqCE>KLsK_jfxD z`_IWjo-B>pP(zaJ(1bN`76yr7g6{<=oB_x;qi%}xCeTh>N*{$Jqy;#|;C z)es+Ag_LM+7_$*Xh+CwFw!JH2W!NfSZI2t4p4S-2PcGrLyT6@5s;b+(#=s?{Wa(rt zwUZd@qWV0bEi+ozk$r#9@SysE6A!2d=Bm15pvKrtJ$JnzkV?XzB9H9jy6@zZR9q{& zz|2`J)Z}^?joH(Ydii0sElg)%AL`QqL+^LGv!mOp7qLe6eN|AonV>H)8#$oaA$D8pG`Jsz zp;o5}Xe#&|b>MMl3W~nW_wkyUoQ&0JatF3O!~ZQ*sJJ${Odhxpl0HLuR+IgS z$#~^2s{fq%spk5K=MIA2jKU^a=*~phcqq(8CbbqsX<0okR?_1VhQ`(4co^7y8zM^I zWr<5w_U`0B%af|pAMAJSO<=V7re0{)y?r@w$sQ&IU(esLubuuS>2+v#m;7L zh>RwQc0>s8+2cW=Xf7AjFlYFuBk}m_lMZ~T{9O^ivpJDF_)H!<>e*#K_IUq2xkD4w zdv2iI&OF+G0)C^kgEer~>SV1|4{W4)S|)l+O;3zP z^v#e6g%*pzt>uQV%?m^GlnZG#ZILa)jahv3K3d)H7h0*}gbsbniv3~zDa^A5vS^t$ zq(^Cg{Z+$t_GE5_nplQ5Q|TKbg0?R)ccS7y|3-V!YG*OVX!#6I`9G6Mq+{q{lMxc8 zIDsQ$7@F4Bi~z1P=#8QRC3!9&W>`EfEKg2OKx7kCk3aO7&#SP+L`0autM(2KcFuT+ z-EawVHEP2XaX=J?VMXfkh2<e8v4|mb(GQK%zSibLz$Q z;M;$#)ziXfWG?v~xlZ{LaSoG;erep} z$w2PJ&I+QT$y%RV9IHRt)KmkDK{z`(xk(o^V^XLM`nz9FtEa$(xkkUf>jnEtcZxQ~ zX{MD|e)EwRmo)$^=&m!esYFU(dvlA%A@KEIGvrmchbX-Hd@43T;c&=g2wVKN#cwcz zXZVWangJ~3KNrc>-;0lScoT5a_(V^;AtJ^`!+|+UgFy@CnG8Kb)>h%lUzhTNqUCJA z_M2?8=fM4Z;2wij0dz!}wK3>ZJzJ}oykC=|ea4BgWzM1cq}s?HpN#Z&^;ue&Ralco*91GwK+wWn%H+Nr-M?vt z!DF`5%*lOyhBR2CAgJppG#gG~*(#uXMC*6ryBlP#sP;;#KXS!tF<-BZ$)krV_BkO} zc=dNzwYEtPCAFliUygIfXdTPXiBe_BXHoP_1~qM=+x}IwC2#V}w1>+8V961FhztNK zqUj9$e&H9$h@kN)b9~Qfw9yj@u5N=T`Hn_}vDD!e!f#L^h=mZe!67bSKf_1Z{g0#d zS>O2yT2#K5Wb1M)DTnmpy>Dgpo9@c^vc|O zS|klV7!!U|sMXfcUQ0?|P7MY&>H^M&yn(b=9O(pq=ghUhIt?F|;^*Ub0 zcQsM_C~-~b&6V66)gwRNj51ft%uAiRABC2>H=WzH_qp1hwfKuSE4)iMT{K%D+Whql zH4P>ST^&kg*XoGq-9mArVy# zK|t~J&b7Sq&3zGHjW~eCal3wY^6Qo6BOU^f^`Tc|I|$qTg=tkVb8fMR*19jtbn~Zj z2bmvOUp;12*qM)xp9Pc~xT?iX1?QK|msV^L0$S!H6A zhV0n{$MhZFD>^d7 zZW<0@(>19ye`1qnSu#`$OMbybK_S>B_Lx1gG#zH2@_pDjhq)k2tjel5hUKSd?a`2K zujlJGec&lrCK=d(eQm38g#8o()Z=-(&neI7S)>d6YXk@bOV+4(yPQqLg$wk=1_)!X z&RT;fRiNf4tXvhlZ-TJHGY5y$PkBA$8?;lafC$@_FgdLBvIZacgx@kMj+-w}Lc?pB z(x@NLtbw#?ded_Y3OkO~Ye z$Fvf)q?tI&=^ohVIMJPzTP^m!AH%-xv}5~EeQ@)#jZjZw{C?s1xcw5D;;uA_vq^|p z=9cGXo+47*bjQemW6;7XVW!PDn-Ak&K;UU>tuv|gzU?&%|5+%%Cu_zVQ0AEJS`TKv z`!w)NKv<_BjI<52NT?F~W_QRE zRSDCLzS~pOn#X9*d!ztvMbm+_wW2?ow!{;E)PQmfOS35sukOqD`&xF^lPI-n&uL1D zSIH~>3jZ-(pb+!Bp#e?8gmv??IyHq1&h!Wgb+V++pJMZWA55uD{z^tlQEfA+RL&M` z7~*}A)h3NsZ*Te<+foxR8tGrFUSX^D9#h4h?rf8_z>Pcq%3V>41&#f~HO;K0?v|h! z2V^PCNsKk2@`m8mQZ?V`IF`S(`(na^0LlEqnFdw7qI~YUt>im~Qg<%hqS3E~Q4ZTe zuO4L0HQeS;q%5hioGvz7{&{X!E_h( zcjT>;KB>0NvNJ_rFY`o*AMbO=t&E-)4t6}j&RnM68o`rP9A4qoqm$`s)~|+K&MZ^j zogo{&LCsHYGGn>;QepC%2IBQ2(~rW`p7AB~7Co@!TE@u~YLQq9{D6zBest^R&dB{a zaOzuWR*;#tO;LK2M+ARyla<-ylJ^VI8Rm)`^;umq;vV`W9Jn&2i2A~Mv%OKE+1Tpl zC->;(Iz@~r#^)+$Bm2C55o_?K3dM_R^%VZYq5+FHD9t~)n>>4IN9~5i0wN^o4oS4Q zS1jA7X?9-gbr+?^+O;rq{mS#<7He?1G$@WaZn0i^VLDeEFM=;(E##{=u@}*^d*L%= zHsA8@b_EaYoxI|d#9k&J%vF6VlGftIDHW47F4}B*q%oNYRE!>bhmH03rjs}2KIE~n zhf&z@e~VSc0cH_OD=<{8Qv&P`y8m=y2`z4zm2Rw~{CO--p6 z+ZIM=-mcu1M3d*KS0zdtTk`+*)!~Tf?wR^6hu(;pjVU!}OPk!w+qP$h=9-E3hXx2k z4@W#QU5)>M71G^B6Vid6b+pTkIp3-RND|_=ZG9Ba1IlD$a?_yTB&3z)89Js?GGlh= zEcWUBVH+^G|Ao6jqd+CRusBMtn}g=xWTFB+CeDU_Xh0LT3olgx5+Uj1Dy0U|J-eiFX>DvP?WZ%e5o$9;g`**osEhz)W*F)$t`5TS=e3gCWbagSzFuphA zL(z}eC%v=sjlxcLGzrrPWA+ixs=N4HBOm-NaQiI_lzddD^|s(FzJki7L?c zitMMn;#Fr}I~8`sa&GU?>CD&=TDpP}+b6_KAL3&bBYmnBfEQAY%^(5L)8YSf2L!oA z3Mb+Bqytk{3S9r&3j*ozsdV55-fGYS*iN&@2sDE1R#(U?Us3=c@Snd!K@p!T`wgBO zN3BXkVG%bY(u4g_xkuW`>Y9`1l%{8%V~-Ejea(LdPLtJmVu4B(1iVVXYDTY1<&Ax9 z(^ace-DCzP)6otjRw<3&TMQC!d%FOO@qDd*zQiS1^A;tHe}2?*ei*CfW0OixwkNm1 zg5~3+8?SOn*hyPY1l@co8((|uG0bA93J)4t5LcADJ>n^nmRe_-HJEWcT<^X?BIxmazFg?`cHsg*JQ>|B~M$Q2Qpf z0&8_#;2&c?I}Ic$kebuqAehEiS+e!0uGWRP3t-SV7e@{7NQAj6L<52^Gu9O@etWC= zV$MEU^WvRh`%~cV#+Xo50IbVT);sBRZwWE$)$%QAr_G1~a1LfqrSa<FByK%qsyL@(F|Du8QbH6;3;)W2lto*C zHjw#Pv+a?p9~f-+Y#}?3o;LM$2jhod)9pn`KjmWtC^Hny>waaCE;7p&)Ql} z4W8*hHzfM&3J2%dQcga_t^+-AGiZ7_0ak%!X5((Pjw8Bf;$8GGC&N2_(Rpq>58B=0 zwBqI_H(n&*o0rZHy;ia%nj2cyt1j;S9gncxyzd%&VU-%r>|q0&&F&h0_dITjVJ6bL zEiCsk^@2_XVkls+ilQJ>NL*uVPd~nQNNR0VUE0*rwBrQ24~W26{hS2=$NO51IWNA| zOGT_Z%`4{t2!n`}oviI*%qzCjyhUINuw8k#NGELHb1y;vdwoqEv7&CB@3=fz6T$X!*v6x(8q4RF5Ck-IT3p|+buD;_(&`stN^X;#?a6R_x zpCu~tHH1^U4tP>3zCqDB2tV`+Umb;=oc{2A37`q9?%m{MyFBYW0>EUQ;$eVf;y?BYcm zXkQWjVp-*M4&bt-+&n=YwXxgW!#BG;5l!T;_xn-HZ`_|9WAcUZRN3|h!ozrnLWvDm zVa|Mfvd=3ZS(Pr(i4*nY>l+d9*Z;j1d+1GZbhn@}O4#DQxj+Q)c`Kl3i8~r10V@qM z4o}|#9K$;0r>chmzk$mOkh}n@I;?C3Yl5-)OLctqzpr~PZW6Shw}{D2ZN5G{S&LHF zW1Ck=wBOAgrh3k)?<~#NlF|pQ`t*I~&p)z2xO@zL$&>w8P6*vo0k5%^-N?uYjQ0)1 z*U7);%n|^JAVY8)F^G_6sT6{E=-UN$jAE?#*x0W|PA10RLAcDCneYYitt|^M{>QHu z`2q3FUG0iZ>&ysnM9go1cljIgnWv>9DQbcXvq|9fbTwl6xy`4UY>`Pu)kKF4x%NCp zYRi*(z}>IhESl)r1MMGT!!4cztIIoOD@=}5Nbqx-PL<5_R<1Lv zn*g}+hIIDyGQoKp?Gg(-Us8IB^&an9iszgwTNLViHLJv4LL_hO0*saM%>zKcc@UK8 z*8%Lzmt1!e2t^BsV9C8r@R&-lF5&faV*8@+;YcLXH?_xPhxbvRlqHrOA)#S1aZ~?&ZOwA8+h3i4UBffhnlco z+=YLda>b^5k`i&ZumSY~5X*6ogjkZBlc=Nzg?P+K!*h$_?gi_qLQonW% z^?(_zPH8vo_e~0fG+tDe{NH!`c7p%ECQIHWkWcI7K?pDjK+H=>Svdwg?hGL5IFJTO z123{zs-0t{B4xZYH}$##X)An??IR4#{6`UELW6VGiViZtz)!wI<`+HLl#>>*M);&K zO#UUm4Ba%aSl1zFn5<)}MI>~q>fGkLSWTY~P7WF__=rZGADG@-rXV~gK7O62I>b(I zk50ghRToPFekRXDkC-zOh%#QCQz+$X9_Q#r4nQ)<@0ZE!tROufOqvy)L@cMXn3$e? z<-HQjW{U9p55O?Bso}?u@k_DMkBT4!fxX@{B-Mg_U3WT{k z$BM{#b9~(pcapjGqJEgR=fK65Rz9&4Y|iJEV!&$_&MDy8{! zdM1vh>3Dn!rTGQVNuH7YQ6um`BRwQdKdI700J}deV7>=!L4Mbdm(oDBl9st?9&C(Z zfFnNM7a%NMiqCt?G%UF}BZg(bw!L%J#eo32JFcb^i3suMZbwc47fomAA-A3!-|h=K zCgH*W4-mr%_cOp4Vwt?0?CKz&_O|vSreT#NSe%MR;dM`_rrC-;J>u*hAquffT72~l zi1Vo-+@a50y~Lk0G+MgdUOrPflrd-wVA0S!k#zBIwe_HS4PQ$LNC^ zvnA=z5~D0qFZ(9GPjfEOh^n3L)%$1A16|vblBJuhKw~7 ztCpGu$>MONZ@P{)pO~IZUNY8vfd_-iL6t4X?AXxLvAt?m@{7vRi`;DN-*P6Dl}JnLMb5FV5k!1xC`p zsQ%C6$#FTW_us;NS5ut_3*HO#@_9;n$@fjU7&5~99bj?^E_`D^t*XG1 zL~*J2wq2Q$l^?J8PJd$KpTde=7j?3a{@#+_1eXbf2Ed0MsBCPmni_5I=3|W}+1-z5 z#P1OXUATM^(0PkgazV?j985qJ?R4KIltTI6X)RF8z*2=F z(ZbjXY7ua}1BQ3RqQAW5ds6%Vm_gfmQKC!64vD2R=Aq&P5wk6!_TgaLJ+=YMXE)cn zc%V$>1^Zq!$UwA{Y3t8zje5P#6G)R0QQ-KLm0EhtL%K*VvlqMkBHS9wcC}{M*4m5d zE!?{Ul$>}d3}endL{u1Rk%H`7y`a3R8|9818;!5%A63*u)C1CXB+GYOC*?d01TXa1 z^e6T{n)XNb#JV(o6MAXh|D$+I9Y0AXQZ(FGqstGIbYNHg8pY090tw5oaDf-5VY=^V)Cqu}fS@(LWQ{XQy&21}W?oW@QffH8={nF);Dq~{ig@#= z{k;&GM$Zmzg+jy4`k3=uz_%$SdgECPQ~UAXVdljRf1*>BkFrM_CN;1N+7=o6Nd+eaI5j%x=z#TRnQPgSGgHfd7%2?Rg4j(-n!CtglcY)##v^sOvfDW>%h-HI3S4M z{tkQ|Dse+M{ikNI?aM;bdA?-hnSF;MIPfCbo9 zTl1y$`fhsI;>f1SZ)mz|w75~N3#n}TFuK{r3=P3K5B79R`0W?O-Qm*5Z%1Zine+@R z@O9Me5@nX=@R@0?FNo{1V-IXePJ^CR_2kt=t9HYHoAUJYioPJA>nr?c%kEq9^o2VB zN=1F2_*N5Vvt*z2C8daP_bmZIUbFGA6`T?{@kc>|{ zI+xHNmwZ3{#ppj)?HK8VywHAd6yQ*FY%#&obeLGBlTqPd8J!u}170*_*MAS%>IoHr^yphiS zu*Y}1by*xzv&9Ael?}FhZ2Fub?GCRyH&&{9|2L`ST+QKiD#DpYESjLszB9nde(5Ov zv=V$)yjculV*2JjXDN2Clb0|AzutMTsqo9}nfP>-tJi2eYW=XK2?4%Dbdy)PN}o9W z#7FOQ2_dawf`tVni=2>4QMPw8>Y<;Xf@;~|BvFajt)9YuHYG(w>}3hneHy##1#y7X zUNbx$i7UblD%Y#Cv>(g&tXv>i$>y_<0~(Ey-2!$5plF(%uDJ){7|=@c2_ zI*BD{$&EFLbQWI^w{7%2u%{=W2mcIUF^9hDayg>R5)W?E+n8RTU07}(+`nU;8&T^{NFk0-hlufe;D>(Ec-CMTi@H z9(V|HfH3MEI`EHJNXj?dtn%v3VUQfo6!AKLDxT%9ArbZcE^BkIY1I&cqD$jF*uU@5 zjE`OLdiuf_mH0YFohW|FYZmgTa|X=;=x%%7p%s4O!FL%{tp4s2_z*>7bl>pRugkK;m3p5AQxipZ*2C+aK0V`$%c#b;LA>b3h z$wlNeAh$Ryu~h~4OIz;gbzJKUKm$WC<$)rC&}|(>n%qnCdGz~xRJ$r6+IZ}E^*+Qx z*_WHvGAIz<342-_42;~sLR=tx-uH?ChV=(buClHBcrzZs`aPKRVml2g0<(u1n#qHP z8x%Fzrb&I|iE(lc~+Vdj#x)nATM}Y_7XrLW84SX1v2C9P_ZzS#a!8nEy{` z1oGYIXlAA_4K3X!$Hs@H51y(gpuAlE&i?pyV)J@i6J4?Fw-K-6mA)LI2q8bIv*Z#n zwsg223a0k^HRxdAKa=>3IH@-a3kj;o$cb?@r*1}^HVb6`P8Q7!aV-+&I)g{8787tC zevT&?onhJ;N*F$utc%e`MH&bRG*J}85?D_88LHS-;dTGZ5~^Y)-I6x64r__31^C3o zV33IL53>U^he1~hsb7WHMT&~3l$$C7SE}^cx%aXC9iO}tQUM=SQmw!1(q=C?J{(zP zgV>uSAgZeIjT(YeAQTwu@m8=(#Ar$9|mJ351y|c46|^49Ant zx*ETP)$jrIo^`bU=`LW1gID!xqH{V#&e|%Vx#$@Jy#Ly>7_MJ|7$V#03iwJJfp8qu zSC?qnc7jnLU)xiBsdgi=8(qlDfw3V5qxS`)g`5z_* z33Ie0?Y+5y9e_}IQcba=U#`I_M+A(U)C)=A%{!vn>**>!L5W6BSe*A%|J$jsIvp}B z%aHdu3nl$?R=189Oxapg&L>qJKcGrcbgOmlHWcq!hWD%gYPiHezJ+8^%iTzqPFGkj z@{qc3XK3o66_x0g}I0N;U^2c;jM+!zU&984L4sw{cSP4LO8#G zS)c`P=KW+elwl7lr_JmhWCB*@1?j~+#c9{&U0w*oD<& zkcQ9COB8X4!_sPgfwY#ToVqGy@CXDClCOBdU)hWFOZBwU|TNY(1eZS11eNu>s7Uv#yF{i>%w9csDI#=-g z#n{t+J%|-a5zTsqn$Ag3C!hWwVJ5g**YTRFYfOJC{y!>qt)l+16YPps-8dUjO}gPA`ewWmW+>4nk9;ytKS`(xO!Nq-iD1jB^=a-P_1n$|yHJSP`H zUufEeICcMhv)D!Qtw^7tE{6xsEw`bINZ)O~E^f3cTo@1ll4RrgaSDxjCk0L{@83UhA) zZMQ~za8wh@c>gR&mw|4CmSiJXeWI2W8&0|p0@JY29n1A?(K+3Ja%FIWc4nYqFDI>@ z-XryFjR+#fP84qRNs}v4<6l`^Y)eDlnAB0VYNSqnrw)UGxE~P{peLslv~#d@ZK!KF znw>5A6NJ+~nuO)y0*>eju%>k|>6%j~qC$i)q#h)ijWq#wBL$ulJz8?C(rYzv_MDG` zA)H`%A|)N&?oH@kWKWOylGmlSGi**IoOD`uSNi2{A$Ul#GBaJlWQWgW;6WP#@wn)l zEjl297{f=BQ42JLxKvY}&Od6rS~C|Bjg0#d?AsSA>RQymsf`GuE&TYAe)9#K7(Zo} zk?HRKXM8&7XZ(z-z-@azIz-Ja#7w)9>rNTP8{|C3Tawq8y*b)(X?UMCYbJ3$1X zc_?I#I`9-$&JD*$m1Zo{h?(Er!667+@j?F_R&Rlh&1mkJy^zYIN@G1Gnu>c0RZ`$X zGHWzNf-KSYiir!1sqs4wz$p=uH3X9OeKnuIdGzTi{!_Y!>&GiX{iJwFP)miDk;DEj zc_PWV)(A1nsGw%_o3 z{vPeviffJse{x+TwF%3M{|hy=j(ubC;M&LzO#bTRK9NGsg)#PL860kx2|YWpa#59b zsMraOeLaOzMNPuo&4y^7uo_M0jLF7QD}APHuLdL4+@5`1&L#mmG47DRXU)F{&EyWRcPnEp1Bk1_Cx+OK*@ZRo zC&oFbM0?!XFy2)Y*UkML>9s*z;WR`pCzJitxDR|b=75!$>0Ny90OAsOyQ`k8x#-bttc35E z*pq|(k&rw3OHjBkHWqJ=S#C9gNfm9HN^j0UZFd0eavBG(x3UFiqE7OtrL6;g+=2LN zBx5YMyCJ=5>a1;8rBEoX>cQ;4$p3)#(gsfY2Pp#={U=^RgTdX+vuSNJ)d!AEwb+uS zFU5GSW{>9!_^4e@KEI2Oh|!baLM$37!C~qk$mw?QeAZ^0lhlr*1yCNPtn_ z!CmX2M?^fXRJG@52$}Yl+AuXoHNr)9M5ZWiv>XU?sjB0%*C>ui49!-d&o;zW{k_DS z4^^XQk_$c9O9y4MCRJ|CQw#`6?^=J;POcSr;zBaf>XCUS2yCptfaTAwvt{7Ga;c#@ z-Szrzd^?Fo3!d9Ci(Wt{&W*H>=icC;wrjstnjg6f93H5yayOTWod#`rv9r%O$i&+Z z0mD{-r#{I_iKiB1nEPeAX(Gj+H>edk2bzR4=T582-NH4@5-XHMs|s{eiFy2~#j`4; z41qF*-YZKjhYBkT>Y6_%H8!gPNdsL0zvKD~h; z@~sx$cG2d22))f2^3FyoXdpl?#8#QB|WN69~-DdGV(tHKeYz*<)ftC>CA)In$FUO;EA^B66{nXt6 zqPwEGQ?g3tDaX4;bsTXS{8VH*kM_ro@PVt(-$WZ`HXDpUs72W;N_kq-USwC|@C z08wgPmiO9JCiYPEXI}%=OQ;sW&0X!wfB5 z-iQ)z(6!C_k(HFYGdp4-UO;-60GrZ}XX}_uc8t08iMj!qIThU0ar)NNQ7Qx1G|Fw3 z1sJtz1q}_efuKtP=5VN0TfRZTBu)o2Wx%+_m*(2ShaK?oR$$yBxS(ge+&~P>g@eBd z9xJelS6`LHn4f=`aZd$AML&3QKHN|QqBTV>KTe~W5FvRCMNl@PK{Z4UY=9b-U`+On8^{XlU>ZbR5-Z1(_fT2xtv}a2wL5F#Do{2 zwr!sn;OC?+OosDI5t%rnkj=-ju5P@h(H{KAoairFpET3DnjSz1!%t3A%Sf~0(aZA^ z5sc-D`#}1|>3TH0o2^2S68^Qs!{?ELeU=+ug{XrGJ|I_!;Y`~~%J06iwgxWfs{|4Y zVfN3Z0B|QjU~n)x9E=AcKR=#TAmJ_WEQN#l2_Hy;Mt*_zD^eppBf5oOGU2sF&H&rX zMm398N!m_X28xE-AK?!LZX;8nH(Ody9+L<2G}95mCff1b5w)^(%v#99h1%OA2;gqu z6BP4b>RFA^ZzPFvouj>mx?asg!Cr}9=iZn6U}$~#=khfn4iM#^JB`ee>CjjV z^El>+fVmAvyZn)$fBsV`wS0|}7e7o5j9y@hl!{G~b z{sk(WR*g>V&VOX|&UJ2a3l3ei5QX25>ThQxU|Ng}e;e2m`~yjKP&3Jy48Og#ax^6ELY1 z=oP16xJ?ig(;qpf{A?B61OoR+z%1FULw@n&Yc+s4#DpCHX|QNcix)6y$vW4R?{M&b zJ#BIqFJwKsNbCJ)6iXg0`RnNK*U^Z+gu&F^_5S_eu7DB`DD@4JAmN|IOY_m34z}Fs z!}l0VIJr0bkv!?i%t%ZxGKm|d;XZ$&VEEkT&O5bY)~F{NTf<}iVTxpMGj!)-dBc>1m+?zhEnTiad*+}1KX|$o+L4id~YLGU&TsAs%(cg)>B1-)DLJ%+ZfH` zLeX-veAY>Ar#?QKzxHz!+Uq)rQb?^W1B`P2wh^TZ3QX(KF z-3SVT(j7{7cZZ~uh={awgLId)lt@T-cQ^le?fd(C@w|BU7<=sD7Ibi(>s)Kic^t=Q zI<~#veCVSd(3J54WFUybqnjpWJo==Nbr;YfC-yG6Cdc-7N|%#j&3nrE-CNg@UZ_oV zPU_|jcl*KI-mOLT%{|%>3IA_(+jwhYN&PwN!P*Q{V-(SnCk`L>L3g=pHxu5jK3%+y zDWd6Ua!TSu?sOwP)ipoyc%^3kxq?xIIxq4tq~mc6v=yuik}Yg0fj*(@+pMAWJ0iLq z%gge$&z}=G){ME+@ojad8rh+e zo-$hQYphN=<80tPu(qZ(1h11WjFX+2~oC26R0dvj&5k6fd2u*48dJgY-SRA zWP>kE{qo%{IA>eOdrRic$iV*|5w9$xWen6;JosHOtsu8fKvE;)&tMEjqW9EzzVPN) zPS?XP0SZlpq2~v0;o&T}Sdc&rT&byxJ3w0yQdSFu1YdI2veh=L4=A~F^=`p)Y4bYl z$ev#R>kuTCCb~OhETA+Knn9p;G!Hj$_Za3;9QGBynml^Qn*Kc^$Z`&@QSZ24X;DVW z%YJ(~)tR8;ii-3#jowxg1fQG(@121Vx^94!u1Q4|Ged!GxLBV$&UHM$4w4d;qT2ib zV=#@bcEQm0qFgVIml(<`y1rNJ3^u$qOZpIwmuV~brdC%D%0v{L1kVcC8592sHl$55 z!~s%DE;0JkKo*)1kPKP36gPa-z=}e3bcy!*a-RV{Og7RaM>F0{tw|pm&gh$kSZU8a z1R)vwqsV@)-zNpk1=v8VxCv@<+7GUn0&dZ<%ht zaWSUZYrq~(DCEgip#WS1=$Y4x$2^RHI4(O*M;GD$Nz0Nn@*<%{oLbj_+0#ce@7v)% zeHR1Pr|p?eKbfgd+V)>4@EMMtJbGRh$7~v|2WQ)%Ua^TnHl}wjo(d)Pe#31L>#WDAyXXs!5 zj-LHT_p7zBJzfXcDFN6yUu&FWZ)%SRmnoxzl>wYE(Y&GJ(2WR#bilzFtBkuq$cd%a zkJe9p((Q&@VJVBXmc=%h6i2=RTo-LtZfI{(#{(o@P4C`>HfE?)Zan}{Q zdYERW*$Y^s)r%vhG|4hm4Gxj!8yPukdBe0jL{SJ zR{9^bN?SJ&Totu3bNlTxb3OJ*8fL^(1ulO_K(9d8%7Q}YM5E-}pK{L-;#bhc zTPo?_vB$KTX{-hEg6KV>=vv)zW7aDUxO@ZAn`NyOu&9O*8&>KQ{&6!|p{_!q$0c;nUOI>ToV&EU5U^o|SX zxPH|Z=dpZRH4S%2Z<0Vn*62{&BTAc3%`%tVT=;=s+e0QPF=!2P#ZM|CXd_yOiKOT!Je z<8KBgB{n$E7YD)Be|?@bb8>(Gd>6F;#-%yAgaT4ju>aq8J5w&V-sbsSsrVLlL$<{( z8C-sAi+{w3Y2k3|_v;vYZ#Ub3J5sWrVSbH@RJXATHP!~We{f=_kah~35DU1|QbjV* zb@Kp+3L6$7vU5-tvW@HZ$I!P`cVRiq3frZ(t{vTcQs5dBVO{tc+LCbkz9*-&_>2K3qy3aSjm=dI&g=0j#v4?L zkp8qbINV?94v|hxga($9#nr2bZwBnLV>Le*7*OD|w3{3jkRDjjV6vf?66}WL{0)eZ z>MlZ}lqv!knPI@It<+}O4|H9g1@+paMOdCZfS`9tcKo zsGJdSKSCI-SxB4Ix7lr#sOrDD-r_>E67dT&Dc!2hg4Al%0gmH)JyS>d@!9+d!l|{r z^R*LR_2SiC2e}~W^+?B_Y?SSJfz2k1?V{6WiGui)nfq7YX+V%-~n|5(+OgR zl%Oeszv89mZmd^*?dXP6I%GE0GfG9QUt)XEPY;_)z8P2O704KYu0wPQ=l9yur`FM6 zDDgDGH}))Kf#=CkbT>;wt*Z5RG~Apckm#If35pvnNx#yiug6cHnmmc@vI6MPlIMD5 z(IVUb<#a2{8*qrHcX(~2;cTUa;_B}m@R#kb5Cs+F1y(BdS#7m*8Xc|N?{z!mE5E^L zbkqq$#Dnr)q_d>?gC!|@oJc6?{{lhY?*(lzhC69oXxu5=veG5tZ9k>gxC{fatS{WF{^))12j zm?E%HT5za~odD|tg)n6_GT8G}9?}YtUD2TU0Xyso>&s0ydY^aUTHrM)D}wH##1p?gnV) z^ZXYuj|}VjB0@EC{LsK=koeOpM7DxYf+Q5Gi()Q!EH50wF62l*5|O&c@eE!B$4)~@ zAx7qboQc8&)RQu3gB5*&3n_9^UzzWw66U|k~^hY>R!O6ZzB(U zcSYk&o1-SjnN)yl(ZjvH9~AU6@Cy4nxbfX%Uvma;1m*G$`K>Ug97*CrEgT!SPAUKv zc>^*HXn~ObZB8-9FFjpF3IotZKquf?&zOTb3rLd^7^5jG9Fpl~pJ#g(G+TCHA;4MO z_RiY*Q3TvxX2#y5Yw@>F{0M z=dbb%xQ5egkQRLb?QLoO{U9^RGM7hKcZKPrSUJzw8-5wQPyI64&mETb0JrMmIV^3f z;;M=8rGs6=F~@cmf=}$nKlSAdEg^$14)or)Mb3L3@Frn&*}u6w;*z_=rN*FgCIs2n ze2qPwkt#RrC-}iAaJK&j&gFkHvpDoZbYs4J`2|B(kkekFvzQArbz+~tN9pYBJUJbz zwX1-GDD=Mwd=Szus$i_f_kBVE?oEo=&y-cEA{D08|Hi~n$g5J7{FG4t)^mE7181yS z*17%qF~rTdIM%_r#DJi}p4H4L-Cw(;fLQG>`XBgkYO}08rF+Bmsp}tcbQGvpXQ(#`Yjhzu>570`o5ZK; z?py^DZJygPFK+H^?e|M-OWiYRDI8S${xFKa?CRNg?}BU|$BYs6a?{b4LDi98;oJLh zN+MOgUN!kX?bWVq^>yucwJo<#MYK*hy!|F@nkVugRg6bj`!tDBW^18yK_>B}_GitJ zfDS)^Q*VW^t&~EFjs}JxK!Rd4z>Wf3?}%IB?y<;Fr}JR@D5}`Zura|Yt>e^Q*%$1s zj(pO8{F#7Pc=$eQs(GwtjZPj4GuS+1;G&<#&VM6TF4WzKmgKubl|gM8^?9QKfa4*7 z@p5q`$gx3UXH~hXyn3RoDKB3vL(D=yWMZsdN%x+g`okrI)(X71dE-K{R5oOMm9K%t zTXCMj(3D`jH-Xmm{6qeiNTsaz7i&?_ZD6T9JtU7e5AQSl*N}a|RmBZPkPHvc@5dm1 zid=i~UKT@}6RiIJ+l|Hs-@hXw%93BhJXjqkh`6#b(^Y%&Am>I)j`r1p+(09BZ~ocLL{6V{t2*%$x$NwjS@BY5 zfwQny$_c`5hcqhy^8j<|dwE?g{SB&}vl$Ac>fL^w@mVRWsa3pbe$xHhXz^K&MUt|^ z{&wDrTU82I);xyhe4w%&*_D&uj^bGDOJt4HgZ;#N=6zr}FU+R)@asCv^}mm>3w zfW$99XKjrhfOAU<2HC0t+xuM>gZC4FzI0r!tpi-hRyF=flucExC!=PH(YyfSa0_~u zeXDm-qqnTE7M~(g;fk51;=(_8qCEb0E3hYSnooFj`BX2~44#~u3EXT+D73X}q4`hE z+WHBr$N732^MUCBP!?A1ioYP~d*3?RMQ#Yr9c_LB8Lz0|>t1};**N*q-Qtw=;~qvf zhiCbI&g=8490KvfQ{`ghJ#(|$GW&hMUu5XNEcm-UPnGt-D2E&GlhJBd^JC8XdMC+x ze~%=Rlvk+|G$1QJ-`9-+8Mwuh>0J)T%y!y`U#-DM>)Z*dZC(r>mFi9^NR`FH_;&B` z2l|&OvsLV=&_XyIOedWDuT5X{e~=3>=dvR6E2{NwRq3L|fdIwLc$*|+U_b-YfstyL zg_`bvYS)`2fdrTm%*SWpR>g{J^k=;lFSpYlAb+j*$k@6fm*mSs_a`A)!_m%JZ=Sxt zLU@jyjDD8?q1$zwd7|UW%6u2AcGZ;ef+`5`6lUKZc_&ErcE{m4nOv&Y*K(Dd4t-~w zP@k`Y{CqL!37n3X(|si%8-ikdL8ADnUge8J=clB;qEkKWMII_fzI?*bzpMVeuB@qF zmXx1`K84Kv()*t5Pja#g2CE4835kfo#00R3Q8(5YKvfj8r65(0BmCU*^N*TW*6Tzo zLWMNg8{FDHl6(SY;FB9b96$L*xl}@>GxK7<@ z!I7mTw8ZU;hK$ZPe@*B=Q!i3Yj;ndW}5_KXGP>AHG1>E zsN*bh4jgb(L6(70J|P5vMjOn0Yj}Bf_$KZ}53=%;Hr;S{0#S$WY$Y{MyCD${2vPR?cKOzmIZbSg*^Z2u!UYV&mD3!C;& zI|VuyQFOG=A)~SH-fbc(*S;7XD)ufWf0h3+G@`a~7>yJK??M)J<*W&Yc0EQHS`6Gc zEdIyW-xgXXQPLy+?%JLv{J27!;&CRHFD@WH{IoeHS3_UX_wNiwKC7RI1K8*gaVK3) zR&<{g6##>g-+7fpvIu=G03Z$u1c`}&P(41s?+i4P!YO>G$$eD@{RhTO*G}3l=JGuU zWj_5Xuk|pRGd*v40Y0>N4=ZD3B86x?SJMq?afXUaVv5jLOFkIL!Ju<}LZ$!;U6XcJ z$#M5)uB{IycOBPt(M3|zBVM1Zcz4Mf^c`S=W8)`nv6tmhIug-jnpq}H6aAcdiQPJu z+OG<5Y8zMR$^f$9vP7>8!)Di9bW3nw zuv=JIz|_8w|3z5;O8vm887MJ)3$_1$0}>s@PD)$9Vhi6x1e$ENfmQX*_LB7P0kbnd zt~opFeA?x(_8uU11ZbZNI1fqzpI8M@f;-ENvs*0{wzf#r!ZcZ(4QZ^IP zT?Nwm^U{x)h1XwSnRxp%X4;(Uo7_SWC3qVP{@R`GxQFV`n-cNbd7(z0z?3UIT0~Oy zW7B~^u1ZLZyMB6gOx-P9f-;5QL|-0em8`<-Rj_ifhS&Bfb}sgp0}xGlVbI22Bl3xlKNPksegPzhI-=s-0rFk!`JQ=;>M? z>tx# z=AuAyJ2`((?!ZSRvNd4-6*~ZU6ODrMzFY_5^YLpn0>Fpt2mc)HNJ49$?8_|mMf6>a zI^6tTuaHoZDurfk!#z08g+rH(XN_B`N6i302X)M@_JL3fD{2q602l-nl;-TKO<-PP;7Cg>dk;z$~bLC{+QVDxBOLFjG-Qqu&o7?+Kx`az(ja&t9d)qCaa&A?rEWh|9UZ*$FDc0 zEx%;6%mRC=mv&8$GT931Xb0rS?I`sj}_gOv+GkZP6@N zel0@`|9FyuPy_XOC*5D2%=@}u;Q-kGUmvGX@c(l2<3;ap!bAXEDsdH9WJKU&f?wSw zE^zmRt{l-o0E6aQ3o)k!j7ET7tSoY>5@@B2r2erDw5}z(F3$lhUj{Q4(=MulNyGhj zj`b`hnc@K%C&B^Hr7cWH5bT5W@Rj?SH*DTJ1}TMyO8O^AipyzncRP;MDi{L`^N9MP zHB;s%*8D`rLOTB_N$NL7+fAko3AFh|HV9SVg~rJ&{I#nlvfW{0cBs!rshIeT@grPM zR-vFp=eZ3*LY6(1S`{m>xK1ybl?%pm^lAm@0Qvq ze-zo?XoTzki#~}Fm)`}OXtR*RYmbphXTN*~>DG?XhrWLXZU*#+m3iMn|YJBs*Ufr=DxFyJ|!31xfeZJZa~HFRLBT{>*%k=N^0= z7{>w2eL{gRm)zvFnF%hr#oIYx&92OBH85hm3~ox1*X-1K;rq7({;^hx2{Jsnp^g}v^5%R1KQ5lG~H2*0sk zl`2cxM@;SQLXYQ8kV+A+u-ENaE*{O&tL^Vg;y`1@_pj@pP6BH@!QrCy&tL|tV%g`< zCt{m=G;{d1^CKG_(b$}g-q^6COHeXCaCfs;4n# zBP&=7T2%OsKb3Y`E3LFfox%(~xTUhK_ci{1# zkWwsj>ws%3TtCx@?5Bo)9TO#F*N;mO9b+|#<@=lwRfhcV?3gObe!f(HciQgsy2}{4 z%DRhWfnfYWlH>=H0~o#%GfIaPvdVRVL5Qz9yn6P8{gg0^g!!$Z+5funX>toD>S-7c zsqtkZs|-nTOB!lVnT77HahvKdn)$B-p)=QU@#fB9v8tIGMH7sa$Yz##Y z7HGTR=wyD?)L@bhS2Fz7B$?&_-5)Jz$6I{qXvqZ|$vf=_`1t$dmJP)^-Y3WSPh)u9 zR?}nXn3&pNS`V@&un!V2V}h(Fu*g+mlHCv=`;v*9u zv8SmN9)c3ngi-*r2Y&s_)fsV64n;TDaPHrMqYLl6N!0Fqg--6{8nzqJtjx;8xmR7L znz?4%4|MvDa`)B<`I+#WwqwV8&8A^ro4ZE69Xl0nX3t%y42J3TdB=7%-Ry2bf0p<9um>%bhGG;2G2OnL#~ggpA$hA)9ZkAI;wV6m)+SIv;`#eY05o^yk`&2 zC7!x6JcjTt&%ZK^NxohGLXxw(P`u(>8ls>J1_QZ@cJEL_T7C3v$W6EK1s&-G^6gF4 zavW@jJbhlB2m;=1is%%E&deWA$YE-t3I0B=%>8UA4StQGtKYK#Q?YAr{onNA-Vyr7 zM41YfX7~2I^0G%E6ceDGCZ=8NQiIknw1{OEfFIgCMuook@U{WWp8-@HiMt~o#&{Rn zd!*C}rj;k=8a`q)v>%S1KEaPFk4xxeG~Gr;isUee%D3JJnS?uD9E3@GM@wVcow2ytx7TMsHHXebAfUn{a9mS`&XLQ8{~iACyj*^YM0y*y&t5 z7%rvXJ6cSUead>+p!-$K$4c*nii`e-?#RZ2ZoBi@YUaP6`k1NWlP8Uw!bJ}T0(t-i zxBHu|ugH1dfXrzIIp7&xgi&g0kI=cjd~hv$`^~sqg{Np+gWw-Pb3icsF>+uR*u%tV z+JQY8!sf5c=pqf*D{A`8p1o7Ju?zxqYV^b(tTwMTj>%N7^F@`lKIu9+LYh4A&s8su z$ZoA6I|91!Rjaz`Hq=0IMZiR?C$FBb9ey#)Ooq3?Lf`Pa@TAdmib%ttN97#Kg5QC=r* z<2;pY@H*KtJp#O z^jQ4ID^F)q-LNod!*k4UFXPNCEQgCKE8WU&e#Dp+E9hI%^?WvkI4(J#d)#tsBD7d%?%*V@LYG{z4RYktmxvT7$>OVg}&)pH)f#vK- z`f)*f`3IjMCI&t;qOr`qp}FR~p`irm6X6Lr+mju#V2ar)a|VTd3ktjYK|YT*gY%}w z7Y!M)8ct4Oi}k>e+^ObMYUb3%eJRsP&%Qy9>F(Ml?mXKv<_=E3RPsXrL z@0z(wb;~wA8z$~7o{mtglmR$ope*t%1+CQuc7DoIAi?xX-drI4UOSwQ6% z6%~^-k*~ILWl_B%cDE-jpHSxbTDA|HdrjoCRZe#PkXUzoL`}(RvC*;}=FWv1xht1D zgrvx6xo&b4rxHX%3)5|{lNP4M7Zn%d@?sB|7yFyG6Vb40!-rbw=O{YteQ3mPMb!0k z@ZA?X62>HT6oi$To9!J@Q)Tj#!U|>@eB<+V^7vNHarU+ z+I^P;;mm|&aDTky_%v%xqUPUv%7_xh_8Sz*!uXp{{+Nu{^a?5r<*u{qQ>H(m)NK~F zc!E(_kQ#A#>~eBI5SrI8(v-Un!@#*|uf*2QZqwC3(Nq{vUyF4VYSR0`n)7>ncV+o8 zzQ(_*$~6ID<*>brTvTVQL+P9%Z{-nLE_SIq0gaEW(DUjuN+8cZp>`-dSMfHrrKTa| z%jdxV{&{obe5Xd2Tt-YU-eF_Zdv5LnlkEvP+(4by^DByMmu0YsU%c7i602w>Dj+re z)=}&1=y@xq!ZTd_X69p4PreDYWAA8R21N3~8nuSnB~!4xj8G>1M~amctGx5QA5d}j z$HdI8pi$*aGDoV5M#ahMD&p%>N2@p09g230R^1vdsNY%R`FVCkTz5w7uCqOzw}$B| zrWecA5h^zOZ}t6?%L_90D#A(+C*rZXQ(}aWc1lxhVFu+zC-cmd1x1<-L;Tsa-h_oS zJG}YTdvU4d-IzahHkGPMzm&z1qXh7Wx3wfSWWEV7IQHjRw#gr_t$C@;$S~4>z{B|W zpE1Xj1{y-R@TIC!4tjQ0eIEDsk3$kaiv%*#M`g)6FxQ!yM_zl6oS_Djetc!OwqciM zew8Ea5uRpoLgH+AB-9_>QZeFG>38fjV;Y06H~)KNkV42y{v9tn>2diDJUqgN13r%W zu>_?uK4bT@i#~;o*IlAjGL0u;^((j=A0kWzs~2uzXFmL}EtN34*g-87^z7mJj%&NX z#LpreT2>EhYio2I90@V8Ni?0flE6T8AO>OW?(ULMh>C}DXw(pMkl^DZ7py25Bu+2h z7N$IzM=9705q)>Ct~kzg!H((RkrMdr>fwTeOl3JUcBggOaRV!{>x$)S6UXUD&Xw8X z3~F{WD(Tce4E1eSb!?vZXPapC$X9MXMtN$`dCfO@;G+Yvi zH1Ww=PyZ#nw`9cQ?Y*gL%Z@39qt!aRYOj9Bn+yxKE4l|VTUOZH{w2lC&BJVnBaajW zqOKg}EZ9bcyIPboifZ{+QU`AxF)U}+z7 zWj4g4j6908!m>bfcP8A!J8r5{Rqik+HHpt1l&HZvIyqQ0LHD_uZcp>B*SMSg!=`%m zi+$4L>kW_6gon!WmCYdmk5|f&-!5F(5=~m1MfclZ*NPXpVeVR43=_WC3Qex}Andf> zpBbyoY_h?dWDverbkeAp$LDs}*qMlHylWsO@}}0tBs%WY(cQ%5q0pEj!Ss8bRVKg5 z2Oa<}g~JJzvRG5FnzS0jerP+!2NUhCjn;pskTwc~EB+G?&YSwG*+qvYW)Ze{1JdJo zg`)XGk1b5p5sgiMQG655y9Z`Ztkgd@^-73k1i5no?#+CorBhB(K|yD}E!1f#*6>%@ z2RrD}fsM7;Xn;~dL7}m$OLF6t7)O!6WjMzhy(zt6haDq9f*7T`kHO9BR+i(1CUG*F zSh4vx^~%HOx@B4xH>anM_^?CODedP)zGzANl;z^c%?aj@(L_qlj=NGa<$Iu|ymiAe zRo4E3zmz<4al^@hL~i|*vZ2PPs6A^$3E@I|t8Qyrb#};00TpcSha004M#vp9N|w!( zME#rH0~cx4MK6w^TW~9<6tpHM3U|7Lh;UT9U6sH_f}$=|nA->+fB%9(BYj>;(;nVr z2&17*k&!m){le&Gai*7n0gN<`yG=V^$?hgDs>yO|oipB{Wev8@&qDF!8$_L*;)tPs z;YhL|{$qEG2gA_Q3A;&mNnw(Q!d8}oMJ92&?}+`R&#>^7YrX&Ec1F;f%{&+*?Cq%@UY|$t@-uEy^b~+C6R}dp zeiM=0+2dtvl&KazXxeRUy%_h-FjIuG#K%3=s?^PzJ5SRcfk-D@jx0SAHImwUT;b^L zx*l+k-9?FCkm$$vsj=@qg1yVLQM1wm^!>Olbzb>~w!7`8Zn9GvmRsiG0qQO@sig6; z-oG*#8JhoX8w#nb-8z2Z4jbNVj(O%4=bf?7D5EZDDXPCw^vEynuBtbD{M|`g3thqIg7=fgN@={DRHk$fG4qIw6xe_`O8>!X)M zjAt)w+glj%Tbd>Rva=8-YDt;i>!h;3+5V=4%$I39Bot*=NgYWYO%Lc;`GYN9{ zThWpq!MGAk8jyuLypu4QH9J}3mPE4Kim#xgn?j$AZS|8M6!T# z{dB~A!VNbc+}*#Nigh*Rd6hqv`>If~C#rLlD-IWa5B;_JO49M`LNjJ}HiF{duIHcDgmiPnK=n;Mvf*v&~sG ztgAs;ojSf8kZZw_K|G zP~3<|wtbwG6Q`Y<=dyYDU6%#>Tl{-x<`v}EcF%&DNWFF6)wp)Q{ZS>C+%&E(Z$(jd z&*cULBT31z?-mqKtE+!5re?u^Nkr;1VYah1L$mT{^bW%6zy{aAQbt@ZjOc|8y?EJC zBd_mwA6~yYU-^3U3_N=_;Z6wcY^#!z=3Mui_lCysI=S_~Rn6X) z(PsRpK_lEpVfc(l{@lhqs#?JVq0bmYw7_zoKM-zmvqSa|YO6naC69^4YY-={1fpA37{$LJ3m)h%J6v8BSC$@-z7(|Lx5JBQ5eH2DC`RFkzM#mF4L zPCXMYxQl?2KvwokH_hkYQ(c~{klMx|Ku#V`BmF&DlNZJyXSkmq8BJ8O%+1Zgt>x2w)019{zgBK;d}vj; zv5zah)|qZ9CbU#1!-17MiO*#wK9p3`TUakC1sm5r)~mXl@u^7*Y?7~Ps;Qk>v1Pqq z4PM=^;t2ELLYbQUaqsLz10i%0g_(<5dKq(ermw>7+?=mps8+!XhjhTP!^DwGB5A;c zb+T4llHgLitDTO`$Gm1V**G5g$oKo4ij=;} zWRyOQVCv6J+cQpo4H|C>^plxmg$tJp9{BQF()=BG%yu|7ZMzJuPP(?NOd8C-A~eE< z8B8Z@k?(vLndMAb91V#CNCzHJSrGOQmj&Tfzp&0Z{APKq(G=XG+QGyWkm-xkM}L`X zz~9H1-`Ks*JS$}K7(p>Q`KY@sprvODqk*iiZMo)8q^8CVT4L@D`~B9^Ef0>~?bYPQ zv|a&J*H0P2z7v6DUQ#6mffp$jQi7AKJ^N@wOVJ#M4i>B=30jSK41GT&BG|BNN6F-Z zZoz?JI$06ga8qk#5?yB4=HkSWCga+M%(J1N?1hqCZ5hXH7ESlvSHy3>7};^fDPka0 zT%QqtG$Flm&gOq$Ll0}#yT^RW&cjWg59{QZRVh>su z;l@!1trH770y~tQKW8D|N~j4#+CQe2Y6Mi2XXxygDxvQdwU+&`S%@*dfq;4yTVEbO zFX1oBBF}7jA8uqZeYd1HY;}mInV1d2{l?bTtt7BD2}Q-->FaI5CD!%GnF5Yv)!Ix%&pOT`^)I92CQzi~n4g5rV0FKfA+iDsLMX1Qq? zKFK!Ld^AniZ=$P9-FguzjXSF!kfy;wQ}9X*Pnr15+MMrX3`Wtc0=9=|HAgM!!&lw% z^KHj9%Vw{|uMG-Dxyq=!>``P4rs_RIzq#&|k6Vlckk@Nql_`Zk#j-)L4B zhavVBJ-5liSA|Ac&ZH<$r#H$!zm8B=@?Oy3_^=ap7?UIS;NGIs%N zX@&|_<8Lg;NYUo^!pLziUM=pa-p0pn8f$H*7^yEZdxYZ8hiR%j?czEW_~|8{qdG~I zMxc1Xi~e=X-arSQ5&{Pd!nq`~kP*)K018Ctq}i^s9b2AYSL!rYHaTuqZpx~r2J|3zlAi`;3-(3S>q3CF%;fq@E^CU z)8$e4zb*$Wj~z&UJk6VRZ6Sj1u(jQgWRbHO8hu`-KeRE;?-LKNw0czBae&k42Qyyg z4JrD@G$w(f&##jQ)~p3;uq+&GZdH#dZ01J1j*wLeGi_bmvHvNa**Jb&7*xVDa>}ys zRjJp_g1YAT#!8FY#74`xU-b|9WIXJ&2alV_$&^^>_qt18b;?sUpD&%GrZ4h(q^v>E90r%=sc@lbhHYw1iUB!1t8sly51N;BjXJYpiv8KbYTYl6LBF|GERIa&AZ|Qq;#v$C7 z#PfrKgDe{XQC8)Vx-O1_d}CAH6ye@k0;fl0;U&|ToV4HA^Kt0t1jY=Y?l{OgLNXC6Ee-t|2byH?#@IMs13 zV{<3bly5FppX6O#l3x8Ir{{{-3dBet(+&&EvOZUGjO#Sl@k8s?`%1AEpqtf?34ct5 zNyNCXmg`cr?Wj8oPPVh*&Qh{YDb5Vqu$Y*h>i?a%`1okr+S+!TV~QJe;(%4Q*xB6` zMjo;q5wh>^PRISfQkasm#426rrl8@=Aw&f)w@nqgtz*V4pmn;+P@W#3jGZcDBi3;j zKE=4A&4A_nRhMk4V<6@f+2}0+8I0fLgg&R9>AEvN@T@9ISe>rdOc*4<8u!%h%ynzi zlRq(Z`&fU*rXCf=>n8?(^(`e2?yXfak9Ver-_G`(A*z)##aSfZQKYBA=2pRcbNf0m z_>q9ax1k0W9@L5PH(Or=;XDf@`j%9}0dIKn&>crN-%B_Y>;JjlHcd1NGe~rZkfb&e zIHvIPTz-Gop`w*DwIz83bM*7Kkky9xmh2IpCYi@AQtl`_a!H7^KR8U#3t} z+yx8Q5!3rYuc3+fmH9$pv;M!n>gJL{QsOxE57)ABsLc5K%ZS6Ba6=_(jrvV_Z?Q}C zy<8@z!;hr5E{&Ye3}VckEzOfL#kASUCg-r9eEZvqcB-#d zTYE(*FcBHdoGLo-s|QtEJV4~$tqp0WUnh$}6`R_kf_zrv>(d1|+XOS{#8nT}@oZUY z)7v%4_ZQvnYH4}i$t4RH8flY>cuh@kK1evB!q7IQ=FfeTNVnIk?Nu**i{HZ_#cg7` zxN4z)e{V0U@ju$^Zj^6`Zx>{CtX$_{dz$ zFB@na9-`FD)_WUg`mr6H9j3h<$@D#zwk<__`&!qXT7%e&9 z{`ze<9zFs{Jf`4#Z5rA}CKfhbzf{sD%TLolgCAMug$?qw+}B)H9RC^8ZM|T_Lg}0E zo+_qU>v2nHRzpIsaDX`Wj%~R=w8HNr{*1Gqz|VPr0;yCbTBtk%L>$iBr6KGuCKf#! z5Jjy+-YXFQrVk8+kzc?1Lr1na^_w5EF?+sQ!DX|6&SN)&_Th=Zu+?Odf6%i49$r#3 ztyQ1I5GEl5U1o=PG#ifhcMC3xZ$S$S`T4M#Uy}60le09JZrZr=E-LvvE2Q>5U+U|l znDrSms(^eE|6iumD(+7(Z!kqO#FNy>VdZNHnQFU^cBB^-e-=5i?5jFbd2!}f+1S#A z(ffk+)sw8TBW?Vt6}jd186&FpJP&W=g%9Fq?jjmGhSaUI-K{DPD^Ke`Vds~78apq# zqHQ@SMIc6PXwcB)F$YekV<#NqyG4s??n0$CW5gJ+F@1a&`N?tB{gkMXe>IyNF?nQ- z@TwNkK3v8~n4)yGhU2Ch$A@C3t5-#DOYM%m1JS|}lapXDkOL zIlg~A_;9mO9Cy_G-Ci>;|4u1Ig38d;ZT}U4S-xHeQ$gc;1NGyQB9bEvLVqCM*_poe z74Sa}U>Dw)R^Dw}(o0)3o%ui|nkhtEJ6aYnLv*m~WnDU|ybyJ=k3dv)@nXw!@g zA;gTB-tKPP-^G6%iC}cwU~yDge8b*e_+UOEA6!5yGsc!59nsKIisPh1_ir}pcHfGy<&E03F%)9B5i#F#zjoNyRHl|nJmvAwXOz1KS*bt80FeSerCwo$=9%xyBu#-y~ZOBY^r;-3mb{JJm z$PEOPiefFF!+giFC6r!$yVbMr ztsZ($vxv3Mq1pp#@&M7OVU4FV0_Z+ob6@c2_)&`a{-d43=t!7+z}1-C~?fv{r$5cdl4)soulK4*;W?5Yw^*0j#esjFtc# z3FK*VGIR4t;K@_iaEkqdItVoXRT0pyFH_ky2UwX7;;@?XKG5I_{N3<1gtbFjT`AD( zabGiaEipyp%v>;M#K|U`5wEMJuVG5fzO|grz_%izW3P>QSY*?b3IONK2<5f?M;;u&xR=V zBwndjPtDJCf4P;djEB_exI7U)T3*T{xabKdvZ0qu1#WmFIF{b!rRe2T!bWGKu2#lM z+zEQ`|Hx{nyuj2~j-pT;ZOm1FAktQM)4d8%B)`SBf*L4R?28~2)Q$gDc-HKjW&c+# z>E>v`uRkW^8k*=UHHR*zCQXwZYQSb-*x-r%^0frTik%h4re6`QfIkQ6Gf8aB5>r+- z9+Lr$)0fYHl2VG@a*B_H$L*HH6e(8r8?7qr$XB;uQr){u&dOWDh+9IGOAlIo5Op#k z<(s@ZQ?<@>ixeRo$nQB_zK81RddnvE!3RZO3UtI)s{gvvr^j~?r9AvRrKa6y)U0^O z3Iq=~Hki$@cjn@BU!_RLdsOy~Bry2CGHC|?`XvT`yT%5WUXx18aR9W_wb)NbA`bRz zI^Is8X;u-1Nvoyym^Sv-g#B2UuPkzVA<4!hWRmV>8ln~Gv}WqNIP&%3~idYquwt8}ZEnMJmDS^q3qb?HI3|{k!q%;fu=3 zJx2C5!H3Y{^p?@|`P^-I1EG9e@Ugt~o+dtt&)+VhR^!aaihl(-7CZaMC7Mos%PegF zKeFCC5bO2}1AmO{J=laaJ+rg2D@4dhLRKPsZxYGgWMpqLf9LM~ z{`Md5>wS&qc|P}j?sKklo$H*NY%x@gpD)|i>o`t4-)S>vCtydHUWc|0e}2v+MTpr-xB%hf2JaVTS=B7;l+e(q(_JL1Cch3&ZkQ9 z7+#C6i%+qZ7;hynX;CThy+!#sP{U_jGn_aNCh#0wtnokVRw&0L;vF6|4iI0Mt*Knb ze0{g6{p2LxZK~LH=Z3Dm+#(~;8}xcNNWchLm;**Or;FD~WEa)Fe4v=Sp|sqT$^Wq? zl*_9hi5RRsynV3j#Z&1lo(b3Vg7^yT`1Ea?CO^qMA7qXANC`K=sn3zD<*tkct+t$g4*8=U4BFQD}OI7hl~@SENx z;$3}6f3s_mt+4neoN;!K;cqAcz(dRQ$dCIb>ht(EcSzeEatm*aJ!n}@=r-+g*yC*; z-4UJf7ZF|`6y6ix!mTI^cbjOw6F^Bx33F)P=)Gt$==9}?0g1Iv8*lFZiL|kTDlaWI z4$k5=VLS|jkX5L+H|CQnFIglz6{Y#xpinG?{9=ALb#UYN5T#fwa9Eutj@h(k?Tj=1 zlnY|p7t_rVMx6;Fu;q8)f~M0~pOU= zD5~Tsf#oD|Z*q9CfT3Ubiq{RIPS#J``<;(sL67vpEGZ{HDl;$m*j`}s4?zse+;a$=?`FU&{n`zs8$sK79sPpVmNw{OYrz7$lH zZSqFW{_f8z5BC@T*BTQnpJ@oo1UN0#GmfM$K5=o^=A|Jz1=mlWcVZH_0=f&33Dr*2 z`Zb0dzQQACAz@@>Y-nyaf{T3&o7ssQ7(6e7r`a@^9wUH5H(xXp*|vJ)oyt_I39Q(a zug@pBQ6qhsE&GNNx2okX#Dn7_F7#}3hWau;`B^y(_hUgQ;2ZQuANu2dbFVbE{*4$d z_PY0$#qJ;L;7qhk5LbuSA7SCIK3DFArS5Sg(0R#|Fz0D*0Mtm}0teya*0)&_I!}!v zz^Tjj6j+0JLqMs6Re!t6$DH__jy6w9e+;L`BF9f1V7Bz0UKL>XP--g*&>^2O2~;>-Yw zREL4z2vB{DQ<_N4`*Jh#%VX)@;tgL58a+{xemaHQITmb%1%9$Bae@aE);2_cfIX3H zS=QwpkBt8HoN4F_2P#1e`#Z_3y%kzGbou0Y=PZ@QSQ+Wjfy4_a?D{6kkTLozE@{xE z$#5rs`piVO<20~0Y`wOzK_y0|oJd1UJJ}M&=>o#B)kT+dm0*~;lw4ABnv9GLAegK( zVzG^CA|53|hqeh8}6xI1H9yRrv?pl_2*R7wjDml_Y5+dz~S35SQ z2NdBknJ2?1c#-!A7!T`~bafDnLmsy)11O&={a_FNKd$Bh& z1nCc;8~{7nGEzx&odpZyUH8F=VoOW-`ZLnFXi{S$n79>P`MtN&98MlNjS|br&K#AL zmisvM-_b@b!WHF`hF#4R0+Tp8gOnk} z?It%p#7XVM=!F9vWu__ldk4OCdviyX%xf+sc2o87Ntj+U6}1buz`+psvLO9iq2vv6 zDQu9}5qKOLVWb{!T+z|lzW()z{{vyy)IWQ>28)mEHNad=)07&!?W~TI zSL%_ctGMbnUx-$icMx3~uV#f|!a_Ep9A%79csoP9Gr3fM>E7+zKzu}v3n=mrxe@E%d22I^3-EeMmflWK-SlA=YtCJPaxsD zPFB^2@w;$YRF7-xeh&@Df-A z`J1HD4G;DEY(+*79(ypAkzW zj6dGrSq_2k6r@G^gCPV!MgZ9&UxRnEhK|vP-v~rq!aPU{$8(auM>#?M|?$FH94?!9Ng?20$%v! zEV8byrP6pUrq>vlL>*eBT7%~G0F=*rmXw#K`T~qkz489v`;szV$6(|_PqW)mZQ4w` zj+EG@wcKe@$qyhk|J@Qrf$JlvsQTk`PR?UXBbOcXT7d7}WQ`N@FNiJJ0hh_c-6rciLzow2A zJ8NbDF~5uzytM4`Uq3*KYD+xEX9bS6$QZT70C#|LH0YCFV`C$`fB==Ao*rONV)z1= zzsl<9Bn&9G$iSyW8+{p=vHk7Ehy zQMh}z`yi!C*lFtQh?oS)fgELIi?LyIY5*5O*E5AEuLb_!iARk3UPppSuCr$f9#T@q zRl;jXr93d``FM}~d2Y(hK$+}b!-Jh=yxVfJyS@N~cGHrRF)%#7B=QaRu2Ma4$=Jbf zAcFmTrl1^If?t|Jz1A(y`nd12rlC9sN0tyW+l`Gnpi{0GHIWodzYXKB+QSBhMb^pb zdO~6%Fmg{r1$89BKuC>#B#F=WS4)2OkeL`{uU&pe$v1@Glr`RgpX=!8tU*pURn)KNKC$`z zoe(*U!pGX!*kFroCyk5@T6qDFPd)`JzxVs~eG2+&mbkbxU5odM$opmT`lVCyt=7yJ zx7|Q>2@CkvhSe%;XL*SIX`8WPg0M0uqh+_v8eh`B6}IL0+0)bgNtqKZ#@3btE*_pC z{NS{tXT^YROK3sBBEI?CPm^cQo}EZ5Wi@IJA5ZBqgJt5_*xIR0Gm@M3BbV@g{rdHQ zZO|t=Kvz%mDk_-57zFEH5K`wG)cWF8S83%0ynoO7@!q=taO5SmdG}>yKhAZf+?*dN zy}GkJ9B4IKCcgF;{TG^fj(#vukcLG?)tv~3rsXHx`pc18c3RMkM&Rnzb(rK4!rZt7 zWXUObm#J5Qs(Bjq+Zk{V<=XD9{PX7(1Ij@ceU&n9+S*=GwB3V~D8j+PaoK4}bbu+;YTiffCoM)S|QyxC+Cu=!@NP{rsL88C1m0cGF3s}{0jrNWBdmW zD7Y}*^XF@eEr-`u%2!`9H(tCYFF&2=JSKhT4k5fDvb7*VooblBkAC4xKrlm)BG=Wy z8K3E?smOQlLQLL!(1E*6HAcGxChYe^;vf|KMlT~D&%LMo*RRv*mN`g=&|Q*khjvWA zZ1p_D_LxhlPuMs(dLTS^&=C10+)sIs425U-e0Ub#g^2| z!_z`vEr;orhrh{NSbY8@5){<3_h;J;Y(*+lm0_zwA)Shlkg#iU8&8~{@)790Iqss4 zk)Q<*2?-W(MRK=q2ch=`2%z=N>idjB=2(8fqPnRmk5-XcqKx!NIk&-`_>W#hrJe;ouN3%((wv4!l$)No4Ey?~QR+O-S7I zII_awsKlU73d+$7VRC+Oe;m80sMZqCQv@#FUAOdwYn{Z1;?DHh)PwhbN=n_$jEwFs zG&UF)Z@G!n@#}?xzo=<#y|X0jvQ*G)@Z^dAllGWkaBo4OZ^0IT@hNloW7xF`7Y5PV zl_s<^Q}td2kfSNEGbt;pN-yTTBCD)S87uPH1ez3hE?#V4^*UsSy5H5$lL3^pw2cg0 zsl$e7g#kCVSS*iKGTym!C-we!H$Mn@NI~tgZU|!uoS5J#Rd}O`5=9+-6B84qf0&z? z1S%zp+Io{HiyJ{{=p5Q7Sw~7h==u4V|AA4Sr#ZJm=msc^A)quVCh znZ}Y;dBMU4bf|i%(Nl_|r*y~zw{_iOz#FQdFvS!6i6CUIAepfk$j8#kQ3I-)OQW7t zo+FA4O?*|9E&9J6_~Ojm948+i*~5>DSF-THko5@2$mGDL;4lZZ5a?F8P{Q|e`mqG! z;^Imi{-{(Yya&es-r?1M-eI89UC7Sfo|2gvf_4Z6Ev=) zFbe`jT_z|~7JfDBK=Eo}CD-){S zcURzlZr{HBDRz@uQ}q9_wUAyDkdVj*k}-qcol?|kDG1mdNUuYf{X1~YOCH0g{B#|n zudiCR;E=>2^p3)HhCbjfTRMQuj7uLF&_IlygoK2leCFeyAMQX~&)(m;_l@B)vJi*U zRZ^gv3L}GjMNirDkVIQ}$I4T4Fx7WK0dHqJCWsW+t`BXTzeSblgLA8=%xSFcjTl_3;xZiM^tg%P64PLq$An(t0q zst<0NLeG{6&$hR}Z@4y5Ybalu16M{f-Eg7EP)ei{BLd_X4B_T1H@8p=Cho(BvpO)J zNiXlF@iXK&<0#Sgs;H?2&|XlR2$C)@MUs@1#8e5V;}I1V6$S$roNT)# zGR{@oigO5%pdID?@32B$1lAB#8or8L)MDYcqFRkn^Z}NYoxm4@qHIAxBV@~|jL2Eq z%_zWM8Ak#7>}V@clXVXXoJHOjgNU@c0i1{J?tQQ zSUD9Hs^i1W`wa~Z@UTgsi$Fo%(?RyrWwKia|K3Znh=uiENQV?;iYrJ!vhlA&~jf!;>zKS4%?Z zFCAg;J!3u~4EB4G9-S_D%&Y5=lud8Uv~yh*TqF$-4?ivDbP*yCNu?e?a!6Fc3h8F~ zPspqv@J0j9FlK{K`$hKSSRlXVmlv>B$~Ykr5zT;}9vc~Pf)jjdZXN=N+0L8f|NmB$ zjEo^_Ia(=TPvCS4!B{?g{5S>Z430BAS2BQV@DbEcygRzn6$B%&Mg#C7Ev>E4 zE=fr#co~vTR#w&*FJ4@mcf5J?=H&A7_ZhNa77BDODF6Ap?2-~;5+-2^;K#r|RQVXM z8`NOHA)y0^Yd%nd_KqBWGgSeek?S4>E{rrcgsjcR*%|zehx_4rb4veW0r&@GrOG&v zhr!N~9xq=Amc>q2X~hzdB@aT681U~AAIZt#0cjHPPifk9359&Prpfr}Q%J-@nZQ=y zNL<86@AnQ)pW+Z?wl;9M{yTj>D6gLkWQCA<5k|xPd&J0}KUE<6{8Sq&QX!qGItBbM z&xH%rGyAe1|KXwkdu1V`SYN9rhk#{;zTV`Eo$lB7YJ_0;_aM?LM9?C^>euDii{}Kq z(B@lBK?r$PP*8xPUbW{@L-GU%-9|wtACX99Laky&$dYlIPL3I)e1@vZ%TdOO`flNXp19BqX%;rw9K( z7nJIT&gMK9oE4b-rxW_#>-fxT?)w3%X8T^z5Q+#SB_&}GP~sZIa9c(O8~L)-Tg1w_ zuYXsC~S~NKy$^5N7-sAU%#p% zfBy@E{5>bF zEA1T^h>wm-R0}~tK-e&xFJw;e%V16r{^4&`k(+8om<_g}tHjm_B+4Cc&Odyhp%Dbe z`_JseFYxoD6Gd_6%z3kLC?hNle6A=98LC~Jg>zzAyF2!-N4*?G}9L>ee z?Im;YaeaS3eYx{$Kw=^@&>fJQ$^{5oFhC4O2=VZ6^9Q<}8GJ!QxU4K5+MQ3xVZU|& zC%ZISP7h-KF~Q~A>2(Gk!xVc<)_Rzpe52dsbUMo+z-GTvf3WqZ9zM_tF|5%YtqPYb@BkflFPFgAEdjcw=hs7MfwGqqJb}Br z`&aXRrC&^tX#~QvLPXBuL=pKvBcTV(7eZiUD{wUI0&d7wuElf(O7098 zA(J7o*x&lIwzgI?P+$agr7fWQ=q_q_htTuOWb_0xH=l(AHvth_8g7|`9FGn*3yo5| zedkW?_F_MJYCK~?4`E4i?$iTdh`?##D(l`jT&pi}+vX)>7Cr0q#?AhB3#G$+7gqJr z4r2f*BiBD%o)1AQ1;z~^$p1zmeZV%~eb;JhWt0&j)=fy}z)Fp`{+ZIn6zF-_y9|)L z1+5@Mf?OJE13}^oNXqTFKDx6JU<0r0i!x_GO7r8@)dNw$C6csJ?b~>=D9P^n-85|d zU~Ft`0EzV;&@m0+DtSZStRvv>Ij&!iss05k7YX^FOp4T8C$LA@7({9S@Pl&?IBOIK zSrHWP0Ud2G=2aipL6sj(;(!PyyClqYGp#!rZYe@d>)_Obf;!s5Bo5*C=)@E%QF65F z2if2UWH>fI`p+r*b9Mc`eftKPL{N)2Alk0v)QIGr)d^&7bJ)N|`)8J<&ePUwPZnp@@-! z@wWx${d^VcO^l|!S+p`TGGG=R_$A+GJs%&vqts4Uf%xR<4OaHfx+)l2!f$NRyysmIvLMj#g8+*zM=b=9gyHvi$jMaln3xjt zJ%-gBR>yRgc<|0eD_~(D?`b0DufSjiip=Tz^7JD%Hte>R2C1P|k0_&Lwd~22m2ikD zQ%i$|E^~>_mmk;Rb|i>UFZSm(Lk@1UG;jqGDVww3Z|3P$UbdZ*Me`kCb%6dsXEZDK zQf&KUOG{E|iix>7bm$MwJk`?DQZk4dN^GYYe#Dh_nH9YievIv@ zS17l{v)$I(jivp~QmCAviXI)VanGZuclPkq+hz;DGWUS9>d2J~X23=PQ4A%PXMmUh z$13{|*D<^RY98_y3c#8`#B9_naBy6;Jw9|be)0r3We9e?kls##QP=qs(^JsVq2rwf zM94pf2bd@>MqS^7*4*oe_FhvZzIOG4+XkL_MxbZNwK}0Y!Y66?Yi}~Sy2|f#)=wLz zc6TO;hd@novYFLO7CtQ&CXk#Jh%FiiQD%6^wRZk$1K`jAb~cNxR@e`3Is)8)6Dg|a zb>yg_sfj>39O0Y57sBzy7(h}12V2-_>9ho_o0;DL4E=ULgbUK&iaPRwr^9G#Ln}Pm z1lP@3v;KH7XRF>F?U~=dYwGKv4ZG)0mSqok&-NMcky1)ZP-GGjIb%Q9c?)PD2%yLU zZXEATw{&zgz>RT1@4c@7@JT>YFwY~$OJj_1?^#mu)4$eHb~!BLE9EbJaQ3H{N;jWs z)_;2qb1EgsxBbV?VJ4^NZkj#01S>$zmV9=b#?VMo#=wAW?D^)s0#3>}rk%;z-SUVa z5-3=-7y~xqxOVN=yu^dg_aQPP!}uTP8pIq3P7lt3G;cJ+=(k4@^2st)iJ}!6AqF|u z{6DsY?ok~}iSwCLaCE=a8G4cttpxTK}aYK zpx^W4$Qko#=kZ#Y=4%B7g{&2OTU)FLB5y6gO6MVA2{RuP?u0bo-`~HtSR7UxjogBd z78M`=5>hUdRRM^O;KB=t){aLsG4wZKHSx7E%M7$;G zc;{fDN!0t;kiH)|0Uic-3#NZD#?C6x=C9;!MoU;5Wjc>;iBR9~#XwA7((Iv&9s_VU>Jx z6Y$3$GZb?II$BdYo1ZLef}gxxr7l4<)cW*+JDzg9n_6iO<7XY?Wdd=1W5UN`qfakFN zyYa->#H9Z%Z6}~Y{>m*X3>&1+z--lh)-1eiGa&`p(pvFgAjuhD$kQ;NmeloJCQr2( z3xM2i7Ik4~Ysk)&%EIVexO7 zy?AiHJj;^tzHakM$X{q~a&b4Hb`jytDjw8Umyo;rX;M40*9_y`(R8Svk>0n%bN=e; zInCe&93;PzbwE=ZxE^mW^uo~CX6pMl*qz=W_kP87!?**=FDD1%C!s6#sz8N_!6wSH zxXLb_E&?tVwE6U6cu~s#F#W%_kf07fE`I7W+SRAnq@K>bsENv(_$^;QIc9tMC)9ZQ$T1{EWw_?Zu%mjUK`Da>T!m_s83o3_DOYM_KoK3DcrCV zf;oC||NIQP6p#eO3VHcV5VDTi7k5a~aa!yPf!2-Vk`-UP(*9So;qB^v&gV^rG#O0t zF=n(3;)lMI@0mD%0jmQjseHi+B@21_)n*5ub<63bW-!8-UcB`0aaS|??6IZ5djK); zXwh-pWjf5gjh>QH*=TK~(sZms1d?jK<*MFnb(~L2MsF0J6j|zf{(Vw341=@tHU+=m zdYXbazS8QAscDr9P-h`(SYy3!sA2C6ttz4LzcndeIo`ZkOztHXj(zlU5<63G^!W3B&^!1HU@2=3>IN8C!Qk9?%#!><~<-km?;-dVgS z#Z~2cmc-xe((H<2eFkT|nx(eahA3SGaWk}hYdD@I21`&S{}Jvwv3QMgv<3<1DMxjW z$kw?9A`#gN&#zyqkVl<4 z_dgs5$hYN&&XExzjBJG7mIwNG1N5}XKy`A6r|75tLtfcN?o@N6(%SqD>=oufEX;@) ziNE!DYsp6~|BtWdI9;ZiTMm!hG2S;HXz%TW_=KrbyyQE=*(JLf>Ad8ub7W8WoF!g$ zdFZ_P*sYC)Z&{uo-N<24>H=9(eOou_(5niwcUB!r#>=48stDb8wF^?Ld4;3S9(!lP z@PfQuI0LjSEqykA+c!!cQM~KBpn@fOO4LyHE;)(0a_Puo*X+vMkw*G#GX45P%6n)Y z!t-iLYD`z(I2QDmLxY^3*Uym4A4@sR|B2VTb%6ywQ%TLOGy&KS zLP6+&vMeP9g*T8F0H2|NgBBg2GA7yNM-m5j=4hA>cxx!53n-y{gXZ6>ME#}M5_NGn z6YNdNkBmLCjW&{(HC-UN`gFJwY`6Zx}zJVRU4{TgqcoE_|r*rixwN^D1qm}p#j4zN^jBsPdL&^MP z`1})7zdW5$A#8J$>Gl4?m|&5`KjpAEOXTsy@|k1w33J~FGxAL{&jsr$ma>>LdWd^H zkHx>fR8?_Jf&)=9q*gr}#!#h_?DM{G`$*h{xVHZp2evneKc|5m{XbW{m-f@j} zp9wQ@==_5Xwry=*06R7YUgRT5SaZc+%n$Fs<#sbf5`TYFgQ}FCp1xvt%pIyNVsa5I zVSf1JQ%Aew&5)5k(9t0Q1pY8XX%dRvh&Fr;Y#R)+0FaHM1Y!^>TsIL%aF&Z}3bLmr zNJ$HxerM3BbfX1AhrQ}}f6=}3Iy7LGl$A}v4+V>JK;DADx8%v8A5`KB`qI@1-~Mc* zXI)nGk4VGT5=WCr1b3dT7zLgzSCJGI?_<(!mn+v6n$DU!xG63#5QnKnJdA66TYi;P z^VW<@Ss?2N6~e>BoNms6(k9n&MkpF=JELe6v9aX}SuX)v?=HE8h(1UAUf$mojK z@$-Ud3d(K|l}uGYHF*P9R&E2i2LYQ3#URkBL_uq!9t>onj3YL8_IhzITY~32gaeGX zy^FJOpff(^n)~blLvvXXGv-?{PL>sY!YF=!Oy%V8No&@@m)#>6h+|RmbxC*5Lg{;1 zU8ZKg*{=V+6FkgB_cP%eqNI77BA2?Mh(&ne!UbVr;faw_dx)2Tm6hUp&)07)4HV#r zIW0+nbYgO9$`_cODG2sR*w6*>C-n3cY*=-9sDFP{N_?4-QZ9@9S!BSyw?h(k2Zf5j68eQqlrUwa>ATlx%g9ZVbToSx3=zi*Z?n{Qz>$zZIY=8~wND^m+P;qN_Wt2U(o$~bQAnqiW z)F#)H-RhIc?7XV1eYlG~x7Y)?5$Kn~f&zEQKW+?cN_~4A?(mr2{2|#4Cds<>EZKdl zt}KLyr-xxHp_-|^4w^?Fw=EP3zfmK=xGaA=TT!-7g{H7DELW0yo~_S}=wD3W)Q&2# z;Sg{>H0au-LS=6uptDoUC+*UWzXEtDfXRaw^(ue_mrH??`~1&zVmP$s6`E4AvQ77a zN9$_wJ(38A&~Z$2c- zPL@`~lpsI3`XoOHSOQ)rWY;CwDR{5t+wI8SS-1n&YcT8+kqQ)h;>4?)!C+3!VU9wAjy%#6wCQrb=E&Tc;KvVGcb>ep)?;UZ$C zl*BIMrQt5Rx!&f7hECA3P+-=ZWwx86U(JF+Cm8W;7`P5=h@qK`Rf4HLa9qdA1{VAD zHPZ*Z-a;5e<)C>P5a9bnhI9I>C!3_oA%{cqYe$KU^g){qQRp6>G`!IKQ2fwFpb*z9 zgN7sH1I*iDoK*0^8((gh4GJW^riGP6rY0Px>6_ucC!N+i{Z9PMJoSPv@!*p^Q+OxW z-E;H?!-G1i^SIEIDtkd6AMSh=#n%0Ng>8g35j$}uGOO&0W##3MIrK_TX^i?Cu^os| z@2Vdb#@hOxG|a<4OvoWU9(l|7(52GJTJp4=n8}-u?{gzEyjwWZKYTC%CP3p0l<<^8 z5xY^w2!v8v5h_Cp3ma_BcLP(#04cP~LS}{yq(#@ve%(fFk}5vPBj*3Bx5&VmXZCCP zgd|K<`Q&;3JhG^x6Oo0-&51G3%QT%yJkf3J^It&(*CJAJitOG{O_ePpZm0`k@ca8x zHJ{XBRG9zg7lP0H<(aD_R;mxgy=hQ@7 zQG84W>2t{H&XbAvTd7%_d_%E5*FDKF-qdH#!61_WKbe=c(C1qfWmH76YIWCVWqjrd zRaYG z#(N~!PAWt;*BcrXVW!oV7AZ!m>(SxC_(D4)ksI@2V5d|~P;56G6&tfYS}abJ)cHF0 z57vhj`&FXMxJTbv=gs+a2j(Z~DR+Kx7+V}QP5i(beGjGLGZh(c1ptc&{>?a8pmnS+N!YxT#96(Y%3@Z?KUzNUVOVY1|%D}2y$&gM&0y%v{` zoO)@FjJ}BQy=>~NMD+~%%bY)1`F9CxhW+A`&8fLNp7US=f4*G%o4dtuFt(qT=gEty z38`d!+&zE!c_B9rhGO2#?|y*Lt-Jc}Ot&v&xng<^{;js~|JG*Z#Z?&Qus(_pD_g54FDu+Z`9KrS)Tp`X|; zx59BSI)jJjVAz~o+mF@M?)*7}>E7Q}EY;`!0)0_n)J#@VTKX8;UC>|#r`54A(EB2C z8#s@8kb+XOu#gpLkwQHf3Ye!62M1vv3MPTIe)vcs0FqS4URUSkA%6&!X&`Szu@Oqv z>wVhR>^wXKr~^BKMSMkM8X`UxX0b1)9@3p)wB7s9E{k0_E)DpdAOOBY`KVzcm#a?e}hOh=2mQs;_6CZ93y}2XeSX&FQQm@7Q#HY@jxs0<|;+3*_?~@ z?FW}ks{R~!oVnfJlPs0fShAznaz3*LjuZ~&WxM0sUxRbUEEWD&{QDPu3lAdy9_)6- z{FYvs!24mPqYybSy);rv{pZi0rTPS6N2 z<6obYdw0%Xyx0&gXubxCNdI)+t-GIJK7T0& zMwYA_3yo)a(1#OYVg4JIl0M|>cIci|7T-6T*W|%UA{cf&`1WnxVv)VeZWf8`*2F;I zI136f%qYZwj@A!sY#I(A}X)nZLB5vI1?r`Rs&SyS}i`S0m#ZUN^ID#TMoT|8s{{~XYeq> zV(MguUy5PiN#Rw{6(@JA@;Pci$zr)$$h1fcAuS^R)P*Xj-a_d^aG=UVY|L#j$LwHC zwB_E(`*gCx!ou}BVtxat6WZ9>2~4DA48gMGGGOFq!@zxioP%^tquNs(PExZ(1s(W; zc_-m-oM#dNkx5bD{?j4z$lZav%iG?Z4+;snsi&8Oidpk~KoYP0kUV+YArL6U6RiL$ z27>hTbnxO-v`C);v!BI~%5ipKDfZ#QOojaT1CEnWB1|Nm5Pj=~Iksnm=5U8e-%-z$ zq}c-YJl!dzzcU*9(=DAK(r~~N7N264VA1~a1Q<;l0-+}6f%G!`@>bQ zlX@UqA8k`J>w26=Lc7$@pGjcJC5o-bk0;27>h5WJ6zg?nA8zmf7Q({h9mTwWq_A#F zzw;(w9UC`FG)4Agck$Fkkx0$O^(Ql;75OV1Pc0Sd$PX^;28$(y$;Tw*##Y4CXz1nT zSsYVwk1+?m`uY&#{j=S~cK@|}mKAk(rFMbgDNH_jH7Uw0#S&V>Lg5C1JrF!zF>D}k zad8P^ZZ;K%62x0^_v^s<`2zzB8con~;(MJO34PVdxkDD&ZC11gvZTNF&VMn504m@N zBgsj@Tm=O0TzCqS!nqWi^`_Fi3=ZRk*pe@QiRUi>B(=#YrkNk)&tQYJ^HW|1r+qWI zslblqhrAP-{SATnU*}9W=F4F|>iA}nC1~q^>JY-b)A?)Z1Pmuw0>zhPx*KoW5apCI zsT=%S#CV4;k4t-8A`E9|rWAN0T_nw%G@2T<`C}H}MXqD~>$BcikE8jt2(zUgfTx%m zusJLYDk4Hr3Dq!37_JfUSFFkOR)K2k6b8ED$su{mc1i+u+lYEorTgw!zio`^&d0Mi z#YL+72TGbAbTBNDQ~Z7y>v3npFwVOP7-gmm z6KL*~lC6@NVY{0TitCFt)EAqb?{Ou^$H9z^#rr0_B61+GkqyMi&u?KL^RHmTgp?fH z!Eai!U^$??)lu_ zE!c#<-rxmb>OM5-1)0^yjkJ71Cj;b^GXI#h(D;Z5awwA{UxBnA-MY}z0{aYdaG50@ zM`*Uea&~rx@4*j)k^UC>i^|gOz!Tu>P-0icour! z%eucD-aPwWEoEupT#H2_TT~n57%67&M8!y3v&XY`9@>c%^liSGdnz+Iix2t1uDq+2 zT+YF>yTnmnpVJX_uK4HZ&wEH1x>3M_=U%S~YoPCKqF!$HfysR6A! z>%)H*oo}c)o@b4?%+XRUEzW0Z{`!6>w5Pngg0n!=g{=-MBlz3$<<|k zth=C>=V#PIMsRvvz?bzseqeI(x1~!`U5i;q6W7~^&W}9L##1o0%Tpyy-s6W(QD5#( zf0JGLNPCxd9C0fS`|c|>l=8VWM#}{jp;n8*l6=2Yg5Qzqx$;mDxo$`7d^p+3PN#JP z3IQW}&90%{1N-voq`}v1=TAfG6qrQ4PdoPSsPHf_d2j+vdH&hYg8%a+2E(c4X3#tR zyHSxpsc6oxtkc=fi!j<%m-^_NQm&U zNh1H8!cV2zzKrurjNfFATdN}rEGfUH`}gkHTaJ=vR(rl!^lAoxr?n%eIeYHIXIJt& z?fQXS{Do%vj6a$PSWX@nu^xEO7U_)-eP@Lk@gEKkum93KZ$t9u#jCBWkp5Phu}bI3 z|7;VAtgs`qi?9kb&kjt^>q23Ir}8--II{lwt46NSF0IKwM+)`f&O|XR41e{ZKwVuO z$a%enk02ckfr8{TaEBOgU?NjM&h+S8W?78Hj&QW5??;wm(;4%KYQ9;c?lBUYtn445 zef%A?tjo7=9R|0Z8FLeItVMm<*6u`lCzlB0{NuAc7|RDI>QicaJ>w64RaHEJlyufY zsVU)mtMGf>0J1QqwS!WfiqdmukeOFKj=oxYY3QBL7dzcv+nXFC`O`WqVIpUe}CqZG7)|G!l}Y0SGH*}b>judfWHw*Ta6XQ5~F z#(D!aQUpjGZ9N=7?Ux|TIGzUZ>Z6r&{STG^NUKf`H_1Vb#0Rr3@er=Jhgc5xX-=0qG1!YN=(>pgXHBDD3z9;=i)0-rA9(UIOpBX~S;@bB1EhyY0;w?gAp8!4tRC*z8QtPea zln-wJ7a#TNl`qiph5$W~cIJVW*2~mXj*B`aNUiByqUAU_If+K5U=TQg8gc3LF|;-K z%Gul3JOU*TD4bWz#0`iyp6AKARH@Ir$I-Q1I~sV3@y>8l?fg>Yq9Y7+=Z>CW_>qHb z4}Miuc_ppHX$BY>i1mHsa&nw@(LpXI&~8uc&%lpiN>3Ubr`M65y3JO%I?RKKP@ zAHxQeSMEZC&E&*H%?w$qA*vIP6p-hYgRUG&bN`b=atLU_ZQ*}S%ua+p-Cu2fy!ddp zR%q~GI@xB^qs#Q^Gibol00f})UIK(g1RyYj9$e6@O#*jYI$Z!HZ4KOcIkB`v0$>_c zq*<1&t*tJT{`6T#P|p8S#Y%PtD^3-=H~r9k>?Be8-b&g1k#!50CA?C`D)<@Sqmr-k zIbHnXpGd8K2}U-`-Uf+^l|5HTCt&n(3r~Wi7YiU&=+B`PdwGYDl+<4(^EXWnG&Jx+ z4MN;)%Uj1P{`Fkjj2*BkIPjYY+?_&>;mpC>0TS*hKI%&nY!UyGqnD>_V!5_|1ATLx zXV2n-goXv$D6^L@M5}q8RaH@ePT)Td^L;stW3F?|7qz|;KtD|uct~8+q9HR$xEE{$ zC(R5B&UM8NBW{&V$8WH2(0gGe?Whg7@Ztj=Q`Gd>@#!oTyn;`yewO2#Y6y8C9wSet zt659`pAWQ!w$J`3zG!&vlkIh}a&>c1kSH)ZL`b06E5u%3lJH(9ZN2|FftvuOkbbx< z`kANJ&8H^oQ;jx`j*Xv`(?IZV2b}9#1ND8j^XCa`Yb~gPgF^lP6X<1KA~t*p?W};R z@e`Hs;}o5Cae|=Hx{<_CndJU)s{kV3CeojdQM8C#QsJG_R)sJzd z96azsbC6)^>I?f(N?j5=87LR#(X}&==GM;M(!WnOX43pah*xkJYPVw65@Z^@<9bNsJnT zC?6dlNIw8ge8~7g>U9cILC-aO>yI$JbZx!!8eoPLV8eTl=nLD|pcX)rRW#2$mV*ovF0d)7ZXMvsLJc3r0*d${nJm~ z_ME;Sb`c$nv#*=NQICqr2$11wWISRt>ic*Wrd3X?T7+eeT6LBQ3)eAsUbjoA(lUF? z-4wd!7>^|D)n11;5`wHI*o3GOLDh9%^5_MKGwYk0pkah-4g{37K)mDw^;mIod;m&J zj#MvZV71^@uYa`#S2obBSy!y%DJ2N|z^Vqr4F8BFFhtzaR(p})$4GhDz#ZW3+tGn`OlZsn=qMVR7e(g%*qEoC z34|DwFM7AlR2?sZSn%@g0L{Xuj}BgSfvN~80v`>VfXw#ob<23^T$7a;rjnOuqK*kR zvzR^<&zjO?be^3=x6`RG(RX9U6!PfVUMtv$===4k-;IjTmIJB`R0%#h zT(!Nab~or|{aXxT_uUja$n-OUFA^WD;UiQzj|Zip)M2=N4eG;fnVPGwK}igChrn}p zW27>AMvpKY9A*^YJ+}e7Lt6uBv;v6FQ}K}Y&Av(Euy$m=9$1ey@PeW7 zvMQU)CqPfu&86IdtN2K^77Pbm1f?NlK1i}Z1?R1{Q2W|*xBCs-N470v-*#d+PZ7B3 z7O{?eaY!HOW}YdHquAPStK2Tjl2oUeOBMdZgPo=q2->Fq5-#>7oOn*xMn<@!DKy`t zDp2uppuN8O$*3w+8-C9ecDXER!zcrW<2$MN9I@?C-{{o(CJxF?0%GFw>2;KQK$8*^ z6Vn1`QtNAlvw|xJwq9IZjytti#g887n^s`hS98_fzf)t zJL-&Ri~y1|9Ej19nZd+Cv7!#CxOvDMKyC`MVtzf{C@T;G&jewY}_pV~YbiA|c7!zlqQlWomV1QW&(LbML?NlsTLpn$j8g%BbO( zFw%JjkWR(&HuTCL;)(l*gy?&B^ilfeV&7}3(sulu+{0ls%mHNni@WV(p!*So`7khU z!mpzVE7N?;4`#+_S1NB;7peQm%fA_bXps#vQd4j{FtLl@X^dwr-M z?k3WGC;Vu1{OZ7&@s;o1?_XmR#o-3`E9CFrreni=uUv*4m5eNdo*e?7kCl)KkzH~0 zbbZP<{V^b;CNfz(T^=9Z64q*ekV3t-lFSv|Jk*lXORVu#2#5RXT{NXxf3;N#uJZg!7cB=c)wmknJYA}M>fXaW6uwShNx3c?Mwikku*Jmr=*nvjm*tLoln*Pw_ z=7Q-P`EZUAQw2Ii&`LW?PUr)NintTh4v}%6724dGL9UAgrqIL{1#N&(1WN@`zYAQ- zNC{pJnlDfn8_+3%)zy+*_me_FYVKZ^#72O|zg@Q;Bgs_N5)45D9+8IFy6P`pW@aYn z^_n5z`9nTJ1uely2M%h7HIRhYx3&`c`1lA`c^3lF!6fGNxzle1Nk1=Y7n8tWI*N8` z6j=>o{~u)i2RPR68$OOdQYxFQ>`_uglwD+{gwn8Ow(MC(CD|z>Ezv+q_Q=l4Y*R!= zk(7{8LWuu)KYiZc|Mz?RKF9Gn-iOljdS3T^-Pbs;^Ez*VA6}ESEyDS!ZJA)1Jiav( z^Pz2`5=_==;!~4*dn)rL9Ys$i`TO*TuRU?hM4c~DF*_hv9FObk$jJF+K1FI%8>kVN43KhVWo2qql3j)F7drg|Xci!|bA*b9sNC_< zIwk@*uN6zsXRge#9Oe0K*kVlCd7CaIA(gxyQgo^^1LlP@=CiZ1_8S}1v9Pd6nH3;s zUI7Xy`U?^`ycaMxI{W93?dST)cDhndkT65JQP>s+%)Fx6^x?w?3?_vfGXIl*MPEE~ zdI`Q)L;8l;`t>VFwT`pW0*~-y>g`@|b4x)$@t+=zL}Id`Q^i#DSRKjc6Fpa)W6qjxRZN@-hT|)y1<>GG0 zxDkHqgMG-b9asWc^tePKF zMJK5r5N0Dp7be;c?z8>#bI`Qd*{t0I^malADDx2!-nVZbC=*(_x%?q%peHXT0c<8b zn>rSCZ2?%CY^7^iN44Mv*IhHD|sIbTET*T$e4SQuO>(kOBBDC*A!XQ~|WoLIc8LmQ% zAwL1W(!Z-_1)TCXnPU5T@hm2+iFf|he#82+_PCnTq+uXTy@GfLWdGc%VdT*hlQ$D8s1wf*?_5%ajE<;K6%Fw;$(h)dDg6Fx*jL8z4+|P zGZ-YaYZ8{D$MJd5+be*%WUQh??YgJg)(s+#JHdv+fW|Uu%hLTLim%hh2-}*>Eh6@9 zO-#^@h|s?*j0x3MH8{Fq;6r2Xgzgt!rw`gayUfjb`E=!+{yUyzK7MT?=(jkxD{+_s zeUb4HsPO#DSQw8zyR!bK+)?^Xn>GpG@H%p#{N~o<%pitFQ+bj+7|B{dM~0EeF!+`E zNz45B{(W`Yfz@aq5*Fo(>He@1bmJI#wm`rxF4V3`JB4T=fEO(bGVr3N;=OZ!=R~$_ zVMPR6NHf%4cjf9;4ok>Q=%ry^fG+LOrb#Tgh`c-}AYM#z8dVY+8xGMCbsWjKd=D?L zI#A^rqBVTn?Y|dbsyrk#G?K5ee%<=@3B7OKvTT2K;PhkG!g!Pi(|MiY9WfY=O98_i zhfs|0d3kZAUtONf=#1l%@5UY@uq!I85PkD9lfjsTXAY)LB6y zhj2t4$jaTb(`(^&8R`f1_Hdv#6$I)RqJz{aj>32_Qo%4M1WW9~MUvM`83MUPKo>%< z8+uuE9S;onNMlTWaTY0N#4t6+;c~_~QdCC>9&&WtM3h+IN8lLcXMgYAyVq#{e$XPu z1MZxBke9~_xCIItzmsxKA(|285hUgtpZ^9jG!}N&_wODsiePeBZZlyB5Ye3;Zrcbk z1$xl^sltWPX1q%7!WW~W@=uQ+AuzOAQcKHz(vPz~Ga1@x#OT_1X)VdZaQbDC_4-qj zET9LAOgvjC7(>KHL^Q8s0!tS?9}LuV>=60|l6`-2xZqQC`C8G^nq$D#k`5QJd+NX= z9TG}sx5}3S^~Thq9j4jA&aePGqqD?K2ovAb(QhL;IXU;q&pMbmxhRl!gbbdbL4e^4 z3;`Mur75ZrK@w^WTim1P2)V!jAtqt?iC! z>=);cPu5~46CwkLZb!$z803+)TaH_~YE}dzLvXbd`nUziO59Nl-a76lgVY!&0X~3; z@*bduEG9`v#K#Aa@~{AuB6?d}TU;~3hAA?Fr9n>xnp(sds0a(&{6rXLi7tb)Rshc_ z8ovfwAy65;XOz0jM@>ORg%>kC^;qU%k+p2Nu=f9bK29dVO(0@`=TGBn#9q4=iUCQY zh0<&X_l$-e0;nW)!r7sij8R2*hAUze1&ilMKw}8XU|_trW0GaZ6l+pa61bdheGZsC zco22gw+^7NgCA&+y!!A>#Fs^zy9tA?sfk(xqePN~;t@sEaR9@b)l(Ai9}z`G6ZAm; z)laY_YCsS@ScnH<9HMtdSkcMJiHsJa$+*>B4qyG<|9-1rJ30YO$1n+*S6Enw(}{PI z#XIHsj2Of1=-^YekGX-&NU>p~by%)Z12GVi8to?zQx|fZq;)GQw;qTbnS_NO9-HS! z3zdks!6Dn!P+uz&uZ0ZO!Hm3C2qxUGtlv9ygfOCTT?(+|wKF`5>RA%#|Bsf7vDFR2dg{TJ>4~bvQ*-|Cq4!DRrAj$%O8~@#R z!?giD5>&FES0-ToF+Dwfm2NbDeguu&&Oy&`GM?q(kq@(EBuZXiBhHrkQqLc?7i?qRD1vTXmA!+-!=@#fr84DC}cSa zwp+b8CkarC(6Jq^gX4}efjZ=Nm990*Y%5&VPYWXlh5vRmio}YwWUvHj0`f_|FK1rA zevRgoIvfF`ry7j*U?H>wFeyIWPewQ$lwnQ7*MwJ*oCz_ne{gX9DgLP601fY@q%6Zo zE3_Ec;>^Gk22=C#O}7*HURV9>aArEy<3QdqOzQrl zOFsB^JVr}7@>mp^i51bpiYisWpotbzkEZg1Ot+!kgL#UOgOAzS+G(Mzg&=48U)NsA zokChJ{GD0Um}_W7;lB|Sv&PaP*89PjAE2`Oi+jt6}89R#lGb3;y@Vn?+0kulfkC@ zd*X%TpVdd(PonHt20;L7Z;OaWK+GFaPj6I$DFb8kNDDvXeEu}8gJbYQg-}YgC`9c! zpv8`Vg;~+wzHb2c8^JXX0zEDGZX#Be=i2q_1uTl9c+HnWWN{-wg3!>Qd7z&ldWvHi z5t#zJb(y4b)_K?^c*SuaK(`tCQ24MxK2I)z5j3PPxD>=M%l7@2IAvQQS4-;o$%jNB zR+mTF)1h)|Cs2V9;EIU(jOSd*i{d+lKYgk>-M9tiN5>$O{Y1`5LVG6k|7@QDSL<3j+0uk--I! zg-ED?0S^x@z`r+!oh_Xtru&hYZUejz>CjUZEJ9t$p}@>+JTaxU%|v$OzyZ5fS_fnj z9vF|s7wP4GzXdWMNw7ylELlYJO{gr@wYPJUo^pv3_SF_SFJ=ev11k)*bGly;~mKB+U5c!w~!{pbi%&Ya`_P1bdMn15!`$JI5kZq9cRL z2r)C7KhP3LrZr#<{}`tK9;*owEvENDC#OeA0#u5eQz_~TTjGWPB?>$ka1fQ1&Htz# z!2*3Ow1~7xF1%us7; zUTBb}_GPtEIZXLilH4Ff-ZBygMiB3)NPR~}91dGIw-{(Jw#^q2K@tqEr?(ILS99SV zdH(l%U&(A~AL(r&v5vBue#U_f72dH!0P9K=7UG_%oSoYSb5h#5>yfsrt27#Xkf%ve zv1Kv=X*^*izB?&9*djNh+Jp-Nm(!rq8nqQYM1s(8)vdHfMA^-VZ4@HgNCaN?W3}pF z(n^lK%|2s2Yd4!e-uXW<8GU2CQdU+LnTF3#jmQ8IAB8@IyC7|21VKSbbkp?lLGLga z77%ht5#&2!JHrz}Yt#OUWUu)i;4``e{V>4u zNbSbpwa7panIb`-g6ih4z|E`p3@;~gL8%eXAr*wS(uIHJ{{Q#UxQvwri3w8U(%Ky4 zf1!}5g6mGH&Dn!Tv;NdMMO*o_|@5oB=6z z?A5F5u(ttEd2>*mlUzBGi`de-uuGR1iSh_?BPRb|^eKpL64EiJmJ#q$+BZ!1KL|Y= z=@hh#LTS3OM_c0%jG6j$Wc2`llK0((I$Cl72RuI+1OgI3_52%=>q5E>g%`w?j4%ZO z#gx@h?})Gc|9=5N@&(#j0I9K{(}}i1sYb3O;43UR4y*=>AaZ+X#4rPFVhuhY{1Z1UkzLV0+!%?k9Kuz+#acC%R6$J9koG^VHhn0Cv5r*o6UZ1=LAuWN_)e zPV3O@kQbJTq0j^fAw3}UZ#MbIKp2Ri4KjbGb3Y&|V_9*EX?|u&Jzz%Q{g<+8nuXWQ z0pnQt6(&}tDaIBSP6D;<89w_f3@s{}l&!vXyfd$`_#xe8T(SQ3`wv}>mAXc3tSg22 z9JGX=jk9cvIDcW8TjSrdoFnpn@rtKgR?e#Zsd`gbQD=>Clb)T=T%w;<7n#LSJ%1pP zD7|1e2r#lrAkH?h2LMI3I65{3&MVE2jZXO-mJRK?*>&C(2m8gJFMvZvde{N zKiW55(%8R0w#6v@8foK^8BEKv14GERxkTVPFv%8#Q=@Xx%;?b}GMKz;S6GWtL{6lA z>B;{+ez4p!dx;Ermzx$PwTHeRMnyHbVMqJ-+AA8~)t zfJ-&}^PXLH?ZVl~gQg+0oK{2fhzNI3FXwC3({9u3E^%8)$yw%e?S*yB3u_@!uLjwk zZPCyLd#ch?kk1Sy(&&`d6C^X7ot<5ET_f9=F#kx-VMCte%7rI2{`VyFGiSB~gqDST zXmV-`6GB-X8Ea2UlJZ6sQ&<9_P4AKSxPb47rmn0cx#p#-NRrQ?dDMeZ@v{BR=7K!t zk~1i*;RRPh<8eLtR|N-9BX<@Yjk2-0c0>Uo2+)}rNT4nJh_H&mKBa^9SZM*Qw4%<# zcl{OPRdtweg~U^6w3gxt0$~9od7HL3lqjMRq1h5;=P-B-<4jH!x>k8SIR;Ob!;_g| zD-r3He=+uhW;vGh$~|Q$Mu_X*JRcIW+|^7EyA|nE>SOcY^Nu1>23-B^Z5Ef|qeFJ6 z^JnpIjVvrAi8&QsgKhyvLGv^o5S@so7$Mzr=c=%+x9VljxR7lP!iSX>Fy}sc#HNBF zDzjufW@`MC5TqG2bv5AsuEfWCKxQf#MG{*P?}+LdZi;$GO`)un=|yvM4FXYTk?VT@nX$_ju4OC0 z2ra^EMZCOjoT&rj0`|Yoo1+k70U@!mtgfebAB(b|?NY)eEkbw&&obs143Mipi^wbi zb#!t{%2VfCJ;bQb}D{4Lkol*(?qb~uH6vcC$Eae27Zia zmO5Ev?&{?$fY~A@Ifr7Uuw1gGPjLLzs9U-Ny3{B!#Lxj7k`M>p1NM3YW}}EnXt&vA z)n&7cptNZeS1cEy3#I{avH^_O{?Sn>Z+=HHOlitE$gQpVJ8eN-+uNT%+d*%&h|5{y6W8DqMID_`d4V3Ug;?EU#9R97wJOG}%Z?(4 zF=Kq+|31fb{j9OSlhuE0c1sRZO!3(Ia+B4 zYHhaIARmAya6E5cdXy|IH&*M*@bE=MkN3p;A0tAANPH7t8K>P)#?)f8>X=PD-~Z>a zD1=ZV*)_+CXzC^_kXsIbKRSP^vLdeO7zXQ}0z(G4g5&Ch8Wb2y|0$H^N?{gZSm85R z;UiF;P^Z4@RF^-|S^NaBXu`UznhEbaL{5>H@K%pxx`9 zlFkzPLMf%CrbFoI97~2C6>{nUg>a}^J<5Y1-63ORdV$bYlqPp455_eKLfEy-J_v0^ z*Kovy5e!gDXzHH40__(}Yc_1&i#&Z4>*8vce-pbKY8TH}MBF0zYA7C)Q}?STpof-` znK_9;zklXS3JiI9dFR-ZBB-=bSR)0&-~2xhjiEj?6z79fu4r%Kq1PcajUxvdIXQk2 zw;EJ>=HRjm^qoCZjg?VWVr1x9vLAGmNUGf7OMUhBPmVv-=G|a z;D__D92QkXEQ(>oy&K*J9{zf*O-;t(OeFjwT#4hl>KLe|P>6&;-#U0;19%5M;vK|) zWqUVEHK0O4K!i-vLj=3qMD`NmO0$68LvYyEDWS4LXifONE?=1~s)5|)1o!Xk&RD_> z$K+=#;HrwxVrzXxuuQZ)lZ`V&qdg4}Dafy)OedeB-L~$1e{nEYcd-BA_BK0QKSf0U z38GG9#Rm4P0yE@)Qd#){+NTQp9=@~1Y3cczcMW+9;o;o6#haWOdwGDTN|qQCys(Qc zF(yT*Q`Nk9!GN5cQ{}l&Lt`URh^#@`_RmpJuq0HM1P5&H+@5i`)OS)8O^^-V9K#fp zr|7l|k=DTQuw(K7E8FU?$lB7gvK)f-B>^@OT|oQ+PAy-P>=}Eq#Bqb^J_{i6&^c3e|Z?|J_xgdNQu9t=>ky0#jFyeYLaBQdd zS#0pCaW;r?q(#HViFA4DA?4C z#39<^$TXvo53b%NP&eTYR7~OVi=97Q03CxIne;lSZe#R1xSRi@Fd!rx#TuI}aKX+T zO%t!+g4vOlbRz+Zh%n!q+G+zKEYw6eF|vScofRP-`o!$jdBJtv2Ytnr6Z!VeS^L)LVe zomfFzGRz5?1{&NOy*Z+wUlWeU=V^p3uaiCF1uG7Lg%D#h+Pi+;IvZ>XkmWUaG?Ztq zUcYXK=toUi`DQ@IEo3h7Ktz_JP3#%EMes|cv3TCU5U?D*i(p9Oi>Rp`A)6!6cw!r_ zKk!VfON1}J*X~G-hMEdt_N6YcE1&J}`5dDd5AP3uHl!em84Y-FH2#@Jk^CSu;pD z)#3mViikId94+yp>gTotS#;_Td1zk|GwQgnZDt*m}q zzG?WoYg9i`gl)G+W(TEiP2IAM2v0ih?pywI#|jZXN>(J5`GZtOUJ~UF5?G!c{*POX zn)Qh}VTqJ{C+?9T)dkMqphCEdHUhdvm+$*Oy_@!u1Xx1a$Pa@aZhfpI28BSV2R{xh9bBkqn! zL;(TgLT z+Ji7W)b=PIk9`oS zk%@Z;n32_@65&wMOT>H!>2dxm7Kz0-?;^iynNw$yV~Gd5Ft!^g=^k$!C}YyN4?W zG|@iB!9*xFj&-}3E@ry117>HA;N?JA%~P49^eS$9+Er=?vtonREF6>+W(bNBc6Xo{QivW zZ99Skxe^C*9=5o-mN;E$9L7&jz{|>cW*{DKi)hnx2LMEu0L*7KqRZDvJdI3h#U&=b zNHhC>3;KLpAO>*;1g-S!Y)%w^Mu~47i;z7BS65fJ7-i`JZo&NuPk%5!>h)kOVTra) znTDS=zd+2#>H!1{L9BstM)yba1V2e1&o@Qia)x%M_`ec?` zo$7WescmhI%PfObRs|f=W}hqJ!B@`*2^u|pcxl^g5Yjq{yNU009*El>T~PS-bVQNApFh{Ui}hO zbH{pM>;(t->wb{q@&l*tJ#nrM-9%WlGT=1IB0RJg(VNUjpFDZ;-$U#1&=F68JGd+f zXJr}U7(1Le5mEwE&fUWUg}hnB+O5X`mi7UPz~RBxlJ5VqWy{E{F~WBm4$~~IAV8gg z&y)n-QiU+ug^Z<~jTp1@4G`bK>(4sHS@NPWT7Uug_Mgv`j!l`T91iZ4=-&xN7LMg}0vIfOWMR;LxI#!WyZK;G!oQw+es+S{ncN zs^jeyiFw8S0Jg^`edNh10tZ|@H8?aB0YM9*B~7BT!%;+q%LD>B0vAjV49T$rJq?}@m8dZg#Y@XP#15H;#rjh*KWKJuq5*Kts}pQ zQe>NBk-@^4J92uhP1ihMpvmmG6+!A4am_-`o??WOlK$vXEj|cef@`-Fm;G2z*(`>t zF`Tmr_)iKSJaqr}?~zDpRtq$ct{D@Wa6MzOU5I*3eLUiheE<7E(SjYMDT7`CP{q*b zw<3^kcE^~H=KoTdta{WTt8N=MVXd04`S|YH6A91l%$wM=iuAo8o4>HIK+N6|h-P|v zdJTdiLENZcfgqYk8romi+sOhCYNT81=*3?rKvEeOIQ~SEBsUj>v{CcvlMpQ(BddyD zxDhVo0BaMJP8os#6)d|k;*LRK6iuPGKz~BmH49&}`jUaG!Wgh3gm&S7MFE!-Xg|n2 z``NR#9r97)lABa;*N6U)gn}(Bq!S9pXv<*x^|1X?*#1Opf7deNV!ClWQFu_R%pYj>xx)0?>$YMIy;g_1i=&e*fXa zE2wTJ5m!OrhfuU?5!JJnWATvylq=&5Ws?Y506=0PSBz0a+;C}&OV;sKB!PDlT!X-$1Md&x>IY;Z(NLw_iBl)g zAdX^%$N&QD#d$DW9o-+@Agk{^U>bhV;>wFbqR>J1K=Rl&-Z-E#5dZCTsN8z;?K+H5 z5fvnImPHw0DqU~kf6y(#w^;rZ-+~KqC0Q$66!Nw>P3sA$2p%v@om!&iI$Cn8iW#5q-`ulC0<`L!un_u)*=y!tsNR zL!^g^$qevtk%K=1$3KnM2=y3TL5+(DBP*ppcEsviZpb1 z!1uy(1?4?^wF{DSjE07Wgq%ja8nRtm)U>F8}OIx)Zl&-w=j|Di-l9h zX?SWIpUCtU9t(vcG**~F*F$Y_EkVJB7wYJf|COyesNul!arS>|x{5w@8MtE3vB`;z z|N92WV}2lgXN&XEgIGBf;y`Rbbkoq$))odi3hD8`{r5a7Fb4f;n7ABts)Ers#wyTY z73f@g%Q-;~x4d<&D-^P?MC*)j^hrq`4ipaYmWsP`APEQV9!i1|y_rO`tt}w6B60%u zkrR*=j}w4T9V;DR#;Z%CuEDMb8$Kjal+A1v@|ruivX1zk5cnPm6dGhVw%D8}9tBWS zKb&!Au}dNe7&+g+Jd2l*kgy59v=ciIVoDr(bqM+U`RktI{5xX#+CGx%#_TSRdXL|? z+t3{2R=L=R91wOg(Igc3Z`(=ThJny%_!J<4StMB5;-wpL^&=5ah1CV46hPd61lo=m zE)RrHCJR>oZ5LG|G}>T4OhHmZ)ytb-wfZULiK$ko=}M;ndUE}Y=PEwP<*WXnn-Q(ZJ_#Y zWE4ko`&()4j${NADo(551%N7~fC`Mg=fmC;|9c&KFAzlCd$S@(PQct_$kw%7TsD)Z*W>90z-Pe%DU zhD4OlVfljtk7DEEg8o5AQjAS`eu%y03fTTQ>h=>3GXa~awZpMAz#!n$YlAXCU3{o9 zR14wC97GE(T;i93-%5~fDGPvb0jLD zdlsQBioMTn{J-9N8+q^RZqOPbtBQLP@ZJ|qsn>V{Z?)ONLuyLxW5C3G$gc_j#8yaw z8jt$lsWq4qq$Nlpe*xR+9*>oX|D6CkkL}59Pr;j`_*tWJ(ZCBBFQA=HVXgn3dkZN< z2!VB*e-g9?Hxl*OWv@m@1h^9**SpkLW<=v^0Os$2B_D<)AO71G)cNI@7`e2BU^+;2 z$a;%HEBN?pezMhoIZ2*rL~EXXy_^5mYA9(V(U6a}{Y8KTeEW6FsV|!iN*nMJz1SNb7Y){Cf(we4AjBF>e z+&>lcMyhbaoT>;kM5n91UK+k=*C0K*C*npQRNz30I5~#9kpij0(wZ%a83;+?>k*q2gsh8J* zGYS=lO(ac|qs5W{j;u#OCRZ9F7J(?rjSO5cDdZV zDgW84iLxN^*}oEK`$kDlvGt!EhHOiUqDHL zro(wO^ID8nozKmcd0JOj)+;>&ktQ> z27!jG%ish95AG?r8WBNh>*&WEzA{o@g8PD=+XtC=R;9HcE`p}p56@5sA_4ghptjd` zbeIg8Y8&s}C>Z`| z-Hv#P7z7OJ7_OQF24#N9zH-w*^i7DH7sIT!BEJbji5^UCZE#v2q6Jykk7A9>jb7 zS2wiVrc19Ra#k#8RRXjJ`LYJ!K{OVn7H}T<(uAmrAiM@OClTa7x1eNVDI!|S$;Blq zD;vioN4~cR@+5pJPT|%jS?4?Oq{Fc2WLywR4_pbpQrjU2c?9XuqS}N-?|?JMK4QEY zU?2D?d6FX&$X#r3r-^CdF(zQ#%MEuG+rhYqVWK-H%pOT@N>oWo7a%6$7Z8xRxE-e* zyao1Cr%o*s7q!?1IGWfxm<#A$QGpD0K+M$EGoQoDO9=L@CrIUul~ZaOIE3+fX;7Q2gG}iJrmQtK4URotcZu8q1RoC40oVqa z2#d{)$jWhhcPc7Xv^^!s^Z>3XL}iu}^?!X!a0jXN@Crk(Z+0NRK>1o$Wa69|z6%EK1-id1e$=Kp=v1u(jiV|xGyLg)4+ zE57#tk2oGF>R3to*cj9Lm%!_I;L0Cj-AJ&u|M?QH*#ufLA5ghnsYXLQ3cBd)5-$`? zA@HtP=elT`VF`uSFQ5TM?ZZ6=l7WBLKHT>ZxBD^(JUa&Uj;YahzQS)WNpn6l+P;vU zfngGNtl0g&74L#%?iMbET210j zkvU+lc%`scphH+aLJOIUpq!fR1E{djP1CqbEH<655wK3@wG&|L*w^FUX!t28A%cgKCZQ?aM6W|-5Sx+__? z=+!_}(8#p}$zmUp2tRbT*}~)JAvw!{Pc)&Yk4MZ#9d6pfYh+<|@oivn%BFFC<6QGl z_^iD{hqM(8z%{@%6_O@7$k*k?kdGr$5M+QH=m@w?9O(V0+h?b~&)6J&s#m%zCgkkH zJFI)0Kl7><+O!-_k@8c#c#zo!my-F8m3<3|cJKmGMb@*F{@FuAJN*zz%^e)9zZ}0Z z+b-XK;BZv-qckP!L!s@N9i)w<*#vtc z)|8A4`qW*~^{rwnWjA&Sv{o_4?lBGYew@KeJDnucFgLJ*q|TGTX6Ru^M5-e=j8VtP zkEw?)BYBEBXtxxb%5?+Tr>oBDtiBlI($H|c<75vaYr(mXQl;(UWQX+_@z^ z0033fvC85}sVY^|^F2?tE2*k72sAw)bDIozeA!!bokO&26q5%!*J-&h8J^v?W--N% zf8Tb11EPr-#Zn;oJQ6r^G0p^xia{Xq)+A;k`oU0Zf)F+?%Zvm|e3d}tOG;DTAhK0T zC-5seCf<;|KT-Xhsaz`2uTz+Fzh>?T^gI)bc4|d#aEpiY zfy*Dy2i(|@{8EHfcWDnbK`TT(9)u3OExI{q-{!S=NWC3Ae80r`gQz=*m9_|N#NH8JP#{Wf=Oc91J7Br_;);T;BUiSk+~vu;nTl3RiX{H@?KV|oLbFu z;Bj+R?CM8XxhoHJ7tReP5mFJ0?h0}30Duq=8knYZiVVX+*99K-bzyOyuGCxLC7mKo zWSX2WOEt@e=%rxr)v=`??xXM+c=YfN?zOH%hn-xAnJi|shP)y=n25%_a1iS-O}5#4 zo!lD!y6ZvqH>JDtY-u?R$0qo<0I5`Xb-Hc)P)4R%ID<{yc(uLzac+gu)w{*n2C5d- zl&VpMphJ@al#6&JrTRDHz<@QM1DW}MC4ioxr`2_ z+@;^uZXsT8TAkgu(gI3Vt2>?|EW=H0a4?%~%yjdVl#n=oH;SFz)o7DMAU8DWS~zyP zo>OxeqtS$~^gJC%*$F0C&7KJ7gP3IljTD@Fl3{|QL~Mava7L0O@puumYU39ip2|9} zRSNrFaN)u-iJrfu@*y=GfnRxYe5%`ezLdWVgM1nFa`KdWX{#vvTV=MkJ2n0))EhH#4QHr=TbCd8% z+CO?eDNF=wwcM%dtde>A8h`Y$^9-|*+Yj9~i}}gMuU@6^4U03Q2W@NSSF%l4SKgel zXTP|~F~@K5#f~uJD%Bh_Io`|X)6a&_C4Zy){CTxYMb)kVVl6Wxe4R_a9MkH8p9c%BCDHrX}@8;18W2B+9wYBwuGc!2tcZ_s+ z2+7;garj(r>B7ve`u{CaxyqBX3?A==J90IoJA$$_RPLbuEWO=C`IcfX$Gu*z5<>;b zDnKkTjmJ`StGnV8=~Izuz5l@+>>=pX2FU#W`K#Zu=TAfasdLw&jZzi+8h)P~s&W@f z`Pdb+Wc84FV|GSEUA4H_=gXBtzh2XvsrvQQ#~MVXeCF>v5#UZVG0=IQUbtkxbozM3 z?P{^Lj!v<~3p%3#S@UUS=}T6>J~_G^-FQVkkS9PWvL@)^T;J0NZ*rGJlVVa%9s6zI z`t_c%+aj%RLy)Dj{-*lt;Q@9dVmtEJm#uoi@@3nf&Vyx~zUQoX)r`Xjh6?}MY*;h! zsrvT!zIul}m*&iq#Vk$6K1uHP^%e84DqgW;se)5Sw?z2c_@IGWvFI<3w^adx{WF1= z&I$2{n%ahMm0awtjf_l}2in#T3K>aQS+-A{Z}Ax774#nxAw3u9^ym=!szmPYe?59x~$%?lU_1SQiRwIq@hUnD809kv2`#jby;MYHqkT+IGc zV?1+Dl6BsgD5g$Kh&wEmWqoM6`?r5Rk7Gv5Ol9!UbQ?=^&%{Rh80WE?dhN^k&wgAP zWf%Cmxb^kmEo+_8(w&iR=Rxmow(t!J!g+bJzs#V=QB3`S5mZ;?bl;3y!`C= z@~_D1ez(zvhpKXMM-GLaH#t4f6s$b7geyG$PAf6eiK#89($ZoI;f~vQ)4(l`tB2ap z7FI0Ga<24re^IfL*E{Ij_2HJmzyKrPhW1BGmJ(VqI`goa#~OPGt>pysC z@S*)_ONwXlQbh2Z>88ok51yXbB5dCqt~5JJ84XO?Kt7|0b3JAM(rmR!U(LaiHlGV_ zStH9kIKSDA*IDg9XuQN=|Dh}}FHnPp$1(S8`zfa{V{@E~_JSc#dxaqXF?-pM)lW6> zX66l-T~5X6|``!V4{Jms}J$=)!TU&=ryHXW3PGdAKrV zrLae7ronl1`94WepDj7qK1ipwM!*y6-`qlTU}MS63NsW^#Pg$riF}nXX)QA9aN1Hk zx>u4-H!c1B!XcDTsb-)ED5wHM{=ryb`_6p}O!8(1-Ul+pd^ zw_&Dx63E&bH9b(x_eRq#{e}DdaouF)_j!UmGwtfL{WkBopZ28RW&UOtw2AdjwA}L2 zO%p9PfeYSBCC{3Sho?*vZ^j&VW4aK3;G(Mjb0tRZ{o%Q(IWMY&4XqkJ%_N&w3 z<$f6}T9xL11=?=~tuDmlJ^}-sNhIMc-vra;0&2eP_S#@|wVx zTFt$S2jgSfO-w`sKbbE#IKy>7b%WCKQ^7IAN4|{BmKkhiE8@!BK%om}^E$Wt>CGJ1 zSqD$00zL1Wi@mG8Cm+{EY%!tKTt65(>uA1C?IurH$&ymc(!x2qk#93KpF7=0Rjn_c zZVcCKPaDhsuH*A53%P3TrpU4$AchqoG@_wFBL$0AIezGUEblCKxe6B<9 zoVyJ6hPU@_l11cL)%1hz-m?O+NfxbNl&^S6w#YO;^H#05TAiRB^7w+@)YNI`>7r+6 z)-ur{1ksj!v}axH+t9*!w7vc&Cz0EJ|%PVQ^?W&Pux~4POR^Ri=(}alA_RrJB1D! zM8yW601YVoFezqmNeTLLTW|?1$+Z&%f@>qn)WvOgALkzRD`0`gy7^0sefwyduT7ph z<=*`auHy`uc_Wb)u~$qYE2`&KmYnv{y_!qQ70u9~IA4(8UmN!Oo6ts`SsObZu3iCp zt85yX#Zz=2)~h(ro5-~BUb;D7xA95IW`U)$%J}cf7KWVtF}nWArFt>nKi%GWFLRlP z?75rooA@MSzG(EmW1z&|vW~o;MBgJY*!m@a_wRQj2QCvuKdBm>pzN3_NAIPE=jIOA zhF~nU-qu&|(q8$sX{7jUrK8%2w+83bF0pd|ZbjdP@_`i{bDr@Dj}n7#G2F<}YB~N< za7gk+>|rU`jC9+yb$o97T(;Z#ub_&-pM8J5qFps| zm*#2be%dAZ2C5*Vo8S{zIiCD3w!~@u6T9K!{M?s@Iy$@9x6gg*{Su%uH+61K6{+%nmvjflU=b)d?t$2SU zF}*kc8BO!^xAl_;o^7SzvR?cmdx6`QvdTHvSG3IH4kS$9VOz&U}$b`X?d~o zzL-@PDlMTJ$p4~lL1S^~OU`o2lPxMdscOm>S;WH<1ee;#i`hmw@9NBqmfqCv*{D(Wy5)=F zW6#@yhI)H~=aV?ukL+-&eg0Zd!{8*%g+}$&l>Pob7l^wVx3Hf(|4jLbdXGOYoAqV7 zsPq1MZf?NGd#m?prKHa{$hgJOEtb3rf15eR{h_au+kNVq(W=B5jlTm;>MJ6j3Ni=v z_U-WI{W+5pd-V zmv7GZx{zG{AX zlQHzrr4#Bay=RJa5w=otPOPdh$Dy$cXm4NU`H079@mbTY`PBYn)~ACy--*#qwhg~y zy47<>(5z(BErghrEB_396Dx~|nykBj=Z>$}h@Zo?Lv?S=f~;A-eSYma=6*n-_jSvm zHQm3jjtmd4+@N#Tr9bQHy%)$x^b&YP{jmC1~ z+X}~Zi`XD0^h}kJZ+oHrjh58Gn&gAqMuW>!7c50_1a9rf)!NVTgsx#3rCv_?=S;T7 zRFspjS61c#?S}1_-@Ulw*z$#I+v3UHKazxtye=&LU=cn2?G3T_H#hev-JKB6h$?`L zr`FhO>;7{uZpU^h%m3*zkk7Y@sVe8E@V=DUGkV*$t)s@&f+@CNbdXM_(-F}PTJWYS3SD_ zhm(H6>dOQ5Z&%+7Ph8A2rnTZom*I&7WtN@=vvx??z!i3 z5uD5h(KL~7lUs{-&OJY{`|1<_q#-$^Q$!62^iPeWN7Gt7x^CaSE427KH&A5z_Jui? zV)l(2H_m>E-xqt$=P!%J=G*AGqB}eR*t+ZG@FD)X_ZXiEY2*!&kH{XdgoYjnB1rNt z@hLLX>kM3z{@w^i)@SvlKP-fxh29_O@-}A?`-Zpm4s@#it`?l1V>=>uJdH!o?CjR4 z+xizSj%@Q=j%Tb{al@TA{9GzN=V}{u zb0@K9&0}71PPn0KP6q7 zzk1~B11=pC{z;E3A_I1RuRKa$B_c~3_S}zUve!(pA$5TpiR<^hDw#QioMMz#h;xcO*kkp*B z^x#}h9=%E>Te9F2mFEXG_ADe%F>2loRdpL({_2dW?_mAomklf1SFC-|-M%7$bxA@jxCiA)KO8ac)Q)5M(*E%3;kx^@+Vt(U@Kl&QR7|CVF zY%J!z!OQ~(fW0r}{Ebxpm3PMiwBB$lD&FvH=pCDuYhEwvV3`#*pLVV1t&DN)3k|ww zoRY~_67LhDk8_RmjaYt~Quxc|_N3}NzyGZ2&UMSePUX01mUlI;*Nts`c`6Nl!a3ma z>C}0vANMD`t`GPcmOa{~e8(z!l}v^Bx2SSo2@$r)*sB&nNc|r)o&J4Hz-|x67)@-2 zZ!!uMx1m2KIS%h9cK#JQYBjl_${XHwR+04z$Lv;(yWo|kr!UjvMHHsvd%xdF^(0+d2io&F@8?=GX*=={IWYE zaw8%R=^9XD|CN?Y87KDXu6@ld|5t0OAacj{$!F0FrSd(Tg41$OemD7DikUv){`*IG z;PJlOlgUbf`Ml~v&l>D}{_3$G^bfEN66gFgDDP|QJ$}quxA)zP$0mMG`eTEkGN+IC zzh7k%A^eoPLfU3*=O=NY%(3s0lhzyT#>?2JCcJ7Yy5D*nS3FG;0e-FdohjbhU&rs( zoSxbqk>-iyCMfvzO}t$M+rjcDwqk{q~iH^`T66Yl@n(W0n2-o_yWxJ0S7Cv4G-*(1V0d#KS$%J_p{aFuV!KH0B5Sg50j6)M}jwq zIlZ{y{=-jFo3;PxSD8LlN7r}b>vvtQS8PgM=O@Fj@_Xmp@M&sb>h$1TAxeS3b}d>y z!S^EHh)#@c2#J0t;@#kdNLwxNu(uMJ^Oi%`ZC(T$O?Y0lURrJ3S6982 z+;P_j<+?D^H}&Y^Y}(S4Ctr{z>%8&*Er~DhA_I?j%0bxm20-rs@52lq23xxF6`|3- zcZ%6PBqo({d@&(9Jbbp!P<+>1gJV*)>1EsGSBh7xz79%g89rh@EFVUfS!uOLl*{(a z;|(9X`~~kCM()i17%{W-{DgPy%aws?19f|BB_6#Uc+t!4Ze2acYvappvfbi#Scl?W zE$4l?blo9GvLA^TUq{l-Amp^QRk3CD;Uc;-`H~EU^NKr695&qO8#}@B5MPZUIEuYVF|sP)xNsP55TYrsRXMSmD(FD3PUv0qQG>N{1<_dWBY z#_V6A)N*0kvhG0bq0tgIOu~18@4kOVYow)l&B+xmGUM<*2c`P?AhW0gi4U|Jq+L76#sTHgQCOw0EctCzKtI*wS1e%>9+ZjYa>thEPF z<38IUa=w+aB+F7O@9+CJe_|2z6ZfIE)Y=2NE)B6FfXXi@olwVmTu|L~W(>F_A*gIo8Bv;~c0s)U{;xMfQvEtCneOkX%#&u? zQ^tAo?F)vjbAKMsb~bM@A!dc&%&yW1v+tbqV6>RwDdx!1Fb(chc@4qRixJKy@b!c+ zrle&0C*#91(4Kw&{_Y?=0mw-2q9)&3qFnun*xf*j*BV8$Tb}#SEid1+B{0@H>W=#T z!ma*)R8z(SR@!V?)-B}ZuQS}y6xP?ybX?Z%8C!%-O{c4%ufIc`zyF~SzFlYiIR;gb zmHXMfK0ha}7-<^vtnx{oLx6bI%`UZy&D&+Sv#uB{;+dPYjnMJ&d3TMw`=#YMc3KtX zeviXeV~UNZ8VjA?5ya3&=0;y~?PBSU0*}8sUo%~qhxxF5fl3P~1={p;e(vyPqOwoI4Z_E>e{);j zS+EgL5~UNK$Z4XX+&=y!=&{?~hM-<$*Yg?e1-8m=eOy`X_S-3z z7Y}t6l57|~4~qV(4%RPe^7d)EnZp!#)5?#ZQg6q0ho3EEgYGtUGv&${#beV>i?rN< z+{)s=C!D8eU%}jbzfT*I{~HMI`}3xTu4KdSsi*h(O7(8MywUTDn~mjM*O&Zzndc~y zJA$`JR4r=R%&6J-eSFw|zxG;FP!=Wf02D>iw#&bQ(5J}yxw5%%8B40-@ymw6p7(bdW4nxAAyI~gvL ze;OrArpM1Zvk6|g6?Tsn%A54-*?BHEo_ck{aGhd)_ua3i19WSHYkgly{_1+#f9XJ} z#CmkeE}Kaj+UdE;G^c$Y5u~*-Kk(`LVzhEtHX6$bwC{zpG@J#XLCvkBUA#d|j24aj z259A?NWA9%dpH!-3@}F&&|b_@BmPr9_o?ItLX?op4>)F&0j*bfudcs8{Cm%;r;b(*x}tUYs= zG@d=$k^WMRN%fQTklefr`HQ$i(HF6)EVr3E()*hie%Ob6ELp|LeIPdTub{{~YbTGY zpM|>p{A{!o_f^rFhhMLS<2o|K(=M6$e&E^RM-mxX7FPDP6FXm;o?B+MeKc?8%*G>C zm1`WQ==h&W)qOeIH2Kx)_GEp=q2*;8WglenO($)x{l1__z|HP~ioVFhk7D;~?|zklUO3lnO0mQ9;9m zDqZBgumb=D9q@s08Py7mt%JLW#^@%{`Y?4uw7ZFBt4N%?g_Ef0-UG(jO~B7cSp-6) zkJ8zrYYFJIczQcw`WL>hda-eTqUpUo4O4FW`tbO^bQ_~x#T$_E7(P)Hz>);lzczw9 z^bST}fHH3Tjt$#?=vZ3HgU76mw;X3L-wDrQsM3m_URnB^XLVD{#|VGd^`n2q_8&}& zLf1KYYIu3%Q|8t;8+NAT>7MKop`3?XVEy}a)0ZFStII0Xy&KB5@G?qlpXJ-0(^V|x z^F3Z^aoXWs;6`h;u0CHE9ocoy%g1He)5NB(t>3J&>-RaOo59~peC-E&B<_DTNy&Rc z*Iu;iB>;@mTgyL(2P^Ze9Si$N6K$%jNYD0hx_NVEt(^~#O4&x+4e`RZN4AX)vhzQe zSV8IG`W}7wS4#fR6M`t~n>V*Z9N}}xnoxvk-QbQu&%>Lei!)X?c|O?3}yVA6#^R}^qTIk zmigPXL!-&{^{31YZZ%sD{xFZE-S<1}?Zd6eM$uxAGaJxzJ=Qn8c|*>0++5d-a(1)w z%o4$JbJHGOftz6wU3(83^3pSBHHBWjn_Qg2Q~sGh;=ZRur51%Ucd^l6!>M!qtb3^? zftLF=N{z_<4H>`Q(om*@dPUgxqTL=^Rj~Xu8?Di;a-pNgnZ;~+F3^1||0vf=g{Phm z7aY2#VfU>EFDH|IkA7qyM_=J>?){dBqmtwVzOb`t_FSX1IK10adPCsL0a-a|W3jF` zG?bQipJ=}8v{TNHsZGa5=uG%rNxiQZgKQ*Uy^+`&woGpEp9TxPT%r)Y>s?!TVLsdMTaiO=UfuIsffkog{;J6NCer(oca zA;38Tssb1<1!5ua%2nv&a#egFy?3;``U@0BOKy8AP=YsFY|rBCX5kr9ieWL6aWoS zWxA!@deThKmUYs}4oRB`DqZF5_@fDCm4TC$tTB5ng8;GOCdaW72M%JY5u7q=234<1 ze$-a60pufV(hDt_m`Gcl#jdHxEXk=iu#5F>ot5{-anvxp*g-|;^E;B}(hx~agja<=l zBMYvtr3}L(RjDFhthI6~z|Shx?*+DDGz3xs47Sn4e+u9r$g+p%rs14Tm}Ukaoy{9h z{tOf4KP_2~+LLIdU44+dqdZ-07W0=gBqteez`*gkIwaLMJs(_Z_;rBigkPxcLDK2G zm-^zDgiVLlUQ)n^?$l<=0Pmb6VS|DozSv{DNvh;CM9Tw8WnyZSocmqAP))q;qTt;^ zlM*TG-Jgp4!-<$4uE2lYfsGskJ?=h*SE3#{P9>HxD`6konp2{E;jw*Dud8sf$ZuA_ zkChCz+_{G8b=xcD>}X9D^U5NdXrd?i2PzzjQsx?L)>w|Hi1P&90IZ!*m7{$>xjPa* z`KSV>JY?P+^q)yz=Eiv*!#*0sU7ceQqXs%>^w9BrCE+7rMElR zx65ZppyQ|NC3B{qHJrem@E9G4x3`AQM#dMDLopKvz;UnY{Dlx|Hy_wEtP9*okL`Sg?Y3 zLHnHEV)fW^lCO&xN`-u5`-_B9z9a^t=gG=h6I6RjS zck|3k^GL?qYa=rHUR0e>ntX8^JE?5UL83c zaJ~jL7V;>=3g^L873q>BjQLRkmN;aDX*CF^klrH*ob(4*5{N@!Q_>;>ioVfy7l^!_ zcqwn2Rg7IUb=$83?IIu&T+B*d4~Q0)u6)I{&=bP>kgc0&o;wNxC}X)(S6=4Qr-ok( zd>dHE#EDXT-BUSHv+wDECFwPxmeBppsGUZ*C)b75K%1WKyd+`?j_ZFw{D8DJ|H)-d z@gA-}tg>jPjULlYcNI6NLI(S9`rrWQA;MFxf6<}t+GuI@fX1GOzl3naY&>4qWV?(&^QT6Cf{O5-U z2&HOoe7CwwQrH_3vWyfh34hIyc!~Ux8Dt{h(=ZB5Qj&(fNMq+uQVj$0So*cs-Sd1|$gex#LeFFdYCiA%lhOoP58< z_>=nY%`JZbN$SPf>-RGJK5woJm}IeKhixCGF2&fDkk9BlyISZnRh|jza@**rI@zHk z&TSuL|LJdbhxt?tzlh8!HX$t1y8cANCPPpR&G~W$SrJnDRT{u|RkDX`;TkL#kvCq7 zK$Iw$3tB%ULzIx6hK1*_)nO)-zDzWwO}DraepcM$Nr>;!yCa@6;lY3ZYmaBDLgcI2 z7?V)`paL3@Wa3*y2SJa=+a<}<^acnPUeGs!eNW2J@bh0)OQbghLJVR)ehh)!ihtnq zItXx3YbRp^-{n1bO@k>28!WLWl&#D_);=u8dbm1dO1EhK!whKG93RGSgz*>_9K+zvt3+CYXOJ};+QffDOVB{>xj zs4{393AqjycrPk-Q}*&0P-!JJHb*M&@#Rt;UOG8cfUJoi6eg6a>P17M#ZBVG@lQzd z)f67u7@Ek;8mp|ED#?CJrSZ)Y%%TZGKmE(dHs;)x-z1NZoxSlE@$(Gw_P6@OhmQ^M zb(wl*BDdCa=^Z|-6urcylysxBaY0pLO9`K}c?Az`(sDB2^s@F^LLJ|g?}^V{#zc|$ zle3#9U#~EORwg5{9~f)ra5`4@BOC5e{OLPEak zym=#l)Fuw;yDNgwaRH`s2SUI#7}t@-lVe=}`IG8WHmXGG)zAmZLi6ueRdls)ARdtW z0#InI62i@Ff#a$h>-WK1%%3A$`cq2L2_p@QnX@D#-`&l#%RBd47>&FlXd;FC@Wp7l znvsZwMLETnca?DuL9|5Rt*qt2!-i_G)ztucnqr4~HkA%?-gi{p?5%a0_vY4CV#vtW zBR0&Nk$S81lOp|X2hHi+f$W{sF`*as931bp*6f&z(%X+dyeT2i5EB|*KT92MB)ZWW zIJy2IhE>YcnYnkU+CQbp+ArCL%#qYVMMu?RC5I3?9h{{$@dwMyF}nwI{Cka^As+Mc zeS3RHul{a?lBl@3G_>PyNzOKeZdZAVp4NC@s}S;%87=E>C}=IniaXppeD(LB$5l#r zm*m?|ueV>C9qc-Fgy`b$Q_M3fi$%_JTn4tu)&)5nKO#;Yj+|Gfkt!Ox3TXI)pZ!53 zP$~8bt*AaWt%zB+IZ#cpwr-q5d&U+3T>H0L>2ZQSMqDyzQO0-6g4>QMgSb{{*KALJqvr*Rt0S3^ z#R@Y$7S0brb(6c2tMkxiN3Z;iOI~==O$()D=&YJMxyP@0?^Xt2V#Ub`u+Dts%4KkY zV<$oY*e(Zpq-*S+zpf#~V^jQ}W%yok)1J}Z=I7echShs%)EvFjE{eEtxo4fPOijE z+S>>HtJcdrA8W(`*R zP;2F8U0HibUIpMO>c9_-^5MrjC+{(lSU{pqcZQ}-)4onMXKAW(VlDL#z;*tJe&7k& zm=Puh)9hHe9Rpb6(=_>N-m(6`hqMMTVN;O54Z;nQlG2I^llJKed0&lvt-Nh!?hao; z|6seGc^)J2Q%-6sa-Z^4a^4{%KLRfq@oRXK`q@85X7x3(ld`gK89T1_-GQv<;sebj z3=O&{2z=a~)*<(A#b=hBGC@^5{99pY0v{(^Df}f?HAAVX7sI^7m4@G|2MC~f(8Y$> z))r$2F7Vj8L?^7q<)fD~yNcr`gfu-Rd;6$4khpCEjn?IWv!Fj~f7RrZ^36_SOn4tk zY@rt09!27@FKAST=2v=LSh>6nQ0r9pz=p&5`itigC2;i$4$_6H%%Jy2AdaKnU~22s z%GmW<18^`hT`k~U;B!>GVjD`SepJ0Y1FJqby)kjxeT_2Y4ww2uvaF&xor%(B)=6Ie zePPtXH{uHi(2`wyfg$n)_S{Z;GC?OXZlr8oF?P@>IdSSZlqDvQnl$hpKeM#Aka&qN zbbb^`P<^F^0s5^6!oNw1BC&jnN-4AVSazlqU)rrp2*CLUlzvYiK<;}YR3=$sET%@T zg1ptqTAId{8|m*G^w~Kl@El_pvcK<0Ihonvo6zSC zUD@0cSy!aDEhb^to4^i`ZTqf8=dveldT5?>aZ<-5=8l7*cQ+#FIltrU@-Og=uZNg+ z$dp+;*;OdZJY~)4;(smcu-!)@Ldd75S>!jJdbPQCWZvnc_(ET|l z$aIPY*~vk;w`$rtGH-_R%>QB3|E^?tIk`tL`+@M*E$55tYKc!F1_E;Rkh5pEw{8Ea zzupqK+?`A%4k`jRFH|F&lXe&jp1=6V4^+Dyj;>CP+Up_)Vc%9ONq%!&U2$!(yv7qs zAk){HtW~#pzlM`_R+!{X5-PRrph{cy#g6J7)U{Pz@zcSQ6ykkM^E5f-4wER(L}5ybC_dp}7B!XD9YFrBk+NBP%%4}?Tk4K{SSP{!IGW^1W_ z`UX8N7s97D?`!>@sg~}0w)ipe#dXt4Hr4)uFzXy8Cto{H#q@bOE!zftM`p?Jyz8fHXO8cI73%L763Z1SOc9#75 zx~V}R^YIWAK#j!4svS)ZoKhkl-(Dlu8c3JO=2LuNc56)`c}r^-NadXuD9;`U7x$D{ zEYys)j^eORPM-4;-oLu->c17hn*0yaR*-;h9;xSE(yaB}J_$#PMPa!x%=#+Rl{iR& zQcA^kx{Sgrc{I#uIiE#=hHC|zTXAAb>s{3u-E?6qA-}bF{;odTTv=4iiO$>^2gR}Z zlW=>AgC98^LPP6z{1Ln^>ZTix^JJP_r~yJ!k;Eei$r5qf z&HCRi-rJ0LH(xpM99m~TIeN)5#(1PyHK#))e6`_*?TR3>W8_V)0ug%G`Swj5o&ikq ztA|NdcoMy{6>x6?!L1yi;GU_W-P~K5KK|;#qm_a7eUzUd;g|cJuU_~PnBw!PtkU8S z^4r732&l$^Zbk<-a@-*t-|TR&K4O9K@5lt6dsm#{C9T+3!FYTSb}n#N&4B!yU*}oi zjphNe@ey?8sILZWOX}9>NM0|`0@u||iU1>!-*_|XaZVvmdUhR}{%xY(ys!F^=MN5L zp7pxMSGnq4iC_Emt))=m=~2zF;e+(p#9l|8Y`*kT_dOPDNUp@Sw}Jxg#=inh&B=)e zInnNA${X;N@UB4kst;&vp!H7izI^j4kXuhsCsOPinWr_y>!HWO#m7S6Q4FTWEaNekD;q(z_jHUcuUh^+cReJG^lyo1wbtj`_eLhcN)mA* za^>CQ9JA^Ecl_m!G#IU2o=CUb9dl9ucRsUpS@UiUpl-QQ`nrfr=6T5Yqg0G;&GcMZ z%dP9FI4@#;EAzBiea<1`Ng(4-4U&e&SAQAY*PiwhFV*Kdqz-y>hFG>WH`V=p11$=C za}WXq1*B^CB{qWpq`qeRwkQ!2u%Ea@2w%^&^IAwmBK5uTbv_+m)e%0%%WOy`GS~sc z6}8h=#&55QNi<0sJTh7em{(qsoB^q!JEHQp7uYaM#3Ro=Eu!~^b}^0u$Ik>QQL7TH zwcv){CB>IyjcK?#ysmJx1j6OQ2@R{uA0@ornfgr@E{-x+hKyuyWtR74&IPkK(T?3e zq&EHb5{0cZ>s#-oa&3zMx@XH4#_B9TsbC5255a?V(pIt*R_1!jPB!!w=+)Eo*bdcA(=ZU;| zUs#%mC!xQj?)E_nD;Y{t06(AG`0v`uj|ZMNgIw&S;ZeYb<$tG*0sN*%^87RRYD5OZ z`1EVWz)}fRLPUvN|FSSjVHid2XBjOEpL$HP2v~?d;7t5tMEK=hOOa%J8kZ2sKp^Qf zr6|HEmlDobsoBaFIZ2J%P1r&JWvi&Yi~4;)Gqt})lKOXc){YHH;0+EIp=1zt#c;l* zUJ~)R_f9kk9e6NX>@9q_!+=AhbvX(KA{G5?u%Te+l^m5W5S$oN>Gw!q0Jr@8*sp^{ z!z@(>%9wIT3XBr zLq-Xp>;Ih3V>mPDAlIHBpg=D%Max+2=|YP5)mauiUE!3yv(j}V)6750FmN+xOzP;oE;%w|q?O)I=00p#rTx)5Df>>khF&p04j zwn5{XHKuo2Mhdc`na0PuMib;nMfHbbf;iHrwC~!ci3dsbHr*)@1Y8TINtc@fy2Ho! zDg82LI1PoLC~a2H485yz6xwMcJl`p|3UodyB5d6aRaN+<{^F7)^M^*(afgr+J} zO+oO|uQ_<}wPw8Bh?D>eRCj$9`QMZoPD(4y9(rorUiT1`T%q!t=eR;Y(l4ur&a%&# z!=S{0?Mad0zQ+om?(q3t5Z4BEZzli*Ee-s~^64?3)xq78=cT77(@-|mihcCYmboSY ziU%;PF%=EY^=HbE1*!)a^4*m}RJsmcY|eo)=&=(U;^QZ6`kHmyv~sVN(>`K4tKZSX_>c)G2xKNS#s{>*|B*k_AeoB`tUiX&>qs^l2zi z5zjnXX^O1*{mE+xfiw5uu z{=HS)z^D`7d^1-$R-%R%5e6v2ts79ys8z2f|#fF#Y{L2Gq0Vb?Xc=?MFE{3U~ z+ab@R6s;APH`jcosFz3POFk03d_`m7nz>)9buIwH401{xD$SD&L%Q_teZ$OVM%gyNE$mcP?ED|x@8P+)xDSj*rg@cF+bsYG0y&^zs=s9AL7PkbW#40KHb;O>ElnE|m$&{G=A^)8!Mw5O| zGa)U~Zb;7s9KA0fHvC!f=M}3JNPJUtF}VKK`?G9pYeUWY&Y3^1_f#r(@g-`0oC5`~ zHQzzi*??16>{CCNdk;Nd{2o|RbFvG}3%7`k(&Nm(SUNB4l8o=B%Mo&`{$AT|e0Vvt ze6qWRySZ266uHu^+Ff92``dl_A!sB9r)%?+ z+cSS*)!fC0BXU_r^<@706N(7P0)ao?46h=0&1H>@d>2ULdkm%C*qVGuA0$l z$|0STT%f(PMROB<;swj;&V8GaXD0vylHgwis7^ONgBBurLWGBEMG7% zS_R&XybVuVtnD<|kWNzQ&#j%GvPR;zf>G#rM}-}#Nx*EHH`b9E8gjPCc`(@6*#A=0 zi4zxRXZ^;kt8jsU`j-;|M4RouKcwT$xQuxmPw>{&dukQQ*}p+20)30bmd-T<=BMq4 zh-sr)17vllq<8v>+WXA`NkuqcrP!W>zk_sL@oJEL2jv9eg@;7rddA!|pWtRt*URul z{9Ul;c;Rl2oyrwql+~pIWkE2X1yYo?_=dXb`7?uDQh=@SPcZ}%;7ghk+As=urA*Y+ zS7_?}FnVn9uZ18{S{`LJ6-IMn5#;%iN5o@x-*{+3KPcc0ZYsrC=Z%t@YU^c>e>n7p zct#H`KR+1+K-XOQ=juVmH*$qTPA_u$CImnsKPG7PC)q#OC+pd6o8Kxo5kl50Q!e<} zRLJ3;aMEAifL#navl!OB z-3hl>t@~HsZd|(Esh<6!$A27gqI;qT%vjYHKGKn?y5G&4HaGqEhJVSDFTod&gOBi? zvp&)A66~$DNZq&=RmY7E%$l`Za+7q4oGx*A-uZr3>3oXxYy*~@Ei zS1&qDPQ^z)-U)T})|sdawz@A(c3e*f#~&y%2fLD5zSUOVY}Ka;_l$Dpp3s7CmeS3d z_K9+0_tsW?dPd78)ai82=JfZP8D|y5?=m~ODI5zDu4ab1n*CX^SPa+u6f&F{D#wwB ziV!}x;4)(s>tRpGH7>!Z;04cM@PD4cy${jR2pSq1@X$YlaKOiKxBnYHLh|FPo0*>8 zf*bM!hV475YrQdm4~2TJY+^CL@aKX$A<~`uYsfiqV#sfF67m7kmzQs)89yu*Vn?{s zZ1!63Z56~_Eb$GW$?+peOL~UK5HKf+3{Tp%d&f8IMW3x6yfl(P_9UQARD}WnjI9Njm9OOE}cD)}Bd1<>}VIqD7{xP6Gi!ufEYrwM^ zL^p~{M&J7nbnp5-g=!WG^Z>x42A1vTM&g}xihJ)m2)(Xa5k5C4M5RBBgs)y!?{Ein zd6f3df#oN}?FCYKfs|*#HRifcaUJeDpxm^-6nWAh4fBaDZs;ZI(l@lO z+9H1GO^6Vzz6fSWF#RJcTBaV%9jNh)g!FCe&8N{|l5Vr*Q`nd|caY;Rpz1VApqxl~ z=KH>AEm3sbEkbG}I+u#*6hEWCZ1&tg=PO?b_l&_Iob^TG%e>a?C0U)OP3)&69p9IO zDJ?te*w-qVwoiu%aw^MF5!T_3k{#X{M$^}WN4Kd6ekg%^7~||$Vb&NJpXAOC1&FE6 z&~9d;owo{Of{yvevhM#+n}G1?NP76i>wurxp$j2{wtn8&1KLo)@DPaY8Yestn`Hk9 zI~1!cnb1OCmWmNX2__lgVi?e3KTj(vpQ9~ae7puge3EOLSl0ewLp4n-G(X=ZpQis* zjd_D@J|z;heI_PThaV~NwYV{XmCKf= z5FjjsXrcmv`d3HSJNlGqEi63TYe?wxqk)WA4s$?3+&2&j`t(iWOVJye&~PKW^W^)$ zoWF8gkIR3wuz7s@#C_hIQ_GDZgD3t7InOfdgD*SQelwnQ>Zdk?xMCBMsn_8z=E0 zg^;_55FJSgJrPTNZ9BzMms;Ohmx`itH1lV>S(ad@MIYyMn;?Fx+x$=$rD(Gy!S6}m z2BT&sQXHUIR+hxvV3kTGT^4xu;Bw8|mcHsjOyR)(y>S=9VE@p?^1#6*mCbk3unDel zfWg4R=}-8pi@Vv1=DnZ-7+|LRv-c>*T^572Y!`z+p!9o^dCtR;cP4iYPecq;z?;qH zP`7FhbycvO)Fqy9g|cuEX!HzOH`dBl&7^{fMY!fdtFLV;!&hf(Y?|^m8W;%c ziBhF!(s2K3>3f?}=hobKpCaz0$O?%sMp$|FYg4IU9e?WnLB1@4Ahb!B(|1`A)^Sie zgN|WN0eCKPxMW?9r*zSOKvoj}kIhE%c?#VBQdF3fP`?(f*onLyV23(yxV~AX=X|4-I>xcEEQcHtV zMky?dl%Z}nHNaPBDB&(Je8#v&iTtDk!bqQ4X5Xua$T4y->m2k*T9A)D{N0=gBH{dm z2uI>Mob`UyO!0hus|D0+KoJoNLYBMp!OWVJqd_^o$kb#MEQa*2&!^@qCdc9@bK)(VTfs@kWi6r1qn z%5uH?K3HHGyPUd>e0!>N$d!#On%&$c2C}H}xIBb1SU|;Fyrhd|RwyU2g}A=|YC%d5 zOwTF$v0*lw8(|igKt`JM{J1m)0k)t@Gh`iiq;tEgP3`qV@DDd<0&(Gf6acsvu zyH~}p75Qk~lR65T+P2qhsw5WM9e!Oi`RYysRFK>{<%@^QsmA*FFUcDv3O&m^Aj+^> z#j|<;=0CDOeW(C%fo%2*g|fIwI)cZhIlmwK8_RXyN;aO#nD|`8emw%?nxbn~eMz-Q zzOC8G4X~AquP#R__34}U2!vY3S@hc`>7U*q))%*pq`Zb`8}N$q^RKJ_oFAZ5G^Q9u zlD5;Uqak3<#9vHJ%Ig(`q;J9;6!N)8PJ-xGD=Z#1=x%gq3`_kvI&~knXB>Brs?Swk z%HTSC2+hhTwfl189Mpg;~}vl3nBYI`8Jpkpwd6&kJIU)D^j-V zjy8r_(<10pYay*O!_AWDc-DBb9Xl` z;Uz+fs+gKGd*;eSd)MwwYK$=r-JbqpIlQs?H9B2)$ni*28zb-~Ev29P=k09_SKJi8 z*;o+DjMam$4zT>9o~+hNDLn9g%`hoWj;-QcDF>q%)vuTMza(BhKS+#B~=8YteNsPNmXV10^4ZF1m_&OAt}G`7Dlu)ZMM4XYPi;P(0mR z)grS~my6cYEbBjmqiBEEMG9b3I|lJo!kh zX{-J9!5Yv!tYXprfG(nNv{s%ZEXks0ER9`PN$X2o{9Nd`+l<0@TsW#((S=ybUn9sQ zUJA>*_5=p$ZO-C?Rtt%$T$YLXtcwI% z69A^3xy&hAm)yjBtdxCWJmDT9=!yE0PUA3G1>X2?<}r%swN$agl3>Qc^Vq2SH8sVn z&qVJXZa<_t~RkE9&@-PTq;^K8hH%QBL{q zlbF{l%6sg;XAZdWKirYW)V>9B&6qkDTu2iUzns+ArsVH&{JNYf298xvb+c5I2n;gE zxMFWKkhd?bA&UVmRwIRdE~OA%uHi=6EbNZ6C|( z?|XW%em8XyBF$6@k`ve6A&_up|66wS$(RvHZX7)=Z%cj9G&0{P5~5%(1*!}pzNP-PeZsR?ReKxB(n&{Q_u;NRJN$nUu78*N@R*+10y33qGO z4^%Qe?Rr?Btx2+>F<|JI9QnM{G@yhn8+eU7KGi2tZ(?8rNk!!HWS6+Q-!m~HjGq`I z!Om@SOd0%&@nh2!1zAW#UIrMgklGI5y(gwS#6U|8MFHo1Q9?<cPfxXn8~*M{O$J!WDUiJ-hyoU+`TVd_Jrv|AM{*;Vo)V+&x4VcXuJ85IK1X z;)-E5$G3DsK(ayT)tDn*EBpJj^cIAF4<`QEV3FK&75#8nV4$kxEjOSenUM&^dy(R7wW~ks z@2?xtE8`@`MyVbAjI~I-q-}JUTaiJAcxYfP@Ng(U*5|QxM6U z+OFBLe7kyvQISXkMx|G-y8oju+ASzPih7{;{mGqcuvhGnPL?UhTclz6C7ihg7F$JY zlLdu5hL_##$FH7}wKzKYgO~`~U_;AAVQ1u-gp)P)##HcFZ}haITRXBf7~Qab`woehx*Yc>)=1`bv?%HQ9IDqTRXDO&Tv>C{{SVI+{vjO*e5xG%nR`nt#|zW z>S03{M|J(2UM0Rin4obtEmkGxxy5RG*NdJoj{=SudD&;#ukiLt1gm>W zII?8Mw=JreGLiDzsDYVsp$;?`*#Xi_&{)hUDx?cLSP-O$d(QR!d{2+eef(Is$(Pjl zNxSzMh7T03W7Ez(Vq?f$M=1CzW+(?cbXzuOhg+7EJ|an;C;E3c*9UDQFsklTh-}Un z#LlezTGVi8Apa5wT6&x=X`Z*>aWesXAtB7GL*|V_iZtzm2LzDUtil{8A)CQceX^K% z3`52tC^`sYybWQZHP~V3S0A`IJG9w`=$ls_fHfEo`wt^>gsvi+?U5^?!iGUfO` z1(#hZ*z5{JMowN~ju<5IWQkRc$)~*!-8n-O`8$$fifaWKi&lrjzYJe~{W!om7U#U@ z^y?i}!UM7h0)x@4X;)|T7AG6eIiiH?M>?BQqr0Rkc68|A)x}KNuU>fue}Az><6C@2 zDhW2#>Ip14g9GQ-r5Gkk!@mPX%h^@45>Ik7gqrN{x>cVG0he0FG&Cto-tDcwNGVEy zBs$Xfm^}ZPJLtKg%W<0fa(-?%jQ;&Hy{8k>pg@}PNvyC)9L%CH#q9I&ymKf(9{v)# zNG2N7UW0Z#e{*#h_(Q;jgekv3e#n=JmXipBO>pRFGH(*)B|{OUlHl#}tPsF^Vazbo|&S?plqav^PnkEL81Z<3rbT z4e|ezu+WxRRHut?4VC?9ci)RpW!U+%!}NIaMzE4dGzekJr|h#=c+}=n5r}|h&*9|? z_>y!Ej=`QPe4sIlu3fc~+t4#yGB^^qLAptgYN?Qc9lCNzC;BuwPqm^wk?L89>vo@6 z6s*Mn1<#Y9uCbGptEwKkekCxMd&Itl$;_R`5{2%OF*DX+n{%FMRc*BhFIgWnz5_s} zW#_Ygj6xpg#G9mZE*IBPj=#n&WgfOdUJ)iw(|^G*PAmPnM2DLb;p^*`$VClNy^=7J z;TVo{+88;QLe%qP?X5Q^7!J+BNjly$Kn2`z`1v>T2&kaZ0J?%u&R>t|>LCHj<44s= z!IO-0%I%q{+j1;90jaT+TlzgXjJLuclosJ%R;^Ipid@4Hdx?(SVF5)qRts+*Yy57i zuhk*7#XiO|uFs@lz@&;+tXz|i!c(naq#4a$B57y8t))t&$*CzNG35sV#C3vg?K1?K2%kn;?RubzDVk>-y}TZE>B8VS zAcR?WWemA;v@T~+n8|umUtPd`4LxZ9;UzaWDI|w?$*( z3$s{kK+)P_hWUvb_^%hpr?FHbvTgT0NZe8Wa%6*t(nS(Rf@=CDXYYX)xE0e0a;LH$m|gwU^}=;Z_8GvwU50eL@2>*l_mo zoh1|)mz+Hf5Rk5Vpts#fN`rzhxpW~wOiS4DK)1RYYG}8J$Rt~va3xhm5r7{x{hii-*?marO8|`)6bY0Tst)DCbaLqqT{*5 zGPydz9Y2czOWSFjD$>3I_8CZNl+Wo9Kr7M3$}YaNjSMj#Ix?jp2<OTI50d&ZTO zd}H&2@hN(VK?YIt3D({4!5bUw7C5Wm^0$HEeJd(=g zeeUj&KfKK-R!%yZcN?_aS!a@2HDM4bAvs7R|6+*-CCY0KsX4pXjdZD12Prg*T~S;| zLXss{GMk4q3a$ReS8sn$eVB#kf=S56nm+|l{<_wt4g^{J#-Z_XG=<5seb2X|*l8A^R* z@i~3mlm!@gYj>#-kGn8fsVQhyL56WumI-k>+A4C=<=_3UX%j%_Im6>>pK<1ClZ-Z{ zv-I$U@<-HSfb-wah%b0$2a-Uxz71%HcuOoY#WD&N=pt`PXb z(<}hl6hGSc-awWO^~IL;;Q^HKwts|!uS9Sv!$-E4ht35;EcJkWWIwO{(mdup5dlF+ zpO5F2$X+{btN!tE7^>&H7_FQYMg|EZ=B~GLpfQzrCH$PcqFOYW z^P#FS7yY697`Ohhc;}6^{S%U4W?%6LXV;)A-jo-VulB`9Gw^$BIByMbwxlB{Bdlu< zG;6@mTDjLHbU}sfB`zZqBziJWBmzd)CkiIamWYW?!Vy)fT4kCZZi&?MugZ2K!_)<0 z993>|iB>KO9sN|X&WeBdou&{82fT0|+V6+a)nBsSqO?x)PH+ zQh;S8rlr22uA8Gc_bvU&h4xLh06NO);Nyc+aXprC4SYGpI_=gOd=zd&ALpK z{$&8h)XC#SCy~giuLnzXM`g(}d=4#CR%S;dp69KWH=d4$4mm$#v-0NB_TRs#Q6uKF z&vQ$5in`NIPFXlf+P!J(z0^fNLO)CF)K-pHCOaST_w(4)l(Bn>WYk%+RL!@jt4u4K zNtRe1Ij%jG&wJ7HgUt~gAu5woUK4!%V&x`(ZLGIGG4wh9LGg-3J%1M>n(^?p^Q7pO z&CgMu8F$Kjpjg_(x*x2*=WoVgESy6J2e|jPEO{ zAM)R{Y6{FAoug3UMH~%j<&Df)B+srUTKzcN7>`PMUr;4F{Bx=+cq-^+xy9V=mih=$ z%)hscRS|!UfXM&!;6?YqD?)xek$n}Bv-Q$TJT%0%kmX67XLNtgXOVlw8{rx5vKA9> z75Xr*Zu7kCEE*MKV%I>k_BumIGu)**VT9k=XwF`S%5wH-2*maG*||!IsTC5FS%T8I z!cR&X@0D5WTn$(3KJ0Ic`Ix)zK=ZVj&nDk>$5Pu*;=RD!)@$a8elvkPJT9qY1-i8= zA})#8Z1gY7Ke^nx-2BN_|Ds^&fwtwlN|%$6O0%fK<8HG7ff_+(#wW4(BO1<^<649H zStA+0$%3gry(=gi(tLGD+KgRNrcnRG{H0pr!gy1(r?tyW<&z2f--S(IW>yH9zrLR2 zuxbrfJu!C?Zq%dbet`I&4}P-fc?<*sFK1WTKy$q9ei(&aI!>x`SBG9^n#1M!7gq85 z(cRg%ubIcbc4%y|9C37aP$%DMe4t*v9Ln2SrE|2Muv_7BS1RC;@~_#bxQkw(C83>r z#M;q!w#SO5i3_c2`Cp$Ykc}xwHl-6k&Q@d5{-j+Iz#WF524O{oT-LmXi zOjdOB_RLHXVbgj{=zKdUjZu`ty0p5A+dwzlEUL)q%_I99>%@;emAXCCnz|pYqUm(fLM)BX{9YV92pY;b*Roc1c;l<9kr_s1 z=jTT0lJN85$SdKiW@f6RWRU%tGuiXE`*#g)x2 z(}~0yiQC@o)%Nr*nHW|{bndSb{5rhvJ|?5{zi$oC45qq7n{_3vY(2m$hfy+eL%I%c z>l&9GVfZ#V-=*Aw)#&n;3SQ3Fq{&mucE%@4N{23hAj5bchW3q@iv_Gh`79kgSLc_P zcndBSt96NSlrM%1qE;v4?r*Qfd5bD9q}JqJ@^u}#Fx7P~zASntBB;H>crh@QhL}iU zpp$euVId~|z}#4tpQkK2^Bh)m|5cIA>&|dKxga`O31!pNBk$3Qbv6x;nnzg1$`++b z`@s&+qo#C7az3WD=B_usd@s5o^Z4n+qK!%TlTi+vhr}r+T#d~l>&6@bXr{^f; zs*3NPID1-nS!w%Atlz46qwhVjeR%73P(Sx*+EpSf`oZ;nKOPc6D>;e81qYeUQ?t{_ zyhl_+d@|i08d(%k)^CdT{`?fY8#o3h_^a@Lr+^IYXMDfS$Py;RvDGcM=U)=IU#_Ne zwhNjYR*6YJ=_fqb3dm%Yk`W2lW`@6dvqnZoq zCdC?(YK@Ku$La@)>*r!ELmX{=AJBhv6j=^RGMbs6reP&hqg9f^hCJ-iwKCZKmLycx zPEMX%tS=eo)-Zsrc(Kqycc!DJo$|BRD|?mKo279{?b#x&X^K~{U+gy_9z0r=yAeW? ziC1qbetMt#rDITM4!>X{N%JErRW~SpzD6w|f6qexZfe5`elELB1x}It2&wR(^&1!W zd{h!{S3mL_L#tq-t}_Bv7p{qvF1RTYt37ul!J zomZvW93G{5vI|l_Gy^)qZVaxzb4xzx6&zt>8%U!*Em!FbU!C72cU%9m?%Y{52CD|E zg7dk~V2-&%-G{N4%Modz1z22lWgBag9evqv21YdpzUtiD?~m@^zOXOC)BJCv6mKp2 zf37lS#eVczyTm9kUDAKQ3`@k6E8)=Vm-uS*7lN9fl}}-I(7NVh>!25}4)Wrjh%9JI zA5bi9+2Vb!eIk5#v%u{F?{-j6=9K}4&y>*&KFOz=?^0_&`8rw%_*=TXeJOUJr~(%{bR6ZH!Fn7q2P zoIWwb-G_tBZrj&Plh3@r@i(aNA2vR}8h#_VU#R@=&1?HN#<@4|Sq&Zto{2v0^+~+- zU7IFBDC>`Xx0>~(o>lhc&g;y}oz;s`=U30g-ntq!ZiVHuV7WXyIA5u-8k{(4)qnQ| zofPL)sh5J8N#a5xpLqyjk(_wSldjPaU~F2mbI-oQvW?hjcMJ8AE7?ak7BvQ6c5|<+DFXXi-oO^ zP~PGq@MOB#SOz4a9xR;Wa#G)V6*&C+)J-Q@|J1q4;NAG|d*&X_?+Yo1BxA2mJhRK9 zHnaCLmejvb)4|62kba%|u7gU*Dz9h3hP?i+{JTUb5Jc|j%f$5gta!)5v< z-+6YQvG~z|RqS|}MN*}es&BkiL0orq7EZ{3UhC28vZ;a_dp~UTGFOxIOTo0>|0KHd;^^zm?9}*H5R8%|j>flGp{e_mgaej0xz7 znO$c}lecI6PUx;og*r!PyK&*X7wv{uK%V_G(RJ#1P&Dg!4t^s)sFQ8W{x$qZ=dbaj zGL+#ZJ~3Y9`$Baaj4Lv8Ns#2JySl|;B#&wCK{6;gkeY_=y-N;voDdc{MiVZd;1RCY z19c3wjUm14Nx8SGH8lfGvw7UID{NF^H=t0R>&;DCc+Hj4ZQp9H#nvN%4{+f#- zf+MlFcXb``D!cwQr9Fw_i@1rRT5;FPx*Ahl$(=DUFKC~W>ZGiYMQ|2*zRqYqUd0t1 z>`-#O=99G#otQqeybAuYBF7!6ZAqz)?>@u&4G%UB;Ad}JbYD%xgb~Bdy{ADolG`^{ z7cd~_W}o9I${$qklKbZ9kQz|tYbIy3k?hLccv$G0=CccJ*F6|8rZg?XeAw(N*L(cp zJbF);5DhlUz0ad3nkCs-<{QdI%Y7odIvwV$Pgxr1JlJ_3 z=eZQch$p39g&nT5xje1KR2PFs)gkHwi~O;c!Bmb+C9GfRbRg0Wsym0;)B&yG7wXTg z5%(X+Yt|~?3P*w}LLzzDE+sm3EZHUq3ura=*6GF6zMvP;{kzj8V06U{*282LE)b9Q zw~kqw3yQFm z8x=-JuMxA&~0wFKbq=a zJTLH1I<*I@R&(7sxOBWiq{s+~Yj=_?u^6lS7+HnqQ`-y zkx3{aq#%?OEIx^1KiUz7z#~6Yvm%c8Ko%z}U6WXfo7rX?gzQL>Ko6WO%cB<|f}qPE%Fi$2;m$W@%RnX**Ckzk~K{zhM?TxYNi>TcT=eb*!Mh;L%u$TbGq3FZJPIJlP zrmyu^b}x5xa5q=Hw(wpo)h2>ST3i9&Fck(bSl#lW8c!lirYi7-qc_2gyt71ja} zEG8jimF<3HHxKs2PL`yPp{mPOLM-9Dol743(rArEmu}m2uDniRmhdSkN@bwdcqZXu zm0>-NV3j_Hjk5Zc_B;1$=wq*TeI`&ZW!l}kyUZ)!Guc+(ak7dv=E_jCXF2v!3xoC) zj~4hq*wVRX3_Bf^d~zZ;uNQ-vBeNS%NtVrtVl#pE7)KZ2L-=f+qwb|C2pG5}BrFp? z%W#g})!lR|9PFAu@Z@&w^Tqvc+G1_Om3B1?7VyF-F(8C~v}?9HBT=Z`z~{6wHWOP! zG~Bk%thLH4XDw&RyjgZ5rzM)zXZl09N(M?C&MO(@vniY~Z8#pYWaBgBB>aR3Z#M-D;OD#}$z9m-#S z<%2ihYbfo$8t_Qav%^uRgAJM_q&xBX#ez+tt=pO=_tCl5{4N8e>&FeoQ;+KpxKHaS zRxWIRVxX1%Xs7~$7a-%jrk`$1YEP8lO$`g)S%>zrCgt+^KzvDu42`JH-}yoam8BR1 zg~s9O@_T0O|T@Y(WOP zW!(_1SGsf3_y8PsL`oFl)^sPPFFL_YwxY5R@HNE)#1W8p$dl5?66IE}F>KQU*cHof z*(T9*JGRF;C~fP$KiK@2eo0#Ygdt3AorGG}D;X?7bECNeMvR-3NnB9X$k&(nj^{W= zN=&lDaI=FS2{VcE0$M$?RWMth3-lNq2#V4=NntsIq|Yor0QG^N6S)WBt^rh2}_{N%rNa3|#-aKN!^4i_xuaye$P;j3ToTPs@vF5X=or zIAn7Yj$WH;vCmI!lkbEGeoCBOl{F z$hQpwSK$l-;yW29IGghR=ak6J>SFPL)*3|G!O`TX1s*o}WTucS#*;6k`Y6^=W~vpU z5kFEJ_X}%9yWi^|&Etf4SEQ3(pAPL-Y#y=i_lLc2uIxr|%a>A?;=uE&2V|q@p_ye9 z+F@Q+1fcy!9UKG?Q#u5JHZLDvz~Z#se3}%84)}#$eC2wvvaIaqBu7D(tMIJ_v~y2< ze$FJjwl_)~?$onhKV7gf`N>@O#lX3V7rq@_@pt?X^HF2Z;_=z~*j2H!t+{JkTHkbhvdSD_^IvQ|!;S4|#q4*sB*SZz%4F%$Hg=c8T8(*ZBK zUM_0ASlRFa6ar1XMe2cs*Mz5m5Q~Mm`JQa*=}4;@(l^!+h5qwdjDypnEUo%XFMH=G ztjS`(VS8pj{p{Br>Zpr{iQuw+_9NK9csLvsZcPMGb*gbhCAFw(Vsq?a>uT7EJ-U;Z zqG5f=$BOH-j$K$3pR`Yy7CMcV4EE>CfG6_zi?}| zuSwoqtoys~f(<4fB2l+qra+5KV{T;#b2Q_;aEe-hY^4D9W7Oz6*{RguU*FO^^f0n_ zyb=oV)DWY3|KKaOG;uMQWH?MCN|W77g8$Z^81k}-*)6=0nn00c~xaYb=VjAl9m9T!*`Cy()am| zehSsp>p94SAGAn|88~d$rr8Qi+_BSt$Fa3idYD||Su3ErfSJH%E}JVaT$+6$$HQf| zTa244usv?J^oM$mFN|j7oaZQdlRAeX)Tm_@1z1##gEXeQ6ZvCsKGh-MF404>%e)Lz z`{JP7YuCnu#sxnX!NObMNM@Mza-Y=e#mJ&y4C^y9+0; zhPi?%*=ddj2mvWss6%SklidRRb7sGAK1cdYH(5nX)i%_vH~ttLerqy<$g zYb~j)|;(of$|7^|1&%WJ6FU#Y^s}3D0 zXXL_DaMW9T=8CD7mXjfRQRn}Qy(Hy`H`pGufy@*FL?#XwR}JSAHbns8ub1p^$Js% zT<3Z@sLg2ZJI_P1D#L(60#nh%kmLiKw&*h^%}>KqO*2G(jTb6tFW6KidGv)65T;9m ze4%+A_L4;^=FwM3BeVb_XS>A)q$=s+_5y{q?zu4|Y%mY!G#pz%9({Xy-u?KM9Hchj zfbcrK`S_gs=}wijf>Ng$VLt?VVdu`b3WIg(PcTwJcJ;}PjQZN1gV$x^6-2YK?j;w2 z{r49a++>l>>wXS?J6yH4UGZXDza)LJJljfsVm?dder=T=*lzlw8;tU$3_bbCW<_*` zOK;w4z(8ymyHEDEX^kn=D%LmspnBHj{Hdp#hUonD(w`ig&6Ty%HujxUB;%yIh@uNr zR+$PF)gQ;7CB@Wv`X_H3I~P|WB1fLOOfN|jqD^$Vw&xAnKCKsnY z5}+wZoKwaj#HnN^s~|C+f!Wzvj1pSW{hO#fq=emHv_jBLm!+F4Z7}&ORduzhq`yjPQ|ranIaaR3OE{xv zNy70*JZ_9SKn}4bp*E0i6D-OUZXC$$om^?TbS{6+h;gOfpuLNQtA0#0)eHoeFu;ud zT)WUHr*-{<5cmwrus7^x=e-3t2)~?FE-$~uJRd-|m0NEwE_g^*MyAn8)1~btM7eil zRTIrCIF~a!eaI2vP5mHWHT0xHgTyVZlVmkjRH)Rpo=7@NMS}ia_3b5Xmk1p$+*Wu{ zcz4z4YUkZB$foA;@NMWrOFZm&W515VF4g!vh_b-nNZesHtiftuZ>)MSd*V?8M#IF- zJQ*u3rXGy!ks%Q&U11*9ZT)|Gf>#92%Lo&63C|9BhIq(>2J6ixe%`pp(?^a?y4T0- zo{PLI2>jzC0588Wa*vizky|p_LeUack6dx)G40~MMCeQ91 z3f`E)p4a$-<_&19)GcWI9`uNMNzc;$yD8z}(~DsmG5B)*kg=U`ZZSX=Bbt+T zn9zeNx9|pLW;2>?Q5O0^*Zhihl{LHQY}WHENjaaG5QT$Ap$9kMvRiCC*D3L>HJpltq-$-zijMAw`C!gp0j$8@Y%ADM|JbfU3N(0Fi*Y=`zuv+jlI#zaAwfQ&gF!Q4Q#*+7BMnk$FxXCiO~S zq)t}HZyvnUewd*Jh(kaMz_h2nUt^LwdTkb89 z@Q@p`ef4q-pYnmfsyd14_UP71Y77kHxV=2NpW-og-iR_@!C~>|eKPm}4EWt`JCC+- z5_NAzehC|S_;oP-@VA!1&%_VOmDVOiXyYRmq^!sKkzAvzr&+`*>y7Y<+?A7q2MWx( zmoJwuC;TMu(fvBT3uir;bhp%QT|A;YY_O)}POTiY$J8)8+>B7@gtOpSdCJ0+BA|zS z>(7<=Wz-A{Z(3RDj=R6%PBh(m1%xjF-@~gTC5H_jeuLb-THrUPAj>c%qh8l&wnkO@ zI`o!`Vu-7Vzgoz%LeJythP&Tc{Ww|-JZ*Zp2(zFQ_om)Ua=3!T8dqp;-bj@VkJ~+} zT=nY_3|p3m-@+>^$lW(lP{-K<6t%=(T}c16@oO5DceNyq8z7op!d9g;0Jh=_x3c@T z77ihQa+0+#$g)lwm3L1bECBkUvL))xW_$7z)FN7*wFtN?7FG1bkV*9LF zal77^A@u>}MpXal@3;@9pzgQ!GdIo*T|cvFfR#^5BlUWX zX$qIw)|`}B(xw~?ofb}72+@oeWa&20D&%T6q2fu_(G!bw4rSv&fLX-5#v`TJG88b1 zPt2Rk#0VVaXM*7hZH8Mt1cq)lOy+wg1ErgMu|+M?t@7#~2Xk}cOzGmr@|sO6dS;CM z$jh8Eaj##A2+7GA7o_{H&UpR#;2-_TDu+>#I~S-DVL*dApD`UfALzQB>7CXJ$I7t~V~rqsnLNR^Gb@j|&-Al~%RztCCDiLC3{VWmMtl z7i4Opg{>$|Sk(g@Byo$C?LCJ;=!j5gk5G5iX0@4FrIL!J3ctvg{Kuinw_**ekEile zRA{Sq7rgX6z74F@Sv+e}yA>PxB~trMPYF?(;yafjCNEj)F!&^=IB%AT{dp{G@H4jn znKEh^u~&vBoM*=j3yx+1?fG+1N%_&Z)g-v#+n*C?us72s_v$U8#%{oRHcaB5PMdNc zN1tL&5Y-1zmbt;V7{n~E>XSFNQo$t=`IC71NyMl|^8oEp47TSoR!rYLAvy}56oc#|)QehD47rH)taKAZ~`4491QLhZ98nQJ3X zcr30kXdNdI1rGPl5{>ut2_$C-9Bu!s46TOIZ+Zf31iIcD-R_@|#R}0pWr-X=C6%8G z?KZFKFc+(Sr$ZEWVl7}`uwm6Bw0KCcIQ7?sYUoVHk>Z{A$YufRA8Gx<6f->+Cw(61guk6a4sOTxj3igq{I2epq0JLS zUg&IOw#QqmG#W`YG%Dv+iUdyz0|vAt|MFBb=WH`~VwP32z)%k$^^GcZw627Q6sRn( zZWXOl8=oS|Mf}`i&rO{~{L->sXYC#~V`Rn$+h!dcQdM;)q9nV`(N|y$Bl59+Ff=IH zB&za0#>1DH+jgh~mbA+H&DtvvCUe~CaZsK~H2OHpGP-h5UA;_kJE&wo^ZV0#Ny2oF zy$eNjGIW`C29R#5qi7xa^q{EjBPPYrbQom+Cl5sVO9{ozClAl=SavSiYCo~Z_?nS( zg{V(!?L4i!FG_VFkpkg7h_V*V@y(DdV zQ^hP({iH!{)>!&?YT?5?wVKCB!CAWr>pjFaZD{gnEgr4Lpsw%`(5EWszsS>*CX<){skR**N?}G`a>m98O>;prc zLGzXPPo-8(0wW_kQr-7**J_^1Lmm2%)#V#g$au&3R0#T^0`Do%#1l-g&(P0)m$la3TthZn1oL(h5CwpN8*X@!qQ;VP(>Ll# z+r%ymP3QMYakKn5ycX!5EYdxq00cgy$e^QS^uQPKLg}SFZaARAl>pL+k2@f+K0ry^ zSCClKT|uQKY<&Roe|3=>!PqntNJ8=To}ogY=78Vi)qH!Zkk`6KeBjhYm0@a3 z+Z7rgfoFpZWAGor===_DrapJ9o_5CEH)Y>o8?qmL^Qu&kUWYRsr$N} zlnk*O#mVe``f~45QjaA?X6e(Fg3MXEBjerjgqYvuFBgAla5GL)?xF5xo&5EU9E zQfQ=0w_W|cndm*Rz?Xk}1t~HsnQvJuPi6;Iu>8Hc;hs|PIm(5h%(y3X=+o=(l7>|f zP$yCxdyKTOfF_ctQF-6X9bY1?s!Dd4!>$Zj-4!0ljJpRL^y0e=kG!D$e!2v9{2a{h z2K7t$8Nq0;YMIkH;x{FVoa~pTUW#CSRQ`Asi)WTKN3X?f*-98$c*~0!2+>Q;Gu9xc zNw?@hjfO{n%hj;~4{5BgGA3s9=nTK)VNQ_N?^Maa0SXq8xa&ajQDy1V)qkWqZ{9Mx z$;6ZG>IC*U0qs7Z>`h z3h_XK!q8~3qx@|pH-urKw~9?hqb~q;aAD^@A1q|-_!~;zq1SG&Vb$e`C}y-?N_zE+ zFJ_+fm*Nhd{|`Yn&QZGVdGghEF3J8j#z4O) zmqkxb5BH{w!1FBOt$i`{%RZklVehCj@FQ(Yhx>J)_Y(fS@V57_;oDNciP=D2_ay2` zbgo0l(*bDzkU=Yn3qIE)d$PTBiEd6ngOW+FN_ye-#y@s(U_$oQ~$TUP4c(|`|I zaG*|sKDko59ndYb=j=esB$E^KFt+47fRwZenn&$WJf#SU4Y~R2`(kpA6FF$6E-d?Tdt?2u&x%OU{mLLShDwj~gqY%Do@14Ds3lpjB)zwfd1SqVJDgzHmL$h`)p z_S?SCr$ehP)S|6Uori}hle#Q{QyStN+EE{K^$iVaClfk=2KMYkOPDTm<}P;e-X-ncE#qr~1k zwY+(+uJJN~-9j>DBu9l275d{x&0da#am)R-976N0$j*0hygg|#f-rcv=W--0$q*!^ zt%-p;XLnapQdxLEH$lN=I;3Z};$GdiY3(YaII zOK>!Yi%nOmOQ`MWU8MWT796_xc{BjuhkA%+8gWX^K8QG+@VRoRF8XLO(k9}EQ&-}^ z6$l@W^S9hH7fU+g)Tq`2QX(=)r>Lk1q!YqpP0gXUL3LpARIn9tU}c$LvWM6DM;KPvrg2vvcmUx6>q$l}5TYOXd4wm(qVgIV0n3 z4(AKp_r_ho%eN^1ln;oE0#}e>b>m$x9FN6Buf9a6?$#L26rdxXj%=z@opouts9fa{n zVPaw;y>Rs{iIe+pE0^mF$pA0$F}OlehgBvd>ZHt8D&QNaee&R_c^A2LbUmFY>x8k{ zxyy4M>s5m-( z0p4pIX&P{FSHOq#!^AaTSlsz_j*uH>IuW)zx&k6lQ#VTm;^rF=H7`$#+`g^C2;IvB zM#Q47yJkAht2fYj-HZxnF11XT92~`qv70tRP|#y%h5M9Y9m?Usx`knIa%mG<}&jtqm}I#7FVxp z3OaEWd=cM3060BKU6lXJ3Q-sDGup_hs|l=WqDcS4NWexv&RRJJCN0x_+*J0vAxAxH z-;-l5?w1=jQ#CGi%h}=NF=;pQQMK%_!oUr7&S;G>;A${2rg=N8EMOegmrvb%iNLw4$OrZ7!4qUbwF7@88he^268jQjK_JdP(`yxn5*%_nf?^ zb`{rgi9V2I20lreKpn><+7$TenROwg472!EV8)_L_JDOQ`a~QldCSUhd2*K#hX3}; zs6edI;duxqnR>+4LEKsas&kKYW%Z1KQC2WX0OIhZ;gQwDqeZ*TD^(4^0eahv-w%;3 zDd+M>`CV_d)=j8D27%v5zrvRTWfO7@hPdcStexE9z zbPp&sTE}gj1_nZ)Kc$nW=v~WV;C~70U>$Hg0agl;jP{W_4s>!csHLdc%covuh^F-T zZg<{>fWD$}tBt*@k04atz7B?$Q>8}fcJHqxLz)UEp`27ger=o6H8wU(>!$Ev^U1N3 zu>SWdIQ-&59^=f)=g*1|DyG|g)WE7pGXqMya^6J0`mt+l?p2;g)_=aCrd`UE{h%+D zeBUMemPRUYf`U7B%KaLh`up&#<%sj#*Xu0d%I#c!C6V$}eUMsDP4~ir<|!E?vgFX2 zfv(7l2|(ZJv$`{GIZCkes)D&5)@trx;`Ih{r(XXINbkuCJS@oXH4I&agpiVOaKf6x zi^s1HZqcg3?2zhQtsxcu4`bPwbNjY!1BZO-QuefN+qCPQXpvtk+_^7pbPEp)nTwUa zqc7X+Q?HID^dWJ_2z3h|hLsq>jA8@zyc8zTv7qT-0IuMl-$VQ9h1DI2%438Pwo7~B zg*GqjL7DslMrZJ9<7b^Lx!5-JD}A49H2 zww|?h-*+com5<}N$_oQHydPNlCYOHYI;_wK>q#W+T>ilRnuvLns24nLvj}+bDmB;7x00hiWYvlUX=Cy zytEgKz+%4%(zyDr4-AYJ0*D}aY7GzP8m~-ideH}9IsLB%4)i}yoib^QSIJ%N;xP== zi1J(p95ioHr6OU9wHBQ_6vA5}Oa0>-o~n8&F^V)`X*+zqiGK)4JPy>_r|#Qrg7~LoCWe=$KwZvZsHS4l^Tuo;Fl3{!5{^aRJ z%wM(INTkxw;jtx(Zp-_7bN+yTuYMqbt7~zvV${^uHU55=t2xjw2hfuYl{*9KztP)q z+T75UYQ6{sqffBr)ZZpRmItUPq@OzY z8$8L;(~A@Q;uzCyx4aoE2J|+-8&FdElFTOP=1Az2e(6P(6IVrJFFo469AE{R#m(n` zu*1{PA^QF5XS)y^c;6G(u#P^7xp$7g& zKL;3nDZ<~|%OHKA_3Q@LsJmoIMoc)s`+;grZd;|hudIPo7LpfC3h}DCBR`pSRlbFp z%Y2}FHbId8Gj=7kP4G6yQX=Sze}fkqZDd{YCc z1cqZ$@s6XBcW0vu%5>?viJaa0ss`^Mzy&b$vEER1uIWl_5b|?J&oicAqvKKoZ2RtA ziWl-%tkrA!Ep|evWyt~G2#lP7eHGHwi4fClR<+>nLGJWA0GzPuv)w~qZ&H3Hl>bhC zDBzwlDK$VEi{nL>=;tPTWC+fEUpbuHppVk@Y1se1bzd_nQ{`eN(5g`1+ZDV;Jw^s1 zuyR8+Tk0O*wuc(xiK}Tp-)xDFTm|;~m8;3kmIc4GI3!En!K%M90v=1oK;!ZJ2;h8@ zkjdfu1DpLRC+>0m7t7e(1RJ>5(heO@3Sc~K9Sm}+bR~;Xu7#0b-l9jIYa67g7VK*D zkDF?pUrlhAw&0d80EGrz^$gljpcaPid>-F7B4nihu1g_ftbF<&6Xg3HWw3Cn*J8&a z9FCY;wsH7n3e05zf6^yOhd(bR<#s@GjHTu&EMtqN>#UlDMx=Sr?kjWr7_a@ftKa$qP-}VfY>AHkR^j=V!^R9+V#$lEX}$p? zk)z)(DQzFTlRSAh9ah;IVu&;(p*PzZ98EI@4%~nwD;Q4lXc<^p&ETLXGB3`IqCi+y z-Tt&cKwv8?D~-(0@ykajr~4nI^1?-vCQ-QnJc`qu6!0YXBL5zWE3bLs*u-upQmaJx z?o1)TI&l&-q4lF}bs(f3II6IT`Oiq6c%JYXwY(r=gyQFRD%aL`s%UFi90GWN;0i0} zKz>XF{w2`nX#reg z(w<}ygFP_zVc~ET@UWVp_lsSBZ^U^Q(c+_`#{~nUYF8Axxz@`&Kx32pxVh&o&j;H4wLl z^}1Y^%SzipN*W=(tEY~q175f5&+tm04J4Ra-C(hXXn-!M46JjacS+AkY;=hmt*qH$ zV#WtZdZEaB4ryMYUpzx!p-SYyYqO#U^Iz*x z;L)T1Y`=Y&_9OsnP}zA(4>IV8v+D<`|6!#Pnw3^0LY@ILL-Ck2qkzlWB~+-}?+zeS zI`{;B@VyGN6xiX@yYI0~|Lph_2Lkw& z)upC${WcrNX0te)rDnWyHtI-N$HX(0K0B5X^3NN(;931)W7QXzo!a5*M9nc{z>_N0{^dn{OxHBeGTA$ zY!~$ZrxgAD^^Yw7-Ob-4@!zreH&Ff?H~)^r%a{M1Q~!*`e-p*OLGk}DQH-9P9+mBK UzW@_@Pd^w;S>qvA@#(Ao1-%*|Bme*a literal 0 HcmV?d00001 diff --git a/examples/classification/interval_based.ipynb b/examples/classification/interval_based.ipynb index ca67f38465..45988a0f70 100644 --- a/examples/classification/interval_based.ipynb +++ b/examples/classification/interval_based.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:05.163967Z", @@ -41,76 +41,32 @@ }, "outputs": [ { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[3], line 18\u001B[0m\n\u001B[0;32m 15\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mutils\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdiscovery\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m all_estimators\n\u001B[0;32m 17\u001B[0m warnings\u001B[38;5;241m.\u001B[39mfilterwarnings(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m---> 18\u001B[0m \u001B[43mall_estimators\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mclassifier\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtag_filter\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43malgorithm_type\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43minterval\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\utils\\discovery.py:121\u001B[0m, in \u001B[0;36mall_estimators\u001B[1;34m(type_filter, exclude_types, tag_filter, exclude_tags, include_sklearn, return_names)\u001B[0m\n\u001B[0;32m 116\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[0;32m 117\u001B[0m \u001B[38;5;28many\u001B[39m(part \u001B[38;5;129;01min\u001B[39;00m modules_to_ignore \u001B[38;5;28;01mfor\u001B[39;00m part \u001B[38;5;129;01min\u001B[39;00m module_parts)\n\u001B[0;32m 118\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m._\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m module_name\n\u001B[0;32m 119\u001B[0m ):\n\u001B[0;32m 120\u001B[0m \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[1;32m--> 121\u001B[0m module \u001B[38;5;241m=\u001B[39m \u001B[43mimport_module\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodule_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 123\u001B[0m classes \u001B[38;5;241m=\u001B[39m inspect\u001B[38;5;241m.\u001B[39mgetmembers(module, inspect\u001B[38;5;241m.\u001B[39misclass)\n\u001B[0;32m 124\u001B[0m \u001B[38;5;66;03m# skip private estimators and those not implemented in aeon\u001B[39;00m\n", - "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\importlib\\__init__.py:127\u001B[0m, in \u001B[0;36mimport_module\u001B[1;34m(name, package)\u001B[0m\n\u001B[0;32m 125\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[0;32m 126\u001B[0m level \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m--> 127\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_bootstrap\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_gcd_import\u001B[49m\u001B[43m(\u001B[49m\u001B[43mname\u001B[49m\u001B[43m[\u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m:\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpackage\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m:1030\u001B[0m, in \u001B[0;36m_gcd_import\u001B[1;34m(name, package, level)\u001B[0m\n", - "File \u001B[1;32m:1007\u001B[0m, in \u001B[0;36m_find_and_load\u001B[1;34m(name, import_)\u001B[0m\n", - "File \u001B[1;32m:986\u001B[0m, in \u001B[0;36m_find_and_load_unlocked\u001B[1;34m(name, import_)\u001B[0m\n", - "File \u001B[1;32m:680\u001B[0m, in \u001B[0;36m_load_unlocked\u001B[1;34m(spec)\u001B[0m\n", - "File \u001B[1;32m:850\u001B[0m, in \u001B[0;36mexec_module\u001B[1;34m(self, module)\u001B[0m\n", - "File \u001B[1;32m:228\u001B[0m, in \u001B[0;36m_call_with_frames_removed\u001B[1;34m(f, *args, **kwds)\u001B[0m\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\convolution_based\\__init__.py:12\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;124;03m\"\"\"Convolution-based time series classifiers.\"\"\"\u001B[39;00m\n\u001B[0;32m 3\u001B[0m __all__ \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m 4\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRocketClassifier\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 5\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMiniRocketClassifier\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 9\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMultiRocketHydraClassifier\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 10\u001B[0m ]\n\u001B[1;32m---> 12\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_arsenal\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Arsenal\n\u001B[0;32m 13\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_hydra\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m HydraClassifier\n\u001B[0;32m 14\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_minirocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MiniRocketClassifier\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\classification\\convolution_based\\_arsenal.py:20\u001B[0m\n\u001B[0;32m 18\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbase\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_base\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m _clone_estimator\n\u001B[0;32m 19\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mclassification\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbase\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m BaseClassifier\n\u001B[1;32m---> 20\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mtransformations\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcollection\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mconvolution_based\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m (\n\u001B[0;32m 21\u001B[0m MiniRocket,\n\u001B[0;32m 22\u001B[0m MultiRocket,\n\u001B[0;32m 23\u001B[0m Rocket,\n\u001B[0;32m 24\u001B[0m )\n\u001B[0;32m 27\u001B[0m \u001B[38;5;28;01mclass\u001B[39;00m \u001B[38;5;21;01mArsenal\u001B[39;00m(BaseClassifier):\n\u001B[0;32m 28\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 29\u001B[0m \u001B[38;5;124;03m Arsenal ensemble.\u001B[39;00m\n\u001B[0;32m 30\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 120\u001B[0m \u001B[38;5;124;03m >>> y_pred = clf.predict(X_test)\u001B[39;00m\n\u001B[0;32m 121\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\transformations\\collection\\convolution_based\\__init__.py:13\u001B[0m\n\u001B[0;32m 11\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_hydra\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m HydraTransformer\n\u001B[0;32m 12\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_minirocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MiniRocket\n\u001B[1;32m---> 13\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_minirocket_mv\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MiniRocketMultivariateVariable\n\u001B[0;32m 14\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_multirocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MultiRocket\n\u001B[0;32m 15\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_rocket\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Rocket\n", - "File \u001B[1;32mC:\\Code\\aeon\\aeon\\transformations\\collection\\convolution_based\\_minirocket_mv.py:303\u001B[0m\n\u001B[0;32m 290\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m X_2d_t, lengths\n\u001B[0;32m 293\u001B[0m \u001B[38;5;66;03m# code below from the orignal authors: https://github.com/angus924/minirocket\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;129;43m@njit\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[0;32m 297\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfloat32[:](float32[:,:],int32[:],int32[:],int32[:],int32[:],int32[:],float32[:],\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\n\u001B[0;32m 298\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moptional(int32))\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 299\u001B[0m \u001B[43m \u001B[49m\u001B[43mfastmath\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 300\u001B[0m \u001B[43m \u001B[49m\u001B[43mparallel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 301\u001B[0m \u001B[43m \u001B[49m\u001B[43mcache\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 302\u001B[0m \u001B[43m)\u001B[49m\n\u001B[1;32m--> 303\u001B[0m \u001B[38;5;28;43;01mdef\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;21;43m_fit_biases_multi_var\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[0;32m 304\u001B[0m \u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 305\u001B[0m \u001B[43m \u001B[49m\u001B[43mL\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 306\u001B[0m \u001B[43m \u001B[49m\u001B[43mnum_channels_per_combination\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 307\u001B[0m \u001B[43m \u001B[49m\u001B[43mchannel_indices\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 308\u001B[0m \u001B[43m \u001B[49m\u001B[43mdilations\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 309\u001B[0m \u001B[43m \u001B[49m\u001B[43mnum_features_per_dilation\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 310\u001B[0m \u001B[43m \u001B[49m\u001B[43mquantiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 311\u001B[0m \u001B[43m \u001B[49m\u001B[43mseed\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 312\u001B[0m \u001B[43m)\u001B[49m\u001B[43m:\u001B[49m\n\u001B[0;32m 313\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mseed\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m:\u001B[49m\n\u001B[0;32m 314\u001B[0m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrandom\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mseed\u001B[49m\u001B[43m(\u001B[49m\u001B[43mseed\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\decorators.py:232\u001B[0m, in \u001B[0;36m_jit..wrapper\u001B[1;34m(func)\u001B[0m\n\u001B[0;32m 230\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m typeinfer\u001B[38;5;241m.\u001B[39mregister_dispatcher(disp):\n\u001B[0;32m 231\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m sig \u001B[38;5;129;01min\u001B[39;00m sigs:\n\u001B[1;32m--> 232\u001B[0m \u001B[43mdisp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[43msig\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 233\u001B[0m disp\u001B[38;5;241m.\u001B[39mdisable_compile()\n\u001B[0;32m 234\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m disp\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:905\u001B[0m, in \u001B[0;36mDispatcher.compile\u001B[1;34m(self, sig)\u001B[0m\n\u001B[0;32m 903\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m ev\u001B[38;5;241m.\u001B[39mtrigger_event(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumba:compile\u001B[39m\u001B[38;5;124m\"\u001B[39m, data\u001B[38;5;241m=\u001B[39mev_details):\n\u001B[0;32m 904\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 905\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 906\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 907\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfolded\u001B[39m(args, kws):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:80\u001B[0m, in \u001B[0;36m_FunctionCompiler.compile\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 79\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcompile\u001B[39m(\u001B[38;5;28mself\u001B[39m, args, return_type):\n\u001B[1;32m---> 80\u001B[0m status, retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_cached\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 81\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m status:\n\u001B[0;32m 82\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m retval\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:94\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_cached\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 91\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 93\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m---> 94\u001B[0m retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 95\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mTypingError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 96\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_failed_cache[key] \u001B[38;5;241m=\u001B[39m e\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:107\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_core\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 104\u001B[0m flags \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_customize_flags(flags)\n\u001B[0;32m 106\u001B[0m impl \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_implementation(args, {})\n\u001B[1;32m--> 107\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[43mcompiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtyping_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 108\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtarget_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 109\u001B[0m \u001B[43m \u001B[49m\u001B[43mimpl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 110\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreturn_type\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 111\u001B[0m \u001B[43m \u001B[49m\u001B[43mflags\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mflags\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlocals\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlocals\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 112\u001B[0m \u001B[43m \u001B[49m\u001B[43mpipeline_class\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpipeline_class\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;66;03m# Check typing error if object mode is used\u001B[39;00m\n\u001B[0;32m 114\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cres\u001B[38;5;241m.\u001B[39mtyping_error \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m flags\u001B[38;5;241m.\u001B[39menable_pyobject:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:744\u001B[0m, in \u001B[0;36mcompile_extra\u001B[1;34m(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)\u001B[0m\n\u001B[0;32m 720\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Compiler entry point\u001B[39;00m\n\u001B[0;32m 721\u001B[0m \n\u001B[0;32m 722\u001B[0m \u001B[38;5;124;03mParameter\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 740\u001B[0m \u001B[38;5;124;03m compiler pipeline\u001B[39;00m\n\u001B[0;32m 741\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 742\u001B[0m pipeline \u001B[38;5;241m=\u001B[39m pipeline_class(typingctx, targetctx, library,\n\u001B[0;32m 743\u001B[0m args, return_type, flags, \u001B[38;5;28mlocals\u001B[39m)\n\u001B[1;32m--> 744\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mpipeline\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:438\u001B[0m, in \u001B[0;36mCompilerBase.compile_extra\u001B[1;34m(self, func)\u001B[0m\n\u001B[0;32m 436\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted \u001B[38;5;241m=\u001B[39m ()\n\u001B[0;32m 437\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted_from \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 438\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_bytecode\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:506\u001B[0m, in \u001B[0;36mCompilerBase._compile_bytecode\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 502\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 503\u001B[0m \u001B[38;5;124;03mPopulate and run pipeline for bytecode input\u001B[39;00m\n\u001B[0;32m 504\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 505\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mfunc_ir \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 506\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:472\u001B[0m, in \u001B[0;36mCompilerBase._compile_core\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 470\u001B[0m res \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 471\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 472\u001B[0m \u001B[43mpm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 473\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mcr \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 474\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:356\u001B[0m, in \u001B[0;36mPassManager.run\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 354\u001B[0m pass_inst \u001B[38;5;241m=\u001B[39m _pass_registry\u001B[38;5;241m.\u001B[39mget(pss)\u001B[38;5;241m.\u001B[39mpass_inst\n\u001B[0;32m 355\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(pass_inst, CompilerPass):\n\u001B[1;32m--> 356\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_runPass\u001B[49m\u001B[43m(\u001B[49m\u001B[43midx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpass_inst\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 358\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mLegacy pass in use\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_lock.py:35\u001B[0m, in \u001B[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 32\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m 33\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_acquire_compile_lock\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 34\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m:\n\u001B[1;32m---> 35\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:311\u001B[0m, in \u001B[0;36mPassManager._runPass\u001B[1;34m(self, index, pss, internal_state)\u001B[0m\n\u001B[0;32m 309\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_initialization, internal_state)\n\u001B[0;32m 310\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m pass_time:\n\u001B[1;32m--> 311\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[43mcheck\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpss\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_pass\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minternal_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 312\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m finalize_time:\n\u001B[0;32m 313\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_finalizer, internal_state)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:273\u001B[0m, in \u001B[0;36mPassManager._runPass..check\u001B[1;34m(func, compiler_state)\u001B[0m\n\u001B[0;32m 272\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck\u001B[39m(func, compiler_state):\n\u001B[1;32m--> 273\u001B[0m mangled \u001B[38;5;241m=\u001B[39m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiler_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 274\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m mangled \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m 275\u001B[0m msg \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass implementations should return True/False. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 276\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass with name \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m did not.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typed_passes.py:112\u001B[0m, in \u001B[0;36mBaseTypeInference.run_pass\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 106\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 107\u001B[0m \u001B[38;5;124;03mType inference and legalization\u001B[39;00m\n\u001B[0;32m 108\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 109\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m fallback_context(state, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFunction \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m failed type inference\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m 110\u001B[0m \u001B[38;5;241m%\u001B[39m (state\u001B[38;5;241m.\u001B[39mfunc_id\u001B[38;5;241m.\u001B[39mfunc_name,)):\n\u001B[0;32m 111\u001B[0m \u001B[38;5;66;03m# Type inference\u001B[39;00m\n\u001B[1;32m--> 112\u001B[0m typemap, return_type, calltypes, errs \u001B[38;5;241m=\u001B[39m \u001B[43mtype_inference_stage\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 113\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtypingctx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 114\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetctx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 115\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfunc_ir\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 116\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 117\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreturn_type\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 118\u001B[0m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlocals\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 119\u001B[0m \u001B[43m \u001B[49m\u001B[43mraise_errors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_raise_errors\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 120\u001B[0m state\u001B[38;5;241m.\u001B[39mtypemap \u001B[38;5;241m=\u001B[39m typemap\n\u001B[0;32m 121\u001B[0m \u001B[38;5;66;03m# save errors in case of partial typing\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typed_passes.py:93\u001B[0m, in \u001B[0;36mtype_inference_stage\u001B[1;34m(typingctx, targetctx, interp, args, return_type, locals, raise_errors)\u001B[0m\n\u001B[0;32m 91\u001B[0m infer\u001B[38;5;241m.\u001B[39mbuild_constraint()\n\u001B[0;32m 92\u001B[0m \u001B[38;5;66;03m# return errors in case of partial typing\u001B[39;00m\n\u001B[1;32m---> 93\u001B[0m errs \u001B[38;5;241m=\u001B[39m \u001B[43minfer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpropagate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mraise_errors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mraise_errors\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 94\u001B[0m typemap, restype, calltypes \u001B[38;5;241m=\u001B[39m infer\u001B[38;5;241m.\u001B[39munify(raise_errors\u001B[38;5;241m=\u001B[39mraise_errors)\n\u001B[0;32m 96\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m _TypingResults(typemap, restype, calltypes, errs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:1083\u001B[0m, in \u001B[0;36mTypeInferer.propagate\u001B[1;34m(self, raise_errors)\u001B[0m\n\u001B[0;32m 1080\u001B[0m oldtoken \u001B[38;5;241m=\u001B[39m newtoken\n\u001B[0;32m 1081\u001B[0m \u001B[38;5;66;03m# Errors can appear when the type set is incomplete; only\u001B[39;00m\n\u001B[0;32m 1082\u001B[0m \u001B[38;5;66;03m# raise them when there is no progress anymore.\u001B[39;00m\n\u001B[1;32m-> 1083\u001B[0m errors \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconstraints\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpropagate\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1084\u001B[0m newtoken \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_state_token()\n\u001B[0;32m 1085\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdebug\u001B[38;5;241m.\u001B[39mpropagate_finished()\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:160\u001B[0m, in \u001B[0;36mConstraintNetwork.propagate\u001B[1;34m(self, typeinfer)\u001B[0m\n\u001B[0;32m 157\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m typeinfer\u001B[38;5;241m.\u001B[39mwarnings\u001B[38;5;241m.\u001B[39mcatch_warnings(filename\u001B[38;5;241m=\u001B[39mloc\u001B[38;5;241m.\u001B[39mfilename,\n\u001B[0;32m 158\u001B[0m lineno\u001B[38;5;241m=\u001B[39mloc\u001B[38;5;241m.\u001B[39mline):\n\u001B[0;32m 159\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 160\u001B[0m \u001B[43mconstraint\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtypeinfer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 161\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 162\u001B[0m errors\u001B[38;5;241m.\u001B[39mappend(e)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:583\u001B[0m, in \u001B[0;36mCallConstraint.__call__\u001B[1;34m(self, typeinfer)\u001B[0m\n\u001B[0;32m 581\u001B[0m fnty \u001B[38;5;241m=\u001B[39m typevars[\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunc]\u001B[38;5;241m.\u001B[39mgetone()\n\u001B[0;32m 582\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m new_error_context(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mresolving callee type: \u001B[39m\u001B[38;5;132;01m{0}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, fnty):\n\u001B[1;32m--> 583\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtypeinfer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtypevars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfnty\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:606\u001B[0m, in \u001B[0;36mCallConstraint.resolve\u001B[1;34m(self, typeinfer, typevars, fnty)\u001B[0m\n\u001B[0;32m 604\u001B[0m fnty \u001B[38;5;241m=\u001B[39m fnty\u001B[38;5;241m.\u001B[39minstance_type\n\u001B[0;32m 605\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 606\u001B[0m sig \u001B[38;5;241m=\u001B[39m \u001B[43mtypeinfer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresolve_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfnty\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_args\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkw_args\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 607\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 608\u001B[0m \u001B[38;5;66;03m# Adjust for bound methods\u001B[39;00m\n\u001B[0;32m 609\u001B[0m folding_args \u001B[38;5;241m=\u001B[39m ((fnty\u001B[38;5;241m.\u001B[39mthis,) \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mtuple\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs)\n\u001B[0;32m 610\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(fnty, types\u001B[38;5;241m.\u001B[39mBoundFunction)\n\u001B[0;32m 611\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typeinfer.py:1577\u001B[0m, in \u001B[0;36mTypeInferer.resolve_call\u001B[1;34m(self, fnty, pos_args, kw_args)\u001B[0m\n\u001B[0;32m 1574\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m sig\n\u001B[0;32m 1575\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 1576\u001B[0m \u001B[38;5;66;03m# Normal non-recursive call\u001B[39;00m\n\u001B[1;32m-> 1577\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcontext\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresolve_function_type\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfnty\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_args\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkw_args\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\context.py:196\u001B[0m, in \u001B[0;36mBaseContext.resolve_function_type\u001B[1;34m(self, func, args, kws)\u001B[0m\n\u001B[0;32m 194\u001B[0m \u001B[38;5;66;03m# Prefer user definition first\u001B[39;00m\n\u001B[0;32m 195\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 196\u001B[0m res \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_resolve_user_function_type\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 197\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mTypingError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 198\u001B[0m \u001B[38;5;66;03m# Capture any typing error\u001B[39;00m\n\u001B[0;32m 199\u001B[0m last_exception \u001B[38;5;241m=\u001B[39m e\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\context.py:248\u001B[0m, in \u001B[0;36mBaseContext._resolve_user_function_type\u001B[1;34m(self, func, args, kws, literals)\u001B[0m\n\u001B[0;32m 244\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mresolve_function_type(func_type, args, kws)\n\u001B[0;32m 246\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(func, types\u001B[38;5;241m.\u001B[39mCallable):\n\u001B[0;32m 247\u001B[0m \u001B[38;5;66;03m# XXX fold this into the __call__ attribute logic?\u001B[39;00m\n\u001B[1;32m--> 248\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_call_type\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\types\\functions.py:308\u001B[0m, in \u001B[0;36mBaseFunction.get_call_type\u001B[1;34m(self, context, args, kws)\u001B[0m\n\u001B[0;32m 305\u001B[0m nolitargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mtuple\u001B[39m([_unlit_non_poison(a) \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m args])\n\u001B[0;32m 306\u001B[0m nolitkws \u001B[38;5;241m=\u001B[39m {k: _unlit_non_poison(v)\n\u001B[0;32m 307\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m k, v \u001B[38;5;129;01min\u001B[39;00m kws\u001B[38;5;241m.\u001B[39mitems()}\n\u001B[1;32m--> 308\u001B[0m sig \u001B[38;5;241m=\u001B[39m \u001B[43mtemp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnolitargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnolitkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 309\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 310\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (utils\u001B[38;5;241m.\u001B[39muse_new_style_errors() \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m\n\u001B[0;32m 311\u001B[0m \u001B[38;5;28misinstance\u001B[39m(e, errors\u001B[38;5;241m.\u001B[39mNumbaError)):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:350\u001B[0m, in \u001B[0;36mAbstractTemplate.apply\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 348\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply\u001B[39m(\u001B[38;5;28mself\u001B[39m, args, kws):\n\u001B[0;32m 349\u001B[0m generic \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgeneric\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 350\u001B[0m sig \u001B[38;5;241m=\u001B[39m \u001B[43mgeneric\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 351\u001B[0m \u001B[38;5;66;03m# Enforce that *generic()* must return None or Signature\u001B[39;00m\n\u001B[0;32m 352\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m sig \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:613\u001B[0m, in \u001B[0;36m_OverloadFunctionTemplate.generic\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 607\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 608\u001B[0m \u001B[38;5;124;03mType the overloaded function by compiling the appropriate\u001B[39;00m\n\u001B[0;32m 609\u001B[0m \u001B[38;5;124;03mimplementation for the given args.\u001B[39;00m\n\u001B[0;32m 610\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 611\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mnumba\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcore\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mtyped_passes\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PreLowerStripPhis\n\u001B[1;32m--> 613\u001B[0m disp, new_args \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 614\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m disp \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 615\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:712\u001B[0m, in \u001B[0;36m_OverloadFunctionTemplate._get_impl\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 708\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m:\n\u001B[0;32m 709\u001B[0m \u001B[38;5;66;03m# pass and try outside the scope so as to not have KeyError with a\u001B[39;00m\n\u001B[0;32m 710\u001B[0m \u001B[38;5;66;03m# nested addition error in the case the _build_impl fails\u001B[39;00m\n\u001B[0;32m 711\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[1;32m--> 712\u001B[0m impl, args \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_build_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcache_key\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 713\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m impl, args\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typing\\templates.py:816\u001B[0m, in \u001B[0;36m_OverloadFunctionTemplate._build_impl\u001B[1;34m(self, cache_key, args, kws)\u001B[0m\n\u001B[0;32m 814\u001B[0m \u001B[38;5;66;03m# Make sure that the implementation can be fully compiled\u001B[39;00m\n\u001B[0;32m 815\u001B[0m disp_type \u001B[38;5;241m=\u001B[39m types\u001B[38;5;241m.\u001B[39mDispatcher(disp)\n\u001B[1;32m--> 816\u001B[0m \u001B[43mdisp_type\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_call_type\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcontext\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 817\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cache_key \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 818\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_impl_cache[cache_key] \u001B[38;5;241m=\u001B[39m disp, args\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\types\\functions.py:541\u001B[0m, in \u001B[0;36mDispatcher.get_call_type\u001B[1;34m(self, context, args, kws)\u001B[0m\n\u001B[0;32m 534\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_call_type\u001B[39m(\u001B[38;5;28mself\u001B[39m, context, args, kws):\n\u001B[0;32m 535\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 536\u001B[0m \u001B[38;5;124;03m Resolve a call to this dispatcher using the given argument types.\u001B[39;00m\n\u001B[0;32m 537\u001B[0m \u001B[38;5;124;03m A signature returned and it is ensured that a compiled specialization\u001B[39;00m\n\u001B[0;32m 538\u001B[0m \u001B[38;5;124;03m is available for it.\u001B[39;00m\n\u001B[0;32m 539\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m 540\u001B[0m template, pysig, args, kws \u001B[38;5;241m=\u001B[39m \\\n\u001B[1;32m--> 541\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdispatcher\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_call_template\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 542\u001B[0m sig \u001B[38;5;241m=\u001B[39m template(context)\u001B[38;5;241m.\u001B[39mapply(args, kws)\n\u001B[0;32m 543\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m sig:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:318\u001B[0m, in \u001B[0;36m_DispatcherBase.get_call_template\u001B[1;34m(self, args, kws)\u001B[0m\n\u001B[0;32m 316\u001B[0m \u001B[38;5;66;03m# Ensure an overload is available\u001B[39;00m\n\u001B[0;32m 317\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_can_compile:\n\u001B[1;32m--> 318\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mtuple\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 320\u001B[0m \u001B[38;5;66;03m# Create function type for typing\u001B[39;00m\n\u001B[0;32m 321\u001B[0m func_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpy_func\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:905\u001B[0m, in \u001B[0;36mDispatcher.compile\u001B[1;34m(self, sig)\u001B[0m\n\u001B[0;32m 903\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m ev\u001B[38;5;241m.\u001B[39mtrigger_event(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnumba:compile\u001B[39m\u001B[38;5;124m\"\u001B[39m, data\u001B[38;5;241m=\u001B[39mev_details):\n\u001B[0;32m 904\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 905\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 906\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mForceLiteralArg \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 907\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfolded\u001B[39m(args, kws):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:80\u001B[0m, in \u001B[0;36m_FunctionCompiler.compile\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 79\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcompile\u001B[39m(\u001B[38;5;28mself\u001B[39m, args, return_type):\n\u001B[1;32m---> 80\u001B[0m status, retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_cached\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 81\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m status:\n\u001B[0;32m 82\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m retval\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:94\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_cached\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 91\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 93\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m---> 94\u001B[0m retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 95\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mTypingError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 96\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_failed_cache[key] \u001B[38;5;241m=\u001B[39m e\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\dispatcher.py:107\u001B[0m, in \u001B[0;36m_FunctionCompiler._compile_core\u001B[1;34m(self, args, return_type)\u001B[0m\n\u001B[0;32m 104\u001B[0m flags \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_customize_flags(flags)\n\u001B[0;32m 106\u001B[0m impl \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_implementation(args, {})\n\u001B[1;32m--> 107\u001B[0m cres \u001B[38;5;241m=\u001B[39m \u001B[43mcompiler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtyping_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 108\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtargetdescr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtarget_context\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 109\u001B[0m \u001B[43m \u001B[49m\u001B[43mimpl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 110\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreturn_type\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 111\u001B[0m \u001B[43m \u001B[49m\u001B[43mflags\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mflags\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlocals\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlocals\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 112\u001B[0m \u001B[43m \u001B[49m\u001B[43mpipeline_class\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpipeline_class\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;66;03m# Check typing error if object mode is used\u001B[39;00m\n\u001B[0;32m 114\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cres\u001B[38;5;241m.\u001B[39mtyping_error \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m flags\u001B[38;5;241m.\u001B[39menable_pyobject:\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:744\u001B[0m, in \u001B[0;36mcompile_extra\u001B[1;34m(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)\u001B[0m\n\u001B[0;32m 720\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Compiler entry point\u001B[39;00m\n\u001B[0;32m 721\u001B[0m \n\u001B[0;32m 722\u001B[0m \u001B[38;5;124;03mParameter\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 740\u001B[0m \u001B[38;5;124;03m compiler pipeline\u001B[39;00m\n\u001B[0;32m 741\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 742\u001B[0m pipeline \u001B[38;5;241m=\u001B[39m pipeline_class(typingctx, targetctx, library,\n\u001B[0;32m 743\u001B[0m args, return_type, flags, \u001B[38;5;28mlocals\u001B[39m)\n\u001B[1;32m--> 744\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mpipeline\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompile_extra\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:438\u001B[0m, in \u001B[0;36mCompilerBase.compile_extra\u001B[1;34m(self, func)\u001B[0m\n\u001B[0;32m 436\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted \u001B[38;5;241m=\u001B[39m ()\n\u001B[0;32m 437\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mlifted_from \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 438\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_bytecode\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:506\u001B[0m, in \u001B[0;36mCompilerBase._compile_bytecode\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 502\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 503\u001B[0m \u001B[38;5;124;03mPopulate and run pipeline for bytecode input\u001B[39;00m\n\u001B[0;32m 504\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 505\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mfunc_ir \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 506\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compile_core\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler.py:472\u001B[0m, in \u001B[0;36mCompilerBase._compile_core\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 470\u001B[0m res \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 471\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 472\u001B[0m \u001B[43mpm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 473\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mcr \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 474\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:356\u001B[0m, in \u001B[0;36mPassManager.run\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 354\u001B[0m pass_inst \u001B[38;5;241m=\u001B[39m _pass_registry\u001B[38;5;241m.\u001B[39mget(pss)\u001B[38;5;241m.\u001B[39mpass_inst\n\u001B[0;32m 355\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(pass_inst, CompilerPass):\n\u001B[1;32m--> 356\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_runPass\u001B[49m\u001B[43m(\u001B[49m\u001B[43midx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpass_inst\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 358\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mLegacy pass in use\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_lock.py:35\u001B[0m, in \u001B[0;36m_CompilerLock.__call__.._acquire_compile_lock\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 32\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m 33\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_acquire_compile_lock\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 34\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m:\n\u001B[1;32m---> 35\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:311\u001B[0m, in \u001B[0;36mPassManager._runPass\u001B[1;34m(self, index, pss, internal_state)\u001B[0m\n\u001B[0;32m 309\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_initialization, internal_state)\n\u001B[0;32m 310\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m pass_time:\n\u001B[1;32m--> 311\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[43mcheck\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpss\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_pass\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minternal_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 312\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m SimpleTimer() \u001B[38;5;28;01mas\u001B[39;00m finalize_time:\n\u001B[0;32m 313\u001B[0m mutated \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m check(pss\u001B[38;5;241m.\u001B[39mrun_finalizer, internal_state)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\compiler_machinery.py:273\u001B[0m, in \u001B[0;36mPassManager._runPass..check\u001B[1;34m(func, compiler_state)\u001B[0m\n\u001B[0;32m 272\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck\u001B[39m(func, compiler_state):\n\u001B[1;32m--> 273\u001B[0m mangled \u001B[38;5;241m=\u001B[39m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiler_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 274\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m mangled \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m 275\u001B[0m msg \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass implementations should return True/False. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 276\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCompilerPass with name \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m did not.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\typed_passes.py:497\u001B[0m, in \u001B[0;36mBaseNativeLowering.run_pass\u001B[1;34m(self, state)\u001B[0m\n\u001B[0;32m 491\u001B[0m state[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcr\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m _LowerResult(fndesc, call_helper,\n\u001B[0;32m 492\u001B[0m cfunc\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, env\u001B[38;5;241m=\u001B[39menv)\n\u001B[0;32m 493\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 494\u001B[0m \u001B[38;5;66;03m# Prepare for execution\u001B[39;00m\n\u001B[0;32m 495\u001B[0m \u001B[38;5;66;03m# Insert native function for use by other jitted-functions.\u001B[39;00m\n\u001B[0;32m 496\u001B[0m \u001B[38;5;66;03m# We also register its library to allow for inlining.\u001B[39;00m\n\u001B[1;32m--> 497\u001B[0m cfunc \u001B[38;5;241m=\u001B[39m \u001B[43mtargetctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_executable\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlibrary\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfndesc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43menv\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 498\u001B[0m targetctx\u001B[38;5;241m.\u001B[39minsert_user_function(cfunc, fndesc, [library])\n\u001B[0;32m 499\u001B[0m state[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcr\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m _LowerResult(fndesc, call_helper,\n\u001B[0;32m 500\u001B[0m cfunc\u001B[38;5;241m=\u001B[39mcfunc, env\u001B[38;5;241m=\u001B[39menv)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\cpu.py:239\u001B[0m, in \u001B[0;36mCPUContext.get_executable\u001B[1;34m(self, library, fndesc, env)\u001B[0m\n\u001B[0;32m 226\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 227\u001B[0m \u001B[38;5;124;03mReturns\u001B[39;00m\n\u001B[0;32m 228\u001B[0m \u001B[38;5;124;03m-------\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 236\u001B[0m \u001B[38;5;124;03m an execution environment (from _dynfunc)\u001B[39;00m\n\u001B[0;32m 237\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 238\u001B[0m \u001B[38;5;66;03m# Code generation\u001B[39;00m\n\u001B[1;32m--> 239\u001B[0m fnptr \u001B[38;5;241m=\u001B[39m \u001B[43mlibrary\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_pointer_to_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 240\u001B[0m \u001B[43m \u001B[49m\u001B[43mfndesc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mllvm_cpython_wrapper_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 242\u001B[0m \u001B[38;5;66;03m# Note: we avoid reusing the original docstring to avoid encoding\u001B[39;00m\n\u001B[0;32m 243\u001B[0m \u001B[38;5;66;03m# issues on Python 2, see issue #1908\u001B[39;00m\n\u001B[0;32m 244\u001B[0m doc \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcompiled wrapper for \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m (fndesc\u001B[38;5;241m.\u001B[39mqualname,)\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:989\u001B[0m, in \u001B[0;36mJITCodeLibrary.get_pointer_to_function\u001B[1;34m(self, name)\u001B[0m\n\u001B[0;32m 975\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_pointer_to_function\u001B[39m(\u001B[38;5;28mself\u001B[39m, name):\n\u001B[0;32m 976\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 977\u001B[0m \u001B[38;5;124;03m Generate native code for function named *name* and return a pointer\u001B[39;00m\n\u001B[0;32m 978\u001B[0m \u001B[38;5;124;03m to the start of the function (as an integer).\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 987\u001B[0m \u001B[38;5;124;03m - non-zero if the symbol is defined.\u001B[39;00m\n\u001B[0;32m 988\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 989\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_ensure_finalized\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 990\u001B[0m ee \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_codegen\u001B[38;5;241m.\u001B[39m_engine\n\u001B[0;32m 991\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m ee\u001B[38;5;241m.\u001B[39mis_symbol_defined(name):\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:567\u001B[0m, in \u001B[0;36mCodeLibrary._ensure_finalized\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 565\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_ensure_finalized\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[0;32m 566\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_finalized:\n\u001B[1;32m--> 567\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfinalize\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:762\u001B[0m, in \u001B[0;36mCPUCodeLibrary.finalize\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 756\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_final_module\u001B[38;5;241m.\u001B[39mlink_in(\n\u001B[0;32m 757\u001B[0m library\u001B[38;5;241m.\u001B[39m_get_module_for_linking(), preserve\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m 758\u001B[0m )\n\u001B[0;32m 760\u001B[0m \u001B[38;5;66;03m# Optimize the module after all dependences are linked in above,\u001B[39;00m\n\u001B[0;32m 761\u001B[0m \u001B[38;5;66;03m# to allow for inlining.\u001B[39;00m\n\u001B[1;32m--> 762\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_optimize_final_module\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 764\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_final_module\u001B[38;5;241m.\u001B[39mverify()\n\u001B[0;32m 765\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_finalize_final_module()\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\numba\\core\\codegen.py:682\u001B[0m, in \u001B[0;36mCPUCodeLibrary._optimize_final_module\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 679\u001B[0m full_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModule passes (full optimization)\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 680\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_recorded_timings\u001B[38;5;241m.\u001B[39mrecord(full_name):\n\u001B[0;32m 681\u001B[0m \u001B[38;5;66;03m# The full optimisation suite is then run on the refop pruned IR\u001B[39;00m\n\u001B[1;32m--> 682\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_codegen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_mpm_full\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_final_module\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\llvmlite\\binding\\passmanagers.py:698\u001B[0m, in \u001B[0;36mModulePassManager.run\u001B[1;34m(self, module, remarks_file, remarks_format, remarks_filter)\u001B[0m\n\u001B[0;32m 683\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 684\u001B[0m \u001B[38;5;124;03mRun optimization passes on the given module.\u001B[39;00m\n\u001B[0;32m 685\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 695\u001B[0m \u001B[38;5;124;03m The filter that should be applied to the remarks output.\u001B[39;00m\n\u001B[0;32m 696\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 697\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m remarks_file \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 698\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mffi\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlib\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mLLVMPY_RunPassManager\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodule\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 699\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 700\u001B[0m r \u001B[38;5;241m=\u001B[39m ffi\u001B[38;5;241m.\u001B[39mlib\u001B[38;5;241m.\u001B[39mLLVMPY_RunPassManagerWithRemarks(\n\u001B[0;32m 701\u001B[0m \u001B[38;5;28mself\u001B[39m, module, _encode_string(remarks_format),\n\u001B[0;32m 702\u001B[0m _encode_string(remarks_filter),\n\u001B[0;32m 703\u001B[0m _encode_string(remarks_file))\n", - "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\llvmlite\\binding\\ffi.py:192\u001B[0m, in \u001B[0;36m_lib_fn_wrapper.__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 190\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 191\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock:\n\u001B[1;32m--> 192\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_cfn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", - "\u001B[1;31mKeyboardInterrupt\u001B[0m: " - ] + "data": { + "text/plain": [ + "[('CanonicalIntervalForestClassifier',\n", + " aeon.classification.interval_based._cif.CanonicalIntervalForestClassifier),\n", + " ('DrCIFClassifier',\n", + " aeon.classification.interval_based._drcif.DrCIFClassifier),\n", + " ('IntervalForestClassifier',\n", + " aeon.classification.interval_based._interval_forest.IntervalForestClassifier),\n", + " ('QUANTClassifier',\n", + " aeon.classification.interval_based._quant.QUANTClassifier),\n", + " ('RSTSF', aeon.classification.interval_based._rstsf.RSTSF),\n", + " ('RandomIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.RandomIntervalClassifier),\n", + " ('RandomIntervalSpectralEnsembleClassifier',\n", + " aeon.classification.interval_based._rise.RandomIntervalSpectralEnsembleClassifier),\n", + " ('SupervisedIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.SupervisedIntervalClassifier),\n", + " ('SupervisedTimeSeriesForest',\n", + " aeon.classification.interval_based._stsf.SupervisedTimeSeriesForest),\n", + " ('TimeSeriesForestClassifier',\n", + " aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -157,8 +113,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(67, 1) (67,) (50, 1) (50,)\n", - "(40, 6) (40,) (40, 6) (40,)\n" + "(67, 1, 24) (67,) (50, 1, 24) (50,)\n", + "(40, 6, 100) (40,) (40, 6, 100) (40,)\n" ] } ], @@ -239,7 +195,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RISE Accuracy: 1.0\n" + "RISE Accuracy: 0.96\n" ] } ], @@ -263,18 +219,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'SupervisedTimeSeriesForest' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[1], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m stsf \u001B[38;5;241m=\u001B[39m \u001B[43mSupervisedTimeSeriesForest\u001B[49m(n_estimators\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m50\u001B[39m, random_state\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m47\u001B[39m)\n\u001B[0;32m 2\u001B[0m stsf\u001B[38;5;241m.\u001B[39mfit(X_train, y_train)\n\u001B[0;32m 4\u001B[0m stsf_preds \u001B[38;5;241m=\u001B[39m stsf\u001B[38;5;241m.\u001B[39mpredict(X_test)\n", - "\u001B[1;31mNameError\u001B[0m: name 'SupervisedTimeSeriesForest' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "STSF Accuracy: 1.0\n", + "RSTSF Accuracy: 1.0\n" ] } ], @@ -307,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:06.471294Z", @@ -321,7 +274,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CIF Accuracy: 0.98\n" + "CIF Accuracy: 1.0\n" ] } ], @@ -345,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -379,14 +332,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DrCIF Accuracy: 0.98\n" + "DrCIF Accuracy: 0.94\n" ] } ], @@ -407,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -428,27 +381,35 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## 7. QUANT\n", "\n", "QUANT is a fast interval based classifier based on quantile features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QUANT accuracy = 0.88\n" + ] + } + ], "source": [ "quant = QUANTClassifier(interval_depth=1)\n", "quant.fit(X_train, y_train)\n", "print(\"QUANT accuracy =\", quant.score(X_test, y_test))" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", @@ -463,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -495,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -506,7 +467,7 @@ "(112, 7)" ] }, - "execution_count": 2, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -527,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -538,13 +499,13 @@ "(

, )" ] }, - "execution_count": 3, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -561,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -572,13 +533,13 @@ "(
, )" ] }, - "execution_count": 4, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJNCAYAAAAs3xZxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAADn9UlEQVR4nOzdd3wUdf4G8GdmW3ojpJLQpHeCIByCKOUQFfRUsKGcop5YAAtg49SfoueJKOJhQQVERbBgBRUVCyC9994SSC+bZMvM5/dHzJpIIi3bJs/7XnmdmZ0NH5jM7DzzbYqICIiIiIiIiIiozqn+LoCIiIiIiIjIqBi6iYiIiIiIiLyEoZuIiIiIiIjISxi6iYiIiIiIiLyEoZuIiIiIiIjISxi6iYiIiIiIiLyEoZuIiIiIiIjIS8z+LoC8R9d1HDt2DJGRkVAUxd/lEBERERERGYaIoLi4GCkpKVDV2tuzGboN7NixY0hLS/N3GURERERERIZ1+PBhNGrUqNbXGboNLDIyEkDFL0FUVJSfqyEiIiIiIjKOoqIipKWleXJXbRi6DayyS3lUVBRDNxERERERkRecaigvJ1IjIiIiIiIi8hKGbiIiIiIiIiIvYegmIiIiIiIi8hKGbiIiIiIiIiIvYegmIiIiIiIi8hKGbiIiIiIiIiIvYegmIiIiIiIi8hKGbiIiIiIiIiIvYej2oRkzZqBJkyYICQlBjx49sGrVqlr3veiii6AoyklfQ4YM8WHFREREREREdC4Yun1k/vz5GD9+PCZPnox169ahU6dOGDRoEE6cOFHj/h9//DEyMzM9X1u2bIHJZMI111zj48qJiIiIiIjobDF0+8jUqVMxevRojBo1Cm3btsXMmTMRFhaGt956q8b94+LikJSU5Pn69ttvERYWxtBNREREREQURBi6fcDpdGLt2rXo37+/Z5uqqujfvz9WrFhxWj9j1qxZGDFiBMLDw2vdx+FwoKioqNoXERERERER+Q9Dtw/k5ORA0zQkJiZW256YmIisrKxTvn/VqlXYsmULbrvttr/cb8qUKYiOjvZ8paWlnVPdREREREREdG4YuoPArFmz0KFDB3Tv3v0v95s0aRIKCws9X4cPH/ZRhURERERERFQTs78LqA/i4+NhMplw/PjxatuPHz+OpKSkv3yv3W7HBx98gCeffPKUf47NZoPNZjunWomIiIiIiKjusKXbB6xWKzIyMrB06VLPNl3XsXTpUvTs2fMv37tgwQI4HA7ceOON3i6TiIiIiIiI6hhbun1k/PjxuPnmm9GtWzd0794d06ZNg91ux6hRowAAI0eORGpqKqZMmVLtfbNmzcKwYcPQoEEDf5RNRERERERE54Ch20eGDx+O7OxsPP7448jKykLnzp2xePFiz+Rqhw4dgqpW73iwc+dO/PLLL/jmm2/8UTIRERERERGdI0VExN9FkHcUFRUhOjoahYWFiIqK8nc5REREREREhnG6eYtjuomIiIiIiIi8hKGbiIiIiIiIyEsYuomIiIiIiIi8hKGbiIiIiIiIyEsYuomIiIiIiIi8hKGbiIiIiIiIyEsYuomIKODpuu7vEoiIiIjOCkM3EREFNBHB519+7u8yiIiIiM4KQzcREQU0Xdfhcrv8XQYRERHRWWHoJiKigKZpGhQTP66IiIgoOPEuhoiIAprb7QZUxd9lEBEREZ0Vhm4iIgpoFS3dDN1EREQUnBi6iYgooFW0dPu7CiIiIqKzw9sYIiIKaC6XCzD5uwoiIiKis8PQTUREAc3ldkFR+XFFREREwYl3MUREFNCcbhcnUiMiIqKgxdBNREQBTdc1KAzdREREFKQYuomIKKDpIv4ugYiIiOisMXQTEVHgU9jSTURERMGJoZuIiAKaqqgQXfd3GURERERnhaGbiIgCmtlsBtjFnIiIiIIUQzcREQU0q9kCcTN0ExERUXBi6CYiooBmtVghOkM3ERERBSeGbiIiCmhmsxlg6CYiIqIgxdBNREQBzWw2A5q/qyAiIiI6OwzdREQU0MxmM0RjSzcREREFJ4ZuIiIKaCaTibOXExERUdBi6CYiooCmqipbuomIiChoMXQTEVFAU1UVChR/l0FERER0Vhi6iYiIiIiIiLyEoZuIiAKeqrClm4iIiIITQzcRERERERGRlzB0ExFR4OM8akRERBSkGLqJiCjgKexeTkREREGKoZuIiIiIiIjISxi6iYiIiIiIiLyEoZuIiIiIiIjISxi6iYiIiIiIiLyEoZuIiIiIiIjISxi6iYiIiIiIiLyEoZuIiIiIiIjISxi6iYiIiIiIiLyEoZuIiIiIiIjISxi6iYiIiIiIiLyEoZuIiIiIiIjOybfffOvvEgIWQzcRERERERGdk5KSEn+XELAYuomIiIiIiOiciIi/SwhYDN1ERERERER0ThR/FxDAGLqJiIiIiIiIvIShm4iIiIiIiM4Ne5fXiqGbiIiIiIiIyEsYuomIiIiIiOiccCK12jF0ExERERER0TnhRGq1Y+gmIiIiIiKic8KG7toxdBMRERERERF5CUM3ERERERERkZcwdBMREREREdG5Yf/yWjF0ExEREREREXkJQzcRERERERGdEzZ0146hm4iIiIiIiM4JlwyrHUM3ERERERERnRu2dNeKoZuIiIiIiIjOEVN3bRi6iYiIiIiI6JwwcteOoduHZsyYgSZNmiAkJAQ9evTAqlWr/nL/goICjBkzBsnJybDZbGjZsiW++uorH1VLRERERERE58rs7wLqi/nz52P8+PGYOXMmevTogWnTpmHQoEHYuXMnEhISTtrf6XRiwIABSEhIwMKFC5GamoqDBw8iJibG98UTERERERH9BU6kVjuGbh+ZOnUqRo8ejVGjRgEAZs6ciS+//BJvvfUWJk6ceNL+b731FvLy8rB8+XJYLBYAQJMmTf7yz3A4HHA4HJ7vi4qK6u4vQERERERERGeM3ct9wOl0Yu3atejfv79nm6qq6N+/P1asWFHjez777DP07NkTY8aMQWJiItq3b49nnnkGmqbV+udMmTIF0dHRnq+0tLQ6/7sQERERERHR6WPo9oGcnBxomobExMRq2xMTE5GVlVXje/bt24eFCxdC0zR89dVXeOyxx/DCCy/g//7v/2r9cyZNmoTCwkLP1+HDh+v070FERERERFQzdjCvDbuXByhd15GQkIDXX38dJpMJGRkZOHr0KJ5//nlMnjy5xvfYbDbYbDYfV0pERERERES1Yej2gfj4eJhMJhw/frza9uPHjyMpKanG9yQnJ8NiscBkMnm2tWnTBllZWXA6nbBarV6tmYiIiIiI6LSxobtW7F7uA1arFRkZGVi6dKlnm67rWLp0KXr27Fnje/72t79hz5490HXds23Xrl1ITk5m4CYiIiIiIgoSDN0+Mn78eLzxxhuYPXs2tm/fjn/961+w2+2e2cxHjhyJSZMmefb/17/+hby8PNx3333YtWsXvvzySzzzzDMYM2aMv/4KREREREREdIbYvdxHhg8fjuzsbDz++OPIyspC586dsXjxYs/kaocOHYKq/vEMJC0tDUuWLMG4cePQsWNHpKam4r777sOECRP89VcgIiIiIiKqmcL+5bVh6Pahu+++G3fffXeNr/34448nbevZsydWrlzp5aqIiIiIiIjOETN3rdi9nIiIiIiIiMhLGLqJiIiIiIjo3LClu1YM3URERERERHROmLlrx9BNRERERERE54YTqdWKoZuIiIiIiIjOiYj4u4SAxdBNRERERERE50RhS3etGLqJiIiIiIjo3DBz14qhm4iIiIiIiM4RU3dtGLqJiIiIiIjo3DBz14qhm4iIiIiIiMhLGLqJiIiIiIiIvIShm4iIiIiIiMhLGLqJiCjgCbj2JxERUUDjmO5aMXQTEVHAE2HoJiIiouDE0E1EREQBobi4GMeOHPZ3GUREdBb4gLx2DN1ERBTQRAS66P4ug3wgPycHR/bt93cZREREdYqhm4iIApqu6xwnVk+UFhcjxGz2dxlERHQ22NBdK4ZuIiIKaJqmQTHx46o+0MrLoOrs1UBEFJT4gLxWvIshIqKA5na7AZWf5PWBuFwQp9PfZRAREdUphm4iIgpoLpcLipkfV/WBuN2A5vJ3GURERHWKdzFERBTQnE4nwGG+9YOmAzoHBRIRBSNFYa+02jB0ExFRQCtzlLOlu75QAQ4KJCIio+FdDBERBbTS8lKoFn5c1QeKyQyYTf4ug4iIzgY7KtWKHfaIiCiglTrKALZ01w9mC8DZy4mIyGAYuomIKKC5NBd0MIjVB4rZzNBNRESGw6YDIiIKaG5dg1vX/F0G+YBiNgEqb02IiIISu5fXip9sREQU0LTf/0fGp3ASNSKiIMbUXRuGbiIiCmhu3Q237vZ3GeQDutvN7uVERGQ4DN1ERBTQ3LoGTRjE6gNxuQC3y99lEBHR2WBDd604kRoREQU0HQJN2L28PhCXE3A4/F0GERGdBRGm7tqwpZuIiAKaJhp0tnTXDw4HxOn0dxVERER1iqGbiIgCmohAZ5+1ekFcroou5kREFHx0flbXhqGbiIgCmi46W7rrCdHcEDcnzSMiImNh6CYiooAmv68ipXNWa+PTdM5eTkQUpIQt3bVi6CYiooAmIoDCCVqIiIgCGj+ma8XQTUREAU0gEJUt3fWG4u8CiIjorLClu1YM3UREFNAqW7oZuusBVQFTd/2QfSIHDi4PR2Qsws/q2jB0ExFRQBPRIQrg5gRbxqeovwdvMrqtW3agoKDA32UQUR2yqhY4uexjjRi6iYgooGmiQ0wCF5eSMj5FgaLy1qQ+MJnMcDh4ThMZiVUxo7y83N9lBCR+shERUUBz6xrEoqDMwQ9yo2Mbd/1hsVjhLGeLGJGRhKhWlJWW+buMgMTQTYazfuMmf5dARHXIDQ0mqxlF9iJ/l0JeJiKcpb6eMJusKCvjmG4iIwkxWWEvKvF3GQGJoZsMZ8fOXf4ugYjqkFNzQbWZUGQv9ncp5HXC2W/rCREFmsYJl4iMQtd1WBULHKXslVYThm4yHLfm7wrIF3Rdxzeff+zvMsjLXC4XnIoLiqLAobFVzPA0HeDMt/WC7q74IiJjKCwsRIQaCt3JG/GaMHST4bg0tpLUB9nHs2AqzfN3GeRlefl50EMr/rucodvwRHMDGpNYfeB2Cdxufl7XBzk52Rw2Ug/knshBmDkUwtBdI4ZuMhRN0+BiI0m9cHz/DiSaylBczHG+RpaVmwVTpBUAUOpmlzXDc7lg0jTOVF8PuJ0Cl4NBrD749acfOaN1PXD86HE4NRfEyRvxmjB0k6HY7XaIKYRPVOsBV94xpIQL9m/d4O9SyIsKy0ugmio+quwaZ0Q1MofDAbPbjSgFyM9jLxYj0zQNbifgcvKzuj4IsagoKiz0dxnkbc6KXkpSxt5KNWHoJkMpKi6BOTQaDge7oRpZbvYJRDhzoSoK3HlH/F0OeVGR649ZUIvddmgau60ZVc7x44hTFcRYrcjLzPR3OeRFWZnHER7SELrLDKeTy4YZXZjVhJwTPKeNTv89bKtO8D68BgzdZCglJaUwh0ahtLTU36WQFx3evh6JYRWXr/DybOScOO7nishb8h1/tI44owQHjxz0YzXkTQVZWYi0WmFSVbhLuOSMkR3PzENEWAxiIlJwYN9hf5dDXqY57Cgv4VAwI9N1HVpZxUPx5NB4HN7Hz+o/Y+gmQyktd8ASEonSMo4dMiqXywU954+LeWKYisNb1/ixIvKWY1mZKA7541y2hFux5wQ/yI1Kt5dAUZSK/2boNrQye8WYT6slBPl5fEhuZLk52bCKA+X2An+XQl6UdSwLsUo4AMBmtqI0l9fwP2PoJkPRdB0mixX2Uo79NKod61YiPaT6QxVT/kGUFHMNZ6PZfHArTDHWatuOlWaxi7lB6SV/nMMa12Q3LJfLBYfd5Pm+rJiTLhnZwX27EBNmgaukAG43x/oaVc7B44gKifR8rxWye/mfMXSTobh1wGSxobSULd1GpOs6HJm7YVaVatvTQt3YvX6Fn6oibyguKcZB18ljAMsSBKu2sGeD0ei6Dqny4MxWWsphQga1Y+texEWme763qfE4fPiYHysibyoryIGiKIgNBXbv2ObvcshLtMLq992hDjMKCzh5XlUM3WQoTrcORVHg1Pjk3Ih2b9mARqaTx4UpigLJPcCn6AYhIliy9nvoCeaTXlPNKraW7EVuXq4fKiNvyTp6FA3kj+t2stWCI3v3+LEi8pa8bAfMJovn+8iwBji8L9uPFZG3lJaWwm2vWInAajah4AQnPjWi3OwchDts1bYlhTfEwe17/VRRYGLoJkNxuCqWHynnYt2GIyIoPrQNIRZTja+nWcuwfd1KH1dF3rBs7S/IjvtjfO+faQkqFm/6nuu+GkjekSOIsf4xlMBiMsGRn+/Hisgb9u87BJuScNJ2V2koCtgqZjg7t25EfPgfD08dRdlwuVx+rIi84ciOg0gKb1Btm6oocOWUcQnfKhi6yVDs5RUtnXYHx3waza7Na5EstbduWkwq3Ee3oayM4/mDlYhg6eofsd18EGpIzQ9XKhWnuLFwxWewl9p9VB15U9VJ1DzbijkRj9Ec2puLiNBYaLobmv5Hz6QGUenYunG/HysjbyjOPgq1ynCwBqEKtm1e78eKqK5pmgYtt+YH4ImIwSHOYu7B0E2GUVxcDLte0WWtsFRnV2MDKSosQPnetQi3/PUlq0lIOTb/9LWPqqK6VF5ejs9+/Qo7bUeghlfvVi6aDvnTkBFFUVCSqmHhmi9w8NghX5ZKXqAXnzxsREqK2UpiIDu27UGY2ggAUFJagJLSgmqvS3k0sjK5/KNRHDq4HyFa9QkRzSYVBZkHeV4byM6N25BuS6zxtShbBHL3Zvm4osDF0E2GsXrzLtiikwEAalQK1m7a7ueKqC6ICLb/shiNQ52n3FdRFCQ7DmPHRk60FUx2HdiD93/7BJkJRTCFnjyO21XshKv45OOvKArKkwVfH/kJ369ZBl3nsJJglJ2ViYgaJk1L0Nw4uHu3HyqiuuZwOHD0QAlCq8xu/GcxEcnYvukIA5lBHNyxEZGhlpO2h6MEe3ZyQjUjEBGUHS2C1XTyca4UVWrDiSw+TAMYuskgdu49gOPlYZ7uiSazBftzBUeO8QlbsNu6+lc0lpMv2G5d4NZPvjmLtKpw7luDggKOBw10uXl5WPTrl/g+dyWcKUqtY7hPRYk1Y2fYEcz7eSG27uHDtmDicrmwf/lyJNmsJ70WZbUgd+MGlHIIQdBbs3IrGka2OOV+UdZm2LiO53Cw27FlI0JdBTW+Fm6z4MiujZyTwwB2b9mJVCX+L/dJCIvDkS0cOgIwdFOQ03UdK9ZuxLoDpbBEVD/xzdHJ+GXbCWzYvINPzoNUQX4e5OhmhJhPvlQVlrlRWFbzEIL0UBd2r/zO2+XRWSorK8M3q77Hwh1fIiuhGErUya3bZ0q1mFCaomNZyVos+PlTHDp2uA4qJW86dvAgNiz6BK202idWagkdOz7/HAd37/JhZVSX9u07BNWZAFX963kaAMBmDUXhCRNyc/J8UBl5w9HDh5C7fxPCQ2q/rieEali+9CtoGuffCVa6rqPoQA7CrCGn3DfKztZuAFCEacSwioqKEB0djcLCQkRFRfm7nDqlaRo2bd2FvVlFcIenwmyx1bqvq9yOEOdxtGwUhzYtm0NV+awpGJSW2rH5u4/QylrzjLa59oob9QbhNXdrKnHqyInviE49LzrrFlSqW2VlZfh180rsdxyFlmg67ePiLKhoEbHGnPrDvZJe6EKiIwbdm3VBo+RGZ1Uv1T232429W7bAfvgQ4oqLEG+tfv66fx8iYP7TdbrA6cKJ8AjYUlJwXsdOsNlqv+ZT4CgrK8OKH3YhIbpVte2FJTkAgOiImlvJjhdtRr9BGfy8DjLbN29A3oFNiAs99XHTNB0nXKHo2rs/YmJifVAd1aUdG7ch9ogJoZY/Ppdz7QUAgAbhMSftvwfH0HVgTx9V51unm7cYug3MaKFb0zTs2XcQR7ILkV3khhKVApP55C6JtXE7y6DYs5AQZUXj5Fg0SU/jB3oAKi4uwr7Na6Af34MmIeW1BrNThW4AKHXpOKo2RHTjtmjepgNMplO3tFDdczgc+HXzSuwrOwJ3ggpFPbOHIGcTuivpBS4kOWNxQYsMJCckn/H76dyICPJyc5G5eze0gjygIB9pqgprLedi7u+rDzQIDa3xdbeu45DLDYmOhRobi4TmzZGQmMgHawHqp6VrEGtrC0Wp/ll7qtDtcjvhth7A+T07er1GOndHjxzC3q3rEebOR7jtzHou5dh12OLT0THjAoSEnPk1nnxP13Ws/2I5WoSkVdv+V6E7uywf4d0SkJSa4oMKfYuhm4I+dIsIso6fwP4jWSgs1VBQqgHhibDYar4ZAwD9965K6inClbOsGGpZDmLDzYgJt6B5egri4xv85XvIOwoKCpB5YA+cRTnQinNhK89FcljFGo9/5XRCd6Vyt45Mpw1KRDxMkQ0QHp+CRo2bwmo9/Yc2dOY0TcPyTb9hV/F+uBLPPGxXOpfQXUnPc6GR3hB/a9UdcXFxZ/1z6K+VlJTg2IEDcOTnQS8uhl5chGinC/Eh1lOe08CpQ3dVIoI8hxP5ZhPUyGgokZGwRscguWnToPzMM5rtW/egOCsKYSEnH4tThW4AKCg5hmbtw5CSmuS1Gunsud1u7Ni6EflZh2F1FiCqhkkwAUD7vfeK6S8aOXQR5NgFIbHJSGvRBikp7J0UyLat24yGWSGw/anh669CNwDslmPIGGS81m6Gbgq60O10OrHvwGHkFJSgqMyNojINbmsMbOExp92KUVZcMQ4sNPL0b6pFBI7iHFi0EkSFmhEZYkJSfDSapDeC2XzuY02p4tjmZp9A/olM6A47tLJiSHkJtLJihOmlSAhTYTrDQHYmofvPylwaTjhM0KwRUEMioYZEQLGFwxYRjfjkRoiJiWEviHN04MhB/LR3JewJGtQaxuSfCWdBOY4u2Y2mwzuce2HZbrQJaYoLu/Ri6+g50DQN2cePI/foUeglJdBL7RC7HTZHORKtllpbsk/lTEJ3Tdy6juMOJ0qtVqjhEVDDw6GEhSE2JRWJycm8pvtISXEJVv98EA2jm9f4+umEbgA4UbQVfQd2Zi+lAFFeXo69O7ehKCcT5UW5aBAmsJzi2BTaKx6aRoef3kPTwlIXHGoYwmIaIim9GRqlNea1OoBUtHKvQIuQkx+MnCp055TlIywjEUmNjNXrjKE7AM2YMQPPP/88srKy0KlTJ0yfPh3du3evcd933nkHo0aNqrbNZrOd0WyPgRy6dV1HZtYJHMnKRlGpG8UODaVOBebIBJhPY1KG2pxN6K6Jq9wO3Z6DcCsQ+XsQb9IoCQ0bxvPi/ydOpxOFhQUozDmBcnsRxFkOcZVBd5ZDHKUQZxlMWjmizW5EhZhPq7XrdOTaXVi4MQt39Eo79c6nyanpKCjXUaJbAWsIVGsYFGsoFGsIFEsoVGsoImIbIDq2AaKiohjMayAi+GHtT9glh6HE1E3AcRaU49Dn23HeTV3q5OdpTjca5IRhYOeLERsdUyc/06hEBIWFhTh+8CCcRYUQeymk1A4ptSNOAWKsp9eCfbrONXTXRERQ5HQiRwAJDYUSHg41LBzmiEgkNmmM2Ng4Xtfr2M/fr0OMtU2t/66nG7rdmgtu2wF061EHD9zojOm6jsMHDyA78xDKi/OhlRYgLswEs+n0P/vONHRXVVLuQolmQWhkA4RGN0DTFq0QFRV9xj+H6s7OjdsQe8SMkBrmUjpV6AaAPUoWug7o4aXq/ON08xYf+frI/PnzMX78eMycORM9evTAtGnTMGjQIOzcuRMJCQk1vicqKgo7d+70fB/MNwUulwt79h9Edr4dhaVuFDt0iC0W1rBEKFYFsAKBNJLHEhIOhITDDSAfQJ5LsGtzLsyuA4gMNSE6zIykBlFo2jjNsE/g3W43CgsLUZBzAuUlhRBXOXRXOeCs+P/K/zbpTkSobkTaTIj/c4um+fcvKADOvEX6VMpddbsus9WkIiFcRQJ0AKUVXy5UfKHi5r30iI4sl2CfbgEsIVDMNiiWECjWEKjWECiWEKjWUETFxiOmQTzCwsLqVThfvXUtdtoOQ/2LmWv9zWQ1oyDFiW83/oBrLhwW1NfWulZSUoIje/bAlZ8PsRdDLylBuMuFRJu1+uRmId6byOyDHbsxpkvdjeVVFAXRNhuiAUB0oKQYKCmGdjwTOds245DJDCU8AmpEBEwxMWh0XouAe1AdTA4eOAKLVjfj7M0mCwryrCgoKERMDMOWt4kIsk+cwJEDe1BenAdHST4izRrCQywINwGIrPvP8b8SEWJBBADoudDzcrDp+y3QzeEIiYpDRGxDNGvRmuPAfcx+pADJltSzfn9YiQn5efmIjat/k+cF7l2RwUydOhWjR4/2tF7PnDkTX375Jd566y1MnDixxvcoioKkpOAdy1RWVobNO/Yiu9CBgjKBKTIRZms0EA5Yw/1d3ZlRFAUhkQ0ANEAZgDIBDh+xY9XOdYgNU5EUG4b2bc6DxeLbD6RzVVJSgqzDB1BWlAe9vATiLKtonXaVQ3WXI1x1I9KmokFN3YMtv395KVCfSrlLR7lbR5lLQ6jFNw8+FEVBuNWEcCsACFDx21Dxn47fv1AxPs1+UMMhl4JyVIRzT6u5LQxKSARiE1KQnGqshza6rmPTiR1Q0+r2o0VzuKE5NWjlbpjqMMznxpZi447N6Nymfk/WVFxUiP0bNkLLyUaIvQTJVQO22VTx5UMOrealAOuaSVGQGBKCRABwlgN55dBys5G5bQv2hoRBjW+ItPbtERf/162xVN2hvTmIDWtdZz+vQWQ6tm/eiZ4Xdq6zn0l/cLvd2L2jost4WXEuQqQcUaEWhCoKEKEiUFYXVhUFDSKsAFyA8zjcxzLx2571MIVFIzQqHo2atkBikrG6LQea48eyEOMKP6dbvuSweBzYth+xvRm6yQucTifWrl2LSZMmebapqor+/ftjxYoVtb6vpKQEjRs3hq7r6Nq1K5555hm0a9eu1v0dDgccDofn+6Kiorr5C5yFbTv3YuP+fJhiGkENM8EW5ps/1+10YPtPC9B1yGiYrXXXPbEmla3hdgC7SlzYuXQdurdJQbPGddfd2ZuWLJiNFKUQCSFSvYXa0zoNBPIlYme2HVHRMdh0rAQ9GgdWC4iqKIi0mRFpA1BDq7kUC4qPaFi9woqQxh3Q+YI+fq23rqiqikhbOArgOPXOZ6DkQD5ioqJRuCsHcR3r7kGklGpodJ7xZlI9EyKC9V98gS6W35dwC/Vvq1G5241yt4Yytxuhfhh/bVIUpIaEANCBnOPYtPgwul07nJMunqasrOMwuet2UlJFUVBWYIbD4eBScXUo58Rx7Nm2AeWFJxBn0xFlNiEqFADq9nfd6dJw4HgBOjZNhM1ad+e02aQiIVIFUArYD2H/qj3YZY5CZMMUdOjaw1APtANF5p4jaBbW8Jx+hqIo0PLKICL1rpdZYDy+MricnBxomobExMRq2xMTE5GVlVXje1q1aoW33noLixYtwrvvvgtd19GrVy8cOXKk1j9nypQpiI6O9nylpfkv/B3MzIE5Jg2q6tuLXt7RXYiMCMOJ/Vt8+ueazBao0ek4dCzbp3/uuejR/wqoTbvjeFQrHDSnYr8Wi31lNuwrBrJK3ChxuKEH8JQPrRqGo6ykEB1TIvxdSq1cmo6CMheOlujYW2LCPkcYDkhDHLI1QUHDDojv2Bdtu17g7zLrVLukVlBy67alMqJJLIrsxYhuWXctjlq5G41c8YhvUL9bMRVFQdPu3bE3JAwHyx2emYb9ZUdePmJiYrDxhP+upboIjpQ7sMdiQ3KXrgzcZ+DIgWxERZzeTfn3K9477Z8bF9UYe3buP9uy6E+2bVqLnSsXI9qdjcRwBRYv9mY5cLwAxwpd2H00z2t/BgBEh1kRby2HOW8Pfvj8A5QUF3v1z6uPpMh1yn0+XP/lKfeJ1sORk5NTFyUFlcBtxqrnevbsiZ49/5hWv1evXmjTpg1ee+01PPXUUzW+Z9KkSRg/frzn+6KiIr8F74t7dsbazTtwJNcBly0etrBIn/y5caktkblrLVo0be+TPw8AHCX5CNEK0DQ+DF07dvLZn3uuYmJjEZNxcuDTNA3FxcUoKshDVmEedEc5xO2EuB0Qt+v3/3dC3C7A7YRoToQoGkIUDWFWFTazWqcTK9UmxKLi5vNTfda1XNMFpS4NZS5BmZjhghmq2QrFYoVi+v3/zdaKMd5mC2C2whoagai4eCRExyA0NLRePNVt37wtokIj8f3uX1GeXDdPsk02MxoPa1tnXcsl342O1ub429+M9cDjbKWd1wJp57VAaWkpDuzYDq2wEHpJMaS4GNG6hjibDSYf/e62josF8vLRKeHcWlNOly6CfIcDBYoKREZCjYiEGhmJ9NatERnJcd1nqtwuON0htk7X6U8MazZZUFx46ht+Oj05mUfQMMw3n51NEmOw7cButOjsm2XATCYV0WYXMo8dQYtWbXzyZ9YHJSUlsDlMp+wI4XCfuqdbfGgMDu0/hoYNfXOdDxQM3T4QHx8Pk8mE48ePV9t+/Pjx0x6zbbFY0KVLF+zZs6fWfWw2W8B0vbLZbOjVrRN0XcfBw0dwKPMo8kvdKHGqsEQlwWT2zhhgs9UGW3i0V7uWu53lcJecQLQNiAk3o1nrRKQktzBMoDKZTIiJiUFMTAyAZqfcX9d1lJeXo6SkGPaiAuSXFEN3VQZzJ6A5obucgNvh+X9oToQqbkRYgHCrySch/c9cmo4Shwa7ZoID5orAbLFBMdsAsxWqxQrFZAMsFigmK8y2EERExSI+MgoRERFs/foL6SlpGB47FL9sXoH9zmPQE0wBcX7ohS4kOGJwftPOSE8JjmEgvhQWFoa2XTM83+u6jrzcXBw7fBjukmLodjuktBRKeRliRRBjq9uZywEgxGxG54SGdd61XERQ6HAiD4AeGgolNAxqeDhM4RGIb9QIaQkJ9WrCQ2/x5nB8XfPez65vuva6GJtW/QKtOAsNws1evT5bLSZYLaY67VpeG5dbQ67TjIbp7Rm469iR/YeRFFY3Q0cURYFeWv8eojF0+4DVakVGRgaWLl2KYcOGAai4mVm6dCnuvvvu0/oZmqZh8+bNuPTSS71Yad1TVRVNG6ejaeN0ABVrPO7Zfwg5hWUoLHWjxAGYznGZsD9r3fvKOvtZAOAst0NKsxFlUxEdZkZCQjia9+gYdJOmeYuqqggLC0NYWBiQkHjqN6Di97mkpKSiNT0vF5qzDHA5oDsrlhjTnaWAsxTRqhMxIaYzXsMbABxuHTnlAocaAtUWDljDoFpDK2Yat4TAGlbRCp1Yj1qhfSk0NBQDul8Me6kdv275DQecx6A3NEE5i2N5rrQCJxKc0cho3BNNGzX2+Z8frFRVRXzDhoj/U2uEpmnIyc7GkaNHoNntEHsp9FI71LJSxCtApNXq1/OpxOlCtq5BCwn9fZ3uiiXC4lKS0TYxiWt1e5Gmn96QJKerHE5XORzOUtispzfpC0N33YmIjESvSwajID8Pe7ZvRmn+cZhcJYgNt3jl3LV6sfu6y60hrxywRMQhNjUNfdp24HhuL3CXu2A21eEETe7AHb7oLfzk8ZHx48fj5ptvRrdu3dC9e3dMmzYNdrvdM5v5yJEjkZqaiilTpgAAnnzySVxwwQU477zzUFBQgOeffx4HDx7Ebbfd5s+/xjkLCQlB+zYtPd+73W7s3XcQx/PzUFjqRlG5wBSVBHMN6//5iqu8FFJ6AlEhKmLCzEhuFIMmjbvyIl6HTCaTZ+4BNG5a4z66riMnOxvHjx2C254PrSQPakkOUsN0mGsIbsUODSf0CJij4qGGxSA0piHSG6UjMtI3QxuoZuFh4RjY/WKUlZXht22rcaDkKMoa6lC9PCxARIBsN1JNDdE5vT3SUnzTtbE+MJlMSExKQuKfemq53W4cP3YUhzIzoZeUQC8uBuwlSFYVhJ3mQ8roM+ytVe5245hbg4RHQI2MBMIjEJ2YiNZpaXww6gcm0+kFtkPHtiEqKhJ7Dm1Au/N6nd7PNvPBaF2LiY1Dt159AQD5ebnYv3sHyopy4bLnI8YmsFnqJib0zzh1r7nTJSIoKHXCpYYhJDIOUUnJ+FurNjzfvUzcp57vo9zlQLnLgTJXOUItf92YJnW85GswYOj2keHDhyM7OxuPP/44srKy0LlzZyxevNgzudqhQ4eqdW3Lz8/H6NGjkZWVhdjYWGRkZGD58uVo27atv/4KXmE2m9GqZXO0+v17t9uNXXv243h+DvJKXCg3RcEWEefVGkQEjuIchMOO2AgLUlKj0bwJQ7a/qaqKhMREJFSZgLC8vBy7N66Gfmw7In5fPNvh1nFASUBCuy7o2rwVW6wDVGhoKC7K6ANN07Bu+wbsytyHwmgHTGF13I1YF5hOaGhsS0GPDhmIjgqsme2NzGw2IzW9MVLT/+hN4Ha7cWT/PhzPyoJWkA9LYSFSrZbqa35X/Rmn6OKti+CowwFHRBTU2FiEJySidfPmHO4RIMzW02u9Sk9pi217VuK89M6n/bNNPMReFRvXALE9/gagojfLvj27kH/iGBzFeYCjGHERFr8MBQOAcqcbBQ4Ftog4hETFoWWXloiPr1/jgf3tdO6tdp7Yi8joKGw6ugM9mnQ+559nNIpIAE9PTOekqKgI0dHRKCwsRFRUcE4Ic+xYFnYdzMTxIhcQmQqT+a8/dcuKK2bHDI08dVB3O8tgKs1CUrQVrZunoWF83S5zQt6Tn5eDAz8uQFqIC3uVZHT/+9X18gIezEQEW/dsx7bju5ATVgxTZO2tFKJVPBFXTLUHMt2tw5YNNAtPwwXtzkfI6c7mRD5VVlaG/Vu2wHHsCBrY7Yiznl7rVLHLjUxbCKzJKWjSoQMiIgJ31YL6bP3arTCVNYWq/PXDk8KSHCz+6S0Mv/Sh0/q5pWVFSGhejiZN0+uiTDpDxcVF2LtzO0oLTsBZkoe4EJz2jOeF9ooJ86LDz+yaXFjqgkMNRWhUPGKTGqFZ8xZsDPGjrWs2IS3vrx9iHy04jrmrP8Z9F406ZUv3AfUEOvY/vy5L9JvTzVts6aaAlpKShJSUJGiahg1bdmJPVjHU6CZQamkNsYWdulVLc7tgKjmMdo1i0K5XN06eE4Ri4+JxsGFz5GTvQIs+/Ri4g5CiKGjfoi3at2iLPQf3YtXBDSiILatxhvK/CtsiAvNxHe0imuGCXudzvG6ACw0NRdvzzwdwPg7v3YsdG9bjPM1Vawu3LoI9oiCmUxd0NVhPLyNq2boJVi07hIbRTer055Y4j6Fb4451+jPp9EVGRqFztx4Afu+RuH0L8k8cgas4B/HhdTcZapnDjSLdhvDYRDRp2wpJKal18nPp3FnDbHCecMH6FxMhh1hsCLHYThm4AQCW+nfvzbsTCgomkwkZndqiQxsnfly5AbkSD0voyWN11VM8BXXZ85Eaakfv/uczbAe5hKZtsOPYIbSOr9/rLBvBeY2bo3l6M6zdvh6bM3fAkaSc3oOUfDca64nom/G3ion8KKikNW+OlCZNsGbx12hZUgTLn67fmgi2m23oPHgwey4EifDwcNjCT71k0JnQRUdUAxM/swOE2WxG2w6dAXRGaWkptm9ah6ITBxFv02Cu4QFpROipxwUUlbngssYh8byW6NKyNR+kB6DUJmk4sH0zGkWe3qpLf0VEoITWvwjKKxgFFavVioF9uqNxeDFcZcVn9F6XPQ9tE4A+F3Thh7cBJKWkIt9d/y7aRqUoCrq17Yrrul+F+MwwaI7a1x4SEVgydQxMuRCDLxjAwB3ETCYTzh98KfbUsILFHqjoOmQIA3eQad4qGQXFmXX283KK9qJdx/Pq7OdR3QkLC0PGBb3RZ8gIOKObIcd+8uRYJlWFqZZ7Lpdbw3GHDSmd+qH3oKFo0aoNA3eACgsLQ7mlbpYQKHQUIy65/g3pZPKgoNSrWyfEq7nQT3NRULezDOkR5ejUruWpd6agoKoqlFOM8afgExISgn/0uQJtytOhl5x8fosIoo5acE3Xy7n8l0Goqor087vjhPOPdVuLXS7Etu/ACdKCUGJSAhBSUCc/S9c1RDWoGJZAgctkMiHjgt5od+EQZJZZcDrTRZU43CiPSEefwf9AWnoT7xdJ58wUVTfX4xy9CCmN6t/QAYZuCloX9+oKU9GBU+4nIggtP4pe3TgezEhEBKK5Tr0jBR1FUdCvWx+c50yB5qz+ZD0sU8VVPS9DZASXgjOSxEaNUFhleb9Mawiatm7tx4roXLTv3BS5xYfO+efklOxBp678PQgWcXEN0GvAUBwv/+twVupww5LQEt1792PPwyAS0iAcTve533epUdZ62aOBv+kUtEwmE3p1bg5XUdZf7qcVHka/7u3r5QluZHm5uYhQT6+nAwWn/t37oUFela7jOW78vV0/djc2KEvDROi/t5BZGjbkNTuIxcTEICSqDLp+9t1Ryx12JDYK5frLQSYkJARdeg9Etr3m1m4RQak1Hp3PP7312SlwNGt9Ho6Unzinn1HuciAsoX4+NGfopqCWnJiAJjECt7O8xtedpUVolxaFqKj6eYIbWdbBPWhgdkDXTx5DRsagKAq6Ne4EvbjiyXqakoCE+AQ/V0XektamNTIdDhQ5XIhJ49JQwa5zRmvkFO+p9XXrKWY4LnEdRNv2Leq6LPKBmNhYRCQ1g1s7+fM5u1SQ8bd+fqiKzpXFYgGiz20unaPObDRvUz/Pa4ZuCnoXZHRASNnRk7aLCGL0bHSopye30ellRYhWHMjPz/d3KeRFzdObIao0FO4iJzqmc8koI4uOjkGZLRQnFCC1McfrBzubzYaGqTY4XTU/FL+45/W1vrfIno3mrRPZ2yGIdczogdzy6jFDRBAal4qwsHA/VUXnKjI1FqW1NHSdDiXWWm/XW2fopqCnKAp6djoPrqLj1bZrhUdwYff2fqqKvE+gQNjSXQ/E22IRajcjNbn+TbxS3yiREVAiIjjO0yDadWiJwvL9Z/w+zZyLtMY834OZ2WxGaGz15aXy7W6c166zfwqiOtG0ZXMc03LO6r2F5cVo0CSxjisKHvxUI0NITIhHw5Byz4yZuq6hUYyKyIgIP1dG3qLYIlEkNsTFxfm7FPKySEs4Ii3hbPWqB9SwcKhsBTMMVVWR3DgSZY6S035PQXEmWrZl4DaCpPRmsJf/MfGWbo1EXFz9WyrKSFRVhbnB2c2rkq0UoVHjtDquKHgwdJNhdG13Hhy/t3a7C4/g/E5t/FwReVNCejPkayH1tptSfRJuC4cFXJO9PlBDQqFyeShDadW6OUqch0//DbZCJCY19F5B5DNp6U1Q7P7jMzokKtaP1VBdSW6ZjuzSvBpfs5ltNW4XEZgbhNTrh+cM3WQYcbExiDI5K/47TIXNVvOJT8YQ3zABpSa2iNUHEaFhgOvU675S8LNGRMCJ+ntTZkSKoqBBUgjcp7HEY3FpHho3Z+A2ClVVYQ2LAgBouo6wKPZMM4KGCQ1RaK15XPe1XYbUuD3Tno3GbZt7s6yAx9BNhhIVVtEaFhXKVjGjU1UVl189wt9lkA9YLTaIm2P364PImBjAzOu30bRt3wJ5xQdOuZ9TTiAtnV3LjcQaWrF6TKHdhZS0Jv4thuqMpUGoZ4nH01EeriE6JtqLFQU+hm4ylBCLCl1zIzKMrdxERqEqCtjOXT8kpaSgS48e/i6D6pjZbEZo1F+fxSI6ImI4XMhoLKEVPdKcsCAmJsa/xVCdadruPBy1n96a3Zquw9IgzMsVBT6GbjIUm1mFs9yO2Giuy01kFLqugz2OiYJbfFIkyh32Wl/PKzqKFq25VJzRxMQ1RJnTBUtIWL0ez2s0ERERcIafXg+0I6VZaNaOy/cydJOhhIaGwFVegvAwTsRDZBRuXePNGlGQa968MYrKjtb6umIpQ1RUlA8rIl9omJiEknIdJivvy4zGHHt6Xcy1CBWhnCCToZuMJcRmge4sg9Vq9XcpRFRHdF2DojJ0EwUzVVVh+4u5L0MjeEtqRGFhYXArJpg5ua3hNG7dFJn27L/cRxeBKfrslhgzGl7hyFAsFjN0t5PLSBEZyJlM1kJEgSskrObbTpfbicgY3pgbkaIoMJmtUE0Wf5dCdSwqKgrlIdpf7pNlz0F6qya+KSjAMXSToZhMJohoUFX+ahMZChu6iYJebIMIlDlKTtpeUHIMjZtw1nKjUhQVisrGECMyRf11z9Iyq6vez1peicmEDEVEoECBsGWMyDAUKOD05UTBL71xIxSVHj/5BZOTYz6JglBkYgzszrJaX1dPEcrrE4ZuMhRd16EoKkM3kYGoqgqe0kTBz2w2w2w5ecZjWwi7shiZQMCLuDGlN2uM4868Gl9za25YY/gwrRJDNxmKpumAyQxN++sxJkQUPCwmM0Q/vaVJiCiwWWqYT8tiZeg2MtF1XsMNymQyQWqZqyGzNBvpzbkMYCWGbjIUTdehqia2dBMZiMViAXSe00RGYLaefOtpsTF0G5noGnSdjSFGpYbXPEme06ojPPwvliyoZxi6yXCEMy4RGYrFbAF4v0ZkCOY/3Z87XeUIj+DM5UYmogPClm6jskTYoNXwUKW2MF5fMXST8fDCTmQoZrMZorGlm8gIQkLNCLVFICIsBgBQUpaP+IQ4/xZFXqUIG0OMLKVpI5woPXlctxrG0F0VQzcZSuVlXVF4gScyCrPZzO7lRAYRnxCLMkcJTKoZAOB0FyM6mksKGZkoAHhfZljR0dGwK+XVtpW7HAiLjfBTRYGJoZsMRVFUzpBJZDBmsxlgSzeRIcTGxqLcVej53mRWoKq8HTUyVTVB4TE2LEVRoIZWb9XOLs9HUqNkP1UUmHgGkKEoyu9rdfOJKpFhmEwmCFu6iQzBYrFAUf8Y//nnMd5kPIrJDNXEA21kaqi52vdOk8ZJ1P6EoZsMxWRSAd3Np+ZEBqKqKruXExmIyfzHg3FmMeMzmSwM3QanWE3Vvw8x1bJn/cVkQoZitVigucsrlhgiIkNQVRV9+/T1dxlEVEdM5qr/zZ5phmcywWyx+rsK8iJLuKXasnCKjaH7zxi6yVBCQ0MhrnKYTDzZiYykYXxDf5dARHXEZKrS0s3QbXhuTRAazkm1jCw2IR4ljlLP9ypD90kYuslQQkNDoWhOf5dBREREtVCrtHSrvDc3PLcuCAnhWuxGFhsXi2LN7vletfLE/jOGbjIUk8kEs8qxn0RERIGqsnVbRMCOacYnIpy93OCsViu0389lh9sJW0SofwsKQDwDyHAs7KpGREQUsCrzl6a5EBJi828x5HWqosDlcvm7DPIyxVxxYhc7ShATH+vnagIPQzcZjsXEX2siIqJAVdml3OEqQzhbxAzPajahrKTY32WQl4VHRiA6NBIlWhmioqL8XU7AYTohwzGpbOkmIiIKVKpJgYjA6XIgNIxjfY1OEQ3l5aWn3pGCmsVmhlk1wa3qsFo5W/2fMXST4TBzExERBa6QEBtcbic03cEJtuoBze2E7uYkt0an/D5BQ2U3c6qO/ypkOAzdREREgcsWYoVbqwjdNhvHdBuduJ3QOKbb8BRLRaxk6K4Z/1XIcBSFqZuIiChQhYRY4XY7IaLDxOnLDU3XdeiaG7rG0G14laeyiffhNWHoJiIiIiKfsdls0HQXFBMflBud2+2GCgGEy7ka3u/LEiic0LhG/FchIiIiIp+xWCwVoZt52/BUVQXjdv2g/N7CrTBd1oj/LGQ4wqepREREActsNkNEg8rUbXgmkwmimKCazP4uhbzMbDXDrWsAW7prxH8VIiIiIvIZs9kMXVwAM7fhKYoCk9kC1WTxdynkZRabDS7NBYUzGteIoZsMR2dDNxERUcCq6HIs7IZaT5gsNpgsnKXe6Kw2C9yam8sI1YKXOzIcnambiIgoYCmKwlbu+sRkhi001N9VkJdZbFa4dDfP7VowdJPhaMzcREREAU0BJ1yqL8ocGqLj4v1dBnmZ2WyGJjpbumvByx0Zjpupm4iIKKCxsbv+cGiCyMgof5dBXlYZujk/Ys0YuslwXAzdREREAU1RuUZ3feFy6whl93LD+yN0M17WhP8qZCi6rjN0ExERBTwFUPh5XR+069AJZjOXDDM6k8kEHRrTZS34z0KG4nQ6AZXLUhAREQUydi+vP1q2auXvEsgHTCZTxZhuqhFDNxmK0+mEqHyaSkREFNAUcD1fIgNRVRU6BBzUXTOGbjIcnupERESBreKzmt3LiYxCVVXoIhCe1zVi6CZDMZvNgGj+LoOIiIhOgROpERmHoigVw0Z4XteIoZsMJSQkBNCc/i6DiIiI/grvy4mMh5M11IqhmwxFVVWY+VtNREQU0BQFvDknMhiBQOVcDTViPCHDsZh5shMRERER+ZIuAtXECY1rwtBNhmNR+WtNRERERORLOnS2dNeC6YQMJy4u2t8lEBERERHVKwKBajL5u4yAxNBNhjNoQH9/l0BEREREVK9oosNkZuiuCUM3EREREfkel/MlMhSdobtWDN1ERERE5HPC1E1kKJroUE2MlzXhvwoRERERERGdE13XYeLs5TVi6CYiIiIiIqJzooHdy2vD0O1DM2bMQJMmTRASEoIePXpg1apVp/W+Dz74AIqiYNiwYd4tkIiIiIiI6CzoosNsZkt3TRi6fWT+/PkYP348Jk+ejHXr1qFTp04YNGgQTpw48ZfvO3DgAB544AFceOGFPqqUiIiIyPsUcD1fIiPRocPEJcNqxNDtI1OnTsXo0aMxatQotG3bFjNnzkRYWBjeeuutWt+jaRpuuOEGPPHEE2jWrNkp/wyHw4GioqJqX0RERESBRoQTqREZjQZh6K4FQ7cPOJ1OrF27Fv37/7F+tKqq6N+/P1asWFHr+5588kkkJCTg1ltvPa0/Z8qUKYiOjvZ8paWlnXPtRERERHVNVSruhYjIODTRGLprwaudD+Tk5EDTNCQmJlbbnpiYiKysrBrf88svv2DWrFl44403TvvPmTRpEgoLCz1fhw8fPqe6iYiIiLxBFECELd1ERqILu5fXhiPdA1BxcTFuuukmvPHGG4iPjz/t99lsNthsNi9WRkRERHTuFEWBwiHdRIbC0F07hm4fiI+Ph8lkwvHjx6ttP378OJKSkk7af+/evThw4AAuv/xyzzZd1wEAZrMZO3fuRPPmzb1bNBEREZGXKBzPTWQ4Ag4bqQ3/VXzAarUiIyMDS5cu9WzTdR1Lly5Fz549T9q/devW2Lx5MzZs2OD5uuKKK9CvXz9s2LCBY7WJiIgoqCmKAkVlUzeRobD7Sq3Y0u0j48ePx80334xu3bqhe/fumDZtGux2O0aNGgUAGDlyJFJTUzFlyhSEhISgffv21d4fExMDACdtJyIiIgo6ikDhDTqRsfCUrhVDt48MHz4c2dnZePzxx5GVlYXOnTtj8eLFnsnVDh06xO4YREREVC8oChi6iQxGYZSpFUO3D9199924++67a3ztxx9//Mv3vvPOO3VfEBEREZEfKKoC9i4nMhqe1LXh8wgiIiIi8ilFAe/PiYyG53StGLqJiIiIyKcqJlLzdxVEVKcYumvFyx0RERER+ZbCJcOIqP5g6CYiIiIin2JLN5EBcXLEWvFyR0REREQ+pUB4f05kOOzBUhuGbiIiIiLyKUVVuGQYEdUbDN1ERERE5FsKwFYxIqovGLqJiIiIyKe4ZBiR8Qifo9WKoZuIiIiIfIs350SGwyEjtWPoJiIiIiLfUniDTmQ4bOquFUM3EREREfmWCFSVoZvISIShu1YM3URERETkUwKBqpj8XQYR1SVm7loxdBMRERGRT+mi8y6UiOoNXu6IiIiIyLcEUDl9ORHVEwzdRERERORjwiXDiKjeYOgmIiIiIp8SESicSI3IWDimu1YM3URERETkcwqbuomMhaG7VgzdRERERORTFbOXM3QTUf3A0E1EREREviUAGLqJDEV0NnXXhqGbiIiIiHxKFx0qx3QTGYswdNeGoZuIiIiIfEsEqom3oURGoigKdF33dxkBiVc7IiIiIvIpXXQo7F5OZCgqFAhbu2vE0E1EREREviUCk8rbUCIjURWVLd214NWOiIiIiHxKF53rdBMZDLuX146hm4iIiIh8StjSTWQ4Chi6a8OrHRERERH5lLClm8hwVIbuWjF0ExEREZFPiehQ2dJNZChs6a4dr3ZERERE5FMiwtBNZDBs6a4dr3ZERERE5FMCcMkwIoPh7OW1Y+gmIiIiIp9i3CYyHs5eXjuGbiIiIiLyMWFLN5HBsHt57Ri6iYiIiMi3mLeJDIcTqdWOoZuIiIiIfIuhm8hwVEWF6OLvMgISQzcRERERERGdE1VRoAtbumvC0E1EREREPsWGbiIjUiAaQ3dNGLqJiIiIyLeUirW6icg4FJ7XtWLoJiIiIiKf4+zlRMaiQGHorgVDNxERERH5HG/Oiai+YOgmIiIiIh9T2NJNZDAiAj5LqxlDNxEREREREZ0zPkurGUM3ERERERERnTOFaxPUiKGbiIiIiHyOY7qJjEUArgdYC4ZuIiIiIiIiOiciAlU1+buMgMTQTUREREQ+x4nUiIxFIFBNjJc14b8KEREREfmWsHs5kdHoojN014L/KkRERETkc2zpJjIWHQKTid3La8LQTUREREQ+JWBLN5HR6KJDVRkva8J/FSIiIiLyLWFLN5HR6NDZ0l0Lhm4iIiIi8i2O6SYyHE0YumvD0E1EREREPsW4TWQ8OkN3rRi6iYiIiMinFLClm8hoNI7prhX/VYiIiIjIpxRFga7r/i6DiOoQu5fXjqGbiIiIiHxKUVSGbiKDEUXY0l0L/qsQERERkU8pigrR2b2cyEgUReGqBLVg6CYiIiIin1IUBbqwpZvIUFQG7towdBMRERGRTymKCl1jSzeRkbCRu3YM3URERETkU4qiQOOYbiJD4WO02jF0ExEREZFPKYoCcMkwIkPheO7aMXQTERERkU8pULhON5HRMHPXiqGbiIiIiHyLDd1ExsOJ1GrF0E1EREREPsaWbiKqPxi6iYiIiIiI6JxwTHftGLp9aMaMGWjSpAlCQkLQo0cPrFq1qtZ9P/74Y3Tr1g0xMTEIDw9H586dMXfuXB9WS0REROQtbOUmovqDodtH5s+fj/Hjx2Py5MlYt24dOnXqhEGDBuHEiRM17h8XF4dHHnkEK1aswKZNmzBq1CiMGjUKS5Ys8XHlRERERF7AVjEiY+EpXSuGbh+ZOnUqRo8ejVGjRqFt27aYOXMmwsLC8NZbb9W4/0UXXYQrr7wSbdq0QfPmzXHfffehY8eO+OWXX3xcOREREVHd4/05kbGw/0rtGLp9wOl0Yu3atejfv79nm6qq6N+/P1asWHHK94sIli5dip07d6JPnz617udwOFBUVFTti4iIiCjQiAgUznRMZCg2m83fJQQshm4fyMnJgaZpSExMrLY9MTERWVlZtb6vsLAQERERsFqtGDJkCKZPn44BAwbUuv+UKVMQHR3t+UpLS6uzvwMRERFRXRERqCpvQ4mM5NIhl/q7hIDFq10Ai4yMxIYNG7B69Wo8/fTTGD9+PH788cda9580aRIKCws9X4cPH/ZdsURERESnS4RDuokMhrOX187s7wLqg/j4eJhMJhw/frza9uPHjyMpKanW96mqivPOOw8A0LlzZ2zfvh1TpkzBRRddVOP+NpuN3TqIiIgo4AnY0k1E9Qevdj5gtVqRkZGBpUuXerbpuo6lS5eiZ8+ep/1zdF2Hw+HwRolEREREPqPrOlvFiKjeYEu3j4wfPx4333wzunXrhu7du2PatGmw2+0YNWoUAGDkyJFITU3FlClTAFSMz+7WrRuaN28Oh8OBr776CnPnzsX//vc/f/41iIiIiM6ZiMBkYtsPEdUPDN0+Mnz4cGRnZ+Pxxx9HVlYWOnfujMWLF3smVzt06FC1blZ2ux133XUXjhw5gtDQULRu3Rrvvvsuhg8f7q+/AhEREVGdEAhbuomo3lBEhEuqGVRRURGio6NRWFiIqKgof5dDREREBAD4fukydOzUDvHx8f4uhYjorJ1u3mK/HiIiIiLyLWFLNxHVHwzdRERERORznL2ciOoLXu2IiIiIyLeUisnUiIjqA4ZuIiIiIvIxdi8novqDoZuIiIiIfIt5m4jqEYZuIiIiIiIiIi9h6CYiIiIin2rTpjXCwsL8XQYRkU+Y/V0AEREREdUvycnJ/i6BiMhn2NJNRERERERE5CUM3URERERERERewtBNRERERERE5CUM3URERERERERewtBNRERERERE5CUM3URERERERERewtBNRERERERE5CUM3URERERERERewtBNRERERERE5CUM3URERERERERewtBNRERERERE5CUM3URERERERERewtBNRERERERE5CVmfxdA3iMiAICioiI/V0JERERERGQslTmrMnfVhqHbwIqLiwEAaWlpfq6EiIiIiIjImIqLixEdHV3r64qcKpZT0NJ1HceOHUNkZCQURfF3OT5RVFSEtLQ0HD58GFFRUf4uh7yIx7r+4LGuP3is6wce5/qDx7r+qK/HWkRQXFyMlJQUqGrtI7fZ0m1gqqqiUaNG/i7DL6KiourVCV+f8VjXHzzW9QePdf3A41x/8FjXH/XxWP9VC3clTqRGRERERERE5CUM3URERERERERewtBNhmKz2TB58mTYbDZ/l0JexmNdf/BY1x881vUDj3P9wWNdf/BY/zVOpEZERERERETkJWzpJiIiIiIiIvIShm4iIiIiIiIiL2HoJiIiIiIiIvIShm4iIiIiIiIiL2HoJiIiIiIiIvIShm4iIiIiIiIiL2HoJiIiv9A0zd8lEJEXcVVa49J1HQCPcX1QeYx/+eUXbNmyxc/VBC+Gbgoqn3/+OebNm+fvMsiLKi/ulR/oZEwiApPJBAB488034XK5/FwRecv27duxYMECf5dBPlD1uu10OqEoih+rIW8pKiqCqlZEiK1bt/q5GvI2RVHw3Xff4fLLL8fevXt5f3aWGLopaKxZswYjR46E0+nkCW9giqLgo48+wpgxY+B2u/1dDnmBpmmem/FbbrkFjzzyCPLz8/1cFXnDhg0bkJGRgUOHDvm7FPKByiA2YcIEjB8/nr1ZDOjzzz/HY489huLiYtxzzz3o0aMHCgsL/V0WedHx48fxxRdf4JFHHsHQoUM95zmdGf6rUVDYu3cvPvvsM4wZMwajRo3iCW9g27dvx4QJE5CRkcFWEoOqbOFevnw50tPT8d133yEhIcHPVVFd27hxI/72t7/hvvvuw/333+/vcsiLqnYx/uabb/DFF1/gpptu8pzrZBwnTpzAvHnzcMkll+CDDz7A6tWrER0dzW7mBrV+/XoMHDgQS5YsQXp6OgAOKThbTC4U8A4dOoQRI0bg9ddfr9byyZPeeLZs2YL33nsPl1xyCW699VZ/l0NeNGfOHPTu3RuvvfYabDYbAJ7TRrJ582b07dsXY8eOxZQpUzzbf/vtN2RnZ/uxMvKGygekn3/+OT788EMMGTIEPXr0YEu3gVRen2+99Vb07t0ba9asweDBgxEfHw8AfEhuUF26dEHLli2xc+dOrFy5EmVlZVAUhZ/XZ4GhmwJW5Qmdnp6OW265BWFhYVi6dCm2b98OgBd4oykpKcF9992Hl156Cfv27YOiKDCZTBxKYFDdunXDvffei7y8PKxfvx4AQ7dRaJqGm266CeXl5bjzzjs925944glcffXVKCsr82N15C05OTn4v//7P8ybNw/79+8HAF7DDULX9Wr3XL169cK///1v/PDDD3j22Wexb98+P1ZHdammz+EFCxZgxIgRWLRoERYsWMDgfZYYuingVJ1Iq/K/x4wZg8ceewxlZWV4+eWXsWvXLn+WSHWo8hhHRETgpZdeQp8+fbB161bMnTsXQMUYQV7Yg1tNrV1t27bFQw89hBtuuAG33HILvvrqKx5rgzCZTPjoo48QExOD22+/HYWFhXj22WcxY8YMvPbaa54uihTc/nyuxsfHY/bs2ejfvz/WrFmDd999FwCv4cFO13XPkL433ngDc+bMwUMPPYTHH38cTzzxBD788EO88sorngctQEWPFgo+IgJFUbB8+XI89dRTePjhhzFnzhwAwHvvvYeMjAz85z//wcKFCz3Bm06fIrwSUgCpPOG//fZbzJ49GwUFBUhISMB//vMfxMfH4/XXX8fMmTNxwQUXYNy4cWjRooW/S6azVHms8/PzYbVaoWkaoqKisGvXLtxzzz3QdR3/+te/cNVVVwGo/sFPwUPTNM+4zieeeAKHDx9Gfn4+brrpJgwcOBB2ux0PP/wwFi5ciPfeew+DBw/2/G5QcMnNzUVJSQni4+MRHh6Offv2oVu3bggLC4PD4cC8efMwcOBAHl8DqHo93rdvH1RVRVhYGBISErBnzx7cfffd0DQNd9xxB66++uqT3kPBoeq5+uCDD2L+/PkYP348hg0bhiZNmgCoCOJPPfUUrrjiCgwdOhQvvvgitm/f7mn95rkeXD7++GOMHj0aF110EeLj4/HGG2/g7rvvxssvvwwAuPbaa7Fr1y7cfffduOGGGxAaGurnioOIEAWYTz/9VCIiImTs2LEye/ZsadSokXTt2lWOHj0qIiL/+9//5Pzzz5ebbrpJ9uzZ4+dq6Wzoui4iIp9//rlceOGFkpGRIa1atZIPP/xQRES2b98uAwYMkAEDBsjHH3/sz1KpjgwbNkxatWol//nPf+Smm26S1NRU+fe//y0iInv27JE77rhDYmJieLyD1LZt2+TCCy+UMWPGyM6dO8XtdouIyP79+yUtLU06duwoBw8e9OxfeQ2g4FP12P373/+Wjh07SuvWrSUlJUXeeustERHZsWOHDBw4UAYMGCALFy70V6lUR1555RVJSEiQVatW1fj6nDlzpEOHDtKmTRvp3bu3OJ1OH1dIZ6vq+bx7925p3LixvPLKKyIicuTIEYmIiJD77rvPc00XERkwYID07NlTCgsLfV5vMGPopoCSnZ0t559/vjz//PMiIpKXlydpaWly1113VbswPP/889K3b1/JzMz0V6l0jr788ksJDQ2V559/XtavXy+jR48WRVHk119/FRGRrVu3yuDBg6V79+7y2Wef+blaOl01ham33npL2rdvLydOnBARkRdffFFiYmJk2bJlnn0OHz4sV199tdxxxx0+q5XqxqZNmyQuLk7Gjh0r3377rWd75U3avn37JDY2VgYNGiS7du3yV5lUx5566ilp2LChLF68WIqKiuTyyy+XuLg42bZtm4hUPDwdPHiwdOnSRX744Qf/FktnzeVyyY033igPP/ywiFQ8UJk7d6706dNH+vbtKxs2bBARkZ07d8qWLVtE0zTP+yhwTZ8+/aRtq1atkp49e4pIxQPT1NRUufPOOz2vr1692vPfhw8f9n6RBsPQTX4zYcIE+eWXX6ptO3bsmLRv317y8/Pl6NGjkpKSIrfffrvn9arhKy8vz2e10rmp/BCu5HQ65ZprrpFHH31UREQOHTokLVq0kNGjR4vIH8Ft/fr1cuWVV1ZrIaPAVVJSIo888ogcO3as2vYXXnhBrr76ahERefLJJyU2NlYWL14sIiJHjx6V5cuXi4h4QjkFj8pr9iOPPHLSa5qmicPhEBGRXbt2SWxsrAwZMkS2b9/u6zLpHFVt5RKpONcHDhwo7733noiIfPLJJxIbGyuvvvqqiIinpXP9+vUybty4kz4DKHBVfv5WfYB69913y3nnnScvvfSS9O7dWwYPHixjx46VCy+8UNq2bXvS8eXxDmz79u2Trl27yu7du6ttX7VqlbRt21a+++47adKkidx+++2ec3/NmjVy1VVXydatW/1RsiGY/d29neqn3NxcOBwOREZGVtseGxsLk8mEt99+G9OnT8fll1+O6dOnAwAOHz6MqVOnwmQy4dJLL0VsbKw/SqczdP/99yM0NBT/93//59nmcrmwY8cO3HXXXSguLsYFF1yAyy67DK+99hoA4LXXXsNll12Gzp0744MPPoDVavVX+XSadF3H/PnzUVpaiuTk5Gqv2e12WCwWvPnmm3jxxRcxb948DBo0CACwaNEiHDx4EB06dEDDhg0BgGN+g8i2bdsQEhKCf/7zn55tmzZtwoYNG/Dxxx+jdevWuPrqq9GtWzesXr0aLVq0QGhoKN577z1YLBY/Vk6n66KLLsLll19eba31oqIibNy4Ed27d8ePP/6Im266Cc8//zzuvPNOlJWV4ZlnnsEdd9yBzp07o3PnzgA4pjsYVD1GZWVlsFqtMJvNngkRn3vuOdxzzz0YNGgQunTpgoULF+L1119HaWkpIiIiPD+HxzlwPfHEE7BYLPjpp58QHh6O3377Dd27d4eiKEhOTkZqaiquuuoqDBkyxHNPBgDz58/3zLNEZ8nfqZ/qn7Fjx8rQoUM9LSDffPON/PjjjyJS8XR03LhxEhMTI4MGDar2vkmTJkmXLl3kyJEjPq+Zzs6mTZtk6dKlsn79+pNeu+WWW+SGG26QtLQ0+de//uVpGSkpKZHLLrtMpk6dKrquc+xnECgoKJBu3bpVa8GcM2eOp5vp7t27JSYmRhRFqdY97dixY9K1a1dPjwcKHvv37xeXyyXz58+XuLg4KSgoEBGRd955Ry6++GJp37699OrVSzp37iw9evTwzL+xf/9+2blzpz9LpzPwww8/yMqVK6W8vFxEqrd+Xn/99TJ48GAJCwuTWbNmebYfOXJEevfuLe++++5J76HAVbV1eurUqXLFFVdIv3795Pbbb5fi4mIREcnJyan2noEDB8rw4cN9WiedvRdeeEHCw8Nl3759IiKSn58vTZo0kR49enj2eeuttyQpKUnuvPNO+emnn2Tt2rUyfvx4iYmJkU2bNvmrdENg6CafmjNnjkRFRXm6tDgcDhk5cqSYzWb56aefRKQiqF188cXSq1cvefbZZ2XevHly5513SnR0tGfsEAW+8ePHS4sWLSQ/P19ERL7++mt54IEHPDdvb775pjRp0kQuuOACzwMYkYqHKy1atPB8KFBgczqdMmDAAOnfv79n26pVqyQxMVHGjBkjO3bsEBGR2bNnS1RUlNx2223yxRdfyPvvvy/t2rWTyy+/3PM+3pwHhw0bNojJZJKPP/5YSkpKpGXLlpKeni69e/eW0NBQeeyxxzwTLi1atEiSkpJk6dKlfq6azlSfPn1kwIABnrG5zzzzjNx5552ea/j06dMlPT1dhg4d6nlPUVGRDB48WPr163dSl3QKDhMnTpSGDRvKq6++Ku+8844kJiZK165dpaysTEQqHox/+eWXcskll0jHjh09D8x5/Q5smqbJmDFj5JZbbhERkRUrVsgvv/wiP//8szRr1kwuuugiz77Tpk2Tvn37is1mk86dO0tGRgbvv+sAQzf51MyZMz2TNHz77bcyffp0sdvtct1110lsbKynxXv16tUyduxYady4sWRkZMhll13GJ2xBZOnSpRIdHS0bN24UEZHCwkL59NNPRVEUmTRpkohUhLVx48ZJx44dZdCgQfLQQw/JtddeK7GxsTW2jFNgys7OlhYtWsgHH3wgIhUPWxwOh7z66quSkZEhd911l+zfv19ERD7++GNp1aqVNGnSRHr37i1jxozx/ByOAQwOGzdulNDQ0GpjuA8dOiT333+/jBs3TtatW1dt5uItW7ZImzZtPBMkUnB45513JCUlxRO4s7KyZMGCBaIoijz00EMiUjFR1tixY6Vjx46SkZEhI0aMkAsuuEA6derk+R1g8A4uW7dulY4dO3omufzss88kOjpaZsyY4dln06ZNMnbsWLn++us9vx+cNC3w6bou48aNkzZt2sijjz4qiqJ45lVasWKFpKenVwveWVlZsnHjRjl48CDnUKojDN3kUx9++KG0b99eRowYIYqiyBdffCEiFQGsMnBVBm8REbvdLuXl5Z4nrBQcfv75Z+nQoYOsXbtWPv74Y7n11lulrKxM5s+fL2azWR544AERqejp8Pbbb8v1118vAwYMkHvvvdfTJZmCg8vlkrvvvlsaN24sF154oTRp0sTTFfGVV16Rzp07y1133SV79+4VEZHS0lI5evRotQ9xBu7gsHPnTomJiZF7773Xs63qsavpOE6cOFG6du0qx48f90mNVDcWLlwonTt3lnXr1smDDz4od911l4hUfIabzWYZP368iFSc/59++qncc889MmbMGPnvf//LIBZEqp6zuq7LmjVrJDU1VUQqAndERITMnDlTRESKi4tl7ty5ouu6ZGVleVq2eZwDW2Vvs0qtWrWS0NBQGTt2rGebpmk1Bm+qWwzd5HWffvqpZGdne74fOnSohISEyBVXXCFFRUWe7VWDd2VXcwpOu3btkr59+8qFF14oiqJ4ZrgVEXn//ferBW8KPiUlJfLJJ594vt+zZ48kJCSI1Wr1PEirVBm877777hpnrWaXxOCwfv16iYyM9LR0Vg3RNYXtvXv3yoMPPiixsbHslhhEKs/H3bt3y4UXXijt2rUTVVWrnbuVD08rg3dN2MIdXJ599ll5++23Ze/evXLppZfKs88+KxEREfLaa6959lm1apVcddVV1c5nXr8D27x58yQ5OVmKi4vF6XRKdna2KIoizZs3lw4dOshXX33lGd5XGbybN28uGRkZPLZewNBNXqNpmmzYsEEiIiLk6NGjommauN1uady4sfz973+Xtm3byqOPPlptrT+XyyU33HBDtfWaKTg899xzsm7dOs+F+pFHHhFFUaRbt27VJs8S+SN4T5o0qdp4bgp8mqbJXXfdJRMnTvRse/vtt6Vz585y5ZVXSvPmzU96aDZjxgzp2rWr3HDDDWzxDELr16+XkJAQ+e9//yvffPONKIoi99xzT63H8pVXXpFevXpJ165dPUNMKPDdeOONsmXLFs81/NJLLxWz2Sz9+vWTlStXVtt3wYIFYrVa5cEHH/SM8abgUfVB2Zw5cyQhIUHWr18veXl5ctFFF4miKDJ58mTPPqWlpTJ48GAZNmwYeyYFmcolVysbvzIzM0WkYs6Gdu3ayddff+0ZDqJpmvz000/SoUMHLtXqBQzd5HWVs9pWru1XecF+4oknpHXr1icFb6fTKf/85z9P6hJDgUnXdXG73SfNXn3PPffI+PHj5fzzz5dhw4bJ999/X+198+fPF0VR5IknnvB1yXSOqk5yV9nqoeu6bNy4UYYPHy7NmjWTn3/+udp7nnvuOc5SHoSys7OldevWnnG8IhWTo9UWvHVdl507d8q8efOqXdcpsLndbrnqqquqjcd/5JFH5O2335bu3bvLFVdccdJkeAsXLhRFUeSVV17xdblUR5YsWSJPPfVUtTHbhw8flrS0NOnTp4889thj8uqrr0q/fv2kQ4cO1cIZBY+NGzdKWFiYfPnll56Hapqm1Ri8dV3nkE4vYegmr6k63iczM1MURZE77rhDjh075tnnySeflNatW8tjjz3GpcCC1J8/fJctWyZbtmzxfL98+XLp0qWLDBs2TH744Ydq+3700Uccwx3EXn31VTnvvPNk3rx5nm2//fabjBgxQpo1a+aZpOXP2G0tOFR2Sfztt99E5I8HbCJ/HbwpuPy5K/irr74qa9eu9Xy/cuVK6dq1qwwdOvSk4P39999zTG8Q0nXdc1+mKMpJD0T3798vN998s3Tp0kUGDBggo0eP5lj9IHf11VdLgwYNZMmSJZ7PYF3XpU+fPtKpUydZtGhRtYduVPcYusnrKk/ud999V6xWq4wfP97TvUWkIni3b99exo8fL0ePHvVXmXSO3G636LouLVu2lCZNmsjq1as9N3MrVqzwBO+qE+VRcPnzzfnKlStlxIgR0qdPH5k7d65n+2+//SbXX3+9NGvW7KSbdAbu4LBjxw4ZPHiwPPHEE9WOma7rnu8ZvI2hasuXpmmSnJwsrVq1kt9++80TsH777Tfp2rVrjb2WRBjEgkFN196NGzdKgwYNpGfPnp7ehVUbTMrKyqoNAeNxDg6Vx3Dr1q3VhvfdcsstEhUVdVLw7tSpk/Ts2VNKSkr8Um99wdBNXlF5Mq9YsUJmz54tpaWlIiKeJUf+HLwnTpwo3bt3lxMnTvilXjp7lce68hiXlZVJhw4dpGPHjicF7+7du8vFF198UtdjCnxVA/fbb7/tGR+2YcMGGTFihPTu3fuk4D1w4MC/nGyJAtOmTZskISFB7rrrLpk9e/ZJr+u67unhsmjRIrFarTJq1Chev4NQ1Z5Kubm5IlIxxKtTp07Svn37asF71apVcv7550vv3r1lzZo1fqmXzk7V41wZoiuv6WvWrJGwsDC55ppr5MCBA579/hzS+cA0OFQep48++kiaNGkizz//vGd8tq7rcvPNN9cYvKsee/IOhm6qc5Un8cKFCyU+Pl4mT54smzdv9rxeOZZ33Lhx1YJ31RnOKThUHuulS5fKo48+6ulWXl5eLm3btj0peP/000/St29fjvUMMlVv2K699lpp2bKlvPvuu56b8fXr18uIESPkb3/7m7z77ruefSuXCaPgsX//fmncuLE8/PDDfzlu0+12e15fsGCBxMXFSVZWlq/KpDpQ9fi++OKLcv/993uG+zidTmnfvv1JwfuXX36RUaNGcUxvEKl6rKZOnSojRoyQfv36yVNPPeVp3V61apWEhobKtddeywm0DOCrr76S8PBwmT59erVVgirdcMMN0qBBA/niiy/4MMWHGLrJK3744QeJjIz0rO9YqTJ8ffDBB2KxWOT222/njVqQqvpwJSIiQp566ilZv3695/WysjJp3bq1dOzYUdasWeO5aeMEHcHrxhtvlLZt28rBgwc9PRsqb+i2bt0q1113nfTu3bvaMjMibCEJJm+99ZYMHjxYioqKPMd279698s0338jEiRPl66+/9rRoV3ZHFhHP2uwUfB588EFp2LChvPfee9Vau5xOp7Rt21Y6dOggq1atOqlrMYN34Kt67Z0wYYLExcXJk08+Kddee6307t1b2rVr51lhYPXq1RIZGSmXXHIJ78uClK7rUlJSIkOGDPGsMFJSUiK7du2SF154QV566SXPvsOGDZO0tDSx2+3+KrfeYeimOlV5gR87dqzccMMNIiJSWFgoy5YtkzvvvFOuuuoqz5PV2bNnS2xsLC/uQWzVqlXSsGFDefPNN6ttr5wsr6ysTNq3by/p6emybt06EWEAC1b79u2THj16yIoVK0SkYi32jz/+WP7xj3/IO++8I2VlZbJlyxYZNGiQPPXUU36uls7WAw88IG3atPF8//7778vQoUMlJSVFkpOTJSkpSR599NGTlonieR2c5s2bJ6mpqdUmTnO5XLJz504RqQjeHTp0kISEBE56GcQ2b94srVq1km+//daz7eeff5Zhw4ZJ9+7d5dChQyJSMU/HxRdfzAcqQe4f//iH3HbbbbJlyxYZM2aMXHLJJdKkSRNJSUmRm266ybNf1YmNyftUENUhEQEAxMTEYNOmTfj0008xevRoPPvss9i2bRvy8vLQr18/2O12jBw5EgcPHkRiYqKfq6aztWHDBjRv3hy33norysrKsHDhQlxxxRW4+OKL8dxzzyEkJASrVq1CcnIyoqOjAQCKovi5ajodmqZV+97tdmPt2rXYvHkz5s6di4kTJ+Lpp59Gbm4uxo4di0WLFqFdu3aYOXMmHn30UT9VTefqsssuQ2lpKa655hrcfPPNuPPOO9G0aVO8++67OHbsGK6//nq8++67KCgoqPY+ntfBad++fWjTpg26du2KnTt34uWXX0bnzp3Rt29fPPnkk7BYLFizZg0GDhyIli1b+rtcOk2vvvoqRo0a5fnebrfjyJEjiI2N9Wz729/+hjvvvBNlZWXYvXs3AKBHjx5YunQpVFWFrus+r5vOXOV9986dO7F9+3YAQIcOHbB161Z06tQJJ06cwK233oqNGzfitttuQ1FRkec9ycnJfqu7PjL7uwAKfiICRVGwevVqHD16FEOGDEGfPn2wZcsW3HLLLbj88stx7733YuDAgVi6dCkeffRRlJaWIjw8HBEREf4un85A5bEuLy9HSEgIEhIScOLECUyYMAGrV69GREQEIiIiMHLkSEyaNAkXXnghevXqhZUrV/q7dDoDmqbBZDIBAN59910MGTIELVq0wL///W9MmDABFosFEyZMwMMPP4yMjAz069cPW7duBQA0adIEwB+/KxTYDh8+jG+++QaapqFDhw7o27cvHn74YSxYsAC5ubn46KOPkJGRgZiYGADARRddhC+//BJOp9O/hdMZ03UdqlrR1lJYWIjo6Gikpqbi8OHDGDFiBLZs2YIOHTrgmmuuQVRUFB588EFcddVVaN++PebOnQug+rWBAtPrr7+O++67D/Pnz/dsS0pKQvPmzbF+/Xp06tQJZrMZiqKgf//+KCgowJo1a3DxxRdX+zmVvysUuCo/Zz/55BOMHz8e999/Pxo1aoTJkyfjiiuuQEFBAfr16+fZLzMzEzabDS6XC1ar1d/l1zsM3XROKk/kjz/+GHfccQfuu+8+dOjQAf369UPnzp2Rm5uL8847z7P/kiVLYDabYbPZALB1JJhUHusffvgBa9aswfDhw9GzZ09cd911WLp0Kbp27YqRI0eiR48eOHToED799FNERUX5u2w6Q7que26qR4wYga1bt8LlcuGWW27BI488ghEjRiAqKgoNGzYEAJw4cQK5ublIS0ur9nN4bge+TZs24YorrkBiYiL27t2L6OhovPTSS7j99ttx++23w+l0nnRj9v333yM9Pd0Twik4VA3cL7/8MgoLC/HPf/4TgwcPRm5uLn788Ufcd999uPjii9G8eXP89ttv6N69e7WWUQAM3AHujTfewJgxY/Dhhx/iyiuv9Gxv3Lgx2rVrh5dffhnNmzdHv379AAAlJSVITExki2eQUhQFX331FW666SZMmTIF1113HSIjIwEAXbp08ex3+PBhvPLKK1i4cCF+/vlnBm5/8Ve/djKOpUuXSlRUlMycObPaRCtV/3vt2rVyzz33SExMjGzYsMEfZdI5+POkaU8++aRs2rRJRCrG/P15bcdHH31UWrduXW12egou119/vbRp00YOHDjgmWil6ji/zMxM+frrr6Vz585y2WWX+atMOksbN26UsLAwmThxotjtdvn2228lJSVFhgwZIkVFRdWWBRMROXHihEycOFHi4uKqrUZBwaVy0rR333232izVTqdTRCrOcbvdLpdddpkMHDiQY3uDyHvvvSeKosi8efOqbX/yySdl+/btouu6XHjhhdKuXTv517/+JS+99JJccskl0qFDB66/HaRKSkpkwIABMmnSJBERsdvtcvDgQXnllVc8yz2uWLFCrr76amnfvn21yW7J99jSTeeschzvHXfcgeLiYmzbtg3vv/8+SktLMXHiRERGRuLNN9/E1q1bsWzZMnTs2NHfJdMZUhQFK1euxJ133omXX3652lix4uJixMXFAQC++eYbfPLJJ/jwww+xdOlSJCUl+atkOgfbtm3Drl27sGDBAjRu3Bj79u3Dli1bPK0nF198MVatWoUXX3wRXbt2xaxZswBUb02jwHX48GFccsklGDJkCKZMmQIA6N+/P1JTU7F7927oug5FUTy9FV577TUsWrQIe/fuxdKlS9G+fXt/lk9nafbs2Xjvvffw7bffolOnTgAAp9OJrKwspKeno6ysDB999BHeeecd5ObmYtWqVZ6xvTyvA9+PP/7o6V3mdrthNpsxbNgwbN++HbfeeisURcGyZcvw8MMPY/369Z45Wb7++muYzWYOHQhCFosFVqsViqJg27ZtePPNN7Fp0yZs3rwZDRs2xIYNGzB16lTccsst6NSpExo1auTvkus1hm46ZxEREdi5cyc+//xzzJ8/H3l5eTh+/DiioqJwxRVXYMOGDbj33nsRHx+P+Ph4f5dLZ2n9+vVo06YNRo0ahfLycixZsgTvvPMOsrOz8Y9//APjxo3Db7/9hvz8fPz0009o166dv0ums2Sz2bB582asWrUK27Ztw4cffoidO3eiQYMGuOaaa/DNN99gwIABSE9PR+fOnQEwcAcTTdPQtGlTOBwO/Prrr/jb3/6GKVOmYM2aNTj//PNx0003IT4+Hl27dsVFF10EVVVxxRVXYODAgWjWrJm/y6ezdODAAXTt2hWdOnXC7t278d1332HGjBnQdR0333wzJkyYgMOHD6Nz58549tlnYTabPeGNApf8PvTrtddeg8vlwpNPPgkRwYIFC3Dw4EEsXrwYKSkpnmM5ZcoU6LqO0tJSz7w6PM7ByWq1omnTpvjss8/w3//+F0OHDsXNN9+Myy+/HI888giOHTsGABgyZIifKyUAUER+n8KO6CwtWLAAb7zxBlavXo0hQ4bguuuuw+DBg/Hhhx9ixowZWLJkCcLCwvxdJp2lyg/0t99+G1OnTsXw4cPx008/wWazwWw2o23btpg2bRo2bNiAhIQEqKrqGVNEga+msOx2uzF58mS89tprUBQFDz74IPr06YMLLrgAvXv3xmWXXYaJEyd69hdOmhZ0du/ejXvvvRdWqxUJCQlYtGgRXn31VXTv3h3r1q3D1q1b8fLLLyM6OhrNmzfHokWLOA4wSFWe4//5z3/wwQcfoEuXLli3bh1atmyJJk2aIDw8HDNmzMCmTZuqTXDKls/AV9P1e+TIkfj4448RExODJUuWoF27dp5rdE3Xal6/g0PlcTp06BA0TUN5eTnatGkDAPjhhx/gcrkwcOBAz+/E6NGjUV5ejrfeesszcR75Fx9r0WmrPOG3bNmCzMxMFBcX46qrrsI111yD3r17o7CwEK1bt/bst3btWphMppOWHqLAV/VDuPL/+/fvjxUrVuDzzz9Hly5dMHLkSPTq1Qvbtm3zzH5cuSwYBYeqN9WrV69GTk4OevXqhaioKDz99NO4+eabERUV5RkmkJWVhYKCAqSkpFT7OfwwDz4tWrTASy+9hLvvvhvvvvsunnrqKVx99dUAgPT0dAwbNgx33nknvv/+e3Tp0oWBO4j8OYhVnp/XXXcd8vLysH79etx+++245JJL0LJlSyxduhSLFy+GiHgCt4gwcAe4qsd5yZIliImJQY8ePTBnzhxERkbi66+/xurVq5Geno7IyMhawzWv34Gv8tgtWrQI//73v1FYWIiwsDBcccUVeOaZZzwT4wEVn9Mvv/wyFixYgF9//RUWi8WPlVNVbOmm0yJVliUYN24cQkNDUVZWhqSkJHz00UdITU317Ltp0ybMnj0bs2bNwk8//cQx3EGm8livXLkSy5cvR0lJCYYMGYKMjAw4nU6UlpZWm7n48ccfxyeffILvvvuOa64Hkao3bCNGjMDGjRuRmZmJ5s2bY+TIkbjxxhvRoEEDABUzlG/atAkPPPAAGjdujEWLFvmzdKpDe/fuxV133QWTyYSHH34YvXv3BgC4XC7erAWhquf122+/jc2bN6OoqAi33HILevfuDV3X4XQ6ERISAhFBeXk5hg8fDk3T8Pnnn3OISJCoGqAnTpyIzz//HPfeey+uuuoqz8oSI0eOxG+//YaHHnoI1157LXugBbmvvvoK1157LZ577jlcdNFF+Oabb3D//ffj/vvvx/PPPw8AWLx4MaZNm4bDhw9j3rx5nuFfFCB8OGkbBanKmau/++47iYmJkTfeeEM0TZMVK1aIoijSs2dP2b17t4iIbN68WW644Qbp1auXbNy40Z9l0zn46KOPJCEhQfr06SODBg0Sk8kkCxYs8PwuiIgsWbJE7rnnHomNjeWMmEGm6ozEt9xyi3To0MEzI3WvXr2kWbNmMnnyZMnLyxMRkXnz5skll1wiN998c40/g4Lbrl275O9//7sMGjRIfvnlF3+XQ2eh6rVZRGTChAnSqFEjGT58uNx4442iqqq8//77nv2Ki4tlzpw5MmjQIOnUqVO12cspeDz55JPSsGFD+emnnzzHsKqRI0dKmzZt5KWXXpLS0lI/VEhnY+3atZ7PX5GK1UKGDh0qU6dOFRGRY8eOSZMmTeTiiy8Wm80mY8eO9ey7cOHCaisTUOBg6KYaffbZZ7Jy5UrP94WFhTJu3Dh56qmnRETk0KFD0qRJExk5cqR07NhRunbtKnv27BERkS1btsjx48f9UjedvcqbrV9//VUSEhLkjTfeEJGKY60oioSGhsqbb74pIhW/Dw8++KAMHTqUywcFiZKSEvn000893+u6LsuXL5c+ffp4HppMnz5dYmJiZNiwYZKamipPPfWUFBcXS3FxsaxatcrzXt6YG8+uXbvksssukwsuuEBWrFjh73LoLFQG6lmzZklaWpqsXr1aRES+/vprURRFrFarvP766yIikpeXJxMnTpQxY8Z4lovislHB5ejRo9K9e3dZuHCh5/tly5bJ3XffLf/97389+11++eUyYsSIkx7MUGBavHixxMTEyPTp06WgoEBEKj6/n3/+edm3b59kZWVJu3bt5I477pDS0lJ54IEHRFEUuf322/1cOZ0KQzedZPv27dK8eXO5/vrrZd26dZ7tn3zyiWzevFny8vIkIyPDc4J/8cUXoiiKtGrVSvbt2+evsukszJ07V/73v/95vi8rK5Pp06fL448/LiIVgTs9PV3uuusumTBhglgsFs8aoEVFRZ4PBAp8L730kiiKIm+//bZnW1ZWlixatEh0XZe5c+dKcnKyfPfddyIicsEFF0jTpk1l3Lhx1dZh542bcW3fvl2uvvpqtpIEkUceeUSmT5/u+b6oqEiee+45z0PTzz77TCIjI+X111+Xhx9+WGw2m8ydO1dEREpLSz3ns9vt9n3xdE7sdrv06NFDxo0bJ0uXLpVrr71WunXrJv369RNVVT2f4yJ/PCjl9Ts4jB49Wlq2bCkzZsyQ3NxcERFPT4UXX3xR+vfv72ncmjZtmnTq1EnS0tLk2LFjfquZTo2Dd+gkrVu3xlNPPYW9e/fixRdfxJo1awAAw4YNQ/v27bF8+XKYTCY8+OCDACqWLLjsssuQkpICXdf9WTqdAbvdjrfffhtz587FnDlzAAAhISHo27cvLr/8cpSWluLGG2/EoEGDMH36dNx2222wWq248cYbMXv2bERGRnLitCAydOhQTJw4Effeey/eeustAEBiYiIuueQS6LqOhQsX4s4778Qll1wCEUFaWhoaNGiABg0aIDw83PNzOOmOcbVu3Rrz5s1Denq6v0uh01BQUIBff/0VCxYswDvvvAMAiIyMxJAhQ9C/f3/s378fEyZMwFNPPYXRo0djyJAhcDqdGDlyJD799FOEhoZ6ZrTmpGmBraZ7K6vVisGDB+Onn37CoEGDkJaWhmeffRbff/89Ro0ahezsbMjv0zZVrrfO63dgqjy+lRMPv/7667jkkkvwwgsv4P3330d+fj5CQ0MBAFu2bIHT6URCQgIA4MiRI7jpppuwY8cOJCcn++cvQKeFs5cTgD8mX6mcPOe6666DxWLBc889h5dffhn33XcfMjIyAAB79uzBvn37PDMaL1u2DOnp6Zg2bRrXeQwi4eHhmDNnDsaOHYs333wTmqZh1KhR6NChAwBgx44dKC4uxqhRo6CqKiwWC6655ho0b94c3bt393P1dCryp5lqGzdujEmTJkHTNIwdOxYigltvvRXh4eEoKytDfn4+ioqKoGkaSktLYbfbMX36dFxwwQU1/jwyJs5SHjxiYmLw3nvv4d5778WsWbOgaRpuvfVWtGvXDgDw/fffIyQkBFdccQUAICwsDPfddx+6du2Kyy67zPNzeF4HtqqT473//vvYsmULTCYTLrzwQkyePBm333478vPz0bZtW897du3ahb59+1Y7tpwkLzBVHt/Dhw/jl19+QWZmJsaPH49XX30VY8aMwdSpUwEA119/PWJjY3HZZZfh2muvxfXXXw8RweLFi7F8+XIuzRsEeAaS54Tft28f/vOf/+COO+7A3r17cfXVV+Ohhx7C9u3b8dJLL2Ht2rUAgBtvvBFhYWFo164d+vbti5dffhmjR49m4A4ClU9THQ4HCgoKkJqaiunTpyM+Ph7vvPMO3n77bc++J06cwIYNG2C322G32zFr1izs2bMH999/v2dtSApMVQPywoUL8fTTT2Py5MmwWq247777cPfdd2PcuHGYNWsWACA0NBStW7fG4sWLcemll6Jz584QEQZuogCVl5eHI0eOIDk5Ga+88goSEhIwe/ZsTy8WACgpKcGGDRuwe/du7NmzB4899hgyMzNx0003wWw2w+12+/FvQKerMiw/9NBDeOCBB3Ds2DHs378fV199NZ555hkkJyejbdu2sNvtWL9+Pf7+97+jsLAQkydP9nPldCqV999btmzB4MGD8eWXX2LVqlWw2+0AgBkzZuDvf/87pk6divfeew8FBQUYMmQIZs6ciWPHjsHlcmHZsmW8JwsW/urXToGhcpzPpk2bpGnTpvKvf/1LJk2aJOXl5Z595s+fL926dZObbrrJM5nSgQMH5N5775VHH31Utm3b5pfa6cxUHusdO3bI6NGj5eKLL5ZFixaJSMVMmFdeeaX06dOn2pjfUaNGiaIo0r59e4mOjq42xp8C38SJE6VDhw7ywAMPyJdffunZfvToUZk0aZJERkbKa6+9JiIVvx+PPfaYjB07Vh577DHPvhwDSBRYFixYIFdeeaVkZGTIjz/+KCIVsxtfddVVcuGFF8qsWbM8+/7zn/8URVGkWbNm1WYpp8BXdcLKL7/8UtLT0z2THM6dO1dCQkLkrbfe8uzz/vvvy5VXXikDBw70HGeO1Q9clZ+t27dvl9jYWHn44YfFbrd7Xq86seFdd90lTZs2lRkzZkhRUZGIiDgcDikrK/Nt0XROGLpJdu/eLYmJiTJhwoRq26ue8FWD99q1az3bOYtxcKj6cCUlJUUeeOABefvtt6sdv6NHj3qCd9Wbtvnz58u8efNk7969Pq+bzt5TTz0lDRs2lFWrVtU4K3HV4D1z5swafwbPb6LAMmvWLGnQoIG8+eabnkkPK2/eawveS5cule+//94TwDhLeWB74oknTnrYOX36dPn73/8uIhVLelZ9YFpcXCwbN24Uh8MhP//8s+e6zeMc+EpLS+Waa66RW2+9tdoDkpomOBwzZoy0aNFC/vvf/1ZbToyCB0N3PaZpmrjdbhk3bpwMHz7c8/SsqqoX/gULFkiPHj1k2LBhbPEMQgcOHJDGjRvL+PHjq23XNM3zIV0ZvHv37i3vvPOOP8qkOrBjxw7p0qXLKY/hkSNHPMF72rRpPqqOiM7Gd999J/Hx8TJnzpxq2ys/y0Uqgvc//vEP6d27d7XgXYktn4Htk08+keHDh58UmOfMmSO33nqrLFy4UCIiIqo9KF20aJHcf//91VYT4QPT4GC326VFixaeByh/9ufjeOONN0qXLl0YuoMUx3TXY6qqwmQy4ddff0VSUhIiIyNP2kd+n/nS4XDg6quvxj333IPCwkIkJib6ulw6S5XjuD/99FO0atUKEyZMqPa6qqpQVRWapiElJQUzZsxAcnIyXnjhBcybN88fJdMZqjxPKx07dgyHDh3yjMn+s8rfiaSkJDz88MO48cYbsXXrVq/XSURnrvJ8/frrrzFw4ED84x//qPZ65We5pmlISkrCq6++ipSUFDz//PP44osvqu3LWcoD29///ne89957MJvN+PTTT6tdqz/44ANcc801nrl3AKC0tBSvvvoq7HY7oqKiPD+Hk6YFPhHBwYMHsXfvXnTq1AnAH7OXV6qc4Pjll18GAMydOxdfffUVYmNjfV4vnTuelfWYiKCkpASZmZmeZQb+vCxF5YV78uTJOHz4MG644QYsWrQIKSkpPq+Xzk7lMVy+fDlUVfUsM1GV/L5kjN1uR3JyMl588UV06tQJvXv39nW5dIakyiRn77zzDjZs2IDs7GyoqoqIiAjPPlWpqoqNGzfiiSeeQHh4OP7v//4Pr7/+us9rJ6JTq7yGL1u2DHFxcQgLCzvpnK68hmdmZiIhIQEvvvgirrrqKgwePNgfJdNZcLlcCAkJgaqqWLt2Le6//37ccMMN0HUdAwYMwHPPPQegYoK8H3/8EStXrsSwYcOQlZWF6dOne5Z/o8BX+bmdnp6O1NRUzJo1C3a7HSaT6aRj+Msvv2D27Nk4duwYAHhWDqLgw9BdT1We8BEREcjIyMC8efOwe/duz4d71ZN+3759+PXXX5GbmwsAnht5ClyVD0+qzk5bXl4Op9Pp+b7qMa4MbePGjcPnn3+O1NRUvPPOO2jcuLGPKqazUTVw33vvvbjzzjuRlJSEpk2bIicnBz/88EO1fatasmQJysvLoSgK4uLiatyHiAKHzWbDkSNHAOCkgKUoCoqLi3HzzTdj2bJlSElJwdNPP+1pAafAZ7FYAACzZ8+GrusYN24c9u7di5tuugm6rmPMmDF4+umn8eqrr+LKK6/EfffdB5PJhNWrV8NsNkPTNK4yEaD+fE+mKAo2b96M8PBwXHzxxfjkk0/wxRdfwOFwnHQMv//+ezRv3hwxMTG+LpvqGEN3PVF5wpeUlHhO6qVLlyIvLw9XX3019u7di5deegkHDx4EUH3dzjlz5kBVVaSnp5/0GgWeqktQvPDCCygoKAAA9OnTB5s3b8aHH34IoOI4Vg3lRUVFOHHihOfBC7unBbaqgfvBBx/E/PnzsXz5ciQlJaFTp04YOnQo7rrrLvz4449QFKVaL5asrCx88cUXSE1NrfYzeW4TBZ7Kc7dv375YvXo1Pv74YwAnX8MLCgpgMpkQGhpa7f3sUh7Yql6bp02bhlGjRiE+Ph633HIL/vnPf2L79u0YOXIkdF3HpEmT8N133+HXX3/F/Pnz8dVXX8FiscDtdvM4BzBVVbF3715cd911AIAFCxagT58+2Lt3L/73v/8hNTUV999/P2bPno2ioiIAwJEjR3D//ffj9ddfx+TJk7kOtxH4dgg5+dORI0ekRYsW8vPPP8t7770niqLIN998IyIi9957ryiKItdff7388ssvouu6rF27Vu655x6JjY2VTZs2+bl6Oh2Vk25s2LBBFEWRJ5980vPa9u3bpVGjRnL++efL4sWLT3rv448/Lh07dpSjR4/6rF46O1UnOJw8ebIoiiILFiwQkT9mrF2yZIl07dpVoqOjZeHChVJQUCA5OTny3XffSbt27WTYsGF+qZ2ITq2mibA2btwoSUlJ0rVrV/niiy+qvZafny/Dhg2TwYMHcxKtIPXbb7/Jq6++6rmWi1TMbj1z5kzp0qWLXH/99TVOhMfjHRy2bdsmkZGRcv7554uiKNUmOs3MzJQLLrhAwsPDpXHjxtKjRw/p0aOHNGvWjBMXG4giwv6E9cnQoUPxyy+/oKCgAG+88Qb++c9/el579NFH8d577+HgwYNo2LChZ+zYrFmzPJM8UOCqbOHesGEDevXqhXHjxuHpp5+uts/SpUtxxRVXoFmzZhg+fDhGjRqFVatW4ZtvvsH777+PZcuW8VgHOKnSwj1u3Di8+uqryMjIgNlsxiuvvIKOHTt69v36668xffp0LF68GOnp6SgrK0NycjI6duyIOXPmAPjj94aIAkPVc3LdunUoKipCQkIC2rZti3Xr1qFfv35ISkrCZZddhiuvvBLr16/HJ598gtzcXKxZswYWi4XndZBZu3Ytzj//fKiqijlz5uD666/3HMOysjLMmTMHs2bNQsOGDfH555/z2Aap//73v3jooYfQsWNHrF69GhaLpdpn+ptvvont27ejqKgIvXr1wiWXXOLpZUrBj6G7ntA0DSaTCcuXL0fv3r0RERGBzz77DL169YLVavXst379euzfvx+HDh1C165d0bp16xon3qLAtHv3brRu3RrPPfccHnjgAc/2N954A0OGDEFKSgpWrlyJxx9/HCtWrIDT6URKSgpatWqF559/Hh06dPBj9XQmbrvtNixatAjLli1Dfn4+nnnmGeTk5ODNN9+sdhyLi4uxcuVK7NixA2FhYWjbti169uwJgIGbKNBUvQF/+OGHMX/+fISEhCA/Px+DBw/Gf/7zHxw5cgRPP/00li1bhsLCQnTo0AFdu3bFjBkzYDab4Xa7YTab/fw3oTNRWlqKuXPn4sEHH8Q///lPTJs2DcAf925lZWX43//+hy1btuDNN9/kdTuIVD2nv/76a6xevRpvvvkm2rRpg3fffRcNGzbkOVtf+K+RnXylsitqaWmpZGVlyQ8//CDXXnutNGjQQD7//HNxOBx+rpDqgsvlkmeffVYURZH333/fs33KlCmiqqpn2ICISG5urhw4cECWLFkihw4dqra+JwWmql3KMzMzpVu3brJy5UrPtiVLlsill14q3bt39wwH0XW92vtq+3lE5H9Vz8lp06ZJYmKi/PTTTyJSMQQsMjJSfvzxRxERKSkpkaKiItmyZYuUlJR43sd1uANf1e7gf16P+6WXXhJVVWXKlCmebZXHtLy83PM7wi7lwaHyeC1btkxeeeUVyc3NFRGRLVu2SGpqqgwcONCzTUQ8Qz7JmBi6Da7yhF+8eLHcdtttsmbNGs9rV111lTRo0EC++uorT/B+8803JTMz0y+10pn78wfv7t27ZdKkSRIZGSmLFi2S//3vfxIXFydLlizxU4VU1z788EP5/vvvPce+6kOzqsF78+bNIsKbM6JAt3PnTs9/VwasESNGeILXxx9/LNHR0fK///1PRETKysqksLBQRKoHdT5IC3xVr8fTpk2TW265RS688EKZOnWq7N+/X0REXn75ZVFVVZ599tka38fjHBwqj9PChQslMjJSnnzySdm6davn9S1btkhKSooMGDBAVq1aJY888ogkJydzXh0DY+iuBxYuXChhYWHyzDPPyMaNG6u9NnToUElISJAXXnhB7rvvPlFVVXbs2OGnSuls7N27V2699VbP9/v375cHH3xQwsPDRVEUWb9+vYgwfAU7XdclMzNTmjZtKkOGDJHvv//e81rV1pLK4H3BBRd4gjcRBaYxY8ZInz59qvVaKS8vl759+8pPP/0ky5cvl4iICJk5c6aIVDxke+mll6qd/xR8JkyYIPHx8TJ9+nR55JFHpE2bNjJw4EApLS2V8vJymT59ulgsFnnkkUf8XSqdg19++UXi4uJk1qxZ1bZXPjTbuXOnpKWlScuWLSU1NbVawxgZD0O3wW3cuFFSU1NPOuGrPlkfOXKk9OzZUzp37uwJaBQcdF2Xjz/+WEJCQuT666/3bN+3b5889thjEhERIfPmzfNsZ/AOXpVPzVetWiXdu/9/e/cd12P7Pn789W6H7KxIIuNGGZkhMrNnZvbeZO9NsqLsTUKoED72ylb23puQKO2u3x++7+tTt/v+fe6Bd3E8/7m51n28H5drHNd5nsdZXmnSpMmfJt779+9XGjRooBQoUEB5+vTpD49VCPHXnDlzRilSpIjSvHlz5fTp0+ryPn36KLly5VJMTU2VDRs2qMvfvn2rVK9eXfH09NRFuOIbOHPmjFK0aFH1fO/bt08xMTFR1qxZk2K7GTNmKFWqVJGW7TRs1qxZSu3atRVFUZSoqChl7969Sps2bZTatWur72YxMTHK6dOnlVevXukyVPEDSCG1n9zu3bsZNWoU165dIz4+Hh8fHzZu3MjNmzdxcnJiw4YNALx48YL06dOTKVMmHUcs/pffF79KSEjA39+foUOH4uDgwObNmwF4+PAhS5cuZcmSJSxcuJDOnTsDKYt6iNTr9+cp+d/Pnz9P7969yZcvH4MGDaJGjRoAKYqx7Ny5k7t37+Lm5vbjgxdC/E/ae3loaCht2rShePHiuLm54eDgwO3bt+nTpw/Pnz8nNDQUExMT3r9/j6urKxEREZw4cULmZU4jYmNjMTY2Vv9++PBh+vfvz40bN9i+fTtdunRh9uzZ9O7dm6ioKA4dOkTt2rUxNjZGo9Gg0WjkuZ1GLVq0iFWrVtG1a1cOHDgAQHx8PAUKFMDX15dTp07x22+/6ThK8aNIqbyfXNasWUlISKBDhw7cvn2bPHnyYGNjQ8+ePWnTpg1NmzalRYsW5MmTR9ehir9A+5L27NkzDh48SFRUFI0bN6ZRo0YkJiYyYsQI2rRpw+bNmylQoAB9+vQBwM3NjdjYWHr16iUP7jRCe56WLFmChYUF+fLlo3Tp0gCUK1eOxYsX069fP+bNm4eiKDg5OaWoXNy4cWP1WPLCJkTqo6enR2JiIqVLl8bX15e2bdsyZ84cjI2Nsbe3Z8CAAcyYMYN8+fJRsGBBtG0kp06dQl9fX61sLVKvQ4cOceHCBerWrUupUqWAL+c9e/bsbN68mV69euHu7k7v3r0BOH36NIGBgRQvXpyCBQsCcv9OK7TnKT4+HgBDQ0MaNmzImTNn8Pb2pkqVKri6ulK9enXOnDlDSEgIGTNm1HHU4keSpPsnor3g379/T0JCAqamplSqVAk3Nzf27t1L9erV6dixIyVLluTjx49UqlSJ7Nmz6zps8RdpE+6rV6/i4uKCra0tRYoUIXPmzJiYmNCkSROAFIm3lZUVffv2JSoqiunTp9OmTRsyZswoD/A0Yvv27fTr1w99fX2sra3Jnz8/Dg4ONG/enAoVKrBlyxZcXFxYvnw5SUlJ1KpVCwMDg696Q8j5FiL1SH59apPmMmXKsHHjRtq3b8/UqVOZPHkyzZo1o2rVqvj6+pKQkECuXLlwcXFBX19fphhKA9auXcv48eNxcXHh06dP6vLq1asTHh5Ou3bt8PLyUj+Ox8TEMH/+fNKnT4+1tbW6vdy/Uz/t+/fevXvZunUrly5domHDhjRs2BAfHx/CwsIwNzdXt9+9ezfx8fGYmJjoMGrxo0n38p+E9oIPDAxkwYIFPHz4kBIlSlCpUiXGjh371fYTJ05kw4YNHDt2jHz58ukgYvFP3L59GwcHB3r27Mm4ceNIly4d8N/zHx0dTWBgICNGjKBy5cpqV/MnT55gYmIic66nMe/evaNbt27s27cPLy8vzpw5w927dwkJCaFo0aK0bNmS6Oho/Pz8KFGiBN26daNOnTq6DlsI8Qfi4uIwMjJS/753717evHmDhYUFJUqUIFeuXJw7d44OHTpQrFgxxo4dS/ny5b86jrRwp34+Pj707NmTFStW4OzsTJYsWVKsv379Ok2bNiVHjhz06tWLhIQEfH19efXqFaGhoX/48VSkboGBgbRt25YRI0aQKVMmDh48SGhoKIcOHaJYsWIAHDt2DH9/f9atW8eRI0fU3g/iF/HDR5GL72bPnj2KiYmJsmDBAuXs2bPK2LFjFY1GowQFBanb7Nq1S+nRo4eSPXt2JSQkRIfRir8rOjpaadu2rdK+fXslLi5OXf77IiufP39WNm/erFhZWSn16tX70WGKb+z9+/dKlSpVFHt7e3VmgUOHDimLFy9WbG1tlRo1aigajUbRaDTKnDlzdBytEOKPjBgxQpkwYYISHR2tKIqiDB48WMmePbuSL18+pUiRIoqNjY1y5coVRVEU5ezZs0qRIkWUFi1aKEeOHNFh1OKfePHihVK1alVlxYoVKZZ//PhRuXTpknL+/HlFUb4UtHVyclKKFy+uVK5cWenYsaP6bJf51tOWsLAwpWrVqmqBww8fPijZs2dXhgwZom4THh6u9OvXT6lXr556rYtfi/RN+knEx8fj6+vL2LFjGTRoEGFhYaxbt47+/ftTv3594Etr6PPnzwE4fvy4+uVNpA0xMTGEhoYycOBADA0N1eXarmfar+Kmpqa4uLgQGxuLh4cHz58/x8LCQldhi79A+ZMxe4qikCVLFnbt2oWzszMNGjRg9+7dODk54eTkRKdOnYiKimLbtm18/vxZiqYJkQolJiby6NEjHj9+TMaMGSlVqhTBwcHs3r2bwoULc/PmTTw8PKhatSqnTp2ifPny+Pj4ULNmTQoXLkz16tV1/RPE35CUlMTjx4/Jli2bumz58uUcPHiQwMBADAwMcHV1ZenSpRw6dIiwsDBMTU3JkCEDgAwdSCOSP7cNDAx49+4dtWvX5vHjxzg4ONCsWTPmzZsHQFBQEJUqVWLKlCnAl3pL4tcj3cvTMO0FHxERQaZMmXB0dKR79+7UqlWLcuXKUb9+fZYvXw7Ali1bsLS0pGLFikRHR6vdkkXacfPmTapWrcq6deto0KAB8fHxKZJvLW9vb1xcXMiQIQMJCQmYmZnpIFrxT+zYsYM8efJQsWJFdZn2BezDhw80bNiQ169fs3Pnzj/9aCZdEoVIPZRkxZUGDhzIzZs3sbGxITIyEl9fX3W7J0+e0Lt3b5KSkvDz88PMzIw7d+5QsGBB6Uqexty/f582bdpQq1YtGjRowOLFi7lx4waVKlWiXr16JCQk0LZtWxYtWkSvXr1S7PtnH2BF6uTv78+bN2+oVasWHTt2ZNCgQYwePRonJyeWLVuGnp4e9+/fZ9q0abRt21aGf/3i5M0sDdNoNGzbto0BAwbw8OFDbGxsuHDhAg4ODikS7vDwcPbv38/FixdRFEUS7jQqb968ZMiQgW3btgFfKmMmJSWl2Gb//v3s378fAFNTU0m40xA/Pz9atmyJk5MTo0aNYvXq1QBqi0fmzJnZvXs3OXLkoHnz5ty8efMPjyMJtxCph0ajITExEUNDQzw9PSlcuDA7duzg8uXL6v1bURQsLS1p3Lgx9+/fJzo6GoDChQurVcpF2lGwYEFatWqFv78/Li4uXLt2DQ8PD6ZNm0aTJk1wdnamRIkSvH79+qt9JeFO/bRtlbdv36ZVq1YYGhpSsGBBSpcuTZs2bbC3t2fFihXqs3jlypVcvHhRpgYTknSnRdoL/vnz54wcOZLKlStToEABGjVqxKJFi8iaNStz585Vt58zZw5Hjx6lfv368kKexmjP9eHDhwkODsbV1ZXt27fj7e0N/DfB0m53/PhxTExMpCJmGmRjY0OPHj1Yt24dRkZGeHl5UalSJXx8fHjy5AnwJfHet28fmTNnpkKFCrx8+VLHUQsh/ow2qda2VBsZGeHt7Y2rqyvh4eFMmTKFiIgINdEqVqwYSUlJfPjwIcVxpKU7dfv9x2/4MovIf/7zHw4dOsSVK1eoXbu22t08MjISIyMjrKysfnCk4lvQaDQEBwdz584dRo4cSdeuXQGYPXs2Li4uHDhwgCVLluDp6Um/fv3w9vZmw4YN5M2bV8eRC12T7uVp1IEDB7h06RJ37tzB09NTbb1esWIFvXr1ok2bNujp6aHRaNi9ezeHDx9W5/gVqV/yLmbaDyYBAQFky5YNNzc37t27x6BBgxg+fDiKovD48WO8vb1Zs2YNx44do3jx4jr+BeLv+vjxI05OTjRp0oTx48fz6dMnFixYwKFDh3j06BETJkzA1tYWe3t7IiIiWLRoEePGjdN12EKIP5B8mEdISAhZsmTByMgICwsL4uLiGDBggNozbciQIURFRTF06FCio6M5duyYfCBPI5Kf5w0bNvDs2TOePHlCnz59KFCgQIreZoqiEB4ejqurKx8+fOD48ePyQSUNio6OpmbNmpw5c4bGjRsTEBCgrouIiGDChAkEBweTmJhI4cKFGTduHCVLltRdwCLVkKQ7jRozZgyzZs3CysqK06dPkzNnTnXdzp07CQoK4sWLF9ja2uLq6krRokV1GK34p54/f46vry8xMTFqgnXu3DmmTZvG7t271fNqbm7Oixcv8PPzkyko0iDtuO2TJ08ycOBAPD09qVq1Kk+ePKFEiRLY2dkRFRVFXFwcBQsWxNfXV/3QJmO4hUi9Ro4cyYYNG1AUhYoVK9K7d2/q1q1LbGwsQ4YMYePGjZiamlKpUiVMTEzYsGGDOnRIruu0Y8SIEWzYsIFGjRpx7949Hj58SN++fRkyZIhaZGvnzp1s2bKFt2/fcvr0aQwNDWX6tzTq+vXrjBkzhrNnz3Ly5EkKFSqU4pp99+4dGTJkQFEU6XkoVFIeMY2aMWMGmTNnZtSoUezYsYM+ffqo6xo3bkyDBg3Q19eXohxpxB+dp4cPH1KwYEEyZszImDFj1OXly5dn6dKl3Lhxg6CgIBISEqhUqRJVq1aVOdfTkOXLl5M+fXrat2+PgYEBiYmJFChQgJw5c/Lx40fCw8Oxt7enWbNmrFu3jtu3b3PmzBlOnz6doi6DvJgLkXokv5cHBweza9cu/Pz8uHnzJocOHWL48OHExsbSuHFjFixYgLGxMZs2baJp06Z06tQJjUYj1avTGH9/fzZv3sy+ffuws7Pj8OHD1KpVi0KFCqnn8dixYwQEBFC4cGF2796NgYGBnOc0QntNK4pCfHw8RkZGFC9enPnz59OuXTvq1avHqVOnyJEjh1rgNmvWrPLuLb4iV3saoL3gIyMjiYmJIXv27MCXL6vv379n4MCBmJmZ0aFDB3Uf7Yu4XPSpn/br6OfPn/n8+TPXrl3DxsaGAgUK4OPjQ/v27bl48SJhYWGYm5sDkCdPHvLkyUOtWrV0HL34J968ecOhQ4cICQnBxMSEFi1aoK+vj4WFBXXr1qVFixYYGhrSqlUrvLy8AChSpAhFihShU6dOgFS5FSK1+X3rdHx8PHXr1sXBwQEHBwdsbW1ZtGgRY8aMQaPR0KhRI2bNmoW5uTkdO3ZEo9GQlJQkiVgaExYWRtmyZbGzs2PTpk306dMHb29vmjVrRlRUFK9fv6Z58+ZUqFCBPHnyqMX15Dynftrn7P79+9m6dSt3796lbt261KhRg0qVKrF582ZcXFxwcHAgODiYHDlySC8V8afkX0Uqp73gd+7cScOGDSlbtiyNGjVi7ty5JCUlMWvWLIYOHUqXLl3w8fFR95OX8bRBe3O+c+cOffr0oWrVqjg7O1OsWDHatWuHo6Mj27dvx8/Pj2XLlhEREZFiXy0ZJZK25MiRQ51WZPz48WpFeoBu3bpRoUIFatSogZeXF6ampn94DLnGhUhdtC/aHh4etGnThgULFvDp0yd1ffny5Rk4cCBlypRh3LhxbN26FWNjY8aMGYOenh6JiYnysp6GaJ/BL1++RE9Pj3PnztG7d29mzpyp9j7csmULK1as4PPnz1hYWKgtptKlPPXTvn8HBgbSuHFjFEWhSJEirF69mvHjx7N582asrKzYvHkzuXLlolixYoSFhck1LP6U/MtIxZKSktBoNOzbt49WrVrh6OjIpEmTMDMzY/PmzfTr14+kpCTc3d0ZOXIkrq6ubN26Vddhi79Im3BfuXKF6tWrky5dOkaNGkVoaCh9+/blzJkzODk5YW9vj4+PDxMmTGDx4sV8/PgRSNmtWBKw1Ov3H0S00/+UKlWKvn37UrlyZSZMmICfnx8AZmZm2Nra8vz5c9KlS6e+pAkhUqfkH0CnT5+Ou7s7+vr6vHz5ktWrV7Nr1y51fbly5Rg0aBCWlpbs3LkT+O89QhKx1O33Vcq1z+C2bdsSHBxMxYoV8fLyom/fvgDExMSwfft2wsPDU3w8led16qQ9v7GxseqysLAwZs6cyfTp01m1ahXLly/H39+fnDlzsnLlSkJCQrC2tmb58uWULVs2RcOIEF9RRKqRmJioKIqivH//Xl0WHR2ttG7dWnFzc1OXff78WfH09FTKli2reHt7q8unTZum3Lhx48cFLP4x7bm+fPmyki5dOmX06NFKfHx8im22bNmi2NraKuXLl1diYmKUpUuXKoaGhsr48eOViIgIXYQt/oV79+6p5z0hIUFdfunSJaVbt27Kb7/9pvj7+yuKoigRERFKzpw5lfHjx+siVCHEP3D79m3F3d1dOXr0qKIoinLnzh2lR48eSqZMmZRdu3al2PbmzZvq/UCkfklJSeqft2zZosyYMUM5evSo8vbtW0VRFGX+/PmKpaWlMmzYMOXx48fKsWPHFGdnZ8XW1lZ9tic/hkhdtNfilStXlDZt2iivXr1SFEVRPn78qBQuXFhZvny5oij/PYdXr15V8uXLp3h6eqrHiIuL+8FRi7RGWrpTCW2r5+XLl6lUqRKXLl0CwMTEhPfv3xMeHq5ua2pqSq9evcibNy+HDx9Wl48dO5ZixYr96NDFP6Cnp8fTp0+pWbMmDRo0YMaMGRgYGKAoCgkJCQC4uLjQv39/rl+/zvr16+nVqxeTJk3Cy8uL+Ph4Hf8C8XesX7+exo0bs2fPHpKSktDX11dbvO3s7Ojfvz+lS5dmwYIF3Lx5k3Tp0uHg4CDd1IRIIw4ePEjRokWZN2+euszGxoZRo0bRpk0bXF1dCQoKUtcVLVoUPT29P5zjWaQ+2tbpcePG0atXL7Zs2ULTpk2ZMmUKDx48oHv37ri5ueHj40Pp0qUZOHAgGo2GCxcuqIUypYU7dUr+/l26dGmKFClCzpw5URSFmJgYjI2NefHiBfClp5qiKJQoUYIKFSpw/Phx9Ro2NDTU5c8QaYC80aUCyS/4ChUq0KJFC0qVKqUmYAUKFODp06eEhYWpF7exsTGOjo7cu3ePyMhIHf8C8U9oq1XHxsZy8uRJ4MuDXZt8A/To0YOyZcuyZ88e4MtUcffv3ydbtmw6i1v8fc7OzmTIkAF3d3f27dv3VeJdqlQpevbsyYMHD3j27BkGBgbMnTuXSZMmATJmX4jUrkyZMowdO5Z3795x584ddbm1tTUjRoygbdu2NGrUiFOnTqXYTz6spW7ady7l/+bYvnz5Mvv27ePSpUt4eHhw4sQJZsyYwevXrxk4cCB37twhKCiIgIAAdu3ahaGhIQkJCTJ0IJVKPsyvUqVKjBo1Sn3uajQazM3N6dWrF1OmTCEwMBADAwP140lsbCwFChSQa1j8ZfIvRce0F/zVq1epVKkSw4cPZ/r06cB/E7ChQ4dy6tQpxo4dS1hYmLrv9evXsbS0xMjISFfhi3/BysoKHx8f4uLimDZtmpp4/56enl6KKaIyZ878gyIU/8TvE+SYmBjMzc3Zv38/RkZGTJ48mb17936VeFerVg0zMzPu378PfPn3oT2etJAIkXr8Uet01qxZGTduHP3796dPnz7s2LFDXWdtbc3gwYNxd3enfPnyPzJU8S8kr0L96NEjPn36RLZs2ShSpAgA3bt3Z8CAAYSEhDBr1iyuXLlChgwZqFixIlZWVmpPBqlSnnppC9mWKlWKoUOHMm3aNHXd0qVLOX36NP369WPo0KE0a9aMUaNGMXv2bIYMGcLRo0fp2rWrDqMXaY3cCXRMT0+Phw8fYmdnR48ePZg6daq6zsPDA4Dhw4cTFBREgwYNuHfvHpkzZyZDhgz4+/sTHBwsSXcaZmNjw8KFCxk4cCDTpk1j/PjxODg4qFPHvHjxAlNTU+rUqQNIApbaJT8/vr6+XLhwgZMnT1K0aFEaNWrE3r17cXZ2Vj+s1a1bV30hu3XrFkZGRhQoUCDFMeV8C5F6JE/Eli9fzvXr1wkLC8PZ2ZkWLVowf/584MvwID8/P5o1awZA4cKFGT58OIDMz5xGaM/zyJEj2bJlC+/fvyd9+vTcu3cPe3t7ALp06YKenh7e3t5MmTIFDw+PFPdwaQVN/a5evQpAtmzZ1Gf47NmzGTVqFCdOnAC+vI/b2Niwfv16oqOjyZ49OydOnJAhneLv0c1QcqEo/y3I8Pr1ayVdunRKvXr1lJs3byqKoiizZ89WjIyMlAMHDqjb37p1Sxk+fLjSqlUrpXfv3sr169d1Erf49u7cuaPUq1dPqVu3rnLixAl1+ciRIxU7Ozvl6dOnOoxO/F3Dhg1T8ufPr7Ru3Vrp1q2bUrhwYUVfX1/p0aOH8uLFC6VWrVpK+fLllSVLliiPHz9WLl68qJQtW1Zp06aNrkMXQvwFw4cPV3LkyKFMmjRJ6dq1q1KwYEGlU6dOSmJiovLhwwfFzc1NMTY2Vnx8fHQdqvgbEhMTUxQ8O3r0qJI3b15l586dyvTp05XSpUsrdevWVc6fP59iP29vb6VLly5SHC+NWr58uaKnp6d4e3srs2bNUrJly5bi/Vvrw4cPSlxcnBIZGamDKEVaJ0m3jsydO1fx8/NToqKiFEVRlGfPnik5c+ZU6tevrwwbNkzJmjWrcujQIXV7bbVj7Q3995WuRdqXPPEOCQlR3N3dlQwZMiiXLl3SdWjib5g7d66SK1cu5fz58+p1+uTJE2Xu3LmKsbGx0rNnTyU+Pl7p2LGjUrx4ccXExESpUKGC0qFDB/UY8uImROp1+PBhpVChQsrZs2cVRVGUwMBAxcTERFm7dq26TUxMjNKlSxelatWqugpT/Etr165VBg0apMyePVtdtn37dqV27dpKgwYNlAsXLqTYXpusy/07bfh9NfnFixcrenp6ikajUf7zn//84bZSgV78G5J060jdunWVDBkyKLt27VIT76dPnyr58uVTNBqNsnr16j/cTy78n9udO3eUhg0bKjly5FAMDQ2/eqiL1CspKUmJjIxUateurU4jkpSUpF6rHz58UObPn68YGBgoGzduVOLi4pT79+8r+/fvV65evaoeR17YhEjdtm7dqtjb2yuKoih+fn6KmZmZsmTJEkVRFOXTp0/KwYMHlcTERCU6Olqu5zSiTp06yty5c9W/P3jwQKlTp46SIUMGZcyYMSm21SbejRs3Vk6fPp1inbybpQ3a83Tp0iXl7Nmz6nRfGzduVPT09JTZs2fLtSu+ORls8oMp/1dkad++fTRs2JCOHTty8OBBPn/+TN68eTl37hy5c+dm06ZN3Lx586v9teM7ZZznz8nGxoY5c+ZQsWJFQkNDKVu2rK5DEn+RRqPhw4cPnDt3DhsbmxTLATJlyoSLiwslS5YkKCgIQ0NDrK2tqV27NiVKlAC+3B9kDKAQqccfFU3T09MjT5487Nq1iy5duuDu7k7v3r0BOHr0KIGBgbx+/RoTExOZFiyN6N69O0OHDlX/XqBAAYYNG4ajoyMrV67k7Nmz6rrmzZvTp08fnj59mqJgHsi7WVqg/N+4bX9/f+rVq8fJkyd59eoVAO3bt8fb25tRo0bh7u4u1674tnSc9P+StF3FFUVRWrVqpWTJkkUJDAxM0eKdM2dOpUaNGuoYb/Fr0X51FWnLx48fFXNzc2X69OlfrdN+WR83bpxSvHhxJS4uToaJCJGKJW/p2rp1q1pH5fXr10q2bNkUjUajrFy5Ut0mOjpaqVevnuLq6iotnmmIr6+vMnPmTEVRFMXDw0Pp1auXuu7w4cNK48aNlTJlyqjDCbSOHj0qraFp1L59+5QMGTIoixcvViIiItTl2uvW29tbMTY2VsaNGyfXsvhmpElFB/T19UlISABg69at1KpVi86dO6do8b5w4QJ3796lffv23L17V8cRix/N0NBQ1yGIf0Cj0ZA/f36CgoLUqb8g5TRi4eHhVKpUSc6xEKmYkqzXyejRoxkxYgQBAQFERESQI0cOtm7dSubMmTl27BhBQUHs2rWLxo0b8/z5c1avXo1Go/lq+kCR+ixdupT27dtja2urLtu0aRMjRowAoEaNGgwYMABLS0v69u3L+fPn1e0cHR2lJ0MaFB8fz6pVq+jUqRN9+vRBX1+fW7duMXXqVObNm0dYWBh9+/Zl5syZLF68mPfv3+s6ZPGT0CjyVPhhlGTTCSUmJqKvr6+ua9WqFYcOHWLt2rXUqlWLdOnS8eTJE2rXrs3+/fvJnz+/rsIWQvwNR44coU6dOrRv354JEyZgbW2trnvz5g2Ojo68fPkSKysrXF1d6du3L6ampjqMWAjxZ6ZNm8aCBQvYu3cvJUuWxMTERF136NAhBg4cSFRUFDly5MDS0hJfX18MDQ2/esaL1GfNmjX07NkTPz8/mjZtCsCHDx/w9fVl3LhxdOnShTlz5gBw8OBBlixZwrlz59i/f79MFZWGJSYm0qFDB9KnT0/37t1Zt24dDx484Pbt2+TIkYO8efPi6+uLsbExHz58IHPmzLoOWfwkZKLIH0SbcB88eJDAwEAePHhAmzZtcHJywsLCAj8/P1q2bEnnzp1Zv349NWrUwNLSkhs3bsiDW4g0pEaNGnh6ejJ48GCePHlCs2bNqFGjhvolPVeuXEycOBF9fX2qVasmCbcQqdTbt285duwYixcvply5cjx9+pQ7d+6wevVqKlSoQO/evTl37hzv3r3DxMQEc3NzNBqNzMOdBmzYsIFu3boxYMAAmjZtqr6jZc6cmbZt25KUlMSECRMAmDNnDrVq1SImJobChQtTuHBhHUcv/o7kDV7wpbepg4MDixcvZtOmTTRq1Ihu3brRrFkzZs6cyblz5zA2NgaQhFt8U9LS/QMFBATQqVMnGjVqhLm5OatWraJr16507tyZUqVKAdCmTRu2bt1KUFAQzs7OX90shBCpn6Io7N+/n8GDB/Ps2TOio6Oxt7enVKlSLF26VNfhCSH+gs+fP1O6dGmqV6+Oq6sr8+fP59mzZ2TOnJkDBw4wdepUxo4dm2KfpKQkKYaYyi1btoz+/ftTp04djh07xvLly2nXrl2KbT58+ICPjw8TJ06ka9euzJ49O8V66cmQNmjfoU+cOMGlS5e4desWrq6uVKhQgadPn/L8+XMqVaqkXrcDBw7kyZMn+Pr6ygdx8e3pYiD5rygkJESxsrJSVqxYoS4zMzNTMmfOrHTu3DnFlEGdOnVSbt26pYswhRDf0Pv375Vnz54pISEhyuvXr9XlyYspCiF0788KYq1bt07Jnz+/kj59emXEiBHKwYMHFUVRlAEDBijt2rWTIktpzJIlSxSNRqP4+/sriqIow4cPV4yMjJRNmzZ9tW14eLiyePFiRaPRKIsWLfrBkYpvZfv27UrGjBmVTp06KU5OToqtra3SpUsXJTIyUt3mzp07ysiRI5WMGTMqV65c0WG04mcm/Z9+kPDwcNq3b0/37t158uQJ1apVo1u3blStWpWWLVtiampK586dKV++PGvXrtV1uEKIbyBLlixkyZIFCwsLdZmiKNJCIkQqoiQrmrZmzRquXr1KsWLFcHJyomPHjtSvX5/379+r3YoVReHatWtUqFBBeqKlEatWraJbt26cOXOGbdu2qWO4tS3YnTt3RqPR0KZNG3WfzJkz07p1a3LmzEmTJk10Ebb4l27fvs3w4cOZO3cu3bt3JywsjLx589KkSRPSp08PQEhICGPGjOH169ccP36ckiVL6jhq8bOS7uXf2a1bt8iSJQtGRka8fv2aQoUK0apVK7JkycLixYsxMTGhVKlSXL9+nQEDBjBz5kx1LIkQQgghvh8l2RCusWPHsnTpUkqWLMnz58+xtrZm5MiRODk5AfDp0ydCQ0Nxd3fn2bNnXLx4UcZupwH79++nXr16TJo0SR2nHR8fn2IGiREjRuDp6cm6detSJN7JyVj91G3z5s2ULFmS4sWLq8tOnTpF3759uXTpEnfv3qVWrVrUrVuX5cuXA3DlyhVsbW0JDg7GysoqxQdyIb41GXj0nSiKwuPHj2nUqBGXL18mS5YsFC1alKioKJ4/f07VqlUxMTEhOjqa8uXL4+npSd++fSXhFkIIIX6A5An35cuXefHiBXv37uXo0aN4e3uTLl06xo8fz9GjR4EvL/BeXl4kJSVx4cIFDAwMSExM1OEvEH+Fvb09S5YsYdGiRQwYMAD4Mi2ndupW+NLiPWjQIDp37syWLVv+8DiScKdeFy5cwNvbm4wZM6ZY/vHjR8zMzHjz5g21atWiTp06al2VkydPsnr1ap49e4aDg4Mk3OK7kzvId6Kdr7dw4cJMmTKFOnXqAF+6mYeFhXH//n3Onj1LUFAQx44dY86cOV/dLIQQQgjxfWgT7q1btzJv3jyMjY0pUqQIAHXq1MHAwIBFixYxbtw45syZQ926dcmZMye2trbo6elJy2cakJiYSNasWenWrRtGRka4ubmRNWtWJk+erH400Q73mT17Nnp6erRt25Zs2bJRq1YtHUcv/ip7e3t27txJlixZuHr1KoqiYGtrS9WqVXn48CG5cuVi8ODBzJs3T90nICCAK1euSME08cNIS/d3EhsbC8CECROIjY0lMDAQACsrK0aPHs2iRYto3749q1atwtfXVxJuIYQQ4gdYsGABvXv3Vv/++fNndZz248eP1eVOTk4MHDiQnDlz0rFjRy5fvkypUqXQ09MjKSlJEu5ULnn9jGXLlnH27Fn09PSYOnUqI0aMAL5MH5W8t8KsWbPw8vKievXqughZ/APa85clSxbCw8Np164dM2fO5PLly6RPn56VK1diYWHBy5cvefz4MWfOnGHEiBGsXLmShQsXki1bNh3/AvGrkDHd39jdu3exsbFR/x4REUHjxo2xsrJi3bp16vLLly+TmJhIrly5yJMnjy5CFUIIIX4pK1asoFevXvj6+tK6dWt1ub+/P+7u7mTPnp3p06djZ2enrtu7dy/Hjx9n2rRpUgQxDZo4cSJeXl4sXboURVE4evQovr6+dO7cmfnz5wN/PAWY9GRIW7Zt20bFihUJDg5m/vz5/PbbbwwbNozffvsNf39/Bg8eTHx8PBkzZiRjxowsX75cna5XiB9Bku5vQDsu7Pr16/Tr14/IyEjmzZtHwYIFsbCw4Pjx4zRp0oS1a9dKBUwhhBBCB5YtW0a/fv3Yvn37Hz6Lt2zZwvLlyzEzM2PKlCnY2tp+tY3Mz5y2fPjwgaZNm+Li4kLfvn0BePv2LT4+PowdO5bBgwczbdo0QOZYT8tCQ0MpW7YsXl5e9O3blw0bNrBgwQLs7OwYPnw4xYoVIzo6mosXL5IzZ06yZs0qLdzih5O7yzeg0Wjw8/Oje/fujB8/HgsLCwYOHEirVq3Yvn07lpaWNG/enFOnTpGYmCiFV4QQQogfaNWqVQwaNAh/f/8UCbeLi4taybh169b07NmTyMhIJk+ezMWLF786jiTcaYuRkRFPnjxJMWwge/bsdOjQgYoVKzJjxgx1qIEk3GnTlStXOH/+PNOmTVM/rLi6ujJ48GAuX76Mh4cHly5dwtTUlCpVqmBjYyMJt9AJucN8A48fP2b69Ol07tyZmjVrEhgYiLu7Ow4ODnTs2JHJkydz+vRpVq5cyYsXL+ShLYQQQvwgL1++ZPTo0ZQtW5ZGjRqpy1u2bMmNGzeoV6+eukybeN+7d49t27bpIlzxDyUlJX21LF26dDRv3pyrV69y6dIldXm2bNkoXbo01atX5/Xr13+4r0j9nj17Ru/evRk6dCgxMTEAxMXFAf9NvG/cuMGMGTO4ceOGLkMVQpLufys0NBQPDw9+++03OnbsqBZQq1u3Lh4eHuzbt488efKQlJREeHg48fHxOo5YCCGE+Pl9/PgRgEyZMuHt7c3169fp378/AK1ateL27dsEBQVhaWlJ8pF2Li4uzJ8/X+12LFK/5F3DL126xOHDh3n48CEJCQm0bduWR48esXTpUs6fPw9AZGQk9+7do02bNvj7+6vF8UTaki1bNtq3b4+lpSVBQUEkJCRgZGSUIvHu3r07YWFhZMmSRcfRil+djOn+F2JiYujfvz+7d+/GwsJC7Yr2++IbCQkJREZG8vHjRywtLXUVrhBCCPFLGDVqFEuWLOHu3bvkyJGD+Ph4du/eTYcOHciQIQMWFhbs3LmTvHnzppive8qUKfTs2ZNcuXIBMoY7LUh+/kaPHs327dtJTEwkXbp0lCxZkkWLFnHmzBkmTJigFtL6/PkzCQkJhISEYGBgkOIYIvVKfp7i4uLUBHvLli1Mnz4dW1tbNmzYgLGxsboevnyAk1mChK5JS/e/YGJiwujRo2nZsiX37t1j5syZABgYGKT4Yqqnp0fmzJkl4RZCCCF+gE6dOlG8eHGqVq3KmzdvMDQ0pGHDhmzatAkjIyMKFy5M3rx5gf/O112/fn28vb0xNzdXjyMJd+qnPX8LFy5kzZo1rFixgvv371OtWjWCgoK4evUqDRo0YPXq1YwaNQpbW1tatGihJtyJiYmScKcB2oR7//799OnTh1q1auHh4cG9e/dwdXVl5MiRPHz4kE6dOqkJt7Z3qSTcIjWQlu6/QXvBv3nzBviSTGfPnp3Xr18zceJEQkNDad++PQMHDgSkEqYQQgihK3fv3sXV1ZV3794RHBxMjhw5iI6OZt++fbi6utKuXTu1iJqzszOPHj3iypUrGBoayvM7DVEUhcTERNq1a4e9vT0jRoxg9+7dtGvXjjlz5tCzZ09iYmJITEwkffr0KfaVacHSloCAAFxdXenSpQv58+dn0aJFWFlZsXHjRszNzdm4cSMrVqwga9asBAQEqC3dQqQG8kT5i7QJ986dO6lXrx61atWiZMmSzJkzB2NjY8aPH0+pUqXw9fXFy8sLkEqYQgghhK7Y2Niwfv16smXLhoODA2/evMHU1JQGDRqwYcMGfH196dOnD82aNePBgwdqwp2QkCDP71QueXuRtpU6OjqaypUrc/jwYdq2bYuHhwc9e/YkPj6e9evXc+LEia/GbUvCnXa8fPmSqVOnMmvWLBYuXMiQIUOIiIigXLlyWFhYYGxsTMeOHXF1dSUmJoawsDBdhyxECvJU+Yu0XVratm1L586dOXjwIN26dWPEiBGcPn0aCwsLxowZQ+nSpfHy8mLZsmW6DlkIIYT4ZfxRISwbGxvWrVuXIvE2MjJSE+9ly5Zx7do1rl27pibckoilbklJSWqi/eTJE+BL8pwpUyZat25N06ZN8fb2plevXgCEh4ezefNm7t+/Lx9T0gDtdfz76XUNDAzQaDS0b9+e+/fvky9fPlq1aoWHhwcajYaTJ0+iKArdu3dnx44dWFhY6CJ8If6U3H3+AkVRUBSFLVu20LNnTwYOHEhMTAx+fn706NEDZ2dnAPLnz8+wYcNwdnambt26Oo5aCCGE+DUk7w5+9epVrl+/zr1799BoNBQuXJgNGzaQNWvWFIl33bp1OXHiBLdu3ZKEO41Ifp6nT59O3759OXbsGADu7u4UKlQIc3NzXF1diYqK4u3bt3Tu3JnY2Fh1Pm6RemnP78OHD/H29ubUqVNqr4ZPnz7x5s0bjh8/Tr169WjQoAFLliwB4ObNm3h6enL+/HmMjY3JnDmzDn+FEH9Mku7fSd5lKSEhQV2m0Wh4+PAhVatWJSYmhkqVKlG9enWWLl0KwNq1awkJCcHa2hoPDw+srKx0Eb4QQgjxS1EURU3EJk6cSIsWLWjatCnly5dn06ZNaDQabGxs2LhxI9mzZ6datWq8evUKU1NTHBwc0NfXJzExURLuVC75eR4xYgSenp707NlTfd/KmTMnY8eORU9PDwsLC6pUqULDhg158+YNR48eVc+zSJ20CffVq1epU6cOR48e5f3792qvBmtra5o2bUrTpk0pUaIEy5cvVwsdbty4kQcPHlCgQAFd/gQh/r/kCZOMNrkOCwvDzMwMExMTDh48SKZMmShXrhwlSpRg3rx5DBo0iGbNmjF//nw0Gg2xsbHs3r2bp0+fYmdnJ9VOhRBCiB9E+1I+efJkli1bxsaNGyldujSDBw+mU6dOhIWFMWjQIGxsbNiwYQN16tRh8ODBbN68WT2GPLdTrzt37lC4cGH1PB88eJBt27axZ88e7O3tSUhI4N27d9y8eRMnJydCQ0NZuXIlenp65MiRg1atWqGvry89GVI5PT09bt++jaOjI7169WLIkCHkyJEjxTY9evTg0aNHhISEsG3bNmJjYzl//jyrV6/mxIkT5MmTR0fRC/G/yd0nGY1Gw9u3b2nfvj1lypTB1taWDh06EBAQAEDTpk05deoUZmZmzJo1C0NDQxRFYfLkyVy8eBF3d3d5cAshhBA/QPKuxleuXOH48eOsXbuWWrVqERgYSFBQEA0bNmTo0KFoNBr69+9PoUKFOH78OLlz59Zx9OKvaNmyJTY2NuqUrIDa+mlvb8/NmzfZvHkzPj4+vHjxgipVqrB//34GDx6c4jjSkyF1UxSFuLg4pk6dSuvWrVOc79jYWF68eEF0dDTFixdnwYIFzJgxg969e5MvXz5y587NyZMnsbW11eEvEOJ/kynDficqKorZs2fj6+vL48ePWbx4Md26dQO+3LQXLlzIxo0biYuLo1y5crx9+5ZTp05x4MABSpcurePohRBCiJ+ftmcawL1797CysmLp0qX06tWLU6dO0a5dO0aPHk3//v1p1qwZe/fuZcKECYwZM0Y9RmJionwoT+UuXrxIyZIlMTIy4uXLl+TOnZsrV67Qtm1bjIyMeP36Nc7OzlSsWJGKFStiZ2fHrl27aNCgga5DF3+ToijUrFkTZ2dnhg8fDsDevXvZs2cPa9euJVOmTFhaWnLw4EHSpUvHs2fPyJEjB/Hx8V9NBSdEaiRjupNRFIX06dPj7OzMmzdvyJ07N48ePSI2Nhb40v1swIABeHh44OTkRGRkJHZ2dpw6dUoSbiGEEOIHSJ5wu7m5UbduXeLj4+nUqROGhob4+Pjg7OysVq/OkycPJUuWZO/evSnqtkjCnbolJSVRtmxZjIyM8PLyokePHly+fBlbW1sWL15MvXr18PLyYvbs2fTo0YMcOXJQvnx5smbNquvQxT8QExNDREQE58+fJzQ0lKlTpzJw4EDCwsKYO3cuc+fO5ePHjwwePJjExERy586NkZGRJNwizZC+NsloH+JZs2YlMDCQY8eOsWfPHmJjY5k6dSrGxsYYGBjg5OSEk5OTjqMVQgghfj3aZ/WlS5d48OABGzZswNTUFPjy4n7lyhUqV66sViR/8eIFc+bMwdHREUiZtIvUKXnRNIBixYrh7u7O7NmzGT16NI6Ojur5jI+PJywsjO7du6Onp0f58uV1Fbb4h5KSkjA1NWXFihU4Ojpy7tw5IiIi1EYua2trFEXB19eXjx8/ygczkSZJ0s1/H8CPHz/G0NCQTJkyUbhwYUqVKkVsbCyHDh1i4sSJTJkyBSMjI1asWEHp0qWxt7eXh7cQQgjxAyR/3m7atIlly5ZhaGhI6dKl1a7iJiYmNGnShHHjxhEeHs6VK1dISEjAwcHhq2OI1Et7jtzd3cmfPz9t2rRh3bp1dO/enRkzZjBs2DDKlClDfHw8mzZtYt26dURGRhIcHKxWKZfELPXTXo/aDyxlypThwYMHvHr1ijx58pAtW7YU26VPn568efOqc7XLtSzSEkm6+XJz37FjB0OGDFGT7qlTp1K/fn1GjhwJwJEjR2jbti0FChRg3rx53Lx5U91XCCGEEN9P8qJpAGFhYbx584YPHz4QHR2NqampWp3azc0NQ0NDTp06Rfny5Vm4cCEGBgaSiKVB165dY926dTRq1AgnJydWr15N165dmTt3LsOHD8fOzo4sWbJQv359Bg8ejIGBgVQpTyO0ifSpU6cIDg4mLCyMmjVrUrduXczNzVNsqy2ydvjwYY4dO5biXiBEWvFLF1LTXvDPnj3D3t6eSZMmkS5dOk6ePMmaNWvYunUrzZo149OnT6xatYrDhw8THh6Ol5cXdnZ2ug5fCCGE+OkdO3aMkiVLkjVrVkaPHk3WrFkZPnw4y5cvZ86cOZQpU4YFCxaQK1euFC3ZcXFxGBkZAUgilsZoz2NwcDBubm6MGTOGxo0bA1/+PXTt2pVKlSqpibeWfFhJW3bs2EGvXr2oUKEC2bJlY8OGDUycOBE3NzcyZMgAwKpVq7h8+TJ+fn7s2bNHaiiJNOuXSrqTP4y1fz5y5Ah37tzh0aNH6hQFr1+/Zvr06Xh5ebF9+3aaNWtGfHw8+vr6fP78Wb0RCCGEEOL7+fDhAzY2NtjZ2VGwYEE2b97MiRMn1OmBFi1axObNmylSpAgzZ84kZ86cXyVe0qU89Ut+jpL3akhKSqJu3boYGBiwd+9edfvjx49Tr149RowYwaRJk3QRsviXbt26RZ06dRg3bhw9e/YkMjKSLFmyMHz4cGbMmAHAo0eP6NOnD2ZmZkyePJlixYrpOGoh/rlfKukGePPmDTly5ADg06dP9O7dG19fXxo0aMCuXbtSbDdt2jSWLVvGhg0bcHFx0VXIQgghxC/r3bt3WFpaotFo2L17N9WrV0+RWHt6euLn50fRokWZOnWqzMGdxiRPsv38/Lh9+zZubm5qcbxr165Rt25d5syZQ9u2bdUE/dKlS5QsWVJattOos2fPMmrUKI4cOcL9+/dxdHSkQYMGLFu2DIDHjx+TP39+3r9/j4GBARkzZtRxxEL8O7/UoIjIyEjs7e1xdXUFwMzMjGHDhtGlSxf279/PqVOngC9fXHPkyMG4ceNo3749ffr0ITIykl/s+4QQQgihE0lJSeqfP3z4QEJCAqampri7u/P69Wv09fXVbQYNGkTr1q05evQoa9as0VXI4h9IXqXc19cXb29vdu/ejY2NDStXruTy5cuUKFGCypUrc/r0aeDLvw1FUShVqpRaNE2kPR8+fODJkydcuHCB2rVr06BBA5YsWQJ8qaM0YMAAnj9/TtasWSXhFj+FX6KlW1tkJS4ujm3bttG3b19at26tfk27ceMGkydP5tChQ+zatYtKlSqpX1LDwsJISkoiZ86cOv4VQgghxM8vecvnkSNHsLe3x8zMjGfPnlGpUiWKFSvGxo0b1V5rWkFBQdSrV09aPtOI5F3Kp0yZwu7du1myZAkFCxZk+vTpnDp1ipcvXzJ69GjevXvHlClTOHHiBGXLltVx5OLv+qMhHm/fvqVjx44cO3aMpk2b4uPjo243evRozp07x5YtW8iePbuOohbi2/rpq4qsXbuWESNGcP36dczNzWnZsiXGxsZ07NgRgGXLlvHbb78xYcIE9PX1ady4Mbt27aJixYoAX1VQFEIIIcT3kbzlc9SoUezZs4euXbvSpUsX8ubNy4EDB6hTpw6dOnVi1apV5MiRgw4dOlClShX69+8PSDGttEKbhN24cYNr164xbdo0NaH28PDg+vXrBAcHM3HiREqWLElMTAw7duyQpDuN0SbSp0+f5tatW5iamtKmTRuyZ89OkyZNePDgASYmJty+fZuoqCi2bNnC8uXLOX78uCTc4qfy07d037hxA1dXV2JiYjh27BjZs2cnLi6OXbt20bFjRzp06KC2eF+/fp1Zs2bh4+PD2bNnKVeunI6jF0IIIX49s2bNYu7cufj7+1OqVKkUBUy1BZi0U3x+/vyZq1evYmhoqMOIxT+xatUq5s+fj56eHtu2baNw4cJfVZp/9OgRhw4d4ty5c3h7e0sV+jRo9+7dNG/enFKlSnHhwgUaNWqEt7c3efPmZc6cOQQGBnLq1ClKliyJnp4eq1evplSpUroOW4hv6qdPugHu3LlDhw4diIiIIDg4+P+beF++fJlFixYxYsQIChcurOPIhRBCiF+HoiiEh4fj4uJCmzZt6N69u9pSlrzbeUREBB4eHmTMmJGhQ4fK/Mxp1OPHj3FxceHixYssW7aMbt26Af8dYvD7+dlBpn9LS7QpRpcuXahcuTKurq48evSIGjVqUKRIETZs2IClpSVRUVFcunSJvHnzkiFDBrJly6bjyIX49n6JpBvg9u3buLq6/mni3alTJxYvXgyknNtTCCGEED/Ohw8fKF26NCNHjqR3794p1sXExPD8+XMKFiyYYrkkYqnf7xNo7TCA58+f06xZMwwNDZk0aRK1a9cGZKq3tEx77l6/fo1Go8HT0xMXFxd1TvVnz55hb29P0aJFWb58uTRyiV/CL1O9vHDhwqxfv55MmTLh4ODA27dvMTIyolGjRmzcuJGlS5cyZMgQAEm4hRBCiB9A+90/+ff/uLg40qdPz+3bt4GUlcxv3rzJ0qVLefnyZYrjSMKduiVPuH19fZk0aRJjxozhxIkTWFhYsGPHDmJjY5k1axYHDx4EkIQ7DdNoNGzfvp1q1apRoUIFZs+ezaVLl9T1efPm5cKFC9y/f5+2bdty9+5d3QUrxA/yUybd2of3q1evePv2LS9evECj0VCkSBE2btz4VeJdv359AgIC6NWrl44jF0IIIX4NSUlJamL1+PFjnj59Snh4ODly5GD06NEsXLiQxYsXq8laZGQkY8eO5eHDh+TKlUuXoYu/SXsOhw8fzujRo7l+/Tpv3rzB0dGRtWvXkjdvXgICAvj48SPu7u4EBQXpOGLxb9y8eZMxY8bQrl07pkyZQv78+Vm9ejVHjx5Vt8mbNy/BwcFERkZKY5f4Jfx03cu1XVp27drFpEmTiI2N5dOnT8yZM4dWrVoB/x3j/fnzZw4fPvzVtCNCCCGE+H6Sdx2ePHky/v7+xMbGEhUVhbu7O3Xq1GHt2rUMHz4cZ2dnNBoNERERfPjwgZCQEAwNDaX7cRoTGBhI3759CQgIoFy5cuzZs4eGDRuyYcMG2rdvD8DTp09xcHCgadOmLFy4UMcRi7/i06dPmJmZqX+/du0aW7ZsISoqinnz5gFfiuE1bdqUnDlzMnr0aKpXr65uL7MNiF/FT9fSrdFo2L17N+3ataNDhw6sX7+eli1b0rZtW3XMduHChfHx8SE2NpYGDRqk6LomhBBCiO9LmyxPnToVb29vZs6cyZkzZyhevDiDBw8mPDwcNzc3jhw5go2NDXny5KFu3bqEhoZiaGhIQkKCJNxpzLNnz3BycqJcuXJs27aN1q1bs3TpUtq3b09ERAT37t0jX758nD9/nvnz5+s6XPEXLFiwgOHDh5OQkEBiYiJRUVEMHTqU+fPnc+PGDXU7KysrduzYwatXr/Dw8GD//v3qut8XyhPiZ/XTDYJ68eIFXl5eTJo0iSFDhvD06VMCAgIoVaoUAwYMID4+noEDB2JjY8O+ffvQ09OTC14IIYT4wT59+sTx48dZuHAhzs7OBAYGcubMGaZPn06hQoVISEjA0dERBweHFGO2ExMTZQx3GpSYmEh4eDhbt26le/fuzJ49m549ewJfppQ6evQo7u7u5MyZU91eWkBTJ+250dfXZ/z48RgYGBAbG0v69OlZuHAho0aN4urVq2zcuJEOHToAYG1tjb+/P9WrV2fZsmVUqVKFdOnSyccz8cv4KbPNmjVr0rFjR169ekXdunWpUaMG58+fp0uXLowePZq5c+cCULBgQQoUKKDjaIUQQohfi6IofPr0iatXr1K5cmWOHDlChw4dmDlzJn379uXz589MnTqVN2/efJVgSyKWuv1Z78HSpUvz4sULOnXqxMSJE+nTpw8AUVFR+Pr6YmRkRJYsWdTt5TynTklJSejr6/PgwQMiIiKwsLDg9OnT9OjRg6dPn1K0aFFmz55N4cKFWbduHVu2bFH3tba25vjx43h4eJAuXTod/gohfrw0n3Rrh6TfvXuX+Ph48uTJQ+fOnTE3N2fZsmVYWloyZ84cNBoNuXPnJlu2bMycOZP379/rOHIhhBDi1/D78jEajYY8efLg6OhIv379aNSoEZ6enuoUYe/fv+fw4cMcPnxYF+GKfyh5lXJ/f3/Wrl3Lxo0biYqKomrVqjRp0oTMmTMTGRlJaGgowcHBtGjRgmfPnuHp6YlGo/nq34pIPbTn9/LlyxQqVEg91+fOnePKlStMmjSJ58+fU7hwYebPn4+BgQErVqzAz89PPYaVlRXW1ta6+glC6EyaTrq1RVQCAgKoX78+06dPR1EUzM3NAbh16xZZs2Ylc+bMwJevqQsWLODhw4dkzZpVh5ELIYQQv4bkVco/fvzIu3fv1HXVq1cnJCSE2rVr07VrV+BLlfKePXtiZGSkFkAVqZ+iKGoSNmzYMDp16oSnpyfdu3endu3a7N69m4kTJ9KxY0f27t1L2bJlGTZsGADnz5/HwMCAxMRE6W6cSmkT7hs3blCpUiUmTJjAmDFjABg0aBA9evTg9u3bjB07lufPn1O0aFHmz5+Pqakps2fPxt/fX8e/QAjdSnPVy39frXTv3r00a9aMRYsWUbVqVYoWLaqu8/T0ZPjw4bi5ufHkyRP27NnDmTNnKFKkiC5CF0IIIX5ZEydOZN++fTx69IgmTZrQunVratasyfDhw9m7dy/p06fHxsaG+/fvEx0dzfnz5zE0NJSxvWnM06dPady4MStWrKB48eJERkbSqVMnIiIimDFjBo6OjoSFhfHo0SPy5MlD7ty50dPTIyEhQcbqp1LahPvatWvUqFEDc3NztVBaTEwMJiYmACxatIgtW7ZQqFAhpk+fjoWFBdeuXWPy5MnMnTsXS0tLXf4MIXQqzSXdycXGxtKhQwesra1xd3dXl2tv3LGxscyYMYOgoCCyZ8+Ou7s7dnZ2OoxYCCGE+DUk72o8f/58Zs6cyfjx49FoNGzcuBFDQ0P69++Pi4sLu3btYu/evWg0GqysrBg6dCgGBgaSiKUxM2fO5PTp05iYmLBu3TpMTEzQaDS8efOGZs2akSlTJvbs2fPVfsn/rYjUJXmX8sqVK1O+fHnu3LlDy5Yt8fT0BCAuLk6da1ubeBcpUoSJEydiaWmZYr0Qv6o0k3SPHTsWRVGYMWOGuuzTp0+ULl2aHj16MHLkyK/20d4owsPDMTExwdTU9EeGLIQQQvzyQkND2bVrF8WKFVO7i9+/f5+pU6dy7949VqxYQbFixb7aT1q4U7/kybKiKCxcuJAxY8ZgYWHBxYsXMTMzIz4+HkNDQ06ePEnNmjUJCQnht99+k27kaciFCxeoXLkyY8eOZdy4caxatYqxY8fSrl27P0y8vb29WbJkCY6OjixcuBA9PT053+KXlyY+H8fExGBtbU358uVTLNfT06No0aI8e/aMz58/p6iEeOHCBfz9/Zk8eXKKaphCCCGE+H60SRZ8eRZrn93Lli0DviRnBQsWZPLkyVSsWJH//Oc/f5h0S8Kd+mkT7levXpErVy569+5NxowZ6dWrFzNnzmTGjBnqv4XExETy5cuHqampJGBpzOfPn+nTpw8TJ04EoHXr1sCXBjH4MpzTyMhITbz79euHoaEhderUketYiP+TJvrymJiY0KVLF0qWLMnBgwcZMmQIAOnTp6dUqVL4+vqye/duoqKi1H127tzJ3r17CQ8P11XYQgghxC9l//79LFiwgJs3b6IoCvb29qxevRoDAwPOnz/P58+f1W3z589PhQoVuH79ug4jFv/Whg0bKFq0KOfPn8fY2BhXV1cWLlzI7NmzGTJkCMHBwVy/fh13d3fMzc2xsrLSdcjib6pWrZraoq0oCpkyZaJNmzZMnz6dTZs2MWjQIACMjIyIjY0FoGfPnnKuhUgmTbR0w5evqYmJidy5cwdvb2/09PSYO3cu06ZN4+HDh7i5ubF3715y5szJ8+fP2blzJ8ePH1crmQshhBDi+1mzZg0TJkygdu3aVK9eXW3N7Ny5M7GxsfTp04d8+fLRv39/smTJQnR0NA8ePOC3337TceTi32jTpg1Lliyhffv2bNq0CXt7e3r06AHA0KFD8fT0ZPDgwRgZGXH06FH09PRkDHcapr2uM2bMSJs2bYAvLd76+vrMmzcPY2NjXYYnRKqVZpJu+NLVzMXFBQMDA8aMGUN8fDwLFy7Ex8cHd3d3bty4wcGDBylRogTBwcGUKFFC1yELIYQQP73NmzczcOBAVq1ahbOzM2ZmZinW9+rVi/j4eAYOHMihQ4coUqQIr169AmDy5Mm6CFn8A7+fQUZRFAwNDTl+/DhOTk64uLiwdetW7O3t6datG8bGxvTv3x8zMzMCAgIApKjWT0SbeOvp6dGzZ0+MjY2ZOXOmrsMSIlVK1Um39ub+7NkzYmNjsbCwIHv27LRu3ZrExETGjRsHwMKFCxk5ciSKohAXF4e+vr5UOxVCCCG+M0VRePv2Ld7e3sycORMXFxd13efPn7l+/TrGxsZYW1vTv39/DA0N6dOnDwkJCYwZM4a6deuir6+fYhy4SJ1iY2PVVsw1a9bg5ORE/vz5URQFAwMDDh8+TI0aNWjTpg2bN2/G3t6eDh06EBMTw4ABAzA2NmbMmDGScP9kMmbMSKtWrTA0NKRSpUq6DkeIVCtVZ6YajQZ/f3/1Zq3RaFi3bh0ODg64uroCMG7cOIyMjJgzZw4ajUa6tQghhBA/iEajIT4+ntevX6foJr506VIOHjzIjh07sLS0pEiRImzevJlevXqhr69Pr169uHbtGvXr1ycxMVES7lRu//79XL58mWrVqvHbb78xatQo8ubNS2BgIHnz5lUT7127dlG6dGkGDRqEh4cHlStXplu3bujr69O7d2+MjIwYNmyYrn+O+MYyZcpEx44dpUCeEP8fqXJAjXYWszt37jBo0CCGDRvGwoULKVu2LM7OzgQGBpIhQwZcXV2ZMWMG8+bNU1u9hRBCCPF9rV69mrt37wJgZmZGZGQkGzdu5OzZs7Rs2ZLFixeTLVs2jh49yvjx43n16hXr168HoHv37ixatIgJEyYwadIkGdubyq1Zs4auXbvy8OFD9PT0MDMz48KFC8TGxtKiRQuePn2qJltGRkYUKVKE06dPM3/+fHVZ586dWbVqFQ0aNNDlTxHfkSTcQvz/pdp5uk+ePMnLly+5cOEC7u7u6vKuXbvi5+eHj48PjRs35tOnT/j5+eHg4ECRIkV0GLEQQgjx89u9eze9e/emZcuW9O/fn0KFCrF37146dOhApkyZyJgxI3PmzMHOzg5zc3M+ffpEhQoVaNWqVYrx2/PmzWP69OncuXOHbNmy6fAXiT+zefNmunXrxpo1a6hXrx4ZM2ZU1z179oz69etjZGSEv78/FhYW6Onp0b17d0aPHk2BAgXkg4oQQvyfVJl0JyUl4ezszIEDB6hTpw47d+5MMQaoa9euBAQEsGzZMlq1avVVYQ8hhBBCfD8LFixg48aNODg4MGTIEKysrHj79i1hYWFfzbn99u1bWrRoQceOHenWrVuKZ3Z4eDhZsmTRxU8Q/0NYWBguLi60bNmSfv36qcsjIyPVsfpZs2alffv2PH78mFq1anH37l0+ffpESEiIOuuMzNMshBCpdEy3np4e27dvp3fv3gQFBXHx4sUUxRlWr17Np0+fGDp0KM7OzmTIkEGH0QohhBC/Bu1UT4MHDwZg3bp1AAwYMIBChQqRPXv2FNu/e/eOLl26EB8fT+fOnYGU3VAl4U7d3rx5g4WFhfr3JUuWcPjwYbZv306uXLkoVaoUhw8fxs3NjdevX1OgQAFWrVqlTgsmCbcQQnyRKlq6tV+94+PjSUhIwNTUFID4+HgaNmzI9evXCQwMpGzZsin2e/nyJblz59ZFyEIIIcQvKXnr5fz589mwYQNVq1Zl8ODBFChQAID379+zYsUKjh8/zuvXrzl9+jSGhobS8pmGhIWFUaZMGerVq0fbtm1ZvHgxd+7coUqVKjRr1oyIiAiGDh3KsGHDGDhwYIp9ExISZBYZIYRIRud3RG3CvWfPHtatW8fNmzepW7cuFStWpEWLFuzZswdnZ2eaNGmiVsXUkoRbCCGE+P60LdxAiqR5yJAhJCUl4ePjA6Am3rdv3yYkJIRChQoRGBiIgYGBJGJpjLm5OWvXrqVFixYcPnwYMzMzFixYgJ2dHdmyZSM8PJxs2bIRFhaWYj9tJXMhhBD/pfO7okajYdeuXbRq1Yo+ffpgaWnJuXPnOHz4ME+ePGHIkCEEBQXRrFkzHBwcOH36NHZ2droOWwghhPglJE+4d+zYwd27d8mePTtly5alVKlSuLm5oSgKmzZtQqPRMHToUCpVqkSRIkXIkiULGo2GxMREScTSoJo1a3L37l0iIyPVXgzJmZmZkS9fvhTLpMaOEEJ8TedPwIiICBYuXMj48eMZO3YsAA8ePGDZsmX4+PhgY2NDw4YN2bJlC507dyZdunQ6jlgIIYT4NSiKoibcI0eOZP369djZ2fH8+XPy5MlD586dadu2LcOGDUOj0eDr68uHDx+YMWMGefLkUY8hXcrTLnNzc8zNzVMsCwsLo0uXLsTFxdGtWzcdRSaEEGmHzpNuY2NjXrx4kWKZtbU1vXv35ujRo1y4cIGGDRuSPn16/Pz8dBSlEEII8evRtlouWrSIzZs3ExAQQIUKFfDy8sLNzY0PHz4QGxtL586dcXNz49OnTzx+/JhcuXJ9dQyR9r19+5aVK1dy8uRJ3rx5Q3BwMPr6+jJWXwgh/gedJt2KohAXF0f+/Pl5+fIlsbGxGBkZodFoKFCgALa2thw/fpz4+HgMDQ11GaoQQgjxy9DWW1EUhaioKG7dusXQoUOpUKECAQEBjB8/nmHDhnHu3Dk8PDwwMjKiXbt2TJo0Sd03ebd08XN49uwZwcHBFCpUiICAABmrL4QQf9EPrV6ufRB/+PCB9OnTY2BggEajYfXq1fTo0QMvLy86d+6sVi9v27YtGTNmZMmSJfLgFkIIIX6wx48fkz9/fu7du4epqSmfPn2iYcOG9O/fn8GDB7N9+3Y6d+5M7ty58fDwoEmTJgAp5uIWP5cPHz6QKVMmday+tHALIcT/9kM/TWo0Gnbu3MmUKVNInz49VlZWLF++nK5du/Lq1Sv69+/PhQsXyJo1K1FRUezevZvTp09Lwi2EEEL8YAEBAcyePZvAwECsra3R09Nj1apVmJub06VLF+DLc93R0RFHR0caNWqk7isJ988rc+bMgIzVF0KIv+OHZLPaxvTQ0FBat25NgwYNKFu2LFevXqVs2bLExsYyZswY1q5dS1RUFMHBwepYoRIlSvyIEIUQQgiRTHx8PE+ePOHVq1fqx2+NRkN4eDhnz54lJiaGtWvXYm9vz7Bhw9DT0yMxMVHHUYsfRT6sCCHEX/fDupeHhITw5s0bLl++zMiRI0lMTCQ0NJRu3boRFxdHaGgoJiYmREZGkj59emJjYzExMfkRoQkhhBC/tOTjr5P/uWHDhkRFRXHw4EH09fUJDQ1l+PDh3LlzB319fczMzLh48SKGhobSpVwIIYT4Ez+kpfvt27d06NCB+vXrExERAYC+vj5ly5Zl9erVGBkZUa5cOWJiYsiQIQMajUYSbiGEEOIHST6MK/mfBwwYQExMDEePHgWgdOnSzJs3j4ULFzJu3DhCQ0MxNDQkISFBEm4hhBDiT/yQlu64uDh2797NjBkzUBSFixcvqusURSE0NJSmTZtiYWHB6dOnv3c4QgghhCBlq/aqVauYNm0as2fPxt7engIFChAREUGdOnUoUqQI69ev/8NjSDEtIYQQ4v/vh3Uvj46O5tChQwwaNAhra2sOHDigrlMUhcuXL5MxY0asra1/RDhCCCGE+D9Tpkwhf/78/Oc//+H+/ftERkYydOhQ2rdvz/Xr12nWrBnLli3D2dlZ16EKIYQQac43T7q1Y7ouXrxIaGgoGo0GBwcHihYtSkxMDAcPHmTYsGFYWlqyf//+b/m/FkIIIcRfkLyFe/PmzbRv355Lly5RsmRJzp8/z759+/D09MTOzo58+fLx6tUrypcvz6RJk2RGESGEEOJv+qZJtzbh3rFjBwMGDCB37tykT5+eGzdu4O/vT5UqVdTEe9SoUaRLl45z5859q/+9EEIIIf6GHTt28Pr1a9KlS0enTp1SrLty5QqHDh1i3bp1XLlyhTp16rBv3z4dRSqEEEKkXf8q6f6jSqXHjh2jZcuWzJgxgx49enDhwgXKly+PiYkJ27Zto379+sTExLBnzx5mzZrFtm3bsLS0/Nc/RAghhBB/3a1bt6hWrRpv377F29ubPn36EB8f/1Ul8ujoaDZs2EDXrl0xMDCQKuVCCCHE3/SPk25t17SwsDAeP34MgL29PZMmTUJRFCZPnszz58+pXLkyNWvWJDExkS1btrBv3z6qV69ObGws8fHxZMiQ4Zv+ICGEEEJ87ffJcmRkJEFBQYwfP568efNy+PBhIGVhtN8XSUtISMDAwODHBi6EEEKkcf8o6dYm3Ddu3KBnz56YmZlhamrKjh07uHjxInFxcZQoUYLatWtjZ2fHsmXLCA4OpmrVqgDs27ePOnXqfPMfI4QQQoivJR/DHR8fT0xMDGZmZiQlJREQEECfPn2oUqUK27dvB6QiuRBCCPEt/e3P1YqioKenx/Xr16lSpQp9+/alV69eWFhYAFC2bFkAzp07R2JiIkOGDAEgc+bMtGrVivz585MvX75v+BOEEEII8WeSJ9yzZs3iwoULnDt3ju7du1OjRg2aN29OUlISw4cPp1WrVvj5+aGvr59iPyGEEEL8c/+opfv9+/c0adKEMmXK4OnpqS5P/oDeu3cvDRo04MqVK5QoUYLx48cTEhKCn58f6dKl+3a/QAghhBD/09ixY1mxYgWzZs1Co9Hg7u5Ozpw58ff3x9TUlKCgIEaMGIGVlZXa1VwIIYQQ/94/Gpj16tUrXr58SYsWLVIk2tr/KopCrVq1aNq0Kba2ttjb23Pz5k1OnjwpCbcQQgjxg125coXAwED8/f1xcHAgODiYhw8fMnr0aLJmzQpA8+bNiY6OJiAgQFq5hRBCiG/oH7V0b9q0iU6dOhEXF4dGo/nDh/Pnz585cuQI8fHxPHz4kIYNG2JjY/PNAhdCCCHE1xRFITExMUXBsytXrtChQweuXLmCn58fXbt2xcPDg969exMVFcWBAweoWbMmJiYmGBoaAkjiLYQQQnwj/6il28rKCgMDA3bs2EGLFi3+8KG8du1aAgIC2L9//78OUgghhBB/TVxcHMbGxgAcOnSI0qVLk5CQwKdPn1i6dCmjR49m1qxZ9O7dG4CLFy+yceNGrK2tsbW1VY8jCbcQQgjxbfyjJ2r+/PnJmDEj69evV6cLgy9f17Xu379PmTJl+BfTgAshhBDibzhw4AClS5cGwM3NjUGDBpGUlESZMmWoVq0affv2ZeTIkfTr1w/4Mge3h4cHCQkJlChRQpehCyGEED+tf9TSbWFhwZIlS2jXrh3jx49n1KhR/Pbbb2g0Gj5//sy0adPYvn07+/fvTzEnqBBCCCG+D0VRMDY2Jn369FhaWvLp0ycuXbpE9uzZARg1ahRv3rzBw8ODdOnS8fHjR44fP86LFy8IDQ1FT09PupQLIYQQ38E/GtMNX8Z6rVixgv79+1OoUCEqVaqEiYkJz58/58yZM+zbt0/92i6EEEKIH6NXr16sWLGCokWLcuPGDeBLQq7RaHj8+DHz5s3jxIkTZM+encKFC7NgwQIMDAxISEhIMQ5cCCGEEN/GP066tc6dO4eHhwf37t3DzMyMypUr061bNymaJoQQQvwA2oQ6KSkJ+DJl57t371i6dCnR0dEcO3aMjBkzpkiqY2Nj1XHfgCTcQgghxHf0r5NugMTERPT19b9FPEIIIYT4i5J3B4+MjAQgQ4YMABw7dozhw4cTHx/PyZMnSZ8+PQDbtm2jevXqardzbdIuhBBCiO/jmwzcSj7+SwqnCSGEED+G9vk7efJknJ2dqVChAosXLwbA0dGROXPmYGRkRLly5QgODqZ27dp4eXmpc3MDknALIYQQ39k3SbqTP7Dl4S2EEEJ8X9qu5ACenp4sXboUZ2dnnJycGDhwIG5ubkRHR1OtWjW8vLzImTMn7du3JzExkQMHDqCnpycfyYUQQogfRAZwCSGEEGmMtoX76tWrxMTEsHLlSho0aABA9erVad26NUlJScyYMYNy5cpx5MgRbty4QdGiRdHT05Mx3EIIIcQPJE9cIYQQIo2IiIggU6ZMAJw5c4bKlStjYmLCunXr1G1atGjB1q1bad26Nfr6+owdO5YsWbLw22+/AV9aySXhFkIIIX4cmYxTCCGESAP27NlD1apVCQ0NBaBixYosWbKE+Ph4QkNDSUxMVLdt3rw5fn5+zJs3Dx8fnxTHkXm4hRBCiB/rm1QvF0IIIcT3FRERQdGiRSlQoABLlizB1tYWjUaDp6cnQ4YMYd68eQwePBj4b0XyY8eO4eDgIC3bQgghhA5J0i2EEEKkctox2J8+faJ06dJkyZKFFStWYGdnh0ajYcGCBQwdOpS5c+cyZMiQP91fCCGEED+e9DETQgghUintd3EDAwOSkpIwMzMjNDSU9+/f06NHDy5fvoyiKAwePJj58+czcuRIpkyZ8tVxJOEWQgghdEdauoUQQohUKCkpSR1//eHDBzJkyKAmzx8/fqR06dJkzZo1RYv39OnT2bdvH8ePH5cpPIUQQohUQpJuIYQQIhWbMmUKx44d4+PHj0yaNIly5cqRI0cONfHOli0bK1asUMd4a8dza/8rhBBCCN2S7uVCCCFEKrVixQoWL16Ms7MzuXLlomvXrqxdu5Znz56RMWNGQkND+fDhA02aNOHevXsAknALIYQQqYwk3UIIIUQqkZSUlOLvCQkJzJ49m2HDhrFr1y569eqFt7c3Pj4+auJ97tw5ypcvj7W1tbqfJNxCCCFE6iGVVYQQQohUQFEUdQy3n58fr1+/5uTJkzRv3lzdZsqUKWg0GhYvXoxGo6F169bkz5+frVu3ApCYmIi+vr5O4hdCCCHEH5OkWwghhNCx5N3BR44cibe3N9bW1ly7do3o6GjKly+PlZUVAJMnT0ZPT49x48aRJ08e8ufPr+4vCbcQQgiR+kj3ciGEEELHtAl3aGgojx8/5tChQ4SGhuLh4cHjx49ZtGgRjx8/VrefOHEiS5YsoW3btin2F0IIIUTqIy3dQgghRCqwZcsWFi5cSIYMGbC1tUVfXx83NzcURcHHx0edj9vS0hKAbt26AdKlXAghhEjtJOkWQgghUoE3b94QFRXF/fv3CQsLU5PrYcOGodFo2Lx5MxEREcyYMYOcOXOq+0nCLYQQQqRu0r1cCCGE+MF+X6UcYMCAAbi5uZErVy6GDh3K/fv31XVubm40aNCApKQkzM3Nf2SoQgghhPiXNIqiKLoOQgghhPhVJCUlqVXKz507h6IoxMfHU6VKFQDWrl3L6tWryZMnDzNmzEgxFZi2YFryYwghhBAidZOkWwghhNCBkSNHsnXrVuLi4oiJiaFWrVp4e3uTPXt2Vq9ezbp167CwsGDSpEkULlxY3S95pXMhhBBCpH7ymVwIIYT4wby8vFi1ahW+vr7s2bOHHTt2cPz4cdq0aUNcXBxdu3bF1dWVy5cv4+Pjk2JfSbiFEEKItEVauoUQQojvKDQ0lBIlSmBoaKgu6969O8bGxnh7e6vLHj9+jJ2dHZ07d2bBggUA7N69G2dnZymWJoQQQqRh0tIthBBCfCczZ86kbNmyHDp0iISEBAASEhK4e/cub9++VbeLjY0lf/78TJgwgRMnTqjrGjZsiL6+PomJiTqJXwghhBD/niTdQgghxHcyevRoGjduTOfOnTlw4ACxsbEYGBjQpUsXTp48ib+/PwDGxsbqf/X19UmfPn2K40hLtxBCCJF2SdIthBBCfAcxMTEABAQEULFiRXr37s2hQ4dITEzE0dGRGjVqMH/+fPz8/AAICwsjKCgIKysrTExMdBm6EEIIIb4hGdMthBBCfGPJp/Tatm0bnz59olu3btjY2ODp6Um9evW4ePEiixcvZsuWLeTIkQMTExOMjIw4f/48hoaGUqVcCCGE+ElI0i2EEEJ8J+PGjWPp0qXMmjWLN2/esGfPHu7cucP69eupV68eHz9+5N69e5w+fZqcOXPSrFkz9PX1SUhIwMDAQNfhCyGEEOIbkKRbCCGE+A6ePXuGo6MjU6ZMoX379ury+vXrExISwtq1a3F0dMTU1DTFfomJiTKGWwghhPiJyJhuIYQQ4juJjY3F3NwcgLi4OOBLd/OMGTMyevRo9u3bp1Y115KEWwghhPi5SNIthBBC/EtJSUlfLcubNy/m5uYsX74cACMjIxISEtDT06NgwYLcvHmT1atXSzdyIYQQ4icnSbcQQgjxLyQvmnb37l1evXrF58+fAZg0aRKXLl1iwIABABgYGGBoaEj27Nm5cuUKgYGBOotbCCGEED+GfF4XQggh/gVtwj169GgCAwN59eoVnTp1okOHDjRp0oTnz58za9Yszp8/T5kyZQgJCSEiIoKCBQuip6cnY7iFEEKIn5wUUhNCCCH+geQt3Nu3b2fw4MEsXryYmzdvEhAQQPbs2RkzZgwVK1YkJCQEDw8PANKnT8+SJUswNDRMcQwhhBBC/Jwk6RZCCCH+haNHj7Jjxw5KlixJjx49ANi/fz+zZ8/GxMSE4cOH4+jo+NV+Mi2YEEII8WuQz+tCCCHEP3T16lV69OjBmjVrCA8PV5fXqVOHkSNHEhsby/z589m9e/dX+0rCLYQQQvwaJOkWQggh/qGSJUsyY8YMLC0t2bt3LyEhIeq62rVrM3LkSB49esSJEyd0GKUQQgghdEm6lwshhBB/wf9v/LWvry9z5szBzs6OQYMGYWdnp647f/48ZcuWlbHbQgghxC9Kkm4hhBDif0iecG/atIkbN25gamqKo6MjVapUAWD9+vUsXLgQW1vbrxJvQKqUCyGEEL8oSbqFEEKIv2jkyJGsXbuWmjVrcvfuXdKlS4eLiwv9+vUDYMOGDXh5eZE7d27mzJlDoUKFdByxEEIIIXRNqrgIIYQQf8GSJUvYunUrQUFB2Nvbs27dOrp3787Hjx+JiYnBzc0NV1dXoqKiOH/+PNbW1roOWQghhBCpgLR0CyGEEP9DfHw8EydOJHPmzIwYMQJ/f3+6du2Km5sbly5d4sKFCwwbNoz+/fun2E/m4RZCCCGEJN1CCCHE7xw5coQTJ06QlJRElSpVqFWrFi9fviQxMZHY2FgaNGhAr169GDJkCCdOnKBRo0ZkyZKFyZMn07FjRwAURUGj0ej4lwghhBBC1+TzuxBCCJHMypUrad26NSdOnGDNmjV07dqVnTt3kjt3bvLmzUtISAjGxsa0b98egOjoaGrUqMHgwYPp0KGDehxJuIUQQggBknQLIYQQqpUrV9KvXz8WL17MgQMHWLt2LR8/fiQgIIDExEQA9PX1iYqK4vDhw4SHh+Pl5UWhQoUYOHAgenp66nZCCCGEECDdy4UQQggAjh49ipOTE5MmTWLChAnqcgsLC6ytrQkKCiJDhgxERkbi6urK1atXiY6OJleuXJw7dw5DQ0PpUi6EEEKIr0j1ciGEEIIvyXWVKlW4ePEiFy5cwN7enubNmxMWFkbp0qVxdnYmY8aMNGnShM6dO2NsbIyiKNSrVw99fX0SEhIwMJDHqhBCCCFSkpZuIYQQ4v/cvXuXgQMHoq+vT0REBJ8/f2bNmjUULVqU4OBgbt++zaxZs4iJiaFVq1YsWrQIgMTERPT19XUcvRBCCCFSI0m6hRBCiGTu3r1L3759OX/+PMuXL8fFxSXF+oiICC5dukSVKlUk0RZCCCHE/yRJtxBCCPE79+/fp1+/fujp6TFmzBiqVKkC8FUXcmnhFkIIIcT/Ikm3EEII8Qe0Xc0Bxo0bh4ODg44jEkIIIURaJFOGCSGEEH/AxsaGhQsXoq+vz+DBg7ly5YquQxJCCCFEGiRJtxBCCPEnbGxs8PDwoFq1apQoUULX4QghhBAiDZLu5UIIIcRflJSUhJ6efK8WQgghxF8nSbcQQgghhBBCCPGdyOd6IYQQQgghhBDiO5GkWwghhBBCCCGE+E4k6RZCCCGEEEIIIb4TSbqFEEIIIYQQQojvRJJuIYQQQgghhBDiO5GkWwghhBBCCCGE+E4k6RZCCCGEEEIIIb4TSbqFEEIIIYQQQojvRJJuIYQQQgghhBDiO/l/d19mhr4h61MAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] @@ -639,7 +600,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/classification/rotation_forest.ipynb b/examples/classification/rotation_forest.ipynb new file mode 100644 index 0000000000..6fc174249d --- /dev/null +++ b/examples/classification/rotation_forest.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rotation Forest Classifier\n", + "\n", + "RotationForest is an ensemble learning algorithm designed to improve the accuracy and diversity of decision tree-based classifiers. It was introduced as an extension of the popular RandomForest algorithm. The key idea behind RotationForest is to apply **Principal Component Analysis (PCA)** to rotate the feature space for each tree in the ensemble, creating diverse and accurate base classifiers.\n", + "\n", + "Unlike RandomForest, which selects a random subset of features at each node, RotationForest:\n", + "\n", + "- Divides features into random subsets and applies PCA transformation to each subset.\n", + "- Ensures all original features are used for each tree (instead of random feature selection).\n", + "- Uses a C4.5 decision tree (this implementation uses the scikit-learn CART).\n", + "\n", + "Rotation Forest is relevant for **Time Series Classification (TSC)** because it effectively captures complex feature interactions and correlations which are often critical in time series data using PCA-based rotations. It works well with feature extraction methods (e.g., **TSFresh**) and is used in TSC pipelines like **FreshPRINCE** and **STC**, making it robust for both **univariate** and **multivariate** time series data.\n", + "\n", + "In this notebook, we will see how to use the `RotationForestClassifier` algorithm for time series classification." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import (\n", + " ConfusionMatrixDisplay,\n", + " accuracy_score,\n", + " classification_report,\n", + " confusion_matrix,\n", + ")\n", + "\n", + "from aeon.classification.sklearn import RotationForestClassifier\n", + "from aeon.datasets import load_italy_power_demand # univariate dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "italy, italy_labels = load_italy_power_demand(split=\"train\")\n", + "italy_test, italy_test_labels = load_italy_power_demand(split=\"test\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((67, 1, 24), (67,))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "italy.shape, italy_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "RotationForestClassifier is not a time series classifier. \n", + "A valid sklearn input such as a 2d numpy array is required." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert 3D array to 2D array\n", + "italy = italy.reshape(italy.shape[0], -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(67, 24)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "italy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.9708454810495627 \n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 1 0.97 0.97 0.97 513\n", + " 2 0.97 0.97 0.97 516\n", + "\n", + " accuracy 0.97 1029\n", + " macro avg 0.97 0.97 0.97 1029\n", + "weighted avg 0.97 0.97 0.97 1029\n", + "\n" + ] + } + ], + "source": [ + "rotation = RotationForestClassifier()\n", + "rotation.fit(italy, italy_labels)\n", + "y_pred = rotation.predict(italy_test)\n", + "\n", + "accuracy = accuracy_score(italy_test_labels, y_pred)\n", + "print(\"Accuracy: \", accuracy, \"\\n\")\n", + "\n", + "report = classification_report(italy_test_labels, y_pred)\n", + "print(\"Classification Report:\\n\", report)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot confusion matrix\n", + "cm = confusion_matrix(italy_test_labels, y_pred)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rotation.classes_)\n", + "disp.plot(cmap=\"YlOrRd\")\n", + "plt.title(\"Confusion Matrix\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### References:\n", + "\n", + "\\[1\\] J. J. Rodriguez, L. I. Kuncheva and C. J. Alonso, \"Rotation Forest: A New Classifier Ensemble Method,\" in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619-1630, Oct. 2006, doi: 10.1109/TPAMI.2006.211.\n", + "\n", + "\\[2\\] Bagnall, A., Flynn, M., Large, J., Line, J., Bostrom, A., & Cawley, G. (2018). Is rotation forest the best classifier for problems with continuous features? ArXiv. https://arxiv.org/abs/1809.06705" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myaeon", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/clustering/clustering.ipynb b/examples/clustering/clustering.ipynb index b0e51431ea..20b861453f 100644 --- a/examples/clustering/clustering.ipynb +++ b/examples/clustering/clustering.ipynb @@ -2,6 +2,12 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "# Time Series Clustering\n", "\n", @@ -23,45 +29,47 @@ "erative [16], Feature K-means [17], Feature K-medoids [17], U-shapelets [18],\n", "USSL [19], RSFS [20], NDFS [21], Deep learning and dimensionality reduction\n", "approaches see [22]" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Clustering notebooks\n", "\n", - "- `aeon` currently focusses on partition based approaches that use elastic distance\n", - "functions. The [partition based](partitional_clustering.ipynb) note book has an\n", - "overview of the funtionality in aeon.\n", + "`aeon` offers a comprehensive suite of time series clustering (TSCL) algorithms, encompassing partition-based, density-based, hierarchical, deep learning, and feature-based approaches.\n", + "\n", + "- `aeon` has many partition-based clustering algorithms, which include TimeSeriesKMeans, KMedoids, CLARA, CLARANS, ElasticSOM, and KSpectralCentroid, leveraging elastic distance measures like DTW. The [partition-based](partitional_clustering.ipynb) notebook has an overview of the functionality in aeon.\n", "\n", - "- `sklearn` has *density based* and *hierarchical based* clustering algorithms, and\n", - "these can be used in conjunction with `aeon` elastic distances. See the [sklearn and\n", - "aeon distances](../distances/sklearn_distances.ipynb) notebook.\n", + "- `sklearn` has *density-based* and *hierarchical based* clustering algorithms, which can be used in conjunction with `aeon` elastic distances. See the [sklearn and aeon distances](../distances/sklearn_distances.ipynb) notebook.\n", "\n", - "- Deep learning based TSCL is a very popular topic, and we are working on bringing\n", - "deep learning functionality to `aeon`, first algorithms for [Deep learning] are\n", - "COMING SOON\n", + "- Bespoke feature-based TSCL algorithms are easily constructed with `aeon` transformers and `sklearn` clusterers in a pipeline. Some examples are in the\n", + "[sklearn clustering]. The [feature-based](feature_based_clustering.ipynb) notebook gives an overview of the feature-based clusterers in an aeon.\n", "\n", - "- Bespoke feature based TSCL algorithms are easily constructed with `aeon`\n", - "transformers and `sklearn` clusterers in a pipeline. Some examples are in the\n", - "[sklearn clustering]. We will bring the bespoke feature\n", - "based clustering algorithms into `aeon` in the medium term.\n", + "- Deep learning based TSCL is a very popular topic, and we have introduced many deep learning functionalities to `aeon`. Autoencoder-based models like AEFCNClusterer, AEResNetClusterer, and AEDCNNClusterer enable complex pattern discovery.\n", "\n", - "We are in the process of extending the bake off described in [1] to include all\n", - "clusterers. So far, we find medoids with MSM distance is the best performer.\n", + "- `aeon` also includes averaging-based clustering algorithms, which utilize centroid-based representations of time series.\n", + "\n", + "\n", + "We are in the process of extending the bake-off described in [1] to include all clusterers. So far, we find medoids with MSM distance is the best performer.\n", "\n", "\"cd_diag\"\n", "\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## References\n", "\n", @@ -141,31 +149,28 @@ "[22] B. Lafabregue, J. Weber, P. Gancarski, and G. Forestier. End-to-end deep\n", "representation learning for time series clustering: a comparative study. Data Mining\n", "and Knowledge Discovery, 36:29β€”-81, 2022\n" - ], - "metadata": { - "collapsed": false - } + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.11.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/examples/clustering/feature_based_clustering.ipynb b/examples/clustering/feature_based_clustering.ipynb new file mode 100644 index 0000000000..ce5385b778 --- /dev/null +++ b/examples/clustering/feature_based_clustering.ipynb @@ -0,0 +1,1489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "17e241ca-43e6-42b5-83f9-33e8dcb6d6ba", + "metadata": {}, + "source": [ + "# Feature-based Time Series Clustering in aeon\n", + "\n", + "Feature-based time series clustering algorithms find descriptive features to represent the characteristics of time series and then perform clustering on the features. Various transformers can be used to derive features from the raw time-series data. Bespoke feature-based TSCL algorithms can be easily constructed with aeon transformers and sklearn clusterers in a pipeline. Currently, we have the following feature-based time series clusterers implemented in aeon:\n", + "1. `Catch22Clusterer`\n", + "2. `TSFreshClusterer`\n", + "3. `SummaryClusterer`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "29b17c39-9706-4c6d-ba02-090f4b79120e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 6, 100) (80,) (40, 6, 100) (40,)\n" + ] + } + ], + "source": [ + "# Imports and load data\n", + "from sklearn.cluster import KMeans\n", + "\n", + "from aeon.clustering import TimeSeriesKMeans\n", + "from aeon.clustering.feature_based import (\n", + " Catch22Clusterer,\n", + " SummaryClusterer,\n", + " TSFreshClusterer,\n", + ")\n", + "from aeon.datasets import load_basic_motions\n", + "\n", + "X_train, y_train = load_basic_motions()\n", + "X_test, y_test = load_basic_motions(split=\"test\")\n", + "\n", + "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "02f062d3-f3e6-4402-ac3e-422aec6f45e0", + "metadata": {}, + "source": [ + "## 1. Catch22Clusterer\n", + "\n", + "The `Catch22Clusterer` simply transforms the data into 22 features based on the `Catch22` transformer and then builds a sklearn estimator on the transformed data. The `Catch22` transformer transforms a `d` dimensional time-series into 22 CAnonical Time-series CHaracteristics derived from the 4791 filtered features of the *hctsa* feature library. `Catch22` is a diverse and interpretable set of time-series features, including linear and non-linear autocorrelation, successive differences etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "27bee800-6b13-41d1-86e4-73f879039eb4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Catch22Clusterer(estimator=KMeans(n_clusters=4))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Catch22Clusterer(estimator=KMeans(n_clusters=4))" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catch = Catch22Clusterer(estimator=KMeans(n_clusters=4))\n", + "catch.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4c1c7875-6f32-43e7-a666-47172d49b98e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 3, 3, 0, 0, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 1, 3, 1, 2, 3, 3, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 3, 0, 3, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 3, 1, 2, 2, 3, 2, 3, 2, 3, 3])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds = catch.predict(X_train)\n", + "preds" + ] + }, + { + "cell_type": "markdown", + "id": "27df1ee9-3ca2-46a6-a948-3b450332f732", + "metadata": {}, + "source": [ + "## 2. TSFreshClusterer\n", + "\n", + "The `TSFreshClusterer` transforms the data using the `TSFresh` transform and builds a sklearn estimator on the transformed data. The `TSFresh` transformer computes 794 time-series features and automates the feature extraction and selection based on the FeatuRe Extraction based on Scalable Hypothesis tests (FRESH) algorithm. The algorithm is efficient and scales linearly with the number of features." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b755814d-8c5f-4514-8cc3-d8b244cd0866", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
TSFreshClusterer(estimator=KMeans(n_clusters=4))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "TSFreshClusterer(estimator=KMeans(n_clusters=4))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tsfresh = TSFreshClusterer(estimator=KMeans(n_clusters=4))\n", + "tsfresh.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1ba7bcf9-dc10-4793-8915-93bd706e07af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 2, 3, 3, 2, 3, 3, 3, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds = tsfresh.predict(X_train)\n", + "preds" + ] + }, + { + "cell_type": "markdown", + "id": "cf0f6f58-9a2d-423f-b5c9-c08c08bdbac7", + "metadata": {}, + "source": [ + "## 3. SummaryClusterer\n", + "\n", + "Like the above algorithms, this clusterer transforms the input data using the `SevenNumberSummary` transformer and builds an estimator using the transformed data.\n", + "\n", + "The default estimator is a Random Forest with 200 trees, but we can use other sklearn estimators or aeon `partition-based clusterers`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9443ccdf-9847-4ae5-bfb9-2006dcde5ac7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
SummaryClusterer(estimator=TimeSeriesKMeans(n_clusters=4))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "SummaryClusterer(estimator=TimeSeriesKMeans(n_clusters=4))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summaryclst = SummaryClusterer(estimator=TimeSeriesKMeans(n_clusters=4))\n", + "summaryclst.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8505a56d-1637-4513-b47d-d82fd7652909", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], dtype=int64)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds = summaryclst.predict(X_test)\n", + "preds" + ] + }, + { + "cell_type": "markdown", + "id": "f73a50de-f3ca-4589-a16f-632148fa00e4", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] Christopher Holder, Matthew Middlehurst, and Anthony Bagnall. A Review and\n", + "Evaluation of Elastic Distance Functions for Time Series Clustering, Knowledge and Information Systems. In Press (2023)\n", + "\n", + "[2] Lubba, Carl H., et al. β€œcatch22: Canonical time-series characteristics.” Data Mining and Knowledge Discovery 33.6 (2019): 1821-1852. https://link.springer.com/article/10.1007/s10618-019-00647-x\n", + "\n", + "[3] Christ, Maximilian, et al. β€œTime series feature extraction on basis of scalable hypothesis tests (tsfresh–a python package).” Neurocomputing 307 (2018): 72-77. https://www.sciencedirect.com/science/article/pii/S0925231218304843\n", + "\n", + "[4] John Paparrizos, Fan Yang, and Haojun Li. 2018. Bridging the Gap: A Decade Review of Time-Series Clustering Methods. In Proceedings\n", + " of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX). ACM, New York, NY,\n", + " USA, 52 pages. https://arxiv.org/html/2412.20582v1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2f743d3-4cfb-49bd-bb41-ada2d9f65f08", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/datasets/datasets.ipynb b/examples/datasets/datasets.ipynb index c1cf05ff05..2f8bd9c2ba 100644 --- a/examples/datasets/datasets.ipynb +++ b/examples/datasets/datasets.ipynb @@ -7,15 +7,16 @@ "\n", "Getting data into the correct data structure is fundamental. This notebook describes\n", "the data structures used in `aeon` and links to more complex use cases. `aeon` models\n", - "abstract data types: single series and collections of series.\n", + "two abstract data types: **single series** and **collections of series**.\n", "\n", "A single time series can be univariate (each observation is a single value) or\n", "multivariate (each observation is a vector). We say that the length of the vector\n", "(its dimension) is the number of channels, which in code we denote `n_channels`.\n", "The length of the series is called the number of timepoints, or `n_timepoints` in\n", - "code. We generally store a single series\n", - "in a 2D numpy array with shape ``(n_channels, n_timepoints)``. Series estimators\n", - "should work with a univariate series stored as a 1D numpy array, but will internally convert to 2D." + "code. We generally store a single series in a 2D numpy array with shape\n", + "`(n_channels, n_timepoints)`, though data can be passed the other way around in some\n", + "cases using an `axis` parameter. Series estimators work with a univariate series stored\n", + "as a 1D numpy array, but will internally convert to 2D." ], "metadata": { "collapsed": false @@ -23,35 +24,190 @@ }, { "cell_type": "code", - "execution_count": 1, + "source": [ + "import numpy as np\n", + "\n", + "from aeon.visualisation import plot_series, plot_series_collection" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-03-20T00:21:22.315887Z", + "start_time": "2025-03-20T00:21:21.284145Z" + } + }, + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-20T00:21:22.690311Z", + "start_time": "2025-03-20T00:21:22.323622Z" + } + }, + "cell_type": "code", + "source": [ + "# Univariate series length 100\n", + "X = np.random.random((1, 100))\n", + "print(X.shape)\n", + "plot_series(X)" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(1, 100)\n", - "(3, 200)\n", - "(10, 1, 50)\n", - "(5, 26, 100)\n" + "(1, 100)\n" ] + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" } ], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.069673Z", + "start_time": "2025-03-20T00:21:22.984023Z" + } + }, + "cell_type": "code", "source": [ - "import numpy as np\n", - "\n", - "X = np.random.random((1, 100)) # Univariate series length 100\n", - "print(X.shape)\n", - "X = np.random.random((3, 200)) # three channel multivariate series length 200\n", + "# Three channel multivariate series length 200\n", + "X = np.array(\n", + " [\n", + " np.sin(np.arange(0, np.pi * 4, np.pi * 4 / 200)),\n", + " np.sin(np.arange(0, np.pi * 8, np.pi * 8 / 200)),\n", + " np.sin(np.arange(0, np.pi * 16, np.pi * 16 / 200)),\n", + " ]\n", + ")\n", "print(X.shape)\n", - "X = np.random.random((10, 1, 50)) # Collection of 10 univariate series of length 50\n", - "print(X.shape)\n", - "X = np.random.random((5, 26, 100)) # Collection of 5 multivariate time series with 26\n", - "# channels, length 100\n", - "print(X.shape)" + "plot_series(X)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 200)\n" + ] + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } ], + "execution_count": 3 + }, + { "metadata": { - "collapsed": false - } + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.138096Z", + "start_time": "2025-03-20T00:21:23.076845Z" + } + }, + "cell_type": "code", + "source": [ + "# Collection of 5 univariate series of length 50\n", + "X = np.random.random((5, 1, 50))\n", + "plot_series_collection(X)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAABQsAAAFfCAYAAADpk5oxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsXQV4ZNXZfsfimbhuZN1YZZVlDSlSCgVaKFCjLbS0SEuFH2gLLRUqQAUpLoVixSkOC6usu1vcXSYZn//5zrnnjmRmMhrZPe/z7JPZbDaZJHfOPef9XtG4XC4XJCQkJCQkJCQkJCQkJCQkJCQkJE56aIf7CUhISEhISEhISEhISEhISEhISEiMDEiyUEJCQkJCQkJCQkJCQkJCQkJCQoJBkoUSEhISEhISEhISEhISEhISEhISDJIslJCQkJCQkJCQkJCQkJCQkJCQkGCQZKGEhISEhISEhISEhISEhISEhIQEgyQLJSQkJCQkJCQkJCQkJCQkJCQkJBgkWSghISEhISEhISEhISEhISEhISHBoMcogNPpRH19PdLT06HRaIb76UhISEhISEhISEhISEhISEhISIwquFwu9PT0oLi4GFqtdnSThUQUlpaWDvfTkJCQkJCQkJCQkJCQkJCQkJCQGNWoqalBSUnJ6CYLSVEovhmj0TjcT0dCQkJCQkJCQkJCQkJCQkJCQmJUobu7m4nxBM82qslCYT0molCShRISEhISEhISEhISEhISEhISEpFhsIg/WXAiISEhISEhISEhISEhISEhISEhwSDJQgkJCQkJCQkJCQkJCQkJCQkJCQkGSRZKSEhISEhISEhISEhISEhISEhIMEiyUEJCQkJCQkJCQkJCQkJCQkJCQoJBkoUSEhISEhISEhISEhISEhISEhISDJIslJCQkJCQkJCQkJCQkJCQkJCQkIiMLFyzZg0uvPBCFBcXs6rlN954Y9D/89lnn+HUU09FYmIiJk6ciKeffjrcLyshISEhISEhISEhISEhISEhISERZ4RNFppMJsyePRsPPvhgSB9fUVGBCy64AGeccQZ27tyJn/zkJ7jmmmvwwQcfRPJ8JSQkJCQkJCQkJCQkJCQkJCQkJOIEfbj/4fzzz2d/QsXDDz+McePG4d5772V/nzZtGtatW4e//e1vOPfcc8P98hISEhISEhISEhISEhISEhISEhKjNbPw888/x9lnn+31PiIJ6f2BYLFY0N3d7fVHQkJCQkJCQkJCQkJCQkJCQkJCYpSThY2NjSgoKPB6H/2dCMD+/n6//+fuu+9GRkaG+qe0tDTeT1NCQkJCQkJCQkJCQkJCQkIiLPSuW4/am2+GvaNjuJ+KhMSJ3YZ82223oaurS/1TU1Mz3E9JQkJCQkJCIhS0HQMOy1xiCQkJCQkJiZMD7U8+gZ733kfPhx8N91ORkBi+zMJwUVhYiKamJq/30d+NRiOSk5P9/h9qTaY/EhISEhISEqMMr3wHaNgFXLcOKJw53M9GQkJCQkJCQiKusLe08rdNjcP9VCQkRo+y8LTTTsMnn3zi9b6PPvqIvV9CQkJCQkLiBILLBbQc4o/FWwkJiRMfDjtGHZxOoF9aBiUkJKKHvb2dvbX5iKQkRiHeuxV46DRgzys42RE2Wdjb24udO3eyP4SKigr2uLq6WrUQf+tb31I//rrrrsPx48dxyy234ODBg3jooYfw8ssv4+abb47l9yEhISEhITHksDU3o3/37uF+GiMHplbAbuaPu+uG+9lISIwqbHjtKJ65bT1MXRaMKrz3f8CfxwLtFRhVePN64K8TgeaDw/1MJCQkRjFcDgccSlahval5uJ/OSYGGrn68sLkaVrsz9p+89RDQvB+wj7J78UggC7du3Yq5c+eyP4Sf/vSn7PEdd9zB/t7Q0KASh4Rx48bhnXfeYWrC2bNn495778Xjjz/OGpElJCQkJCRGM2pvuBGVX7sClmPHhvupjAx0eWQMd9UO5zORkBhVcDpd2LemDr0dFtQeHGVqt0PvAdYe4Ji3k2jEq6CPfAg47UDd1uF+NhISEqMYjs5OrlRmZKFUFsYbJosdVz66Ebe9tgfv7W2I/RfoVj6nsQgnO8LOLFy5ciVcdIMNgKefftrv/9mxY0f4z05CQkLFka1NaKvtxcyVJUjNlJmeoxqkYuipByacOdzPRCJKWI8eZYfOvs2bkThhwnA/neGHJ0HYJZWFEhKhor3eBKvZwR73tPVjVNmPxeu+cS9GDXqbgD6eMYbueowmrDrYhFUHm/GrC6YjyaAb7qcjIXHSw97W5uU4kYgvfv/OflS29bHH1crbmILOaIT0YpzsGJFtyBISo7oFtHpTXD71mhcOY9v7VXj2159jw6tHYe61xeXrSAwBXrgCePZSoOXwcD8TiSjgNJng7OOblP49o+iQPGRkoYfKUCJusPbb0d06isglCb9oPNapPu5uVaz8owF0qHJxkhNNo2gd9CQ2RxFZuPpwC77/7214bmM1PtovFUwSEiMBDiWvkODs6oKzX96T44UP9zXihc3u/WVrb4ytwtY+wNzFHxulslCShSc6SAWqyKIlhgDPXgI8dV7MN54OmxNmk019vOOjajz7qw3Y8k4FrOZRGCp+MqOnCeigXCcXUC8V1yfKJNm8R+YWDiAIZWbhkOCDx/fhP3duRFt973A/FYko0HCsy/3SGU3Kwo4q9+Om/aNnz9m0Z9SRhXvruvCj57bBoemGLuUoKltNw/2UJCQkaD/Y2ub9d2lFjguae8y49TW+dhdlJLG3rb3W2H6RHsWCbEgFEo042SHJwhMdz18OPDAPsI2iKfVohaUH6KwCXE6g+UBMP3W/shBqtRpc8KNZyClJY3alzW9X4Llff45dn9TAblMm+ycROv77XzT89rdwjZbDCaHR44BC4bkSoxb2lhb1seXoMTh65cHNiyzsa+MTWom4gYZHtQfb4XS4ULlbsVRKjH6ycDQpCzvdOeWwmZRh2CjAKFMW1rT34TtPb4HJ6kBK8RtIKX8cm5o/He6nJSEhwZSF3mShTZacxBwUg3fLK7vRbrJiWpERvzh3Cnt/S6yVheJ+QKpCjQYnOyRZeCLD1s/Dm9uPA21HMFpgdzjxu//tx49f3MEej8rpekdlTD91fw9XFSanGzB2Vi6+dvsCnHPNKcjIT2b/tu6/R/CfOzZi/7p6OEfTzyzKm0bz3X9C5wsvwnIw9k2Gx3e2YNv7lUEzWiNCo4cCLcakssTQwt7iQc64XDDv2zecT2dkwLfURKoL4wpSExJRSIhHKUbXW2+h7he3SEtVnGHqtKCnzU0QUsnJqLmX05A00EBsJMPTMj3C16nOPiu+/dRmtPRYMLUwHbnZ3LJ+2PzOcD81CQkJ5jRp9/57s1QWxhrPbqzCZ4dakKDX4h9XzEFRRnJ8bMhCWZguLcgESRaeyPCc9vaOjgmHzeHEj1/aiSfWVeDNnfXYVjWKGgE9CcIYT9b7eriyMNmYwN5qtBpMml+AK+9chDO+MRVpWYnscPHpcwfx/G83sTIUlzPGJNcIA0n8RV6cp8IrFqBD2sdP78fGN46jtSbG1j5JFp4wsLd6K7mkFdmDLNQq/WmyETmuaKnuUR83HutiSsNYovWhf6H77bfR8+GHMf28Ev5VhTlj0qDTa9n9m+7po2uvqRk9uYXktmn1GKL3t49YB47Z5sA1z2zF8RYTs909/Z2FMDv59dKvPY59rXJIJSEx3LC3ee8HpQ05tjja3IM/vMPPTLedPxWTC9KRl87PxK09cSILjbLchCDJwhMZnko3U2zJlHgRhTe9sAPv7HZXoG+p9J7UjJrpeqyVhd0KWagsjAI6nRbTlxbj63ctxtLLJiEpzYCu5n58+Pg+vPTHLajc0xp7ZdwIgfX48YBZIdGipboXNqWVsrMpxjZKT9VFVzVg7o7t55fwi3i8Duytyrqq58RY/+5RoqiJp5pd3GsKZ/G3kixkuPfDQzj3b2uYQiiWaK5yk4V2mxNNlbFdT8QgxrQxPsVdEhwNSrlJ8cQMGHN5DlPXaCmtEXvN0oWjpxG55QArZWlNzcbq9AxKEHa3X44gOJwu3PzSTmyt6kB6kh7PfHchctJ06LO7h5jP7n9uWJ+jhIQE4FCUhdqMDPZW2pBjB6vdiR+/uBMWuxPLJ+fh26eNZe/PTUtkb7vNdljsMYzi6pbKQk9IsvBEhidhNcKVhbQQ3PD8dry3txEJOi3OPaWAvX9z5WhVFlbGR1mYbvD773qDDrPPKsU3f38aFl44DglJOrTV9uKdB3fjtb9uR93hUfRzDBGW4xV+iyZigfoj7lbKrpYYHtgsvbwxm5CQzt+2HIrd55fwixtf2IEpv34ft7++B1VtppgrC1MXLmBv+/ec5GRhV507FLpwxqiw9w0VUf3vz6twqKkHmyva46IsTEjmhHXtodit9aTcpsZvgmnTxhN28DQSQKpQQuHEDKTncGtVz2jJLRSD0qkXjB5lYeNemDUafCsvEzfkZmBHYuKIyy2k1xtF8oh98aPfnM/UNO1m7zXkg8oP0NYf2z2QhMRg6LP14Vinsp+VgF3JLEyaNo3/XSoLY4a/fXwY++q7kZViwF+/Oovl9xMykg0w6PjjtliWnIjBkVQWMkiy8ESGp9LN1DyiicLrn9+OD/Y1sRyCR741DzeeOYn92/aqDjZZHX2ZhVR04opDZqG3stAXCUl6LLhgHL75+yWYe04ZdAYtGo934Y37duDtf+5Ec1X3CaksdPjI/6NF/VE3WdgdS3VHE9mFXHxaVcoJJllyEl9Q7ukH+xrZOvP8pmqccc9njDzcV+8uE4gUDiWzMG3FCmofgr2hAbbmkbvWDlm5SWYpkFHm/b6TGPVdZnT18zW8tiN26wlZjtvquMJoxvIx7G1dDMlCzyGMvb4BtlqpEo0HbBYHWpS4i6IJmaqyMKb3nnjBbnWTbFO+6H7N94/wAWXTXjyaaUSNxs7+WmnQjziy8PG1FXh6Ax8833P5bJw2IYc9FmShzpkBR38p7C4bXjn8yrA+V4mTD3/a/Cdc/ObFWFe3brifyoiAQ3E4JU2fzt5KsjA22Hi8DQ+v5qT03ZfORIGR3x8JGo0GOamJsc8tVJWFhbH7nKMYkiw8aZSFI9OGTLLhHz63DR/tb0KiXovHvjUfZ0zJZy1H6Yl69FrsONDQPfp+3pZuoC92CpJ+RVmYMghZKEB25CWXTsQ3f3caO0TSFKZ6fzv+e/dWvP/IHrQ3jP7WVktFfGzITqcLDUfjpCxU8goPuMrxSo1QFsa+nEXCjZqOfkYU0vqyckoeaPbw9q56XPDPdfj2k5vZRiRSxZRQFhrKypA4YQJ7bN47ClQ18YIgBjNKgIwx3mrDkxgH6t33sFiShaLcJDFVj2lLuF2msaILdqsj9gU+zIq8MSafV8IbTRVdLKOQsofTs5NgVJSF3R6FJyP7Ne8C9MlAzkT3kIANxkYujjZux1MZRvXv7TrdiCIL39pVjz+8y/O5fvnFabhotlvhIsjCJK0R1vYl7PHLh16GzckHEhISQ4HtzdvZ29ePvD7cT2VEwN7e7kUWntSD4xiBhqw/e3kX095cPr8E580YaAvOFbmFsSQL1YITqSwkSLLwRIan0q23aUSGNl/37DZ8crCZHeSf+PYCrJicx/5Np9Xg1PKs0ZNbSCuZCPkWwf4xtCILsnAwZaEvUjMTseKqKbjqt4sxZVEhyx8/tqMFL961CZ88s390KBcCwBonG3J7fS8sfVxtQIjpz0ghCz/uLMQmE7faO0b4oWq040gTt2lOKkhjwfDv3rSMHbzIxbD6cAuueHQjLv3XBny4r5ERxZGQhfrcPCTNmske9+8+iUtORD4hIwtLvN93EsNz4FXb0RfzvML8snRk5Cez9d5pd6HhePSqWa9MTgV9MrcwruUmhRN41tWoUhaKfU9mGck83PEDIzi30Ol04C57LewaDbRKKUubTjtiyMLPj7Xh5y/vYo+vXjIW1ywb5/XvgixMN2TB3jMTiRojmvub8Un1J8PyfCVOPtiddtT18EHgmto1zJJ8MoMiO1z9fL1Omq7YkFta4HLEMEfvJMSdb+5FXWc/ynNScOeFp/j9GJFb2Kqck6OG0+lRcCIzCwmSLDxRwcirkVtwQkThD57dhk8PtSDJoMVTVy/A0km5Xh+zYOwoIgspE9LeTzXFQPHcmDciu23I/jMLB0NGXjLO/s50XPGrhRg3O5ddHgc/b8R/7tyINS8dhqlrlLQuKnD0mrwk/rG0IYu8wtzSNPa2t9MCuy1GN/wGTiTtd5bjkLOUPTbV7JFZYHHEkWZu75uUz5Wc04uN+OeVc/Hpz1fiG4vLWPTBjupOfP/ZbTjn72vwyrZaVrY0GFxOp0pS6/NykTyTF3qYT+aSE5UsLAWMQllYG9NIhtGIA43xURaKvMK8MiOz44yZkhlTK7Igw3W5/N5s2rRJrlVxzCssUsnCUaQsFPtMIgsJBQpZ2DRy18HX9zyJHQl6JDuduGrqFex9bUxZOPwq6MNNPfj+s1thdThx/oxC/PpL09lr2x9ZmJOcDbj0yHauYH9//sDzw/KcJU4+1PfWw+7iQ3Wzw8wIw5MZYi+oSUpCQlkZi6WB3R7zPPWTCW/urMMbO+uZeOhvX5uD1ERFiBOALGyJlbKwrxVw0rWtAdK4qONkhyQLT1RQXgxZYUdgwQkRhdf+eytT9SQbdHjq6oVYMtGbKCQsGJvN3m6u6Bj5BxShIjSWADmT4qYsTDGGpyz0Rc6YNHzxh7Pw1f+bj5KpWczCtufTWjz368+x5Z2Kkf9zVmCt8CZiY2lDrj/MycIJp+bDkKhjDqueWBzaHDa4mrmtaJ9rLC4//2z22Ghvxwuf7Yj+80v4xVGFLJyYz8lfgfKcVPz+4plY939n4IcrJ7DYA/rYn/93F1b85VM8tb4CfVa3wtQXjq4uthkk6LOzkSyUhXv3MiLx5LYhe5CFNEQZ6fllccZ+LxtyXxzIQk6El0zJiilZ6FDIwrSVK9ghiP5uPSYD7WMJUjNTrrDIKySk53BlYX+3FbYYWcrjrizMKudvR7iysLW/FffueYw9vsGWhFPyZo0YG3JjlxlXP7kZPWY75pdnsQMyHZR90Wbm+53CNL5vtrQvgl6jx47mHTjQxvcYEhLxRFV31YCSnZMZDjE4zsmBRq9nbwl22YgcEUhN+Ks3+D3khjMm4tQyvrcJShb2xIgsFPeBtHxAF5lA50SDJAtPVAiiSpfgwZQP/6az3+rA957ZgrVHWpGSoMPT31mghjb7YnZpJmuAoxyCyra+0TFdpw1z9riYkoVE4LnbkKMjCwUKxhnx5Z/MxZd/Moc9tlud2Px2BSp2xrYoJF6wKnmFCePHs7eOzk64FOIm2p+1KDcZMykTxrzk2OUWth6GxmFBjysZEydNxzeWT0d3Mrdqvv3xKpadJxF7HGlWbMg+ZKFAfnoS/u+8qVh/25nsLW08qJDit2/vx+l/WoV/fHwEnX0D7Q1kMSHoMjOhSUhA4qRJ0CQmwtndDWuV90b6pEGnR2ahIQlI5bESJ3PJicliR1W7+/7VbbarZSexKjfJL+dk4ZjJfEPdXNkDq9kes8xCQ3ExUk49lT02SStyTNFeb4LV7IA+UYecMansfUmpBrXdesQ3IncEUBbSYMwR/TUYa9yz9R70OPoxzWLFVdlzkZOU47YhC+vZMKDbbMPVT21m957xeaksvzvJoPP7se39XFlYlsFVLw1tCTi7nA8fnz8o1YUS8Ud1Dx8SlKZzh8zaurUw2UZ/Fnq0eYU6hSTUF/DXpr155EWAjXRQqelPX9rJhiZzSjNx45kTg358blqMMwvVvEJpQRaQZOGJCkFUFZLaRUOeuZgWbkQCUul85+nNWH+0DakJOjzz3YVYNN4/UUigjdKsEm7L2VLRPip+3p0ZxWhMzYopWWjtt7McKkJyWmynHCVTs/GVW+Zhzhf4Rn/T28fDzm0bDliUJuSUeadyub/LBUdH9GqazqY+ZvmmFun8ciMyhB0sBtlR3ZU8DHq/qxw/WMnVp+llXI02ETW4/j/b2TRNInaga1koCycVKIUyAWBMMjCFISkN/3DJDJRlp6Cjz4a/fXwYS/60Cr/73340dPUPIAvJgkzQGAxqsLV5T2wseE6HM3YW+HiD1JTCxifyCtXcwuG39w0XDjb2MBd2fnoiclL5prYuBlZkz3IToUQj+yo9ZiVNirU1GrgzOXORsmgRe9y3SZacxBKNx/hwqnCcEVoirBSouYVt/aMks1BRFmaNAxLSAIcFaDuKkYQN9RvwzvF32MHnztZ26ItmIjsp260s7GlkDoChBhVwUdEfrRV56Yl45jsLkaWsFf4glIXlmQUw6DTMsnxO6VfZ+949/i46zCe3klsi/qju5q/7s8vORrmxHBaHBatrVuNkhRpJk53tRRbaZCNy2Hh87XFsqmhngqK/f20O9B73RX+gNTOmZKFQFhpluYmAJAtPVAilG7XTpfDFC6bmYVVXXP3UFmw83o60RD3+/b2Fqs04GBaMyx4duYUdVSDj4dX9+/Hlg4+gkwisGJGFIq/QkKSDPsH/pDkaUB7O/PPLkZiiZyqHo1ubRk25SeLEidBlcXI2FtkgIq+QDm5EGMZSWXh453r2tiV1MhYq17Umn5NLi9Oa0GayssIfsulLxAZEvpptTpZLWJrFf5eDgYYUX19UjlU/W4H7r5zLmtn7rA48sa4Cy//yKW55ZRcjIB0+eW4E1Yocg9xCInze+ucuPPmLdaMjU5RycR1Wltv6XP1n+NHHP0K9MR8ne8mJKDeh66hEuQZjYUX2LDfxzDRTrcgHoycMPAt8UhdzstC0eYsMbY8hGnzyCgXURuSRriz0zSykvY9yX0PTyLEim+1m/H7j79njKy1anGK1MhVkTjIfWNOezUGZI0NcBkhuhv97dbc6RKf87tLslKD/R2QW5qXkoCSLf2yKczym50yH1WnFq0deHZLnLnHyoqqHv+7LjGU4d+y5ONmtyMKGrMvl64mhgO99pA05POyt68I9Hx5ij++8cDrG5nK1fTCoBSe9MSo4kcrCAZBk4YkK1RpSDqTmD2tuYS8jCjdjc0U7ywUjonBe+eBE4agqOemoxL6EBByzdaHPYUGlQc8PyPboF69YW5D9ITHFoKoLyY7sCKHgYaTYkNVskBjkFtYpeYXFkzLVYphYHNiILHcq5SblpyxyH+6VQ9XZOW3ISjFgT10Xfvn63lGTHTlaLMjjc1MHnU76gj7+wtnFePempSwuYdG4bNgcLry8tRZf+NtqvPTBTv5xeXluonHGzJgpC/etqWPZczazQ800G9FQCEFnehEe3PUwsyV901aBY7QWdp+8ZOF+L7KQH+xrYqAs9Cw38cQYQRYejiFZmJeLpFNOgTY1Fc6uLpgPHoz6c0v4koX8niOQPhqUhbZ+N7mWNdb9fjW3cOSUnDy6+1HU9NQgPzkPNzQoasjCmchMzIQGGjg1Gj7kHeLcwr9+cAiv76hj2YQPfWMeZozxJo2DkYWkiiQFPKGmvR9XTb2KPX7p0EusrVZCIt7KQlIVCrJwXd069Fq5k+Nkg72Nvyb12YoNOV+xIUtlYcggocRPXtrJ9tnnTC/A5fO5xT10sjBWykLZhOwLSRaeqBCqNsrQS8sbtkbkHrMN335yM7ZUdiA9SY9nr1kUNKjUF0QqEq9CmYXNPSN4wt5ZhU9T3cqltgSahrhiktWllpvEkSwkzDqjhLUtk4ru0MZGjFRQNqG1kpPhCePGQ69M8qJtRGZ5hUe8yUJhBYtWWfji5mpMcXE15PS5S93/kDeVvUlsP4QHr5wLyjJ/dXst/v35yMu8o9fyNc9sxZn3fsbylUYDjjSFZkEOBiJ2V07Jx0s/OA2v/nAJvjC9gNlKm6v4ofL9ehs+P9bmpSw0HzgAFylXIgQpCTe+cczLHj/i0cUPD8cyCtBr4z/3ZqcZ3y4qwO72k5dcEspCauGOpbJQkIUir1BA5BbSv1v6bFGth542ZAptT1mwgP29T+YWxgS9HRZWnkV7nILxRr/KwhGdWahklLoS0vDPg8/h8rcvx+GOwx6NyCNDWXis8xie2vcUe3zbhMuRRrE8NERPy4deq2eE4XCUnDy7sQoPfcbX+T9dOhMrJrsHT8FelyKzkMjC8hxOFlIu6nnjzkNWYhYaTY34tObTOD97iZMVNqeNtSETytLLMClzEsZljGOq1pP1unMXnHjbkGVmYej403sHmWuHbMV/+sqsAS3wg2UWdvbZYIuF0KVHuQekSxuygCQLT1SohRtjPZSFQ7toEaHwrSc3Y1tVB4xJevznmkUsrDQcZCQbMEU56G+pGKE5LKQe7KrFqhQ3WdiarlgTO7xbe6OxIRORF08kJOlx6rk8d4iakSlAfyTCVlsLl83G2jkNxUXQ5eTGRFlIhzZTpwVanQYF4zN8lIX9cEWY5Ug3r3fWbkKGpg8OjR66AsWiRcidBGh0gLkLSwpsuP2L09i7KR9v0wgqPCGi/muPbMTHB5pwvMU08jNEFRwReYUByk3CxbzyLBY8/9HNyzE7lVsx9/XrceVjG1HRaoKhrAzajAxGFJoPH4n466z77xFWeiDQ2TyC1UU+ysKdKdw2MjN3JmallqBLp8M1/QexoW4DTjaQlfxQIyf1phele5CF/TErNxFNyAJpWYnILEhhhLYYfkT03Knt22bzCm1PEVZkmVsYEzQoeYU5JWns/uuJUZFZqOQVPp9bhMf2PIYD7QdY/EBTVsmIaUR2upy46/O7mNJuZclKnOU0eKsf6eef7FFyMkRk4Yf7GnHnm/zn89MvTMZlIapoqESCSBlCVlKWqiysbutDoi4RX53MswufPyCLTiTiAyIKHS4HknRJyEvJY6SOUBd+WPkhTkaIGCSdoiw0FIrMQmlDDgWfHWrG0xu4yOmey2YjO0hmqy+yUhLU1vi2WFiRpbJwACRZeCKCWo9FKyXZkKn+e4htyNT2+M0nNmNHdScj/J6/djFmlYRHFAqIfLcRa0XuqkG1XodjCe7FrS1ZsZK0x4IsVGzIxvgqCwkzlo9BakYCetst2LduaO04ocKi5BUmjBsHjVbrtiFHmVkoLMhUbGJQsiHTspOg0WrY4byvO7Kb0P921yO3l2dwaEhJqPf4PeoTea4ooXk/vrd0HL48pxh2pws/+s921I+AwpPKVhO+8q8Nqp2SQEHsJyNZKEBKxVkpnMzTKpmFFa29bNOcPFNYkbntPFxU72vD0a3NTG00V4kG6BoVykKFLNRxUn1J8RI8Nv+XWNLXj36NC9evuh7vV7yPkwmk9qG8y0S9FmNzUlUbcrRkob9yE0+MmczvtXWHIicLhapQl5EBrXJvS128mL3t27qNDWwkokOjsCArw6lRl1nYWYkNSUn4SyK3fxkTjGjqa8L1+x6BSaMFehsBU3SK/2jxxtE3sL15O5L1ybh90e3QNO3j/yDUj4pCz60sjH8Z0/bqDtz04g7Q/PGKBaWDtn36syCn6FPY9yTIwqp23kR7+ZTLodPosLVpKw61832HhEQsUdXNxSilxlJo6XUO4NxyThaur1+Pbqt7r3iywNHOzx8H0ICffPoTmDL5fVnakAdHW68Fv3iF75evXjI2JIW1J7RajUouxsSKLJWFAyDJwhMRNBl12gCtgbf5pA6tDbmrj4jCTdhV04nMFANTFIaSwxIIoghlxJKFHZX41ENVSGhLVP4eg5ITQVLF24ZMoAKV+ReMY4+3vVcJm9UxYvMKE8fx5xkrG3L9UW8LMkGn0yI9OzFiKzJZhh5ZfRzTtXxzpS2ePfCD8rmaEM0HGNn0p0tnYXqRkRWeUEPicBae7KntYkQh5SHRoeTKhVz9INRSIxn0sz/axJ/npILYkoUEeytfT9OK+AS5pccSdcmJ3erA6hcPs8ezzijFpAX8c3c2jx6ycJeDHxTm5M9BSvYEPNDUgvNMfUzZc8uaW/DSwZdwsmB/Pf9ZTClMZxmYsbIhByo38c0trI0it1AlC5W2b0Li5MnQZWbC1deH/j3Drxo7UfIKCycO3B+JzEJrvx1m08gkZqta9+Pn+bms3O2iCRfhpS+9xIi3Q51H8LMxpbANc25hW38b7t16L3t8/ZzrUZRW5LZGF/J1mpCTJJSF8bchH2/pxfee3sKKt86YkoffXzwjZLudb14hoTwnVVUWEgpTC3FW2Vns8QsHX4jDdyBxskPNK0xXGtABTMyaiImZE5lF+bOaz3CyZha+0bYKn1R/gk/6d7C/O3t74ejlRL6E/336ba/tYftnGurfej6PZgoXIrewJVqy0NrHnF4MUlmoQpKFJyLUdrpS/P7dQ7h3AydBmhtq1ANt3L50nxVff2Ijdtd2scKG569ZHBVR6EkWUvYT5aaNOHRWYZWSVzg+Y7x70xkjslBVFsbZhiwwbUkRU6sQSbn3s/hP2cOF5bi73IQQKxuyb16hgDHXbUUOF58dbmEqvFk6d6D6AIjmyOYD7E1ygg6PfHMeI9p31XbhV28MT+HJ2iMtuOLRzxlpeUqxUc3rIxxsHPmT44YuM0xWB/RajXqgiiUcLZxMSVQKTpq7+dqapCgL+yNQFm57vwrdLf1IzUzEwovGISM/WY0iiCZ/bkjQWY12rRZVVv46mpU3C0grgEFrwJ+aW/G1cRfABRd+v+n3eHjXwydFiY+aV1jE8+jGKGRhj9nO1PdRl5uUe+fc+eYWttX2oj9CW469xd2ELEBK7pRF3IrcJ63IUcFqtqO1ttdvuQmB1O3CTRDJvSfe6LH24MaWNejRaTErqQB3nHYHStJL8OBZDzLF23qDC3/IzYZrGMnCe7bew1ROU7On4uvTvk65AIA/ZWGyUBZq3U2YcQCpXq5+ags6+myYVZKBB646NezirTZzm9dzFsrCbrOd7b8JV03jRSfvHH8HnebI1cUSEsGUhdSE7Ilzxp7D3p5sDgLKUXd08MFcrY7fmw9balghGEHmFgbGy1tr8OH+Jhh0Gvz9ijlIMihn5zAhcgtbo+U4xPpvSAUS/e+vTkZIsvBEhEJQuTLL8cz6Suzr5BPqlsYaLPjDxzj7vtX49Rt78e6eBib/jdmXNVlx1WObsLeuGzmpCXjh+4tZqHu0KMxIQml2MrNsUP7hSENb6yHsTORTja9M+gp/n8bp3Uodk8zC+CsLCTq9FgsUdeH2D6qYsmEkwarYkBPHeysLo7EhU9A8kTQ04C+a4E1uG5XcwkiUhY+s5uHl8xKVNtjCWUGUhfvVd5Vmp+CBK09lhSevbKtlQehDibd21eO7T29hZNvpE3Pw4vcXs9DhsbkGaPSdLLfQah+ZmZa+FuRxuakwhHkgGwxOqxUOynQja2ZxvtdEU9iQrceOw9EbejNgR6OJvd4Iyy6fxDLM6E9KRsLoyC3sqsWuJL4OksKALImgdlFjMWj798vyL+O62dexf39w54P485Y/szyxk4EspCZkQkqCnt0bo1UXNld1q8pCf0gxJiC7mB9U6pV4hXDhWW7iiVSRWyhLTqJCc2U3y8GljMn07IFWckKGyC0cYVZkh9PBVMIVLgsK7Hb8Y+r3WF4eYUbuDPx52Z+hhQavpqfh8eoPhuU5fl7/Of53/H+s6fjO0+5kRSZkmwa1teoSeF6wAqHS48rC+AxI+6x2piisbu9jBN8T316A1ETvnMpIlIU0XMxP5z/7KkVdeGr+qYwgNTvMeO3oazH9PiQkqnvcTcieEFZkeu11WRR11kkAR2cnSeSoDQ/VOn6/Pdpx1F1yIq3IfkE53799m597fn7OFJxSHLmwKE9tRI4ys1Aoy0lVGIbi+0SHJAtPRCgElTmtFJd3J2BG53xYnUko1PWwa5/ahoh8oEy0eb//GOf+bQ1+89Y+vL+3UZ1Mhot2Igof38RyzYjhJ6JwamHsWPmRbEVe074XTo0G0xJz2UaZ0OYwu4nbKBU0bmXh0JCFhCmLClhIPtmfdq2KvtE5ViA1kq+y0J1ZGLkNuf4oJ6FzS9ORkOy9gc/IjYws3FnTiY3H25Gr7UWGtXFAqPpAZeFBrnxQsHRSLm47nxOJd729H5uHqFTkiXUVuOmFHbA5XLhgVhGevHoB0pMMzNL1o9VXIW3iX+HQduFYS+hE2HDgSBwtyKL5DgYDMvNzvJSFRK4YiovZ6968V1GxhHBdr37+EMuhK5+Zg/Fz3WquzPyUkd+IbDUB/e3YoQxNZud52O0zeNmBpruOWQFvXXgr+/t/DvwHt6+7ndmWThaykBBtyQnlp7bXm/yWm/hTF9Ye6ojKZu9LFqYs4rmF/Tt2wGmJr1PhpLAg+wynPJEucgtHWMnJ37f/Hevq1iHJ5cI/mlqQm3+K17+fUXYGbi2/iD3+p7WakXZDCbPdjN9v/D17fOXUK9V9mVq4QtnBOsMAGzLPLGzwug/HAnaHEzc8v4M5Bchx8/R3FrDhWyQQTcjiORM8G5EJZGu+aipXF1LsA5G7EhKxtiGXpnuX8ozPHI9JWZNgd9mxqnoVTjYLsi4rEx02vq4f7TwKfT4fJNskWei3+PEnL+1kmc6Lx2fjmmX8PBcpcpX1NOrMQqEsTJcWZE9IsvAEVha268eg2KFFokuPdns5ctCFHb86Cw9/Yx4LERUtw4eaelgL0XXPbcPc332E8/+xlpETH+1vCskqRerEqx7byA5GlBvwwrWLMVn53LHCQkEWjsBG5FVmvrickTtHbdVrtdINQwNYe4C+6OyxfT1Dl1kooNVpsfBCrtzb+VH1iMlMIqk/a+nUaJBQXu5lQ3a0d8AV4Sa//kiXXwtyNDbkR9dwVeH3JpnczeRJfg6G2eMAUmXY+7nywQPXLBuHi2aLwpNtaOiK36GRCKs/vXeQNTETaI24/4q5SNTrYHFYWGhzXW8doHFAm9g04nMLaShCmJgf27XIS3WVk4M8Y/KArJSkWbPCsiIf2tTICnb0Bi2Wf22yV4YVkfYjPrdQ5BUmp6h5hb5kofgYsgPevexu6DV6ZpO7adVN6Kdr/wQDDd7qu/jQaGqR+xosUWyDkZKFfstN3vs/4P75QK87l7hEyS2si5AsdIhr3COzkJAwbiw7BFHjd/+OnRF9bgmPcpMgZKFoRO4ZQcrCN4++iaf3Pc0e/66lDadYbUCmtx2RcOWc7+PbXZws//X6X2Nzw+Yhe47UzEzqp/zkfNw490b3P/jJK/QqOCElNA0v+lpjel/99Zt7sepgMys6evzbCzA+L/IBlmpDVp4zoSxb5Ba6s9HOH3c+MhMzUW+qx2e1J1+GnER8YHPY2DXlT1noqS78oGp4FMXDATUvPct9fui19cKWw4eEdtmIPAAPrDrKeg3Sk/S47/I5aptxpFBtyNGShaqyUJabeEKShSdwZmG9wz316XIUAC4HMmHCeTMK8ZuLTsEHNy/Htl+djYe+fiq+ubgcE/PTmAiOSL8n11fg2n9vxZy7PsSX7l+LP7yzH6sONg3IDKQMxCsf28hy2cgKQXZFagqNNRYojcg7azthsY+cKSkdcjdqOZl3ZvkX1Gkvvb9PLDZR5BY6HE5YTNwGnGwcmsxCgYmn5iNnTBqsZgd2fKRk7g0zrIqqkJRb2mRO0uiz+aEYDge3A8Qwr5CQkRc+WUgNwu/t5WrCrxS3B84rJGh1QN4Ut7rQA0Qa/fkrs5gyieT11z23PS6FJzTl+/l/d+NhxTb9i3On4M4Lp7OWMTrs/GbDb7CzxU0MaHQ9ODDCcwvj1YRMsLe4VVf5Rj7RbO5xH+jVRuQQSk6IiF//ylH2eP4FY1VyWkDkFo7oRuSuGlZmsDeBq3Ln5HmQhcYxyscoVnwAXxr/JfzjzH8gSZfEFErf//D7J5xtSbSHU4SGMcm9dkdbcqKWm5QbOalcvwPY9DDQdgQ4/J76ccXUiKwhe3sfTF2WKDILvclC+popwooscwsjgtPpQuNxQRYOvOcMGFSNEGXhrpZd+O3nv2WPvz/hUlZcxAZgyX6+h4xS/LTPhS8o5UY0bDrWye8v8cTxzuN4cu+T7PFti25DWoLH+i+UhR55hQQx5G0zKAPZGJac0KH4hc01LFLkn1fOxbxyZb8SIYQNWTxnL2WhYkMmJOmTcOmkS9njFw7IohOJ2KC2t5bFh1AuaV7ywNbac8dysnBT/aaTJi9TKAsdmd57zS4jp1ikDdkbFCf2wKd8z/uHS2aiONN7zxtNwYlUFsYHkiw8gW3I9f3uzUSPRpkAmbwnHDlpifjizCL87uIZ+PinK7Dll2fj/ivn4qpFZRifm8rIQ8ogfGxtBb77NJGHH+HLD67H3e8dwAf7GhlReLipFwVGThQS4RgP0HOhrCfKSaOG1pGCDZUfw6zRoNhmx+TSZUg1pKrZPW1ZJVGTheZeTs7SmTApZWjJQo1Wg0UXcXXh7lU1aivzcMLXgoytT0HzyZ2sodNT8RWuzbujgU/kiyf6URYqZCFlR1IofSh4dO1x9to5c2o+8k283RaFfpqQB1iR3bmFApRJ9KgoPKnpxB1vxrbwhLKUvv/vrXh1ey2b7v3lK7Nw/RkTVXXb43seZzYynUanFvho9T0jWllIP5942pA9iZS8NCUTtsei/l7URuQ9g5OFn792lL3OKWNuzhcGKnRUG3KMMwut1dXoXb8+Np+sqxYHEhNg1YCpWbwUB0JZ6JMFtrxkOR4951GkJ6QzIvrq969Gc9+JM4E/0MCvv2k+cRwlWdEpC1uUvELVgryK2y0Zqt3kXVKqAbkl/Nqvi6AVWW1D9iELCamKFbnvJM0tdDhdLKM5UpCNnIZwhkQdcsYELl8y5oyczMJGUyN+vOrHLDbgzNIzcX0uJ4yROVBdxKDRQFswA3e3tGJOSgl6bD340cc/QkufW/0aaxCJQWQmkZMrS1aqrcAqmvb4jQNxKws1cMWQLHxzZx3u/Yjf/2lAf+4phVF/Tt/MQs+SE8pD9MQVU66AVqPFpsZNONJxJOqvLSEhLMhl6WV+W7zHZoxleZlkRaZW4JMBjnau9rUYvbNnG1P4+c0mC05U9FrsuPmlneweevGcYuacigVUslBx4kUMqSz0C0kWnmiw9QO9XNHUaHIfUrqgHEJ7gy9alKNy4exi/PGSmVj185XYdPtZ+McVc3DFglI2vaQXOBEWj6w+jh88u41Z/QqNSXjx+6dFZa0YDHRTmj+WT2Q3j6Dcwk+V1q8zrS5oktLZ88xN5oertnSeV4EOXsgRCQRBl5SewMi7ocbYWbnIL0+H3erE9veHtmRj0HKTtmPAOz8FNj4InWIFVbPkwkD9UT79JLImKW0gIZuYrGcH71DVhUQaUSkJ4QfLxwMNu4MrC71KTngjsi88C09e3lqL5zZVx7SU6NNDLcwi9cg35uHyBW5F8kdVH+GfO/7JHt++6HasKFnBHmtGOFlIvwNqh6SfFxWcxBpqnltenpo9ZbY50WPhZHLS9Oms3MPe2AhbEAtKw9FO7F/PJ5krrpoCnZ8iFtWG3NQXM5KYPk/ND65DzfeugfnQoeg/YVetWvJEqkKvQ4RqQx6YfTo3fy6ePu9pplCgjJ9vvfct9TByIuYVxiKzsFlpQmblJlWfA0c/dv9j9edeHztGtSJ3RlFwMlA9IkpOiAx39LqtjycLfvziDlYWRwHtkaDxGP99FIwzssiPwZSFPW1mVoYyXCCnBMUFkAWWMskoRkArXs9ZAchCQsEMJLqAf6adwgYIZF+8/pPr0Wfri5tFenvzdqZ6IlWh1zpk7mKN7eJ5eUIQb2YN0Ef/J0YlJ5T/S7h22Th867SxMfmcIrPQiyzM8U8WFqUVMWKX8MJBqS6UiF8Tsj914QeVH5xUysI+n/NDVRJ3t0gbsht3vb2PrVNjMpPx2y/7yXCPEFJZGF9IsvBEg9gMJRrR0e2uIO8mGzLBI9MoFBQYk/DlOWPwp6/MwupfnIENt56J+y6fjcvmlTB7FVn8XvrB4rgcyAOWnAxR0cNgoOn16uat7PEZCW71hbAit6VkRa0sFOUmKelDqyoUoM32oi9zNdneNXXo7RhehYOlQlEWjhsPrL0PUNpU9cn8IGVvbYupBTmSRuRnNlQyBezcskwsLEkGWhVlYZGfJuQBykL/ZKEoPLn1/Kns8W/f2hd12U9dZz+++vAGVsSSkWzA89cuwtnTlXUCwL62fbh97e1qztzlUy5XrU9EFjZ0mdHVNzKyLANZkMfmpLLMxVhDJVLycpnyM11ptSSSkqBNTUXixInssXnvnoARA589z4m6aacX+VW1ioIdOr/aLI6YqXutFRXsD3t++waqWcNGZw12Kk3Is/N9FLQqWej/AD45azL+ff6/WVg6ZWISYXiw3duO7wnK0a32sNuNVOyv908WlkZhQ/YuN0kDVv2O/8MpZDfUAO3HgV73waRkcmS5hS6bjeXD+sssJBjGjIGhtBSw29G/fRtONlBxFeXI7q6NMPbi6OB5hQRqSqYhocPuHDZlPw0W7lh/Bw60H0BWYhbuP/N+pBhS3HvNQMpCDwVfVvNh/Ousf7H/T5/nF2t+wfZPsQQVcN2z9R72mIqUitN8lCFN+9yxCCluoo1A3w8RjO6Sk9goC4Ut+KvzvIsgYq0sLFeUhY3d5gExJVdN40Un5A440aIeJIavCZmUhYFwTvk57O3mxs3q9XoiQ5Qr9qTxvaYxgd/zD+n4+6UNmeP9vQ1M7ED7WeIR6NwRK+Qqmf7tfVZWKBUxqOCKIJWFXpBk4YkGQUxllsPa4z7Id1uz/dqQwwVlC1x6agn+etlsrL3lTHz00xUoz4k/UUhYqOQWbq3qYArH4cbO5p3otPfB6HDg1IxJ6vuzk/nzbEtSfi7t0ZCFtiFvQvZF6bRsRqTRgWXre1UjQlmYkJcC7H5Rfb9e2xtxI3IoZKGaW9gSnCw1Wez49+f89/2D5ROgaTnAskKRkhN8UiWUhUQsOgITcNcuG8+Uv3RQ/eFz29GoFCiEC1IFXvrQehxrMaEoIwmvXHca5pW7Dx9Npibc9MlNMDvMOL34dPx8/s/Z+4VqNimJH4IOjtDcwsOKBTlesQgOH4tmnpJbKMhCQpKwIgfILdz1cQ0jfki1uuQSTiz6g86gVYssYtWIbFq7Vn1srYx8fRJwddVgV2LCwLxCT7KQFDFW/8+/JL2EEYZTsqYw9dJ33v8OtjbyQYwvbnxhB86+b7VaYDMSQRmg4vmdUuxNFo7J5Af7HrM9pAIxf+UmdM2kd24EqtYDugTgnN+51VIeVmRa04hsoiFHT3voa4W9vYO1eUOnUyMefJGyaCF7azrJrMhkoxLqBdGAHnm5SeB7DoFUh0QYRlKwFcuykPcr32eFRPetvA9j0sYM2GsGhFDTN+1FaXoJ7j/rfhbTsqZ2De7edHdM4zTu3Xovuq3dbA2h4dYACLLQR1UoIMi3NlJ6CnVJFOg229TXt1ATRwsiWDstnQPIwuzUBKQl6tlL1ncIMb9gPlODkjr0jaNvYLjRVNGNTW8dZ4MPidEHofz3V24iQKrDadnT4HA5TgorskNRFnak8Gt6YSG/N+5FvTpcdtljOxwZbWjqNuPW1/he+LoVE7BovDsmLRbITklgJCStgUQYRgQqyFScmVJZ6A1JFp6geYVkDXEqxRiEXksKHC69l+pgtGF6kRGpCTp2yBoJFshPaz5lb1f0maGnpltfZaFeHzNl4XCShUxdeBFXFx5YVx+Sui4ecFossNVye29i07sAKROyJ7C/61xtEdmQLX02tNb2Dq4sVFopuwY5sL24pYbZXylj8wuk0msUGUmzePBkIBChkpDOmxjJXh0AvPBkJqYWprMDKzWYh1v4s7miHZc9vAFN3RamDH71h0u8SomY5ezTm9Dc34wJGRPw1xV/hV6r9yILDQm9apN6zEA2sbptQF977MpN4pBX6K/8IU+xQDR7kIXJM7mS1OynEZkO/lv+x4nvJV+Z6Nf+7s+K3BWj3MLeNR5koaIwjAYNPTVo1uuh1+hwSu4p3v9IBQh0bROC2Pvo2nrqvKcwr2AeaxK87uPr8Gk1X2MFiFzYWtkOq8OJNYfjl30WLY619LLnSIpTX6KAlKiiuS9cdaEoNyFVoeZTJatw/vf4+lG2aABZmJCsV7MNw8ktVG32OTnQUEOsH7hzC0+ukhNPVSspucJFb4eFEbd0OygY700kB7v3dLcNvaqfDvr377ifPb598e2YXzh/QJGevyZkFXnTqA0L6GtjBNzsvNn487I/QwMNXj78Mp7a91RMnufGho14+/jb7PPeedqd6v3KC+q92D9ZqJacMGVh9DbkGsUSTFnbqYryPFoQUeiCi32flA3ruS8QuYWeJSfi366aepVqRXY4h7cgcP0rR7D13UpU7old47TEMCgLg9iQTzYrsr2d71nbkvhwYGbeTKZUbkm2AjR8cDojylM/kQq9fv7fXejss2HGGCNuPntyzL+GXqdlhGFUuYWmFn6u1GiBNLfLSkKShScexAYuayx0Fs/JnQY9jnz+YhiloMXgVKVJbmvV8Erb6dAqyMIz+vrYz3vAplOjTM1p42m3RGlDHj6yUBBpZdOz2aK/9Z3oyYVIYK2sYmMjbXoadEdf4e+86J9Acjb0CZaIbMgNpPBw8cbZ1AxO+ARXFvYFVRM9sZbbpK9dPp6VhYSUV0igk2P+1IAlJ55ISdDj0W/OZxJ+shDf8ca+kBUaH+5rxDef2MQITWpl/O91p3k1kVFA/C/X/RL72/Zzy9lZ97MCCgHRfufUckUhtaDHDM99BXjsTOAv44C/jAeeOBd483pg3d+Bg+8ALcFVl5442iSakGPfzO4vzy1fCbb2VBYmz+SH0v69++CiiaUC+l2tfekw7DYne11NPW3w0Hu15CQGykJnfz/6tmyJnbLQ6cBOKyeipmZMUO18XgiSW+gJutYePvthVk5gcVhw82c3sxwygY4+G/qs/LC7rTr80o6hziucWsRzbH0xRik5qWnvj6zcJK2FE+tkB132U/6PZaf5zS0smZIZthVZKGd9m5D9KQvNBw5E3EI/GlHdbvJSS4SLBiWvMKckDQlJg5NIxpzkYVEWHu44jNvW3sYeXzn1Slw2+TLvDxA25GCZhYYkIHeSVxPxWeVn4ZYFt7DHf9v2N7xX4W7wjgS0Tvzuc27Hv2LqFeyg7hdN/puQB5Sc0OE+BjZk8douUUi8WEBYOrOSsqDTesdr+GtEFrhg/AXMGkkxD2vr3IOioQbd+9qVMrnhIL8looPVYUWDqWFQZaEnWbilcQta+09sokzcLxsTzOo+eWLmRLi0Gtiz+EDoZLYiP7epCmuPtCLJoMXfvzYXCfr4UE9R5xb2KOt+aj6gi82A50SBJAtPNCgqNldmGZJsnEDQ6vhhpYtyC0exstAzt5DUUcMJCuOv6alBggs4vd/stWFWlYV2E2AgK7KLZXpFgj5hQzYOT2ahJxYq6sJDmxrVDd9QwqrkFSZmG6BxWoHypcDYpeyQrE9yRGRDrj88uAXZM2i+K0gr5du76lHfZWY3rEvmjvFWMxQFaUIOseTEExRo/sBVc1mBx0tba/CfEApPXthcrSgRnThraj6e+94iZCqTOIEHdjzASk1ImfG3M/7GcuQ8IYhwq8sEaGw4qJAiUYPWpVo3gcWUKDUbgR3PAR/fCbx4FfDgAuAPhcD984DnrwA+/BWw7RmgagPPYlUIUzqQHG6Onw2ZPr9nZqG3stB9fSROmgRNYiKc3d2wVrnt+xU7W1G5p42ty1Rq4o9MClhy0hw9Wdi3eTNcVis0Kfxz0nNzOaJQm/Q2YWcC31jN8VQeeSJDeT10cWVwMCTpk9i1d9GEi5iN6Vfrf4Vn9j3jpdYhbK/qGHV5hQNLTvoiKzdp/i9/x6IfAGlKkVYZV/qhYRdgNQ0oOak91BHyUEFtQvaTVyhgyM9HwoQJ7HVn8iCfT3R4kjGR2JBVC/L44HmF8VAW9q5ejcbf/4FlUg5GSlGhCanMFxUtUsk9Ff2dXAlOyBgkj0+Qc6KJGMA3pn8D35j2DfaYhlPbmiLPvXxs92NM7ZSfnI8b597o/4NITde0P+jgTt23iczCKC3S4rUdKwtyoLzCwUpOCDTAuXQS5ZoCzx94HsMFitWx9HHHk6kjyiICiSFHbU8tGyin6FPU10sgULTIjJwZ7OM/qTpxrchsP6goC+sUxw39bIgsJJgy+d7QdpKShZSh+s9PjrLHt543NW7RQARRNhgxWajmFUoLsi8kWXiC2pC7E0uR4uKH0LzydHfJSZSZhcMN0YhM5Q6xzLsJF0JVuNhsQQo9Dw9lodqGTBu77HFRNSKPBBuyQMFYI8bNzmV7aGGhHEpYjivlJjplQV/xC/62fAl0SVy55QhTWSiakMcMQhYKZWFvmxlOP+G5dC1SQzjhu0vHIsmgUw4oe0NTFnqVnIRWOLFsUh5uOU8pPHl7H7Nn+gM9t39+cgS3vbYHFPV5+fwSPPLNecwO6Ym3j73N8qkIvzntN8wO6gtSJxi0nLjW6HpxuKk3Nq9DIvzEwfL2euAHa4CvPAGsvA2Y8VVu4ybinSwCbUeBw+8BG+4H3r4JeOp84J6JwJ/HAo+fDcsrP8BVlldwnm4zJqImYlVvIDhNJrjMZtWm6blJ8VQWagwG3orMrMj8oGw127H2ZV54M/cLZcguCi3vNZbKwt6169hb4xfPZ8+RiENbg5LTEmkTsig3KZjr/2MGKTnxBZHVvzv9d/j29G+zv1Nxwd+3/d1L1UUFO/WdwxOJMBgONPSo0Rn+EEkjst3mcJeb9H3KSsyw5CbvnzERN5SRWuvOe6RcPCKme9st6A4y7Ahmsw+E1EXc+tw3AnMLH9/zOL7+ztdjXupQ6UEWNnkMB8JSs9MtYWJoZGG6oizsiVJZSK/z+ltvQ8dzz6F3zZqAH2dz2PDTz37KVGg0LLp3xb0Dbb1CVZiSCyQOcvgTtl9FWShAObhnlZ0Fm9PGiMnjXfz+GQ6Odx7HE3ufYI9vXXSrlwreC1T8Y++nJjQgmw89AyoLSbFHbc2CDI0Q4rVdqqiI49WELCBsyP7IQqG61Gq0+Lzhc/ZzGw50NrnX795OSRaOZgtyKENO1YpcdeJakV19fep+sEbfow7VBVnYrixJJ2sj8us76hh5V5yRhK8vDq5GjRYi3iVqZWG6LDfxhSQLTyTQoV1RFtaZuT3OBhcKx/JNabedlIWj14ZMmFuaBYNOw/LWwrVwxRKrqlext2eYenkmj7FkoA25v81NIkaYW9jfPXLIQgLLLtQAR7c1o7W2Z3jKTdKtQMlCYNwK/g/lpCzkBJ49jMxCIm5alAywokHIQrIo6/RaZsPuaR94I/rscAvL76NMza8vKncfUOjQQQeUnMAFFpEoCwV+sHw8LphVBJvDhR/+Z/sAWxwVAd3x5j7c9xEnqK4/YwL+/JVZzNLvW9Zz54Y72ePvzvguvjzxy36/Hm0Q1dzCxF4W9h8O4REQVNRAKD8dSEjlSsyZXwVW3gp89QngurXA7XXAzfuBb70JfPEeYNF1wIQzgQzKztEA5k6mTkza9xJuMbyEhw1/R9Jjp3M14j/mAG9cDziiD5m2t7SojcdaRZ2X74csJCT7lJxs/l8FyywjtdD8L7oHDIMho8Ddxk3XYCzKTdJWrIChvCzq3MK+9qM4nGDwX24iINbHEJSFAnSw/dn8n+Enp/6E/Z1IgeeO3kt0rfox20egFZnIc2FDDqws5NdNOK+dtjoTLzfRmZCubQFOu2FAq6uqLvTILTQk6tigJ5zcQlU5mxOcLExZzMlC06aRlVvYZ+vDI7sewe7W3dhQrwwi4mRDDmdYQvcckZE7WLmJr6o9VKI3EHo++0xtuBbZv76g7+WPm//IlH6phlTWfJyR6IfUDCWvUKDAXXLiCbLR3r3sbszKncWKSX708Y/CsivSc71r412s9GNFyQqcXXZ24A8WCv+C6WS18fsh6r4tgSs5o7UiCxV0aXbslIVU/kTwp+oqz+aDp6o2/64PKqahnxPh+YPDoy7saHQTmaZOaUMebajqrhq0CdkT54zlrchUVnaiWpHFmUOTnIwmZ6ebLMzie/66JH6d25tPPmUh7VUfWyNEFONg8Dl3xM+GHGFmoVQWBoQkC08k9HcAVk5+1PXwDZ7ZoIExL8lDWUgBnqO3hYzUUDPG8O9tcwAlVbxBTbH72vaxkOmVff1c0eGRb6DaWczRk4V9qrJw+G3IhJwxaZg0j9veNr01tOpC61FOeCWm24EVt7gLQwpnQ6/cJOjGHerhjVr56GaWlp2o5kIFAjWKqnYwPwqPhz/jpSRXLSpjWYKqHZBQcErAA4pfZSEjGUMjEYi8++tXZ7HCEyKqPAtPSP5/4wvb8ezGKvaj+s2F0/GLc6cOmAjX99bjx5/+mCk8ziw9Ez8+9cdBv6bILSzK5la2mJQNCWVh+ZLAH0PPm+ys41cCC68Fzv8z8M3XgZv3AL9sAK5bD1z2NHZOvB6vO05HRcJkXqzhcnJl787ngNrNUT9Vf3lu/pSFhCSl5KR/z2601PRg9yp+SF9+5RTofZSdwZCelcTJaocLPVHYEa3V1dwSrdcj9bTTkDhuXNS5hXubd8Kh0aBIk4DC1MLgysLu0MlCAl2r35v5PVZaQOTh/t6PkDTmP9Bo+bW3bQRakekaaDNZWUTAlML0mNmQWxQLcp7uMDREEi7+4cAPUslC79xC1Yp8MEyycBBlYcqCBex1aT16TCXRRwI+q/mMNbkTKruib/sOZEM225zo7g99ANFc2Q0X3XOyEpGerZBSg0Dcd3o7zHD4UbWHiq7XXlcf2+r9E2EvHnoRrxx+he1t/rL8L5iQyQvEBiCUvEJfZSEpwn3ua2SPpVxcUjCSkvHGT25kRG8ooGZfIjXpc9y+6PbgSqdB8go9923tsSILldd2TJWFwoacPFBZKDILazoCD5SumsaLTt469hZ6lLPCUMJTGU9DM4kTrwnZE8VpxWwYQKU8FG9zQpOF2Zle5UOTs3iJR3VS70lrQ/7oQBOOt5pgTNLjioWhEczRIFfYkH324SGjRyELZRPyAEiy8ESCIKTSCtHSzjewjkStezJNZCG1rZICZxRjoZJbuGWYcgvpIEKYlVKMXNq8+2yYxYTaZDOhX2R1RUAWEulFGS8joeDEEwu+NI7xNpW7W9FYEVuLVyBQQYTlOCfkEiZOAiZ6qAh0eugmKpZZux3OrtCeU/0RYUHmB+nBYFSsyL5t0DuqO7Cpoh16rYZNz1SoeYWzBn4/LhezhXkhNQ9IoWvHBbQcQqigwhOyFRNJuaO6E795ax+6zTZc/dRmvLunkSlx779yLq4+3eO5KaBr9PpPrmeHkKnZU5nSg0iZYBDXd14mf/4HG6PMLaT246Z9g5OFwWBI5ofSUy7Ba+lX4Wbb9Xhx7r+B22qAnx3iSlTPtvgoIEgRzzy3fOPANmRPZaF5/0F89twBRhRMnJeP8lOC5/34I6uphCfa3MJeRVWYMncudGlpSBg7Nmpl4c7OI+ztnKQg7XGqDTk8slDgq5O/yuyQGpceBuM+FE4iZYxjROYW7lNUheNyU3kcgR+UKmRhXUd/yMONlkq+XuUbjgFLfwIk+VEtipITyv/0UNEKspCUhaF8PbUNOUhmIfv3rCwkTuNRCKZN0RPxsYJnA2dFd+yGWla7U7W+swKrMK3IwoJcNCE0CzIhxZgAnUHLjCO97ZENCmxNzV7WY39kITUK/3nzn9njn8z7CZaXLA/8CTsGKgvXv3oUT/x8Lbp8S8CoVZLsyjS08ROxQZbah856iCkY97btxf+t/b9BG3vpfnXvNlIZA9fPuZ6REkHROHgciLD2ssxCQhSNyPQaE86X0jgUnPizIRdlJLE9CF2jgVq6FxUuwoSMCSyL0rM4ajjIQlOXNWqVvMTQoqqnKqQmZH/qwvcr3seJCIeSV+jMTFfLhyi2gYYPRBq2pblOWhvyo4qq8Cez7Ejb8FevLOV4KgtbIs4sVO6LRmlD9oUkC08kCEIqqxxdyqZSk6r3IAsLeWbzCVJysmWYGpFX1SgW5ASusPLMKySkGdKQoOXkXltqdsRkoc3igMPmHFE2ZEJWYSqmnMYnL5vfGprsG3vlIbioBVXjQsKXPFSFCrQTlkFrCM+KLMjCwcpNBDJUO1i/3xvil+eMQVGGh0KxMXATcu0NN+LIWWfB1tzs04g8PWwrMqE8JxX/vJIXnrywuQbn3LcGG4+3Iy1Rj6e/sxBfmjXw5kcHslvW3MLKeshaTJazFGpXHQTChmxM7Y9NIzKzTLqA3MnusoYocMSzCZl+pumFQN6UkNp4I2lC9iw4aTdZWSu2gKG0FLqMDNTlLURzVS8MSTosvUxpBw0TscgtNK3hZGHq8mXsbcLY6JWFO/t53uHsjAAqJK+Ck7qIiwPOLj8bxq7r4HImoFd7ALqU49hX380UtCMJwoI8vTgwITQmk/8ueyz2kJVpzYe4qiMvvRVYcK3/D8qbBpBt1NoLNCsEPC1B441MmdrXZQ3p+nGEmFlISF3E1Yx9I8SK3Gvtxbo6nssZa2UhKUGJ30g26DAhLzXsRmRRblIYogWZQIo5Y45QtUdGFna99SZzlGgS+D7CVudNFtZ01+Bnn/2MFQpdOP5CfOeU7wT/hEJZmOkelB7Z0gRzrw2HNvrkn9IaHCC3UGBsxlh2/6F9Ew1j/7zlz0FJ7Xu33suyKKdkTcHXp30dgyIUZaEyBGvXOL1VJhGAlMX9Ngf71oszQ1OQRptZSNEiQrHsrxFZXEtCXfjCwRdY+cRQosNj7aHBmYjZkTgxlYWeuYU7mncwV9aJBruSk241Jnu9Num1RrmFbWpm4cj+3ila6xvvfoPl1f5j+z/w+pHX2e+M4rQiySWnDHVyfiTotPh637PA6j8Be1/D0GQWRriuSGVhQEiy8ESCyJHJGou+Tv5iSUw3qBtNqysFFlf6CVNycrzFFHmQaYQg68bmRq6gONOmHbBhFjcJNf8mSblTtFeEfUgW5Sb6RB3LnRpJWPDFsSw0v+ZAR8g5WNHA8v6D7G1Cph6aUy4c+AGeuYUKmTNYWQDZkMMhC1XS3UNZWNFqwvv7+OHoBys8gtPpd90gyELvJmS68ZrWrWMH8q433wyQWxhayYknVkzOYzZjAikLaMr24vcX4/SJ/g/89227D2tq1yBRl4h/nvHPwBbSAGRhQqIpNjZkNa8wQlWhD440C7LQI3hfvEbFGhkF1PKHPDdZmJWSwFQdhDaPjQqzxs1aiGPjeQbk4i+PR6rSjhcuMkVuYYRkodNigWkzX7vSlilk4Tg+6LBURqa+osPmLie/DubkByg3IRgVspBKBkhJGsnXcrrQ3FIGey+39xgzWmF3urC7dmjUzeGWm0wrSg8apyGm4MKuGAz2PhPa23jURd6y84CEAKS+VguULhyQW6g36FA4QcktPNQRehtyKGShyC0cISUnVD5mdVqRbkhXc7ZiVYZWpeTQUZlEgZHvqyg/OdTrt+F4+MpC79zC8PNh6XsXFuTMr31tgLKQyNUbVt3AcgNn5s7EnUvuHLy8QM0s5Ouqpd8Ok1JYQU3vgRuR/ZOFhLn5c/HHZX9Uiax/7/+334/b1LCJ2WjJ7kfxBAPKV3xB641QCVIkSACIQ36Xyw5blMpCkVdYaExCol43JMpCQllO6oBcTV98afyX2GuDyio8SfV4w2F3qmS33sD3ztKKPHpgcVjQaOJ7XYoNCBW0r6QsY7Loflz9MU40ONr5etevuEvE/phAZGF7Ol9LSRgwnKWcg+GJPU9gV8suZhencrA7NtyBb733Lax8eSVOf+F0XPm/K/F/a/4P/9r5L7xz/B3sa90XNMrgEUVEcempY5DYW+uOohiSzMJo25ClstAXkiw8kSDUa5nlsPdy+2pqViLLxkrN4Ix7F1mRR7myMDMlAZMLOBEQqAE2Xlhft54Fao81jsW47ma/ykKvRmQ9bWQ1gM0EmMIL+HVbkEdGXqHv4WX66XxB3fTW8fjeBM1dsG58lz1MmDRlgKqQYcw86JL4c3BUHQgpO4o2r8nGBNXeORhEI3KXx4HtsbX0vQNnTc3H5AIPcqCnEehrJf+omwBU4OzuhsvCb2bdb73l/bOLoOTEE9etGI9rlo7DaeNz8OoPT1PzPX3x6uFX1cPY70//PWbmhdDW7HNtO7WcbKVMEpGTGF1e4emIFh0mq7pRmOBFFpZ5K2KigL88N61Wo25Umn1siYeyVsKuT0GmrhszVriLkMJFhlAWRmhD7tu6Fa7+fkZyJk7hSssEJbPQXt8Ap9LoFw5ItdWtcSHZ6cTkogWBP1CfCKTmR6XuJGsJWexg5aR2dmb7iMwtHKzcJJJG5LaPX4ATOiTpepG+nCuDAiJQbuFkJbfwUPAYEmdfH2v89iXEAyF53nxAp4Otpga2usgJlljh/cr31fZXnUaHPnsfmvtis+epVhRbZTmeZGFor5v2+l7YzA42+MsZE1oLuoCqLIwgr7R/x04WM0AB/Dnf5YpBR2cn+z2Tupxsv9REnJ+cj3+c8Q82PAoKul/5ZBZ2NJi8sjUFcahCqOsDKAs9VUjUkiwa0D+s/HAAYfG7jb9jj7825Wuh3bcEQUnEpj/rvgKyQdP1wr4fsiJHkVlIuYGxziv0LDgJSBZmB1cWEsg9cPGki4e86ISIblITsuu/hN+be2XJyahBbU8tI/yo+MhfwU5Ircge8RAnCuxtfB/Sm8rXDiEUIUzKmqS2IVNrsrOXD7JHGuhMe6iDRx9dN/s6traeVnQailOL2VCmx9bDIiLerXgXD+16CLeuvRVXvHMFlrywBCteWsFIxV+v/zUjGYls/OTYTnx0oJYd1a5dPp6fh2I0rA8GkR1ODp+wIw7IIm1RBs9SWTgAg4zkJEYVRI4MkVd93NqUqUykKW+NMkKoEbmASk5GOciKfLipF5srOnDejKKhb0EuOwOoeDwgWaiWnNi6uaqGgv2JzE0b/PAl0DfCmpB9QW2uBz5vQMPRLlTvbw87hy1kbHoUVpbBmYjEWQHUZ4Zk6LOMQEs/7Md2ALg6xLzCzMFVFH6UhUTwEXnxyjY+MfvBCh8LpsgrJGutjwrIM+jYcuQoLAcOIGm6Yj8WNuSWg4gE9L386kvK5wiAzQ2b8fuNv2ePfzT7Rzhv3HlhfQ1BFvbY21lwcbfZjqPNvTgliO0yICw97iKYGCgLj7bwzdiYzGRmwVaRWRpXslDkFpKi07PkpGZ/O6q7M1le17S6t6DV8kNaJMgsEGRhZO3TprVcRZK6bJl6zesyM6HNyGA5n1R8kqSQiKFiZz0npGZYrDD4WQcH5BaSqp0UO8UBWpNDUOtk6EpAjzSJTSOOLCRL9HHlGpweAlm4s6Zz8JITSy9aNpP6djzyi/TQGAaxNYrcQlIWErGj/K5LpmRh89sVPLfQ6WI5mEED25OSWOP3YNClpSJ55kz079zJcgszL70EwwWypor2Y1JQ0eGUFFSkLixIDZKpGSIECTM2J0VVjIVKFtJ9klAwzghtmK2Q6cq9pycCZWHX69z6ZTznHBiKiqBNS2OHVltDAx7qfMutLj/zn8hLCWF/Qko9srkTMvi62u5BFhIq97TilGWKmthLWbjP65r0h29N/xYjJqhs5ba1tyE/JR9z8vl6QYdR+l1SydZNp94U2g8ghLxCAmX1Ut4Ytba267TIj4YsVNYqMRCItbIwEFkjGpGrla8fCFdOuRLP7X+ODb8ruiowLmNgnnG8mpDpPkYFP00V1IgslYWjsQk51D2zwBfKv8CiBcjWSurEUF0so0lZ2JXiGvDaJLLQatCgL0mDFLML9sZG6NIDOw6GC8c6j7FBDEVo/XD2D71yy+n9FFNBv3/KrGRvlT9srTS3sz/0u/VE+lQgAdm4e9urKE+yY6o2FRd3VCKe0pfsVH5Wdjhd6OizIkcZ4IelKkxICzpUOlkhlYUnpA25HHorX7hyFTWKV8nJKFcWEhaOU3ILh1BZaHPYsLaOZ36dWbzMPXn2RxYq0yVaTCNtRBY25JFKFpKdcsYKfiDY9Gac1IVEJm18EJZuTvwkTAici6Yv5Kote/WhmOcVerZSWs0OmE02PLOhkimd5pZlYoFijVfRuCvgAcXW7J1d8vaDP8f2pu3855c31a2+MkdZHOIHdIO/+bObYXfZcf7Y89kUMVyoqtn+NkxVCJGIrcg1mwCXg6s+RAlGDPIKJ3qqCj2VhZSZN0h4fshkoU/5g8gtFCUnZHVf/QK/Fkvq1iD50EY4eiK3bIvMwp52M/vckZabpCl5hQTa9CeqJSfhZ7vtbODW0zkODWVeBP9gNbcwspITYdctTuUH2y47KRRdrGBopNh76HVAA+2c1ATkK1PuQChRVEeDKgs3/QvNJn64yjslhLzLMacCWgPP3/GY5OePNUKfoGW5cr7kjl+bfW5uyIfCFMWKPNy5hTTMI5UEHdLGZ45nWXiEyu7Y5BZWtZlUu2eBYjsLmSyMoNzE994TrrKQ1IPd73BVfsZXLmVvDcXcEbBu6+t4cu+T7PFdS+7CKbmBLbpeENdUWiGgENdCWUhFLH6tyDQ0o2uSlBuDDGzomrt14a1YWbKS2clvXHUju2+R+pHIQgL9ezo13YeCEPIKBwx5o1QWitd0SQzLTaglmopJArUhC8VrKGRhqbFULbB58eCLGAqIrFRGFio5jtKGfGLnFQrQoObU/FPZ4xOtFVlkFrYm2wcoC0WbfKtSckJFUyMR+9t47NG0nGkDCg5pkDQxayLOKj8L353xXfx2yW/x9HlP49PLP8XnV36Ol770Ev6y/C+saIoGdNOyZsDl4HyDFe3Y1LQFLxvTcVduDj4yx9d5YNBpkZViiCy3sEdZ76Wq0C8kWXiigA7AndzeZU8tRZJyliwu4odmz5KT0Z5Z6Flysq++C72W0ALio8WWxi3otfWyDeUsPW34XYAhVWmwDdCs198WA7Jw5NmQBU49p5xlKpL1qGJXeDbrkLDlCaC/A1YTP5gljg88AdeV8iwzR2Nwm6PD4UTD8fDyCgnMzq/kzTXW9eLZz/mh6QfLJww8VAtlYeHAJuTGSn5jNiu/1pKNFfjOu9/CZW9fhldrPkG/yHeLUF0YTHVzwyc8m2pW7izcdfpdYU+IPclCIsKnKHEAEZOFMbQgE4409wzMKxQbAMq2ojZ4YYmIEGpTrI+yUFgghLJw+/tVrDk7JSMBUyxbmaLGvM9dOhEuaB1ISNazZce3kXswkD3UeuwYy7RLPU1RnilQG5EjyC3c0ca/nzn6EBrFFRVSpGRhrdIuOj6jnOWUmR39SEjsZmUCwWx3w2VBHuy1FZINub8DWH8/Wmz80JE3NiO0ZvDiuQNyC6ngpGgiX+9qg+QWBrq+gyF18WI1t3A4iVthQT5vLFdLU1wIgdRTscwsLM9OQX6YmYWi3KQojHITAWNOZJmF3R9+yAhDQ1kZUhYs8CIL39/IYyiumXkNvjj+i6F/UjWv0N2I2t7Afy7Tl/CDVu2BdtipkExAn+AehAXJLRTQaXX48/I/45ScU9Bp6cQPP/4h7lx/JyOCieQipVLIUO/Fg5OFYt9GykKYOyNu7xRqYdF6Hgt0WDrUw3uK3j8JWa6QhaGsh1dN5XEGbx57EyaKyRmicpOswhQWj0SQZOGJ3YTsz4os1ugTBXZFWdicaBmgLDQmGFGQUqDmFo7UkpN9yj5uenZwV5Iv0hLSMD1nOs4fx4UHdy+7G/MS7kTv4Tswoe9ePHv+s/j9tO9htpn/bCqcFuaUGJG5hWpeoSQL/UGShScKaApKB2GtAe1Wvhm1wYXi/FTvybQjH+gd/Tbk4sxkZjUkFQcpS4ayBXll6UpoPTN7/BwK1WY9so1kC7IwvANLn5JZOFKVhYQUYwJmn1miZheGnRMRDNY+YMP9cNg0sCt7WZGx5g/6cVzFZ+/qcy/8fkDEpt3iQGKqHtlFYWZHKa+jDzfXMvvt+NxUnDPdj71NLTcZqCysr+SHpaOzc4BMIzJNwLxqA8sM+c3nv8HZOQbck52JmlqFSIsBbE4bfrb6Z0xhQxaQf5z5DyTpI2tpFNc2fc6x+fwWciBSsrBSKTcZGxuykOzQhEkKialCq3OXbERhRXY5HHAoGTW+5Q9CSUaZhR2NJmz7gG+ul10+GWkzuL23f7dycI0ARD5lKvmaXU3hkQa9igU5ec4c1s7sCfGaCldZ2GHuQGU/HzzNSgsh8FwoR6NUFpbnGFUSaFxx94iyIrvJwsFVT26yMMjBfsMDsJv70G7nao68shDVVGWLBpCF7GtOyRq05CSQcjYY6Lqipl06DEXTrB0N6F5L5RdeZGEMlYV0bxOKrXKPzMLmEJSFRIqQIpi2CgXjw7c4ifsO5RjbLKGrirte5RbkzEsuVslrWz7fH2Z12tle5sa5N4b3ZHzyCj2VhRPnFzCLqd3mHEhID9KI7C9b74GzHsCYtDGo6anBzpadSNYn4/ZFt4c+5HLY3EO3EJSFQrHXJqJDguwjQrEhl8ZQWejZhBzo+6fiHUJXvw1dfXz/GAiLixezdZSIwjeP+hStxQGdPjZkgrQhjx6QFVXYkCMBEfyUf7e7ZTfqeyNX7Y40iP1gfYJpgLKQQKq8DtGI7OMqGik40MYz0on4iwYk3HluI+17NfjR8lksPuLLaeOxoo/vV+spuiPOuYURk4WqslCWm/iDJAtPuHKTUtTW85tyr9aFjGTDQBvyCaAsJAjr55aK+FuRSS1BLYuEM8vOBDqVn3eAnC5PqyayxkWlLEwZwWQhYc7ZZUzx1F5vwtFt3jfDYy29eHj1MTz46dHwSzC2PcVKQqwuTjLo8nKhMwY+aOmLFBuyWQtUbxjcgjwxM2Bu12AlJ1v3csL9+8vHs3ILL5B9WBDDfpSFnXX831JKypF1AW92vqNrOQt2p4NRN5x4JsOICw4/wZSAlCtErbPRXLt/2vQndpCmw9YDZz7g1dgWLkjZQBNTQl4mv0YPNUZgmbb1A3XbYtuErNqQ/ZAqqhU5soINgqOjg1gDNiDQZ2f7VxZ2m7H6hcNw2l0oOyUbE07NQ/JMfh2Y9ygk8hCXnPSuUyzIy5YO+DehLAy3EZk2/YRxVhsyxRoXDIKsjbBltEZRFpZmJ2NSJrfj5mVzQmLbEA2MBsP+EMtNPG3IdR08A3UAaKi38V9os5fzcpM0A9KzQyT4PXML/ZSc0BoYaLDjCKMJWUCblMQIQ0LfpuFpRf646mM4XA5My56mql8EqUxFPNGiqcfMYieo9ZwGldR0K2IHBhuSNRzj9xwqdkhICj8qPDHFgMQU/v+620IbFFirq9G3ZQtbqzIu5lmpZrsZ/zPx38+4/nT8admfBtjOQs7GVtZTq9nOiFACDd/GzuLXTeVuH6eBmlsY+sCE7lMPnfWQer+hjF26R4aM1iOAwwqQZVlpbg4GoQpqT06PeK2ivKy6zv7Yk4WDNCETUhL06n2oKkgjMoF+71dNu0ptn45mjxG+DVkoC2XByWhTFkZiQyZQHuq8gnknlBXZZbezsihCra7Hb57o5MzJaE8bmFc+EstNoiULX9xcjR4SUeSl4uxpioiipxGFdu7+a6TCzxjkhgdDro/DJ2SIwVD6iZOnGUtIsvCEyysci+YmvkmwJmjVCWSGCMh25MHREwe76DBggZJbuHkIcgsp04EaFYlsWVS0yKt5Omj2jTkGNmTjyLUhE5JSDZj7Ba4sogD97ZXt+PP7B3HWvZ/hrHtX40/vHcRfPziEKx/dOKApNiBsZmD9P9lDaz5XiSSOGx/0v+hz+M/cQWShsLf6QUMEeYUCgnTX9TvYpvziuX4OLsJmReRI6kCLurWJ22DzyqYg48sXscfmVWvwjfKv4p1L3sGDY7+K0/v6yW2K1bWrcd3H1+GiNy5igeQ91vAVfNR4+PLhl9lU98/L/owp2eGVWPiDIBuNaf2qFa+zL8yMkNotXA1NFuFQyKZB0G22sYIRv5mFnq/VKCabQnWly86GhjWdu5GXzskDbU0/U25RftfyK6awNTh51syolYVeJSfKwSsUuKxW9H3OSaPUpe68Qn/KwnAspKT0IcyhZu9Q8iajtSF3itKAFDULSJ/EB1/bR4CykH52Bxv463N6cShkoXJPttjR3e8nSmP93wGbCS0pK9lf88vSQ1dUlSrKwpYDvJBCQV5ZGgxJOlj67Gir7Q2awRSODdkzt5CsyMMB0ZzrWdgkyMJ6Uz2sRBpFgcpWfv2NyUqGXqdFbloCUwranS5mhY+XBVkgXWlE7mkN7R7a+frr7G3qkiWs2ISuz99+/lvsNfAD6zxXKWs2DRvisKesp2ItSjYmMEJbJQv3tHmvJ2EqCwUoe/LFC17EPSvuwbdO+VZ4z1XNKzyFRTAMBjU+JkGxD1PuZ5igDEubwwWDTqMSyrFsQvZVLvmCLPKhWpEvmnARuwZIefu5UlYVD/T3WlnOs8jeVW3InZYRkzcrERg0ZKBikmhsyCdiKzIbHtP1q9WiRtcZUFnotiE3j+hyk2h+tzaHE0+s4wPn7y/zEFH01KNYEYowZaEYNsUJdF+OKrPQKJWF/iDJwhMF6rS3HO1Kro0zWetlF9XpNXBBh95uF1/gRjkWKrmFO6o72cR/KCzIS8csZcoqr+ZpPxA3DK/MQrKKEwkWIshyNNJtyAT62XeXJcNh0KCruR93/GMz/vXZMRxrMbEN87JJuaw1d3t1Jy66fz121fCbalDseBbobQSMJbA4eIZEQpC8Qk8ljN2ig6vSP1lICpB6pZUyGrIw06HBd04fiyQDb8QMNa+QrJtJHUpI/rg5SJo5kym7XGYzej76iGU1LZ94IR5uasHbbWZ8Y9o32E2cAt6pTe6s/56F333+OxztOBrS811Xtw5/2fIX9vjmeTfzFu8YQJCFJnuHSnocDNeK7JlXGEF2YiALMhUPCEW1F2LQiOxZ/uALIo8TncDEOrvaFi6UqKztWqtlNs1oQq4zC5LDVhb27dgJp8nECM6kUwZOjhPKy9jP39ndzTe/IWJns0IWmkMlC8e4D+CO8HJm7Q4n6jv52lmalcI24IRuJyceDzX1oMcc3HYXCkjpIooowgVlDxLxl6DTYkKeH7LaB7R2CMuMsFiroHvF5sfYw+bML4ZnQSak5vJSCfbJN6vvphZeaoBnz/dgxyBt3yE04/rJLSRloYvUt0MIyk/d0rSFPT6n/ByvdYrIEFJNkZU1GlQrSi1h9eSEYWglJ9GUmwio7pAQlIUUl9D1BreWZirFJq8ceQX/O/4/tGXwe5amyaeEJMLMQlGWk13Efy5jJmeyHGOymLbWeBDSBUokB6nuqbgsDFAhBxENYasgw8gr9Nq36QwRKwuFBZmicnRhOheiVRZ6Xp+DlZwQ6LVx8cSL1aFivC3IZD82JOqQmpFITkWmvqfCJYmRDbF2phvSkZUYQj5xAJxdfjZ7De9p3YO63viWXQwF7O38NanJzIBDw8/U1KjuiYmZRBaOXGVhsHKTcPD2rno0dJkHiih6GlHkcCsLnWGKZoY8s1AWnPhFRFfGgw8+iLFjxyIpKQmLFi3C5s3uzag//P3vf8eUKVOQnJyM0tJS3HzzzTCbpfw8phAvwKxy1RKiS3UfmMluqeYWWrN5eHMM5MuinW04QOohaj6y2J3YUxfZAS9UCAvyGaVnDPh5B9t0UiGKOSGV17GzZoLQDyx93SPXhkwHc7o53PjCDsz73Uf47nPbsEbHn+9SiwEXzijEP6+ci22//gKe/d4ivHnDUvb7IuXXZY98jtd3BFEX2a3Aur/zx0t/AmslP5wkjg9NWehyaOCs91bUCLTV9cLab2fqmtySwQ/0vjjax19bWS4tvr4ogB0jSF7hrpZdyFbOSRkl45hSSKgLu95ScoPySPmnwdjuZvzf9O/ik8s+wa8X/5ptOuj1RirBS966BN/74HvMekevw0ATw1+s/gU7KNOB4OpTrkas4Nn2PbWQ74QOKhbMkFG1PqYW5KOKBXmSPwuypw1ZKYKKBG4iZSBZSJmFy80GJDl5iPvcL7intNqUFCRO5ASXee+eqBuRw1EWmhQLcurS06Hxo64hCykpjwih5s1RXuXe1r0eysIQMgtT83kjKtndwlTs0CaU7H1ExNHPmV4LhJqeSozJSmSzr52hDCEGwXsP78Frf90WEWEoLMi0zlErXygImFu45h7AYWF24hYl8CivPAyykFDGyTtUeyuGxojcwsMdMcssJCTPmAFNSgojnC1HjmAoQbY2Wudm5s5ESbqbuKb1NVZWZKHUGpvjVuOJRuRginmy6bYqKs7CaMhCRVnYHYKy0PT5RtgbGqA1GpF21lk42H6QRVEQLl56DXtrb26GyxYmUUMvNJ/MQpFXmF3Ify56gw6lU/k1VrnHw8VCKntxEGviB9S4I4wmZK+CE/HyjaARuUYpLKKhRizBBs+hkIWiETnE0qcrp17J3q6tXas23sar3EQo46lsSexrSV0oMbIhrgtSnkVSiuc5vJlfMP+EUReKe6UrkzsJMhMzYaA9jgfGZ4xHe7rWy1U0EsnCcMtNPEHq4EfXHGePr17iI6LoaUC+3QGtC7BrNGjtOIZ4Ii/izEJRcCKVhTEhC1966SX89Kc/xZ133ont27dj9uzZOPfcc9Hc7F8t8fzzz+PWW29lH3/gwAE88cQT7HPcfvvt4X5piRBtyOZuvgFMyvAmmYx5Ke7cwhiUnHz3g+/ivFfPG5ImNX+gm9Z8RV24NY5WZJqqHek4Ap1Gx5r4fH/e/kATOHHTaLO0u22W7aHlgjkdTtW2MVKUhRTk/p9NVfj2k5tx6u8+YkQhEYakpqFp0uSlRdCn6pHu1OD7JQW4aHYxjEn8ZzAuNxWv/2gJzp6Wz5SIN7+0C3989wAjAAZg1/NAdy2QVgjM/SYsFfwmlDCIDVmbnMxIGdWKXLMpYF4hKTxIZRMuXtjHDw9pTg1S9QH+f6NCFhYNVBbubNzOCk0I+nye6WG8kJOFfRs3wdbYCBC5LK6rlgMs6P3yKZfjtYtew5PnPomzy/h0dnPjZtz82c04/7Xz8fiex1XlAYEeX//J9YysppyYOxbfEdUmL1gm59RCo6ruChlECNdsiUsTsl8LshdZGI2yMHBTrLPVjDlWbk2e/9WJ7EDkiaQYWJEFWUiqY4s/66of9K4ReYXK2uUHaiNyRWjr0+H2wzA7zDA6nBhrs4dGFhJRKdSFYVqRhfKOLKBkbylJK2EKb3oO08vsMSk5IatccxW/ho5sCV8BsL8+9LzCoI3INIja/gx7aF/+K5YFS8gvD7MYI4TcQmqGj0UbMoEKTlLm8Uyqvo3eXzPeeL/ifS+bmydEyUlFd0VMyELROEsoUKIHGrsCH0yaKrvhcrqYqirkzMlgysIQGpG7XuPFJhlf+hL6NDb87LOfweq0YkXJCly15EfQGAwsezVslXNvM4UC0/SZqf49m5CzPMrCYplbGBWE5dnP4C6ostBli5wsVMtNYteEHI6yUG1EHiSzUP14YzlzzLjgYtmF8YAYbmUphDIhVcktNMlG5BGP6p7qqMpNTlQrskNRFtoyU/zmFRKoSDC5mO97nO0dLBZmJGF/+/6o8wpXH25hzqLUBB2+4Sui6GkE7YrzdXw9bFCupXghVzkvh0UWkhOip3GAspDu2xIcYZ+W77vvPlx77bX4zne+g+nTp+Phhx9GSkoKnnzySb8fv2HDBpx++um46qqrmBrxnHPOwZVXXhlUjWixWNDd3e31R2IQeGToORSSyeizMXWXnBRGXXJic9iwo3kH28CQgmm4rchb4kgWflrNVYVEumQkZgD9HYC5y5uA8AERM95WZGUBDVGCbTbZmRCRrBpJqeEHoscKZO0kS/ElD63Hwj9+gl++vpfdGCiTZ0JeKn64cgIjATfddhb++NVZWHIhJ/S2vlsJm9W70CQ9yYBHvzkfN57JVUE0ibr6qc3erX3UXrj2Pv749B/DpdHDWsVvLomD2JC9rMhmnVu55q/cJAILMrVur6vpgEWxG/hVeBAJ1nwg4AHl6PGtbMLm0mqgz+HXbkLJGCTPn8dUG93/+x//wHzlxi0+l3JNLShcgL+d8Te8f+n7uHbmtcwSQlky/9j+D5z937Pxy3W/ZPbQmz+9mdk8iFT528q/wSBsVTFCXjK3KLb0t2CKUBaGY0Ou3wGQKjklR1FSRo8jgZqQB2Tm1fDNQQRQiZR8b4smkS6fv8St4XsS7NDmDyQFYlFyQkVCFClB6ArBikxkgOXQIWYzJmVhwM8rcgtDVBaKvMLZFgu0NBRJ89MI7g8KwRCuvU+QaYJcI7s+TewJhbn8NU0xB9GgQYknIBzb0Rz2RlE0IYeSV+hbcuJFFq7+C/nzgAlnok03i0UnUBacaBANW1lYv90r/oIU1VSWQa26LQo56qkQcASx2g+G1GHILWwyNbG9SECyMFbKQh8bMqEgI2lQG7I7rzByVaFnZuFgykJHVxd6Pv6YPc649BLcueFOdtgvSi3CH5b+ATqdHvpifiCy1YdpBRSDFmqM1Cf42JDdRFD5DL73IfLd5EmkRphbGBGI2GT7XA2QPy2k/6IWnDh4ZnBkykJ3tmosEboNOTUsZSHhqqm86OSNo2+gzxZeeVa45SYCYj2TysKRD4rBIUSTaedrRSZFm2hYHq2wt3G1r1kp1QhUHFhcPBV2LaBxudSBcySgQQSViIRdFhms3KQ9+nIToSq8YmEZMlIMfu29RSn57G1DX3NcY9BUG7KS+R8STC1kSeNDMI+97Nb3KvHyH7fg2PaRlzU5oslCq9WKbdu24eyzz3Z/Aq2W/f3zz/2H4y5ZsoT9H0EOHj9+HO+++y6++EWew+MPd999NzIyMtQ/ZF2WGKRVtFdRQmSNhbafH4Szcrwnm6LkpIspC6O7+Jv73f9fBN8OB+aLRuTKjkEbCaPNK3RbkKvctjpSgQVAbpJnI3J4JSei3ITKQyJRwEUK+hlur+5gpSRn3vsZzr5vNSsroVxIwtyyTPzfeVPx8U9X4JOfrWSP55ZlqWG205cWM/UEWaj3rh54EKGP+9k5U/DgVaci2aDD2iOt+PKD63BEqNJ2v8xVm6l5wLyrYautBWw2aJKSoFeskqFYkVkjctXnAw7CbrIw/NyVR1YfZ+cOKORtd4sfhUfrIV7aQaSyT/kNEexNVZz80+R4F2RkXKRYkd98iwd+i8NNs3+7VlFaEW469SZ8dNlH+OPSP2JGzgxmDX3r2Fv45nvfxPbm7Szr8IGzHhiQoRILiE2Rpw35cGNP6K9BTwtyjBSPRwazIVPhjEbH2zEjHJaIplhfImX3qlpmcbdogdVJNr+2RLXkZM/eqDLdwik5ERZkysbUZwW+DtRG5BCVhV55hWTbCKE8gEFkG4bZSF2rqnXch01RcpKYzDffO6qiuweItYHQ12VFw/HwrMgHlEbwaUWh24UH2JBbDgO7FHXPGb9CS3V3+OUmAqRmp3sUXe8N/PclIkmEutDXiky5lcKaGk4bskDKIiW3cMsW1hQ5FPiw6kOmipqbPxeFqYUBlYVU4hApaE12KwtTBygLg9mQhaW9MIpyE9/MwmClEF3vvMPUK4lTpuB17S7289Fr9awghA07ARiKudXKVh8mGaY6Kvi9zW51qEpHT2UhZdLlj+WkedWeNj/KwiEgC0VeYc6EoPs0T4h7pd3lQDftaSIgC30HG7EmC/2pl/wpCxu6zSGTCqePOZ0pDMmJ8PaxtxE3ZaEnWSgbkUedsjDSJmRPENm9sHAhe/xB1ehWFzoUstCUxvfy2cn+ifyJOZM8cgsjP3v/9u39uPW1PextLMtNKLs0UiJ4T20XNhxrg16rwXeX+gg6rCbAogzLlJzpBti44CbOZGGbKYzyJFFuQvslnftcdmx7C1qqe9hg9WRHWCxEa2srHA4HCgq8VQT090ayz/kBKQrvuusuLF26FAaDARMmTMDKlSuD2pBvu+02dHV1qX9qakb39CHuENPeRCPseiMMdv4CyStM8T+ZthdwJj0KUDOwQIMp/Ma4WGHGmAxGOnX121RlUSxBhRRCtaCWQ/hsmANBVRZG0IjcJ5qQh8iCTBa6X76+B4vv/gSXPrQBD68+huNKQcmKyXn4wyUzsPn2s/D6j05nasJAVk+yXi74Ev9et39QxfKa/OGCWUV49YdL2Ia6sq0PFz+4Hh/trQfW3ss/4LQbgIQUWI5XqMonf3lrvtDnepCFdECmm5WCjsY+FqZNLbX5YeZ/HW/pxQf7+RpXWsoPQV3+7GCeeYU+h3vKjUpVciiTCryJT+N55zEbH2V9WQ4e9CAL3cpCfyAr5oUTLsQLX3oBz3/xeVw4/kJmf9dr+OFQECqxhmdm4djcVJYlZ7I6vBVSIZGFsbEgmyx21HXyrz0pkA2ZNgFEGEZhRRYFJ55ECmXEbn6bT1arSgzo1wItPQPVEpRZSKS3s6dHzeGMBJn5ySGThb1r17G3aUuXBv24SJWFLK8wgLraL1Qbcl3UOWAit7DLUYOUBB2LQ4jmHtBwtFMd0BDCmSZTE3dNO3+O06OxIX92N890nHIBUDIPzdU94ZebCND6M1hu4aEOvxlM2owMaBPCv/ckTZvKcvKcvb0w7x+aXLr3KwNbkD2VhUIdEwk6+2zoUe5lXspCJbOQ2uD9gcjrxuOxURaKzEKb2QELOQ8CoOtVbkHuP/c0/HXbPezxT+f9FLPy3LEYUZOFymueZdG5+GsmOd1bUTJ2Jr9HVHhakYXanjIL412CE2ZeobifUoQMoV2n40MlcgtEOdiIqbIwACEhkJOawOyAdE4Wa9JgIKWXyC6kopNYNhST6r5LGaxmepxJRCOytCGfXMpCz7VaNNiPVtjb+GuyK1UTlMhnJSfKttTeHHnJyV4lm//5TdV4f29D7MpNsiMvN3lkDXcWUuzUmEyfAYmw9hpSUaQM7RpoHx7HkpMcpQ2Z3G/EC4RVbmJ0n8uoRJAEACRwGavEapzMiLtk6bPPPsMf//hHPPTQQyzj8LXXXsM777yD3/3udwH/T2JiIoxGo9cfidAsyKYuvrGxwYUiJaNQQDRzdsdAWdjU1zQilIUUJE9qN8LmOFiR19SuYcHpU7KmYEzaGJ9yE/95hQLeNuRxESkLU4yxtY8GwrX/3or/bKpGc48FaYl6XDi7GPdfORfbf/0FPPPdhazMI98YWt7SlEWFyMhPZsTc7lWBiX6y6711w1IsHp/NiKa3n38AaD8GV3IWsOB77GOsSl5hokJmDAadoix0uLK4la92ywDlUOF444A8ucHw2NoKtvmmzMUxJemBlYVCzeAnr5BIZ1FuovcZuOgoiP6MM1R1oZcNOcSN+8y8mfjjsj9i1WWr8O6l7zK1QLzgmVlIr0FBHh9U1FVBQc1o1ZtiShYea+EkUW5aArJSg5AcUeYW+muK3fDqUditThRNzIBrLFew+CMLKSeMtSJHaUXOEMrC5uAHQVJ2mTbwxum05cuCfqxQFtqqqlmTajDQek9/dNBghsUaWhPyAGVheJmFQnnnqdYRZOHxrmOYXZIZVW4hDTValObWhRfyteb4jpaQrcgHG/gLuygjCZkpCRHZkF00aNhHRI8GOIMPU2mqHVFe4WC5hVMyVeu1w+YMqe07FGh0OqQsXDBkVuT63nrsbtkNDTReLcieEDlbnZZOdEZY7FbZZlLJweQEd3h7gTG4Dbm9vpeRe9QAmzMmNHVbIOgTdEhRcqgDNSKbDx2Ced8+QK/HHWm8/OqssrPwjWnf8Pq4iMlC4arI9C43ySpKGaB8FQes2oPtTIHIkD0B0CcBlHNNrchDklcYOlnoSca1GRTbf2/o+1vKYyZFX6wLTmgPSoPrUGzI9HsQRKVo8A4FX57wZaToU3C86zg2NsQuc7Sn1QynwwW9QauqCQlpWfy1I23IIxtUqifEIeXp0SsLCbQmUQb8gfYDUQ1xRoqysD3Z4XXm88WkzEnoSOfrozWAsGowEPFFBZEC//fqHtQrA/Koy00itCBT1MG7ezjRdu1yP5nyag5gIYrT+D2nQa+LKjd8MCTqdTAm6cPLLRTKQorXUED7P7FXSvIoiz1ZEdaJOTc3FzqdDk0+9d/098LCgfYPwq9//Wt885vfxDXXXIOZM2fikksuYeQhWY2d8Z4sniwQG7isclXt1KN1ocCY7FdZaHGlw9LZEXVOkD/icDiwQOQWVsSeLFxVvcpbVehnwxwIYspE6itke5CFIZA//UpJzVAoC/utDlWV9di35mPbr89mRCERhpQzGC7INi0O3Ds+qlGLWvwhOzWBtSVfvbgUN+jfYO97O+VimMCvXctxpdxkkCZkAX2OklmoU8i4Kk6WEFQL8sTw7GBkMXt1Oyc3frBiAoyCdPenLBTlJoV+yk1adiK7x+U3846gtiK/8z+4aAqn1QOW7rDz3TKTMplNOZ4QmYUdlg5mrxZW5EOh5BZSuL21h1u1C06JqQU5YLmJQGapt0ImTPg2xZICo3o/X3eWXDoReQp54I8sJCTPjF3JyWDKwv7du5mtVJeRwWzIwWAoLmLKVrKgDkYgCFXhFF0aUmgtC4csFJmF4RacKAoZT7XORMXWUtFVgbllnEyjCIVI0HScl1DQPXLa6UWsLb23w8LKKcLKKwxDVehJfvZa7LB/8gf+zhmXMoLDbnOgvc4UWROygKos3Oil5KJ8OVKC2W1Or+8xWNt3qEgVVuQhKDkRyhTKE85LGbimEqggStiTI7UiVytqsXIlDy5UslDkYBaMM8YkTsSoRMsEyi0UxSbHZ2TjsKuRDTjvOv2uAUSeQQnct0epLPSXV+iZjUm5dDRIqRUKVlKVCNW8GKzFXVkYWrnJgNxCam8mhGFFpsM7LYnkdqHBVazQbemGgzK1aJufOHisiLAih5NbmJaQhosmXKSqC2PdhExDLopAGGhDlmThSAYVPBLSE9LVGINoQXb/xUWLR33RiV0pOGlJtARVFpIis9PI1//OWn6mCRciqokGVrNLMxl5+JMXd/oviRyicpPH1x0HfXlyn/ktdhMNw+lF6j24geKXItx/h4pcJUOyJdTcQj/KwmMKWTh+Ls9aPNkR1u4lISEB8+bNwyeffKK+jwg/+vtppykTbB/09fWxXENPEOFIiKXU/aSGh9KtsZHfmHu1LmZH8ERCkh7JSXzD0d1mjp0NuXf4bMiEhePCa0RuefBB1N1yy6AKGrPdjM8buH3rzNIz3f8QrrKQbMisXEHDJ+ohWMCH0oZc18mvGVIUknKOJjPRYtK8AmQXp8Lab8fOj4JPkUiZ9ptJxzFZW4duVwp+WXc6vvKvDSzM16rYkEMpN/GyITvTvchCr7xCJa8rVDyzoZIpBk4ty8T88ix39qevspDWM3EA8ik3oa9POW/ZikvSkD/wBkRWUV1mJisYMG3ZDuRMCsmKPBygTSNZncX1HVbJSaWwIJ9GzHJsy00C5RUOUBaGH23hNJuZhdiTTCEinK5xcTjOVzYppND1B7UReU8MyMLmvqD30N41a9jb1NNPZ4qvYCCLf0J5eUiNyLuad7G3c5xKtkskykJqOw8RlLvVpGTClXooC6mwIVmfzLI6ywr4GrY9QmVh/VH3IEFv0GHszNywrMiCLAynCZmQZNCxJvk5mqMwHH2fB2yvvI39W1utKfJyEwEaWhhSAVLUUZ6qAiKPhBVZJXKiaEL2V3LSt307nHFufhQW5PPGnhf044QVmYjlSCDyCss8mpA9bcitvVbY/DRLi7zCaC3IAsbcpICDKsop7HqL5829NKmVxVHcu/JeGBMGXpOqsrAuXLKw2iuCpcNPE7LnNVauvI6GPLfQbgFaD0emLFSUe+0pylAxjGGdu9wkOfyM0RAsyPS7DKWsTORqVikkd6i4chq3Iq+uWY3anvAGOoHQ2Tgwr9DThkzKQnkWHLmo7lbyCtPLY3pNnwityPY2PlxrTOwPqiykzFiNMmDurovsHnRIIQunFhrxzyvmsPMauekeWHU0sufutONwO18jT8kJf2jfbrLi5a18H/0Df6pCL7KwEMWp/J5TT2ShENzEu+QkZGWhm9Rkf203o5mGqBpg3GxpQSaEPer86U9/isceewzPPPMMDhw4gB/+8IcwmUysHZnwrW99i2UOClx44YX417/+hRdffBEVFRX46KOPmNqQ3i9IQ4kooWbojUVLM5/02hK1aumEJ4yZ/H3dHa7Y2ZD7hs+GTCAbsk6rQX2X2R0UHwCOzk60PvAgut96G+YDB4N+LFkxSIJPh9Kp2VPd/xBqZmGShw2ZmgPFQTkEK7JqQ/bJAYoHRF4W5U3EajNAE+RFF/EbyK5Pa1nhSUCQ4mXNX9nD3rnXIDEti5FOF92/FqYjR8NSFqo2ZGqaIJAN2W5lSgxTpwVanYapPEIFKX6e/bxKVRXSz8etLDR72xTp90pKQF3CgIbfelM9aw7O7uU/X33+wPZYUnYZleKnrrfeGrTkZDhB+SaqXau/DVMVkiQkG7JQe1K5SYxwtLkneBNyDGzI9tY29fekTeekZJdiBSYyh2yCRPwEVRbO4opTy4EDERMpLE6C5g5mR9DXlUnJK0xdFtyCHG5uoVpu0t/n3TIdCsQaSAHXHnmiwVDX4VbrkBLZ8xqckMEzOZNSOcl1vNXENrHhwrclfeKp+WrAdSgH2UjJQkEs/FT/X/6X2VcBuXxIEFW5iQApuUrm+88tFCUnHmRhoAKfcJAwcSJbh11mM8y7OLEcD1CT5r62few6oIbNYFAbkSNUFgqycKwPWZiVksByfQO95tUm5DDV7IOXnAwc9vZ89hkcHR3oSAN2jdfglgW3BDwEGsYoZGFDQ+hlS06He8gSgrLQM7ewck+r+3UkBmnxbERuOchjSJIy3Tm1IUId8iamhK0s9KeAjgXYwDkEC7KAyNUMR1lIoIb5JcVLWGHQiwdfRCzQ2WQakFfoqSy0WxywmmWBwMmSVyhwZtmZbOB8uOMws74PJSjeY/2rR3FoUyMjhiIBrWcOJbOwRt8TlCwkpBSVRGVDFu4ZGszTMOB3F/O1/R+fHA5ZKOMJ+pmbHeaIy03+/XklzDYnZo7JwGkTAnzfwoZsLFLdTj06LXo74vv7zguXLBRrPJX10c9mZ4s65KOyLokIyMKvfe1ruOeee3DHHXdgzpw52LlzJ95//3219KS6uhoNDW6l2a9+9Sv87Gc/Y2+nT5+O733vezj33HPxyCOPxPY7OZnhkVnYKSbOyf6JWGMOP2x19Rpipiwkm62VGheHCSkJeswo5oe0LYMsmiZq5VY2rbb6upAsyCtLV7oPa7SxVqfroSkLxVQ4nJKT/p6hsyELC3Ks2/toIkNFIrQZ3P5hkEnS4fe4yiAhDcXn3Iy3bzwds0oy4OzshLa3By6NBoay0G5m4pBr7+oDUnKo6QSo34H6Ix1q9pfBI3NqMLy4uRrdZjvG56XiC9P4GpeWncjIUIfdqWaEMghVIZF8PtN/UZJT2M9/n3o/ykJPK3LPxx/DaRTKwuCk9nDBXyMyldWYbUE2/vT6qRZkYewyFYWycFAbsiC2IiALHR6qK7EeCHUpZXQS8pV21EBkoaGkhKlHye5rOeRWeoUDKugRZQddzf4Pg/a2Np5dxhSrof2cQ2lEpuEJFfUQ5nQ1h08WJhlZERd/8nVhDTNKswcOM4QVuaGvEhPyUiNSFzo8rLiUO0koOyUb+kQdO0iI3MBAsDucqqI2nCZkgZWJh7FctwcOUuquuEV9v1puEqkFeZDcwhJFWdhY0aVmyqmZhYoKIhLQ7yh10aK45xaKJk1q1gx2SPNqRO6KlCzkhEeZRxMygQay4jXva0UmeyVdP3TJhjOgCgYRJdPjR1nY+srL7O3qGRqcM/58fG3K1wJ+HqZs12iYGtGhWOkGBR38nDYej5FezF43Yv0LRBaWTM2CPoEOhxa01vYOnbJQzSscWDQWsrLQkOBtUQtDWeipgI4lWTjYde5rQw5XWUi4aupV7O1rR19Dny38/x/IhuyrLKThWmIqV6fLRuSTownZ152yuHjxkBed0ID/oyf3McfTx0/tx79v34B//3IDPnl6P/avrx/UsSHgNPXBRQVv9LrXdQ3aVJ5VxvcqmtbI4rJExM/kAr4fuGRuCS6dO4bZgH/84s7QyzxiUG5C0VX/VkQU318+PvAw00OxR6SkUS+a2uOXWUgQERCRKgtFXuEEaUFWEVGIyg033ICqqipYLBZs2rQJi5SNoSg0efrpp9W/6/V63HnnnTh69Cj6+/sZmfjggw8iMzM2k9aTHrSoeWQWmjo5eWEIoEjLUA7T3X2pIRcnDJZZOJJyCzdXBD8omj53qyuCZXM5nA6srl2tTsC8FhUiRmnDPMjE2qvgxJMsbK8IWVk4JGShUBbGeINLNxChLtz7WZ3/DSFdg6v/wh8vvBZIyUZRRjJe/sFpuKqIX59NyZm47d0jzI44GPSKspDIElW5Vr3Bw4Ic+rpD1uMn1lWoMnuh1NXptEjP5tOm7ta+0PIKFTVWlsgsLPB/E0qaNYvZQV39/eg+Zh2xykJfspDst5kpBpafcjRYI23LAa4qI2tk0eyYPA8iJ0WmWMg25K6asNc/keem8yBSaGNJyFDKpISykHIuA70mVCvy7t0xyC30H3BtWsdVhYnTp0Gf5z/LLRJl4b7WfbC77MhPzkNhX5d3w3HYJSc1YR7AB6p1RMnJ0c6jmFeeFVFuYXNVNyM+KMMvUznQ0kF27Ay+lhzd1jxo+YXF7mSNzML+FzJcLlza+RR7uC3nIi+1enOVUm6i5DFGjLJFfslCIrhTMxPhtLvQoDT2qtd4FMpCQoqwIm+KH1n4fkVoFuRYNCIL0qXcj2IsX21E9n7NNxzj95yckjQWARNPZaG1qQn9a3m8w+ElJbjztDuDqlFJHS0GViGXnAhHBe17dHp+qHa6kJCsV4tXfEGW/pKpfG9WtUdpRRY5tfT6p3vBCGlC9iUL24QzJxwbsnKdiOKiWKG9vz0sZaHI1qT7IkUZhINlJctY1mWPtQef13urkSOByNYVa6sn0jI5+S0bkUenstB8+DBaHngQfVu3hq5QHmYrcs2BdnQ09rFhYP5YIxv897SZcXBjIz599iD+c8dGPHPrenz4+F7sXVOH9nqTX/LQoViQNcnJ6DM4ByULi8bytSiZOSXCP3sfVmzIkz3cM3ddPIMNBkjwcftre8L6vNGUm7yyrYY5OGiAe/4M/30VvgUnhKIULrhoILFRHDsrVBty2JmFxcyt06DE0oybIy3IQ9aGLBFnMDuXonzILINVkEwBpLPGIr7Z6LblcstkhM1szf38AEWZUcPdiExYoOQWDqYs7NvgQRYGyevZ1bKLKQIp1JfC01UIVSCpaQbJWxM3jh5bDywOS5jKwqHMLHTbkGON0unZTK1DKrxNb/mRnh/9GGjYCRhSgNNu8Mry+n45X55q0/Px8tZaXPnoRjQHCJIX0CkFJ66+PjgLFqi2V1+bYSh4a1c9GrrMjAC6eO4Yv83iXS0ez0fNK/RPFhrsLiQoky5/mYUEZnNW1IXdGxTlWcshbgEbYRAlJ2Svpuc9pSCE3EJhQS5dOEB9GU0TMu2RiKwcNFSeDro0RSXFaQjZoYM1IQsbsrgeRGZhR5+Nkc3+kDyTXx/mKEpO3I3I/pUfvYoFOW1paBZkQsJYkVlYOWi5yRzjeHJCA2RFTwiTIBNDlhAP4cGsff7IwnAbkUVeIVlFPQmWCaoVuTnoJny/0oRM9iCKwwgLRz9Bac8umF0GvJR8ufpuKjfpqI+y3ESgZAG/5ons8bBU8tzCTC8rsr9rPBKkLlZKTnbtgrM/usZGf6DswUMdh5iNbTALsqeykFQyNAgMB31Wu6oUFootTxSqJSeWAHmFsRuMC0UxtSF7RmB88tgd0LqAwyVa3PrV+1lZxWAIuxHZJ6/QbUEe2ITsiXFKK3LFbmVompwJZCjEQxNXP8cc6r04fLJQdYTAEbYN2VMFHUsId0qoZGFxZhJbi+geJPJeQwWpjGbnzfZSlUUKyvQVLhm/ZKFHbqHEyATFPXi2ynui+a/3oPWBB1D1jW/i2BfOQfPf/66WEoaCM0rPYHl+dP8+2hFZ9l642LWKZ3FOP70Il906H9fctwwX3jQb884rZ2cVrV7DHENHtjZj9fOH8MJdm/DkL9bhvUf2YNcnNWip6WEEvF2xILuyM0LKEx0/gceB0DnA0uGR4RoCSCHXZrIykbSne4ZyC/95xVzotRq8s6dBzRAMBRThEQlZSIKAx9ZyEcU1S8dDH6y4S6ydimKvSCGcG+hm1dsU94KTkJSFFIdjUQbf6UWo2EXRM+RCS1cLxSQkWTj60aGo1NIKAUMynH08bD9DUT75wljIF7ZuRwHQ2xLZlzR3sHBUDTRMwjwiyEJFWUiqpkCZVba6Olirqrz+Hgif1nzK3i4vWc6CwsPNK1RvHsr/ZerCMMjCPmWDlWIcwszCGCsLCXSIWPIVfqCnyZ1qRfJVFc7/LpDqPcURRQvTF82CMUmP7dWduOiB9dhVww/3/qBNTYEmiR+o7Gk8Z7L3+GGWL0g32qLxA4Pm6cZPNrP39zbgvg8P4ZpntuL0P63Cz//L87a+e/q4AaUvqsLD0w7WoCjFirzJwl5rL450HkGWwqGxzLuMwIH3GRdxstC0bRds1mTA3h/SNTPUEIcq1vbtkdd2KFhuYdX6mFuQhZJxUn7a4NlulB2qbFzCtSLbmweWP/jakDOSDWqGWaCNSnIsS078NCJTcZNQFqYtD50sTFSUhfbGRjj7+oKXmyQVeLdLR6QsDC1AX+TQ+otJmJA5QQ1hn1nCSctdtZ1+yyYCQTTW+rakMyuyQcvWjtaa3tjnFdL6t+p37OG/HedgX4+bdI1JuYlAYro7J85HXejOLez02/YdKQylpdAXFQE2Gys6iVexCdnYQmnopNzhRF0iK8Op7w2v1EOolum1nZkycBgRqBG5McblJgS6FkgJQ2pQEYGxrXErDO+u5f9+8UWYku2dlxsIYZecCAeLT16hv3ITT5QruYUUGG/qsniTePHILaTXVSyUhQ5zBGRhnJSFClkYTLnkCTrAi+FvuLmFBFIWEqItORH3p9SMBL/qWrXkJEbKws4+KzYeD4+IkQgMsqELYYg/G7KtWtlDGQzsPNX28CM4/sULUPGVr6L9mWdgbwl+xqS1+/Rivhf8sCr+VuSORhOq97WxzOeZK/k+hK7Lsuk5WHzxBFz683m49r7luPjmuVh44ThWAkZ7AHOvjVlT1/33CF7+wxY88bO1+OjtdlSVno32nAnQOrWDRgQUZ5ejJ5nvDWuO8aFruKpCyiKl2C1PUDPyz8/la/5v3tof3Nnjp9wkXLLw/b2N7J6YlWLAZfNLgq/DqrJQIQvT+D2ngc5TcWxEDqvgRKgKacCWZFQtyOPn5uEvW/6Cr7z1Fbx7/F2c7JBk4WiHakEey9QIOiufNmfn+9/ACZKjx5EHZ09kzL6wHNPiWJpeOiLIQgq+FxOXQGGvpo3KQclgCDpRJxWJyCukyZcXQmxCJhBxoW48GVmoNPoOQvzYLA6W8zfkNuQ4KAsJjakVSJliB1zA5697TA8rVgO1mwFdIrDkxgH/z1LBJ5TjTz0Fb92wlJFBjd1mXPbI53h1W23An7lqRdbmAgnpqO/l12heWTrsOm5T/M+mKvzqjT2sdXnmbz7Air9+huue245/rjqKjw80qWrLU4qN+PrigRNVUXKiNiKbWoEeup4ooMo7VH53626mxp3s4AdwfUFBUFIroaQEyfPmsZttd0vJiG1EFjZkYbMftBGZNg+iCXlsDPMKleDniYNZkKMsOXETKR7KwpY+L/KOrOpioxIotzBpJidurMePw6G0K4eLzAJ+/XUqykZPUFYhFTlp09KQPDt0qzdlKdIf9tw8hipejd5CWahJCj+vUEDYlkMkC2uU9cnfAbwgpQDphnRmjdYltLKhAoVuCwJvMBAhJywnvqpjdohQrMjBWpH310dIFh78H1NVOw2peNh+IRvaCAWjWm5SHkW5SRi5hUTkWE0WNb8umoIT39zCvjjkFn5Q8UHIFmShlhI2uoru8NooK5WoCX+qQm8bsvv1bjXb1cFYYQzJQq1nBEZbP1t7//XczRjT7oItQYcV37495M8VsbIwk+99OgYpNxGggHi6jglVe9t8cgsjH5gEBJF75LjR6IA8j2K6ECEIuXabcujupaxGR0gKVGrFjkfBSbjKwmhzC8W+vq43dAt2UAuyT7mJb8mJKUaZhT98bjuueHQjNhxTLO8SUaGmp0Yl9XyHMnSvsjXxs+C4V1/BmL/dh7QzzqDsMbYHabr7TziyYiWqr/0+ut5+O+AA0tOKHO9W7D2f8j0HRYyIPZsvKIKESMIFF4xjpOE1f1uOS38xD4svHs8GiIYkHaz9dtQ2aHBswiXYmX0VvrPlTzht++XY+m4lczFRrIm/e2KfYruvqwxv3Tvsk1foi+8vG4+lE3PRb3Pgphd2DBrZ5FluEk4WJf1+Hl1zjD3+5mljBxCXXjB3caGDhw15qBqR3ZmFIdiQ2bmNE5qkhK492KHmFeY+9xGueOAAEtfxzPmTGZIsHO1QySvKK+SbVRtcKFBIQV9QRpFW44ATBpiaWqMqN6GDWmEqXwQaTKGHQMcLC8ZmBbUimxQLcjrd0IIoC2khJfsFqQKXjlkaYLpeHh6hQgHVgmCkxclmHtSCTEUGhsT4NoZ72lRiPQ0X+Nnqn+HxlD+yvXv1vnaWGcKwmjcgY97V6s3E67kd54e6hPHjMDY3Fa9ffzq+ML2APeef/XcXfv+//axcIFAjcktNE1qz56Deysm7dV09OOXOD3DpQxvwy9f34rmN1cyyaLI6kKDXYsYYIy6bV4I7vjQdL1y7GDvv+ALeuWkZjEkD1Z0ZvspCkVeYPZ4refyosWZrSoOWm/hTF3YdwYgnC4WyUJCFIoh5ANqOAaZmTg4Xnxqz53FENCEPVm4SK7JQIVJoY2Ex2b2GMJ5W5OYAZKE+OxuGMZwwEyUk4UJsdIms9M2k6l3DVUapS5ZAowxGQkWw3EJqku20dDKF1lQl2FtVCYYDQTCGqixUDrv+rH20ARfqwuPdx3GqyC0M0YrcVtfLmjjpAEDZcr4QrchHg1iRBTE5PRyykMiHVX/gDxdeh3YYWfN6Z5/NK6+QBhwxQdliv43IdN1SaQZdQ7U7azmhr9OppHEscgtNMc4tJLvasa5j7P58RpnPMC+URuQwS06q25VykwAEUIGfghMqzCGbMCkB07MVYj1GSFesUfTav23tbZizhRNwmeedB73S0h4K1EbkcDMLVWWhUlwxCFlIGKtYkSt3t8ZfWShUhbmTAUP4P/tsilagddTeB4tWy1uVQ4isEA4NGliQCjUuZKHy3OLZiEwoSefrem1vbYzKTfxfI7G0IdO+43NFVbinVrEVSsQkr7A8feB5x9nVxRrvCQn9+2A8/3yU/ushTFq7BgW//hUfVDqdMK1di/pf3ILDS5eh7pZbWESKy873TaJAktZyOneRHTlesPTZcGAjF7XMOjP0IadOr2Xq8HnnjcWFN87BNfcuw2W3zcfsoibktu6CDmYYnIkwNhezqKXX792OJ29Zx/YWvnDk8vtqWzVX9YWKQ8pA3DOv0BM0pL7v8tlMNLO/oRt/fu9QSHmFU7OnhlVusvF4O3bVdiFRr8W3TxvkDCxUhdRIb+D3rEJyQNKyz5SF8Ss5UQf2vZbBCWg1r7CI5erSXii7OJXFJmQfa8WMahdyHNKOLMnC0Q51A1euSvl7tC7VGuNvUUlP4gtPd1N3VOUm+Sn5Klk43MpCr5KTyoEHRQrfFeUmmV/9Cnvr7OmBo7s7oAV5UdEiNnkJRM6GAq+Sk5RsdxNoEAl2n5pXaIiNqiQIGrvM7HxIi/+geW8RoMvSxcjl7qQ2ZJ/Kv5cNrx2Fq2IdULUO0CUAp/94wP9zms2w1fKNauL48Wo+xyPfmIebzuS25sfXVeA7T29hOYZ767pYXsdv396HXb18WfvT8xvwZE0R6q1cZr/PRjcOfiNZNimXlZb844o5+PDm5dj323PxvxuX4a+XzcZ3l47DaRNy/FrOAioL1Ywkxe7npwl5op1fn4YA5SZen/+8cxnRY2k0wdyhH5ElJ56ZhZ5TTyLJ/EYBCAtyyfyIDnGDNSFPCrCRilUjsq9FU+QVksXKk9QXJSeBlIUEd8lJZMqatOwktoklO2Jvu/fggTbn7Hkt8xl0RNmILEp6Tsk5BQZhzcuIrw3ZZLGzrJ5gwwxBFrLcwjIlt7A6cFSBJ4SqkA4DosDI10JJP2f6XVPQuS/aei3seqdlWjSCh4S9r/Gyn6QM6JfepF4zgnAQTchRl5sIlC52Eynmbr/qwtr9yvWdnQ2NLvohlVAWmvfujVhBG8yCTPY1ivoIFZGWnFQpZMvYAOU1hRkDycJ4WJAFjLn86326fx22V23AkgP8MJTzVXfmZXyUhe4IFofDiS6FCBpMWUgYO5OvmTQoJAeMqiykIZjDTRwMd14hgZTKIj6mXQwxQ8hXFeUmsVYVDoeyUNiQSVkYbsZnqOUmQrxAEEKHaPDCZvf9vDICglRiIERmpb9yE1sVJ/Z0iQ5o3/gev6fR/SMrC9lf/zrGvvQiJnzwPnJvuAGG8jKWI9791tuoufZaHFl5Bpruvhv9e/chzZCG08ec7rW2xwMHNjQwxxYRQdTSHo26O7/ciInaY5i191GMMT6Hl2f9CebFlSznODFFz5SHxxQ7qycSCnh8i6k+9GxB73KTwHuMfGMS7rmMRyA9ub4Cnx5sjnm5iVAVkv04RyHkQlHsCXgpCzvjF60k9lMkLOmx2EN8nsXq74wsyGTVzmzl9/TcCd5usZMRkiw8gWzIPcqhkchCWjgCISPN4k10RGhDJmUhZQGNHGUh30jtq+tilhBPWI4cYRYraq6i8HVddnZAdeGn1Z/6tyB7bZgHtyF7WlqYspBOlIJkDGJFFoHQKUNgQa7t7FMtyPEgJkmNpGJeKxKSdCz/6/Brb/H3zfm63zZVZoN0uaA1GlWlIIEO9D89Zwr+9fVTWfvo2iOtWPjHT/Cl+9fhlld246n1lTju4DeKbGsvWtIWoMPBCY3bvzkbW355Nrb+6mw8+71FuO2L0/DlOWPYDdgQLKTXD4SykLJMaGMQKK+QNtpkQyaMMStNq3mDk4W6jAxu6aDXaWXKiFQWehLhNL0jMleoGQ76yy2MQ14h2S3EYX7QJmR/jchhwN7qnVkoLMgZPnaWPEVpFKgR2avkZE9kjcj0OhCEtWduob2jQ21ZTlsWel5hKMpCKn0izMmf4yb6IlEWehacDDL1FXEAwdQ6k7ImqYqzcJWFoviIyk38gVmRT1HycP1YkQ8o5SbUkpuaGGLjrcMGfPZH/njJTazwQeQxUuZZTMtNBIxF/J7lcgK1W7z+iSxXhPoK04C272hgKCpize6kLunbsjUmn5PWGdGcee44bl8LFaLkxOueFEZmYVkAG3KBnzZkUW5SGMNyEwERun6w8hgWHXIh2QoYysqQskAp9IoHWUiEXpeyV8osY+Q5KTBoSBJKpmZuaRojhuxWJ+oOd/JIFsqIouK3thiriaLIK/SNj2lPzws5t1AlC+Pg0Ai3DZlQJhqR2wYOOQYD7e2pPIgOzGIYGB8bclJMMgv7rQ68tr12gBpYIjpQFnCgchP7xv+yt/oUJwUlA69+D9j1ktfH0Pqfd8P1mPD++4w8zLrqKqZad7S2ov2Zf6Pyq1/F8S9diK9t0iOv04UPKz+MixWZ1qrdigV51hklMTnr2Nu5irUzxYn21AYYT3XgvO/PwKKLuLihqWKgujV9DD//OZoDE3m+oJ+HIAuFeycQzpxagKuX8PscZa4HKoQUZCENfkMF7ek/PdQCmqlSscmg8GlCJgi+oEWngy2OOexUkEnnEUJrkKG9p7LQmjIG1fvbVQtyQ1cdcpVfYd6kgSKQkw2SLBzt8FC6tSrNmD0aF/KCsP7GDL4Yd3c4orMhp7ptyEJtOJygA1dRRhLsThd2+ChLhKowZcF8VjARaKNM35sgd0ge7wVbP9DT4JXbE24JRCglJ0PZhBzPchNfFUezswGnnsdvlhuPnQq7JhlYerPf/0d5bqJ0wd+N/fyZRXj1h0tUcoqIhNPG57Ayknlz+I3sR3Oycd3ZF7DHOfpKnFnSp06cokVCsp6VDxC6yIocoAmZ1E4mm4kpVNOVQHrKLAwFGaIVuSoZrpYjgD2E/I0hhCDCKfuEvsdBrciiCbl8ScyeA+WJUTtbeqJePbTHw4ZMGzZHi7cN2bfcJBxloVpyEkUjcqbydT0bkU0bNjACLnHSJBgKB1r7o2lEFsrCOXmeZGEEykIjrb0a3kjd1xa1WkcoC8maSmHftJklkpFU04P9TusDlJt4fX61FXngoXl/Q1f4eYW7XgDajwMpucCi67xUk7Qei3ITUpZHXW7iT11Ys8lvyUl7uwt2XVLUeYWeSBGtyJu8sxIjBTUgE9lHVni/w7w42JArFbKFCGF/EIPZbrOdkRb0u2s8Hj9lIdL5MDHdnIPLjvA1OPOSi8M+ABOZqzosBlN+MmLfwZ0AaYVqXmFWYfAmZAH6mLFK0QmzIpO9N3+6N7kXKwhrc4TKQoKaNZ2S6W1VCyFbNdZNyFaHFT22niFVFuq0OhQrZQQity5c0OtA3JuyAigLxfpm6bOzrO5IQU2w9PoT5WJigCgRm/37AGUh5RXu4CpAQ/lkYO43+SDq9R8AO57z+/onW3LhHb9mNuWSfz0E4xfPhyYxEdZjx5DzzPt48F8OXP3QURx66n44umJrI6c1p6fNjMRUPSYvCn9f5A+OVr53aUm2eZ31CsbxvUBTBY+i8ERuGS8iSWjvRb/I8xsElNPeY7azxuPxuYO7Z249fyrbj5Ajg+KafGNqaABwqP1Q2MrCR9fwM9n5M4pYLNSgEGdltt+D+jMyaPRwajRoVojoeEE45YLtw/nz5BxAdecEljVJQ/icMaloOL4bOhdg02uQUBCba2Y0Q5KFoxlkDxDqmKyxaFWULvZELctgCwRjNmfcu3t0USkLPW3ItJmh1tfhBN2QhLrQN7dQkIWpp3GiQmSG+SoLP6v5jL2dlTuLfX9e6FR+1gnp3FIcjrJQKYEIjyw0jPpyE8+DGVnVZ59ZirSEXvQ687HbeFtAO7dFIQsTFAuyP9AN8bOfr2RqQcoXfOH7i3HHhdMxcwb/P672dtQf54ea4oT9QLVCVsUIGYqyq7uhE2g74pcsFAQLXU8O0aYbQmahUIbRFNZu1sHUoIu9AiNKpBhSVJu+IMOFFfOgorhSQcQcrVVaPVC6MOZ5hRMLQmhC9kcWhjjFdnZ3w2Xjm0JdrrcNWVwHoWYWEpKmT2cHZntTkxoSHi6Etauzyb3pNIm8wjBakP01IpOy0HPCT3ECRMYRZtM0WmwEI1EW6hOBtPyQrMihqHUmZk5UVRB6nR1TC/lmnYqMgoF+f/3dVmYzzh+bHjRvTavTMILE14oslIUh5xXaLe4G+GU/BRL55t9TWdhc1a3mFcZU7R0gt5AO7HQtuaBBZ+ZE6HPdBT7RIlXkFsao5OT9Cn5AXTZm2cCIkBCVhdTuKYYbg4Fates7OelcHsCGTIOKZINOVRO31/fCJnIwx4T3HAcDHfSeqXmMPc615KPgUAtzLGRcfHHYn0ubmsoU7ARb/SBkmHBU0HBAq1WbkEOxIPtakengztYWNbcwhiUn1j6gna9TKJgZvWo+KS1sG3K8mpD1Wn1YtnsxSKUc1K5+fu8aykbknrZ+FpNB6yvFZgQautLrJForsrAgf30R30/Wd/YzC6JEbGzIA0owKtbA3sLPNPpJc4EL/wnM/x7tuoE3rwe2Phnwc1K8DuXGj7nvPkxavw5Ff/wjGyq5NMD0GsD1l3/hyNJlqL3xJvR8+mlMlIa7V/Gz2ylLi2FIiE0OvF0pA2tM6Pc661H2MeXNEwHuOcglZJbws0l2j4tlNIYCMXgngi7Yud5TVXf/lXOQZNAy59Xj67y/TkVXRdjlJg1d/XhrJyfVvr88BFVhAGUh5SMWKufqhr6W2EdQ+G1EHkRkoQyCjtfzfc+EuXls39V+lKsve3KSoaHh1kkO+RMYzaANDIUvU75KepFqQ9akBF8MjXl8g9dtSo664IQppog8Gym5heMGkoUuq1W1QaUuOS0oWSjyCv0Gp3tk9jBLcTibTrIhh0oWdis2ZOMQNCErNj9xWI2nspCs6vrW3ViUzDcS26pmMRuvP3iWmwxmxyQll+ehWp+rtCG3taJO2AwN+9zKthhBlFp0VRHp5ARS84F0b9XgjpYdqnXT1sxJIX1+aIdxUsDS9JV9jcrkEZlbGKjk5KBim1AhWpCL5wIJqTFvQg653MST4LL1Daps880rJFu8NpFvQsRGMCMvJWxloTYlhan/COY9e6IjC5XnQbmsvevXR2xBJpCdkdY2pjZqc/9sdrdwtTVtLrOt/fx6p6Ka1AiJpRBzC91NyIHXJ9qkZyZmwgUX2wjPU6zIVF4UDPVKXiERhXqF7PGHxGQ9Sqfz+8qxHc1+y01CVhZue4aT5unFyuEKPmRhP1qqY1xu4tuIXLuVW6E9MGYyV1B1ZE6JrbJwIR8MWA4dUg9XkYIOjSLTKlwLMoGIFqHMCtWKTMM0Ui7TwUsMAXxB9x6haiY1a4OiVi0cZ2T5VrHEv3b9C5tM69jjBGs6nBodKzISKsFwoVdLTgYhw4QKWxnuqcrCMMhCygnTG7TMctpWZ/JoRI6hspDiOti9OG/AvTgiZWFCUug25DgpC8XekZ5TOMMDikUQ6hpBZEZSchJpI3JHo4jpSPabB+vbiNwbYSMykSm01pPy6kcrJzDinsRUNHiRiBw0UBH7ugHKwi2Pw9bH75mGMXyAgAvuBRb9kP/7/24GNj0y6NfQpaUh89JLUP70U6h95g48e4YW9YUJbDDb89FHqP3hj9QM5khBrfQUfaDRajBjRQTDzQAQ+6M6Q6/XWU/HMg3TVXWhJ/SK2yO7BzjSIdoLQ9vjTgmSV+iLifnpuPNCbjH+y/uHsLvW7bTb17Yv7HKTJ9dVMMfe4vHZzL0REsRA2SOzkFCsrCsNeg3QHV2BUmhk4WDKwgbYXQZUVurVvEKCqZqTrJbC2EeJjEZIsnA0Q23mLaPUVfQpNseEQUgmo3Lxd5uNUSsLPXMIGvsaR0wj8vaqTqYKIFCGF4XrUk6hOKD7awKkm+OmBq6A8GtxUi3foVmQvdqQVWWhQn61DywQGFhwMgRk4RDakBmZvPqvmJy0GjlpHbCaXdj6nv9Dm6VCsSFP4BbDcCAyDs3tvWojGVMWVsVJWVjX4jev0Nu6ORt2RVloCNGG7NmK3FObBGd1ZPl2Q0kWClXXkaYeb/uDmlcYOwsy4agoNwk1r1Ao28QGJkQrst3HghzMhixIhcHsD9FakdVGZIUstBw8yLKANCkpSDk1srZpIkLFIMUzt3BnC7+OZ+fN9rAgj+GHhEggcgsHIQvFgS+YDdmzEZmVnIRIFjYog4RgFmQByrAhHPPILaS8THH9TSs2hqZ6WnsPf7ziF14lP0I5SWRhzMtNBKgdNjmLk+Sivd0nt7Aja3JMyUJ9Tg4SJ09mj/s2b47qc9Ehh4iLZH0ylo9ZHtHnCNeKLCycpNIKRniIQrmmHkvc8grX163HY7sfQ7+hBxo9ra0amJOykfmVSyP+nCHnFqp7zXKvJuRwlIX6BJ1aLMCsyKIMLJaNyE17osorFBAH/3ZR9BMCWaiuVbFWFip5hUK5FA6EurBqGBqRRV4hWdWDIdpG5Oc38Wvz7GkFLBIgGvu1xMC8wqzELG9FKymxDr4Dex+/9+uFRZOI7PPudhcWvncLsP6fIX+9pXO/jA9PT8ZPvuOE65l7eZsy3WcPh9ccHEhVOH5OXsya6YnMdHTy/UONvnvA67NwHFdsN/qShYqryNgPHG8O3lgscCiEchN/uGJBKb44s5CRfDe9sAO9StFHuOUmpEp+YTP/Gf5geRjnMRHd4KEsJBSKngOd3n1fiQNylfNzULKQ3Jk9jai1zIbNyteignJ+rdtr+LqnGRPZIO5EgyQLRzM8lW50cSvFGGJSFwjGMZys6HekwxqmPYGsxsLCQ8pCgrAij4SSk8n56Sy/rt/mwL56vlCbNigW5MWLVTmx2CRbPZSF6+rWwea0MfXM+IzxgclCZcMcdsGJr7IwgLx+KDMLhbJwTGbsQ7mdLqdqYyA0mRrhPPQOtBoXllzKSds9n9WimzL/PEAKKZGZJgoXwoE47LZajcwVkZmfhFS6odPrJYQG1rCVha0Wv03ILX0t7HBL07sZiePh6u8Py4ZMSJo9GwkFmXA5tOhZG91heyjIwrE5Kcwq0Wd1oMZzsq/mFcau3MTXhhwWwswtVJuQlWvL0mdTVbG+NmRPZWEwC03STIUsjLDkRCgLu9vMLGulV1iQaZ1LiHzt8NeIvKs5RuUmAiLrcJDJck17aGodYUUmsvBUpRF5X30XzNS8OoiysGjS4KTOuNm5jCwiRVRHo0md+NNGnO43xUojblBsfhTobeL3jznf8PonoSxsaO+LfbmJAN37RG5h9Ua/uYW9aSWwG2NHFhJSVCvyxphYkFeUrGARCJFAWJFDbUSuUvIKRVnEYGQhBco3HHM3bMcKNGi7be1tTD17+ZTLYUzl+xhLdinSzjor/mShWCczy+B0ON1EUBhkobD0Eyr3tCqZhRqgtxEwKZnOIyCv0HPf1g6nd2NmAHT12ViuWDxtyOHkFQoI63xVBIUf0dqQB2tCFkjNirzkhBWb7OB7+KsWlaHP1oe8LL7vr2qVJSfRQOzdS40+ucTbn2H5pTY7vz8ZCj2G30QYnv1bYPkt/O8f/RpY89eQvh6t6ctL+BDoA/0hNe825Lb2AGepw5u5uGX2mbFTFVKRHINWi84k/rrPTna/PgvGi9xC7+xFihVyGriCrbFqf5hNyGk8suHj3wK2wVW4NES9+5JZLGKK2sHveHNvRGTh85uqGdFIysaVU8Jwkqg2ZHdmIaEoTSEL9To3hzFcykJTC7uWj1m462LcnDymQCUYGviZPbk8/DPoiQhJFo5meJBX1KAIC9/YZPocXn2RmFuIRA1fgHoa2yOyIKcb0tUNe2FK4YixIdOBbr6iLNlS0e6dV6hYkL1tyO4b0arqVaqq0K/dIwJloZhQ91h7WFA1OyST9JvCbXubhzWzkOxVlO0SL2UhXSsU4kutekSY2Vx2tJMt65SLUbZkFkqnZcHpcGHjG0rGkAJ7YyMn1gwGJJSURKRmIXQk8P9bPCkLKOJTSlR553VFg4w8Jdi+W+8/r1BRY03KnITETpPbxpoc+s+arkPjF7iltGtrfAOBY0EW6nVa1RIs8tzYpoHlSGncuWkxACmHK5QDQVg2ZE+yKmSy0LcJuV+NCqDGXH+bFKvDiW5qyg6A5FmiEXkvI8jDBa0P1C5OhDg9n951nCxMW7YU0cC3EZly0kTpEy83UX5mGQMbEmNvQw4tB0yQhcc6jzFikX4HNocLe+v8B6XTwbS71czONkXjByd1klINqirq2I4WHwtyCNmCLYeA1X/mj1feSjIrr38uVjJjU/ud8Sk3GSS3kK7jNCv/vlqtsS3lIPKa0BdFbiFrQa5SWpDHhm9BjlhZqCiyhFopEIQNubnJhN52C7uuRNB9tKAB5i1rbkGHpQPTsqfhloW3IEkcxOavUGMR4ksWugfT9Lpx2J3MUpyeE55Sp3wGXz+bKrvRZ0kAssfFNrdQbUKOrr1StSE7LW5lYZDBj1inaN1JjlEmWizIQqEsrG4behuySigPQhYKcYMpArLwf7vrGUlLa/7Sibn4w6Y/YCduhy65UioLY6QsLE/3EEdQfMW2p9lDt7LQxylDi9+ZvwTO+BX/+6rfA5/+MaR86HPGnsPeUuO9obgotDzVINi3tp6tVRTpURjD4Y1DRGpkGuHSaNh5mEq3fJWFbbW9XsU9tE/Q5PHXcWet97nHH2gvIGzIkyni552fAevuA/bwJurBkJFiwN+vmMNK317bXofXdlSHVW5C7omn1vOh8bXLx4ceg0D7WRoC+VEWFqfye06DXh9WyWC4EPvwFuU87Rfd9XC4dKiwLFLzCgXSFNdI5nheSnOyQ5KFoxlCwps1Vg0HtsGF3MEyUxJSkaHnB4Ou2tAr3P1ZkD0nBSOBLPTNLXT09jIbMiH1NA+ysJiThc6uLvYxtCFfW8cP22eWnRmSkjMUkHyfgqlVKzIdEo0lQXML+xSFaLyVhRTGTsoYnVaDghi1BHtCqDdo05mbyNU7jSQ9X/4L9vi0SyYy/ujI1mZ2eBCwiLzCsjIWhhwuiJCj/0dh/YRiUg6VKfbXGJacGHP5JrjHamQ3HF+ycEezO6/Q3twcVl6hJzK++nX21lTnhK02fpO4WJCFnlZktRFZWJBJeZkUuw0bHeSJEEpJ0KE4IzkyZaEoiBoE9haFLMzzKTfxsSCLgGlSm4nXWCAkTpwITVISnL29XpbfUEEbN6HaaK9sRf8OTk6nRphXGKgRmbJ1iPSnDTGz+8ZEWShsyHVB7S9utU7oykL6ucwrzwxqRW5QVIW5peksZD8UTJjnbUUWZPigeYVkP/7v1dz+O245MOtrfq8Zsq8XOLTxKTfxzS0kZaHP4S2zg9u9mjpjO6RKmT+fKTDoGrc1RrZH2NWyi+0vUvQpWDpmafRkYXd4ZCEppkNRFvbW9akh975DhEhx/4772b0kzZCGe1bcA32vGYY6/rtyTBgYfRFfZWG5Wm6SWRjcmu0PRICzLE4XULW3Nba5hXQ9N+2LrbLQrhBO1Nze3xFCa3vsh67RKQujsCGnlaj39lCbWz3RoSoLg6tPUzMjtyGLYpMrFpSxa3Fb0zZWsqFLOxgRQSoxSBPyofdYxptDnwtnP/996fMDxOpQ1MYX7uKPaVD28W8GJQwpXoJiJoigrk+1RaUsdDic2Lu6VlUVxvJ+aleakB2Z6V6iEM9rmtY6+nabPc42hKRCvvfRtHaw4rhgoFgScsmRW6c8xQbUbuH/0BE4xsoXVPp501ncyXXHO6tYuQndR8W9MBje3FHPivoKjUm4aLa3QjAoKAuc+hTogCfK7BSoTkR9nG3IoSgLexpQbz0FFmcqG9AWKZE05KDM6eAkb8EkRWhykkOShaMZqtKtXJXw92hd6sY1GIxJfJHqbnIHn4ZVbpLqvkEIO/KIIQuVRuStVR3o27yF7howlJepakJTlwXdvXA3AdbVs00Gqf9oQ0bNtQNAq74HORsq6AalTqlVK3J5QLLQ5XTBrExCUuJMFoq8wqKMJKYIi9dmg25KhVb+PTWWLwIKePAuHRqmLOQ3jg2vHlUtm1alCTlxkHKTYD9zV24hetK5eqyYLHYiKy+GuYWpGQkg7tMFHXq1ZUC2t3Xd07ppa+KvG0OgjVUQJEydjeR8J6gurvu//8bIJwv5BupQU3dcLchHhQU5Py3sQ2u4NmTKAiTo8zjZ26U0z/takMMpOdHo9Ug6hb8WxEAjXGQouYUt2w6zdY5UgZGocQM1InsqZGflz+KB2DEhCwdXFooDOIX0pyToQyIL6ZBBVrTBcgtVC/LE0MlrsiKTRaW1ppf9/kMuN6HsJionogKkSx9n+cL+QISoJ1kYFxTP4cU0ZL9pd7ckOvv7kdXCiZaG2vCbU4NBZzSq13nfpsjUhaQ0EcVjSfrIc6eEDZnIwlBaNlUbcoAmZAGx53K18uFAUYzyClfXrMZTe59ij+86/S52cO965x0kmfj9pE8TpqLaB2JoGvRATg3eIrMvs1y14WcXR1ZUNXYmP1hX7mmLbW4hDXMt3YAugedzRgFhKWwjoi4ld9BG5Jo45RV6kYUeNsdwycLqCFR2GYkZbEBEqOsJT11o7berGepEKgeDUFCH24Z8sLEb26s7WbHJZfNLYHPY1CgkXXI1KpXXrkQMm5C3PsHe2Md+mb3VpqdDlxZkHaD8wvP+xB+v/zvwwe1BCUNPK/I6x6GoyMLj21tg6rIi2ZiAifMiLzvyB0c7P8tZlSG1L1lIKFBzC70JwcTCIrXkhIaboeQVTsxLg756HS9vIoQZp3TDGRNZnr9Zx3+nU7OnDVpuQqrGR9fyPcJ3l44NqYlZhYhtoKIpnffwsThNKAt1cHWGPyQPFXmhZBZ21+OYWbEgz85TzxG1NfuRoggSM8ZFdy85USDJwtEMVek21ossDNTa5wljKieKuptN0SsLRcHJCCELZ47JQKJei3aTFfWr1gxQFb5x3w68+PvNcJZMVBuRhQWZ8pB0/g5zNFWmjagn0RBubqEoORHWGz/TIXOfTb2XJsXZhuzOK4xPuYlQb5QnZKCwhx9uGsZ7q0IWfXk8dHot6o90oooODx7lJgnj/ORGhoiegulwUVNkCniosVDUtBwETKE14A4GIg6M6Vz51J2+0KvswWw3Y3/7ftW66VYWhp5X6IkMpWCh672PMdLJQrURWSgLRRPy2BjnFSr2DCILw0ZmaUQFJ7pcH2WhTxOygFiDaSobDMlKbqE50pITRVnYdpSvy2nLo1MVemYWWmtq4LLbPUp65vAP6KyJniwU6mqyqvg08/oWBoSSAZaZlKlei2RFFrmF26s7/JJCQlnIVMchIjktQW0NJnXhfoUsnB6MLNz1IrDjWT5h/8rjQRta6fsstPM1JF8J2Y45qNxnzKkDcgvtbW3I7DzCDiMdzWY2UIslUtXcwk0RZd9+WPkhe3ze2POieh6kctdpdEwpJfYyAb+u06WSLOVBCnY8ycLULkfM8grre+tx+7rb2eOvT/s6vlD+Bfa469XXkKwMHimvNBoIq5+jpRVOZaA3AOxg6gIodiY1V1UWhlNu4i+3sGZ/Oxw5MVQWCsIxb8qAA2q4EHs2sn47jUXegf1hZKuSBdE3kzmaNuRwIYqh6rv6maVwqKzIQlVIRA21yYdUcBJmGzJlqRG+ML0A+elJ7DnSWkHQJdeipsPkXbImEZ2ysPUIcPwzdi+z5S0bmFcYCIt/CFxwH3+88SHg3Z9zm2oAiJiJ/5n4/YmcF44eZS8ZBnYpxSYzlo+BzhBbqsPexgn8fuWM5q98qFDNLfQpOVFs29k9LhztOBp6XuGxT93/IPZgIYLEIH+/Yi6SU/ka1t/rbQ32h1UHm1mBW3qiHlcuDDNyRs0rHPh1hLKwX6tF1xDYkIksDDQYdHY34LgfC3LzUT68784wRBXzcSJBkoWjFWRtorB0Qma5eqPt1bhYI9hgMBr5Yt3dbotMWaioCT1f/EQWhjKtjzdoAjJHqXdX8wpP48oyq9nOslScdhfMhXxiYK2rxac1nwa3IAsVYFohYAiPXFMbkf2VnPigv5v/PhJT9dDFQe3nK3Efiibk8ro9KLJzUq2RQm09QETeLCV4eMPrx1h4ulXYkCNUFhI6M3hrV75ROfSm5gB5U/3mdUWDjEROOnQlzhjQ3ElZb3nJeSwo3N7UFBVZaFw2DxqtC5bqZpgPhdaiNtzKwspWE8xdzUDLAf4PgrCNEY5E0oQsIEqKaNMVwprlLjgRysLANuRQlYVejch7Im1EVkp2Ovi6kbo0erJQX1jI7NGw2dgghSygarkJ/azEVDvMoYkX2MQ5gU/KexqCHsAHsyALeDYizxiTAYNOg9Zeq/p5BMwmGysqiUQBNuFU/vo9sKWJ2aRJ1TIpULkO5RT+72Z3TuH4FUE/d4kxCblOTXyVhQFyC8lmb7D3Id3K7+/1h8NzHAyGlEX8a5o2bQx7j7C9aTua+5uZymlJcXRt6gatQSVABis5IaLfYneymI7B7pGUWWhwARlW/r1Fm49FKqmfr/45uq3dmJEzAz+b9zP2flr7zfv2IcnO1So9bdERUbqsLP5ap2ugoSH4UJpe7xoNOpQm5KzCyMjCvNJ0pGQkMCKtrm+c+7ViD5ItNYR5hWL4QCDyqVMQ/CEoC30HG58+dxDP/fpztCgN59G0IUdCFualJbKYDnrJCSfJUDQih5pXSEjL5Ndff4+NFXWFWmzy+nZ3sQnBs0xPo7XCrqtHY3d0ZPrJCrJhCkVrWbpyn9/6JH87+VzYe13eTciDYcH3gIse4EOzLY8D//txQMKQYibIilxpa4QrIz0idSERdPRHq9cwsjDWcLTx/WCPUjQVXFnY7XXP0xfwPUR2L3CEBnRBIKJ8WF7hcQ+yMIKiRhKFlBfz3+mOo2nYcDR4qdSja7ho46rFZUhPCnP4IvZ0xoHWZcp2zEnka1m9pQ2wRXcPG4wsNNucMFn9D0oaq63od2YhwWDHmCl8wEzoPs5jPkyRnC1OUEiycLRCMPKJRiA5C53KhLlb61IPqsFgzOK/+u6u8C6BJtNAZSERhxpoYHVa1RvMcGPhuGxkmbuRXFfFNripixay91M4t4A1iy9kzcf3MqKTblCLixbHLK9QQNxIVGVhMLJwiCzInmRhSZyUhaoNufJzFCpTbX/q03nnlTNytKPBhAMbGjxsyJErC9sN/Heba/A49AorcgzJQiP4hK9bUxYwr5Bs0faWZq+NQrjQjZ2NtGJ+7Xa9+RZGGllICgyHk/+Oaf3JTk0ADfWb9tAkmt45lalS4kMWRqAsFKo4a0/QLKoBZKGSWdipkIWZig04sLIw+GElSZScHDzoX9lz4H886yfAxlooC/v0/NCfsnABogU1xieU83Wu+eBOtdF7Zu5M/rOymQJuBEMGqXDF/w+w8RXKQqGOGQyeuYWUAUiEIWFbtfc9qeFYl/qzo2KPcDCe2vI0FE5ugtGhwYS8NCT6DED85hQqOa3BUOjSQUuqDb0mPuUm/nILfa7vXBdfp2oPDf6aCAcpp85lhVX2+gbYasM76Lxf+b46yEsggjlKhFpyIizIdMgyDDK4I2VTkV3Lfn8pmYlczR4F7tt2H/a07kF6QjruWXkPDIpSruu119jbvPnTVIKFBqCRgu5Ng+YWiviVzDIWk0L36WiUhaTIHztTaUU+ruU5tk4b0BrlEEyUpESZVyhI5UwlZ7k9VSHphBU7WGahB1lIPytqfSaegJwTkULsqf2pl0L5/YqSk0gKPyJtRFabkAexIKuDcUX5Faqi+W0qNrHY2fd2+oRcr0IOAV1yjbQiRwhBvBJBTWsQu5/t/A//xwXXwNbE9/H6UJSFAqd+E7jkEV7wuP3fwJs/ApQ9oyfoHLayZCV73JWVEBFZKFSFk+YXhH2PD0dZ2KFc3v5em3mladDqNOjvtqLHQwFu8FAWUh50KMrC2amdXrEhbHDhCG/dJ/FCs4ULMRz9Y/CTl3Yy950/kCNjc2U7G7h+9/QIRBtBlIWEojTPkpPwVJKhIjVRj2QD35u1BhjaH6/ha/y4cTbmcBOwVPN7nqM4/Iz5ExWSLByt8CSvNBq0KxlaFoMGaYmDB2tn5HKCqNuUwDY1oUJYd4SakEAbWUEaNPaNnNzCOS18IU6aPp1V1hM8LSFm5Tk3H+NZTacVnRY4DymCJuQBNuT/Z+8twOQ6z7PhZ5hndndmmUHMkm1ZkhnDYAcabpOGmjZpoA19xa9Nv7+QpJA0iUOOw4kxdhJjHINsSbbQot3VMsPsMMN/PS+cOTNzzswZWlnO3tfla9craWH2nPe87/3coEBZGBKakGtPFgo25BooC7EwZoq3qcUT0LL5Nlmy0GDWweWvoQ+kww+MQHTZm9XKWiqwGXwlSYmC+rhILcFLTnjhRqVIp8Eeo6o5b8wpnVfIrJvxCm3I0LQZHD0sOuDBByGdLN1SVAvUG+oJkYQKDCQM+QFlQzOdyEUvPFWTvEJs8r6wyMhCOWVXIaA6GDPkEEWsEOl4HJIrK0IbMuYx4QYQYa8gs5B8G+3tdG2KxyF67lz+X0DLzjNfyZ4qS2QWxvR20F2xv2qWCX7vTZ6httH19evBorNkiD1UBpaosJZtpJYpOZlkwwylOWDiRmTEni7p3MLZodItyBx48OD/bn1cQ5qQK8kpFMMWooSw20DvoZqhkw7OYHkIILiUlcnZZKGH6+kqk4Vqs1lo/w4+nyEplRxwHh1/lLz/qt7KLMillpwobUJGYANun4ruu+yd5ZFoHI+PPw4/PPtD8v4/H/hngbBJx2LgfeBX5H3XW94ABjP9euKDaDkoShaKyk387ggk4ilysLK7KsiOZFbksZPLkG7aWp3cQkFZWDlZKFbyuU32gmQhqob44FVsQ0YrbjxCn9M857FU4OeupOCk4kZka3nKwpU55cpCXOt4IzKPU1JcbHJFp5AzxgkuXiiIuYVrJSflgROvgqrwpbsBIl7qyOi/ERJz9ByoU6os5NjxdhrHodIAnPgJwD0flIwhubWXWpHHTcGSyULMvrzwIt1v77iB7TFysXAWYPhxRa4SKSRYZuGyMS6rLNTqNeDqsOZZkbkak2cWyint48kUjCzSn39TGIt7cIHZC4DXdzqZaRtWiFHvKInfIOUmjm6inP/rX56Q/Prf+j0lJt+8q11RB4KsstDGIhxywEtRKVlYw5KTArmF+HOPLNF9bt+W7HVKNcMy5jsry/9+JWGNLLxUwYkmZqfjD1mtVVkDn7XRDipIQjKlISGwlWQWZlmRS1zAaoXd3fWwm5GF6d0ZtU0WWaijm0C02hW0IGdN18tXFgpWzfrezIKaI8HmykJsZqo1pkvIBCv5cz/335CENJhSKWg88GlovezDBXMt0SqAhw9sgp7ovIEUSWhs5UnA8cGcSqtBH/WCSaxa6maKmtmTANHybUECfNPgSNGHqtevy3oI8VIIYt3EzQUvOGFTxZLRtBEsrRFQ61Mk/7CUA3ctgfme/BCzGKKNweLcQtv84WxVZ5WASo5YIkWyScu+fhU2IifcK3RTqdEQYo9bkPEelctjQqURYrFQuDI7KBnlrMhhT2bTNUOVqrnAr29I0+8ntZMGg1cDvBHZO0RzN3c07sh+rSrJK8wrOZksqNZRakMWKwv5MwDx4rhHstykrYRyEykr8oa4Bja32SvKKRRD7aFr/0Q6Uds4D1M9GT7QL/Z8ViZnkytNlF94jSMxVE1Y9tJsoFAJuYUvzL9AyBIsW9jbSv99peAlJ6O+wo2S425WbqJQ2dqdooSwprEyVeHXT3ydvH3f5veRQhcO/5NPkqEFPhutV10Fdj7wrRZZOC1HFmaUhUITcrMZ1BXEpHRsrCdqMrzG3OYrK88tjPgye+IqkYWCI0Rvyg7tzwGu8WhXR86qTeTSWBj35ZFnpcIf95PBa7kFJxU3InMbcrnKQgVkYVZuoaf4tYzFUsd4scmeDBnEyULMHUeoTRNlqSnXIJFXyIpN4LL3E1dAWcpCjq23A7z1+wBqHSUhf/n+vAgCtCIjqTVliZZMFr701DTJqsTysqw4D4w6ePL/AXxtL8DXrwT44W0AZ+4r04ZMCfx5Q7Sg6re5L7/kRMfcRfUBAF/UC4vhzL45V9keS6ZIjED9LBM49N8IYGe26hIVeWeW6V5uY8NG+J93XAZ6jRoeO7sAdz2fTdaNLAbg4TP09/uha8p0d/F8VzllIes5wJITKdHMajQiYzSEP14PWlUEurZlu2SMrPjV1kP3lGtYIwsvXeQ080YY4We0K1OWqO3NYNPQRUppADPm6PAppyxZ+DJRFlr0GrhsmR4aL3RRyw7Cxw76iFCSvlY2d4Soo3gLV82UhdyGjIc1Azuo5lTHo61oNZSFeBitWcHJiZ/C+DP/St7t0deD6vovCO3Z+GDE6ygXeHC48k00c2yi82ZI97HDbBnglp867xAkl5ezyQkkiHAqN8lIrEowdwrsWkqe+5YzIbqoWPFEPSSbY1PDJkinUiQTrCJlodEB6voOsHcxdeEDD7zscwttEILm0FBNlIXcgow2UMwUKwsKG5ETS+x319AAKo0mk1coU24iVhYu+IorJUzbtkuXnGCgOMcsJZ9zkQwEwcgUL/Ee2jhbDfBG5PTEdBbpnWlClpnYlwK+6ZXIAstW65hLyizEgRZmvfFG5PNzPghEqWUHc9IWx+mgoHWgvMbavl2NWPcAbUk19FuMFeUUihGaoz/vFCRgJVTdRuI8oEJBFMnAbcimpnrhgDU9WGUrMi85OXRIMRnKW5Bv6rqJWEOrAeU2ZEo09BRpQkbg4bSB8RyROmUDWykE40HBmva+Le/L+jPvPfeSt443vZE0qdud9NqrtEBDsbKwvltUblLZgFGn1xDCEDEW5I3I5eW2EqCKlytZMJ+4mspCVL8UUBbyTNRWR7ZdfUGkJuKFH+XmFVp1VrKfKAe8yXuCkd+lgKtaMYpC6T2LTiXPQmlkoYUpC4MrMcWqwlu2NGdFLk36KHnyur7XkbcawyIMLxUuMVqDgibk6RfpsBIjIHa9m3xcUBa2lKgs5Nj8BoC3/5B+zrMPAPz8vbR1nQGv9StaroAlh6pwnqqEq+j003Q/sf36ToClYYDf/xvA1/cDfO0KgCf/hZYccpy5v6xvHwvBEDP6oKyyENHSm19ygsMehC4JYAuDbMnJICvw29BkBtXo7+kH+28QOTKmyiILNzs3kyHn519DM9z/6aGzhIDnuOPpUTIbv2lTEwyUm9knKAulo2oyjcg1VhYysnAxkL+uXDhCr5Nu/VHQOjPfJ7qkHEuMBB6o3p76UscaWfgKaELGBTLF7A42p8INhbUJ7BpOdCjbbPIJCG7a0X4oRRbOBpQt6rVGbHQU6oIeiKm18IwxE3DrFWUWBsP0QWQPA+x17IB6Y/bPVPXMQm5DRosZ/zw5U5XVsiFjVgUGvyJaWcB0VXD6XoD7PgpjOrrB7u7YR35e3Hjr1XpIQ5qE1UthYE8T1BtCkNQaYdiZ3ZpcFlnoGRYe6gI4aVWN3MLZk8I9hHajSIAe8Hl77BbnFmLRJ4Ql2oZVKmJjLRtNm8DRQzfhvkcfg1To5TE1z1POIlnYaoc96vOghhRAQx8Ab5SsEoYW/OVbkEtsROYWTQ3LK/SyyAe5cpMsG3IRZWHBkhNxhtcMtbXnInT4EJjZgCaQqsz+KNWIbJ8LZDchC8rCzioqC/M3vcvBGITjSbJUtilcnzBbiRdvjXhGiH0GByGYsnFikq4J86NeQuygksXGyJZSoTJpYEpLn7emhZhMTuG1inIKORKxpKA+mtciUVrje5vnFk4eyivw6dhQVxMrsmnnTlAZDOR+il2gVvFCQEXVY+OPZTVkVlNZiG3D0aT8/cmbkLsU2JCXpwOgTQFEIQ3L2vJVoXigw2ck7qcazZm8JIyxCDxFIx0cb6aRHjauLKyULGxXnlnI8wrry8wrFEPILZxtyCgLy1XUcqKxSqpCMVm4rEoXJAszre3Zz4P5scwBHGMrsFipVFRqQRY3eZejLMRDPeaRo31RaR45qkWxqATz2jihXS1lYSiWyBSbXNGdtVbwxmbM1m000D3/BR8rV1tD+TbkI0xVuOXNQu50Yo4pC8t1yiA2vArgHT8BwOinwd8A/PSdWU6rdls7LDHhvqzqOQdDR+aJ4MJqjkLfobcA/M8egN/9E8DCaWrfXXcLwJu+AfCuu+k/QCuyhHihEJA05yKESa23MFnIlIWLk36hvEel14PGSf++0ydfcsLLTa63zwBEPFRg0rYrs2/1TpRNFiL+eH8P3LCxiTh0Pv6TY6Q0CGNz7j5K92MfuoYOX8tCkcxCgS9AZeFqNCLnxAERC/Jxymf0W48BGDKk6OLKNDSwAp+W9Wzfu4Y1svCVYEPGjAZEHNLgrFd4ALI0ZshCkdpOqQU5N1OpxfzyUhbyFuTTzl54fiozURVvrAOeGIRN1Dp0k57Z7KSAIbxc8l2GslBoQ+bKQvHnySELeRaaucY2ZK7awSIGyYD+cnD+NwB3/ylpOB1r3ZSZTOJCo1IL6kI5KzJeU5sTL5D3x0KtZeX8JJMpmBthBQbeYYHoyTskjx+EijF3ErSqOFjMdLPBFWe5FmSeV6hxOYkipGw0bQKTMw46pxnSoRD4H6MH6YsNlzGn7Rvz3JqtsFdNJ7iRNpnSoAowPF9BuUnJykJOpLBykwWuLJQnC3nBiScUhygr95GDcRslC7HYJ+kX2eOXaCObsDEM5R/WkEAws4Z6bv2qJlmIjX3t6gZBYZJRFlbThjwta0FusRtLWp8G6qlthG/AubqQ5xbODHsFVWG5uYDn5vwwiLIAXAJOu/NzCq3N1H6sIKeQY2k6QBQ5MQ2AX5VRVda8EXnmOCE5M9e4U2gFxJKTatqh1Xo9mPfsJu8HFViRD88eJgptJEoub6m8uEes9EelFpJyXI0kBWxzV5pZOMdKc2a1KZIFVS5eWqJWXFImJAJRkqdSYNq1Cwx9VPWbURbWMLMQD+9BNtyrQ2VhqKJyEzF6ttED89x0EsKpOoDQcuaQWSq4hbkK5Sa5jhB3mpEJUZ9kfIlQbiJSQCMxsDRFn1G8vKOc9bkaZKGQWegOlXw/Y6EQ37cpzS3kPyc+H5Va1S1sIBQskln44IlZodhkf3+GoEGRQjKdBKPGSEj2zU56/yzGBmsb6fAKVxZ26euoVRhx+Z+SN6lwGJJeb2XKQo6BmwDe+XMAnRlg+DGAH78dIBYUzpSLTFkYL6YsXBmD9NNfgRO/oIV629Q/BTXmhiNBiF/jjV8D+KthgHf9AmDnOwD6rwcwu+g9LSr6UoJUMEjyYxHLxkRBGzIOJDGuJpVIE8IwrxG5QMkJLzfZp2KDkN6rATTazL6pBBsylg+eXzkviBgQuP/5t7dsJ3tVdOr800Nn4M6DY4Q83NVVB5f3FBDPFPxicYDgYsHMwjYLfebM4Hkox11XTTRapTMLUSHvWUqAGuLQ3Zj9zJkZpJE/YaMKDPXVLWW8lLFGFl6KwIefyIbM8woD6jQ02U2lKwuZSkcpWcjVG1KBpXJE0MUiC481roPz837whGLkMCZWUSZiKZhh0+DLoYBiECfKmBuDNiiZxa8Q+IMErXGCBVeOLOQ25Bo0eNW03AQndGglSCUAtr0Vxus7ssjCrGlSUP7Bbxt7AVxLJyENKnju3uLqk1ygxRB/rwaTBizBObKpwYKKPGXh1AtZtoeyMHeSvHE4dVlENG9C3tW0KzuvsLFMCzJH02aitHJsNLysWpG5AkacWWjWa+FqPSW7JmzVn85xG3LZNglx/miRTRfPc0PVFcK7UFxZ6DDpSCYMYknCAiEG2pt1HfR+ibz0krQNWcKKjIeg4NPPgJkpdfn3VQ1oHA6I2+kh86pUf4ZY468Vn27XKLOQl5sozSvkGHBkl5zs7qrLJgsrKDfhODPjE8hCbFYmwzqeU4hNj0gUWku717k1OubQkqjDmisLkShHixA+12aOZqz2Lhe09tcRVVDAHa2YiMqFeS8lKUOHnlfcgnxz981CaUE1gNdysZIT3C/4WMuwksxC3rA9rU3BvILoATlgA7L4QMfvc8/dtAXZcdubhY/zzEK/QmeIHHStdE8Tn5/PL87igxS9DdLGuqoqC631RnB1WgE9/eO6V1eWW8jLUaqpLGQZgcsxfyY2hmdxSdiQxUVMSP6nkmkwWnTQ2k//LbdwrzZZiHs8jOrAXMVyiOxSG5G55bq+Rfk1klEWFv7+fswsyO+4oksoNhGTW5ixiIPpK9vpUCKhG5NtfF2DNPwxv3DddY8eBEhEAJq3AXTQgU1inp4DVWYzqMvMFc8CRnW8+24AvRUA7bY/eish5VusLYKyEGN8OEGXtTY9+18A37oe4D93wMxvfgHL4RbQQhQ2b4oAvP6/AD4zRD832qcx/okDB3nrbqbvD9LnjFIIAgSzCWI6FSl+kyvGxGdNc68jz4qsa2KNyIFMxnIu8NyKWOdnkUlIcCLKsCGLy03EZzKn1QBffttOcq740aEJuONpmsH+4Wv6yi9ZC+D1kaZErdlZMLPQrdFApKYFJ9KZhSPH6H6ny3Ac9PXZa6v7AlVg+lzm2hbNXWJYIwsvRaDCBDcwiLougSz0IVkoyvAoCL0V7Hr6QPCxg3cxzAflyUKuLCxEBK0W0okEhA7RBXZx3TbhsBj0RsmEBzcZuIkjf15HHyCOQlkpgoqzsyS1CIfdYActa0rMb0QelSk4qTFZKBzGq1BuMvYMwE/fBZCMAWx6PZH5jzMbAz+UiR8QcoQyknqxiQnoH7mfPLxGTywJZQRKIZAB6+tBxciahFuk/nH20yZXtJ9NH4WygeUT7CBlb20QlIWeiIc8mMWlEFhIUrFlA9FE1ZqOljmBEOeqxZdbZiFOhzel6CbomLq6uR9oIx1ma1ZFNmS+6SqmLOR5k0xZqCSzEDcZmdzCiHIrsji3EPPvELy1GVVgOVELWM5kjrkFRUc1VRRLjXQN2hHJ2CGrqizkmYVosYlmP4M4Waa0CTk3t5DnAO3ppvfmsYkViMeTxIaMwPDzcoH5PgE1QLJeR/bEF55+KZNTeO3nAHpLL5pZmKDPcz1rmK25shAXWKYuTI8fhKRAiLtAZ9BAc4+9JrmFFp5bePhIwUZ3HKo9PvF41S3IuVZkObKQWzZxP4WDj2KYvUCfO9OaFMz7I1VVFoaPHyf3uspkAvurXy0iCzPKwkrue5Kjq9GQRnauMJXKK0QnBmZ+4v6p0KCkLCtyfG/5uYXo/OCZhS3ZisyqKAuROOExGhL5qpN8rRI1IS8wC3JTj10gVj1llJzw/WK55SYIzFHkUQ7jFTQic5tvtctNssjCAspCHNIcn6TFJm/Z0yFpm+VEyJ5mOqDUmCZhdFnZ+WYN2a8lXv+Wo7SVHS7/AH1m4NosNCE3Eyvng187IWRUlg0swHvPvQAGO8D4swB33QatWhv4zAAxXH7TaTLMIPuP574G8O2bAL66DeDRvyHDLhzSnQSa8bphXysYP/AzgD3vAzAXuG/Ws+fKIM3FVQp+nkjV2bL2v3Jo6bPnlZzwswAqC3GwiTl5YkTiSXKvmiECjiVWbtfHyELBhqxcWXjGnSk3wVJCMa5a54IPM8sxDhR6XRa4eXMFilGuDre2kDIcKWBhmUlL18u5uJ8WVNW04CT7fH+BkYV9hufychWD45QwjbWUqax8hWKNLLwU4WEbXFS56YwQWKGb04AKlYUKyUKVChw2Zp9cVjZ5W2B2t9xyE7GyEAkD3t52sRA5fRpSfj+o7XZo2UM3DYfH3ILyy+o0ChvtFVtDViNysXzIcoCTTiH/hluRG3oLZhaaa00WVqvcZPIItQ4kwjQP5PbvQigVE3IJhTY1EcksRxbGpqbIgcWa9sKmA/R6Onj3cEkHIaHpdF0dUWwhsg5AuOHhzbwTFViR+aHG0QWOFko84PV1cumkQJLyDEyBLCy33ISjcQNpWNXrlsG0fQuxpfkefAhejpmFMHUENJCE6bQTXvRUQOjJXLuYZ6fTqIQ8prLAN11RLyV/ZSBYNBsbyWE5xMqkCtmQxVNNzIEpBiMrOQmfotcPUb3ytWHbWySVhcGnnyZvnVu7iRotFkkKyuRKQTJd7PS50uPRZb4n3nZfjcxCo12k2JmWVOt0lPj7XVe/jrzl0/qNrTYw6TREJXb85AJRHeOgqKEE1UsueBh4w0aqTrzw5DFRTuFnyvqcXFnYgEqr1SALRZEMqcGDgvpawwhxwYp8rrpkoXHLFlBbLJDyeiFyThQ0n4ODMweJwqXR1Ai7m6hKqJrgQyw+2MkFb1FVYkHG/ReqMPEenEEbcpnKQlw/cdiKGXE8VwrhvYeqCu233AIaa2Yt5ZmbuCaVk4fHgdEYePCXzAYTBqWZvEJHsxk0FTQhi9GznV5vE+4OSKa15SkL3aP0/kN1T0MFOVs5EPZsSNjZ22RzCzlZKB688rzCph4bNLSYyy454QUnlSgLEd0NFqFhtdaNyOWQhbzgJIQD/WQ2cZJbbHLrlpasYhPEpH8yk7HHngOqtB5UmggcnRFl/65BcRNyt96BMiuiKka3EEdC1IT84m/GYfzUMjz0tZMVrUEEnVcAvPd+AGMdwNRhaP3158l+Xcgt/Pa7AL6yBeDhL5D9JVlwu68CeO1/gO9PTsHoCn32b7+Zvi0KLAtB9dvyEMCychcTz0GPO8wFLcgcXFnI45HENmRXQA2RZASm/dn7n5HFICRTabjeOAQqPE+jCwazv3OVhQrPRrl5hbn49C3rYUcn3c985Nq+8ksDxeUmBXLKcZie1YhcI3Vho4SyEDPHl6cCoFKloNf4Qt73mZyivwtVe3Vz1i91rJGFlyKEwOnurJwPVBbmPkQLwV5HF4RQIA3xWOFcLTFZyDNMxMDNDFqFcEIitiNeTAuyZe9euKyfbkaPjLrBu0gPvw6XEcz1lIwLsslTwQBdUT5kucgrORHbkNmCjyH3WJSBwJyLWoIfRiuyIaPS6Ye3A8QC9KD8trsAtHphs4HXBE6Q8hqz5cjCUXpw0/f2wBWv7wOtXk2k+xeOLipWnM2KbIb84JvViIzo2l95biEnC1u3g52RRqg44xZkoT2WZBbOZ20QyobOJJDMjqupgsL7MmhFlsoshLFnyZvDqY1wnuULVgtcVdjnsoK2kkOr3kJza4pMaTNkoUtQFSLhxNXJcuAqbyXWL64sFBqR3SO0tRsn7XwCnqMsDDxFyUL71VeBrcFY1dxCPHyN1lFStI5lNAqEHk6EZewl1bIiy5UGFEOfo0+4FlciK0RVs6OTrkEvHV8QVIWqMjfDuMZgZiFi+5V0MzkT7IGQcaDknEIOXPe5RbGDBaLX3IaMYMrCxDBVWONwTW0wZJGFqCysploViSnz5dTOFiqQW8gtyLf03JKnhFgVZSHLK+xiJIsSC7Kj1QJxFd7vEXKdlKsqxGvYipY8vN5CIfD9+jf0899Oi004tDoNWBz62uYWcmVhXfWakMVo6rKB2a6HeEIDM7EtGTtxKZg/lVHeY6ZXlcD3bFRZKE0WJpIpmGGlHFLKQlTo1rHBBCdbV9uGLC7p4aU9ZdmQS8wsrGckqRLgcBwVq7jUhFhud26xyX3HWLHJ3swAmoPvOTvtlEjBs0i9hhLHxzG7bg2KwS3dnQE2KMKMP0NmSBFnsTraphZhTcDf+cN3vEQywysC2sff9yuyv3DOnABtOg2LdqZoHMVYG1TE7wd49b8BfPocwJ88RLIUTx0Ok2unc1M9NLQpHAQaHZkM86FHFH+L/DwRZoIOuXITjqZuG9Eo4ECJ9wvw4Ux7mK4Zg55BybzCV5vPZizI3BLL90x47gorG+adXjpdkCzEfdKP/nQv/PhP98LbLqtwEFyk3CRXYDRbw9xCqYITripsr5sFo9qfpyzUzdLfr6mrPHHQKxVrZOGlCE5esUZdn5spC4kNWXnDo8FhB72KLvZ+BZtNccGJlHpOKDm5yLmFwYOMLNy/TwhpPTXtBfdCUMj68RvoJkzFvmfZJkCEKB+yYrKQKwtxOoT5VpgHQjIeAMKsTRfzovSm6m16pSAcxstVFs6fAbjrzVSVhQ9cbDXTGbMnk6JsDCU2ZCx4QBh6+8DiMMDOm+mm8Pn7LkAyUXwTgtMiVFfpjBpwddpAyxrHEku5jchcWXiIWpgqyCtE25PDZRaKgngTMs8rFCsLdZUqCxFN9GFv22QD0Okgeu4cRM5nbzReFjZkRsQeSm0i2Ss4Ja12E/JAJRbkEkpOxHluSvIK8xqRlSgLN+FBV0OuFWK34eUmrnUArax8CaevrOQEQ8ZDR3C6DmC9+ipBxVGxHYgBS3p4SWlibDzfglytLBcHL06ZlhxmlGpDNuvMwuGWqwt3d9FnwBLLDMJyk3KBirNQLAkGrRq2hB6BJh3mSqphZP1/lJxTmFtuggOi3i5OFoZrH8zfvIWoRhI++lqLm9rROqXRqomKtprFOQgztyLL5BZiQ/HvJn9XMwsyQsgs9I5Jvs6lKAs5WdixDktz0EKdBncoVjZZuNWVyd3zPfIICdTXdXUJJKsY9lo3Intq04TMgaR9Nys6GY1eRlU+okbUi5VXKFYMYdZXCEuLEP7s12fOFyHPNsynbWZ772g4IagIm7rtAmmGvyPeiFoqWVhMvVTLRuROGyUPctVPUohFEoKVuBRlIV4H5jq9bG4hLzbB+3FfX/5rkassJO9bN5K3I35mUV+DIgiW7mV2zrzsA1l/zpuQIw2d5HrG54TWoCEq9Kd+WoVCmdbtAO97EDSWRmhOJGGJ6Q3i7a8C+NQZgPf/BmDvhwQyCq+5M89SNdv2G0okuta/quTcQq4sDFi1ioh8vVELDW3WrNxCLcssdAXVWbEpuXmFl6dOZFuQuWhAGHJPlVVuIgXfZACizy1CNESzeitWFhbJ988oC7U1Uxa6WMFJMJYkbc/ivMJ+G3ttc5SFlkX6nKvro+vHGijWyMJLETm2WA8jC0NagHqzckWayiYqOVGw2RSUhRKZhUoLLGoNPESHj1F1l2XfPhJOjgof3MCPj/uEDfZ4ki7Odea+EmzIFSgL2WZPUF9pdJkJESN/xXmFtQ5WrajgZGkY4AdvBECLTNtu2maGKi0GrtbIJQuLXR/REaYsZG2Pu27uIkUvqOY6/fS04rxCEtCvVoHWyWzIy0v5h2RUbGHuZzkZSQj+71pQWcia/LwxODtPrXU7GzPKQl5wUnFmoSi3UBsaBeu1NBvN+8D98HIoOAnGgxBCOxjaVYlNBOC4ajNE4qmyFA1yGKpGE3KuFbkAWSjOc8vkFRa/b7iycDEnXFkKarMZDOuofSZ88qSILFxPg7n5oGKWbnBChw+TwG9tWyvo+/uhromRhVUidpD0nmmga1BsjBEq1cwrzFMWZja9qMrimapitY5SDNRll5yQRuQ0gNYdr7jchFuQb3StgObXn4Z+AyXFL0yUn4HILciNXXZhPUZCciVU4zgPVOx1Xg6JiCaPLETVWks/yy08X+3cQlZy8sKL2eVTDM9MP0PWEtxn8NzXchAdHqb3kgR4PAaWjmHjci4mGKlSShNy+7o6cFroPT+vIKdUSV6h9557ydu6N79Jck9gY3Eq/uXKlIVaQVk4LZtZWM0mZKncwvHYXkinUgALTE1TchNy9fIKEZiphe26iGWTTVJZyOMS8L7lhRuLuM9MA1F7o2oS/8PhLy6hnsXQxVEWcrKwjOcwtyHPheYyBX0y8DIVutFaXHmfC2uBRuQfyRSbIBKphKB6FJOF25w02mMxfnGHqZcaxv30vNOFhSJo823KJk3IMBPJMhPdzzvbLXDLB7YQ0d+Zp2fg5BPKizdk0bwZ4INPQEtdDyxxZaG6PaPwFeH883MQCyfIALd7i7M8shCdMBJN51JILtN70sseDcWUhYjmnNxCXQs9C9hYpE1uycnQvB+awQ3NETwTqfJzkEvILeTlJrie5Z7JxHj2F0OEdD32SOEM76LgJVDFlIViG3KNlIVWg5YMdrkVGSNDCGGLL6mGOnPEysJILATOFUqWNq+vfjHjpYw1svBSRI4tlj9cdVZdaSSTJUMWeouQhWgvLtSGrMRmuhoIvXiUHEDwEK3r7iavx+W9dKPlZqobm8sAJyIvkPctmlZBWo5EY2ElZxWVheLPh5k7kLFf1NqC7IvEwc+aHkvOLMTX4gdvAAgu0IY0bBrD7DER5JSF/PrAAxohleSUhX19wkTuitdR4vDIQ2NkYl8ImaZTenDXOJkNOVdZiIdkZsEry4qMZNgiy9tq2UY2xXojPXDrQxZiveY2t6pmForIQjxQOd7wBvKu71cPFiwLqDWwYY2HFZPre/pFWiBjaQJd03ry8fNzvqo3Ia+rpAk5T1kovelCVQ9aAREaV6NIWVicRMgUnCjLMDNtE1mRF0VkIYKrCxlZGHj6GfLWetXVZI2ra2ZWeG4ZroKycL4OIK1WkdeAlLzUgizkJSeizEIsiIglUyTIvsWuXCmfV3LCNuC7uurBlVKBPoUpCWpoZLmA5ZKFRojC34T/jeSk9a+j9930oEcY9pRbboJ2JYNWA80sd3h1rMj7IBFR55GFiPb1LLfwfGklU8VgWL8eNHV1kA6FIHwq33b68OjDgqoQHQulIDY+Dkvf+AaMvP4NMPK618PY294uSRjiesUPK1JW5HE3VRd0OwsTY6hqWZqkvz9sveW/u1JzC5GM503IWxupQi42OUmGAiRf+k1vkvx3dmeVlIVyNmR2iEs7OmFlLlgTsrBzUwNRJ/kSjeBOdJaeW1ihshBfeynSGtdVwYrMbZi52aoScQmZvEK78Hm4unCFEa4lF5xUyYY8WQZZiINuJE3xDFBMCLAyHyzZglys5OT0jBdOTHpIRnFusQk/byBhqFfrsyKS9nfQrNOYahYCaNlcQ2nKwkSCFpvkgCsLfUCHbg3tVujd7oIDt9Mh3TO/HIKxkzkD+nJQ1wUtzTtgkc3hErP51x4q8k/+ju5Ltl/fUXq8iGuAZgFiLuAFqmZXqix0mxKKVb8tvfZsZSETDmgDEdDF03lkISoLr1Kzda1tV35RC9+DyexbpcpNNjVsko30wLPnAhtannt+VjY3tDRlYT6xK68srJCglAGuvdyKjEN7LOQhX7vPDpbIUJ6ycHr0JOiSAAk1gLOb7b3XQLBGFl6KENliE/EkJJhs2MRCghXDmiELi9mQMf8JH8gYvu3iEugcFLOZrgaCzx0UVIWcOL2ih+USeumGcF4zBbMqujilQ3pQs9BwSStyLCTYhCvKLGQPlCyrZn12yQkvJ8BJdC3BVTuoQrUYSrA7o1XwztfTDbNrA8B775NsG+NkobgJGWHT28CqswpT6twNe5RnFjKyELH5QCuxs0QCcTj6sPz0Cf99hiykh1zBhpybWYjgWSXllJyg8iGVoIovRwe5znhuoT3iImoYfshNxWKQXFmpIlnIMkcWzoH12mtB7XAQMjJ0SD7/q9Yghyp+fUeWaJsdons/bGilm6Szs8qmtsWAv+eqNCFz8HtaxgbBrx1sIlVbzCUpCxtFmxQlMPJG5FOnspWFiNadWSUnvNzEes3V9Mdoqp4NGYsl0BaT0KpAw+yJqC4UNnTVKDfhEMK6J/PUOq11xrIyKbmykG/AGyx62Gmkvy9DiwnUFeRcIln499o7oTU6AmBtBse7vgyNXTZyaMH29sqUhbasooTVKTm5EpJhRhY2Zj/XO1hu4czQCvn5qgWVWg3mvdSKHMqxIqMC4smpJ8n7r+phqo8iQFfA8ne+A6O33Q4Xbn0VLH71PyE6xA4CSD48Q4n1QlbkrO8hloR5RvYVK1DCwx8qxqwNBrDWG6GZkdulKgvRRolDNCQ81tfRe957L1UVWvbvB12rtKVLaESuUFkoSRai0oYVbITUbcSehlsqvtZUC9i+zTMyx6KXl5ZbiLEMvqmMY6BEoJJx5jN/BYN7r4TYxIR8yYlOJ6ksnGLkW6foOlkYo/czbxRH1POYCEamKQEWBXox5qXCNmQx6e0OxsAfiZf8fFeaW8gbn0uxIHNYOFmYY0PmxSa3bGkRDv6SGXu2zqzhwva2TkjF6gFUaTicUw62Bmng9caV1l36eoCNr8v7O1xZ6IvS34WTZQTuuLETNl/VRlS1j3znNCxNVU7Q4plSKDiRyJWfOOsmbgoc1m/cV2YhBVcXDj1cUmbhgjGmXFnISk4wyxSJOLXNRvaUiAY/fQZx1W4wmiB7oKs0pzJ5hblwdClWFhYrN0FMnqXrPAKjR8ZPZ/6/ZpmFq1BwIi4axNxCbkHu22SkmeC4Xlgy57KFIfqae+v1oEYScw0C1sjCSw2YscYXiPpuCHroghWHNNSXShZaGsGhnVOkLOSqQlwYdWrdy1ZZKJSbXMnIIMx96GkAXRpAn6AHnsOhZyFgoARO2BcHdVunPFnID8hY1IHkULUKTnJLTnJsyKtBFpZkQfbPU0Uhvh44iXvfAwAWlySZw5UauWRh1jXCW1UZkm43acjE04i+O0PK4sF+35upUujE45NC83cucGKPbWxanZoodBBaFycLJQ7x3Qfo2/HnFDeKSeUV8uw2B8uOskddOXmF9OGk0umImqZiYNsj3n8xP6jDC2B/Fd3oeO9/4OWTW8jVmt0HYGML/V2cZ6UQlQIzogLRBGlr6ymi+imJrJKZbArlJi4XOTR5mHJPSWZhEyMOFhUSB6bt1DYVOXUK0otDogZsZMAZWThznBxqUUEFWBbBLJ38cIbKwnLKFcQ4tXgK0pCGDmsHmHopcR8bHcsoC7kFpqqZhZlDKFfUlZpXKGVD5vlJ69gza8VS2Zana/IB+CPtk5DGTSYWmliboH83teEPH6UK4nLLTfi6xVVKq6IsbN8DiSjdFGtybIOojEIlJg6x+PdYLVh4bmFOyclTU08RwhDJCXF2Xy4wZN/9gx/A2B+9A4ZvvAkW/u3fIXLmDMn9tFx1FbT+8z9D419+gvzd8FEaS5ILrv4e9WU3IvPIBLtRC3VFYl14XiFGXyC4shDXqVLAVYUbnRtBp9ERpbjn3vvIx+pyik1qoixspWRhYmY2kznG10RTPbhX1IKiWqOr/rGhdzt9Vo9jbmEpysL505nDs6n056v7e98D30MPEfV48GD+4JAPwdxcsRRaBohnfreTEtmqCyzuprk3o3znOY8rjExTAk+EkjZIgNUZ6iq24zkt+rJzC5U2IpfThCx8j+z8EhTt8ZA4ue8Y3Ze/84r8YhOxEo6Xm3DYjTrQxOk9/uwkLXFaQ2Hw7EdXIgnm3e8jhYViYPRJku2JVpjg3NlOh7a4P7rmHesJ8Y8N7Q99/QQEveU1w4sJpUUHsyHPztKYAhFOPkG/303724gTqSzwArnBRzADpehfT7gpkTarCylWFuKwAKMIEvEULE8HyWvFM8w7wyZIpBPCc4gOw9NwteZ0prU5FyXYkJWQhRNnloXBDeLsswUy/KuUWdhmpc+cOa0WUiiAqlFGcyPLLVxYCguCkr5e9rzELFpRKZaXlOgABJur4Fp6hWGNLLzUgKouVDXh4cfWKpAnpNykVMuWSFlYbLPJ8wqlyk3yiKAc1dhqIbGyAtEzNO/Gso/ZTAFgQ4sNWtmUQGvUwO8XnoCINggqLWshbuuXzy0U8gq7Kgr1L2hDZmRhaJXIQn4IVWxBDi7TjMLlYbopf+8DslOjlegKUSahAjV381boGuEWZF17O6iN2ddx7w4XaTDFMOVDv8o+2HHMDNOHQHOfg1iasmzILGMkCyjt1xoBQksASxkVSql5hRwZZaEzK2dLbEGuSg4lbt6w9IJbkd/IrMiPPirYZS9mbuEiDgqwOAbRvR82ttizApurlVfY4zSDnv2eq2JDltl0JUR5hdgYz9vs6hqV25BRWagk9NvQ30+mzWj7jS1HAdTazBrBlYUroxB4gjb3mXfvBg1TRVsbjKDWqkgRUIBl2FZiQeaN3obe3oyysKaZhdPCZpErC8slC3sdveSAjQoJHM7ga2/zU7vw+UR5VmGEb/Il+Ez8m+T96IG/EnKE+nfRZyIGvKMCuhSg+oKXm1jYYTlDFq6CslBvgUSaEhFayF4ncR3lZTBTVc4t5MpCzBdORTLX68NjGQty7nqJh7SVn/4Uxt/zXhi+7jqY/9K/QPj4cfJcNl9xBbT8/d/Duqefgq5v30EINus19PeDf0cqpkFOWTi+nLEgF1uz5y7wnFyqHMkoC6PllZs4KUEafP55YrvDhmrrjTfK/jshs9AdqUj9qWujBzt8hpChXVYTsqjcpAx7qRJ0s9zCufh6CE+PKT84crKwpXQLcujFF2Hhy18R/j86TDNOxeCKvuVkhO4XxIdhka2XZ6vi8wFttHjZYMkaByfPSiELeV5hvaG+ZDt+tRuRBbKwiLKQF7twJWUpQGVurrLwwZMzZDjYI1NsIlYWivMKORq0dK90cnGtEVkJxqeo0rsLLch7/jjvz+Ns+J0ymMHrjmaRhQiNRg2v+tBWcr1j++9vvnGKDMTKBZ4X3DaAlIoRlYyoQ2AswgQq4FQA266vYE+C7cp6G41WmpUeLEkpCyd1fsXKQrRHNwtWZG+WFXljqimr5AT3yhtVk+ACD4DOAtBxhaKsZ8nvNZWEc+5zBctN8LkxeYa+rvuZlXzs1HJ5RC+WU7EhRzFlIZ4ZcF2Lq1SwnArTQUwNwNXIK4Me8lhBB4ddPS9JaHJ1eaqVnmfWkMEaWXipWpDxkKvWCPkevhKbkHMzC5EsLHSgLVZu8nIoOAk9/7yQiSTOX0IV0m4n3bglzGoY8Y6QBwwesBFxV5c8WViFvEKEy+gqQVmoW51ykzoFG7qwB+CuNwEsnqUL6/vuL6gs4hZknBoZNAbF6tPcchMx8MDGH2LnnpuVtDfMDK7klRdklIXL0qRbB2uX5LZZpZg9mUcWpm30PnREG7MUMYmF+eqVm+TlFp4B065doOvsJPlf/scfh4sFwYaM10k8CGCsI5ZpJOoRY8tBoY3sZZNXiODXcngFIOIr2ISMbdcIg1kLBotWcRMblit5FJRVqLRaMG6m09+wW0cVvFiEhEC7PyM2g49TstBy9VXCv8XQd0djdazI4kZvfQ9do2IYEVALspDk2qhoxmVwSTYHrBQYtUahwROtyL6lCKRDSUhCGp5Z8UOinDyeWAi0d/8JmFVReEG9HYw3fFb4IzwY4YGJWJFP0utFKRYnMuUmnJhaVRsyXuNx+hzUxvMJc25FrnbJib63F7SNjeQASAg/VpCEykKxBTnp9YLn7rth4gN/CkNXXwNzf/8PtAU8nSZrX/MXvgADTz4J3T+4E+r/6O2gbcjYNXEfgMVBqUCAlJ3IkYX8mcXBlVecXJEDKnjnWAZVSw5ZuFCmspA/O7x330PeOl73OlAbDAUJFrz3U4l0RSoeHNBpWGyH4LAQ9pq1KzfhwDIQLEpIgwbG/euUZ1jNnyorrxCJ5+lPfRogmSTXISJ6YVheWYjEHS9XEFmRM2uVOSuvEJWEYqWTkFk4F1RM6gp5hRVakKvRiMxtyIUakfHnqkhZKJFZ+OND8sUmHJO+/CZkjl4r3SuNBs7Wvl3+FYCJoYfI225zi+RzPjHPmpA7txC7MRbZ5J5XMMP7tX+2neyTMKbhiR+U/9rjeSGpUYHXqs5zf/GsQixIUhILIws8C3CrL6oLCwCzTfGZhFjkNmSFTeU8t5A/M/iZoCdqy4pNGZzzw9VqdsboOZCn7sx2xBRWFqLLq1i5yeKkn7gHdEYNbDrQSkhNvJexOKZk8EEKZpgbC5e+oUORi49mUMxTo5ITThbGx+nAq29XY+b7zCnMUU3T85quq4rumVcI1sjCSw0CeUVvfEFZqEoLShbFsDaCTYOHmxQkYikhM08KnNxRoizE3AtcoFYbwYPMgrwvY0HmWGdmrXYqVnKis4GDHQai9hbZTAzxhrkS8OkTvjaYRZNFFqIlNxbKZBbW2obMyMKih3HMLPrRW6jtFnMqUVGIBEYBcJWG3IOpxSxNKAvlJsz2mPfveh3Qv7uJbFCeu3dYNq8QGyk5eGYhZgZKFoB076dvJ+h1owhoU+A2qdYMWTiroZtaV7xVKPuoermJRMkJEgyO17/+oluRuQ152T2YeW3VarImof0J94qDVVAXDi/4q5dXiDDYMvECEupCwYbcmN2ErEQlimUV3MaoNLdQKDlxo4I0J2C5dSdJoQieoOpp69U0r5CjjlmjPfPlr704iT65RDeqqJBFUgcRG70AQNZ0VaaUpBrAjTBaQRAseyyj1ilfxSTkFq4MwyxTHS/q0hBIJOFcOZb43/w1mD2DsJCugx93/A0tSRKBW5EvHF0su9yEg6/L5ZQRlINEkK6N2sD5vD/jJSe4vlZqbxcD7x9uoQ8+S/M3n5x8EqLJKKw3dELrM4Mw+ZGPwuBVV8PsF/8PBJ99lhA7xq1boemv/goGnngcen7yY2h473tA19wkS76bdlKVd/joUVkbMiqTMI85r9ykyPW3PB2AeCRJDllcXcNtyFjSoxS4Hzi7fFZoQsbDqP+xx8j/O26TtyAjkEDBvEQEkuJVzS2UUhbWiCxE9O6g99BYKVZknm9YgrKQ5BT+9WchMT9P1re2f/3/yMdjUspClllIyUJexkRfn0g8k23Zye5ZwYIsyivkzgO1RkX22bmZfLVuQuboYrEdE+z6LgUYSVFMWYg/F/58eE1yp0Up4MpqVGciWfHStBdOTHlJscntEsUmecpC1nAuxmbnRkintBBN+YW/twYZxIIwvkQtq12dLKYnB3FWbhJ2UTcWEvxSeyEki1/94W3kWhh6YYEUFJYDnms3b6cDvvgMPTdEQ3E4x8isHTdUYXjJcwsHf1vwryXcbGimUUPQRMv9zDpl+xR0PYlLTvhzqyVEr/uhlSFBWXi1mg1B+iTyCsVkIaohRbEIuTi9fLpouQlRZ+IatrGBKEM3H6DPgbMHRZEUpeYVYmmIgj1ydslJedeIkqG9IQWgW6bkbr+YLMxRFprmKRFs7aH7xzVksEYWXmoQbLE9WU3IVFlYIllosINGqwGreqmoFZkrCzkhiPA++BAJFucLChJwFpRNX6TcQiGvcH8+WdjMcgmmUm5hY4HTbESU5cHUUlmIDbkaFV2s3Sw0nKiF+PTFM/7yyizEYpcfvx1g6gglU957P0Bj8XYouSZkjlardAlOdHQkr9wkF1e+qY9suPHhJg7kxes26I2RP+NSf4SmHsOtVYTg4yUjkiUnpTQir4wCYLMeWpKczA6MD/okfSibwo4s5QAPg9Y2VVHWLpSc0I2d4w2ULMTMpTgjJy9aZiFXHnAiFm0WrdXLLeQ25HXVzBQRGpHzDxOkBZg0IbsExZ6SJmSOplIbkXnJybIunyxs2wmhRQOkYwmihjFs2JD9Y1Sh5ASn26jwwnUcCTdBWTg1A2ncryOxpy3xOVMMOZYarqjj1r5yIG5E5hEFKRf9vl8cL1Eld+KnAMfughSo4RPxj0FnZ/6zgAwyWFA4HmTKLTfJVRbWWg2DQ5Skj14v2vAwVZKL0NhlJWQYllvMjTB7apVgueIy8jZ07zchNXMGhu65Ez59TxL+4Z8nYPazn4PAk08CxONEIdj4l38J/Q//Fnp/+QtwfuD9ArFVDKZdtBE1JJFbiHsZbHlFonAmkBkUcuVVsUzUOZZXiIoRrnri7o5SbMh4SIylYqQADPcl3h9+nSguDRvWg3GLfM4Uh91Vo0Zk0V6TZ1bWSlmI6N5Gh3sT0V2QnGb24kJIJmjZWInKwuVvfQuCzzwDKqMR2r/6VTCyAQ2u9Vw1lFdwgio/frD0z2QNXc16DSlR4gUG4iZkDjyEc/UTb5UuBr5PrBpZ2FAFG3KBzEKuKkSiEH/eUmF26MksKpVMQzgQF4pNbpUpNuHDLZ6zJ0UW9rnqIBWhJO+JNStyYZz6BUyo6fOmu1uapErM0f1sgF0Pzjb5oS1mF177LrpHOfLgKAweKf1MyEsRl+2qrLXpzLOzkIgmoaHNIpQjVYR1N9O3WITDCS8JJN1U7Zt22CEtaktXAj5AwPsE40q0TXRI2uCnr/mQh5KF43NuuEJ9Tr7chJ8dOUmZ09Becl7hafozdW2h68zAZU2gNWjI98kzeaudVyhZclIrZaHNAP0JDeCljddLfYsFwMeVhZnvE/daDoz/ISXZpcdavNKxRhZeashRuvkZWUgzC0s8xCGRQnILKcHAlTOFCk64sjA2OQkzn/0sCRYP/I5WzuOESU45Vmvg9xOfmqKh/5fRQ4gY+gidTC0RJWU2WRgCugGOzRTKLKyMLMRsBr7pI42xElbksG+1bcgyh3GcVP30ndSea7ADvPsexZN7Xm5STFmYSxbGmA3ZIGFDFpMhW6+hG7+D9wwLpNz0IMsrJIH8mixlCSEM5azInVfQXDhUlCm1PfFyEyTsRMG4x0KHIalKgiqpzrKD8YITXS1syIvnSeEREjqmHTsIKep76NdwUclC1qQnFMhgZmgz3SSVpegSAR/mGRtylZSFWWRhvrIwyTMLGxtLakLOzy1Upvox8pITjw5SjPAS0LoDgrP081muvjpvoi+UnLBDWzngB6rtru1kEo1WGdLal0xCLKCprgU5r+RkGuLJFMx6K8sszG1E5qpjPtk/OlECWYj32IOfJO/+2PQOeC61BTaxhm8xkERB1RUedMdOLpVdboJoq6PPpXA8SdpLawmSAYVqaRWARp+kwyERsGCKE6GH7h+pKnlpTtDYkPCyFgZf/Xa49dunYO/5NGjiSaL4cn3sY9D30IPQ98D94PrIh7OKr5TCtHuXrLIQn8mcYODPLTGZUsyGzA9SLazchLzvoL+7pUCUXMul5hXi9+T75Y/I/9ftG1CkYLY7jdUlC7nDgu19wvoumsWJTcg1yixENHfbwWRMQDxthplBBQpdzFDG+AK9FaBeft8gRvDQYVj8r/8m77f8zd+AccN6kvuqbaH7kugFOrTk4GQAIe5ybMjCUKPeTH5PuB9ZYOR/rrIQQQ6pJeQWcmWhUptjMXQ7K7chY2M3/ieFSizICCQYzXZKui4tBOH+44WLTfi5BJW5WrVW2Fvm/szJMP33JxfZ3m0N+cB1/ci3YUJH97RdDum1Ns5syH6tMy+vUAqoUtt5M339n7jzXFkDJxzqLDJNBZacoML91JOUtN5xQ2dZWeDn5nzwn48NQTTBHEfWJlL2RTAkb0VOLNFzRNxhLvneRHs2vzcwroDbkE0eet9MB6Zh1ueFjuBJMKrikLK2ADRulP5k+DPzvViBs0sxshBLIfnvpHMzPZ9ifMLAnqbyik4UNiHnkYV4lqpRIzIOGtbH6bmwb2dj1sCHxuBQuBcnwBqh+5v29XTIuIYM1sjCV4gN2V9OZiECyUJt8ZKT3IKT5Tu+TQ6Q5P1vfks4RLTgAoeLYZAFiK6yqhBtR2pL/vQ7sExfJ59pTiCzBLIwrhOIgVRUpAjAn6lKysJiJSdp91jGhsw2TLUA5sYtBWLyNmQsAPjFHwOM/I6G677rlwDtyhdOriyUakLOzSzk1wwG3HNVZyFlIeKy1/aA3qiBpckADB6h19gsIwNaRRbkXCsyt5NmQW8hBAz9xp8rMa+QqhEQqMQ67z0PAb07j3SviQ25rodmguBByU1JVjsrOvE+cHGsyC60qeMmH58oeHgT5TkKjcjz0ocMpUArrzccBxTx9LqqqHDB0h6ExGZF3IaMTcNKm5A5+Jq86FemNMKCH40xTRK9o96cdaB1FwQYWWi9km1sRahrNlWsLOR5hVhugsCNuKAu9GtrRBZmmv1mPRHAGQCW18ipSUohC6cX5unvTQWwY2dzacpCVFfjWhgPQar3Wvgn32vJhzdLkIWITCvyYtnlJty+zu2stc4t5Ne3xqoH0qEgEclwxet6SQMukq5KidCiOP9b0J//DuisCYC0CtLRBMzXAfzuunroufce6Pv1Q9D4F39OSn8qgWnHThKHgM8XbFDOBX9OjXrpOooEH3/NObkih9mcchNEg1kPWrWKbB2QMCyJLHRthaTXDeE5aom2tSo7WNu4spDtcaqmLFyhh1B3tEUgJXWiYVy1gSUAPesoWTE2oWB951ZlHNyp1Yqu9enPfJqQ4443vSmrZZpfZ7m5hVnKQsGGPC1ZboLPfVTgYjFQQ3v+989zCz0lkoXVUhZyW/2MJwyxRGm5rWi15N+HXG5hJeUmuY3ITx6fyxSb9MsTMtxajDZpKZtll4gsPL6wpiyUxdQL4F04DV4NfQ155q+cstCXoNe31HWei31v7oee7S5Svvbr/z0JvuXSnml4ZlgSKQvHTiyBfzlCyLf1V5Q+hMdzx1/8+Bh85bFB+PkLUxJWZFqyVUhZGGHZtKUoC7NyC0e8gg05vegWhu1Pjb0kWJDV2IJciAgV9k1TRctN5MhCLDqjg0YAAQAASURBVGXDZxWuTXZnZl+7eT8l8YZfXIBYOBPRURQ8z1WhspA3ItdSWVin10JPXJ21R5NSFs6cp+4Dn1UNJltl7fOvRKyRhZcacpRuXFmIZCEP1C8JliZwaOaKbja5shALTjC3wnPvvfQPNBoInzgBocNUkXCxlIWCBfnKfAsyHsj4z+Y3Z5rTeMFJ0JcAldmcF6ALITe1nIoX5grAp1BSZGF0YUrIhDKV83ssUVVo0WvAYdLl23ru+SDA4G+ozfadPwXooq2VSoAPpwnfREFlYbOFPtwjyQjJb0TExscJMat2OEAjCqiXAr42u19FP/fz91+ARDwp2AzFeYUcGlZywhvM5HMLD5bWhCzKK8SJdSqdgqglkE8WCjbkKioL8WDUtDHLimx/9asBdDqInj0LkUGWG7iKEAp8NBpIYXubSHXJS07OzVamLBxmFmRsKTXqNKtjQxbIQlQWMuVECTbkxhJtyKqIF4z19O+GJ7Nfr7gnAjGfDkCVBktP/vfA7dG4kcbm8IqakBtZ+zLJERWRhQXKjcoGP4R7p4SmdhxkyAXaKwGSQFqVFuzuZkEBsXtdAyGakQyaV1JA8Zu/pveXtRlGrv4qRJIAVoNWNut1gFuRz7gVbbB5uUlTd6bchGO1Sk6E67uBWbkmqNpPDByq7biR/t4P3nMBkuUUxIiB99m9Hybvtr/WCa6tPvj5u3XwFx/RgOoj7wHTpk3VaY4nJKhFsOuHj8nnFnJlIRIpyVSakNXNBYavOKTFtk9xyyUCr1kePaDUiszLTTCvMPz7XxPyVGdJgM59SFErsN1VJWVhOyMLZ2epHZ09n90BW80tyBw9e/jvYz2kJQqnJJ/FClwPaLef/sxfkYGwfqAfWv72b7L+3DBAycKYjLIQm9UTNp6tOlOw3ASt+1I2XHHJycUgC/FZZNJpyDCG7wOr2YgsKAsrUJ/yRuRDpxeEYpNCawHfb0pZkBGNVgPoEz2CzTMUX50c2EsOR74N40xV2GRqks3hQ2VhTGeFCFNqKVkTcE28+f2bwdlhJYKIh752siQCCtVngrJwZgZOPEEdIJuvbstyEinFcxeWBYfKwWHR8GvdLfTthd8BJKTX7sQyvSeDVm1Zqt9MbqFXUDNj/MGAna4/L86eyeQVylmQOfheTCJrO7fcRE68MXGGW5Czfw4s7EIVJGaQImFYurJQGVkolKKSzMLaZIrGJoOgAxV41Cmw8EG/YJcWKQtHaKSFz1U79fyljDWy8FICKh0CTLFX100OhFG0h+BGz6oDbRk5IVhyIjQiy9iQUTmF/3GycPk73yVZQmj3rXvbW4UcmEJtt7UEBlaHnnteNq8w5IvRw7MKIGTJZOrZWDA4Eq7CRllccsJVhbig6MpQbcopCyUakcPMrqo3aYmKo/blJtQ6IwCtaPd/DODMfehHA3j7jwB6rynpc8+F5kj2ErZccXl5LrAhmW9+OaGcKTfpVXRIROsBNufhYQ0Prxjsjv+MN1KKoXW6suwDeejaX1puIbchi5RznGAxNWjyDm0ZZWEVMwuzcgvpA05bXw/Wa+jvy3cR1IW8sTGpUoGnKzsGYH2zjfx+loMxxQo7KfAN3kA1LchisjBn04XrimBfr2sQWhpLsiEzdZzSghNYGgKTkyp/I2ezSd/A08+Qt/jnmgDNtxEDFcmYL4f8grcM4mApvETyn1Sggm2NGeWsoCz0obKwBmQhVyv6poUDeCUWZIROoyNrfKuPbsLbBurAZtTBhhZK7Bwtpi5kOYVEbnf7t+EUU3luarXJkpiYh4MbbFRQjJ1aUlxuIs4r5OCFCZw8rRUS3Gbfwgjb6RclD0p7bu0mCkgkBM4+M1PBF0Tl+p+gzx6gbTeYPvlz0G8PwX0d+HxWwa29t0K1Yd5FrcghqZKTnEZkbtFEFVYhsppbkF0d1qzWW0QTU50oIaRxX3XBc0FQFoYO/p5+z40xAO8EzcgtAq4IwSFBJdC1tmYGpnwobWmElYV4zctNODp2doFaFQdfsgVWzpxWpixUkFe49PX/hdDzz5NIhY7//E/Ski2Gvo8rC7NLThx6B7GGI1aM1ixVypSbZ6uaC+YVctSVaEPmQ+VqkYW4t+K5hePLwbKtyHK5hVwxWa4NGWFhjcg+dwT0GjW8pUCxCULIK5RoQhZ+ZkcbpOIOSKWTQuHDGkQILgOcvhfGkbApQLxyZWHA0iYMKXLXPjng38OGZNyjuGeC8Mh3TisuzMKzBFcWrqzQMkMc0my7tryite8fzEROHBp1Z74PdBkhyYVn3TG618pFYpnl+1tUZSkL+WAJS040DU46+E8kYIua/iyjSy/BVjX7/vquKynrWc6CvLFho6TqFhWWvNyE5xWK7xtsRiafpxQrcok25DZ2LZE2ZNx/4zm0yphjP+OgLgluzJOOBgCivjxlYWicnkNjrdVZb19pWCMLLyVw5t3gIKUTvFUtDmmwO8q0bFkws7CwDZmrCrHARO8NgecXvyD/7/zoR8D5gQ8QdSG2FYZfOi2QRKtJFkbPnYOkx0M2gLxNVAx+cLbU6wEMVIVmUjWDFbOhVECIxHRbX37JiSfb8l0bGzLN2gm7vauTVyhXbvLIFwFO/hQAS1je+n2AdTeV/LnHvePCxk2ueQuRe41EGVlYzILMgdPEK15P/+6p300JB26pjYtgQ2YP+Tx00UZOWBoECBSxD/rnGVmvAmjekmfdbGqtzyLdk4EApEJ0A62rpg0Z0ZitLEQ43sCsyL96ULr9uYbQqbRQzzZdS03ZxRsmvUYoC6ik5GSINyFXnSxkBFjOZJOE3SfoBDyUMpMmbrTAG63K71GeI6tUWYjXoamBHs7DJ9mEmSHwDG2NtbZGAWboNZe7wRNKTsrILeR5hQP1AyRYnENoRK6ZDTmz6Z10K2xqV1hywsnC1gE6SNjdVVfciizKKYRrP0eGJmeZKlYqr1D8+pfSirzImlOlyMJVVxa2ddHG+0QEYDbfrodDrMtfS6+Dww+OlmZNEuPxfwCYfoEWe+Fzpq4LHm9ohYRKBestHdDnUPYMKAWm3TRGIyxRcsLJwjEvfdaPM2tpUQvyMM8rzB9QCY3ICshCPNClIU2GrI3mRgixpnNzE1svRp5UXHCC+8FyFcViGzLmWKbm2DCirgvcTAm3GspCfIZ31NHD6dgx+TKN7Cbk/D2fGFj8tfT1r5P3W//h7yWt7VxZmGtDxn1MvYE+1918YByYIy6MzGDDlKUsRKWwFLiyEIfXSkqQBGUhG8RVAzyHs6ySE9aIjNlquYjHkuBnkUjVsCHbUiq4dWsLOItEURRTFiLWcguL4PgPSaTNRENHQVdQOpEgz4sgI3iK5RVKKdRf82fbiRhi/KVlOPjL7HutoA2ZLbPjDjqIxucsV6GWAhy+PXaWnmeRjMZM4EG2ryQTbV50ImNFTjJloducLEtZ6GyzgFavhlgkCZ6lmHA+WZ+k+wZfmO7nPfb1NEdRUXzORFl5hUjaYvO4VqeGNglX1sYrWwkpi8Qm/t2aFJywwku/Rg2BVDzz76sEfB6OvbQskIVLKFbgXwP3uIbM3is1Sdc1Vbuy7/0PDWtk4aUEIT+viyxsPK+QlpuUqXwjBSfzBTebPH8Q8wrdd94J6UiENMhZ9u8HfUcH2F/7GkFdKMiKV9GGzC3I5iuuAJUu/yDPSVBDPZ0GpRNmOD+TJA8tng8Yb+zOtyEryCuMxJMwthQk0vZ7j03B158chvuPTxe2IUsoC0Neunkz17oJmYXpZpWbRLwAh6kyFG77FsBGms1VKoqVm3AI6tPQnOJyk1xsuLIla7Mi9bDLsiHLKQuxVYyr9CTyuiRtT84BmnfIrNd8AzrQSR/e3qVIlqpQbbVK5mhWU1mIsF5/HajtdmJ9Dh0+DKuKpUFwJegBaMmWr6LcwNqLMVi68ibkKpOFXC0XWgaIBfObkOvqwOeJC1bfUiySpSsLB8HopF8rNjoKSR99vbAdVVBPI1mIrX0S4KqOcnILhbxCkQVZrCyM1pos9M/BtNuXpdapBP2m9eAMtWWtD3u66wuXnIhyCqH3WoBrPkM+fHbWV5QsJF+TWZHHTy9DLJIoUm4Syis34ehYLWXh0qJgsxcGJzLrINq+8PpCK9nRh8vIFzr3EMBz/0Pff9P/CkO439rpSfBVxtps0s2s5CRy9qwwvMm1IS+GFyEQC8D4Er3/uxoKr9c8+kKcV8jRUoKyUFxugt9beJw+p8x99YrJQhww4gEUhxl+d/nqQnx28OdUnFmy0MHCr9PVUBYierqpsnqsEJcQXKKkHQ7u+LNQAphTifZjlFvXvfWtwkAtF3xQmZiZhWQg+2DMybplVZoWomEtfGBeyCxEYh/VxJijLFduQr6GUStkkxZTF6Lqp9oFJ+LcwnJKTniOnZSy0IvPmzSAwawtaZiWC3RIIaxpFbzjiuIqdp5ZKKcsROCgMhmmn2utETkHqOR64bvk3Qlnb+G8QnRZJJOZJuQSyUJ+b9z0x/R+RTvxS0/JN/mKzwthgwq8VjvMN18muIvKwY8OTRAb/oEBJ+zto/c1nt3ycwt/KxkBkWCZhUvGeFnKQiwM48OEObQis5KTrjC9L30q+jyOy7RRl6Ms3OLMCBvE4KrCtvX1oJWI9cGzcQ9rqD9zUIG6EF8vgSxUpiy06Cxg19tFVuTq5hZOnnVDPJKEiBZLVNI0R5jnKopUhQjtLP3dmror7yd4JWKNLLyU4MluQua2OJ86LWRjlQxLI5jUXtCqY7KbTV5u0gUNsPKjH5P3XR/9iHBodn3wg+St/9FHoXkxLqgRq9mcWAjBg8/JWpDFSq+Elb5NxZ1wZJQulLzkJOpozVMWJpYp+TWZaoRfvjgF//PEEHzx3lPwge8fgdf859Ow6x8fgY1/81u47t+fhHfc8Tx88mcn4F9/ex4+8dPjMMoOHUWVhbjgqzQQTtCHhanGZOGUlLJw+DGAVALAtQFg21vK/tzcytUt06TGkUsoR0dLUxYi0CK2/7b+omShYEOWyyzMyi0sRhaezMsrxLbVQDwAZq0ZNvT0Zl9vtSg3yW1ExkZIZhtU6/VgfxXd7HjvX2Ur8viz4GJqxqVYfjC/kFtYgbJwWGhCzidXKoKpjiqdchqRk1x11ejKNCGXUG6SrSxUeIhfGgStIQW6Jvr9RF6iZELo2HFIBYOgaagHY32c/t4lMr3q2PdXTiNybrkJh76dbmiTEQ0kNTWwaKCiTYOvUxrCy1NVsSEjWgN9oAI1hM1esDDlPScLX5r2kUFPHh79WyGnEO3HoEZbdxrOzCgjC9GWam80kaEbqieKlpvY9VnlJqutLBRf4xmyMD+3EIE5bHzNPf74ZGnEFA7e7vsoff/KjwkDKSREDqfps/LWUGU22kKKOZIPlUzmqXVRQcvJGHx+cWVhT4HcIlRjLE8FCE/Vvp6RepI25KjivEJiQT52DCAFoDUnQHfjh+hfGH2KNN4XAu7DuLqw1PKA3M8jlJyMU6YuYuqDsC+WpYyrNbq30WHT3Eo9bWEuNLhr6AUwWGWVUDOf/jRRSho2boTmL35B9mtijIfGRfcKMbYfySs5ia4IipnQ8gSsMHUgFpyg8gYJQyTLCj0jMrmFhddnzBrDXGfx119NZaHvkUdg6i/+AkIvvphvQ5bILOQ/D/58leSNHlui+4MGlQb29RUmYjAnupgNmfwZURbSPSkOdlfrXHJJ4MITdG02OGBCoyo47E/M0eF+kCnaMHajHGDT7t430L3+Uz8dJIROIXAn0mjXfkipdeBsyNh5SwE+7396mJLL793XIxTnZJGFOCDEvQietdFtlAMuOpjThcoiCxEtfRkrMicLG4OUiglok7CiVoN9C1M4KnHEYOFSjn0XRQxn3WcLKgszeYXy68umA/R5cP75ObK+FQRae3kmqEJlYVYjcg1KTi4cp+Sr14lZ36x0TEb9aGVn9vo+mQbqP3CskYWXEviNxNRogrJQhU3IZZKF1ibawq6jhwaprCtuQz5w0Eum3xgYbr0uk6dgWLcOrDfcQCYLmp88KGx2eIFFLYHtxXxDY9knQxYypVfQRNUkqZiLZFUg+RDR0QfkMQ+daJ45dg5e9dWnYMc/PALPs8/71Rdj8JlfnIB/f2SQTKYeP7cAZ2Z9wmbRqFNDn8sC+/ud4LTos5QoRQtONDpCGIaTjtW1IYuVhed/S99uYFO1MlGsCVnKhoy5cLHRsSy7o1J0bm6ALde0E6KwY6P0A0/LlIUFycIudt2MP6swrzBje+KT6u2N26Ge2WMjwThEw4lMuQlrPasq7G00jiCdJDl3HA7Wiuz77W9h5nOfh8WvfY00JIeOHiNKuZptlMcPgottJjD7LheY9VaJDXk5ECWZh7hW9TdWWVkoU3IiNMWKm5BLyCtENFpZ+UAkIU1O5YJtUE2b1pG3nNwIcgvy1VeDytGefWCWVBaWRhrEkjEhzylXWahJ+0FjpN97bJ4qqqoKzO3B6xnX5pXJrIbRSmBcpGvClG2IHCwRmNmFRWCxZApOz+Q8n5CUwaxCxBu/JtiAMGcTrz2MsOMKWTngQXlAgRVZKDfpskkerjPKwnBND7dCZiESJXwdRLJQ5mtiqyWut0iGHnpgpMScQi9A+2UAN/298EePjT8GSUjDpmgMumYzkQq1UhcWKjkZ9Y3CBFNc8Ww3KaBqFIEKEe5MEKO5DGUhlpuEnqPPH0tTDFS73g1gsAOEVzLPnQLApmLxXqdcCGQhG5q6gR7qrQ0GxflklcK+fgs4tWOQBjWMn1osO69w8b/+G0IvvEDiadq/8mVQGwu7b4RG5OELkvs2ovRjB0z3LN3r1Jl1JAtVsCD35JcViVHPcgs984Vtfdx9guUEcmUT5YBf1/w6l8PS/34D/I8+BuPvejdMf+pTxHHDC05mAjOEjJAsN6nAgox4cHhBsCEXA4oYoskoKbLidkYpdDdYIBVpA0hryOsqZaP+g8WRb5M36R3vgPHAZEFLd3xuHtKggqCxqWxlIceeV3fD+r3NZGD222+9VLD0B3PyNSktrDhpJvf6Zm9ZhPSvTsyQMxuefW7c2CSQ0Vm5hTh46Lkqoy7MQcJNic0pfbBs1W9zb6bkhDciq5Y80Kijw4qzehMY+tn3UAi4FmGeajKW6TJQWG6Crgeuju/OKTcRo3tLA5gdejK0GTu5pCyvEAfveuXrAL9356qsLMQittET9PmRbqf7qaVATKQszJSbxCJBqPPQNa1lffb+dw0Ua2ThpYQcW2xQpCwsmyy00MXKpp6TLTnBh7IxmoZNj9NNlOvDHwIVHvBEcH2IqgsDv3oI+iOOLJtpLRE+dpzYojWNLtAPDEj+HW5DXtbRiUIq5iQKp5u+/Ht4dJwugDyP3ri8SP7MG45DJ5OEJx1dcPU6F7x1Twd8/IYB+NKbt8H3/vhy+O1fXg0n/vYWOPuPr4InPnMd/PiDV8J1G5qyVFBiuEyufBsyor4HwilGFkocPmpTcGLKNCAPPULfX//qij63Uhsyb0RGeztOK9PhMGnyRUt7KcANw3Xv3ABv/vRu0BmkMxI1LBOEK2gKKguRfCnUwCi0L2aUhccWjglqLDxMcbIX76M4UxZWPa8QgZslri4UWZExnwsVmnhPeO+7D5b++39g5q8/C+PvfCcMXX0NnN+9B0Ze/3qY/OifwdyXvgTuH9wF/t/9DqLDw5DC30M5QGJhTKQslCALebHE4LyftI2WCn4/4XWLGYhVh5D/Mi5BpDQKtl5HY2mHIbtJS5pVEUXLXZBUcVNLvnHX5eRt+BS95gJPUbLQctXVAK1sMyNhRXaUmVmIlpV4Kk5ULHk2JO8kGGzUUhsbq+7kN9dSYwrPZinrKkFokl5nU9bz5IDL14xdXfXSuYV4f8f8lKTpv0H4MA6GEL0ui6JrT7Aiv7REsryksFAgrxDRWmckt3g4niS5SrWCmBAn65rWhAG6WQMIMfD12387fc6ePzQnkJ4FgWrNmaMAxjqAt34PQ2eFP3p4jGZDvSoYpGUeocIqk3Jh2kVzC3FoUii3cNwdFBrX5TDBFKN4kJKC0sxCXCdRXY+FQqj+CD1HQ/XNnUZSPAc9Vyu2ItuYstBfgbIQIRS9zdPrYiVKn9UNrTUY0MihoR96TPT3NHZ0oqy8wsBTTwmle63/9H9JeVoxcLIwNnJBWlmI+zZ2wAwsTmQpoDN5hYWHCVxZyK3dq9WEzMGva1QWyg0h8OOxsUwJhO/Xv4ELr34NaL7zC7DENeQ5gbb9apOFp6a88OIC238l0xANFc5F5arCdls7aNEeLgOSP5rWQSpKf3druYWQGYwO0fXXs/Nt4MdnXyEb8vwchI1OSKp0oNGqBRdDOcDnyA3v3gQtfQ6Sf/vg107KqoixrGyH/ypIaxygj3qgLVH6HgSv6Tufo9f0u6/sJmWg29odYDVoyVmPP+OzrcgP532OJBMdLBiiZSsLuSpyeSYIaSdrRJ6fh7YEfW48b+kC0Cl4bVFowtt8c6zIxcpNZgY9kEqkSUlNISU02qYxu5B8zmdnq5pXmCsemamyshCLcKLBBIlFsHZYMntwie9zdugEIcMiOoDGdjqoX0M21sjCSwmeHGUhKzipLLOQqiAcqinZkhMkdW45lgZdMEryq2y35jcWmnbuBPPevaQQ4I1HYNVKToLPMwvylftkp01cLTmtog+L9WhdwbXCoAWjgx5c2hmp5Iz64M737IRHPrEfujT0wfDVj7wJ7vrAXvi3t+6AT92yAd65twuu39gEG1vs4DDrsr4uz1Pjza1i8AeLJ+ohGy4BDb0CWVjLzMJ4MiUcYAQb8uQh2kxpqgfooARFOUBlEj+QF80sNGdsyFGWV6jv6pLMm6wURDHDJoKoYpQEHgDwnkL10aRM1h82aC1fyG9CZtbNXY1UucLtYGhbTcxzGzI9cFUdnCxczJCFeC12/+BOaPu3f4PGv/wEOG6/jWR5attwCqkixGx0aBgCv/sdrPzgLpj/0pdg6qN/BiOvez2c37WbEIpj73wXzHz2s7D43/8DnvvuI8pdzH6Sff1wiOGfASf7YymyEBUNqMCNYlNsGU2MQ7WyICtQFuI1VK4NGX8finML3SNUKaq3genyA+RD4ZMnyGsfPX+e/P4sVx0AaGNkoUTJCd/AY4h+KSUUXCG7o3FH/jrqmQQ9JwtHi7ezVkIWtquWwaLXQL25srUAMwEXxujhZ9Z+QWiczcotHM9RSfIYArTjijbYSspNxEACEOMtErEUTDAVWi44ySZHFhq0Gmi2GWtuRc5c442UxOu4rGgkA+ZOrbu8mcSWPHv3cGHl45kHAA79L33/zd/I3GdMYf/C/Avk/Vu07NAlk8VZKUxcWXj8eN46xsnC88sjEImniII0S3mfo1aYYLa57q302SKfWVj4fj+9RJW8WOpiTmogfI5eo+ZtA9ltmEpKTqqtLFym16c7QPclDa2rY0Em0Gihp42+xhPng+Q1L0VZGJ+dJQMyRP073wH219BM7WLQD8goC9m+zY0kup2quuM5CmjehCyXV8hR16JsmEO+Vg3IQryu1WwIITe8wvgUMsDVaKDnFz8H82WXQToaBff/fgO+ckcCDpxOwSQrFuHgP099c/m5lj8+PAFJFUCCuX3Q7q+k3ESO3OJoqzOBTqOCRIj+vZNLa2QhwYvfp3ve3mtgHIkapuJDNZqcspCXm9S3mgmRVAkwM/7VH9kGNqeRDNd/881TslbXTTN0P9Q+8zQkZ0tXhh6d8JDoERzcvv1yeh0gYXh5D90LPD8izi28JaOwR2U3QyoQgHScntt8Zqb61Za+LmIkCv7M+Pz0GihhlViYh74gXUOGrNLPlYJWZO9ESeUmfF/StdlZVKW5aT/9HifPLAtuxmo0Iec2Ilc7s3CEOTv6drjAxZ7J2ZmFGWXhwjAdyq849QXLOf+QUdbd/rWvfQ16enrAaDTC3r174XCRMH2PxwMf+9jHoLW1FQwGA6xfvx5+/etfl/s9/2ECN+RcWZiTWeivRFmI036NXtSInL8YLHtn4XWH6CLu/PCHQaWRvpmcTF2459AK2ELpVSk54eUmchZkVHaEvFSZMZSiC+j/e/0NcOYfb4VT/3ArfPY2utnstFlBZTSCKp2GfdYErDf6QIUHd42+pEnJALNISikL6wx1oMG2YVyUIiJVS30PhFJ1Nc8snPNGSLgvPjBdFna9DP6Gvl13C9mklwuc8mKro1VnLSrN55MkVKxGWPtgKeUmpUDbwDbbySRtt5VD94HCVuR5PNil6bXACHYkxTC/B5Uh2xqpwkHIjloK1zazUKbkhHw9lwscr38duD7yEWj7538m5OG6J56AjSeOQ/9vfwOdd9wBLX/3t9Dw/veD7ZZbwLB5EylhQaBVOXz0KMk8XPra12D2c58nVqTha68lZOKF17wWZr7wRVK4IWD8IHnTyNR5UmShRq2C9c3lW5EzeYU1UrhwEsM7mUekqBpcEGD5bKXakEtqROYZOa51YNy8mRzWkotL4PklbZ/HUinM1iqkLDSYdYK6tZSSE7m8QgLvFOjtXFmYUZzUgixsVS2TcpNKcq+40ieVTEPCFAGfYRmGPEN5ZOGLEyvZRBe/97nSmEFpuYnSVuTschP5zym2ItcCqUgEUn5/JrMQUSS3kOPKN/YRdcn0+RX5bEZUyd7/5/T9/X8BsCFbuf74xOPEHo4B7B2te+gHp/NtwtWAccMGUJnN5OfFYYmUDfmCZ1QYpHE1cC7mhr0kMB3vMTkVGR/comKlUPSAOK8QSUxIpkBrSoJu/Y5ssnD8OYB44WtA/NypClkYoPfFyop6VctNOJp660mWdiymgtkhT74CGxvLES3ZZCEe5qc/+SlIejxkDW363OcUf01DH29ElrYhi5WF4JsVlIVo6XPPBgUbciE0MBsyDp8KZYDVSlmI1zWSZwiez5kLIRamowNM27ZB110/gPavfpVcG3XeJHzigRRo/uxvBdU7rqErFSoLA9EEPMCKAW31hqzzjRzG/eNF8wr53oOU0LBG5BMLayUn5B46+gP6/uV/msl+LNAqjS6ggJU1IbdVZx+GMQ6v/dh20Bk1RAn25I/P5w2f5ka8YPM0QQri0D7zDBkGlIofMFXhG3a0QQOLikJI5haicKBxIx3cDj+etx9Mm00Q16nIvVnuPqWFqQtXWPwUErG7gvT6nzGVQMvIlJwUIwvHz7iL5hVy4D2N0SP4azn3XIHXnpNwJSoLW6xMPIKEtUyzc6lAe/sIyyvs291EomcQcpmF/lG6Pww1lZ6F+YeCksnCn/3sZ/CpT30K/u7v/g6OHj0KO3bsgFtvvRUW2ME4F7FYDG6++WYYGxuDX/7yl3D+/Hm44447oL2d5S6tQRnQnhMLZB1sOcvvJ5mFZSoLcbGzZBqRpQKy1z89AXW4F2htAsfr5JtysR3ZuGUL6OIpeM2RVM2VhdgUGjlFJ8yWfeyQkwM/Iz91Jg3MJaaFAg6zXptVcIJkgI5dkySvRyiT6aKZWgrBlYUXFgN5dku1Sg31xvr83EKxDbmGmYWTrFmTTJdxvCzOK+TS+ypYkIs9QNGOjTkzyXQSfEOU6NL3Ki83KQWoVtQ4HMqtyHKKGom8Qk6wrKtfR4LyxWQSqllrmlmIEGzIyrK+VHo9UQZbr74K6t/xDmj+67+Cjv/6T+i75x5Yf+QwrH/+Oej5xS9IvlPjpz5F2iPN+64EHdrDNRqiLoiNjID3nnvA98ijeSSLq3mbLFmI2FhBycnQAv03AzUjCzvzlYWsDTlibSIbJbS6S2WUFYNiZeESOwA3bgC1yQSG9evJ/7rvpJt661Usx4YrC9EqGvVX3IiMm/Pji9JNyAJZyJSF0bHaKgvbVMuZiIQKMMvyePRtCRJsLVYWov0IlSaorBGIOPwFIymD6MomC7lFaXOb8o1k/x56z2POD5KD8uUm8tdTrRuREyysXWUwCMMC6CzciCwmp7bfQH9nB++5AKlc9ReWLmGrNOYWd+4FuPHv8j7Ho+N0Dbm5+2aANmoThpl8m3A1oNJqwbRju2RuIVcWzoXwwJwiGWfF8gqJIoM/Q3NgN2qJirrYgCArr/AItWOYm6Kgambtla511GaWjFIHQAGgnQzhW65MWahtZUVvQQ05TLnnWMnGKpOF6tZt0G2gqtOxU8v56yQ6MzAbizfZMyx85auEeFXbbND+n18lpV9KYWDKwvjkJCHS85SFSOCxBk0Di9jpaDBTlXAawFpvEIqU5ID5X0iM4P3Pc3BXkywUbLkFGpH5QAj3CkKBzqtuhb5fPwSnbttOrHqms+Mw9ta3wcznvwDeCzOEQCfZ52UM0xAPHJ+BYCxJsr+bmDqxoIoJ97O+4gSX+GfmZOE59zmIJGpTpnTJ4OwDAMFFACRqNrxGyBsvRLzG5zPKwob26q0HSDze+sGt5Po5d3AWjj2STRidfIL+nucsL4I+HiD5maVgwR+BX5+iBNEf78/O79vXR4dkh0fd2ee19cw9xyOa8PzA8gqTddayLci5uYVLPnreS8zNwpYoHTjMJUvIFufrn7iYT1xu0pBPFuK+EJWcao0K2jfkF3RJYdMBuu6dPThL1q7CysLybMhEWYhlLUmZUqsSgAQzumv0Ji10bKgX9uA0s3A2rw05OkGvuVRbjc5qf4hk4Ze//GX44Ac/CH/yJ38Cmzdvhm984xtgNpvhu9+l9eu5wI+73W6477774MCBA0SReO211xKScQ0lwDOWuRF1RhIwHvbHM8pCpl4pC9ZGsGsZWbiYHageCwfhxmfogdT2/vcWtIripsL5Ydrid+vRNCwtVWdKIIcQKlpTKVKMoWOb3FzwSbuhTkUOjbj54sQOgsjB8QDhj4O6jS685GGUkw+pFDjBxOltLJGSPOTxKXUWoZJFFtZOWZhXboK22uUhAMx7Gbixos8tNCEXsSAjUObdaKbKm/AFOtHR10hZSL4etyIrKTmZfhEgHilAFkrkFYoIFr5ZJpmFizXMLBSThXitxkq39ubeu5q6OjBt2wr2V7+aZJC2/t9/hO7vfQ8GHnsUNh4/Bv2PPkJsXQjvPXfnk4UdBwqShTy38PxcgVxIGQzNM2VhkYKJ6tqQKVkY0tYJFuRyJsm8qb5oZiHPiUOSANeCrVQ1k/LR18t6Dcsww+INklWTli45YbmFhQ6jYmDgO/7OMPdJchLtFdmQx8YVbWSf/NE5uOv/HFSudLKLycLKLY8zw1RF3NjPlN6ejJrMqNPA5jZHdm4hvvahJQCtEaCNWlYRqAwbWaTX3maFykJuR0TyIB5NwgSb4nMsjBcuN1mtRuQku75RiSx8H50YRaGi+YF88y+DPa/qBqNFByuzQXKIyMLDX6TKV1MDwFu+S/OVREBl/ZE5SpDd0n0LQPvumioLEWYhtzD7a5DMM5UW4ukoqLQ+oTFWClxF2bVVnsTB11IoOfFLkxJ4DwnKwsatEDxE3TnmplhmXcffiUIrst1JnzuY+4VKt3Kha6MD00RYAxHLgGAFXW1lIdqLezhZeHIpe82ZE1mQRfeP/4knwM3OIa1f+mfQdxa2p0rlG5PBImb2ieIWOGFHyUL6+tji9NneWW/KKjcpBrw2eMnJSoGSk1qShZmSE+mvz392ThZyYEGM/x23wCc+rIELV9L12nvvvXD2A58UrPBoLS0HP2Ette+4ogus9WyAX8yG7FdmQ0Z0N5ghHa8Ho9oBiXSCEIZ/0DjyHfp2zx+TtZlbugvt34mykJGFlZSbSAFLNq56Gx2OPnffBRg5tiioS7k6/2Tr7+n3MT9Pms6V4ieHJiGeTMPurjrY2k6f+xw4ALQZteCPJrILz7h4AslCVubDzw9RtraXU27C0cwakRfm4riLg1QoDN2hBKjTAKFEQCgVLUdZiGcxXm7S68g/V02cpmtL60Cd4tIqzGHWGzXEeTg9mJP1XGFmIbchL2g0EEdbvMjdU2kLcs92J3FAuNge3O0PZcpgeN4jrssz9GO6ztJy8/+QUNLKjirBF198EW666abMJ1Cryf8/x+yguXjggQdg3759xIbc3NwMW7duhS996UuQZGH4UohGo+Dz+bL++4NHrgWZPUhxqdGacJJdgc+eKAvp5icWSZJQUI6ZX/4YXH6AFStA61vfVfRT2W66CeKdTWCNALQ/SjN5aoXgwcIWZHFeYdoWk5ycGcxa0LJyjHgz3RzFUFnIg1bZ660UaHngba2c4BCDT6PEJSdJWxdE0/TfmI2VT1UUl5uc/03GgotT+mo0ITNLl9JpUmqcPuQMfbVRFiK0LI+SK2kk0dBHp6zYLIaEYS5mJZSFi/nWTcEOhpmFvCCjVmShxSUUFMFibTe/OCTAw1fD+z9A/j/43PMQm5oG8OK9MkZa2Zy915I/w6BsbCmslrLQG4rDAiPaaqcsZOsCTtuZ5Q8twORDSXq4Klc1wVXfizLEQb4NeQN5Y9yeudbwEIs2ZAGtbNg2e0JeWaiw5IRfxziFNiJZJkUWWpJEYZ0OhQR7vRyC3iiceWaGbCyf+umgsim5oCxcIjbkSoAqt7kLdOO/cQv9vY54RrIaPPewkpOjEyvZFmTMbRUVcGAhDw7S0bpUStQHsSLvovfmhWPZr9fiROFyEw6+TnNFeLUhzuQUgM8BngNXxIqMlvfLX0fX+0O/Gs2QVKfvBThyB33/tm9lDjQi/G7yd0RZvqlhE3TaO+kQBpsd/TNFScpygeVPiHBOyYlOrROaXtX6RUIsSMHvjoB7Jkj4qa5NhQ+KPG8Soz+kgLY/X8wHerUeBoxdEDl5UmhCJhY4jr5rFZGFqKAwWOjBz1+BuhDt6Cq0YKdVsBShh3dLnQEMptVpQhbQvAU69cdBDXFi2c1ayyTyCvFZNPO5z5P369/7HrDffHPJXxLvWV6SJ84tFApOwsuQZgdhZxL3EmmyVinNK8wtOVkpUHLC94e1IQszJScFlYW9+Xs5vE9WbCr42dtaoOdnPwXjju0Q1NC9o378JfA9+mjJ7e1YbHJq2gt6jRpu39MBVqa2LpRZiF+DW2eVDKi7SLGLCizpvqyM3j9IzJ8BmDgIgJFIe96XbemWUWlizmt0cRnC5qaq2pDF2H59B2y7tp3MQB/93mmi2H3p91OkqdjUmYZx1zQkNCoiEOGuHSU57T86RH+29+WoCvl5bW9vQ74VueMKGtGFmYVTdKjFy01CzP1VibKwscMGaq0KIsEERBsY2R1Wgz1B9wRDK9IFY0ric04v03P3hvoNkvl7E2eWFVuQOXR6Dc0pLlR0UmZmIb6O+AxOqVSwWIWSE1wbeF5h/056vbqYslCLjj60l+O1j0N3BtM8XcNtvdIlqWsokSxcWloiJB+SfmLg/8/NSW/wRkZGiP0Y/x3mFP7N3/wN/Md//Af80z/9k+zX+Zd/+RdwOBzCf50lTghfkVjJKTdhEn0sN2msRFWIsDaCVhUDiymWTbAlEhD83g/J+7+/ph40xuJWZ9KS/O7byPu7n5yGVLSImqYaeYX75clCrmwJmbySD0PcIPKMlFi9yIZcprJQTGgMM0WKZCOyyIYcTtC/r4IkGCKlh/eWrSwcZBbknCypcoBNkmJLVzFgI7Ipkgatmy7SqA6tFbQu1oi8XMCGjCfA7n1ZGXwCUBbPcwFbqbIQbSw8F0RMFnJCyb8SgVSCkhPaRqqirAkkGpFrCX1HO7Emo/oCVQXCa9WyHey2dnL4lVMXcrIQDymhmPLJ8PAiJRfbHEbSXlcT4MaQK449k5CKxYSMS39EW1YTcknKQjxgCcpCekg3bc+oWC0HDmRnxRYsOSmRLGR2+h1NEmp/LIPwTpO9lb69NSvTSg7DLyyQH4crsUZPFLjvOBx07XWoQtBjlR8kKgHafFHRh4OgTQO9YNQYIZaKCYfLrNxCrizktlvZvMLCKkAp8NzCsRNLxAmQW25SrDm11spCoQmZ5xVy8NzCItZXxJar28maF/bFqH0M1er3/wX9w6s+CbBOmrR5ZOyRjAUZYbAKJHmtrMimnTsI4R2fmhKa6jn4c0ttWJJtQuaqQrSQYcNiITQ7eMmJNHHHVYUbnRshceoMydrTGpOg6+oE0IvWGTaAIfd5kaZori7kZUzlAPdvWva9LwXbVr/chMNUB/r6JmjXU2Jw7KToIM/V1CyvEPNzpz/1KaLANm7fDs2f+UzZX5Y3IkdFjcicsMNSuoDBCmlQgV6VgAbwk70UL1JSoizMIgsLKQt5wQkqc2tlQy5GFuYoCxGcVMesZtOOHdDzk5+A6pbbyceM7nGY/ouPw8Qf/wlEsJBLIX58mJ5tXrW1hQxlLHX0+gsWyCzE/QWqpzADnCuTCqGH/cyJECUW/6DJwheYqnDja0gGJ5IrgrLQJk28ogU3qHdBWqUhz9VC8RmV4Kq3rYPOzQ2kHOyhr5+E009Ty3HfVXWQVqlgxUEpC6VW5N++NEcGzUgWvXqrtOLtyj6WWyguOcH89oGbslqRE8v0nvRZ1BUrC1GB29hJn/+BNrqOJUIaMOv68pwQpdqQ+blki4vFWYiQiCdh+tyKoOYsBZsO0PsMVZ+RYLwkZWEyECBRG1KDBIznarFQgnGmCiUnS5MBMtjT6tXQyQjROpOOEMMtKvYMtTYLJXb4PdWxAZtrQDSQX8PqtiGnUiloamqCb33rW7Bnzx54+9vfDl/84heJfVkOn//858Hr9Qr/TU5WLku95CGQV9nlJr5Kyk04mDrJbgpkEWy+X/8a1DML4DMBXLhGufKr8Y23wZIdwBFIwcrdIrtiFRGfm6N2CbWatL3KgRe2rBjmZaeQPLcwyqyx5EEkNE+XpiwUlzBIKgvFYdkMYT8laTHQW+WtXhuUnLKQNCGHPRmip8K8wtzMQqXKwnb2EiCZprHVyF5K7EWu4spCcckJTl1zFV+olEMyqa5HeCAnUglC/nZYM8oZzLTDjQCq6SNGJ7E21aLluVjJSS1Rdxs9HHjuvQfSo8/QD/ZcRcgUToZLkYVOq4Fs2nC/MChxb8iB30cDtbIgI5AIElmR+RQZdDrw+ZJlNSFz8PWZqyNlw6ExkxYjAVhbOx5cVSb6NS1XMwsyR4GSE0cz/TeehexICTnwg9Oupoz9VgBac/HaV6lBzwoAYkVyCwePzGe9Xk//bJCQdwVhsIEPKEnTrcspNCgRGJSOaO13gEajgb46+uwS5xbu7q4TyMBgNJFZC3kcAcOZGV/JFmSOlj4HyShDtf7kObdQuMXLTRq7Cn9OcWZhqWodJRCUz2JlYVbJSeHcQgRafPbdRq+L449OQOBHfw4Q89PX8fr/I/lvvFEvHJo9lE0WImpsRdZYrUIOaK66kCviibJQxobMGyS7txY/ZDUXued5XuFW51bpvEIOzFUiSsM0wNjTinILK1EWInRsmXUH6y6OBZmjBa3I9LUZO8WeJ3gf5CgL5//934kyU+1wQMdXvkyyecsFzy2MiZSFqLa26OhrsBz3Q4I94zZb/ZAKJ8nBFJ37GCugBNyG7Jm7WMpCbkPO//pIWsempuTJQrbX4WQdkssh5hJpunwDee1Dhw7B6Jtvg9m//3tIsJy3QsUm9x+nxM8799LnL8Y3kD8roCzkThbcR+pyIg6kwO9pz0rrHzZZiBnHJ35G37/8T8mblegKBOKBLDI4F1jAIbYgV1pAJgdsWMb8QlxzUFmKpBSez7ZcRs8V8zY6dFNacsKLTfDakiut4iUnR0bdRIkogJ+LGFmYdNN70mNOVawsRLSw3EKflQ5Kp0NOaLVvLpEsZL8vzAaOeIuWm8wOeSERT4HFoYeGttLWdRxuOtstpJhpiO3xsobKBZSFc3//DzD+nvdC4IknCucWaipXFl44uiCQoaiIRGBGv9Oiz5CForxC7/Qo6BMAKRVAx4DEHngNpZOFLpeLbL7ncyTA+P8tLdLSU2xAxvZj/HccmzZtIkpEtDVLARuT7XZ71n9/8BAKN7qzJPqBSspNOJgc165fEchClJ0vffNb5P8fukINDfXFp3ccTfY2eHAvVeMsfefbJeVLKAXaIBHGrVtBU+D64MTnjFpeZm9luYURLd3sxafLzywspiwUbMhhabJQ+Lo1wJRYWTj8GJVj40GEkRPlAu1UPGNHKVmIk6T2ZXoA1tfQgpxlQy6UWSgmCiYPAyQTEkqGbULZjTivULxxwtB7bkUOG521Kzcps+SkGrDdfBOo7XZIzMxC6ODTWYqsQmRhlhWZKbaUYKjWTch5lo6JjEXT6QTvIj1815VJFipSFvJyE7TDs8MPljI4//QDYL7ySvKaZ0EoORnMy6sk6lYV5s0mhFxbOQTjQRhcofbnHY0SykI+sba1CiVEhZSFGJ6Ntjy8D97w8Z0kExYHW0ceGi16aJxONQhW5KqQhQOU7BioG8jbgLc6TESpihbjc2dPUxsPErWd2YOns7P+kpqQxcDXQLAisw3sssJyE/I91hkJhx2Jp2A5KL1Xqo4NuVF6HcTohWhxUr9vZyMhZvEQcnh0F4DZyXIKpVXAaEHG3DAshsqKreBZkTM1zC3cvUuy5KTZSBUaav2SQKaIgcrQSa7IUEIW2pUpC7EJmWQvC3mFEpmhJeYWVtyIbKTXmidkuyjlJlK5hbMXvFTNggfS0DK1rDdtAt/Dj8DKD+4if6ftX/5FKKkrF/oijci4zwka6D29xRoQLMj1zWZiBS9JWTgnPwTg+6lK1EvFiDNcU3DdFYMQhckkGVJpcxxkCLveDjbGJs8EKMnnYQrJzne+Hvp+/WuwvepVhDzw/PRncOHWV4H7zjsJCSmF+49PQwiLTRotgh1UIAsLKAuVtPfmqrRxLQ34WkGt0sBCaKHmBYwvS5z8OR3mOAcE1TJXFeKeXDKGhOQEzgnlJs4SSaZSgZEHr/2z7YJ6e9t1HeAyO4lrZZE9hpUoCzGD8MjYCmjVKngXI6KlsKnFDnVmHSnYQTu8AMxxx3Vm4TTZB3GxwZIhUZV7k+cWenT0PhsNtcDWxvWl2ZBRkW9iJSXeqaLlJuOCBdlZMuGLf3/TfnoN5GUUoxIaS6ckyEJ0F/ofp63S/FlXsOSkAmUhrqcXWN5lH3N2cKBQoVm1kqd+nB2kA3e3Qw1Wc2VxXK9klEQW6vV6og58nP3iuXIQ/x9zCaWApSbDw8Pk73EMDg4SEhE/3xrKtCHjNJOXm1SsLKQ3ldCIvBgG/6OPQezCBYib9fDb3SpoYlkVSoA5CS/tawavGSA1PQu+37BsvCoi+NzBonmFuHDgz4IYTZ2XldnbWKByKGXKBOj6FsvKLBSTGhcWAnmbQXH+DUeIkYVmQhbWpm0Ucz9mvSJlIbcgV0FVyDcbjaZGYQJfDC3mFmhzc7KwdhZkhMZJX/NEIRsyAg9qmNmFCi9eaFJCXmGuFTlsagRd42qRhaunLMSgc96K7jm2nEUwSJHhYmwoI7dw9cjCTCMyV12pXU2CUqdcGzIvn0KyEO9DSeRYkDkaP/Yx6P7+94gqKgu4IcOMTZSw5pScaHUaYU0r1oiMpEUqnYJ2a7v0Gs+zcBwdgtKkUCMynzh3bqwnpPk1b6c/z4nHJmF5Rp54QvXcTJoSzeZw+Yc4XG+RWEC0rZMnCxG7mRV5+eyTGbWm3pL1uc6yMp5yyEKxFRmt2DiRV1pugjBoNUL2XS2syJKZhdwS7uiiw6RpStYUAv4c+3fSZ8DZ8A2wdPW3iL1NDlktyGKIlYU1UFIiTELJSbayUJ+mBzatcQksElEHM8MeSESTRDnu6rAqvuelMgvRzsoLFrbaNkD4xAlBWSis5+WQhdVoRE6nQaen17w3arnoykK7dhEajPOEYCfKTq4qdK6D2OwizH7xi+R/Gz7wfrDdcH3FX1JQFo6PE3tz3r4tsgwrWnpPDxh8MD9eWl4hwt5oIkoXVFtL5fLhgd8T9VRFvSQFm1FH7L6I8ZySE7EFWWp9wo8JVmT/FLE18usNs3IxpqTjq1+B7rt+AIZNmyDl98P8v/w/GHnjmyDw1FOyxSbvvKJL+HqYkUm+l3BCtqynlHITBGa6tyKBn9ZDp4UOvU4uivZ4fwjANZUXm1z2AaEcSCgnlLEgcycXVxY2VLncRAq4h37zp3bD/tsHYPsNHeTaaLW2whLjcuIzxZWFPziYsbfz4Y0U1HK5heYGgM699P2hhyHBlIVzxkhV7s3mXrpmeNXNkFTrYDlkh30dWwUXhDhjWWnJidJyEyQLy8H6vc2kRRmjVBYn/fkWZOQScpS+ocNHIB2m+5fIGelzCv5uEbMVZhZiDizG72AeZM/W7H0NlpxklIWZ/cnKCP2efGXu7/9QULIN+VOf+hTccccdcOedd8LZs2fhox/9KASDQdKOjHjve99LbMQc+OfYhvyJT3yCkIQPPfQQKTjBwpM1KAQuGvzQxm3IbJNRcROyWFkIE0Jm4dI3qU389HXdEDaqSMZcKXDVt8OvL6OX1/K37iBKxWoBD3FCXmEBshCr01HtgCqbadWY7CTS1kBfv2AoDSqDgUxF42ENzTEz0QNnKcDMI8xHwKntXI6yQKrgJOyL11xZiHYobAQjuQ1WLcDQo9XLKyzRgswfDtyGbGCKpVpBy2zIyWI2ZFQNdknkFnLikOUV4vV3YuGELFnID21hk0tyOl9V8DB8fFgXybSqJhzMiuyfMkHSvolurETKwsUwI9tllIXnSyALh+fp313XvErKQiQLWVNszNVFDqqYf4KW0nLgtND1JZFKgyccL1Jukk0WFkSh3EJuRS6SWyjkFUqpCsUte0gWslxRfqDMBd4Xg4cpWbj+Cnrd92x3Qe8OFyFJn/qJfNnJpDsMM2lnXrNfqUC1DjbCajETiNkC++v6JclCnluon2ZFHjyzlAEJOn8kATpNprSqVKC60WTTQTSUgOnzK5lykyJ5hRydDRkrcrXBr3EstchD115FJScES8PQcuTPYcCIKmM1HDwi8fkYsPzo4AxdW2/tvjX7D9FWqtZRlUKFuUXFlIWRs2chxQ4wiGSUqRA0KySPVr4F2UkUo8XQwg6nUjbk4ZVhUgBl09ugccxDSCmNMQV6W5IUe0jGY2BoqHuk4EHK5qqCsjAwDzpjFBIaA4TS9PDUwGyzqw5mM+7RPie0IvPBSMq5Gab+8i8hFQiAadcuaPrLv6zKl8TntdpiIeo6JAxz922YJTifps+6Tp1HUBYqzStEaDRqIaIB16tceGNeMsBB1BlK339WYkXGtnuEvkd+L4eDJZ5b6F0IE4c8qiqRSOcwX3459P7yF9Dyj/8AmoYGiI2MwOSHPgwTH/oQREdGyN85OeWBl6Z9pNjktt0Z+ys2tHKVplzJiZL23ryfmSkqmwzr/zCtyJhBiyo5rQlg5zuED3OysJBKMzE3D0FrbZqQ5YA22V03d5GoCy4wWLKrFCkLPaEY3Hd8WrbYJBf7WG7h8+LcQsR69owafBiSLLNwRheoirIQ7dVmm4bkQPqtnZCIaOGKznWSGcsFgYM9hGeiYLkJxiWszNKCro6NTI1YIkxWPfTuoM/Ks+KiEx/PK8x3mAZ+T1usEZFz5yT3gDx3tFJlIS+T69zUkKf0dlnRhpyvLAyN0fUo3lL9yIc/aLIQMwf//d//Hf72b/8Wdu7cCcePH4ff/va3QunJxMQEzIryBLCc5OGHH4YjR47A9u3b4eMf/zghDj/3uc9V9yd5JcM3DZBKAGj0wkXOJfpUWVipDZn+7uxJar3wTnkgeuYsqMxmeHIf3SiWoixEILn48B4VJE16iA4NQeDJzIJRKWLDw6SpVGU0gmlXPlmTm1dosKshpU4SIkNK+WZlmYV+dxR0rfT1jQc1ZeUVIjAbgwcq5+YWCpmFq2xDnvaEhEOMdvowQMQDgOHZ2P5ZIYTJZAkbN6IsZDZkVU9t6+p5wUlRG7K44IDndeGDbS5bWYg/L+a8oC0C2zzllIURo6t2TcgcRgwH7VyVRuSsL7tlMxja7JBOqcC7mPn9obq0sA2ZHqrOzfkU5bD5I3GYYeqcgcYaZhZmkYVoOaHff4RNIFFVWG5OD64HXMmxINeIvHi+dLKwUCMyKznxFlEWFlLI5pGFTFkYn5rOUt2Ig6WRnMTMzt6djVmh5UjeoT34/CFp1eCkOwSzAlk4XbEFGS0+/JDBlYU41EBVVy5Z2BU4kZ1ZynCGWeXXNdlk846KAVULfcyKPHx0IVNuojDfrJYlJ7ztO09ZWEpuITaH/+J9RI29b8M5MtGfPOMW8v1y8eTkkyTrtd/RL2RJCtAaMmRZjUpOtG1tdICTSED4ZEaRu+zTQTppBFClBdWSZF6hQkWG2Iacu84JFmTnVgizvEIL5hViCzfGEEit8R2X0fdH5fdRdhangkqvsjMuPROgsyQhZKZ7QrTLFytzqRnqewH0VujRU8J6/LQbkrP0MDz/ex/Zo2rq6qAdcwqrlAtMG5HzrchcWYj24IkEJfCa0m6YL4MsFDfWr8wFZctNkCjUYjRCLcnCnJKTQuUmHGJlIR9G4c+T+3zEQq76t70N+h/+LTS8//0k/zf41NMw8oY3wvy//hv85Hn6tV69jRabiFHMiizYkG3KbMiIbtYCbUr9gSoLj3ybvt12e8a6KlJpFtq/h+aXIWqoXxUbshzQJr0oKAsLk4U/OzIJ0USKZA1fxp7zhbCvnz4DXxhbgVhCJGxZx8jC0aeEPeGCIZo1GC8XeL+0NND9gM/eC82JMOg0GtnhphJlYaG8Qv4MIwVdlvLXy80H6Bl58PAcURYXKjfB55CYLESlMYn5ygEvOCFkYXARIFbecHTkOLMgi/afHI1oQ4Z8ZWFyin4/KlbgtwZplLUD/vM//3MYHx+HaDQKhw4dgr1792Y2g08+Cd///vez/j5alJ9//nmIRCJw4cIF+MIXvpCVYbiGIuAEEpICbFrA25D9qurZkB2MLAwGUpBSqaH+j/4IxtWUiW9mm0elwJs/ZFTByA308Lv8zW9WLaSdqwrNe/aAGpWAMuATdpU9XnBjwQtO8DXVsswbShaWnleYl1vILJQc/AGDNhM8NOWTheM0LLZWeYVoQT7PbOHrbhGup0owzkpZlDYhI+xqM7SwIY+Xqc1qnVmIpRVFr8Gu/RllIWmCnaTBwah4adyUlVeIeVN6JPBzIGQWorKwqYZNyBcxtxA3OnXr6PXrPbGSp8CQIwtRHYjCnJVQvHCGH8OFRXqYwjXOYa7xoVVolpuAJNsYhhj5WW65iXijgpD9mWVsyAVRsOSENyLLk0yoXjm5cFLI3pSEYEPuJNey2mymqhsWgi8Gbh4RvdtdRB0izlK77LV0bTh497Bkkx6uT9MCWVh+odnscHZeIXnf0gpmrZmst1yRwq3FbTo/9AEjJ7nlKK8JubLM5AFmRcYWQfdMUFG5iVTJSTWB66CsDTkrv/VIdn5rLn7zWWoNNbvA/q4vw/br6MHl2bsxeiZ/rX1knLUg90i3JNe65ATXLZNEbuH4cghSscas4RcHtgujAgwVhZ2blCkyuNsD89hyc+GEchPMKzxCbd7mxihtg5Yra1BgRcZ8UHRRoF0a1bVlYWUcdOYkBM308NbA8vUuClDp37QZmnVDYDSmiC11diQE3nEjeJ6k1rG2f/tX0MnkpZcLQ/+ALFmIjpALEXrv6kJxiAYThCB3lai24iUnUspCnldYi3KTYo3InCw0FCILWckJKgtXGFmImY1ywPK65r/+K+j/1QNgvf56QtS7v/tdWHrw14IFORfciixFFuLaxe/RTrsyGzKi28UakYP06yGxEk+WcJ+MPQvw678GiFdWIHRREFgEOHN/VrEJB38mFrJ0uxfp62QxpRRnc1YbxIbMlYWzs7J7+WQqDXc9T6+P9+3vVjTkxYgbJKzD8SRRvGbtrR1dkI5GCNGF8FgADBqD4silQmjW0j27194DzrA3a7ipOLeQx+d4JwuThWe4BbmydaVjUwMh89Etwcm5TLlJNuGGJaTxyUkyzNF3UzI6ei7fitzGVKtIFpLfqid/YFcM+JzGgTU+p/uY+jE3s1BKWaiboySqqbu2cViXOmrehryGauYVdgth2zy4vio2ZJwyqXVgVntAo05DWqWGmLUZ6t/3XpgPzZdFFvLA0ueudpGGNMzlweyCaiB4kFmQ98tbkMVkYcTiLzg5s+AUU4XizTSkWph6JqgtK6+QA9Uo4rw1DpwWY1V8GtKwEqELV4j9Lk0aP20eDczVjCzsqBPlFW6oPK+wXBsyqpO02BisA1iwVL8ARwwNOwxjyDZ/4BdUa+nM1AqH1lCeV4h2X1R+iOwrO5qkrZtCZuFqKAsvUm4hhFfA3jACKnUaIiMzxNYnJsPlMgsxO6jHaVGcWzi0WhZkBL/fA3OQWKDrXkjjyPqdlouCJSdIRvN73rWudBsyKkpzJrFcuVIos3DEMwL+uJ/k22DZRDGykKhu2CGSNNGLgMQQzytcd3n+s2LnTV0k2B+fW4fup7YPMSZXRMpCVNKXCcyWE+cVIvD7FjbgnswGXKdRw+1O+vN5bOsEK30+WVjZMAO/F1Rn4eYazzdKyk3yycLqKgtxHeTqUL4+ZgEHIwYHPggzOXFSYflH78RXGOD2b5OGwT2v7gGDWUtI0XPPZedKBWIBODh9UDqvkKNtd02VhQizkFsoIgvdIUhF6esw5h2TVGRgiYtB4cDCrNeCjRHm876opLJwm30jhI8dE5WbSOQV5pGFv5cdJmJWqcVhyHJVlAwPIwvZ/q2u/iIP9Vu2glqVgp5murYMTnXB3BF6bzs//GGw5rbEVwHYQo/AzG4OcRbvmSBdDwJeem+6OmxETV0K6lszJScXgyyUtyEXVxa229ollYXFgJ+z83+/DvXvfQ/5/+3TZ6C/0QJXsLw4MayMLJSyISNhG0qEQAUqgbgsRVm4sGIl+3C0evLsUEV4+PMAh78JcPoeuORw7C6AZIyur7xIihGvSpSFHj9dBxpcF0llzNxIy2zGhhl4SY+I1BPhd+cWyPMSS0veuLNdsQPgyj6J3EIkGtffCokovb/TGg2EjNQhVnEjdCoJLcFHBWWhOeAlZaB8LybeqygZcqc8E8L1nEsWJpMpmDpbWV6h+LXauL8124osoyzkjkKMJTDtps/dyNlzssrCsFoFXhwSlWFFxmEson093W/lwmXTSxac2BbZwKO/wPN3DWtk4SUBT065CXuAxiENYRWqViq0IeOiZ2kElSoNpji9mdS3vAn8di1RY+BD2WV2lbywI0a0bnDcfht5f/lbtF25EiDhwxuVCuUVIni5icewWDCTAzNk+CY71tBRVWUhlpxkfS21BuqZnJ/nFnJlodnGFjh39UtOpj30tdhqXABYHqZKuf4bK/684ilvt0M5WRgbpYTBjBNgNpzdrl5toPpUzQoieKOZLJAQ5Jav8Wcz5REsr1Cc87arMbPpylN4pFOQ1BohYavMqqAIvEFzNcnCiUOgNaTA2ksPxJ6771HUhozY2Ko8t5Arczn5XlMgWcSmxYk5ak0IJE1VIQu5+lsqw0xQFeIGBi2HSoF/39JES05yCJ2MDRmb7dMFLcjbXdvl7W4iGzJCLrcQ7b9Bb4wQRVJWTbQEX/uODeT9l56eFux7YhvyDLgyNuQy1NW+5TAE3FGymW3pzW61G6gfEILDxbjWSF/7M7pMeVFuEzJamSqBWqPOssU0dRcvN6m1DZmrCrHVXFKdT/JbC+QWLg4C/IrlxF371wD9tFwC7U2XvYY+Nw89MEJKHDiemnqKHNBRgb6ubl1hZSHmcNZAYY/gh5bw8RNClvKESFnIh1+5eYVKWpCLNSJj+zi/BjfMayAdjYLGogO9PVGYLGy/jK5NoSWaO1a05KTM68UzTuIRw1hyg5+PWe4uGlhu4YCFKjAvhK+EZEJDDp+Nf/HnNfmS+n5qU40O5ysL54JLMJmkZOWyz1lyuQlHfbNF1obM94W1VRbSrz/uznz9VDBIyv0QXAUkBU7QTQemwTOnnCzksN5wA3m7e2EQ3nE5HUJJDvBFZx0pCzIKEqScHcXUlJiPu72R7udOLim0IqOacP706u+zqpV5/+L3JFWFeK3hmkSIV2YvlyyKTNLrxdlZ2bOwEuDvO65Vgd+qKWhFvvM5un6//bJOMpxWCp5b+JxEbmGSkYUphxXSKlV1iodmjkNj6gSoIAlRYz1E9XYSlcSfjcptyJQsHAtMExJdqtxkfsQHsUiSkGhKI1AKYdO+VjIjnDq3QkU5grIwW+XNLcjW664F4yaar86FBWKgUpNHdM2UWXLC8wqlLMiIZkMcbCr2XLRTsjAR8IM1SPcoretlcrvXQLBGFl5KNmSmfAl66OYzoE6DXqcGezVk4dZGCC3pwOChqg7VlTfBQojefLgw6pBcKgG83WguOAfOD3wAGTkIPvsshF+S3+gqQfjUKUiFQiSrxrCRlTvIAItaEPOayaKTM25FjtqaK84sFJOFgwv+PLl8rlVTsCHXs0W8BrmF0+ywuTtyiH6g50BpxIQM8GfABxSqJTutyi0hPOR6ukFFrpFaI2NFLtKILM4uQytyTl6hN+qFC94LBZWFmnQSDFFqKQgyZdqq2ZBr1CKah/FnyJu66+im2/urX0EqGs3KLJSziWxotitXFjKykN9PNQUeWlhuYWKRDhgCEbq2Ohj5VhNlYTnlJvz7lSk5QcIaW+uwgdfPIitky01krmOiVgwtZ5OFvBE5R1k4xCzI/bsaZVU27RvqSZse+kx+/+Pzgk0VrxMkw+bS9ZDGHSiqq5EUKRGzw/Sec3XZQGfIPiRgRp4UWbg+QocBT4Tpn4uzMnmeV6U2ZPL1mRUZwYtXSrUhVyvGA8HbviUtyMVyC/G6wJxCVB32XA1w7Wez/njbtR2EtAp5Y3D8sYl8C3L3zfJkKVpxUdkd8wMsK1RWlAjjxg0kjznl80F0eBgi8SQpIhPIQpGyMBFLkmKacsjCFgmyEG1i6CpAJYX+JL3vzXjuwpdDqtxEPMTCZ3YRKzJa/isqOWH2ryALnLcmpdU7qwb23O2IPgw6iEJM5wB/8zpo+49/BxXmW9UAhoEBQT2NSh8EP8guhpZhjhWcLETpWtjUU/rhu47Zu/EeiYYTF82GPOOJQDxJCXNe6KKpryf7azmgZRDJpXA8DO55SjaialwpLjT2QUSjg4aoH95gl75OBWWhxLOL22YLFXIUKjhZCsRgY/3W0kpOkChksUFCvvClguHH6H2NhY1bqXAj97VEIg4JGymkvF4IGOm5yDWwCrE6MmixUiJqgT2OpchCHC4/PbRE1tN3X1na+W1fP73HXxxfgWhC1ETcczUk4vTaiWE5ZBXKTQhGngCdOgp6Ld1r+mw9hKzng0383WARllIb8um4Vyg3yR3+cnU8Fn8oKegqBoxa6thARS9nD84C+GfyFHtJvx9CL75I3rdee61wXo9I2JDFbsRySk4wrmB+1EcIzL5dMmQh0Od4AEwABrpmzw3S+99vAmhpyt4DriEba2ThJWVD7hGKODLlJobK5dAISxMsn7GBidkHgwm9YEEutdxErCzEjU+6tRHsr3kN+f/lO+6oTl7hlVeCCtUPBcCtOGPpoaJhyLwROaJ3iMjC8jMMsD0Tfy2eUByWgzHZkhM8AIY4WciKOGpCFjJlYe/y0+yUXHkLsliFgQ15Orm8JQnERijZMONUwVyo9mQht9opKjkRNyJzG3LL9qzNJapj5DbzqNoxRegGIMjIppoCSSaVmliDsc1yVcDaoi03vR60LS10Q/n44wIRjkUSvli2eoxjA8uoxJKTYhhaYDbk1SALEXWdhG9NrHhJbqvfz1opK80sLKQsLKfcpEhuIarruBrSK5NbyJWFRZuQDXahFV5KWYixGBeYBWT9FYXzww7cvo5kHmHRx+mn6GDKG46TXLcEaAHYgaCcRmTBgjyQT9BL5gBFvGDzUkvMAys9pEGRgxPZrQ4j1OeE75cDJEoNFroWNHUrJx9bHSbyHInEU3nPkao0IRckC/dllIViovI3f0UHE6hqvf07ebm3SBbvezN9vY8+MgFBbxRC8RA8M00HDLf03CL/NTVaYa2tWW6hVgum7fRrhI8eE0hhU5pee6O+UYGYnR7yQCKeIhlq2M5ZCng8jNiGLOQVOjGvkMaymOtY6HohZaHS3EJBWVimDXllHBJpPYQ09H63hLKt5IXwk8MT8J7vHCL3c3VV8yoInHaDc45atgP7Xw26GsZ76NraSHkeulhik3TQ3IBlcGRY6IYIGMAHdliM95WtLDSYtGBx0HWFq/M4eIQH/5q1AJ4bDFo1yXfjg2QlFmTy5xo9OROY4jaIh5PkgF6K8v7Hx+bgpIsezDUvsuF1Dqz1RlllIbfNllJugrAbdUKRSqNuXWklJzOitWgVi+SqgmM/pG93vRtAl/17UtKEHJubgwAbHri6a9POXcqZcsFO92QJUZEqx11MVXjjxmboZFb7Us5suE/DYpRjE6Ihic4ICTt174QNdG2rirLwAl3H3Rp6/3kdvRCfmyMDd7veDsl0Eka9Clxm6PrTGOCMQVc0r7C7wrxCMTYfoNcExo2kfPN5ysLgswdJPimuJ6hUNm6iz7fEzKykhVwQGBFlYWlnYJ6diI4S7hLMhTNF19W5VD0k2IBk6QJ9Hrud+pqVSb1SsEYWXgrgNw5TuvEcj6qUmzBEvCYIzBjByCwQ3sWIoCwshyx0GBxEDo2YD86D84NU/u5/5BFBVVYJWVjMgowtTfx1mtUUfyDyRuRwnN4S8bAG0ixHoRyY9BpBFZJbciLk30SWiTQcsxLJv2luqQlZiAcf3BDaIQD2xReqmldYThMyIsaVhWhDDio/kFSqLCxqQ0ZgQzQ+OHBa5mOkRcvWbDWWHMGCX2NhAUxMNVr2oa0U4AaQt2iuhkUmGhCUbKreq8Dx5jcJVmQ8SOBGp5AVmWfAoWqQP7SlEIolBPvluuZVsCEj6roglVBBOpYg7X/o8EXyQ24DUrqyMFKdchMOGWVhsdxC/N3gvYsKEfkmZJ5XmLEnZTILM2vU+OllkseHhEqrKCtQCma7Hq58I71Wn79/hBBJaAvjAdSqukyzX6mYHcrPK+Tg03q0sAnT+snDoEqnYFrVAgtQD8cmPVUvNxFHXdz4vs2w6+aukjbs2MLMFWrVtCLzAp+CZCFmW6GjAPM0+TPp+E/o4ROHE5hTyJT4UkrK5l47Kds4/KtReGr6KfK6Y4g+Kh8Kon0VcguFkpNjpNwE0Ym5nKACf8xP2u5zLcilDmWlbMhCXqFjE4SO0p/P0hgG0NsyBUvFyEIc1CSiBZWF/nKUhWhX9E7BSqKNSB118QCoF6YU7zH+45FBoup55HQVh38GK0BDLyyftULTAiVsJqL9stEK1QAOoQ19fVm5hXzAG0kFAVQJmFZtgwQYQK9PC5EPpaKuRdqKzJWFVVEvyQCvZZ5byEtOogrJQgRaVusiTUILt1avzO7pDsbgvuPT8GITXQPQbSSFQm3I5SoLyb9hP7Mh2UPudbRSF4pMESBei1ClV2Zb60UBHyRueHVZrdLekTlIak2gSicFRezFgFlnJufKJa4szGnVRTfAL1+cEopNyrknruRWZHFuIT4vTXQ98OtC1VH94h56khLlL2msQm5hYn4hO2NZSckJimYcHXDGoJckC0O+GBnOIjo3V29N6d3pIrEzeI9OutvyWoYFC/K11wpFR7oOur+LnDsvqyycKUNZOMIsyGIHRy7scUooooMF1yGEb4Sq+8PNq+AAu8SxRha+3IEPpeBClg054I6IlIUV5hUyLD1NN3jOtpRgY+H20FLLTRC44PF/h5/HuH49zSpJp2H5298p63vETBXMGVJSbuJnJA1GmkS0QToJZeRlIRty0B8lpQ2QVkFiuTILjlzJibgEIuyjixba5nSN3TUhC3FhxJav69QnyQOfhNdXkMcoRRaW0oSMBwtuY5x2qgiZXGtomWozocSGrDdngvYR+FoZHVlqrF1N0nmFiPh8hizEhq5VwWqWnOAmB68jRxdR4tXdRq0twYMHiTWkWG5hZ70ZzHoNxBIpGMsJWBdjZDFIBE1Oi15QBNQcdV2QCNPHYqSebqBRNVGpdYOv09KZhWzj1FiOsnBHRu0Qz77W+CGWh9CLcXSeHrwxTJuTu8XyCsUHSWwWT/oooSYUm1zWRBSNxbDlmnZixcV204P3DJNyE0RngwnA3l5WyQlGOfCygNb+fLIQp/U2vY1M6wWbKWaS4hrkoPf60fGVPLKw0rxCMbAlev/tAyTDsBTUohFZaEJudBUeQvAwfFQXLpwDeOhT9P+v/RxAHz0EyD3/D7yFqnfOPjsDTx5/rrgFOa/kpDbKQoSJl5wQspCSNb0N9cKBhV8j5eYVIprZgEBMFp5eojEs25ctkI5EQGMzZ/IKi70uqLKzNALEQwBTRwpnFpZTcIIh9ak4rCTpPW4OzkFiTtkgD1vrlwJ0bTs9U1wxXgqSjk0Q9WqhYeU86CEIoZAGZkeo5a5W0A/0Z+UW4hqpVVHliUoThMU0tUc3uqJlPxu4dTe35GQ1bMhiKzJX1ipVFvLcwrpwU8l5hT8+NE6UW/5t7P574UVIhcOybcjY6o2DfyllYaH2Xjn0sJ95zqOC/rp+5VbkLJVzumYRCVVHLJhplsV9fw6UKAuXRulz0aryk6HXxQSuz4sOlaQN+d5j0xCMJaGv0QIH+svLCpfLLUxo6LXuZRmuFSsLceCTioPH0AanGFnot3ZCbJ6e9XnJidLcwpSjHc7ppcnCyTP0Z8E9Fw5rqwUs1Fq/lwpczoZuxIWRqhzxDkmlIPDUU+R96/XXZSJAeG5hkUZkWJkoae+HmdmF8goR6gB9ls1DAyyyZ1Vskn6dVNvFs9dfKlgjC1/u4As9NhNia7FIml+VJmSyIRoG/wl6MGxjDiAkC7mysByyUNxwxJVjrg99kLz1PvAAxCUk5MUQeuEFImvG6YS+s/BGgW+W1Q5qkyimfONkIVq8tWa6OYlNl9/KWajkRLAhR5YzeYVYbsIJvJXRmliQX2M4XlVVYblNyEg0YF4UHo7mGiiZXGtoeGahEmUhontfXm4SWmtPLVJliKwaiysLI0uVZUeVXXJyZtUsyDxDC+9F8969ZBDgue8+gSxcDNNJXi6QUOJKwUJWZG5BXpW8Qg5HJyQjVCURcXZXpdykYGZhIpYpNCpHWYjkGm7QkLzlAewMDmadllIWHmUqnd1NIlK8QBMyh8ZqAW1jo3C4jEUSMHpySZEFWfz7v+5dG8i6PHhoHibOuQUSWSAmS1QW8rxCtIpKNeEhQZUXHD5OCaxk5z4hq4jjzEx1lYWVoBYlJzyzULIJWSq38MLjLKcwRBVu13ym6NfA9mCc9CPhrzrUXNyCnKssxHKpZBUtrSKYdu4gz5/4xAQsjM8ImWY9jh7huYYkO5akYfZnx0a69yoFLY5sZSEOT2aCM0TR1DZIr3lzfwPlCItZkBH4F4tYkTFLCuF3R4RM0FLjbtyaTYIFWa5EIBfPiw7XL01Xl8iLEFJKBQZzBHpNrOjkRTZArxEM/VTZEx25IKwfnLxTaf3gSVKiqdmRWTNKRX0xZWE1rI4F0MXagScYWR4bo79/fW9xshAbkR0lkoU4HPzBc/RrvOF1+0Db2koa2ZEwzAUqlrQs+1bciIyD5klf8QxyOXSxYpcJd1BwhxS1IqMKjA/0GvovrdxC/n3ikMGSfz0paUJ2sxiTOmP1YjDKBZ4pBWWh6AyJ18WdB+lZ5H37ehQNLQvlFh6f8JAsW44k27ctsySKiu/NC0+QNycNu2FFDaBWJSCl0cPSLN2rlVpyMmZzQUitBqNKk1duMn6atSBvrv7wYfMBOlwbjV4OYdMAVTnimn36NDnnqS0WMLNCMQTPLYxKlJwIfAHakDHzPaxMrDN6AvPRKRnKn3+S8M0KykLMLUWoZuhzRFeET1jDGll4CVmQu4TJM5fm88zCSrGELcVpAFtHGFz1lLhBW9mShy4yzZbyyEI+pedkkGnnTjBfcQUh/Ja/x9q5SkDwoDILspikiVmCivJNuA054AfQW5KSMvdSwUkOTnrk2ZBRWeinhyGTTU/sNgTBRbpBqRLQgqyFBFwFx6uaV1iuDZnb0LXtbaTdLBAPEOtXLaF1lpBZKC45QbTQTeWgexAiyQhRGeQ+kMVILMy/spWFTJEF3fuFD9WxxnPv3feAy5C5vuWwsbl4I/LQPGtCbl5FsrCuGxIRtuFhjWmVlpsg+FDHH0lkbULJYACJPrQhisKhyys5OSZtQy6gLNzTvEf+c0soC3NzCzErBjML8Wu5OpX/njC3b+s1VEUYfX4JUMxNlIVlkoWZvEJ5GzRXkpCSE1RhTtNDavNW2uR7fNJDbPGY43V+3p9lmb+Y4MpCbIyuurLQVWSiznMLT/2CqlcxU/K2b+flFMrhyjf1A/5yO1Y2wo7oftjckJ+nlAeMVMDhaCJSs+EHWqIM6+iBLPUSJQu6G8yCQh6VhVxV2DpQB3pj6XlGTYINOZqlKuxz9EHiKFUzmVsYoVeo3ESMImQhKrKQ3Ewl01kkSymD6ZU0tdxZgnNlkYVnZn3kHqoWwot0PTY5Y9DfTIkizEitpRXZwJSFMVEjMt+3obLQG6EH22aTcgXMy1VZiDZ8JFtKVhYyG3K9QrLwoVMzRFmPZ5bXbm8H61V0jxV8hmaZioHkLG9EFl/HnqgH/HF/wfbeQsB7nP/MislCLLnDPBJbG0DvNZcYWcjyFRvzyyDxd65EWbjCOJv6+ipk41cht1BKWfjs8DJRN1v0GrhtN3MnlAFUnjbbDRBLprKcBgk3vScXrOrqRASM/I68eSK2mQxNbTZanrO0osmKTVFkQ8Y1V0/vlQ1qS1b2Hg6MJlleYdeW6g8fXB02aGxKQgp0MBijLeeIwJPUgmw5cABUTPGI4LmFkbP5uZ9tLBdzFsu8EAqsyHi+OvLQaFFVoaCcJ2RhAywx8tc0T4fCtl66F1iDPNbIwpc7+A0jso0GWEMYzSyszIYcm5gA30O/Ju87NwdAH56mKjdid46VnVkopSwkX+PDHyJvPT//hbAAl5xXWMSCLG5C9huYjaiospDZHqIYss9yC2eqoyzMyyxkDxrSJMyUhUQebsyoR0vNbCimLLxMPQjWdADA7ATouKwqnzeRSgiZJ6XYkHm5iaG/n2SQIGqtLizJhozo3EvUDGJl4bEFSsbgJhPbn5VkFmLbYTyWbaOpqbIQN4cp+RzAiiEiWcSEqu2WW0Bts0F8eho2jFECvFAW0EZGwhRqROb2fW7nXzUbMlMWhoyuqikLbQYtCZTPUxcK5SbritsQSyw54WQhRjJgKzJHIBaA8yv06+5u3q2ALMyeuoobkYcOUwvy+iuaS8502/uGPvKs0QaTcFlUSxV05ZKFzIbSuk4+e0bIAfIM0Ws4FScEbc/AFvL7CcWShCQcXQqSQhGTTgPdTIlyMZGxIVdRWagks1BYBxlwzXvLdwCsyi07aIX3DdBn2d6x1ysraxcT4DUqOUGYWG6hY5gSkvi75spCLDnBLM5yLcjizMIFf4QczIW8wrrNxP6MMNtYBIcSZSGil1m/8fqN5Cv4UFHDB58lq9rZnsMdbRaUhVhclQxkK99ygT/bodHMXg7vI7yHqoXwGP3cJmccugYMoDNqCIE0h+2XNYKeZRbiYBMtdYg6A92b6dRB8AfoutCkOVcxWYjq1STL7o0kIhDElvFVIAu7RDbk5MqK4PbQdxXPAkQLcCk2ZLxGvvMM3fe9d183yWJFIgERPKg8t5Ar4VDAINfeWwg9rgxZuL2R2qhOL58me1lZ8CEcRjJw0q3KJSePjz8On37y0wUHrNUmC3F/Fk6EyV4WyV85eCN0PWlovfjPQizB4MrCpNsNqQg9C9/Jik3esqcDbEblRYu5wD2MlBWZnxtmOFnIijDLgm+G/F7SoIL7vXRP0tpJr0t3zJa1V8GzM+7XiuGMip4jN+ccNRbH/RAJxkFv1EBzX21cEpv76fp8ZuUyoRgsN68wlyzEdTUVjUqKi5Y1KoiqRMWuMsDn231fOUrWB1xLt17bXvx1x+EdURZGSYGVna0tTQP0jLcGeayRhZeKspDlFaKKg6vRUFnYWKENefmObwMkk2DZuwtMDXGSj8ilvDE2WCnXhiwoC0Vtt5b9+8G4ZQvJ63HfdVdJh5voIA0jJZbHIsANGGJeN60oDBkbOnETSr6Wta6qykJUFvgi8bwJNU6QMzZkNk0RrMjVyy3EQ+aNanboWneLYlVIMcwEZsgmy6gxlqQ+jY1SZaGht09oOKs1WViyDRnbX7e/jVpDmYJOSV4hIr6wANpEEHTa9OpZkVGNg2UEuLHg9tFaAA+pyRhVGPFSFXyQGI1gfy1tPB94dqIoWcgbkQspCznJvmpNyAiLCxJRei8GVdYsO28lwE2oZCPy0mD5FmQOQVmYnb2EAwjMQsX9m/gaxOs4lU6RA0LBQZBEwYlYWegbnYHJc/Qhse7y0p8RRosODtxON8X7I1poxok4zywsgSxEK/TSpL+ospBvwImykFvpu/aRDMGdXfTfoZqA5xUioa2pMKuyujbkWigLixBhaF1rZhvp678A0HNVSV8HyY/fNNwFUU0I1G4zDB5SuM6vSskJ/Rod00OC0ooPvSaXp2Bm0FMRWdhopfd7PJmGlVBcaEK+wtsA6VAINA4HGNQT2cOeYqjrBHAOUKXTWL4iixdOlJVb6JmAZFoL3hBd92zqoKKh6chSkAxAcBiytZ0eSE/PVMeKjIfP8Bm6RppcMdC0bSbZn7W2ImO0hkqnI/tUrmAys4botrQW0mkVmNVusMaU2QTlVKC4PqP6h+9XVyJ0PdWpdWDV1fa5x1V2SBbyDGldayt5lhdDi7EV7BF6X1ibiuegHR51w0vTPnKNvHMvPctYrrySkJPRoWGIz+fnVlvrjPlkoa+8JuRc6/WMNwxtli6w6WyEMCuo4OJrUDuSheurrizENfIfnvsHeGT8Efj68a9DVYE5s4imfLKQE694TsNiOingkDGgons1V2/pUQzVBn6vQSNA1MAFHbNEcf/4WXr9vGdf5Vns3IosLjlJLlNCbI6Rha7l8ks6uSo83LgdVtJWcJh00LOZDuA82iay5qGIosnUpNiKfCZO143N4ewhzQTLK+zY1FCzvMl1TRdAA1FwBxtgYcwPicVFiLxEn3XWa67O+rvalhby3EN3IcafyZWizmkKl5z4lsNw35ePQcAdJcOKN35yF9lPKlYWBqIQmBwDTQogpgFo66YFlmuQxxpZ+HIHZ9cZicTzChOQhrAqsyEtB1jTjhljCBdrK4bwCtid9HPqg5aKyEJOIIkLLPDQ7PwQVReu/OjHkAwos9sGn6fNUYZNm0DbUHziyjfKk0BtJN22wspC/L54bmHUQj8/qqQqgd2oI5L2XHUhJwtxYxhkFiWu5qw5Wbi++nmFSMQWUtrlIsqUhfq+XoFQrnUjMlfQoA2ZT7+K4rZvAfz5EQCjnfwbriwslFdIvgY2muFhy05fE34QqCk0ugzhVEsr8pjIgpyjJKu7/Xby1nloEMyRdGFlYYtdOKgEovlTfbTq8uKBgdW0IatUkEjZyNQ3lDRXTVmIaJJqROZNyOWUm+QqCxfPAsQjWWualBWZW5ALqgpRTeOdzhAUIuh76Fo6uWAkVsCmHnvZjaDrrmiGKS2aWFTgfmYuo2IMzNM8RwWYG/ESQhTLHaz1xqI25Cn/FIQ50cIGAXu664XcQrRRvlzyCnOVhYrXrgJIJ5NElaFIWYh4y3fpf1d9uuSvdXDmIKyolmCo7zmhAVuR0no1Sk4YWdjvmQarKkFapzlZmJo2kYOyzWkUFGClAtVTWM6EmPWEBWVh3xi9R01bB+gSamkiQwrFUJhbiAeqkrAyDp5EGyHCcHhqYYruYlZkbkHe1VUHe7rqq5pbGJ+aIqo3lUYFhvoU+dn7d9ND9IVjCzWzIqu0WmEowg+16hR9PbpS9Hpo1g2VXMSU9TVUqowVmWWViS3IpSq1yxlC4JdAJejyuWHFFmSELmABNWggpo6AT1PcIfTdZ+me77bdHUJZmaauDozb6CAi+Cwb3ojAbcj8zFNpuQnCZdWTcjVcRmc8UdjWuK24FZmrm8XKQveI4udTMTxw4QGhff2e4XvIEL5qwD2BTLmJEuIV9w1plQa0iRA4+sqISakyyHlBpQK3QyOsTT88NA64DFw14KpKvvW+ProWn5jyQCiWIM9c7oLzWQB06TTYLtDyjrJwgVqQJ+uo6GVDsw3adnaSAVDY6ILg3EpJJSc4+D0boMPVLd4Fkh3OMcHV8TWwIHMYotPQb3yevH/m4AwEnnqavG/culXIt+bANQ3P8IjouXN5f5ZpRNbIKgsxjxeJQnyLg/w3fXIXWBxFeJBkgu4pBbIwBvND9J5fqldDHXf0rUEWa2ThJWZDDnoyTcjISFRScLL8ne8CxONgvvxyMO+/jrYZ4eHYQS0R9qiTTDexsr5aNmSE7eabyEYMbQ+en/1M0ecKPndQcV4hLu5cSTOvnSL5Jp324psLWz3dxESY3URpXk8hcAvlMMtfQ9Qb6gm5loY0+DxBaWUhLz2oBpaHoE89BylUnvVnciUuRl4hIsYyCw19fQKhXHMbMlMWolIgFSxdoYPfHxb+aFQa2OoqPIVCG7KYZCqrmbKi3MIztVMVnr43L6+QAzcHmAOmjiXgwJnCZCEeGLjSbpDlw4mBNjbcAOLUtZKBSDlIxIwQMdZDCtSg1qoKElAVl5zw4PRKlIWo/DM1AKCVaiG75KSOl5ywkHJxuUnBvELcWKFNF58JqCIVwcAO0dMqet+vL0NVyLEUjMEjphgkIQ3z5zwwivtiYi9LA/hnSrMgF1AV8iENHsJx3R2ZPyZNFk5klIUvF7Kw1WEih3psEeXB3JWAEIVIBqvVoFEweCNE9tbbhfDyUvDo+KPkbe/+OkK8oX30xGOTypWF82fyWr6rBV17OyQbnKBLJ+FAYpFYePF5hEp5zFjkh6xKSBtuRT69NAK+mA/0aj1YT9Ehm6WPHVCaFaoKFZOFrKytDGWhO0FVxA2tZtC3tSkkC+lB+so+J2xpp/Y8VJFVA+HjVC1t3LIV1J88RiJBurY0EEUeKs7mx3y1zy28QAfOqQQbnkfputCEZGF4BSBWvuK3jpOF80Gh9G41LMiczG5zsOfDYGlkoXeB3pMe0wJMBQurwCeWQ/DIGXpQf/+B7M9fKLfQyhqRgxLKwnLKTRB4L/NoCRxGciuybCMyliy4L2QGGJgrjPnCmDPMP14BkOi56wx1WKGqCp063zr5LagKMPec5ZB+/PEwuIPZzw4leYWLF+gezhKcBX1L+c/5aoGfKedtdOAUnpqCnx2hz5P37a9cVYjA7OT2OhNRhL8wxuz5ceoM85kBnMkkqIYeKe+T43OX5RW+qKMOpfUtVjA57WCJ0ELA2ZPTWU6IYmQhijZCyQgYUynoRWVhkP7O0H48z6IaOmtQbiLAPwObTI+Rd4eOzIPnyWckLcgcRlZyEjlzVt6NqJVWFlKi8CiJ1sHz1Zs+uVtoTi+IIJKoKUipNLAEDqIsdI/Qc5K/0VLzwcwrAWtk4csZOCHIsSFjWy95q04DOqScFkPZNiTPz39O3nd99CP0IICNWUiamelGAG0G5aoKEdxiihks4gILlVoNzg/SZuTl738/L7tAivwrpdwkEohDPEpbkAMGN3nAKMk3sVqowilsaBLatlCFUZXcwsUMWahRa6DOQA+2fh99rc0CWdhbdWXhJh9dvMNt+4hKrlrgm41S8gpT4bBw+MBcIP7wrzVZqDabQWWmG/Ok0txCEbiqcGPDRkEqL4VUMAgpppZ1tDku/ZKTWBDgxTsBvnktwB03sEm1SpJ0Jkq2t1B14fUnUgXJQsTGAlbkTF6hddUf5ImwCsImuhY6XKaym/VywfNlBRsyru9cWVgJWZhVcpKdW+jgykLWiBxLxoRG78JNyOwAaG8DQEtIDskStjSD19ZDvvTAZeVl2iIm3WFY1qThHFuWnv75EMRtbD3hykaFTciFLMi56sJhdRLAWCcoLnZ21pGfBb+fF8eYpedlUG7CD/WtjHSqhhWZW5CRKFRpqhNJIQW81p6cpITWLf03wz4sO0Gy+uFxCPmKkJ5oR0fFHR7KsRW5BsB1xddHf/+7GQmBQzx0IXR5NlVkQebgzgJ+z21ybIDIUZZX2JwozYLMgVZwVPJjhIHEPWJ3lqEsxNZp3xSsJOhQtb7VAjpGFiYKkIW4N+PKQiQLt7Jn3kszqPatXPUXPsHIwp07AerpHlir00APsyIPH62hFbmfXq/RC3S4GYnStbQhSEneZtNklr2tskbkHGUhDn9WAV3Mihzmbg+FZCFXqnuRLPQXJgu/d3CUPOquWd8I61ixGUcmt/CgkA2Zn1mYIb15Rna5NmTZkpMlGWUhzwHG85e5gT5rGzdULbfw95O/J2QP2qG/fN2XycfuH76/6GuqCCzixA0OeGAoCne/OFVyE/ISIwutsUXSbHux4TK5yMB+wU7XljPHBsETihP1/Q0by9+H5D4XcC3juYUJZkFOWUykkNGJ+aLLQwDLZZDFOMzFAkudBZ4M9gjKQkR9ipGFQ+6SSk7OLFPSa0MSQCuKj5k86yb3XUObRXDN1QT+OWjXnwa7Iw3xSBJGz9PBh/U6GbJwEyMLc5SFPJMSMYNkYY6yEIdD933lGBFf4EDsTZ/aJawRRcGakONGFxEB4MA+NEbX9VjL6qy1lzrWyMKXM0JumkGGqOvKagbDchOX1VB2ppL7zjshHY2Ccft2MHMCjgWXO4z08G6PusouN0GgIpEXWOSqCx2vey1oW1shubgE3nuZWkkG8fFxSMzOkgwZ854CB9ycchONNQVJdaJoXiGHzUhf65ChEwAXq0RCUIlV3Iico57iVuSI0IZcGxuyPxKHq1IvkPe1m6rXgiy2IZcy5Y2NjxOSBHMrNPX1krmWtVYXKm5EFqGUvEJOTta12VeZLNxcPbIQP8ev/wrgPzYC/OrjdNOMuTbb3gbwp48DOOlBKhf217+e3DsDcwD2yRWI4yG0DLJwmN0vq9qEzJAIJjJkYRWakGWVhRi4jOs7ZvWJ8h+rWnLCvn9+uMMw91gqRpQrBe9bmbxCBK7DSwO0QbilRV3cAlIAnPxy95jA2mAgk+MX/Lcpzi3EDF8+PW8dKB46LuQW6nRUVcjUchiKzjft/miCnAk3MKv8ywGZ3MLw6pWbVIjnZp4jTfeYvYQKHiSVm7ptZJB35MFRBQT4rpqXnEy2U6vXwELm4LdOtQVsUSeAJg3tGyqzJ3Fl4bCXHugOBFohFQqBGvMKtVOllZtwoGWKvzajNEheDBtTFpakaEc7bToF7lS3UGbAyULMBSuWV4iENhLuuF7rNWrS+o7Ee7XIQtMOSupwDHAr8tGFqpCSUjD007UieoEqewIhIxjjFrBGmLLQGc4Kzi8HGRsyUxaygouK21ZLbERWTdH1Xt+rjCxcYc8Tj3EBpgPTBfefv3iBXucfuIoNwkUwbd9OSKikx5OnNOKKfrENmQ+olTiFlLRAb3NtEz4vz4uUzisUnTuEkhOWN1wBvn/6++TtWze8Fa5qvwr2te6DRLpK6kJGZp5L0hxgcQmRUmfQ8hQbfOuql5dbCbDpF8+kS3Z67h0/S58j77myu6r5wuLcwqSb3pNxB71unOxMC+WoC5kFGXoOwJkFuj5zAr3BQNeAxRn28TplNmROFm5WW7L2bhO8BbmWqkKEfxZUqjRsuoz+HNMNe0DjcpFuAimIbci5AwIhlgptyKiKZWs78h5YZoKRTpQo3F2a44e5VJKMjESHRmqKfkzdfvHt9ZcC1sjClzM4YYTSdx17cLozNuRyLcj4YMa8QITrIx/JKHdwko8Hfh2bJkUboNlUmfRcIINylGNYp+78kz8h7y9/+zuQTiSKtiCbdu0iJEwxcAtywhpWlFfIYdPQyY4fWknQczVyC6WUheLNYCyYkrYhowS7Cq22s3MzsEdFNzWGza+FaqIcGzK3IKOqEK+71VIWZpGF7LBcCo4vUBJmR1P2oUUqr5B8reZmsAs25FVWFqK1FTM6SkUiCnDqlwDffTXA168EOPwtgKiPql1v/keAT50DuP0OgA55+yrmiVpvuF5QF3JblRQ4GcNtn1LKwoHVbELmeW6+CIRYuHS18grFmYWCspCXm+Dri5mTlUBGWViXoyx8cf5FQVVYULEpkIX5hzI8nM/W0/ugyyZxwCoBnPxqd1ng6rdRdeXxmcuoHdJXnCycH/eRbDkctihp5RSsPXodKTcRYzezInP1idWQrah8pTQiJxZXhyzEwH7ETd03EcUeXm8H3kJf/9PPzICbESQXs+TkdAN93jaNDwoHl3Y3VQ7FmleI3bUSNDGycDpM1SFb2SVt3rMHVFyZ1CR9qCrXisyVhUFvlJDpisBUHCup3oyykO+BCigLDzEL8u6uOjDqNKDTqIWm+1MV5hai44SrT0w7snOC0YqsRSuyO0pC9WsBQz8d4MSGL5A1z+3TQ2OADp5xrTHU11dOFjYzZSFmw+HXEGUWrgawEVmdToFpcbY0ZSFTQhIbcgEVHFpEMZMY98HXrMtfb4gAAItOSG5hdisytxeiChnbor1RL7HyIwq19xaD2IaMYgbujOGZorJ5hRxCyUllykJUG2MkCBJg79z4TvKxP9v5Z0KO4aSvwqI6NjQeSlOy8MiYm5TpIPBa4yrNQvmP7mW6j6yz1YaQLwd4ZlhkfJ1heZ6U5rztsvLJYylc2dcgrGGBeXoujLD9m5OfdwZ/W/onZhbkaNe1wrN8PSMLXQ30NV7yqMnvqa+uj0Ro4ZpQqCVbIAv5Wd07RX6/PK+wq4Z5hSQjG6MYcPh/Fb4uafDWDUB6/63EQSgFjLHB8z+6sDCTVposRLFOGCCwQJ5jqCjE6ANUSGKZSclKSaYsVDvoAMwdjIJujq61pp78IcYa8rFGFr6c4RnLJpBEUzZCFjJbW6lw//BHZLpt2LABrNezTSfCSg/IlvQspNUp0KQ10MQeNOWiUNtt3VvfQtRluGD4fiO/8AoW5P3FLcgI3yIlVANGNllRqCy0pikxGIjXi6bqleUW8iZXfDCERcHuqCxUpdWQwpYa1lwq2K9QaYSNswozuwohdvYR0KpSMKbpFmw81QA2yPHfaSk2ZHG5CUJMFmJ+Sy2hYc2fyRKVhaF4CM6v0Gy5nY1Fyk0WGVnY1EQsrNwOxjdpNQVaZTBfFK8dDOBWCszHfPTvAL68GeDuDwBMHKRZdRtfB/DuewD+4ijAgU/QZlQFqH/LW8jbq0+nYck7W1xZOO/PU4eIbcirCQzTx2lm2OSqOlmYpywUyk2YrakaykI8ICDpm5NZGPLGSGuwonITsapPQlm4PB2AANhBnYpDc6gydQU2GXIyrHeHC3q2OSGVVsNTvg9B2lOcLJwd9ggWZCV29X57X4Ys7KYWOA5ezvByyivMJwurZ0OuJVmIiuLfTdKD0S09twgfb1tXT37PWEzx3L0XLnrJyQvaRohodKANBYRBlnGGqornnOU33XJgaQpAEjxJ+txrOkcPnuadm2mOUrn3v5gszFk7kThHIo3EfrLhsrImZA14Yo0ZZWF78T2Q2ILMsUVkRa4EkTOYVxkHjdMpfC8cWr2GrBW1tCLru7sBNBpyqE3Mz8PCig6aGFmIClmhub2CkhMM6FepVcS+h2v0apOF3Q0WcIU8oE3EAXQ6Yd9bDFypTshCVq6Qi2QqDd8/SM8w7z/QK7s+y+UWmqw6UGtU5DrG14bnFaJSudwc9SxlIXv2cCuyZG4hH75lkYUbq9KIfOeZO8nb1/S+RsjuxvK8A+0HIJlOwjdOfqOiz59m399Qmj7DveE42WuRbz28SPbwOMSRI16j4QSEIpQiaFDQeL1awDPDkoNeS03hFXjjzjaoZ6U51VTyY3YhXsMjg/S6C1rp4MjZuCVT9hf1l0asjdPs/VHHFcKekBf+oF1Yk4hAIqUhSmOMO+JErpy6EM9M59yUtN5sZ6SXZxKWp4PkntHq1YocF2WDRzBojWBtaYLGEH3OzTbT8hYp4IAAs80RkbPnpMlCPcsrnRqD+79yjKw36DxB6zEfhpX2fdJnmK6ertl4HrMt0vu/vr9EZf8fKNbIwpczuGef5RVy336GLCxdWYjtw+67aKCu6yMfzn6AM7JQHV6EuJndSLHKlIWFCizUJhM0vO+95P3lb30rT5LMlT7BQ7QJ2cImkMXAlVxLutmSlG+2OLObREygbWuvirLQaTVAvVlH9vMXROpCl9FFLC0I/BUYeO075oNxNU8VrMiWcRo8e8aWX0hRCfjGDSezdZj9VUa5CQItBTg9i6fiwka5VtA6WSPyUmlkIebZ4EMZH2Sc3JQDt61rm5tIngbm3aUSaSE+oKbASR7fyBYrOUHl4bmHAH54O8B/7QJ49qsAoSUAWxvAdZ8H+ORLAH/0I4CBG0suNsAcIp9DB/YwgP93zHYhAVQboHMEM2cEtR1eI4kUjC0FL4oNmRMpYTM9NNe5qrcJzWQWRnLKTejGqSJgTAXaE7GUZD5TcmIw64SIA/dcQFDIVkIWDh6mYfXO5ZcgPV5ZyPskI786G7CZUwVXv309aDQpmI5tg6Hh4pvCmSGvonITjoFESphcB5x9ssrClx9ZWE0bMiWstI21Iwufn32e5BRjxlTugGX/bQNkXRw7uQRT51eKKwuRVI9Uv8gCBxRj3iicr6cEUOjoUUKox6bpofCM5UjFXwMzC9WGOUhDHBxaK8BJqvYx9zoyg2BDGWtcxxUAmJ2LRUQ5Cie8j+xObkVWeL14xsGbbIVUWkPUlPjsEjILFxchHYsVzSvk2Npur0ojstiCLEU01dqKjOoXQhjifvv8ICx5kSyk/2/v0NE81wqVhRqtWhhIueeCwh6Ix9TUGkicdWCGGpKjXV2KMkzDgRgpT0B4C9iQHz0zR9Yr3P/etltedMBzC0PHj0MykFEbI4nK1YW4f+IZe0qH/8VyGqfcYUIGyZacYFGEdyJ7GCcm95eHy3NwoGAxMC2UP713Mz0DcXxsx8fI2wdHHhTcO+UgMUf3gCOqLtJUjjjMrMj887ZZ2kAn42pwT9PziiGyAuaW1bkelQD34Uvs8ewKe+G9V1RXVcixj61ps6P0/vZa6BrkbBgAaOiney1uK1aCiecAEhHiFDwZoecIHn2CwAIZu5/+XuZGvFlOCLncQvw9YicAlnL1uRiJ6Z0UVIUYo4EZrzWDn53rbS0Qm5yE5hF63hydM0IKsx1lYOC5hWezzyltVrqmzqlVEEg64P4feEmeKz6PsMzEzgQYJYMpCzX2NrIeOWIBMMRSgN9hSz+9/9dQGGtk4csZnCwSKcJ42C9mFpZDFq785CeQ8npJG7HtlszEX2xDRulvwEQfKtawciKoFBsyR/0730kyS6JDQxB48veS02Vso1JbraRtVQn4BnlSNVLS5sISPgcqSEIqpYJkM/03sQrJwqxGZKaW4ptBc5x+3IgTVHHeRkOVSk6ScWhdpNPauWZqDb3YTcjRUaYs7KUHdZ1aB40sH24+SEmI2mcWlmZD5gQLTn2LIT5PfwZdUxOoNWrSAIrArI2XRW4hPjSf/P8A/nM7wE/fCTCMD/c0LSx5+48A/vIUwHWfyxyEygAeOAavpIcDzUPSjZ0ItK31uix5VmS0ByVSaWIDpcqc1QNaNNOggrCRZRaaKjvwSikLSV4KKk25DbmSchMOPEwXyS08PzoK/rgfzFozbKgvombyTGZl5XKgIgwb7xDN80cgNlbZGsXJr06mnMPN4GVMQP7Mhb0QDclnXuJrOHeBKQvXKXtOOWZPQCOLvLgQYAdBhh6nGZxsyr/5FawsTK6CspAfhG/supEUeomBFs4t19D14eDdw+SakoTFBeDA6y+dd01XA4uBKIRiSTjrpMr48NFjMHVuhXSqeI2LMKYahADPjK4gs1BjosT79eEeUn6lttnAaPWXV27CgbE03fvkrciCql25snCFNSFjjh6Sc6QAx2gkysX43JxkY/2CKK+Qg5ecnJ7xVUTiyeUVcnRtdRLlDDZjLk7UyopMs3kXT50DSGsFslDbkqDxQBUWnGRFRcyFLooNuT1AyUJNV3dJFmRLvR4SmniWPViM7zxD93rv3NtFnvVyQJJS19lJVKShI4dlSk6qRxa21ZlAp1FBLJmCWW9YUBa+tPQSJFOiQkMef+Bcl10MiGsSEvXJqGRjqxL88MwPyQAaMwo3NGQ/i7c1boNrOq4hf/7NE98suwlZx2zGzp5tcNOm5iyyUEmr9PIMK6oIzoD2ZdCELD5Tuq0ASRWANp2CDfoiZVkV5hauzFABgNuUzBD5619F/9LgwyVbkKHvejjPnTOiYbi2qRnsPnrP8BxmXnIipyzkFuT1DetBy/dqSBaeYRbkzTUmefnaZ2sj53fX8kugT4ch5I/D+EvyogzjRpZbmKMsbDQ3ErWrJmGFe1f+EVZWtGRggIrCilw+3KVnbyNdD61h+kx22wDa2fN/DYWxRha+DLDwH1+G5W9/G2IT2YcX4UHEbMiYPxNmhRioLOSHz1KaaN3fp9J354c+lD9FZMpCtMe4dfRAqA1W1oDFlVi5BSccGrsd6t/xRxl1Yc7mMvjc8+Stee9eUGGOgQLwgpNl3SxZeDqtyiZPas8oWNR0gYs6ijcBKkU/zy3MIQtNjCwU8go5qlVyMvEcGJMBWErbC+bMrVYTMipHY4wsNDAbspJr5GLbkAWysIgFGZFYYKqdJpb/yR5w/JpcvUZk0cQOFbs4Af3ZuwG+sgXgyS9R6xQ2Lu7/OMDHjwG8516ATa/La74tF/PX0UOw9dgQaRWXw0aWWyguOcnkFV6EJuSlJYga6iCl1oEa4mCF6l2TTquecHqoZlgJxTIB6a4q2JARrexQPXtCshF5eHxCIL0xJ6kgZApOZi94yMFNb1CD032aKK9TEqojJcDXYcbDyEKm9kDsurED6jRTEE7Y4NADowXD12ORJOiNGnB2KFRnTTwHA3H6DL3gyVZF4rX292/YAu++sguu3UDJ4pejsrBSFRVXVmMIeS2AKvEnJp8g79/SnTOQZLj8tT3k94YkzyAjnyXRXruSEyw4QMx10vsvdOyocMBZcI1klXiVC8yVVrPW3D0zJlFe4dnKyMKiuYUlKgtXxsHNmpDRDsfvh0K5hc+zvMJdnTSvkGNDi42UDbiDMZj1llCykoPIiZMFyUKdXgPdW1kr8os1siIPULIwMDgM9pQKTAkrJFVJSNQHqmJDRjS0spITJAvDq0sW2o066GMEZbi5vaRyk4YWi/B9TvuzX4OTUx44MrZCSLn37iu+R7QcoM6X4DPZuYVWpixEkQQnuApl7CkBXpudbD2dWA4R9RZaPlGhNeIdKVxugkCnBXcDlJFbiOTq3UN3k/f/eMsfS/6dP9tBswsfGn0IRr1FyqCkwAaRi2k7XLVjA1zR2yCUnODzY5wp2AoRrxg3grAEp0HXUthRs5pwGZsgrVbBMrMFFypgqgT7+ujakmIFJwuGaCZvfj17rg09rDxbnqsQ+6+HQWYHFysL0Y3kYGThHCMLecnJkEdaWYiFdYjNDZsFR1rMvQizw14h27WmECkLA7//PajTSehtpfvBM8/K/16MmzdJNiKjeKRD2w2vP/Mx8CW6wGIIw5s+iURhhUWDTFmIAx7kTdoi9Jm84tSDQVN+Qd8fEtbIwosMzENx/+AHsPDv/wEXbrkVRm67DZa+8U2a7cbJImZD5nmFOFHBqLvGEjMLPb/4JSFKdO3tpI04DxZ6QIoHFmBRxx7+Pm1NlYWIhve9j1g+wsePQ+hItvUn+BzNeLDwxuYiwLB7btX2GZbJ15eT2WcBsydCy2DTUNVFFImUqikLWSPyQoYQwQeOKW6tLVl4nuZA/i65E9rqrRe9CRkbrdORCM3G6ciQEKtVclKODRmnu9yeUqwJmXxupizEKSFCyC1cXGWyEDex2KZ+8L8B/ucygLveBHD2V+jrB+i8EuC2OwA+dRbglv9beROvBAw9PXC6C0CVBvDef39JjchD8xcnr5BbNENM6WrXLIC60pBxETD8v8FM7/UltIIG2PXuotPjiiFXcsJyC5fmfEK5SdG1MEIVe8KBOMeC3Le7CXQmA9koY1t9OZjzRSCeTJMDJW+NRWgaOuAaO22DfOn3U7AwLm1BnWF5hS39jmxlthyQZBs/CP2xuKy15/U72v5/9t4CypHrTBt+xdxqZu7pHma0x8xJ7Nixw07s8Cb5F8K8QQc2ye5mA7vZzbcBZwMOOnbiOGYez9gz4xkPYzOpQczwn/dCqSSVpCqp1N2T9HOOz8gD3WqpdOve530A7rplPXmvlhKanWZi2Y/Ek0SZqk5mYWUI0RcmXyAHYiQStjZJD6nwnrf1FfQ+9/y9ZyEuyvPNAM8Kq0DJCScLY6vWEmVudGgYhg7TYU+qg65BJR3URai3mUDPlIWtp+l1bN2+Pa38VtqELEUWDj5DXASSysIZJcrCDqHchEPIbh6TIgtzLcgIJA752l2qFTk2PU0JSo2moKNkxdbKWpF5I3Li/Dloxo03kijWMZiPz6liQ0ZU85KTibQNeaHIQkQPKyGbrWlSlFeIz5vn3WVbkX/IVIU3bmjNWNvzwX7JJdIlJ6JGZEFZ6ChPWUi+hii3EJXPvBU5w4osVW6iQm7hb079huQF9tf0w0Wt0ueatfVr4YqOK8j+8/uHlGcXzg4eFvIKr13TDBvanUQBPOOPkAZzXp5S6LXkZCFRFjYtHWXhmQl6npthSQ7l5soXuuei88UZoa/DhInu4TFaAzovBjA6ANDCPyHj3oSW9snDwrrNycIBtv9FICFbxc5VuBagqwKvEcSZ+TOS6xtXFuL1wge7Y542SCZSRKjAXSWVVhYmjQ0Q3EdVweteRe9pOHjDchIpmAZWCuel+Fw6firsj8HlL90BdcFW0Onm4ZbV98gqrpP7PLmysDVE996hpgrmOf6VYWntiP8WodFA0yc/Qcs7dDqIHDsOrm99C8698pVw7udBcB2xQ4Rk7mPuGd34BXGgoqFTa7lA9cfsD39IHte9+10kZDQHTFnoCs2A10Q/wOG58konBCIomL/AQt/QAM7bbiWPZ//nB+nnHA5DaP8BReUmaElB15JGn4KQwSefzGL5kHYj3dCHdVTxFB+fkMxSLKkROY+y0MoyxXLIQiyeKBV4Yzn1Z/Lw0eQWwca2mDZkodykqzNDJbpQykJ9Hd2AxxUoC1H+74/5yeSZ37hlZRYumrJwTTrn619XATz0GYC5s3Rjs/1dAO97DuCdfwHY8HqhYb0SQGv54xvo7cX929/l/QyhCgVxIkNZ6FuUvEJu0QwxstCpnyCHaDXB1eDBcTZRRSubWaUNi1BycgwgniaU+GYryvZkxfMK2cEPn5fIfoWDGF4mMLCzmURZICIlWpF5uQnawlDtIcBkhw7nCPSbnyLL2JM/PylZECSUm8i0IJPhi28C+lluYbaycCkDD3rckl+uFVkgCyuUWVjIgizGhivbSWg5DveOPDW24CUnw7PUZtfU2gCmFSsgYGuFgDcGeoMWanvNqigLQ4kgaI1ToEmmwHaMfi3rDjFZWIaysGk9VYejVXqMtpxz8PgL36yM+w4WIvkmaAM5KzfhyFf0li+vkGNdGy85KS1rMnyYHqwxCF9nz+9u6UIrskFLSNGZkfIs44UakU2jQ9ASp/czl30YZpFg44MU/3QOWasEaPvmmbLxVHzBycJmL13TR+31CslCK7Q56GsgbkSe9IThj4cnhGITOUDnEJ5/MNYiOjqWoywMzEfSBFeZNmREN2tEHmRrALciH3YxQkc8oJAkCwdKIgux+Onnx39OHt+55k7BOYGkyrmDrgxCiKsL/3z+z3DOraCwDgmjk3S99DlWkAINk15HFMDcilxMWYjPQ1AW+seXlLLwwZfoOXjGSe/jsYnKkIV8batmZOGUMZy2IeuNACuukm9F5urvpnXg1lbDlDeSMxDHsk8jRMAcogMrbHnH9wddIMF4EMYD4/nLTerWAFiqAUxVMByh12vXmgVYQxgJFxiJQSoWI3ECTdtWQnNvFYkXOfm8tAAE13RDF732IkxdiDmof/iPg2Dz1kLQ4AVjx9egOiJROqQUOPzmcSKOFmpDDtB7V7JlaTlIljKWycJFhtZqhZo3vhE6f/hD6H/maWi560tgu/RSShx69DBzpArOveldcO5VN8LYz6lCxw10Cq8ks9Bz770Qn5ykLa2veY30X2KZhdMxL3jNM0KTazngGQTxZLxggUXdO99JfmZsRAsdodLq0IEDJFgbp1r8YFoM3HaTcIQJoSp7Csks3w473awFYwbyfHABxByzcsBJj8HZIClv4MpCM1MWmuwG9ZWFSBbNnYNISg9PJ9dXjCxUYkOOnmflJiyvUIn6VA1gq6I4s0uJBRlDsItZN3GDxclCQ1Pj4igLHc30AImMOWbqNK8HuPFbAB8+AfCqfwVoYiHIFQZOX/eu1EDEpIXYyAgEX3ixoA0ZifQYC0TmpDrP+lxI4GddIAt1E2k7rspkYXz6hHrlJuJ1A8uGsA1bZEPn02VboAb0Gr2goihuQc5cO0eOzUEkECfN7W0DNWDspp/96PnBMvMKJSbHznbY7fgxGI0pmB7ywbFncsmK8dNuReUmvImwz9l3wZGFapWcJCMRkgFcqcxCvM8/OvQoeXxt17UF/y422u64kd7X9/95CKKheH61LJL2qM5QEbwNFUseLFu2wGwtXRvbVtVAdy299sspGBCUH5oUdI7bQRsIkHxmc4sdIOoD0BoA6spQFaMdsvdySSsyVxbKGlJ5RiGZ0oA73pZLFvJG5KwoCdzL8LxCXp4gxrpWuq4fLVFZWCyvUHh+JrQi11XMikz2nRoNGEN+6IhRImfaPkz3stY6+h6S2unJssnCoCcGhrgJHAYHGHUL0z6LIoIqD/1cnTbUKiILa5qsgrJQ3Ih8955Bkjm8o7sW1rfLG4TpHA7hvQ48l1YX8oITz1wA5lE1oZaysDZtQ0bwkhOBLES1KCr/NVqA5g0FlIXKbMh/HvwzTIemyTAVW5ARSKrc/51D8Of/fhnOHqBEEWJ13Wq4quMqSEEK/uvQfyn6PjFWbuLsTN/rd3Ir8rkZgXjNN+zHQploKAGaVALs4CU5q0sBqFR+aSgCqaQRXBVWFvLcwuowHV57bEDOAFVGNkDtv57+eoo6uORbkOn+tq3aAg5z+uyn0WrBgMIZwYrsIbbcHmePoC7MW27ipGeqVFUHDEXpgK1z7QKU0rB1z3+MXrf2yy8nBPjqi+l94/hzE3kV3zy3MHz8OCUKv3WQDHxSlhjct+a7MGl10aI9cY5oORZkUxUZRNc7jNDs9wp5qcuQh2WycAlBX1MD1a99LXT+4H9g4BffgJad82Dv1hIVILbIzj5Dp0VrJ1+Gtx/9E1QNnZZlvUjF4zD7g/9HHte98x2gNeUhGa215OY4qdeDz0SZd8xIxIbAUoGLHZFtFyGDjO3tUPVKevOc/QFVFwb27BEsyHKzyzhZGGTFBPKVhfTA66imhJDPHQUDk96X24iMihAsa8CMLixvQNSYa4SCE401IU0WYjstTkVKAVMV7k2uBjDawWmRYcWWCXfYDe6IW3F+TIQ1IRtZE7KU+rSS4IfjZDBI8jvVzitMuN2EXCbfq6FhcZSF+Dm5+bsAu/4/gHc9CvB3TwNse3tpjZtlAD/zEaMG9m+gm3LP72hGTzaQxLYadSRsHBuQ44kknHMFMhS5CwlUXWWQhRVSFurmzqhXbpJRcsJzC9NWZBoMnQJTwgobHVvBrDeXlFfIs+X6tzUR26+xh5GFZSoLJQcZznaw6eZh59Z5waoa9KbVkp7pELk3YZtoU1eVMrKwg7Zv4qEN7bIXCvjrxBukSwEflGDsRyUOgPun9pNDfbWpGrY1byv691fubCYKJTwsHHpMgphHdSsWDFTAioyEF6Kr1grWLZthto6ShV1r64Qh2KCnPGXh0Rk6+Fx9jhJwlm1bQTPHs0r7qUKlHOTJLayqp59xJPclSVgx5gfBm2iCBBiJSs9Rm14f8mUWclVhdl5hrrKwVLKQ5RVuKkwWIvoqaEXWms1ELYOlVw1J+nNO24dgNjRLydqqlrKtyNhYjwMYRHW4CWpZBM5CIDY8DBp0LenNcCpafI+YSCTJ2ouobrZCuyOTLAxFE/DzffSe+Y5L5A34OWyX7M7JLeQFJ142IMF9hdVQvi0RBwTiKAI+QDvrOUvLWvha04D7Z2t+shCH8jKdR3ht/vjoj8njN69+sxCPhPdVzN9FnDuYSXi/fxNVF/5l8C95G3GzMe0NQ2OErlv969Jr8E6mAN47PAjhRBh0Gp3QPpuN2TG6B7MGp8HUWL/g2dH58JPn8OfSgE1bDzNOTcXJwp1tdrBjgzHuOaxU8Su8Fv04DNPQjGhOSEkB1yRRuYlgQZZwzqAwxsnU7ELJCW9EzsotzCg3YUIGj3Et+BK4P0tB60B55aSy4EMyEMB/6KxAFpLnvK0R9CYdGSxMnJW+B5hZI7L32Bm47z9eIkShxWGA2tf6wW2dggl0PybjZWfCCuUmrJAKlYXNPvq5d/SoOKz/K8cyWbhEoYvPQHVPCDpuXwn9e56D1m98A5L99IZWE5iC159+HMbe+EY4e/U1MPW1f4HgwYN5rX7eBx4g6h6UOVe/7nX5vylahqz1MI12AH0YEqxlSnbuTR7ItZmiPRrhe+ghYlkNPMfJwl2yv5eHPdc546QyywK3ITN7gm8uTLId1SAL8ebCS054eQOZUCVryOO4KZx7QOIbRva8Ss0rfCS5BdpqLKre7LmFocnapGjjFj2XW26SQRbyDLcKARu18ZCsxIr8kusl+XmFTFVIWiTZ9xEf2go1u6qKVa8CuOErAO3bKIG0CCBWDQB4aB0lwr1/eQgSvlziG0knsRV5eC5IiEOLQUcmrwuNuCudWejUT5bcdpgPjSxn1uo5o265SYHcQlRwJWzU9rLRuL3418BpbhZZiAOj84fo9Lh/Bx2ipJWFpcUlcNJLXG4igFn81rUeh/oOO0SCcdjzuzM5eYVNPVWgM8jcxgxTstDec4WgZr6Q1IXpRuSQCnmFlTkAcgvyVZ1XkUFhMWBj/I6b6P3gpYeHSWZRDnjBgMolJ9yG3FVnA+2ajeBh6oyOfjt0O7sF9Ua++BQ5eHnmZfLrmhG6Dtowr3DqaPkW5GyycPSFjMGi0awHs80gzx3iHhbKTTCvUCOKBMhnQ+ZkIScgsrG6pYrcetBuN+1Ttn/E4XboyBHy2LJBQtWVBVQW4hrgcYVghpEuajciB2zNoNfoIGVIgtsylXbJqFRywtWF1aHGBbUgR9jaPWavh6G54uuKbyZMIiGwhRotwkJmISs4+d3BUXAHY9BRa4Fr1yjLubPvZmTh88+Ta0BMFka8CYCURhVVIf/MI3B4jyQe7lf44PuI64io3CTPvq+mh6pKYwEAb1pVWQh7JvYQwg8jbV438DqhtHLvfWmL8fCxOULIcmBTMiq0lagLHzt8Hjo09F5d15MecqMCWK/VwHSYPt82e1veNVpcbrJUmpDnA1H4wyG6DvXWtMMMmxFiVFSlUBune5S4RgMBMys3Ecd2tbFM3tMP5f8iSCjj+oBFGl0XS+YVisnCKpGyEK/NgZoByUZkThaSchOGoTBdL1vrPeQeUHH4JiHi1kN81g0ai4VGbLD7D8+TPf6sNJlrXr0aYnoLPDu/nhSdme0GuPkDm6G9i+6/J0xsaFXuwJ4TuWyw02BIQW2A3o8bGKeyjOJYJguXKrgFtaYLdHY7OG+6EWD9TvJbjzcPwIHereTDiZu4uR//GIbe9GY4c+VVMPnlr0DwxRchlaAfBiQQZ/6bhsXXvu1txPZcEPZGmNLTCaqmKqasUS8P5NpMzQMDYL/qKjKJmf63f4XwMboYWnfJyysUP9dx7VBpysJmulHzq0gWIlY05OYW2uJ08hMx0kOLalZkLLYYoS3Sjya2qE66lGJBRkTOF1YWukIu0qRZKeDhWEkj8kxoBkZ8I6ABjWBTkVduwlrF2U0Tp2VqkO4XEria+FhTFPR9PaTYxvunB4qWnIibkGWVVqiM2MxsprIQFSOJ0pXV+ZSF1cFB9W3I4tzCrEbkeTO9NntSA/LJwuq0avj8oRmIR5NEpdjYRd8vE4uGKFVZyEmvfMpChNY/Bpe/eSUZ4J94fhLGT1OlYdqC7JRvl5nD9UcD0LED+qr7JDfgf+02ZE4W6iqQV5hIJuCRoUcKtiBLYcWWRkIIY7P1gYeGCpScqEcWesMxmGfDGyw7mPRYIKXRgTUwCcaJM+QgjQM9VOBMBQq0NRfBkZkjRLm1dpJer9YdO9QpNxHvE/A/VGAw5SwHH1QVve+4h2Ce5RXWsGbebLKQlJOxYXRmXqE0sWUz6aG3nhIyRxXmFkbOnIFUMEiGe8Y++jktBLzHohoUcbYSrcjdPeB10L2OuVkDKU2KZhYiVCo5qWmmr1VNqHlByUK+do/ZG2DMHRKiQIo1IaMaGEllrizEgpN4IiEUm7zt4p7MHFoZwCIbbVUViUkIM7IYFZdkpoFN1DF72U3IHEhm4tcNRBMwG6CiCL7HIyUnhcpNEDp9OkJAZm7hT47+hPx6a/+t4DTR+9bRZ8ZIzjr+nEju41BsMkuJ9d6N7xUGMSfnin+vY4dpSWQQbeXoFmOwGvXEFq410mu3oyr/azk7zsnCCTA0LY28wnteHCExTuvaqmBlfQe4RMrCSpQbicsQPWYzGbzzIbiAgeuL5xZyVWHnLgCDRSjzG5CI2cH4Irt/DLSaJBEYoIqXKwuzbcgCWYh5hQzD8/Q97awur5hLFsJekgXoHzcLDkCxa3HNxS1CPISUul3TPQCHNvx/4DE2g9mmJ0RhXZsdWm10TZ3gJXOlCmZylIX069Z46P08YAJoa1F5WP9XjGWycKmCK1o4aYQEFmv5fam2Ff586z/AwHPPQtt3vg1VN95IsnCQrJj/6U9h6C1vhdNXXAETX/gCzPzXf0H07FlyE665/c3Fv6+9kSgLEcZqjSpkYbNVfoFF/XveTX71P/IoIQ2NK/qE/Dc54M8VlYWFZPZ5MwvbKUGIN21oaldN5s5zCzkZgjDH6AYxoPcWIAtLWPRPP4wsMbisK2AMGoiyUE1wa5aScpOE1wsJlv2YnT+Jm2OccOL01BVMZ7ZUtBFZBll4aJoSLitqVoADC0Jkl5tkhuZSGygQ5cPfCnB6bjfYqbLxxqvJ77l/9zvJv7uyiSsLvaK8woW3IGOhUjiigaTOSB1mhnl6COdNaioAc2b1EIf62Lj6NmQEtyGjeomF7iPpPakfFmxuReHOtSGfZhbkgR1NgiLN2EU//4n5eWLBV4rRuQLKQic7yHhGobnHCWsuoev4Ez8/RZQXQrmJwrxCkuFpdkJ/df8FSBZyZWHpNmSev1uJJuSD0wcJiYKZTjtadsj+d0g67Hw1HSC9/PhoboOiUHJykFq6VADPKqu3G0lECCp6EHVzRyF44CAhCjkxcZ4pPZQCP3cYSt85rQFHNAIRgxnMa9ak80TVUBYWsCI76ngjsnxloTivEEFaULOym9G+jYpBo04LWzqpO0IK65kVWWluYegllle4YT3J8ZKDvq30esYCJrWJA29TO3jZXqe+k76mc6G5DGtbufcItPQuhrKQk4UTVY0kJmeClSjmg3syTRZydwnm4OKQ9/6jJ+CsK0A+T6/flhlhIQcanY4QDgg/a0VG5bHVSQkIe7RalXITBBZ+tLDCKG5F3lAvIguFcpMCZWANK2WThUjyPTf+HMlvf8vqtwhq/RcfoK//9ht7oGs9JaIGX87cm6Ky7PpuSkoVa0Z2B6MQGqfKZY3EMGJHTy1ojfRz3OXIv3/nNmS7f3xJKAvx2vzpHnpGu+OibiIwmGVb8mQgAEkJ14oq33eOkYUsxilDWSgmC3HtjeX57Jx9jP7adyVZm7iykDtqxNA3NoE2lYBqDd3fTJ33CGThOc85QUyBavfjc8czyMJ4NAFjU/RrdhrTzpKK5xVO2jIsyBzNfU6yTuCQ+fSLmQM3JA//fM8YeKt6QB/zw/WvckB9O93zt9jpmuqFJARwr1muuydLWQhT9PM6Va2BOnNlSt7+GrFMFi5VcEVZdXpB98/TxcinSRGFitZigaprr4W2b34D+p97Ftr/8z/BecsthBhEYsb9i1/CzHe+S/5N7VtuJwrForCllYX2epMqiij+4ZdTYGHZtIlO3/nTuehi2d8HF2JeJOE1zRaU2Wf9Q2F6YWzqApOVyrejtZVTFuJzNUTYZl47p66ykOUVHrTsylCkqAXeEKmELORWRVTdZV+HuIHCTefCNCLXZShsih1+5eYVImJMWcizLnPC5l3ltZheqOpCzxWbAPR60nAZPsXyukRYyUpO0IZ8mm2kVixCEzJOkbkFGQ/a2mo2aFAxtxDX7S7NFOixpMpoTytT1EJtLwCqFrDchimY8Dr2WCgJH5pNKLAhUwIh5IsKZEr/9vS1jQMqQiaUoC5EhcCEN1xAWdiW8VwuuqWP2FTmJwLw3G/OkHsS7iObe53KyMIuej+5EJWFnFQdmw+VTIiIbchq46EhasW6suNKeffdLCspvpfxWBL2swO0ACR4NToA/1TZCi4O3oKKdkQsGBg+Qg+FdbNHSbEaotzcQp5XuGuCXqPnmvtAo0kBzLA1sKmyZKGgLJwtsn+bR2VhR4bCjUOj14OeDWtj43QfxFWFmzql8wpzcgvHlCkLQ6wJ2Vyk3ESM7vX1JL8UlTic6FALU9Wt4GPESnMPHU6klYXq2JBr2eteHWpaYLKQ7nujzZTcG5pjrx2WCkiUtrinAhlkIbad8/393S/Qz80btndkFDeolVtoU5EszLYiIzY20uvtZdchSCIZjGtYoUI4gSwsXnJy97G7ya/XdF4jqDFfemSE5O7iMHn17hZyDZPn83Lu3vR9G99HHC6PDD8iNOBK4dHj09AH9J5pact97rt66kDDyMJ8ryUO4+YnGVkYGFsSTciPHp8iytcaqwFevbGVuNUwEzto01c0tzA+S/c9bitd56y6rOEklt/gwADt6EPP5H4BHNgOst/vuwpm/FGiaMe9i1QmN99PVYfpzzN53ksELzh8R6KQF9PwchOTziTsZdBtkUhgnuMM1EYXgiycgHhYCyEX4wsuvyzjj0nRye4WoeiEA0lyLPSZOucFQyoCmw99B2yudB6jzWATSmQm9HoVlIUTGYOd2Bj9XpNOE3jL6GP4W8MyWbhUwT8gNUy9EUuSGwvCp03lNCGj/Ndx1ZXQ+rWvwsAzT0PHD/4HnK+9DXTV1aBvbYGat75V3ve1N8A0Suxx48xk0mopC+Xaeere8x7hMZ80ygEJ8w7Tw7DPNCd/Y+GfBoiHaPOZswPsNSxnztagGlnIlYVnXX4yJYtFEqBN0EV2XitBXNX2lEYW4s3pDG2jfCJFp6IVsyGzXCc5wAxKKQtyKYRyOVBiQ+Z5hZsa5ZGF8WlKyOgbMpWwvOTkr8GGjFmeU4PyDn/csjFjjoHjSnqg9fz2d3ltyGivPDjiXrQm5MSMC0JW1mLdaAGo7qwIWbhCw1WF/epnSpKSkw0ZJScHpg6A2zyd0WSZF3hI5AdfpiwkxQHJFDR0OnLIBJ5bGFHYiDzuRsILwGzQQoNdonCLqxrxuSSTxKJ18a10wn74cXoYqu9wgNGiL4ksRLXwhZZZ2Ow0Azr7IvEkuPxZ6juZiM+4KkIWotJBsCB3y7cgiw8Wu26m94ajz4xn7jmwYICr8FSyIg+Jyk0w5w7Lc/BcUu05CyGW/8zJwlIbkXle4eYxSpy8hPd0tMJjW7nBltM2XjK68ZCmoYpF31TOkMpXZP+Wmh8RbMjZykKp3MK9ggW5cNvm2tbSSk6EJmQZeYViK3Ln2lphvVIT58114LdRUrAT7wv4HOMhCMaCqtmQubLQGW6AWuMCtJgy8CGPjrWCks9FNAjwo1cC/OtKgAc+ChAL5diQsQmZg+cWvjx9jqxPb7tYWTyNGPaLLxYIY3SjkN9jjci2SLVqmYVSJSeo4MNmWW/MD4MGPSUK9XnKIBUoC6eD0/DAeRrBcufaO8mvuN5gRiti5829oNNpoWNNLYlemZ8M5gyWkRC6oecG8vg/X/rPvN/rwaOTMKAZzXx+ImztrhFsyE49U1plwTMVgmQ8BbpUDMzhOYHAWkz8ZA+9Tt+wvZMMKHi01Vy1rqJkIVcWBh30+/gwuDB7v9V/XX4rMmbJRv2kCwCa1guqwu46m+SghbvoHMxyPHnOQ8QU3AnBS064BXllzUqh3GT4KCU2O00HQYMZoirG50jCNwmBCfr5MK1aJUkqr9rVQq5pLGtBazsShX/87iHyc6Eo57KOc+Dwj0L4BIvmYODv7zgKl8pWFrJrg63VkWF6LU3a7TBT4j7qbxHLZOFSBN6sA9MZCjO/m17USS1ASJPOvpIClivYL70UWu+6iygOVzz6KGlaloOktQGmmbKwublOHbJQZsEJh233xeC4/nqSYWLbRXMa5YA3zqasUUjoYvKVb3wxwimx3giOWvrahg10sxvDvJ4yrS2o7jPqtURRgzYyTvzGtFGYjbnUUxbioTjiBbA1wJN+urFS04aMh8Jh77DizEJs85YqN8kmlCtNFgo2ZJZFkg84xTs+S29gGxvkKRwEG3LW5spZ/9djQ/7T9w7Db7++X5g8y1EWoh3Peeut5LHnvvsgFU232iJqbEZoqjJlbNzVtiGjyueBc9KZiWLVlVBu0mBNZ/bxdmAVgEOePkYWxmsr1MSWVXKCDbUey7RwDSLxV3AKm0pQVYW9OaMFGS3I2Si1ETmdV2iVLtogU2ANJVawFZ5sPJszMgplW5Axw5VbPzvp8KnX2UuUGlhUQJpNLwAYdFpocZZXciIoC1XOLETrHmbOYvTArhb5hWRitK2sgY7VNZBMpOCFP2VZf3nRgEolJ9yGjHmFQ0foa9Kxug50Jj0kPB6ihOfDMK6kLzWvsOMcJcv2OXsgOv5yOq9QpsW2KGx16QHB+SeF366qk6EsjAbB501BHEyg02sENWI+spDmFc4VzCvkWNNaJVyraI+UAySIMDYHYVGgLET0bWkUMrLUtCKPT8YgpdWBIeqFKs8MUfMgsPU7rSwsj6xA9RzuWXUpHTjCC6MsJNEwbGhq66NE/cisD+C37xIyr2Hf/wD89+UAE4czhk1cWYhoc9DXQGuYg+vWNEvHSsgE5oSTmJpEAgJ795LfM1ZpBRuyWpmF/LOPwFI18r21BsHSeRiz13ixUj4IjcgnC8Yj/Pz4zyGejMOWxi1CLuL+BweJYAAHcJjZijBZ9NDS75S0IvPsQiSOHh95HI7OspIkEQKRODx1ygX9mrF0k3MW7CYd6I308zszzxpC8uQV2kOToIHUoisLz0z74Nkzs4SIfsuuzsxSRDslxGIVKjnh54Qw23ZMzEkoZgduSJOF2dfBWd6CfDlZ73leYb79LT872EfpwARV0nid8OEmd0JI5hUeo8+1y3yY7uEqXBiJe0X/hFnSgsyBWZzcXo9DXjw/TJzxkCHvTf+4CVo2sWHz8RPSPQcVUBYmx+j/T9hrSh66/i1imSxciuDkFdrJLJTkC7AskbBBQ85QjSxvoxgw80VJ6+G82Q4xDR6jADramgRFVMEDZhHwhR1JgxjL0Sr4nDUaaP+Pb0HPb35dvJBFBE5qhqx0Iil7CimUydCFy1FLX9tg3EQW+FQkAgkZttVCwLDnPmZFPj3lJ9Y+8lwNPpiNzOYnC1HVhGofuThFW5CTK66DCfY92lVUFuKUFEPfMadGdh6kuNykp1cVQrlUCDbkIsrC857zEE1GiSRe7gY1XXDSIK0svMDJQmwqxZY8XAt4wUQhNDDijZAIl14K+oYGkm/neyLTLie2IiOQVC/nwCGFDz/5Yfj40x8nuUGFiBSh3IQoC9mwQcVGZMxzGtDRg6XfLv1ZUK/k5CXwR/1wcv4k+EzzoNFpiEKdD54KWpBxCqvVkiZV3NzhDWHFVgmysMRGZN6ELGlBRugM6TwwRtZitt3lb1optLXyg1Xxb4aHzhRAXT9tMGSZmhhTcaGpC/ngp1SykOfGqq0sfGgwbUE26mgTfCnY+WpqqTr5/GTmQELlkhNut0R10dARenju2lAPlvW0HTF44EBZNmQkq1BZ2O4CMPhDENIZ4XR1O4TGjqhXblLEisyVhbgvykueifIKkQDCjLhsiEtOcJgz6Q0XzStEOC0GQb0lt+QkdJiSqYbOTtDXKiPNujfUg1avIYTW3Lh6VuTABF2rqrxDEDt3XrAJkyEDz8HCAykrgCkFuOf12+h1aPItjKo+OkTva3hfbmvFfVEKLjr5LwAn/0SbW6+7C8DeRMmwH1wF4ce+Iwy5xWRhnZG+BlrjPLzzUulhsBLYeCsyyy2MWpiaMdEgKztaLlDdJY4kEA+GD5uM+ctNOLDgBN1IYQ+NSJAAqk9/depX5PEda+8QPo9HnhoT4jXE7eOFrMg44HpFzyvI4/96KbcZ+clTLtDFg9ChdeVdY3D/ntLEIJXSwmmmeM7XhGxljgr9IpOFd7OswmtWNwmRSk02uheZqkouiLLQ56Df5+ykxFkaiUD8vOA+MVtlystNeq8kv5yezp9XKCYLTb5psFUZyF7bNewVcguxTVuKLMRrChWppHSofiYze7pCSLnHwM+UhfnIQvIcd9P7x7Gnx8m5wWjWwav/cRM0dVeBeTUl3DGeiJeyip1mRFmIa2u8RFIP1ZX8s8mUhYZJus5OWBqJLXwZ8rBMFl4AFmRxuYlfSzd92TZktTCNTD7aUZIaqK63k8UnEU9CwFP6hwo3V0atkRRYTAVLbxaUSxa6jS6FTcjs9WbkgJ2RhX5PVFi8VcktZNOkM64sslBK2YITa5SXo7JG7tQaDwQnaV7hXNuVgPwuburrpWx+JYKrLDB3hcvf5SAq2JB7CpKF5TRPyoGe25CLkL88FwZl/jjNlYOYa1oys5AXnGDmKH6WLlS4htMh0tMyrMiCDTk0Q7KvME8V4f7tb3P+7mrR5glJdaVNioWA6jFsa0Tcf/b+vH8PA/wFsrChMjZkPBSu1NOJ74xFfuanIvBDzuQRODS1n6iBWx0tUM2uw4JW5Ky8Ql5s0jZQLWRHiVFqI/IILzcplKfKrcj8OeE11WaHq966CtZc2iocrJRakDmyp/V/CyUnlcgsxOsLmzoR13ZdW9bXauqpgp6N9eRWtve+8xUrOeEK5laLiYTIIzrX1oFlC/0+oQMHBWUhDrDCcWUREiO+EfBGvbBhRCvkFSa0OkhOMkVQoSy0cslC9vqQoacGg+/TETaSZGFCutyEw9DClIVj4+m8wo7CeYUc67gVWWbJSejwoZJUhVyZ1bmmTig6UQ1zdK9W5RuEyNkzQskB3lcImYb7AyzCCpRXzjZnpvcFjVueEKBc8DUbBz6Y3/d+3X1whfc+qui+7QcAF/8DwPv2AKy6ESAZA/cjNHfPVqUntm+Os+OMMLB5YFuXPAeT3NxCJLn9xnny/zVx+UWHctDJBpJcZYzYUL+O/HoIlYWFyk0QaFGu6SloRf79md+DL+ojZ5Er2ulndN/954nNt31VDbEei8HvaWOn3MSymY33bqDqwidHnyTKZTEePDIJK7iqECOURE3IHDxSIRWthRcHPYXLTQLjxKWGUVaLBV84Br/dT+//d4rs7ajuxc/hTBVrRJ6obGbhvJWev8dm9DDty7oXGG0APZfSx6dFVuTQPMDYfvq4j5KFQhMyK/XLBsaJ8de7oZF+xibPpclC3KtIlZvwTOnm3iow1TWo7oiRQujYGUjGtKCzm8GyMX9kBMZDWJ10gGgw64iiEO/zfO3RmM2QCgYhOjycoyycMOLakiqd+ESHZipJM49tDYSQdMzQz/u4pRVmfMvKQrlYJguXIgSlWy5ZOI/y4gqShVOMjGxKxEmOBrfklmNFxgOyIBuvoM2UK7cmdXRhkZ1ZmEdZiPlshja6UY6qkVvYmFYWYmYJImTwEzIlB1pdmqyQa0XG4HRsT9YZ4WzVTkGJgpkRamHIM6TYgoxNivxGYOpdXGWhTmYbMrcgr65bLftnTDDLApa4ZEvx9QYtOcP5ioXNL2G4RtJk4dSQT7YNmZPhzltfQ34NPP2MUAbDIZ60qm1BxiZCjkeHH6VZUxKIicjC6kZrupFXzQltKgVdKbr5Hderl7+UATzAoAIjEYEDgzS/dGvTVnDiz1SULMxsQk63IEurCwRl4dAQyXmTC66M66gtoHoWSk4y195VF7XAlbevIoUGZZGFog34hQKurChFWYgHb04W6lRsQ0YFHQ4BrXorXNwmv5AsH0gzsoZmzwkDCiTXUL2BKh7M/SsD4ViCqOMQBleErMu1rTZy37duoUQ7lpzUmGpI0DoOOZXmFvKD/A7WFDnRRe8jxtmTlVEWor0eXx/M+Jyl17POoBXy3lAhLAk3lpvQz3pNPrJQZEPmZGExCzLH2jZ6KDwiV1lYQl6hGCu20Ov67IHyiDvxZ8YRTArKwujZc1BrESkLUQGNhGGZJSfouHGZ6H0hPrswx7Ioy5nFNXyN6wH4mOEe8v+pG74KsObmtMX9Df8HcNO3wQ2UGKuOHgZ4+Tf0a8ST8NRxeiYxmOcVuZjywbZ9O4DBALHRUYgND8OcjhK/tqhMJblMcNXrbCBKSCnEBj39HmeMBgjwGBI5VmQJshCtxz899lPy+I41d5AyGMxHPbmPnoF23UJV1GKgYhNdDRjFMHI8t/gQBxg39t5IHn/vpe8Jvx+JJ+CxE9MiCzJ7Xlng61gyVgcnp3wwH8gVgcxxG3JgjIgl1HhPSwUShYFogggtLu7LzPJEQsnFLonKFZzQ9W7SxCKuEnbYy2IY8lqROc4/TYkqdDQ421kTsr8gWYjgApU6Bz3zY95ffw2NrMEIKFQXZpebDB+lz5MMS4Qha2XJQv8Req3Ztq4mTeb5gGp1VNCi5f6mf9iUUUqH/840MEAeR44fz1EWTpjYMNk9WF4TsqOZnKnjk5OgS6QgrgVwGTqWMwsVYJksXIrgtrdqKbKQblzk2pCVYjpFp1mNmCuWiGdYWcqBQBYGK0cWeliBxLxpiqje+HRC9utdk6UsnIvkhHurpyykm5OQ3k+ybxJSVmM+tZRLFjJVIXRfCiMBbUXKTUpqQh4ZBYjHQWO15g1LFjIqKnh9iJWFxchCrixcVSu96coG+Xp46tTrQZdln8LNVrrkJPRXoSxEm1c8mpCdWchVaJZtW4ldy3PvHxaFLMRgemwUlEJgxg8JnYnk9Dgw74uT9ahsUxIFUAi+CbCmQhBPaWEoVaHgcMxCa6HKnANTL5JfNzduFqxj7ukCZCEnRqs7iB0JVQZo7evd1JA3YwoPdhjVgDZFpTZkpcpCxYgGhKKXfGThhWRDTisLla8jSb+fvE/idVANPDxIVYWXd1wu5LmVA1SPDrDW7b33MWIQSRlsRebqwjKAqkxcqjESYBYt9qyNGWHZtEkgvxNzcyWXnJByk1QKes8zt8PAOrBAGKwBpp7ghS1qwWAB6NyZY0Um6xgpOckzpHIPCTbkvMrCNhFZeFZeuUm2svCoDGUhHqbDh2g2nmWTcmWhYEXWaUhruhpW5HFXAKoTlCyp8g1D5OzZTGUh+YPyS05wDzhvoYMZv6t4VI+qykJHHBof/zB5/N/xV8Hsundk/kUki7beCfPrP0b+t0YzBPDbdwL87j3w4P6TMOum925/fJ7cX8uF1mYD62ZK2vuffRbGU8wOGzSrmkWJjc11NmOG0rhxbgha4nFIajRwZL54yzE0DORtRMbBJDoaqk3VcFPfTeT39v7hLBFK9W1pIDZMKXB1oVRuIeLvNvwd6DQ6eGbsGZIVi3j2zAz4I3HYZJ4oOIzgeeNVOrrffmEwk/hCNSMv4rMFxnNcMguJZDIlWJDvvKgrh7TEMyVXFsYrlFnIMz09Nozm0kIqYYE9bGCSAV5yMvw8VRSKLch9V5Ffxj1h8h7ptRroqZdeaxG8fb5WS78OloHUmmrJ8AoHV38898eMchN0K42epH+XlDxVYsgtAf9peh6w7y6eUYwD3td/aju09OUS/uZVzIosyi0UlIU8FqNUd4+Prcks0sZ9np4Fpp14JK1bJgsVYJksXNI25LR6Cy2MvAnZatSRjW4lMBUPCMpCCM6qTxZWUlnInqPPNEsa2mTbZPMoCzHbS9fapl4jMiNBzk77IchsyGGDn8jK3RF3+SUnLK8QVr4CxthhslJNyF1OBWQhyys0oeQ8T6g7vz48EU9e5ZeamYVJrxeSWUUbHPh+cLJwde1qZXmFDQ2SP2PVX0HJiZgsxCwVnJLLIQsxs5Cj+tbbyK/u3/02Y+OPRDq3HvPmcLWAmX0I3GwVsiJ7PfSQZrMBVa3hBgPXkGSMNL+pAlT/4uco1QRTgQpa0ls3AV7dLwfourWlaQtUsyZP91RIhg25XSg26VpbR9qIpYD2cmNHh+JG5JG5dMFJXlTxRuQyyEJsI0SLIH4tTv5KKAvVPIQuVRsy2uwRWocDtGZ1ho34unEL8vVd14Na2H5jD4lAGToyCxNn2L2RFw6UWXLCiYHOGovQIMnJQp3TCaZ+el1gK3KpJSeoLGyfweypCLFZaVatIS3oOIggzZgsO3PBcgvzKAtTc6gs7CisLGxhwfCBAPhn50m0yeYieYUca1nJybmZgKDeygdUkSXcbmJ9NK/MbXKVA5PVIFg71bAin2KlAX5dEgzxAFGmNCTp/Wk2zEgDnq3Kg/RLABKPbkYWuieDC7IeCWTh+Z+BJhmHh7WXwNfibxI+H9lwe+kgoHrlKmq9PnwP7PzLq2FragiMGrqOj/vVUXilcwufg8E4zWmDhBYiAXUbXrNLTnBt2RCmBMJhFyWu5ZWc0Ps6B75/Pzn6E/L4javeSDJyx8+4CQGI69qum3NVhRzdrBACcwulsuLRMcXJR55diBZkxE77dGFloY/u3/vY2WLv+UyykBPsZkMcjLHAouYVPnNmhqwbeNZ9zRa2F8g6M3BlYdzlyrufLxVEiT9HXx+PFcBhwDVPKwxMMoBCEyyUQeffmUczy02YBZk3Ifc22Egudz5wgtYRmiCuMHShBdxRITaFk4Xc9YRkYiycAIvDAA0dDnWGrEWAql/sdwJNCuxXXF3W1zKvoT9H+ESaLGy10QHMNMSB3DVKLTnhykKWLTtzmir+Z2oNACnTcmahAiyThUsRWeSVWFmIZGGhJuRyMcUO9k3xBPH783Y8Pm1aqmRhIpEE/xx9jl7zrHzlWxwzAccylJyYr4CLNN6oE/UdQl5PucBcGCREcLo0zw7LYIlnbjxLJQux8ZME+aPG/XoYcwdVb0IWk4VKbMgR1oRszGNBRmBwNZaJVFpdqHU6iRJKPDXMxphvDPwxP2nH662WV0IR403IWeUmOY3IF6iyMBKKC0Qn5qIgporkFnKycD48Tyw5iKrrryOlRbGhYQi9SFVvCJNeR6xtFoOuaHC+UnDi9+82/h35de/EXslsTB9T4zprWUGDTp9uu1TL0uGih4qzqdbc7Bs10bIJjpmMEIEkyYztqeqh1upiykK2wUxVtcPpfYUtyBxGhbmFoWhCmOgWtiGrsOnNY0FGIBGE+U+YLScmtJcyuBITh0FKCYX4jEv1vEIMWh8PjJPD8O42esBXA3itrt5NN/jP/+Ec/VlVKjkZZGTIarOJlDZh4HqzSPFg2UxJySDmFpZQchJLxkim1Jph+v5YNm+Cxlo7rNSOVMaCnE0Wov2NKaGFRuQ8+ze/yw2xFEaVpGipkwS0Fouglm8MzpO8QouxeF4hos5uglYnfQ7HJ3yyLMjmtWsJYVgqeLss2tjLxRhrsg449GQQiGidoa/tXIgrC3kjcukDZbQ0e8wuSEESouGEEFNTKeDnKTrIcqQtfuJG+UnTxyAFWhhm5T/ZEJqQL70F4O0PQtjeAU3JafiV8S7oRuUvDjF86hAUPLcw+PzzMOobhKCBXjt+VvSoFrpYbqFAkI4fhI2RqHyysF5aWXhw+iBRF2NW+xtXvpG83nt+RxXsuK6JC2Ky0bKimqxJ6D6azhP38p4N7yElg8+OPwsvTh6Ah4/Re3VnYrggWTjipWvQlhZKPO3LIgt5uUmVjv5qaF48ZeHP99Kf5bVb2yXFMag+81kA4ga6b1PibJCDpMdDHFEIrxVLVepJIzMSmFMsxiIDA9enrchz52kkFA6buy+hv10kr5BD38hec9ck1HfYBUKQDze5U2dtHc295QMvHJKQshxun6+gDdn3MHWxWeqjoGulFulSISgLTxzPyDvH8xeO011YclJqyaCgLKTko3eQDh589fQ9WFYWyscyWbjUgJtiwRYrIgtZg6VPk6pYXiFvy0I0YjORf1p1ZWGlMumQKMSXLqVLQtDglZ9XiAsqWrv1FmHaj0ShjYX5R9jvqWFDxmlSN5tkzjNi02CjairJkhOBLJTRNHr6IfpzNK0jCprRCigLMVeHF0UosiEXKTfJsSJXUH2KVgbeshhnGYPZ4OHBmBOCNyw5iDOy0MBv9Fm40BuRZ5iqEFW3HSxEfnqoMFmISj4kY9A6gYQhtxhVveqV5LH7t7/L+Ps/fvsOeP6TV6sasRBJREizNeLqzqthS+MW8nweOP9Axt/DzXwgRj/zziaRwkbtkpOZNFnoqmS4cusm2G+mP8/mhk3kuucHFN9MKH/RDiPmJj1NNLPVpBOUDvlg7O5S1IjMBxkOk540pirNLFSLLETLbKej84LKLWx2msmBJRJPgkvhRpeXOqlJFv5liGY0XdZ+GZj16kajbH9lN1H4YoPi6PH5dOHAxKGyYgGGWftpW4jeeztW15J8Zg6LKLewFGXhmfkzZN3ZOEoJNduOHdBUZYaVmpHKlJuIW9DNToCIB2CcWu+L7d/mZ+g64KzTZ7wG2eBxLI2hedl5hRxr2+SVnIReKi+vMNuKjCqpjEbtEuAZpf9eV28G4wqqBqufCqtuQ8avldQmIGKnJA02m1YS8eFTkAyGiDLI2NNPcgnb6uiQTkpZiJZQPmSqwftI5074WP334Nfxy0CnSUGnh+7rRyfLiwjgMK9eDbqaGqJmrTnrgoDRnSGaUAs4wEcM4ZqAbqrJw7CBRTWgxbfoQIaThVhugwN7Bq4qRAUgEh+Dh2cI4YM5ottfWXgfjGse32MNSrQiIzocHXDzCpor+fW934b5YAxaLXEwMyeB1EACHTNYvIS4egVdg46OezIUv7NMWeiIsntF0+IoC4PRODx+ku6pX7ctV1UonCk1GnDX0D1ETGWykKsKEzYzxPUaaLTWw1oWq7BHSl3IcwvPPAxwhkXdtG8HMFFiCjMiZZGFzIYcm56Cph6nkFvIyUKOdLmJKK8Q4RTF51RIoex/kqom7V06AH3pgx0EySzUaCDhmiEKUQSeGwTOAIf25SoLMbMQX1OMxML3tpkOfpYLTuRjmSxcAsAbkkCQBGcBoszex7IHErEkhNikEZWFjY7KtaXxtuImnKgEXGn7ZJlkYaWJIK+LbuCiNj8JR+9ydCnPKxRlYnArcthYLdiQ1bCG8NxCbkM2O/TqKAt5XiG7YY25uc1PPbJw1D8KiVSCqEgaWBGEHESZsjBfuQlHk61pYRqRmRU5Piu9EVNqQSZfa4orC6XtZWmFbuiCLjdp6HJAYxfd7EwPFlaKYKA3qtoQ4hIf5623kl+9f/kLJPxpK7NBpwWnVR45KxeYR4fXrNPkhCZrE9zYR8PB7z+XaUVGW3rQREmU6vZqCbKwxM1KNmaoJfpMsg2mK7lRqe2DA1Z6ENpiaxMU03qTjuwfJa9DLI9AogGn4CfoWti7uQH0RVREShuRuQUZVc8Fw9N59o5/EiBewmuFqnG0IechCzOsyPMXBlmIn5EWZ2m5hUITckO9ehbkQXVakKVgrzHDusvptfv8H85Cqm4FgNEOgDEVedpH5WCIWQ6ts/SQ3MksyBxW1ogcPnoUukytgrJQ7v2f5xWuYdygdft2smcTyMJKKQuxFK3nsoy8rIL3nbAX5oKUIKplRSRFycIgkoXK8i6FRuRxj7xykxLzCjkwMqF9FbMi7y9dXYjvd4IpMqvabGDqo2tFFStrEfZsgrKwPLIQkXSy3O2J8vMW8yIWhuiPqMre4NCA5o7fAliq05ZcCbLQNxsiDb5IduHeGMm1+0/64aPx98LE9d+H9hS9R4zu/S7Aiz8sm6TAKBfbxXTN3nguCVFzoEJkoUhZiPfmWBBWp0xkSIw5kkWVkiZ7mpxhaxK6bx4foZ+/O9beQYhWVEeTn+WqdrAzIUIhdG8oTBYi3r3h3URdeNy9H3SW8/DGHvYZtzVKNiHj2SuajJJ4pvXN2IBtBXQ57x+aT1+HTFloY6SifpGUhU+edJGBGDZWr2mpKnimdLE/VsP9JQZ3HkWr6P0WSd+LWMmKJFmIxKC5mmYWPvdt+nu91ILMiy3lkIXchoxnCt4aPHXeAwM1AxmDTnQ9BTwRmBmhZ95O3qzNh6zII/D8RBWRDIUgeIDaee0ry88+RqcRL8oTW5H5+zuuhrKQDXS04/R+YOykn1m0IV8oETSLjWWycJHhj/rhsnsug+t+cx1pOBIYdMxBMdCNHi4IiJQWAIfhDQuoLOT2yaAnCrEihQaF0GytrLKQk5ke00xZTcjZZGEwZSEkYiocJoHn5aK/kd4o4kEqb7dWmYorC5FADnsLH4p5TsbKV5DNyQSza6hpQxZbkOU2pIWOHiX/IUz9/Yt6jXDoWLh/PhsyVxbKLTcRKwvzFbg4mbIQS3guxJsTzyvETBQezI22JLQnKyk54SUCaElPhULgfSBT4ac2eLnJqppV5Jq9rus6chDARjlx8QkSKUH2XKtZzlYmWaiSpWPm9IIoC5MagIMmurZsTdCDHFEX8tzC6VBeVWHC3ABnXqKbzIEdxQ8LQiOyTGWhUG7CLGB5Ya0D4Gq1Ug7iWGwSD9OvwxUgWeA5QGc9F07JCV/TR3jOFgB8a/+34A1/fAMcnaFrbaHMQp1KykIcquAAyawzw6Vtl0IlsOX6LkJwox3v/MvzVD1XphUZyRBrEpWWESGTUwxDRwd5jbDhvmnYBxht74v5pAd6efIK22bxwB0DjckE5g0biCI0bUOukLJQIrfQUWcRytpwX5AB93A6r5Cp//IhVEuHg81ht+y8Qo51jIg8OpZ/D5MMhyF8kq7Hlo3lkYWIFVvLb0VGYkoXRWNwCtp6qsDElIXm0ZksZWFL2WQhv7b0tXSPPV+osb4cYGP9798D0TP0tTau3iyQC0jMiMl0MbjSEe8faHX88XNIngNcPtAALRe9Cdp204KUUW0K4I8fBPjFm9DjXtZTtV1C7Zsbz6dAZ6fXboA5rNQmC0lmIStOMrZuEvLgDs1QAltJyQk2IKN74fL2y6HX2Qun9k4SlavJqofN18kTMZA1SQOECMpHkLbZ2+CWFa+hz7nhYbimnhFDDSsL7t95nvuO7tqM3ELcl2KhGcI6TYdnhkXKLHzwKBWV3LCuOe9ZgzfmjtuilVEWsvNB0EEH2FhsdBEblEiWnKACrv/aTCcKKzdJJFNwetqXU+YnBX6GwBx0HvnjGvZDty2tSMVyE9zHjhyj711jpwMsDmO67MrWUDErcmDvXkhFY6C3xsHUzYjJMmFenb/kZFKvp2fgSOGM9MLKQvq1LFP0HlS7gn5GookkeMPq5qD+tWKZLFxk2I12Yt/Bmwvm/wgV4RLlJjGTltxAGhnBpDaQrMSsNnFmocmmJ/kZBRv1ZIBLin1RX0UKLPjkfFo/qswmO5/bPI2w19LXOOCJC2oxVUpOmuzo/ABtlG5+nE5bfrLQXEUPuohCk5WhZwGiPjpRbN1C7Gm4CGI+YrOKlk6h3ETma4uBwxOf+ATJ/XBce23BzMKFsiEj9HX1BW3ISpuQydeaniqYWViFhzYNCqQSQhP2BUkWsk0JJ9NdRazIUmQhIa1uo+pCT5YVWW3w93Kglm7oUWF4RQc9UN939j7h78WmXRBiallOqNF/0KGeDRkJfxaCj2ThbCBKNpGVAJKhGFlhTSZh5Xx6Ey00IksdRhlZOKq9lGS5YWB2+8rixADPLMTNOh76i4Er4go2ISPwkFBOHhiui4jOizJU42L0VfddUMpCqUZkDDz/3yP/S/YPb3vwbfDoEBsc5VMW1stXhRcCLza5tP1SsBqKvJclwlplhE1XdwjNyEmeW1hiyQl+3pCs7o7TPQ1mQtmqM/dTuD7xNtbYoZeh1d6qqBEZlYVCXuGmTaA1GqFRF4AmDbNSOvOXG5QNrmTB/OJokPxsaMlFopDvIwW4h2Eu3l6wCZljSE8PuP3gl51XyLGOEZF4WMa8UimEjx0j+wTMBdSzQpVy0LOxgcTJYAab5FonAzxmw6VLQWejHUx99H3TDo1lZvGKbcglDgJ5/qG1gb627jLt05LA5/aXTwEc+wNEA5RYMK5cJ02c5csrbLKCNxyDX71ASYh3XkLX/vYGSoCP1bQD6IwAp/4M8F8XAZx6qOSny5WFvRMAdqsmI45JbRvyuCcEidH99DdbN8GGemqFPzQthyxk+0TXSXJN3HvmXvK/d669E+KxBOy9/5ww+MhXFJYN3GM1M1XZ0JH86sKL618PqZQO9LZz4I0fktWEzPfvO3pqM3ILMSczHIiRW6Vp7HjB4XclEY0n4bHjdPh+/dr8ZCW6VpAwczk1qkVFSZGFPptWUBZu664h5yr8jHD3lqQVGWFyCjm7ONgLx5Jg0msFUj4f+GuemJ8He5UOzHYDiY2JTGuEczQns4eOMgty1sCrkiUn/iefJL/aWyOg4YOSMmFaTX+eiCi3UCCDzez1KkVdyEunqlpJeZaZiRta+zeQGBzEcm6hPCyThUsA6+roDZuoArjSTURe8clSiGW8VsqGzC3Idq0BbLix8E+TjbOQt1aGhRJJUYfBUTEyyCtSFmKoMF9Uy1UW+ubDggVHjZtRX4MdLCnCGxHU1lDLY17VAn9eGJibDxioixi4DgMXhUMkEoX6AjlESsFzm+SShTPf+Q5ETp8h4ejNX/h8UTVipXMtOfT1+W3ISGrhf6gmEcv+5RacGPLYkNG+Y2eH0gutETkajgtKByQLEYIVOU8AdyGyEOG8+WYAnY5YzyJnKkfUSBG/N/XSJkHMLeTFK4HxWUhgbmkqRYndSmQWMlVhyt4Mfo2VEBdzgcoE2R+YpmTKxkgE9JPpoPaCJSdsCn0qcBH5dcW2JtDKWD/w840Nu/jaRYeKv05cEVew3ESNTa+QV5i/eKO/ul9QFl4oil/eII3rPGZQ3fX8XcKwJZwIwwef+CDJzMr+edJkYfnKQvzaDw09VDELshibrukgqhxU55z27yxLWTjuDkEskYIVjCzMVhVyWJgVOaSw5AQHoec85wSy0LpjO/nV5qGf/dFUPUxFyst4KojaXjrgSEQBhvcQwkzYy2QNe1Pz6SbkYmThkTi95tojhYdDUsCM7Xq7iVgeT0x6C+YVmjdukO1aKG5FrinLijx5nj7XSV2SqKCNjCxMjk+COQpkwO+OuIXwfIiHSrb9cZUiH+ZUJLNwz3cB9tL23Kh1c0aEBKKrll4DqHjHzLh8ZCEShYFoAvob7XBpf72gVkOMJkOQetdjAI1raI7fz18H8KePEOJaKQxNjeBuc5JDaouf7pkC2YR3maizGcFmpNEcsRG2prRuho2NVN16eEZOIzJT8s2chHtO3kPySjFPblvTNjjy5BhR9dqcRthwpXT2Xj50raevLTYo58OLZ1MQc9M15vvzB7BrPW+5ybBvWMg7ROzsoWvf4VE3IfGFcpNaI+gw9kOvFyJ7FhLPnZ0BXyRO1o3NHaJImCxgrh1Gywg2ZJXJwsQs/Uy6rWly0mE2CMMPSSsyKgmxKRzRcylVG4ryCjGGCsnGQtBVVwsFTwmXSyCNMfOSk9hbm7aSARBXFgoW5Jwht7rKQrzvp8nCcLoJvkyYV63OURbyRuQJE3sDlOYW4nCeR7o5WiA8TP/9vA2gvbEP6plDczm3UB6WycIlgLX1dCp3ZPZI+gMh0YTsRUka23xV1IJsYKuvn/6/WrmFPJOuEmQQb/vzmmbJzRBvJIozC0Wwsw02FqcY2tpUUxYiWWhjVKHRqod6W11+ZaGc3ELc5eAUFzHwCvLLKLP5qVluIj4sySELsUly9n9/SB63fPELQqnIUlAW6tgGKCGhLDw+SydbGGyvRC0Tn3YVncRyK/KFlls4O+rHkxFRqaDSB9HIrMjFSk7ykYVIWNivuEKy6ETNjc2p+VOCbYPjkrZLoNpUTZ4TNiOT58DytCzaECF2c8hCJNLQwqVCuYmmvp8cUhCVsiIfmKIHny3hCMDky0IhBD+MeqTUNu4RiCVNcG6GrjkD2+WpCvBwr6QRmduQOelVEbIQf95h1g7fRclPKWBcBVqyUFVf6XVHbWXhyLwPPvH0J8hz39y4Gf74mj/CG1a+gZAY33zxm4RE5GS42pmF+LlCpR0O5rDcpJIwWQ2w+Tr6Ody33wkJzEebPFJSjiUqQnAb1cPJwqy8Qg4rLzk5iGRhl+ySE1R3JpMJWDeiEfIKCaaPkV9OJDukmzTVAhJtvZdnWJGF3MLZzPtOcGoSIik7aCAlDBGkQJpc/VQRVeWdKeEpaQQr8hGW95c3r1AFCzJH31bWinywNLJw9AxVgk4bUnTwWltLSjdwz7XS70jv2zAuiDtAuJKlRLKwgeWz4Z4fh3Sq4eXfADz0Gfr42i9BdC6aESGBwLxgXjiVrS7kZCEKB370LP0cvOOSHoHYRfUtDlhD8RDMVbcCvPtxgJ3vo//4hR8A/M8VtJhIIU730+uyeWKqIpmF+Pw762xggDgYeYRD6xbYWE+vw1Nzp8jPVBD1dG8RcZ2EX5z4BXl855o7IRZOwP4/0/PF9ht7imb/ZqObkYWjx+cgLqHIxc/lX45MQnTmStBpDLAfQrAPS83ykYVZykIc1rU4zWR4cnBkXrAg17BEAnTJaHTKnrMa+AuzIF+3tokMOwoB1WczXFk4obKycI6eD2YtsYy9LLciPy9lRcasSD6c5JZkklfILMhF8gqFEkaRFTldcuKBT+78JPzbFf8GN3TfANODXogE42C06IVswxyyUGUbcuT0aYiPT4BGrwFbYzQdwaCSDRn3j8lgMFM8gpmFpQzs+VpsqiLZorNn6ed7ukZDSOZ6u1HILVxGcSyThUsAvAI9Q1koIq/4DXKWHfgqZUPmxRJNZrbxwcmgiCwsx4ZcaTKIEzBe86z8vEI5ysJZdclCtPB02ujX1lp0JAdDlrIwH1mIocr4Z2j9YHlFlSg3yc4sLARc7Mc/+QlCrqCCzHHNNbK+Pr85oMK1kiofwYYskVlYSl4hBv5iQQb52nmUhRmk+wWmLBTKTZiqECG35CQfWYgQrMh/+APJB1Mb2NyNsQpoVcHsIA6DzkA2W+KiEw8rSHKYsp4HWsxw8IBKnUDpYfnichNUIjQwdfi0T33iAD87AlkY19BCCEZUCsrCPDbkwch2iCf0hGDI2YCq1IjMC04qqiycOkrLWowOgKb1ef8aXht8PTvtpuqvpQ5u3z4d+T0cdh0miv2vXfo1MOqM8Omdn4aPbf8YObj/6tSv4O8f/XuSi4yIz7hUUxZyC/Lutt1gMxRWpamBDVd2gKXKCN65OJyI3wSQjNH3WCEGZwPQktCCMYkkpMQhS9TGinmDaF1a6XPIVhZiXmHLHIDTnyTqEIH8YmThqVSFyUKxFZnnFgqNyJnfF5WaCGdVLHNAIvF5PZqk77HWPS8raiBfycnRPI3IocOHVScLezbWk3w9zH2TVFIXQApV3zgkw/t7jUFQA3Ercr/bnJlbyNWFJeYWCmRhTR25zhGl2qdzcP5pgHsZcbfzvZDa8T6IjozkkIU5hR8icKXjyWCY7C9rbUZ4zeZ0XhmuPY3WRuG+SwjUV3wN4C2/A7A303vfD64GeObfFTWZ7+uiB/naEycrYkNGdNdZYaVmGLS4plhqyJ4b96JY4hdPxWk8lIzMwj8m3eR9xHPOtd3XwsGHh4mtFwd0qy9WTqrUtdlIGUo8loTRk7mKVVSrDc4GwQA18Joe6pb4zxonpPKQhUM+un/n5yMkpbgVee+5OaHcpMpI78+GRWhCRrfFQ0fpOfSGtcVfM3ytZ9gSjiRWqtyBrvi5sPPBlDEi2JARBUtOEK/+DsArvwmw6S3Cb53k5SZF8gqzG5EJWchyC1HpjPtpVPLjezfMLMgdq2tyHSDVlSELuarQ2mEArT6lmrIQ9yQ6HGKmUhA5RfeqPP5jAuJUMavUhszXYvYc587Ss523wUbKF1Htjli2IcvDMlm4hJSFGBbu9kjZkOnmzJWgk8YGdpFXTFnIFIBcWehkk+lylYWVspniDRknLAifaVZ+XiG2f3LbSHZmIWssi4YTkGrkZKE6k6tuRhbG9BrhBiRFphDU9BQmC7mqEFsQsZkNN2vMhqxmuQmqV1whetgsRsZO/+u/QWxomEzHmj79KcXKU5zkelgr60LbkLltdU3tGsXlJhqrFbR2+h5IQbDzX2hkIbMaN3Skf7aGLrqB8c2FIcSavaVQ6Pq2X3YZ2SBgcRDfhKgJXmCCuXRIEIpxUx/dXD82/BixDno99ADjyH778N/x3LxyrcjMhoxlG7ykqhLKQjysTYemaeshJ73HXyK/OFkeY8ATzVWueEbhVJgWVQzsyB8sXk4jMuZdeUIx+crCUjMLh/fQXzt3ClagfOCNyNicfSEAh0A6yzkIWGn8xD9f9M/Cxhrfs7eueSt868pvkdb6Z8efhTsevAPGPaOCrUpNsvC67utgIWAw6WDbK+g9+gXfbRBPGUqyImO5SW+Mbns71tTmtdkTom89JZk7B4OylYUZeYUbN4KWlQzBlFhZWOHDCW9ExviBwCxU1UkrC+dn6POsaSj8+UAFjd9ggYiB/iyxceX7t7SyMPe+HpuagjiWE2i1YFmXztArFxa7EdpXUhvj2QPKBj0Yu5GMJiEKKXA0pfdRRlZy0jWryyo54WThWEnDHf51ai21UKOmFRmvu1/eToddq18NcP1XqF0zHgeN2ZzjhOB5auJG5GgoTvLsEL88Qd/723d2gtmQqTprdzArsrhBeMXVAO97DmDVjZTgf+TzAD95tSx7ZDgehuca3BDVAZjH6WcP1Xr4fNQEtkBv0LIhF2bMaTRkHd3YwKzIriJWZEsNJO3N8BMnvcZvX307xPwpeOlR+jPuurlXVpxHNvA5cHXhkIQV+cEjVHRxWX8DvK/tUjAmU3DAbIY9ntym+EQyIbwv4vOROLdwlg0PqlLzi9aEjM3MmOWMCtedvcXdSKgQm8MEFCyhjMXylhaWgji7X06Y6LrJhR3bumpAr9UQ0lxcMiagtgdgx7sz9h2nJukeeqAp//lADEMjfe1jk1PQhHttDRWu8LJTxDC3IEtFaQjKQnUzCwULcgsbGDnUI5TTVuTjGXxBKJUAj1ar3IYs5BVSsjA0TNeQeDO9rpbJQmVYJguXAKqMVcICfjQ8k9eG7NOmyCJVYzVWNLOwycHIoOAMmQJyRVS59slKKQv584qZwhDXRRU0IbPFBy0kjGjjMJr1pNwFEa2ii1ZsvHxlIaLFTN+/gDYl3IAwGDmZSipXFp58MCdYlysL1bQhc1Uh5nZgSUQ+BPbsgfmf/Yw8bvnyl0FXJV+dZNKZyNdHTAYnF9WGvKpOvrIQDzsIQ0NDQYLlQrUhSykLTRa9YGmdGsxvRcbpfD6yUKPXQzVmF1bIinxi/kSOBZljff16suYiMf3I8CPgC9GDj7NOYhCjVm4hU/dBfb8QJTFdAbKQ5xVibpKlhWavcQsYZnlhcQnCk9WIHJ6bg+EI/fv9Mi3IShuRR5mqsMZqADsLmK6IslBcblIEQsmJ+8IoObGZo2Buuwc0mhRc33kjvKKHxk+IcVXnVfCjG35ElAhYdvPe376Z2ui1WpIxWQ6QVMVcPlRlYuPnQmHtJW2keCwQtcOR4A0AY7S9VAlQMdVbxIKcnVtYfYoeOvCwHUPCo4iyMJ1XuIP+Jqrkp48vnLLQ3gjQxEi3809KO0OQoPLS369lRF4+ELudRgPRuqaSs8HWMmXhyUkfKTCQsiCbBgZAa1W3KKdvS2NJrcho8SO/krzCtHLW1EcHC80z8cz4GHHJiUIE40GSc4eoMdVATTMnC8ssOfGMAfzstVRhjevgrT8A0OqEgY6xqws0eAiXUhbOpb83zys22PSwd9QNBp0G3rqrS7Kdl4seMoBRO2/4P4BXfxe/CMDQMwDf3104hxsVrb4RiBk0cLpLD7pkFAy6ZEWsyJjVuEHDBkW8QAnVzA2s5MRV3D79TEM7nDcawK41wW39t8GLfzpPyuzQgdG7ufRCqa71dI0afHkmx23DyUJsDG70TMHrfXSf9p8v/WfO30WBBq5duGY3W9MEz05GFh4cmoe5Cfqe29jeezGUhfxnunp1IxhkEKxoQ07oNOB3GlXPLeTEo8dG8xExugZhM+lhI8tSzKsuFCGWSMK5GaYslGFDRohtyGgz5pmyUyxHFQvo+L47J69QvG9SMbMw4fFA6CAdOjvq2X5eJWUhwrwqsxEZz4P8fEysyCUrC+nanBql93FtO/3/ZbJQGZbJwiVmRT6CuRZoKRV9CLn0HtstUZFSLMehbLLQiRsBDXoxAIJzGWRhOfZQPilQmwji9hq/mU5auhxdCvMKpW213IocttDFODo2roo9to5lMMwnEmSSjEikEjQsOxv8uSFRkW3fCMwCjO7LJQsroCyU04Sc8Plg/NOfJo+r3/gGsF+Sv1ig6DVSwfwwrqxBi5nY/opN3Xyju7pWulGu1LzCC9WGjFk5cxO83CTzUNnYXbzkpJANGeG8lVqR/U89JZTEqK0slLKUI6l7Y++N5PH9Z++HQJy+N06pya8ajciJGMDcOSHjqJLKQm5B3tq4FaCF2fom6CYP4WyQKDlJxODsTA8kQQ/1reaihQfZkJtZyPMKsTBAFkqZkOMaLaPcJLvk5EIgC/H+8+V9XwKtwQPJaB3c0vn3BfcUP3/lz6G/ph+SM/TemHDays6hemiQFptc3HoxONDmvUBAq+z2V9HrbL//NoiOHFH8NSanAtCUoNvezjXFyEJKHKQOnyAqTbQkjvnyDwxxjRv3j8FaThbyvEJUm0U8kNTo4FyqBaYrrSxEsEgStCLzwqaMIVXYDfMRer+q6WoueL3xbC4TO2SVMjRFNSyqhTAf7RTL76pkXiFH76YGYkV2DfsU3Xf5QXxCT8tNOExMWVjHVH9pZSFXQCsnKzjhiNcY5iTXNNO1112OshBdMz97Hb32MFPvjT+n1mDRGp1tQRYrC8U2ZG6H9rDZzk0bWqGxKrdkkSsLiQ05GzhE3fJWgPc9A9C4lj6/Az8p+CPwQo6xNZRsM8fpexJQ2Yrcna0slCALi+37f2yg5PFrLR2Q8Ojg6NP0OrjoNX1lFfa0r6wBvUFLCFKeKYgYnAnAiUkfscdfs7oRwHUC3uHxggm05Pk+N87uf1mvJb5HaMEUZ6hjfrIlmoJELEm+l3lmaFGUhSSDkeUV3lCgBVlKgDLnpGt6DBXKKoHHFHmslMQXv248t3CPVG5hFvC9wnUPi3TkCji4DTk2Tc/kzb08t5B+BkaOz5Ec8dpWtKqb8w+4MTonps5wyv/MMwCJBJh6u8FgjQNodAC20onwbJjXMGXhiRM57++4Xk/FPUrO31nKQsMka5zvppFE9Q6eG76cWVgxsvB73/sedHd3g9lshp07d8K+fYywKIJf/vKXZOG85ZZbSvm2fxu5hdiChB90NvHDBTzELACoLKxUuYk4s7ARK8sxqBURmKakmQaJgySEfKVni1WKCOKb4BkDXRwUKwuzLMjZZGEI6OYtFQwSgqlc2NnHbjISJZM+PrGSLDnBibXWQC0c2RaX0w9RQhczuVhGBd5weRuymspCbsEqlFc49bWvkdwQQ0cHNH30oyV9Hz71rGQjss7pFD5f8bn5HAsy3qAKqSezgdO/YnmFYmUhWnpiEfm5PYsJ3KBifhOq0WzVmYrmRmZFdhUoOeFkIaon0O6bDVNvL1g2byabEMwurARZuLI2V1mI4GTh3vG9ENDRn6Wmq7YyykJUUWDZhNFOPtM8SqISZOH+qf3k1y1NWwBaN9HfnDgsKjmx5GZi+SbgVPAS8rB/J1PJKICxs1OYPsfn54s3IcuxICOc7BCOLax4wJSD2bM0b1dnAmhjykoZysJz7nPS6u4lhHvP3EtbiFNaCI29EWZ9xZUXd99wN1xsprEKIwafZFOyEixUC7IUVu1qhup6I4RTTjg81A8Qla++wp9ZO0UPTs42m1DWlA/WTfSzExschNXatqJWZMycbpoHqPUDaAwGsGzieYVUVRiw90AUDDBZaWVhNlnYYBaiB+Ixdt+ZH4K5OCV3ajtoa3C+vMJxT5ioyWp7u0o+kItLTo5mWZHDh9TPK+SwOIzQNqDcisyVhRO6VEb2M29Etk35QB9PpbOmedB/CWShYEFmrgquLJwrlSzE4h+0Hk8fpXmBb/lNej+PSylTf0uThXSvK7ZX8vvEGZZVicUmUhAakcU2ZKm27ss+Qh8fvbfg4X/ES1VRwS00E9DooecGv1vdzw9uYwY09HslWtJkISrz9Ro9GQIUOrMcnT0KL8TmQJ9Kwe0RDey97xxpqsWYg/ZV5am4sRSlfXWtoC7k4KQaklbV6DRznYCGRBLeUEvXrO+99L2MNV4oN8kSUvDcwoYEJTSRfIozgsrQvLDKwqPjXuKKshh0cNmAPBKKk0kTjriqUVHJSASSfr+gLORxOhzi3MJi91LehIx5hXKJY4OgLKRrFs/WxZITBM8rlLQgIzB7kxc0lhCNUNCCvJ2p1u1NRKmsFkxMWRg5eRJS8biwf0FMosAm6lPWNu9l9ylHCySjUbCxc3FNHyUll5WFFSYL77nnHvjQhz4En/vc5+DAgQOwceNGuP7662G6iCpkcHAQPvKRj8Cll9JMpGVkYl09/QAeNSFZmF7QhYwCrQZCGhCC8SuZWYg5EGBjxId/mkz07dWmsi2UYrJQzQILnqXoMbnArDMLQcullptkNyIHfHEavqrSzcgYpz+7KxonGV4FS05wMeZlN9lWZJ5XuDKtKpwPxiDEDgSt1QunLPQ99jh40Eqq0UDrV78CWltpoffCzaGCykJU1nArXkKUW8jJQiXlJuLMwmJkIVpAMVT/QrIiu4a9ggU5e6PDycKpIV/ez7NVbyWKiUKN39WvvY38itePWusCZl6OB+hndaCGHjaygVP2LY1bwBKxQFxHn2NNX1NlyEKRBRk/I7ykSm2yEF9jTmhgQy7mI5JNYywAMEuVc9w+LlYW+kZGYDxG70H925UfEtA+qG+hn93o+fyECh9ktMspN0EYbXTjy211SizI7dsA9MWHax2ODtLqG06ECyrHFhtYsPHVfV8lj/sNr4VkuEN4PQvBbrTDe9vfQB67bRrSlPzlvV/OaEqWC7QfowIT8zCv6GCE1AICs7923EKVoAcDN0P4nPyGVWw9bA/TNax3Q/HcRl11tZBRt23aXrTkBPMKuarQvHEDaM3mjHKTWB0dWlTchoxA2ykOGd1DYI6MksxHBOZeIULjw4RwRXlKNSOnpMBVhRvbq8HSTgnTeIlWP15ycmQsPVzCQ2HoCFWIWjZSJZfaSFuR5ZGFOKCfYeUmk8SGnH599A0NoHU4QJNKkSKb3MzCEpSFbN/H94H8/fBMByGZUDi8wKiBe98PMPg0LXe6/dfp+xdDdHAoL1nIbci4rsTZ9+bZibOaFCGW1rVJD1IlMwulMHA9AO4J5s8XbEjmajjHqrVk/20MzlbEhtwcOgt6TRJcKSdMJNPEOe5bBmoHilqRcfiCuD4QBN1oCE6/QMm2i26ha0e56GZW5CERWfggIwuvX8fu1dN07/r2VW8iZyBci54eezpn/y4lpKBkIaUCatvsEJ+clOWUqZQF+YqVDTl5mEVz8DlZqJINGXO0ESm9DoKm9GeTY0tnDRmg4OAHS2YKQcgrbJSvwhfbkBHNvBF5yAeJRBKGhLzCPGQ07tXVcMQwpBIJCDxFryf7hg7V8wr5wBlz31ORCESHhjKVhRZn4TguKfjYtVDVCrHRMdCkAMIGgNZ2er5bJgsrTBb+27/9G7z73e+Gt7/97bBmzRr4/ve/D1arFX74wx/m/TeJRAJuv/12+MIXvgC9velWymWkgQSFFjQwrdfDtLM5p9wkZdESdV+lmpBjiZiw8SFkm71BshG5HAslISFxcpCIwHykyITA76IKERnghRE+8yx0VHWQfAllNuQ8ykIm7/bNRcDYql4jcixAb2xBTQrOTPuFqVU+MkUytzAeBTjzGH088IocCzLaHOXecOVgyJO/CRmVRBOf/Sx5XPu2t4F127aSvw9XFlaSLEToWW6huBGZk4VKLMjka7joIcTArAN/TVZktG8hGjpyNzr1HXZi8ULlc74NPBKMXF3IC3Ky4bj+BrJJQHtU6IDy0gIpnJpnjWq21oIqUSw6afXQtc4UngNTg5SyUIVmOd6EjOSdqKTKpfJG5eD0QaG0g/zcOGxoXp9RcsIbkcWZhaf3089Bq2NUUFQrhZxG5FFmQ5ZVbsJR1a5sQs7LTboulvXX0V7UW927pK3IeH/++NMfJxmbO5p3wO6G15Lfl0MWIpIsrL29ax1pSr7n5D3w94+lm5Ll4pGhR8ivu1p2KVJfq4kVWxqhzjYL0ZQNDj4i354+6PJDNys3kUMWIqybWYbnaKKosjAjr5BbkEXlJroW6h5BG7Kaw1JJYAZzB81M1JDcQl5yQveTc8NsX2fxgwGjb/Lg+fN0XdjVWweG1tayBqZrGckkLjnB5stUOEwIOB5lUBErsobGZcgZ0iFRmEykyP7Mo01lqKDxfsYbkdtnUqLMwjbVlIW479QbteQ5ZDdYF8UjnwM48hsArR7gDXcDtOQSsGkbcu6+t7nKDEa9FuLJFEx42LXCshPndEl4Zx5VoVhZiDFDBbM9cQCEhCHi6O+LkoWdzi6wX7wbzCymR+1GZN0EvWceSvbCEMvU5eAlJ/nIwgn/hBDLcKfHC8+PXU0e929rzMh4Lgdd6+qFNlwsk5v0hOHgsJtc09evaQKI+AE89LWqb9sBb1z1xpzsQv5aSg37kSysT2oF1wGPg+HqtoUCJ0Axg1EubAYbicKYqdKoakOOszzzaJWFEG/ZykKLUQebmSK7WG7hKYVNyAg9KzhBIQK+h6g2Npp1JAfz9L4psufWm3TQ2kdV06rmPedpq0dXnbaqCixtZtXzCrmIwzxA98fhY8czlaNmtgYryS0UKQv9g3RfN1mDQ42OjD04koUVvx//rZGF0WgU9u/fD9dcc036C2i15P/37GEbdAl88YtfhMbGRnjnO98p6/tEIhHwer0Z//21A7NKerX0Q3iUT6RFU7SIkb5VlbIh40E+BSlii60x14iUhVOZTa5lKKKMOqNAHBQkg/CD+8PrAL6zFeChf6a2igLgz8lrnpWfVyhLWUhfa/9cGAxt6pGFvD02qAU4M+UXplb5G5ElyEIMiUZZNkrBRTkrY+6g6hZkXEgLKQunvvQlSMzMEItOwwf+qazv1WxfYLJQVHJyfO54ScrC2JQ8ZSFCrbKghYJrhG50GrpyNzp4yETbCmJahhU53/Wts9ug6oYbVC06KWZB5sA21xYf3ZhZ4m7pPDexsrDUTYXQhExVUTzzaVpllRG3IG9t2pr+zRZuRX4pU1k4FRQ2SadP0vvLQCdd70uBnEZktDUiOpTkqQqb3hHVy00ulJKT77z0HTg2e4wQdF++5MvQWWvPIF+LAddnxMr+nfDvV/47bUoeo03JStZaoQW5a2FakKWAA4pd2+iw8fCx6oyGyEI4c3wWTKCBmD6tii4GXnLSdIZ+v/MeaSIcP0dHRE3INl5uIlIWWtspaR9NJMEdLD3OpRQrsoPlFvrYfWee5dDWVufPa8Kfae+5uVyysGRlIX3Nj094BdWakFe4YUNO2YZaQLt5K7Min5GhLuR5hagqRFKg3p5pV+dqUyQLc5SFWCaC5I0CzIXo1+CEBF7ffI1WVHKy978Bnvs2fXzz9wD6rsr5K8lgUFCOSSkLMQ+dr82YW4gRJLzgxFRrgmtWNxW8z2MpAUY5TPqLrClrb0mThXnuqdw62+noBNsll4CJCQwCKisLYZyShS8jWZilEuO5hYdnpBuR/+/4/5G88Z3NO6Equg2Go1tIws2OV6snirHXmMhgFjPqho7OwkPHJgV1G9lH8EEkntmstfD2dW8n6zvao58cfTLztZRQFq5qroImRhYGEgEAzPHWaIiKdqGAogn8D9V6V66S6QxjQEJphi3n6ikL6bkgxMQ52cpCxC5mRebq63zgGa0rZZabIAyN9LUnDc/z82RN4FbkFx4YFPIs0fWXF2oMuRn8TzAL8iW7QcMciGorCxGm1azk5MTxDKfZBGYWIuQ2IifiNK8RUdUKM2eoen221iAMOXlmYTiWhED0woiFWkwoujvPzMwQlWBT1sQB/3+S3YCy8cwzz8D//u//wg9+8APZ3+erX/0qOJ1O4b+ODnbR/5VjHbOnHoFoDlkY0NE/a6yQDZlbkFFVSJR52KhHngD9fSefTJdJcshSjqGikJQBpOjm53+uoJlbEkCbBir/yHMzzcjPK0S7Bpdn58ssrOPKQiQLy9soi8FzHwOoLHSJlIVSNmQxWShuj+MtyP3XCfl7iNEKlJvghtgX8xFFCio3xfA+8AB4H/gzgE4HrV/7KmhN5ZHZC6Us1NXXZWwKwvEwySxDrK5bXZoNWcYk1snyo7gadikjEU/C7Jg/r7IQ0cRIxOnB0ktOxFZk74MPQsJfZgukSCVajCzEJvpVQPPc9Mm5PH8JySoMbQ0LSmvFcHFlIX0+vOAENymBiHI7aLEmZLRXC+C5hUxZyLMzI8E4hAMxmBsPwMy8FbQQg77+0iesQiPyYH5CZVRpwQl5wm3ybcg4Rcd1HcO3mbJKDlCJuVTJwj3je+BHR35EHn/hoi8Q6xVXZnIleTHEXTNCudPVnVfDj65PNyW/+U9vJgfLYsADJ36udBodXNlxJSwmurb1QZPhJMSTetj/oLwDxPRJqmiLNsgvibOykhPTmVEwxNNDs2yg9dI05YF6XAb1erCwvENyaGGffUPLOqi10QPKlG8BcwtRWcha3rlSbW6GknW1DfkbyXEvgRlieIDf0lWd3gNNTQmZUkrQXWcjIf94ODs3Q9f4UAXzCrPVqHJbkfnga0KXJHmF2fEbvBG5fZbujcjAxeSgtl9xsH6JykIELznhFuCiOPYHgD9/nD6+6p8BNlJ1WTaiw8OCxV5fI51V2VVnExqRPbMhbN+DOKTgtZd2k0KNfMDXiTcij/iLEBS4b8V4DFQKMbJODHQf8T0gxkTYLr4ITExZ6GPXjmpg3/9QCpWFmV97Yz29Lo/PHodoIpNYx0K8357+LXl8x5o7YI/vLeTxmtVhQb2vFrrX0z3U4OHZdAvy2kwLMjSuEq6jN616k6AuRJUnL+5D4jUbyVgSqlhm4cjkqHCfwNzVhQLPYNy9oh6qzMq+L94PZ5wqKwuZEt9vp8PjbGVhdslJPmVaOJaAwVl6TQ00S5Tn5YHGaAQdFzQwK3ITsyLzs4NkC3K55XDF8govvzynOERNmFfTs1eENSILykJIKLNUo8gJ86dZCYv/PN3XhRvTbgirUQ9WpqqfqUB2+F8bKtqG7PP54K1vfSshCutZA6kcfPKTnwSPxyP8NzKiXv33Usa6AD1wH43O5ZCFHkhWVFnIG4q5VVhoOcqyISu2ReTLmChUYDH8XJokw+eBk/kfXAXw9L/SzbcI+Prg9DOpTUDA6C3Y1pv5D6fo4R8XE65cyQK342H7mq5FHWVhLJoQyi2C2hScnvIptyHjjekUIwtXpi3IGZlgFcgrbLW3kskxR9zlgskvfJE8rv+794BlPbM7qnB9YDN3Irv9WUXo6+ozlIVIEuCEGFvPhM+ADOAmQW7BSYYN+QJQFiKJhFYozFnkxHk2GruriioLiypn8bC4eTMYujpJiVAAW9fKxMl51oRcU1wl2sCC/kOpCekcN70xrRxxl3Avws+roCykNgu7Kb1RUSu3MBALCCQpKTfJVhZOYslJkoSmc9W0eyoEp1+k12+n6SCYG0vfABZrRMY8VT7BVaR8VmKnGWIOB7Tf4QH+AicL58Pz8OlnaMP86wZeB1d3UZsbL10YdYdImH4xxJmyUMf2YWvr003J6Cp4+4Nvh8eGWaxFkWITtEFXmwvYnxYAmrbNsMv+f+Tx0afGwIukRhEkxijxYpdQSeeDobOTHNo0sTj0TtKBHpIEhfIKUSWntbDrG3PZEhGa0VbdLezf0EpYcbRuoQRWaB6qjPMZw945LyUzaljpiBR40+eG9mpysCJKIyQQEglhQKYESNCuFXILPVlNyJXJK+To2dRA5j1YXFLsWpnO04Sc3YjcNpMipBauu5m5hWMqkIUKlIW45v323XS4vu0dAJd+OO9fLdSEnN2IPDwbhKcP0H26Vwfwhp3FB/EFG5EVWJExOxadTnaDnbwu6ARxtNPXxy/jsy4bEZ+QJ3wElYUzwZyfB78/Em7cecLx21O/Je99n7MP2mZWwVSgFfSaMGzrlRY2lIMulls4fGwWXmBq3+s5Wehiz6shvdd529q3kbxofM6/OP4Lsq/BXF6+vxZjbiKAHw0iXhg5R68P/QKXm3CyUPiZSlQWJj0eVYbNXESATcj5yMLNndXEso/7t7Mu6e+Jakm8PVdbDYLtVS6ERmSBLMxcq/OWm3ColFkYm5yECDYUazRgw84J32RFbMhishAbkfFshTFCiNlkGCIaBTZkTmii+lGrg+go3ben2jLPaMu5hRUiC5Hw0+l0MMUuXg78/2aJxeXs2bOk2OSmm24CvV5P/rv77rvhvvvuI4/xz6VgMpmgqqoq47+/ekQDsM5LF6gjviFhUsEzC12JRIYiRW1MM8muUA6SpSxUyz4pkEGseVkSQ4wsXPdagPc/D7DqRtoG/OgXAX70iowsQ066BM1uwARTqcmZJPiig6oVnfQky+owglanIWf9WE2rKmQhtyBrdBqiHyXKwkIFJ4iankyy0HWCPn8k7rh6gAGVAAhxg1+5kLIg4/U58c+fJe2npjWrof6971XlezVYGohyBYm7QuRSudAzZWGcFZyILchyG8v45iQVpe+pHNsGV3WVS7ovaF6hRLkJB7fzYR4UkvalKgvx6zuuoGol/9NPlZ3vdtZ9VpayEJGK0IlvQDsBeyf2Ftl4KchM4cDNFRIMOJjANkgGvparlVuIuUpoAcMNVsahAAlKJCswn26Ovi5c+eCeCsCpfXTzN2B+Ou/gRJGycGiYBGJngzdsNirNU1UyIecW5K7d8r++iCxEm2kpxR+VAK6xn33us4TM63H2wEe3pxvmW5xmovKJxpOyNrqcLNTXN+Q0Je9u202yED/w+Afg7qN351VJcAvytd0L34KcA2sttDf7od14iAw0XvxT4eBzdAeYAglCQHSszd/+K7UucXXhlklr3pITJAsl8wqZBZmofrRaaHby+IEFOJzo9ADdtOG8KnwsnVmYSsFciN7/arvzH/i4vW5XLyVp0CbMG1JLdVisZeQklpxgBhbPNzVvqCxZaHOaoHVFdVF1YSQUF9R8aEOW2kfxzMLWOQBdQsKKrDC3sCxlIapWf/FGSkivfCXAK79Jyw3UIAvngvDUfnrotjeYyYCrGGQ1InOsfU3eVmSesYeqQr73qGMtrNGYBuJq2QZJwUoKwtYWmAEnDIlaoBH4vTfU02vz0HQ6txDJQ7QgI+5YfSfs/QN1pWy03g82P7U8qommriqwOAwQCyegOaqBNS1V0MnKaATXgogsxCip21ffTh5/5+B3hNdSKs+du0dmdEmYHaTvm6F54fIK8cxyeNRDLttrMYNRIXCvEzJpIGKl57jYePlRUVxEMGeme4F6c67YCfcwWztZbmEeK/LpaVZu0iS/CZnDwHMLJzNLThDORotwlihuQy5PWeh/8ilhCKbHYkghC7ACNuT+fnKfxIKZ+LSLWIZ5QeIk3s/k2pD5GswITe04XfNNnZliIh4xsUwWqkwWGo1G2Lp1Kzz66KPC7yWTSfL/F12Umw+0atUqePnll+Gll14S/nv1q18NV155JXn8t2IvlgX3MPRHo6BPpcAT9QqTOa4snMQcCZJ1Zaq4DZkAs/AQgUyyEMOFsS2uospCThZ2XQRgqwd4w/8B3PJ9AFMVwOg+gO9fAvDC/5INBpdkzxnpgipbWVgkrxCBORGYF4KIWOuETXI5YaghL30f8caP4zxUAjoMNRnZNTngBSz452EPwEnWgtxzGZ3QijBWARsyD3UXv7ae3/0e/E88QawKrV/9GpHNqwEsG+DXIFe7VgJc4p9gm4ITs6wJuU5hXiFTWKCtR44FW0y6y1EELRWyMB9q22yg02shGornLW1psFKCohj5a7/8MvKr/6mnyvqMYWMrbuZRlcBtUYUQCNNDkM80A/edvU/6L5XTiMwzhWp7qEqRgauM1CIODkwdyFUVInCT1bwus+SEZWKd3DtJiGtURHSbXkgTcyUAM81wPUDyXMoONFKKBTmjPGBU9XITDlRN46YUrxt+UF1s/Orkr+CJkSdIjvDXL/u6sGlG6HVaUkYgfl1lkYUN9TlNyd+96rvw+oHXEyLtGy9+Q7IpGQ//mJmIh020MS8JtG6BnfafkYcn9kwUVGENH6Xr/LguCb0F1HRSsLCSk3UT+rwlJ1husnqEkYU7cstNoJGWmzSxGJkFaURGsGFilftZ4b4Tnp6EUJISZ9UrumXlFXKUn1uYLjkJvfwyeWzs6spriVUTK7YWb0XmCvmoSQshLWar5q5V2PqOhVz6JECTWzTkLbHkRCALLbnKQnGurOQQ6v9eCxB2A7RvB7jtf2mhVQHwpvpCZCFvRN57fk4gK9esLKJiYuD3W1lk4YprAQw2Ws4xnllsJpWxV33pLtAhKSoSUZSNMfp94810IDA8G8h5vaVyC/8y+BfifsFBf//0VvI6mcwp2Gy7N03eqQg8i3Sto+9BX1wLrxCXgAg25Mz4nDvX3kkKQMIJ+lrli2iaG6PrptsAYPPQa1HftHDKwr8wW/X27lpB6aUE3KrqqaFkYVwFK3KcKQunzdG8ykLERTy3ME/JyclJv+K8wpxG5Gl6tjXbDYQklKUqRPDBLyqdMXarXAvyFZdnqfbUVxZqzWYw9lJxTPj4MUKwClZkvY7uv+X8LCKrdCqZBOs0Xdcd3TQznINfby5//uzeZZRoQ/7Qhz5EbMU/+clP4Pjx4/C+970PAoEAaUdG3HHHHcRGjDCbzbBu3bqM/6qrq8HhcJDHSD4ug2F+CPDVWJmiG9Ijs0cymr/ckCSTl1IWUznAG5+kDRlbiRm5hQ1t6HTAKX2p4B/8vJl0mEuF6h2cgLWzzCn8wTe9CeB9z1GCLBYE+NOHAP7vNvCyiQHmFaLsnquYioJPKPLkFXJw+2VISxf7pN8PyTIKd7iy0FZlghqrgQxUgyFLYRsy2ums9ennLViQaSmElLKwrdpaMWUhqiunvvIV8rj+H/8BzCuptVItyCKU1bIhszbkkpuQFZSbIOy1ZqJWRTWMahveCsE1Upws1Om0NHy7gBVZjrIQYdm2jRzCEq4ZiBzPtPyUYkEeqBkoOs3FzL5okm4y5y2zxIop2MpUIwszLcg5ykKV8suEvMJsslCq5IQpC8dO0hyoXtNeMGgj6XzAEoDlMGglz9eIzCMSFJWbZNiQi2x6AzNUda2w3ASBJFivk6o+uSp1MYHPAYk7xAe2fECydEmwIhfJLUxGIsI9ixc7iaHX6uEzuz4DH9n2kbxNybwFeXvT9gwF1KKibQs0G09Ddx06MQD2/TF/C/fZw3TtOWdICplscsGVhZ2DQTKgzC45QYLZde4oNKKzVqcD6+bNEspCel9pYsPeBcksFJGFDtfDQk7p5DF6P3foZ8FosxbMK9RrNbC1q0aCLCzt3ryONSIfG/dC8CVmQd5U2bxCjt7N1Io8dd6bdw/LLchzLHWjozZ3rSKNyL296ZITPuTlGV4qKAuRFMBbF75fQa/EQRYv+N+9mxJttX0Ab7oHwFh8zydHWcjJwrlAFGqT9P7Z2SWv+Vy2DZk8CWt6D5tlRRaakEVOIevWLWCKUvv6/MtnVc0rNHdtJa83xmTMZBEHvBH5sIuShUgmogIb8ab+2+HAA9TiuPXqRjBpg9T5FFeffGhaRT+HfTFdujFY1IQsVhYiUJX1ltU0R7GQkGJ2nK7z9iYL1IfcC64sLMeCLD4vTDtSquXKJ1hm4aQpJI8szJNbyMtNBprk5xXmsyEjVu1qIefw1RfJIOocrfQcjVmbrKhUKXDvEGDltSSvEMtG+XpXAbIQYV7FcgtPZOUWGoxUQS3nZxGUha0kr1kfSwLGcjb20nxyjnq2B1/OLKwAWfiGN7wBvvnNb8JnP/tZ2LRpE1EIPvjgg0LpyfDwMEyoFDL6NwWmdFtnoBPfozNHiYIPK9IRPm0Kaq1GMOi0lVUW2rJsyJhZmESiUqNK3hpf2POqxrgypHkDgLkqV1b91j8A3PAvAHozwNlHwbv3QaEJGSdnsqXeMpSFCEcN3TX6/UlBjVaOFTnIyEKLwwj9jZSEmffSBQsn1GgjlAR/nmMvAozso48HMslCfyQOnlBMdWUhJwu7q7rJlGb805+BZCBAQsnr3vEOUBuyrOqq2ZBnSTbiqflTJTUhKyk34blNnIBeylZkLA6aGS1cbpJjRc5TclI0k5NBazSCbdcu8tj/1NNQbhOynPfSM03XMmPEDfpGJ5nCc2JEtWY5odwkiyxkg59pFTYqaL3mh5mtjaIm5HwlJ2xCzTFgeYoOiAzlrRtCIzJTsEjZkHk5h2zgphQ3vRhFUahght87GlYTm6pSLJXcQsxB+9hTHyO/7m7dDW9Zkz7wicFfx2JkIW9CRtWnNk+kC943UYmCTclmnTmnKZnnFV7btQQsyOJMPgCqLtQAnHlxWhhwZBc1jZ+kmX1zDq0sO6UY5jVrQGMygckfgZa5XGXhmfkzsOI8/Qyb168HrdWaSxY2rcloQZ+U2eBcNhpWAtibwZj0gJl9tIeO0sNejY0SA4UsyBs7aF4hh6GlpawDeV+DDUx6LdmrzL/IiJoKW5DFVuSWPmdBdSG/hw2n4gXXKm5Fbp8RKwuV25BRwYu5pNlkod6gAwfbb7ulrMhnHwU4/xSNorn91wA2eco/gSzsyb/vxZ+Zb6NrE9oMpaNcspAXahTFmlskrchcWYjWWfH+wGqm++O5/aUPEzPAFI369q3Q6qSv93BWyQnmu+IwCYfXeE7aN7mPxNag0nv95GXE/YUOpPXXr6YZoakEK2hUF2e0ccAghdqkFurmtgP3AADnUklEQVRT7ByY1YScjbeueSs4DI6c11LKhtzTVw116FxaQGUh2j9fGOQZjKURlJxMGrNHyxpkiMFFBG5LigzQqk3SGb0b2p1gNmhhNhCFU1O5LegnJ9M2ZKUwcGUhEyQgtr2yG/7u21cUHOBnOEqQMCzDihzc9wKkQiEihjBhniDPK8R1x1IZNbiQW8hLTngjsq1GfhSQSFkYHaZ/f8aJa1t3HmXhMllYDCUxT3//938PQ0NDEIlEYO/evbBz507hz5544gn48Y9/nPff4p/de++9pXzbv26wD8Bae4dgawmwDSXm24U0lcsrFCsLeRutoCzEG19oPtNCWUaTK1/Y8aYrmQ0lWJDz2Miw+XfXewH+7imA1s3gjdakyUL+3OWALzhFyEJUgiH8pBGZqm6iZZCFXFlodRigr5FOmybn6WYcc/o8EXqzzgF/nvuwVTwF0Lw+J1+MW5CdFoPiA1E+IJHGN244mZz/xS8g+PzzoDGbofVfvkbURGpDIJQDC2BDnpuD8/NnCUmEmz/ZNnYGbhHQNxbPK+RwqvA5qjTQVoPDCoNZVzQbpbHbUVhZyPJeCpLhDPbL0lbkBSELXfQgZg25YP3ApeTx/efuV1lZeEqSLOTEgRoFJ9hmi+QSFvRgvl1+ZeEhMvzhNmSE2ZIi2W/l5BXKaUQe4cpCCbVO8U1vS/FNLy83wfiKEiCQhfOLSxZ+a/+3yPACyYO7LrlLMmdK/Dryhumi5SYN9UWHaWgx/vENP85oSn506FGSyYeHJl6wsiTQgoofDdRHXoD+jZQE2ndf7iF94owbEtEkCfC3NSsnwzFiw7ye2vhXjeY2IovzCm3ivMJYKE0aNFKykFvHpxdKWYjvN7cim+nBdfA8vZ5qq+lgUQrPCxbkTAJCaEQukSxE+/zqlirQpJIQO0JtyJaNbG1aAKStyNJDhymmLDyLg4kCkQlGVnKCysJcG7L8/aE74ib2fylCQig5mcr6fKO6+pEv0Mc73g1QR59LMcTn50nGNHn+nfmzvTGLDa9TQwrAkaLrhfh+ISezEPexUkVAOejnVuQRgLH9OcrC7P2Ygw3Y3afLb3iF4FxaNNC6SchqHJrNfL3RysvvDTiQ+8nRn5DHt7TfCsceofu/HTf1gB5J9QZ2j+cKdxXx8CkXjKD3HZ/jkVnJJuRsoLrw8xd/Hi5rvwyu72aFMiKgajXki5Fhy9b1jVAf4mThwigLHzk2RQpA1rc5lQ8RRTE3eI+crlJTWcgKTmwakv+I6nspmPQ62NZF18g9ZzOdMzgQ4U6vUshCPc8szOqIUARhyD1cdgsy2TuIi0MUZjDKhXk1vZbDJ45ncAbjZuYIkJNbKFIWzp+jX2eqOm1p5mjgmYXLysLFbUNehgKwm9baWrqpxHwg7xxdaDQ2PVnM+eFSbeABPiezEEs/+OSA5RYKJEcZykJUGeHCi99T0pbI1SHFbGQ4MX/nw+DR0oOx1zQLXScfBTgtoQoqR1nIVGC+uYhgwYmXcTMiN2ZBWUjJwnOuMLmpF1RfYd6ZWKkwkNmCLD40qllugtPUaDJKcrPqXFGY/sY3ye83fuQjBa0s5YAT1hW1IWNQL97skkk4PUSnyytrVuY9mBfLLORTQDmoalj6jchCXmGHg+TlyFEW4r9BRWI2MIsJD0NIhnMVRT7YL6OEXeill0gAvlKgFeTE/AnZ5SY8Z9ESmoaLN95IHu+b2JdLVPO4AiQLleYpchsyrlkSykI1pprcgry5cbM0IYQWJVRj4wFu/jxU1ZmJyhWxossDOk1CJbIwfyMyX5+kcsBUyS0ssdyEY0XNikW3IT81+pQQnP+l3V8qGKshV1koVW5SCLwpGQ/IWK7ygSc+QH5/a9NW+TEfCwGTXfhM7djkIuvU4MuzMHkuc+DGD9bnDAnorlduB0NYWW7hytEUGZ6Jhx442F0j5BWy6BSuKMa/h1l0LAO6qWqBMwsRnCxM0QOjP0APSDUN+qLKwp09mYq1cjMLEevaqqDVPwO6gI8oNtWOMSmE3k0sD/mcJycGJOCOkP+IVVmXhCqzngxepWDqo2tF22wqvWfjAw1+oFZgQUaiMJuQEEpOJrJiMY79njbbo4rtkg/J/l5c7Y2Zi0Jbdx4geVODvj0WQWS2Sb8O2bAarIJCUpYVGZXsK1+RYUVGlTzf+2Xn7DlX0OvP7/JDMlTm/olFcpACQUsNdNezAqMsslBsRf7d6d/B02NPk/3MtqnriU28psUGK3e1ZFqBVc4tDMcS8PjJaThroMUuuM7la0LOxnXd18H3rv6ecMaQsiCjEGRzby00MGXhJObDL6AFWbBVlwA8m+B9iTciS+UlKwG6p5BYR3hsmYrfwlbkzMz508yCjNnUNTZj6TbkEprnSyqHk9hLL2ReIYdpFb2WY0PDpNlaiC7TG0pSFrrP0fOAr94KRl3m+7Dchiwfy2ThUgFjy3ubNxOFUzAehMFx+gFPmrUZgfiqf+vwPMnd4W20ArglmWUEVDWUb59EMobnIuaQQTjp42SYjID6SAQgEmXlBKgsDLoBfnYbwB8/SLM88gFzF/jkoVhmIbMhY8YNn6qroSy0VBlhBSMLz0zLaUTOIuYK5hVWoAnZ1gFTn/o0pMJhsO7aBTVvfhNUCkVzLVWARq8npSSIocFDJVmQS8ksVEuhuxTKTThqmqxEgRiPJWFuIii5mcPprJzcQjyMmvpXEBI38BxTGStUSKOqARu1+6qLKy7cE/TntIRc0Na5DrY0biFKjwfOPyBNVmFeKq5TchH2AvjYWlNHD5gcDVXqFZzkLTcRq/Oa1go5TVqdlipCNQCrmpmSzimzSb4AuL0tkmVDxjIfTmqVpCAQcgvzbHojPnqALiGvkIOrR3DNwwPrQgM/G//87D+Tx9hkiWqQQpCbWYiZPQh9vXyiD60/P33FT4kNmmNJWZCzrMjVoQOw+iJ64Hz+3rMZ+VECWahPCOohpbCw3MJVY0BU6OJ708iZg6ToIqXVgiUjr/B4WlXICHyeWYhq4sRCFVz10oNeVSyTwKjNU/SCcQFSeYXZZGGpJVRYcrJqnhKX5nXriD1+oYB20bQV2SWpKjTVmiCmKVzEZGLKwrZZgPlglrIQoxJwj1liXmFBZSGuS4/dRR/v/kfZ9uPMvMLi7omv3LoePn/FgCJVYUmNyNmtyMkkIRmRjMdzEN8Xc1R10bND2FAFwRfTSsRyyk0w+xTRWWsTSk6ywUtOkChEXNfwSjj/NCXWdt3cKwzeBPcAtwerhKdPz0AwmgA/K/GYOO0mzd2CsrAAWVgIvNykrtUGxqAfTOy+t9+vvmMoG95wDJ49M1uWBVl8ZnBVaVRRFhL1bZy63rzW/HmFHLwA6vnzsxmlhem8QuWqQrEAIenxQDJc4pmb75vcyuNzMHc6NjJC1mceDyTYkCvQhCwWcnBla+TkibSyEAfacshCvC8Jjc2tEByi616sJfd9FDILlwtOimKZLFwKwIubfQD0tX1CycIgq4AP6zUVJQu5qhBvzAZUFHLw3EJWcqJGZmFBm+nw8+kbLrYgFwEnLSOGAMR0Eejqp8ogePGHtDF5eK/0PyQLZ4raH4p8H3utSbAh61vpZjA2Nq6KDbmfhd7iJLOGbRbzKgvFZCGqFFpEh5IFaEJ+9b4UUXtpbTZo/fJdoEE7eIWwEDZkcW7h1Ci1ia6pywy/VZRZqIAs5LbefO3BFxpZiIqexs7CVmS5uYUIG7ciP/lUyRZktOKaMFdFJlloTXhBa7PCTX03kf+//+z9mYdhg5lkf8mebHLMnk5/Zi3VFVEW4sHq4PRBQf2VF1klJze8Zz287hPboEnHBjRqKAtZZiE2EoqVH/gzRuNJwDNVS7VZfbJwZC9VceHwp8SSFhxiYYN2PBWHbx34lnCQXwjge/iZZz9Dvmd/TT98cOsHi/4bThbiul+oWT2tLFSmCiRNyVd/F9629m3kunpV76tgyYEd9PHgv+1VPaDVa2DslBtGT8wLLgiMVMBXZ0iP5SalkYW8tKR1NgWOYAoGPfS+GIwFwXaEWu4Na1aCzm7LW26CqLObQKfVEOvd7EKpGTBLr34lOHSZdraanpaCqkLM47JlxZmgKg2BQ8MEU9+UUnKyig3HLRvWw0KjbwuzIh+cliw3SVQbiiqgMZImZdCDMQ6Q4BlpmBnH7zky1YW8HEXchCwewiEyWr4P/pRa27Hwbtf7QQnklJuIFTc8F08pWdjmUNCIjFhxDYDRTlXjY/szyk2yVfI8FihiqobAs0xJXma5CcYZIfjaMMSydaWUhRwXTdxEhqPNvVXQs1G0rlZIWfggawzevbmZkMi43o8cm0t/n6wmZLngeYV1bXahSMNttMHz7PcricdPTEM0kSQ5piuYvbwsstCZ3pOnYqUP+zCaiHwduxkSOk0OYZ0NXCetRh24gzE4wTIKETzDsFSyEPOFNUwBXLIVubp0ZaH/iScFtTye+zIVeywLsUIwM3Uh5ha22un3mkwEISnHhhzxAvCSQmxDHqXPWdee+5yXlYXysUwWLgXgZJI3Dzo7iA0IMe2imzGfjh4EKpVZmGNB5uC5hcyGzMlC30yo5Klywbbb4ecUKUO4HdptomRm53VfBbjjPoCqdmK1gx/dAPDI53OnvG5uQe4qmrvANyexSAJSDeWFeyOC3rQNGXNhMFsQFQZmjVO+snDgeprdmIXRCikLO1wp2PFHas1r+tQnhezGSoFPkvC1iGKTV4WgY43I7onB0pWFAlnYpJgsLMfOX0ngRtQls9wkp+RkyFcwt3AmXFhZiLBfysjCp58mlhAl4K3Wct9Lzyy9vhzmODmYoGXHqDWSkgv+tcrKLXRJ5xUiGpnKCEmDclRG+Fy9US9RYhT8ubNKTmzVJvq+8Y2kCmQhqnW1TrqWRYeGcspNWpyW0kq6ipGFQtZtaRZkBL7/V3ZcSR7ffexuuOG3N8A3XviGcH+sJH52/GekVAQJ7q9f+nVZRDfeP5B4wgNXIcI5PuMqiSwk/0arhw9v+zDJMZSysS0VZSEWFThqTLDuMnpvev4P58geZfgovZ+6TAARbZoQKOW6NrJSi4GxFJz3nhfiYlYP0TXKuSvLDZFVbkK+jlYjDAkmF9iKXCUiC23aGTA1dRXJK8w9JGPJhL6hoayhKQ5JVzNlYbCvNJKjHPRtoc9/4qyH2o6zlIUeq7ZonAtmNSc76eHTOMzWB9xLKiw54QMJKUKC25D9cxGIhuMA0SDAE/9C//Dyj1EbfglkoUlmfIybKRpLVhbKLTnBQdzKV9LHR38vZGRnW5AR9mr62YkY1SQLt2SShRI2ZMxOrDLSPc5Oy6UwtZ/uGy56TV8mocmjRjB6JMmUUGUilkjCI8fpZ/eGtc3QtZ6u44MvTeRtQi6FLIxPUkJyxlINe7MstUvVgiw+M3htAAk9FqElISYqBSm13CTMztvFlIW4n9nezXIL2aBFrCxc2Vxa9AVeV4bG3Ebk0mzII2XlFQpYAGUhwrSGlZycOC5kUsZSCZjFvWOxYT1XFeJ+xWgD4yS9li3dtMFejHqWWYiq3WBUokNhGQKWycKlAM6UY3ORwQxr6yhZyDNV5thNp9Fhrmi5SZMti/BgOTvgZ2Qhy++LhhMQCcTVt5kqPPBxhSPmFaIihFg50HLz/ucANr6JKk2e+XeAH1wFMHU0N6+wiAUZYTDqSGYLIsps2TE1bMgOI7kZ8JITSDgK2zQxI4LnLUjkFYqVhWpmFo7Mn4f/7/4EaONJctNw3norVBp4KMVGzoo3IrOSE5M3TA7F3IYoF6l4PK3aUVBwwnMwMe8mHFh4u2MxeKaDEI8kQG/UQrXMFsSGLqYsZAeubPCsM1ewQJstg3XLZjLJxAlv+Cg7cMvEyfmTQv5kMUSCMYiEKUlXVUVvhXgguKLjCumik1IakfOUmyDqbCaitCMqo0CkbAsyWqXyBXFnKgsPZ+YuqkgW4prGD6Pi3MLRUstNZJOF5ZWbcGChyLeu/BZRGYfiIYE0vOv5u+RlcJUAVMP++/5/J48/uu2jQnainLKIFqe5aMmJsEY1LKG8QbXQvA5Aa6ADV/cwbL2hG/QmHVmHzh+aESzIJ7R0ne2qEyn/FALXJV5ywpWFJK+QlZtYxeUm2TZkEbgVeUqF+AFlZGH6AF2rH6NDVQnsPT+blyzMsCJPlEYWGmJR6PbQA92ZhsrkHheCvcZMFGEoN+XqwhQOyJiafozZ3QrZkBGGPqqidoyJsnVLJAulbMhmu0HYe3qmQwD7/gfAP0mHVlvfBkqRbkKWKMCSALc/c9JSLhQ3IoutyMfuhWEWfYPKQikbOSJqdEDozLnSSRTfFCuiQan7hoy1YS4QBV84c1+GZMWl7TRP+YrJ15HrpWtdHbT2ZzXC4nuD2cCJSPqcUSaQuPOEYlBnM8K27lroXl8ntJonUf2Zpwm5GPBnmGN5mHVtNohN0tdy1uIkMQTFirPKzWB84iTdC16/tnziCc+uKY0G/Cw2KsZceeWUmwTsdC9VTFkozi3cc3Y2pwm5v0RlIUIv0YhcElmo0Iac8PkguH9/Zl5hRnFI5TILEeZVlCyMHD9BYoy4kGlCrwfwjAEkCvAPPPanqgUSfj+YmcW4VmIwhWIdExLMpORk2YpcCMtk4VIAquC40g1tGvW0eS/lp4vVNMtP4EqUipGFLEtQgJ0rC5kywagDq9NYthWZF1hkkIXRAG3qVHDg8zEbstc8Q6aQwoTP7AR4zfcBXv9TAGsdwNQRgP+5AuCZb9FpHydni5SbiDeXiJCeKiuSXi8kvNKkSLGbc8ifVhYiVjRQsjASsRa2aWp1dJq89laAFdKNlGVlguXByj+8DL1TAKkqOzR/6YtF2zTVAH6PvOrTCtiQqwMpQhRmWPBlID47R5sJdTqBeJQDo1kvfI7mxnPzcRYbXB1Y3+5IZ/EUQVN3lTCpxhblbNSjbUpGZiFvH7VdTFU6/qfodFMuuBpQSbmJMeIBc33aIsytyA+ceyCzsb0UZWEBshBVRmhLLDe3kJebbG0sYEHmViVUrGHjOm9pjUfT02L+86nViHz+fI6ysOS1qVDTaCwMMPZi2cpCfijERuBfvuqX8P1rvk8KYzDP956T98CNv7uRZApyokgNICH5sac+Rr4HktSvX/l6Rf9eTm5hYobeU3QlKAuXPPSmtHJv/ABYq4yw8cp2QV3I7ciYV2gz6siBu1RYRCUnPMv37Ol90Ix5hRoNWLaKPn+h+fS1mmURbFyMkpPu3eDQIzFF1+Ya2zzNMc0Cfk7xWpLKK1SrETl89CjosODO7IRD4crsaWVbkVluId4LcHinM2jhdDQia7Bh7af3mIapcDrjVEWyMKPkBNWLz/wb/c0rP02vewVAhT5XesuxIeNelSsLuR1aqbJwzKeAsOm7iha2eMdg2HUkr7IQCVSMGgCNFiJGJwSeVZ5rnKEqRCWgySEQB3x9kFIXfnbXZ+Gn234NnmMpwjHuuqVPep9e36+qFfnBo3QPfN3aJrJnaO5zgsmqh3AwBdOx/rxNyMWAZ7h4NAk6vZa4XeJTdB+QbKCfjRcGK6cu5BmMrU4zKdNRzY3k1AoxKKWC7OtZEzJCTqnXRWywgoMWdIm4g1GYZg27vMiyPLJwsrwhK+75WHmNHJDPVTxOBgsZzekLpCzkjciR06eJpVzILTSaAVKJwkV3Ql5hC8lcJL9lAWhrXCF51qxXsWjwrxnLZOFSAJfVMqUbTtQcBgdYI/QAPhqJVjSzkKu3cshCoeAkPdVQoxFZMpNu9AUAPJjjtFvmoZU/B695FrocEirBNa8GeP/zVImHdtZHPgfw41fR7yUiZ4vBwazIgSC1I5W6UcbNKG7CEHxizHMLfQFLYRsy4rKPArzuR5IbRZzU8dwFtWzI3kMH4Jon6A2m6lMfESTxCwHhGglOVtyG7AyUakGeEux9aEtSgtYV9DoaObFwuWhy4RqRn1coVktiY2IykYIZZmGWsiHLySxE2Fgrsv8p+bmFgVgARnwjJTQhuzJUV7vbdkONqYZ8Fp+fYDmq5ZKFDdKNn+XmFqLVsmi5CQeS4bzkhDdBEkIjRdUQOFhRAVy5IlYWjpTThCyekGPZVnasxPgBur7j/ao212qiBJ777oPxz3yGbFDxOvjJDT+BH17/Q9jVsotkGd575l64+Q83w8ee/BicmmfvbRn45gvfhHOec6RY7IsXKx/GyGlELjWz8IKBYEWmBMCmazvJYRpbZDFXTG/Xg0ubgs46W1nDLq4s7J1AUo0S4Yn9tFQnOdANOrvoUMiLB3A/g8NLCWXh9EKShWYn6No3gl1L19/aamlF+97zc3nzCrNzC0slC0OH6FD4RE0nHBmXf4CtBFk4fsYNAU9EsCDXd9hhhKugi6xVVQNrhEZkId9UIVnI93tSmYUIruyff/FpethvWA2w/nWgFGgxTWEjoMEgKEMLwe+OECIJh4WOenNJmYW8qES2FXkVtSKPeOggq8PB1nwR8PMrWJHLyS3MyivkKGRFxqbn4UfotTGwvQnq2/OQQNwSrELJCUbC/OXoVIYCT6fTQucaer0MRrbRa6KMcpOaFispPOPKwiqW7baPrQWVAM9gvH5dsyoCBMGt5oiXHRWVmKOfyXlLQpYNGbG2tYqQzb5wHI6Ne4W8QjyLOcylFzgZeCNyqcpCjCqw1CjOLZS0IGeQhZXNLDS0txN3USoahci58+n3187ei0K5hYKysBVCg/Q+PVmTVjznLzlZJgsLYZksXArIUrrh4rm2dh1YY0ytU2Ebct7MQl5wwjILM5pc1SYLBQty8RZkDq5u9JlmJaeQws/wpl8A3Pw9Orkc3kP/U6IsFJWc8Ly+Um5GQSZzxoMMTvPEysIZj1ERmZKNcZZXiEG71dby2wWTkQiMffwTgHGZ+9YYoO1mZYqXC6HkhKsBSycLpzOmf0rQwTZ7JKR6iWFGKDeRPxHFNYu06+YpOeHTWTmZhQg7KzkJH34Z4jKD9Dl5g+tYPqVGtt2ak4Vi1RXaHm7ouUEoOsklC2VaOlBtwhV8EspCcQ6tq0Rl4XhgnCjD9Rq90NhYEDy3kKu4xRZklVTDXLkibkQu24aMNiskNKUO4kPPpu8dZfwMSBBOfuku8Pzmt+B/5BHhut7evB1+cN0P4Gev/Blc0X4FOQD/efDPcNt9t8E/PfZPcHRGFHGhAI8OPwq/OvUr8vjLl3xZaAwvTVkYzEsmp23I8qMSLiiISk4QOLTYfF16P5BqNhMlUHeJeYUchq4u0NZUgzEBYD07QciQljN0/a7etbtoXiFHE9vHLWhmIaL3Cmg3HQYtxKCtPVGw3GRnHgtydiNyKQi9xMjC2k44MuYpK/+6VOAAuKmHWpHPHXQJZGF1mx18kbgsFbS5nyrI2mdw3zZTXAFdirKQl5wMMbXM1Z+l6rVSLcgdHaBBK18RcFVhVYOFkFNKgKIDvB+hWlpR3uva1wBS2GPxQF4bstjpQ8jC555TnGtcmCykSs6huVzHBw52R47Pg1angZ2vLjCUql+pmrLw4Mg8aU53mPRwcV96jyLkFka2pnMSFWJ2PJ1XiOCZha0r6OteqdzC7AxGNc8LY7ZI2WQhVxa6zDHZNmSMBNnRw3MLZ+CkkFdYXnELz0IvueAkI7dQHlmInyc+pM+wIEd8AFGWSe4or726GLBA08RKTiInjqeVhRb2ehbKLRQIzRaYPUvvwzM1+rzvYwPLLVwmCwtjmSxcCuDZFiKl21oTbd9KahMQ0gC5WViMuorakPMWnLA2ZEQVmzJ6y2hy5Qv7fGSe2LAyycKLZE/cfLPchjxLAojzAg+Qm98C8L5nAbouSf++TBUKVxb65kVk4ehYWXmFHFxZODGnL64sLADMGOGTLDUmda7/+DbA4Ai4bQBPvmHlgtiPxVhIG7IzkBIayEsrN1F+CO9YTTcWmK21lHILxdlNDZ10WCEXQsnJYAGyUIYNGWFoaqKbhVQKAs88o8yCLCOvUMiBQh4KlYVZqqubeqkV+bHhx4hikcApUhbKOeDiuo5qaWxd54fILHC1eKnKQq4qxIw9LDgpipaNGSUnabIwV8WhhrKQEwFcWViyDRnXn3y5hUJeYXkWZGx7T/rotR/Yuy/nz5GM/c7V34Ff3/RruK7rOtCABh4beQze+Kc3wnsffq/wXshV83/uuc+Rx29f+3a4qLW0rMViysJkIECaaxFKohIuSGUhEuCMONhwZYeg3p9z0n1TZ5lkId4DrcxqjFbkP537E6wZShUuN5FoKW1iOZMLmlmI6L0Crqr6Lryz8U6obqsrSBbmyytUhSxkysJTtV0w40/b9RbPijwt3LM0dXQ9Rmtasf02Em8JLQByCu7hM5lZXgrbkPMdZGtamA051gzQvgNgpXRedTFEFDQhl1NugsDc3BZ7i7JGZETfVTBhrYaERgNmrZEUG0gBi7kQUUcTaeQOH2PZoHKB9yRUo4vXjixl4bCEsnD/n+lruPayNkE0IQlO3rmyCtLKUOBdvboRjExgQJ7n2jrQQBJm4z3gM5VZbtJKzyA8/3HFml5yuz03E4Bpn/oDDVQsijMY1UC1qZrknLuc9JwS4w3lJSA+S/eoU8awbGWh2IqMBVGnhLzC0i3ImTZkFchCmY4YjInA3EZU9lm3bMkl4VB0w6z7lYR59eqcRmSSWVhMWchtyFUt4B+k63K4yZn3DCs0Ii9nFhbEMlm4FMBZcpHSrddAbzghk49MxRsqlFeI4NO/3IITrix0CZtwnDQiPCwvsBRgiYBVb01boDE3a5RlTnXKUxZiix1aHpOaBASM7vzKQjGQjL3zfoAbvwVwzefzqn3ykoWzIrKwhJKTkI/nFRoyDnu4CYhGWLhyCEOLlU9KeblJmwrlJhhsO/ejH5HH//0KLTS2SmSzVBh5S3BUhJ8FGFcH5NlWs8E3V6XYs/Gawo047lvHTslTzi0EUK2LBUaofEV7ihI0stzCaUY2SmYWBuWRhQj7pcyK/ORTsosiFDUhi23I9ZkHE8yN7a7qhnAiDI8MPZJZcILTVcwkKwauLMAMozwbFa4sLNWSuH9qvzwLck7JySF6aOJlLSqUm3AYuzrJz0uyXefmIJ5Iwrg7XJ6yUPwcxWQhBl2P7FWl3ERseQ/uZV9TAnh9/esV/wr33nwvIZV1Gh08O/4s3PngnfD2B98Oe8b3FFRLJZIJ+NQznwJPxEOGFP+w+R9Kfs7FMgvjLjro09rtoLWoV3y1pIC2PyTKI16AWXo4MJh0cNM/bILL3zQAp3VUKdZVW3q5CYeV5RZiycmTL90LrfMASQ0IJGKxchNE02JkFiLat4PGaAWjNiQZ9cLzCjEXbVuevEKEoZXugeIltCHHJifpkA3bhPvpOo3qwsVA32a65o+fdgsDsoBNK3ud0hgM4G6k98jg6VNZykKFNuR8ykIzvV+6462QvOpzJSunowrJQqHcpASyENFmT1uRZUNvguGeXeRhhwbLv6SPp9yGnOiiezbFVmRUfeKZBsvAsCBJgiwcnM1UFnpnQzB20k3OYpuvLXLW4DZk1yl5Q8U8wHvIg3kag82GCDQbKBk5NFWaYnx2LF1uIlYWOrvaYVUz3cu9cF79vSknQK9dQzMY1cw5d7H5dlk2ZKYsnLexfFeZin9ecoJk6PEJOnxYWUa5SYYNmQkTSoJQzCePuPc/QS3Itt27SX64AD4AqXBeYXZuYfjEibR4RJOUoSzkJSytEGWZhanW/EpIgSxcVhYWxDJZuNjAww63tYnaeZtT9APuMbgANLGK5RWiasYf8+fJLGQ3oWQMIOxWzYYsLrAgmXSYn4UKQ8zMkimp58pGbEJOaVLSmYVS0GoBtr0d4JIPyt542RlZSGzIZUzVg146ubCKlIV4s+xrsEMqwawAqTh48dCjEOlyk/IOhKhEGf/EJ8km59zubtjfry2s2qwQJEtwVMY5Hd0UOIMAVp3y1y0+7cqwCvw1WJH5oQk3kErtR42sERmzwqLhuKSy0BfzQTgu75Bsv5xakVFZmEpI2+akyEK5xK/bJVIWZjXF4hp1Y++NmVZkgyWd4ypnSlug3EQ1ZSErN9nSKJMsRPICW9XDbhgfPAGTI6dVVxZqzWZhncSN3oQnTEK/jTqtYMEsCby9VRxuPXkYIOqnuXASxIwSiElpPFwjsVEIvdW98JVLvwL3v+Z+eO3Aa4mi5sWpF+E9D78Hbn/gdnhi5AlJ0vDHR38M+yb3ESXo1y/7uuJiJTH4eo/DIlTbZyPx155XiMCiDtZoKiiGWObqusvbYYjdGzkRUA4sLLdwYCwFjiP0wBLuaQFdlUiFje/51NECZCEbECy0ok5vBNhyB4C5GqCHDmKU5hWKC04SHg/ZL5RiQTatHICVXXR/eWRM+X5HDeBeFu9Z+Hbh4BnjYSZwr6sgW9XXxjKsz53PzCxEFU6hxk7cD8aCgrMmH1noOPgN0EMEkmAAr13mGl+QLCy+l8M1iysteWaiUpTUiIyKvkZ6r+wMegSBQjZsrBE5VtdeGlnI4gqI6hfv6SJ0soFCtrLw1F46GG4bqBHEA3lR20OJSHQkKMiJy8axCS+MzIXAbNDCZQNZhKDrJHSbqMBi8KTys1g8mhBiWNCGjK2x/LOMBNVOZqndx5rR1QLNYEznFaotMJipYsrCiYmS4w3iLLPQY9UQxSLG0sjB6pYqqDLrwR+Jw4tDlGQdKJMsFJSFLldpdvuMIeuISnmFC0MWCjbk48eF8+BEIlR8/y1SFurGXekBdh7UL9uQZWGZLFwKeMtvqdpNVEeuD7KcPKMbtOZxaKhQXiG3INsNdrChXU4MLNLg4dys5IQXnPjnI5BIJMu3mfon0hbkzotkE3g8rxDJQlQqVuMGuEIQCk68UdA2t5ahLMy1ISNWYFtWSg8mrb1kK3LahlzegWjqm98kDVIYYv6HV9ENAyqsFhrN9sqThceTlPDVJenBRym4NUBfYvFLJ7MiDx+bW5TcJinMlFBuwmFzmohFCH8U/nU4sLDJqDUqur4tmzaB1uGAhNsN4ZdfLvh3sbX4tPu0bBtyNBSHECPvacFJ7nT+xj5KFiKxI1yHwpR2pOxyE/JHbF3HXCKlwLyr8x56SMXWXtmEASMw/t+vfgenTh5XXVmIsO7aKWw8R0WqZ7nt2rKVhTx/tmNXSVleHDj8iZw6RYZJXH1TSF0oBgbxf+6iz8Gfb/0z3L76djDpTPDyzMvwD4/9A7zu/tfBXwb/QtSEiCMzR+C7B79LHn9yxyeh21ne2tpcZSYDp2giKUk+qVFuMheICm3WF0rJCQeS1KNz6pGF5rVrIWnQkQHT1YdYWdmObbmHKhyuarAZNfezzwlzfF0j8eJDEFVxw9cAPjEk+XnfK8OCjMAiFy0jR5UOTbkF2bJxI6xlLaiLVXKC6NvamKGMH50PKxq6Rjvov9cNjqXdOPi+Y2OnKOtbChjDg8D7Ys7eGzFxGDRHfg3Vevq13ZOlfwaj5+UrC8+95IKp817iLuBxKUrBG5EV2ZBxcGqir3tnyA8wIioXE8HOyMKIiSq+ggcPKiOt8+QVIniu6YQ3TEoDEbg3O7mX3v9X7pRBlODwp25F2bmFf2EKvMsHGsBqzCLvXSegi5GF2PgeiyhbR+Yng2SfZrLpweo0CqpC/Fyj/ZSThXyAoBZeGnWT+xTNYFQ3FgPPlLNsZoPRG2hRLwWJGboOeq3y8go58D4sznrFrQ4525UBct9GgUs8TqzB5dmQi+9ZkZQMH6Ft5HZWMpijLOQDkQrDtGIFgF5PzmaNPkpVeRMhCCBHkM+GjAMatu6mLI1gnqVCKGc3aygvsAdfJgsLY5ksXApT8b4rqdoNFwUG/3xUIAt15tGKKQvzlptwcCUN+wBaq4ygM2hJtpl/LlK+zRSVhfzAh2ShTHBlo69YXqEKQNswKSRJAUSrmlUgCzMnVf3shqJPVZVccqKGDdn/zLPg/sUvyePWL98FJ2N0o7eYykJUvfp4qK7KOO47DX7GwZdyI04XnJRGFrYOVJOwbLS3c0vsYiOdV1jaRJSrC6cGfTlKPaW5hRjEjlYIOa3IQ94hiCQiRK0l1aKYDf56G6I+0CfCoK+tlbRSbW3aCimg+WSKG5HlKAvLUBkdnKKHnhXVK5QNS1jJSYPvOLRq6HV/JKAsn7IYHFdeSX71P/Y4jLCw+HJVz+BkFj/PWFnFWFLwP/W0QGI4rrk6b25hscPKJ3Z8Ah687UF4x7p3kKiNk/Mn4SNPfgRu+cMt8PvTv4ePP/Vxoh6/tutauGXFLVAuMFi9hWXgSZWcxF30s6bLUs4Wgzccg1+/OAJ3/HAfbP/yI3DFN5+A587KjxBY7JITjglPiBCpBp0GWpzl27C1RiPEBuj9cB3LK2zdfU3mX5pmqsK6PtrymgUsIOP5Y9MLnVtYYBj7/Hl5ZCFCcFhMTJRMFq5rpWvO0UWyISNWsNxCRFN3Vbq1vVYmsdxD7zWmUfbZwIEFV95whUuRvEJsQpbM03rsS+SXmjp6rcxNKlNxciSjUWG/WowsjEUT8OyvqZUfS4KKquhkNCIrwRBTInbEYwBHfy/5d+zV9DkFgykwdHQAxGIQ2LdPFbKw1mYkrbZIpPH1dHrIRzIc9QYt9G2RafnlDqkyGpG5BfkV69IiEgGu41CrHwGHJQSJeBJGT86XnFeI1x5vQsasaMR2RhZiUYc7qF6WGydAr1zVCCa9uhn8eKaM6zUQYvfDUnILk+GwQDxjXrvcvMLs3EJelmM2lPczYtQBzxouuRFZgQ2Z74PM69blDtAXWFmoNZnA1Et7BXRnh8GBWYk8t9A/CRCTODP5pzB4nSh7Y54YaJMpiOoBmjpXyVAWLmcWFsIyWbhE4WcTzoCpsmQhyQwsRBbamzKUhRqtBqrqzGVbkXk+4iQqCzlZqODA52WZiV7zjLy8wjKAN1PeiBw20UM5TjsS/kCJmYUSykJSnmpTQVlY2oEo4fXCxGc+Qx7XvPnNkNi2TmjqWwyy0GqwEsVoJdWFWIjhYWeCOJsmKgHPESklsxBhNOuhude5ZKzIxH5ULlnIcwsLNSIryS1krch8E1PMgjxQMwA6GQozThZaQ9Ogq67OzGaRKDr547k/UvWnXLKQyCtPZ7YjSqCB5aWUoizcP71fmaowK7dwneY8tGnoe/GJR+clQ91Lhe3ii8lrGhsdBfexk+WVm+RTFuJrrBpZyNr/Lr8crDtpblbweWllSzHgdf7BrR+Eh177ELxv4/vIJnfQOwiffe6zMOwbJqQiKhHVKo0qlFuYVhYWP+SGogm4/9A4vOfuF2Hblx6Bj/7mMDx1ykXUefjfZ/9wFKLx0t0EC6IsRFs6tpAz8GsabaVq5WNxKzIiheVzO6iKNjevULo0C9/3tBV5gXML8wCJEbQ8Fssr5CgljgXbxjE8n5OFaxhZOO4Jw+wiqTrQiszvwTi84wpauTZkQx89zFaNe9PuAK68KdKIXLAJefBZgNMPkUNv9dqNZSkL0SmCll6t1Vq0Ef3AX4bANxcm+90tN5S+7+uwd5SmLPRR9VNnLA5w7A/oD8+rLAx4omC9mK77gWfZfUBWuQknC7dIfjY7GVE8xNYOrirs2dRA9myyUF9eyclZlx9OTfnJkAOJtRxMnyC8f3c3XesGX54pjSzkTcjcJdPcLOS49TXYyMv1wqA6uYWFMhjVAHeruWuoGCM2rlzQgRnLiKReByGTMmWhOLcQMVBmuUmOFXl6qjxlISoDsR+gFAuyOIdV5IBcuJKT49BqYyUnZnt+pSRXP9qbIcpKSKeqAdqq8gsI6hm3MrNIRVsXCpbJwiUKLPAQbMiWUUGBUrFyk+y8Qg4721xgIDCDGrmFgrJw/ixA2ANgtAM0s9whGfCKbMiy8wrLAJ+wBsMa0DqdJd2M8tmQubIwFLaWpCzEAoFJFpZeqnpn6mv/QqwIhq5OaPzIh2HIQ2XeDZYGaYvMBV5yglmdqEbz2DIb0BRNIJl1md/My8otPL74ZCEeEiKBOFE78oY8pWhSsREZYb+UtpejNYITH1I4MX9CYblJUGRBzq+6urb7WmITO+M+Q9uW5Vo6cAqL2aNoScMMozzgBSfBaIJk3SgBb9+VXW6S1Yi8XXsSLBq6Jp0KVcG77n5B8XPIBzyYWi+ipJtp37Pll5tkZBaOpS1eqM7Bcgte3FKi8iawZ4+Qk2lFMkivJ0RIdLT0zCmnyQnv3/R+eOi2h+CftvwTIQXwWvrqJV8lf6YWOLEhqSwsYkNGG+zDx6bgH39xELbe9TD8wy8OwkPHpogaD4dYH7p2AO77+91k+n5m2g8/epZlsy011PYC4GuKeaicrMMDPyN/ym1CFqNxJx1gIDwdNaBj+wE55SbZVuQFb0TOg73n6P1nfVvhvEIOQwu9N8cUlJyET56CVCRC9k+ocHOYDdBTT2/AR8cXJ7cQccPfrYOb/nEjtK+sEQh3uWuVo3eAFNyYQ3GhTChNFo7LIgtzCAlkaB79An285Q6o6W0XrKPllpsUGlDgAO3gX+gQbPdt/WAo0gYtJ7PQFXLJzijGKBGecdiltVCVEBcSiGCpMhLRArqbtFt3K8stnD9PIwIwtzfP57O7Pk0WYtTS6RcoSbNyewOJRJEFoRG5NGUhz/W7uK8enBaJzDxGQnatp9fO0OEZRVE2s+OZ5SaxKfr9DM3pveyOnjpVcwtPTPrIa2rSa4m1ulJk4XRVquRc+ThzGEWwhEqjUawsxEKTGqtBlXIT1RqRsXtAh/vMVLr8QwIp3Aexz5H9CgmycIGVhQgTKzmJnDghnAcnqhryl5wIhGYzeM/Tz95UtUYoXCpUcOKLxIXogWXkYpksXKLwzTOy0DQPWuMMOCyJimYWFrUhM2WhWmShUHDCJonY1Ecs2UrJQvNsxZWFOSUnLOBbqRU5yMhCtHKLgXJ1vVYDsahNMZmCEBcIcKWSEuAm13PvveRx61e/Sg76qIRZLFUhR0YJjsrgSrQwa6ZUakPmBwON2Uxy9UoFzwQaOzlfVgaoGpgZptPm2lYbiRooBQ3MhozK37A/rfDJIAvD8q9vVEKY19BNvf/pZ2QpC+XAM51uQtYVyHNDdesVHVeQx/efuz9dQlVMWcgtyNhwj9mveYAHcxs7lClRF2I4PiEvAWBrY1YTaxF4qgYgmtKBWUPfn4StEaoddqJk+OA9L0kWZZRjRW4+9qJKykK24UMSFgdMQ+yA2LGdZjGWiOC+FyAVCpFrDUO1MbPJsmFDWepCMexGO7xr/bvg4dc+DI++7lHY1pyVcVcm+OsqrSxkJUyiaxyHS0+fdsHHfnMItt/1CLz77hfhvkPjhLBGkuT9V/TBn//pUnj4g5fBP17dDxvaq+HjN9BN+388eppYe5ccMMKldWNOyQlXB3XXqTfwcmzdnv6fzWtz/0KBcpNFb0TOg+dl5hWWoywMHXqJ/IqfLU5arWXqwsXMLcSs3c41dWT9jcSTJGusVaZDo9bZBJMsASJ69mxWI/JYaU3Ipx6kDe84BLnsY1DTTK/d+clASdnGAlnYk39ohXj2N6eJpbV9VY18u22B+ybmoCPG/fKuERwKI2GIA5XG/lfR3zxK96ViYO6tzUnX+2TfOtKsHT1/Xt5+nMcUNK/Pe8/gJSdDswEYPjpH9jFmYxIiH3oLnLp4N8z+8EfF3wehEflkSY3IvDFYUoEX8QmZyW1bV4PeqCUqy5kRun8rSVnIbMj6pvT3S5ecqDPI5gTopf0NsgYSSsHJpDEb3UfFFUYkkH/DzgFBFhWllCzEa/OV6+nz2L1CnVIxoRGZvUeKgWstd2UUGHIHDxwgFmzcD2M2bw6ENuSFySxEmFdxZeEJaLEzstDMzlvzg/mfY1ULuM/RM4G33kKcavmApTR4dkYs5xbmxzJZuASBN2wevh/QpkCjSYEvKfHBUJEs5MRMfmWhmCykG12Pq/SNrjAliHpw3qHIRoZNq9zS60NlYdXCKQsxX87Y1qZ4qp5pQ86cFGJ+EYavp+KlFZxwC3JrtbmkAgHP/X8kNhUslLBuoSolVN0tFbKQlOCojONzxzMO0UptyOJyk3LshGj3xZDpaDgB0+cXT12BcJVRbsJhthnA2UAPWtPD3rKVhQgbb0V+On9uISfO5CoL3dMiZWERi+ZNfdSK/MC5ByDOD4JyyUIZ7e5cXTitgDg45DoEiVSCrKN8EyUXT5/3wqlU2pahq+6E/37rVrIOocrs3x9hz71M2K+gJGv7xDlwRnzQUW5modEGYKlNW5GFrNtyLchPCtcZ/yzbWEGL0tzCQjDqjBUp4pJjQ9bW1cELg3Pw2T8cgV1ffRTe+r/74FcvjoI3HCcRJ+/Y3QO/f//F8NRHr4SP3bCKNDuK17XbtrTD1q4aQih++U9p5d6SQmtubiEe+BHcWqgG9DU1EOmm96b+a27L/EO0TXI1UQGykDtFuCNgsZHOK5RXaCEMTBWRhem8Qo51rOTk6CI1IovB8wox29LADo/FgKrAsXr6OQmfOZNp0+MH12I2ZL6m8evn0S/Sx7veSw691Y0WAA1AJJje95aqLMyH4aOzcP7QDNk/Xvr6gbIjEvDfK21ExogGBGYOa9fdSn8zjxUZi9QQoahOuJ78ctSFBfIKOXgR0ojLC0d+8wJ53HD2cUiMUjv39Ne/DpNf/CKk4gVU+FhwotFSFaNIaCF3P3941EM4nmvXSLhWXOz+bG8CvbNeGDgPHZG3r0LyM+ih58valkLKQvp1j4x7VXEcFCRA1Twv2BMlKwsTs/Qz6bPRz79SGzLiszetgac/dmVG2Uk50DeWqSyUmVvoffBB8qv90ktBI+pPIEDCexGUheZVdP+McTbtQOMxxg0GGcrCVggN0XUv1lL4fSB56su5hUWxTBYuYQuyTq8BX5Qu2JMRln+1WAUnfnVtyNz2HIIkeJHgKiGvMKT3Q1QfXhBloYNlFqLis5SpejyWIA2sUjZkRH+jA1JxR0k25HLKTXBCylWFzlvSgfucLFyMJuTsmz8ntNXE8Vl64LU1tpVkQ46XmVfIgZvzjlWsFXmRrcgY5I1o6CjPPsFLTqazSk74lFYpWSjkFj7zrOQGHb8eHry0Gi301+RvPZPOLESysPAEeHfbbqgx1RASf0+YfeYjHoCQW0a5SfHn08gbkRVMNQ9Ml2hBxtz8E9PwclKkMnG2w+bOGvjarevJ/37nsTPwx8PKN9rZMDQ3g3HNGtBCCnZMHpdfGiCr5GRUtbzCwJPpvEIOK8uhQ2XhUmkqL04WZtoU8XmHp+h9+z1/PA+v+/4euHvPENkQo1XqzTs74Zfv2QV7Pnk1OeDgNZCPJMB16os3ryWqqz8enoDnzsws3ZITUSMyVxaq0YQsxqpv/Rc0f+ELUHfN9Zl/gGqHeAhAby4YP4At1otScJKHnBDyCrtlkoUl7IHChw6TXy0b03Ez61oXvxGZA18DpVEuqAocZbcP/6nj5duQX/4NwPQxALMTYPc/kd/SG3VCTjiqC9VuQkZxwtO/oueL9Ve2E2eBGuCNyDyHsBiGvYwsxHyx3ssBcLCCIgW+zkvkFvrnI2DbrSC3sEBeIUdXlQGuH9wLd/zgszA6SY/IbeGT0Pjxj0PjRz9KlFpYBDjy/vfnzy3HYiN0FZSQW8hLQLZ31woWyQy4jmcMIrvX0wtw8GV554bZcb8ggDBa9HmVhaiuRaU5upb2D5WXW4hDG7Qh4xpzzery9sz5YNabyedxhqVClFJwwpWF85ZkScpCBBa3qLLXUSuzMCPvWfqz6P7d78H9y3vI46pX3JD7F0LzAInIgpOFmCeub6XDl44pSgJPatl+TKoRWaQsTI3Rx9r24krI5dzC4lgmC5cg8AaIMFUZIRmhH/IznuMVLTjJn1nIycL0QsWVQ+WQhbiw1/ACC7yxtm1V3oRsmiWHeF6EsXA25DbFNmQ+EcZDl8maK8HHfKhkojxlYSnlJpHjxyFy6hQpIxDfJJaSsrASmYVciVbX1psxUZQL3kyGysJyIeQWLmLJCRILLqYELEdZWKjkBPMvlRaccNsa5oIlvV5BnSL1XuK1im3IxRCLJITJukUGWWjQGuAVPa8gj+8ffhjAWldw45VJFipRFkaU5xU2KiMLceP/xEkXHEllkoWIW7e0w3suo5+Hj/z6EBxRoaU0uYtmTl48fRzqbKVbhQXwzEg8QKLNT6unERYlAlU30aEhAIMBbBddJPy+ZfMmsiZi3ABa3JYy2tnBBO8DaCE/PeWDf3voJFz9jcchyQLbT8ZN4DDpiULwx2/fDvs+fQ185TXrie1UbvHH2lYnvHUXvR989r4lWHbCCQAkW2JhsqYNz1WGLDSvWgU1b3h9LrmK35sf5AsULS0lG/JeZkHGvEJsglVCFuLQDLOuiiE+P08/Z/jZWk+HEmIbMpK6npBy1Zya4GS7koM+qoVnmug9J3T2jCIbstCGzG3IWD7w+F308e4PAFjSRTNpK7Ly3MLIUGGy8NCjI6TtF7MAt99Y2KqsBDwnTG7JCVcWdjo6AXQGgNU30j+QaEXmjcgorLDvZrmFe/ZAKlEgrgkVihOH8ioL8Tqe/9WvoPa9t8MHXvo1xK1dkNQawGmJwqb774a6t78N6t75Dmj/zrdJ/Ezgqadh6C1vgdhknv0pv/fzvYBMCCUga/OQMpx8bKAWza51dD8yNeiFIHOkFcLsWGZeIQLzyrOVhYgd3erkFnILMrYFV1tV2AfkAZ5jZ6o0ZSgL6c85Y46VTBaqDcGGXGobMsKZv5jP98QTMPHP/0we173rncJwXpKEQxV0gVidSsC8mir060ep+GA8Eczv7mEDmpS9BUyTlOC2ddM9bSFwUn7Zhpwfy2ThEm5C1tkNkAjRg9zRWZaDoyJiiZgw3SyqLBQVnDjYlBNtEZFg6Ru8Zh3dZE02DQAYLEs2rxDhqGE25Lkw6FtLIQt5uYlBUr3R32RP25BLVRZWKz8QuZmq0H7VVUJQOx6yhMxC5+KRhYJVPaCuDTmaiMJZN80Xau1YkzFRVKosLKfchIPbSLAUJBxYnAMTkmdIaGNweH17eS1ujXlKTkrJLERodDqwXXJJ3lZkThaurClOzIlVhYZUGAxxzKorni3DrciPDz8OAW7pKGRF5lah+gHZZKFcZSGu24ddVKWztUlZXuGhUTfMBaJw1rAi/Zu84RmAZNNh+Hg4liStuKW0NIvhWk+JvM1TJ2WRCkXBD+KowOGHPqO17BZk69atoLOnr3utyQSWzfRAGdy7F5YymhwmmnmbSMG1//4kXPvvT8G3HzsDsxPToEslIQUa+OrbL4MXPnMN/OvrN8IVKxtl2yyz8aHrVhLSd0mWnSDpjWHuyTjA5MvkOkf7HN5uy87LlAuh3EQi80nChrwUyEKleYUIXV0daEwmYk+LybDHhQ8fFnLzUC3CUWMzCkPOY4tYciJWFsptQuYIttP7d+LcYJaycKJgXl1OZuH+H9N7ir0ZYOd7M/5udbO1pEbkhN8PCRe93xq7c/dySLa9+AB93he/pg9MTGmmBrgNecwvb5884h3JHFCvfQ399fh9KH/M+Ls2kbLQvH49aKuqyDAx/PLL+b/B7BmAqB8A88tE92VCEt7zKzhzww0w+dnPQWpyAubMDniplw4I19ywGnTW9DXhuOYa6Prp3STbDYsXBl//BtLWmr/kRL6yEO+3GBeBuD6fXXeafb3GVYIlmwx4U2hFnpWtLKxleYXJUAgSvKyPtSGrnVvILcjXry1/v1zszDDDtCOJ+XlIBpV9XuJsuDZljpRsQ1YbZRecFLAhBw8ehLEPfBAgkQDnzTdDw4c/LP3vhbzChWtCFg/mELbzlIOYjnohns+GzJ5nImUHfSQOOM6s66GkeiGkbcjLZGE+LJOFS1hZmLJoIRFuE26482F1Kuw5sKksBSnQa/VQY05PMfO2IbONj9GsF3L3uCW4FDTH6AZgslYZIeV1pcnChVK+2ZkNOR5NQrK2WXleD88rzCo34ehrsKdtyOFZRda3UXewpCbkVCwG3j/+iTx23nJzxnURioeIrbPDnr9yfsFsyIEpVa2A2GwbT8WJIrWhbUVZNmQ1lIVoB6lptpKP19gpdT/jcuEaplM7fB5oeyoHuHHFAzqGbvNIhezMQqXvJ7bUiskdMU7NUWJuZa1MspDlFVpj9LUupixErK1bSyz54UQYHrbZCpOFGEDOW+dk2JAFslAmMXds7hh5HtWmauh1Fp+aivHYcXrdNq3YSlV5YosKkgBaDXz7TZuht94G454wvO//9pelIDtf0w4zZieY4lF1SDf+XL2jqliQ/dyCLDFNF3ILn1/aZKFepxUKGc66AmDQUavX166gewd9XS3csLENzIbyPtcIbOb8xCuWaNkJLjpcXTh+AAaZBbmlyqzKzy4LQrlJ4QNKWlm4+IeT51kT8k6ZeYUIHHgKjcgy7H5SeYUc69ro6f7oIluReWah0n1UrKOJHEq1bh8lG/iBGm17wbnimYVIFkb8AE99nf7B5R/LGYDUikpOlCA6SA/USGzpJIrYnvvdGaK0b+qpgpU71bUXlpNZSNBzOVVX4tmDl1ll2ZBxf0GGiUwVXjC3kGeZtmwkZYqEJPzlPZQk/NznID4+AbqGemj61Cfhm6+9CywG+nkY2JH7uqA6tvuXvwTjij6yFxy8/S3gf5Jm30qWnMjEI8dxrwuwod2Z3ykkKAvT+czdvBX55eL72Dmh3MSWQUJhsaFWNDATrwmHRjwlN8XiQOTAMI1suS6fWlIlYH5zwAwQN9PzaV7VZxFloWBDXkJkYdLvJwUkatmQI2fPwuh73wepcBhsl10KLXd9KX9WKc8rrFoEspA1IsOZ88Tlg+PPab2OWqPDogETfnBwQIPrHruVzFUBtNV2K1AWLmcW5sMyWbgE4WcH7Cg2kiYtYIbmiqgLeV4hSreRGCqoLExEaVivirmFLQG6WZqwyd+kIjyMoPSaZqhlYQGgN+gEoi/MJsGJuTnZk6u0sjA/WQhJdvNOxsEb9VY8sxDbZfFnwI2knSm3xBZktJEY0A6ySEC1qwY0EE1GhY21GuBKtNW1q4Vyi8SMMoKWb7C4RaBctK9eXCvyNCMLy7UgIwwmHdSw4Gy0xnBwS4fS6xtBlIUaDbHNZ9sxTswrKzfhykILi1Yo1IbMgZsori78ozZUuFmO247sTQCW4oUWWDCBmJZJFnIL8ubGzYqD6DGvEHH5mnaAnssAdEZ6eMoihH5w5zZwmPXw4tA8KcUolawfmQ/D3maq3vU99lhJXyPzyaWJzXLLTXDtDu7bl0FGi2HdyXIL9+2DVHKJWW6z8LEbVsKr1rfAv9y2Hl749DXw/+7cDpfWaWWT4UqAVuYtndVLs+yE2wvHDsDwHCs3UdmCLE9ZmL/cREwWovJRjfKAUoHWdbRqk7zCrjwDYxVyC0MSeYU5uYUqxB6oQRYqzRtzOhuFnDTSiIwtu6hwLWBFTqaSwvCfkIV7/4uSYjU9AFvuyPn7XFmo1IacLjfJHaqPn3bDqX1TpDzlsjcOEFeBmhDbkIvdPxLJhJBtKLiFiBWZ3nPhWGYrsp0VnHAXlqzcQpZXmGzcAPO//CWcuf4GmPz85wlJqG9ogKZPfQpWPPww1N5xB6wG+nrrWy0CMZkNY3sbdP/852C7+CJIBYMw8r73w9zPf57+Cw0DisnCtAIvD6kmakLOIAs30DV++NgcyaDMB3wfZseZDbnVntGyi6RU9n4Ci6GaqkwQTSThICP8lOIhZkHGewZf9yqFZmsz2Sf6WGyU0hJK7jDy2mij92KefzjQ8aBlA+qSrchOkbIQ1eCTkzD8rncTRal54wZo/9a3QMOLQwoqCxcur5DDxBqRI2fOQpupKZMzEKsLI16AGL22I6zYbLJaI2SnyiELleSG/61hmSxcgsBcPPKrjt5g6w195NcjM0dU/T68OCKvBRmBeYImZ96SE0+pZKFvEpoZWTiJUwIFENuQFzJTz8E2DcGoHrRsSitXXRhkZKE1D1loMeqgvboKUgmzIisyZlSNu8MlZRYKxSY33ggafdp+IliQFzGvEIFTJJ5zp2ZuIS83QXJJX08JLJwy4+ROLmIu9ZSFiE5GFuJmbzEKFbiysNxyk+zcQv51eb4Tzxd1BdNriRzoa2uJ3QgReCZtRQ7GgjDoGVRmQ+ZNyB56kMODghy8qvdV5Nd90RmY1OmkbRCImdOyLcilKAtLzStEFdixCS8RYKHVGN50D8CHjucScGx4gQpD/Lu/fGGEFGOUmgPGyUL/40+Uf21nPFcNQCcl9EoBKgZRXW1obwdjb65C07JuHWisVmJnipyuTMGYWrhxQyt87/Yt8IbtnUImVHzGVRGykJadrFuaZSeikhNebtJdp05hQ1HEI9TqiGgqTBZiNiDPB1TSgl6pvEJsJXYwNY5c8OD52HhhmykS7SFmQ5ZWFvKSk8WzIccTSWEfhaUOSkBLTjSCWkdOyYk34iVt9uTf45L47LfpH1z1GUqSZQEV/zwGJxaVr/DieavZeYXJRBKe+iUdaq25pFWIDlETrfZWMuxFl8p8ZL7oWSSWjBGXEyF8OLgV+VimFVmwIbsj5J7CcwtRwZrwZRarcSSH98P8aSuc/eqTMPn5L0B8gpGEn/409D38ENTe8VbQms3k6zXN0dfY11Q4X09XVQUd//3f4LztVtKUPPXFL8HU1/6FZify+z+WtBRQmHJgZudzZ2cKNwaLmpDBmhZZ4L7NWmUkKlEkgfPBNxuGWDgBWp1GIKDjrAlZn5VXiEDycEcPzy0sbZD9l6NTFW1BFqMZLfx4fnJSaiM2oZAsnKProdumWRJ5haqVnJD4Fg1APAyJ8XMw/K53kesfYyE6vv99oiotCO/i2ZANba0kZgBiMVjro/eKcUdDbskJf44mJ7iH6ZnAVaMtzG8wLBecFMcyWbgEwa17bmJuAOiwDlREWVi03CTHipyealTVm8uzIQ89B81xekOeUFDokUqmwDubLjhZqMxCcVYjbtiEqbrM3MIQCx7m9m0pYCNyUmRFlgPMWMCpHx7cmp3yp3YJtxv8jz9OHjtfk25BRgx5Fr8JuZIlJ1xZuKpuFdkc8qldfEbeoRc3k3EVC04QrQPVZAOHmzmufFtIzIwwspA1GZeLJqEROU/JicLcwoxWZGYd5ZZyjFLAAxu3OctWFoZcpNiCZ3XKUUpgRiCe7f5kt+a3IStoQs5oQ/YVX0tRkVJqE/LjJyh5tLmjGupwkkoUMPlfsytXNsInmeX0i388VhIpNDIfgpcaVkDSZCZq3PAxVgChBlnYtDajBEAp/E89KVxXUgpNLDjBLMMLIbdQCgm2nqlNFnKC5y1LseyE25BnTsHU9MzCKgvxc48EEDbZyjhUpXMLI0sgr1CZu0OJshAJq6TPR0ohTAO5A5S1zIZ81uWHYHRxVJYTnjApfzLqtNDE1mO5QKsib0RG9YuckhPulHAYHWB47jtUFdO8HmDtrZJ/32I3gtluUJxbyJWFpiyy8OjT4zA75idle7tuVhZlIRcmnUk4qBcrOeEWZFQB6cTFQN2X0VIFLEUbekb4bZuTfnaS8RSE/TFSOojEB2avBZ5/PuNrJyMRmPvpT+Hs/4zA5P5qiM96yb6t6TOfgb5HHobat76F7AM5ps57QR9MQBRScM5UfLiFiqyWu+6Chg9+kPz/3I9/DGMf+AAkk/q0okuGuvDxE9Mkd7a/0U6dRlLIakIWnoNWA13MijxYwIrMVYVIPutYbi1XFhpETchi7OC5hYPKS07cwSjsYWtMXrVkBXLOJx1xxVFRONRIzFFS22tdGhZkDj1zMJWcW4h7PUczifMd+acPQfTMWfIZ6Px/PwB9jYw9FLchL4KyEPdmPLdwYIYO2CatbLghHtjz6J+qFvAN0uFuuMmZuZ7kwXJmYXEsk4VLED6WWehik7SBGjqlPjpTGRtyUeadW5H90+rZkIf3QEs8nqFwlIOAJ0I2CAlNAvwm94Kq33gjsk/ciCzzZiRkFuZRFvJGZKUlJ6OsCbm5yqwotN7zwANEVWNatQrMKzM3HtyGvBTIwiZbk6olJ2h3OTl/UrAhI3RMXcjzSooBDz6Y86EmWYg5oM29zkWxImODHslJ1UDZ5SYcDUypMDXkzVCTiXMLlYJbRQPPPkuuXQR/L1ElKteS655Ok4V6DOrXyv/c3NRLbVH3222QyteGzA8GMpqQxcrC2UCUKFwKAYt50MKNrc+r64oHN4vx2Am6zl61Sv41++5Le+E1m9vIQfr9Pz8Aw0ytJRejc0GI6Qyg2bZTUBeWBVQO8MiMMvIK8ZoU8golLMgctp07LojcQinEWbGBnAKfUvDha9NlJz9+bomUneBgkxzQUxAafpH8VlftAikLp46ly01krEWclJqWMSSoFPYytZCSchOlZGHopUNppa7IwSAelmAUA94mjk94F9WCjFEuqJwtVVkYPcfIQk4Wc/teFvgwuM7oBNj7P/Q3r/4cynbzfh+uLpyfCpRgQ07v5UL+KOy97xx5vPPVvYSIrBTkNiIPeylZmLOf1+nTVmRRK7JOrxVigXjGu423IrPcQkIS/t/P4Ox118PUl78C8aAO9NYkNH2GKQnfcjspssrGyb2UGDltSMA5lgdeDLj3qP+790Drv36TDJl8Dz8CQ3fcCXEzI2JnTsq2IBdU4GU1IYvRvZ6u84OH82dCI0GMqGUW5GLKQnHJyf6hecVDoUeOT5O9w6pmB3QtgMKbk4Uj1rBispCUvLA2bUIWLiFloaGxKYPYLQUpRxuM7amB0JFTRKnX8f9+IJxji0KwITPF9CLlFrZPUs5g3GDMryx0tEBsmK43qVZ5e92G5czColgmC5cYMG+Cq9DGI/RAvL5+DckUxOIJrgZcMBtydskJg5OThaWqoIb2CMpC/JmQxJEDTk76jXNQa6kBm2GBDgKiRmS/mCyUqywsklkokIUJuyJl4SjLK1Ta9ui59w85xSYcS6EJOWdSqJKycMg3RGwxZp1ZIEP1dXSTFZ+R95oLgdBOZ8ZEulx0rGG5hccXlix0MVVhdaOVkJZqoL7NTpSSkUA8Q33MN2BKG78R5rVrQVdbS0Kegwdo/tDJuZOKyk3QwsWV21YkCxWqrq7rvg6MWiOcNRrheMKfGbCcY0OWpyystRmJMhj390gYFsLBafpzb6jfQGz6coHh5M+eoa/5VavkNxLiIeirt66HjR3V4A7G4F13vyA7Yw1VQvznqbn2avIrVzOXDDw88g1rGWQh2orRhoONrtYdlBCUgnXnLvJr8IUXqLXsAgJXSsvJ5CwFTqsBPs6Up9965DRMeha/2ReRbKG5hY2+Y/D/t3ce4HFVZ95/p0szo1GvtmS5V9wrtimmGEINSSCUhZBselgIG5IQEmA3yQdJdkM2CaGETUijhGRpDiV002xjGwM27kXd6hppZqTp3/Oec8+dGWl6vaN5f8/jRyNZlkfS3HvPfc+/lBt1SanmkqJHDAvjG+JjFhiSq59b59Aos2onk1eY0LBQsiBjNlYkZCtyR26Ghe1SE3Ki5SZIRXEFtFeqxikLo9uQ5XKTsRFehDJtPcCss6P+P+W1ieUW4sAo3LBw29PHwOnwQOVUMyw8Lc5hQYYbkUVeoVxuEkwEK7LILRTXczm38M23+JDwnHOh+0c/Yus1bXkJ1K4YgplfnwUV11wTdkgo7r8O7+Tru4/1XpbnmUh0RukFF0DTw79njd/YzHzijx3gtGpjKgtHXV54/VBPbAXeuCbkYKbOKwe1VsXWW0PdjrjKTUKUheOakAWodMQ1ypjbBx8lmCsa1wA0jaAaUKvSQo+F/84wjzJehFjAZTaAV6NSmLIwtUZkfA13veEGW0cxqLQaaPzNvVAURuWtRGVhcG5hZRu/PnSp/FGUhQ2g6eKzCkNTfPewIrMQowAU45JQGDQsVBjiwoc7Zx2j/EZrSlkpzCyTcgv796a/4ERSbyWkLKzmiyq0TWJuXkKMDgF074Uqrxc0KjVrpo13OGbtHctJXqForUVGBpwsRwFxxTksFJmF0WzIySgLkyk3cR47BmO4gNdoWF5hMFg+IXaBFWVDdqRnWHigny+25lTMkeXpqC5LpBHZLTUh69KkKhQ0ScPCjoOD4I2hMMtIXmEayk0EGp1aVin2tAynRVmICkDzRl7EY39za4ilPN68QrG5odf6QOexJzwsRNvYmU1nyurC4HY5htcNMHAsrFUoEnizLgcsx8hM2dW9KykLMlqBRt1eqC8tgvn1if2esUn2wX9ZwdQ/h7pt8M3H98R1zhcbGViUUn3OJqa2Gtu3D9zJWmkEm24DWHIlwJzzk/4SdqlV27h2DaiLI587ixbMZ/m0qCYe+1hhZR5xDgtFiVMm+HRw2clzuf/54E3Ry1a+wbRMc5wVvTDLfTbLTWLkFQpqpdiQXNmQtx9PPq8Q0TVMkW/IoxUARWtCFixqsOS05ASzVZMpN0FwqNAhXUawGdc7PBy3DbnS2hVQFcZQo4rSsMEuR9wxBKw9Va0GXVOTfC3++C1+U42lJomqKDPViCyUhWFjhZo3AhgrAUYHAE4EIkhE8YgohDThpo9Oxzbw2ZCwpwe0dXVQd8ftMPPba6FitgPUTTxWIhIte/vZBifm/7XpfOy8lqjayLh8OTQ//hjop00D96ADTrxcBfYd/LodiTcO9bJhHA6rF0rHQrxNyALc6J06hw/9T3wY/v5BLjeZEqQslBqDxUAq3IbhqubyhHML7U4PvHm4N2sWZATX9Hg/21eqSlhZ6Onn39uoJOiIN9YmmzZkd5KZhb2//CVYd/UCqPww5doVYFy5Mv5/jEIeqQwwF5mFwcrC4uMn2a56l28sorLQZ6gGg6QItkyPbyCKpX5a6VzYbycrcjhoWKgwZEl9uUF+0eJN2qLKRWm3IgtlYezMQjEsDJyoTGUGtouFN40DnfEXQzDa0NLlB23FTKiWVI3xKseCy02ymVcYnFnIlIUJNAEG25BxERLPsLAraDAbjQ7ppJhIuYlQFZo3bpwwLOm0dbLhLSrv4gmGzdawMF025OAmZEGiNmRPj1QckOZhYVVjCRhMWnCNeaHnePYUFn0ZGBYiIjQ9OLdQZBaiSjoZTEG5hZjfd2jwUFJNyGadM2mLprAiP28ygWeQKzdk8H2fGwAVzwlYNoQVOZYlMfm8Qn4+OXNeTcINygi2GD7wLytAr1XDSx93wz0vS7mMUWgbkG7Ay41sIC+GBSlbkZdeBfDJ+3n5VpLIFmTp9RQJlUYDxlWr2GPH9tAsrPwZFmbupie47OTZDzrlgP5c8eDWY/BwCz+fn25uhxVJKOZSVxbGOSyUbMjdObIhbzsqWZAlm2Gi6PAGVq1mkRCR8n5xWOU8xM8VxUuWRvxaC3NccoLZquJclShoQx41qGCgJKjkJF5lIaqV534irqKmMklZOBSnDVmoCtEFo9brWd43KzXxA8xZXQsNs8og04gm0ngzC5tKwqzpmRX5Yv5431MRG5Exe9p8KlcXauvroe7OO2DmP1+E8iuvBHXPntBM0wgIC/KcNXVQJ62pW6Rm1UTAQeG0xx6F4kVzwOdWQ+ujbTD0979H3OB4YiffdDxvYV3k63OEJuRgpgkrcpjcQlRNirzL4GGhvPkdQVmIBEpO4neE4ADU6fHBtEojsyFn856hV5q34sZkvI4AryQWsJn4WERRNmRZWZh4GzKqbPvvu589rltphZKmBHNh0VGIWbwY/yJa3rOMAQvodDpQ2Ueh2grQ5bKy7HCWGy6Uv5JV2jXKj1tbEUB9w+y41zGVIrdQEvYQodCwUGHYpEY2Q4kOULyBi3DcGV9UtSitJSd4gZKVhbGGhaaJNmQ8uJoX8QvTBy9HyO6KRMs7/O20dXLzWbzDIDEsHDH0ZV1ZaK6Q2pCHXaCqFQUnnXH9rOOxIVuKdFCik6yowz0ZURbihdP6zDPscemlocUmwRZkHMSi9X2y2ZD3DwSakAXJ2pAj7cQmCx5TjfOkVuQsWpGFDTntw8JmqeSkJdBOKBZgySgLEdZ6qFYzG2nLkd3MUo5B6vGeC6xSXqEJRpK2aJ465VSoAA30azXwbgfPR5pYbjIrav7UeHBDKJayEAf5eBygzQZtyPGC559X9vPzyaa5yQ+4lzWVw92X8UbqX716BLZ82BnfsFBqFzWfyRWZI6+9CrkElT+O3bvjGhaG5BZu3wF5OSzMUGZh2LKTp/eBO4uq6GBwWHnX8wdgr286e9/oaAewJx53kBQYRyBu5OO2IRfltA15m3Tjn0xeoSh2kO1xXeHXb6N797GWWBzesOFiDBvy4e4RFpmQbcS5KhkbsrimtUk/RlfIsDD8z2VAyrCrwGNl0w/i+n/K67iycKh7NC5lt3OcBfnAtpOsvENn0MCpl82CbBCPDRk3/YQNOeywMNiKvP9Zrt4PakS2SwILpP7uu6Dxfx+CmS++AOWf/SwbkoJ7LJAn2sAjCsIxZnfLQ7a5a+rYkAsRreqJgsURTQ89AJZpDjag7brt+9Bzzy8mqHD/8M4JeOVAD3MYXLY8qMArzibkYJqlkpOuo1b2/QSD9nV83eiLtbIq0+9yBYqwogwLRW7hzhODLIMwIQtytAFohoaFg2YAH2a4e71MYZqIsnBI2i+YDDbk4eefh+4f/5g9rrrqfCif6Zjohok3rxAdhji4zwGYA2qYxc9Zzd1+GPWOwTA6w9x2AEd/yMaMe4Svu7vLApmp8SDcPVRyEp7cTwOIEGwD/IWqMvGDEgeFeBFZWLVQHhYmkqERiUHnILhRAROk9omtLAw96S7fzG8QDu7oTqzoRB4Wrk94GBSiLIy0sMgQRSYdaHVSg5jUIsqsHlLZRSRcox7wef0xbchIg1QJ32OPb5jSIRWcxKssxFZPtB1gwK35zDMm/L0oN8n2IDaWshCHS+L1mix43IRTFmolZaEnbmWhaEJO/y6bnFuYpZITXFCKTMHqxvSUm4xXFqLNWdzcCGtHMpmFCGYBCYVax0vPsrezy2aDVh3fImaoly/8i50DSauuMCvwvGJ+7nm2jw+dki03Ga8sjDYsFBZkLDYx6uJXwBzusbHzhEGrhvWzUhsc4c3Ml07joe3feuKDqLbB8XmqJZv4sNDx7jbwOZK7AUsH9nfeYTcR+hkzQN8YJiNrHMa1Um7hrl1ysY7S8blc4MPA9gwrC8OVnfz+7eyXnaA97t//yu2un16/CKBSGoZ08ozPrFmQUU0cZ0O3nFmYg2GhyCvEzeiVksUwGWI5LEY/lCzIi6NvbjSUFrF8SY/PD4e6A5tL2S44ScaGbNaZ2TUhpBFZ2PVcI2Fzbfs7+MZDRd3SuG3r6GzBeA9UiI30x15vB+cVOkc98O6TR9j7Ky9oZs6gbCCUhSgGiLR+Q9GC0+tkm2D15gg2R8x0RNECWpGPS6pwkSEu2ZDFgA43FNmQUNCNA2s3tzKXRb5nOLKrh5UnouoOI1REIUeLNEhOBnVZHTScbYCqhfw13f/AA9D5rVtY+Qry3okB+NE/+Lnj1vPnwYKoFmTRhBzZRYHFkxUNJqYiHb+GFOUmlQ0meXjnllwyOIzBtVUk5tdboMSghRGnJ64SIqfHK7sZNmcpr1CA95R+tQpGpcG/O8JGxng8A3xN2l/sUZyyUCsVnOAGoF8qBo0FtoJ3fvs7THlXftWVUPXF6/lfDCU6LJTuzS25sSALiubze7b5/fy477TUhlqRpaGmXcqf7S5XyZsV8SBHAdGwMCw0LFQY4sLnNahDFCdzyuawBYnVaY0p6Y8HUZSCuyc6jS6+zMIgZSFSO93CQnXxwrTnJW4jiIl7NLCAb1oXyKSLc1holQYbw4bsZxbiBVY0ItvdelAb+cLSHSNEF5WIiL5IA1pd9Br36eX852F1DcQ1/ArckMc3LBx6its4LJ84P2zI8wnrCcXkFQqLD77ucfe515GcdTXYdj/kHAKNSgOzygM76xops1DssMZC5Iak24aMNM6vkK2743eGM6kqtFQVgcGYeG5VrIwlrV4NbqdXtr+kklkoEO213rd3JFRuEqwsLLadTCnP7aJqbmd6bawTbC5bmHKTBMKjpUZQpCfKsFC2INckZkEWqsJ1MyuhWB/9/BMP3zlvHpw+p5plLH3pjzsjDjjlG3Dp3KSfNQt0U6cyNQMb2OWIeC3IAsPs2aApLwe/wwGjH6UvMziTiHMZqr9wYyjTBJed/E+Wy05wQPnFP+4El9fHVCy3XTA/YDfsHDfMV0i5SbCyEDML07EBnExe4SlJ5hUKdPX10YeFceQVirVVrkpOUMkociPFuSoR8LnjYEE0IjMbssEMUFQa3op87HUYkDK6K065IiHnAZaQxVty4jrBb6L1zdPgvS3HWRQOWpmXbIq9QZIu8HqPyn9cv52UrrnjEarCKSVTIm/6hViRnxxnQ45xcy/OAagqjKJwO7iNP7+5a/kaPKAsTNyGHIyqeg5UnzIC9V+5GECrheHnnoPW6z8PJ1u74Gt/2c0G5BcurocvbOCK6JgbElGGhcHqwvFWZBEXVTElXBNydPUfK0GSNhVEg3o03jnazwaLeP+6dGrm7e7BCAHKULkubvcX4pWcRd0Gp/KUhSho0GiYSjseUcPYxx9D+9e/wTY3SzZvhtrbbgNVuTQox4G7K4HXtNyEnONh4Tz+up/dx88RXRbp/mvoBFcbS2KmoZPS2woDWPTxr31IWRgdGhYqDMzDQ8Z0qpBhIQ70RIh/OqzIwoIcVy5dsLJw3KJ2xfl8qPTx211gt8ZxkLXv5Lt8eOIpb05oWIhDB9EUjcrCsM1p2cotHHTG3Ygs8gqjWZAFc6v5Tv2o1xrzBgLbSTGAGWmIQ1notdlh5KWX2eOyMBZkJSoL0QotbPKpWpH39/PF1oyyGWwBO8GGnGBmocgRSXeJTnmdkR1mHYcGIXvlJukfKODNjbA2i5ITMSzEoa1bshMlimnjRva27KM20Hr8iQ0LJWVh0UBrShbNhXUrodnlhjHww0stL020IVfPSbuycHd3anmFZ81Lz3Abbxx+eeUymFFlgk7rGHz1z7vCNsi1STu8Qq3DNltkK3KKrchJghYw25tvssfmM06Pu1hHNCbnS26h3IRcXZU1CxiWnSxrKgN7FstO8Hj53O93sBZD/L9/8dml7PUp2w07diuy3ASpkZSFeOzg888m248NpGRBnqAsDHNDzuJXxLBwafRhIbKwQeQWZrfkRLgzjHoNa31NdlNTDAuZDRkRJSeipRPBC/vL/wEDeOOP/64yMQU6rg3iHxbyjV97WRN8+BoXGGy8fDYrT8wWeO4RVsBIJSei3CTmen7hpSFWZKGORIFF1LWyECdEsSDjuuDkMSubJWKeIzKtIjUbsow03CtbaICmh37LCrNGd++GI5d/FnRd7TCn1gw/+dTi2Odp4VoI04QcLrewZV8/+IIiIfo7pHKThuAm5JNxr2UTyS18UbIgY7FJpkt0xiPuKXulVJ14c+U9A5INudgnt5wrBcxO1lZXx2VFdrW1QeuXvszyYnHd0vDTn7B/zzYvDNI635qA4EhEKeSoCXl8ycmUTj4D6DKWBpSFrE8Bc9u0MNbBX3vu+sSubVXS/TllFoaHhoUKbUMeVvtDFCeIsCLv7dubvXKT4GGh1wngDN31nTKnjCkM0Rrx4attCViQT2W7fIkMC4UF2alxQFlJSUJWvHRRImV9jGDJSdzDwth5hYLF9fxr+lUeGHYNx7XIxR0RbCyNxciLL4J/dJTZUooi7PSLzEKlDAvTWXIi8gqDLcip2ZAzUwAj1IXZsCIHhoXptSBHKjkpNZTK6oF4G9DD2RFwCKJ3eWFeuz/uchOP2yurEPTdR1OyaKrKp8HFNr743nJsC/8g3rDImYWJKgtFwUn4YeHg2CAcs/KW5WU1kW96xjPkcMHOlgG53CRdYHvcb69byZqOd7YMwu1P751wwxauYVRYkW2vvxG1QTVTjO37mKnuUBWOrZXxYlrLCwjs27CcS/lkowl5PHhT+MMslp04XB74wh/eY+r65kojPHTtysB1cMryLNuQEys3QQxaDbPe5qIRedux1PIK47Ehezo7wdvbx9RURQti/1wWTeHXin1ZbkQOLmJKdrCOw8IO6UeJPwvWQiyUOMHKQhx0de4ODAsTHEgEhoXRlUFoVXS1trISgJ0f6Zj7Z/qSKmhamH21VKxG5JYRvkEdM1ZIWJHHhgCOvSFnFnqcXlYKF3tYGPl8f3A7vx+aOr8CTKX86wobcmsKNmRGtTQQ7j0IprVrofmxR8FWXgOVQz3wi62/gt+cogaTIY4YFbkJObp6uW66BQxGLWt1PhlUlCfbkEOakLtj5hUKVku5hRj5EG04i5mGWIKGnJdlC3Lw/UK7mZ9T3V1xKgul9f8QdtPpSkIEBYpqRI4yLMTrfusX/pWtcQzz5sHUe38d6iArbUzciqwQZSF+P4h5cAzMDj906aXva6glMNA014FfGhZqGuPPK0SqSVkYFRoWKowR6WZ2wO8J2X1GRMnJ3v40DgtNcQwLdcUAemmbxhZqBcXFlVAXfvRGBzgdMXbIW6VhYdO6EMl4PIOgXDYhC4QN2ZbUsDC23WdBXSX4vfx33jkS3XbbnmC5ifXpp+Vik3CLYofbIb8ulGJDTmfJSbhyEwSbWhEcpLJFfoyCGE+vaENOv7IweFjY+nH0RVl6h4WZaasTJSfdUskJKkWFvSNZKzIqvXSn8uHNsqN+mFMe32BuuHeMbT6yOICRFJtiSxvhAum18t7J9/hrE3c3cTMFi4EqeK5fupSFwoI8o3QGlBeVJ9RIiHGRc2tL5OzAdDGz2swUhjgceuy9Nvjjuy2Bc82oG4bHPBPyVI0rVoDabGYL87EPP4RsY9v6BntrWn8qy2mKF+Ma/nobff99OW9KyXhwSJOlvMJIZSd3ZLDsxOP1wQ2PvA8ftluZGuzh61ezfGeZusUoxwBA62MiKopkwHM05qIlaEMOtSJnz7bdZR2FE2nIK0R0UyIPC0el47to7lxQF8VuLkdLNLL/5EhWS3LkJmSpiCkZ8JpmM6rAVcrPsc5jxyc2Ins9AK/+CHA1OCKVXyVqdZRLTmIoC9nvw+2G3vpV0HnCwbION3wmvmbQdCMrCyPEJ7UNS+Umsdb0WGiw4BL+eN+ToNNrwCBlu4tG5Amg3VIM2SIoC3GNJVqQsdhE0CTZkAfsLhgec6dlWIi8MFIEX1zzVThQ3gglLgd4vvl1sD4rbThGIqQJOboaVa1Ry0PhFsmKjPdlYqMUMw0FbsmGrKurjev4LNKpYdDhZjnIkdh5YgD67S62oSgGjLm4X2g3OZNSFg6beKyA0tDVRG9ERudY25e+DO7WVhb30vjgA6ApGbeuL5OGhYmUnIjMwhwPC/F7we8Lmdbjh05JUMWUhZJ622+uA33PEHtsmpbYGpxsyNGhYaGCQHWesNn2SCGmQnGCLKzkysKP+z8Gr8+bPRsyYhaNyBNPVM2LKtkFyD3mhY9ejzI4Q9th23sBZWHQLhCqjFze6PJfUcQwnIMm5GCbqKwsjBHuLXAIG7Il9s0p3vCofHyH/aOT7XEpC6fGYUF2tXeAY8cOpuYsvfiiqNkxqP4qK8puzkg0Es21jES4chNEbTKBqrg4LnWhFxcUXi9rutVWZmYh1DCnDNQaFYz0j4G1N4HioATB4h2R4ZexYaGkLOxrH2Hnt3TlFvYv4TcWq49rwaQLLH7jsSCXlGpAJf3eRe5owhSXQYPGDCtH0Yjs5+pCYRMqnw6gNSQ1LOwZGQs7IE7WgvyqZEFOp6owmDPn1sB3pay6/9zyMbxzpC9ErYOlF8GqCRzQmU/jNvKR116HbGPbyvMKTXHmFQr006czGxDmLY6+vweUjqevNyfDwuCyE7yhfPhtrlRPJ3h83PnsPtYgiqU9v712JTRXjTsH6I0A9ZJ6/q/XAjgyqNLGeBbMgcKzSow8sfHUSMPCbJacCAvyohTzCpFoa6DRPfHlFQqaKoxMqYy2bMyhzBbtchNy8pspQiE4LBVUOI8eCdiQh6U18YePAfQdhAETH0RgoUeJ2ISPk7I4bchoQfaq9XBk5mXs/WXnNrHyi1wgSk4iNSK3jnAbclyFhaIV+cAWAI8LzGVShnik3MKuDzF7gg86IhQ0nDw2DMO9o6A1aGDG0oAS22zQQpWZr9lbU7Eii3PC4Ak42N4N3/37RzBUVALHvvczKDn3XJYr13nLLdB3332RN4fjaEIOpnmxyC2USjs6+cYmtiBjUeMEZWFtbAWgXquGFdNi5xa+sI+v08+eXws6bCTOMnhMYelQn+S4jdTUHinn12rkSmGlEWhEnngfhOuSjn+7gWUVaioqmN1dF875VDo1b4eFwSUnzT0AJ33OCcpCj78S1F4feNQAVU2JRTzQsDA6NCxUoAUZM0W6RvngrDrIhoyqkmJtMYx6RmW7aKoFJ3HZkINLTsY1IiMqtQpWnMeHdx+82sayBSNeuLHqHAdRkpS+zFAmy73Fc4qEVQHKQjEsTCyzkP8ujXHYkJFiNR/UHeyNfpETNr94yk2szzwtK2TEAj8fLMjpGhYOjQ3J/z6cbVWoCz1SyHEk3NKuHn6+ShtfA2+i6Iu0UDejNONWZBzgIeYKAxRLi+J0U1pdzCwx2DI4IC1Y0zEsPDDLAF4VQG2vG1zt8SmHxODVYvSlZ5BS1gQXCSvy0S3gEQqGBC3IwcNCLA2xOSe23b3f837C5SaovkJlIXLW/MwMC5EvbpwBly2bwuxHX3tkN7uxks9NYdpFRW6h7dVXIZugcmDsw4/4czgtvrxCASqxhbrQsWN7HtmQsz8sDC47+cXLh9Kumntw6zH487ZWli/2P59dKt/ATuDCn/Nm4o5dAA9fELjpyZQFGdXE6MJIgDrJOdKTxWFhuizIwQUnPpsNvMOhsSmJ5BWKY2yhNGyL1rKeboRDI5kmZIFQCPbWFgVyC2VlYReAewzgtbvYuwMr/oW9xYEEKu0TAQtKcCaN5WdiXRlpWNjSdC6MaS1szbp8c+7WdLINOYyyEIdjYpM6rjU9OpJwYIZW5ONvsOHX+EbkRPMKhapw5rJq0BlC43zkRuRUhoVonWYN6X746Z//AaNuL2ycXQU3XbAYpvziHqj4/OfZp/X+zy8jXxPjaEIOpmlBJbsvwzUXOrIGJAtyRUNo3EwiykJkdbPILQy/LsXfp8grzIUFOfieobdUanzu6Izp0PGhq8jBf8dWk7KakGPZkDHOpfO7t4L9nXdBZTRC4wMPsKipsAgbciJqe5G5muPMQsQg5RY2d/uh0zUUsFQP8+/H5eSv754ygKmlTcllFtooszAcNCxUEEImjlkcPdJCQNxEIhq1RlZFpZpbmLSyMMywEJm1ooY1qo7Z3PDxW52xLciSDQMXiLLN1HEybhvytJJpObUho7JQK4d7py+zECkz8JufE4PRfx4dcdqQ8UIZsCBLNo4o5SZKsiCHDAtjvD7isSBjiLZZb448LOzviy+vMAPlJsE0LpByC/dnbljYI1mDqxszoyoUx3fNNMmKLOUWimFh72jy7db7nCfg4NRQtVgshIrSpOM35ph7mBJljXCO3QEGlQaOWo/CWQcfhP+sLIdtljLw+CYO/KJh1GuZkiFcbiHGA4hynhW1K+L+mu+3DbESJLQDLWssy+jv+P9ddgosaSxj/9+//vE9OHByJOJGhhkLajQacB4+HPegNx3Y33qLWUYN8+eDTlp4J0I+5RYKlUSyBT5pLTv5R/rKTp75oBPuep4P5X9wwQI4b1EUtQMOCK5/nuUYsYHe787jlqVMDQsTKDcJ14ic/WFh6uoZVGZjUzjiDlLwoNIFVS6JKAuRRVLJyb7O7DQiH++zw1ZpQwULm5JFKJE6q/i61nkkeFjYCbDzf/kNrWUKDMw8PekCBbTeig3raOrCgSMnoaXpbPZ4/WdmsX+XK6IVnOCGIYofNCoNNJjCb2JHsyKL3MKIjcjBTchh8Lp9cGRn9wQLskCUnJxIpRFZpQJ/FVc5mYaPsliO//nsMlbEhJEqtd++BcqvuYb9/dDf/y+lJmQBqgfrZ5bK6kK53GRK6Gs8EWVhaG5hf9gB3EcdVlZ6hmVBOBDN5T1Dv6QsxCGgz2qN7RjCtzoNjOqV1YQsECU0wTZk/B103303a9gGnQ6m/uqXUHwKjysLi1AWxptZ6HECOPqVoyycx+cf07v90O8cAifmn2Nhaid3e7hG+Br6ZJlK3qRIVFk46HCxjXYiFBoWKgjbEL+JNZcZ5OyqYBtySG5hisPChApOgpWFYWzIIidD7F6+/1KrbDkMoeVd/nYazysUiNzEWMoxtAogI4YcZhZiA5uKLzK8Zfxnghl2Plfk3QjHcPyZhUiNkV9kO0fC/6zH25CDM8HCgTlb7pZWtutkOeeciJ+ntCbkdCoLhQU5UhmGRlLgiJDjSHh6ujNabiJokoaFHQcHwZuhC1dvW2bzCieUnIxrRO4fTa7gRPw+35/JL1/2N7YmZEM2+UbSU/5Q1gQlfj/cVrqYKaQH/C54wlICX+x/Gzb9dRPc+c6d8E7nO3EPDmsi5BZ+2PchePwedhw0mOO4oRpnQT5jbjVoM2wHwmKJB/9lBfseDnXb4NevHpFLA8ajKSuTy0Vsr2avFRlLVRBzghbkCbmFH34oqxCUnlkozmvZJrjs5Jk0lZ1sP9YP3/orV6t9fv10+PyG6bH/EWYIfv4FgLJpAIPH+cBQxAXksNxEfnpZziw8aR0LyitMj9UuXCPy2MGDbGCIx7quKf61Glqjs6UsxIIcbHIfcXqYOnVDCsMNoUQ6XsHP9c5jQcNCtMlt/S/++IzvwoBHUnklaXUUuYXRSk72nKwDv1oHdZXuEGttLhA37VanFUZc/No7fs2JggGdJk5L/ALRirwFzBY+HLBHyiyMUW5yYm8fOB0e1qw8Ze5EhbLILUzJhozRUW4+aJmr7YD7rlk+oXW7/LNXyBufnsHBiV9AuBZiNCEHM+2UQG5hf+fEchMswZHzt+PcPMMNIL1GzTY3whW/vChZkHHNEU/pYqbA15NLpwKXpXjCRkY4RPzQGK7BVCp5jaokREZ6cBty/0MPweAf/8QeN9x1F5jXr4/+RcqaElMWspZhXEjo47K/Z60RuQ9A5/HDyXIp6qF9J3tjHeL32r3lARFSvJQb9ey6iDNwzCklQqFhoYKwDUhNnRYduKQBQbCycHxuYbLY3XawuW3xF5wENyJHUBYi89bWg7FUz+zUQtovg82XQlmIrWZBxFNygm1uw/1CWdjHFGK5AIOijVL2oMNXFMi6i5JbOCplFsZrQ24s5b+TvhhtsfKwMIay0PrkU+yt5dxzWU5bJJRqQxavjyHnENuFTmcTskBbURGXDTmgLMzssLCqsYTtDmPLX09Qo1066W21ZXdYeGIkLTZkp9cJJ6wn4P2Z3GZi3749rtKJIUlZaHT2p82GjHzSpYZXL38VHhj2wqeGbVCuM8OgcxD+fvjv8OWXvgxn/vVMPjjseAfcuAsagaoIw0I5rzABCzLymjQs3JShvMJwKqkHr13Jso082KoSpTRAtiK/np1hId4Y2d5+m//fpyc3LMRwbTYY8XjAsUtSrCiUXNqQgwc/V69JT9nJkZ4R+OIfd7J10XkL6+C2CxIoEqmYzgeGqMpBS9Xvz5eVCGlBqH6SGBbWSsd8toaF24/3y78bS4p5hQJdQ/2E3EKRV1i0ZHFCDcOiEfnjrmEWa5ApUJFz6/99xBTQqCj5zdXLU8pXE0qkg6V8gOJuawefXlInYekVZlpWzgZYchUMsHzLVIaF0XMLT3zUB93qqaDyeWHdWZVJNzynC8wUFt/r+NxCYUFOaM3ZtJarhZ1WMLmPRbYhjw4B9B+Jqiw8uI3fp8xZXcs2OMbTLGzIA8krCzEK5P/a+de5uMEGi6dOVPkbZs3ijeEeDww///zELyI2OGI0IYc891P4ub/90CD0tYthoSn0GoH3ZVqt7KyJBQ4AlzSWhmSfBvOCZEHevLBOEfcMI5LyNFauvBgW2sx8wKloG3JPDzt/oQq1979/zj5We+t3ofTCC2J/EaEsxBxVLFyKO6+wjg1Rcw22dmtKS0HjB5jaC9BlkeYX0n2hrZdbkx01lvg3HyRQ6VthktbglFs4ARoWKgj5glfMT1hoHxu/OyOUhaiscWNhSAqqQryIx1sOwHI3EHtv1EHasnP4DfTuF1rAF7zY6zsIMDoIoAsKHk9AOYbqPK/bDz7wMetBkTZ2u152cgv57rErihU5URvyzEr+87B7hljYdzgw1wwtf7GUhb6xMRh+4QW5BTkaSrUhY2CxeJ0mqy6MrSyMz4aMF2okbHhwGsGF69R5fKe7NQNWZMwVHZKUCRkfFkqNyANddnC7vCkPC48OHWVKO+vUUmYH94+NgWOHVJwUAVQCY4M5UjTSldZhIQy1gs49Bqf2d8Cd/QPw6iXPwIPnPAifmfMZdpOEQ242OHyZDw5vf/t2eKvjrQmDQ6Es7IkwLEzEgowbCXgjjPc/p8/JnrJkaWMZ3H3ZKfL74ZSFSMkmPiy073gPvCOhapNMwNSAViuoS0uhePHipL4Gyy1cu5Y9dmzfBkoFbyTkYWF1blVF3zp3LlPRpFJ2gqU/n/v9e6xdG5Utv/jsUrawTwhUeX3uOT40QFvVHy4CaJE2L1MBb7Z7DiQ9LKwrza4NOZ15hdFKTuS8wgQsyMj0KjMU6zTgcHmZRThTYHP703s62evo3quWyXbwZBHDsDYtP8fg68J1chBAG7Q2O+sHGEgOA2OZGxbide6tx3kZxtSO16Bm2UxQApEakUW5SUKb/0FWZHP/25FtyF0fBK7TUqlMMBiZ1LK3P6IFOVhZmGxmIRZ93fjY+3DYx7//Rg//fsNResnF7O3w088k3YQ8/nWC0VCYF43lk5hhWF4bNCw8ydfS2ppqUGniVwEKK/L4khPc0Dnaa2fKw2xtUEZCLs4s5d+Xu7MrLhvysJFfV5RsQ/Y7HDC85R/Qdfvt7P3Kf/0CVFx3XXxfBIfsah2A3wtgi+NeSjS5K8CCLNZghgWi5MQPXUY+uBZ4uvmw0D8luZgoUWhEuYUToWGhghA3s26DOqwFWVxUcXji8rng8NDhlPIK47YgIxgqzJ5kdGvsgg0NYDBpWZnA0d1BnysW5lNX4lQx5N/UGetiKgtFuYnNMAhNIqQ1R8iNyP1BjcgRhoVox0abA1JsiW+nY0a5tHDRjEBLhKwUkVeIA+VojYYYmOwbGQFtQz0YV6+KWgCCNhEkV6rNaIjXSDLDQsx8QyUaMr8ygrKwUrIhx1IWioITyRKQldzCDJSc4E4zyu1RCWwqTay5N1HQ4oNqXFQH97WOpDwsPDjAd9nnVcyXm3Vj5RaiKhm/Xwww1wx0pCfPTZyHMP+lTzoXm2pAa6qGdQ3r4PZ1t8Mrn3kFHjr3Ibh8zuXs5hCPsSePPAlfffmrcMbjZ8AP3v4BvNn+Jtv4qQ6jLMSBItqQE1UWCgsy2uvKjJkpr4nEZcunwp0XLYBLljZEHEpgADc2DKOSgmUJZhibZFU3b9iQUjGRac1q9ta+fQcoFZ/dzgboSLyKkUyWnXz3vOTLTtAq+oWHd7ISiuZKIzx07crk7W04MLj2Ge5sQLXXny4DOPwypARaTLG0DUvasOAkQcSQCpUMmVTSIVaHG577iF8/1yl0WIjDuwVSycm+zsxYkXe1DMAPt3Bnzq3nz4M1afhZlBfxjT0f+EEznW8iOY8eC1iRcUg9nw+D+iXHSKo25KHuiWvDPa+0grVvDPROK8wceJvZwJVApEbk1mGpCTnRWCGpFdnc+WJIOWQi5SaHd3aDz+uHqkZziD03XGYhtpWPuSMUN0YAP/+rf9nFNvV1tdK6c+AoTnTDfr7lE59gWe547LhaWiaqCuNsQg4erAh1oSjHQUGHwC3lFerizCsUrJ4ulZycCF0rv7iPf731sypTbllP17Cwq8QTn7JQWvcPFHsUqyzEfFi1hZ8bO7/zHQCvlwlAqv/93xP4ImqA0inx5xYGKwsVgsgtxJKTLn3oJo9aGhYWNSbnjhNr8L5xG/YEDQsVhbjgOaS1cI3UlDehMU6yIiebW5hwuUmwDTlCZmFwk+viM/lN9K4XWgIhuC3hLciIXHASZRAkl5vkMK9wfMkJ2sblRuQIFyPcvURQwV1kjO8CKoYpKq2NKTLC0THkiCuvcOgpbkEuvfhiFqYcy4KMA2Qjqj8VRp05+WHhocFD4Ac/+7lGyiLRysrCOG3IGVYWIo3z+cKw58Qwaz9MJ72t2ckrlEtOmkVuYeiwMFZLXTSV6JyKOWCS8ufsMYaFogm5tKY4UP6QLmUhnhNPfhi2CVmr1sKa+jXwg3U/gFc/8yr8bvPv4Iq5V7Cd62HXMDx15Cn42itfg9P/ejp85HwANOYDcHI4cMxjsQla7y16C8woi38Y8ep+vnDfNC/zQ+1wfG79dBbijpbkSJgldeFIFnILbW+8kZIFeXxu4di+fVlRRCaDyKFSm82glmIycsmnVyRXdoIh4zc88j4LzUd14sPXr4ZKKYQ8aYosANf8HWD2udy69OhnWVFCynmF1XOYaixRKk08JwkHhf32zN6g/Ob1I2AddcPc2hI4LY1qY7noTVoDYeu4u43fiBafElAZx8uiDDYio0r1a3/ZzWISLlhcD1+IJ/cyDvA8j7m1iGcaX886jx7h+dyY93Xuj2QbX+rDQr4+G+4fA48rMMCyDY7Bzuf4Om7msafA2FSXcwvy+NxCYTsWyE3IJQmu6RvXMLWT2cu/X9yQR7dE+HKT8JtsIiopkqoQwfNOiUHLNhrbB+NXF+K65gdP7YW9HcPsa/zntZsBsFgP84sHuHV6PKgCN0mZc9Znnp2YVxhnuUkwwcPCCeUmIn87ziZkAW5A4jmrbWAUOqUoJCVZkIPvKVuNjrgyC70D/JjsLnIpdliIyMVsPh+YTj8N6n/4n4kf43IjcjzDwi5FKQuDcwux5KQzaHnpVZWCzsGvoaUzQtfhiZac9JENeQI0LFQQQkpvVXHraU1JeGuEsCInm1vYbU+w3CTYhmzr5QmgUVh85lTQGjTQ327jMn/8/NZ3A03IEXaBxPOKWm5S1JfzTL2SCn5CGRkcA70YFgaFewfjkCzIRSV6ZgOIB3GhUmlscLh7JOkmZLTM2t/iNo3SSyK3IIdYkEuVZUFOh7IwVl5hQm3IUrhwNoaFqGDFGwM8fDoOhQm9Tke5SQabkIMRjchYciJe35g9KLJTE+HgoFAWzgPTunWsBQ534l0nTsRsQi6tLgZvusofissB9NLP78grgaFBBLDNflXdKvj+2u8zxSEODj8797NseIrB7wftr4Gx8WF4bfRrcNtbt8Hrba/Dtq5tsqpQrYrvcj3q8sI7R/niN9d2oGiUiNzCrVtZpmCmcHd3g/PAAXazbtqwIaWvpaurA/20aWyx7niPh2orDXkYnmNV4fiyE5VUdvKu9NqMdbN957P74JUDPWDQquGh61ZCcwpttSHoigGu+AvAwst4k+LfPg+wm4fEZ7PcBMHiIXGD0pNBKzIOOn7/Dj8/fvcT8xK3cSegLBSqQv3MmaCRlDCJsFAuOUlvVi9mZn7jkfeZ5XtWjRl++qnE8hRjIayLjqn8rQsbkS/6JcDNBwCaA+cdkVmY7ECiyKxj7h3wYw5vYID1zt+PgMflg0rTKNR17wBDs3LWcuEakfEYF+vORkuCbhbc+F5wKejVo6DTuMOrC6MoC4e6HdB9fJidk2avinwfhK8PYUU+0Rf/sPCRHa3wxK52NlT71ZXLYArGcYiNRDH8CwNu6iPWZ58NbKTKmagJ5LRKNMwuY24KpLIhVD2ZrLLQbNDKRUQ7JCsynl9wUwe/37MX5GaDMhi8t1WBCk6WeOPMLOTfx6DRp1gbMqJrmCJnwU695x5Q6ZJQcCY0LDypuGGhYR4fFk7DPXpvwKng8vK17oAZoL6yOUUbMg0Lx0PDQoWAdlUxWOrHHJwINmRkUWVqjcgiszApZSHuxrui3+BjMcOi0/hJbdfzLeAfbOGBqlhzPnVVxGHhiHsEbBG+9nAfPykMF/UnvguZqczCgbGAsjCCDVnkFcZbbhJ8oVKpPXCgJ3xGZHscTcjDz25hN7XFS5eCAS1/eZhXOMFWEMWqnmxeIaIRNmRp0RAObLz2Dg2F7vBlSV2YbityNpWFSLCysFhbDGadOSkrMi6ghQ15bvlc0JjNgWbdrW9G/HdW6aYKM3xQ+ZKWNmS80yiTFl7HXg+rLIw1OLxt7W3w8qdfhofPexhOr/sk+Nwl4FONwjNHn4EbXr0BfvX+r9jnL6+N34KMzbNOj4+dG+bUhrdXKQE8L6FNDrMEHbszVxgiLOpFi0+Ri4xSQem5hSKvUJOqzT6N4M3lNVLZye1P741ZdvLA1mPw522t7BBDherypolNpSmh1QN86iGA5ddhexrAM98AePferJabjLciY1Nxpvjvfx5i+cenzqyEM9KcYSqGhTikxqKpZC3IgkUN0rCw05qU8jwSP3n+ABtu4LDj/mtWgMmQfBxBOCqK+bnFWs+vqc5jx3i+3ri8vFQzC3GAVSE3IvPrGm4mHt7ZA6ACWKz6AN+wqAelIJSFHSOBdTIqLB0ebOZWyzblhFjIM7jN6h5ZWSlj72NZwoxxGenBqsLGBZUxY1imidzCMO2/4Xi/dRDufGYfe3zL5nmwflZVqDIwSht7yVmbQGU0gru1FUb37BmnLIw/r1CAtuO5a+uYUKFpYejrTc4sTFBZiKwZl1soLMirmivkzY9cguUWuAnbZ1HFOSzk18xhI8/yz2UmfjSqb7oRKr/6FWi8/35mS04KUXIylJ/KQryX9et0UOwCcHYH7otcLn7d6C4LnG+SVxZSZuF4aFioENiumB+dLGrodrrCNiELFlZxG/KRoSNJtcOKYWFCykK9CUCUocTILUSWnt0Iaq0KTh6zQte2nYEdPv3EExzaXtFmF005Zg2yIedaWShsyCM4LAyT1xPM6LAoN4l/BwgvVAY1/zkd7u+KqiycGkFZiItsq7Agxyg2UXITciJW9UiglTNaXmGwDdlns0Vs1vVIg1uVXs9DzLOAyC1s/XggbTdOHrcXBjuzU24yXlmIO/pOhzvp3ELMPEI1Itq+ZpRyW65ZsiLb3owyLJSUyRaTn2W94BRCW5n64Ei2ImMGWgLDwvGDQywv+frib4H9yK2g7f4GXD3/ankzB3fIT204NeG8QlQVKsWGFg7MDhS2YNtr0rA1AwiLuvn009Py9ZSeW+iRlLMpD8MzWHbyB0nlFg5UH979PL9Bvv3CBXDeogzZ2nCQc9H/AJx6A3//xe8BvHZXTOdECN2pKQuDh4XdI5kZFqKd98n3+ZDm1vPnp/2cgAN/HHAgnq6uwLAwySKh2bVmVpIwMuZhVsd0sOXDTnjorePs8X99ZjFTFqYbscnbXcM3hlHt7neF3nTiNTzVYSFSJkpOuuzg8/rgTanUZOHGKWBs/4g9VuSw0NYBPhzOB1mQcW2nR6t2wl90NUBJA5hVvRMbkUXbeeUsgOKyCb+DQzskC/La2PdA06RG5NYI+eHBoCIJbe5urx82L6yFr5weFB0iXAdRhoU4ALKccw57bH3mmaSbkIPZePls+PzPNkDNNMsExb1QyyeKnFsotau/uE85FmQBvq76pGW62MiIhBAJWI3KVRUiRfPnQ82NN4K2PIXNM7HBbQ0tG4o+LFTO7xXVlNrZ/LgytfSDTyqRGnNICv0yVXKbD2RDTv+w8N5774Xm5mYoKiqCNWvWwI4dkRfNv/3tb2Hjxo1QXl7O/px99tlRP7/QLcjY9CvaMCMNC3HIhyc0r98rq2ySKjgxJbijZK6Oe1iIu3XzT+WDtF3vuCNakONVjg318gu1rWgg6V2DdCsLR0fcoKqpl+2p4xeGiGPEnVAT8oR2PWt32OBzDHyPNix07t8PzsOH2WDL8onz835YKDdmOxIbFmJBBA7VYykL1SUlsqRf2PjGI2e81NZmbQiDNhK1RsXKdMTAK1X6O+ysqRztTOby7OwCF5v1UFLJj5ueFEpOxPluVtkstnuMiIGTY/t28I1O/Bm5xjzs/0RMGv73moqKlIouJgwLBUkMCwX8fK+GwYGp8O8rvg0vffol+NP5f2Kqw7kV8akK8EZIHhbOV64FWWA+cxN7a3stM7mFqAa2v83zcs2npWdYaFzNh4VobfYMpjceIB3ITcip2uwzWHZyz0vhy062H+uHb/2VD5swT+769enJlIsInsfP+SHAph/w99+4G+CFW3nLcSw8LoB+qdioNpVhoSFjjch4Prjreb5ZhoVDp0xN/yYXXgt1DXwd5GrvgLEP+bCqeGlyykKdRg3zJHUeqgtTBaNcvv03nin75dNnwHmLMqOSEWu2bpMb1CYTK29ytYa23+JGF65Jgj8/GUSr7WC3A/Zu7WDXdLQmr714hhzHoaRhId6zaFVa9r2L+w9RbpJ0oR5akRdeCiZN/8RGZDmvcKIFueuolTmVdEUamL4k9oaKKDk5EaMRWWSsdlnHYEa1Cf7rM0tC14lxKAuDW5FHnnse/LaBpJqQg1Fr1MztNeH5CmWh1LKbCKua+bAK248PnhyB907wYdvmTG3sJHnPMFIM4DXo5I2MSAi3idWkUmxeYdoQysI8tSEjpgVcMDX1pBsGyvn5w2rllvP+Sl3S59aqMCWDRJLDwscffxxuvvlmuOOOO2D37t2wZMkS2Lx5M/RIwf/jef311+HKK6+E1157Dd59911obGyEc889Fzoi2DYLFdsQXzibywzyCzVSZiFegERu4b5+LndPBJENmJANGTHFV3IiWHZOE5O/t/bVQo97BsC0U2Mrx8IMg9wuL4wNS43CFdrkdiHTiMGoZZmMyKjKBCqDgakR3NLFN1UbMlInDWa9qmFoC2N/6JBtyMaoxSbmszbFzA1qH2mHI4N8oDa7bDYoelhoP5mQwu7Y0DG2QC3RlUTdbcJjSmTYRSo5yWa5SXBhUN2M0rRakYMtyNlUnomdbSxsSXpYOBiwIAswGwvbvnFYb9++fcK/ee8fJ1jRkKW6GCq0w+kdpAQ3s2MxkEVqmkuCCqNezhLrt7mYPWtpzdKELMgHTo6wm5UinTqtjaeZwrRhPc+cPHECnMe48iedjO7aBT6Hgx3bRQuSU2aMB187htn8POlQoLpQqcNCUXaytJGXnfy/50LLTo70jMAX/7gTXF4fnL+oDm77RHp+XzHBc+Bp3wI4/2f8/e33ATxzA2bDRP93OCjEwgKDJaXjXigLexJsio6HrYf74O0j/Uyph8rOTKGr5xvD9jffZG3cqDQ0zJqV9NdbKKzIKZacjIy54ct/3gUOl5dZsG/J4M9A3KAOOAfZNQlxYm5hEEJVmKrVsbyer/swd2/7M/y8ufaSmaB12+SoFJatqhDQCSDWcKIRuXVEakJOJVZo4SfBrObrNXuw8k/OK5x47Ty4ja/TZy6rBp0+drO6yCxsjWFD/tmLB+HdY/1g1GvggWtWTGwEFsO+vkMAPm/UEi1cY3qtVrD9469JNSHHwu/zsUzzZJWFZUY9zKvjA/0fP7efibEXTy2NWbiYTdjrTaUCh/T7i1Rygj8LrzwsVLayMC2UNgWUhdHupZy2gGNGQcpCpHg+35yb3g3QWcavvWOSgMZdX5n0fU0gs5BsyCkPC3/+85/DF7/4Rbj++uthwYIFcP/994PRaITf/e53YT//L3/5C3zta1+DpUuXwrx58+Chhx4Cn88Hr7wiBcITcrMuYq4IKAvDtSGPtyInmluIgxOxYEnIhsyeXE3cykJRJjB7KR8Q7LZ9CqCJZz1FVRbaJp7QR6S8QqdmFOorcq+WwRORnFs46AxYkcMMwMWwsNiSWBBtcCPykXGNyGNurzxQDldw4ne7YXjLP+IqNkEeP/g4awteV78O6s3K2kESiNcq2u6xRTbRchNUZsW6gMglJ30RhoXCtpGlvMLxVuS2/QPpLTfJkgVZUNMsSk6SVxaGy5/E36uwItvH5RYOdNrhw1faZDuOf7A/vYOUYGVh1WyudkihCEIsVrC1MxmEqnD9zCoo0sW+Eco1mDlpWrUqY+pC2xuSBXnjxqht8Mm2Ijt2TBxO5xpPH7flaRWUWRj8Gv/Rpbzs5Ok9gbITfL1/7vfvwfCYB5Y3lcE9Vyxln5tV1nwJ4NL7sVkMYM+fAf52PYAnisIguHgghU0XoSw8meZhIToS7pIGsteumwaNkkIqE4g10PCLL7K3xYsWpaTcXjSFrxuxNCFZcFPxlic+hGO9dqgvLYJfXrmMFcpkCqFI6h/tB4MYFmIjchDpsCAHNyKj48A16oGqRjMs2NAA7haePa2tq0s+0yxDCEcQbk4HKwubLCkMC6esBJOJq4BtwVFAEcpNMILlyC5+jZy7Nr61brNkQ8YSD1QPhuP5j7pYzirys08vgdm1YdZWZdMANAYArxNgiP+ewqHSaMBy4YXssfUfLybdhBwNNhxzu9l6Jdm1kMgt3HqoV3EW5GABylCZLmpUFBuuS0pyVCJOfmWhtLGF/QCjUZwRNqlwFFu8ixIvqsq0HRtp7vZD16KLAc74Hvj7+fVT25i887BasiEP2J1hHX2FTEJXTpfLBbt27WJWYvkLqNXsfVQNxoPD4QC32w0VUYLGnU4nDA8Ph/yZ7Ii8DUOJHmxOT9SCk1RKTvocfWwwhDt95UUJ5h6IRmR7+NKNcCyfz0/QR51rYdAaWV0nNyJLeYrBDEt5hSOGfmgSuyI5Rm5EDi45CXMxQqtyKjZkHBZixlMwqBxiX1OngXLjxCEkZrfhYgDVNOYY7Z8OtwP+fvjv7DHmpCkV3IUXP5NEcgtFXmE0C3K8jchiJ1Zbnd1hYZM0LOw4OAjeGMUA8dDbkt0mZEFtkLJQLMiStSGPt+UKi6ntjTdk5Sm+3fr4QWa5nr6kCppPqQoMUjIyLEzegiwQavJkbRD5ZEEWmDdxK/LIa69mrNxEWNXThWktHxbatylvWOiVNjuUqCwUZSdXr+HHzR3P7AXrqBu+8PBOFq3RXGmEh65blbtB99IrAS7/AwC6F/Y/A/DolQCuCFll3ftSzisMySxMsw0ZcwpRaWwp0sI3NiWv8ktkWCisjcVLkssrHF9ysq9zOOms3ge3HoMX9p0EnUYFv7l6ecaLF2Rl4dgAGGbxYaHr6Dhl4Wh6hoUllcUs31xw2hVz2HDdqUAL8oRhodSInBZloVoN5pl8bWfvlQbLw108a02lBqgPfR2e+LCfDVcxfmXK7NAsw0jUWYpAr1WzHEKx9h6viP7WEzw64Ysbp8MFi+sjZ6TKjcjxWZFtuw6B16VKqgk5GqIJGa8RSTXqBuUWCpQ6LOy18POHuyPCsFByEjnNevBqCsCGrCsO3MtHsyIPdypSVYgY5swFvwqgwgbQaxsF37qbQDcgRQ1NC8oJTRDMVMZ9P5wTDjpIXZj0sLCvrw+8Xi/Ujss4wPdPhrFghuM73/kONDQ0hAwcx3PXXXdBaWmp/Aety5Mdu5S34StSy4MgbG2LpSzErLkRFz9IEmpCLq5hNreEQCl8AspCpNL2Nkw34A2VGna/2BKXzTRiuUlR7stNwpacRGlETtaGLC5YKs1EZWFwuUk4tZz1SanY5MILY+7ubzm2hb1+MDdm49SNoGSSaUQWSrRo5SYCjVRyIhYP4/F0Z9+GjFQ1lrDMGdeYF3qOD6fcut7facuJsrAaS05UXI1b7qtOeFiIitJOO1/AzCmfM2F4g4tePAZdx7ktCxUEHQeHWCPghs/MDsmjTJvqKmRYmLrFTeTUJjMsHLC7YHcr3yk+c27+DAtLzjyDvR3d/X5aMwBdbW3gwkZSjQZM69dDOjGiGlKlYl9fbCIorg1ZocPC4LKTQ902OO8XW5mCDN9/+PrV7G1OmX8RwFV/5bECR18B+NNlAGPWjDQhZ8qGjO6D//4nH0jgoBBtg9kYFgqSbUIWzK0rYZEMeE4LN6CJxTtH+uAnL/Br/x0XLYRl6W7TjqEslG3IR7naLLgBOB3DQhwMltVyV8ncNXVQP4sPvgJ5hcpYJwczxTxFbkTGAXDbcFvqykJciy/i53abXQPgHg2oClGNh8WMYVqQ56zmDcHx/qwbJQdPy7jcQhR2fPlPu1iswtoZFfAdKZM1IsKKLBqOI1A0dy4Y5swBv9cHw63FSecVRsLTLZqQkx8ErZoeOKawMCgTpUHpuF9oNzmj2pA9UrmJ3cyHppPehhwcnxOt5ESheYWIxmwCRy3fUBo7cICt+1V+gFE9QE1D8htjqDwvl66VlFuYwzbku+++Gx577DF48sknWTlKJG699VawWq3yn7a2OII48xzboGS11atlC3I02yQuNhpMDSHqqYSakBMtNwkuOElAWQgtb8NyE1euHdreDcP9o1F3gcINgoSycNjQp5hhoWxDjjEsdAgbcqLDQumCpdaOsJ3LYNAOEcmCjDfbI6/zZtHST0ZvQcYF2yP7H2GPr5x3ZeLD4yxTZ4w8UA4Htu7Jw8KK2MNCbWVVdBuyUBYmEQidCrhYnTqfL8xaU7QiD2B7osfPcjctVclnJiWbv1hey61RxYPlCQ8LhaoQz3ulhtCgfrRcsQGOpCbDUpO3n+DlAyvOmwaWquKQpti0DVKMlXyoIGzIKSLU5CKKIhHeONTDImjm11ugQUHZQbHA86dh7lxmBRLNxem0IBuXLwdNSXoH45rSUtkKo6TcQsxfEpmrSmtDDgaHV985j98A4zDIoFXDQ9ethOaq0Jv7nDHzTIBrnwYoKgVo2wbw8IUAtnHrnp6PUy43CR4W9ttd4PKkrhxHfvf2cfZzxQyxa9dlXmWmmxI6LCxKsglZ/vc6DcyWhg+J5hZ2WUfhhkffZ+qQTy2fKqtYM02wslAMC3Hjyu/xpH1YiKy6cDrMXlkD6z8duDl2nWjJC2XhkHMIRtwjoAJVyoWF5jncajzqKwXvgZcjlpvgxn3r3n55wJoIworcMmAfZ3P/gJV8oPrwV1cuj21zl4eFvL06HnWhtQWHhelWFvI1tC6FtSy6IGZI5+vzFKYqDB4WthaPRrUhCyfRiInfb096ZWFwyclQW141IQfjnsHvu1VHTshFUt1leJ5JTVwWyC2kYWEwCU0HqqqqQKPRQLeU3SXA9+ti7FD813/9FxsW/vOf/4TFMRYSBoMBLBZLyJ/Jjmjysmv8MS3IE3IL+/dmvtwkuOAkXmXh2DBA916o0x+GqbOMzA6456W26DZkezcb8gQz3BtQFqZkWcjAsJApC+XMwtCLES4mRoeFDTm1zMJgK06g3GTiQGD4uedYFolh/ny2OxmN7Se3w1HrUSjWFsOls6IPFpVANPVpONpG2sDhcYBBY4DppbFbNbVV0W3IgYKT7N+EN86vSEvJiSg3QbViNstNxpecQE9x0sPCSM3AwmqKA6ed/zgBdquLDUSXndsUpvwhTb9D/BliyzuG1TfyltxcKQtf2S9ZkOcpd0gUCbOkLhx5jW90pAPb1jcyYkFWcm4hy1/yetnrUluReTVVKnxmRSOsnl4BWrUK/uezy2B5FtRfCYHH8+f+wS1bJz8E+P35ASWGcySQO5bijTxGiWABSSpZpcGgGu++17j99Vub52TF0h2sLMTHujSo79GujuztjF9N7/R44at/3s0GrwvqLfDjTy7K2nVObPCOecfAXVMGqqIiVrrlbm9Puw0ZmbmsBs7910UhG9FKbEIWNJob5czCluEWWbSA67NUMJh1oFHzwhD7nn9GzCs8vLOH3YOgo6KiIbFNCVFyEqwsRJv783slm/s1y+VrdzqUhYjlbLxu+WG01wAud3rvgT3ChpyCslColrEZ+eq1yrgvCwaPMb1aD73SvnIkZaFXUhYOGH2FoywUjphoNmQFKwsR3Ty+OW8+3stcJEh3WeqbDyKugoaFKQwL9Xo9rFixIqScRJSVrFu3LuK/++lPfwo//OEP4YUXXoCVK1cm8l8WBGgNFAq0IeAnrHguPKIROZHcwh5HT3LlJsEFJ3G2IUPbDpQ6AJQ3w4oLuW3w47c7wTE8MQsAh5e4y+jyueQQaMFAL7dN2osGYUpJ8q2DmcksdMq76q7OUGWhe8zLfrdJKQuFDVlrYxaHYCuOsCGHUxZan36GvS27NHaxiVAVXjzzYijRZ9eSmgzR1KfRyk2w4RkzOmOhkTILReZXMDisldvjsqwsDB4WYt7fmJ0PoFNtQs4FouRkTPoVDo4NggcbReNAqEQjDQtNUslJ7942+ECUmlwxB7RBN8sZaYq96nGAm/cDWELVNckgzvuJDg3cXp8cNL5pXvZfn6lSIuUWYpsq3mCnim90VFb8iddFulFibqGsnC0vTzqLKpuK6T9+fjW8c+smOG+RMtULUHcKwPUvAFim8vbj350H0H80kDmG0Sym1G4ucZglyuzSkVv4q1cPw4jTw4ZllyzJznpJW10NIEWeFKWYVyhY1MAHJPsSUBb+aMt+2NM2xHIa779mRVazL406I9t4RQadQ2CYwbOznEG5hWJtmwn1EqqKXVLBiUGBw0JhQ+4d7YXDQ1z1n47Nf1ZwVsrPdbbDewE6doVtQhYW5ERVhcg0qRyoRWpcDra5337Rwvg3OkRRCTYix8ji1GmsYKzl18Lhl9KnuEfckg051bK+y5ZPhSe+cirUlyrPyYCvCyxsFMNCT1cXO0bG4xng6/1eg6vwlIXWeJSFyhwWlizkURdV7SNgP8HPsd3lAA3mhvQMC6WZDMFJ2Hd48803w29/+1v4wx/+APv374evfvWrYLfbWTsycu211zIbseAnP/kJ/OAHP2Btyc3NzSzbEP/YbKE5bIWMHctN/MACi3td7pCg+2gsrOTKwn19UtB2AsPC5JSFkmJlvB0nEi1v87fT1sOUueVQ02wBr9sn38gHo1ProLq4OkT9KIY0tj6+gC6q0LDPUwLmctGGPAbaehHu3c2aiAViAKw1aEBn0CRpQ8bjxB+SW9geQVnoPHYMxj78kGV0WS64IOrXx93d19u4iueqeVdBPpCosvBAv9ScWxlfk5xsQw6TWeiz28HvcOQks1AoWbEBEdeXHYcG0zAszE2+jFAWDraNgRrUrGwJB4bxcGiQW3fmlYf/faKaQtvYCAenf5IpCJoX81KTYORhYTqbYjU6AGPqSpFgRXmiysJdLYOsSRbz3pY2xhfcriSKFi0CTXUVO87s772X8tdz7NgBfqcTtA31YJiduj08HMUrVrJzrbutLWwERS5Ie4FPhsFhTjxrnZxSNQvg8y8AVM7iN1c4MPzob2nJK0x3biEOM/68jQ+MvveJ+VlrlMYGV52kUko1r3CisjC+YeHfd7XDn7a1MLE3KlWFGiybCMUg2o3l3MIjE4eF6VAWhnM++EdH2dBWROMoCYwOMev4umNb57a05BUKTFV8XWFzGnnDK94n1HExBTJ40s42WjGncPaqxDfTpkl2W1QWdg6F2tyvScTmXjEDF/W8iXY4xjWjZz+UNvM1p/WZZ5Mu+omqLKxV6CZNGqOLBs0AfrWK3ZuJ9V8wIqN8sLiAlIVxZRYq24Zct2Qtf9vvA+v+j9hje02JvGGTLKQsTNOw8IorrmCW4ttvvx2WLl0Ke/bsYYpBUXrS2toKXUFy3/vuu4+1KH/605+G+vp6+Q9+DSLUgmwqN0CPNGCKR1m4oJIvVDH0f7waLzOZhdKQxG0HcMYx7G2VGrKb1rFdHswOQ/a+0Q5OhzuuYRCqEFF45AcfVNcq5yYYf1esNcnjB7fBwlUcPh+4pRKM4CZkY4IW5JDdLZUbQO0MaUQOFJwYwxabmDdujHmz+NiBx9ig5tSGU2FGWfLtUYoeFiaQVxhqQ544LPRI0QtqiwXUxbnZRU3Viuzz+qC/nb+OappyE+1Q1WhmN7BjNjc0wgxZaRALt9cNR4aORFUW4jnGuvKTMFg+D9Tgk0tNBD6nE3zDw4oepgSUhYktVF6TWpDPmFPNigHyDZVaDSVncCuyLQ1WZGzFRsynnZYxGyKGbBcv4jekdoXkFsoFPgp9fectZY0A1z8PUHsKd1Zsvy/Nw0KhLExtWPizFw+y1tbT5lTDhtnZfQ2YTj0VVAYDmE/nzfSpgtmreOii2jKW0npfpxW+9yS/YbzxrNlw5rzcFDyJQQMOCw1yyQm/bmV6WCiKvfRTpypSVYznYWER3NYlDQvTFCuE7caIzVcVyBHVGiaoCpsWVoDRok9BWeiAr/4lBZs7bixWzIzPitx7AEqmjoFKp2G/27G98TvI4lYW1uWfCyHRewZsOHaW8yG1J0xuoSg4GTIBGzShQrggrmdxZxYqU1lomdIMVpMK1H4A9Qf8WPI3pP56rpJcgL00LAwhqUaDb3zjG9DS0gJOpxO2b98Oa6TsHuT111+Hhx9+WH7/xIkTbEdk/J8777wzmf96UmIb4gshcxkOC8fizixE62izpTkhdaE8LEzGhqw3A4ipfSwrsnssYAeYdip7M31xFcsKwVbXj97oiKvtdriP/zxs+iFoErshCkCjUYOpTLIiD7mCcgs7JjQhJ2pBZv8GL1paftFSsZITPuTxeH1wUrqhwDZkgd/rBesz3IJcemn0/EGH2wH/d/j/2OOr518N+YJ4faA61uvjGTWRwHOMsCHPq4hPWShsyD6rdYIVMpd5hYLGBfwGo/XjgaR2mQe7HeBx+5jKtbQ6NwNPtARXTOG79E3OOXHnFh6zHgO3z82UCcLONB4sNflojA8Sp/e9OaHARQxSVHo9G/oqEaGyQmVhIr/jV6RhYa5uktOB+cwz2Vvbq6+mpKJganSp3MR8WnoGF5EwruW7247t/OY312REOUsENks/twWgMbDeTbXcZPxx351CAyPab7d82MUGbN+N1cqaAer+406Ys30bGKbHzgeOB5NBKxco7IuSW2h1uFlOodPjgzPnVsO/bcqMkjjRkhPDLKnkJEvKQiXnFQrEtXvYxX+f6R4W2r0VE/IK/T4/K1dM1oIsNuZxD27U7YUP2oagtFgHD/xLkjZ3ObdQijKIRO8B0Oj8ULJyTkjEUKrg9dEjiRpSzSxUOmhDRmxSbFS4khOhLBw2FYiqEBH30ngfj/fq48H1l5xZqMzXCA7pe6bwe2S1l6tCi5pSP/cFlIVkQw5G2fWnBaYsxAuesJ/VSLaUuHML4yg5wYtESjZkXIWa47Qi46DQ6+KlKCi9Z+oRFSzfzNWFaEV2u7wxlWNyEzKWm6TJspB2KzLLLZyS1mFhsLpQrcGSE24fxUGh1+dngejV0kkNsW/bxtRv6tJSMG/iN92R2HJsC2uiayxphA1TNkC+gDZ1jUoDHr9HbhWMBL7OcWGODc+zy2fH3XAqcpc8A6HqPbekLExHcHuyNMwuA7VGBSP9Y2CVSn+SKzcxs2MxV2AcAXtr5+eC/tHov8tgC/Kc8jkRd/J3PX8CHKMARWP9MPXjJ8F5mOcihcsrzEW5SyLKQrzxRVtxPLT2O9hmAioKUVGUr5jWrWPKJFzQOw+F/u4SwXXsGDsPo7pG5ApmCtOa1bKyMJ02sWRJe9s3EUpxGcC/PAkw9xMAxRUAM7gaNl025O6gbOJEwNfeXc/xzbHLlk2FBVLeXzbBc6q6KL2WcmFFjpRbiHETNz3+PrQOOKCxohjuuWJp1qzX0dZseE2TbcjYiIwt5T4PawEO/rxCGxZONYeWDzRa0iMAMJXx151d0zghr7DzyBArItQXaZhgIRn0WjU0SLE/3Oa+FBoltWHGhoU9XC1VesFmubwwOOYoWdhm+NhYziJ1sm1DRvpK1RFLToSTyGpUFUZeIVJcDqCTSn7C2eHHhgA8Y4pWFiIj0wLHs1eFvS2SajcFxH11Xwobd5MRGhYqalhYJNvP4lEWJppbOOgcZOoc9vWLk7xIiEbkWMrC1ncCqsKgG/PZK2uY4gdtiB+/1RmHslAaFhr6YZqFDxeUQqDkZEwuOQk3LEzGhjy+ERltyHhDICzI9WVFIYti61NPs7eWT5wPan3k4SR+DVFsglmFOEzLFzRqjTzkjlVyIizI0y3T486wQCuktoLvTHvGlZx4eqQssJrc2Tb0RVqom1GatBU51+Umglopt9BirY1bWSh+n5FUophJtOdlbqk4Rb8PND43K8sIxtPLf4eYjadUUKlQUqRNKLfw1QN8kL1yWjlTPOQraO/HgSFie+3VpL+OUBUaV68GtTGzlqLiZcvYUNJz8iS4pXKBXJL2tm9iInoTwJWPAtxyNBAUnyJ1pZINOck25FcP9MD24wNg0Krh38/lSqTJwKIGKbewI7yy8FevHoHXDvay7/u+q1dAmTG5jdlMKAv1jY3s3IA5gu7OLnlQiGuuUr3UupBGnPkwLBzXVIob1mm1IZuXAqz8AsApn5lgQZ65oga0+uQLb06RBtc3nTUHzpibwpBNlJxEGxaODQMM8zw50+bLmOvFOzAAtrelHPgUEBvfmooKUBtSa6LOl1LEk2a+8eruCGNDloQBVmMBKQvxnlxcu4ZaJ/79cFfQUFG5mcLemYHzBxbZTClPfUZAmYXhyZ9JwSTGLg0Liyx6GLC7EhoWCmXhvv59MZUNojgEFzQ6zM5IBmz/Q2wxhoUt74ZYkAVqjRqWncsP6D0vtcptwSEndkdAWWjt5QG/I0V9MK1EYcPCSqEsHAsoC4Nk7o5hd2rKwqCSkyGHm+WkdIQpN/Ha7DDy0kvscVkMC/L2k9vhqPUoG6BdMit2Y3K+5hYKC/L8yvjyCgUaKbfQ298XwYac251YYUVu25/8sLAmx8PC6mn8/9f3WwD8qrgyCw8OHIw4LMTz3tbHDoHP64fmUyph1mmzQ4ZG+TZIEerCuIeFB/nP76z5+a8SEFbkkddeSz2vME3ZabEGnKLQQQm5hRlp+ybCo07f8rlW2JCTaEPGaJK7nuebKZ/fMF1WQE0GFk6xRCw5ee1gD/ziFa44//EnT5FViLkkWFmo0mpBL1myrU8/JSvoywxlbOMzY8rCNNnAM0FwhAhu/KZaRjBhWDhqALjw5wB6vknkcXnh6K6elCzIAnyNPfrFtfBvZ81K7clWzQlkFka6Z8O2ZMRcB6qSarBc8An27rAUNZQKuLGFaCd5XiFSZ+a/81bjaFhloc/hkIsLraaAQKMgKItScqLwvEKBbl4gv7y7TDVBuZxKZiHeb6NyneDQsFABYKMu4jPyX4dWrYLyOHdIMewfrZmozhF5hJEQFuSk8goFwoZsj3KD7/UAtG3nj5u4UiSYeevqWMgwKirFrl+kQVBfN99RthUNyfkTSrMhc2Vh5mzIFjN/faDVMFBuElhkjbz4IrMV4I5y0eLFUb/mX/b/hb29ZOYlLPMyX20FsYaFsZRoMRuRxysLpd1YbW1uBzJN0rCw4+AgeKWcjnjA3J6+Np57WZXjYSHmlmp0alC5NVA6Vh1TWYjDwAOD/Pc5p2Kiaubo7l5oPzDI2uQ3XD4HzKdtZB937N4NXpttgkVT6YMUsVEUK9QfsTs9sO0of61uyuO8QoFZKjkZ++BDWQmaCPj7duziWbnm00+DbKCk3EK5DVnB6lliIiJ2JpmCkyd2tbO1QblRB189I3UblpJYKCkL2wdHYcgRyJBqG3DATY/tYbOWq9c0wadXpEfhmU5lIVJ+Dc+E7vvVr2HkH8+FfE46wYxld3tHXikL05VXiIj8cIfVyYrcBMc/7GMZ6eYKAzTMSq0gscKkh3UzK1OPMKnCzUwVt3pGuo/q2R9iWS69mG/sj7zyasiaJhncUhOybpI3IQffL7SbnWEzCz0Dg+ytV6eBMX1m4gEUi1AWWsOUnCg8r1BQPnsBOLkRB3rKJyqXk6HSxM8lGPc1NJq67X+yQMNCBdmQxzQqWQYbb+4K7szNLJsZlxU5pXKT8TbkaMrC7o8AXDYAQylALbdJjy85WHo2Xyi8/89WeXovhoW9jl7ZLi0KToorNKBVS2cFhVBSEeew0KJLSVloMfGfAVqRcdGMTCkL2OusTz0lF5tEW8i0jbTBG21cdXPl/Cshn3cK4x0WxtuELNBWhm9EVoqysKqxBIpMOrYA7jkeOfR9PEM9DnA7vaDVqaG81pjzcqDqRt5OV2NriplZiOctq9PKNkVmlYXu6uP39PbfeL7dss1NrLhF39TEb5g8HrC/807eqa6qg0pOYvH2kT5weX3QVGGEmdX8Z5rP6GproEhqGBYKwURgv2+PB/TTprE/2UDkIioht9CbJwNxInwb8siYBxyu+LJKEfzcn7/EVUg3bJoNlqL8jSEIB8Yq4LktuORkzO2Fr/x5F1hH3bC0sQxuvyg9JTMZGRZefjlUXH89e2z8yf/CwhZfRqyOLhwUer2gMhpzWsIWiwZzA6hwUIbDwjRmkBtL9OyeCU+/juHAUFmIEVBVmMuc5hB0xQDlzdEbkcXHa/j6tWjhAtDPmAF+pxNGXvxnSv+9p7twlIXYbFxqKIU+iyrssFA4iBwYFaVSFY4NObjkJKqykMdrKZV6yxRokW7JessDMVWpgPmkIs6HrMgBaFiYY1AdJIZKw9gBznaZE8uRCLYixzMsTOmAwkbAWJmFwoLctAZ9x2E/ZeFpDWAwamGo2wHH3u8N2KPVOvCDnw0MPW4vuEf4LmFFrfJuhM0VQQUnog25uxv8Hr7Yd4ykaEOWdrn0Bjt7exSVhcKGLCkLXe3t4HjvPXahK73k4qhf77EDj7Gf7fqG9TCjlJfOTEZlIQ6WOmwdsvI2EbQRbMhuaVioq83tAgsXxFPnl7PHrQlYkXvbuAW5cqqZRQHkmhoptxCHhbGUhcKCPL10Ohg0oefGnc+dYJstGAmwQipPClaV2bZuzbumWBGwHM+wEK14QlWo1NKWRBEFTSOvJm5FFr9vU5ZUhQiquVVFRaxV0XXkCOQKn8sFXiu3a9KwML8oKdKBScpTS8SK/NCbx9l5Agdq16xVVkxLulgkrMgdVjaM//5Te9ngsNKkh/uuWQ4Gbfotvckihg3BBWw1t3wLSs4/D1QeL3zr7z6Y0a/NYLnJNEVfB/D6XW2sTmteIYKDQGOZPkR8gUPD1n0DabEgp51YJSdiWCjlG+LvtPRivr63pmhFLiRloYi36pP6nnwjI+Ad4WthxNPPXx8jJr4mLihlYVlT5MzCPFEWNpga4J/L1dBaDdC+tCFtGfwiCohKTgLk/q6xwEGVzZd+eTpc88N10O9xJ5RXOL7kZG/f3vhsyKZUlIVxtCGLcpMwFuTgsobFZ3LJ8K4XTrBFIB7oQvWIBRbY+opyfZd6DKZUKu+kJTILx+xu8FsqAHQ6pmoRKrRAwUlqmYWg4Re3wz0jEzILrU/zYhPj2jWgq49s03a4HfDk4SfZ46vmXwX5SrgSnEjDJczHwV3FRNBUVE6wIbMmQ8kSmWtlIdI4vyLhkpPeVpsi8grHNyJX2xtjDgsjWcp5qQlf6Gy8fHZIeLlpIx8W2be+Kau9ZIumwgcpYrMo1rAQvy8sNpgsFmRBiZRbiCpBn9TaGA/487BLOZXZyCsUYKGUcTlv37Rvk+I3cgAOKxk6Haix2Z3IK2oTtCLj+eGBN46yx7dsnssUEZMRYUXe2zkMj+5og7/tagcUif3qymVQX6qsfMYKbMiWNiyFOwaL0xruvhsG5taByQmw+dc7wd0dI/M7yWGhQcEWZIFwB8wpT28Rj1k0Ig/x6+bhnd0sfqVmWgmU10nNr/kyLJSakOUyFBSCXXQhe+vYsSNsq2+8FFJmobhnGDOowGPm5wosGxJ4B/g1c9DoL7xhYVQbslAWKu++Oxj8fb27xADf+lctGGakL6u1yszv2XtJWSgzOVcXeQbactE+1ysNl4QNLV7iLTkRBSdpURbaIuQj4v8vl5usj/qlFp/ZCFqDhmWpiR1AkUuIyjFhQR4u6odppcrbNTcUa0FfxAcUNqtbHtahFRlzU3CImA5lodPP1SKHugPKQswsxN+19eln4io22XJsC4y4R1hOzIYpGyBfkUtwoigLRblJonmFwcrCYBuyd3CQDYFRvSlsykoYFvacGJZfY7HobR1WRF6hABfwSKV9Koy6x9gwOxIHB/mCem55QCWKr/03/3qYlZpMW1QJzYtDB4DGVStBVVzMBvfOgwfzyqIplIU9MYaFqK5BFZJRr4E1M9Kfg5UrDPPmgba+nuWw2rfFnwPo3L+fDfXx925ctQqyiXENtyI7duRuWBhss1eyuoiIvkkQ77Dwl68cBrvLC0umlsIFpygrzzmdiOKStw73wp3PcPfMt8+bB6fOUt55HMtLhLplcIznoSHYOvva19ZAeyWAccAObV/+csrZc+GVhcofFn5/7ffhxxt+nPZ1qFxyIikLD26TLMhrFXhsyI3IB6I2IUNNYA2LUUfGlSvZPZZ1y5aU25B1dcoeBKXbjeSo5HEG7q6AFVmIAvqL+Dq6MG3IHQA+X14WnOC5VghI0lFuMrERORBpUOjQsFBBiJvDRJWFs8tmM/vusGuY5dJltOBEZBZGCubtOwzg6APQFgE0LIv6pYrMOli0sUFWF463mQ738cHYiKE/rfkmmbEij8lWZFdHB4zZPQA4t1Xx7zOVYeGIewjHI0xJ4PL42K56XWkRjL7/PrhbW1lOTck550T8OjhYEcUmV867Mm1S7VwgLgxo83F5XWkfFmoqJ9qQRbkJNiWrUD2qgKzM8jojm8t3HArckET7/QtlYbVChoVlNUY2aNf59FDhqIuqLhRK0WBL+bE9vUxZqdaqYMPlsycMR/DmzCQVT2ArMv4MxDBFo/A25HiVhUJVuGFWlaKseKmCv8uSM3nRiS0BK7JsQV63jqn9somcW7jjPaZEzgX5UuBDRFcW9sRhQz7aa4NHdnBV9XfPnx93xnU+srCBq9AHHW6Wz7p5YS18+TRlxqjg2qrcUB6SWyg4qbXBXZdrwF1mBueBA9DxbzeC3+0uuGEh2o8vnnlx2tehJjEsHHLCQJcdeltH2HExe5UCVfdVc0NbjyM0IUMxfy0JLFLUELYiJ5uPKysLcxypky2EAGWoXDcht9AjKQv7jd7CUxbiIFClAUAF9Hjxj2xDVvawMFhAkplhISkLBfk7NZiE9Ertl4lmFuo0OnkwEi23MC0FJ6INGQtMXI7IFuQpKwG0sW/YsOgEb/i7jlih8/BQiM10qNcRUBZalKcsnFhyIuUWdnbKIcvFZl3SC3mxy+X0jkFNkKuszlIEOo0arE/yYhPLueeC2hi5tGJb1zY4Zj0GRq0RLpnFW9XyFdy5L9IUhShlx3OgP7lyk+Ab7WAbspxXWK2cRWciVmRU6LpGPew4q6hXhh0HM4aqJXVhdZTcQrvbDq0jrSHDQiw1eeuvvNRk+bnT2OAxHKIVGYdImFWDjZF5kVkYZxvyZLQgC8ySFdn2+utxD99wKMz+7WnZyysUFC1cCGqTCXxWKxsE5IJ8sdkT0YeFJ+NQFv7shYOsrfGseTWsoXUygzdu9aX8ZzOjygT/9ZklilbOioHD+OIuHB72lqlg6EdfZxu8GLPQdfsdaSlFch0/njfDwkxhlhqR7YNjcrFJ06JKKJYshYqiWrJg45DGMRC1CTkYy+bNoNLrwXn4SFLXGVSz+ux2ReRvZ3uY1FMixdEEWbi9UmbhsFHF7ivwHqlg0GgBLA0TS05wvSWGhRblDws/OfuTLNLgzCa+ZkwHlFk4ERoWKlJZmJgNOZ7cQrT52dy21DMLDRYAUTIQruSkRRoWTjs1ri9nKjPAvHX8hLTrhRZ5WIiDoJ6TXDVlLx6UFYdKHRai9SG4EVluQk7SgiyavLDtGplWHbhhxnITzPIafv55uQU5Go/sf4S9xUFhiV4ZyrJkwZsE8Ro56ZhoRR71jMLxYb5wnl+ZxLBQKAuHhuSiGo+UL6SkndjGBXxY2PrxQMybDdxhR6qmmEGjoFyreEpODg3yXfaa4hq5aXLX81KpSUURLD8v8iaCyC0c3bMHnEd5tpfaYmGqQyUjzv9MSeMJPyjDHc8P2lFxDHDmJBwWoq0XN0DQRj627+OYn+8ZHITRDz4IGRJnE5VWyy1iOcwtzJe2byK1zMKdJwbghX0nmcPgO+cnrp7PR647tRnm1ZXAA/+ygpXBKJnxjcgC8b5l8VKYes/PMbAcrE8+CX2/vjel/89rs8uZytlqgFci5nKxce+EQ0EtyIrEUAJgmRpeXTiuCTkYjcUib6SJCKJkVIWYaRtNYDAZh4UdZn5P5u4IUhZKcUNWIx/yK3kTIrO5hUElJ+ga9KPSUhVwEiqYC2dcCH+/+O9pFRSJzEJSFgZQzp0jIdvOErUhB+cWRhoWClWhSWdif5IGT6bm2sglJ3JeYeRyk/EsP7eJfdnWff1QNjJRWVhUrgZNhFblXGOu4L8rLGPRy8PCzqBhYWoLW6EurK0I2FWw3GTklVfAZ7Mx67NxdeR8LrSlv9H+hmxBngyIYXe43MLDg4fB5/exBXt1ceJ2U015OVYOs1wYzwBf3IvCGiWUmwgaZpeBWqNirztrL7frxxwWKsSCPH5YWG1vgt7R3rgsyNie/r5UaoL2Y11Qqcl49FOngH7WTKyclxsE82GQUlasA62kRu63h1+svH6wl9nQsSlUDBkmE2gjNq3nmbe212Jbke1vvc12xA1z5shxENnGKNne7dvjz1lMJ948afsmwlMrOUqi2ZBxY+j/PceVR5evbIQ5tco6p2eKr5w+E1646TSYnQffb6xhIQ4lsICp7o7b2ft9994LQ3//e9L/n6uFW5A1FRWgKeBiIxQeIN3HrWwzUV+shebFClbdCnXh+JKTcU3I4ymVrMjWf2yRN7QTbkJW0Fo20whxwYki2wQbsigFs5oKLK9wQm5h+8S8QuwnQPVhAUKZhROhYaFC8Pl4Jl2wBDYZZSHmtXl9PH8h3LAwpXKT8Vbk8crCoTa+Q4E5CFNXx/3lSquNMGslHwANb9fJg6CxQf59lFUrwzoZ04Ys3aTixWh0JLVyE0FVMb/xKy9xhigLrU89LWeYYNteJB478Bj4wQ/rp6yH6aXpa4tSwk5huEZk0ZyLFuRkdglVGg0fGLKmNDEs5MeOtkY5WXfYJl4/szQuK3JvGx8WVjcq60arppk/nwpHA/SPDMRsQmalJo8fAp/HD00LK2D6kthDEbOkLhze8o+8GRZibIFsRY4wOHj1AH9NbpqnHLVrujFv2sTejsQxLBR5hebTs29BFpjW8Gve6Hs705ZFlkxmoSYPXuNEFGVhlPiBF/edhN2tQ1Cs08A3z0lvmyyRORsyOnvQ9cD+XhpKlF9+OVR+5cvsMdqRbW++NenzCjOJKDgRRotZK2pYeaRikUtODoZvQg6jLETMGzaApqyMFbYlqmL3dIsmZIUqLjMA3kNpVBo4aeEujeAmaSEIsJpUhZVXKChrDNy7T8grLJzXyHgos3AiNCxUCIMOF3h8/pAXaiLgIAgtq8yGaeU2zLSXmwiENNk2bljYKqkK65cAGMwJfckVkp2we+8olI7WgBMLQtxq8IMP6muVe/MjF5wMYmahpCzs6gKHdSwtw0JxATMWB9RjzTAK9rffZo/LLomcQYgL1CcPP8keXzXvKpgsyDbkMMrCVMpNxluRRW6hnFmoIBsyMlXkFu4fiFFuIg0LFaYsZIP2Yg9o/BoY6hyLqSw8vqeP2a4xe3Hj5XPiGgaL4RFmFubLsBARw8JwJSdoTX7zUN+kzSsM+d2pVKzlOHiBPx6/1wv2N9/MWV5hcIsz2rt8DgeM7YucHZwpyIac39RK8QMnrWNhoyXcXh/85AV+PvzixumTUlE8GRDKQixhG68qxFw0ES2DVN94I1eKeb3QceONMPZx7MiFiMPC6YU9LDSW6plzUqBYC7JAZBIGNyIHNyGHySxEMLPQ8onz2WPrM1w0EC9uyYasq1PWWjaTaNVaJpLp40YW5hTC/GpcN3gHB0NsyAWHbENuC9OEnBuHhhKoktbf/TZXWjJlJwM0LFRYXmGFSQ/6JHLF0Ka7oHIBe7y3f6IVWZRBpFdZ2JtSXmEwlVPM0Ly4ijUIr+o6Dyxj/IbHrrdCU4W0+6HwzEI1tqxqtQBuN/RueZl9XOcYTKkdU+xC6/Q8lBhp3vMms9wVL1sWdTf52aPPwoh7BJpKmmDDlA0wWQhuzI5UbjKvMoVhYVVoI7KnRyoOUJh1o0nKLew4OAheb/jXGL4ux2xuplarnKIshS4O+wx1/ELsOjnxnOfxeeDwEC8ymWWaDW8+wbN9lp3TBGW18eXtGJcvD8nmyReLZrW0YSSuC+Mzy0acHparsnjK5LWdaSsq2Dkulrpw7KOPWMaouqQEipcuhVyBCm+TFAlh374jh8NC5SigifgRxXZOjw+GRyfaCx/b0QrH++xQadLDl06fmYNnSCSyZgs3LMRBYvAmFz6u/+EPwbhuLdtkaPvyV0JskvHgOtHC3ha6slCjUYPRwjfnLVVFsvNCsYRrRI7ShBxM6cXcijzy0styYUk8BPK3FT5IzYDAYNgE4NdpmfQUBQC4ZsD7KL8KYMRYqDbkpjA2ZFIW4jUWcXnDX4sLERoWKq7cJPnw/UWVkXML09KEHK+ysCn+vMJw6sIZPUuhYXgWezxsUG4TMmIq1bNmV5/XD6N2n7zjNyrZB+1/fBAOr98AHf/+LRh66ik5iDpexG6XwzvEsgpV4AfL1pfYx0qjqApxN+QvB/7CHl81/ypQqybPoR7Jhuz2ueVCjGSakAWaytBGZE+3ZENWmLKwqrEEikw6cI15oef4cNjPEarC8gaTIi05pVP5RVnVO3H41zrcCk6vkykxet7xgW3AyTJCV5wf/00R7sIbTw2cj/LFoikGB+GUhaIF+Yy5NUk3recL5jPPYG9tr70e8XNG3uCZrKYN60Gly235gXENzy10ZDm3EM/38rAwTwbiRChFOg2UGXVhrcg2pwd+8TLfOLnp7NlgNhRmllQ+INZsA6MBxb+wJAvV4fhr1NRf/pLlreL6sPVLXwKv1Rr3/0c25ImNyHNW17F1uaIRykFUdTlHQpuQa6JvdhctWQK6aU3gHx1l+eXx4u4uPGWhGBb6VSoYqyqRc+VFucmYSQc+3EwvZGVhiA1Z2qwoUX4TciavxSVF/BrbS1ZkxuSZIOQ5PVIDXjJ5hYKFVTy3cF/fvszakDH4FLHxIQrD3h+Q0yc5LKybUQpT5paB2q+B5e3nso8NFyl7WKjWqMFUppetyFN++lOY9crL4J/Oh1UGjYdJ3Yf/8Q/o+u6tcHjjaXDsk5dBz3//N1OfoBw+rl3q0X74w+dXwWNnlIPv6JEQK0I43u16l9nRjVojXDIz8lAxHwluzA4Gv1+Xz8UKfBpLGlO3Iff3s9+PyC5UmrIQB0VT5/Pd59YIVmSlWpAFtc189984WBExr3CxbiXseYkvZjZ+Zk7UUpNwBFtT80V1FVAWjkUcFp41iS3IghIpt9CxbVtEBYX9DSmv8LTTIdeI3ELH7vfBF+Pcnk58dge7cQw+fxH5a0Ue34j84BtHod/ugulVJvjsakkNQuSfsrB44nUO0ZSUQOODD7ANSdeRo9D+jRviOn/gJoEYFhpoWAjLzp3GHEqLz5SGIErGWBEQXghFYYxyk2BFaulFFyfciuyRCk4KTVkoBAY2qTEb1btiXW8z8fVkYSoLpePEaQUYkzYoSFkYsgan3EIODQsVpywsSllZeHDwILi97swVnJjC2JCFqhAvcqbkT7orzuMLHr2P/xwcxUPpec5ZKjlBMLvQpefDmVm/+TlM+/OfWJB10UI+zMUMrv7fPgSt110Hh9aug7avfR0GHnkEXK1B9fUSYrerb6wPZtWUwLRdXEVjPmsTaCxSCEcYHtn/CHt76axLwaxPLD8yX4aFaLG2uXjDWfBwaW753JSUlME2ZKEERcUShkorjUaRWxih5ESp5SaC5ln8d1niqIRRR+hF+cDgARZLsOjgWbzUZEEFTF+auGoqdFiYH6qraimPbLyyEG2Ix/rsoNOoYMPs/PheUkE/YwbomppYYYhNymkNBu1EIufLvDH3UQv6WbNAU1kJ/rExGPvgg6z9v94+fp5Sm0whtnsivxCK4u6gYiMcHP72TZ5D/Z3z5oJOQ8v2fGlDFnlXwTbkSOjq6tjAUG02g+O996Dr1u/FjLDBgQfL41Wp2Hmy0MFSkwu+tjjlrPDs5xYeClUWxhgWIqUXX8Te2t99V87VjoWnADMLg4eFfaX83Onu6pSdQ5hXiBSkshC7BYTdXViR5czCwlUWIlRyEgqtOhSCuCkUi8VkmFoyFSx6C7djDh0Kryw0pVNZ2JM2C7Jg6rxygJrArrq+DBRvoR0/LERGR/iusLG8GIwrV0LNTTfB9L//DWa//RY0/OynLNQabyoxp8b26qvQ/Z8/hKPnboYjmzfDyf/8IcvoQiWNaENGZSGq3Ia3bGHvl116acTn0zbcBlvbudrmynlXwmTDqDOy1/n43ML9/XyhNb8yeQvyeBuyWIShqjCZduVsDQt7TgzDmN0dUVlYM02Zw8L66moY0Q+CCtRw/OjJCeUmzYOLQNdRDmqNCjZeEV+pSbibMNP69WyIUjQvfGh4vmQWClXh6ukVUFKUW8ttNsDfd0kUK7JdahAtWrRIEYNgfL5CXZhoU2UqULnJJGtEDlIW3vPSIRh1e2F5UxlsXljYao98QKgHMXMXNzTjHRYiRXPnwtRf/ZJlX6Mbpfeee6J+vlAV6hoaQG1I/t6ByBHjS05EM3KEJuRg9E1NPKPX54PhfzwX8/N9o6Oyvb2Q2pBDShHN7iBlIR8W9hd7CldZiJQ2jhsWSutwS4EPC6UNh74wUUCFiLKnMIU4LEzBhow3KgsrJ1qRcXgoMlPSoyysmagsbJFUH9PWp/Sl8XuoPDXQPlRarXyVhNyI3M8X+G6nFzwuviM8focTLWKlF10EDT/5Ccx+cytM/7+/Q/U3vwnGVavYAtHd0gqDjzwC7V/9Ghxcuw6MN/8ELt7mA9OJXrBt3coszZrqKjb8iMSjBx8FP/hh/ZT10FzaPLkv/o6TE5SFqTQhBysL0Yas1HKT4EF1eZ0RM5uh4xBvdhPYrU5wWF0oOmAFQkpEp9aBtZTvZLYfC83zPNJ7FNafuIw9XppAqUk4pt77a5j1xuugra7O68zC16Rh4Zlzlfl6zATmM7kV2fb666zBMBiblFeYyxbkyLmF2R8W4rWByF/qxg0LD3WPwF938giG731iviI3rIhQDBoDmHX8eivW3cKSHGtYiJjWrYP6H/2Q/7vfPgSDjz4a8XMprzDPEQpCHBLG0YQ8HtakzVqRY1uRRfY2bpqierUQlYUtRh7V4ensAk8/H+APFHsLV1mIlEmK5KFWAI8rcF9PykL2ts+WvTgZJUPDQoUgsqlSySxEFlVNLDnpc/Sx4RFWyMezWIlbWegcBnCPAThtAF0f8o9NS01ZiExfXAXtlkNgNfRBTaPCG82ClYWDzhBVoUanBp1BE7U9s2jBAqj68pdg2p/+CHO2vcuGGmWfvYJZmbFV2btzD1zzmg9+9FsHtN94E/t3pRdeBCpsXQ6Dw+2AJw8/yR5fPe9qmKyMLzlBuw8q0VItNwnNLOxTbLlJPFZkoSosqzNFfR3mmrGKIfa2tyVgKe8b7YOmo8uhxFkJpnI9rEyg1CQc6qIilguVLwhlIYYrCysblhxsP85vOjcVQF6hwLhiOWs6xo2S0Q+k6wwe82432CVrsvmM3OcVCkxr17C3jg8+YGqObODppSbkyUCtbEPm68GfPH8AfH6AzQtrYWVzGtZuRNatyMFv411/o3Ok+sZ/Y49P/vBHMPLqq2E/z3Wc29NpWJinVM3hb/sOBlSFMZqQgyk57zwAnY5FG40dCnWTjcct8grr6gpu00GIC1pMdllZiOt7xGpSgV6tlwf8BZtbiEU7oodArQOIkK9aKJANORQaFk6izMKQkpP+fRPzCotr0mPpLSoF0EiKOXsPQPsOAL+X17CLE08KNJgbYMuC38Cjy34I0yqSL6rIlQ3ZISzIJfqELsoasxlKzjoL6u+8E2a+/BLMeP45qL3tNtgzSwNj6DhEVY1aDaWfjGxBfuboM2Bz21gpDCoLJyuyslCyIbfb2pnlB5VqM8pmpMWG7B0YlNvjtDXKvQlvXMAv6q0fBzKSQstNlL0IUtfwc99IB7eDIB8c2Q9LO88KlJooeNiZCcSmkcvjg+FR/nN563AvuL1+VnIwo1rZv9N0gnmhQjloey1w08xKROx20FRUMBuyUsDsMGbzcrth9P33s/J/kg15clAjKwud8O7RfnjlQA9o1Cr4znmpqeWJ7CJUSkJZKIaFiVgdK7/yFSj7zKeZzbTj5n+H0Q8DGyUCJykLJ4eycPAEQNeeuJqQg9GWl8vXxuFnn436uZ4CbUJGMLaoWFsMfRZ+P+bu6gKvyCw08eO10AaoYW3IweUm6sIeD9GwMJTCfjUoBLzB7xlO3YYcXHJydOgojHpGQ4aFackrRPCkKqzItl6Alnf442mnpuXLs+ep8gOoAJosyg9tNlfw35lNGhaOjvBcjOKS5DPF8MJlmD4dKv7lGvjD9VPhCzdpwHXP92DaX/4MRXOk3cgwr6NHDjwiZxUqPesxncNCYUGeVTaLDQxTQVsh7ep6veA8dJg91ClYWdgwu4xl+o30j4G1d3RiXmFT5CIcJVBcz1+nXqsaRm0u9jo+tMUKWr8ORuv6YMYy5Q5qM0WRTgOWIq4e7rXx88or+wvPgiwwn3kme4tZrgLbVsmCvHEDU2krBZ5buCaruYUeqeCEhoWTI7PwpHUM7n6eZ/BetbqpoDYHJqWycHQgYasjnkfqbr8dTBs3ssKktq98dUIJHtmQ8xx0aRWVAfh9AAe2xF1uEkzpxZIV+dktUQtx3AXahCyOJXQj9ZcA+FUq8Dud4DxyRC44Kdi8QkQIfIbaqNwkiCozF0T1kg2ZoZwVdgGD9jIMsE614IT9e2MNK8Xw+r3yAEWUm6S1VdgsGpF7AFreTZsFGdFr9GzoWaIvgbkVyi8kKCnnC3ynwwOuMQ+MDvOTS7ElPY1suMB0a1XQu6AejMuWRfy8d7vehePW42DSmeCSmZfAZGb8sDBd5Sbjm49F06pSMwsRfZEW6meWTrAi54uysKK0FIaK+Dmqp2UETnzYB74WI3hVHrBsGi3YHV+hLsSNJJ/PD68d5AOhs+Yr97WYKVjTsVYLriNH5Rtm+1Ze4mRSUF6hwCiGhdu3ZVdZSJmFkyKz8OTwGHzQbgWTXgP/dtbsXD8tIkHE8AGzCn1+Hww6eZ5wojFAuBaZcs89YFgwnzUft33xS+AZ5F8L81sx4xrRT6dhYV6CaxuRT3j8zaSGhRjBobZYWNOxY8eOmMpCbQEqCxEcFnq0KvCU8/Wwu53nQ1qNKrlIsiApi6AsLHCqpPU3FZxwaFioIAuy2aAFoz58Fl284I21UBeK3MJue3f6h4VCWYgnmI6d/HFTepSFyB/O/wO8+KkX5dZbJaMv1oLBqJWtyMKGPL7cJFmqigKNyNF4ZD9XFeKg0KxX9oAoVeqM44aFA/vTUm4i0EglJ95+/jPX1ih7gSWsyG37B+TcTJuUoVk1VdlZffj67jXzm57Ow0Pw5l+5mvOD+tdgwcyZUKiISArMLfyow8rsEHiNWFWA2WWa0lIwrljBHtteew3cHR3gPHyEWWXMUcqecoVoRB7buw+8tkAWZyZAO/bYhx+xxxopb5XIXzVD8N7Il0+fmXKONZG7RmRUFFqdVjYwRMpQRZYgGrMJGu+/nzUeu1paWPmdb2yMWSkxt1Wl14OunpRAeYsYFmKUU5xNyMFgC7Zl82b22PrMszGVhboCVBYGCwwclaaQjw9LNuSCRdiQUVU41MIfk7JQzg3Hdbc/KN6pUKFhoQJIlwU5Um6hUBbWGtM48BDKwkMvAnjGAIxVAFXp2wFHdSEqC/MFuRF5wCkXnBhTsCGHzb+RGvXC0TrcClvbt8oW5MlOsLIQT+RCRZtquYlAK+UWyu8rOLMwuOSk4+AgeL0+6G3jqsLSmmI2zFYy1cZq6JGGhXtebmV2apt+EHZP/SfMLVe+sjgbysJXpRbkjbOrQK8tzMt2ySZhRX6dNcMjxcuWySpgJYEFVbrGRhZl4NgpbaalGRwU9P7yl9ByzTWs/EU3rQlMq/mQkshPtBq1nJWEx/+/bpye66dEpGhDFpu8pYbSpCNSdDU10PjbB5mCbHTPHui85dvgOnqU/Z1+WhOoNIWV6TupqBq3xomzCTlcK/LIiy9GLNVC5WEhKwvFPcNQWeh6eMiUuOJ3UmGqBtDi/asfoGM3/xgpC+XrsNPjY+7PQqcw7zomaRPy+EbkfX37QjML0zksFMrCY68HLMgFahccX3ISyCxMnw05lrLw0QOPssbrDVM2QHPp5Lek4GtZBSpw+VxwaPAQa8/F9+eUh89zTLYROXixrmSqGkugyKQD15gXeo4PB+UVKn/gjhaQXhMfFvo8fAfvneanwGIyF7Q9RGweobJQDAvPLKAW5Ei5hTh8s275B/+YAi3IE1qRt0e2hiULZpWduPoa6PvNfawAofSSS2D63/4GaqMx7f8XkV1mVHHly83nzEnZaULk3oacaBNyJAwzZ0Ljvb9m1uSRl15iLckI5RXmOcG24wSakIMpXr6cbVBh4Vek5mx3t6QsxPKtArUhIz2WgErMo1ODU1fgykK8bxe5hZ1SIRspC6FYr2ExIEgf5RbSsFAJ9IomZCmvJlUWVnJl4YnhEzDsGk5/wYkI5kV87rRbkPORknJD0LAwvTbk4IVnOOxuOzx15Cn2+Or5V0MhoNPo5EHS6218YI1DUqPOmFYbMqI2m0FtCrUuKA21WgVT5/NFZuv+AXlYWJUHw0JcqPWZ2sEH3Iaja3LBsYo9zFJeqHmFwZtH+zqtzIZcqOUmAn1TE+jRlu7xwOiuXexj5tOVOyw0rlmb9txCVFEPPvEEHLvsUzD24YdMaTTlnp9Dw0/uBk2J8o91IjY/+dRiuO/q5fDZVZI9jMhrZWG6hoWIcdUqqL/7rpDMNRoW5jnBSsIEmpCDwYIvy0UXssfDYazIPpcrEKmj4LK+bAwL202BDDq7WcuGZQU9LETEsFAqRQULDQtDcgttlFtIw0IlDQvTpCwsLyqHKeYp7PHH/R9npuAEpcvBpKkJOV8J2JADw0JjmpWFqJ4LxzNHnwGb2wbNlmY4teHUgrv4i2FhuvIKx9uQlVxuEs6KjCUngXIT5Q8QcOjr0bihtfxjlv3Zt3wfa0LPh3KjTCLKrt4+whf5S6aWFnx+mbAii5sew1zlvkaMq1ext879B8A7NJTy18Nig/YbboCTP7gd/A4HK1GZ8fRTYDn//DQ8W0IpNFeZ4PxT6gt6oyTfCXaDiE3edFkdSy+4AGpu+Zb8Pg0LJ8GgRidtRlcnH6MjWpFtb70FHmkwKPD08HI0zLdUYmxHNm3Ix4sCGcJWEz/HFnQbcnBuoYCUhSFW5D4qOaFhoZIKTtJ5IyjUhW93vA1uSf1XU5zONuSg3SnMFqw7BQqZksqADdkhbMgWXXqVhWFsyBicjRZk5LPzPgtqVeEc0kIpu7d/b1rzChFtkLJQW5tfw8KeE8Mw3CdFGzQqf1hYoisBvVoPL8x9CM6+rRn2+/awjxdyXiFSbQ5Vmm+aV5iKgHBWZPb4tNMUPVDB6AKmhPT7wf7eeyl9Ldubb8Kxiy8G28uvAOh0UHPLLdD0+99RsQFBKBAxGMRNXFHCls5ctIrPfx6qvvY1MMybp+goBiLeRuQ5SecVCgwzZkDRokUsJ3f4H89FaEKuU/Q1Mxv3C50lkhsOlb/F3M1CysLxw8LCtKqHKxxD+khZSMNCJWUWpktZGJxb+ErrK/JCBa2babchI42rAdSFHbAsZxb2j8FYutuQJbttOBvyts5tcNx6HEw6E2tBLsSdQkE6lYWaioq8ySsMfg2W1xlxNsGwVBWxHEOlg4tXLDlBNeGgtx8ODh5M++8zn5WFgk0FnFcoKF6yRD42lWxBHt+K7Ni2Pal/j62nJ3/0Y2j74pfA29sH+lkzYfpfH4fKL3yeWc8IglAeFr0FtGqeN3l46HDa1UvsmvlvN8CMp54EbbWyy9eIODj9uwALPwmw8NKUvoxQF1qfDbUiu6VyE12BWpARg8bAjsE+C0wcFha6srAsaFiIUU6GoB9SAfPZVU3w408ugnUzC/z1QcNCpbUhpyezMFhZ2DbSlv5yk/E25AK3ICPmcsmGPIg16/xjReb0tiGPekbB4XaE/N1fDvyFvb101qVg1puhkBA2ZEF6lYXBNuT8WWAJdWG+qArHv8bf73mfvc5xYTfNMg0KmWrJAiE2khY20AIOWz8bfvZTqL7pJjBv2pTrpxN3bqFjR+LDwrH9++H4pz8Ng3/+M3u//JprWIlJ0fz0necIgkg/OMwTSsIjg0fY24JuXCWiM/c8gM88nFS5STCWCz4BoNHA2EcfgfPYcfnjnpPdsrKwkMF7BkeRCnxGfr9mNQFrKMfhfkEjMguFqrBA1afjwULBq9dMg1k1+XMvlSloWKggG/J4JUkqLKhcwNphBWkfFuJFTSMp52hYCMZSPSuZEBhMWtBo0nN4GbVGKNIUTVAXtgy3wNb2rezxlfOuhEIjWFmIj8uK0pfFEtyGnC+ZhUjjgsANST6Umwiqiqrk2ARkVtksWZlRqJQZdaDTqORik+DzSyFjXr8eqr7y5bxQ1sm5hYePgKcvfObsePw+H/T/7+/gxOVXgOvIUdBUV0Hjbx+Euu/fBuqi9G0oEgSROYRaSRQMFrzVkcg4uG41bVjPHlufeVr+uFuyIevq8mfjO5P3DM4qvja2GvlQv1Ct2WFtyCUNuXwmhEJR/mp7kuPx+pgvvqRIm1YbMqrMsB1WkNZyEwRPrutvAlj0KYCp3GpVyOCNvLki8PtLV7kJghey4MBswWMHHmNvN07ZWJAqrDpjYFiYbsuqJk+HhQ2zy0AtDZjyodxkvNV+d89u9rbQLcjiuK8rLZJ3OIn8Q1teznLFEMeOHTE/393VBa3Xfx56fvYz8LvdYD7rLJjx9NNg3rgxC8+WIIh0UVEcqiQkZSGRDYQVefjZLWzjKURZWFvYykIxLOxv4sKC9qrA2rOgsWAhqjQwpbxCIgyFLd1QAFqNGl759zMy8rUXVS5ieXYZGRYim25L/9fMcyuyKJZIV15h8C51h61DHhba3XZ46shT7PHV86+GQqTeHLAhL6hYkNavrTYYQG2xgG94GHR5UnCC6Iu0sPqi6dDTMgJT56RmackmVUa+YPP4POxtoTchC+64cCF80D4E5ywobEVAPmNaswacBw6Afdt2sHziExE/b/j556HrjjvZOUdVXAy1t34Xyj7zGVI9EEQeMj4HjYaFRDYo2bQJ1CYTuDs6YHT3bjCuXEnKwnHRRa9+ZiZsvGQTfNj/EJxGil8ArZ4PCUe6aFhIhIWUhZOYhVU8tzC4CYrIfMlJRoaFQlko2ZCfOfoMa9prtjTDuoZ1UIjg4ltYVTOhRKv43HVgOv00KFoYOI7ygRXnNcP5Xz4FNLr8Ob2P390lZSHn7AW18O/nzgUNWZDzFuOaNeytY3v43EKvzQad3/kOdHzzZjYoLDrlFJjx5P9B+eWX06CQICbLsHCc0pAgMoG6uBhKzj2XPbY+/Qx7S8rCUGVhm7cX2qcZmUOu4MtNxluRS0Kz4AkCyZ+7SSLpkpOMKQuJEEJtyOltoQ22Ifv8Pnhk/yNyVqFaVZiHMX7f6+rXsaHh8trlaf/61V/7GjQ98ACodMpvFM53RGahYE75nJw9F4JIJ8ZVKzGnAlwtLcxmHIxj1y44fsml/KZOrYaqr30Vmh/5C+ibAxEiBEHkH8FKQtzULNHlTywIkd+UXiJZkV94AXwOB3h6e9n7pCzkg7Aue5fs0qIsUYnZ5wJoiwGaeeYlQQRDNuRJDKpztCotePyekDIIIgvKQkv6bchCWfhu57twYvgEmHQmuGTWJVDI/PqsXzPrql6U7RB5SbCysLGkkb22CWIyoCkpYepkbKi0b98OZZdeyvIIe++9F/of/C2Azwe6qVOh4ac/AePy9G96EASRfYKHEFSiQGQT4+rVrPnYc/IkDP3tb+waA1ptSBZ3IUcX9Y32BYqHSFnIOf0WgA03AWhIHEFMpDAlSQVCkbYIvr3623DdgutgumV6rp/OpCcrNuTRfvjL/r+wx5+c9cmCH6qgupAGhZNrWEgWZGKyYVorrMg7wHn8OJy48irov/8BdhNXeumlMP2pJ2lQSBCTVFlIAwkim6jUaii98AL2uP/3D7O3upoa9vFCPyZ1ah1zZ+3v388+RsrCIGhQSESAlIWTHLSpEtnBHDIsTLMNWVpsftT3EdsRU4GKfrfEpCF4wTa3nMpNiMmFcfUa6P/tQzDy8svMGuYfHQV1aSnU/8edYDnvvFw/PYIgMjgspHITIttYLr4Y+h/6X/BI0ReoNCx0UFxQa6yFdls7dNo72cdokE8QsSnsbQaCyDNloZDOb5iyAZosTWn9PwgiV6A6tNRQyh5TEzIx2TCuWM5sYL6RETYoNK5dCzOefooGhQRRIDZkgsgmRXPmgGH+fPn9Qs8rHG9FFpCykCBiQ8NCgkgTOoMGSiqLsGALLJXFGW2LvXr+1Wn9+gSRay6ZeQmzIK+sXZnrp0IQaUVtNELJWWeBSq+Hmm9/G5p+97+gI6UHQUxayovK5cc0LCRyQelFF8mPC70JeXzJiYCUhQQRG7IhE0QaueiGJTA64gJzeaAZOR0EX9CaLc2wrmFdWr8+QeSaW1bdkuunQBAZY8o9P+f2Y1Nh58wSRCGA2Wiolrc6rVBRTMNCIvtYLrwAev7rv1g2rra2JtdPRxGgDVmABaAWgyWnz4cg8gFSFhJEGimvM0HD7MCOcrrAIhODhg8gr5p/FcveIAiCIPIDDJenQSFBFA5CUUjqJSIXYKlJyVmb2OPiRYty/XQUZ0PG45PupQgiNqQsJIg8QKVSsSHhoYFDzK5JEARBEARBKJOFlQvhuPU4i9cgiFzQcPfd4PpqCxQtWJDrp6I4GzLlFRJEfNCwkCDyhJtX3Jzrp0AQBEEQBEHE4Ifrfwg3Lb8Jak1ULkHkBlSz06AwAA0LCSJxktLf3nvvvdDc3AxFRUWwZs0a2LFjR9TPf+KJJ2DevHns80855RR47rnnkvlvCYIgCIIgCIIgFI1WraVBIUEoiDpToOiF4gEIIkPDwscffxxuvvlmuOOOO2D37t2wZMkS2Lx5M/T09IT9/HfeeQeuvPJK+MIXvgDvv/8+XHrppezP3r17E/2vCYIgCIIgCIIgCIIgEsp/L9GXsMekLCSI+FD5/X4/JAAqCVetWgW//vWv2fs+nw8aGxvhhhtugO9+97sTPv+KK64Au90OW7ZskT+2du1aWLp0Kdx///1h/w+n08n+CIaHh9n/YbVawWKh5iKCIAiCIAiCIAiCIOLjU898Cg4NHoJbVt4C1y68NtdPhyByBs7XSktLY87XElIWulwu2LVrF5x99tmBL6BWs/fffffdsP8GPx78+QgqESN9PnLXXXexJy/+4KCQIAiCIAiCIAiCIAgiUUTh0KyyWbl+KgSRFyQ0LOzr6wOv1wu1taEZHPj+yZMnw/4b/Hgin4/ceuutbMop/rS1tSXyNAmCIAiCIAiCIAiCIBi3rbkNHvnEI7CuYV2unwpB5AWKbEM2GAzsD0EQBEEQBEEQBEEQRCoYdUY4pfqUXD8NgpicysKqqirQaDTQ3d0d8nF8v64u0DAUDH48kc8nCIIgCIIgCIIgCIIgCCIPhoV6vR5WrFgBr7zyivwxLDjB99etCy/nxY8Hfz7y0ksvRfx8giAIgiAIgiAIgiAIgiDyxIZ88803w3XXXQcrV66E1atXwy9+8QvWdnz99dezv7/22mthypQprKQEufHGG+H000+H//7v/4YLLrgAHnvsMdi5cyc8+OCD6f9uCIIgCIIgCIIgCIIgCILI3rDwiiuugN7eXrj99ttZScnSpUvhhRdekEtMWltbWUOy4NRTT4VHHnkEvv/978P3vvc9mD17Njz11FOwaNGi5J81QRAEQRAEQRAEQRAEQRBpR+X3+/2gcIaHh6G0tJQ1I1ssllw/HYIgCIIgCIIgCIIgCILIK+KdryWUWUgQBEEQBEEQBEEQBEEQxOSFhoUEQRAEQRAEQRAEQRAEQTBoWEgQBEEQBEEQBEEQBEEQBIOGhQRBEARBEARBEARBEARBMGhYSBAEQRAEQRAEQRAEQRAEQwt5gChsxtYWgiAIgiAIgiAIgiAIgiASQ8zVxJwtr4eFIyMj7G1jY2OunwpBEARBEARBEARBEARB5C04ZystLY349yp/rHGiAvD5fNDZ2QklJSWgUqlgMk52cRDa1tYGFosl10+HIAg6LglCkdBxSRDKg45LglAedFwShPIYVshxiSNAHBQ2NDSAWq3Ob2UhfgNTp06FyQ6+YOhkThDKgo5LglAedFwShPKg45IglAcdlwShPCwKOC6jKQoFVHBCEARBEARBEARBEARBEASDhoUEQRAEQRAEQRAEQRAEQTBoWKgADAYD3HHHHewtQRDKgI5LglAedFwShPKg45IglAcdlwShPAx5dlzmRcEJQRAEQRAEQRAEQRAEQRCZh5SFBEEQBEEQBEEQBEEQBEEwaFhIEARBEARBEARBEARBEASDhoUEQRAEQRAEQRAEQRAEQTBoWEgQBEEQBEEQBEEQBEEQBIOGhQRBEARBEARBEARBEARBMGhYqADuvfdeaG5uhqKiIlizZg3s2LEj10+JIAqGrVu3wkUXXQQNDQ2gUqngqaeeCvl7LIy//fbbob6+HoqLi+Hss8+Gw4cP5+z5EsRk56677oJVq1ZBSUkJ1NTUwKWXXgoHDx4M+ZyxsTH4+te/DpWVlWA2m+FTn/oUdHd35+w5E0QhcN9998HixYvBYrGwP+vWrYPnn39e/ns6Lgkit9x9991sLXvTTTfJH6PjkiCyz5133smOxeA/8+bNy7vjkoaFOebxxx+Hm2++Ge644w7YvXs3LFmyBDZv3gw9PT25fmoEURDY7XZ23OHQPhw//elP4Ze//CXcf//9sH37djCZTOwYxZM8QRDp54033mALqG3btsFLL70Ebrcbzj33XHasCr75zW/Cs88+C0888QT7/M7OTrjsssty+rwJYrIzdepUNozYtWsX7Ny5EzZt2gSXXHIJ7Nu3j/09HZcEkTvee+89eOCBB9hAPxg6LgkiNyxcuBC6urrkP2+99Vb+HZd+IqesXr3a//Wvf11+3+v1+hsaGvx33XVXTp8XQRQieEp88skn5fd9Pp+/rq7O/7Of/Uz+2NDQkN9gMPgfffTRHD1Lgigsenp62LH5xhtvyMegTqfzP/HEE/Ln7N+/n33Ou+++m8NnShCFR3l5uf+hhx6i45IgcsjIyIh/9uzZ/pdeesl/+umn+2+88Ub2cTouCSI33HHHHf4lS5aE/bt8Oi5JWZhDXC4X251FW6NArVaz9999992cPjeCIACOHz8OJ0+eDDlGS0tLWVwAHaMEkR2sVit7W1FRwd7idRPVhsHHJVo7mpqa6LgkiCzh9XrhscceY4pftCPTcUkQuQPV+BdccEHI8YfQcUkQuePw4cMs5mrGjBlw9dVXQ2tra94dl9pcP4FCpq+vjy22amtrQz6O7x84cCBnz4sgCA4OCpFwx6j4O4IgMofP52PZS+vXr4dFixaxj+Gxp9froaysLORz6bgkiMzz0UcfseEgRnFgztKTTz4JCxYsgD179tBxSRA5AIf2GGWFNuTx0PWSIHLDmjVr4OGHH4a5c+cyC/J//Md/wMaNG2Hv3r15dVzSsJAgCIIgCMWqJXBhFZzzQhBE7sAbHxwMouL3b3/7G1x33XUsb4kgiOzT1tYGN954I8v3xaJMgiCUwfnnny8/xhxRHB5OmzYN/vrXv7LCzHyBbMg5pKqqCjQazYTmG3y/rq4uZ8+LIAiOOA7pGCWI7PONb3wDtmzZAq+99horVhDgsYcxHkNDQyGfT8clQWQeVEPMmjULVqxYwZrLsSDsf/7nf+i4JIgcgHZGLMVcvnw5aLVa9geH91jMh49RqUTHJUHknrKyMpgzZw4cOXIkr66XNCzM8YILF1uvvPJKiOUK30eLB0EQuWX69OnspB18jA4PD7NWZDpGCSIzYNcQDgrR3vjqq6+y4zAYvG7qdLqQ4/LgwYMsC4aOS4LILrhudTqddFwSRA4466yzWDQAqn3Fn5UrV7J8NPGYjkuCyD02mw2OHj0K9fX1eXW9JBtyjrn55puZhQNP5qtXr4Zf/OIXLCz6+uuvz/VTI4iCOXnjLk9wqQkusLBMAYNmMS/tRz/6EcyePZsNLX7wgx+wsNpLL700p8+bICaz9fiRRx6Bp59+GkpKSuT8FiwXQusGvv3CF77Arp94nFosFrjhhhvYAmvt2rW5fvoEMWm59dZbmbUKr40jIyPsOH399dfhxRdfpOOSIHIAXiNFnq/AZDJBZWWl/HE6Lgki+3zrW9+Ciy66iFmPOzs74Y477mCO0iuvvDKvrpc0LMwxV1xxBfT29sLtt9/OboiWLl0KL7zwwoRCBYIgMsPOnTvhzDPPlN/HEzeCQ3wMpv32t7/NBvhf+tKXmFx8w4YN7BilbBiCyAz33Xcfe3vGGWeEfPz3v/89fO5zn2OP77nnHlCr1fCpT32KqZo2b94Mv/nNb3LyfAmiUEC747XXXsvC2vFmB3OYcFB4zjnnsL+n45IglAcdlwSRfdrb29lgsL+/H6qrq9n947Zt29jjfDouVX70+xAEQRAEQRAEQRAEQRAEUfBQZiFBEARBEARBEARBEARBEAwaFhIEQRAEQRAEQRAEQRAEwaBhIUEQBEEQBEEQBEEQBEEQDBoWEgRBEARBEARBEARBEATBoGEhQRAEQRAEQRAEQRAEQRAMGhYSBEEQBEEQBEEQBEEQBMGgYSFBEARBEARBEARBEARBEAwaFhIEQRAEQRAEQRAEQRAEwaBhIUEQBEEQBEEQBEEQBEEQDBoWEgRBEARBEARBEARBEATBoGEhQRAEQRAEQRAEQRAEQRCA/H94uAPHEMMXowAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.209004Z", + "start_time": "2025-03-20T00:21:23.143127Z" + } + }, + "cell_type": "code", + "source": [ + "# Collection of 5 multivariate time series with 26 channels, length 100\n", + "X = np.random.random((5, 26, 100))\n", + "plot_series_collection(X)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 }, { "cell_type": "markdown", @@ -83,19 +239,6 @@ }, { "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['run' 'run' 'run' 'run' 'run']\n", - "[1 1 1 1 1]\n", - "[0.6 0.6 0.6 0.6 0.6]\n", - "[0.8899 0.8899 0.8899 0.8899 0.8899]\n" - ] - } - ], "source": [ "import numpy as np\n", "\n", @@ -118,8 +261,25 @@ "print(reg.predict(X))" ], "metadata": { - "collapsed": false - } + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-03-20T00:21:23.228783Z", + "start_time": "2025-03-20T00:21:23.218268Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['run' 'run' 'run' 'run' 'run']\n", + "[1 1 1 1 1]\n", + "[0.6 0.6 0.6 0.6 0.6]\n", + "[0.8899 0.8899 0.8899 0.8899 0.8899]\n" + ] + } + ], + "execution_count": 6 }, { "cell_type": "markdown", @@ -144,13 +304,6 @@ "metadata": { "collapsed": false } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } } ], "metadata": { diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index f31155333d..0433b44962 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -27,15 +27,7 @@ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "\n", - "from aeon.similarity_search._commons import (\n", - " naive_squared_distance_profile,\n", - " naive_squared_matrix_profile,\n", - ")\n", - "from aeon.similarity_search.distance_profiles.squared_distance_profile import (\n", - " normalised_squared_distance_profile,\n", - " squared_distance_profile,\n", - ")\n", - "from aeon.similarity_search.matrix_profiles import stomp_squared_matrix_profile\n", + "from aeon.similarity_search.series import DummySNN, MassSNN\n", "from aeon.utils.numba.general import sliding_mean_std_one_series\n", "\n", "ggplot_styles = {\n", @@ -158,9 +150,9 @@ "for size in sizes:\n", " for query_length in query_lengths:\n", " X = rng.random((1, size))\n", - " _times = %timeit -r 7 -n 10 -q -o get_means_stds(X, query_length)\n", + " _times = %timeit -r 3 -n 3 -q -o get_means_stds(X, query_length)\n", " times.loc[(size, query_length), \"full computation\"] = _times.average\n", - " _times = %timeit -r 7 -n 10 -q -o sliding_mean_std_one_series(X, query_length, 1)\n", + " _times = %timeit -r 3 -n 3 -q -o sliding_mean_std_one_series(X, query_length, 1)\n", " times.loc[(size, query_length), \"sliding_computation\"] = _times.average" ] }, @@ -172,7 +164,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -233,17 +225,20 @@ "for size in sizes:\n", " for _query_length in query_lengths:\n", " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", + " X = rng.random((1, size))\n", " q = rng.random((1, query_length))\n", " mask = np.ones((1, size - query_length + 1), dtype=bool)\n", " # Used for numba compilation before timings\n", - " naive_squared_distance_profile(X, q, mask)\n", - " _times = %timeit -r 3 -n 7 -q -o naive_squared_distance_profile(X, q, mask)\n", + " mass = MassSNN(length=query_length).fit(X)\n", + " mass.compute_distance_profile(q)\n", + " dummy = DummySNN(length=query_length).fit(X)\n", + " dummy.compute_distance_profile(q)\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o dummy.compute_distance_profile(q)\n", " times.loc[(size, _query_length), \"Naive Euclidean distance\"] = _times.average\n", - " # Used for numba compilation before timings\n", - " squared_distance_profile(X, q, mask)\n", - " _times = %timeit -r 3 -n 7 -q -o squared_distance_profile(X, q, mask)\n", - " times.loc[(size, _query_length), \"Euclidean distance as dot product\"] = (\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o mass.compute_distance_profile(q)\n", + " times.loc[(size, _query_length), \"Euclidean distance with MASS\"] = (\n", " _times.average\n", " )" ] @@ -256,7 +251,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -281,7 +276,7 @@ "id": "f10127f4-6515-4ea5-a1d9-e14671eb70be", "metadata": {}, "source": [ - "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `ConvolveDotProduct` value for the `speed_up` argument in `TopKSimilaritySearch` to use this optimization for both normalised and non normalised distances. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algortihm." + "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `normalize` parameter of the two estimators to set this option. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algortihm." ] }, { @@ -300,34 +295,22 @@ "for size in sizes:\n", " for _query_length in query_lengths:\n", " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", + " X = rng.random((1, size))\n", " q = rng.random((1, query_length))\n", - " n_cases, n_channels = X.shape[0], X.shape[1]\n", - " search_space_size = size - query_length + 1\n", - " X_means = np.zeros((n_cases, n_channels, search_space_size))\n", - " X_stds = np.zeros((n_cases, n_channels, search_space_size))\n", - " mask = np.ones((n_channels, search_space_size), dtype=bool)\n", - " for i in range(X.shape[0]):\n", - " _mean, _std = sliding_mean_std_one_series(X[i], query_length, 1)\n", - " X_stds[i] = _std\n", - " X_means[i] = _mean\n", - " q_means, q_stds = sliding_mean_std_one_series(q, query_length, 1)\n", - " q_means = q_means[:, 0]\n", - " q_stds = q_stds[:, 0]\n", + " mask = np.ones((1, size - query_length + 1), dtype=bool)\n", " # Used for numba compilation before timings\n", - " naive_squared_distance_profile(\n", - " X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds\n", - " )\n", - " _times = %timeit -r 3 -n 7 -q -o naive_squared_distance_profile(X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds)\n", + " mass = MassSNN(length=query_length, normalize=True).fit(X)\n", + " mass.compute_distance_profile(q)\n", + " dummy = DummySNN(length=query_length, normalize=True).fit(X)\n", + " dummy.compute_distance_profile(q)\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o dummy.compute_distance_profile(q)\n", " times.loc[(size, _query_length), \"Naive Normalised Euclidean distance\"] = (\n", " _times.average\n", " )\n", - " # Used for numba compilation before timings\n", - " normalised_squared_distance_profile(\n", - " X, q, mask, X_means, X_stds, q_means, q_stds\n", - " )\n", - " _times = %timeit -r 3 -n 7 -q -o normalised_squared_distance_profile(X, q, mask, X_means, X_stds, q_means, q_stds)\n", - " times.loc[(size, _query_length), \"Normalised Euclidean as dot product\"] = (\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o mass.compute_distance_profile(q)\n", + " times.loc[(size, _query_length), \"Normalised Euclidean distance with MASS\"] = (\n", " _times.average\n", " )" ] @@ -340,7 +323,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAADM0AAAOrCAYAAAD+45B0AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAewgAAHsIBbtB1PgABAABJREFUeJzsnQeYHVX5h0+272Y3PYHQIYTee+9dkCpFkCZNQEEBEcU/NhQEBQFFRHpRFAQpglJC6B3pLSS00NLb9vJ/3rOem9nJzNy5de+983vzzJO7u3OnnHPmzPc753zfN6Svr6/PCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCFFBVA32BQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgiRb+Q0I4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIISoOOc0IIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIikNOM0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEKLikNOMEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEqDjkNCOEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEqDjnNCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiIpDTjNCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCi4pDTjBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhKg45DQjhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghKg45zQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQoiKQ04zQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQouKQ04wQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSoOOQ0I4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIISoOOc0IIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIikNOM0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEKLikNOMEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEqDjkNCOEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEqDjnNCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiIpDTjNCCCEqmpVWWskMGTLEbh988MFgX05FoDIVQgghhBAiO2RL5x+VqRBCCCGEEMVBtnf+UZkKIYQQQgiRHbKl84/KVAhR6chpRgghhBBly8KFC82VV15pdtxxR7PccsuZ+vp6+/9OO+1k/vjHP9q/l8v5+/r6zDvvvGNuvvlmc9ppp5mtttrKNDU1pQQp4lQIIYQQQghRulSKPnn00UdTOiTutssuuxT03oQQQgghhBCVqWc6OzvN888/b8997LHHmnXXXdfU1NSktMbRRx9dkPMKIYQQQgghSpfB0idvvfWWueyyy8zBBx9s1lprLTN8+HBTW1trxowZYzbZZBO7nuvll18uyLmFEIWnpgjnEEIIIUSJssMOO5jJkyfbz5MmTbI/lwtPP/20Ofzww820adMG/H769Ol2434uuugic+utt5rNN9+8pM//wgsvmJ133tnMnz8/79cphBBCCCFEuSB9Ur7nF0IIIYQQIulIz2TOFVdcYc444wzrOCOEEEIIIYTIH9InmfHggw+a7373u+aNN94I/PusWbPs9uKLL1qnmq997WvWeWfUqFF5Ob8QojjIaUYIIYQQZccrr7xidtttt1TkALz6iSZAVIGPP/7YPPLII6a7u9tMnTrV7vfkk0+addZZp2TPz3HkMCOEEEIIIUR5Umn6xMsyyyxj9t9//7T7rbHGGjnfhxBCCCGEECJZembmzJlymBFCCCGEEEIMuj7BGcbrMEPWy/XWW8+sttpqZuTIkWbGjBnmiSeesP/D3//+d/Pmm29ax6TRo0fnfH4hRHGQ04wQQgghyoquri5z4IEHpgTS+uuvb+666y6z0korpfb54IMPzH777WfFFM4o7I+4qampKenzI7RI57npppva7Z133jE/+MEPcr5mIYQQQgghRGGoZH0CEydOtNGfhRBCCCGEEJXHYOsZx/LLL5+aF9lss83MlVdeaW6//fa8HV8IIYQQQghR+pSCPtlggw3M8ccfbw455JAlnGFw+L/kkkvMj370I9PT02PPe/LJJ5vbbrstL+cWQhSeqsG+ACGEEEKITLj66qvN+++/n3IyeeCBBwYIJODn+++/3/4d3n33XXPttdeW7PkReu+9956ZPXu2+c9//mPOP/98K/KWWmqpvFyzEEIIIYQQojBUoj4RQgghhBBCJIPB1hPHHHOM+fzzz81HH31k7rjjDhtEjCjSQ4cOzcvxhRBCCCGEEOXDYOoTMsrgoPPyyy9bR5ig7DF1dXXm7LPPNhdddFHqd3/729/M22+/nfP5hRDFQU4zQgghhCgrfv/736c+n3nmmWbppZcO3G/8+PHmjDPOCPxeqZ0fMbfqqqvm5fqEEEIIIYQQxaMS9YkQQgghhBAiGQy2nlhxxRUVPEwIIYQQQggx6PrkgAMOMPvuu2+sfb/zne+YZZZZJvXzv/71r5zPL4QoDnKaEUKIHOjt7TU33HCD2XXXXa2h1tDQYD2aMaLwPnbssMMOZsiQIXZ79NFHA4/F99w+pBJMx9FHH53a//rrr0+7f19fn7nzzjvNUUcdZb2jhw8fbq+XlOdktOA+uru7I4/Bdblzej25n3jiCXPccceZNdZYwx6Xv59++unW+9rtj1NAe3u7icOCBQtMc3Nz6ruvvvqqKWaqx5tuuskcfPDBZpVVVjEtLS02otXKK69sDjvsMFuGlGUU1LG7dure8cgjj5hDDz3UHpeyxyt9u+22M1dccYU9b1za2tpsusett97ajBkzxjQ2NpoJEybY63v44YdjtSn3+8mTJ6d+t+OOO6Z+793itK9PPvnE/PjHP7YZU0aMGGHLjPbw7W9/23z44YcmX0yZMsW8+eabA56DKLx/px1NnTq1rM8vhBBCCBGF9Ek/0icDkT6RPinU+YUQQggh8on0TD/SMwORnqlcPSOEEEIIUcpIn/QjfTIQ6RPpE6iurjabb7556uc4z7UQokToE0IIkRWfffZZ3+abb461HLrtv//+ffPnz+/bfvvtU7+bNGlS4PFWXHHF1D7Tpk1Le/6jjjoqtf91110Xue8rr7zSt8EGG0ReK9vqq6/e98Ybb4Qeh+ty+3K9HR0dfSeeeGLgsU477TT7nY033jj1u5tvvrkvDldffXXqO5tuumlfLmRSrtTNhAkT0pbTFlts0ffJJ59EHsftS91TTscff3zkMTfaaKO+GTNmpL2fV199Ne01UiednZ2R957uHr2bv335j3vnnXf2DR8+PPT7jY2Nfffee29fPrjqqqtSx11ttdVifWfixImp79C2yun8lL33mRNCCCGECEP6RPok6jjSJ9InmZzf32aEEEIIIQqN9Iz0TNRxpGcqU8/EfSb5LIQQQghRTKRPpE+ijiN9kjx9EsQBBxyQOvfJJ59c1HMLIbKnZrCddoQQohyZO3eu2Wmnncxbb72V+h2e51tuuaWpr683b7zxhnnuueesF3pV1eAm9XrsscfMPvvsY+bPn29/rq2tNZtuuqmZOHGi/Yy3M5EB8Pp/5513zFZbbWWefvpps+aaa6Y99ne/+11z1VVX2c/rrruu9SrnmO+++27qvk844QRz4okn2s/XXHONOfzww9Mel/0cRCwoBn//+9/ttTkPf7z1t9hiC+udz71wT5QL0ReeeeYZW9fPP/98rLTxlAGRGzgOnuZ43BOVguNQ5vDSSy+ZI488MjJlI171O++8s5kxY0bqd5T7BhtsYI/93//+17zyyiu2ToiIEMUpp5xi/6eNfvrpp/YzESaWXXbZJfaNagsPPfSQOemkk0xPT49ZYYUVbLkMGzbMTJs2zUZYoLyIhECkhtdff90+J7ngfeY22mijWN9hv/fee2+J75fj+YUQQgghgpA+6Uf6RPpE+iT/56e87rnnHluXs2fPtlHkaGe0nQ033NDU1Gh4WQghhBC5IT3Tj/SM9EzS9IwQQgghRCkifdKP9In0ifRJNK+99lrqM1mdhBBlQg4ON0IIkViOPfbYlLdwXV1d3zXXXLPEPs8++2zKA5t9BiOyANEPxo0bl9rvyCOP7Pv000+X2O/zzz+3URDcfuuuu25fd3d3ZGSB6upq+//yyy/f99hjjy2xb3t7u/1/wYIFfc3NzXbfIUOG9L3//vuR90VkA3eOoUOH2sgMuRCnXF9//XXr/e6u8cwzz+ybM2fOEvtx7dtss03qeHvuuWfayAL19fWpCAlvvfXWgP16e3v7Lr300gFe+JMnTw48Jvtut912qf1Gjx7d98ADDyyx38MPP2zrnPvwtruwe48T9SKqTLk/6ummm26y1+gv12WXXTa17zHHHNOXK5S5O97ZZ58d6ztnnXVW6jt77bVXWZ1fmWaEEEIIEQfpE+kT6RPpk3ye39tmorZlllmm76KLLrKR7YQQQgghskV6RnpGeiaZeiYKZZoRQgghxGAhfSJ9In0ifZKOJ598ckDdPv/880U7txAiNwbX3VUIIcoQPMyvu+661M9/+tOfzLHHHrvEfptttpn5z3/+Y5qamkxnZ6cZDH70ox+ZL7/80n7+zne+Y73bx48fv8R+eMfjWU+0BOcNffvtt0ceG09y7g3P8m233XaJvxNhAZqbm81hhx1mP5MF8tprr40dVQBv9HQe8vmAssH7HX7zm9+Yiy66yIwYMWKJ/VZZZRXzwAMPmLXWWsv+fP/995tnn3028tgdHR02isMjjzxiIwp4GTJkiDnttNPMQQcdlPrdX/7yl8Dj/Pvf/7ZRIoAoAv/85z/N7rvvvsR+1OF9991n9ylGu+MctJUjjjjC3o+XtddeOxV5AmhjRBrIhVmzZqU+x4nqAEsvvXTqM5GRy/n8QgghhBB+pE/6kT6RPgHpk+Kenyh1Z511ltluu+3MF198kdOxhBBCCJFMpGf6kZ6RnkminhFCCCGEKDWkT/qRPpE+AemTYMgkdPrpp6d+JsPQJptsUpRzCyFyR04zQgiRIRjxGPtOCB111FGh+6622moDDKViQsrGm2++OWUkXnjhhZH7V1dXm/PPPz/18y233JL2HKeeeqq9x3Qcf/zxqc/XX3+9FVhBkArzpptuKmoqTtJXImBgww03TFtfQ4cONT/+8Y8zKqcLLrjAisUwvCKbNK7pxOOhhx5qtt5669DjYYyT2rMY7L333maPPfYI/ftee+2VEikLFy7MOR0mx3CQMjUO3v283y/H8wshhBBC+JE+WYz0ifSJ9En+zj927Fhz8sknmzvvvNNMnTrVtLa2mvb2dvuZCdhNN900te8zzzxj9tlnn9TkoxBCCCFEXKRnFiM9Iz2TND0jhBBCCFFqSJ8sRvpE+kT6JJif//zn5vnnn7efcWC6+OKLi3JeIUR+kNOMEEJkyKRJk1Kfv/GNb6TdP0pEFRI8/p1n+QEHHGAaGhrSfgfvZ0QAPPHEE2n3x1iPA4tpNthgA/t5+vTp1ks+iLvvvtuKO8B7f6uttjKF5l//+lfqMxEQ/N7xQbgIDHHKiXJn8VAUiDPHBx98ELjP5MmTU5/x4k9HnH3ywde+9rXIv1Oe66+/ftr7iwuLtBx1dXWxvuOiXECui7gG+/xCCCGEEH6kTxYjfSJ9In2Sn/Mz8fbJJ5+Y3//+92a//fYzK6+8sp184rt8ZlKOKHveCUQmiYjUJ4QQQgiRCdIzi5GekZ5Jmp4RQgghhCg1pE8WI30ifSJ9siT33HOP+elPf5r6+eyzzzbbbLNNwc8rhMgfNXk8lhBCVDxEFMAT3bHlllum/Q6e96NGjSp6mvKnn3469fnVV1+1UQAyYc6cOWbRokUp0eSntrbWrLvuurGPd8IJJ9gotc5LHo/zKO/5b37zm6bY5YQA/vDDD9N+x0WWgI8//jhy39VXX92WVRSjR49OfZ4/f/4Sf0dYOvHoxGwcYYpA8V5rIYjTBtLdXyZ4xX7cdKOkRM00GkGpnl8IIYQQwov0yWKkT/qRPpE+ycf5oyLjOajPn/3sZ+b99983t956q/3db3/7W/ODH/zA1NRoyFkIIYQQ6ZGeWYz0TD/SM8nSM0IIIYQQpYT0yWKkT/qRPpE+8ULgMJyvXLnvsssudo5ECFFeaAZTCCEyYN68eQMMsxVWWCHW99iv2CLp008/HeD9HidSQJBQChNJI0eOzGghzOGHH27OOussK7zwvMboHzt2bOrvRLF1EQfwGC9WOklvOd1///1ZlVEUw4cPT3sMr4jq7u5e4u9egdTU1GRFdzpaWlrsuefOnWsKSab3R8rVXPAu3oobJcC7X5zFX6V8fiGEEEIIL9Ini5E+6Uf6RPqkmOcHJoWc0wzt75lnnlFkNSGEEELEQnpmMdIz/UjPJEvPCCGEEEKUEtIni5E+6Uf6RPrE8eabb5o999zTtnHnuHTnnXcqgJgQZUjVYF+AEEKUEwsXLhzwMwZrHMKERqEFXa4EGezZemgPGzbMHHLIISlD+cYbbxzw9+uvv9709vbaz/vuu68ZM2aMKYdy6unpifx7nPSembS7uG2uWBMW+bi/TPBGKfjiiy9ifefzzz9PfY4jMEv5/EIIIYQQXqRPFiN90o/0ifRJMc8PEyZMMCuttFLq57feeivnYwohhBAiGUjPLEZ6ph/pmWTpGSGEEEKIUkL6ZDHSJ/1In0ifwLRp08yuu+5qZs2aZX9ee+21zQMPPKAgAkKUKXKaEUKIDPAbPK2trbG+5zyN84kTFHGE2W9/+1ubHjDTzbvwJR8cf/zxgak3Odd1112X+vm4444zxcJbTv/4xz+yKqditru4ba5Q7W6wIb2pI07qVPjoo49Sn9dYY42yPr8QQgghhBfpk9yQPskO6ZPS0QeDfX7H+PHjU59nzpyZl2MKIYQQovKRnskN6ZnskJ4pPT0hhBBCCFEKSJ/khvRJdkiflLY+mT59utl5551TWYsIIvbggw8qgIAQZYycZoQQIsPUg6SKDDK+ovj4449zTsmYqUf8UkstFehZPZhsscUWZr311ktFn3366aft50mTJpmpU6fazyuuuKLZZZddinZNpVhOfrxRFhBJ6VKAumgEhU7FORisueaaqc8vv/xyrO+89NJLgd8vx/MLIYQQQniRPskN6ZPskD4pHX0w2OcPmqAbjMiKQgghhChPpGdyQ3omO6RnSk9PCCGEEEKUAtInuSF9kh3SJ6WrT8h2g8MMmWZgueWWMw899NCAIGJCiPJDTjNCCJFh6sH1118/9fMzzzyT9jvvvfdeKkVfunSVjjj7v/baa5F/33zzzVOfn3zySVMqBEUX8EYZOOaYY0xVVfFeT6VaTl4wvL1C6dlnn037nRdeeCFW1INip9PMlR133DH1+Z133jGfffZZ5P54+/MMOnbaaaeyPr8QQgghhBfpk9yRPskc6ZPS0QeDfX43kce5Hcsss0zOxxRCCCFEMpCeyR3pmcyRniktPSGEEEIIUSpIn+SO9EnmSJ+Upj7hOd11111Tcx/jxo2zDjP5ztAkhCg+cpoRQogcjLSbb7457f433nhjrON6Dav//ve/aQ1g58kcxu67725qamrs56eeesq88sorphQ44ogjTGNjo/182223mU8++cSmwQTE0bHHHlvU69l7771Tn7kOPMVLke233z71+ZZbbkm7f5y2CQ0NDanPXV1dptSZOHGiWWuttVI/33DDDZH7e/++7rrrmlVWWaWszy+EEEII4Uf6JDekT7JD+qQ09MFgnx9uvfVW09HRkZqE22677XI+phBCCCGSg/RMbkjPZIf0TOnoCSGEEEKIUkL6JDekT7JD+qS09Mn8+fPtM+ac10aOHGkefPBBs/rqq+fl+EKIwUVOM0IIkSHf/OY3B0QWiDJGp0yZYi655JKMPdyjDD9SdZ522mlpj7fssstaQQJ4mB955JHWsItDb2+vmTFjhikEI0aMMAcffHAqZeRBBx1k2tvb7c+77babWX755U0x2WyzzcwOO+xgP7e1tZlvfOMbprOzM9Z32S9Oasx84BWPLEqKimpB+sl04sExevTo1Ofp06ebcuDkk09Ofb744otDhS3pVfm745RTTqmI8wshhBBCeJE+yQ3pk+yQPikdfZDv85M5hmcuDkRx+8EPfpD6mWeGiGtCCCGEEHGRnskN6ZnskJ4pHT0jhBBCCFFKSJ/khvRJdkiflI4+YX7kK1/5innxxRftzy0tLeaBBx4w6623Xl6OL4QYfOQ0I4QQGbLaaquZo48+OvXzcccdF2iQ4v1Pqr5FixaZurq6tMc95JBDUmkon376abvwpKenZ8A+eOHjCU+kgPr6+rTHPP/888348ePt51dffdUKgv/85z+h+3N8RB3e0Xj9FyMlpze1JGU5GFx++eWmubnZfsY7nMi4USkv3333XfPzn//cRoMoVgrPPffc02yzzTYpEbvPPvvY1I9+Hn30UbPXXnvZthOn3a2zzjqpz7fffnusFJ6DzQknnGAmTJiQSolJ2Xz44YcD9uFnymH27Nmp59Y7wBFUbkRFdhs/F/P8QgghhBDZIn2SO9InmSN9Urn65LnnnjNrr722ufLKK82XX34ZuA/1yYTxlltuac8J1O+FF14Yep1CCCGEEEFIz+SO9EzmSM+Ujp4RQgghhCglpE9yR/okc6RPSkOfdHR0mP3228888cQT9meyJt1777322RJCVA79edqEEEJkxG9/+1srZN555x1rNCGafvazn9kFI4iXN954wy40weA84IADrCE3efLkyGOuuOKK5qSTTjJ/+MMf7M8sNvnLX/5iDXZSJr7//vvWIMebfZdddjFLL7102pSLyyyzjPnnP/9pjcWZM2fa6yWFIFEHMOrGjh1rUzDyt9dffz1tis98sfXWW9tFOJSTg2i0X/3qV81ggFCgrBGqeI0jkLbYYgtriG+00UZm1KhRNvoBC4YQm4PhgY/hfu2116YWJVFniPD111/fbLDBBnYfUq66VK5nnnmm+fvf/54SD06A+6F9/vCHP7Rt9b777rPe8VtttZX1lncceuihZpNNNjGlQm1trbnjjjusaCQ6xcsvv2zTdO688862bSP2H3nkkVR60WHDhtn9XXraUj0/gwQMrnhxIg8+/fTTVF17+fOf/1xS9SOEEEKI4iN9khvSJ5kjfVLZ+uTtt9+2Ed1OPfVUs+qqq9rng3ZHvRHBjf6GOndUV1ebG2+80da/EEIIIUSmSM/khvRM5kjPlI6egaB5j48++ij1+e677w7cx9WPEEIIIUQ+kT7JDemTzJE+KQ198uMf/9g6VjnWXHNN87e//c1ucbL6/PSnP835GoQQRaBPCCFEVkyfPr1vk002wQ07dPvqV7/aN3/+/L7tt98+9btJkyaFHrOtra1vr732ijzm3nvv3Tdnzpy+o446KvW76667LvJaP/jgg76dd9458rjebamllup74IEHljjOtGnTUvusuOKKOZXfpZdeOuCcZ555Zl8h4DrdObj+KP773//2bbzxxrHLaaWVVup7+eWXlzgOdez2oe7j4D1uumtceeWVI6/rhBNO6Ovs7OxbZpllUr+jzYRxzjnnRB7P374yKVPIpK1mwlNPPZW2LFZZZZW+p59+Ou2xvHWW7jktxPnB209kssW5ViGEEEJUPtIn0ifSJ9In+Ti//9zpttVXX73vySefzKishBBCCCH8SM9Iz0jPJFfPZKI/4tatEEIIIUQuSJ9In0ifJE+feO8l0y3XZ0YIUTyUaUYIIbIEr/1nnnnGRlO95ZZbrMf5vHnzzFJLLWW9vY866ihz4IEHWo/wuBBBgNR+eLmT4vOll16yx8TrnmMSweCggw7K6JguagGpG4mGgLf5Y489Zj7++GMzZ84c622NxzOe2XiP77bbbmaHHXbIa5SoMI/2008/fdBTcXqhjMn0QcrSu+66y0ZyILvH3LlzbcQIIjGQqnTzzTe3ERrw8s+0LvJxjUSB+OMf/2jrktSgREMg7SrRIkh1ioc9UL8uqgDe9WH88pe/tF761113nXnxxRfNF198YY9Z6lD+PHc8g3j2UxZEXaA9k37z4IMPNkceeWQq1WqlnV8IIYQQwov0SW5In2R/jdInpaEP8nX+bbfd1rY7ns+nnnrKRijkOGxEVhw+fLhZfvnlbbsjOuAee+xR9HYnhBBCiMpDeiY3pGeyv0bpmdLQM0IIIYQQpYT0SW5In2R/jdIn/UifCCEKxRA8Zwp2dCGEEBZEh0vHOWnSJPtz0kEEIvoAA/3xxx8f7EuqKN577z0rFGCNNdYwb7311mBfkhBCCCGEKBGkT5ZE+qSwSJ8IIYQQQoh8IT2zJNIzhUV6RgghhBBChCF9siTSJ4VF+kQIIbKnKofvCiGEEFlzzTXXpD7jDS/yy2233Zb6vOmmmw7qtQghhBBCCFHqSJ8UFukTIYQQQgghCof0TGGRnhFCCCGEECI+0ieFRfpECCGyR04zQgghis7LL7+ciiQwatQomzZR5I9p06aZiy++OPXz17/+9UG9HiGEEEIIIUoZ6ZPCIn0ihBBCCCFE4ZCeKSzSM0IIIYQQQsRH+qSwSJ8IIURuyGlGCCFEUWlvbzff/va3Uz+fdNJJpqGhYVCvqZzYbbfdzAMPPGC6u7sD/37ffffZ9Kbz5s2zP2+wwQb2O0IIIYQQQoglkT7JDekTIYQQQgghBg/pmdyQnhFCCCGEECJ/SJ/khvSJEEIUnprBvgAhhBCVzxVXXGGmTJli5s6dax5++GHzySef2N+PGTPGnHHGGYN9eWXFgw8+aLeRI0eajTbayCy//PKmrq7OzJw50zz33HOpsoWWlhZz4403mqoq+cgKIYQQQgjhkD7JH9InQgghhBBCFBfpmfwhPSOEEEIIIURuSJ/kD+kTIYQoPHKaEUIIUXBuv/12M3ny5AG/q66uNtdcc41NxykyZ86cOVZwhjFx4kTz97//3ay77rpFvS4hhBBCCCFKHemT/CN9IoQQQgghRHGQnsk/0jNCCCGEEEJkh/RJ/pE+EUKIwiGnGSGEEEUFj/itttrKnHPOOWbrrbce7MspO15//XVz1113maeeesp8+OGHNqLA7NmzbUrTsWPHms0228zsvffe5tBDD7VCVAghhBBCCBGO9EluSJ8IIYQQQggxeEjP5Ib0jBBCCCGEEPlD+iQ3pE+EEKLwDOnr6+srwnmEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEKBpVxTuVEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEMVBTjNCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCi4pDTjBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhKg45DQjhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghKg45zQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQoiKQ04zQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQouKQ04wQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSoOOQ0I4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIISqOmsG+AFHezJw5syDHra6uNiNHjkz9PGfOHNPT01OQc4nBR/WdLFTfyUF1nSxU38miFOp7zJgxRT2fSLY+KZV2L4qH6js5qK6Theo7Wai+k8Vg17f0iQhDGkXkA9V1slB9JwvVd7JQfSeHUqhraRQRhtZ5iXyg+k4Wqu9kofpODqrrZFEK9T2mBDSKMs0IIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIikNOM0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEKLikNOMEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEqDjkNCOEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEqDjnNCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiIqjZrAvQAghhBBCCCGEyAfV1dUFO3ZVVVXkz6KyUH0nB9V1slB9JwvVd7JQfQshhBBCCCGEEEIIIYQQQogw5DQjhBBCCCGEEKIiGDlyZNHONXz48KKdSww+qu/koLpOFqrvZKH6ThaqbyGEEEIIIYQQQgghhBBCCOFQuDUhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghRcchpRgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQlQcNYN9AUIIIYQQQgghRD6YM2dOwY5dVVVlhg8fnvp53rx5pre3t2DnE4OL6js5qK6Theo7Wai+k8Vg1/fIkSOLdi4hhBBCCCGEEEIIIYQQQgiRGXKaEUIIIYQQQghREfT09BTtXCzCLOb5xOCi+k4OqutkofpOFqrvZKH6FkIIIYQQQgghhBBCCCGEEI6q1CchhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghKoQhfX19fYN9EaI4tLe3m7lz55rW1ta8Rdnr7Ow0hWDIkCGmtrY29XNXV5dRU61cVN/JQvWdHFTXyUL1nSyyre+qqipTU1NjGhoaTH19vf05W8aMGZP1d0XpRP9euHChmT9/vtUV+dAohdInoH4uWai+k4PqOlmovpOF6jtZZFPffIetrq7ONDU1Dfh+pkifVAbSKKJUUV0nC9V3slB9JwvVd3LItq6lUUQx1nnRHgsBbZc5QEd3d7f6uApG9Z0sVN/JQvWdHFTXySLb+q763zqvxsZGu9Yrl3VeI0eONIONnGYSAFX82WefmXnz5tnP+axyJpEKhffhKuR5RGmg+k4Wqu/koLpOFqrvZJFrfSPIRowYYUVVNmjCp7xZsGCBmT59ekqf5EujFLrvUT+XLFTfyUF1nSxU38lC9Z0scq1vJn2GDx9utUqmSJ+UP9IootRRXScL1XeyUH0nC9V3cshHXUujJJdCrvMSQgghhBAiE5xzP9uyyy5rWlpaTLmy2G1IVCyzZs2yQgoh7oQUAj0bYV1McvFIE+WH6jtZqL6Tg+o6Wai+k0U29e0d2Of/OXPm2EgC2TrOiPJejIY+8U4WSqOIUkP1nRxU18lC9Z0sVN/JIpv69tqjbW1tprq6uqwnfER2SKOIckB1nSxU38lC9Z0sVN/JIdu6lkYR5bzOSwghhBBCVA59fX1L2KOMo5ez44ycZiqczs5OM2PGjFTDJQrF0KFD8zYYQ4qmQuFPBSUqG9V3slB9JwfVdbJQfSeLbOobexT7dNGiRaajo8P+jrTy48aN02RhQkCXeBej4TDV3Nxs6uvr8zLZU+i+R/1cslB9JwfVdbJQfScL1XeyyKa+sUlbW1ut0wQsXLjQRnP2HktUNtIoolxQXScL1XeyUH0nC9V3csi2rqVRRKHXeQkhhBBCCBEX7FHWd6FL2tvb7e8YT19ttdXK0j6VqqpwaKiu4TLRU67eXUIIIYQQovxhwRELj+rq6myWGYSVE1hM+ohk6BMXjYLFaKNHj1ZkNCGEEEIIMWgwqcO4OfYpzv2APtGCtOQgjSKEEEIIIUoJaRRR6HVehXLYQ0eRGcnR09OTikouKg/Vd7JQfScL1XdyUF0ni1zqu6amxjpyo1HQJnz3s88+M01NTRldw8iRI81gU35uPiIjENGuYWfaQIUQQgghhCiUGCMqlsNFIxCVz/z581P6hMkeLUYTQgghhBClgNeJ32XFFMlAGkUIIYQQQpQi0ijJpVzXefkXXWrRbWWj+k4Wqu9kofpODqrrZJFrfQ/xrfMiOybOM5lspYCcZhKQtpPGTYOtra0d7MsRQgghhBDCQrYZtxipUFGtROnqEyDrkBBCCCGEEKUAkdKcPimVyRtRHKRRhBBCCCFEKSKNkly0zksIIYQQQpQidRWwzkv5Oysc0iEBDVUR0oQQQgghRKng7FMG/p3NKiofN7lXVVVVdvrEf72u/YrKRPWdHFTXyUL1nSxU38ki1/r26hO26urqAlylKEXKWaMIIYQQQojKRXMoyUXrvIQQQgghRCkypAI0ipxmEoKElBBCCCGEEKLS9QnR94qFFlImC9V3clBdJwvVd7JQfSeLbOobpwkgku/IkSMLcFWilNEcihBCCCGEEKKUkEYRQgghhBAiv/TPAgkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghRQchpRgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQlQcNYN9AUIIIYQQQgghRD7o7u4u2LGHDBliqqurUz/39PSYvr6+gp1PDC6q7+Sguk4Wqu9kofpOFvmo797eXrt1dXWZOXPmZPTdkSNHZrS/EEIIIYQQQgghhBBCCCGEKB5ymhFCCCGEEEIIIdLgX3SpRbeVjeo7Oaiuk4XqO1movpNFPuub7+J0I4QQQgghhBBCCCGEEEIIISqDqsG+ACFE4Tn11FPN2LFjzUYbbTTYlyLKDNoMbYc25Oejjz6yf2P7y1/+YirxHpNGMeqb77rjcEwRjyeffDJVbnz2o35eCCFEOaH3lsgW6ZNkIX1SukifCCGEqDT07hLZIo2SLKRRShdpFCGEEJWG3l0iW6RRkoU0SukijSJEaSKnGSEK9LJjO+6449J+x70A2UQ0v/71rweU73XXXRfbONx3332Lco0iefif+zjbj370o8G+bCGEEEIkAOmTwiJ9IkoR6RMhhBBClDLSKIVFGkWUItIoQgghhChlpFEKizSKKEWkUYQQIrnIaUaIAnL33XebN998c7Avo2K59NJLTUdHx2BfhhBCVCyKGCGEEJWF9ElhkT4RQojCIn0ihBCVhzRKYZFGEUKIwiKNIoQQlYc0SmGRRhFCiMIijSJENDVp/i6EyIG+vj7rNX/99dcP6nVcccUVdqs0Pv30U3PDDTeYE044YbAvJZGssMIKZsaMGYN9GSXFMcccY7d0jBo1ypQbqu/SplL7eSGEEPlF+qSwSJ8MLrJXl0T6RAwWldrPCyGEyD/SKIVFGmVwkc26JNIoYrCo1H5eCCFE/pFGKSzSKIOLbNYlkUYRg0Wl9vNClDpymhGiQIwePdrMmjXL3HfffebVV18166233mBfUkWW7+9+9zvzjW98wzQ2Ng72JQlhxowZY9Zcc83BvgwhhBBCiCWQPiks0ieiFJE+EUIIIUQpI41SWKRRRCkijSKEEEKIUkYapbBIo4hSRBpFCCGSRdVgX4AQlcrxxx9v6uvr7ecLL7xwsC+n4jj11FPt/19++aW57rrrBvtyhBBCCCGEKGmkTwqL9IkQQgghhBCZIY1SWKRRhBBCCCGEyAxplMIijSKEEEKIwUZOM0IUiGWWWcZ6xsN//vMf89JLL2V1nN7eXvP444+b8847z+y1115m9dVXN+PHjzcTJkwwO+ywg/39J598klZ4jB071my00UYDfn/xxRfb37O9//77aa/la1/7mt137bXXNj09PYH7/Otf/zLHHnus2WCDDcxyyy1nr3OXXXYxF110kZk7d67JF/vss4+9Drj88svNokWLcjrem2++ab73ve+ZzTbbzKYnXGmllcw222xjzj33XPPRRx+Ffo+/uTL8y1/+Yn937733mkMPPdSss846Zumllzb77rtv5L6UK17rK664oq3Tq6++2nR1dQ1I/3rHHXfY47Af17fTTjvZdLD8LQzK5M477zSnn366Pe4qq6xi284aa6xhvvrVr5rf//73ZuHChVmXWdD9+Pn888/Nz372M3u97vxrrbWW2Wqrrcw3v/lNc+utt5oFCxaEnmP+/Pnm0ksvtW2f6+a5ot4PP/xwc/fdd0fev+Ohhx6y9cH3KbvNN9/c/PjHPzafffaZKSVcWZLqNwraAfu5dhVVP5T9zjvvbFZbbTVb9vQfe++9tz3HBx98kPf6Bp5zzrvlllua5Zdf3kycONHst99+5q677srofO3t7ebPf/6zOeCAA2yboe5p/wceeKC5+eabTXd3d+h3Ozs7zb///W9z9tlnm1133dWsuuqq9v4ph913393ePxFMoqC/5F7dwM2UKVPMd7/7Xfv7ZZdd1l7TUUcdZV544QWTD9ra2swll1xin1X6Aq6Vdn/TTTfZ90A6wvp5L0TEOfLII21EHO6Bfm6TTTaxbeJXv/rVgPfUk08+aY/3ne98J/W7jTfeONUG3MZ+Xp5//nnzi1/8wh7T1dvKK69stt56a3PWWWeZd955J6P7mDdvnrngggtsf0y58E6h/7/99ttNHOjj/vCHP5j9999/wPXsuOOO5gc/+IF59tlnQ7/Le+6vf/2r+frXv277c8qMevnKV75irrzySltnwpi2rp5YfbEQYvCRPpE+kT6RPsmEStUntFnpk+ToE8rD6RPam/RJMujqSd9GhRClgTSKNIo0ijRKJkijDEQaRRoFpFFKH/phaRQhygdpFGkUaRRplEyQRhmINEp5ahSt80omvb3JXedVM9gXIEQlc9ppp5lbbrnFdri8qP7+979nfAwED0IkyMh844037IZRzcuCDj4TMFJcdIR//OMf9kUXBp7+iDrgpVRdXb2EAYWIcvs4Ojo6zCuvvGI3IgXceOON9uWdK0OGDDHf//73rTEzc+ZMK0AQDdmAsU79+A0WXvpslO9vfvMbc8ghh0QeB8P+lFNOMX/7299inZfr90dPoD5/+MMfWgPlmmuusQbjt771LXPPPfcM2O+1116z9UVK2N/+9reBx8cAeOqpp5b4PUbk008/bTfOj2GMY0O+4fiIHr9YmjFjht3eeust2+54Rnbbbbclvv/YY4/ZSB6zZ89eoi0yQMGGUKfum5ubA68B0fTHP/5xwO+mTp1qf4cxFiUKyhmE8vnnnz9AlANlifHIRhv75z//mdfzvvvuu1bsIKK9omjy5Ml2O+yww6zISsfrr79ujf6PP/54wO951mkXbPQliKpx48Yt8X0GRm677bYlfj9nzhy7IRp4vjgG4jodiJCTTz7ZtLa2pn5HG2bwCNGGYU2/mC1ffPGF7Y8pPwfnQpiw8fzTD2QLouCEE06wAxB+0cmgy4cffmjbxMMPP2wHH7KF58krvhy0Q+6NDXH4y1/+0r4v0oGApd/1D2g988wzdqNsoiL80OZOPPHEJYQz10MbY6MdUJd+GKQ84ogjbJ/sL7PnnnvObq7/ROAlkc6eXvPp/FYzt7XTrDiqyQytky++EOWA9In0SRTSJ9InSdMn2L5x9ID0ifQJSJ+UNr19fWZRR4+Z295hFrR3mYnjWkytJIrwQX/99ttv23cvzzJ2Df0pk6di8JBGkUaJQhpFGqVQSKNIo3iRRpFGKQTdvX1mzoIOM6e10/T09pmJY5sG+5KEEDGRRpFGiUIaRRqlUEijSKN4kUaRRsk3vO/aunrNwoVdZkF7t6mvqTLLjejPrpc05DQjRAHB+/zoo4+2L9pHH33Udv5bbLFFRsfAmF5qqaWsJ+qmm25qPTBJB/rpp5/aF8m1115rX4YnnXSSfRHiHRkXPMLxKH3xxReth3uUmMJ72EUdOOigg5YQTBhQGPaILD5j5OLtzUsLoxrjlRcWi+YfeeQR65mcK5TJ+uuvb4UaYhKP9paWloyOQflhdMKYMWPMt7/9bRuFgHvFYLviiiusUcPvR40aZb2Zw/jTn/5kX7zU8THHHGNfsHjPBkUwuOGGG2y5U068sInWQJ3+7ne/s7/HeMM7n8gIGFKUKcYWbQExgMB+7733rGGCiMbLPKjt4HGLxzURIWiPvAAxUDECMaQxojBaJ02aZBoaGky+oE1gvCGkEDqUBx7IlDFtAkMFY4T7DALDjqgB7Is38nHHHWc9gLl/DE/aI4MTGH54LCN4/dDmnJDi3hncwKsZ457vXXXVVbbNVJoXMcIfj20YPny4LXu8t0eOHGkHYXhOiXzBgEQ+oa4PPvjglJAi6gCGMGWPUcwziuHJ4oQoaN9EV+BaeZ4xup3XP2LwgQcesCLo5Zdftm2X56O2tnbAMXh+8a6nj9hwww3t81VTU2PbPs81zxbHon/mZ9pYGIh+nhXaHoKGZ4nniGfmsssus+3pjDPOMNtuu61t35nCc8qggxNSRCCgzrhfnhMMds6VSwQXjuGEFOKRPofyaWpqsuKSfoZ+mTJ3cJ+Uzf33328Hm4CBIurTC/28915GjBhhy50oI7yvOAdtggEgBj4QNnj+M4BDmYXBc0m5UE+I4+222872JbRfBhnpL+m/6d+IcOLniSeesH0I18R7iUgve+65py1X+icGynhnMijjh3MSQWH69On2fUt5cT/cK+9b6oN7mTZtmj0Hxxk2bJhJCt29vWbmwi4zp7Xb9Jn+yAOfz283q4xpMvntVYQQhUD6RPpE+kT6JOn6hPrDjuMZZWKPNh2F9Enl6BPeMbyvpE8qi47uXjO3rcvMa+u2i9Ics1s7zVLNA59FIZh4x/6jP+c9FDS5KoqPNIo0ijSKNEqxkUaRRvEjjSKNki9o+wutM3+3Wdjek5pDAX7fXD9wsboQojSRRpFGkUaRRik20ijSKH60zksaJR/gvL+wo9ss6OixQcd6PJm+unp6TW9fvalK4EIvOc0IG4lvblt4+rMoeiLSpuVKdU1fQc4zvLHGVOXZiIgCIxzDGYMcT0nSKGYCnTgix28sICJ4MWBk7rHHHjYFIZ70GCyZgJGO8U7azv/+97/2BRoEnuKAQPDvw4uNFxyGG6KMa/OCuECAcb0YwogXv1d4tpCWD097DAKM4zPPPDP2d/Fm/ulPf2o/YyBgpHkjC2J08KImRRz1h8FEWfnrwoGQwphEgKUzVDkOnrGkt3NQbttvv701fDH6+Bv3xf/s692PFzvlSko6hESQmMLQC/KMRUBj6GKocL04NOCNT1vLFwglZ1RTL/4IA65N4Insj1CAgMLbm/8xkjAEMci8cDwylmBkIcgYrMAIdSDcnQGIcMcgxBh2UH6k7eP+o9I/ZgptCuM7HaSRDGtHucBz6NJ+Uvc8t6Qp9EL7onwxFPMt4twxf/SjH6UigiBi6DMQSJQ3dRUF4hijft1117WCefTo0QP+Tr1R/zz3PEekVXQpkr3RPRAL/ueQ6+B5Rqxg8FNfpAY955xzIsuUZ46+2ztYQyQV0j8isGjDPEMMamUK7wcGhABxSDk6OC+DJQwEIACzxUWa4NlnIII68UK/w33Q3ziGDh1q06TyXnDQprziyQ+DQwho97y6Z4s0odQZEUVIGUxfyfswSkxRN3j78+ySctdbJgzMcM0IWfoHv5ji99wP5+daKDu+44VBM9pN0HNAFBh+T99BvSMKvXAs7oO2RPpbUkfT5pOQmnN2a5eZtahrgJByC9XmtHaZUU1alCYGX6MUUp9UikaRPpE+CUL6RPokKfrEtTn0CQPjTCpEIX1S3vqEd4p34oPnS/qkMrTJ/I5uM7e127R29S/88ENGzLFDaxM54SPCwX4ZP368nVSn/8mkLyvVORSQRpFGSYc0ijSKNMpipFH6kUYZiDSKNEqhnPm98Hc5zYh8UqoapVD6BKRRFiONIo0ijSKNkg+kUaRRtM6rMrWJc5Rp68SRP9yWbO3sNs31yXMhSd4diyVASO102XMmKTx48kZmZBEXdTIJh5cznS0ekWwYM3GJenEBhhKpIs8991wrBvCMzcSzGKOa1IZ47GIMBIkpvCwxWoKiD2DM4wUKeJX6hZSDlxJiBCMHT1gMBl7WuUJEAIyaF154wUZ6QFziARsHsl64NHw/+9nPBggpB0YAhgyGOYIVz32MwiAQkxgIccqfc5133nlL/J4XP8YIAhUvWO7NK6QcLioFHsFEtggiXSo5jBGEOPeEwZJPMUVqTQeiJwyMOn/UCIwXojYQEYH0k34h5cAQIm0jKRgxqL1iisiRrm4RzF4h5cCQ4xj+1Km5wLHiHI/nKd2znQ2UF+lnaYNExPALKS9B7T1bMHqJLgBrr722fWb8IB6JsEGb9qcTdRCthMgqwKCEX0g5GDzAkEUk8Bz7xRQiJwoic9DeEfq0/SgxBVx3UHQTBqNoXwwc8BxmI6Zce+Fd8fOf/zxwHwag8JRHYOTyPBLFxi+kvBClIhdYeBJ1fCaCGABDNBJlhD6OyC5h8E7xCilv9BwG5+grOI4f+kU3mIMw8gupqOeAvgfBCUTy8Asp77vBvdvpfypVTAF2xbz2bjNjYZeNNBBEddUQZZkReUcapXBIn8TTJ/5r5mfuJR3SJ+WnT7zlgy2DzeKta+mTytInrr7RJ0xAErFM+qRy9UlUXy59Un60d/WaOW1dZn5b9xKO/EvQ1z851FhbldW5aCtEsxOVBc9KtiRNn4A0SulplGyRRik/jeJFcyiVr1Ec0ijJ0ChRSKOUX9TmBe3d1k4Mc+b3gj7J1AYRIgpplMIijdKPNMpApFH6kUbJL9Io/UijDETrvKRR4oIN0dr1P0eZ9h7TGbKuK4hFnb2mud4kjuxmjYQQGYFHLenGwHlFZwuerqRafPvtt62nM5szNt3fMoEXOEY18ALBEAuLPgCkjvTy1FNPpVK9YdxE4YxqDCnncZsPeDkD15FJBAZS0jkRRIq2MLwiw30nCKIVuHpOB17FYR7oGKOOMOEGpLEE0vmRHjQdGGJEmnDths0Zq3gE5xOveMnUc5pBAdde0qVBdG0KMe1l8uTJ9n+ENUZXGHixVwo8u6QPBIzHXBYCZArPs0sryWBA2IAC4s4rev38+9//TkVoQPDEqXs85NNFkeDaGBTy9ps890D6xjBxB1yH95n0wn0SKQHwRM8UDH7O7571sIED+hU83nN9Hilf0mYWC1JcIk685e4VW1H9DmWLWA3DDdwRNcHf/7lUnJSnX2in48EHH7SDi3w3KLKLF5eGm3okxWqlgbBCVE2d1WY+ndcR6DBDPY1prjerL9VSVIdkISqR6poa20fme/Mu9uSz+z2RgtxAIYNH3u9UVS0eJohzDgawidxCSnnSULM5mxh9wt/834k6B4Nzzl5g4JR9/fu4gS8gopT3bwy0OX2y//77R167m+TCFnj99ddDy85ffunug8E84DqIvOb9m7cP9R/v8ccft3/DTmHSK+y6ST3u4Dv+vzuYOMEejioDB1qusbExcB+vXYkeDDuW2w/bCzsgXdthP/SrazdsLp06dkLU9Qa1C+/f/fXlnWRg4iWTuna2BTY2keui7olIZ06fBNUt9UFZh33fa7uE3WO6LZtF3tmUtduc7e9v03zP6ROetY022ijje8m2vnmenT457LDDrPYOKh8G1olwFlYOru5JOU/7jrpWN4DvInhF7cukNFEHvf2mm9hAH2CHhd0r+gRbOOi43Kd7Dnm2Mi1vxgycPqH/ZDIkaD/aMX1UWPm7+g8rV54joHyx5bNt35nuT7/kf1/V1y+eDUC3hN0H7RutG3YeJg2dPvH3f2gMYEEB0fAyuV+eIadPGHOK0wbRJywGyLT+S3UbUlVt5rX3mGmz2szUWa02w2WUw8zQuhqz/Mgms+b4YaalsS7j81HnbDxPPJeZbJUMzyoLA3iHEk2UyUVsMDYm9jOBqKE33nijtUd57/FcMMnN4puOjo6C3YMofTSH0o/mUBajOZRwNIdSuXMoaBTNoSxGcyiaQynJBWmdPXbO5L0ZrebT+R2RDjNkxBjZVGdWGdNsJoxpksOMEGWGNEo/0iiLkUYJRxolO6RRwpFGkUaJSxI1Cg78ZLmcPrfdvDuj1Xw4u83MWtQVy2GmsbbaLDWswaw6rsUs1VJnkogyzQhRBPCwxIscr3vSGZK2zp9mLAoWUzAByQuCz1Hg0Um6ukwgqgDXREpNFvM4ceUgFadL+YbnpxevKHLGfaYe6rmCccZLDQ/gq6++2noBR3m1Olx6RYzOqBSK48aNs97iGAVRKRnTGX9xowM4Iy/dft703Sy68X7PwaJBygQR6E3JF9Ru8gkpT2mHGJhEx6ANETEBA5jFJF6Dyo9rU5MmTUotmsu0Pbl6wtCNOhdttq6uznrQ5wNS7BJlYzBgIMUZlc7IKxbe5yIs9a+DxXJu0ZAft8CMVLJx6x4hRNv27//mm2/aRaoIzKj+BhGK2Ao7H8IuCrcwBuGQS7m5RVZR5eaivWQKApfoDghK0lUymOP6zagoFdmAWON9dc8999jBm6iI/FHCjoGeqH7cG2nG3/+99tprKcEVJlDDcG2QBeBuMV8caGPLLbecqRTaunrMlws6zaLOnsgU6OOHN5m6mv6FhN3d8aMVCCEGH/pYbOaLLrrI2ou8L9MNInnBLiZaEIPg6fQJ/X2m+uRrX/uavSYGrLBj/YOxRE4DonX59Yk35XRQJJti6BMW4WP38v4lKhQp0zPRJ7zD8qFPwgaEC6VPvPsx0RekT9BslMmjjz5aVH2C3eP0CQtzSQ+PTYSTC3YWmiCMl19+2f5Pm4y7KNrfnrBNwTlehIF+yac+YeKRqE6DrU+iotIVAlfecexsxjncYLwf15/gZBG37sP0CRMJRE186KGH7NhLtvpktdVWizy/u05s5EKX25///GeTDTgyMTE/depU+/wxMU8/TzvJZ6S8YuoTb/vw93+vvvpqXvRJuslmfx9EJNByhXpCi8xZ1GnmtXWZ3jRZZWqqqszIobV2MVpDrbLDFILjjz8+L8dhMQSR/Nra2lK/w1GG55ONdy3v6Uz0uKgcNIeyJJpD0RxKGJpDyQ7NoWgOpVTmUIiOrTmU8oKgYixKI7NH3IVoIxprzKjmBlNd1b8ANd3CUCFE6SGNsiTSKNIoYUijZIc0ijRKGFrnFZ+kaBQyVxL4eGFHj3Xkj541GejIP7Su2jTXV5uW+hrTUL/43ZlUjaJMM0IUiW9961upzv7Xv/517O+xoIKorNdcc01aIQXeSce44KHtXjhuAZrXsGWBCAR5gxIdMBuyuc4o3EIgXqos4IuD85iOs/AAQeX9ThBBYiYMojiH4Y3IGmUIePfDY9YP7YzICkTojhJShagPxCkpNd2CHhaakXqQ68E4RcCzUC3ourNJTei//rh1i9CKm+a11PEK4qA0pYXE277SiaCov2ebltJf/7Q9Fv6S0jPOwE17e3tWzyq4aAtBbTkd3v4kXVuNKy6DOPzww20EV9o7kVooFwb4EBuk8vy///u/rCIo+OF9gVi75JJLrCCOElK5lntU/+eehWyeg3y1wXKls7vXRiIginOYwwxiauXRjWbZ4Q0phxkhRHlyyimnpOxXss3EhUFRBsgZsI+jT6L6+zAY+HN2MDaj/31DdgDnXFOq+sSldGbxNCnAM7Gp4ugT956L0ieZ2LlxdUfc/YIi29HOXOrtwdAn2ECrr766/fmll16yKdO5HiaBCq1P4tYt9lqlZCvwDpwnXZ/cdNNNdjLjlltuiXSYCft+NnZyNvokX+WWDqI8fu9730vpE8qFBflMdjKJxIRsPvQJEyWDoU/8/V8u+qRU3mnFXIj25YJ28+4XC8zUGQvNnNbOSIeZloZas+KoJrPG+BYzfnijHGaKBO8yFxUwE5jgvfTSS237bGhoMIceeqj5xS9+YccknPM2WZKI3luubVjkjuZQcr/OKDSHojkUL5pDSbZG0RzK4M2hELxDcyjlAVpkfnu3+WhOu5kyo9V8ubAz0mGmpmqIGT201qwyutHOoYxsqk05zAghyhdplNyvMwppFGkUL9Io0ijSKIvROq/4VKpGccHFvljQYabMbDXvz2w1X/wv+HE6hxm0ycjGWrP8iAaz2tgms/zIBqtPaqqlT2z5FKUGRUlDlItHvrNZVt/tKaC3WbXHazif5yEy+mCAoY2gYqHQiy++aKMJ7LbbbmkXmeBNjyfk0KFD7cI2ohazqAfvcxcNl6gB/nSamUBKuD322MOm57zvvvtsxGkmL70pO6urq81+++23xHe9iwHw9I3y5PeSb69X0hRuu+22tiwQnpSbE0DpyFcqZMqoVCDiAPUItBeiWxMVAA9ZBJrzyqc9EhmjELAgjesgVSCb84DG6KCtsJFmlbSeXkPRGUYYw+edd15O16A018UnlzJ3dU9UdKIwx2X8+PGpzwz+EAkCb2jaFf0mA1JEEaGvc30UC7MQGJDO6C8GhW6rLNw98sgj7YAZ/STvId4tiCjKmijRv/zlL83RRx+d1fGJ4nHcccdZIUMZn3DCCfYdRxQVBizq6+vtfpwPAVcq5R7WBomAwILeuNC+ypnu3j4za1Gnmd3aHVov9TVVNjUnTjPqW0Upa5RC6hPafpXH3uvt6clbX9ZcW5hIHt4BJz57z4G+cPqEqN//+te/bN/tte/914Q+YWFzJvqEY/iPE3UOQIs4fUJUmwsvvDClT/72t7+lbG+yE/i/7/05U33i/S717bXvewLq238f3u9jezt9wnuWgUyvPuFYUXWerj24a/EfJ6o8os7hbx9x21HYfv7yQBdQj5nok6hrp+yj7jXoOpnUmTx5clp9wsCzd6A7W30Sdv1x6zboHuPgrYdsj+El3THC2mI+ryPT+vY+m/7v+p/tqOfYfc5Un6BD3HfRJziIZKJPgvrNqLLw/91//XGJKrdM+4V010EmCZxn/PqEZ5KIZmSkCtInUf2MX58cc8wxA/QJCwlWXnllOz4XpE+yeV9lcl3p+v0g3P7Z6JNyiRBGD7Koo9vMaes2C9u700721FZXWTtxRGOtqf3fBA82WW/Md3c6qHM2F+0wEyrF6TEIFkWg753GZ+L61FNPzegY119/vX02qSOc47yZs3CaY2yHyXEcZ7D/Dj744CWOceONN9q6iQvRUL1jRpU4h1Jp8yiaQxmI5lAKi+ZQ+tE4X/HRHEp2VOIcigvkoTmU0qK9q8dmlJnX3m16eqPrgFbZXI8+qTFD66ttFGchikWpapRC6ROQRhmINIo0SiGQRkkm0ijZUYkaReu8Bhf0B5lkbEaZzp60esRLQ02VaWmosRll+Ky+NBw5zQgr3kc1xTOA/XR3F+7h8qbaK+R5igkLpZj452XDgqF0YooJQpeG74YbblginaYj00ncIIgugHAiEjJC76tf/aoVSu5lwrmDPGC9E8IsLMq3SMo0CgFGAgbCZZddZqMVRsELniivcaIoOC/mcvFWJ5Ktu977778/1Ls5H20nncBkgpoNPv/8c5si9rrrrrNRX9nOPPNM2769bYr9mARfc801sx68oM7S1S0Gd1RUiWKCsYJxGRSV2wvtOwhvisM40Yvzife5oNyj0s1G1Yk3BWa2dc8iR+qVtkf0jYkTJwbuVwr17o1akq6tZhvtxcvyyy9vvvvd79qN54vIIJQRi06IBkDKWSI7k8o4U+h7XRSDiy++2Aq3oEVaxSh3noVPP/00q+fAPUdEs2EBTykNkhUqUtqc1i4zc2GX6QkRt7VVQ8zYljozvKFGokqUhUYptG6oNI0ifRKOf9Avm0FA6ZPy1CdnnHGG9EkF6xP/syx9khx9An6NIn0yuFkuWYg2t63LOvFHMcQMMS0N1f0L0SKc+PPx7vZ+N5tIg5VKkANLJhCl8K233rKfWSjkdZhxELV00qRJZvr06dZWYOGQ1/Z2GQ87Ojpin3eLLbbIm9NMqc6hgDRKcjRKPpBGKU+NojmUytYofqRRFiONUhikUZaExWg4ycxr6zZtXel1QF1NvzM/8yY49gsxGJSqRqk0fQLSKIVFGmUx0ijSKMVEGiU7Kl2jaJ3X4MyVLMBJpqPHtHb2mr60ocUW9z/Ml+Ak01JfLV2SASopIYoI3q94w8Krr75qvf2jePvtt1MGRpiQAgzSXNlpp52sx6U36sBTTz1lo/u5iIJBrLvuuqnPzz77rBlMSBfHfQDGOQZ5FM5Yoy6iInBixLiUqdkaeMXmnXfeSUVmiEoHSJq9YrL00kubr3/961awkzIQ+OxNeefaFO0ar+ZsWGuttez/r7/+emTdvvHGG1mfoxD9QzpjE7FFFIcgVlxxxZRxTrSHYuJ9LtL1RxjwYbi6//DDD7MWhK7tE8UgTEjFuc5i4NppunKJ8/dMIUoAfSbpdP/4xz+m2heDeF7iOkm4cof9999/UMvdiUH6t7DBh3RtkEU4pdBGCgV1Pa+ty7w/s82m7wxymGGwfVxznZkwpslGcZbDjBCVifRJYZE+WYz0ifRJMZE+yQ7pk8IgfTLQaZ9FaB/ObjNTZraamYs6Ix1mXLbLiWMbzXIjGmwEZ+mS8uS5555LfcZpJoiqqqqUfckkO+/EoMUjZB2Mu9H3ivJDGqWwSKMsRhpFGqWYSKNkhzRKYZBGMan6JOvl9Lnt5r0Zrebz+R2RDjPMmTBXstKoRjNhdKMZM7ROC9OESAjSKIVFGmUx0ijSKMVEGiU7pFEKQ5I0CnXW2tljvljQYd6f2WrnSli3taizJ63DTE1VvyZZfkSDWX1sk1lhZIN1opYuyQyVlhBF5pvf/GbKk//Xv/51ZNRDF9GQTj3MK5kXxd///ve8RHwg6gA89NBDNvLBHXfcYX8mzSMpooNA5PF3uPrqqwc9DRtRCABv2ksuuSRy3+22287+z73ee++9ofuR3s/dl/tOqeMERJQhgYgkdd9ggCGH0HPX6iJtAClkYf78+dabPBtcPRFhgZShUXVbKri0f1EC1z2bYYsLdt1119RACPVbLBDGLgoBixLC+gEGZx599NHQ47i65/tEaylU22egJapdFHNwwUVXvfvuuwcMKnhhsQh/LxTefo0IOV5cuk2IGnjwDlqElT3vMRcdpZC46D5cB9EVMmH33XdPCcirrrrKVCKk8Zw2q81Mn9dhunp6A6M4I6pWHdNoxjTXmaoqLUoTotKRPiks0if9SJ9In5SiPiFqlfTJYqRPCoP0iTHtXT12ARoL0abPa7eTP3EWoq0yutGMHlpnajThU/a4CVie4VVWWSXWpLN30lYkD2mUwiKN0o80ijSKNIo0SlykUSpLoxDJecbCTjNlZpv5cE67zTCDg38YTXXVZpnh9Wbi2Cb7Pz/LmV+I5CGNUlikUfqRRpFGkUaRRomLNEp5aRQyW85v67bzI+/OaDUfzG4zsxZ1mY7u6CxV0FBTZR32Vx7VmNIkLQ01WsuVA5pxEqLIDB061Hz729+2n998803z8MMPh+7rJhF5IZBaLUhskX4tnad9XFyUAcQbQsoJDAwc5x3tB49nBCI8//zz5txzz41MO0hKv0K+TDfccEP7MoSbb7450pv7sMMOSwnB8847LxVtwQse7Jdeeqn9PH78+FT6yVLHtR2iQkydOnWJv8+cOTMVDaMQ4AEfdF6vUfbkk0+mnglvlIRDDjnELLvssql6QRhE8cwzz6SO5T1GY2Oj/fx///d/qbSrXvhOMQy7uGy11Vb2fwRuUDQPPPJ/+MMfRh7j5JNPtqIKMXLCCSdY8RJG1N8yBYOb58k9M1dccUWgsU1/FWWQE22UtJHw+9//3tx1112R56UP9Ysi1/Zpf95Ipg7605NOOilUuBSbo48+2v5PG6W9B/HjH/84p7SdDLhFReKYNGnSEqLesdRSS6U+h0W/AO+il1tvvTVwH1IpF0Pkf+1rX7P9NfzqV79aon+Ieg5WXXXV1MAiaauvvPLKyHMRLcNF7SmHhWofzW4zH81pN+0hwmtYQ42ZMKbRLD2sXgvThEgQ0ifSJ8VA+kT6pBT1yWmnnSZ94kP6JP8kVZ8wCTSntcs67E+d1WZmt3bZ34XRWFttxg/TQrRK5ZNPPklNKldXV4fut8wyyyzxHZFMpFGkUYqBNIo0ijSKNIpDGqXyNUpvL1kvu1JZL3GaCQoq5o3gzMK0CWOarEM/jv3VWpQmRKKRRpFGKQbSKNIo0ijSKA5plPLXKDjr4xiDBnn3y1bzybx2M6+tO3KeBJgXaa6vtuu2mC9ZZUyTGddSZxo1Z5I3avJ3KCFEJi9PDAWMo1mzZoXut++++9qUaoib73znO+a1114zO+ywgxk2bJhN6fnnP//ZeiuTei3IaMgUjkPqP14Ov/zlL1OezgceeGDk984++2xr8GIE4jXMi+sb3/iGWWeddaxY4Thc72OPPWbFIyn++Huh4HpIBYnRFmW4YcBjvLA/L9Sdd97ZljPlgOHB9WIU4v3LS+c3v/mN9ZwvBw4++GBrZGI40o64L5cmE9GLgYDxtummm9qf883jjz9uy2uLLbawXvFEiiQtLJEh3n//fesV7DztDz/8cBsBw2uY07b3228/W/YHHHCATQNIFAzaJ2KdZ4fv/+tf/7IGNQaTi2gA48aNs9EoqN+PPvrI7LLLLtaQx1DnGvDkx7sYgwujGnGZDzjOW2+9lXa/hoYGs/LKKw/43ZFHHmmuu+462/aOOOIIc8YZZ9jyow3zfFNn/A2jNUyoknLw+9//vrngggtsOeNZfuyxx5ptttnGpv8lqgP9CCmDWaSQTqxkwplnnmkHfXiWfvazn1lRRTtkQcSUKVPMH/7wB/PSSy+ZDTbYIDIdIikkGRAhesTxxx9vbr/9dtsWuG+uGVHBPfCMv/DCC1ZAugEU4Jy0H9oJKWIZNNh8881tmdNmOD7ll69+M1eOOeYYG2mDe6L+6X95R7BQhLK89tprbdSGdOUWBWXEs7D33nvbZ36llVayzxllOXnyZHP99denBjb8KZpJgUnZ8dzQrugDl19++ZQhzjPEwAVCmOg6HJP3Fs8dzyzPPeXN4BZ9ajHKneulvSGq6AO5Jz4zGMb18ky99957th+gn5w+ffqA71900UW2rXzwwQd2MOb++++37WqNNdYwdXV1tm2S8veRRx6xfR3HpZ8qVZj4+XJhpxVfYbAgbanmfpElhEgm0ifSJ4VG+kT6pFT0Cc8Zdp70SfL0Cecjcp70SeFgYretq9fMbes289NEa4bqIUPM8MYaM6KxxjTUSotUKrTxBQsW2M+8e6NgMQ/9AbZmlE2aLdh92IBAv+B+R4Rdzk2/kwlRE9fFwj9Rys+DHSE4X0ijDNQo+a5raZTS1ijYKzfccENKo2CPS6NUrkbBbmUhkzRKMNIo+SdJGqXd6pOu/sVpad6bQ8wQ09JQbR1khtZXm1yXo+X73c33oxzQhRCFRxpF8yhJ1iiaR0mWRtE8SnI1itZ55T4/sqCj2yzs6ImVRcbrtN9cX2Na6qvN0LpqZZEpMHKaEWIQ4MVz+umnm3POOSdyP16mpPYkygAvsssvv9xuXjAyECbpBE9ceCGQ7tIJKV6EO+20U+R3eClj8Jx66qnWSOMl49JnBtHS0mIKCQblV77ylchUnA4MTe4VIwEjAE/foPtDGLiUiOUAHrR4hGOkEaHC77mOUfrzn//c3nshxBRgzCKyoyIIYIQQtcLPJptsYg394447zho6tC+2TNoUBiRRKUknS3QJf5ukbV9zzTWpCBr5AGOYLR1rr732EukrMdQweGmDRM7wt0XEEBNltNWo6A6IMKIQ0HdQvzzPQSlsXcSDfMEgz2233Wb7IoQ6Xtl+z+xDDz3UnhdxHwYiE+MVkYEwxdiNSrHpr3uikDBAcuGFF9r7Z2AoqG1Q3qUgphhIwGOfAQOcizDQ2bwwiMY1Y9BnC/1bVPuk/hgMc9E/HCwaQdTy7iF6AKLEC88pAxkIMQafjjrqKPu+QqA5keZgP9rvtttuawoNAwik5T3xxBPt80RfGDcNMM8a7w/6HyKcEFGFbbDeadlCdIKZizrN7Nbu0ImY+poqM665zkYpUEQCIZKN9In0SaGRPpE+KUV9wuQLE1jSJ4uRPikMla5Punt6zbz2bussE2ciiEkfHGVa6ms0+ZMAeAa9k5/pYB8WFnm/ly9Y7MOkspd33nnH2mr0f3GuzwsT4nGYMGGCKRblsJDSe4189i6y8fdn3/ve9+x71It/f6JpYhvzPg/TKLzXeCegVcLOi70Sdg4/TNxzTq9G2W233SK/x9+IdsnCj3vuuSetRuFdGHW8sLr230fYMbBR9tlnH3P33XenfsfYUND+RLrF+Q07JkqjEMmZxRV+vMeMqvNM9o3bjrz7+csDrYmdwzs6TKNwz7y7nUaJunbK3v/3qPth/7gaBbvcf2zsWNoSdmocjcLCfP8xeG5Y0MPCszCNgg3lougG3WMcvPUQV6OwWJOFLP7f/fSnPzU/+tGPQjUKi3qoNzRKWJumX+H32IJRGgV7Mex+M61vGDVqlK0j+iQWDIZpFM7rIogHPccTJ060i81YoBdHoxBJ3nsMFlwx/sMixTCNwngKC2OdRonqT9K1i0z61yD4DtqOhaMskgrSKIwTUWZuLCqbfj6ORkGzs+jT/2zRT1522WWBGoXnFD1APbBoksWUYRqF/dDOTh/n8r6K6v+crvvrX/9qdUY6jeL/LgvrHnjgAdv/OH0SpVH8bbAY+mRua5eZ3dpp2rt60u6PA/+opjozoqnW1FQvLt9SsdNcnbPYkX5OCDF4aB5F8yiFRvMomkfRPIrmUbxoHqX051FYl7WosyflKJMui4yXhpqqlKNMQ22V1mwVETnNCDFIIIB48fg9H/1gfJBGjIgFvPgxDhhYxRjDWEZMRaUkyxQmfLyGF0Z5nIEsXri8OHnxYFDxP0Y8L1VeNHi8YuQgSPBULTQYU3inR6UQdSBWmdTCsGYQnsFqXkTLLbecNWJ4GftT2ZUDDNBitODtjzd4V1eX9czfcsstrYDAGx+DuxBg+BF1AI9jPKtpC87Ln2vYeOONbWpNPMfDojIiqEhfyaAtxjTHmT17thUKCCEmBhAETO7xjASBIU17Q1DhvU20ATyQiUiA+GfAopQgneRqq61mveRffvlle71kaiE6Bul+aZNxoE3z7GI4UweISo7F4D3HJ4Im5Z9vEChPPPGENbwZWKF/o2+gLTCBQ38Vx6BlMQGpJIlowIQCdUe0FlIVY+hS30QVQIy7yBr+aAj8HnHAd/FCJxICfRATfDzXcQ3rYkAdI6CYMEGc4PmOpzttHAGFQIky5tNBv/bggw/a54ljI6x4l1A3nINnhHLh2QwCYY/I/dvf/mYXkxDJgrrww8Ab98EEOefkmWcyhDbHu4VoI7TFYsH1EKWC54BJRKJycN9ExuF+6AvDBiJJV0rb43ssaOA4DBLQj3JPfJ/JRfqwfA9M5ArRnOe0dpmZi7pCBRlRCsY219mFahJeQgiH9ElhkT6RPgHpk9LSJ/QvN910U9rjSJ+Urz7hPNS/9En+wTGfySAcZRa095g+Ez0ZhAYhYjMapK6mcAvRROnhjQ4bx4Zz+0RFlc0WFtO6RcheWIDL+byLUEVpwDsHGzKdRqFP531Mn8/7hf4V+wyNwt9YxIBNkC9YbMDiKwcaKE77RodgC/Mexabkf+xS7CL+Rn+OTYwWSLfALR+w+IgFBXE0Cgt4eM8Q+RV7juvmmcEe5N39rW99qyw1ChoYjcIiJjQKfQE2CO9TFpigE1gcUgiw/92iK7QFC8LCNEoYvP+JDM5iHRawcxzsVKdRVl99dXsv2OLYWUFwf7Q3bFUiCNMe0SVoZWx+/+KbwQZtx31hq3LvTqNwvSziInpuHGjTPLvoburAq1E4PjY6QcDyDY4oPPuMoaNROK9fo1Cf6aDPw8bFNsROpO6cRmGMhr+zsI2oxEEahUjWRD3GYYrvOo1Cu2OhG891nOsoFuhmHF8Zi2IRn9Mo2PY8J1xz1MLOdPBdbG3Gjjg2trbTKJyDZ4TFuWEa5Sc/+YnVjfTtOOmGaRS0dNAcCm2Odwtjc8XUKFwPGpXngHEOFvw5jUJUcPoPf9Rqr0ZhnIvv3XHHHXbhrlej8H0iUu+xxx4DIsgXUp8saO82c1o7bdbLdBldqq0+qTMjh9aZRi1QE0JkgOZRCovmUTSPAppH0TyK5lG0zquU51E6u3utgwyOMq2dvWnnRhy8o4bWVpnmhn5HmdoCOuyLaIb0VUqudhGId8IHAyLfhBlB+cBrwBfyPKI0UH0nC9V3clBdJwvV9+CBSc9k0JcLu0xXT/BAYtWQIWb00Fozuqk2LxGd81HfDGAy8ImtiojNBAZJRPlRzvoE1M8lC9V3clBdJwvVd7JQfecXtAaOMmxhusOB4iBSGo4yxcpumWt9S5/Eg8lGFikAE/ZBjigOJmSJ8AdMRhKVNwoWqTMRyqJnr1NCKWqUuG2smJHVhRBCCCGSREd3j5mzqNMGEkunT8z/9MmooXVmWGOtnS8pB3CqRKO4RZgiGZTzPIrGYZKF6jtZqL6Theo7Oaiu06/FauvqTWWT6ehOrzu8wcTQIMyPDK2rts77g02N1nkp00wlgEcunol4q+LtSXoqvAqJSoK3tyZlhBBCCCEql0Ud3eaLhZ2mvStYnA0xQ8zIphozZmitqVG0AiGEEEIIIUSmUZs7+rPKoD3SReCqq66yjjLDG2sULU2YhoaG1Gci1abD7eP9XqkSd96lkJPNOKNVV1enfiZipOLkVSaq62Sh+k4Wqu9kofrOnd4+YwOIzW3rMq2dS0bL9lP7P30ywqNPent6TPylboNb1yxGYyNC9pw5czL6LlHdhRBCCCGEEJnR09tnFnX2pBxl+DkuDTVVKUcZZbUsTeRNUQHcf//9NrUZXlukKRs2bJiNOEEaXFINkt6ssbFxsC9TCCGEEELkkfauHvPlwk4r0sIY1lBjxjbXmfoaLVYTQgghhBBCxIeIaSxEm9fWbbrTTAox8TOsvtouRGuqK05WGVEeEBG7paXFLFiwwMyaNSty34ULF5qOjg77efTo0UW6wvLGv/BSi24rF9V1slB9JwvVd7JQfecW3RlHfhxmetOUG1lkWgZZn+S7rvk+jjdCCCGEEEKI/NPZ3WvXXuEo09rZa/rShg/rB60xtLbKNDfUmOa6alOntVklj5xmKgCcYn7yk5+YtdZaa8Dv33rrLfP666/byaZyiMwmhBBCCCHS09XTa2Ys7LIL2MJoqq0241rq7ISQEEIIIYQQQsSht7fPzO8gajMTQ+kXZOGcP7Kx1gxrrDE1VXKUEcEst9xydq7i888/twv9vBG3vXz66acDviOEEEIIIZINcyHzbFaZbruILR2NtdU24+XwhhpTLX0ihBBCCCGESOOY7xxlCCIWF+ZCyCRDRpmhddXSHmWGnGYqgM033zzw92uuuab58MMPUylbhRBCCCFE+ULKz1mLuszs1q7QSGpELViquc4KNEV3FkIIIYQQQsShravHLkQjq0ycqM0sQiNqc0NtlXSHSMvqq69unWbIIjN16lQzceLEwP3efPPNAd8RQgghhBDJXLzGwjX0ycKO7rTxnVmgtlifKIiYEEIIIYQQInzN1aLOHqsz0BzdvfGzQRJArKW+xq7FatS8SFmTeKeZefPmmSlTptjt/ffft9uCBQvs37bffntzyimnxD7WjBkzzP33329eeuklM2vWLFNTU2OWXnpps+WWW5rdd9/d1NfXm2LjHk49pEIIIYQQ5QmL1ua2dpsZizqtiAuLZDC2uc5ODMnuE0IIIYQQQqQDbWGjNrd2mfYYUdTIZjmiqcYMq68xVYqcJjJgs802M3fddZf9PGnSpECnGYJ+TZ482X4eOnSoWXvttYt+neWIX//zMwtNReWhuk4Wqu9kofpOFqrvcIjsPLetyzryp1u8RikS1Zn5kOaGGvtzpdc13w/L2CiEEEIIIYQIhoyVOMjgKLOoqze2TT7EDDFD66qs7sBRhgDGojJIvNPM8ccfn5fjvPDCC+byyy83bW1tqd8ROc054jz88MPmnHPOsU40xWLmzJnWAaipqUmLJ4UQQgghygzE2oL2HvPlwk7T2dMbGuV59NBaM6qpVik/hRBCCCGEEGk1RmtX/2I0tEa6rDJoDBaijWistZHUhMiGVVdd1ay55po22wxOMzvssINZbbXVBuxz7733munTp9vPe+65pw1IVikU8160kDI5qK6Theo7Wai+k0XS6xtH/rmtnWZOa5dp7exOu399TbUZObTWjGyqM7XVVYmo66qq/vusra01I0eOzPNVCSGEEEIIUXlzIG1d/Y4yCzq6rXN+XJgPaamvto4yQ+uqtQarQqmcmYc8MGbMGLPsssuaV155JaPvTZs2zVx66aWms7PTNDQ0mP3228+ss8469ucnn3zSOsx89tln5le/+pW54IILTGNjoyk03d3d1omHiGxEZpPTjBBCCCFE+UBK0C8XdJq2rp7Av2PZjWiqNWOG1pbd5JAQ5YoiQSYL1XdyUF0nC9V3slB9L4ZIzURsZjFamEO+Fxe1uaVEozYXur4VxXkgb7/9tvn8889TP8+fPz/1md8/+uijA/bHKcbP0UcfbX784x/bOZNf/OIXZv/997dzF/z81FNPmYceesjuN378eLPPPvsU9H6EEEIIIcTggY2+qKPHzGntNPPautI68hM4bHhjrRk1tM401VVr3YsQQgghhBBiCWd81li5jDLpMld6IViYc5RprK2S3kgAiXeaOeigg8yECRPsNmLECPPll1+aU089NaNjXH/99XZyh4m0c889d0CUNJxnmOi5+eabrePMPffcYw4++OAljnHjjTearq6u2Ofca6+97HGD6O3tNX/4wx9s5Lbtt9/e1NfXZ3Q/QgghhBBicGjv6jEzFnbZiAdhsHBtXHOdIj0LEYCiOItCofpODqrrZKH6ThZJq+/+zJXdZnZrp1nQ1m36TPREEc74LEQjanNdBWiNbOpbUZyDISjY5MmTA//2zjvv2C2d08zKK69sTj/9dBvoq62tzfzlL39ZYh/mO84555yiBB0TQgghhBDFpbO71zrKsPE5HUPraszIoXXWYUYRnkWl8thjj9kgBVOnTjUfffSRDY588skn22DPQgghhBAinK6eXrMAJ5n2brOoqzd2AK0hZohpqsNRpsY011dXxFyIyIzEO80EObBkwpQpU6xzCuy4444DHGYce++9t5k0aZKZPn26uf/++80BBxywxGKuBx980HR0dMQ+7xZbbBHoNIPDzJVXXmmeeOIJs+2225rll1/eOvQIIYQQQojSFnQ4yxBZLUzKNdVWm3Et/dHUhBBCCCGEECIIFqDNXtS/GA2dkW6CaFhjjXWWIZKaoqiJQrLJJpuYiy++2PzrX/8yL730kpk9e7adJ1l66aXtfMcee+xRkQHAWPhWKPxZkXp6ehKbUavSUV0nC9V3slB9J4uk1Te3RoCwua3dZmFnepuopmqIGdFYa7NeusVrfb09JoaPTcXWNet/2AjAO2fOnIy+q0AApc9tt91mZsyYYVpaWmx98VkIIYQQQiwJtnR71/8cZTq6TXsGIgEnfBxkcJQZWlctp/yEk3inmVx57rnnUp9xmgmLUEfGl1tvvdUsWrTIvPHGG2b99dcfsM9NN92U87W4DDNEI9h6663NKaecYt5///2cjyuEEEIIIQqXJnTWoi4zu7XL9IZMmNRVV1lnGVKCahGbEEIIIYQQwg9aYn5bl3WWYcIoHfU11f/LKlNraqoVSU1EwzwDWz4YO3asOeqoo+wmcse/8LKSF90mHdV1slB9JwvVd7JISn23dfWYeW3dZl57t50DSefI39xQbUY09Ed6rpQ5kHzXNd/H8UZUFieeeKINloxOuuuuu+yaMiGEEEII0U9vb59Z1NmTcpTpTqMtvNTXVKUcZRprqypGZ4jckdNMjrzzzjv2fyKgrbLKKqH7rbXWWgO+43eayafDzFZbbWW+/e1vW2cdIYQQQghRejDBMaet28xc2Bkq7IiqNqa5zoxsVMRnIeKiKM4iX6i+k4PqOlmovpNFEuq7o7vXzG3rMnPb0i9GqxoyxAxrqLFRm1PZK/t6TXc5hm0uUH0rirMQQgghhBDZ0d3ba+a39Vh9EifqM4vY0CbDG2rkyC8Sy3rrrTfYlyCEEEIIUVJ09SzOJrOoszf2GD/O+E11OMrU2IDELnOlEH7kNJMjn3zyif1/6aWXHjAp52eZZZZZ4jv5gom8K6+80jrMbLHFFnKYEUIIIYQoURB0CLwvF3Sazp7e0MVso4fWmlFNtUoLKkQJkZRIkKIf1XdyUF0nC9V3sqjU+sY5Zn57t3WUIYJzOhprq+1iNBxmKllf5LO+FcVZCCGEEEKIeHYzkZ/RJsx7pLPBmfsY3lhjs8o0KNqzyIF58+aZKVOm2O3999+324IFC+zftt9++4wyZc6YMcPcf//95qWXXjKzZs0yNTU1dv3XlltuaXbffXcbQFkIIYQQQhQGNER712JHmTgO+A7mO2w2mboaM7S+uqLnP0T+kNNMDnR2dqaE1+jRoyP3bW5utmKqo6PDCq18cvvtt5vJkyebhoYG65xzxx13pP6GmGtsbDRNTU0lE6E5Dv4BEn6ulIltsSSq72Sh+k4OqutkofpOT2tnj/liQWfowjZKcERTrRnbXGezzCSpvv1RoYUQQgghhBCLwdZu6yKrTLd1mOlNY3tXu8VojSxGk50thBBCCCGEyB+dNuNlt5nX3m0jQadjaF2/I39LfY2pKvG5D1EeHH/88Xk5zgsvvGAuv/xy09bWlvoda7qcI87DDz9szjnnHLvuSgghhBBC5Ife3n7ne+co090bf60RGSuto0x9jWmUI77IAjnN5EB7e3vqMw4r6WAfBJb3e/mAyAfuev7xj38M+NtBBx1kxo0bF+v6vHz44Yex9pswYYIpBlpEmSxU38lC9Z0cVNfJQvW9mPauHvP5/HYzv60rdJ9hDbVm6eENZbugLdv6dtkRa2trzciRI/N8VUIIIYQQQpQ33T29diEaC9I6YkRY02I0IZKFApgkB9V1slB9JwvVd7Io5/pmHdsCq0267AK3dNRWV1ltwsbnpKHAY8VjzJgxZtlllzWvvPJKRt+bNm2aufTSS22wZNZT7bfffmadddaxPz/55JPWYeazzz4zv/rVr8wFF1xggxULIYQQQojswNm+30mmx+qJuLbxEDPENNUtdpSpq0methD5RU4zOYBYcpCiMx1uH+/38gGpRcPSi7733nv2fG5BohBCCCGEKJ7o+2J+u5mzqMv0mWDB11RXY51lmutllgshhBBCCCH6s8owaYSjDJNI6SaPyFI5orHWLkbThJEQpUecuaN8oYWUyUF1nSxU38lC9Z0sSr2+0SKtnT1mTmunmdfaZXrSaBMcO4Y31pqRTbV2zkNRnxejwGP5heDBBPhlGzFihPnyyy/NqaeemtExrr/+eruWiro599xzzWqrrZb6G84z48ePNzfffLN1nLnnnnvMwQcfvMQxbrzxRtPVFR4wz89ee+1ljyuEEEIIUemgJdq7nKNMt2mPERTMUV01xDTXVVtNgbMMPwuRL7Q6Lwfq6upSn7u7u9Pu7/bxfq9UWXHFFQf7EoQQQgghypKe3j4zc2GHmbGgw/SGTCKxmG3pYQ12AkkTR0IIIYQQQgic7nGUYeNzFCgIJoxwlGHSSJpCCCGEEEIIkVdt0tppZi/qMh3d6bPKNNZWm1FD68yIpjotaBNFIciBJROmTJli3nrrLft5xx13HOAw49h7773NpEmTzPTp0839999vDjjggCUc4h988EHT0dER+7xbbLGFnGaEEEIIUbH09vYHBHOOMt2kq4xJfU1/NhnmPZpqqzTnIQqGnGZygBSdjvb29rT7u3283yv36GdxnIXykV62pyd+Si5Rfqi+k4XqOzmorpOF6tvYXDJzW7vMjIWdoeKPCaOxzXU20tqQ/5VTkuu7t7fXbkTimjNnTkbfVVQ1IYQQQghR7mBDL+zoMXPaus2iju6Q/JSLqauuso4ywxtrTG21ssoIIYQQQggh8qdN5rd3mzmLOs2CdrRJjIyXTcx11JnGutLOmCOEn+eeey71GaeZsCw/22+/vbn11lvNokWLzBtvvGHWX3/9AfvcdNNNBb9WIYQQQohSd7hnjgNHGbJUhgUW9sN6qSabTabatNTX2MDDQhQDOc3kABljWlpazIIFC8ysWbMi9124cGEqwsDo0aOLdIXli3/RZdIW3SYN1XeyUH0nB9V1skhyfbuFbl8s7DSdISlFq4YMMaOaas3oobUVEWkt3/XN98vVgUgIIYQQQohM6egmq0yXmdeWPtoaDuvD6qutswyTSIqwJkR5UajAY6AAJslBdZ0sVN/JQvWdLEqxvvu1SbeZ19aVXpsYY4b+L+MlC9v6pUlfQe2dckWBx0qbd955x/5fX19vVlllldD91lprrQHf8TvNCCGEEEIkDWza9m7nKNNt2ruC10gFwVqpZusoU2OdZSph7ZQoP+Q0kyPLLbecTdv5+eefW6HrFb5ePv300wHfEUIIIYQQ5Q+REr5c0Glau4IdPpB4wxtrzdjmWkWCFkIIIYQQIsH09vaZ+R3ddkEaOiId9TVVZmRjrRnWWG1qqqQlhBBLkuQAJklDdZ0sVN/JQvWdLEqlvnvQJu392qQtZG7DizJeZo4Cj5U2n3zyif1/6aWXDl3jBcsss8wS3xFCCCGESOLcxqLOnpSjTDpne7+WaGnod5Rpqq1SUDAx6EjR5sjqq69u/yeLzNSpU0P3e/PNN5f4jhAid0499VQzduxYs9FGGwX+nd/zd/ar1HtMEsWo748++sgeg+0vf/lLDlebPFy5/frXv17ib6Sudn+njEWy2XfffW1b4P9cePLJJ1Ptis/Fjr728dx288HstlCHGUTfyqMbzTLD6zWRJIQQCUH6JFlIn5S3PiHiqvSJKIY+YRHaZ/M7zHszWs2n8zoiHWbIUImjzMqjGs0qoxvNqKG1cpgRQgiRE9IoyUIapbSRRhGDqVGeeOIJu9Bt+rx2q03QKFEOM2gTHGVWHNVoJoxpNGOa6zTPISqCzs5Os2DBAvt59OjRkfs2NzfbbDQwa9asvF/Lww8/bH7/+9/b7emnn0797sMPP7TXmGkWJ/aPsxUK/yJYLYqtbHKpb2mU8qPU61saJb8axdWvNErlE/Vsd/f0mTmtXebjOe3m3Rmt5oD99zPrrbqcOeWoQ9Jnp6yrNku11JtVxzSZVcc22c/8juMP5jqvpJNvW23I/7JrZrKVAso0kyObbbaZueuuu+znSZMmmYkTJy6xD+lSJ0+ebD8PHTrUrL322kW/TlEc6Mj322+/1M8MZP35z3+O/A5G32233WY/z5gxo+DXKJIJhkYm0E89+uijBbseIYQoNxgkuOiii1I/b7jZlubSa24N3LexttqMa641Q+trzLPPPmv23nvvAX+P+77nfG5woqWlxTphNzQ0xL5mMh3efPPN5rHHHjNTpkwx8+bNMzU1NXZgY8UVVzTrrruu2WKLLcwOO+xghg0bFmnfYKu8+OKL9phtbW2msbHRjB8/3kyYMMFsvPHGZptttrEDWlVa0CdESSF9IkoV6RMhRKVD5ObZrV1mbluXae/qTbt/U221XZDW0lBjqquGZKxPsMfvvPPOtN+rZH1ChOBVV13V6pPtt9/erL/++tInQpQg0iiiVJFGEUJUOjjwj5rdFkubkFFmWExt4pBG0RxKudDe3p76HKe9sA9BlL3fyxdvv/12ai2Z45133rFtj7aYSXsGnG3iQLssBqWyMDIdOBXus88+qZ/3339/c+2110Z+5+STT04tzJ8zZ07Br7EcyKS+vX0hbT1qv6i/lzJx7zEK3keZsM4665jHH3/cJLG+vd/j2sq13Qwm6cqfv6lcKxsyG3b0GLOgvdvMb+uKlZHSgW5oaai1GoL/o3SEt/8o9PN6wQUXmAsvvDD187bbbmvuvvvutN975plnzJ577jngd3Hf95yP8zqN8u6772Zk002fPt3cdNNN1kZ87733Uhpl1KhRVqOst956Zsstt7QaZfjw4ZH2zV//+lfzwgsvWI3S2tpqmpqa7DyK0yjMo/B/phrF7V9bW5vxu6oUUE+WI0zErbnmmuatt96yTjM0xtVWW23APvfee69tzMDDpBdIcqCTZXBmrbXWGuxLEUKIioRoBhhwcNlll5nDDjtssC9J5HGS+qyzzjLf//73TakIRC+vvPCs+fLzz8y4pccPSCs6trnODGvoj5AAf//737M+59/+9rfUZyJK3X///XagNg6IqHPPPdcKHy9EkPrss8/shtC7+uqr7WIV/vezcOFCc8opp5h//etfgX9DoLE98MAD9ncIrp133jmLOxVCFAvpEyGEKCzSJ8nWJ17NMH1uuxkzvyPyuEwcjWiosc4y9bW5LSR56qmn7MTHMsssE7lfJesTFrmxoU/OP/986RMhygRpFCGEKCzSKMnUKL19fWZhR4/5wqNJunoHznF4qakaYh1lRjTWmvqa/Dh1SKNoDqWUM8044qzdcvt4v5cvaD9sfmg3nE9OVoMDgbPPOOMMBcQWQogCahQCHgHZ1r7+9a8P9iUlFqsbcJJp77LOMl096QOAOeprqm0QsGENBBSutk4dcPbZZ5sf/OAHphTB2Z11/Msuu2zkfi54TzZg83s1yn333WcOPPDAWN+94YYbzA9/+MNAjYK2YiM74VVXXRXq5IsOOemkk+x5/XA9CxYsSGkU5lHQY7vssotJEon33sBz//PPP0/9PH/+/NRnfu+PEoRTjJ+jjz7a/PjHP7ai5Re/+IVtkBjP/MxgwEMPPWT3I5KE1ztdVD5MlhPd5Prrrx/sS0ksL7300mBfQkmxwQYb2EHxdGQataRUUH2XLoicgw8+eLAvQ5QI//znP005vcvntnXbKNGOuvp609nRYR6675/m6988yS52GzO01oxsqjVVnvSV2ILuXulXM4lCxWTMBx98kMpUuGjRIivM4kz4/OMf/zDf+973UudlInTHHXe0k1PcDzbuf//7X/Of//zHvPbaa6HHOfbYY61TOKy88srmG9/4htlwww1ttAJEGhGriALHRJQivQpRHkifDD6yVwcifSIGU5+wMdAsRK76pLun18xt77aRmx1RU0vNddVmRFOtaa6vHqAfssVpjdtvv9185zvfCd2v0vXJ1KlTbdQ06RMhygtplMFHNutApFHEYCGNIvKhUdq7eux8xrz2bpv9sq07XJkMMUOsJsGJn/9dILB8II2iOZRSpq6uLvU5Tp/r9vF+r1Qh+rjIHfoBIsTjXCcGh1dffXWwL6Gk4N1yxRVXpN2PLGfliOq79DWKqCxwjFnQ3mXmt3WbhR3d1nEmDpddd5tpqq9OZZNpyDEQWLFxegMnkdNPPz1So+BA6/1OXHBocRqlubnZOrDgRBPHaQbt5K6L8/Ls4XDvNApO/WiUf//735H9Jr4MDz/8sP28yiqrmCOPPNJmvBwxYoTVTFOnTk28Rkm80wwNxJ/u0pv2ki2d0wwCmAZ7+eWX21SrLgWjFxxmzjnnnLI1UIqNf1CGn/0R3kud0aNHm1mzZlmvPToqUmOJyq3vcoE0a2THGkxU38lBdZ0sKrG+F3R0my8XdJqO7l7jDcK29Q67mEn/vs/859477YTP6KHB6UWZUJk7d64VNEy4IDoyjZBGxLgTTzzROmbjzP3ll1+acePGhX6vp6fHOnM7EUbGw6BISHvssYeN7kAqUCK6Bl27m+zh2hkYrq+vH1C/W2+9tRVqv/3tb80999yTNhpDFByvXNK0C1GuSJ+IUqQU9IkQQmQDWmdhZ/+CtIXtPabP9EVGbq6trrKL0YY31Ji6PEVuduy+++52oRmTPVEL0ipZn8Dmm29uF6mhT7B3ctEnQojiII0iShFpFCFEudHd22cXvM1t6zLtEU4yDjLJOG1SU12YLBbSKP1451DIrieNUhp4HV/jLIJ0+5SDw2yczDlQKAdN/1wfz1s5zBVznX6NQt+As3GYRuntXdzfJtXhNZf6TkL55fMeWWu62mqrle3zXYz69h6Xa6vUdlVIqCdXboXuz1Vfxae9q9eufyIbZVvX4vdeOqqHDLFZZFrq+x3tF6+L6gutN29bSvfeLXT9e/sfp1FwtD/11FNDv0MWySCNEuc6nd+AV6OgGcgQk06j/OhHP4rUKOuuu67ZbbfdbKZRp1H814RGcQ4zYeu8tthiiwHrvJZeeumM64ByZevq6jJz5szJ6LsjR440g03inWbyxSabbGIuvvhi+9BgOM+ePdsKEhoVDQ1R7RfJlUBc0ZUr5bKI0nuddHy/+c1vTEdHh42U5k295cWbUrVY5VnqZFLfSSi/fN4jL79SK6dSq2/vcbm2UiuvcoB6Ciq3QvTlqq/SJR/1HdaWgs6Rz/pv7eg2n81rN4s6g4XB7l89wDrNTJvyrpn58RSzTMiAKZEAADvQ6zid7joZgGcCBQ444AAb5eyXv/ylFUp33nlnYKp2x4svvmgnhVwEAZfWNoy11lrLbn4QUw5SchKpLQzKfr/99jPZ4Pr12trakhBHQlQyxx9/vLnkkkusPrnwwgvNLbfcMtiXJIQQQpQdnd29qcjNRGSLgsjNLQ39kZuH1uU3crMXsroy2UNG9ahF5yxYAyZWMgns5NUn2P2cz+mTO+64w3zrW9+KpU+OOuqowMVoXlgAELQI4IEHHkh9/tnPfhY5zo4++epXv6rJVpEIyj2AiTRKcupaZIbqO1movpNFvut7fnu3eW9Ga6xjtNRXm5VHN5rGIkSElkYZWNdOo+RS1wo8lj/IGNPS0mIWLFhgnSOiIDo4tqpzphDR+Nt4Ob7PpFGSVd8iPqrvZKH6Ln96e/vMos4eG/gLR5l0cxle6qqrrIMM+qGpgPMaxUYaZeCzXF1dbfbee2+TCxzP6wRVLhQmdEQZwaJDolDE3aLAQ4xG+7vf/c56aV133XXmV7/6ldl3330r0mFGREOkENoDkBaLzi0XZs6cab0Pt9tuO5tWFYcsOm+cc0jtFQX7sRD15JNPtj+TqovPLKRdaqmlBixS9e/7yiuvWGFIZ0zGJNJ14dnoH0AgbReLc9dZZx17baSHPO+88+xgQxh4HD722GM2mgvenBMmTLDPEfe37bbb2t9//PHHOZWb/36CXlhXXXWVfQmsuuqq9vxkj9p0003NQQcdZH7/+9+bjz76KPT4dPx4iR5yyCE2+hjlSWozFkjzXbJPpYOMVlwfZUzZ8f9xxx1nHfBKCcqIskz3wiRVLfulW/xM2yCFKIOEq6++uvWoXWGFFWwbxyuWtNr5rm9XZ3/+85/NLrvsYs/Htv3229tsYW7QKy5EQfS2e9ounroMoOB1HMXzzz9vn2nK03v/OFqSEhwjLQrukXt1Rty8efOssbXlllva/odr2WuvvdK+uzIBw5DrXWmllcxyyy1nz8V7jnOn49Zbb021i7Bnir7p29/+tnVE5R7c80CWtzPPPNM6pnoNOI7ldQjgne7O4TbaoxfSINLuDj30UFt29GtseGSTRv2hhx7K6D7ox66//nprLFMuXDeRo3CkJfV6Ovg+jhWkQ6QdueuhDOh7MdjxzA7j8ccft4b1BhtsYFMy0oa22mor23+SmjFbqFvukTJJVw7012Ft3O3jL9ew/sQ9ww6eJX+dRj3fgFMJthd9OmVJf/5///d/sbzbO7p7zIezFpkpMxaGOszAjpttmHr2iEIQBA7UDz74oP2MGMoEIhW454rvUreunMOccB2ffPJJ6jPvo2zJ13GEEKUDfQnR151jXK62JvoE2wPbBzse2wCtQD+dzo5jP+xuF8UFzcHnjTfe2L5L+VvUvieddJK1AZZffnnbz/Pe8+uT5557zr7HeEdybezH4BATnVHvZd6t6BjsKK8+wR7h997+MRv89xOkT/70pz/Zd9kaa6xh32UTJ060NiKa48orr0yrT3hXECEG24LyZNDsK1/5iv1uHH1CdBqujzKm7Pgf3fnyyy+bUoIyoiz5PwqCWLCft11F6ZP999/fOpTyzKANaeNEFkXz5ru+XZ1de+21VhNzPt67O+20k9WTmeoT7GVvu6cNo3suuuiitPrkhRdesM805em9f2zbs846a4lMzH64R+6VewZsGWzxbbbZxj5DXMs+++yTcirOBwwyO7vPjSNgP2aiT7jmsGeK/ua0006z2Ss4vnseSIeOZmXw2atPOBb9mINouq7tuY326Ncnf/jDH8zhhx9uy45+jY0xFcYFXESoMBiPcMd2+uTGG2+0fZgrF/QuEaPi6pN//OMfVufSjtz1UAb0vQz4R+mTJ554wuoy9AzahDaExv/JT35iPv/8c5Mt1C33iEZKVw5BWcKdPnH7+Ms1rD9xz7CDZ2nZ8UuZtVdZ1my11opm+3VXNr/60ZmB5xvZWGMmjm00Lzz6gDnysIPtmBFlSX/+05/+NOPoW1HwvkDTeid0gvSJ02WZ6hPaunuuvva1r9l3E892lB5yTJ8+PfWZ9pAt3vdvLscRYjAgcEahNv/CSRdQpJQ37zXTL7o5FDQK796g7/gDKYVt2DvYH9hSXo3Cu4n3QNR3XZ/P+5ufX3/9dfvZq1HC9n3jjTfs+JzTKJtttpnVDvSd3nMwR8T73atRfv7zn1sbPey6uPennnrKvku9cyjYjNipvFMYA4y6t3Tl578f/4YT4jXXXGMn/L0ahfFDxliZXyFiZtj5WVzB+wl7h/eV0yiMC/JdbIt07eb999+31+fVKNgmLHLIpI1Ebf7AY9lslBFlyf9R+zF27OySqOfEq1EYJ8dGd3rhhz/8obXh813frs4Y82aei/OxYQOjKdEv3vJK1+8wP+pt97ThXXfd1QYdRJ9HfRcNyjNNeXrvHzsEe3zKlCmR33f2OPfMz4sWLbL2OLal0yjMT2H/Zlvn/o2xaa4XW5z5As6FDcm5veXmgkV569tplDFjxoQ+U/RN3/3ud61N6dUo6D50K32pt07iaBTao/cc2F1//OMfzRFHHLGERjnhhBNsJo+oMmBOyh2b++Beb7755iU0yqWXXmo6OzvTlinfv+uuu1Ja110PZUDfyzwduizs+8yhu3knNAptCI1C/zljxoys69ppFMokXTnQX/vrG3g3Ud/s4y/XsP5kQ59GueryS8x266xktYnbvBrF6yAzblijaWmst9GEDzzwQNunU5b054yZ0efk0v69/Q9jUm4OBe0etP/8+fNTGoX3SSZ9OXMvTqPwXe7DzaFQ9lHf9c6d0Q9ke7+0bwfvxULZaZQLmws8lskmwqH/BMYqohb6eevZfUdUNppHyWwexatR+F/zKJpH0TyK5lE0jzJ48yi0I+7x8KOONe/OaDUfz203c1q7Ug4z9991e0ozfPOgvVLfwyVmaF2NGT+8wcz98E2z5VormnUnLGeeeWLyAIeZTOZR/HUa9XwDmo+Awk6jaB4lOzSPEg+FYxeigDBwiQMVBjVGE4ZINjzyyCP2Ret3QMGhhI3BH4wAjBrvgFIQGJFnn312rGiLiAQMGwYtHdOmTbOGCAKRVGA4ieBsgPDxGjkYLJdddpmZPHmy3Y/UYX4wcrhmPwySMfDLxvU6p5Z8g6GBIe93TsAIZmPAHcOK/Zi88kPZI6K4Ti+UF0KAjevnxYeRFQQD6Azqeo14Bl9oKyyWxyCrRBj8pc36RTlG42uvvWa3q6++Oq/GDyDuMWr8jmZMrrFR7rTbdNA+mNDF6csL9YjjBxsTiUQeYWAhSFgEZYrg/hFRbBjoiCPKKR3vvfeedfLyixTuk42Bd4zSbKG/YHESRqoXnh02+iD/3zKFfoWBGm9qRPc8sCG0KFOeu6D+JA4ffvihFU1hhiMbzyRtBHHtncQKArGE0U4/54UUiGwYxDzHYRk6qC8mnmjvfpgIZkMMM4HhDGnvQBBtCCHm56233rIbzrs8R3vuuafJFEQ9dcukfBBPPvlk6jMLA3hW/QPwbh/KEUFTaGg7DET5HcXoy3lPMXnGAAjvLT/dPb3mywUdZtbCTtNngqNk1KTSjBrTUFdtB9xcv8HgoX8ChLrhfUDkKQZZqMe4OMcYBvfcgAntkrbGO4cyD4ssQIQs72BdtjAB4z2OE49CiPIG2x77BH2C42vYgFA6SCP8zW9+M1SfcFwGnDlHOn3CQpxzzjknlj6hj0djefUJuoPFHEyW896ln+c9zkCaX5/wPqAvZb8ge4KFIkE2E/qEvpeN68VuYfIk36A7sOn8g+pOn2AboA3Zj/vzgy2DbcF1eqG8mPxiwz5gcJiB9yCw6Ri49OsT3msM8uZiU5arPnHaFFuUBTX51idklPNPkDpNRLkT2TAdtA+eOSYrvVCP2NFs1D0ag0F4P7QJJib8cP/YAWyMbTCuEeZU7be/sJX8+oT7ZEOfBI0FxIX+ArvPRWzy6xNs6FwnlehXGOcI0yfYgZQpYyS56JMgvejVJ/RXDK6jU9PpE/p29vXrVKdPWLBIm4rSJ+hc/xgHTJ061W7YvvQT6AW/PqENoaei9AmT2UxsZgqLuKL0iff33Gs6fcLkVRwyiaBXNWSIaa5brAmIwvadb5+6xLuWvpyJZfQJGiFIn2QDesH1G0yu+fUJ9eb0CRPKmegTp7EY23KamvcVbY13DmUelL0ySFdki1/nSJ8IUTloDkVzKJpDCUZzKJpD8aM5lOTOoRAlel57l5mzqNPOZaSjqmqIGdfSYEYOrTXzRzTmZQ4lGzSHIkoZAlrSD/BeYryDthQEfZX3OyIZaB5F8yiaRwlG8yiaR/GjeZTBnUeh/+7o7jULOvqzyay5waa2b3zhuWdMb8Dcxn9fWOzU9v67b5u+9gVmuaXGmuFNdaamuv89dNOzz2Q8j5ILtB0cSTWPonmUYiKnGZETcQzyfKSXxWO4HFK9eaNQ8JnoMEzU4ImOUY6nqH/Qy2s4BJUnHTXGHx0yHSSiiuhOTU1N9m+88DEQyJxBSjCi6YdBFAQ6aLzkeeHg7co5Mbb85+bYvMTxOmRfOm0MUQaqeVFhvJ177rlW5HBODEaMYzp+DGQGGfG8xKhkYifourgnXmocg+hrRF1qaGiwBgbGH5MlRF9ioJmJl6C0YunKz7uf/+94ebvJHgwiroPMFrS9L774wg7aE+0/6Lt4ljKIiqcnmaQQVWR3wAOYa0YAUwYYQxyb6x82bNiAYxBhgAhEHJtjYKwyKMhnos4RZemMM84YcN+5PnM8R9kewz2D6Y6Rrk54DigT/kZZY5DQpmmXiBEMOMqLSUX/93Opb6C83WQPg6h4eBOBgBR3DLAiFk4//fQBz7H/OFwjXtMY91w/0Zhc1hrEEMdHGCAEuTeefbyg/ccYMWKEvW8W/3ANPNMIdzfZxXOE1z1CPCiThysLDHqiKNEmyVCD9zlig+tjwAIhQv9A9DYMumwgu5Sb0OEZZwCAwWYGPigzhN8xxxyzRPmHpQrnb95yxTh0kz148NPPEdGDMqLfob9h8oDn0ftdDEvKzHl3M1Dkn9ygH3b7U+4YiES5IEoAg5ucww2e0Oe4CSzqk8nxqH4eMcOzipClTZAtiD4Bw50+jL/R/3FvfmhziBwXJYA65jgMylJu9Os8K5Svvx3yDJKZxmUx4Ticn7JjQI2+nvcO4pB3ECKC/j4TaJcMaNAXIsz8g8Vcm/d6GFigD/XWt9uHaGL07f57cP97f8/7hXcD7RhoV962BdSZ+463PsjcRLkTDYI2QQQLnkPqlbKiP6aNIDAdvX3GzG7tNDMXdgWKRqitrjLjmuvMyKbFIoXzE+GNdxtlxPuOSBleXKQA9qNO4/ZfXDP9BtC/uH25L97zPPO8i4MG+cArslwWpLBsQFEgnlzqTjI93XDDDfZ5yretRrmw0X9mOsmvSGlCZA62rtMnDG6jAzJ1bMRWwfZNp0/ofxlQjNIn2NsMyAbpEz/YCwwihukT3uUMbvE+4v8wfYKNxKKqoOvi3OgT+lwGYLHReIfxfueanD7BhgzTJ7nAe8pN9ETpkyCwBVkkl06fMDCN7RimT1iMFqVPsE/zfd+DjV+f8BmbMkif5BtvRMEwfcJ7OAquEZshjj5hbCFIn3DvcfQJkeKwC6NsC2wVon1F6RMXES5bfcIkjJvoCdMnYRlJ4kB/4yZ60ukTL3H1idd+iqNP6OO4jiB94oXJ8Ch9gk6g/wvTJ/Q5cfSJH2xAJgHj6BP2y0afMLnEZBHXyYJHvz7xLlTjemj39OVevPokaoKup7fPLGjvNnPbus0FV95gn6NjDtjD/m3fQ44w+x1yxID9x4weZZYeVm+GN9SYWc2LJyRY8BClT+iPqQuvPskFFiPy/qOMeMf79YmbdEKfeCdg0sE18w5xEzwOIh7SJnnm0T5h+sQ7KcOEM/1MNvqEemPCEuiL/PpECFG+YO/yfmDBFnYKfTj2SCb451CwA3gHY89gg2DHujkUfhfWZzmb2M2hYGMwyY2t5HcgABZIMIeC7cS+TqOQUYFjYDPQ19Nnoj/QGFwb7zFsKxxdeCcwh4KdRD/uh3NTRryn+b6bQ+Ed7xxOOCfHpf/P92JO+no3h8L7jHtxGgW7AY3CAusg/HMojGfyTncahfqmDNwcCtc/fPjwAcfAjvDOoaBXGOd2GgXbhjmUSlvEylirV6Ngl2FTYE+wyAaNgr51Y3f5BB3o2jvRfylz5iiwMVgkxjwBdmc6jYLNQdvm+rEhqDfsQ2wrbDcWUbo5FBZl0i6CNAr3jbblOWOMgQwRPNe0HTeHgkZ148lBYK/QRzD2iL4iAjrH4jgsQnNzKNgpfhsqLjzrbg6FZ5y5A6dR+D12UJyFc2HQ33jnUHjmsbMYH2UhLv0N7cavUdAtPKvoRDfX47eTvRGBnUZBq6FTeLY4B2XHOSgnN4dCf4TmSbfomGjclD9tgjEXdMHvfve71BwK/R/6yw9tjjpxWUGoY47j1ShoMBbI+UETsJDN6XiOw/m5ZqdR0EluDoVnKcxZKAycdNwcCouR/GMl/jkU2r1/4bDbh0jcYRqFRXBvfTbf9Pxv7P3iq24M1CiUSXN9tRneWGvGjx1tlh7esMSxzj//fLsQmOtw2VkoZ+qVsqI/JosV95UPePbdHAqagrGKoEVlaJlMNYqbQ6GvdPCO4hnnmWdMJciZE1wGHDeHwrsqqg8Jg3pz/TBrDnj/S6OUD6xNcf027TPIaYY+1zn98d4Ic8QSi/FGo3c/l8M6Lz+aR4k3j5JunZfmUSp7HiVqnVe+0TxK8edR/P15EJpHGZx5FF6rizp7zIKObuso47LIwAabbmHuuu1mM3vmDPPh1PfNiqsMdPx75YVnBlzPh2++bNZZmXVeVUvMtaSbR/ESd51XEJpHKe48Sr5ttSEh60JLHTnNCGP6es2QtuwiEQ3pKYzTjD12tad59nTbVGD5oK9hpDFDor308wkpn+mAiKbD4GuQt2gUDPjzYqGDIZoBRoCDATxSh2PUY4AyscQLhHRiQbAPooiXs3fyIcgzlAFYJmEQXxh53kFABuXxpOReMHo5PwNq3k4QQ4XfMxjKdTPI5vfmRYAwiMRLxvs3jDQMIsSZGxBFWDCIni+4B/eSwMAOeilhoPDiClrEy/1gNGEkUw4YMV6Y/KFuePm5aAwMRntBKGFUc/8MDCLGHBj6lB/3749wkAu0QxbAp4MUkv4JqnyVuxOQtCsMAv8AA8KalLfe1HP5AHHmjHHEDwLA2+6YtEF8pPPUZx+ECuXDpCiDol64Hwwg2jCinEFoxJUXzo8I8z5bznhhgTvChPZD3XM9UYYQKX3pI7g377PPddEOeRYpdxYYZSOm8HTm+XbX548ogtFLXxGUSjGuYUV/gpCiPJhkRZR4QXDSJhBv3jJbc801B3j3k9qQ34XB4A3ih0ETP9wHg19MZiGs6W9oq/5BEC8Y7uznHZR3aUZpTzxrDJIj8vz9H8+/E1IMNvGu8EL/jhGOse53sOCYiAP6Dtqx34hncMtNYiMO6XsQVJng7Y8Qst7BYkQag14Yv9wnzxb7eCfbEK4u9a4/ekIU/mgtGO9RdeqvD8qaQQUvlA/lgeChrfHMEBVgXlu3+XJhp+nGcyaA6qohZszQWussQ9RoP7RTJlh5DyKcvPXA5BLvP0AkZwL9iqtzr5jiueO9xCA++9BugsQH7yP6EeqFZx/RR3vi+pjo5jP3nw4GabAreG8wkcZ7ib6LtsFxeFd7n8dcB735vtcJSohsNUoh9Ylfo+TzXMXUKNIn8fQJuL/zfqdvlT6RPskn0ifF1ydxkT6pTH2CTZtOn3R295r3ZrSmHOqXX2mVAX8fOWq0WWXi6qZ6yBAzrLHGjGisMY211Tnrk3wsrJI+WXtAFDUhSmkOpbuQcyhMTFZ5Alr05i/4WKE0ij/wGH0MmQCwDXjGicjq1yjpApG4TC9BGgV7ATvWaRQWQfCuDNMovKuCNArvs6DAY0EaBVuByXD6eSJXsugsSKOgZZxG4R3KZLb/HU2WFjQYfZz3uyxEoX91i+/QKL/5zW8CNUq2gajot529GqRRmNDn3YuGQqP4j419EaVRsFO5f6dR0Fh+jcK9h2kU7BxsXO7fG+E118BjLJYLymoRR6PkI/AY5e4chZxGoU15n23efbQNyjefgcd4XzsnqCCNgv3OAiWvRgkKPEb2FxxmwjQKzw02gdMoZKDwaxSeY+xPv0bh+cSOoO07jUK/4W0b/rJwGoV78z77PEe0Q6dReEb5nI1GcQt4gjQK9id9iFejeMs/aJGJP/AYNleURkG30ibQKPQX7rss1mQRq1eDBC3gdPtjl4VpFO4D5zenUehPWSTq1yjefh67za9RsNuwW51GYUEQYx3+/g872jnMBGkUypr+g4V6/vKi7dKewzQKNiga2GkU+t9MNYp37IqFfyyajNIo7IP97Orbq1Fov+76ma9g7qK1s78cySzjHGaCNMrYMWPMVhuvZ534mdNwBAUe43x+jeLqw2kU3n9ow2w0ir//GTVqVEqj0Gb47NUoaCZwC3Dj9l/0i+7v9CXuM0HHnEZBExH8M0ijsLjXq1FYrJiNRmFBLeNr2C/Mh9EmvRqFPoZrcijwWGnB4mHGbOiHWOBI+/T3j2QCdOOBvLPSRa4vJ40SfwlobuS7xPoaC6NRvH0Fn6lrFlXzjsI2JEimfwG0NzNMUNtgAb/TKPSB3nFQry3Ee4jxDvqUsHFEN4+CDeC1P4PGuLCLsQuwv712FG2c68FOcfMo2FIsSvbeP30h10UfjbbiHey/P8YseZ/4FxNj8/FuxT6gn2XRP46yODv7SVd+3v28f/fOo2BbBTlIsqAdXUGf6T82v3caBb3m1yjYgtQN44poFOrGv3DeZSXl/rF1vfVA2fNe4f3vnUfJtf9AX8bJPMA7zq9R3AJp/k9X1mHXS7mjCb0axZ/lj/cfi+Sxg/zfz7a+gfp2upRyxQHMuw/tlWeUhff+59gLf3fzKNgKfmcE7odxUxf4Fo2BE4wX/sY+fo0CtBmceWh/1D3X5B2b8JeF0yjYI95nHxuCdki7otxdkNRM4RrcPArPpj+7LX0SWog5B++1RdUPf/P+HfsZG4U5Ee7Dr1GYR6JNzJs3z7S0tKTunbEEbzvFSSIqGwbtGn0ZpFG4D+Zu6Q9oGzid8Nn/HHj7Ofo39Kd3nJxyp36pMzePwrPvLw+efzePwlwJ41Fe6N8ZQyfgL8+L9/vMzbt5FK7V71BOfaDp3LsBW9Y5aOMYs6C9y8xv7zYL27tDAwJvsMlijfLfF54xK60ywTTV1ZiWxhozf+YX5rPpn9i+gDbFs4WzGO8Ch1ej8Ez47z+sP/EHMqEtRNWpvz7om/2Ob9QHZYkDG/01TlLZaBR//8NYDvXMWB3jed6MPt55FOqC/eP2X/QrTpfgdOn2dY52vHsZIwzKwOnWynEt1IubR3FzIIwn8DmORkGz++dRqG/6aI6DRqn22T3Z4MqF9lyOmqPErHoxGCCkhv+pP21tEpj1zedMX2P6TiRfEKGHwVsGTfAmZfMbj2HgRYqXOrAgI8igonNlMoTJBYwRRA3GVxgMVsdZbMQLjkmKIGMPj1ZeSLws8cbGw9bfifIz18zLBM9ohJw/+oY/cpQfXlRMmDlDgIGkON7MceBlyuASpItc5+/cGeh0kUcoT7+QcjA45uoeIeyd8KFeiXDgXlhBA/ossGEiKpcIuX44Z5xoOUS2QJjnGya2nAGJgcmzEDbwieGbT5h4BSI8kJozyJhhoJgBj7CFe3jD84wBg+j+yR4HIpsJPSYGOR7PqH/xVBRMMmBs0zYYaOUZYnA5DK4laKKXQXoMaowv58CQKYgwN1DNsx7kSY6YcIMr2YAHtzMC/ULKS9QCsThQB2EpNIH+BQOVwXQMSKIHMbATBoLXO9njoI3x7GPUB/V/RFRwk4+IZ/9kjxd/edMP8nwCfUNY5DveDUwW8RxjCBNhISx9cNi7i8FhBl9YcMbgl4OfnfBBQLlFaf6+hucFMo0Omi08j0FRDqlXxD4TPvQ3jz/9rFlvyx1thLYg2H9UU40ZM7RuwCRTEEwk0e4ZqOF+XX256APetJtxcdHVENT+9wvtjfcPk8lBUQ/8fbizIfjffQbaAoOj7BPWj9EHMxDEJD0Dwm6xh0s/Sjt3C6g5DgMdQuQTaZTCIX0ifSJ9shjpk36kTwYifVKZ+oQI3P4+tru318xr6zFtXb2pBWphE05QWz3ELDu83rTUM1kyJC/6hPcSkyb5IOn6hHvjGeE45ThRI0qbpOkTkEYpLY3iX2TrfpZGkUbJJ9Iog6NR4iyil0apHI3i6tuvUVy2S6JF9/Evol145y0I+jWqKd7yd2mU8p5DUeCxgbCY1L0vAadBB7+n/r14nbYcPLe8a1m0zOLW/fff3/ZF/EyUdRZTundSVF83WCRRo8w74SXT11Q8jcJCbJw+yObGFjfiOw6o6BTAVgkKHMR7CHsLOwQ7gixfBEcK46KLLoqtUejjgjQK716cZpxGQcsEaRSytbGImXc0zxoLbDOZR6F/5N2NYw39fz41Co4wuWgUF6ABPZhOo1COZFr0Os149Sd9SJDjEhqNPiWXLIN+OGecQKUslGahd75BqzlnZqdRwsABIp+4DHi8V3kew5zUeP9HaRTnQEIAurAs6LRtjsXGs8Iz4rWNqdsoeEY5PsF/yIyTTqOgf4Oc5dAo2MAssA/KKBVX2zmNwn0EaRQW9uP45d53mRJXo+QaEC+ORuGZw5kLmwyN4nUE8cM7PSiwFG0M/YDmDer/3nvvvZSjPTrH7zCTTqPQfsFlpwqCdwN6CxsaffrsK2+a0eNXMK2d8YKDjBw9xqy0yqrmg6lTzLuvPG/WPO1bpuZ/WWQeue/plEYhQAXOGd7smH6Nkklw5FzgeWRsIKhecdbDaQaNgmbzZ03NFuqfdo/m9GoU2hAQtA07PtN+0jlP+t8vbjwAGzUoA6fDBSN1NgT/u89OOzHuSR8TplHog+k3acvcGxqF/sxlaPVrlJEJnUcpXroNIRIMXqyug/V6N6fDpXsFOryoaDYu8gWRaqLESdzFy0QqCEsX6R24xMM5rAP17kcKunQwmMF+GB4Ys2xOzPFSjnOMuHDNLgIlg2uZRB/D8xchybWlS9XuohTz4sOjPqhuoyZWMLQKEVF5sHCpODFoGSAoFtSXSyHIoFiQB7rzhI2Kusox3KBbusEx96wh2vF6jwLDHZHubftesRcVzRtDkYhYYThDiQEEPPgzxfUp9AlhRhfkIr6JbgZMLngNvkJD3RDhhPO6cudZdcI1XRR1b5RdP96y8vdd9CFuwgNBlAlMHhHVJJM2CM4bPhPcRLR7dhzuZ/7uhBLR9LwRrtwEEANrmaarzhaeg7ABN299vPr2+6EOM8Mba8yE0Y1mqZb6tA4zwEAm73cmCL2R6IhIAEETglHQBl0ky6DvMqjqoie4SaUgiDDA9TBJy8CeHyYAEUqIMYQmfVAQRBygvhFU/qgFDKYSeYLBHCa1nIAUQpQH0ieZ6RNnJ0ifSJ/kC+kT6ZMgpE+Ko094Ry3q6DafzG03781oM18s6IhckFbj0QUjGmvN8MbatA4zmegTV375IOn6hElkp0/cJJUQonyQRtEcijRKP9IowUijSKNUskYZM2Fd8/HcdrOgo9s6zITRXFdtlhveYCaOXXIhdBykURajOZTyB2csMlm5jYjw3n7A+7ewbOErr7yyOf30021GICJ6szgeR2QWq3odZlj4780aJJID2dWcwxvZu+PiddrCST7KDnbR+b22b5BGCXIgD9MX/oj/Du/ib+y9MI3i3S/TeRTed2zumcm3RsEGyVajsEDcaRQylkThyhtHkY8//jiwbqP0J4uRK0mjuOw+g6FRnM3EQvEwx3o0SpRm5BhOo5AJKE7dYwe7IA7pNIq37Xs1ijcjqh9ssihb2Tn2ZKtRXJ+CRglzEoIjjjjC5KpReO/iLFgsqBsyRnFeV+48q06jRJV7OvvTW1b+vgut7jSKN0NPHNCx06ZNs5/DHHoI5kU2mZXWWuysMfnJp2M5zNTVVJkxzfVm5THNZoft+x08n3/2mZTDDLhnCac3p1HQc4O9zovnIEyjRNVHLjCOxPsdjUIWJr9zPo4rmUAbJJNV2HcZF3MaJUoToCcIiIOjW9DYAsElcNrnHY5ODtMoBEBgroR90mmUvyZ0HkWZZoQoAryY6YiIlITnI16QQdEEgl6agNHv9973gxcgg5akCiP6hRMKXjCG4hIVTcdr3Mfdz2Uc8IPAwFMS48IrNoLAk3ellVYy+QDPSTxneeER8cWlycQA5nOUgHGGMS/PsImDMC9r51XvPNzT1S1pzEhZ5/fuzRbuz3mPDgZuIJOXe1B0i0LBAC/1BekiFkX93Ttxk+6ZDPKw9zJr1iybGhIDjOc2apEO+4aBgRMVnQBPdO9zmIk4x1ji2iBKSEGmXtZeSCuIVz3nwzilf2Sggslsor/lK/KIE1CkUmawnPZIfxnV50SBd3nccg96Dni+SX+YCV5hzmRDLm0wTn9BlDy+S9QEd79uwgchRZ+GlzwCxZu60/VZ9F/FykCCV30Qnd29ZtGQxYPZrYuWfB8Nras241rqTGNtZqknGfBj4o2BdNoUE8aID/oc2m2mEz5OINE2ggZsGGTh90zW+COz+eEYDMSyMZFJ/dCHMWDB5qLwcN38nf+DUm8SMYX0w0TnoO3yXY7Dfbr+gYEaFrcwcFWI6DVCiPwjfSJ9AtIn0idepE8WI31SOfrERR5zfdaaa69jvuyoMZ2t7ZHnooab62vMiMYa01xfnVd9At5FCWETG9kgfbJYnzBxyfMkfSJE+SCNIo0C0ijSKF6kURYjjVI5GmWPPfcysxd1mP880r+QcdU11jb1TcE2NFC3Y5vrrDap9Sx6ywZplIFoDkUAfQzZPYg0jlMi/RrtCPuFxaIsOsQmEqVBd0+36cvASSIu3ixOfHaOGGRwI5sUGoWI/9jjTqO4DA72unzXxOJZZ8eSRSLKsQO7ioXfOOthi4VplLjOIWSoCNvXm6kh7n70X0H7oUtwSMOZIp1G8dr4jqjy8+/n/Tv9sNMo2Ow8t3E1inM8ppzd4uU4sBjfOWs4h2HqCRss7Np5x3k1SibOPblqFP+5nA3N/+nKOuwYbjG4V6PwrMTJmJju2P79vH93z4WztaO+611g7n2Owet0HpR9Mqru/efMRKPQ9v3fd2WBRqGPCbsnbwZJssBGZVrxg2ag7DItN2/504b9dg9/8x6LZ5GMWZyP92UmGsV7HH995apRZs6cucTxvP18VP/nXdvk7/+c1sWGpO/O5Ln2OhXhdB2X2TNnBP6ekmVtU3NDtWmprzH1NYt1wlZbbmmuv+46m3mRcRWnUVx/ROAA+jSnUfi9CzbgXefFs55LfxL192zqA8e3bPpSf//DRh3i3IhGwWkExx2vRiHYgDtXnP7r1ltvtf9zXMoyaD+nUbA5eabDNArnxymTLUqj8B6kjwrTKDi1oU9wBi/EPEpvb6/duB6v41UcSiG7jTLNCFEk6GScgU6qxzi4ToWBw6AUg15cqjteTHSu6QYg0xEVLQMv7Uz3C0oXTIQOPFh5KaQTUkDKsHxywQUXmN13391+5vxXXHGFfQkQHQ5Djp+9qXy9BlY2eK/fW7dBLy9/6tdKwQ2gO4/zYuF9QacTwFFpI2fMCDZIM227GCIIWyY5ECvpxCTRbcJIF9km3XMYBX2Ju7Z05ZZLO8VIv+qqq+zzgPHIgNNZZ51ltttuOzuQRBQnDMF8tAMmSX7wgx9YgzBKSMXpc7Lt/9wEHvcbNPAVRT76n7h4I9a4iAJElXNCwf3d/e/24X5dmthipewE/yRyd2+v+Xx+h3l/ZptZ2LlYyHhFTUNNlVlhZINZcVRjxg4zDhcpgLTcTpQ4sbn88svHPg7lRvpbF2kgbCLXnY8BorvvvjvWsRncJ638T37yE7vIgQE90sW6dsq1k943CvZl0IRU0vRdDA6TOtQbYeK8884LXeAhhCg9pE+kT6RPgvVJPhcbhSF9Mnj6JE79Sp9Ulj6ZvbDdPP10vz5ZZ6PNTGdPcNZJ4DjjmuvMxLFDzfIjG0xLQ03WfULUItdcnoN0JFmf8B7Plz5xE8OZbEKI3JFGkUaRRtEcShI1ShykUSpDo/xn0mPm3S8XmQ9nLjT/ffF5+7sNN918ieNWYYv+L7tlY22VdZrJ1WEGpFHC0RxK+XHKKafYxYJxt3T981FHHWXr7aabbjLXXXedzXzI4kY5zAhplHCN8uc//1kaJcEaJa7DTC5IowyeRolTv9Io8cm2/+nwtCE0wrCGGrPM8HqbeXKl0Y1mzNC6AQ4z2WgU5yhTKuu8vEijlL5G6evrs2WQyVYKKNOMMH2NI828E17K2pO/UNRU1xTkPH0Ng+OthpBCUGHA8xLHWIjrPZqvRTvel8lggzFx0kkn2ZcBHtHf/va3rdcxaXB5IToDgw6eCEqFAI9UUvXiVY5XPsYCafrooIlExEZ0hBtuuMFsuummqe+5DhzP7zvvvDP2+VZYYYUlfleMBVkif2XuXWxP2me8hONAlCEHBvxxxx1nhSXf5zNe90QcxLh2g28YrK7dFUNwpqPQbRVv6+23397cddddNpIkhhrCgb4C49RFoLrsssuy7stIL+i8/4k4zOAJkVkQig0NDal7JNoC6TxLodz9eA3IW265Jbaxnkm0FAeDHkQeI8Uj/ePRRx+dipBGWmd3TMQUHvhOcOGlTqpl97di09vbZ2a3dplZi7pMT0gdMsE0trnWDM9hEZwDwUhabNoMEQRclJdMI6SRIpdIAUDKzTgDaQz0ZxOVDM99UszTxnmmAGGW6TXzrNx+++124INoBAzAcB9EOxRiMDVKOVLdONKYIfm31b2D9nz2TtBgyzK5SCRE9AkTHdgk3vesf0LH/Y3/0032+I8TtH/Y78OOF2ffqP28v/eXh1efEN2F6I9OnxDRyemTxx57LBXJ0n+MoPvO5Drpn4loQ31gEzFQyXvVr0/QMERNczibhTqNO9AGRBFy15BJ3XrfnXHrL+pY2R7DXUe6Y8StE0cmC5+zre+oZzPqevz7eu1V3sFxyxLbxe3r1ydET2QCAjvQr09cROmgdhK3LLz3k0kf4D9uunLz2s3p2nXQdTAQTR+A5kefMLnj1yeHHXaYnYQNu/d014g95vQJ9hORo9Zee21rB3r1CRG6sTWD2nrc8oy6rrjPUhDeNogmCBr3CIJ7zPRctFsm4ojgjC6h3bpJHPSJi2K/7bbb2muZ/PgT5t0ZreaNV18xCxf265P1N1lyQZq97/8tQGuqqzZLj4iepMn2ec32mFHfC6p7dLXTJ0Rhdvrk0EMPHXCedM9sNvqE5+LII4/M+F44NhM/1MWll15qf8ckEM9YJmy88cZL6BPepUSNi4srF/rDUoh4JkqHUp1DqbR5FM2hDERzKP1oDqX4aA4lOzSHUhqU0hzK8JGjzMyFnWbCupsgmMyzTz9lTujrM1PeftMsCtAoBPYiowyL4fyL38oZzaFoDkUkS6MUSp+ANMrgI43SjzRK8ZFGyQ5plMGnt6/PtHX0ZweBX13xZ7P0MgOzb4WBvTuqqdY011fbuQscZyp1nVexkUa5PdEaRU4zwi7O6msandVXC5H6MnVs70KQAp6nmJx44onmT3/6kzWiLrzwwrRiyk3O4jWLV27UJLpLG40xkEmkgcGCyW/SfAFiBW9LhzdNWaYpvLKBlOguLTqekxgEGAb33Xef9Tg/5phjzPPPP5/yNnZeoexLtIJsokm6OuL+EGdRx8jW6z3fOAPWKyiCcOkxg6Ds8GAmDWAx8T4T6cozKgW7d8EEhqRXJMWFAQKEEtAPkPY7iLBIIsXEm742Xbnlo52yIJUFNm6RzbvvvmvTpxOlBCOTBT+kgaQvzRSMe4QakFqRlKlhFLrsGYhx50FcZxKFwOuVTnkRoaGQIIYQU05EOcHkFUkuygCppunTXCQC+gyvd3qhsRF42rrMjIVdpisievTQumozYXSjqfpfpLZc4b1Lm8IrnwVeRAxBnLsF1XFJF+kqCOrlk08+WSKldVzof5yYmjZtWlbHYECU9KQXXXRRTscRIp8aRcSHyY0//vGPVp8w6cMAbxybiv0rTZ8wGOb0CREFd9hhh8D9iqFPWPTL5mwY3q3oEzQUNheRD5kQcvrE2ajoEwY8s9Enzu6jbitNnyxatKhi9YnXNsTGZIA3U3AEc/rk4osvDl3wXoy2n099ElVumZyPCQU2eOedd2zq9KuvvtpGnuK5XG+99WxfmilEO3STswxoM1YUhuubCq1PqONc9AnlxWRVIUGHOKcZrz4huiWTT/PauswKa/Y7d7379lu2T/vvC8+k+oz1Nlo8Ud5QW21GNdWZEU21FbUgjfcuUcsuueQS85vf/CZrfYITZ6ZQH0T4zCQSmxfeb85pJl/6hEkfISp5DqUS51E0h7IYzaFoDqWYaA4lOzSHUhjKeQ7lif9plPU23txMmdFqWLK37sb9gU/ef/dtM3/e3AEaZaNNNjOjh9aaETjK1FZm9kLNoWgORSRLo1SaPgFplMVIo0ijFBNplOyQRhl8jdLd02sWdvaYBR09ZlFHj+mtb079rbllmFll4uqh322qrbZOMmzMW2Tj+FRO67wGC2mUoYnWKJUzIyhEGUDEYqI5w6uvvmqN9SjWWGMN+z8vWzzjo3j55Zft/6usskrGaeAGg7fffjtlnOJ1HAZRAIpdR6TyvP76683xxx9vf4fhjze0A2MOOjo6sr4+N0ibrm4RlunqvphlE2fBDmkow2BBEVBuUaIr36y00kopMeyelTCi6tTVPXjbRCaw0MobvTib6ygWGIT0KXGuJ125ZgODFaQWxBvbpWR03t2OuAKBhTJdXf3e+/vtt1/ofizAilpYmQ/cc8D1MFCTCd42+Nxzz5lC44QSgwzexWnedJwsiCLCCk4r/N0JLiJiI5CLxWfzO8yn8zoCHWa87cRGYMiTw4w/laZLscuic6LcxIXBOQYOAG9+Bl6jNha/AWWejQhzuGjcuUYZyddxhBDFh76KSGBAlJ577703tg1LxJkocOoAoiyVmz4Jc5gplM2Tro7I+nHjjTemBpQZZHbZFby2Bfok2+tzC93j6JN0dV9sfZJuIDxKn5CSejD0CZH3nD5xz0oYUXXq6j4XfeLafjp9Uuy2H6ZP6FNyLbdswSntu9/9ro0oyYAyZBKVMEyfRJU7E0zZpkWPi3sOstEn+WiDmYBzjBunoWyc9lhrw83MW5/NNx/PbjUtY5Y2Sy+7nLWVX3nhOfPfF/qva9XV1zLDhg03o4bWmVXHNpuJ45rNmJb6VJaZSoLofV59wnskE23GZCQOYsCYHROcURsOOkCZMwmaD12RC9InQpQ3mkNZjOZQNIeiORTNoaRDcyilNYfy1MtvmMmP92uUtTfazDrMAJGkB2qU/utaa+11zIYTxpulWuor1mHGoTkUaRQhyhlplMVIo0ijSKNIo6QjiRoFm7O9q8dmmfxgVpt5d0arXbu0oL3bBvuauObaqX1ff/nFAd8lewyZJpcZXm9WG9tkVhrdaMY019mgX9najeW0zmswkUZZOi/HKUcqb1ZQVAT+B7GSHsxvfvObqVRcv/71ryPTwnlFBqnAwuBl7Aw0OuFywKW+RJD4y8DVN8Y2qfoGC29ZEjXCgdhy13jVVVdldWxv3UYtKEBwl4InujftKGIpbNEOKRZJ2RaGi7pB3RJ5oljPN9E7nMf0o48+mkqN54foClH1Qb05o57owtmkdfRG2Agz2rkOIpyXAq6t4l3OIFAYQX1UvuqWiNlucZz3WQSX5tQNTsQp9yghz0BKodl1111TZRMVUTpMiLnIF7QRZ7wXCm+kgTvuuMOKUq7dn47T/Uyk8qeffnoJwZWNkHfviCg6uxc7yHT2BD+PwxtrzKpj+gdTCgXCf5NNNrHtkc2Jq7iQMtO1SyKZM9AStRGlY+21+4W1/z2ZSb/kHSRZccUVMzqO9/mOOk5cOB7ReDLZhAjr7wu1OfvVwc+FPF++rznseom25fTJr371qwHf8e+77bbbpv7GeyjsvLwL3OQJ3/H/3WvzpLuHuPtmup+/PNygK+8ebIqg+iYzhNdWDCpTb7SuXK4zaHOLxV30J/d7r23xhz/8Iau24q3bW265JXQ/Bra9+iTbtul932R7DJfJAH3CNQXtw8QY9n/Y9VJ2fn2SybOdbX177adJkybZiEJB36ctEh0vrM3RJpw+IWsU7TjTcvTa0LTxsH1c+WTb9t0W1cfE2ZxOR5/gOBO2n1dPecvf/2xncx0MJLtJJzSw92/eyJFtbW2hx/DauejrsP2uueaayH4jk/IMqz8iQmbbh2CTOn1CG4m6l3xs3ghnN97yF/v8c+0T1tnI9PQutl832KR/v5eee8q8+mL/BNa2225jVhs31CzdUmfqqgeWg7N9w/okp0+i6tRfH+n6kqh6jbNFPXPoZ68+IZtRJt/HGczpE7K/EF0tajviiCNS+oT3pPdYmfRLL7zwwgBdkelxXPl79QnvikzLlY3zEWEvk00IkT80h7LkHEpYVGDNoSRjDqUYaA5lcOZQ8oXmUAZnDgUNsuaGmw6YQ/nkw2n22jfYZPMB+zqN8vZ/nzVvvNyvUbbfblszpMBzKKVCEudQ4h5HCFEeSKP0I40ijSKNIo0Sl0rXKDjDLOzoNp/P7zBTZraZqbPazJcLO01r15JzUKutuY4Zu9R4+/me2/9iers7zaimWrPCyAaz2rgms9yIBjOisTZvAb5KfZ1XqSCN8t/Q41Q6cpoRORsohdj8CyH5uVDnKtR1h10zafDwqHXGycMPPxxanptttpnZcMMN7d8wrPDq9O+DMXbmmWemUqQdd9xxS+zj4O/p7iHuvpnu5y+PVVdd1f6elwcvEX9987L+3ve+N8DoDSpTl0oyrD2GXSeLkvAgj7p2ryggErD7PZEhnAczCwkQVFHHmT59uk0X6K9bF80V4w1B7P/ezJkzzU9+8pO8PHMOyjXbY7iFdBisLNzx/50XL3XGIpKw6z3ssMNSg9W/+MUvbJsOe1ZY4Jav+mZjIMMZZ2eddVZgWZC+jucyrM2RbpFnDKiz//u//4t8BjD8ERne302cODF1fDyHg753/vnnDxAu2bT9oH4pm3pnIa0z/OlrKD//Pv/4xz/MQw89tET5hy1q93//3//+t+3Lwq6BfoDUkS6ahPdv48aNS0Vd+fDDD0OPQbm7+0AwB5Xpgw8+OGBRWlDdZlKeYfVHhOq9997b/o3ovSxMCzsG5c3ghfuZe+U5A9K/kh2Awamw79PHXnvttVnVPRvpIJ2QZQABuH4WCgb1D3/9619TUUpYxBl2XFcXYX3SUkstZf/+0UcfBf6911SZT+d3mk/nh4ut5voas+q4FrPi6GbTWF+X0bswaEv3zNF+aKtsRHLO5PtOEDFYw4BdnOtx7yGeDcSM+z0Lb4mgQ18W9X2iR//oRz9KXdNXvvKVAX///ve/b1ORsl/Q992zwPncYl4iru+0004ZlytbbW2tjUqUySZEsfEPMmQzqFqK8Oy6bDN+feKHVPcbbLBBSp8wiOaHRfdefYItUQ64BfC8O3EM8dcvA7NkmAgblM8V3usuik8Y9Ln+iQ5AW331q19N6ZOo9OTOZsJ+89eti5KEPvFmsnFw7359Mpi4wVz0ibNTvLDgmTrz6hM/LCQfP378AH0S9mx/+umnJp+4ZwN7j2cmyJmDlOBefeKH8QWnc9An5557buhkpYsq5Z+0cW3f2XJBUDZREyvFhEFlZ0ueccYZgRNUt99++wB9kmnfjY0eFXkPje/0ifdZhFGjRqX0Cc91GIwxuPug3IOuC53k1SeFAnt/r732St37FVdcEbov5e2d8KWfP/3001P3S9TNqAkRbEsyk2QDZTRs1Fizwkor259vuK6/bFaaMNGMGDV6wL5ugdqD99xpFi6Ybz/vtP22JtuEk06fRNVpqUGEM8a+2JyDYFxclDP0CU5Vcdhnn33s/zwbL764OFoezyL9VLo+BKeTH/7wh6mfiermhXEcMtpEOafQRrz6hOuPinoaBceiX85kE2IwqNTgY9IowRrFX9f0PdIoydAoYc+2NEplaJQ4fbc0SmlplEWdPWb63HYbQbqvcaRZbsWV7O//cev1S2gUIkePbKw1u+7QHwjl7n/cnqpL+oxs393SKKWvUVzd5kujKPCYKAcqVZ+ANEo/0ijSKNIo0ihJ1ih7ejTKTy64xHw0p93Mbu0yXT0D21Nba6tZ4Ckb28+fdKr9/OknH5lLzjvLjKzvX9OEXsjnPAp4A79513mNGTMmsH/wrvPyO9ZkgjRKeqRRqk0psHhFpxCiaBx77LF2gA9jGY/tKFiwussuu1gDFo/GE044wXZ4dFh0lBh/7mVz6qmnmrXWWsuUAyz2/fnPf24NW677tddeMzvuuKNN8UZUarxyWQS8+eabZ50eMQpedryIcIBhoTDOSc7Ix3BDJLG5VI14lnohZRppCil7DGoMokMPPdQeD+9TnCVIt4lYRgSzQP6ggw4acIyLL77YLqxGhBxwwAHmW9/6ln0B832ia/ISo32Q+i5fqTsRr1FiwQv34l3kTfQAInR+/PHH5pe//KW9R+4LT2HqDOch2uSmm24ami6dfYmAzP1yLbSDQw45xA5+42VOeyA1IIvPMUxwnMkXlDXPDikg2fhMmWPYEqkb5xbqnLYQlYIS44PUhNQR9/LEE09YwUE7YbCEwfm33nrLOl1h1PBM8ncHC8qJQsI5cY7BKYByxCEH7+4bb7zRfrdQbT9TuC8chTCkKReuH8c/7ovBHBzCiCaRrtyiYECCvo02hvMFxjp9AWXJMekPnEj3Dxix6J5zU1ZERWeQgmtmAT64xfWILp6v//znP/a5pA3SF9OmqYt77rnHtgGcchADOK0VEp5/2tBnn31mzjvvPHtNOJXhyY5RSbt4/PHH7UAMbcIbWZ7rJpLGvffea8v/lVdesV7rG2+8sS036oXniLZJW6dPoXyzhUgCRB7huOC9Fof7nduHviOXCAQ4FjIQRT9w3XXX2eeB/qO7t9d0mnrT1zgsdMEjaUqXHt5gWuoXO+eUMvSpbnCP972LcpIO3mH0IU7EuvcUAzy0CzbeHzxXDOAhUBl4oL0zgMdzy2dg4Jb254U+nsE+oiXx7FCfpJzmWWLhOP0V9cN53KASTjhJSdUqRKXBe+T3v/+9tb3S6ZPf/va31o5Cn9B3YCfQ12AHYdPjhOz0CQsTXLr6UodI+fSr2KPf+c537L0wwOX0CbYu71zeUc8991zez48GwTbGDsI2pm/26hP6WzcJRf/Oe9/LRRddZK+Pssexmz4a/YhNT//P4Ngbb7xhHnnkEWtjcA7sIS/0+dil6BMmQU488UT7bsKWYOAO7cn7gSg4HCsfoAmwneNA2Xj1Ce8np08uuOACe21oO6dPsF+pR96R3uwFXtgXB2bu1+kTPlM+lD/tHLsKu56Bd+oiX+Aoy8Zx2bh2ypyBbGxR3u/UO23BG+3Hz9lnn231CXWE3Yxd8Y1vfMO2E+wKbFvKA12Kzckzyd8d6HCnT8g2RXly/06f3Hzzzfa7hWr7mcJ9YQ8zCUK50A6YsHb6hKAc2M/pyi0Knne0Iu0ffYIDPn0BZckxmaxw+oT+069PODdlhb5Am3DNzqneq084PtqX55J2x7GcPsHWpw0Q3Yn7KrQ+4fmnDTFO9dOf/tReE2McLvgA+gR9QZvEPvfa+lw3GpYIipQ/4wJkJ0GnUW5M8DABQNtEn/DcuWAUceju6TXz2rvNnLZum2ly3Y03Mx99MM0sXLDA/t0fwXloXbXZc6ftzQU/ZnIpP/qEcQ70Cc8qdjTPg4uG19LSkoo0WgkwXkafAuj/TPQJfbELVOHeU+g22gUb7w+eWdoG+gTdTttGzzMR7fQJQWZof16wTyh7tDTPzpZbbmn7M54nJsunTZtm2xfvSqdPzjnnHFs/Qgw23sAqhaZUJh6jCAo8FgTvCuw0/xxK0P5oEDeHgkZJN4fC+zkMF2wlDnH3jdovKPCOA3vdq1GwwdPNoQSVqT+QS9zrpOyxj+POoXAd3rE45je8GoV+Ot0cij+qJ/Mwbg4FeynOHEquzxx23rvvvpvVHArX6tUojG+nm0PxX29zc7PdL9s5lGzrG6hnN4eC3cO1p5tDCWpzjBESpZY6op1iW6SbQ/GO+1PHXo2CfZJuDiWXth8UKCsTsP/dHArPJGMk6eZQ0vUf/uugHKmLOHMoLLTxftc7h0IdYmsFzaEQoMzNoaAHeB7TzaEE3Ufc8ozq/9z4E9lFmUNBo7CoJ+4cCvVBv4Kuwg5lbCDdHMpJJ50UWc9okTmtnWbOok7T6VsUt/4mm5tPPvxggEYZWl9jRjXVmeGNtaaqaojZZacdA+dQwt7d/sBjYXMoPKvY0m4Oxa9R4r53s30X+r+Xy7MU9szSpzqNwvs+7hwE/afTKAQuo4xcmTqNkskcChk2vdfFWJvTKHHnUFhTkGlAMFcuLvCYEOWkUcpBn4A0ykCkUaRRpFGkUYJIokZh6+juNfPbu8yCtm5z/Jnnmeeef8HM/PILc+Vvf2WefXKy2WPfA80KK0+wfc3n0z8xLz/3tHnkgXvNLy650my33bampaHWDGuoMeue9i3z5otP51WjRIE+om1613n579s5Sng1CnUZVj7SKNIolaJR5DQjxCDQ2NhoPbVZ3JIOjAI8+3hRstAAZ5ugiDoYOyy8LhcwnBETCClSY+McxOaF1GQstHDR9AsBIoAtDAZfeZH7F17T4WOkYCRjWPMidC/DIIIm6Vm8hbMAiwkpA8QTm4OXGi8xjMR8Oc1gmMZdKIJQxOPewUsYIYTwwwudAQE2ryGDOOAFHOY0AxhYLP6hzWIs4+jAVgwwyrl+yhQx5KIJOHC4oA522GGH0GNgmDIIT71hgFM3ZGSIW/cILuodw4V6Z1G6P1UkxiqLlnLx4M4nDHwwAMH9Isa5dy8s5CKbicuMlQ2Ia7fQP8zgYsELotgP/SmDTQxikGHDC/3sD37wgwEDGAhYnE7YvJBVhUWBmaZczAbEHc52X//61634ZtAlKMpMEPRHlDf3xYI1FgdF9f/+aAGZQp+ByHcEOc0QGcINtgCLobz9R6aw8BExwACLy6zj2OOrB5pzzr94ie/UVA0xy41sMiObasvCWcbBgjLnAOSi28SBQQcGvXiHMRCEMyP99IgRI2w/Qz9N/5Tu/cFAJgs//eLQDTAymMsCSLYwELo8nwyKCCHKV58QhZNnOY4+4X2JHYU+8duEDgYtGWQpF8iGiP2FXYGNdvnll9vNC7oEZ4MDDzywYNfxzjvv2C0MFrBjOwbpEwZasbEZNEOjuFTacfUJA7M4T/EeDtJovCsoI2zpfDnNMFi/3XbbxdqXRfd+fULbY3IGWxKHdjavPiGyF5ojzGnG2TZefYL+dtF1Cg3Xy/XjYIHDgt/RmefNLRCP0idEBGNylfc1dePs37j6hDEGtDf1zmAqm98eZBDXZRccbAjAgT7hfplEYFzBr0+YDGKiL1tcVlx/ZlyvPkEHuujHXph8Qu+hT3CE8kd5cvoRZzcm1tAnTKh5s906fYId7nduLpQ+Qe8xUYg+YfEZWxzojyhvJh7pnxhLYFFbGHEcTLCPid48t63bLGjvMX1mscP8hptsYe6747bUzxtsuoWprRpiF6ONaKwxdTVVxoxaOVCfMCmQDSeffLItHxfR0AvPcFTk63KD6GjZ6BPGz9AovMNwXKH/pZ+m3FkQwjNF/5Tu/cF4jMvoHKZP0NJs6fRJLpOKQojBR3MomkMBzaFoDkVzKIvRHErx51B6+/rM/LYuM3tRp1nYEa4l/Bplr113NBPGNg/aHAp1HTRWV65oDkUIUSpIo0ijgDSKNIo0SjI0Cg7z73yxwDrPO0aNGWsuv/5v5offOcFMm/KOeenZp+wWxIpjhpoVRw8d8Lskr/OSRulHGqU0kNOMyIlsJ5vjpm5yEDEwLKJ8KeFNA8jnqPI5/PDDbXQBb6TasP1ZRMQiGgxZPJrxyqRzY6HBFltsYQUH/+MBGJVCkL/FrbO4+0bt5/19UHmwsIBUeizM4v7w+sULm5cw3vyIKG9qzaBjeO836nr918niGYw2IhOxOIl6wBOTFzcvIq6BxStcB8Zz0LG5Vl72eDTzIsM4J00jEQUwIrg3zkP0YIzioGNwj3h00hYY7MUY5Lh43/JSYuGad7FboZ65IDiX/3zcD20QwcH1EiEBb1R+z/Vy3YiAqOvl+SZqNwvkiA6MhzEL4Kh/FlJQbkQMZTFivurbO5BBvSNeWAiCMOB68DqnLljQRB2me445DsYsCxIxhPgfsYE4QjxxPIQFXrsYKv5j4K1NlAXqnYVIlCNtBvFORiL6Bwz+qOuIWxbefimoTjOZYMDLGaOabEUcB/HBIjHEFc+N99r4u78vD7sOFgtSHjzvLLChDngWePYw0GkP9HM8l0HXT3tC4CKWGTCgPHkOvdfiUlASFYQFsC7FojsH98EixaD7yKY80/V/QPnRB1GuLiIz941Ry8Jdnn+i9fJ8+b9P2V544YW2XBjwYTCH+8GAxpBG3BCNgbLBAz2XvoP3i/e8eLoHHQ/R5VLRIrii3t3u9/wfdCz6RSbEeD888+yzZuaMmaazsyPyOpcZ3mCG1VcFpuTN9l3o/14uz1LYM0sfArRF6iuT4zK4gJii3dCm+ZmBOjeJyDNFtByiBTDQRdnQR9Hm6aMYtHQDbP7zssiNRWZE7KCP45i0sYULF9poAfRZiDnqmvc54ivbcmXjmQ1LERpGOUYsEKKUwRmESZs4mTSwb+Lok3KDhRirrrpqbH2STyivTPRJEESbYfA7rj4JgkhhnCtKn5RCNkRvuWHfxdUnYXj1CZNmZNkL0if5hghtLC6Pq0+ijsMx4uoTP2STyESfDDbYItxvXH2SKfRtcfVJENjfYfrEP8mciT4pNLSTOPokqH+nTnjWWAgQR5+EwQQUWWVwlunyRW/2RnF28Lzstct2ZsWxTUtMgtPPOVs7aEIoE1jo4PQJ7wfXP1ci1D/QFukzMoH3FM8M7YZnCH2CfnTZrjLRJ36YPMJ5KUqfMOHEBD76hP6gmGNoQojCwPvWP4cSBvYMGQlYxEIfxEIhp1F4d7Moiv/LDewwNAr2Ana4V6PwN/pOopAWAsoLx3zsFbSF0yjYmIzJEPmSvh8dFaVReIcyB3DHHXfYRVhejUKWRWx2IgeHvQOwRzkX0bidzc/iEd4xvBsYCysljUK5YVOxsIfrJdKt01RcL9ftoopGETWHQrlh61A2+YZ3M/XOfAC2lFej0N7QWXE0CsfBVmd+izFj/kejEGmYv6GziJqKbcjzG3T/vPepdzQK5eje9yyYYwyj1DQK90uZEYQAR2FsEewcbCIWD+Vi27MYhnbA8+40Cs8Ci1ywe2gP9HM8K0EwDoD2ZC4GjUJ5BmkUjoUzP+WORmHxFM839jz3wXhxsTUK9Y9eZswEjcJ9o1HQUzz/aJSg/p064TkkqAt1Q9l5NQqLBInMS5AKysfBnEVbV4/NKDO3rcv09EavT+D52HabrQf8vMN2wQEn0CUuSEc+NArjP7wn6QMrWaNQ/0BbjNKTQdA+3BwK5cV7C01LAA4WXKJR6GvRKOzjNAptHo3CWFBYXdGXM/7Ae5I+Do3CM+M0Cs8KfZabQ+H5EkKUP9Io0iggjSKNIo1SWRqFbPcL2rvNJ3NaU79b2N6f+d7PMsuvYK65/T7z4L13mUf/8y/z3luvm3lz55jqmhqznEejbB0wD5utRskGb//F8xLWn0mjZIc0SnkzpK8cPBFE1vAwYXTjuek8yfJJIScfvZ5wmuSsfFTfyUL1nRxU18kin/VN5LYvF3Sa9gAhCkPMEDOyqcaMGVpraqoXp8QU5VXfpGfGaQZblUHKTMg1uoYYHMpZn4Dea8lC9Z0cVNfJQvVdWhC9eWFHj5nb2mUWdkY7wENddZUZ0VRjRjTUxNIBqu9kkWt9S58kk3LWKOUafExkjuo6Wai+k4XquzTo7u0z89pw4O8yHSFzEl4aatAltWZ4Q42prhrowB+F6js55KuunUbhWJnaqgo8Vr6Uq0ZRH5csVN/JQvWdLJJQ39j8Czq6zcL2Hus0H/fuqoYMMUPrq01LfbVprq8xNRlogVIkCXUtFiON0o8yzQghhBBCiJKhvavHOstELZgb1lBjxjXXmboaOcsIIYQQQghRjnR09diMMnPbu9NGb2YiqqWh2oxsrDWNtVVLZJURQoik4p/U1KR25aK6Thaq72Sh+h48+pwDf3v/Yrm+NEvlqocMMcMaa8yIxhrrNJONLlF9J4d81zXfZ1GbEKWM+rhkofpOFqrvZFGJ9U3wrtbOHmv/s3WGZLoPora6yjT/z1Gmqa7azldUCpVY1yIcaZR+5DQjhBBCCCEGna6eXvPlwk4bzS0MBOhSzXWmsW6x57sQQgghhBCiPOjt7TPz24ne3G1au9IPpDfUVpkRjZlHbxZCCCGEEEKIqKjS8/6fvfsAk6ss+z/+m7K7s72XZDc9m15oCSEBQhFC76CvqMBfeRGpKigoICIKCCqCgu0VFERFQHroIfQSAum9bXazm+19Z2an/K9zQkI2M6k7bed8P9c1V3bmOXvOk9w72XPmPPd99/Sa1yVGh5m9yUx1mNclRiJ/Mi2QAwAAAJKZLxDYkSRjPIzEmX2VnuLYkSiTdoAJ8wASE0kzAAAAiBujqnRjl1fN3b7dZrEbF6FGZxnjopSLUQAAAGDgMM7x3b0Bc0Fam9u31xtTO1dvNm5MAQAAAEAk7kN07EcCv1FN2rgmMRL46XgPAAAADIx7EV5/UB2ebZ0ke3qNbpL7xkiON5LljTVJxsO4HgCQnEiaAQAAQMwZi+VaunvV2NVr3rAKx2m3qTgr1bw5RbIMAAAAMHAYFZvbe4xFab1y+wJ73d7oKmmc9+ekOWWnqwwAAACACCya6/k8gb99HxL4jYVyRiVp47rEuD7hngQAAACQ+Of83V6/Oj7vJuP17/1exHZGYsz2JJnMFAf3JQCLIGkGAAAAMb1oNW5Q1Xf2qnc3F6zGzanCzBQVZqRwYQoAAAAMsBtU5qI0j3+3nSR3TpLPNbvKpJjdJQEAAACgv4z7Dm1mAr9vnxbNGR0ucz/vKuPgfgQAAACQ0HyBgLo8XyTK7C05ftdz/+2JMi6nnUR5wIJImgEAAEBMdHl82trZK3evP+y4TTblZzhVlJkiJ+1OAQAAgKRblGbcgspMcyo/3WnemOKmFAAAAID+MhbKGQvmjGsS4z7E3pbNmQn8LqeZLONKccRolgCQ3Hb9jMd4vreCKhi4iLe1EG9rScR4e3wB83y/w+NTj9e/1/P9nQv2ZqY6lO0yEmWc5nUAEjvWGDjxttlscjgG3vU0STMAAACIKiNJpr7Ta17E7k6Oy6nirFQqTAMAAAADQHCnRWmd+7AoLcVhV57ZVcZpfg0AAAAAkbj3YFyTtLl98gf2fFViLA8yFsrlfp7AbyygAwArcjpjs1RwIC6ixIEj3tZCvK0lHvE27j8Y3WTa3b3qcPeaSTP7yrj/kO1yKseVoiyXk/P+/cB721ocBxhvu33bPb6UlBTl5+droCFpBgAAAFGrON3Q2avWnt7dbpOR4lBJdqoyUrn4AgAAABKd1xcwF6UZ5/i+vS1Ks9mUneYwE2WMam50lQEAAADQX0ZyjJEkY3S77NlNV/udpX6ewG8ky5DADwAAACQmXyCgTrdP7W6fmSizt6T4naWnOJSTnmImyrhS7NyLALBbJM0gIdH6y1qIt7UQb+sg1taNt3HxanSWae7qVWA3MTc6yhjJMtlpnI4ORLTtBAAAsA7jnL7DbSTK+NTl3fuiNONc31yU5nLKyaI0AAAAAP1kfPbc7d3WVabD49/tfYftjGrSRnd747oknUVzAAAAQMJ2jjQSZIxEmW6PX8G99rT/4nzf6CJpdpRJTyE5HsA+Y5Ui+oW2nYgG4m0txNs6iHXyM25UNXd5Vd/u3m3VaafdrtKcNBVkpnKjKolYtW0nAABAst+wMhalGVWc91bVbfuitPx0J5XcACBGKFhjHcTaWoi3tRDvvXez39bp0md+vTdGR3sjUcaoMG1PwEsS4m0dFB5DIvP5fFHZ764/p36/n//jkhjxthbibS3RjLexlx6vXx0eo5uMX959OMffea1RtsthdrfPSHV+cb4fDMjn2/f94Au8t60lUvEOBALmo7e3Vy0tLfv1vYmwLoykGYvgPzMAABDN84y2nl7Vtbvl3c3FqLGIrjg7TUVZaXIk4t0qxAXnqNZF7AEASExGcoxR1c1YlNbTu/euMukp2xelOTnPBzCgResaJVaFxwwspLQOYm0txNtaiPe24lztPb1q6faaHS/3xqgqnZ+RovyMVKWlDKx/P+JtHQcaa2Nhm1F8jMJj1jTQ7qPsOt+BNn/sH+JtLcTbWiIdb+OeQ6fHp06P33z492N/RoGu7DSnstIccjkp1hVpvLetJRrx9vv3fh8x0ZA0Y5GLbyOzy/gh5xcHAACIJOPitq6tR93e8CfCNtlUkJmikhwXLVERtvoAFdKse31iPLZ3GwIAAPFjfGbY02tUcO5Vu9tvLlLbEyM5JtflNJNlXANsURqwp/eBcX5q4BzVWrhGAQAgca5JWrq8au3x7rXTpW17p8uMVGW7nKyBQFJfo3APxXpY5wUAGOg8vsCORBljLdG+Ls03ivEa3SONbjJGogxrjIDEEvj8M3TDQD1HJWkmyblcLrnd7m0fNPX0KCMjI6L7p20nIoF4Wwvxtg5infwXufUdXrNt6u4YN61KslKV6rTTEjXJROL93dnZaX7P9g/8B2LbTuw/43pk+/VJV1eXsrOz4z0lAAAsy+cPqO3zrjLG+f3eZKYaXWVSlO1ymDevgGRinKNuZ1RxhnVwjQIAQHyvSVq7e9Xc7ZV7HzpdGkn7BRmpystIkZMFdEhyxvoeg3H/JD09Pd7TQRKt8wIAINKM31ndvdsSZTrcfnn9+742yGm37egmY9yDsNPRHkhY3d3dO75OTU3VQETSTJLLyclRW1ub+fX2hYjGBXWiZ3nR+staiLe1EG/rINbJqdcfUENnr9p6endbDSIj1UiWSTErQCA5Hej729jOSLo2PuQ3FiPtfANgILbtxP7Ly8szr0uM6xHjOsWIu3HDx1iYmOjXKAAAJANzUbjXbybKGDevgnup8ZZidJVJTzG7ypjJ8EASvieMxUjbP0M3sCDNWqJ9jRKtwmMGCtZYB7G2FuJtLVaNt1Fx2uh0uS/XJA6jq0y60ekyRekpn1+TDNAiXVaNtxX1J9Y7X6MYX2+/d0LhMesYqOu8AADWYnSHNM7rzY4yXv9eu0XuzOW0mx0jjUQZ42t+xwGJK7ibdV4DNbGbpJkkl5mZaV48GT+wxsV0c3Oz+brdHplfNttbLUWDMcdYHAeJgXhbC/G2DmKdXLz+oJk0Y1zqhssXN6pNpzntcvps6mgJqiMOc0Riv7+3d5bZmXGuSiVn6zASpHJzc80bPsbPjdFxyHgY1ybbH/0R7d81/F6zFuJtHcTaWqwab+MMrNcflM8f1LYz+vDn9Abjt7HDbjMTZhxBm4LdUssXxaMGFKvG26oOJN67bmdURxuoFdJwYLhGwUBBrK2FeFuLVeJtfCzcGzCuSQLa/rfc01mX02aT02Ez/1SP1G48NPBZJd448FiHu0ZxOp0UHrMQ1nlhoCDe1kK8rWV38TbO6X2BoJkg4zfWfuy0CH1PC9G333MwHkZnGZtfCnRJ7cYjin8P7B3vbWuxs86LpJlkZ1wwDR06VFVVVeYF1fYf4HA/yAciVv9R8h+ytRBvayHe1kGsB3obVb8Cuzl3MJJlstMcynCl7Pge4m0tBxrvrKws8wFrGTRokHmjr6GhwXweyWuUWP7fw/9z1kK8rYNYW0uyx9usTusLqMcbkMe/97+rcbMqPcVhVm92mJ+bG+f1yVP1ONnjjf7H2zhHNaoxU9nQerhGwUBDrK2FeFtLssXb+D3a0xswH959uCYxusoY1yNGF/tkvCZJ9ngj8rHmGsWaWOeFgYh4WwvxthZ3r0/u3oA8voCZMLOvthfdNTrJpDmNwjTGq0EFA3vrNYl44b1tLQGLrvOyBen3aglGmI3WSB0dHTuqEUSC1+tVtC4Cd85E6+3tpTVxEiPe1kK8rYNYD3xNXb3a1OI2L4DDsduk8tw0Dc5zmQvsiLd1HOj726ha4HA4lJaWZj6M6mgHqqio6IC/F4nBuJYwKjgb1ynG15H4ECZa1ycGfq9ZC/G2DmJtLVaJt7EgbWuHVw2dXrO7zJ4Y5/SFmSkqzU5Vjiu56itZJd7o/zWK8X1GZTRjQdqBLkbj+iQ5cI2CREWsrYV4W0uyxrvD41d9h1eNnV7t5ZLEXDy3/ZokN8muSawSb0Q21lyjYDvWeSGREW9rId7WYkS2zRNUU6dHTV1es3v9vspMtSs/I0UFGSnKSnNEdZ7oP97b1sI6r21ImkG/NDY2RmW/xpvMqJixXUtLC+1mkxjxthbibR3EeuD6rLpDv51fpSW1nbtdWHfm5BJdNrNcRVmp5mvE21oSId6JcDEF61yfJMrPPWKHeFsHsbaWZI53j9ev11Y36+kl9VpUE/48fmdjSjJ09uQSnTS+UNlJujAtmeONxIs31yfYHa5REAnE2lqIt7UkU7wbu7x6YVmjnl3aoE3N7r1uP6E0U2dMLtaJ4wqTLoHfCvFG4seaaxTsDuu8EAnE21qItzX4A0E9t7RBv39ns1q6ffv0PakOmw4bmqOjR+XryJF5KstJi/o8ETm8t60lEeJdlADXKNb49AEAAAB7tbGpR/e/vVnz17bsdpujR+XpyqOGaGRRRkznBgAAAKAvoxbSiq1denpJg15e0agu7547ImSmOnTy+EKdNaVE40ozYzZPAAAAAMnJ5w/onfWtemZpg95b37rXrjK56U6dMqFIZ0wqVmUx9xgAIF4LJqPBqEK+p+dILsTbWoh38vusul2/fG2Deb9hb4wukUeOzNfs0fk6fHieMlLpKDNQ8d62FuK9DUkzAAAAFmdUgPvTezV6ZnH9bm9qTSzL1DWzh+qQITmxnh4AAACAnbS7fZq7vFHPLGnQ6obuvW5/cEW2zpxcrC+NKZArhRtYAAAAAPpnXWO3WYX6xeWNat5LFWqjc/0Rw/PMrjJGUa4UhzUX5gBAoti5wng05ebmxuQ4SAzE21qId/Koa3Przrkr9PRnW/a43fhBOTp+XImOH1+iqRV5shsn+Ug6vLetJdei8SZpBgAAwKK6vX498nGtHl1Qq57e8FWpK/LSzM4yx48pkM3GhS8AAAAQD4FgUAs3t5uJMq+vbpZ3LyWcCzKcOm1isbkwbXhBeszmCQAAACA5dXp8emVls55dWq+ltXuvQD0kL828Hjl1QrFKslNjMkcAAAAAe+fu9ev/3tmg389ba64bCmfmqEKdPKlMx40vVXke9xgAJAeSZgAAACzG5w/o6SUN+vN7NWrq7g27TV66U5ceUa5zppZQ+Q0AAACIk8ZOr55b1mAmy1S3evapgvNZU4p11Mg8OTmPBwAAANDv5P0OPbt0W/K+xxe++NZ2LqddJ4wtMJNlDirPphAXAAAAkECCwaBeXb5Vt7+wQlXN4bvYjy7J0i2nTdDRY4pjPj8AiDaSZgAAACx0ATx/bYvuf3uzNjW7w26T5rTrwsPK9I1pg5SVxqkiAAAAEGu+QFDvbWjV04vr9e76Vu2lqYwG5aTqzMklOm1ikcpy0mI1TQAAAABJqq7do+eXNeq5pQ2qadtz8r5hyuAsM1HmhLGFykx1xGSOAIAD09LSEpX92u125ebm7nje1tamQGDPyZYYuIi3tRDv5LC+sVt3v75BH2xsCzuelebQt2cN0VcOG6yigvwdrxPv5MV721oSId75+V/83xIvrIQEAACwgMVbOvTb+VVaVNO526rUp08q1mUzK1SSnRrz+QEAAABWV93qNjvKGAvTGrvCd4Tczmm36djKfDNZZvqwHNmp4AwAAACgH4wuMkbRLaOrzIcb27SX3H0VZqaYifunTyzW8ML0GM0SANBffr8/JscxFmHG6liIP+JtLcR7YOlw+/Sn92v0+Kdb5Q+EnuUbdxaM7vWXzxqigswUOXa51UC8rYNYW0vAovEmaQb94nA4opbVtqfnSC7E21qIt3UQ68SwqblH983fpDdWN+92m6NG5evq2UM1ujjzgI9DvK2FeAMAAERuYdq8Nc1msszHVe173X5kYbrOnFysUycUKS8jJSZzBAAAAJC8Vm7tMhNlXlrRqHb3nhfMOOw2HT0qT2dMKtYRI/LMZH4AAAAAicdIkDHO8x94e7Naeny77Rh5/XHDNb7swNcKAcBAQtIMBkS7pJ3bQiH5EW9rId7WQaxjq6HDo/teX6PHPqoKWy3CMKUiVzecPE4zRxVF/PjE21qIN5I9qd9Aspi1EG/rINbWkkjxXl3fpacX1+uFZQ1qd4e/YbVdeopdc8YX6awppeZNLBtdZQZcvBF9xBsAAGDftfb06qUVTeYiutX13fuUvH/G5GKdMr7IrD4NAAAAIHF9VtOhe97YqJVbw5/rF2el6JrZQzVnXCH3GwBYCkkzAAAASaTb69Nf3t6gP85fpy5v+KpwQwrSdf2ccTpt8iDZqQQHIInEKqnfQLKYtRBv6yDW1hLreHe4e/Xcolr9++MqLapu2+v2Bw3J01emDdFpUwcrK42PcfuL97e1EG8AAIC+jOJaH21qMxNl3lzbol5/+GJb22WmOjRnfKHZVWZiWSaL6QAAAIAEV9/h1X1vVZkJ8uGkOGz62mGDdMnhg5WRGr1ilACQqLjbCgAAkAR8/oD+80m1fv3qarPLTDh5GSm66rhKfW3GUKU5uQAGAAAAoi0YDGphVYv+9dFmPb+4Vj294RPbdz5nP/vgcn152hCNK8uJ2TwBANFBN0xEArG2FuJtLbGI9+YWt55dUq/nltZra4d3r9tPG5qjM6eU6rgxBUpP4T5CJPH+tg5iDQAAYsnjC+gfC2r10Idb1NMbCLvNMaPzde0xQ1WR54r5/AAgUZA0g35paWmJyn6NDw12rgbY1tamQCD8L3QMfMTbWoi3dRDr2C3Ce2tdi3775iZtaOoJu02a066vmtUiypXtcqq7o13hm7AeOOJtLYkQ71h2FAEAANhfTZ0e/ffTGv3r481aW9+51+1njS7Ul6cN1YkTSuViYRoAJA26YSIaiLW1EG9riVS8jY70c5fU6T+fbNYH65v3uv3gXJfOO7RC5x06REMLMyIyB+wd72/rINYAACBa64Xmr23Rb96sUk1b+OK6Iwpc+v5xwzVjOOcjAEDSDPrF799zdcxIMRZhxupYiD/ibS3E2zqIdeQtre3UffOrtLC6I+y4TdJpE4t02awKleWkma/xuxvRQLyR7En9iZIshtgh3tZBrK0l2vEOBIP6cGOb/rtoq+ataZYvENzj9sVZqTpzconOnFKyo7pbT2e7wqfCY3/x/raWeMebpH4AAJAIC+Y+3dyq/yzYrOcW1arT49vj9qlOu+ZMLNP5h1Zo1ugiOezGHQUAAAAAA8H6xm79at4mfbipPex4VppDl82s0PkHlcjpoOsdABhImgEAABhgNre49fu3N+u11buvEDdzeK6umj1UlcVUhQNgHbFM3iJZzFqIt3UQa2uJVLzr2j16bmmDnl3aoNp27x63ddikI0fl66zJxTpiRJ6cny9M4+cu+nh/WwvxBgAAVtHQYXS5rNbjC6r3qcvlpPIcXXDYEJ0xdbDyMlJjMkcAAAAAkdHh9ulP79Xo8U/r5A9Tt8u443DWlGJ958ghys9IiccUASBhkTQDAAAwQLR09+ov79foiUX18u+mavXYkgxdPXuoDh9Ga1UAAAAgWnr9Ab29rlVPL6nX+xvatOeeMtKQvDSzq4zRCbIoi4VpAGAVdMNEJBBrayHe1nKg8TauR95d36pnltTr7bXNYRfL7SzX5dQpE4vNa5KxpZnma0FPl1o8Xf3/S2Cf8f62jkSINd0wAQBILsYaIaNwl1Fgt7UnfFfJqeVZuv644Rr3+Tk/AKAvkmYAAAASnLvXr8c+qdPfPtqiLm/4D9UH5aSalSLmjC+U3batWjUAAACAyNrY1KNnljTo+WUNatnNjant0pw2HTemQGdNLtEhFdmycZ4OAJZDN0xEA7G2FuJtLXuL94amHnOh3IvLGtXU3bvHfRlXH0cMz9UZk4t19Kh8pTrt5uv8PCUO3t/WQayRSBwOR9SSxfb0HMmFeFsL8Y6/z6rbdddrG7Rya/ik95KsVF177DCdNL6o3/chiLd1EGtrId7bkDQDAACQwJUijMV4f3i3Wg2d4W+A5bgc+n8zynX+QaVK+/ymFwAAAIDI6fH69frqZj29pEGf1XTsdfsxxRk6a0qxeYMqx8XHrwAAAAAOXKfHp9dWNZvJMou3dO51+/LcNDNR5tQJRSrLSYvJHAEAA0esuhDt3G0JyY94Wwvxjp3ath7dOXelnvlsS9hxIzH+f48aqcuPGaXMtOjciyDe1kGsrSXXovHmri0AAECCCQaDendDm+6bX6X1TT1ht0l12PTlQ8p0yeGDWYgHAAAAROGcfOXWbj29pF4vrWhSl3fPFWEzUx06aXyh2VVmXGkGXWUAAAAA9Ot6ZGF1h55b2mAmzLh94TvQb2cU1PrSmAIzWebgimy60QMAAAADmLvXr7+8vV6/n7dOPb3h702cOKFUN506QUMLM2I+PwAYqFhhCQAAkECW13XqvvmbtWBze9hx41bXyROKdPmsCg3KpUocAAAAEEntbp9eWtFodpVZXd+91+0PKs/WWZOLdfyYAqWnOmIyRwAAAADJaWu7R88s2Womy1S3eva6/eRBWWaizAljC5QVpcrSAAAAAGKXPP/K8q26/YXl2twcvsBuZUmWfnL6RB1ZWRTz+QHAQMcnJwAAAAmgutWtB96p1isrm3a7zeHDcnTV0UM1rjQzpnMDAAAArFDF2egq88bqZnl8wT1un5/u1GmTinXmpGINL0yP2TwBAAAAJB+Pz6/Xltfr8QWb9faaBgX2fDmiwowUnTKxSGdMKtYIrkcAAPuppaUlKvu12+3Kzc3d8bytrU2BwJ47pWHgIt7WQrxjY11jt+5+bYM+3NQWdjwrzaHLjxyi8w8uU4rDzv/n6DdibS2JEO/8/HzFG0kzAAAAcdTa06v/+2CL/vPpVvl2czdsTHGGrp49RDOG58V8fgAAAECyauj06tnFW/XMknpt3ksVZ6Pj4xEjcnXW5BIdNSrPvCkFAAAAAAfKHwjqsU+26KEPP1ZLd+8et3XYpCNH5ZuJMrNG5MrJ9QgA4AD5/f6YHMdYhBmrYyH+iLe1EO/I6nD79Kf3avT4p3XyB8PfmzhrSom+c2SF8jNSjDJgMf33J97WQaytJWDReJM0AwAAEAfu3oD+tbBOD3+0RZ2e8CehpdmpuvzICp08vkgOu3EpDAAAAKA/fP6A5q9u0L8+3qw3VmwNexNqZ2XZqTpzcrFOn1Ssspy0WE0TAAAAQBJb39itn760Xsvquva43YgCl86YXKKTJxSqKDM1ZvMDAAAAEP0k+meWNOiBdzartccXdpup5Vm6/rjhGleaGfP5AUAyImkGAAAgxhe+c5c36sF3q7W1w7vbtqqXHD5YXz64TK4UKsYBAAAAkfDG6ib9et6nqmnt2eN2TrtNx4zO11lTijVtaC4J7AAAAAAilsT/949r9ef3a9S7mwz+zFS7ThxXaHaVmTQoSzYb1yMAAABAMvmsukN3v7FRq+q7w46XZKXo6tlDNWdcIdcDABBBJM0AAADEQDAY1Acb23TfW5u1piH8hW+Kw6YLDirVJTMGKy/daKsKAAAAoL/aeny6542Nmruiaa9VnM+cUqJTJxQpP4PzcQAAAACRs7q+y+wus7uFcYePKNCpEwp07Kg8pac6Yj4/AAAAANG1tcOj++Zv1ssrw9+rSHXY9LVpg3TJ9MFcEwBAFJA0AwAAEGUrt3bpvreq9NGm9t1uc9L4Ql0+q0Llea6Yzg0AAABIZm+va9Htr2xQU1dv2HGX064TxhXqrMnFmjKYKs4AAAAAIqvXH9BfP9iiv364xexEv6uRRZm667wpmja8QC0tLfL7/XGZJwAAAIDo8PgCenRBrR76YIvcvkDYbY6tzNc1s4eqgjVDABA1JM0AAABESW2bRw+8s3mPFa0PG5JjXviOL8uM6dwAAACAZNbh9unX8zbpuWWNYcenVOTqzIlFOn5MnrLS+IgUAAAAQOQtq+3UbS+v17rGnpAxu036xvTBuuG0KXKlUEUaAAAASDbBYFBvrm3RvW9WqabNE3abkYXp+v5xw3T4sNyYzw8ArIY7wgAAABHW1uPTQx/W6N+fblWvP7RynGFUUbquPnqoZo7IpZo1AAAAEEHvb2g1u8ts7fCGjOW4nPrpmRN11kHlam1tpYozAAAAgKhUkv7Te9V65ONahWkuYy6Mu+WkkZpakUvCDAAAAJCE1jV261dvbNJHVe1hx7PTHLpsVoXOm1oip8Me8/kBgBWRNIN+cTii8yGe3W7f43MkF+JtLcTbOqwYa+NG2L8/qdVf3q9Whyf84ruSrFRdcfRQnTqxWA6jlFySsGK8rYx4AwCARNTl9eveNzfpv4sbwo4fOTJPv/rKoSrNccV8bgAAAACsYVFNh9ldZlOzO2TMYZMuPnywvjmjXKlOPlMFAAAAkk2722cm0P/n060KV2PXWCV01pQSfefICuVnpMRjigBgWSTNoF/y8/NjcpzcXNrPWQnxthbibR3JHOtAIKhnFtXonpdXq6a1J+w22WlOXX7sKF0yc4TSU5O/clwyxxuhiDcAAIi3j6vadNtL61XbHtpdJjPVoe8fO0xnTS1VAQkzAAAAAKLA3evX79+p1r8+qVO4/vNjSjJ0y5yRGleaGYfZAQAAAIgmfyCop5fU64F3qtXW4wu7zUHl2bruuGFcEwBAnJA0AwAA0A/vrGnUL15coeW14Vuqpjhs+tqMYbrquEoVZKbGfH4AAABAMuv2+nX/W5v1n8+2hh0/fFiObp4zUmU5abLZkqfTIwAAAIDEsaCqXbe/sl7VrZ6QMafdpm8dUa6Lpw+S00F3GQAAACDZfFrdrrvf2KTV9d1hx0uyUnTNMcN04tgC7lMAQByRNAMAAHAAlm9p150vrdRbqxt2u81pUwbp+jljNayQKhEAAABANG5E3Tp3vWraQhempafYde0xQ3XOlBJuQgEAAACIii6vX797q0r/+aw+7PiEskyzu8zo4oyYzw0AAABAdNW1e3TfW5v1ysqmsOOpDpu+Pm2QLp4+WOmpjpjPDwDQF0kz6JeWlpao7Ndutys3N3fH87a2NgUCgagcC/FHvK2FeFtHssa6tt2jB96u0gtLGxTczTaHDsnRtccM06TB2ZK8amnxKtkla7yRuPHOz8+P6fEAAEDicPf69ft3qvWvT+rCnpMfOiTbXJhWnueKw+wAAAAAWMEHG1t1+8sbVNfhDbs47rJZFbrwsEFmpxkAAAAAycPdG9CjC2r18Idb5PaFXydxbGW+rp09lPsUAJBASJpBv/j9/pgcx1iEGatjIf6It7UQb+sY6LHucPv00Idb9K+FdfL6w6fLjCxM11VHD9GRI/PMatYD+e9r9Xhj/xBvAAAQK4u3dJjdZapa3CFjLqfdPB8//+BS2ekuAwAAACBK9wp+82aVnl0avgv9lMFZuuWkkRpekB7zuQEAAACInmAwqHlrWnTvm1Xa0u7Z7bqh644bpunDvihCCgBIDCTNAAAA7IHXF9B/Ptuqv36wRW1uX9htijJT9O1ZFTptUjFV4wAAAIAo8PgC+uO71Wb1tkCYHPap5Vm69aRRGpJP1TYAQOJwOBxR7QK7p+dIHsTaWoh3YntrbbNuf3m9GjpDu8u4Uowk/mH68iFlcuzjfQLibS3E2zqINax4jcLPvbUQb2sh3tusbejS3a9v1Eeb2sKOZ6c5dPlRQ3X+wWUDet0Q8bYOYm0txHsbkmYAAADCCASDemVlkx54u3q3FSIyU+36xvTB+uohZUpPjd4iCAAAAMDKltV26ta567ShObS7TKrDpu8cNUT/sx8L0wAAiJX8/PyYHSs3lwqmVkGsrYV4J4aWLq9ue365/vtpTdjxGSMLdNe5UzSsMLNfxyHe1kK8rYNYw4rXKPzcWwvxtharxbutu1e/eW21Hvlgk/xhKnoZTe//Z/pQff+EMSrMSlOysVq8rYxYW0uuReNN0gwAAMAuPq5q033zN2vF1q6w48ZivHOnluhbM8pVkJkS8/kBAAAAVun6+JcPavS3D7fIH6a7zMSyTP305FEaXpgej+kBAAAAsICXltbqpqeXqbEztLhWZqpDN54yXl+dPlR2kvgBAACApGEkyPzr4yrd8/IqtXT3ht1m2vB8/eT0iZpUbs3F5wAw0JA0AwAA8Lm1Dd26760qvbchfDtVw/FjCnTFUUM0NN8V07kBAAAAVrJya5fZXWZtY0/IWIrDpstmVuhr0wbJycI0AAAAAFFgJMn85JllemFJbdjxoyqLdOe5U1SeRxI/AAAAkEw+2tCsW59dpuW17WHHB+W6zOT506cMks1oNQMAGBBImgEAAJa3tcOjP7xbo+eXNihMAWvTQeXZumb2EE0enB3j2QEAAADW4fMH9NcPt+j/PthiVnLb1bjSDN160iiNLs6Iy/wAANgfLS0tUdu33W5Xbu4XlUzb2toUCASidjzED7G2FuIdf8FgUC+taNQvX9ug1h5fyHhWmkPXHTdcZ0wukS3oVkuL+4CPRbythXhbRyLEOj8/P6bHw8ARrWuURPi5R+wQb2uxUrzr2j36zbyNemVlU9jxVIdNFx1erksOL1d6qkOtra1KNlaKt9URa2tJhHjnJ8A1CkkzAADAsjo9Pv3to1o99kmtPL7w6TLDCly66ughmj0qnwoRAAAAQJQ7P/5k7jqtqu8OGXPYbbr0iHJdPH2QnA57XOYHAMD+8vv9MTuWcZMzlsdD/BBrayHesdXY6dUdr23U/LXhFxQfNTJPN54wQiXZqVFZXEK8rYV4WwexRiKJ1c8iP/fWQrytJRnj7e4N6NEFtXrowy3y+MKf5x9bma9rZw9VeZ7LfJ5s/wZWijfCI9bWErBovEmaAQAAltPrD+jJRfX68/s1agtTKc5QmJGiy2ZV6IzJxXLaSZYBAAAAosUXCOqRj7foj+/WmF/vqrI4Q7eePFJjSzLjMj8AAAAAyd9d5vlljfr1vE3q8IQuGsl1OXXdccN00vhCimsBAAAASXQdMG9Ni+59s0pb2j1htxlZmG5eC0wf9kWHBgDAwETSDAAAsNQF72urmvW7tzerpi38BW96il3fmDZIFx42SBmpjpjPEQAAALCSDU09unXuOi2r6woZc9ikSw4v1zePGKwUussAAAAAiIK6do9+8coGvbexLez48WMK9IPjh6swMyXmcwMAAAAQvc73v5q3SR9XtYcdz05zmIV2zzuolEK7AJAkSJoBAACW8Mnmdv12fpWWh1mMt31B3tlTSvStmeUqykyN+fwAAAAAK/EHgnrskzo9+M5mef3BsNXbjO4yE8qy4jI/AAAAAMlfZOupxfW6b36VuryBkPGCDKeZLPOlsYVxmR8AAACAyGvr8emP71Xryc+2KsytCdk/Xzt0+awK5WWQOA8AyYSkGQAAkNTWN3br/rc36+11rbvd5tjKfF1x1BANL0iP6dwAAAAAK6pqcZvdZRZv6Qx7Q+rr0wbpf2dWKM1JdxkAAAAAkVfd6tbPX9mw26rSJ48v1PePHcYiOQAAACCJCnn9d3G9Hny32kycCeeQimx9/7hhGluSGfP5AQCij6QZAACQlBo6vfrju9V6dmmDAmGqQximDM7S1bOH6qDy7FhPDwAAALCcQDCofy/cqt+9vVkeX2gl52EFLt160khNHsz5OQAAAIDoXJM8/ulW/e6tzXKHuSYpykzRjSeM0OzR+XGZHwAAAIDIW7i5Xfe8sUmrG7rDjpdmp+qa2UN1wtgC2Wy2mM8PABAbJM0AAICk0unx6ZGPa/XogrqwC/EMQ/NduvKoIWaHGS54AQAAgNhUcr7tpfVaWN0RMmackX/10DJdfuQQuVLoLgMAAAAg8jY19+hnL2/QZzWh1ySG0ycV63vHDFW2iyUUAAAAQDKoa/fot/Or9Oqq5rDjaU6bvjFtsC6aPkiuFEfM5wcAiC0+8QEAAEnB5w/oqcX1+vN7NWrZTSvVggynLj2iQmdPKZbTwWI8AAAAIBaVnJ9aVG/emOrpDU1qr8hL009OGqmDK3LiMj8AAAAAyc0fCOqxT+r0h3eNjpfBsFWlbzpxhI4YkReX+QEAAACILHdvQI98vEUPf1S722K7x1Xm69pjhmlwblrM5wcAiA+SZgAAwIAWDAb1xpoW/f7tzapqcYfdxuW06+vTBulr0wYpM5XqEAAAAEAs1LZ59LOX1+ujqvaw4xccXKqrjhqidM7RAQAAAETBusZus+PlsrqusOPnTi3RVUcPUVYayyYAAACAZFk/dO+bm1Tb7g27zaiidF133DBNG5ob8/kBAOKLT38AAMCA9Vl1h1mxekltZ9hxu006c3KJLptZrqKs1JjPDwAAALDqjalnljToN29uUpc3tIrb4Jw03XLSCB3GTSkAAAAAUepM/7ePa/WX92vU6w/tLlOem6ab5oxgoRwAAACQJNY2dOueNzZpwebwRbxyXA5dNrNC5x5UKqexmAgAYDkkzQAAgAFnY1OP7n97s+avbdntNkePytOVRw3RyKKMmM4NAAAAsLKtHR79/OUNem9jW9jxc6aW6JrZQ+kACQAAACAqVtV3md1lVtV3h4wZS+O+ckiZvnNkBR0vAQAAgCTQ1uPTH9+r1hOfbVUgGL7Y7jlTSvTtWRXKy0iJxxQBAAmCpBkAADBgNHZ59ef3avT04nqFKQ5nmliWaS7CO2RITqynBwAAAFi6u8wLyxvNSm6dHn/IeGl2qm6eM0IzhufFZX4AAAAAkluvP6D/e3+LHvpoi/xhVssNzXfplpNG6qDy7LjMDwAAAEDkGOf8/11crwffrTYTZ8I5pCJb1x03TGNKMmM+PwBA4iFpBgAAJLxur1+PfFyrRxfUqqc3EHabirw0s7PM8WMKZLPRShUAAACIZXL7z1/ZoLfXtYYdP2NSsb537FBlpfFRJAAAAIDIW1bbqdteXq91jT1hK0t/7bBB+t+ZFXKl2OMyPwAAAACRs3Bzu1nAa3VDaHfJ7UW8rp09VF8ay/ohAMAXuFONfnE4otO22m637/E5kgvxthbibR2RiLVRGc7oKvPHdzerqas37DZ56U5dNmuIzj2oVCkOfp7ihfe2tRBvAACwvbvMyyubdPfrm9TmDq3kVpSZoptOHKEjR+XHZX4AAAAAkpvHF9Cf3qs2i26FaS6jkYXpZneZSYOy4jE9AAAAABFU1+7Rb+dX6dVVzWHH05w2fWPaYF00fZBcKdFZ1woAGLhImkG/5OfHZtFDbm5uTI6DxEC8rYV4W8f+xNpYfPfK8q2666WVWt/QFXYboyLct44cqctmj1S2KyWCM0Uk8N62FuINAID1NHf16s7XNuiNNS1hx0+eUKjrjxuuHBcfPwIAAACIvEU1HWZ3mU3N7pAxh026+PDB+uaMcqU6KfgDAAAADGTu3oAe+XiLHv6o1kycD+f4MQW6ZvZQDc5Ni/n8AAADA3etAQBAQvlkU4vueHGFFmwKv/jObpPOP3SIvnvCGJXlumI+PwAAAMDqXlvVpDtf26jWntDuMgUZTv3ohBE6prIgLnMDAAAAkNzcvX79/p1q/euTOoVpLqMxJRm6Zc5IjSvNjMPsAAAAAESKUXD39dXNZneZ2nZv2G1GFaXr+uOG6bChFPoEAOwZSTMAACAhrG/o1C9fWqWXltXtdpvjxpXohyeN09iy7JjODQAAAIDU2tOrX76+Sa+sbAo7fsLYAv3w+OHKy6ATJAAAAIDIW1DVrttfWa/qVk/ImNNu07eOKNfF0wfJ6aC7DAAAADCQrW3o1j1vbNKCze1hx3NcDn17VoXOmVpqXgsAALA3JM2gX1pawncB6C+73a7c3C+yf9va2hQIhG+th4GPeFsL8baOfY11U5dXf3q3Wk9+Vid/uLJwkiaUZeraY4Zr2jBjf76o/f7BgeO9bS2JEO/8/PyYHg8AAKubv7ZFv3hlg5q6e0PGctOduvFLw/WlsYVxmRsAAACA5Nbl9ev++VV6YlH9bu8hGN1lRhdnxHxuAAAMJA6HI2r3Dvf0HMmFeFtLrOPd1tOrB9/ZrP98WqdAmDVERn7MuQeV6jtHDVVeOgW8Io33t3UQa2sh3tuQNIN+8fv9MTmOsQgzVsdC/BFvayHe1o11j9evf3xSp79/tEXdveEX25fnpuk7Rw0xK1bbbTZ+VgYQ3tvWQrwBAEhe7W6fWc3txeWNYcePrczXDV8aocJMbk4BAAAAiLwPNrbq9pc3qK7DGzKW6rDpslkVuvCwQVSXBgAggYrS7Vx8D8mPeFtLtOLtDwT12EdV+tUrq9QapniX4fARBfrJ6RM1YXBOVOaAULy/rYNYW0uuReNN0gwAAIgpXyCo55Y26I/vVquxK/yFbq7LqW8eMVjnTS1VqtOamc0AAABAvL27vlW3v7JeDZ2h5+05Lod+cPxwzRlXKJuNxWkAAAAAIqvD7dNv3qzSs0sbwo5PGZylW04aqeEF6TGfGwAAAIDI+WB9k259dplW1nWEHR+c69KPTh2vUycP4n4EAOCAkTQDAABiIhgM6q11Lbp/fpU2NLvDbpPmtOl/DinTRdMHK9vFaQoADFTNzc16//339emnn6qmpkatra3KysrS2LFjdeaZZ6qysjLeUwQA7EGnZ9vitGeWhF+cdtTIPP34xBEqykqN+dwAAAAAJL+317XoF69uCJvA73LadeXRQ3T+QaVy0F0GAAAAGLBqWnv0ixdX6IXFtWHH05x2fXv2KPORnuqI+fwAAMmF1agAACDqPq1q0c+eW6aFm9vDjhu3tU6bWKTLZlWoLCct5vMDsJ/8vVLtIqlmoZESJ406O94zQoKZO3eunnnmGZWWlmrq1KnKyclRbW2tPv74Y/NxzTXXaObMmfGeJgAgjA83tem2l9Zra4c3ZCwrzaHrjh2mUycWUc0NAAAAQMS19vTqV/M2ae7yprDjhw7J1s1zRqoizxXzuQEAkAxaWlqisl+73a7c3Nwdz9va2hQIBKJyLMQf8baWaMTb3evX3z7cooc/rJHbF35fXxpbqO8eO8zsMuPuape7q1+HxD7i/W0dxNpaEiHe+fn5ijeSZgAAQNRsaurSL19apReWhK8KYZg5PFdXzR6qyuKMmM4NwD4KBuRoWS9n/WI5ty6Ws36JnI3LJf/nC2nzhpE0gxCjR4/WrbfeqgkTJvR5fcWKFbrtttv05z//WdOmTVNKSkrc5ggA6Kvb69dv51fpyUX1YcePGJ6rm+aMUGk2Se4AAAAAIu+N1c2667WNauoO7S6TkWLX1bOH6pypJbKTwA8AwAHz+/0xOY6xCDNWx0L8EW9r6U+8g8GgXl/drHvfrFJdmMJdhtFF6bruuGE6bOi2xd38bMUX72/rINbWErBovEmaAQAAUfH04q2689UN8uymKsTYkgzzJtfhw77IYgYQZ8Gg7B3VnyfHLFbK1iVyNCyVvXcPZVtaN8nW0yyl8l7GFw4//PCwr48fP16TJk3SokWLVFVVpVGjRsV8bgCAUAuq2s3uMlvaPWEXpxnV3M6aXEx3GQAAAAAR19zVq1++vlGvrW4OOz7DSOA/cQRd6gEAAIABbG1Dt+5+Y6M+2dwRdjzH5dC3Z1XonKmlctq5FwEAiDySZgAAQES5ewO66/WNem5pQ9jxQTmp+s6RQzRnfCEV4YA4s3XVK2VHBxnjsVR29/63ZXdsXSzfkKOiMkcrMtqgrl271nysW7fOfHR0bPvwcPbs2briiiv2eV8NDQ2aO3euFi5cqKamJjmdTpWVlemII47QnDlzlJYW+8UGDoejz58AgPjp8fr1u7c369+fbg07Pm1ojm6ZM1KDclmcBgAAACCyjCrTL69s0t1vbFJbjy9kPCvNoe8dM0ynTyoigR8AAAAYoIxz/T+8W60nF21VIBg6buTHnDu1RJfNqlBeeko8pggAsAiSZgAAQMRsbnHrh8+u0eqG7pCxHJdT/2/GYJ1/UKnSnPa4zA+wMpu7Vc76JduSY8wkmaVydNX1b6cFI6XBhyiYlhOpaULSpZdeGpH9LFiwQPfff796enp2vObxeHYk4rz++uu68cYbzSSaWGlsbNSSJUuUn5+voUOHxuy4AIBQn9V06Kdz12lza2h3GZfTrmtmD9W5B5WQ6A4AAAAg4ho6vbrj1Q16a11r2PGjRubpxhNGqCQ7NeZzAwAAANB/vkBQ/11UbybMtLlDk+QNh1Rk6/rjh6uyOCPm8wMAWA9JMwAAICLmrWnWrXPXq8vrDxn72oyhuvTwMmWmsOAOiAlvl5wNy7Z1kTGTZJbI0V7Vr136s8rkK5liPgJlU5U99igpPX/bWEuL5A9976P/ioqKVF5erkWLFu3X923YsEH33nuvvF6vXC6XzjrrLE2aNMl8/u6775oJM7W1tbrjjjt05513Kj09XdHm8/nMJJ7e3l5deOGFsttJoASAeHWG/MO7m/WPBXUKU9RNB1dk6ycnjVRFnisOswMAAACQ7N1lnl/WqF/P26QOT+jnibkup647bphOGl9IdxkAAABggFpQ1a5fzdukNWEK7hrKslN17TFDdfyYAs77AQAxQ9IMAADoF58/oN+/Xa1HFtSGjGWmOnTnuVN0+tTBamlpkZ9F9UDk+T1yNq78vHuM8VgiR/Na2cIug903AVeBfKWTP0+SmazekikKZhbvGHc4HDsSZhB55513nkaNGmU+8vLyVF9fryuvvHK/9vHwww+bCTJGrG666SaNGTNmx5iRPDNo0CA9+uijZuLMc889pwsuuCBkH3//+9/NBJd9dcopp5j7DScQCOiBBx7QihUrdPzxx+voo4/er78PACAyltZ26ta567Sx2R0ylua06Yqjhugrh5TRXQYAAABAxNW1e/SLVzbovY1tYceNBXM/OH64CjNTYj43AAAAAP1X2+bRb+dX6bXVzWHHjfsQ35g2WBdNHyRXiiPm8wMAWBtJMwAA4IA1dHp143Nr9VlNR8jYqKJ0/fEb0zW6JCsucwOSUsBnJsRs641NPFwAAQAASURBVB6z2Owk42haLVug98B3mZIlX8kk+Uq3JciYnWSyyyUWy8ZNuASW/bF27VozOcVw7LHH9kmY2e60007TvHnzVFNTo7lz5+qcc86R09n38vDVV1+Vx+PZ5+POmDEjbNKMkTDz4IMP6p133tFRRx2lSy+99ID+XgCAA+f1BfSn96r1949rFQiTVztlcJbZXWZYQfQ7jwEAYCVm0Yko2bV7J908kxextpZkjLfRXebJRVt177xNYTvVF2Sk6IYTRuiEcUWymmSMN3aPeFsHsQYAWI2712/ef/jbR1vk8YUv7vmlMQW6ZvZQDcpNi/n8AAAwkDQDAAAOyIKqNv3o+bVq7vaFjJ08vlA3nzRag0mYAQ5cMCB76yYzMWZ7koyzcblsPveB79KRJl/xhB0dZIxEGX/eCMnGDZtk8tFHH+342kiaCce4STd79mw99thj6urq0rJlyzR16tQ+2zzyyCP9nsv2DjNvvfWWZs2apSuuuIIbhAAQYyvquvSTueu0vqknZCzVYdO3Z1XowsMGyWEnYRYAgEjLz49dl9bc3NyYHQvxRaytZaDHu6qpWzc8tVjvrWsKO37WQYN1y+kTVZCZGvO5JaKBHm/sH+JtHcQaAJCsjAT511c36943q1TX4Q27zeiidF133HAdNjQn5vMDAGBnJM0AAID9EggGzeoQD75THVKlOsVh0/ePHaZzp5bI6aSVKrDPgkHZO2u3JcZsT5KpXyq7t+PAd2l3yl84Vr1m95htHWT8BZWSIyWiU0fiWbVqlflnWlqaRo4cudvtJkyY0Od7dk2aiWTCzMyZM3XVVVeRMAMAMdTrD+ivH2zRXz+okT9MYbcJZZm69aSRGlmUEY/pAQAAAEhigUBQf39/o+56aZV6ekO7y5Rkp+nnZ0/WCRNK4zI/AAAAAP2zpqFb97yxUZ9sDr+mIdflNIt2nW2sH6JoFwAgAZA0AwAA9lm726efvLhOb69vDRkblJOqO0+v1MRBdJcB9sbW3Shn/RKl7EiSWSJ7T/hqi/siKJv8+aM+T47Z1kHGVzRecroiOm8MDNXV1eafZWVlcjh2n8A4ePDgkO+JZMLMgw8+aCbMzJgxg4QZAIix1fVduvWl9Vpd3x0yZtyc+t+Z5frG9MHcqAIAAAAQcesbOvXDJxfr440tYcfPP7RCN502QbnpFPcBAAAABprWnl498NYmPbmoPqTQrsG47WAU2r1sVoXyOOcHACQQkmYAAMA+WVHXpR8+u0Zb2j0hY7NG5Om2U0YpN51TC2BXNk+7mRRjPj5PknF01vZrn/6cIV8kyJRMka9kooKp2RGbMwYur9erjo5t1XwKCwv3uG1WVpbZjcbj8aip6cCTtsJ54oknNH/+fLlcLjM558knnwzZZvr06Ro+fPg+7W9f57enJKH+2jXphySg5Ea8rSPZYm10l3n4wxr96d1q+cLcrRpbkqnbTh2tMSWZsqJkizf2jHhbC/FGomppCb9gPBKMn/Pc3Nwdz9va2swCBkg+xNpaBnK8/YGgHv3Y6FK/WR5f6JzLslN180mjNHNkvgLuTrW44zLNhDKQ4439R7ytIxFinZ+fH9PjAQCSn88f0D8/qtI9L69Sm9sXdptDh2TruuOGq7KYDvcAgMTDylYAALBHwWBQTy2u1z1vbFKvPxhSIcKoDnHJ4YNlt1GlGlBvj5yNy+TcaiTJLDY7yTjaNvZrl/6Mkm2dY0omfZ4gM1nB9IKITRnJxe3+YrWBkbCyN8Y2RtLMzt8XCQ0NDTvm89RTT4XdpqSkZJ+TZi6//PJ92u7xxx9XrOx80xPJj3hbx0CO9eqtHfr+44u0pKYtZMzoKHPFsaN15XGjleJgIXkyxBv7j3hbC/FGovD7/TE7lrEQM5bHQ/wQa2sZKPFe19it215ar2V1XWHHjUrTVx09RFlpzgHx94mXgRJvRAbxtg5iDQAY6N5f16SfPrdMK+u2FW8MlyB/7TFDdfyYAtlYOwQASFAkzQAAgN3q8fp1x2sb9eLyxpCx/HSnfn7aaE0fxkIUWJTfK0fTKqXs3EGmeY1swQOvFhZIy/08MWbS54kyUxTIKovotJH8nWa2czr3frm3fZudvy8SrrjiCvMBAIhNNec/vbVev3l1tbz+0POQsaXZ+tUFUzWpnPN2AAAAAJGvNv23j2v1l/drQopuGcpz03TTnBGaNpTrEQAAAGAgdre//ZUNenpxfdjxNKdNF00frG9MGyRXiiPm8wMAYH+QNAMAAMLa2NyjHz67Rusae0LGppZn6Y7TKlWSnRqXuQExF/DL0bLOTIxxbk+SaVwpW+DAEw2CKRnyFU9Sb8lks3uMkSQTyBkqUXkF/ZCa+sX/yz5f+LbYO9u+zc7fl4gefPDBeE8BABLSuoZOXfefRfq0qjVkzOgKefkxo3T18ZVKc3KzCgAAAEBkrarvMrvLrKrvDhkzPuH8yiFl+s6RFUpP5XoEAAAAGGg63D5d/8waLdjcHnb8S2MKdM3soRqUmxbzuQEAcCBImgEAACFeW9Wkn728Xl3e0ErVFx5apquOHiKnwx6XuQFRFwzK3l5lJsaYXWSMRJmGZbL1dh/4Lu2p8hWP/6KLTMkU+fNHSXZuGCOyXC7Xjq/dbvdet9++zc7fl4gKCwv3abuWlpaozcFutys394uqqG1tbQoEDryzFBIb8baOgRpro7vMPz+p1e/eqpLHFzrfEQXpuu3U0Zo0OFvdHe068LOY5DJQ440DQ7ytJd7xzs/Pj9mxAABIhGrT//f+Fj300Rbz2mRXQ/NduuWkkTqoPDsu8wMAAADQP3XtHl395CqtbwotsltZnKHvHztMhw3NicvcAAA4UCTNAACAPje77pu/Wf9cWBcylplq1y0njdLxYwriMjcgWuydddsSY4zuMWaSzBLZPW0HvL+gzSF/QaXZOcboIGN0kvEXjpUcid3JA8nB6BiTnZ2tjo4ONTU17XHbzs5OeTye/UpKSXR+vz9mxzIWYcbyeIgv4m0dAyHWm1vc+ulL6/VZTUfYas5fO2yQvn1khdKc9oT/u8TbQIg3Iod4WwvxBgAgOpbVduq2l9eH7VBvdLs0rkf+d2aFXCkU3QIAAAAGopVbu3TtU6vU2NXb5/WMVIduPHmcThqTI1uQ4kQAgIGHpBkAAGDa2uHRjc+t1eItnSFjo4vS9cszx5gV4oCBzNbT/HlijJEksy1BxtFd3699+vJGfN5BZrJ8pZPlK5oopaRHbM7A/qqoqNCKFStUV1dnLhR0OMJ3NNqyZUuf7wEAJLZAMKj/fLpV97+1We4w3WWMc/WfnDRSU6nmDAAAACDC3L0B/fn9aj3yca3CNJfRyMJ0s7vMpEFZ8ZgeAAAAgAh4d32rbnhujXp6+96DKM5O00MXT9Ok8ly1tLSIWjUAgIGIpBkAAKAPNrbpphfWqrXHFzJ22sQi3fCl4XKlhF90DSQqm7dTzvql2xJkjESZrYvl6Kju1z792YO/SJD5/BFMo+0wEsvYsWPNpBmji8z69etVWVkZdrvly5f3+R4AQOLa0ubRbS+t14LN7WHHv3JIqa48agjn7AAAAAAiblFNh9ldZlOzO2TMYZMuPnywvjmjXKlOussAAAAAA9V/F9frzlc3yL9LkvyIwnQ98q0ZqsjPiNfUAACICJJmAACweLXqv36wRX98t1q7FodLddj0g+OH68zJxbLZbHGaIbCPfB45G5ebiTHbk2QcLetlC/nJ3neB9MIdCTK9pdv+DGYURXTaQDRMnz5dTz/9tPn1vHnzwibNBAIBzZ8/3/w6MzNTEydOjPk8AQB7FwwG9d/FDbr3zU3q3qWym6E8N82s5nzoEJJ4AQAAAERWj9evB96p1r8W1oX9lHVMSYZumTNS40oz4zA7AAAAAJG6D2Gc9z/04ZaQsUOHZOvX54wnYQYAkBRImgEAwKJae3p1ywvr9N7GtrCL7+46o5KbXUhM/l45mteYyTEpZpLMEjmaV8sWCO2UtK8Cqdk7dY+ZIl/pFAWyBkkkjGEAGj16tMaPH292mzGSZo455hiNGTOmzzbPP/+8ampqzK9PPvlkOZ3JcWnocESvw4Ldbt/jcyQX4m0diRzrunaPfjp3rdkVMpzzDy7VtccMV0Yq3WWSId6IPOJtLcQbAIDIWlDVrp+9vF41bZ6QMafdpm8dUa6Lpw+S08HvXAAAAGCg8voC5nn/3BVNIWMnjS80k+TT05LjPjIAAPxGAwDAgpbWduqGZ9eorsMbMnb0qDzdevIo5bg4TUACCAbkaN3wRQeZrUvMjjI2v+fAd+l0yVc88YsuMiVTFMgbJtm4wYvEsHLlStXV1e143t7evuNr4/U333yzz/ZGUsyuLr74Yt18883yer26/fbbdfbZZ5vdZIzn7733nl577TVzu0GDBun0009XssjPz4/ZsXJzc2N2LMQf8baORIi1UdXtP59U62fPLVeHJzQpuDwvXXedO0VHVtIBLxnijdgh3tZCvAEAODBdXr/un1+lJxbVhx2fUJZpLpwbXUylaQAAAGAg63D7dN0zq/XJ5o6QsUsOH6zvHFkhG0VGAQBJhNWwAABYiLkA77Ot+vW8KvkCwT5jdpt0xVFD9I1pg7jwRXwEg7J31HyeHPN5kkz9Mtl7Ow98l/YU+QrHbusgU2okyUyRv2C0ZOc0GInr9ddf1/z588OOrVq1ynzsLWlmxIgRuvbaa3X//ferp6dH//znP0O2MRJmbrzxRqWnp0dw9gCA/tja7taNTy3RGyvDL1D7yrQh+vGp45XtSon53AAAAAAktw82tur2lzeELbaV6rDp27Mq9NXDBpmdZgAAAAAMXLVtHl3z1Cqtb+rp87rDJv3wSyN0ztSSuM0NAIBoYbUg+sXhcERlv3a7fY/PkVyIt7UQ7/jp9vr1s5fW66UVjSFjhZkpuvOMMTpsaOQqsRJrazmQeNu66s3kGIeRILN1kRz1S2TvaT7gOQRlU6CwcltijJEgUzpF/qJxktPVZ7vonL1YC+/vgeGwww7TPffcoxdffFELFy5Uc3OznE6nysrKNGPGDJ100klKS0uL9zQBAJ8ntz/9WY1+8swytbtDu8uU5qTpznOn6Nix3KgCAAAAEPkK0795s0rPLm0IOz61PEs3zxmp4QUUXgEAAAAGupVbu8yEmaau3j6vp6fYdefplZo1Mi9ucwMAIJpImkG/5Ofnx+Q4ubmRW8SNxEe8rYV4x8ba+g59+x+LtbY+tGPH9BEF+t3/HKySnL6JBZFGrK0lJN49LdKWT6WahV/82bGlfwfJHyGVHyINPlgafIhsg6bKkZZFUkwc8P6OrCuuuMJ8REJxcbEuuugi82EFLS0tUdu3kRy28896W1ubAoFA1I6H+CLe1pEIsW7q8urnL6/XvDXhk4dPm1Ss648foRyXM6r/z1lBIsQbsUO8rSXe8Y7V5+QAAETa2+ta9ItXN6ihs++COYPLadeVRw/R+QeVykF3GQAAAGDAe3d9q254bo16egMhhXZ/e85YjSvNjNvcAACINpJmAABIcs98VqMbn1pidprZ1WWzR+r6E8fK6aBLBCLI0ynVLf48QWbhtj9bNvRvn9mDP0+QOchMkDETZTIKIjVjAEnC7w/9XRctxiLMWB4P8UW8rSPWsX51ZZPufH2j2npCu8sUZqToRyeO0OzR2xZi8zMYeby3rYV4WwvxBgBgz1p7evWreZs0d3lT2PFDh2Sb3WUq8qJbbAsAAABAbDy1qF53vbZB/mDf10cWppsJM4Ny0+I1NQAAYoKkGQAAkpTH59fPX1ihv7+/KWQs2+XUr86fqhMnlsVlbkgiPo+0dWnfDjKNq6RgPyr6phf06SBjfp3NzyoAAEgerd29uvO1jXptdfjuMnPGFer644cpLz0l5nMDAAAAkNxeX91sLpZr7g5N3s9Isevq2UN1ztQS2W10lwEAAAAGumAwqAfeqdZDH24Jmyx/z5ljzDVEAAAkO37boV9aWlqisl+73a7c3Nwdz9va2szqgEhOxNtaiHdsbGlz64fPrNbS2s6QsbElmbr7rLEakp8Wtf/HDcQ6CQV8sjevlXPrYjm3LpJj62I5GlfKFug94F0GU7PkK5kkf8kU+UqnyF86VYGcCmnnG7LGvdso/qxiYL6/8/O3Vd0HAGCgmbemWXe8Gn6BWl66UzeeMELHj6GjHgAAAIDIau7q1V2vbzSTZsKZMTxXN504QmU5VJgGAAAAkoHXF9BtL6/XSytCO0yePL7Q7C6Z6rTHZW4AAMQaSTPoF7/fH5PjGIswY3UsxB/xthbiHXnvbWjVzS+sU5s7dBHeWZOLdd1xw+VKscf8351YDzDBgOxtm5SydYmc9Yu3PRqWy+brOfBdOlLlK5poJskYCTK+kiny54+UbLt8CENy1YDD+xsAgL1r6/Hpnjc2am6Ym1OG4yrzdcOXRqggk+4yAAAAACJbWfrllU26+41N5nXJrrLSHPreMcN0+qQi2eguAwAAACSFdrdP1z29WgurO0LG/t+Mwbp8VgXn/wAASyFpBgCAJOEPBPWX92vMR3CXsTSnzVyAd/qk4jjNDgktGJS9s1bO+iXbusiYSTJLZPd2HPgubQ75C8fKVzJZvSWTt3WRKRgjOVgECgAArOftdS26/ZUNauoK7dCX63LqB18arhPHFnCDCgAAAEBENXR6zU6Xb61rDTt+1Mg8s9tlSXZqzOcGAABiz+FwRGW/drt9j8+RXIh34tvS5taV/1mhDU19i6I6bNKPThylcw4q3ed9EW9rId7WQaythXhvQ9IMAABJoKW7Vze9sFYfbmoPGRuSl6a7zqjUmJLMuMwNicfW0/R5cswSpRh/NiyRvbvxgPcXlE2B/JFyDJkmDT5YKj9Era4h8ttIkAGQHDd7DHyIYC3E2zqiHesOt0/3vL5Bzy5tCDs+e3S+bpozSkVZLFCLBd7b1kK8rYV4AwAQ2l3m+WWN+vW8Terw+MMm719//DDNGVdI8j4AABaSn58fk+Pk5ubG5DhIDMQ7sSypbtP/+8cyNXR4+ryemerQ7y48RMeOLenX/om3tRBv6yDW1pJr0XiTNAMAwAC3eEuHbnh2jeo7Q6tWH1uZr5+cNFJZafzKtyqbp8NMinFuXfJ5B5nFcnRs6dc+/dkVZgcZX+lk+UqmyFcySfb0vL4fsra0SP7Qm7EAkAw3e6z8IYJVEW/riGSs569u0A1PLlZtmztkLNvl1E/PmKizDy5ngVoc8d62FuJtLcQbAGBlde0e/eKVDXpvY1vY8ePHFOgHxw9XYSZFjwAAAIBkMm9lva54bKG6vX3XahRnp+mhi6dpUjmfmQEArIsVtAAADOBKcf9auFX3zq+SPxAMaal61eyhuvDQMhbhWUlvj5yNy7clx2xPkmnd0K9dBjKK1WsmxmxPkpmsYHphxKYMAACQbDo9Pv38heX650ebw44fM7ZYd54zRWW5rpjPDQAAAEDyCgSD+u/iet03v0pd3kDIeEGG00yW+dJYPt8FAAAAks1jH1bp5meWhqwfqizJ0kOXTFNFfkbc5gYAQCIgaQYAgAG6EO/2lzfotdXNIWNFmSm68/RKHVSRHZe5IUb8XjmaVivF7B5jdJJZLEfzGtmCB97dJZCW83nnmG3JMb7SKQpklkkkXgEAAOyT99Y26vonFqumtSdkzOj+ePNp43XBYUNIbAcAAAAQUdWtbvOewYLN7WHHTx5fqO8fO0x5GXSXAQDAylpaWqKyX7vd3qfra1tbmwKB0CReJAfinXjJ879/q0p//aAmZGza0Bz96uxxypRHLS2eA9o/8bYW4m0dxNpaEiHe+fn5ijeSZgAAGGDWNnTrB8+uUVWLO+wF7+2njlZhJje+kkrAL0frejMxxkyQMRJlGlfI5vce8C6DznT5SibJVzxZvWYHmSkK5A4jQQbAgBatmz2J8iECYod4W0ekYt3t9eu++Zv074V1YccPH5arn5wyWoNy0tTa2tqvOePA8d62FuJtLfGOdyLc7AEAWHeB3OOfbtXv3tosty8QtsjWjSeM0OzR/K4CAACS33/gBRj3h3FNHqtjIf6Id/x4fQH99KX1enllU8jYyRMKdcuckUpx2CIaH+JtLcTbOoi1tQQsGm+SZgAAGEBeWNagX7y6UZ4wN78uOXywvj2rQg47SQ8DWjAoe/vmbYkxWxcrpX6JHA3LZO/tOvBd2lPlKxr3eQeZKfKVTpY/f7Rkd0R06gAQb7G8qLfqhwhWRbyt40Bi/Wl1u26du141baFV2tJT7Lpm9lCdO7XE7C7Dz1Fi4b1tLcTbWog3AMAKNjX36LaX12tRTWfY8dMnFet7xwxVtoslAQAAAECyaXf7dN3Tq7WwuiNk7P/NGKzLZ1XQ9R4AgJ3wCRkAAAOAkSTzq3mb9NSi+pCxHJdDt508SkeOolLcQGTv3LotQcZMkjG6yCyR3XPg1ceDNrv8BZVfJMgYfxaNlRxpEZ03AACA1bl7/fr9O9X61yd1CoYZP6QiW7ecNFIVea44zA4AAABAsvIHgvrHglr98b1qeXyhVyOl2am66cQROmJEXlzmBwAAACC6trR5dM2TK7Wh2d3ndYdNZqfJs6aUxG1uAAAkKpJmAABIcDWtbt3w3Fqt2BraaWR8aabuOqNSg3NJiBgIbD0tZlLMtiSZJWYnGUd3aCLU/vDnDldv6ZQvkmSKJ0gpGRGbMwAAAEIt3tJhdpepaul7Q8qQ5rTrqqOH6IKDS2WnihsAAACACFrX2K3bXlqvZXXhO5MbXS6N65GsNJYBAAAAAMloRV2Xrn1qlZq6e/u8npFi151nVGomyfMAAITFp2UAACSwt9e16Cdz16nd7Q978+t7xw4zF+Uh8di8nXI0LFPK1s+7yNQvkaN9c7/26c8a9EX3GCNRpniSgq7ciM0ZAAAAe+8A+cd3q/XogloFwrSXmTI4S7eePEpD8+kuAwAAACByfP6A/vZxrf7yfo16/aEXI+W5abppzghNG8rnxQAAAECyemddi1l01+0L9Hm9KDNF954zVuNKM+M2NwAAEh1JMwAAJCBfIGguxnvowy0hYy6nXT86cYROmVAUl7khDJ9HzsYV25JjzCSZJXK0rJNNYVZS7qNAesGOBJle48/SKQpmEHMAAIB4WVbbqVvnrtOG5tDuMqkOm75z5BD9z6FlctjpLgMAAAAgclbVd+mnL63X6vrukDHj6uMrh5TpO0dWKD3VEZf5AQAAAIi+Jxdt1V2vbQwp6DWyMF33nTtWZTlp8ZoaAAADAkkzAAAkmKauXv34+bVasLk9ZGxYgUt3nV6p0cUZcZkbjGwWnxzNaz5PjlmsFCNBpmmVbAHfge8yNWtb9xjzYSTKTFEge7BkY8ElAABAvHl9Af3lgxr97cMtClPQWRPLMvXTk0dpeGF6PKYHAAAAIEl5fH498HaV/vpBjfxhWl0aHS5vOWmkDirPjsv8AAAAAERfIBjUA29v1sMf1YaMHTYkR3efWalsF8uAAQDYG35bAgCQQD6tbteNz61VY1dvyNgJYwt005yRyqRaXOwEA3K0btypg8xiORuWy+b3HPgunS75iiZsS5Ap3ZYg488bLtnsEZ06AAAA+m/l1i6zu8zaxp6QsRSHTZfNrNDXpg2Sk+4yAAAAACJo0eZWXf/EIq3e2hkyZlx+fO2wQfrfmRVypfC5MgAAAJDMRb1ufWm9XlnZFDJ2yoQi3TxnhFIcXBMAALAvSJoBACABBINB/WNBne5/qyqkerXDbtN3jxmqLx9cKhudR6InGJS9Y8u2xJjtSTINS2X3dh74Lu1O+QvHqnd7B5nSKfIXVEp2TsEAIBocjuglltrt9j0+R3Ih3taxu1j3+gP66/s1+sv71fKFqeg8vjRTt506WqOLM2M2V/Qf721rId7WQrwBAMnC4wvojrkr9Oe31ivMpYhGFqab3WUmDcqKx/QAAAAAxEi726frnl6thdUdIWPfnDFY355VwRoiAAD2Ays2AQCIs06PTz99ab3mrWkJGSvNTtUdp4/WlMHZcZlbMrN1N5qJMSnbk2Tql8je03zA+wvKJn/B6G0dZLYnyRSNl5xpEZ03AGD38vPzY3as3NzcmB0L8Ue8rRXrlXXt+v7jS7VsS3vIuNFR5qrjKvWdY0dRvS0J8N62FuJtLcQbADAQtfb06rqn1+izmtBFcQ6bdPHhg/XNGeVKdXItAgAAACSzLW0eXfPkSm1ododcF9x44gidNbkkbnMDAGCgImkGAIA4Wl3fpR88u0bVrZ6QscOH5ej2U0crPyMlLnNLJjZ3m9k1xuwe83mSjKOzrl/79OcMla90snzFk9VrdJApnqhgKtX9AAAABiqfP6A/vrVe9762Wr27tn+UNK4sW7+6YKomDmYhNgAAAIDIqml16+qnVmnTLoviDGNKMnTLnJEaV0qnSwAAACDZLa/r1HefWq2m7t4+r2ek2HXXGZU6YkRe3OYGAMBARtIMAABx8uySBt31+gZ5fH0X5BnNU791RLn5cNhppbrfervlbFi+LTnm804yjrZN/dqlP7O0bweZkskKpseumwEAAACia219h77/n8VatLk1ZMw4J//OMaPMDjNUdAYAAAAQaSvqunTtU6tCFsWlOGy6dGaFvnFYmZx0ugQAAACS3tvrWnTjc2vl9gX6vF6UmaLfnjtWY0tIpAcA4ECRNAMAQIy5ewO6+42NemZJQ8hYrsupn506SjOpDLFv/F45G1fKWb9kR5KMo2WtbMG+HyDsj0Ba3rYOMjsSZKYokFUa0WkDAKKjpaUlavu22+3Kzf2iu0RbW5sCgQP/fYPERrytw2az6Yklzfrly6vk3eUmlGFkYbpuO3W0Jg7KVldHm7riMktECu9tayHe1hLveOfnU1gDAHBg3tvQqh8+u0Y9vX1/b5XluPR/Fx+mwekB+f3+uM0PAAAAQGw88dlW/fL1jQoEQ+9T3HfuWJXlpMVragAAJAWSZgAAiKHqVrd+8Owara7vDhmbWJZptlLlQnc3An4zIca59fMEGePRuEq2gPfAd5mSKX/xJPWWTJKv9PMEmZwhxurJiE4dABAbsVxEYizCZNGKdRDv5BQMBnXPvCr9e2FdyJjR8PHr0wbpf2dWKM1pJ/5Jive2tRBvayHeAICB4Jkl9frFKxvk32VR3NjSbD38/6ZpUG56VAuEAAAAAIi/QDCo37+9WX/7qDZkbNrQHP3yjEplu1jmCwBAf/HbFACAGHlzTbNufWm9Oj2hizYuOLhU3z1mqFIc9rjMLeEEg7K3bTITY1K2J8k0LJPN13Pgu3Skylc04fMOMpPNJBl/3kjJ7ojo1AEAADAwEmZ+/Wb4hJmh+S7devJITRmcHZe5AQAAAEj+65E/v1+jP71XEzJ22NAc/d8lM5SbnhKXuQEAAACIHa8vYK4jemVlU8jYqROKdNOcEawjAgAgQkiaAQAgynyBbVUhHvk4tCpEeopdN80ZqTnjCmXpBJnOWjnrjeSY7V1klsjuaT/wXdoc8heO2ZEg01syxXwuR2pEpw4AAICBuUDtvrc265+f9E2YMZoNfvXQQbp8VoVcKdyEAgAAABB5Pn9Ad7y2Uc8saQgZM+4T3HZqJQkzAAAAgAW09fh0/TOrtbC6I2TsWzPKddmsctmMGxcAACAiSJoBACCKGju9uvH5tfo0zEXuiAKXfnnmGI0oTJeV2HqatiXHbF2iFDNBZrHs3Y392qcvb6TZOcZXMkm+kilmRxmlWOvfFQAAAPuWMPPAO9UhCe12m3Tf/xysWUPS5feHdoYEAAADV3Nzs95//319+umnqqmpUWtrq7KysjR27FideeaZqqysjPcUAVhEt9evG55bo/c2tIWMfWPaIF159BClOEngBwAAAJJdTatb1zy1Shub3X1ed9ikH504QmdOLonb3AAASFYkzQAAECULqtr14+fXqqm7N2TspPGF+tEJI5SR6lBS6+2Rqj+WahYqc+OHcmxdJEdHTb926c8u/7yDzBT5SifLVzxZwbTsiE0ZAAAAyetP79XooQ+39HnNKNT2my8fpNOmDFZLS0vc5gYAAKJj7ty5euaZZ1RaWqqpU6cqJydHtbW1+vjjj83HNddco5kzZ8Z7mgCSXFNXr659apVWbO3q87pRN/q644bpy4eUxW1uAAAAAGJneV2neW3Q3O3r83pGil13nVGpI0bkxW1uAAAkM5JmAACIsEAwqL9/VKsH3tmsQLDvmNNu0/ePHabzDipJ6jaq9o4tSl/6qLTs35K71Xwt9QD2E8goMpNjes0kmW2PYEZRxOcLAACA5Pd/H9Toz+/3TeA2zsjvPm+qzjyoPG7zAgAA0TV69GjdeuutmjBhQp/XV6xYodtuu01//vOfNW3aNKWkpMRtjgCS28bmHl3z5CrVtHn6vJ7mtOn2U0fr2MqCuM0NAAAAQOy8va5FNz63Vm5foM/rxVkpuvecsRpbkhm3uQEAkOxImgEAIILa3T79ZO46vb1uW6LIzsqyU3XnGZWaNChLSSkYlLP2E6Uvelip61+RLejfr28PpOVsS4wpnixf6RTz60DWoG2lvwEAAIB++PtHW/TgO9Uhr9980iidd2hFXOYEAABi4/DDDw/7+vjx4zVp0iQtWrRIVVVVGjVqVMznBiD5Larp0PeeXq22nr5VpHNdTv367DGaWk4XdQAAAMAKnvhsq375+saQ4rujitL123PGqiwnLV5TAwDAEkiaAQAgQlZu7dIPn10TUi3OMHNErm47ZZTy0pOwYqXfo7Q1L8q16GGlNCzdp28JOtPlK574efeYKeotnaxA7jDJZo/6dAEAAGAt/1hQq/ve2hzy+g1fGq6zp5bGZU4AAAwUbW1tWrt2rflYt26d+ejo6DDHZs+erSuuuGKf99XQ0KC5c+dq4cKFampqktPpVFlZmY444gjNmTNHaWmxXxzicDj6/AkAkTRvTbNuemGtPL6+q+LKc9P023PHanhBetzmBgAAACA2AsGgfvfWZv3949qQselDc/TLMyuVlcYyXgAAoo3ftgAA9FMwGNTTSxp09+sb5fX3vfll9Ei5bFaF/t+MwbInWccUW3ejXEsfU/rSf8je3bjnjQcdJE/RRHmNLjIlU+QvGCXZOQ0BAABAdP17YZ1+82ZVyOvXHzdM5x1EwgwAAHtz6aWXRmQ/CxYs0P3336+enp4dr3k8nh2JOK+//rpuvPFGM4kmVhobG7VkyRLl5+dr6NChMTsuAGt4/NM63f36Ju1SRFrjSzP1m3PGqCgzNU4zAwAAABArHl9At85dp1dXNYeMnTqhSDfNGaEUB8VlAQCIBVarAgDQD+5ev+54daNeWB6aNJKX7tTPTxutw4flKpk46pcqfdHDSlvzgmwB7263C6ZkyHbw16Tpl0lFo9Xd0iK/3x/TuQIArCWa1aHtdvsenyO5EO/k8MRndbr7jU0hr3//uOH66rTB5tfE2lqIt7UQb2sh3rFRVFSk8vJyLVq0aL++b8OGDbr33nvl9Xrlcrl01llnadKkSebzd99910yYqa2t1R133KE777xT6enR77zg8/nMJJ7e3l5deOGF/MwAiEkVaaMj/Z2nVyojle5WAAAAQLJr6/HpumdW69PqbR17d3bpEeX635nlsiVZ8V0AABIZSTMAABygTc09+uGza7S28YsKmdtNGZylO04frdLsNCWFgE+p619T+uKHlbLl4z1u6s+uUM/Ui9Q76cvKLxsWsykCAGBUiI6V3NzkSorFnhHvgeffH1fp5y+vD3n9hpPH6duzR+32+4i1tRBvayHe1kK8I+e8887TqFGjzEdeXp7q6+t15ZVX7tc+Hn74YTNBxkhyv+mmmzRmzJgdY0byzKBBg/Too4+aiTPPPfecLrjggpB9/P3vfzcTXPbVKaecYu43nEAgoAceeEArVqzQ8ccfr6OPPnq//j4AsDteX0C3vbxeL61oChk7c3KxbvzScDmpIg0AAAAkvZpWt65+apU2Nbv7vO6w2/SjE4brzMklcZsbAABWRdIMAAAH4LVVTfrZy+vV5Q2EjP3PoWW65ughSXHzy+Zuk2v5v+Va8ogcHVv2uK23/HC5p14s7/DjJbsjqtX+AQAAgN158pNq3fDUkpDXv3/CmD0mzAAAgFDhElj2x9q1a83kFMOxxx7bJ2Fmu9NOO03z5s1TTU2N5s6dq3POOUdOZ9/bV6+++qo8Hs8+H3fGjBlhk2aMhJkHH3xQ77zzjo466ihdeumlB/T3AoBddbh9uv6ZNVqwuT1kzKggbVSSpoo0AAAAkPyW1Xbqu/9dpeZuX5/XM1Ls+uWZlZoxPC9ucwMAwMpImgEAYD/4/AHd99ZmPfZJXchYZqpdN88ZqS+NLdRA52heK9fiv8m18r+y+UI76WwXdKTKM+ZMs7OMv2h8TOcIAAAA7OqZz2p0/ROLFAz2ff3q4yt11fGV8ZoWAACW9dFHH+342kiaCcdut2v27Nl67LHH1NXVpWXLlmnq1Kl9tnnkkUf6PZftHWbeeustzZo1S1dccYV5bADor7p2j655apXW7dKV3mGTfnTiCKpIAwAAABbx1roW/ei5tXL7+hbgLc5K0b3njNXYksy4zQ0AAKsjaQYAgH1U3+HVDc+t0eItnSFjo4rS9cszKjWsIF0DVjCglKq3lL7oYaVWvb3HTf0ZJXJP+ZrcE7+iYPrATxICACSHlpaWqO3bWEyXm5u743lbW5u56A7JiXgPTK+ubNQNz65WYJeEmf83o1wXH1oU9v8IYm0txNtaiLe1xDve+fn5MTvWQLNq1Srzz7S0NI0cOXK3202YMKHP9+yaNBPJhJmZM2fqqquuImEGQESsbejW1U+uVH1nb5/X01PsuuuMSs0cQRVpAAAAwAr+8+lW3f3GxpB7FKOL0s2EmbKctHhNDQAAkDQDAMC++WhTm378/Fq19PRtn2o4dUKRbjxhuFwpDg1I3i65Vj4l1+K/y9m6fo+b9pZOlXvKxfKMPklypMZsigAA7Au/3x+zYxmL7mJ5PMQX8U5889Y068Zn14TcjPr6YYN0+azyfV44TaythXhbC/G2FuKdOKqrq80/y8rK5HDs/rOzwYMHh3xPJH8eHnzwQTNhZsaMGSTMAIiYj6vadN3Ta9Tl7fs7pzBjWxXp8WVUkQYAAACSXSAY1P1vbdYjH9eGjE0fmqNfnlmprDSW6QIAEG/8NgYAYC8Xt3/9YIv++G61dll/p1SHTdcfP1xnTS6WzWbTQGNv36z0xY8obfnjsns7drtd0O6Ud9RJ6pl6sXxlB8d0jgAAAMDevLWuRTc+t1b+XU7Y/+eQMl09e8iAPFcHACAZeL1edXRs+8ypsHDPnYqzsrLMbjQej0dNTU0RnccTTzyh+fPny+Vymck5Tz75ZMg206dP1/Dhw/d5n/s6xz0lCvXXrok/JAIlL2KdmOYub9AtL6yVb5fM/eEF6frd+eNVnuc6oP0Sb2sh3tZCvK2DWAOAdXh8Ad06d51eXdUcMnbaxCL9+MQRSnHwewAAgERA0gwAALvR2tOrW15cp/c2tIWMDc5J011nVA68SnHBoJxbPlL6ooeVuuE12YK7r7gdcOXLPfErck++UIGsQTGdJgAAALAv3l3fqh8+uyZkodr5B5Xoe8cOJWEGAIA4crvdO742Elb2xtjGSJrZ+fsioaGhYcd8nnrqqbDblJSU7FfSzOWXX75P2z3++OOKldzc3JgdC/FFrOMrGAzqj2+t151z14SMHTosX3/5xmHKz4xcl3bibS3E21qIt3UQawBITm09Pn3/6dX6rCa0SO2lR5Trf2eWc48CAIAEQtIMAABhLKvtNBff1XV4Q8aOGpmnn54ySjmuAfRr1OdR2urnlL74YTkbV+x504IxZlcZz9gzJeeBVcMDAAAAou2DjW26/pnV6t2lxczZU4rNjpDcjAIAIP6dZrZzOvf+Odr2bXb+vki44oorzAcA9Jc/ENRPn1umv7+/KWRszsRS/fYrB8uVEr0OUwAAAAASQ3WrW9c8tUqbmvsW/nDYbfrxCSN0xuTiuM0NAACEN4BW+wIAEJsqcU8uqtev5m0KWXxnt0nfOXKIvjF9kOwDZAGevXOrXEv/Ideyf8reE9oOdrugbPKOOF7uqRept/wIaYD8/QAAAGBNC6ra9P2nV8m7yzn76ZOKdeMJIwbM+ToAAMksNfWLTgs+n2+v22/fZufvS1QPPvhgvKcAIMbcvX5d/c9P9cryrSFjF88crptPm2AukAMAAACQ/EV4v/vfVWru7vtZR2aqXXedUakZw/PiNjcAALB7JM2gXxyO6FRLstvte3yO5EK8rSWR493t9evnL6/Xi8sbQ8YKMlJ05xljNG3YwGif7ahbpLTPHlLqmudlC+x+UUIwNUueCefLM/ViBfKGbfteC8QakUe8rYV4AwDiaeHmdl371Gp5fH0TZk6ZUKSbTiRhBgCAROFyfdHB2O3uW3k1nO3b7Px9iaqwsHCftmtpaYnaHIxr8dzcLz6rbGtrUyAQiNrxED/EOv5aunt17ZMrtXhLR8jYd48Zpq9PH6T2ttaIHIt4WwvxthbibR2JEOv8/PyYHg8ArGL+2hb96Pm18vj6/r9ekpWie88ZqzElmXGbGwAA2DOSZjAgLrR3/kAByY94W0uixHttfYcu/8diranvDBmbPrxA93/1YJXmJPgNe3+vtOJZ6YM/SNUf7Xnb/BHS4d+W7aCvyuXKkctCsUZsEG9rId4AgFhZVNOha55aJfcuN6ROHFeon5w0ksrOAAAkEKNjTHZ2tjo6OtTU1LTHbTs7O+XxePYrIWUg8Pv9MTuWsRAzlsdD/BDr2KpudevqJ1epqqVv8l+Kw6ZbTx6lOeMKo7oQmnhbC/G2FuJtHcQaAJLD45/W6Z43NinQt56XRhel67fnjlVpdlq8pgYAAPYBSTMAAMt7btEW/fDJxWanmV1ddvRIXT9nrJyOBO6i0N0sffKw9PFfpPaaPW878hjp8MulyhONMkexmiEAAADQb0trO3X1kyvV09t3QdrxYwp02ymjSJgBACABVVRUaMWKFaqrqzMXCu6ue/2WLVv6fA8AJILldZ269qlVau7u2809K82he84co8OG5sRtbgAAAABiIxAM6v75m/XIgtqQsenDcvTLMyqVlcYyXAAAEh2/rQEAluX1BfSLF1fo4fc2hoxlpzl1zwVTNWdimRLW1uXSh3+QFj8u+Xp2v53TJU35stlZRqUTYjlDAAAAIGKL1a58YqW6vH0TZmaPztfPTx0lJwkzAAAkpLFjx5pJM0YXmfXr16uysjLsdsuXL+/zPQAQb++sb9UNz64J6XJZmp2q354zVqOLM+I2NwAAAACx4fEFdOvcdXp1VXPI2GkTi/TjE0coJZGL8AIAgB1ImkG/tLS0RGW/drtdubm5O563tbVFtbU54ot4W0uixLu23aMfPr1KS2o7Q8bGlGTo7rPGamh+WtT+nztgwYBSNryhtM8eUsrmd/e4aSBrkNxTvi7vpP9RMD1/24sx/PskSqwRG8TbWhIh3vn5n/+/BgBIeqvqu8yEmU5P386QR47M0x2njU7srpAAAFjc9OnT9fTTT5tfz5s3L2zSjHE9OX/+fPPrzMxMTZw4MebzBICdPb24Xne8ukH+YN/XRxel675zx6kkOzVeUwMAAAAQI609vbru6TX6rKYjZOx/Z5br0iPKZbNR0AsAgIGCpBn0i9/fd8FKtBg3zWJ1LMQf8baWeMT7/Q2tuunFdWrr8YWMnTGpWD84frhcKfaE+jm0eTuUtuJJpS/+mxxtVXvctrfsEPVMvVjekSdKjpRtLybA34X3trUQb2sh3gCAaFnb0K3v/Gel2t19f88cMTxXd51RqVQnCTMAACSy0aNHa/z48Wa3GSNp5phjjtGYMWP6bPP888+rpqbG/Prkk0+W05k8t64cDkdUC1rs6TmSB7GOnWAwqD+8s1l/eq86ZGz6sFzdc/ZYs0t9NBFvayHe1kK8rYNYA8DAV93q1tVPrlJVi7vP6w67TTedOEKnTyqO29wAAMCBSZ47DwAA7IU/ENRf3q8xH7sUiFOa02Ymy5w5uUSJxN62SemL/6605U/I3hvaFWe7oD1FntGnyD31YvlKp8R0jgAAJAoWpCFSiHdiWNfYrcv/syIk2f3wYbn69Tnj5Erp/3ueWFsL8bYW4m0txDt6Vq5cqbq6uh3P29vbd3xtvP7mm2/22d5IitnVxRdfrJtvvller1e33367zj77bLObjPH8vffe02uvvWZuN2jQIJ1++ulKJrHskrpzR1gkN2IdHb3+gH701BL955PQhJmzDhqsX543NS5J+8TbWoi3tRBv6yDW2FVzc7Pef/99ffrpp2YBgdbWVmVlZWns2LE688wzw3boBBA7S2s79d2nVqlll3sTmal23XXGGM0Yzv/rAAAMRCTNAAAsobW71+wu88HGtpCxirw0s0r12JJMJYRgUCk178v12cNK3fiGbCEpPl8IpBfIPemr6pl0oYKZiZXwAwBArLEgDdFCvGNvXUOnvv3vFWrp7ntTasbIAj108XSlp0YnSY5YWwvxthbibS3EO3Jef/11zZ8/P+zYqlWrzMfekmZGjBiha6+9Vvfff796enr0z3/+M2QbI2HmxhtvVHp6egRnDwD7ptPj03f+sVBvrW4IGbv8mFG6/sSxstttcZkbAABIPnPnztUzzzyj0tJSTZ06VTk5OaqtrdXHH39sPq655hrNnDkz3tMELGn+2hb96Pm18vgCfV4vyUrRb88dp8rijLjNDQAA9A9JMwCApLdkS4dueG6ttnZ4Q8aOGZ2vW08eqay0BPiV6HMrbdUzSl/0sJzNq/e8adF49Uy9WJ7K0yVnWsymCAAAAETbxsYuffXPH6ix09Pn9WnD8/V/F02LWsIMAACInsMOO0z33HOPXnzxRS1cuNCsrOx0OlVWVqYZM2bopJNOUloan3EBiL36drcuefhjLdvyRSctg5Ej89MzJurrRwyP29wAAEByGj16tG699VZNmDChz+srVqzQbbfdpj//+c+aNm2aUlJS4jZHwIoe/7RO97yxSYFd6toaiTL3njNGpdl8bgEAwECWACuEAQCIjmAwqH9/ulX3vlkl3y5XtQ6bdOXRQ/W1w8pks8W3Qpy9s1auJf+Qa9m/ZHe37Ha7oM0u74gvmckyvsHTpTjPGwAAAIi0qqZu/c+fP9DW9r4JM4cMzdNDl0xXZiIkuwMAYCFXXHGF+YiE4uJiXXTRRebDKlpadv9ZX3/Z7fY+XZXa2toUCPSthIvkQKyjZ0NTt654fIVqd7n+cDnt+sUZY3RsZW5U38fhEG9rId7WQrytIxFiHcuu7Nh/hx9+eNjXx48fr0mTJmnRokWqqqrSqFGjYj43wIoCwaDum1+lRxfUhYwdPixHd51RmRiFeAEAQL/w2xwAkJS6vH7d/vJ6vbqqOWSsKDNFd5w+WgdX5CienHWfKn3RQ0pd+5JsQf9utwukZss94QK5p3xDgZyKmM4RAICBhAVpiBTiHR9b2tz61mPLQhasTRyUZVZx6+3uUEt3ZI9JrK2FeFsL8baWeMebBWnYHb9/95/5RZrxMx/L4yF+iHVkfFbdoe89vUrt7r7/lrnpTt179hhNHpydEP/OxNtaiLe1EG/rINaRZVzvrV271nysW7fOfHR0dJhjs2fP3q+k/4aGBs2dO9fshtnU1LSjG+YRRxyhOXPmxKUbpsPh6PMngOjy+AL6yYvr9Nrq0LVFp08s0o9PHCGnwx6XuQEAgMgiaQYAkHTWNXbrB8+u0aZmd8jYoUOy9fPTRqsoMzUuc5Pfq7S1L8m1+GGlbF20x019eSPknnqx3GPPllIzYzZFAAAGKhakIVqId/Rt7fDof/8VWuF5XGmG7j93rDKctpjEgFhbC/G2FuJtLcQbALAnr69u1s0vrJXX37dDfXlumu47d6yGFaTHbW4AAGDPLr300ojsZ8GCBbr//vvV09Oz4zWPx7MjEef111/XjTfeaCbRxEpjY6OWLFliFmYYOnRozI4LWFVrT6++//RqLarpDBm7bGa5vnVEuWw2W1zmBgAAIo+kGQBAUpm7vFE/f2WD3L7QaqKXHD5Yl82qkNMe+4taW0+TXEv/KdeSf8jRXb/Hbb1Dj1LP1IvVO/RoyUbFCgAAACS3hk6vvv3vFapp65swM6Y4Q787b5xyXHx8BQAAACAy/rWwTr96Y5P6pstIE0oz9ZtzxqowMyVOMwMAAPurqKhI5eXlWrRoz8Uqd7Vhwwbde++98nq9crlcOuusszRp0iTz+bvvvmsmzNTW1uqOO+7QnXfeqfT06CfU+nw+M4mnt7dXF154odlRFUD0VLe6dfWTq1TV0rcYr8Nu080njtBpk4rjNjcAABAdrDoAACQFry+gX83bpCcXhSakZKc59NNTRunoUfkxn5ejcYXSFz2stNXPyub37na7oDNd7nHnyD3lG/IXjI7pHAEAAIB4aezy6tuPr9Dm1r4JMyML0/X788cpL50FawAAAAD6LxAM6r75VXp0QV3I2JEj83THaaOVnuqIy9wAAMC+O++88zRq1CjzkZeXp/r6el155ZX7tY+HH37YTJBxOBy66aabNGbMmB1jRvLMoEGD9Oijj5qJM88995wuuOCCkH38/e9/NxNc9tUpp5xi7nd33VIfeOABrVixQscff7yOPvro/fr7ANg/S2s79d2nVqmlx9fn9cxUu3555hgdPiw3bnMDAADRQ9IMAGDA29Lm0Q3PrtHyrV0hY+NKM3TX6ZUqz3PFbkIBv1I3vi7XooeVWvPhHjf1Zw+We/LX5Z7wZQVdXHgDAADAOpq7evWdx1dqU3PfSm7DC1x68ILxys8gYQYAAAxsxiK8aNm18jSVqJMXsY5M0a2bX1irV1Y2hYydM7VUN544Mi4d6sMh3tZCvK2FeFsHsY6ucAks+2Pt2rVmcorh2GOP7ZMws91pp52mefPmqaamRnPnztU555wjp7PvErtXX31VHk/fQkB7MmPGjLBJM0bCzIMPPqh33nlHRx11lC699NID+nsB2Ddvrm3Wj59fJ48v0Of1kqwU/fbccaoszojb3AAAQHSRNAMAGNDeWdeiW+auU7vbHzJ2ztQSff/YYUpzxuaDSJunQ2nLH1f6kkfkaN+8x217B09Tz5SL5R35JcnOr2MAAABYS2t3r77znxVa39TT5/Wh+S794YLxKswkYQYAAAx8+fmx63ydm0tBHqsg1vunrbtXVz++QB9uaA4Z+/4JY3TlcaNlsyVGwkw4xNtaiLe1EG/rINaJ5aOPPtrxtZE0E46R6DR79mw99thj6urq0rJlyzR16tQ+2zzyyCP9nsv2DjNvvfWWZs2apSuuuIIkKyCK/r2wTve8sUnBXV43EmXuPWeMSrPT4jQzAAAQC6zSBQAMSP5AUH98r1p//WBLyJiRJPOjE4br1InFMZmLvWWD0hf/Ta6VT8rW273b7YL2VHkqT1XP1IvlL5kUk7kBAAAAiaatx6fvPLFSaxv7JsyU56bpwQvGqSgrNW5zAwAAAJA8alp7dMlDH2n11s4+rxtdZe44Z7LOP2xI3OYGAADiY9WqVeafaWlpGjly5G63mzBhQp/v2TVpJpIJMzNnztRVV11FwgwQJYFgUPfNr9KjC+pCxg4flqO7zqhUVhrLaAEASHb8tgcADDjNXb368Qtr9XFVe8iYUZn6l2dUanS0W6YGg0rZ/I7SFz2s1E1v7nHTQEaReiZdKPekryqYURTdeQEAAAAJrMPt01VPrNTq+r7J5oNz0vTHL4+nkhsAAACAiFi+pV2XPPyRtrZ7+ryemerQA187VLPHxKboFgAASCzV1dXmn2VlZXI4HLvdbvDgwSHfE8mEmQcffNBMmJkxY0ZEEmaampr2abs9/Z37Y9f5kwCU3AZSvD2+gG56fq1eWxX6Hjlzcol+PGekUhyJO/9EMJDijf4j3tZBrK2FeG9D0gwAYED5rLpDNz6/Rg2dvSFjx48p0M1zRkS3AkRvt1yrnpZr0d/kbFm7502LJ8k99WJ5Kk+RHCz+AwAAgLV1eny66smVWr61q8/rpdmp+sOXx6ssh3NmAACQXFpaWqK2b+PGZm5u7o7nbW1t5uI7JB9ivf8+3Niq7/93lbq8/j6vF2Wm6P7zx2tcsTOq78/+IN7WQrythXhbRyLEOj8/P6bHGyi8Xq86OjrMrwsLC/e4bVZWltmNxuPx7HNCyr564oknNH/+fLlcLjM558knnwzZZvr06Ro+fPg+7/Pyyy/fp+0ef/xxxcLO7wEkv0SNd0uXV1f+a4EWbAo99//eCWN01XGjZbPZ4jK3gSxR443oIN7WQaytJdei8SZpBgAwIASDQf3jkzrdP79K/mDfMYfdpmtnD9VXDimN2gWtvWOLXIv/Ltfyx2X3tO1+njaHvKPmqGfqRfKVHSpxgQ0AAACo2+vXNU+t0tLavgkzJVkpZoeZwbkkzAAAgOTj9/ddsB9NxkLMWB4P8UOs9+zF5Y366Uvr5Q/0vZEwvMCl+84dZ157DKR/P+JtLcTbWoi3dRDrxOF2u3d8bSSs7I2xjZE0s/P3RUJDQ8OO+Tz11FNhtykpKdmvpBkAfW1q6tLFD32sDY1970k47Tbdee4UnXdoRdzmBgAA4oOkGQDAgKhIbdzkmrcmtPqDscjujtMrNbU8O/IHDgblrP1E6YseVur6l2UL7r4CUCAtV+6JX5Z78tcVyP6iVTMAAABgdT1ev659apUW1XSGVHl+8ILxqsjb+w1qAAAAANhb4a2HP9qi379dHTJ2UHm2fnXWGOWmc2scAACrd5rZzunc+3nB9m12/r5IuOKKK8xHJD344IMR3R8wkH1a1aJv/W2Bmrr6vnez05x68GuH6sjKorjNDQAAxA+fDAIAEtqahm794JnV2tzqCRmbPixHt58yWgWZKZE9qN+jtDUvmMkyzoZle9zUlz9a7qkXyT32LCklI7LzAAAAAAY4d69f33t6tRZWd/R5vSDDaSbMDCtIj9vcAAAAACQHo6vM3a9v1BOL6kPGjqvM189OHa00pz0ucwMAAIkjNTV1x9c+n2+v22/fZufvS1SFhYX7tF1LS2ih0kiw2+3Kzc3d8bytrc3ssoTklMjxnre6ST96bo3cvr7zKc1O1f3nj1dlkSNq74NklcjxRuQRb+sg1taSCPHOz89XvJE0AwBIWM8vbdAdr22UZ5eLWcO3ZpTr0pnlcthtETueratB6Usfk2vZY7J3N+5xW++wY9Uz9WL1Dpkl2SI3BwAAACBZGOfx1z2zRh9Xtfd5PS/dqQfOH68RhSTMAAAAAOh/ov6Pnl+rt9a1hoz9zyFl+u6xQ2XnM3wAACDJ5fqi47Xb7d7r9tu32fn7Bjq/3x+T4xiLMGN1LMRfosT7Xwvr9Ks3Nim4y+uVxRn67TljVZKdmhDzHOgSJd6IDeJtHcTaWgIWjTdJMwCAhFxcZ1SFe3pJQ8hYrsup204ZpVkj8yJ2PEf9UrOrjNFdxhbYfWvlYEqG3OPOVc+UixTIHxGx4wMAAADJxusL6AfPrNEHG9tCzucfOH+cRhfTpREAAABA/7R09+q7/12lpbVdIWPfPWaoLjxsUFzmBQAAEpPRMSY7O1sdHR1qamra47adnZ3yeDz71cUFQHwEgkH99s0q/eOTupCxGcNzdefpo5WVxjJZAACsjrMBAEBCqW5164fPrtGq+u6QsQllmbrr9EoNyk3r/4ECPqWuf9VMlkmpXbDHTf05Q9Qz5RvyjD9fwbTs/h8bAAAASGK9/oBueG6N3t3Qt9JzdppDvz9/nMaUZMZtbgAAAACS517CVU+s1ObWbYtZt0tx2HTbyaN0wjgWtwIAgFAVFRVasWKF6urqzOraDocj7HZbtmzp8z0AEpO7N6BbXlyrN9a0hIydMalYPzphuJwOe1zmBgAAEgtJMwCAhDF/bYt+MnedOj2hrd/OP6jUrAyX6uzfxazN3SrX8n/LteRROTq++KArHG/54XJPvUTe4cdJ9vAflgEAgMSxu5tbkWC32/f4HMmFePcvYebHL6zTW+v6JsxkpTr04JcnaOKgxEpCJ9bWQrythXhbC/FGouIaBZFArEMt3dKhq59coZZuX0ii/m/OHadDh+RqoCLe1kK8rYV4WwexTmxjx441k2aMLjLr169XZWVl2O2WL1/e53sAJJ7W7l597+nVWrylM2Ts27Mq9M0Zg2Wz2eIyNwAAkHhImgEAxJ0vENSD72zW3z6qDRlzOe26ac4InTS+qF/HcDSvlWvx3+Ra+V/ZfD273S7oSJVnzJnqmXqR/EXj+3VMAAAQW/n5+TE7Vm7uwF2Ag/1HvPeNzx/QNf/+TG+sbu7zemaqQ3/75uE6dFjs3qMHilhbC/G2FuJtLcQbiYJrFESD1WP9+oqtuvJfy9XT27f41uBclx7+f9M1pjSxEvX7y+rxthribS3E2zqIdWKZPn26nn76afPrefPmhU2aCQQCmj9/vvl1ZmamJk6cqGQRrcR+ksWsJRHivbnFrSv/s1xVLe4+rzvtNt1y8iidPqkk5nNKVokQb8QO8bYOYm0txHsbkmYAAHHV2OnVD59ZpYXVHSFjwwtc+uUZlRpZlHFgOw8GlLJpvtIX/U2pm9/e46b+zFK5J18o98SvKJheeGDHAwAAACzKHwjquv8s0guL+ybCZ6Q6zIVrAyFhBgAAAEBie+zDKt309BIFgn1fHz8oRw9fMk2lOa54TQ0AAAwQo0eP1vjx481uM0bSzDHHHKMxY8b02eb5559XTU2N+fXJJ58spzN5ltfFKrGfZDFriXW8F1a16Fv/WKrmLm+f17PTnPrD1w/VrNH9K8qLPeP9bS3E2zqItbXkWjTeyXNWDwAYcD5Y36Qr/7FIjV29IWMnjivUTSeOMBfZ7Tdvl1wrnzI7yzhbN+xx097SqeqZerG8o06SHKn7fywAAADA4gKBoH745GI9/dmWPq+7Uuz6v4umadrwgrjNDQAAAMDAFwwG9etXV+v+N9aGjB05ukgPfu0QZbtS4jI3AAAQWytXrlRdXd2O5+3t7Tu+Nl5/8803+2xvJMXs6uKLL9bNN98sr9er22+/XWeffbbZTcZ4/t577+m1114ztxs0aJBOP/30qP59AOyfl5bW6Zp/fSqPL9Dn9UG5Lj10yTSNK8uJ29wAAEBiI2kGABCXG1x/fGu97n55lVmRetdWqd89ZqguOLhUNpttv/Zrb9+s9MWPKG3547J7O3Z/fLvTTJIxkmV8ZQcf8N8DAAAklpaWlqjt22hPu3O1jba2NgUCfT+QR/Ig3vsuEAzq9pfW6b+L6/u8nua06zfnjNO4AntU35v9RaythXhbC/G2lnjHO1aVejHwcI2CSLB6rHv9Af3spXV6bmlDyNipE4v1k5NHydfTqZYeJQWrx9tqiLe1EG/rSIRYJ/M1yuuvv6758+eHHVu1apX52FvSzIgRI3Tttdfq/vvvV09Pj/75z3+GbGMkzNx4441KT0+P4OwB9MdD727Qbc8vVzBM58mHLp6mslw6TwIAgN0jaQYAEFMdbp+u//snem3F1pCx0uxU3XVGpSYNytr3HQaDSqn5UK7FDyt1w+uyBXf/gWPAlS/3xK/IPflrCmSVHehfAQAAJCi/3x+zYxk3OWN5PMQX8d59Mvxdr20MSZhJcdh0z5mVmjYke8D9uxFrayHe1kK8rYV4I1FwjYJosFKsOz0+/fDZNfpw0xcV5Le75PDB+s6RFbIpmNT/HlaKN4i31RBv6yDWiemwww7TPffcoxdffFELFy5Uc3OznE6nysrKNGPGDJ100klKS0tTsolWYn8iJIshdmIdb6N416/f2Kh/LKgNGZs5Ik93nTlGaYEetSRLJn2C4f1tLcTbOoi1tSRCvPMTILGfpBkAQExvcP2/x5ZrfVPoherM4bm67dRRyktP2bed+TxKW/2c0hc/LGfjij1vWjDG7CrjGXum5KSyBAAAABCJhJl73tikJxbVh3SOvPuMSh0xIi9ucwMAAAAw8DV0enXNU6u0ur67z+t2m/SD44frvINK4zY3AAAQP1dccYX5iITi4mJddNFF5sMqYpXARbKYtUQz3u7egG55ca3eWBOa8HXm5GLd+KXhcjps/LzFEO9vayHe1kGsrSVg0XiTNAMAiJnfvbU5JGHGJul/Z5brm0eUy24znu2ZvXOrXEv/Ideyf8re07zb7YKyyTvieLmnXqTe8iOkfdg3AAAAgH1LmLn3zSr9+9O+3SMddpvuPGO0jhwV/yoxAAAAAAauDU09uuqJlarr8PZ5Pc1p1x2nj9bRXHMAAAAASa+1u1ffe3q1Fm/pDBn79qwKfXPGYNlYCwQAAPYRSTMAgJj4rLojpAp1QWaqfn7qaE0bmr3X73duXSTXooeVtvZF2QK+3W4XSMmSZ8J56pnyDQVyh0Vk7gAAAAC+SJj53dub9Y9P6vq87rBJvzhttI4ZXRC3uQEAAAAY+D6tbtf3/rtaHZ6+1S7z0p2695yxmjQoK25zAwAAABAbm1vcuvrJldrc6gkp3nXznBE6bWJx3OYGAAAGJpJmAABR5/UFdPsr6/u8lp7i0FOXz1Suw7v7Vm/+XqWuf1npnz2slK2f7vEY/txhZqKMZ/y5CqbuPQkHAAAAwP7747s1+ttHtX1es9uk204drePHkDADAAAA4MC9tqpJt7y4Tl5/sM/rFXlpuv/ccRqS74rb3AAAAADExuItHWYifWtP34K6makO3X1mpaYPy43b3AAAwMBF0gwAIOoe+nCLNja7+7z2/RPHaHhRplpavCHb23pa5Fr2L7mWPCpHV98K1rvyDpmlnikXq3f4MZLNHvG5AwAAANjmL+/X6C8f1PR5zSbp1pNHac64wrjNCwAAAMDA99gntfrNvCr1TZeRJpZlmh1m8jNS4jQzAACA5OFwOKKyX7vdvsfnSC7RjPcbq5v0o+fWyOML9Hm9LDtV958/XqOLMyN2LOwb3t/WQrytg1hbC/HehqQZAEBUrWvsNpNmdja5PFcXzxwesq2jaZXSFz2stFXPyObv22J1Z0FHmjxjz1LP1IvkLxwblXkDAAAA+MLDH27RH96tDnn95jkjdcqEorjMCQAAAMDAFwgGde+bVXrsk9ACWkeNytMvTh2t9NToLO4EAACwmvz8/JgcJzeXTiBWEql4//WdDfrZC6sU3CWTfsKgHD10yTSV5tB5MhHw/rYW4m0dxNpaci0ab5JmAABRvdn181c2yBf44orWYZPuPHeynI7Ps1WDAaVueF2uRQ8rtfq9Pe7Pn1Um9+Svyz3hywqmx+bDHAAAAMDqHv24Vr97e3PI6z8+cYTOmFwclzkBAABYuYqzgeqA1pHMsTaqR9/8/Fq9uqopZOzcg0p1wwkj5bQb/S2tI5njjVDE21qIt3UQawDYP/5AUD9/YYX++u6GkLGjxxTrgQsPUVYay1wBAED/cDYBAIiaJz7bqsVbOvu89vXp5Zo4OFdyt0uf/UM57z8oR9umPe6nt+wQ9Uy9WN6RJ0qOlCjPGgAAAMB2//ykTvfOrwp5/YfHD9fZU0riMicAAICBIlZVnK1cHdCKkiXWrd1eXf3vT/TRxuaQsevnjNV3jhklm81aCTPJHG/sG+JtLcTbOog1AOyeu9eva//1mV5aFtp58ivThuhnZ01SyvaivAAAAP1A0gwAICq2dnj0+12qUVfkpenyKXZp7g+lT/8heTu0u1qLQXuKPKNPkXvqxfKVTonJnAEAAAB84T+fbtWv5oUmuF933DCdf3BpXOYEAAAAYOCrbunWxQ99rLX1fYtuGV1lfnneFJ1zSEXc5gYAAJDMWlpaorJfo6PSzglibW1tCgQCUTkW4i9S8W7u7tV3n1ypxVs6QsauOGqovnlEuTrb2/o9X/QP729rId7WQaytJRHinR/DAlO7Q9IMACDigsGg7nxto7q8O/9iDeoPYz5Ryb/ulXq7d/u9gfQCuSd9VT2TLlQwk8rVAAAAQDz8d3G97np9Y8jr184eqq8cUhaXOQEAAAAY+JZtadMlD32s+g5Pn9ez0px68GuH6KjK4rjNDQAAINn5/f6YHMdYhBmrYyH+DiTem1vcuvrJldrc6glJpL95zgidOrGYxdsJive3tRBv6yDW1hKwaLxJmgEARNzrq5v19rrWHc/L1aD/y/+bxi1euNvv8RVNUM/Ui+SpPF1ypsVopgAAAAB29dzSBv3ilQ0hr19xVIW+Nm1QXOYEAAAwEEWrinOiVAdEbCRTrD/Y0KrvP71S3X0KbklFWSn63XkTNLbIGdX3zUCQTPHG3hFvayHe1pEIsU6EKs4AsDtGZ5nv/ne12np8fV7PSnPo7jMrNW3oF/+HAgAARApJMwCAiGp3+3T365s+fxbUlx1v6uaUR5XV0xO6sc0u78gT1D3lYvkGT5NstlhPFwAAAMBOXlzeqNteWq/gLq9fNrNclxxeHqdZAQAADEyxrNZn1eqAVjRQY/38sgb97OUN8gf6Xm2MLEzXfeeOVVlO2oD8e0XbQI03DgzxthbibR3EGgC+8MbqZt384lp5fH2vC0qzU/Xbc8ZqdHFG3OYGAACSG0kz/5+9+wCToj7/AP6dmd293nvl6B0EAZFeBBVRsf4TS4waNYotxhISNeVvi5pYMEGNJkaT+I9Rk6hIUUC6FBHl6HDH9d7rtpn/M7PH3Q13wh237Xa+n+fZZ3d/szPzwnsLN7u/9/eCiIjc6aWN+ahqtiMZVfit+U+YLX3b/Qsn3AjMeghNiOSHhEREREREfmDNoSr8atXxLgUzt05NxW3T0n0UFRERUQ+11gOl3wLhyYAU5+toiIiojaIo+POOYqzYUthl28T0CDy/ZBgig/mVNRERERFRoPvHVyV4YUN+l+8ghiWGagUzCeEWH0VGRERERsBPIImIyG1259fjv/vKcbW0CY+b3kGk0NzlNXJ4CsQlfwCGzHcN1NR4P1AiIiIiItL5/HAVHl95DKcs+oybpqTgx9NZMENERH7G3gxT5QGYyvfBVJ4Nc/k+oCZH63qMGQ8A597r6wiJiAiAQ1bw7LoT+PCb8i7bFgyPxa8uHowgk+iT2IiIiIiIyDvUbpMvfpGPd/eUdtk2LSsKT182FGEWySexERERkXF4rWimudk1cTo0tPsWesuXL8d7772HyspKDBw4EHfeeScuvfRSb4VHRER91GqX8drar/Cm+Q+YL33d/WtGXoOW2Y8hJnmA1+MjIiIiIqLufXGsGr9YeRzOUwpmrj83GXfPzIAgCL4KjYiICHC0wlR5sK1AxnWTao5DUOTuX1/8NXCut4MkIqJTtdic+Pknx7A5p7bLNvVa4745mRB5rUFEREREFPBziR779Bg2HO26oO6SsQn42QVZMEkspCciIqIAKZr5+OOPsWTJEoSHh6OwsBARERG67bfccgv++te/trfoPnLkCNasWYMnnngCy5Yt80aIRETUF4qCnav+grdaXkSU1LW7jDMsCY1zn4Q9ay4kiatDEBERERH5iy3Ha/Czj45pK7119j8TknD/nEwWzBARkXc5rTBVHoapIrujQKbqCATF2fNjqEUzyimVoERE5FXVTXb85N+Hsb+0STeuXl38ZG4mrjs3xWexERERERmZp+ZriKJ42ucUWHqa7+pmO+7/4CD2FTd22Xb3rEzcMjWN30H0A3x/GwvzbRzMtbEw314smlELYNRimMsuu6xLwcyWLVvw1ltvab8AqV1ohg0bhkOHDqGlpQWPP/641m1mzJgx3giTiIjOgtBcCaxZhiuL1ru+8TpF6/Ar0DTzMSjBUb4Ij4iIiIiIvsP23Fo89NFROE4pmLlqfCIenDeAX1YREZFnOe2Qqo/qOsiYqg5DkO1nf0zRBMQMgGCtA8z67yKIiMg7Cmpacc8Hh1BYa9WNWyQBv1k0GBcMj/NZbERERERGFxMT45XzREVxfoiRdJfv3Mom3PKPvcir0i+8a5YEPHv1OFwxId2LEZI78f1tLMy3cTDXxhJl0Hx7pWjmyy+/1CZazJ07t8u2119/XbtPTU3F9u3bkZ6ejoKCAsyYMUPrSvPaa69h+fLl3giTiIh6Q1FgObYS4Rt/BbG1axtVW3AcWuc/BdvAC3wSHhERERERfbedeXV48L9HYHfqC2aWjE3AIxdksWCGiIjcS3ZAqj4OU4VaHNPWRabyAASn7awPqQgSnLFD4UgcC2fyOIQNmQ4kjgbMwVBqagBnL7rTEBGRW2SXNOL+Dw+jtsWhG48MlvC7JcMwIT3SZ7EREREREZF3fJVXjR/9dTdqmvULo0QEm/Dajedi2uB4n8VGRERExuWVopny8nLtfvjw4V22rV69WpuIcc8992gFM6qMjAzt+cMPP4yNGzd6I0QiIuoFoaUK4V/8EkHHV3W7PTvmAqRc+QyUEO+sUkJERERERD23O78eP/n3EVgd+oKZxaPj8fOFAyGyYIaIiPpCdkKqzdV3kFELZBytZ31IBQKcsUPgSBijFclot/iRgDlE2y5JEsK8tFouERF1b+OxGvz8k2OwOmTdeEqkBS9fNQID41z/ZhMRERERUeBata8E9/9zb5frgtSoYLx1yxQMS2JnYCIiIgrgopmKigrtPiJC/0vP/v37UVlZqRXNXH755bptkyZN0u7z8vK8ESIREfWQ5dhqhG98DGJLdZdtlUokXgz6Me78n9ugmESfxEdERERERN9tb2EDfvLvw12+sLp4ZBweu3AQC2aIiKh3FBliXZ5WGGNu6yAjVeyHaG/q02Ed0YM6imNOFshYwtwWNhERudf7e8vw7LoTkPV1+RieGIqXrhyO+HCLr0IjIiIiok5q1M6sHiCKIqKiotqf19XVQZb1n0FT4PiufP99VzF+t/4ETrkswIikMLx01UgkWBwe+xkkz+H721iYb+Ngro3FH/Id4wcLn3mlaEZd5U1VXa2fYL1lyxbtPiEhoUsXmpN/Oa2tZ7/6HBERuY/QUoPwTb9C0NFPut3+ifM8PG6/Gb+94jwEsWCGiIiIfODktaenPkQ43XMKLIGa72+KGnDvh4fQYtd/ALZwRBx+s3gYTKLxCmYCNdfUPebbWJhvD1AUiPWFkMq/hansW0hl+yCV74Noa+jTYZ1RA+BMUotjxrnuE0YDQZG615zptzzmm4jINxRFwR+3FOIvO4q7bJuaFYXfXjYUYRbPXasTERERUe84nU6vnEedhOmtc5Hv2R1OPL8uB/+3p6zLtmlZUXi67bqAPxOBge9vY2G+jYO5NhbZoPn2StFMWloajh07hr1792LOnDnt4ytXrtS6zMycObPLPmoVkyo+Pt4bIRIR0WlYcj5D+BePQmyu7LKtWgnHY/ZbsFKeiivHJ2JCun5SAxEREVEgrkzReRUOCnyBkO9vCmpxz78OotmmL5i5aHQyll83AWaJk4sDJdfUc8y3sTDfvaQoQF0hUPx1x61kL9DSx9VAozKB1HOA1AmuW8p4SKGxWlGMO3sQMN/kL1jYT4Gca7tTxq9X52Dl/oou2y4bk4BHLxrM64wAyjd5BvNtLMy3cTDXRGQkLTYnHv7vYaw/ol9MXbVkbAJ+tmCgIRfsIiIiIoMWzahFMUePHsUrr7yCG264QSuE2bVrF1avXq1tv/DCC7vsc/DgQe0+OTnZGyESEVE3hNZahG3+XwQf/k+321c7J+NR+y2oRBTiw8y4d1aG12MkIiIiIqLTyy6qw41v7kCD1aEbv2BkIl7+PgtmiIgIQH2JvkBGvXWzeEqvRKa1FcecA6S03YdxkSwyFhb2U6DmuqHVjnv+tgdbjnX9v+LeeUPwkwXDtIUTKTDyTd7DfBsL820czDURBaqqRitu/etu7C2o7bLtrhnpuPm8VF4XEBERkbGKZu666y689dZbyM3NxaBBgzBs2DAcOHAADocDsbGx+J//+Z8u+6xfv177pWnUqFHeCJGIiE5hzl2P8A2/gNRc3mVbkxiBZa0/wEfyNLW0Rht75IIshAd55b8VIiIiIiLqoQPF9bjhzR2ob9UXzMwdnoA/XD8RFhMLZoiIDKexHCjeqy+QaSzt2zHDEoG0iZ06yJwDRCS5K2IiIvIjpXWt+OFfduJQaYNuXBIFPLFkDL4/JdNnsRERERERkXfkVDTi5rd2Ia+qWTeudpX55UWDcPEoLpxCRERE/sUrs5snTpyI5557Dg899BAaGxuxZ88ebdxsNuNPf/oTIiIidK+vq6vDypUrtcdz5szxRohERNRGsNYjbPMTCD70QbfbS5Nm4dK8/0EFOlZJnDs0BnOHxnoxSiIiIqKuampqPHZsURR1KwKq162yLHvsfORbgZLvYxVNuO3d/aht0RfMnJ8VjacXD0ZzQz30X2cZT6DkmnqG+TYW5ttFaKmGVL4PprJ9kMq/1e7FxpI+HVMOiYUzcSwcSePa75WwJKDzyqHqfz0e/N3M3/LtzW4iRES+dKSsAT/8804U17XqxkPMEv5w/QTMG8GCSSIiIiKiQLe/pAH3vH8INc123Xh4kITnLx+KSZnssEVERET+x2stAX7yk5/gggsuwPvvv4/S0lKkpKTg+9//PoYPH97ltV988QUmT56sPV68eLG3QiQiMjxz3iaEb1gGqZvVRWVLBGqmPYqrtg1GBWzt42EWCQ/Pz/JypERERERdOZ1Or51LnYTpzfORb/XHfOdWteCOfx7oUjAzOTMSz10+FCZB6Xd/Jm/oj7mms8d8G4sR8i201sFUkQ1TuXrbp92khsI+HVMOioIjcQwcCWPhUAtkEsdCjkjVF8hoL/SvgiQj5Jv6Bxb2UyDlend+HR748BAarPp/X2NCTVh+9UiMTrJ49GfeKPwl3+QdzLexMN/G4Q+5ZmE/EXlKq92JBz483KVgJjnCgpeuGo7B8aE+i42IiIjIL4pmVGPHjtVuZ3L55ZdrNyIi8g7B1oCwLU8j+MA/u91uGzAbjXOfwou7bSiq169Geu/sDCSEW7wUKRERERERnUledQt+/N5BVDfrC2Ympkfg90uGIdgs+iw2IiJy32c5UsUBmNuKY7QCmbq8Ph1TtoTDkTCmvThGK5CJzOhaIENEPcbCfgqUXK89VIVfrjoOu1PRjWfGBOPlq4YjPTqYP38ewve2sTDfxsJ8GwdzTUSB5P/2lKG8sWOhXdWIpDC8eMUwxHPuEBEREfkxrxbNEBGR/zEXbEX4+p9Baijusk02h6Np5qOwjrwah8qb8fevsnXbJ6RH4IpxiV6MloiIiIiITqewthV3vncQVU36Vd7Gp4XjxSuHI8Qi+Sw2IiI6S/ZmmCoOwFSxr72LjFSTAwH6icu9oZhD4UgY3d5Bxq4WyEQPAAQWVhIRUQdFUfD3r0rx4hf5XbaNTQnH768YhphQs09iIyIiIqLekyTJYx2WTvecAkNdix1v7dTPLTp3QAxeuXoEgk1cdCVQ8f1tLMy3cTDXxsJ8u7BohojIqGxNCNv2W4Rk/737zRkz0DjvacgRqXDICv53TQ7kTnMxzJKAXywYCJGrjRIRERER+YXiOit+/M+DKG/UF8yMSQnDS1cORygLZoiI/J+jFabKg+3dY1wFMschKPJZH1KRguBIGKXrIOOMHgSI/H+BiIi+m1NWtGKZd/eUdtk2e0gMnrxkMILN/L+EiIiIqD+JiYnxynmioqK8ch7yrle/PIhGq75z1q8vG42UBObbSPj+Nhbm2ziYa2OJMmi+3Vo0s2nTJnjCrFmzPHJcIiKjMhd+ifB1j0BqKOyyTTaHoWn6MlhHfw9oK4j5x+4SHC5v1r3u1qlpyIoL8VrMRERERET03UrrXQUzpQ023fiopDAsv2oEwoO4bgoRkd9xWmGqPAxTRXZHgUzVEQiKfvJBbyiiBY6Eka7imIQxrgKZ2CGAyP8HiIio51rtMh7/9BjWH63psu3q8Yl4aH4WJJELahERERERGUVJXQve2npCN7Z4XArGpBlz0i0RERH1P279pmzOnDkQ3NxxQD2ew+Fw6zGJiAzL3oyw7c8h5Nu3u91sS5uKxvm/hRyZ3j5WWNuK17bpi2sGxYXgpikpHg+XiIiIiIjOrLzBhh+/dxDF9Vbd+LDEUCy/egQigjlRmojI55x2SNVH2opj2opkqg5DkPXdwXpDEU1wxg2HvXMHmdihgGRxa+hERGQsdS0OPPCfw/imqLHLtrtnZmjfDbj7+2AiIiIiIvJvL31+FFZHRydkkyjgwYXDfRoTERERUW+4fdaEoijuPiQREbmBqXgXItY9DKkuv8s2xRSCpuk/Q+uY6wBB7BhXFDy1NhdWR8e/7epXYY9eOBBmqeN1RERERETkG5WNroKZwlp9wcyQ+BD88ZoRiAphwQwRkdfJDkjVx12FMRWuDjKmyoMQnPpuYL2hCJJWEHOyOEa7xQ0HTEFuDZ2IiIytuM6Kez84hBPVrbpxdULc4xcNwqJR8T6LjYiIiIj6rqamaydBdxBFEVFRHR1H6urqIMsdBRbUv+VWNeO93QW6se9NyUBWfJj2mPkObHx/GwvzbRzMtbH4Q75jYmLga26dObFhw4bv3Gaz2fDoo49i165dSEhIwLXXXospU6YgKSlJ215WVqZte++991BeXo7JkyfjySefhNlsdmeIAUn9u3333XeRk5OD0tJSNDY2IjQ0FMnJyZg3bx5mzpwJk4mTZIgMy96CsC9/j+Bv/gIBXQsb7alT0DD/GchRA7psW7m/Ejvz63Vj10xIwrjUCI+GTEREREREZ1bVZNcKZvJrWrt0hvzjNSMRHcLPVIiIPE52QqrNbesgc7JA5gAEh/7f5t5QIMAZOwSOhDEdBTLxIwFziFtDJyIi6uxQWRPu+/Cwdp3RWZhFwnOXD8WUAR1frBMRERFR/+R0Or1yHnUSprfORZ73ysY8yJ2mGwWbRdw7b2j7c+bbWJhvY2G+jYO5NhbZoPl2ayXF7Nmzux1XOxUsWrQIu3fvxq233ooXX3wRYWGuSuPObrzxRjzzzDO4//778cYbb+D3v/89Pv30U3eGGJBaW1uxdu1aDBkyBBMmTEBkZCSampqwd+9erFixAtu2bcOyZcu0SjEiMhZTyR6Er3sYptrcLtsUUzCazn8IreN+oOsuc1J1kx0vfKHvSpMUYcHSmRkejZmIiIiIiM6sttmOu/51sMsK0ANig/HHa0cgNowFM0REbqfIEOvytMIYc3m2di9V7Idob+rTYR3Rg/QdZNQCGUvXz8+JiIg8ZXtuLR756Cia7foVJhPDzXjpqhEYmhDqs9iIiIiIiMh3sksasf6ovkPR9ZNSkBgZ7LOYiIiIiM6GV9qPvPnmm1izZg0WLFiAP/3pT6d9rdoh5fXXX0deXp62j/r49ttv90aY/VZ4eDj++te/dukmo1aBPfHEE/jmm2+0ApqJEyf6LEYi8jKHFaE7XkDI3jchKF3bqNmTJ6Jh/rOQYwZ+5yF+tyEPda0O3djPLsjSVpUjIiIiIiLfqWtx4K5/HcLxyhbdeEZ0EF69diTiwyw+i42IKGAoCsT6An0HmYpsiLbGPh3WGZXpKoxJaCuQSRgNJYgdfYmIyHc+zq7AE2ty4DylUb3awfLlq4YjOTLIV6EREREREZEPqQulv7KpQDcWFWzCTeel+SwmIiIiIr8umnnrrbcgCALuuuuuHu+zdOlSfPbZZ1oxCItmTk/tINNdFxlJkjB58mTs378fpaWlPomNiLzPVPYNwj9/GKaaY122KZIFzVN/ipbxNwPidxe/bMmpxZpDVbqxBcNjMXNwjEdiJiIiIiKinqlvdWDp+wdxpKJZN54WFYQV145EQjgLZoiIzqpAprFEXyBTvg+ita5Ph3VGpOk7yKgFMsHRbgubiIiorxPg3vyyGK9uLeyy7dyMCDx/+TBEBHvlq2QiIiIiIvJDO/LqsLugXjf2w/NSERHE6wQiIiLqf7zyG8yhQ4e0+8zMzB7vk5GRodvXU+rq6nDs2DHtdvz4ce3W0NCgbZs9e7ZWvNNTFRUVWLVqFfbs2YOqqiqt80tycjLOP/98XHjhhQgK8u5KTLIsa11mOv99ElEAc1oRunM5Qva81n13maTxaLzgOThjBp/2MM02J575LFc3Fhks4cF5WW4PmYiIiIiIeq7R6sDd7x/CoTJ9wUxKpEXrMMMVoImIekZsLIOp4pQCmZbqPh3TGZ7cqThmDByJY6CExLktZiIiIndyyAp++3ku/v1tRZdtC0fE4VcXDYLF1HXBPiIiIiIiMgZZ7TKzWd9lJinCgmsnJPksJiIiIiK/L5ppbW3V7gsKCjBhwoQe7aO+VmW1Wj0a22233eaW4+zevRvLly9HS0tL+5ga+8lCnHXr1mHZsmVaEY2nOBwOfPjhh9pjtfAnOzsbRUVFmDNnDsaOHeux8xKR70nl+xChdpepPtJlmyJa0Hze/WiZcCsgnvmf/RVbClHaYNON3Tc7E3FhZrfGTEREREREPddkc+LeDw7jQGlTly+p1A4zKVEsmCEi6lZjOVD8NYKPb4NU9q2rQKa56wTh3pBD4+FIHAd7pyIZJSzBbSETERF5krpw1rKPj2Frbm2XbTdOTsE9szIgCoJPYiMiIiIiIv/w+eHqLgt43T4tDUEsriciIqJ+yitFM0OGDMG+ffvw6quv4rLLLuvRPuprVYMHn74jgjvFx8cjLS2tvTtLT+Xm5uLFF1+EzWZDcHAwlixZgjFjxmjPt27dqhXMlJSU4Omnn8YzzzyDkJAQjxXNvP/+++3PBUHApZdeiuuuu84j5yMiP+C0IXT3HxCyewUExdllsz1hjKu7TNywHh0uu6QR/7enVDc2OTMSl43hxA8iIiIiIl9psTlx3weH8W1xo248IdysdZhJjw72WWxERP5EaKmGqTxb6yJjrsgGKvYD9UXatrP9RFYOjtW6xrR3kUkcCzksSf3w1a2xExEReUNVkx0/+fAwDpTpi/HV/9V+Om8AvjfRc4v/ERERERFR/+BwylixRd9lJis2GJeM5twhIiIi6r+8UjRz7bXX4ttvv8WaNWtw11134fe//71WXNIdtTvLT3/6U6xevVor+vje977n0diuvvpqrTBHvUVHR6O8vBx33313r47x1ltvaQUykiTh0UcfxbBhHZPT1eKZlJQU/O1vf9MKZz7++GPt7+NUb7/9Nux2e4/PuWjRIu24nal/p++99x5kWUZNTQ2++uorvPvuuzhy5IjW5SY0NLRXfy4i8m9S5UFEfP4QTJUHu2xTRDOap9yDlgm3A1LPOsTYnTKeWJMDpdNYkEnAzxcM1P49JiIiIiIi72u1O3H/vw9jb1GDbjwu1Kx1mMmIYcEMERmT0FoHU0W2q0imfJ92kxoK+3RMOSjKVSCT0KlAJiKVBTJEdNbU7408RRTF0z6nwOGuXOdVt+Dufx1AYa1VN66uFP3k4qGYPzyuT3GSe/C9bSzMt7Ew38bBXBNRf/effRUoOOW6YenMDJhEfkZGRERE/ZdXimYeeOABrWjk0KFDeO211/Cf//xHKxyZPHkyEhMTtcnYZWVl2LVrF/71r3+htNTV5WD48OHavp7UXQFLbxw7dgwHD7omrM+dO1dXMHPS4sWLsWHDBhQVFWHVqlW48sorYTLp/+o/++wzrWCop6ZOndqlaKbzBXdcXBwWLlyIiIgIvPDCC/jwww9xww039PrPR0R+yGlHyJ5XEbrrFQiyo8tmR/woNKjdZeJH9Oqwb+8qwbHKFt3YbeencxIeEREREZGPtNplPPCfI/iqQF8wExNiwoprRyAr1jOdbImI/I1ga4CpfL+rOEYrlNkHqS6vT8eULeFwJJzSQSYygwUyRORWMTExXjtXVFSU185FvnU2uf4qrwY/+ns2apr1C/hFh5rxxg8mYVJWrBsjJHfie9tYmG9jYb6Ng7kmov7W+f5P212dm08akxKGOUO8d31LRERE1G+LZtQOKGrRyCWXXII9e/ZoRTHLly/v9rWK4upxMGHCBHzyyScICgqCP9u5c2f7Y7Vo5ruKWGbPno1//OMfaGpqwv79+zF+/Hjda9555x2PxHfyPAcOHPDI8YnIu6Sqw67uMhX7u2xTRBOaJy1Fy7l39ri7TOcV5t485aJ3WEIobpiU3OeYiYiIiIio96wOGQ//9wh25tXrxqO0gpmRGBTPbrJEFKDszTBVHGjvHqPdanP6dkxzGOwJo9uLZOxqgUz0AEDgar9ERBT41u4vxT3vfq1dY3SWHhOCt26egiGJ4T6LjYiIiIgCoxsmOywFjn/uLUFVk77Y/r45WboFwplvY2G+jYX5Ng7m2liYby8WzaiSkpKwY8cOvPrqq1ixYsV3FnGMHDkSd955p3bzZNt6dzl8+LB2rxb3DBo06DtfN2rUKN0+pxbNeEp1dbV23x/+LonoNGQHQvb8CaE7X4Ig6y9OVY644a7uMgmje39oRcGTa3Nhc7qKFlVqR9VHLxwIk2TM/xyJiIiIiHzJ7pTxyEdHse1EnW48MljCH68ZgSEJLJghogDhaIWp8qCuQEaqOQ5B0U/q7Q1FCoIjYRScSeMRPHAqkDoBiB+Kxrp6OJ1Ot4ZPRETk797ZfgK//Gg/5I6P/zVj0iLx5x9ORmIEO80TERERGYm3umGyw1L/VNNkw193FOvGZg9LwILxWafdj/k2FubbWJhv42CujSXKoPn2WtHMycKNpUuXaje128y+ffvaizrUX8rHjh2LlJQU9CeFhYXafXJy8mkLU1JTU7vs484YEhISunTlsVqtePvtt9s79xBR/yRVH0P45w/BXP5tl22KIKHl3B+jefLdgGQ5q+P/d18F9hQ26Ma+NzEZo5K5uhwRERERkbc5nDKWfXwMW3JqdePhQRL+cPVIDE8M81lsRER94rTCVHkYporsjgKZqiMQlLMvZFFECxwJI7XuMSe7yDhjhwCiSfusNthLE0GIiL5LTU2Nx46trgbY+cvNuro6yPLZFx2S/zqbXKuLZS3fmI+3dug7zKumDYzGs5cPh9nRgpqaFo/ETGeP721jYb6Nhfk2Dn/ItbcKI4gosKzYeBwNVodu7KELh/ssHiIiIqJ+WzTTmVpkot76M5vNhoYG10TzuLi40742PDxcK2pRC1mqqqrcGse2bduwcuVKjBgxQiueCQkJ0YqR9u7dq8Wndu9ZvHhxr47Z0xjZtpPcgfn+DrITQV+/gZDtv4PgtHXZ7IwdiqYFz8OZPB5n+06saLThpY35urHUqCDcPWsA39/UZ8y1sTDfxsJ8ExF5hkNW8IuVx/HFMf3kyjCLiFeuHoGRySyYIaJ+wmmHVH2kvTjGfLJAppvuuT2liCY444bDrhbItN3Uz0bOdhERIiJv8GaHK3UiJjtqGcOZcq12rvzN6hysOtj1u75LxyTgFwuyYJK8+/NJZ4/vbWNhvo2F+TYO5pqI+oPi2ha8te2Ebuyy8akYk2bMleiJiIgo8PisaCYQtLa2tj8ODj5z+3L1NWrRTOf93OHcc8/VVis7cuSIdlOPHxoaiszMTEyfPh1z587t9eT3O++8s0eve++99+ANRm0FZVTMN4DKo8B/7gIKd3bdJojAtHshzVmGSPOZ/+05nZ+v/AqNVv0HdE9fNR6pSfHwFubbOJhrY2G+jYX5JiLqO6es4JefHse6I66OvCeFmEW8fNUIjElhJ0gi8lOyA1L1cVeBTIWrSMZUebDbBUB6Su2sqxbEnCyO0W5xwwGTvtM2ERER6TVaHXjov0exK7++y7bbzk/D7dPSIAiCT2IjIiIiosDthukPHZaob55ddQw2R0fOTKKAH52X3O3PDPNtLMy3sTDfxsFcG4s/5DvGD7phsmimj51mTjKZzvxXefI1nfdzh8GDB2s3IgoAshPY8Sqw7jeAo5sCu7ihwJIVQMbkPp9q7f5SrMou1Y1dMSENs4cl9PnYRERERETUu4KZX6/OwZpD+pWgg00iXrpyOManRfgsNiIiHdkJqTYHpvLs9i4ypsoDELr7DKOHFAhwxg6BI2FMR4FM/EjAHOLW0ImIiAJdWYMV931wGMcqW3TjkgAsWzgQS8Ym+iw2IiIiIvIP3up6xA5L/UtuVQs+2leuG7tiXAJSI809yiPzbSzMt7Ew38bBXBuLbNB8e7VoxuFwYOXKldi8eTNycnLQ0NBwxr90dbWjdevWwR9ZLBbdn+1MTr6m837+asWKFb4Ogch4qo4D/10K5G/vZqMAnL8UmPeoWyaN1Lfa8dh/s3VjMaFmPHrJyD4fm4iIiIiIek5WFDyxNhefHqjUjQeZBLxw5TBMzIj0WWxEZHCKDLE2D+aTxTFqF5mK/RDszX06rCN6kL6DjFogYwlzW9hERERGdKyiGfd9eBhlDbYunSufuXQopg+K9llsRERERETk3/64pQCyol/Q69bz03wZEhEREVH/LZrZsmULbrzxRuTn57ePKUqn37a6KZZRt/tzi/Dg4OD2x62tZ15N8eRrOu/nr+Li4nr0OrbtJHcwfL4VGUHfvI2Qrc90uzKrMyoLTQufhzN1EtCobj/71VtPemrtcZTVW3VjD8wdANHejJqavk1+ORPD59tAmGtjYb6NxR/y7Q9tO4mI3FEw8/RnJ/BxdoVu3CIJ+N2S4Zic2fFvLRGRRykKxPqCju4xWpFMNkRbY58O64zKdBXGJLQVyCSMhhLE7llERETutDu/Dg/+9ygarfqFCmNDTXjxyuEYlRzus9iIiIiIiMi/ZZc0YsNR/fy/6yclIz7M/xcFJyIiIvK7oplDhw7hoosuQktLi1YIo3ZaGTp0KGJjY7UJd/2V+ueIiIjQOuZUVVWd9rWNjY2wWq29KkjpD9i2kzzBSPkW6/IRse5nMBfv6HZ7y/gfomnqg67uMm76O9lb1IB/fV2mG5uaFYWLRsT65O/dSPk2OubaWJhvY2G+iYh6T/185Ll1J/Dvb8t142ZJwHOXD9N+Ryci8liBTGOJvkCmfB9Ea12fDuuMSNN3kFELZIK5qj0REZEnrT5YiV+vzoHdqV+oMDMmGC9fNRzp0f6/kB8REREREfnue4rlmzoWQFdFhZhw4+QUn8VERERE1K+LZp566ik0NzdDkiT8+te/xr333ovw8MBY1Sg9PR0HDx5EaWmpNlFQ/TN2p7i4WLcPERmcIiM4+12EbXsGgr1rZxdnZCYa5j8DR9p5bj2tzSHjiTU5ujG1reqyC7L8urMXEREREVGgfRH1+w35+NdefcGMSRTw28uGYvogTjInIjcWyDSV6brHaAUyLdV9OqwzPLlTccwYOBLHQAkJnIWCiIiI+sM1xTu7SvDypoIu28alhuP3S4YhOtTsk9iIiIiIiKh/+PJEHb4qaNCN3XxeKsKDvDKllIiIiMirvPIbzvr167XJ2Pfddx9+/vOfI5AMHz5cK5pRu8jk5ORoHXS6c+DAAd0+RGRcYn0Rwtf/DJbCbd1ubxl7I5rOfwiwhLn93H/ZUYwT1a26sR/PSEcaV5sjIiIiIvLa5DZ1Ytu7e0p145IAPH3pEMwaHOOz2Iio/xOaK9sLZMwnO8g0V/TpmHJoPByJ42DvVCSjhCW4LWYiIiLqHaes4Pn1efjnKR3lVXOHxuB/Fw1BsFn0SWxERERERNQ/yGqXmc36IvykCAuuOSfJZzERERER9fuimcrKSu3+iiuuQKCZMmUK/vOf/2iPN2zY0G3RjCzL2Lhxo/Y4LCwMo0eP9nqcROQHFAVBB95D2JanINobu2x2RqShcf4zsKdP88jpcyqbtaKZzkYmheF7E5M9cj4iIiIiIupaMPPHLYXaitCdiQLwxCVDMHdorM9iI6L+R2iphqk8G6aKti4y5fsgNeoL8npLDo7Vusa0d5FJHAs5LAlgd1oiIiK/0Gp34uH/Hsb6I127xl07IQk/nTsAknqBQUREREREdBprD1XhSHmzbuyOaekIMrEAn4iIiAKTV4pmEhISUFxcjJCQEASaIUOGYOTIkVq3GbVoZs6cORg2bJjuNZ988gmKioq0xxdffDFMJrYwJDIasbEE4euXwZK/udvtLaO/h+bpy6BYwj22QsQTa3PhkBXdStaPXjgQJn6BRkRERETkFa9vK+pSyK7+Ov6bRYOxYEScz+IiIv8ntNbBVJHtKpI5WSDTUNinY8pBUe2dY9oLZCJSWSBDRETkp2qabPjR27vxVV5Nl233zsrAjZNTIPD/cSIiIiIiOgO7U8aKLfrPFgfFheCS0fE+i4mIiIjI07xSvTFjxgy89957yM7OxsSJE+FPDh06hNLSjhUY6+vr2x+r41988YXu9WpRzKl++MMf4rHHHoPNZsMTTzyhddRRu8moz7dt24bPP/9ce11KSgouvfRSj/55iMgPu8sc+gBhm5+AaGvostkZnozGec/AnjnTo2F8sLcc3xbru9vcMDkFwxPDPHpeIiIiIiJyefPLIvxpu2tBjZPU6WyPXzQIF43kF1FE1EGwNcBUvt9VHNPWRUaqy+/TMWVLuK44RiuQicxggQwREVE/UVDdjJv+vBM5lU26cXVRrF9dzGsKIiIiIiLquf98W4GiOqtu7K4Z6exaSURERAHNK0UzDzzwAD744AO89NJLuO666/yq08q6deuwcePGbrcdPnxYu52paGbgwIG4//77sXz5crS0tODdd9/t8hq1YGbZsmUB121HkiSPHFcUxdM+p8ASqPkWGssQum4ZLCfWd7vdOupaNM96FAiKhGfeSS5l9Va8srlAN5YRHYwfz8j02HvYiPmmrphrY2G+jYX5Jn/lyd9t+HNvLO7O91s7irqs2qZ6/OLBuHxcUp+OTX3D97ax+GW+bU2QKg7AVP4tpDK1QOZbSDU5fTqkYg7VCmScSWpxzDjtXo7OAoSOP6/69bf3PxHwLr/MN3kM801EgexEVQtu+7+vUNmon9QWZpHwuyVDMSkzymexERER0RnIMqDIvo6CiKhds82JN05Z4GtcajhmD4nxWUxERERE3uCV6pXJkyfjxRdfxL333osrr7wSf/7znxEfH1grHk2aNAnPP/88Pv30U+zZswfV1dVacVBycjKmTp2Kiy66CEFBQQg0MTHe+YU5Koof+BtJv8+3ogDfvgeseghoreu6PSIFuPRlBA1bCE//q6AoCh766Cs02Zy68d9ePR4pif7x73C/zzf1GHNtLMy3sTDfZLTrExV/7o2lL/l+Y3MOXvoir8v4k1eMwfXnDehjZORufG8bi9fzbW8BSvcBxXuB4q9dt8rDfZs8YwoBkscCqRPab0L8UJhFCWZ3xh4A+P42FuabiAKFrCh4fOXRLgUzieFmvHzVCAxJCPVZbERERKQn2BohVR2CpeoI0HAcKM0Gyg9AuuZ9OGOH+zo8IiLNu3tKUdVs143dPTMDAjtSExERUYDzStHMb37zG+1+ypQp+OSTTzBgwAAsWLAAI0aMQGjomT/Mffzxxz0W29KlS7WbOyQkJOCmm27SbkRkUA1lwCc/AQ6v7H77+O8DFz0NhHhnQueq7FJ8frBMN3bNuemYNsQ/CmaIiIiIiALZX7edwBMrD3YZ//Vlo1kwQxToHFagLLujOEYtlCk/CCj6RS16RbJ0KZBB/HBA8p+u3kRERORen2RXYl9Jo25sSHwoXrpqGJIiAm+xPiIion5BUSA2FMJUeRBS5SHt3lR5CFJ9frcvlyoPAiyaISI/UNtix9s7S3Rj0wZGYWJGpM9iIiIiIvIWr3yj+qtf/aq9Glm9b2lpwccff6zdesKTRTNERG7rLpP9AfDpg0BLTdft4UnA4heBEYu8FlJdsx2P/3e/biw+3IJfXDLSazEQERERERnV33fk4Zcf6X8fVz16yUjcNC3LJzERkYc47drKsR0FMl8DZQcAWb9iY6+IJiBptL5AJmEkYLK4M3IiIiLyYw2tDryyWT/5NisuFH++YQxCTVwFmoiIyCvsLTBVH9EKX9TCGK1QpuoQRJu+qPV0pIqDwLAlHg2TiKgn/vJlMZpsHYv6CG1dZoiIiIiMwGvLECrqhPLTPKf+qaamm+IANxBFEVFRUe3P6+rqIMuyR85Fvtff8y00VyJ0w2OwHFvV7Xbr8MvRMvtXUNTuMh56z3TnN6uOobLRqht7cF4WFGsTaqxN8JX+nm/qOebaWJhvY/GHfMfEeKdrG/Uvnro+8Zefe/Kevub739+U4Terj3cZv2/OAFw1JsajP6vUO3xvG4tb8i07IFYfg6nsW0jl+1z3lQchOG1nHZciSHDGDYUzcRwcSWO1e6faQcYUrH9hg3ot77vr+f6G729j8XW+eX1CRJ7w+rYiVDc7dGO/vGw0IoJMcDr70L2OiIiIuu8e01TWURxT5eoiI9XmQlD6dm2hdZoh8hOSJHnsuvx0z8n3Suqt+NfeMt3YxaPiMTKl911mmG9jYb6Nhfk2DubaWJhvLxbN8MvIwOWtD+XVnyF+AWAc/SnfaqFM+BePQ2yt7rJNDolF45wnYBt8oWvAi3+m3fn1+Pe35bqxGYOiMX9otN/93fanfFPfMNfGwnwbC/NN/sKbP4f8uTeW3uT7k+wK/O/qnC7jd85Ix42Tkvlz4+f43jaWM+ZbdkKqzYFJLY45eVMLZBytZ31OBQKcsUPgSBgDR+JY1y1+JGAO6fpi/iy6Fd/fxsJ8E1F/d6yiGe99Xaobu2BkIuYOT2QRPhERUV85rZCqj2vX+GqBjFTluhdb+/5/rLowhhw7GFLqeCBpDJA8Bk3BmW4Jm6g/LfrQeWEL8g9Pff4NbM6ORc7NkoCfXTIWMTGhfT42820szLexMN/GwVwbS5RB8+21TjNERIFEaKlB+KZfIujoym63W4csQqPWXSbO67G12mU89VmubizULOJnF2RBENTmqkRERERE5AmrD1bi16tzcGpv3dvOT8OtU9N8FBUR9YgiQ6zNg/lkcUyFetsPwd7cp8M6ogd1FMecLJCxhLktbCIiIgo8iqLg+fV56DSfDRaTiMcWj/JlWERERP2S0Fzp6hxTeRBSVdt9zXEIsr6b29mQg6LgiB8BZ/xIOOJGuh7HDoUUFKorTFDUglcW9RORDx0ta8AHewp1Y9dNyURmXN8LZoiIiIj6CxbNEBH1kiXnM4R/8SjE5sou2+TgGDTO/jVsQy+Br7z5ZRHya/Sr3t41MwPJkUE+i4mIiIiIKNB9frgKj396vEvBzM3npeL2aSyYIfIrigLUnID56BYElX7TViSTDdHW2KfDOqMyXYUxCW0FMgmjoQRFuC1sIiLqfyRJ8tixRVE87XPqv9YerMTugnrd2B2zBmFAnKvwlrkObHxvGwvzbSzMt4fJDog1OZAq1K4xByCpxTEVByE2V/T50GrXWDl6IJwJI7UCGa1IJmEklPAU4JRFK9Xf/phrIvI3z605DLnTlxehFgl3zxvqy5CIiIiIvI5FM0REPSS01iJs8/8i+PB/ut1uHbQQjXP+F0poPHzlaEUz3t5VohsbkxKGa85J8llMRERERESBbsPRavzik2O6L51UN05OwV0z0tnxkcjXFAVS1REEFW0DSnYAhbuA1lqE9+GQzoh0OBLHdHSQUQtkgqPdGDQREQWCziuMe1pUVJTXzkWe02xz4MWNe3RjqVHBuGvOkPbnzLWxMN/GwnwbC/PdBy01QGk2UJbddr8PKD8EOK19P7YlAkgaDSSPAZLGAMljISSOhGQJ0wpizgZzTf6kRu185AFqcVjnn/W6ujrIsuyRc1HvfFPUgLUHynRj109KgcnRjJqas+uyzXwbC/NtLMy3cTDXxuIP+Y7x4mflflM0U11djb/85S/4/PPPkZ2drT1XxcbGYsyYMbjgggtw8803a8+JiPyFOXc9wjf8AlJzebdtl5tm/QrWYZd2WUnGm5yygv9dk6PdnySJAh5dOEi7JyIiIiIi99t0vAbLPj4G5ykFM9+fmIx7Z2WwYIbIR4SmclgKtsJcsEW778vKss7w5E7FMer9GCgh/OySiIiI3O8PG46hpE7fSf7RxaMQYvFc1yIiIiK/pU7iqs5xFcV0LpKpL3TP8aMHaEUxruKYtiIZdYydYSiAOZ1Or5xHnYTprXPRd1MUBS9/cUI3FhViwvXnJrk1P8y3sTDfxsJ8GwdzbSyyQfPt1aKZ1157DQ8++CCam5vbfzE7qaioCMXFxVi7di1+9atf4Xe/+x1uv/12b4ZHRNSFYK1H2OYnEHzog263W7Pmo3HuE1DCEuFr731dhgOlTbqxH05JwZCEUJ/FREREREQUyLbm1OKRj47CcUqLGbXT4wNzM1kwQ+RN9haYi3e6CmXyN8NUfeSsDiOHxsOROA729iKZMVDCEtweLhEREdGpTlQ24U+bcnVj0wbH4eIxyT6LiYiIyGusDUDZgY4CmVK1e8wBwH52XRB0TCFA0qj2zjHavfo8mB1giCiwbcutw57CBt3YLeelIjzI6+usExEREfmc134DeuaZZ/CLX/yivVBGbfMzYcIEJCe7PugtLS3F119/rbX8aWpqwp133ona2lo8/PDD3gqRiEjHnLcR4et/DqmptMs22RKBplmPwzr8Cp92lzmppM6KP24p0I0NiA3GLVPTfBYTEREREVEg+/JELR767xHYT2kxc8W4RDw0fwALZog8TZEhVexv7yZjLv4Kgmzr1SHkkFitKKa9i0ziWMhhSX5xnU9ERIGhpqbGY8cWRVH7ru0k9fs1dYVA6r8e+/dB2JwdOTSJAh6Yk4H6+nrm2kD43jYW5ttYmO82igKxoRBSxUFIlQc77uvy3HJ4OTwFjviRcCaMhLPtXo7KAsRTura1yEBLTcDmOiYmxqvnIyL/IysK/rBZP48oOcKCq89J8llMRERERAFfNJOdnY3HHntMK5hJSUnBc889h2uuuQZms1n3OofDgX/961946KGHtK4zjz76KC655BKMHj3aG2ESEWkEWwPCtjyF4APvdbvdNmA2Guc+BTncP1Z3U/9tffrzXLTY9R+0PbpwIIJMbB1NRERERORuu/Lr8NP/HIHtlIKZy8YkYNmCLIiccE/kEWJDsVYgY1GLZAq2QWzt3eQWZ0QapKHzgUFzgPTJqJPD4TTiBCUiIvIap9PptXOpEzG9eT5yr83Ha7RbZ9dOSEJWTFCXSbbMtbEw38bCfBuLIfLtaIWp6ohWFGNSb1WHIFUegmjTdz04G4pogTN2CBzxI1xFMup93EgoId0Ui6gf4fnw79oQuSYiv7P2UBWOVOi7df14ejrnEREREZFheaVo5pVXXtEuABMSErB9+3ZkZmZ2H4zJhO9///uYMWMGJk+ejIqKCm3fFStWeCNMOguSdMpqHG5ceeN0zymw+FO+TflbEPbZwxAbi7tsUywRaJ71GGyjrtFWjfbMT3/vrTpQobVU7eyq8UmYNMA/V4/xp3yTZzHXxsJ8GwvzTURGtqegHj/58AisDn3BzKJR8fjFwoEsmCFy86IW5qIdMOerRTJbYKrN7dX+siUc9rSpsGfMgC1zBoTYwYiJje14gQdX/yciIiLqKatDxu826FfXjws14/Zp7CRPRET9qHtMU1lbccwhmKrU7jGHINXmQlD6vliFHBKnFcaoBTJq9xi1OMYZMwiQ9Av1EhGRi90pY8WWQt3YoLgQXDwq3mcxERERERmiaGb9+vXaBPNly5Z9Z8FMZxkZGXjkkUfw05/+FOvWrfNGiOTnLV07t66lwOeTfFsbgM8eB3b/ufvtg+ZCuGw5wqIzEAb/UdNkw+/W79aNJUYE4fEl4xEV0j8+JOT72ziYa2Nhvo2F+SYio/imqAH3fXgYrQ79l/0XjojDLy8aBElkwQxRn8gOmMq+hblgq9ZNxlS2F4Ls6PHuiiDBkTS+rUhmOhyJ43UTaCQWtREREZEf+vvuEhTWWnVj98zKQHiQV77GJSIi6h2nDVL1MVfnGLUwpsp139tusN91Xa8Ww3TuHKM+VsIS3BI6EZFRfPhNOYrq9NcYS2dm8DsMIiIiMjSvfNpaVFSk3U+bNq3H+0yfPl27Ly7u2u2BiMitcjcB/10K1OZ33WYJBxY+AZz7Q8APJ9c8sfIgqppsurHfXD663xTMEBERERH1F9kljbj3g0NosesLZuYPi8WvFw3ml01EZ7sSbV0eLAVbtU4y5sLtEG0NvTqEMypL6yKjFsqoXWWUoAiPhUtERETkbqX1Vvz5S/13oeNSw7FoNFeAJiIi3xOaK12dYyoPQqpqu6853qsFLr6LHBSl6xyjPY4dCpiC3BI7EZFRNducePNL11zNztcYswZH+ywmIiIiIsMUzUiSpN07HD2/cHY6ndq9KIoei4uIDM7WBHz+a2Dna91vHzgLuOwVIGYA/NHmoxX4YI++neqFo5Nw0ZgUn8VERERERBSI9pc04u73D6HJpi+YmTMkBk9eMhgmFswQ9ZjQWqsVx6hFMpb8LZAa9Ne1ZyIHRcOeMQ22jOlaoYwcme6xWImIiIg87aWN+bpOluqVxcPzsyD64SJeREQUwGQHpJqcTp1j2rrHNFf0+dAKBDijs1zFMW3dY9QuMnJ4il8uWklE1N/946tSVDc7unSyFPhvLhERERmcV4pmMjMzcfDgQaxbt67H3WbU157cl/xXTU3fW+x2Ry2WioqKan9eV1cHWdZPTqLA4Yt8m4p2IvSzhyDV5XXZpphC0DLj57COu16NTv1Bh79psTvxs/e/0Y2FWyQ8MDvDY+9Ld+H72ziYa2Nhvo3FH/IdExPj1fMRkXFlF9XhrvcOoNHqWtzjpJmDovH0pUNgkrjYB9FpOW0wlX4Ni9pJJn8LTOX7IEDp8e6KaIY9ZaKrk0zGDDgSRgOia4EeIiIiov5sd34dPjtcrRu7YnwiRiSF+SwmIiIKfEJrXXvXGK1IRr2vPgLBaevzsWVzOJzxw+HQusec7CIzDDCHuiV2IiI6vZpmO97Zpe9kOWNQNCakR/osJiIiIiJDFc0sWLAABw4cwPPPP48lS5Zg7Nixp319dnY2nnvuOa3CeeHChd4Ikc7SyY5AnqZOwvTWucj3PJpvewvCvvw9gr/5S7eTdOypU9Aw/xnIUQMAWd3unz93Kzblo6jOqhu7e1YG4kKlfvde4fvbOJhrY2G+jYX5JqJAdai0Hje+uQP1rfpV2aZlReG3lw2FmQUzRF0pCqSaY+2dZMzFOyHYm3t1CEfsUK1AxpY5Q7tO5+QaIiIiCjQOp4xn1+kX9YoMlnDXDHbRIyIiN1FkiHV5WmGMWiAjqfdVByE16CdTny1nZIbWOcYZ19ZBJn6kqxuswM/LiIh85S87itFk03eyXDozw6cxERERERmqaOb+++/Hq6++isbGRsyYMQOPPfYYbr75ZsTFxeleV1VVhb/85S948skn0dDQgODgYG1fIiJ3MJXsQfi6h2Gqze2yTTEFo+n8h9A67gd+/0HeobIm/P2rEt3YOWkRuHJ8os9iIiIiIiIKNEfLGnD9n3ZoK7N1NmVAJJ69fBgsJv++biDyJqG5EpaCbVqhjLlgK6Sm0l7tL4fGw5Y+HfYM100OT/ZYrERERET+4F97y5FT1aIbu2tGBqJDzD6LiYiI+i/B1gip6nBH5xi1QKb6cK8XseiO+j26I3YYnFphzCitOMYZNxxKUIRbYiciIvcoqbPiX3vLdGMXjYrD0AQuSERERETktaKZAQMG4LXXXtMKZdTCmUceeQQ/+9nPMHDgQCQmJmodZcrKypCbmwtFUbSbOqbuk5mZyUwRUd84rAjd8QJC9r4JQelYUeEke/JENFzwLOTogfB3DlnB/67JcTXBaWOWBDy6cCBEQV0jgoiIiIiI+upEVQtu+78DqGqy6cbPzYjA75cMQ7CZBTNkcI5WmIt3u7rJFGzRVqztDUUK0jrIqAUytsyZ2mQb8JqWiIiIDKKqyY5XtxbqxoYlhuKKcVwYi4iIzkBRIDYUdXSOUQtk1O4xdfluObwzLLmtOMbVPcapFshEZQGi5JbjExGR57y2rRB2Z8dkIpMo4MfT2MmSiIiIyKtFM6of/OAHWmeZO+64A8XFxVphzPHjx5GTk6NtV5+flJqaitdffx2LFi3yVnhEFKBMZd8g/POHYKo53mWbIlnQNPVBtI7/Yb/5oO8fu0twuFy/ItCtU9OQFRfis5iIiIiIiAJJo9WBH/9zPyobbV26O75wxXAEm/vHtQORWymyNhlHLZDRuskU74bgtPbqEI6E0bBpnWRmwJ4yCTAFeSxcIiIiIn/2yuYCNNmcurGH52dBEllETEREnThaYao60tY5Ri2OUbvIHIJoa+jzoRXRAmfsEK0wRusco97HjYQSEuOW0ImIyLuOVTRj5f5K3dhV4xORFh3ss5iIiIiIDFs0o7rkkktw4sQJ/Pvf/8bnn3+O7OxsVFdXa9tiY2MxZswYXHDBBViyZAnMZrYfJ6I+cFoRunM5Qva81n13maRz0HjBs3DGDEZ/UVjbite2FenGBsWF4KYpKT6LiYiIiIgo0Hz4TTnKGvQFM+NSI/DSVcMRamHBDBmH2FjqKpAp2KoVy4gtrs/wesoZnqwVyGiFMunToITGeyxWIiIiov5iX3EDPs6u0I1dPCpOK9InIiIDd49pKmsrjjnk6hxTeQhSbW6333P3lhwSp+scoxbHOGMGARLn5BARBYo/bilAx3LlQKhZ1BbgJSIiIiIfFc1oJzSZcM0112g3IiJPkMr3IULtLlN9tNtVc5qn3o+Wc24FRK//E3jW1G5cT32WC6uj44NRdc25Ry8cCLMk+jQ2IiIiIqJAsv6ovjBgXHoUXrl6OEJNXPWZApytCebina5uMvlbYKo51qvdZXMYHGnnuYpkMmfCGT0IEPi+ISIiIjrJKSt4dl2ebkydzHbfrEyfxURERF7msAGVh4HSbITk7YZYcUArlBFbe7dQRXcUQdKKYTp3jlEfK2EJbgmdiIj80zdFDdh0vFY3dv2kFMSGsTiSiIiIqLP+M2OciOhMnDaE7v4DQnavgKA4u2y2J45F4/xn4Ywbhv5GbaO6M69eN3bNhCRtxWsiIiIiInKPsgYrskuadGMPLhyOiCATnM6u1xhE/ZrshKkiW+smY8nfClPpHgiyvce7K4IIR+I42DOmw5YxA47kcwDJ4tGQiYiIiPqzj7IrcLBMf71x27R0xIfzdygiokAktFS5OsdUqp1jDsJUdQioPg60XXsH9+HYclCUrnOM9jh2KGAKclv8RETUPxbgfWVzgW4sOsSE6ycl+ywmIiIiIn/FohkiCghSxQFXdxn1w8ZTKKIZzVPuRcvE2/tVd5mTqpvseOGLfN1YUoQFS2dm+CwmIiIiIqJAtOFoje55ZLAJUwfFoamhzmcxEbmTWF+gdZGxFGyFuXAbRGvvfradkZmuTjIZM2BPPx9KcJTHYiUiIiIKJHUtji6T2QbEBuN7E5N8FhMREbmJ7IBUm+sqjGkvkjkEqbm8z4dWIMAZneUqjmnrHqN2kZHDU9jdlYiIsDWnFl8XNujGbp2ahvCg/jc3ioiIiMjTvPIb0r59+3D55ZdDkiR88cUXSEtLO+3ri4qKMHv2bK0aetWqVRg2rP91hSAiL3HaEbLnVYTuegWC7Oiy2ZEwGg1qd5n4EeivfrchD3Wt+j/bIxdkIcwi+SwmIiIiIqJAtOFote75BSOTYDGJ0K8FTdR/CNZ6mAu3w1ywFZaCzZDq9AsynIkcFAl72vmwZ87QimXkqAEei5WIiIgokL26tVArnOnsoXkDYJZEn8VERERnd53tKo5xFchoj6uPQnBa+3xs2Rymfaft0LrHnOwiMwwwh7oldiIiCixyN11mUiItuGp8os9iIiIiIoLRi2b+9re/4cSJE7jwwgvPWDCjUl+jFsqsWbNG2/c3v/mNN8Kks6AWQnmCKIqnfU6B5WzzLVYeRthnP4WpPLvLNkU0oXXy3WidvBSQzOiv5SVbjtdgzaEq3djCEXGYOywe/RXf38bBXBsL820szDcRBaKaZnuXFdkuGpPss3iIzorTDlPZN7AUbIG5YIv2WFDkHu+uXks7kie0dZOZCUfimH7ZsZWIiIjInxwpb8IH35TpxuYOjcHUrGifxURERGegyBDr8nSdY0xVByE1FLvn+NGZsMUO14pjtCIZtXtMZAYg8LN2IvIszvMKHKv3V+BYZYtu7M6ZmQgJMnv83My3sTDfxsJ8GwdzbSzMt4tXvvXeuHEjBEHAZZdd1uN91M40q1evxrp161g048diYmK8cp6oqCivnIf8wxnz7XQA214CNjwNyPau2xNHQ7hiBUJSxiME/VeT1YFnPv9aNxYZbMITV52DmIhgBAq+v42DuTYW5ttYmG8iCgQbj9VAVjqeh5glzBqW4MuQiM5MUSDV5moFMmo3GXPhlxDtjb06hCNmMOwZ02FTi2TSpkCxhHssXCIiIiKjURQFz63P011rBJkE/GQOO/gREfkLwdYIqepwR+cYtUCm+jAEe3Ofj62YguGIHaZ1kJETRyN04BQgaTQQHIWmmho4nU63/BmIiHqK87wCg9XhxKtb9fOJhidF4PrpwyCJgtfjYb6Nhfk2FubbOJhrY4kyaL69UjRz5MgR7X7cuHE93mfMmDHa/eHDhz0WFxH1Q+WHgP/cCRTv6bpNkICZDwCzHgZMFvR3z689jKJa/aoQj14yCokBVDBDREREROQv1h+t1j2fOyIBweb+2rOSApnQUg1z4TZY1CKZ/M2QGkt6tb8cHAt7xjTYMmZoxTJyRKrHYiUiIiIyOrWT/KkdLW+akorUqCCfxUREZFiKArGhGKbKA67OMWqBjNo9pi7fLYd3hiVrxTEnO8c440fCGZUFiFJ7d4dQL01WJyKiwPbujnwU1ujnEz180XCfFMwQERER9RdeKZppbHStcBke3vOVKk++tr6+3mNxEVE/IjuB7a8A658EnNau2xNGAEtWAGkTEQi+zq/BW9tO6MbOHxSHayal+ywmIiIiIqJA1Wh1YGee/vOHC0cn+yweIh2nFeaSr2DO3wpLwWZIFQcgoNNS5WegSBbYUybBnjEDtswZ2qQdCMZsuU1ERETkTU02J17aqJ+InRoZhB9MZtEyEZHHOVphqjoCqaqtOEbtIlN1CKK17/NPFNECZ+wQrTBGLZDRCmXiRkIJYUEMERF55/uM5euP6cYmDYjBvBGJPouJiIiIqD8weau1Y2VlJUpLSzF+/Pge7aO+VhUREeHh6KgvampqPHJcURR17Z/q6uogy7JHzkW+d6Z8izXHEbb2QZhK9a1FVYogovXcO9B63n2AKVj9oUR/Z3fKePhf36qLHbWzSAIemZ+J2tpa9Hd8fxsHc20szLex+EO+vdU+noiMYUtOLRxyxy/gZkngF0zkO4oCqeoIzAVbtCIZc/EuCI7WXh3CETcCtozpsGfO1ApmYA7xWLhERERE1L03txehotGuG3tgbiaCzSxgJiJya/eYpnJIVa7CGLVARu0iI9XmQFD6/pm1HBKn6xyjFsc4YwYBktkt4RMReRvnefV/r20pQFWTTTd214w0r84nYr6Nhfk2FubbOJhrY/GHfMf4wTwvrxTNDB06VCuaWb16NS688MIe7bNq1SrtfvDgwR6OjvrC6XR65Tzqm9Nb5yLfa8+37ETwt39F2PbnIXTTXcYRPQiNFzwHR/I5roEA+Rl568siHK1o1o3dPi0d6VGWgHwf8P1tHMy1sTDfxsJ8E1F/t+5Ite751KxoRARz8gN5j9BUDkvB1rZCma0Qmyt6tb8zNBF2rUhmBmzp06GEJXgsViIiIiI6sxPVLfjHV64FAk86PysKs4f4/sthIqJ+y2mDVHO8o3NM273Yqv9c52wogqQVw3TuHKM+5vU1EQUazvPq32qa7Xh7Z5FubOagaIxLCfPp3zfzbSzMt7Ew38bBXBuE0wE0lkKWffu7Q0AXzaiFMtu2bcPrr7+O22+/HSNHjjzt6/fv348//elPEAQBF110kTdCJCI/I9aeQMS6R2Au2d1lmwIBLRNuRfN5P3F1lwkgedUteGO7/gJ3WEIobpiU7LOYiIiIiIgCWavdiW25dbqxecNifRYPGYS9Bebina5CmfzNMFUf6dXuiikE9rQpsGXMgD1jBpyxQwFB8Fi4RERERNRziqLgd+vzdN0sTaKAn84boH33SUREZya0VHXqHNNWJFNzHIKs7+B1NuSgSDi1opgRbV1kRrquq01BbomdiIjIU/78ZTGa7R2rwqtXF0tnZvg0JiIiIvLHjqxlkGpztXnY6r2pLg+ozwNqTrg6p965H0bklaKZO++8E88++yyam5sxb948rSBm8eLF3b72o48+wh133IGWlhaEhoZi6dKl3giRiPyFLCNo71sI2foMBEdrl83OqCw0XPBbOFImIdDIioIn1+bC5uz4Ik0UgF8sHAiTJPo0NiIiIiKiQLX9RB2sjo4vmSQBmD2ERTPkZooMqWJ/ezcZc/FXEGRbz3eHAEfiGK1ARusokzIRkDiZh4iIiMgfbTxeo11ndHbducnIig3xWUxERH5LdmgTeE4WxriKZA5Bai7v86HVa2k5akBbYcwIONsKZOTwFC48QURE/U5xnRXvf1OmG1s0Kh5DEkJ9FhMRERH5jtBS47qernMVxkhtBTJSXR4Ee/N37yjbITSVASHG66zqlaKZ+Ph4vPrqq7jxxhtRXl6Oyy+/HIMGDcKMGTOQkpKivaakpASbN29Gbm6utgKTutLSihUrkJSU5I0QicgfVOcC/70boXlbuv1Qs3X8D9E09aeAOTC/WPpoXwX2FDboxr43MRmjU8J9FhMRERERUaBbf7Ra93xiRiRiQs0+i4cCh9hQrBXIWNQimYJtEFtrerW/MyJNK5DRusmkT4MSEuOxWImIiIjIPVrtMn6/Pl83Fh9mxq3np/ksJiIifyFY69uKY9o6x6j31UchOK19PrZsDoMzbrira8zJDjJxwwEzJxITEVFgeG1rIeydFuE1SwLumJ7u05iIiIjIw2xNbUUxJwtj1Fueq4uMtfasDyvV5MDBohnPuf766yHLstZ1Ru04c/z4ceTk5OheoxbLqMLCwrSCmRtuuMFb4RGRLykysOsNYO3jgL2py2ZnZCYa5v8WjrQpCFSVjTa8uFH/RVpKpAU/5gUuEREREZHH2J0yNh/Xf5g0dygLE+jsCLYGmIt2wJyvFslsgak2t1f7y5Zw2NOmat1kbJkzIEdlceVbIiIion7mnV3FKK7XT/6+f04mwiySz2IiIvI6RYZYl9/WOeaA1jnGVHUQUkOxWw7vjEjXdY5RH8uRGYAguuX4RERE/uZYRTM+PVCpG7t6fBJSo9iNnIiIqN9z2iDVF7gKYmpchTHiyUIZtSOMB4h1+UDqeTAarxXNqNROMwsWLMDLL7+MlStXIjs7u71QRhRFjB07FpdeeinuvvtudpghMgpFRtjqnwJHPup2c8vYG9F0/kOAJQyB7Ln1eWi0OnVjyxYMRCi/SCMiIiIi8phd+fVdfg+fMyTWZ/FQPyM7YCr7FuaCrVo3GVPZXgiyo8e7K4IER9L4tiKZ6XAkjgckdjkiIiIi6q+K66x4a6d+QviE9AhcOCLOZzEREXmcrQmmqsMwVbV1jqk8CKnqCMRuFkrsLUUK0rrFtHeOUbvIxI2AEhThltCJiIj6iz9sKUBHjxkg1CzilqmpPoyIiIiIekV2Qmws6dQtRr25CmPEhkIIauMBN1NMIXBGZ8EZPRByzCCEpI0G4oZoN5tVAJz6eRJG4NWiGVVycjKeeuop7eZwOFBdXa2Nx8bGwmTyejhE5GOh25+DpZuCGWdEGhrnPwN7+jQEui+OVWPdEde/hSddPDIO0wZG+ywmIiIiIiIj2HBU/3v4uNRwJEZYfBYP+TlFgViXB0vBVq2TjLlwO0RbQ68O4YzK0rrIqIUyalcZTvQhIiIiChwvfJEHq6NjKpsoAA/NGwCB3QOJKFCuiRuKXR1jtOIYtYvMQW11WkE3jffsOMOSOxXHuLrIqNfQELnAIBERGdvewgZsPl6rG7thcgpiQrkAExERkV9RFAjNlR1FMVq3mJPFMXkQZJv7Tyma4IzM1Apj1AIZua1IRiuUCUsC2j6XlCQJITExHTtaa2BEPq1SUYtkEhMTfRkCEflQ0P5/InTP613GrWOuQ+O0R6BYwhHoGq0OPPv5Cd1YVIgJD8wd4LOYiIiIiIiMwCkr+OKo/sOguUPZZYb0hNZarThGLZJRi2XU1ti9IQdFw54xDbaM6VqhjByZ7rFYiYiIyP+oX0Z6iiiKp31O3vVlbi02nHJ9cc2EZIxMiezzsZlrY2G+jcVv8+1o1brFSBWuAhmp8gCkykMQrfV9PrQimuGMG+oqilFvCaO0YhklpOtnMoFWLuO3+Sa3Y66JyF0URcHyzfm6sZgQE66flOKzmIiIiIxOsNaf0i3G9ViszYNob3T7+RQIkCNS2wtjnJ0LYyLSAJENS3qKf1NE5BPmwm0I3/i4flD9x/vad9CcdD4Ug7T++sPmApQ32nVjD8zJ5IoQREREREQetreoATUtDt3Y3KGdVlchY3LaYCraDYvaSaZgK0zl+3rVDlud/GNPmejqJJMxA46E0VwVl4iIyMBiOq/e52FRUVFeOxfp2Rwynt/wjW5M/Yx/2eKxiA51fydL5tpYmG9j8Xq+FQVoKAXKsoHSfW332UDVUaAX18LfKTQeSB4DJI8FksZqj4X4YTBJZk5U4fvbUJhrIjpbW3Jq8U2RfvLtreenIczCz5yJiIg8yt4CqS5PVxRzsnuM2FLtkVPKofGuYpioLH2BTNQAwBTskXMajdc/izh69CjefvttbN++HaWlpWhpacGaNWswZMiQ9tdkZ2cjPz8fYWFhmD17trdDJCIPk2qOI2LVUgiyfoIaFr8AjFgE1NQYZpLev/aW68bOGxCJRaPifRYTEREREZFRbDiq/zBrWGIo0qNdHzaZctcDn38C2NQvowSE2e3tq7icbGGs0R6fvHV63s1rlNO9pv356V+jnPY1p4x/R3xKD15zpuO0x6E9Pd1xTnlNW7xKD15zur+Ljjx0c+72l333a5ROrxHV1d/DwoHGMuD4BkSf2AzB3ozecMQO1QpkbJkzYE+dAphDe7U/EREREfVvf912AscrmnRjD180wiMFM0REZ81hAyoPu4piOhfJNFf1/diCBMQPBZLUApkx7QUyCE865bMNIiIi6imnrOCVzfrO56mRQbhyXKLPYiIiIgooTjvE+kKtEEZXGKMWyjSWeOSUsiW8vUvMycIYua1IRgmK8Mg5yQdFM7Is4+GHH8ZLL72kPVbbB6oEQYDNZtO9Vi2YWbx4MUwmE3Jzc5GWluatMInIw4SWakR+/KOu7bun3wdM/AGMtPLck2tydGPBJhE/XzBQ+3eRiIiIiIg8R/1MYsNRfbH+vKGx2r05bxPCP75VK804iVPdjEPo4So/tvTpsGe4bnJ4shciIyIiIiJ/VF7fihc/P6IbG5sWhWsnZfgsJiIiNFXqO8eo9xWHAdm1KEifBEd1FMWcLJJJGAmYueotERGRO606WInjlS26sR/PSIfFJPosJiIion5HkSE2lnYUw3S6F+sLIChO959SCtK6w5xaHKPelJA4Li5hhKKZO+64A3/+85+1iSlqEcz555+P999/v9vXLlq0CAMHDsSJEye019x3333eCpN6SVJXZPUAURRP+5z6KYcVEZ/eCak+XzdsH3IxzPN/Zah8v729GLnVrbqxO2dmIDMuDIGO72/jYK6Nhfk2FuabiPq7/aVNKGvQL+Axd2gMBFsDwjf8XOsLQ9T5g021g4xaIGPLnAln3HB+mElEREQ9UuPBrurqtXhUVFT787q6Om3ROvKuX39yFE02/ZfrD87NRH1drdvOwVwbC/NtLH3Ot+yAqE72qTgAU8UBSJUHtZvYVN7n2NQur7I6qSd+JJwJI7V7R/xIKBGpXa+JG9UJvfpJvdQV39/G4Q+5jomJ8er5iMj9C/G+trVQNzYkPgQXjojzWUxERER+S1EgtFa7CmJq2gpj2rvHnIDgtLr/lIIEOTL9lKIY12NtwUWB86gMWzSzbt06vPnmm1r3hJ///Of49a9/rRVbnG5y3TXXXINnn30W69evZ9GMH/PWhXbnDxSon1K7S314O1CyWz+eOgHma/+sfnJkmHwfLWvAm18Wdll5bukFo2CSjPefZaDnmzow18bCfBsL801E/c2Go9W655kxwRgUF4LQLx7zWKtl6l8cCaNh0zrJzIA9ZRJgCvJ1SERERNQPOZ3uX6nwu6gTMb15PgL2FjVg5f4K3dilo+MxOjnUo7lgro2F+TaW0+VbsNZDqjwEU+VBmKoOacUxpqojbpn8I5vDtAUi1KIYZ/wI7d4ROwywdLPYH4s83Ibvb+Ngromotz74phwl9fqFv+6emQFJ5GJORERkXOoCmNrCEVoxTF7bvesm2ho8ck5neHJHYUxUp8KYyHRAMnvknNTPi2Zef/319g4yTzzxRI/2mTJlina/f/9+j8ZGRF6y8Vlg33v6scg04Pv/B1hCYRSyrOBnH+6D3dmxcrV6UfvMVWMNWTBDRERERORtagfc9Uf0RTPzh8XCUrQdIfvf1b84fTIwYjGaW5qhqJNC1MUA2rvQKB3P23+9VyB852s6da85w2sE3WvaXtfdcTrv2+k1rhjatp96Pt1xviMmdTUenCHuzvGd8hptX10MpzzXHp7uz6b+GU73mpNj3/0aXbeg0/z51QVeJHURB9EMpIxHY/JkWFOnQgmN1/8sEBERERF14pQVPLvuhG4szCLh7lmZPouJiAKEIkOsy4dJLZCpOtheKCM1FLnl8M6IdDjiR7R3jlEfy5EZXAWXiIjITzRaHXjzS/3/+xPSIzB9ULTPYiIiIvIahxVSXV6nbjEni2RyITZXeuSUcnBsW6eYjqIYV5HMAMAc4pFzUgAXzWzfvl2bhHDrrbf2eJ/09HTtvrS01IOREZFXfPsv4Iun9GOWcOC6fwIRyTCSv+/Iw1d5NbqxH80ciNGpXKGfiIiIiMgbjle2oKBWvwrr/IFBCF//c/0LzWHAVW8CMQNgranhapABSu2E3LmLrr2mBgpzTURERERn8O9vy3GkvFk3dsf0NMSFcYVJIuoFpw0o3A2UfAOUZSOiaC/EysMQ7U19PrQiBcERN7yjc4zaRSZuBJSgCLeETkRERJ7x992lqG1xdOkyo869JCIiCgiyA2JDUVsxTOfCmBPauG5xRHed0hwGudvCmCwowZy7axReKZopLy/X7rOysnq8j9ns+lDZ4dD/Ekj+paZGP/nfXURRRFRUxz9EdXV1Wsta6n+k4q8Q8d+7XCslt1EEEY0XvQxHULr6Q2SYfJfVW/HMqkO6sYzoYNx0boLH3kv+yCj5JubaaJhvY/GHfHee4ExE1Bvrj+q7zCRHWDAx51VI9QX6Fy74tVYwQ0RERERE1Fltix0rthTqxgbFheDac5J8FhMR9ROOVphL98JUvBNm9Vb6tTbW18kbzrDkTsUxri4y6sQfiJLbQiciIiLPq26y4++7S3RjswZHY3wai16JiKifURSITWWuQpi2TjFacYzaPaYuH4Jsd/8pRQucUZmnFMa4HiuhCQALUA3PK0UzYWFhqK2tRUVFRY/3KSx0fdgcGxvrwcior7y10q46CZOr+vY/auvw8E9ug6CuktRJ04xHYc2crf4AGSbfiqLgqbXH0WTT/7mWLciCRfTee8kfBWK+qXvMtbEw38bCfBNRf7LhqL5g/YdpRQj59m3dmD11CsyTet4tl4iIiIiIjOOPWwpR16pf9O/h+QNgkkSfxURE/kmwNcJUssdVIFO8E6ayb/s0MUgRzXDGDoEjzlUgc7JQRgnhnAoiIqJA8OaXRWi2dyxUqE7tXTozw6cxERERnY7QUuMqhOlcGNNWHCPY9V2a3UFdsF+OSOsojIlqu48ZCDk8lYtHkO+LZgYNGoQ9e/bgwIEDWLBgQY/2WbVqlXY/evRoD0dHRJ4gWOsR+cltEFv0qzi3jL0RreNvgtGsO1KNTcdrdWOXjknAlAFs7UZERERE5C0FNa04WtHx4VwQbLix8ve6Fs+KFITmC36LKJET3oiIiIiISO9QWRP+/U25bmzB8FhMyuRn/UQECK11MJfsgqlILZLZBVPFfgjK2S02JIfEujrHxLk6x2hFMjGDAMni9riJiIjI94pqW/HBKdcal4yOx+D4UJ/FREREpLE1QarL61oYU5sL0aqfE+suztBEyO2dYjp1jonKAKQgj5yTAp9XimYWLlyIr776Cn/4wx9wzz33QDzDxBO1uOatt96CIAhYtGiRN0IkIndy2hGx+m6Yao7phm2Zs9A081EYTX2rA8+tz9ONxYaacP/sTJ/FREREROQpNpsN7777LnJyclBaWorGxkaEhoYiOTkZ8+bNw8yZM2EyeeVSlKiL9Uf1Rf0/D/4QYY3639Wbpz4AOWaglyMjIiIiIiJ/JysKnl13olPJPRBsEnEfP+snMiyhuVIrjjEX7dDuparDuoU5eix+OJA8Fs1Rg2GPHe7qHhOaAAjq+vJERERkBK9uK4RD7vg9wiwJuGNauk9jIiIiA3HaINW0dYypyYXY3j3mBKSmMo+cUg6KhDN6UJfCGDlqABRLuEfOScbmlZlK9957L15++WUcP34cP/7xj/HHP/7xOydJffbZZ7j55pvR2tqKuLg43Hbbbd4IkYjcRVEQvvGXsBRs1Q07Yoeh4aKXAdF4EySXb8pHVZO+1fpP52UhKsR4fxdEREQU+NRrubVr12LIkCGYMGECIiMj0dTUhL1792LFihXYtm0bli1bdsbFFIg8YUOnopnxwjHciE902+1J56Bl/M1g02YiIiIiIjrVpwcq8W1xo27slqmpSI7k6pZERiE2lsBctBOm4p2u+9qcXh9DEUQ4EkbDkToFzoypCB95ARAaq22z1tTA6Ty7zjRERETUfx2taMbqA1W6sWvOSUJKFK81iIjIjWSndl2rFsOY6/OBlmKg6ph2i67Nh6DIbj+lYgpu6xBzSseY6CwowTFcLIK8yisztpOSkvDqq6/iBz/4Ad58802sWbMGl1xySfv2l156CYqiYOvWrTh06JD2WJ1ApXabCQ9ntRhRfxKy900EH/inbkwOjUf94jegWCJgNF8V1OPf31boxmYMisbC4a4Pv4mIiIgCjXoN99e//rXLQgnqF/5PPPEEvvnmG62AZuLEiT6LkYyprMGK7JIm7bEFdjxrfh0iOj74U0QLGuc/A4gsmSEiIiIiIr1GqwPLNxboxtKjg3DDpBSfxUREHqbOWajPh7loF8zFbZ1k6gt6fxjRDEfSONhTp8CeOhmOlInt35lKkgSExnggeCIiIupP/rC5QNerLswi4pbzUn0YERER9VuKonVF1brEtHeLaesYU5cHwWnrdre+lK4oognOyEzI7UUxHQUyclgSIHBBVfIPXmtzcP3118NsNuOOO+5AQUEBXnvtNQhtFWJvvPGGdq8Wy3SeZNW5sIaI/J8lZy1Ctz6jG1OkINRf8jrkyDQYjdUh48m1ubqxULOIn12Q1f7vHxEREVGgURdA6K6LjDoJYPLkydi/fz9KS0t9EhsZ24ajNe2P7zb9G8PFQt325in3wBk71AeRERERERGRv/vTtiJUNes7yj84dwAsJn7pTxQwFAVSzTGtOMZUtNNVJNPU+8+w1O9G7ckTYE87D47UyVpXW5hDPBIyERER9X9fF9ZjS06tbuzGyamIDjX7LCYiIvJ/grW+oximrTBG1B6fgGjXd0p2BwUC5IgUXacYV/eYgZAj0wHRa+UIRGfNqz+l1157LebPn48//vGP+Pjjj7XVhR0OR/v20aNH47LLLsN9992HxMREb4ZGRH0kle9DxNoHIOjWPgAaFvwOjqTxMKI3thchv6ZVN3bXzAwkR7J9KhEREXWvrq4Ox44d027Hjx/Xbg0NDdq22bNnY+nSpT0+VkVFBVatWoU9e/agqqpK6/ySnJyM888/HxdeeCGCgrz7O4ksy1qXGVVGRoZXz02k2nC0WrsfLZzAXdJHum2OhNFomXCbjyIjIiIiIiJ/llPZjP/7uqxLR/kZg9kdgqhfk52Qqg67usho3WR2QWyt7v1hzGFwpJzr6iSTNgWOxLGAZPFIyERERBRY1AXGl2/Sd7KLDTXhunOTfRYTERH5EUcrpNq8Tt1iOrrHiC29v37tCTkkrlNhTKeuMVEDAFOwR85J5C1eL+2Ki4vDY489pt3USVPV1dVwOp2IjY3VOtEQUf8jNpYg8pPbIThadONN5z8E25CLYURHK5rx9q4S3diYlDBcc06Sz2IiIiIi/3fbbe6ZtL97924sX74cLS0dv59Zrdb2Qpx169Zh2bJlWhGNp6gLJHz44YfaY7XwJzs7G0VFRZgzZw7Gjh3rsfMSdaem2Y6vCxtgggPPml+DSZB17aIb5v8WkPiZBBERERERdZ3E9vz6PDjljgXDzJKAn84d4NO4iOgsOO0wVeyHuXgnzEU7YSrZDdHmWqymN+SgKNjVDjKpU7ROMo6EUVxRl4iIiM7KpuO1+LZY3w3g1qlpCLVIPouJiIi8zGmH2FCo6xjT/rhRP//UXWRLOOToQTAlDQdiBwNxQ1BvSYQ9IhNKUIRHzknkD3z66Y0oioiPj/dlCETUV7YmrWBGai7XDbeOvBotE++AEalfnj2xJkf3JZokCnh04SDtnoiIiKgn1GultLS09u4sPZWbm4sXX3wRNpsNwcHBWLJkCcaMGaM937p1q1YwU1JSgqeffhrPPPMMQkJCPFY08/7777c/FwQBl156Ka677jqPnI/odDYer4H66/ld0scYLebptrWceyec8SN9FhsREREREfmv9UdrsDO/Xjd2w6QUZMRwZU0iv+ewwlT2jatIpngXzKV7INibe30YOTTe1UVGK5Q5D864oYAgeiRkIiIiMg51TtEfNuu7zKRFBeHK8Yk+i4mIiDxEkSE2lnYqiOkokFELZgTZ4f5TShY4o9q6xJzSOUYJiYNkMiEmpqOLsrOmBorT6fY4iPwJlzwhorMnOxGx9icwVR7QDdvSzkPjnP9VZ0bCiN77ugz7S5t0Yz+ckoIhCaE+i4mIiIj6h6uvvhqDBw/WbtHR0SgvL8fdd9/dq2O89dZbWoGMJEl49NFHMWzYsPZtavFMSkoK/va3v2mFMx9//DGuvfbaLsd4++23Ybfbe3zORYsWacftTC3Yee+997QOozU1Nfjqq6/w7rvv4siRI1qXm9BQ/m5E3rP+SDWGCoW41+TqfnSSI3YYmifd5bO4iIiIiIjIf7XanXhhg77oPinCglvOS/VZTER0GvZmmEu/hrloh1YkYyrbC8Fp6/VhnOEpsKedpxXJOFKnaJOKjPqdJxEREXnOqgOVyKlq0Y39eHo6zBKLc4mI+iVFgdBa7SqGqTkBqa5T55i6PAiOVvefUpAgR6Z3LYyJyoIckcIFH4h8UTSjTrY6evSo9lid/BUUFKTb3trail/84hfahKrKykoMHDgQd955J+655x5vhEdEZyls6zMIOrFON6b+h9tw8R8ByQIjKqmz4o9b9CtBDIgNxi1T03wWExEREfUf3RWw9MaxY8dw8OBB7fHcuXN1BTMnLV68GBs2bEBRURFWrVqFK6+8EiaT/tLws88+g9Vq7fF5p06d2qVopnOH0bi4OCxcuBARERF44YUX8OGHH+KGG27o9Z+P6Gw0Wh34Kq8G/zS/CovQsTqOIohonP9bw167EBERERHR6b21owSlDfoJ9/fPzkSIRfJZTETUQbA2wFSy29VJpmgXTBX7zmp1XmfUAFcnmTS1m8wUyBFpLJIhIiIij7I6ZLy6tVA3NiwhFBeOjPNZTERE1DOCrQFie7eYzoUxJyBa9d2K3cUZntzWNcZVFCOfLJKJTOd33UT+VjTz73//G9///vcRGxuLwkL9L3yqK664AmvXroWiKNrzQ4cO4f7778fhw4fxyiuveCNEIuql4H1/R8g3f9aNyUHRqLv0DSjB0TAi9d+wpz/PRYtd1o3/YsFABJlYtUtERESet3PnzvbHatHMdxWxzJ49G//4xz/Q1NSE/fv3Y/z48brXvPPOOx6J7+R5DhzQdyok8qQtObW4SViJc8Qc3XjLhNvgSBrns7iIiIiIiMh/Fda24u1dxbqxSRmRuGB4rM9iIjI6oaVa6yCjFckU74JUcQACXPMLesMRO9RVJNPWSUYOT/JIvERERP5IkjxTAK5+93S656T34Z6yLgX698weAPMpi9z5K+bbWJhvY2G+2zhaIdbla8UwYk2u/r65wiOnlINjIMecLIoZBGdMFmStSCYLMId2u09f/ldnro2F+Xbxym9aa9as0SaTL1mypEuXmZUrV2rbBUFAeno6Jk+erE30Ulc9XrFiBa677jpMmzbNG2ESUQ+Z8zYhbNOvdWOKaEb9ohXaf9RGteZQFbbl1unGrhiXiIkZkT6LiYiIiIxFXXhApV53DRo06DtfN2rUKN0+pxbNeEp1dbVHv5Qh6s6B/d/il6b3dWOO6EFonnKfz2IiIiIiIiL/9vsNebA5OybjSwLw4LwB2veZROQdYmMZTG0FMmqhjKn6aK+PoUCAM2GUViDjKpSZBCWEK7gTEZFxxcTEeOU8UVFRXjlPf9TQasefvyzSjU0ZGIvF5w7st9cbzLexMN/GEtD5djqAunygKgeoOua6VR933dcWaFeUbmcOA+IGA3FDOt0PAWIHQQyNhVrG4KvyyYDONXURZdB8e+X9tWfPHu2XOnU141P9+c+uThXDhg3TimUiIiJQV1enFcqoHWfeeOMNFs0Q+RGp6jAi1twDQXHqxhvnPQVH2hQYVW2LHb9bn6cbiw8z495ZGT6LiYiIiIznZGfP5OTk0xampKamdtnHnTEkJCR0WTDBarXi7bff1h5PmDDBreck+i6tNjuuKnkeQYJdN2Gmcf4zgEn/M0pERERERKTamlOLTcdrdWPXTkjGkITuV/UkIjdQFIgNRTAX7WgvkpHq8np/GNEER8JY2NNcRTKOlHOhBHFxOyIiIvIff9qUg5rmju8sVD+7eES/LZghIvJrigI0lHYUxWiFMW1FMtW5gKz/99gtRLNWBOMqiDl533ZTO53y33uiwC6aKS8v1+6HDBmiG5dlGevWrdN+6bvnnnu0gpmTFUx33303li5diu3bt3sjRCLqAaG5EpGf3AbR1qgbb560FNYRV8LIXvwiHzUtDt3Yw/OzEBHcP1qnEhERUf9ns9nQ0NCgPY6LO/2KmeHh4VpRi1rIUlVV5dY4tm3bpnUUHTFihFY8ExISonWY2bt3rxbfyJEjsXjx4l4ds6cxerKDDdvV9k+Vm1fgXMHVgemk+jE3QUmfctp21cy3cTDXxsJ8GwvzbSzMNxG5i80h43cb9BP1Y0NNuH1ams9iIgpIigKpNtfVSaZI7SazE1JjSe8PI1ngSDoH9jS1i8wU2JMnAGYWuBEREZF/qmiw4o0tubqxhaOSMDHTOx2AiIgCVnM1UHW8a8cYtYuMvckDJxSA6MxTOsYMBmIHu8ZFz81bIKKz55XZ3JWVldq9OlmqM3XSVH19vVY0c8kll+i2jRkzRrsvKFDbXJG/8tSkNH7J6YccrYj49A5IDfoWobZhi2Gd9lNIgmjYfH95ohaf7Hf9O3fS3KGxWDAywWcx+bP+nm/qOebaWJhvY2G+/VNra2v74+Dg4DO+Xn2NWjTTeT93OPfcc1FTU4MjR45oN/X4oaGhyMzMxPTp0zF37txeX0fceeedPXrde++9B28xarvafqU6F6GHXtENlUkpSLrsKcAS1qtDMd/GwVwbC/NtLMy3sTDfRHS2/vFVKfJr9NfJd8/M5AJZRH2lyJCqjsJc3NFJRmyu7P1hzKGwJ0+EPXUy7GnnwZE4jp1kiYiIekH9/sYT1O8KO1+L19XVaYtpk97zn+Wg2eZsfy4KwO3np3gsL57CfBsL820sfp1ve7O2+IJYkwup9gTE2hxINbkQ1bFWfcdgd5HDEuGMzoIcPQjOmIGQowfCGT0QclQGYPqOORl19egP/DrXFJD5jonxfZGwVz7hVVcwdjgc7cUzJ23atEm7T09Px4ABA3TbTnadcTo7flEk/+OtH2J+yelj6j+OHzwAlO7Vj6dPhuWaN2Ax6wvijJTvFpsTT3+m/3uJCDLh6asnICbqzJNVqX/lm/qGuTYW5ttYmG//6TRzksl05ku9k6/pvJ87DB48WLsR+ZSiQP7vPQhS9JPd9k78X1zYy4IZIiIiIiIyhvIGG978Ur9w2JiUMCweE++zmIj6LdkBU8UBmNoKZNRCGdFa1/vDWCLgUAtktNsUOBJGA5LZIyETEREZgbfm4amTMDnnT6+wthUf7C3TjV0yOgFZMUH9/u+K+TYW5ttYvJ5vpw1SfYFWHOMqjFHv1VsepKZSj5xSDorUCmFctyw4o7JchTHRWVAs4aeJNbDeB3xvG4ts0Hx7pWhGLYg5cOAAduzYgfnz57ePf/zxx1qXmVmzZnXZp7q6WrtPSGCnBiKf++IpYP+H+rGoTOB7/wDcXDDT37z4+RHkVzfrxh65eASSWTBDREREXmaxWNofq4sWnMnJ13Tez1+tWLHC1yFQf/PVXyDmbdYNveO4AAtmXOqzkIiIiIiIyL+9tDEfLfaOFRYFAA/Pz4IoqI+I6LScNpjKv4W5yFUkYyrZA9He2OvDyMGxbV1k1CKZ8+CMGw6IvetYTEREROSPXttaCIestD+3SALumJbm05iIiHxCdkJsLNGKYlwFMW23uhMQ6wshKO7vfqGYgtuLYbTCGO3mKpRRgmMAfvZDZAheKZqZO3cu9u/fj+XLl+OKK67AyJEj8dFHH+GLL77Qti9atKjLPtnZ2dp9SkqKN0Ikou+y911g03P6saBI4Pr3gPBEGFl2UR3e2JKrG5ucFYPrpmT6LCYiIiIyruDgjqLd1lZ9d43unHxN5/38VVxcXI9e58n29f7QrpZ6RqgvQtTax7QJbicVKvH4JOE2LJZbUFPTcsZjMN/GwVwbC/NtLMy3sfg6397qyE5EnvNVQT3WHKrSjS0Zl4BRyadZUZTIyOwtMJfthbloJ0xqJ5nSryE4rb0+jDMsydVBJm2KVizjjBnCyUpEREQUcI6UN2H1Qf31xjXnJCE5MshnMREReZSiQGiu1AphdIUxaqFMXR4Ep839pxRNcEZmQFaLYdQCmZiTBTIDIYclAYLo9nMSUf/ilaKZe+65B6+//jrKy8sxZswY7QskdTKToihIT0/HVVdd1WWftWvXal1oxo0b540Q6Sx5alKar7/kJBdT0Q6Ef3SPbrKZIkhovHg5HOZk9QfAsPlWV3948L1v4ey0CoRZEvCz+QNQV1fr09j8XX/MN50d5tpYmG9j8Yd8c1JaV2rHmIiICDQ0NKCqSv/B+6kaGxthtVp7VZDSH3izfaxR29X6PUVB5LplEGz61WyX2X+EqcPSzjpnzLdxMNfGwnwbC/NtLMw3EfX28/7n1p3QjUUESbhrRobPYiLyN4KtQeseYy7eBXPRDpjK90GQ7b0+jjqBSeskk3qe1k1GjsxkkQwREREFvFc2F6BjdhEQZpFw89RUH0ZEROQegrVe1zFGbH984qy6j/aEMyLV1SVG1zlmIOSINEAye+ScRBQYvFI0M3ToULzzzju45ZZb0NTUhOrqam08Ojoa7777rja5q7PS0lJ89tln2uN58+Z5I0Q6S9764pFfcnqfWJuLsI/v6PKBd9OsX8KaPkNNvqHz/bddJThU1qQbu+W8VAyICfL72P1Nf8g3uQdzbSzMt7Ew3/5DXZTg4MGD2jWVmhNJkrp9XXFxsW4fokARdOgDWPI36cb+6ZiDzfI4/HQoi+2IiIiIiKir9/eW4VilviPlnTPSERPKiRZkXEJrratApniX1knGVLEfgtL7RXMcMYO1IhlHqquTjBzByaFERERkvK6W23LrdGM3Tk5BdAivN4ion3C0QqrN6+gW06l7jNjimgvubnJIXHsxTPtNK5IZAJiCPXJOIgp8XimaUV1zzTWYPXs2Vq5cqU3gSklJwWWXXYbY2Ngur/32229x3XXXaY8XLVrkrRCJqNMH4VEf/wiiVd8xpWX8LWgdez2MrrC2Fa9uLdSNDYoLwQ/P4wf9RERE5FvDhw/XimbULjI5OTnaAgbdOXDggG4fokAgNpYhbMuTurFSJQZPOq7HsIRQpEfzA1QiIiIiItKrbrLjtVM+7x+aEIorxyf5LCYiXxCaKmAuUbvI7IRZLZKpOtzrYygQ4IwbDnuaWiDjKpJRQuM9Ei8RERFRf6AoCl7ZVKAbiws14/pzk30WExHRd5KdkCr2AYcOAlXHtFtUxVGIjcWeOZ05vKNLTHtxjPo8C0pQpEfOSUTG5rWiGVViYiJuvvnmM75u4cKF2o2IfMBpQ+Snd2kVwZ1Zs+ajafrPYHTqBe1Tn+XC6uhYTUttGv/ohQNhlkSfxkZEREQ0ZcoU/Oc//9Eeb9iwoduiGbUz0MaNG7XHYWFhGD16tNfjJHI7RUHYxschWut1wz+334p6hOG6YV0X7CAiIiIiIvrDlgI0WPXdcx+ePwAmUf3knyhwiQ3FWnGMWiRjUrvJ1Ob0+hiKIMGRMNrVSSbtPNhTzoUSHO2ReImIiIj6o43HarCvpFE39qPz0xBikXwWExHRqYsSmgs2w5K/GeaCrRBba/Tb+3h8RbLAGTWgU8eYju4xSkgcIPDzFyIK0KIZIvJzioLwDY/CXLxDN+yIH4WGhS8AIi/aVh6oxM48/US8q89JwrjUCJ/FRERERHTSkCFDMHLkSK3bjFo0M2fOHAwbNkz3mk8++QRFRUXa44svvhgmU+BcFkqS535fFUXxtM/Jt8yHP0JQ7ue6sX87p2O9PFF7fMHw+F79fDDfxsFcGwvzbSzMt7Ew30R0NrJLGvHRvgrd2EUj4zAhnauZUoBRFIh1eTAXq51kdmj3UkNh7w8jmuFIGtfWRWYKHCkToVjCPRIyERERUX/nlBX8YbO+y0xaVBCWjEvwWUxERHBaYS7eDXP+Jq1Q5my6jJ5KEUTIkekdhTFRHYUxcngy55wSkd8InNlRRNRnIXteQ/ChD3RjzrAk1C9+HbCEweiqm+x4YUO+biwpwoKlM9N9FhMREREFlkOHDqG0tLT9eX19R7GuOv7FF1/oXq8WxZzqhz/8IR577DHYbDY88cQTuOKKK7RuMurzbdu24fPPXYUFKSkpuPTSSxFIYmJivHauqKgor52LzqCxAtj4K91QhRKJX9t/oD0eFB+Gc4emQujDSkXMt3Ew18bCfBsL820szDcRnYmsKHh23QkoncZCzCLunZXpw6iI3ERRIFUfdRXJFLs6yUhNZb0/jCkY9uQJrgIZtVAm+RzAFOyRkImIiIgCzcr9lcitbtWN3TkjHWaJC30QkZevD2tz24tk1IUUBEfLWR3KGZbc1ikmC3J755iBcEamA5LF7aETEbkbi2aISGM5tgph25/TjSmmENRf8jrk8BSfxeVPfv9FHupaHbqxRy7IQngQ/yklIiIi91i3bh02btzY7bbDhw9rtzMVzQwcOBD3338/li9fjpaWFrz77rtdXqMWzCxbtgwhISFujJ7IR1Y9BLRU64Yes9+CWri6QV40JrlPBTNERERERBR4Ps6uwIHSJt3Yj85PQ2IEJ3lQPyQ7IVUdgrlop1Yko64aLLZW9/4w5nA4Us9t6yQzGY7EsZz4RERERHQWrA4Zr23Td/YblhiKhSPifBYTERmHYG2AuXAbzPmbtUKZXncajRsKpE8C4gajMTgF9ohMOKMGcNF1Iur3ONObiGAq+wYRn/1UN6ZAQMPCF+BMHOOzuPzJ1pxarD5YpRu7YFgsZg323mrmRERERD01adIkPP/88/j000+xZ88eVFdXw2QyITk5GVOnTsVFF12EoKAgX4dJ1HcHPgL2/1s3tNI5BavlKe3P1aIZIiIiIiKikxpaHXhlU4FuLDMmGNedy2sH6iecdpgqsl0FMkU7YSrZDdHW2OvDyEHRsKdOgj3tPK1Ixhk/EhA5fYCIiIior/61twxlDTbd2N0zMyBygS8i8gRFhqk8u72bjKn0awiKs8e7y5Zw2NOnw5Y5E/LAOYgaMLZ9m72mBk5nz49FROTP+KkXkcGJ9UWIXHk7BKdVN948/WewDVrgs7j8SbPNiac/y9WNRQRJeHD+AJ/FRERERIFp6dKl2s0dEhIScNNNN2k3o6ipqfHYsUVRRFRUVPvzuro6yLLssfPRmQktNYj85CcQO421SJH4ZevN7c+TIyxID5V7/bPBfBsHc20szLexMN/G4ut8x8RwYR2i/kRd8bmmRd9V/qF5A2CWOl9dEPkRhxWmsr1tXWR2wVyyB4KjpdeHkUPj27rITIE9bQqcsUMBgT/3RERERO7UaHXgL18W68bOzYjA+Vkdn1sQEfWV0FSuFcho3WQKtvaq26i6mLraWdSeORO2AbPgSDqnfQEFSZI8GDURkW+xaIbIwARbAyI/+RHE5krdeMvo76HlnFt9Fpe/WbG1EKWnrABx35xMxIexJT0RERGRP/HmKjfqJEyuquNb4Rt/3eVa5kXzrahExxdPc4fGumXCLPNtHMy1sTDfxsJ8GwvzTUTf5VhFM/71dZlubPaQGJw/MNpnMRF1YW0ECncCedsQfmwjTKV7Icj676l6whmR2lEkkzoFcnQWwNXNiYiIiDzqnV0lqGvVF+nfMysTAn8PI6K+cFphLvnKVSSjdpOpPNi73UMTXUUymTNhz5gOJSTWY6ESEfkrFs0QGZXsQMTqe2GqPqIbtmVMR9OsX/FD8zbZJY34v69KdWOTMiJx+ZgEn8VERERERGR05hMbEHz4P7qxurQ5eO34JN3YvGFc9Z2IiIiIiFwURcGz607AqXSMWSQBD8zJ9GVYRBrB2gBL7ucIOr4ayN8EOF1FMuZeHMMZlaV1kGkvkolM81i8RERERNRVZZMNf9+tn2M0d2gMxqSE+ywmIuqnFAVi3QmtQMaStwnm4h0Q7M093120wJ46qa1QZhacccM5H5SIDI9FM0RGpCgI2/y/sKgfunfiiBmChoteAaTefAQfuBxOGU+syUGn78+0L9B+vnAgV4AgIiIiIvLhRKLwDY/qxmRLBP4v4V7geGv7WGyoCeNSI3wQIRERERER+aPPDldjT2GDbuymKalIiw72WUxkbEJrnatQ5tgqmAu2QJDtvdrfETsU9rTzYE+dDEfKZMjhSR6LlYiIiIjO7M3tRWh1yO3PRQG4a0aGT2Miov5DsDXCXLgd5vxNWrGMVF/Qq/0d0QPbi2TUa0WYQz0WKxFRf8SiGSIDCv72rwjZ9zfdmBwSi/pL34ASFOmzuPzNO7tLcKyyRTd227Q0ZMbwCzQiIiIiIl8J2/oUpCb9Sm1NM36Bj7+WdGOzh8RCUr+RIiIiIiIiw2u2OfHiF3m6sZRIC26akuKzmMiYhNZaWHI+Q9BxtVBmW48LZRRBhDN+pKuLjNpNJmUSlJBYj8dLRERERD1TWNuKD7+t0I0tHp2AgXEhPouJiPycIkOq2O/qJpO/GabSPRBkR493l83hsGdMay+UkSPTPRouEVF/x6IZIoMxn9iAsC1P6sYUyYL6Ra9CjuTqBiflVbfgT9uKdGNDE0Jx4yR+gUZERERE5Cvm/C0IPvCebsyWMRP56Zci+9NvdOPzh8V4OToiIiIiIvJXf9lRjPJGfXHCT+YMQLBZX3xP5AlCS3VHoUzh9p5NghJNQOoEtCadC2vKJDjUIpkgdlMlIiIi8lcrthTCKSvtzy2SgNunpfk0JiLyP0JzpVYgY1YLZQo2Q2yp7vG+CgQ4Ese0F8k4ks4BJLNH4yUiCiQsmiEyEKnyICLW3AdB6WgFqmqY/1s4Us71WVz+RlYUPLk2FzZnx8WsukD1owsHwiSJPo2NiIiIiL6bJHluspMoiqd9Tl5ga0T4hp/rhhRzGFoueBqbjtbrxiOCJEzJioF0lr+/M9/GwVwbC/NtLMy3sTDfRHQ6+TWteGdXiW7svAGRmDuUhfbkOUJLFSzH1yLo+GpXoYziPOM+imiBfcAsWM65Bhh2ERASjZaaGjidZ96XiIiIiHznUFkT1hyq0o39z4RkJEcG+SwmIvITThvMJXtcRTL5m2CqPNCr3eXQBNgyZsCeOQu2zOlQQuI8FioRUaDzetHMN998g82bNyMnJwcNDQ1n/JBPEAS8+eabXouPKFAJTeWI/ORHEO1NuvGmKffDNuwyn8Xljz7aV4E9hQ26se9NTMbolHCfxUREREREZxYT470JT1FRUV47F7VZ+STQoO8GKSz8DaIGjMXGNdt14wtGJSMx3n0fGjPfxsFcGwvzbSzMt7Ew30R0kqIoeH79CTg6rfgsiQIempelfQdJ5O4Vg4Ny1sJy7FOYi3Z0WcSuO4pk0VYItg1ZBFvWPIih0bB48fMNIiIiIuq7P24u0D0Ps0i46bwUn8VDRL4l1uXBkrdJK5QxF33ZZb7m6SiiGfaUc7UFFdRrRWfcCHUStUfjJSIyCq8VzRw+fBi33HILvvzyy159kM2iGSI3sDcj8pPbIDWW6oZbhy9By+S7fRaWP6pssuGljfm6sZRIC348Pd1nMRERERERGd6JrcCuP+nHsmYC596CqkYrdubqW5dfOCbZu/ERERERkd+y2Wx49913tcXcSktL0djYiNDQUCQnJ2PevHmYOXMmTCavrzFHXrI5pxbbcut0Y9dNTEZWXIjPYqLAIjRVIChnjatQpnhXDwtlgmAbMBvWIRfDnjUPioWLthERERH1V7vz67HthP6a46YpKYgOMfssJiLyLsHWqBXHnOwmI9Xp5x6eiTMqC7bMmVqRjD3tPMAS5rFYiYiMzCvfAhQVFWHWrFmorKzUCmFU4eHh2irAoih6IwQi41JkRHz2U5grsnXD9pRJaJz3FCuRT/H8ujw0WPUdsJYtGIhQi+SzmIiIiIiIDM3WDPx3qX7MFAJc9jIgivj8YBk6LRqNELOE2cMSvB4mEREREfmn1tZWrF27FkOGDMGECRMQGRmJpqYm7N27FytWrMC2bduwbNkyfl8VgKwOGb9bn6cbiwsz49bzU30WEwUGsbEMluOrEXR8NUxqoQw6XZR+B8UUDNuAubAOuUi75yQoIiIiov5PnQe5fFN+l2uO70/kwl5EAU2RIVUehEXtJJO/CeaSPRBke493l81hsKefD7vadTRzJuSoTI+GS0REXiyaefLJJ1FRUaF1jfnRj36EBx98EMOGDfPGqYkML3T7c1or+M6ckZmoX7QCkIJ8Fpc/2nisBp8f0a9QffHIOEwbGO2zmIiIiIio52pqajx2bHUCXVRUVPvzuro6yPKZV4+lvgvZ9ASCa3J1Y83THoRViFGTjo++LtBtmz4oGi2N9WjpwzmZb+Ngro2F+TYW5ttYfJ1vdYEw8l/qIm5//etfu3STcTqdeOKJJ/DNN99oBTQTJ070WYzkGe/sKkFRnVU3dt/sTIQHsbMQ9Z7YWOIqlDm2GqaSr3pYKBMCW5ZaKHMxbAPmAOZQr8RKRERERN7xxbEa7C9t0o3ddn4aQrgwL1HAEZorYSnY0tZNZjPElqpe7e9IGK11klFvjuQJgMRuVERE3uaVT4VXr16tFcz84Ac/wOuvv+6NUxIRgKD9/0ToHv17Tg6KRP2lb0AJifVZXP6o0erAbz/XT8SLCjHhgbkDfBYTEREREfWOOunNW9RJmN48n1GZSr9G0Ndv6sbsyRPRPOZGNeHa7/E7TtTpts8ZEu323DDfxsFcGwvzbSzMt7Ew33RqUVV3XWQkScLkyZOxf/9+lJaW+iQ28pzSeiv+sqNYNzY+LVxbKIuop8SG4rZCmU9hLv26R/so5lDYsubBOmSRNhkK5hCPx0lEREREvnHqNUdGdBCWjE3wWTxE5EZOO0yle7QCGUv+Jpgq9vdqdzkkTusio3WTyZgOJTTeY6ESEZEfFc0UF7t+QVSLZiiwqF8qecKpX2B194UWnZ4pfyvCNz6uG1NEE5oueRWIHwZ/WtPAH/L9xy15KG/Ut0l8cF4W4iOCvR5LoPOHfJN3MNfGwnwbC/NNRF7hsCJ83SO61XsVyYLG+c8AouuKZktOLRxyx3azJGDGIHaKJCIiInIXtWvPsWPHtNvx48e1W0NDg7Zt9uzZWLp0aY+PVVFRgVWrVmHPnj2oqqrSOr8kJyfj/PPPx4UXXoigoCCvF1epXWZUGRkZXj03ed6LX+TD6ujoOCUKwMPzs7RF/ohOR6wvRNDx1bAcWwVz2d4e7SObw2EbOA82taOMWihj4ndLRERERIGu1S7j4CldZu6Yng6TxO9NiforsS5fK5Ix52+CufBLiPbGHu+riGbYUya6imQyZ8IZPxIQ+O8BEZHhimZiYmJQXl6O6GhOXAk0am69ISoqyivnCRgVR4BVdwGyQzcsLH4BEeMugb/zdr6/yqvGv/bqVxKcOTQeN8wYxi/QvIDvb+Ngro2F+TYW5puIPCF013KYao7rxpqn3A9nzOD25+uPVOu2nzcgCuFBXvmog4iIiMgQbrvtNrccZ/fu3Vi+fDlaWlrax6xWa3shzrp167Bs2TKtiMZTHA4HPvzwQ+2xWviTnZ2NoqIizJkzB2PHjvXYecn7dubV4fNTrhWuHJeI4YlhPouJ/JtYX4CgY6tchTLl3/ZoH9miFspc4CqUyZgJmLxb+EdEREREvnWiuqXTkl8uswZ7Zx4dEbmJrQnmoh1aJxn1JtXl9Wp3Z1SmtnCCXe0okzYViiXcY6ESEVHfeWUmyaRJk/Dpp5/iyJEjmDBhgjdOSWRcTVXAP64BWuv049PvByay29OprA4nHvlgH5ROV7LBZhFPLhnLghkiIiIiIh+RyvchZM/rujF74li0TLi1/Xmr3YltufrrnrlD+YUUERERkafEx8cjLS2tvTtLT+Xm5uLFF1+EzWZDcHAwlixZgjFjxmjPt27dqhXMlJSU4Omnn8YzzzyDkJAQjxXNvP/+++3P1c9/L730Ulx33XUeOR/5hsMp4/n1+kkuUcEm/HhGus9iIv8k1uV1FMpUZPdoH9kSAdugBbAOvhj2zOmAxEIZIiIiIqM6Xtmse54aGYRQi+SzeIioBxQFUtUhWPI2ubrJlHwFQbb3fHdzKGxp58M+wNVNRo4a4NFwiYioHxbN3HvvvVi5ciVef/11/M///I83TklkTA4r8H/XATUn9OMjLwXm/9JXUfm1FV8cx7FyfSvFny4Yjsy4UJ/FRERERERkaE4bItY9AkFx6lqaN877LSB2fIyx/UQdWh1y+3NJAGZzFTciIiIit7r66qsxePBg7RYdHY3y8nLcfffdvTrGW2+9pRXISJKERx99FMOGDWvfphbPpKSk4G9/+5tWOPPxxx/j2muv7XKMt99+G3Z7zycxLFq0SDtuZ2rBznvvvQdZllFTU4OvvvoK7777rrbgm9rlJjSUnwkHgn9+XYacqo6ORqq7ZqYjOsTss5jIf4i1uVqhjHozVR7o0T5yUFRbocxFsGeohTIWj8dJRERERP7v1OuOwfGeWQCCiPpGaKmCJX+rViRjKdgCsbmiV/s7EkZr3UW1bjIpE3lNSETUj3mlaGbBggV45JFH8Nvf/hZ33nknXn75ZZjN/HA6EKhfLHmCKIqIiopqf15XV6d9kUWnoSgIXfMTBBV8qRt2JI5Dw9xn1b9E+Ctf5Vtd9eGV9cd0YyOTw7BkdLTHfraJ728jYa6Nhfk2Fn/Id0wMJ8YTBaqQr16Fqeqwbqx50lI444frxtYfrdY9n5gRiehQftZARERE5E7dFbD0xrFjx3Dw4EHt8dy5c3UFMyctXrwYGzZsQFFREVatWoUrr7wSJpP+66vPPvsMVqu1x+edOnVql6KZzte0cXFxWLhwISIiIvDCCy/gww8/xA033NDrPx/5l8omG17fVqQbG5EUiiVjE30WE/meVJMDy7FPXYUyVYd6tI8cFA3b4IWujjLpUzkpioiIiIi6OF7Johkiv+S0w1S2Fxa1k0z+ZpjKsyFA6fHuckhse5GM2k1GCY33aLhERBRgRTPqCmAjR47EtGnTtG4z6kph6upkI0aM6NHKXT/4wQ+8ESadBaezY+VfT1InYXrrXP1VyM7lCDr8H92YMzwZdZe8BkW0qMlCf+GNfMuKgt+sOgaHrOhWpv7FgoEQFPX8Hj09dcL3t3Ew18bCfBsL801E7iJVHkLo7j/oxhxxI9By7h26MbtTxubjtbqxuUNZTEdERETkb3bu3Nn+WC2a+a4iltmzZ+Mf//gHmpqasH//fowfP173mnfeeccj8Z08z4EDPes4Qf7tlU0FaLLpP594aF4WJFHwWUzkG1L1UViOrUbQsU9hqj7So33k4FhXR5khi2BPOw+QuCgDEREREX23nFOKZgbFs3spka+I9YVtRTKbYC7cDtHW2ON9FdEER/JE2DJnaUUyzoRRgCB6NF4iIgrgopkf/vCHEISOD6RLSkqwfPnyHu2r7seiGaLTCzr8EcJ2vqgbk81hqF/8BpQwrqDWnQ+/Kce3xfpfkK+flIIRSWE+i4mIiIiI+kaSJI8dW53Id7rn5AayAxHrfwZBdrQPKYKE5oXPQ7LoV2j7Mq8ejVb9ZLj5w+Pd9jPAfBsHc20szLexMN/Gwnz7r8OHXR0Eg4KCMGjQoO983ahRo3T7nFo04ynV1dUev5Yg7/i2uAGf7K/UjV0yKh7j0yJ8FhN5kaJAqj6idZOxHF8NU/XRHq8gbB10IWxDLnYVyohe+eqciIiIiPq5ZpsTxfX6bqjsNEPkRfZmmIt2dHSTqc3t1e7OyAytSEbtJqN2F1Us/OyAiMgIvPbJn6L0vMUZEfWcqWQ3wtc9ohtTBBENF74EZ/xIn8Xlz8obbFi+KV83lhYVhNunpfksJiIiIiLqu5gY73UaiYqK8tq5DGPz74HyfbohYcb9iBw+s8tLt+YV6J5PzIzG8Mxkj4XGfBsHc20szLexMN/Gwnz7j8LCQu0+OTn5tIUpqampXfZxZwwJCQla4U5nVqsVb7/9tvZ4woQJbj0neZdTVvDsuhO6sTCLiHtmZ/gsJvJSoUzVYa2bjFYoU3O8R7vJofGwDr4ItsEXwZ46BRBZNEdEREREvZNTpe8yoza3zIpl0QyRp6//ThbJmIt3Q5BtPd/dHAp72lStk4x6k6Oy1NX8PRoyEREZtGgmN7d3lZxE1DNiXT4iV97Z5ZfAphmPwp4112dx+TO1gO+3606gySbrxn+xcCCCzfxihIiIiIjIJyoOA188ox+LHw7MerjbCXFr95fpxi4a47mCGSIiIiI6OzabDQ0NDdrjuLi40742PDxcK2pRC1mqqqrcGse2bduwcuVKjBgxQiueCQkJ0TrM7N27V4tv5MiRWLx4ca+O2dMY2Q3TO/6zrxSHypp1Y3dMz0RSZGBMWmOuT5koVXkA5qOfwnJ0FaTanB7tJoclurrJDLkYjtTJ7YUy/vitEPNtLMy3sTDfxsFcEwW+45X664/06GAEmfheJ3InoaUG5oItsKhFMvmbITWX92p/R/xIrUBG6yaTci4g6RdTISIi4/FK0cyAAQO8cRoiQxGs9Yj85DaIrdW68ZaxN6J1/E0+i8vfrT9ag43HanRjl46Ox5QBXH2SiIiIiMgnZCfw36WA09oxJojAkj8C5uAuL991ohpVTfqFAy4czaIZIiIiIn/T2tra/jg4uOvvdadSX6MWzXTezx3OPfdc1NTU4MiRI9pNPX5oaCgyMzMxffp0zJ07t9fFLXfeeWePXvfee+/BW4zaYam22YY/bNZ3ohySGI47LxgJsxSYk9YMl2tFAUr2Agf+67pV96xQBhEpwKjLgVFLIGach2BRxJn/JfI/hsu3wTHfxsJ8GwdzTRR4cir1nWYGxwdGwT6RT8kOmEr3tneTMZXvgwCl57sHx8KWOUMrkrFlzIQSluDRcImIqP/xStEMEbmZ046I1XfD9P/s/Ql4XOWZ5/3/alFVabcsyZZ3W5t3HLMYsxpMwg42BBxCIKST0NM06e7MdN55h5kk/X/fZjq9ZKbTk+ZNd5buNGsIq4FAWIxZwhIWB/BuLV7wLsmyrKX2Ov+ryljSI8u2bKvqVNX5fq5Ll+q5darO7botW0fnuZ+ns9kIR6YtUe9F37UtrWzXHYrp71dtM2IVhV79xSVTbcsJAAAAoyc5ES5dkqsBDr652dXVpUTC3L0Qp8a/5ucq2vm+EQst/IaCxXXJoh51/MoPtxvjxnFFKnNH1Nk58m3YT4R6Owe1dhbq7SzU21nsrndFRUXGzpVrO80c4fWe+HbUkWMGP2801NXVpT6Qn/7XS1vU2Rc1Yv/P9XPztmHGUY0yu9cMNMp0mvd2jqlsUn+jjCYnd5Th7wEAAABGX0vHkKaZyiLbcgFymfvQLhXseOPwbjI735Y7cnjH4pGwXB7FJpx5eDeZKRcpNm7e4UX5AAA4BppmgFxjWSp5/a/k+/QtIxwb26juK/5JcvNtfSz/540d6ug1b55957LpGlNYYFtOAAAAGD3xeDxj50pOwszk+fKV++A2Fb7zQyMWL5+mnnP+IlnQo463LEuvbukwYksbxqa9FtTbOai1s1BvZ6HezkK9s4PP5+t/HIvFTnj8kWMGPy9b/eQnP7E7BUhav7tLD/3ebKq/en6NLqivsi0nnGajzK4PpfVPSRuekbp2jOx55VMGGmUmnUWjDAAAANKutb3PGNey0wwwMtGgCnb//nCTTHI3mc6Wk3p6vHTy4SaZ5Mfk82X5S9OWKgAg/zC7HsgxhR/9QoENjxqxRFGVDl37c1k+fhA8lg8/PaSnPmkzYhfMGKPLZ461LScAAADA0ayESl+9R65YyAh3X/Z3UsHwN5g27O3Vvm5z5fFLG1jZHQAAIBsFAoH+x6GQ+TPfcI4cM/h52aqysnJEx7EbZvokG+q/++Q6JayBWMDr1rcunJTW990OeV1rKyHP3j/I1/S8fE0vyN2ze0RPi5dNVrThGkXqr1J8/ALJ5Tr8ha4u5bq8rjeOQr2dhXo7RzbUmt0wgfTpDsW0v8dcsLeOphlgeJYlz4EtnzXJvKGC3e/LFR/5DsOWt1DRSeemGmUiU5coMWb6wPUfAAB2Ns0sXbo09dnlcmnVqlVHxU/F0NcCnMzX+pKK3vpbI2Z5/Dp0zU+VSG45j2GFYwn9z5e2GrHCArfu+cL01L8xAAAAADIvsO5hFex+z4gF59+u2MRzjvmcV5sOGOOpFQHVVnIzCgAAIBsld4wpLS1Vd3e3OjrM3QKH6unpUTgcPqmGlFzAbpjp88KGdv1hZ7cR+9q5EzWu2Jv370PO19pKyLt3jfzNL8jX8lt5evaO6GnxsqkK11+lSN2Vio2bPzBRKs8nnOd8vXFSqLezUG/noNZAfmnpCBpjj9uVulcB4DBX6KAKPv3dZ40yv5Ond2TXfEfEKmcqMvXiw7vJTDxb8vjTlisAwFlGtWnmtddeS30eOgk9GU/Gkqs+jdSR45nQDhzm2b9WpS/9F7lkfh91f+F/KZZcRQvH9It3d2lHp7mS4d0XTVFNGT9UAwAAAHZwH9qp4rf/7qgt1XvP+7+O+Zzk7whe3WI2zSxtqOD3BgAAAFls8uTJ2rhxo/bu3ZuaKOjxeIY9bvfu3cZzgOPpjcT1T6/vMGKTyv26/ZwJtuWEE0jE5d3zofwtyUaZF+Xp3Teip8XLpylcf3WqWSZeNYcVhQEAAGC7lvY+YzytIqACj9u2fADbJWLy7vv4syaZN+Xd/4lc1sgXN0gEKhSdckFqN5nolIuUKBmf1nQBAM41qk0zF1988bCTVY4VBzAy7p49Knvuj+WKmasVJCeUJbeex7E1t/XpP97bY8TmTSjWzZ/jB2wAAADAFpalktX/Q66oeWOpZ+nfSL7iYz6tpT2oTw8eXn38iKWNY9OWJgAAAE7fzJkzU00zyV1kWltb1dDQMOxxGzZsMJ4DHM/P39ml9t6oEfvLpdPk9zJRLask4irY/X5qNxl/y2/l7msb0dNiY2ak7n0lm2XilbNolAEAAHknEonokUceSV0jJRcYSO68WVRUpJqaGi1dulQXXXSRvN5RndKHUdTabs7dqqsqtC0XwC7u7t2pBplUo8zOt+QOHxrxcy2XR7Gazx1ukpl6sWLV8yT38IusAACQ9TvNjDQOYAQivamGGU/ffiMcmn2Tgmf+J9vSygXxhKW/frE19Xnwtqjfvbw29RkAAABA5vk3Pibfp78zYqE5X0qtInU8rzaZu8yML/Vp9vhjN9kAAADAfosWLdLTTz+derx69ephm2YSiYRef/311OPi4mLNnTtX+eJYO+uMBrfbfdxxvtra0adHPtxrxC6oHaNLGirzdgG/nKp1coXhXe/J1/QbFTS/KHewfURPi1fUKdJwjSINVytRObO/UcaJ06Zyqt44bdTbWai3c1BrnEgoFNJLL72k+vp6LVy4UGVlZert7dVHH32kn/zkJ3r77bd1zz338HcnSyUX+BqsrqrItlyAjIkGpW1vSS2rVLblJXkONJ/U0+OlExWZenGqSSY6+TxZ/rK0pQoAwLHQlg5ks0RcpS/9Z3nbB1bZS4pMOlc9l/w1q2udwGMf7dP6vb1G7I5FE1RfzQUrAAAAYAd3z14V/+5/GrF4cY16L7jnhM9d3dRpjJc2jM3bSXEAAAD5IjkJbPbs2andZpJNM5dccokaGxuNY5577jnt2rUr9fiqq67KqxWVKyoqMnau8vJy5TvLsvSPT25RbNBCWQUel/76hgUaO7ZETpF1tY7HpG1vShueljY+J/WNrFFG1bOluculOcvkGTdbyfW5WaM7B+qNtKLezkK9nYNaY6iSkhL9x3/8x1HXPvF4XPfee68+/vjjVAPNmWeeaVuOOLaW9j5jXFvJT7HIU/GoCj79nQo3PyVtXSXFQiNe3MDyBhSddG7/bjLxMbXMcwQA2C5/7jwAeaj4rb+Vf9uqo7al777q/5M8PtvyygV7usK6781PjdjUioC+sXiSbTkBAAAAjmZZKln9XbkjPUa459J7ZflLj/vUTztDamozb0QtbczcBEQAAACn2rRpk/buHdjV49ChQ/2Pk/HXXnvNOD7ZFDPU1772NX3ve99TJBJJTQC74YYbUrvJJMfJFZRfeeWV1HETJkzQddddl9Y/D3Lb6s379WaT2ZDxzYtqVVvtnIaZrBGPSlvfGGiUCZo7gx7TuLn9jTKqnpnuLAEAALJScgeZ4XaRSe5Uec4552j9+vXGdRiyx4HeqDqDMSNWV0XTDPKIZcnTvkGBTU/Jv+UZuYMdI35qbGxjf5NMdOI5ktef1lQBADhZNM0AWSqw9iEVfvxvRizhH6ND1/5cVmCMbXnlympzf/vKNgWjCSP+3ctnyO9l+1oAAADADv4tK+XbvtqIhWbeoOj0S0/43NVN5gSssUVenTHx+I02AAAAOH2rVq3S66+/PuzXNm/enPo4UdPMjBkz9O1vf1s//vGPFQwG9cgjjxx1TLJh5p577lFhIZONcGzPfLTbGNeUBfStS+tty8dxYhFp6+uHG2U2/UYKmruBHtP4+dLcZdKc5VJVQ7qzBAAAea6rq0vNzc2pj5aWltRHd3d36mtLlizR3XffPeLXamtr0wsvvKA1a9aoo6MjtfNLTU2NzjvvPF1xxRXy+zM74TuRSKR2mUmaMmVKRs+NkWntMBf38nlcmjwmYFs+wGhx9+xLNcn4Nz8lb4f5u55jSfjLFZ1ygSJTlyg69UIlSmrSnicAAKeDphkgCxVsf0PFb/w/RsxyF+jQ1T9RYsx02/LKFS9tPqC3th40YjecUa0zp5TZlhMAAADSL7kKW7oMXfVtuFXgcGyu3v0qfuP/NWKJoiqFLvmrEdVtdZM5GevShkr5CtL3Kw3q7RzU2lmot7NQb2eh3tnv7LPP1g9/+EM9//zzqQlpBw4c6J+QtnjxYl155ZUZn5CWCZ2dI2wqOAXJv+fl5eXG5MHkJLt8tm6n+Xv/286uUaSvWxFz3lresbXW8Yi8O34nX9NvVND6stzhgd2mjic2bp6i9VcpUn+1EhUzBr6Qxu+JfOHE720no97OQr2dIxtqXVGRv7t033nnnaPyOh988EF/Y/8R4XC4vxEnuYBAsrE/ec2SLrFYTE8++WTqcbLxZ926ddq1a1dqMYL58+en7bw4dS3tA39fkqZXFsrjdtmWD3BaokH5tr6swKYnVfDpW3JZJ/i/yuWWJp+j4KTzFZ58oWLj5kvu9N2bBQBgtNE0A2QZT8dmlb74Z3JZcSPes/RvFJu0yLa8csXBYFQ/XLXNiFUWF+jPL55qW04AAADIvxuBg2964gQsS3rpz6RwlxF2X/cjjZkwaPLUMezpCmrtnh4jdv1Z06g30oJaOwv1dhbq7SzUe/QkV2k+mZWaj6e6ulp33HFH6sMp4nHz9/zplJyImcnzZVo0ntDWA+YEtVnji/L6z2xbrWNh+T79nXzNz8u3dZXckcMrt59IdNx8RequUrj+SiXKpw18wYE1Gk35/r0NE/V2FurtHNQ6faqqqjRp0qT+3VlGauvWrfrRj36kSCSiQCCg5cuXa968eanxW2+9lWqY2bNnj37wgx/ob//2b9O2I2ayaebxxx/vH7tcLl133XW69dZb03I+nL6WDvOapK6S3VKRY6yEvLvfV2DTU/I1vyB31Lz/NpxYzUJ5z7pNmnujVDRWoc5O/l8DAOQkmmaALOLqa1fZc3fKHTF/IO07+26FZ91oW1655J9e26HOYMyI/dfLpqs0wD93AAAAgC02PC1tfNaMJX+xPvu6ET39pfX7jHFZwKvzaitHM0MAAAAAWW7bgZDiCcuI1VcxQW1UG2V2vJGaNJVqlBnBxKmk6PgFA40yZVPSniYAAMBNN92kurq61MeYMWO0f/9+fetb3zqp1/jlL3+ZapBJ7oL+3e9+V42Njf1fSzbPTJgwQQ8++GCqcebZZ5/VihUrjnqN+++/X9FodMTnvPrqq1OvO1iyYefXv/51qrEquUvlhx9+qEceeURbtmxJ7XJTVFR0Un8upF/rkJ1m6qqoEXKD++BWBTY/Lf+mp+Xp3nnC4+OlExWeuVzhmTdIVQ15vYMZAMA5mEUOZJHSV74jT/cuIxZuuEZ9537btpxyye+3d+nZ9e1G7JL6Ci1t4Ad3AAAAwBa9HdJvvmPGiiqlq/9hxC/xwro9xvjzs8fL53WPVoYAAAAAckBzW58xnlDmU4mf25ynJRaSb/vrhxtltr0qd7R3RE+Ljl+ocP1VitQlG2UmpT1NAACAwYZrYDkZzc3N2rhxY+rxpZdeajTMHHHttddq9erV2rVrl1544QXdeOON8nrNnz1ffvllhcPhEZ938eLFRzXNHOF2u1VZWanLL79cpaWl+sd//Ec9+eSTuu222076z4f0sSxLLe3mdUktjfzIYq5Ql/zNv5F/05Mq2PuHEx6fKChOXeclF/aOTlokuQ7fi/NkIFcAADKB3yYDWcLTtl6+HW8edeOh+7K/7/8hFMcWisb1Ny9tNWLFPk9ql5nkFrYAAADIf8mV2NIledOqvLy8f9zV1ZVa/Q3HV/zCt+XrMxvbey7+K0UjHily4nod6Ivqva0HjNiF00vSWusk6u0c1NpZqLezUG9nsbverLYJpF/TkKaZelZ0PjXRoHzbX5O/Jdkos1quaN/InlZzpsL1VytSd4USpRPTniYAAEC6vPfee/2Pk00zx7rGXLJkiR5++GH19vZq/fr1WrBggXHMAw88kJb8jpxnw4YNaXl9nLr23qi6w3EjVldJ0wyyTDya2kXUv+mp1C6irkTkuIdbcik65UKFZ92gcO0XpAKutQEA+YumGSBLFK59yBgniqp06Jp/kbwB23LKJT99e5d2dZmrePzZxVM0rtRnW04AAADIrHjcvFmRTslJmJk8Xy7ytb4s35ZnjFjyF+6huquTxRrRa6ze0q6ENTAOeN1aNLUs4+899XYOau0s1NtZqLezUG8g/zQPWdG5vpqJPCMW7Us1yPiTO8psf02uWPCET0lOnIpNPFvhuisP7yhTUpORVAEAANJt8+bNqc9+v1+1tbXHPG7OnDnGc4Y2zaTLgQOHF5HyeNjbIdsM3WUmeb9iQrnftnyAfpaVWqw7sPkp+bc8I3fQXIxuOLGxDYcbZRqXcb0HAHAMmmaALOAKd6d+aB0sOO8rsoqqbMspl2za16uHPthjxBZMKtGNC8bZlhMAAADg9C3fi1/7vhFL+MvUs+T/lU5iJ8jVW8wdZS6oHaNAATtxAgAAIDekc6JbcvXr443zTXOb2ejROK7EMRMJT6nWkV4VbF0lX9PzKkg1yoRG1igzaZGiDdekdpSxSsan4skrOGe809nBad/bTke9nYV6Owe1zm47d+5Mfa6pqTnuz5MTJ0486jmjmUN1dXWqcWewcDis+++/P/V44cKFo3pOnL6WdvOapLaqUO6TuN8BjDZ3zz75t6xM7SrjPbDlhMcnAmMVbrxOoVk3Kl4996Tu1wEAkA9omgGygH/Tk8bKXpbLo/CcFbbmlCtiCUv3vtSq+KDVpws8Ln338louTgEAAACbFL/1N/L07TdivRd+V1bxyBvbe8Ix/X57lxG7tKFi1HIEAAAA0q2iInM/v5aXlytfHeyLaH9PxIidXV+jiopSOdExax06JG15UdrwtNT8ijSCRhm53NK0C6S5y+WadZ0KSserQBL7+GSPfP7extGot7NQb+eg1tkjEomou7s79biysvK4x5aUlKSaWpKNLB0dHaOax9tvv63f/OY3mjVrVqp5prCwMLXDzEcffZTKb/bs2br22mtP6jVHmmO6Gs+d0CzW2mH+fF1fVeSYRn4n1jtrRYPytbwo38Yn5P30LbmsxHEPtzw+RWd8XpHZNyo6bYnkSV7xndzCCNTbWai3c1BrZ6HeNjfNJLvm9+7dq76+Pp1zzjmpCwDAkSxLgXUPG6FI7RfY+nCEHvlwrzbtM7dA/fq5EzWjkn9TAAAAADsUbH9dgY2PG7HItCUKz7rxpF7nd60HU03y/a/rcenC2jGjlicAAACA3LBp7+GJjUf4PG5Nryq2LZ+sEuqSNr8gbVgpNa+S4uGRNcpMvyjVKKNZ10olI1/cAAAAIJeFQgNND4FA4ITHJ49JNs0Mft5oOOuss9TZ2aktW7akPpKvX1RUpKlTp+qCCy7QpZdeetLNGHfdddeIjvv1r3+tTMjHZrHtB81G/vlTqzK6UEI2y8d6Z5VEQtr+lvTxrw4vkhDpOfFzJi+SFtwi19wb5CsaK98opkO9nYV6Owe1dpZyh9Y7o00zyW74v//7v9cvf/lL7d69uz++du1azZkzp3/8q1/9Sk8++WSqKD/72c8ymSKQcQW7fi9vZ7MRC8271bZ8csnOgyH9y1vmNri1lYX62rkD2+QCAAAAyBxXpFslq/+HEUsUlKjnkntPepv3V7ccMMbnTitXiZ8NcwEAAACn2TykaaZ+XIkKPM5cDTEleFDa/PzhRpmWV6W4OXlvWC6PNOPigUaZ4qpMZAoAAJB1O80c4fWe+HfNR44Z/LzRUFdXl/pA7rAsS037zOuShvEltuUDh2hvlj75lfTxo1LXjhMfXz5VWvAlacGXpUr+jQEAYKiMzTZpamrS1VdfrdbW1tQPkke4hpk0s3jxYt12222p4+644w5deOGFmUoTyLjAuoeMcWxMraKTz7ctn1yR/PfhBy9vVTg2sM1k8l+T/3H5DGffLAMAAABsVPT238vTs8eI9V7w35QoPbnG9lA0rre3dhmxSxtYsQ0AAAC5Jbl6dLq43W5jRcCuri4lkqvP5qGPt7cb4xlj/Wl9b7NNqta+hLTpN9L6p2W1viZXInrC51lur2JTLlCk/mpF6y6XVfjZNVVyzmfEOe9frnHS9zaot9NQb+fIhlqz+8XwfL6BvRZisdgJjz9yzODnZauf/OQndqeQ13YdDKo3EjdiM2tKbcsHeazvgLT+ycO7yux8/8TH+0qlucsON8pMPT/5n1AmsgQAICdlpGkmuY3kNddco5aWFhUXF+vuu+/WxRdfrGuvvXbY46dPn57aavLVV1/VM888Q9NMFjvZ7UBP5pcIxxvnC1fvfvlaXzJikTO+Is8IVrTIJ6dS72fX7dfvtx8yYivOrNGZU8eMen4YXU75/ga1dhrq7SzUG8BwCna+rcJ1DxuxyOTzFJ57y0m/1jvbuhQa1CDvcUlL6rjRCwAAgNwSj5uTqtIpOREzk+fLpKb9vca4vqowb/+sQ7n62lX40S+ktQ9K0b7DseMcb7kLFJ1ygcJ1VylS+3lZgUH3TBzynuWbfP7extGot7NQb+eg1tkjEAgYc9lO5Mgxg5+XrSorK0d0XLqaz7OhWSyd1rQcMMYlPo/88aA6O0/89ygf5Xu9My4eVcH21+Tb+KQKtq6S6wS7iVout2JTL1R41hdTCySooPDwF7rMxehGC/V2FurtHNTaWbKh3hVZ0NjvzVQ3e3Nzc6ph5s0339TnPve5Ez7nqquu0qpVq/TOO+9kIkVk+V/iwd+seeWTn0uJQatXeAtVdN43VHRktS+HOlG923vC+t+rtxuxmrKAvnv9GSoNFKQ5O4y2vP3+xlGotbNQb2eh3gCSk7dKXv3vRsjyFqpn6Q+SW8ye9Mu92mTegDpzSpnGFPGzPgAAAOA0CctSc/vhZpHBTTOOaJZZ81MVrntIrtjxJ+JZbp+iyQlT9VcpMv0yWQF+TwMAADCc5I4xpaWl6u7uVkdHx3GP7enpUTgcPqmGlFyQqQaufGsWG9rIX1tVyMTiPK53RliWPG3rFNj0lPxbnpU7ZN4XG05sbIPCs25UuHGZEiXjB76Q4feeejsL9XYOau0sCYfWOyNNM08++aRcLpf+4i/+YkQNM0kLFixIfW5qakpzdoBN4jHpw1+asflflBzeMDMSf/3cBh3si5qx5fNomAEAAABsUvzu/5Ln0KdGrPe8/0uJsikn/VrReEJvthw0Ypc2cJ0EAAAAONHurrCCUXMyWkN1kfKVq7dNRX/4mQInaJaxPD5Fpl6syJFGGX9pRvMEAADIVZMnT9bGjRu1d+/e1ERBj8cz7HG7d+82ngNna2kPGuM6BzTyIz3cPXvl37xS/s1PyXvgxPNiE4VjU00yoVk3KF4155QWqgMAABlsmklebCRdfvnlI37OkS79gwfNiTJA3mh6UTq004yd8027sskZqzfv18qPBn45kXTN/An6wpxBHfQAAAAAMsa75wMFPv4PIxadcLZCZ9x+Sq/3/o5D6gmbq5pcUj/2tHIEAAAAkJua28xdZsoLvaosLnBms4zLI828Sj3TvqDwtCWyfDTKAAAAnKyZM2em5rEld5FpbW1VQ0PDsMdt2LDBeA6crbXDvC6pq8rfRn6kQbRP/taX5N/0lAo+fUsuWSfcTTRS+3mFZt6g6NSLJE/+XQMDAJC3TTPJLSuTSkpKRvycI1tcFhTwn3426+zsTMvrut1ulZcPbB/f1dWVd9talrzzLxr8tzs2foG6C6cn31Q5zUjr3ReJ678/8YkRK/V79O2LJ6Xt7yJGnxO+v3EYtXYW6u0s2VDvigpVHiV6AADZp0lEQVR2nQCyQiykklX/zfgFv+Xxq3vp30ou9ym95Oomcwv6+RNKNK7Ud9qpAgAAAMg9zUNWdG6oKpIrj1bWTTXLrPnp4WaZ+OF7o0NZLo9cC74sXfyX0thaRTs7ZcXNhQYAAAAwMosWLdLTTz+derx69ephm2aS97xef/311OPi4mLNnTtX+eJYO+uMxr3D441zWTxhaWuH2dheX12ctvcyF+RzvUeNlZB357vybXxSvuYX5Ir2nvApsQlnKTz7i4o2XCMrcPhefDb8LaPezkK9nYNaOwv1zmDTTHLXmOS2ltu2bdOZZ545ouesX78+9bmmpibN2eF0JLcqzYTkBWmmzpUJ7oPbVLD9DSMWnHtrXv0Z01Hvf35ju/YcMm8a/cUlU1VR6OG9y2H59v2NY6PWzkK9nYV6I1uk8wYFv0QYXuE7/0feg1uNWPC8v5Srqv6UfpGfvPn0WrPZEP/5mZUZv/lEvZ2DWjsL9XYW6u0s1BvZimuUNDTNjMuPyWmu3v0KfPAv8q89frNMZPYXFTn3WyqdtiDvaw1nfW/jMOrtLNTbOah1dquvr9fs2bNTu80km2YuueQSNTY2Gsc899xz2rVrV+rxVVddJa83I9Pr8mpRusGL7+W6be29CsfMxQPPqp+oilK/bTllm3yq92lrb5I+fkT65NdS16cnPn7MVCm5SMIZX5K3si4zk3lPE/V2FurtHNTaWcodWu+M/D+bbJR5/vnn9cYbb+jGG28c0XPuv//+1EpR5513XtrzAzItsP4RY5zwlyvccI1t+eSC9Xt69OiavUbsrCmlWjav2racAAAAkF0yuQORU3+JYNj5obTmZ2Zs0lkqWvodFblPbRLb71s71NkXM2LLz5mhiopi2Yl6Owe1dhbq7SzU21moN7IF1yinr/WAuaLz56ZX5fbus917pd/9SPrw31M7dw7L5ZE+d6tcF/2l/GNnyO+QWmN41NtZqLezUG/noNaja9OmTamFm484dOhQ/+Nk/LXXXjOOTzbFDPW1r31N3/ve9xSJRHTvvffqhhtuSO0mkxy//fbbeuWVV1LHTZgwQdddd11a/zzIfpv3dRvjiqICVZX4bMsHWajvgLTuCenjX0m7Pjjx8b5Sae7yw80yU89LdldmIksAABwvI00zN910k37zm9/opz/9qf7Lf/kvmjp16nGP/9GPfpRqsEk2zXz5y1/ORIpA5sRCCmx43AiFZ31RKii0LaVsF4sndO9LW5WwBmI+j0v/4/La1L8TAAAAADIsFpZW3p3aXr6fxyctu086xYaZpBfWmY3ysyeUaVqlvQ0zAAAAAOwRisZTqzoPNrOmTHnbLOP2Hp40ddFfSmNnZDpDAACAnLBq1Sq9/vrrw35t8+bNqY8TNc3MmDFD3/72t/XjH/9YwWBQjzxiLnx7pGHmnnvuUWEhc3mcrmlI00zj+FLmKkGKRaTmlw/vKrP5t1IievzjXW6pbunha76ZV0u+okxlCgAAMtk0c/vtt+sf//Ef9cknn6QuRu677z5deeWV/V9P/iBpWZY++OCDVMPMr371q1TsoosuSm1zCeQTf/PzcocPGrHgvFttyycXPPDBHjW19RmxO8+fpKkVAdtyAgAAABztjR9KbRvN2JL/Ko2bfcovmfy9wIvrzaaZq+bVnPLrAQAAAMhtTft6jMW0kvPSGseXKKcc2iO99SPpg3+X4uFjN8t87tbDzTIV0zOdIQAAgCOdffbZ+uEPf6jnn39ea9as0YEDB+T1elVTU6PFixen5rX5/UP3/Mt9nZ2daXldt9tt7KrU1dWlRGLQols5bO2nB4zxtDG+tL2PuSKf631cliXP/rXybXxCvs3PyB068d+DWOUsReZ8UZGZy2QVjzsc7A0f/sgRjq23Q1Fv56DWzpIN9a7Igt3DvZl6s5955hldeOGF2rZtm6699loVFRX1d10nG2m6u7sVDof7J8rU1dXp17/+dSbSAzIqsPYhYxyZcoESFawYdiw7OkP62du7jFhDdZFuP3uCbTkBAAAgO6XzJkU2/BIhW3ja1qv0d/9bg9dRi1XPVfecO5JFOOXXXb+nW3u6zBWXz59SZMvNJ+rtHNTaWai3s1BvZ7G73tlwswfZiWuU0/Nhyz5jPHlMQOHeboXNzWeykqtnrwIf/Iv86x6WKx4Z9hjL7VVkzs0KnfOnSpRNORwc5u+ME2qNAdTbWai3s1Bv58iGWufzNcrdd9+d+hgN1dXVuuOOO1IfThGPxzNynuTf+UydK91a2swLkNrKQN782UZLPtV7OO6ePfJvXin/pqfk7Ww+4fGJwkqFG5cpNOsGxatmH14BIilP3qN8rzdM1Ns5qLWzJBxa74w0zSRNnTpVH330kf7sz/4s1QzT2zvwA2VbW1v/42QjzYoVK/STn/wkry/i4Eye/etUsO8jIxaa9xXb8sl2yQa6//lSqyLxgaXk3C7pf1w+Q16P29bcAAAAkH0yeVHv1F8iKB5V6Uv/l1yJmDHRq3vp3you92n9wv/lTe3GOLmz5LQKX1a8z46ttwNRa2eh3s5CvZ2FeiNbcI1yerbsNyen1VcVZv2f0d2zV4Uf/osCGx49brNMaPZNCp51lxJlkw8HT+LPlY+1xrFRb2eh3s5CvZ2DWgO5KxZPaNsBc7Gv2spC2/JBBkV65W99Sf7NT6ng07fl0qBtUIdheXyKzPi8QrNuVHTqRYd3FAUAAFklo/87jx07Vg899JD+5m/+Rr/5zW/0wQcfaP/+/amLw8rKSi1cuFDXXXedGhsbM5kWkDGF68xdZuLFNYrMuMy2fLLdynVt+vDTbiP2pTNrNG9CiW05AQAAAE5W+Iefytu+wYgFz/oTxavnnHbD/KtbDhixpQ0V/TvUAgAAAHCe5rY+Y1xfVaSsb5ZZ/6hciWM1yxQoNPuLZrMMAAAAgKz16cGwYgmzWaK2iqaZvGUlVLDrXfk3Pil/64tyRc1r0uFEJ5yd2lEmUn+1LH9ZRtIEAACnxpaW1mnTpulP//RP7Tg1YBtX+JD8W541YqG5t9BZfgztvRH902s7jNiEMp/uuoAbSQAAAIAdPB1bVPTePxux2NgG9Z19+tf3Le3B1M2nwZY2jj3t1wUAAACQu5rbzQlKDdXZ1zTj7tmjwg//dQTNMkd2lpmU8RwBAAAAnJqWIdckVcUFGlNYYFs+SA9PZ4v8m56Sf/PT8vTsOeHx8bIpCs+8QaFZy5Uon5aRHAEAwOljtj6QIf5NT8oVC/aPLZdH4TkrbM0pm/1w1XZ1h80tiu/5wgwV+Ty25QQAAAA4ViKmklX/tzEJzHK51XPZ30se/2m//Oomc5eZ8aU+zR5ffNqvCwAAACA3dfRGdaAvZsTqs6hpxt29W4Vrks0yvz5+s8ycmw83y5ROzHiOAAAAwFAeT3rm3Ljd7uOOc9XWAyFjXFdVlLb3MJfkQ71dwU75tjwr38Yn5N338QmPt3ylijRco/DsLyo+8WzJ5ZIr+T2l/JcP9cbIUW/noNbOQr0Po2kGyATLUmDdI0YoUnu5EiXjbUspm73WdECvbDEnzV05u1LnzxhjW04AAACAkxV+9O8q2P+JEQsu/KZi488Yldd/tanTGC9tGCuXK3m7AQAAAIATDd1lJuB1a1L56Tfsj0qzzIf/osCGx07QLLNCwbP+hGYZAAAAZJWKioqMnKe8vFz5YEdXqzGeO3lsxt7DXJIz9Y5FpKaXpI8fkba8KCWixz/e5ZHqL5MW3CLXzKvlLyiU/Vel9suZemNUUG/noNbOUu7Qeme8aSaRSGjDhg1qbW1Vd3e34nFzJ4nhfPWrX81IbkC6FOz6vbydzUYsNP9W2/LJZt2hqH7wknnRWV7o1V9eynaWAAAAgB3cnVtV9Pt/NGKxMTPUt+gvRuX1P+0MqanNnBC3tJGbTgAAAICTDb1GqK0qlMftyoJmmeTOMsNPrLLcvs92lqFZBgAAAMgHW/Z1G+PG8SW25YJTZFnS7jXSR49I656QguYizsMaPz/VKKP5N0ulLIgNAEC+yFjTTF9fn+699179/Oc/V0dHx4ifl1xZlqYZ5LrAuoeMcWxMraKTzrMtn2z2Dy9u1v4ec3W2/3zJVFUUFdiWEwAAAOBYVkKlr/7fcsXDAyG51HPZ30rewKicYnWTeYNibJFXZ0wsHZXXBgAAAJCbWoY0zTRUF9nYLPOTz3aWOU6zzNwVCp75n2iWAQAAAPJEOBbXtg7zuqSxhnsXOaNrp/TJo9LHv5Lat5z4+OJx0hkrDjfL1MzPRIYAACAfm2Z6enp06aWXas2aNbKS3buAg7h698vX+tLRu8y47FsRLVt9uP2AHnh3uxE7d1qZrplTZVtOAAAAgJMFPnlABXs+NGKhBXcoNuHsUTvHq0OaZpbUj7V1BWkAAAAA9mtqNyen1VdltmnGfWjX4WaZjY+foFnmSwqe9Z+UKJmQ0fwAAACAU9HZ2ZmW13W73SovL+8fd3V1KZFIKJdt2d+reMKc51jti6XtPcwlWVvvSK98Lb+Vb+OT8n76tlw6/jxVy+NXtO5yhWffqNjUiyT3Z1NpqXFu1BtpQb2dg1o7SzbUu6KiQo5omknuMPPhh4cn2SxevFh//Md/rAULFmjMmDGpQgD5LLDhUbkSsf6x5Q0oPOuLtuaUrSs0/Lcn1qZ2xTzC73Xrni/MSO04BQAAACDzCwAUv/MPRixeNkW9i/9y1M6xrzusdXt6jdjSBvt/WQIAAACMBo/Hk7bXHnp/LZ/ut8USlrZ2hIxY4/iStL6fR7gP7VTg/fvk23CcZhmPT+G5tyh09l2ySicoeQcjnZnlc61xNOrtLNTbWai3c1BrZLN4PJ6R8yQnYWbqXOnStL/HGNeU+lTodeX8nyvv6p2Iq2DXu/JvelL+lhfligVP+JToxHMUmnmDIvVXy/J/tntQcr4atXXM9zdGjno7B7V2loRD652RppnHH388Nen96quv1sqVK7kghHMkYgqs/5URCjdeL8tfZltK2epfXms96oLzTy6YrMljArblBAAAADhZ4bqHj7q50LP0B1LB6K3w/FqTuVpXqd+js6dyvQQAAID8kMnV8wavFJjrmvf3KBwzVzo8u36CKkr86TvpwR3Sm/9L+sND0jGaZeTxSWd9Ta4Lvq1A+STZdfcin2qNE6PezkK9nYV6Owe1BnJTS7t5f6S2qtC2XHA0z4Fm+Tc9Jf+Wp+Xp2XvC4+PlUxWaeaPCM5cpUT41IzkCAAAHNs3s2rUr9fnP//zPaZiBo/i2vXrUD+bBeV+xLZ9s1drep/tWNxuxWeOL9OWzamzLCQAAAHC0eESB9Y8YodCsGxWdfN6onmb1kKaZi+oqVODh9wYAAACAk23e222Mq0v9qkxXw0zn9sPNMh8lm2Viwx/j8aeaZXTht6WyienJAwAAAEBWaWnvM8Z1VaO3oBhOjSt4QP6m51LNMgX7Pznh8QlfqcIN1yg86wbFas6SXMl9QgEAgFNlpGlm3Lhx2rlzp6qqqjJxOiBrBNY+bIyj4xcoPm6ebflkI8uydO+LrYrEB1aN87ik715eK6+bixUAAADADr6W38rd127Egp/7xqieo7MvqjU7DxmxpQ2ZW4kbAAAAQHbatNe8TphVUzr6J+nc9lmzzMPHb5Y5+4+kC5LNMhNGPwcAAAAAWau1w9xppq6SnWZsEQ/Lt+01+Tc9Kd/21+Q61vXbZyyXR9GpFys06wZFZlwmee3aIxQAADiyaWbRokWpppnNmzdr4cKFmTglYDv3wa3yffqmEQvNu9W2fLLVuj09+sOQiXK3nTNRs8YX25YTAAAA4HSFax80xtGJixSvmjWq53i9pVMJa2Ac8Lq1eHr5qJ4DAAAAsFNnp7mz4mhyu90qLx/4+bmrq0uJxMDiVLls7acHjPH0Mb5Rey/dXZ8q8P4/y7fxiWNOtrI8PoXn36rQWXfJKhkvxVPFlF3yudY4GvV2FurtLNTbObKh1hUVLE4EnI5QNK5dB8NGjJ1mMsiy5N3/SapRxr/lObnDB0/4lFjVnFSjTLjxellFLOwOAABsapr5z//5P+vJJ5/UP//zP+tLX/qSXGx1BwcIrHvEGCf85Qo3XGtbPtnq6bVtxnjSmEL9pwun2JYPAAAA4HSetvUq2POhEQuecfuon2f1FnPS2QW1YxQo8Iz6eQAAAAC7xOPJbovMSE7EzOT50qlpf68xrqsKnPafzd21Q0Uf/iQ16erYzTJ+heZ9WcGFf6xEslkmKQvf03yqNU6MejsL9XYW6u0c1BrIPVs7Qhq05peSMx1nVLJjSbq5u3fLv/lp+Tc9Je/B1hMenyiqVmjmMoVn3jDqC78BAID8k5GmmfPPP19/93d/p//6X/+rbrnlFv3rv/6rxowZk4lTA/aIhRTY+LgRCs++iS0fh+iLxPXypg4jduu5U1VY4OGXRgAAADhpHo8nrasDHm+cT4rWPWSME8XjFa+/clTf3+5wTO/t6DJil82sTGsNT4aT6u101NpZqLezUG9nod5A/uiNxLWry1zRuf40VnRONct88P8dbpax4sdplrlVfWf+sazicad8LgAAACDbpet38Pl2Xd56IGSMJ48JqDjgsy2fbDOq9Y70yNf829RuoN6d78pltCsNf/0WrbtC4dk3Kjb1Qsl9ePprdtxdyk/59v2N46PezkGtnYV6Z7BpJuk73/mO6urqdOedd2rKlCn6whe+oMbGRhUVnfgX3d///vczkiMwWvxNv5E7bE4AC877sm35ZKuXN3eoLzqwDbHbJX3xzMlSImhrXgAAAMhNFRUVGTtXeXm58lLfAWnzSiPkPucbqqga3Yljb3y0S9H4wI0Pn8et686aodJAgbJR3tYbR6HWzkK9nYV6Owv1BnJXS3ufMfa4kis6F57067i7tn/WLPPUCZplvqK+M++kWQYAAACOkKn7KLl+Xb6rZ68xnjmhPKP3oHLNSdc7EZe2viF9/Ii08Vkpal4HDmvaBdKCW+Sas0y+QLloYbJPrn9/4+RQb+eg1s5S7tB6Z6xpZv/+/XrqqafU1dWV2np05UpzEs7x0DSDXBMYsjJzZMqFSoyZYVs+2Wrl2jZjfOnMcaopD6izk6YZAAAAwBYfPZTaObOfu0A662ujfprfrjNvOF3YUJW1DTMAAAAAMqe5zbw/MLUiIL935Csfug9uO9wss/npYzfLeAMKzT2ys0z1aecMAAAAIL9s2ddtjGfWlNiWS17Zv+lwo8wnv5a6d5/4+LG10oIvS2eskCqmZyJDAACQxzLSNNPR0aGLL75YTU1Nsqzjb6EH5DrP/rUq2PexEQvN/4pt+WSr1vY+fbK7x4itOGeKbfkAAAAAjpdc2ev9n5uxOcuk0vGjeppgJK7XNpsN9FfOrRnVcwAAAADITU1t5grD9dVFo9ssk9xZZmFyZxmaZQAAAAAMb8tes2mmcXypbbnkvN52ad0T0kcPS3s+OvHxgXJp7o2Hm2WmLJJcrkxkCQAAHCAjTTN/8zd/oy1btqQe33TTTfrTP/1TLViwQGPGjJGLH2yQZwrXPWyM4yU1ikxfals+ubLLTFWJX0tnjbMtHwAAAOS+zs7OtL222+02tqg9sotqPvFufVWlnduM2KHZX1Z8lN/XV7d0KBgdmMTmdklnT/SntX4nywn1xmHU2lmot7NQb2exu94VFRUZOxeQ71raT65p5nCzzH3yb1553GaZ4LyvKJjcWaaoalTzBQAAAHJJun4Pb/d1+WjqCce0uytkxGoKray6h2G3E9Y7FlbBttXybXxcBdtekysRO+7rWS6PotMvUWT2FxWdsVTyBg5/4eDBtP0Z4Mzvb5wY9XYOau0s2VDviiy4j5KRpplnnnkm1Rxz22236T/+4z8ycUrAFq7wIfm3PGPEQnNvkdwZ+VbLGdF4Qr/Z0G7EvnjWJBV43LblBAAAgNwXjw8/QSodkr9AyOT5MqH4o18a41jVHEXGLUi+saN6nlc2m9cCZ04uU5nfndXvZz7WG8Oj1s5CvZ2FejsL9QZyk2VZR+0003CMphn3wa0qev8++bckm2USx26WmX+bgsmdZWiWAQAAADJ2rZzL1+VN+3uMscclTSn35eyfJ2P1jsXk3feR/Juekr/pObnDXSd8Xqx6rkKzblC44Trzmo33Oqvl8vc3Th71dg5q7SwJh9Y7IzP5d+3alfr89a9/PROnA2zj3/SkXLGB1QYst1ehOV+yNads9EbLQR0MmqsIfOnsKbblAwAAADhdcnVm3443jFjwjNtHfdv7ZAP9my3mymBLG+1fUQQAAACA/fb3RNQdNm/W1lcVHb2zzPv/fIJmmUIF53+FZhkAAAAAJ621PWiMp1QE5POyCPAxHdyhwHv/oYKNT8h7cOsJD48XjVN45jKFZ96geNXMjKQIAACQsaaZqqqqVONMaWkp7zryl2UpsO5hIxSpvVxW8TjbUspWKz/Zb4wXTi5TbXWJbfkAAAAATle49iFjnPCXp1b2Gm0f7DikniGT4C6pHzvq5wEAAACQe5razMlpxT63JpT5+sfefR+r/KlbjcXLjm6WSe4s802aZQAAAACckpYhTTO1lYW25ZI1LEuuSI9cwQ65gx3yhjul+CFp4zPStjd1oncouQtouPZyhWfdqOjk8yW3J0OJAwAAZLhp5qKLLtKvfvUrrVu3TmeeeWYmTglkXMGud+XtbDFioXm32pZPttp7KKx3tplbcC4/g8YiAAAAwDbRPvk3PmaEQnNulgpG/0bQq00HjPH8CSUaVzowCQ4AAADIJx5P+iYCud3u445zUWuHOTmtvrpYXu/Arczid344bMNMslkmvOCrCp15eGeZ3H8n8r/WODbq7SzU21mot3NQayC3tXT0GeO6Ibtf5o1YSO6+w00wR5phUo8/i7mDBwa+1ndArkTkpE8RnXiuQrNuVKT+Clk+FlsHAAAOaJr5y7/8Sz3xxBP64Q9/qBUrVigQCGTitEBGBYaszByrqFN00mLb8slWz65rkzVoXOzz6AszK23MCAAAAHA2/+aVcke6+8eWXArN+8qonyeesPRaU6cRW9rILjMAAADIXxUVFRk7V3l5uXLd9q5txnje5IqB93D3R9LOt80nFBRLi+6U6/w/U6C4Sk65+5gPtcbIUW9nod7OQr2dg1oDub3TTF1Vjuw0E4/KFeo83PCSanw5MEwzzKBGmGhvetIon67QrBsUnrlcibLJaTkHAABA1jbNJHeX+fnPf65vfvObuvzyy1OPGxsbM3FqICPcPfvk2/ry0bvMuFy25ZSNEpalZ9e1G7ErZleq0Me2mwAAAIAtLEuFax8wQtFplyhRPnXUT/Xxrm51BmNG7NKGzE0iBAAAAJDdNu8daOZPmlUzaCXit/+PeXDxOOlP35GKqzKUHQAAAIB8dzAYVUdvNDt2mrEScoW6huwEcyDVEGM0w3wWc4cP2jcfzF+mcMO1Cs+6UbHxn2O+HAAAcG7TzNe//vXU5zlz5uh3v/td6vMZZ5yRapwpKjr+D5Yul0u/+MUvMpEmcMr8G34tV2Jg8pflLUxdCMD0/o5D2n0obMSWz6+2LR8AAADA6bx7PpC3Y7MRC55xe1rO9WrTAWPcWF2kyWOcshY0AAAAgOOJxBJq3t9jxGZNKDv8oHO7tP5p8wnn/icaZgAAAACMqtYhu8x43S5NGeMfnRe3LLmivYcbXlK7vhxpfvls95fPYkeaY5LNMC4rrmxjFRQpUVgpT+l4qWKaNGeZuqrPVdyVkWmoAAAApywjP6388pe/TDW/JCU/JxIJffzxx6mP47GSPyzSNINsl4gpsP5XRijceJ0s/2c3c9Dv6U/2G+OG6iLNHl9sWz4AAACA0xV+cr8xjpdPU3TqRaN+nuT1/eqmTiPGLjMAAADId52d5s/Ao8ntdqu8vLx/3NXVlbr/lqua2noVS1hGbLw/nnoPC1//3woMmiyWnKTV1XCTrDS+v9kk32qN46PezkK9nYV6O0c21Lqigt+9Yngejydtf++PN84VWzvNhYBnVBbK7ys4/pNiIXk6muQKtg/aBeaAXH2Dxp99dsXN188GlrtAVlFlqhHm8OcqWYVjlSiqlFVYqURRcnzka5VSQeFR/865u7ok/k/LW/ny/Y2Rod7OQa2dhXpnsGlm6tSp/U0zQL7xbV0lT+9eIxac9xXb8snmLUxfazZvYC2bX82/DQAAAIBN3D175Wt9yYgF598muUb/FyQb9vZqX3fEiC1tHDvq5wEAAACySTyeuVWBkxMxM3m+0bZ5r7nLTE2pT0UFLiV62uVf96jxtdCcFYoVlCTfYDlRrtcaJ4d6Owv1dhbq7RzUGtkkUw1VgxsqcsnOQ7uM8ayJY47/njW9LD3+DSncpezhkooqpeLqw7tzpj6qB42PPK5OHecKlKfmbrkdWG+cGurtLNTbOai1s5Q7tN4ZaZrZtm1bJk4D2CKw7mFjHB3/OcXHzbMtn2z1woYOReMDq8T5PC5dNbvK1pwAAAAAJ0vumOlKxPrHlrdQ4VlfTMu5VjcdMMZTKwKqrSxMy7kAAAAA5J7m9j5jXF9dlPocWPeQXLFgf9xyeRRc8PWM5wcAAAAg/23Z122MZ44vOfbB8ai08luZaZjxlw9qeDlGI0zRkc9jJXd6dhQCAADIZRlpmgHylfvgVvk+/Z0RC8271bZ8spVlWXp67X4jdknDWJUX8k8QAAAAYIt4JNU0M1ho5jJZgfK0XA+82mTuOrm0oYJdJwEAAAD0a24baIxJakg2zcTCKvzkfiMerr9aibJJGc4OAAAAQL5L3ssY2jTTML702E9ofkXq2XtqJ/MGpOJxUnHlMXaBqRpogkk+9vpP7TwAAADox4x14DQUrnvEGCf8YxRuuMa2fLLV+r29amk3b3gtn19tWz4AAACA0/laXpS7r82IhebfnpZzJa8FdnSGjNjSxrFpORcAAACA/Nhppq6qUP5NT8od7DDiwTPvzHBmAAAAQP7o7DQXuBotbrdb5eUDi3J1dXUpkUgol3T0RtTZFzViNYWJY75nxe/9u3yDxlZBkeLlU2UVVilRVCmrsFJWUaUShVWyisYqkRpXpT6roEgaycJiybewO3mtZF4v2S0f6o2Ro97OQr2dg1o7SzbUu6KiQnajaQY4VbGQ/BsfN0Kh2V88vBoADCvXmpPxJpX7dfbUMtvyAQAAAJyucO0Dxjg68RzFq2al5Vyrmw4Y4/GlPs0eX5yWcwEAAADIPYdCMe3rjhixhqqACl/8hRGLTLlA8eq5Gc4OAAAAyB/xeDwj50lOwszUuUbLln09xtjvdammpGDYP4err10FW1cZsZ4L/rvC8748spPl2aTkXKw3Th31dhbq7RzU2lkSDq232+4EgFzlb3pO7nCXEQvNu9W2fLJVMBLXS5vajdh186rlHsmKCQAAAABGnadtgwr2fGjEgmnaZSbp1SZzFbalDWPl4noAAAAAwGea28xVk71ulxq73pL34FYjHlzILjMAAAAA0qO1I2iMZ1QWyuMe/l6Gf8szciVi/WPLG1Ck4dq05wgAAIAs2WnG4/GkPicnv8RisaPip2LoayG7nE5tT7QV1PHG2aBw3cPGODr1Irkq65SedyR3vdrcod7IwAoJyevJZWeMN/7u5EK9MXqot3NQa2eh3s5CvYH82mUmXjROkdrL03KuTztDahoyAe7SBvu33QUAAACQPZrbzWuG2spClXz0D0YsVjVb0SkXZjgzAAAAAE7RctR1SdHwB1qWAhseM0Lh2itk+UvTmR4AAACyqWnGsqyTiiP3VVRkZrJTeXm5ssquNdK+j41Qwfl3Zez9yCXPbdhojC9urNbsaTW5VW+kFfV2DmrtLNTbWag3kDtcoYOpFdCO2jHTU5CW861uOmCMxxZ5tWASN44AAAAADGhqM1d0/kJpqwp2rzFiwYXfTK60l+HMAAAAADhFS7t5XVJXVTjscd79a+U9sMWIhefclNbcAAAAkGVNM3/1V391UnEgZ33wC3NcNklquMKubLJW8/4evb+t04jdcs4U2/IBAAAAnC6w8XG5YqH+seX2KjT3S2k73+om83pgSf1YeZLbTwIAAADAMVZ0viH4lDGOl0xQuP6aDGcFAAAAwCmSC4If1TRTOXzTjH/TE8Y4XjpZ0UmL05ofAAAATh9NM8DJCnZKa80LIJ31R5JnVL+d8sJjH3xqjCuLfVo6a7xt+QAAAACOZiUUWPuQEYrUXSmreFxaTrevO6y1e3qM2NIGducEAAAAMCBhWWpuG2iameHao7rO3xnHBBf8Udp2xwQAAACA/T0R9UbiRqyuqujoA2Nh+bc8Y4RCs78oudzpThEAAACniVn+OC2dneaqwaPF7XarvLy8f9zV1aVEIqFs4P/Dv6koFjRWZu6qu15Wmt6LXBWNJ45qmrlmbpV6u7vUm0P1xuij3s5BrZ2FejtLNtS7ooKJ98DJKtj+ujyHdhix4Bm3p+18rw3ZZabU79HZU8vSdj4AAAAAuWdPV1h90YHfKdzp+Y1csvrHCV+pwmncHRMAAAAAhu4yU1TgVk2Z76jjfK0vyR0+ZMTCs25Me34AAADIkaaZN954I/X5nHPOUWHh8FsXDhUKhfTee++lHl988cVpzQ+nLh43u+zTJTkJM1PnOi7Lku+TB41QpPZyxQKVyTfDtrSy0etNB3SgL2rErp9bNaI6Zk29kRHU2zmotbNQb2eh3kBuKFz7gDGOVc1RrOastJ1v9ZCmmYvqKlTgYbU1AAAAAAOa2gd2malSl77ofdP4emjerbJ8JTZkBgAAAMApWoc0zdRWFcrlch11XGDj48Y4Mvk8Jcompz0/AAAA5EjTzCWXXJJajfqTTz7RnDlzRvScXbt29T8vFoulPUdgJAp2vSPvwVYjFpr3FdvyyWYr17YZ4zMmlmh65cia5gAAAACMLvfBbfJtf92IBeffJg1z02c0HOyLas1Oc7W1pQ3sEAUAAADA1Nw2MDntDu+L8mtgMS7L7VPojDtsygwAAACAU7QMauZPqq0sOuoYd/duFXz6lhELz7457bkBAAAgh5pmkizLyujzgHQIrH3IGMcq6hWddK5t+WSr/d0Rvb31oBFbPn+cbfkAAADAGTweT9peO7mgw/HG2a5w/cPGOOEvU2z2DWl7z95obVdi0OV8oMCt8+vGprVGoynX642Ro9bOQr2dhXo7C/VGtuIa5cRaPlvRuUgh3e552fhaZNZyuconKjeuItInX2qNkaHezkK9nYV6Owe1BnJPS4e500xd1dGLAvs3PSmXBm58JHwlCtdenpH8AAAAkENNMycrkUikPufKhBrkP3fPPvlazRs2oXm3pm1l5lz27Po2Y4JcUYFbn5851s6UAAAA4AAVFZnbyaS8vFw5I9IrbXzcCLnP/Koqxk1M2ynf3NpkjJfOGqeJ46qUq3Kq3jgt1NpZqLezUG9nod7IFlyjnFjrgVDq8wrPaxrj6jW+5r/0O/Jn8D3MFblaa5wa6u0s1NtZqLdzUGtkk3TNw8vlZrGEZal1SNNMw7gS872yEgpsesI4Jtp4vTyBEjlRLtcbJ496Owv1dg5q7SzUO8ubZrZv3576zMUjsoV/w6NyWfH+seUtVHjWjbbmlK0Xk8+sbTNiV8yuVJGPBjgAAADAFmsfk0JdgwIu6ZxvpO10h0JRvdXcYcSumFuTtvMBAAAAyE2haFxb23vlUVzf8LxgfrHxSql6pl2pAQAAAHkpU439uTTf79MDfQpFDy/ufcRZ9RNUURYYCGz7ndS1wzjGv/gbNPnnYL1x+qi3s1Bv56DWzlLu0HqnpWlmxw7zh8Qj9uzZo5KS43dYh8NhtbS06Hvf+55cLpfmzp2bjhSBkxOPKrD+V0Yo3Hi9LH+pbSllqw8/PaRdXWEjtmz+ONvyAQAAABzNsqT3fmbGGr4gja1N2ylXb9qvSHzgBpPP407tNAMAAAAAgzXv70ntWn+d+/ea4jYX49L5f25XWgAAAAAcZPPebmNcXligcaV+86A/PGiOq2ZKk87KQHYAAADI6qaZGTNmHBWzLEuXX375Sb/WV7/61VHKCjh1vm2vytO7z4gF53/Ftnyy2cohu8zUVhZqbk2xbfkAAADAOTo7O9P22sntaQevttHV1aVEwlx5LBt5d72n0n3rjFj3nC8rlsb36pk15kIai6aVKRbsUWdQOSNX642TR62dhXo7C/V2FrvrnamVepF7uEY5vg9b9ifvIOqPvc8Z8dj4Beoum518A23LLZvkQ60xctTbWai3s1Bv58iGWnONAozclv1m00zj+JLUQt/9wt3ShpXmkxbeJg0+BgAAAM5smkk2yJxMfDiBQEB//ud/rq9//eujmBlwagLrHjLG0fELFa9mF6ShDoVienXLASO2/Ixq82ISAAAASJN4PJ6xcyVvcmbyfKeq6OP/MMbx8qkKT74w+Wal5XyhaFxvtR40Ypc2VOTEe5UP9cbpo9bOQr2dhXo7C/VGtuAa5fi27OvRee4Nmu/eZsT7Ft6pOJOI86rWOHXU21mot7NQb+eg1nBCY382NIudqnU7OozxtDE+433yrXtUxdG+/rHl8qhr2hWyHNzkn8v1xsmj3s5CvZ2DWjtLNtS7Igsa+9PSNPPv//7vxviP/uiPUpPm//qv/1qTJk065vOSxySbZSZMmKCFCxeqpKQkHekBJ8XduVW+T98yYqH5t9qWTzZ7YUO7IvGB5jiv26WrZ1fZmhMAAADgVO6effK1vGjEgvNvl1zutJ3z3W1dCsUGfrnidklL6uz/5QcAAACA7NPU3qc/9Tx3VKN/pPZy23ICAAAA8lmmGrhyqVmsuX2gISZpxtiAkbtv/aPG1yPTL1UsUJm2xclyUS7VG6ePejsL9XYOau0sCYfWOy1NM3fcccdRTTNJy5cv15w5c9JxSiBtCtc9bIwT/jEK119jWz7ZKrmT1Mq1bUetKD2mqMC2nAAAAAAnC6z/lVyJWP/Y8gYUnvXFtJ5zVZO58+SZk8u4JgAAAAAwLNf+TbrE87ERC37uG5LbY1tOAAAAAJwjnrC0rSNoxOqqivofezpbVbB3jfH18Oz03mcBAABADjXNDLV69erU5xkzZmTidMDoiQbl3/S4EQrNuUny+m1LKVtt2tenLW3m6gvL5lfblg8AAADgaPGIAusfMULhxmWyAgNb7o62aDyhN1sOGrGljewyAwAAAOBoB3qjWhFbKQ3qj4n6KxRKc6M/AAAAAByx82BIkbhlxOqqCvsf+zeac8YShWMVmXZpxvIDAABAjjXNLFmyJBOnAUadv+k3cocPGbHQvFttyyebrVy73xjXlPq0aFr6JuQBAAAAODZf60ty95k7QQbPuD2t5/xgxyH1hM0tfC+pH5vWcwIAAADITTs/bdX17reNWDh5zVIwMEENAAAAANKppd3cZWZskVcVRQWHB4mY/JueMr4ennmD5Pns6wAAAMgpbrsTALJZYN2Dxjgy9SIlyqfZlk+2CkXjemFjhxG7fn613C6XbTkBAAAATlb4yQPGODrxHMWrZqf1nK82HTDG8yeUaFypL63nBAAAAJCbytffrwLXQNN9SL7DTTMAAAAAkCGtHWbTTG1lUf/jgh1vytNnLiAcms3OmAAAALmKphngGLz7PlHB/rVGLDT/NtvyyWarthxQb2Tg5layVeb6edW25gQAAAA4ladtgwr2fGDEgmm+loknLL3W1GnELm2oSOs5AQAAAOQmV7hbc/Y+Y8TeK79CViE7VQIAAADInJb2PmNcVzWw82Vg4+PG16LjzlC8cmbGcgMAAMDoomkGOIbAuoeNcbxkgiLTLrUtn2y2cm2bMV48vVw1ZX7b8gEAAACcrHCtuctMvGicIrWXp/WcH+/qVmcwZsSWNjLhDQAAAMDRAusfVqE1MDktbrm0tZZFywAAAABkVkv7kJ1mPmuacQUPyLd1lfG18OybMpobAAAARhdNM8AwXKEu+ZueNWKheV+W3B7bcspWOzpDWrOz24gtm88uMwAAAIBt1zJbnjn6WsbjS+t5Vw/ZZaaxukiTxwTSek4AAAAAOSgeUeDj/zBCv02co+rJDbalBAAAAMB5ovGEtneGjFhdVVHqs3/LSrkS0f645fEp3HBtxnMEAADA6KFpBhiGf9OTcsUGLowst1eh2StszSlbPbN2vzEeU+jVxXUVtuUDAAAAOJl/4+NHX8vMvSWt57QsS682HTBilzZwTQAAAADgaMkmf0/vPiP209i1aqg+PDkNAAAAADK1SHA8YRmxusrC5E0PBTY8bsTDtVfICpRnOEMAAACMJppmgKGSFz/rHjJCkborZBWze8pQsYSlZ9e3G7Gr51TJ5+WfFgAAACDjrIQK1z44zLXMuLSedsPeXu3rjhixpY1j03pOAAAAADnIslT4h58bod8nZmln4WxVFBXYlhYAAAAA52lpDxrjcSUFKg145WlbL2/HJuNr4dk3ZTg7AAAAjDZmtgNDFOx8W96DW41YcN5XbMsnm73VelAdvQPbkSYtm09zEQAAAGCHgu2vy3NohxELzv9q2s+7esguM1MrAqpNrsYGAAAAAIMUbH9N3gNNRuxfY9eqvprrBwAAAACZ1dLeZ4xrqw7vfhnYaO4yEy+dqOjk8zKaGwAAAEYfTTPAEIG15i4zsbENik1cZFs+2ezptfuN8fwJJar77CISAAAAQGYN3WUmVjVbsQlnpfWclmXp1aZOI7a0oUIulyut5wUAAACQewr/8DNj3JSYpNWJz6me+woAAAAAMqy1w9xppi65GFgsLP+WZ4x4eNaNktuT4ewAAAAw2ryj/opADnP37JVv6ytGLDTvVokJX0dp74no7daDRoxdZgAAAAB7uA9uS+00M1hw/u1pv5ZpaQ9qR2fIiF3aMDat5wQAAACQe7z7PpFv1++N2E/j18iSWw3VNM0AAAAAmeDxpKf5w+12H3ecjZL3NwarH1eswPZX5Q53GfHo3BVpe99yVS7WG6eOejsL9XYOau0s1PswmmaAQQLrH5XLivePrYIihWfeYGtO2eq59e2KWwPjwgK3vjCr0s6UAAAAAMcqXPewXBr4AT3hL1O48fq0n3d10wFjPL7Upzk1xWk/LwAAAIDc3mVmnzVGK+MXpB7X0zQDAAAAZERFRUVGzlNeXq5sForGtfOguSDYwtoalbz+tHng9ItUPn1BZpPLQdleb4wu6u0s1Ns5qLWzlDu03s5sFQKGE4/Kv+FRIxRqvF6Wv9S2lLKVZVlauXa/EfvCzEoV+1hZAQAAAMi4aJ/8Gx8zQuHZN0sFhWk/9eqmTmO8tGGsXOzUCQAAAGAQd9cO+Vp+a8T+PXalIiqQ2yXNqEz/tQsAAAAAHNG8v0eJQQsFJzUGuqTmVWbwc1/JaF4AAABIH5pmgM/4tq6Sp3efEQvN4+JnOGt2duvTg2Ejtmx+tW35AAAAAE7m3/KM3OFD/WNLLgXn3Zr28yZXYdvS1mfELm3IzCp1AAAAAHJH4Uf/JpeV6B/3WAE9HL8s9XhqRUB+L7crAQAAAGRO0/5uYzy5olBFqcXJBnXS+EqlOddnPjkAAACkhTc9LwvknsC6h4xxtGah4tVzbMsnmz09ZJeZGWMDOmNiiW35AAAAAI5lWSpc+6ARik5bosSY6Wk/9atbDhjjsUVeLZjETp0AAAAABriCBxQYsjPmI/GlOqTi1OOG6iKbMgMAAACcp7PT3D1+tLjdbpWXl/ePu7q6lEgMNM5nm4+3tRvjGRV+xT98QJ5BsXDDNerrjUjJD+R0vXF6qLezUG/noNbOkg31rqiwfwFWmmYASZ7OVvl2vm3E2GVmeN2h2FGT45bNHyeXy2VbTgAAAIBTefd8KG/7RiMWPOP2jJx7dZN5c21J/Vh53FwXAAAAAEd4PIOnXI3+jc7jjbNFYP3DcsVC/eOYPPq32FX944ZxxWl9n/JBrtQao4N6Owv1dhbq7RzUGtksHo9n5DzJSZiZOtepaGnrNcaXFDbJs3ubEQvOujGr/wzZJNvrjdFFvZ2FejsHtXaWhEPrTdMMkNpl5mFjnAhUKFx/tW35ZLMXN3UoHBvYjtTrdunqOVW25gQAAAA4VeHa+41xvHyqolMvTvt593dHtHZPjxFb2mD/yiAAAABANsnk6nmDVwrMGtGg9MkDRui3Ol97VNk/XjhjfFasMphLsrLWSBvq7SzU21mot3NQayD7tLQHjfHFvS8b49iYWsVqzsxwVgAAAEgnljMAokH5Nz1hhEKzb5K8fttSymZPr91vjC+uG6OxxQW25QMAAAA4lat3v3wtLxqx4LzbJFf6L/VXN5m7T5b4PTp7alnazwsAAAAgh3z0sNTXboTuC5sLls2qKc1wUgAAAACcrC8S1+5D4f5xkUKqbVtlHBNOzhtzuWzIDgAAAOlC0wwcz9/0nNzhQ/1jSy6F5n3Z1pyy1aZ9vdq0r8+ILZs/zrZ8AAAAACcLrP+VXIlY/9jyBg7fyMmA1U2dxvjiugoVePgVAwAAAIDPJOLSO/9shA7UXKiN1rT+cYnfq0ljCm1IDgAAAIBTtXaYu8xc63lXnvhAzHJ5FJp1gw2ZAQAAIJ28aX11IAcE1j1kjKNTL1KifOCmDQasXNtmjMeX+rR4OlsJAwAAABkXj6hw3cNGKNy4TFYg/T+fH+yLas3OgYUHkpY2VKT9vAAAAECu6ew0m81Hk9vtVnn5wM//XV1dSiQSyhYFzb9VyYFWI7a64iZp28C4trJQXV0HM59cjsn2WmN0UW9nod7OQr2dIxtqXVHB72uBY2ltN5tmvuL/nTToWzQ69WJZxSwgDAAAkG9omoGjefd9ooL9a41YaP5ttuWTzULRhH67sd2IXTe3Wh4325ECAAAAmeZrfUnuPrOpPXjG7Rk592stnUpYA+OA100zPQAAADCMeDyesXMlJ2Jm8nzHZVkq+eBfjFCseq5eCc5K7jfTH6uvLsyenHNIVtUaaUe9nYV6Owv1dg5qDWSXlva+/sfTXXu0ILHB+Hpo9k02ZAUAAIB0c6f9DEAO7TITL52oyLRLbMsnm61uOqDu8MAvcpKtMtfPr7Y1JwAAAMCpCj95wBhHJ5yteNXsjJx79RZztewLascoUODJyLkBAAAAZD/vng9UsO8jI9a38Jtq7ggZsfqqwgxnBgAAAMDpWjsGdpq5yfOG8bVEYKwiM5bakBUAAADSjaYZOJYrdFD+Lc8asdDcL0tuJnsNZ+VacxXrc6aVaWK537Z8AAAAAKfytG9UwZ4PbNllpicc03s7uozYpQ0VGTk3AAAAgNxQuOZnxjheOkl9M67U1kGT05IaqosynBkAAAAApzuy04xbCX3R86bxtfDM6yWPz6bMAAAAkE40zcCx/JuelCse7h9b7gKF5qywNadstfNgSB98esiILZ8/zrZ8AAAAACcbustMvGicIrWXZ+Tcv2s9qGjc6h8XeFy6sHZMRs4NAAAAIPt5DjTLv22VEQt+7o+07WBMscTAtURSXRVNMwAAAAAypzsU0/6eaOrxRe61muA6YHw9NPsmmzIDAABAutE0A2eyEipc+5ARitRdIauoyraUcmmXmfKAV5fUs5o0AAAAkGmuUJf8W1YasfDcWzK28tmrW8wbSIumlqvE783IuQEAAABkv8KPfmGME/5yhWavUPNnqzkfMb7Up7IA1xIAAAAAMqdl0O6XN3teN74Wq56reNVsG7ICAABAJtA0A0cq2PmOPF3bjFhw3ldsyyebJVd+e2692TRz1ZxK+bz88wEAAABkmn/j43LFQv1jy+1VKNk0kwGhaFxvb+0yYksbaaYHAAAAcJird7/8m542YqF5t0q+YjW3mU0z9ewyAwAAACDDWtsPN82Uq0dfcH9gfC00+2absgIAAEAmMOsdjhRY+6Axjo1tUGziObblk83e2XpQbZ9tTXrEsvnjbMsHAAAAcPSOmesePGrHzETJ+Iyc/t1tXQrFEv1jt0taUkfTDAAAAIDDCj+5X65EpH9suX0KnnFH6nFT28CKzkkN1YUZzw8AAACAs7V8tgPmMs9b8rtixrVLuPE6GzMDAABAurHvORzH3bNHvq2rjFgoucuMy2VbTtls5Vpzl5k5NcVqqGYFOAAAAGQfj8eTttd2u93HHWeCd9sb8nTtMGLhBV9L6597sNXNB43xWVPKVFkaUD7KhnojM6i1s1BvZ6HezkK9Afu5Ij0KrHvIiIVnLZdVXJ163PzZ5LQj6rnPAAAAACDDWjsON/Ov8LxuxCO1n5cVGGNTVgAAAMgEmmbgOIH1j8plxfvHVkFR6sYNjtbeG9GbrebEuOXsMgMAAIAsVVGRuV1PysvLlXHPP2KOx89T2dwvZGQBgEgsoTdaOo3YdQunZPQ9t5Mt9YYtqLWzUG9nod7OQr2BzPNveEzu8CEjFlz4zdTnQ6GY9nUP7ECTVF9F0wwAAACAzO80M9u1XfPc24x4aPbNtuUEAACAzGC5NThLPCr/+keNUKhxmSxfqW0pZbPfrG9XPGH1jwNety6fNdbWnAAAAABHOtAqNb1sxhbdmbEdM99p7VB3KGbELp9Tk5FzAwAAAMhy8agKP/43IxSe8XnFK+r6J6YN5nW7NH1sfu5aCQAAACA7dfZFdaAvppuH7DITL6lRdMoFtuUFAACAzKBpBo7i2/qKPH37jVho/ldsyyebWZallWvbjNjnZ45ViZ8NqgAAAICMe/8XyZ/SB8aBcml+5lY+++26vcZ44dQxqilnkhsAAAAAyd/8gjzdu41Y8Mw7+x83tZlNMzMqC+X1cIsSAAAAQOa0tAdVoJiWe35nxMOzbpTcHtvyAgAAQGYw+x2OElj3kDGO1pypeNVs2/LJZh/t6taOzpARWz5/nG35AAAAACfS2dmZttd2u90qLy/vH3d1dSmRSCgjokGVr7nfWPUiNPtmBXsjUvIjzZK7T764bo8RW1Jbntb322621hsZRa2dhXo7C/V2FrvrXVFRkbFzAVnHslT4h58ZoWjNQsUmnN0/bm4PGl+vryrMWHoAAAAAkNTa0afL3Gs01tVjxEOzvmhbTgAAAMgcmmbgGJ7OFvl2vmPE2GXm2IbuMjNtbEALJpXYlg8AAABwIvF4PGPnSk7CzNT5/Bufkjt8yIj1zf2yEhk6/5pPD+lAX9SIXVI/JqPvt90yWW/Yi1o7C/V2FurtLNQbyJyCT9+St32DEQsuHNhlJql5yE4z9dVFGckNAAAAAAbvNLPC85oRi048R4kx023LCQAAAJlD0wwcI7DuYWOcCIxVuO4q2/LJZj3hmF7ZfMCILZtXLZfLZVtOAAAAgGNXbV77gBGKTFuS0Zs4q5vMHWUaq4s0eUwgY+cHAAAAkL2G7jITL5+uyIzP948ty1JL+5CmmSqaZgAAAAA7eDyetO0Ae7xxNujcv0tL3B8bscjcFWl7T/JZLtQbo4d6Owv1dg5q7SzU+zCaZuAM0T75Nz5hhEJzbpK8fttSymYvbupQKJboH3vcLl0zt9rWnAAAAAAn8u79UN72jUYsOP/2jJ0/OcHt1Sazof7ShoqMnR8AAABA9vK0bZDv098ZseDCb0jugUlnew5F1BsZuN+Q1MBOMwAAAIAtKioy8/v98vJyZZPkvY4zOl+Sx2X1x2LeIhWffauK/SW25pYPsq3eSC/q7SzU2zmotbOUO7TezmwVguP4m56TO9LdP7bkUmjul23NKZutXNtmjC+qHaPK4gLb8gEAAACcqvATc5eZeNlURactydj5N+zt1b7uiBG7tGFsxs4PAAAAIHsV/uHnxjhROFahWTcasaY2c5eZsoBH1SXcbwAAAACQOW2HQrou8aoRCzUuk2iYAQAAcAyaZpD/LEuFax80QtFpFytRPtW2lLJZ8gZWcmLcYMvns8sMAAAAkGmu3v3ytfzWiIXmf0VyZe5SfvWQXWamVgRUV1WYsfMDAAAAyE7u7t2pBcsGC57xVckbMGLN7WbTTH1VkVwuV0ZyBAAAAICk3evfUJ17jxErWvRV2/IBAABA5nltOCeQUd79n8jbtt6IBefdZls+2W7l2v3GOLni2+IZY2zLBwAAAHCqwPpfyZWI9Y8tb0Ch2Tdl7PyWZenVpk4jtrShggluAAAAAFT40b/LZcX7x5a3UKFh7r00D9lppr66KCP5AQAAADhaZ6f5O//R4na7VV5e3j/u6upSIpFQtij46AFjvMs9UUVls5JviG055bJsrzdGF/V2FurtHNTaWbKh3hUVFbIbTTPIe4G1DxnjeOkkRactsS2fbBaOJfT8hnYjdt28anndTIoDAAAAMioeVWDdI0Yo3Hi9rEDmGtpbO4La0RkyYpc2jM3Y+QEAAABkJ1f4kPwbHjViyQZ/q7DixE0zVTTNAAAAAHaJxwca39MpOQkzU+c6oWif6tpeMUIfjrlC5zMxOD/rjbSj3s5CvZ2DWjtLwqH1dtudAJBOrtBB+ZueM2KhuV+W3B7bcspmrzV36lDI/Ifw+nnVtuUDAAAAOJWv9SV5+sxdIIPzb89oDm+1HjTG40t9mlNTnNEcAAAAAGSfwLqH5Y729o8tl1vBhd8YdqGuoY34DdWFGckRAAAAAJL8Lb9VwAr2j+OWS3unXW9rTgAAAMg8mmaQ1/wbn5ArHu4fW+4ChebcbGtO2WzlWnNS3jlTyzR5TMC2fAAAAACnKlz7gDGOTjhL8eo5Gc3h3e1dxvjC2jFyudiFEgAAAHC0eFiFH//SCEXqr1KibMpRh27tCCpumbFadpoBAAAAkEH+DY8Z4zcSZ6hm4lTb8gEAAIA9aJpB/rISKlz3kBEK110pq6jKtpSy2a6DIb23/ZARWzafXWYAAACATPO0b1LB7vdt3WUmFI3ro53dRmzx9PKM5gAAAAAg+/g3PyN3X5sR61t457DHNrf1GeNJ5X4V+zxpzQ8AAAAAjnB3bZdv93tG7LH4EtVWsgMmAACA09A0g7xV8Onb8nRtN2Kh+V+xLZ9s9+y6dmNc6vfokvqxtuUDAAAAOFVgyC4ziaJqRequyGgOa3Z2KzJoSWiP6/BOlAAAAAAcvljZH35mhCKTFis+bv6whze3m00z9dXsMgMAAAAgcwIbnzDGnVaJ3vYu0vhSn205AQAAwB40zSBvBdY9aIxjYxsVm3C2bflks3jC0rPrzZXhrppTpUAB/0QAAAAAmeQKdSmweaURC839suTJ7A2cd7d1GeN5E0tU4vdmNAcAAAAA2cW3bbW8nS1GLHjm8LvMJDUN2WmmoYqmGQAAAAAZkojLv+lJI/R0/AJNrSqTy+WyLS0AAADYgxnxyEvunj3ybV119C4zXPQcc0Lcvu6IEVs2v9q2fAAAAACn8m96Qq5YsH9sub0Kzb0l43kMbZpZPK084zkAAAAAyC5Dd5lJLlYWnbrkmMc3tw9c2yTVVxemLTcAAAAAGKxg59vy9OwxYo/Hl6iuiusSAAAAJ6JpBnkpsP5XclmJ/nGioFjhmctszSmbrVy73xjPHl+smeOKbcsHAAAAcCQrocK1DxihSO3lSpSMz2ga+7rDau0wJ7edN2NMRnMAAAAAkF28ez9Swe73jVhw4TePuVhZZ19UHb1RI1ZfzU4zAAAAADIjsPFxY7w+MU3rremqreS6BAAAwIlomkH+iUcVWP+oEUo2zFi+UttSymYHeqN6veWgEWOXGQAAACDzCna8KU/XDiMWPOOrGc/j99sOGeNSvyfVWA8AAADAuYbuMhMvrlG48bpjHt/c3meM/V6XpowJpC0/AAAAADjCFeqSr/UlI/ZY/PAumew0AwAA4Ew0zSDv+La+LHdfmxELzfuKbflku+c3tCuesIwbV1fMqrQ1JwAAAMCJCj8xd5mJVc5SbMLZGc/jnW1mU/2iaeXyuIdfPRoAAABA/nMf3CZfy4tGLLTgDsnjO+ZzmtrMppnkas5cVwAAAADIBH/Ts3LFI/3jiOXRyvj5qce1NM0AAAA4ktfuBDD6nn76aT388MOpx/fee68aGxvlJIG1Dxnj6ISzFK+aZVs+2cyyLD29dr8Ru6yxUqUB/mkAAAAAMsndtV0F218zYsH5t0muzE4qSzbUv7fd3GnmvOnlGc0BAAAAyBcejydtr+12u487Hk2FH/+7XBpYfMvylShyxleO++draQ8Z44bqorS+H/ksk7WG/ai3s1BvZ6HezkGtAfv5Nz5ujF9OnKVOlam80KvKogLb8gIAAIB9mBmfZ3bs2KHHHntMfr9f4XBYTuM50CzfrneNGLvMHNsnu3u07YB542rZ/Grb8gEAAACcKtn8P3gSWsJXqvDMZRnPY/P+XnWFYkZsMU0zAAAAwCmpqKjI2LnKy9P0c3tvuzRkwpnr7K+rombacZ+2tdO8R3XGtKqMvh/5LG21Rlai3s5CvZ2FejsHtQYyy9O+WQX71xqxx+JLUp/rKgvlyvBiZQAAAMgOLGeQR2KxmO677z5Nnz5dixYtkhMF1h3eYeeIRGCswvVX2pZPtlu5ts0YTxnj15mTS23LBwAAAHCkaFCBIZPQwrNvlgqKMp7KO9u6jPH0sQHVlPkzngcAAACALPHez6TYoMW33AXS4rtOuIPlln3dRmxWTVm6MgQAAACAfkPvt+y1KvRm4ozU47qqQpuyAgAAgN1omskjTz75pHbu3Km77rrLmdu7Rvvk3/SkEQrNuVnyMMFrOL2RuF7e3GHEls0fx4oKAAAAQIb5m56VO2w2qwTn27Nj5u+HNM2wywwAAADgYJE+6b2fmrH5N0tlE4/7tO0dvQpFE0Zs1gQW7AIAAACQZvGI/FueNkJPxi9SXJ7U49rKzC9WBgAAgOzglYN1dXWpubk59dHS0pL66O4+vPLVkiVLdPfdd4/4tdra2vTCCy9ozZo16ujokNfrVU1Njc477zxdccUV8vvT27jR2tqqp556SitWrNDkyZPlRP4tz8odGVi5zJJLoXlftjWnbPbypg4FB9208rika+dW2ZoTAAAA4DiWpcJPHjBCkakXKzFmesZT6QnH9PHuHiO2eBpNMwAAAMCp6uzsTNtrJxdPKy8vN+55JRJmo8rp8n98v4qCB4xY17w7lDjBn+vDFnPBrrFFBfJE+9TZ2Teq+TlFJmqN7EG9nYV6Owv1do5sqHVFRUVGzwdkC9+21XIPuYZ5LL6k/zE7zQAAADiXo5tm7rzzzlF5nQ8++EA//vGPFQwG+2PhcLi/EWfVqlW65557Uk006RCNRnXfffdp+vTpWrZsmRw70WzdQ0YoOm2JEmVTbEsp2z29dr8xvqB2jKpKfLblAwAAADiRd++H8rZvMGLBM75qSy4fftqteMLqH3vdLp01pcyWXAAAAIB8EI/HM3au5ETMUT1fIi7/mp8boci0JYpW1Cf/YMd96pZ9ZjN+fXVhRt+LfDfqtUZWo97OQr2dhXo7B7UGMse/8Qlj/H6iUVutCf3jWppmAAAAHMttdwLZoqqqSgsWLDjp523dulU/+tGPUg0zgUBAt9xyi+699159//vf12WXXZY6Zs+ePfrBD35gNNWMpkcffTR1jrvuuiu1YoUTefd9JG/beiMWnH+bbflku+a2Pq3b02vEls0fZ1s+AAAAgFMN3WUmXjZV0akX25LLu9u6jPHnJpWq0OexJRcAAAAA9vK1vijPoR1GLLhwZIvRNbebO8rUVxWNam4AAAAAMJSrt02+7a8dc5eZyuICjSkssCEzAAAAZANH7zRz0003qa6uLvUxZswY7d+/X9/61rdO6jV++ctfKhKJyOPx6Lvf/a4aGxv7vzZv3jxNmDBBDz74YKqp5dlnn9WKFSuOeo37778/tVvMSF199dWp103asmVL6nVvvvlmTZ06VU5V9N7/Mcbx0km2TTTLBSvXtRnjquKC1E4zAAAAADLH1btfvpbfGrHQ/K9IbnsaVd7ddtAYL55ebkseAAAAAGxmWSpc8zMjFK2ep+ikxSN6elOb2TTTUE3TDAAAAID0Cmx+Si5rYFeniDug38QHrmHqKtllBgAAwMkc3TQzXAPLyWhubtbGjRtTjy+99FKjYeaIa6+9VqtXr9auXbv0wgsv6MYbb5TXa77tL7/8ssLh8IjPu3jx4lTTTHL71vvuu0/Tpk3T8uXL5VQFO9+Vb8cbWTPRLNtFYgk9v6HdiF07t0pet8u2nAAAAAAnCqx/VK5ErH9sefwKzb7Jllx2Hgzp04PmdSlNMwAAAIAzeXe/p4L9nxix4Jl3Sq4T30foi8S1a8i1BTvNAAAAAEgry5J/4xNG6P3CC9XbN9AoU8d1CQAAgKM5umnmdL333nv9j5NNM8Nxu91asmSJHn74YfX29mr9+vVasGCBccwDDzxwSucPhUKpHWySbr311mGPSe5+k/Sd73xHixYtUt6xLBW98w9GKFFUpeD8221LKdu90dKpruDAxLyk6+ePsy0fAAAAwJHiUQXWPWyEwjOXyQrYswPku9u6jHFFoVeN47iBBAAAADhR0ZBdZuKlkxWpu3JEz23tCMoaNE6u1zWDFZ0BAAAApJF330fydjYbsV/Hlhjj2iquSwAAAJyMppnTsHnz5tRnv9+v2traYx43Z84c4zlDm2ZOVUFBgZYuXTrs15I74CQbas4++2yVlZVp3Lj8bIrwtb6kgn0fGbG+c/5MKmBy17E8vbbNGJ85uVRTKwK25QMAAAA4UfJaxtO334gF599mWz6/39511C4z7hGsIg0AAAAgv3gONMm3fbURC37u65J7ZLcUm9v6jPGUioACBe5RzREAAAAABvNvfNwYx8qm6rk2cy5fHU0zAAAAjkbTzGnYuXNn6nNNTY08Hs8xj5s4ceJRzxkNPp9Pf/InfzLs1+67775U08zy5cvV2NiovJSIqejd/2WE4uVTFZrzJdtSynZ7usL6/ZAVpJfNr7YtHwAAAMCpCtc+aIyjE85SvHquLbnEEpbe237IiJ07vdyWXAAAAADYq/APPzfGCf8YhebcPOLnNw1pmmmoYpEzAAAAAGkUDcq/5TkjtHvq9YrvNxcGq2UHTAAAAEejaeYURSIRdXd3px5XVlYe99iSkpLUbjThcFgdHR3KBSPN83jNQqfD7XYfd5zk2/i4vJ0tRix43nfk8bFryrE8t6Fd1qBxid+jL8yuTlsdR7PeyB/U2zmotbNQb2eh3sDp8bRvUsHu94xYcP7ttuWzbk+PeiNxI7Z4Gk0zAAAAgNO4e/bJv3mlEQudcZtUMPLGl+Z2s2mmvpqmGQAAAADp4299Ue5oT//YkksflF8uaSA2vtSnEj/TJAEAAJyMnwZPUSgU6n8cCJy4SSN5TLJpZvDzstldd901ouN+/etfKxPKy4dM2IoGpff+yYzVnKGSRbcnZ21mJKdcE09Yem79H4zY8oWTNHFclbLNUfVGXqPezkGtnYV6Owv1Bk5OYO0DxjhRVKVI3RW25TN0N8qG6iJVlfhsywcAAACAPQKf/IdciWj/2PL4T6rB37IsNbcFjVh9Nas5AwAAAEgf/8bHjXF0yoVa21NmNM3UscsMAACA49E0cxo7zRzh9Z74bTxyzODnpdPdd9+d+shb7/1U6t5txj7//6Nh5jjeam7XroPmzapbzplqWz4AAACAE7nChxQYunLz3C9LHvuaVN4d0jTDLjMAAACA87gi3Qqse8iIhWbdKKto5AtvtfdG1RWKGbH6KnaaAQAAAJAe7kOfyrfzHSMWmv1FtW4w50fVcV0CAADgeDTNnCKfb2BCUyxm3gAYzpFjBj8vm/3kJz9R1goelN7832Zs+kVS3VK7MsoJj77/qTGeM6FM8yYxGQ4AAADIJP/GJ+SKDdyssdzew00zNjkUimn93oHV1pLOnc51AgAAAOA0gfWPyh0ZuDaw5FJw4TdO6jWa2vqMcVGBWxPL/aOWIwAAAAAMFtj4pDFO+MsUqb1cLW9sNOK1Vew0AwAA4HQ0zZyiQCDQ/zgUCp3w+CPHDH5eNqusrBzRcZ2dnWk5v9vtVnn5wEStrq4uJRKJ1OPAW3+vwtBB4/hD5/6l4gfNGAZ09kX14vq9Ruy6uZVpq99o1hv5h3o7B7V2FurtLNlQ74qKioyeDxgVVkKFax8wQsmbN4mS8bal9N72LiWsgbHf69LCyaW25QMAAADABvGoAh//uxGK1H5BiTEzTuplmoc0zSRXc3a7XKOSIgAAAAAYrIT8m54wQuHG6xWyvNp1MGzE62iaAQAAcDyaZk5RcseY0tJSdXd3q6Oj47jH9vT0KBwOn1QzSq6Ix+MZOU9yEmbyXO6efQp89G/G18J1VypSPT+ZTEZyyUXPrt2n2KCZcD6PS1fMGpux+p1qveEM1Ns5qLWzUG9nod7AyBTseFOeru1GLDj/dtnp3W1dxvjMyWXye9225QMAAAAg8/xNz8nTYy68FVx450m/TlO72TRTX1102rkBAAAAGD0ejydtC+4db5wO3h3vytO9y4hF535J2w9GNGitsJT66pK0/dmdyI56wz7U21mot3NQa2eh3ofRNHMaJk+erI0bN2rv3r2pSYLH+uF69+7dxnNw6grf/7FcsYGdfSyXR32L/9LWnLKdZVlaubbNiC1tHKuyAN/+AAAAQCYVfmLuMhOrnKnYxHNsvVYY2jSzePrALlIAAAAAHMCyVPiHnxmh6ISzFJtw5km/VEtb0BjXs5ozAAAAkFUqKioycp7y8gzca3h1pTkeN1dlMy/S3jVmI83UsUWaOL4q/fk4WEbqjaxBvZ2FejsHtXaWcofW25mtQqNk5syZqc/JXWRaW1uPedyGDRuOeg5OnrtzqwIbfm3EwrNvUryi1raccsH6vb1q7TBvVC2fX21bPgAAAIATubt2qGD7a0fvMuNy2ZbT9s6Q9nZHjBhNMwAAAIDzdsT0dmw+7V1mYvHEUfciGthpBgAAAEA6BA9KG58xYwu/krrnsmVftxFuHF+S2dwAAACQlWiaOQ2LFi3qf7x69ephj0kkEnr99ddTj4uLizV37tyM5Zd3CgoVnrlMlg5PKrM8fvUt+nO7s8p6T3+y3xhPKvfrzClltuUDAAAAOFFg3UNyyeofJ3ylqesbOw3dZWZcSYFqK1kJGgAAAHCSobvMxMbUKjLjslNqyo8lBq55kuppmgEAAACQDuuflGKhgbHbK53xpdTDo5tmSjOdHQAAALKQ1+4Ecll9fb1mz56tjRs3pppmLrnkEjU2NhrHPPfcc9q16/C2j1dddZW8Xt7yU5UoqVHP5/9BwYXfVNG7/1vxirpUDMfWF4nrpU0dRmzZ/Gq5bVzNGgAAAHCcaFCBDY8dtWumCoqyqmnm3Olj5OJaAQAAAHAMz/518u1824gl78HIdfJr7jW19Rnj8aU+lQW4JwYAAABkk87OzrS8rtvtVnn5wE72XV1dqYWm06X0/V8akx6Tjf+9EY8U6dTGPea9j0kl7rT9uZ0q0/WGvai3s1Bv56DWzpIN9a6oqJDdHP3b6k2bNmnv3r3940OHDvU/TsZfe+014/hkU8xQX/va1/S9731PkUhE9957r2644YbUbjLJ8dtvv61XXnklddyECRN03XXXpfXP4xTxypnqvuZfJYt/oE/k5c0d6osOvE9ul3Tt3GpbcwIAAACcxt/0rNxh8yZNcP5XZKdILKEPdgxcAyctnj7wSxIAAAAA+a/wDz83xomiKoVnLj+l12ppDxrjuip2sQQAAACyTTwez8h5kpMw03Uuz4Emefd9bMSCs25Kna8nHNPeQxHjazPG+jP253aqdNYb2Yd6Owv1dg5q7SwJh9bb0U0zq1at0uuvvz7s1zZv3pz6OFHTzIwZM/Ttb39bP/7xjxUMBvXII48cdUyyYeaee+5RYWH+3SDweDxp62o73vizs6fl3PnkmXXtxviC2gpNGJN9fw9HVm/kC+rtHNTaWai3s1Bv4CRYlgo/ecAIRaZerMSYGbLTJ7u7FYoNNNgn95c5d2qZrTkBAAAAyBz3oV3yNz9vxIJn3CF5/af0ekN3mmmotndnTQAAAAD5yb/xcWOcKKpWdNrFqcdbO8xm/uTiwtPGZt88KQAAAGSeo5tmRsvZZ5+tH/7wh3r++ee1Zs0aHThwQF6vVzU1NVq8eLGuvPJK+f2ndpMh22Vqu6TB20JhZJr3d+vjXd1G7Lbza7Nii6sTod7OQr2dg1o7C/V2FuoNHJt37xp52zcYseAZX5Xd3t1m7nwze3yxxhQV2JYPAAAAgMwq/Pjf5LIGVhO0CooUmnfqO2I2D2maqa+iaQYAAADAKItHFdj0lBEKJXfLdB+eAtkypGlmSkVAfi+L/wEAAMDhTTN333136mM0VFdX64477kh9ANng0fc/NcZVJX4tnTXOtnwAAAAAJwoM2WUmXjZF0amHVzyz0ztDmmbOnU7zGwAAAOAUrtBBBdY/asRCc1bICpzadUF3KKa93REjxk4zAAAAAEabb8frcgc7jFh49k39j1vazaaZukp2mQEAAMBhtFIDeSgSS+jJNbuM2BfPmqQCD9/yAAAAQKa4etvkb/mtEUut3Oz2yE4HeqPavN9cBfo8mmYAAAAAxwise0iu2MBkMsvlUXDB10/59YZOTPO4XZo2NnBaOQIAAADAUP4Njxnj6PiFio+t7x+3tpv3PurYARMAAACfcfROMzh9nZ2daXldt9ut8vKBSVtdXV1KJBJpOVc+emVzhzp6zVXdrmwoT1u9Thf1dhbq7RzU2lmot7NkQ70rKioyej7gVAQ2/EquRLR/bHn8Cs25WXZ7b4e5y0xRgVvzJ5bYlg8AAACADIqFVfjJ/UYoXH+1EmWTTvklm9rMiWkzxgZYxAsAAADAqHL1tcu3/TUjFpozsMvMsDvNVLHTDAAAAA6jaQanJR6PZ+Q8yUmYmTpXPnjq473G+HOTSjVljC9n3kPq7SzU2zmotbNQb2eh3sAw4lEF1j1ihMIzl8kKjJHd3tlqNs2cNbWMCW0AAACAQ/g3PyV3X7sRC55552m9ZvOQ1Zzrq1nNGQAAAMDo8m9eKVci1j+2vAFFGq7pH3cFY2rvHVjILKm2kqYZAAAAHMasGCDP7D0UPmoS3PL51bblAwAAADz99NNasWJF6mPLli1yAt/Wl+Xp3WfEgvNvk90sy9K7283rhfOmD+wcBQAAACCPWQkV/uEXRigy+XzFq+ee1ssO3WmmvoqmGQAAAACjyLIU2PiYEQrXXSnLV9o/bu0wr0u8bpemVgQyliIAAACyG00zQJ55bn27rEHjYp9blzWOtTEjAAAAONmOHTv02GOPye/3y0kC639ljKM1Z572RLTR0NIeVMeQldYWT7d/9xsAAAAA6efbukreg61GLHjmH592Y34LO80AAAAASCPv/rXyHmgyYuHZNx11/2OwaWMD8nqYGgkAAIDD+MkQyCMJy9Iza9uM2BWzq1To89iWEwAAAJwrFovpvvvu0/Tp07Vo0SI5hSt8SAW7fm/EQvNvVzZ4Z5u5y8ykcr+mjHFWQxMAAADgVIV/+JkxjlXNVnTKhaf1mnsORdQbSRix+qrC03pNAAAAABjMP2SXmXjpZEUnnXvcppk6dsAEAADAIDTNAHnk/R2HtPtQ2Igtm1dtWz4AAABwtieffFI7d+7UXXfdJbfbOZefBTvekCsR6x9bHp/CtZ9XNnh320FjfO70crlcLtvyAQAAAJAZ3j1rVLDnQyMWXPhN6TSvB5qH7DJT6vdofKnvtF4TAAAAAPrFQvJvedYIhZK7zLjM+06tHea1SW0lzfwAAAAY4B30GECOWzlkl5nkam5zaoptywcAAAAnp6urS83NzamPlpaW1Ed3d3fqa0uWLNHdd9894tdqa2vTCy+8oDVr1qijo0Ner1c1NTU677zzdMUVV8jvT+/uIq2trXrqqae0YsUKTZ48WU7i2/qqMY5OPk8qsH9Fs1A0rj/sPPz36YjF08ptywcAAACAfbvMxEsmKFx/zWm/blObOTGtvrqIxnwAAAAAo8bX+pLckYF7G5ZcCs+68ajjhu40ww6YAAAAGIymGSBPHAxGtbrpgBFbfsY4bk4BAADkkDvvvHNUXueDDz7Qj3/8YwWDAzcIwuFwfyPOqlWrdM8996SaaNIhGo3qvvvu0/Tp07Vs2TI5SiIm3/bXjFBk+lJlg2TDTCRu9Y89LumcqWW25gQAAAAg/Qp2vit/60tGLLjgjyRPwWm/dvPQppkq+xcMAAAAAJA/AhsfN8bRKecrUTbJiB3ojepgMGbEark2AQAAwCDmPoUActZvN3YoOmgCXIHHpStnV9qaEwAAAE5dVVWVFixYcNLP27p1q370ox+lGmYCgYBuueUW3Xvvvfr+97+vyy67LHXMnj179IMf/MBoqhlNjz76aOocd911l9xuZ112evf+Qe5wV1Y2zby7zcxr7oQSlQZYSwMAAADIa9GgSlb/dyOU8JUqPPdLo/LyRzXNVLOaMwAAAIDR4T60SwWfvm3EwrNuOuq4lg7zusTvdWlSuT/t+QEAACB3MDsGp8Xj8aTldYdOrHPaRLuTZVmWVq5tM2JLGytVWRJQLqDezkK9nYNaOwv1dhbqnT433XST6urqUh9jxozR/v379a1vfeukXuOXv/ylIpFI6mf17373u2psbOz/2rx58zRhwgQ9+OCDqaaWZ599VitWrDjqNe6///7UbjEjdfXVV6deN2nLli2p17355ps1depUOY1v6ypjHKuarUTpRGWDd4Y0zSyeXm5bLgAAAAAyo+i9f5Kna7sR6zv327J8Jaf92uFYQjs6Q0asoZrVnAEAAACMDv+mJ+WSZS4AUHf5Uce1tJuLxE0fWyiP25WRHAEAAJAbaJrBaamoqMjIecrLmcx1PB9/elBNQ1Zzu/38uozVZ7RRb2eh3s5BrZ2FejsL9R49wzWwnIzm5mZt3Lgx9fjSSy81GmaOuPbaa7V69Wrt2rVLL7zwgm688UZ5veal4csvv6xwODzi8y5evDjVNBOPx3Xfffdp2rRpWr58uZzIt221MY5Mv1TZYH93RK0d5k2j82iaAQAAAPKad98nKvzoF0YsWrNQofm3j8rrb+0IKj4wfy2lroqmGQAAAACjwEoosOkJIxRuvE7yBk7YNMN1CQAAAIaiaQbIA49+8KkxnlxRqPPrKm3LBwAAAPZ47733+h8nm2aGk9wZaMmSJXr44YfV29ur9evXa8GCBcYxDzzwwCmdPxQKpXawSbr11luHPSa5+03Sd77zHS1atEj5xN21Xd7OZiMWmX6ZssG7Q3aZKfV7NLvm9FeWBgAAAJCl4lGVvHqPXFaiP2S5fepZ+gPJ7RmVUzS3m4t5TSr3q9g3Oq8NAAAAwNkKdr0nzyFzPlR49k3DHtvaYV6b1FYWpjU3AAAA5B6aZoAc1xeJ6ZmPdhuxFWdPkZttRgEAABxn8+bNqc9+v1+1tbXHPG7OnDnGc4Y2zZyqgoICLV26dNivJXfASTbUnH322SorK9O4ceOUb3zbXjXGicJKxcafoWzw7nazaWbRtHJ5uWYAAAAA8lbhH34qb8cmI9Z3zt2Kj20YtXO0tA1dzZmJaQAAAABGh3/jY8Y4NrZBsXFH33OxLOuonWbquTYBAADAEDTN4LR0dnam5XWTq1+Xl5f3j7u6upRIDKyGhgHPrN2vnnCsf5yc9vaF+tK01SYdqLezUG/noNbOQr2dJRvqXVFRkdHz5YqdO3emPtfU1MjjOfbqvhMnTjzqOaPB5/PpT/7kT4b92n333Zdqmlm+fLkaGxuVj3xbzaaZyPRLJZdbdktYln4/pGlm8fSB72EAAAAA+cVzoFlF7/2zEYtVzlLwzD8e1fM0DdlppqG6aFRfHwAAAIAzuSLd8rf89uhdZlxHLwbW1hNVTzhuxGqruDYBAACAiaYZnJZ43LzoSJfkJMxMnSvXPPXxPmN83oxyVRd7c/r9ot7OQr2dg1o7C/V2FuqdHSKRiLq7u1OPKysrj3tsSUlJajeacDisjo4OZbuR5ni8RqHRaBY73ljhbhXsfs8IxWo/n9acRmrTnh51BQca7ZPOr63Iityy1QnrjbxBrZ2FejsL9XYW6g0Mkoir5NX/Jlci0h+yXG71LP2B5PGN6qma28ymmXompgEAAAAYBb6m38gVC/WPLZdHoZnLhz22ZUgzf1GBWzVlo3vtAwAAgNxH0wyQw7YdCOqjXYcnRh6xbP442/IBAACAfUKhgZsHgUDghMcnj0k2zQx+Xra66667RnTcr3/9a2XK4N2WUta/JiUGNaZ4fCo54zrJXyK7ffyR2XRUW12sudMn2JZPLjqq3shb1NpZqLezUG9nod5wssDaB1Ww9w9GLPi5byg2/oxRPc/Bvqjae6NGjJ1mAAAAAIyGwMbHjXFk+qWyiqqGPbalPWiMZ1QWyj3MjjQAAABwNpZbA3LYM2vbjHFFoVcX142xLR8AAADYu9PMEV7viddHOHLM4Oel0913351qamlsbFRe2vxbczz9oqxomEl6Y4t53XBxQ7VtuQAAAADZ4Omnn9aKFStSH1u2bFG+cB/aqeJ3f2jE4uXT1LfoL0b9XM1DVnP2eVyaXHHiBRwAAAAA4Hg8B5qPWgggPPumYx7f0mE2zdSxAyYAAACGwU4zQI6KxRN6br05+e2auVUq8NALBwAA4EQ+38BW87HYoB1PjuHIMYOfl61+8pOfKKsl4lLTS2as8Uplg55wTB9u7zRiFzUMvxobAAAA4AQ7duzQY489Jr/fn9p9M29YlkpWf1euqNnM0nPp30gFhaN+uqY2c2JabVWhvG5WcwYAAABwevybnjDGicJKRaZdcszjW4Y09CevTQAAAIChaJoBctTvWg/qQJ85GXLZ/HG25QMAAAB7BQIDK/qGQqETHn/kmMHPy1aVlZUjOq6z02wOGU1ut1vl5eX9466uLiUSidRjz+4PVBY8YBzfVbNYiTTmM1KvNx9QLGH1j5OT2GaN9aT1vcoHx6s38gu1dhbq7SzU21nsrndFRUXGzoXTk1w84L777tP06dNVU1OjN998U3nDiis2bp4Kdr4tlxVPhYJzb1F08uK0nG7oTjP1rOYMAAAA4HQlYgpsesoIhWYulzwFwx9uWdo6ZKeZeppmAAAAMAyaZoAc9fRac5eZMyaWaEYlF34AAABOldwxprS0VN3d3ero6DjusT09Pf0rKo+0ISUXxOOHJ4ZlQnIS5pHz+VteMb4WG9uoaPHEZEKy21stZnPMgkkl8nsy+17lg8H1Rn6j1s5CvZ2FejsL9caxPPnkk9q5c6f+7u/+TitXrlRecXvVd953FK6/SqWv3iNXX7v6zv9vaTtdU9uQpplqmmYAAAAAnJ6C7W/I3WfOhwrPvumYx+/pCisYNRfNqK3k2gQAAABHo2kGyEH7uyN6e+tBI7ZsfrVt+QAAACA7TJ48WRs3btTevXtTkwQ9Hs+wx+3evdt4Dk6Pb9sqYxyZcZmyxe+3dxnj86aPsS0XAAAA5J7krj3Nzc2pj5aWltRHslE/acmSJbr77rtH/FptbW164YUXtGbNmlSjv9frTe32ct555+mKK66Q3+9P459Eam1t1VNPPaUVK1bk9XVQvHquDt70hNyHdsryl6bnHAlLre1DV3NmYhoAAACA0xPY+Lgxjo5foHhl4zGPbx2yy0yp36PqkuF3pQEAAICz0TQD5KDn1rcpYQ2Miwrc+sLM/FkhHAAAAKdm5syZqaaZ5C4yyQlhDQ0Nwx63YcMG4zk4de5Dn8p7oMmIRaYvVTbYdTCkHZ0hI7Z4erlt+QAAACD33HnnnaPyOh988IF+/OMfKxgcmNCUvG450oizatUq3XPPPakmmnSIRqO67777NH36dC1btkx5z1OgRMWMtL38rq6wQjFzNecGdpoBAAAAcBpcwY6jFik73i4zSS1DmvlrqwrlcrnSkh8AAABym9vuBACcnIRlaeVacyvSy2dVqsg3/CriAAAAcI5Fixb1P169evWwxyQSCb3++uupx8XFxZo7d27G8stHvq2vGuNEYKxi4xcoG7w7ZJeZikKvGscxkQ0AAACnpqqqSgsWnPzPulu3btWPfvSjVMNMIBDQLbfconvvvVff//73ddllh3dp3LNnj37wgx8YTTWj6dFHH02d46677pLbza2x09Xc1nfUtUZlMas5AwAAADh1/s3PyJWI9Y8tj1/hhmuP+5yWDvPapI4dMAEAAHAM7DSD0+LxpKdRY+hNK25iDVizvSu1ittgNyyoSVstMoF6Owv1dg5q7SzU21mod/aqr6/X7NmzU7vNJJtmLrnkEjU2mtvWP/fcc9q1a1fq8VVXXSWvN38uC9P5M/Gx/t77t5vNSdEZl8pT4FM2+P32Q8Z48YwxKsijeqcT/845B7V2FurtLNTbWah3+tx0002qq6tLfYwZM0b79+/Xt771rZN6jV/+8peKRCKpn9e/+93vGtco8+bN04QJE/Tggw+mmlqeffZZrVix4qjXuP/++1O7xYzU1VdfnXrdpC1btqRe9+abb9bUqVNPKncMr2lI0wy7zAAAAAA4LZalwMbHjFC47gpZ/rKT22mmsjAt6QEAACD3MVsGp6WioiIj5ykvL8/IeXLBb17cZowbx5fo4rlT8mp7UertLNTbOai1s1BvZ6Heo2fTpk3au3dv//jQoYGmh2T8tddeM45PNsUM9bWvfU3f+973UpPSkqs333DDDandZJLjt99+W6+88krquOQEsuuuu075JFPXJ/1/78Pd0s53jbh/3vXyZzCPY4nFE3p/h9k0c9ncSRl9j/IJ/845B7V2FurtLNTbWaj36BmugeVkNDc3p5r6ky699NKjmvqTrr322lTTf7K5/4UXXtCNN954VHP/yy+/rHDYXEzqeBYvXpy65onH47rvvvs0bdo0LV++/LT+LBjQ3D5kNWeaZgAAAACcBk/bOnk7Nhux8OybjvuceMLStg6zaaa+iqYZAAAADI+mGSCHdPVF9cK6gUmUSSvOzq+GGQAAACdbtWqVXn/99WG/tnnz5tTHiZpmZsyYoW9/+9v68Y9/rGAwqEceeeSoY5KTx+655x4VFnLz4LS0vColBq127S6Q6pYqG3y886C6QzEjdlFDlW35AAAAwJnee++9/sfJppnhJHcGWrJkiR5++GH19vZq/fr1WrBggXHMAw88cErnD4VCqR1skm699dZhj0nufpP0ne98R4sWLTql8zhN89CdZqpomgEAAABw6gIbnzDG8dKJik4+77jP2XkwpEjcMmK1lVybAAAAYHg0zQA5ZOXHuxSJJfrHBR6Xbjxzsq05AQAAIPucffbZ+uEPf6jnn39ea9as0YEDB1IrNdfU1KRWXL7yyivl9/vtTjP3jZsrXfQdacuL0r610vQLpECZssEbW9qN8ayaUo0vC9iWDwAAAJzpSON/8vqjtrb2mMfNmTPHeM7QpplTVVBQoKVLh29sT+6Ak2yoSV4/lZWVady4caNyznwXjMS186C56099NQsyAAAAADhFsbD8W1YaofCsL0ou93Gf1jpkl5mKQq/GFhekJUUAAADkPppmcFo6OzvT8rrJleXKy8v7x11dXUokBppFnOqhd7YZ40vqx8oV6VVnpFe5jHo7C/V2DmrtLNTbWbKh3hUVFcpHd999d+pjNFRXV+uOO+5IfThFuq5Pjvn33lMpnfmt1Iere7dc4UNKpDGHk/HqxsOraR9xzpTStL4/+SYb/p1DZlBrZ6HezkK9ncXueufr9clo2LlzZ+pzsoHf4/Ec87iJEyce9ZzR4PP59Cd/8ifDfu2+++5LNc0sX75cjY2No3bOfNfSEdTgtZzdruRqzjTNAAAAADg1vq0vyx0+ZMRCs794wue1tJtNM7VVXJcAAADg2GiawWmJx+MZOU/yBmemzpWtNu3r1eb9ZnPMsvlVefm+UG9nod7OQa2dhXo7C/VGtsjk38Oj/t4XjT/8kQXfC4dCMa3f02PEzp1WxvfpaeDfOeeg1s5CvZ2FejsL9c4OkUhE3d3dqceVlZXHPbakpCS1G004HFZHR4ey3UhzPF6j0Gg0ix1vnC6tHSFjPKUioOKALyPndiq7ag17UG9nod7OQr2dg1oDJyew8XFjHJm0WImyKSd8Xkt7nzGuqyoa9dwAAACQP2iaAXLE05/sN8Y1pT4tmjaweiIAAAAAJL2/45ASg5Z+9ntd+tykUjtTAgAAgAOFQgPNFYFA4ITHJ49JNs0Mfl62uuuuu0Z03K9//WtlyuDdltJpx6HdxnjupDHstpRhmao1sgP1dhbq7SzU2zmoNXBs7u7dKtjxOyMWnn3TiJ571E4z7IAJAACA46BpBsgBoWhCv91krlx33bxquV0u23ICAAAAkJ3e3XbQGC+cXKZAAasZAgAAIPM7zRzh9Z74dtSRYwY/L53uvvvu1AdOzua9h3cPOmLm+DLbcgEAAACQ2/ybn5JLA6uAJQpKFK678oTPi8YT2t5pLrhQz04zAAAAOA6aZoAc8GrTAfWE4/3jZKvM9fOqbc0JAAAAQPaxLEvvbusyYouns5IhAAAAMs/n8/U/jsViJzz+yDGDn5etfvKTn8ip1xub9h4yYrMmsKslAAAAgFNgWQpsfNwIRRqukQpOvGPMjs6Q4omBZpuk2ip2mgEAAMCx0TQD5ICnP9lvjM+dXq4J5X7b8gEAAACQnZI3ivYcMlfmXjyNphkAAABkXiAQ6H8cCpkrAA/nyDGDn5etKisrR3RcZ2dn2nJwu90qLx/4Wb+rq0uJRELp1NYTUWdf1IhNKLTS+ueEPbWGfai3s1BvZ6HezpENta6oqMjo+YBT4d39vjxdO4xYaM7NI3pua3vQGFeXFKgswDTI/397dwIl11UeiP/2on1ptXZ51S7vxsbYssE2GBLMbjaTMJmwTCB4zBCSgZmQhENyIAMMMCcZ4pCBLPwnxGAHMGExEDYb8G5kGy+yrN22ZG2tVmvvVi//c8vTrX6t7la31FWv6t3f75w+qvuq6tWt+uotn+p99wYAAIbkbBFq4KK3Vc/uyyy77nyzzAAAwEANDQ1l/aFzuHa1uO/pbO4wZ+r4sHze1FBXF+erpGjx5uSJdVrEOy3inRbxrk5xxphp06aFffv2hZaWlmEfu3///tDe3j6qgpRa0NV1dAb5cosXYpb79dZsy+Ybk8bVh/nTGiv6PqlMrKke4p0W8U6LeKdDrDmeb33rW+Hmm28u3f7EJz4Rli9fHlIwcJaZzuYloXPeC0b03PUDimYWzzLLDAAAw1M0A1Xu249mZ5lpmtQYrlpiVBAAAMhz9Lz+IwVWk189uy7TvnrF3DBz5szc+lMU1Rpvxp5Yp0W80yLeaRHv6nHaaaeF1atXh23btpUuFByq0H3r1q2Z51Cd1g24MG3J7EmhXoE+AACcsKeffjr867/+a5gwYULfQAIpqOvYHyasuz2zrP3sN4cwwvxifcvBTHvJ7Mlj2j8AAIrHcGtQxTq7e8J3Ht+VWfbqc2aH8Y02XQAAIKujszvcsyE7gveVy2bn1h8AAFixYkXp33jx14YNG4Z83BNPPHHMc6g+a3dmL0xbNseFaQAAcKI6OzvDTTfdFBYuXBguvfTSkJLxG38c6jqPFuX31DWEwyveOOLnHzPTzGwzzQAAMDxX3kMVu2vDntBy4Ehm2RvOm5NbfwAAgOr1q82t4WBHV187Dsh25TL5AwAA+el/4dfPfvazQR/T3d0d7rzzztLtKVOmhHPPPbdi/WN01u0ymjMAAIyVb37zm+HZZ58NN9xwQ6ivT+sSvnHP3pNpHznjytAzZe6Intve2R2e3XM4s2yp3AQAgONoPN4DgPz826M7Mu3zFkwJS43cBgAAg2ptbS3buuMPVk1NTX3ttra20sV91eRHjz6TaZ81b0qo6zgQWjsO5NanWlUL8WZsiHVaxDst4p2WvOPd3NxcsdeqNUuXLg1nn312WL16dalo5qUvfWlYvnx55jHf/e53w5YtW0q3X/WqV4XGxuL8dNXQ0FC2dQ+8qK7cF9kd6eoOG1uyozmvmDe1rO+RfGJNvsQ7LeKdFvFOh1iXT8z11q1bV/pbv3596W/fvn2l+66++upw4403jnhdO3fuDN///vfDqlWrQktLSykPmT9/frj88svDK1/5yjBhwoQyvpNQmonztttuC9dff3047bTTQmrGbbkv0+4448oRP3fT7kOhuye7bNEsM80AADC84vzyAAWza39HaaaZ/q47f2SjKgAAQIq6uo7OslJu8SLMSr7eSNy9MVs0tPLMpqrrY62qxnhTHmKdFvFOi3inRbzHzpNPPhm2bdvW1967d2/f7bj8jjvuyDw+FsUM9M53vjN89KMfDR0dHeETn/hEeOMb31iaTSa277777vDjH/+49LgFCxaE173udaFIKllQ1b9wrBzWbt8XjnRlr0x70bJTwozJ48v6ulQ+1lQX8U6LeKdFvNMh1mPnPe95z5is58EHHwyf//znw6FDR4vC29vb+wpxfvKTn4SPfOQjpSKacjhy5Ei46aabwsKFC8Mb3vCGkJr6fVtDw97sIGBHTr1sxM/fsCtbzL9g+vgwZbxifgAAhqdohpNSrhHEjLwRwvdWt4T+vz9NGlcfrj1nbiFHbRPvtIh3OsQ6LeKdFvGG6tN68Eh4cvvBzLKVC/0YCwDAyYkXi915552D3rdmzZrS3/GKZhYtWhQ++MEP9l2U9tWvfvWYx8SCmXhR2qRJRgeuVqu3PT+Cd6/50ycqmAEAIDezZ88Op556anjkkUdG9byNGzeGv/qrvyoV8U+cODFcd9114bzzziu177rrrlIO9Nxzz4VPfvKT4VOf+lRZcpRbbrml9Bpx/Sn+xjZu6/2ZdveEptA1a8WIn79+V/a3kCWzJ49Z3wAAKC5FM9TEKGmpjbzR09MTvvPYw5llr73glHDa/NkhBanFO3XinQ6xTot4p0W8IX/3bW7LtGPR/QWnTM2tPwAA0N8ll1wSPvvZz4bbb789rFq1KuzevTs0NjaWRm5euXJluPbaa8OECRPy7ibDWLPt6CxD0Yr503LrCwAAaXrLW94SlixZUvqbMWNG2LFjR3j/+98/qnV8+ctfLhXIxAFr/+zP/iwsX768775YPBML+r/yla+Uilq+853vhOuvv/6Ydfzf//t/S7PFjNSrX/3q0nqjp556qrTet771reGMM84IKRq35b5M+8gpLwqhbuTFQ+tbsjPNLJ5l8AUAAI5P0QxUofs27g6bWrIjI/zWpafn1h8AAKC63bspWzRzyenTw7iG9EaoAwBgbN14442lv7EwZ86c8I53vKP0l4rW1tayrTuOSN1/EIu2trbQ3d1dttd79OndmfbCGePK+v7IL9bkS7zTIt5pEe90VEOsKzUAbqUNVsAyGuvWrQurV68u3X7Zy16WKZjp9drXvjb87Gc/C1u2bAnf//73w5ve9KZSwX9/P/rRj0J7e/uIXzcOFBCLZrq6usJNN90UzjzzzNIMN6k6pmjm1MtG9fwNu7JFM0tmK5oBAOD4FM1AFbr1gWcy7SVzpoSLzyjmf2oAAAAnP1PlwKKZlQvNAAUAAHmLF8VVSrwQs5yvt3bngWMuTKvk+6Nysaa6iHdaxDst4p0Osa4e999/f9/tWDQzVNHT1VdfHW6++eZw4MCB8Pjjj4cLL7ww85h//ud/PqHXP3z4cGkGm+jtb3/7oI+Js99EH/rQh8Kll14aiqZ+//bQ0LY5s+zIKSN/n4c6usKWtmzB0pLZk8esfwAAFJeiGU5KuUYRq4aRN/Ky73BnuP3R55PkXq8/b3bYs2dPKKqU450i8U6HWKdFvNNSDfEu6ihpcCLW7zoUdh04klmmaAYAABgr+9s7w3N7OzLLlhrNGQCAGrNmzZrSvxMmTAiLFy8e8nHnnHNO5jkDi2ZO1Lhx48I111wz6H1xBpxYUHPJJZeE6dOnh7lz54YiatyanWWme/y00DX77BE/f0NLdpaZujgL5ky5CQAAx6dohpNSqdEwUhp54/bHd4TDnUcvOm2orwuvOmtWMu8/tXgj3ikR67SId1rEG/I1cJaZU6ZPCGc0T8ytPwAAQLGs25W9MC3+buHCNAAAas2zzz5b+nf+/PmhoaFhyMedcsopxzxnLIwfPz68733vG/S+m266qVQ0c91114Xly5eHohq35ehsP1HnKS8KoX7oWByvaOa0GRPCxHH1Y9Y/AACKS9EMVJl/e3Rnpn31khlh5pRxufUHAABqxXA/co3FDEvDtfN03+a9mfbli2aExkbpflHjzdgS67SId1rEOy3iDeW1bufBTPvM5olhfKPtDACA2tHR0RH27dtXuj1r1qxhHzt16tTSbDTt7e2hpaUl1IKR9rNcv6OMNC8fP2Cmmc7TVo6qTxtaDmfaS+dMKetvQwzO/8OkRbzTIt7pEOu0iPfzXEUDVWTNjgNh9fYDmWVvOL+YU64CAMBYa25urthrNTU1hWpw+EhXWPVstmjmFeedWtHPIgXVEm/KT6zTIt5pEe+0iDfVoiiF/esHjOa8fK4L0yrJj9ppEe+0iHdaxDsdYl2dDh8+WmwxceLxZ2qPj4lFM/2fV81uuOGGET3u1ltvDbnl5fu2h9C6IbNo8tmvCJNH8XvG021rM+1zT5vp95Aq4P9h0iLeaRHvdIh1WpoSjbeiGajiWWbmTRsfVi5Mc+cEAAAc3/0bd4f2zu6+dn1dCFcsnZ1rnwAAgGIV9m/c3ZFpn3/GLBem5SjVH7VTJd5pEe+0iHc6xLp6ZprpNZKZ2nsf0/955XTjjTeW/gpt813Z9vhpIcy/cFSreGr787MF9Vo+f9pY9AwAgAQomoEqcaijK3z/iV2ZZa87d05oiFe9AQAADOLnT2UL719w+ozQNGlcbv0BAACKpaenJ6zZlr0w7ewFLkwDAKC2jB8/vu92Z2fncR/f+5j+z6tmX/jCF0LNFc2csTKEhpFfurj38JHwXFt25p/l86aOVe8AACg4RTNQJb6/uiXsa+/KLHvdeUaIBgCAkWptbS3buuvr6zMjAra1tYXu7qMzvOTljie3Z9ovOn1qWT+HVFRrvBl7Yp0W8U6LeKcl73ibcYMi27LnUNjXnr2ocMX86bn1BwAATsTEiRP7bh8+nC28GEzvY/o/r5rNmjVrRI8r1+8HI8nLp6+/MzT0ax+cd3FoH0V/Hn52b6bdWF8XZjYe8ZtIgv8PQ2WJd1rEOx1inZZqiHdzFfyOomgGqmSktlse2pZZ9uJFM8KpM2oj+QYAgGrQ1ZUtQi+n+B8IlXy9wezc3xHW7TqYWXbZmdNz71cRVUO8qQyxTot4p0W80yLeVIsiFPb/at3uTHvqhIYwqftQaG09/oWGFOdHbSpHvNMi3mkR73RUQ6yr4YK0ahNnjJk2bVrYt29faGlpGfax+/fvD+3t7aMqRqkVlcqVB+bldYdaQsPutZnHdCx40aj6s27ngUz79BkTQn3okf9XAf8PkxbxTot4p0Os09KdaLwVzUAV+NUz+8L6XYcyy9528bzc+gMAAFS/eze1HXPx2jnzp+bWHwAAoHiF/Wu278+0l86e7ALfnKX6o3aqxDst4p0W8U6HWFeP0047LaxevTps27atFJOGhv7znhy1devWzHM4eeO2PJBp94ybHDrnnDeqdawfMIjYktmTx6RvAACkoT7vDgAh3DpglpkzmieGlQuPjjwCAABwvKKZS8+YHhrr63LrDwAAUDzrBgz4tWzOpNz6AgAAJ2PFihWlf+MsMhs2bBjycU888cQxz+HkjNt6X6Z9ZP4LQ2gYN6p1bBiQmyyeLTcBAGDkFM1AzrbtbQ93rmvNLHvrC+aF+joXuwEAAIPr7ukJ923OFs2sXDgjt/4AAADFtG6n0ZwBACiGSy+9tO/2z372syFnBrrzzjtLt6dMmRLOPffcivWvyMZtGVA0c+rRWIzU+gFFM3ITAABGo3FUjwbG3Dce2RG6eo62J42rD687b3aeXQIAAKrcmu0Hw55DnZllZqsEAADGUkdnd9i8e+BMMy5MAwCgNi1dujScffbZYfXq1aWimZe+9KVh+fLlmcd897vfDVu2bCndftWrXhUaG4t1aV1DQ0NZ1ltfXz9ku+5Qa2hsWZO5v+v0K0bVl9aDR0LLwSOZZcvmTinb++HE403xiHdaxDsdYp0W8X5esc7soca0d3aH2369I7PstefODlMn2DQBAICh3bt5T6Z9RvPEcErThNz6AwAAFM/G3Ycyg35FS2ZPyqs7AAAk7sknnwzbtm3ra+/du7fvdlx+xx13ZB4fi2IGeuc73xk++tGPho6OjvCJT3wivPGNbyzNJhPbd999d/jxj39cetyCBQvC6173ulA0zc3NFXmdpqZ+g3xtuyt7Z+OkMH3FVSE0jh/x+ta0tmTa4xvqwwWLFoTGhjQv+Kw2mXhTeOKdFvFOh1inpSnReLsyH3L070+2HDM69PUXzc+tPwAAQG24Z2Nbpm2WGQAAYKyt23kw0z5l+gSDfgEAkJuf/OQn4c477xz0vjVr1pT+jlc0s2jRovDBD34wfP7znw+HDh0KX/3qV495TCyY+chHPhImTVIwPiY2DyiaOf3SURXMRE9t35dpL54zRcEMAACj4n+2ISc9PT3hloeOjoARXXrm9LBolqQbAAAY2oGOrvDrrfszyy5XNAMAAFWnoaGhbOuur68ftj0W1rcczrSXzp1c1vdEfrGmeoh3WsQ7LeKdDrGufpdcckn47Gc/G26//fawatWqsHv37tDY2Bjmz58fVq5cGa699towYYKZ3cfMpl9k2wtfMupVDCyaWTF/2sn2CgCAxCiagZzEi9ye3J4dpe1tZpkBAACOY9Uze0Nnd09fu7G+Lrzw9Om59gkAADhWc3NzxV6rqWnsC+k37VmbaV9w+qyKvicqF2uql3inRbzTIt7pEOuxc+ONN5b+xsKcOXPCO97xjtJfSlpbW8uy3lgc1v+73tbWFrq7u0Pd4bbQtO2xUNfvsftmXRg6R9mPx5/dk2mfPr2xbO+FE483xSTeaRHvdIh1Wqoh3s1V8P/KimY4KeUaUSyFkTdufXhHpn1K04Rw9bJZoaG+f6qYhhTizVHinQ6xTot4p0W8qVa1PorzSN27eW+mfeGp08K0SeNz608RVVO8KS+xTot4p0W80yLeUB5PPpfNPYzmDAAAta2rq6sirxMvwoyvNf7Z+0JdODoIWE/DhNA+57zYkRGvq6enJ2zYlR2UeNHMiRV7L4w83qRBvNMi3ukQ67R0JxpvRTPUROVX0Ube2L73cPjJmpbMsne+eFGYPWtmbn2qJkWLN8MT73SIdVrEOy3iTbWo9VGcR+qBp/dl2tecs6AqRuUoMvu5dIh1WsQ7LeKdFvGGk7f7QEfYsa89s+zsBYpmAACAkWvccl+mfWT+C0JomDCqdbQcOBLaDndmli2ZPWlM+gcAQDoUzUAObr7v6dDZfXQkhYnj6sP1l5yea58AAIDq98zug2HDrgOZZVctm5NbfwAAgKG1traWbd1xRqX+BWJtbW2lEQLHyoOb2zLt8Q11oanhSFnfE/nEmuoi3mkR77SIdzqqIdYGWaJajNt6f6bdecplo17H+pZDmfaExvpwStPoCm8AAEDRDFRYR2d3uPn+pzPLrnvBqWHG5PG59QkAAKgNv1i7K9OeOWV8OPeU6bn1BwAAGFpXV1fFXiteiDmWr/fUjv2Z9qJZk0JdT3yNMXsJqiTWVDfxTot4p0W80yHWJKu7K/SMnxp66seHuu6O0qIjp55A0cyug5n24lmTQn1d3Zh1EwCANCia4aSUa0Sxahh5o1y+/8TOsHNfe2bZG8+bmfTobEWON8cS73SIdVrEOy3VEG+jpFG0UZxH6iePb8m0Lz1jemhr21PxfhRdtcSb8hPrtIh3WsQ7LXnHW35CEa3bmb0wbensybn1BQAAGBsNDQ1ly8uPaY8bHw68+ashdB4OjdseDo3P3hu6T33hqPuwseVwpr10zuSyvQ9OIt4UlninRbzTIdZpEe/nKZrhpFRqNIwijbzx1Qefy7QvPm1aWDJrYmHe31goUrw5PvFOh1inRbzTIt5Ui1oexXkkOrt7wn2b2zLLLjtzuu2vAuzn0iHWaRHvtIh3WsQbTt7aAUUzy+YomgEAgFpXqUEf+g9sUTJnQQjnvypMOoF1bdrz/Cw1vc47fZbBK6rMMfGm0MQ7LeKdDrFOS1Oi8U6zVAhy8sS2/eHR5/Znll1/0bzc+gMAANRWPrG/PXvx58qFaf5nBgAAUD7dPT1h/a5DmWVL55zI5W0AAAAnrqenJ6zdnr3Oavn8abn1BwCA2qVoBirollXbM+1508aHly6bmVt/AACA2nHvpuwsM0tmTwpzpo7PrT8AAEAxbdnTHg53dmeWLTXTDAAAUGFb2w6H/e2dmWXL5ymaAQBg9BpP4DnACdh94Ej49zUtmWVvvnBuaKyvy61PAABA7RbNrDzTLDMAAMDYW7frYKY9Y1JjmDV5XG79AQAAxkZra2tZ1ltfXx+amo7+ZtHW1ha6u7OF+Cdi1fpsf6eMbwiTug+F1tbDJ71uqi/eVCfxTot4p0Os01IN8W5ubg55UzQDFXLbozvCka6evvb4hrrwxgvm5tonAACgNuw73Bkee25/ZtnlixTNAAAAY2/tzmzRzLI5k0NdnQHAAACg1nV1dVXkdeJFmGPxWmt3ZH8XWTxrkgt6q9BYxZvaIN5pEe90iHVauhONt6IZqIDOru7wjYe3Z5b95lmzQrOR2QAAgBG4/+m9oftoDX6pCP8Fp07Ps0sAAMBxNDQ0lHV0wOHaJ2P9rkOZ9rI5U8r6Xsgv1lQf8U6LeKdFvNMh1jB2NrRkc5Mlsyfl1hcAAGqbohmogDvWtYYd+49kll1/0bzc+gMAANSWeze1ZdoXnTYtTBznx1YAAKhmzc3NFXutpqaxm4lyw+72TPsFC+dU9L1QuVhT/cQ7LeKdFvFOh1jD2BX0L1Y0AwDACXKFDVTArQ9lZ5k5f8HUcM78qbn1BwAAqB09PT3h3k17MssuXzgjt/4AAADFdaijK2xqOZBZtmL+tNz6AwAApKm7p2eQmWYm59YfAABqm6IZKLO1Ow+GVc/uyyx728VmmQEAAEbm6dbD4bm9HZllKxcanRAAABh7T23fF3p6jrbr6kJYPk/RDAAAUFlb29pDe2d3ZtmSWWaaAQDgxDSe4POAEbpl1bZMe9aUceHly2fm1h8AAKC23LupLdOePWVcWDLbD0MAAFDtWltby7bu+vr60NR0tJi+ra0tdHdnLyg7Eas2bM+0T58xMRw+sDcczk4+QwWVK9ZUJ/FOi3inRbzTUQ2xbm5urujrUTsaGhrK9r0frn0iNu5uz7SbJjaGudMnhrpY2U+uyhFvqpd4p0W80yHWaRHv5ymagTJqO9QZvr+6JbPsTRfMDeMa0tzhAAAAo3fv5rZjZpnxoxAAAFS/rq6uir1WvBBzLF7vqe3Z6pilsydV9H1QuVhTG8Q7LeKdFvFOh1hTTSpVUNW/cOxEbT2Qvd5qxfzpYeZMgxRXo7GIN7VDvNMi3ukQ67Q0JRpvRTNQRv/26I7MVKEN9XXhzRfOzbVPAABQVOUaIS3PkTeOdHWHXz2zN7PsisXNZX2vGGklJWKdFvFOi3inRbxh7KzbdTDTXjpncm59AQAA0vXU9n2Z9vL5U3PrCwAAtU/RDJRJV3dP+PrDOzLLXrF8Zpg9dXxufQIAgCKr1AhplRx5494NLeFgx9FC/OiVF54ZmqdOqMjrk/ZIKykS67SId1rEOy3iDSemp6cnrN2ZLZpZpmgGAADIwZpt2aKZFfOm5dYXAABqn6IZKJNfbGgNW/e2Z5a97eJ5ufUHAACoPT9/amemfd6p08MsBTMAAEAZtBw8EvYc6swsWzpb0QwAABRFa2trWdYbZ3ztP4BFW1tb6O7ODgg2Gp3dPWH9zv2ZZfMnl6//5Btvqpt4p0W80yHWaamGeDdXcBDcoSiagTK5ddX2TPuseZPD+QtMFQoAAIzcL9buyrSvXDYnt74AAADFtm7ALDMTG+vDqTMU7QMAQFF0dXVV5HXiRZgn81qbWw6FI109mWWLmidUrP9UNt7UFvFOi3inQ6zT0p1ovBXNQBlsbDkU7n96b2bZ2y6aH+rq6nLrEwAAFF05RxjLY+SN3QePhMe2tGWWXbxgopHUEhlphcoQ67SId1rEOy15x7saRkiDsbBu56FMe8nsSaHe7xoAAECFrW/J5iazJo8LMyaPy60/AADUPkUzUAa3PrQt054xqTH85lmzcusPAACkoJIjYVRi5I17NuwO/cdRmzSuPpw3f3KSI37kLdWRVlIk1mkR77SId1rEG07M2gEzzSydMzm3vgAAAOlav+vgMQX9AABwMupP6tnAMfa3d4bvPr4rs+yNF8wNExptbgAAwMjdtyk7y8wlp08P4xrkFQAAQHmsG3Bh2jJFMwAAQA427MrONLNY0QwAACfJ1TYwxr7z2K5w6Eh3X7uhLoQ3Xzg31z4BAAC1paenJ9y7OVs0c9nCptz6AwAAFFtnd0/Y2JK9MG2pC9MAAICqmGlGQT8AACen8SSfT+IaGhrKst76+vph29Wqu6cn3Prw9syyly6bGU5tlrwVMd6cGPFOh1inRbzTIt5Qfut3HQo79x/JLFupaAYAACiTZ1oPh46unsyypS5MAwAAKqyjszs8vac9s2zxLAX9AACcHEUznJTm5uaKvE5TU21cHPazNTtKPyz1956XLq/Y51QUtRJvxoZ4p0Os0yLeaRFvGHsDZ5lZMH18OLN5Ym79AQAAqmfgsXIMaLG+Jfvbxuyp48KsaXKQamDwkrSId1rEOy3inQ6xhpPzdOvh0NWdLehfYhZMAABOkqIZGEP/392bMu2z5k8Lly2amVt/AACA2nTvprZjZpmpq6vLrT8AAMDoVXJArZMd0OLZfTsy7XNOmWFAsCpl8JK0iHdaxDst4p0OsYbRWd9yKNOeN218mDrBJY4AAJwcZ5QwRjbuOhDuWLMzs+wdVyx0YRsAADAqh490h4ee3ZtZdtmZflgFAADK58lte48ZFAwAACiWcs2GOZYzLG0cMAvmktmTyzqLJ6NnRq20iHdaxDsdYp0W8X6eohlOSmtra1nWGzfI/qNttLW1he7u7lDNvvSzjZn2tAkN4eozJ5ftMyqSWow3J0680yHWaRHvtFRDvI12S5E9vGVfaO/s6WvX14VwqaIZAACgjJ7cti/TVjQDAADFU6nf105mhqWn29Zn2uee1ux3wSpnRq20iHdaxDsdYp2WpkTjrWiGk9LV1VWR14kXYVbqtU7EwY6u8G+P7sgse8P5c8L4hsp9RkVS7fFmbIl3OsQ6LeKdFvGGsXXvpj2Z9rnzp4bpE6XvAABQa8o5qNZYDmixv70zPNt6KLPslMnl7T+1NXgJlSPeaRHvtIh3Oqoh1goMqGVPbc8W9C+fp6AfAICT56obGAO3P7ErHOg4eqFoXQjhLS+Yl2ufAACA2nTvprZMe+XCNEf5AACAWlfJASZOZkCLp7bvz7Qb6kI4Y8Z4A2RUKYOXpEW80yLeaRHvdIg1jNzhI11h8+6DmWWKZgAAGAuKZuAk9fT0hFsf2p5Z9pIlM8JpMybm1icAAKA27drfEdbtyo7wrGgGAAAop7U7sxelnTlzUhjfWJ9bfwAAgPIo12ySYzXD0upt+0NPT3bZnPGdZsGsMtUwoxaVI95pEe90iHVaqiHezVUwG6aiGThJDzy9N2xoyV7U9raL5ufWHwAAoDizzEyd0BDOXTA1t/4AAADFt35Xtmhm6ZzJufUFAAAon0rNenSiMyyt3ZGdBfPUpglhfENlZ/Fk9MyolRbxTot4p0Os09KdaLwNEwUn6ZYBs8wsnDkxXHbm9Nz6AwAA1K57N2eLZl50xvTQWF+XW38AAIDiW7szOzDY0tmTcusLAACQrvW7srnJYrkJAABjRNEMnIStbe3hF+uzU4Bef9G8UFfnojYAAGB0unt6jplpZuXCo1PkAgAAjLWenp6wbsBMM8vMNAMAAORgQ0u2aGbJLEUzAACMjcYxWg8k6V8f3h66e462p4yvD685d06eXQIAgGQ1NDSUbd319fXDtsfCU9v2hz2HOjPLXrx4ZlnfF/nFm+og1mkR77SId1rEG07c9n0dYX97V2bZ0tmKZgAAgMpbP6Cgf4ncBACAMaJoBk7Q4SNd4d8e3ZFZ9tpz54Qp413QBgAAeWhubq7YazU1jf0MMA8/0pJpL5o9JZy3aMGYvw7VEW+qk1inRbzTIt5pEW8YuXU7sxelxd845k8fn1t/AACANB3o6ArP7e3ILFs820wzAACMDcOtwQn6wZMtYe/h7Ohr1180L7f+AAAAte3nT+3MtK9cNju3vgAAAGlYO2Ak56VzJoW6urrc+gMAAKRpY8uhTLu+LoSFMxXNAAAwNhTNwAno6ekJt67anll2+cKmcKZkDQAAOAEH2jvDrza3ZpZdtWxObv0BAADSsG5n9sK0ZbMn59YXAAAgXesGFPSfNmNimNDo0kYAAMZG4xitB5Ly8JZ94amd2WTtbWaZAQCAXLW2ZotOxlJ9fX1oamrqa7e1tYXu7u4xW//P1+0OR7p6+tqN9XXhrJkNZX1P5BdvqodYp0W80yLeack73s3NzRV7LRhrawf81rF0jqIZAACg8jbsyhb0L5lt4GIAAMaOohk4AbcMmGXmtBkTwhWLZ+TWHwAAIISurq6KvVa8CHMsX+/uDdnimAtOmRomNlb2PVG5eFO9xDot4p0W8U6LeMPIdHR2h82thzPLFM0AAAB5WD9gppkls+QmAACMHUUzMErb97WHn63dnVn21hfMC/V1dbn1CQAAqG33bm7LtFcuPDpSOgAAQDls2n0odHUfnfEyWmo0ZwAAKKyGhoayzQA7XHskNrRkC/qXzZ1Stv6Sf7ypHeKdFvFOh1inRbyfp2gGRukbj+wIXf1+Q5rYWB9ef96cPLsEAADUsOfa2sPm3dkfgxTNAAAA5bZu16FMe8H08WHqBD8dAgBAUTU3N1fkdZqaRvcbR9vBI2Hn/o7MsouWzA/NzdPGuGdUQ7ypbeKdFvFOh1inpSnRePufbxiF9s7ucNsjOzLLXnPu7DBtok0JAAAYm1lmmiY1hhVzp+TWHwAAYGyUc1TksRgdcP2Aopllc4zkXI2MBJkW8U6LeKdFvNMh1jB6T+3Yl2mPa6gLC2f5nQQAgLHjSn8YhR+taQmthzozy976gnm59QcAAKh9927KFs1cdmZTaKivy60/AABAbY3ifKKjA27aszbTPv+MmRXtMycm1ZEgUyXeaRHvtIh3OsQajm/NtmzRzKLZU8L4RgVnAACMHUUzMEI9PT3h1lXbM8suOX16WDpncm59AgAAaltnd0+4f8BMMyvP9CMqAABQfk9u25tpr5g/Pbe+AAAA5dfa2lqW9cYZlfoXiLW1tYXu7u4RP//Rp3dl2gubJ5Str+Qfb2qLeKdFvNMh1mmphng3V8FgTYpmYIQe33YgPLH9QGbZ2y42ywwAAHDiVm/bH/a1d2WWrVyoaAYAACiv1gMdYfve9syys+dPy60/AABA+XV1ZX+PKJd4EeZoXmvdzuz1WItnTapYX6l8vKlt4p0W8U6HWKelO9F4K5qBEbpl1bZMe/608eHKJflXvgEAALXrnk1tx/wQNHfa+Nz6AwAAjJ1yjox8sqMDPvh0NhcZ11AXmho6jOZchaphJEgqR7zTIt5pEe90VEOsq2EUZxiNDbsOZdpLZk/KrS8AABSTohkYgV0HOsKP1uzOLHvLC+aFxvq63PoEAADUvnsHFM2YZQYAAIqjkqP1jXZ0wDXb92fai2ZNCvWhJ8kRBmtNqiNBpkq80yLeaRHvdIg1DG/3gSOh9VDnMQOMAQDAWKof07VBQd32yI7Q2d3T157QWBeuu2BOrn0CAABq277DneHx57IXql2uaAYAAKiAdTsPZtpLZ0/OrS8AAEC6NrRkc5PxDXXhtBkTc+sPAADFpGgGjuNIV3f4xiM7MsteedbsMGPSuNz6BAAA1L4Hnt4bunqyPwRddNq0PLsEAAAkYt2u7IVpy+YomgEAACpv3a5DmfbCWZNCQ31dbv0BAKCYFM3Acfx0bWvYdeBIZtnbLp6XW38AAIBiuHdzW6YdC2YmjmvIrT8AAEAaunt6wvoBF6YtnT0pt/4AAADp2tCSzU2WyE0AACgDRTNwHLes2pZpv+DUaWHF3Cm59QcAAKh9PT094d6N2aKZlQtn5NYfAAAgHVvb2sOhI92ZZWaaAQAA8rB+wCyYS2bJTQAAGHuKZmAYT24/EH69dX9m2fUXmWUGAAA4Oc/saQ9b97Znlq1c2JRbfwAAgHSs3Zm9KK1pUmOYNWVcbv0BAADSHWBsw4BZMM00AwBAOSiagVHMMjNn6rhwzbLm3PoDAAAUw72b9mTa8QK1pX4IAgAAKmDdgKKZZbMnh7q6utz6AwAApGnn/iNhX3tXZtniWX4rAQBg7DWWYZ0kpKGhoSzrra+vH7ZdCa0Hj4QfPtmSWfaWF8wPE8Ybba2I8aZyxDsdYp0W8U6LeMPJu2dTW6a98swmF6kBAAC5zDSzdI6L0gAAgMrb0JLNTSaNqw8Lmibk1h8AAIpL0Qwnpbm5MrOuNDU1hUr76iPrQkdXT197fEN9ePfVK0LzNMlZEeNNfsQ7HWKdFvFOi3jD6Bzp6g6/enpvZtnKhbYjAACgMtbtOpRpL50zObe+AAAA6RqYm8RZZuoNMAYAQBkYEhoG0dnVHb5yz+bMstdcsCDMUTADAACcpEe37g8Hj3Rnll12pqIZAACg/A4f6QrP7jmcWbZ0tqIZAACg8jYMLJqRmwAAUCaKZmAQP169PWxty/5o9I4rFubWHwAAoDju2dSWaa+YOznMnDIut/4AAADp2NByKHT3HG3HMZyXzJqUZ5cAAIBErd91MNNeMltuAgBAeTSWab0korW1tSzrra+vD01NR0dabmtrC93d2ZGYy+nvf74u0z5vwdRw5tSesr3f1OUdbypLvNMh1mkR77RUQ7ybm5sr+nowlu4bUDRz+UKzzAAAAJWxbmd2JOfTZkwIk8Y35NYfAACgchoaGsr22+Fw7cF09/SEjbuz+cmyOVPK1kfyjTe1S7zTIt7pEOu0iPfzFM1wUrq6uiryOvEizEq91rqdB8ODT+/NLLv+onkVe30qG2/yJ97pEOu0iHdaxBtGbs/BI2H19gOZZSsVzQAAABWydsBIzkvnTM6tLwAAQCjkoHT9B98byjO7D4aDHdlB+V649JTQ3DSxjD0jr3hTHOKdFvFOh1inpSnReCuagQFueWh7pj1zcmN4xfKZufUHAAAYmXKOPjZWI2888Exr6OnXnjiuPlx0+ozQ0JDmSB7Vykgr6RDrtIh3WsQ7LeINI7d+54CimdmKZgAAgMpbu2Nfpj1tYmOYN31Cbv0BAKDYFM1AP3sPd4bvP7Ers+yNF8wN4xv9yAoAANWuUiOknczIG6u2Pp1pX7Fkdpg3Z9YY9YpySXWklRSJdVrEOy3inRbxhqGtHVg0Y6YZAAAgB2u27c+0V8ybFurq6nLrDwAAxaZoBvr59qM7w+HOo1N/NtTXhTdfOC/XPgEAAMXQ09MTfr52Z2bZlctm59YfAAAgLS0HjoTWQ52ZZcsUzQAAQDJaW1vLst4442v/ASza2tpCd/fR668G89gzLZn2mTPGl61/5B9vapd4p0W80yHWaamGeDdXcBDcoSiagf+nq7sn/OvD2zPLrlnWHOZOG59bnwAAgOJYu2N/2L63PbPsymVzcusPAACQ9iwzExrrw6lNE3LrDwAAUFldXV0VeZ14EebxXmvdzgOZ9qJZEyvWPyofb4pDvNMi3ukQ67R0JxpvRTPw/9y9cU/Y0pa9gO1tF83PrT8AAMDolHMEsrEYeeOHj2zNtOdPHx9mNnYYOa0KVcNIK1SGWKdFvNMi3mnJO97VMEIajMS6XdmimSWzJ4WG+rrc+gMAAKQ7sPGm3YeOyU8AAKBcFM3A/3PLQ9lZZpbPnRwuPHVqbv0BAABGp5IjYZzIyBt3b8gWx6w8s8nFuzUi1ZFWUiTWaRHvtIh3WsSbatHQ0FDWYrHh2oNZvyt7UdqyOVPK2kfyizW1S7zTIt5pEe90iDUcXxzUuL2zJ7NsyazJufUHAIDiUzQDIZRGL7h3U1tm2dsumhfq6oywBgAAnLz2zu6w6tm9mWUrFx4dDR0AACieSs5C1H+2paFsbG3PtC88c7aZkmrQSGJNcYh3WsQ7LeKdDrGGY20YMAvmjEmNYeaUcbn1BwCA4jOcAYQQbh0wy0zTpMbwyrNm59YfAACgWB56dl9m1LT6uhBedIYfSwEAgMro7OoOT23fn1l21vxpufUHAABI1/qW7CyYi2dNyq0vAACkQdEMydvf3hm++9jOzLLrzp8TJo6zeQAAAGPjvgEzW54zf0qpWB8AAKASNrUcDB2d3ZllKxTNAAAAOdg4oGhmyWxFMwAAlJcrdEjedx/fFQ4e6c6M+PyWC+fl2icAAKBY7t2cLZq5fOGM3PoCAABURmtra9nWXV9fH5qajs5e2dbWFrq7s0Ux/f1q3a5Me/aUcaH+yMHQ2nqwbH0kn1hT28Q7LeKdFvFORzXEurm5uaKvBydbNLPITDMAAJSZohmS1t3TE259aHtm2VVLmsOCpgm59QkAACiWXfs7wtqd2QvRLlt49EdTAACgmLq6uir2WvFCzOFe76kd+zPtpXMmV7R/VC7WFIt4p0W80yLe6RBryOrq7gmbdiuaAQCgsuor/HpQVe7b1Baebj2cWfa2i80yAwAAlG+WmSnjG8J586fk1h8AACA9Awv5l852URoAAFB5z+1tD+2dPZllimYAACg3RTMkbeAsM4tnTQqXnD49t/4AAADFLNbv79Izp4fGBuk4AABQOet3HTpmphkAAIBK29iSzU2mT2wIsyaPy60/AACkwVU6JOvZPYfDLzfsySy7/qJ5oa6uLrc+AQAAxdLd0xPuHVA0c9mZTbn1BwAASM+Bjq6wpa09s2zpbEUzAABA/kUzcZYZ12oBAFBuimZI1r8+tD30n+xz6oSG8OpzZufYIwAAoGie2nEwtB7qzCy7fKGiGQAAoHKe2nEg026oe/7CNAAAgNyLZmbKTQAAKD9FMyTpUEdX+PZjOzPLXn/enDB5fENufQIAAIpn4Cwzp8+YEE6dMTG3/gAAAOlZvT1bNLN49uQwodFPhAAAQOVtGGSmGQAAKDf/I06Sbl+9K+xr7+prx0k+3/qCebn2CQAAKH7RzEqzzAAAABX25PaDmfZZ86bk1hcAACBdPT09YdPubNHMYkUzAABUgKIZkkzAblm1PbPsikUzwunNRnsGAADGdobLh7fsyyxbuXBGbv0BAADS9OSAmWbOmjs5t74AAADp2rG/Ixzo6M4sM9MMAACVoGiG5Pzqmb3HTPX5tovNMgMAAIx97tHZ3dPXbqivCy88fVqufQIAANIr5h84kvPZ8800AwAAVN7GAddrTR5XH+ZNG59bfwAASIeiGZJzy0PZWWbOaJ4YVi5syq0/AABAMd27uS3TvuCUqWHqhMbc+gMAAKRnzc6DoV8tf6ivC2H5HDPNAAAAlbex5XCmvXDWpFBXV5dbfwAASIeiGZLyXFt7uHNda2bZ9RfNC/USMAAAYIzduylbNKNYHwAAqLQntx/ItBfNmhQmjmvIrT8AAEC6NgyYaWbRzEm59QUAgLQY4pakfOOR7ZkR1eI0n689d3aeXQIAAApo2972sGl3dsS0lWcqmgEAAPItmjlr7pTc+gIAAOSroaE8BfT19fXDtntt2p0tmlkyZ3LZ+kT5jDTeFIN4p0W80yHWaRHv5ymaIRmHj3SH2x7dmVn2mnPnhKkTbAYAAEB5Z5lpmtQYzprn4jQAAKCyVg8omjl7vrwEAABS1dzcXJHXaWo6dhCxnp6esHHAYGMXnDm3Yn2isvGmuMQ7LeKdDrFOS1Oi8U6zVIgk/fuTLaHtUGdm2dsumpdbfwAAgOK6Z0DRzKVnTA8N9XW59QcAAEjP4SNdYWNLdiRnxfwAAEAeWg50hD0Hj2SWLZs3Nbf+AACQFkUzJCGOVnDLQ9syyy47c3pYOGtSbn0CAACKqbOrO9y3OVs0s3JhmiN1AAAA+Vm782Do7jnajmX8y+dMzrNLAABAotZu359pj2+sD6c1y08AAKiMxgq9DuTqka37w5odBzPLrr9ofm79AQAAiuvhLfvD/vauzLIrFs3IrT8AAECaVm8/kGkvnDkxTB7fkFt/AACAfLW2tpZlvfX19aGp6ejgYW1tbaG7uzvzmF9v2n5MfrK3bU9Z+kN5jSTeFId4p0W80yHWaamGeDc3N4e8KZohCbesys4yc8r0CeEli120BgAAjL27NmR/5Fkxd3KYM3V8bv0BAADStHp7djCxs+ZPya0vAABA/rq6sgN+lUu8CHPga63flc1PFs2cVLH+UPl4U1zinRbxTodYp6U70XjX590BKLed+zvCT9dmR0p460XzQkN9XW59AgAAiuuXG7L5x5WL8x8xAwAASM+aATPNnD1P0QwAAJCPjS2HMu1Fsybl1hcAANKjaIbC+8bDO0JXd09fe0JjfXj9eXNy7RMAAFBMz+45HDbuPpxZ9pIlZrkEAAAqq72zO6wfcFHaWXMVzQAAAPnYoGgGAIAcKZqh0Do6u8M3f709s+zV58wKTZMac+sTAABQXL/csCfTbp7UGM6Z78I0AACgstbuPJgZUKwuhLDCTDMAAEAO9h7uDC0HjmSWLVY0AwBABSmaodB+8tTusPtgZ2bZ2y6an1t/AACAYrtrQNHMFYtmhPq6eHkaAABA5Ty5/UCmfcbMiWHK+Ibc+gMAAKRr44BZZhrq68LpMybk1h8AANKjaIZCu+WhbZn2xadNC0vnTM6tPwAAQHEd7OgKDz6zN7PsyiUzcusPAACQroFFM2ebZQYAAKiSopkzZkwIjQ0uWwQAoHKcfVJYjz+3Pzz2XPZHobddbJYZAACgPO5/ui0c6erJjJS2cmFTrn0CAADStHpA0cxZcxXNAAAA1VE0s2jWpNz6AgBAmhTNUFi3PLQ90543bXy4emlzbv0BAACK7a4NezLtF5w6NUyd0JhbfwAAgDR1dHaH9buyF6WdZaYZAACgSopmFs+anFtfAABIk6IZavYHn+HsPnAk/GhNS2bZmy+cGxrr68rcMwAAIEU9PT3HFM1cuVjRPgAAUHnrdh0Mnd1HZ8GMzprnojQAACAfGwYUzSycNTG3vgAAkCZD3lJzHn52X/jT760Ln3jNknDRadMHfcw3f70jHOk6+oPQ+Ia68MYL5lawlwAAQKU1NDSUbd319fXDttdsPxB27D+SWXbVspll7RMht3hTHGKdFvFOi3inRbwh68ntBzPt02dMMAsmAACQi4MdXWHbvo7MssWzJuXWHwAA0uR/yKkpX394e/jMTzeHru6e8N+/vTb88388L8ybNiHzmM6u7vCNR7Znlv3mWbNC8+RxFe4tAABQSc3NlZvZpampKdN+YNWuTPuMmZPDRUtOCXV1ZrssgoHxprjEOi3inRbxTot4k7rV2w9k2mfNm5JbXwAAgLRt2p2dZaa+LoQzmhXNAABQWYpmOCnlGjV5sJEBf/pUS/jUjzf1Ldt9sDN8+N/Whn94+3lh4rij/fjJU61h54ARnn/7klOM8FzFjASZFvFOh1inRbzTIt5wrJ+u2ZFpX3PWXAUzAACQuLxmw3xyR7Zo5twF0/xGUsP8P0xaxDst4p0W8U6HWEPWhpZs0cwpTRPCxHG2CwAAKkvRDDUxknMcGfC6F00P3358d7jzqZ19y5/YdiB85o5nw+feemHfBWlf//XqzHMvPmNGePHZp1ekn4wNI0GmRbzTIdZpEe+0iDepa9nfHh5+Zs8xRTMAAEDa8pgNs6OzO6zfmb0o7dKl8yvaF8rL/8OkRbzTIt5pEe90iDWp2zSgaGbRLLPMAABQeYpmqBkN9XXhf//WReENN/0ybGo52Lf8m6u2hHNPaQr/6SWLwuNb28IDm1ozz3vHFQtz6C0AAFBpra3ZXGAsxdEA+/+42dbWFrq7u0u3b39sR+jpOfrYSePqw4rm+rL2h/IaLt4Ui1inRbzTIt5pyTveihGoJk9t3xc6urLf//gbCgAAQDXMNLNY0QwAADlQNENNaZo8Lnzxdy8Jb7zprnCgo6tv+f+4fXU4a/608O2Ht2YeP2fahPCq8xbk0FMAAKDSurqO5gjlFi/C7H29n6/bnbnv0jObQkNdT0X7Q+XiTbGJdVrEOy3inRbxJmWPbWnLtM+YObn02woAAEAeNg6caWamohkAACpP0QwnpVwjJw83MuCc8SF8/DVLwx/dtqbv/q7unvCfv/KrcLgzO3ramy+YGw7sawsHytJLijISJJUl3ukQ67SId1qqId5GcqZadHZ1h3s2ZS9Ku3LxjNz6AwAApD0b5oMbdmQet2LOJLNg1rhq+H8YKke80yLeaRHvdFRDrP2GQrVo7+wOW9raM8sWmWkGAIAcKJrhpFRqtL6BIwNetWRGeM/lp4Yv3bOlb1nb4c7Mcxrq68J15882omANMhJkWsQ7HWKdFvFOi3iTske27g/727Pf/xcrmgEAAHKaDXP1tv2Z5SvmTpazF4z/h0mLeKdFvNMi3ukQa1L2dOvh0N2TXbZQ0QwAADmoz+NFYSy854pTw9VLhx4d4xXLZ4bZU8dXtE8AAEBafrlhzzEXpM2RhwAAADnNhLl258HMsrPmTcmtPwAAQNo2tBzKtOdPGx+mjG/IrT8AAKRL0Qw1q76uLvzFqxaHRTMnDnr/2y6eV/E+AQAAafnl+tZM+yVmmQEAAHK8IK2jKzuM81nzJufWHwAAIG0bd2WLZhaZZQYAgJwomqGmTZ3QGD573fIwdUJ2FIKz500J5y+Ymlu/AACA4tuy53DYuPtwZtmVS4aeDRMAAKCcVm8/kGmfMn1CmDFpXG79AQAA0rZxt6IZAACqg6IZat6ZMyeFv3zN0tBYX9e37IaXnBbq6o62AQAAxtqDz+zNtJsnNYZz5k/JrT8AAEDantx+MNNeYZYZAAAgRxtbskUzixXNAACQk8a8XhjG0osXzwj/93fODfdsagsvOHVauPDUaXl3CQAAKLjXnzcnXHjKtPDLDXvCLza0htNmTAz1ivcBAICcvOuyU8KLzpgentxxIDy57UC45PTpeXcJAABI2J/+5qKwftehUvFMnHVm+VyF/QAA5EPRDIWxfO6U0h8AAEAlxNktF86aVPr7nRctCD09PXl3CQAASNjcaePDNdNmhmuWz8y7KwAAAKVBjw18DABANajPuwMAAABQlCIaAAAAAAAAAACgeiiaAQAAAAAAAAAAAAAAoHAUzQAAAAAAAAAAAAAAAFA4imYAAAAAAAAAAAAAAAAonMa8OwAAAAAAAAAAAACUT0NDQ1nWW19fP2ybYhHvtIh3WsQ7HWKdFvF+nqIZAAAAAAAAAAAAKLDm5uaKvE5TU1NFXofqIN5pEe+0iHc6xDotTYnGO81SIQAAAAAAAAAAAAAAAApN0QwAAAAAAAAAAAAAAACF05h3BwAAAAAAAAAAAIDyaW1tLct66+vrQ1NTU1+7ra0tdHd3l+W1yJ94p0W80yLe6RDrtFRDvJubm0PeFM0AAAAAAAAAAABAgXV1dVXkdeJFmJV6LfIn3mkR77SIdzrEOi3dica7Pu8OAAAAAAAAAAAAAAAAwFhTNAMAAAAAAAAAAAAAAEDhKJoBAAAAAAAAAAAAAACgcBTNAAAAAAAAAAAAAAAAUDiKZgAAAAAAAAAAAAAAACgcRTMAAAAAAAAAAAAAAAAUjqIZAAAAAAAAAAAAAAAACkfRDAAAAAAAAAAAAAAAAIWjaAYAAAAAAAAAAAAAAIDCUTQDAAAAAAAAAAAAAABA4SiaAQAAAAAAAAAAAAAAoHAUzQAAAAAAAAAAAAAAAFA4imYAAAAAAAAAAAAAAAAoHEUzAAAAAAAAAAAAAAAAFI6iGQAAAAAAAAAAAAAAAApH0QwAAAAAAAAAAAAAAACFo2gGAAAAAAAAAAAAAACAwlE0AwAAAAAAAAAAAAAAQOEomgEAAAAAAAAAAAAAAKBwFM0AAAAAAAAAAAAAAABQOIpmAAAAAAAAAAAAAAAAKJzGvDtAbZs9e3ZZ1tvS0hKuv/760u0vfOELYdasWWV5HaqDeKdFvNMh1mkR77SIN6nlJ5HvfVrEOx1inRbxTot4p0W8qVZyFMaCWKdFvNMi3mkR73SINdXMdV6MBfFOi3inRbzTIdZpEe/nmWkGAAAAAAAAAAAAAACAwlE0AwAAAAAAAAAAAAAAQOEomgEAAAAAAAAAAAAAAKBwFM0AAAAAAAAAAAAAAABQOIpmAAAAAAAAAAAAAAAAKBxFMwAAAAAAAAAAAAAAABSOohkAAAAAAAAAAAAAAAAKR9EMAAAAAAAAAAAAAAAAhaNoBgAAAAAAAAAAAAAAgMKp6+np6cm7EwAAAAAAAAAAAAAAADCWzDQDAAAAAAAAAAAAAABA4SiaAQAAAAAAAAAAAAAAoHAUzQAAAAAAAAAAAAAAAFA4imYAAAAAAAAAAAAAAAAoHEUzAAAAAAAAAAAAAAAAFI6iGQAAAAAAAAAAAAAAAApH0QwAAAAAAAAAAAAAAACFo2gGAAAAAAAAAAAAAACAwlE0AwAAAAAAAAAAAAAAQOEomgEAAAAAAAAAAAAAAKBwGvPuAAy0c+fO8P3vfz+sWrUqtLS0hMbGxjB//vxw+eWXh1e+8pVhwoQJeXexJrW1tYV169aV/tavX1/627dvX+m+q6++Otx4442jWt9DDz0UfvzjH5fWs3fv3jB9+vSwZMmS8IpXvCJcdNFFI1pHV1dX+MlPfhJ++ctfhi1btoTDhw+HmTNnhvPPPz+86lWvCqeffvqI1hNfP35nHnjggdL3J5ozZ0540YteFF796leHadOmjWg9Tz/9dPjBD34QHn300bB79+4wceLEcOqpp4aXvOQl4eUvf3loaGgItSDGJMbnySefDM8++2zp84l9j5/tihUrwjXXXBPOOuusEa9PrKvXwYMHS/Hp3abje4mfUUdHR5gyZUo47bTTSjGKMR/JZ7NmzZrwwx/+sPTdifuMyZMnh4ULF5b2EfGzGakY5zvuuCNs3ry51MempqbSd+7aa68Ny5cvH9E62tvbSzG69957w7Zt20JnZ2eYNWtWuPjii0vfmRj3kUjlmPKVr3wlfPvb3+5rf+xjHwvnnnvusM+xbVe/66+/fkSPO+ecc8Kf//mfD/sY8YaTk8rxpJLkJ2nt5+Qo6cRbjnJ8qRxT5CjF2rZ7yVHSijfVLZXjSSXJUdLaz8lR0om3HOX4UjmmyFGKtW1H8pN0Yk31S+VYUmlylHT2dfKTdGItPzm+VI4p8pNibdu95CjVH++6np6enrK/CozQgw8+GD7/+c+HQ4cODXr/ggULwkc+8pHSgZCx2yGPJpnq7u4OX/ziF8NPf/rTIR8TT9ze+973hvr6+mF3pJ/85CdLO/XBjBs3Lrz73e8u7QyHs3bt2vCZz3wm7NmzZ9D7m5ubw4c//OGwdOnSYdcTDzD/+I//WDphG0x8/h//8R+XDj7VLJ5ArV69+riPu+qqq8L73ve+0onlUMS6umMd/frXvw6f+MQnjvu4eGLyX/7LfwkveMELhnzMrbfeGr7xjW+EoU4LYhLzR3/0R2H8+PFDriMmcZ/73OdKJ22DqaurC295y1vCW9/61mH7G5On+J157rnnBr1/0qRJ4QMf+EB44QtfOOx6UjmmbNq0qfQ+4onuSJIp23b1b9tjmUyJd+3Em+qVyvGk0uQn6ezn5ChpxVuOIkeJ5CjF27Z7yVHSijfVK5XjSaXJUdLZz8lR0oq3HEWOEslRirdtR/KTdGJNdUvlWJIHOUoa+zr5STqxjuQn8pNIflK8bbuXHKX6461ohqqxcePG8NGPfrR0MI4VZNddd10477zzSu277rqrVPHWe/D71Kc+VTqYcmI75NmzZ5cq9B555JFRJ1M333xz+Na3vlW6vWjRovD6178+zJs3L2zfvr1U/RrjGMX4vf3tbx9yxx53+rHKObr00ktLFZBTp04t7WS/+c1vlqqf44lX3BEOVRm5a9eu0v29Ffavec1r+k6ufvWrX4Xvfe97pZOLWP0cvzOxenkwsTL505/+dOkkMj72TW96U1i2bFnYv39/aWd9//33lx4XK6hjv4c72OQtnjDHWMSDUaywjn2O8Y6f+VNPPRW++93vlqo0oxe/+MXhD/7gD4Zcl1hXd6x7k6kvfOELpZPmxYsXl2I9Y8aM0vuL1faxej++pxiHmDj/j//xP0ojCgz0ox/9KHzpS18q3Y4xfuMb3xjOOOOM0NraGm6//fbw+OOPj+g781d/9Vfh7rvvLt2OfYqVxPG7GKuEb7vtttJ3J4onbfF7MJiY+MRY9yZS8cQsvm5M4h577LHSdzJWPceRAz7+8Y8P+n5SOqbE2P7pn/5p6QQ3fqfj9nS8ZMq2Xf3b9sBj92/+5m+W/oYSv+Nz584d9D7xrp14U51SOZ7kQX6Szn5OjpJWvOUochQ5SjG37V5ylLTiTXVK5XiSBzlKOvs5OUpa8ZajyFHkKMXctiP5STqxpnqlcizJixwljX2d/CSdWEfyE/mJ/KSY23YvOcqsqo/30KWnUGFf/vKXSwe5uHH92Z/9WWZqt3gAjAe8OC1ZPMB+5zvfGXFVHs+LVb9xeq74F0+2duzYEd7//vePah1bt24tffZRXM9f/MVf9FUjx0q/Sy65pLTTigf1+LhY0ThYZW+czq93hxwPDr/3e7/Xd19cT9wJ//f//t9LJ1X/9E//FC644IJBp9762te+VtohR7EaOSYPvc4+++zSyWU8uYs7+PjYwRLGWLkYXyPukOPJVDw569/nWLH993//9+Hf//3fS33++c9/Hl760peGahWT5N/+7d8OK1euPObgEbepOPJAPMGM21E8ofyN3/iNUuXqQGJd/bHu3TfGZGooV1xxRemk4rOf/Wzp/X/9618PH/rQhzKPiScf//Iv/1K6HZOxv/zLv8xU7MYTnVgtHE904ncmnkANdpIeE53eRCo+J1YQ934He78z8SQqnkzF14vf0XgiNlA8uetNpH7nd36ndOLX/zscXzt+9+K0nvG4MVTVdSrHlDj1YdwO47YfpzzsPWkeim27NrbtgeI2Gf+DY7TEuzbjTXVJ5XiSB/lJOvs5OUpa8ZajyFHkKMXctgeSo6QVb6pLKseTPMhR0tnPyVHSirccRY4iRynmtt2f/CSdWFN9UjmW5EWOksa+Tn6STqwj+Yn8RH5SzG17IDlK9ca7NsqvKLx169b1TTX4spe9LHPQ6/Xa1762dLDoPXgMNU0Tg4snCvEEJyZSJypWIvdOC/eud73rmOn7YkVwXB7Fx8Vq98H07tjjidR//I//8Zj7404xVkD3Tt/XW0nYX5zu6xe/+EXp9oUXXpjZIfc/kYz3RXFnOtgUYXHdvVXR8TUHO4jEPk6ZMqXvRK+axZPV+L6HqraMB+Tf/d3f7WvHCvXBiHX1xzoaSVVtrBQ+5ZRTSrcHm9I1VuIfPHiwdPs//If/cMwUd/E14olT72sN9bn0xjqeQPV/fK+43rj+6MCBA4NOIRj363H/HsX9fdzvD7RixYrScSJ64oknSsePVI8pMTG95ZZbSrff8573DDsNby/bdm1s22NFvNOKN2MvleNJXuQn6ezn5ChpxVuOIkeRoxRz2x4r4p1WvBl7qRxP8iJHSWc/J0dJK95yFDmKHKWY2/ZYEOt0Yk15pHIsyZMcJY19nfwknVhH8hP5ifykmNv2WBHvUPZ4K5qhKvTf6HoPkgPFA3OcXrL3QNw7jRyVESv9HnjggdLteAIy2MlJFJf3nrg9+OCDpecNrIbcsmVL6XbckcYd+WD6VwsOtlPuv+6hvjP91xMfG58zUO97Gvia/cU+9u70n3322dJ7qGX9q8d7D0j9iXVxYt2rd0rKI0eODPm5xMdcdtllgz4/Tp13/vnn9400EKuM+4vtRx99tHQ7Pm6oqfbi+nv7Mlis4369N7GL+/uhksXjfWdSOabEKus4hWl8H4ONJDKQbbt42/ZwxDuteFMeqRxPapX9XLH2c3KUtOIdyVGKeUyRozwv5W17OOKdVrwpj1SOJ7XKfq5Y+zk5SlrxjuQoxTymyFGel/K2PRSxTifWlE8qx5JaZl9XnH2d/CSdWPeSnxTzmCI/eV7K2/ZwxPvyisRb0QxVYc2aNX1f/jhl01D6Hyx6n0NlxGk+W1tb+6bWGk5vnHbv3h127tyZua932q/+jxtMHCkhTqs3VKxHup7+9/V/zsBl8UAy3OgMRfru9a+0HuxkVayLE+sonkRs2rSpdLu34r7/d6G3gj+eUA1Xwd77ucSELE7x119s936vhotRXH/vCV3/54w21nH6wd4TusFilMIxJU6RumrVqiErwgdj267deJ8I8U4r3pRHCseTWmY/V6zvnRwlrXjLUYp5TJGjHLue1Lbt4xHvtOJNeaRwPKll9nPF+t7JUdKKtxylmMcUOcqx60lt2x6OWKcTa8onhWNJrbOvK853T36STqwj+Ukxjynyk2PXk9q2fTzifU5F4q1ohqoQq8OiOPVSnPJtKL0Vcv2fQ2X0/7wHnpCNJk6jWU/v/S0tLaUq28HWM3ny5GF3ps3NzX0Vz70VlL3iOuO6B/Z5uL4Mtp5aE6c6HC4GYl37sW5vbw/PPfdcaQq+j33sY33T9r361a8+JtHq7u4eVYwG+1z6x/p4n2/v/bFPcXq/odYzXH/icaJ3qr7BYlT0Y0ocMeHLX/7ykFOtDsW2Xbvbdpxi+Q//8A/D7/zO75SmXv7ABz4Q/uZv/qY0IshQxLt24031KPrxpNbZzxVrPydHKX685SjFPqbIUdLbtuUoacWb6lH040mts58r1n5OjlL8eMtRin1MkaOktW3LT9KJNdWl6MeSIrCvK86+Tn5S/FjLT4p9TJGfpLdty1GqN95DlxpChXR0dIR9+/aVbg811VuvWGkZK0rjiULvxkRl9P+8jxen2bNnD/q83urGXjNnzhx2Pb2vE6fuis/rv+PsXe/x+tLbn2eeeeaYvozmPfW/f9euXaFWxRPnb33rW33tK6644pjHiHVtxvqOO+4If/u3fzvk/dddd114yUtectIxisby8z3ttNOO6U/cz0+ZMuW469m8eXPYu3dvaVSEcePGJXNM+cpXvhL27NkTVqxYEa655poRP8+2XZvb9mAJTvyPiPj385//PLzoRS8KN954YylJ6U+8azfeVIcUjie1zn6uOPs5OUpx4y1HkaMcj227NrftSI6SVrypDikcT2qd/Vxx9nNylOLGW44iRzke23Ztbtvyk3RiTfVI4VhSBPZ1xdjXyU+KG2v5ifzkeGzbtbltR3KU6o23ohly179CbeLEicd9fHxMPPANrGyjeuLUO53ewOdFhw4dGpP19LZH8p3pXc/J9KX//bX83fve977XN03jpZdeOuiUhmJdjFj3WrhwYXjve98bli5desx9YxWjsfp8e9czmlj3rqc3mSr6MWX16tXhpz/9aWlkhfe85z2hrq5uxM+1bdfeth3f9wtf+MJw/vnnlyrq43uI/4EQR5L50Y9+VPqPgwceeCD8z//5P8Of/dmfZabeFe/aizfVpejHkyKwnyvOfk6Okla8IzlKsY4pcpS0tm05SlrxproU/XhSBPZzxdnPyVHSinckRynWMUWOks62LT9JJ9ZUn6IfS4rCvq4Y+zr5STqx7iU/KdYxRX6S1rYtRzlc9fFWNEPuYrVor/47gaH0Pqb/86iuOPWe1Ax8XhQrhcdiPb3tkXxn+lcmn2hf+t/f/3m1JB58b7755tLtpqam0onYYMS6NmMdq5A/+9nP9r3/7du3h3vuuSfcf//94a//+q/DO9/5ztJJWTlidKKf71DrGU2sB66nyMeUzs7O8MUvfrFU3f2a17wmnHHGGaN6vm279rbtv/u7vxt0NI4LLrggXHvtteGTn/xk2LhxY2n//u///u+ZqXnFu/biTXUp8vGkKOznirGfk6MUO95ylOIfU+Qo6W3bcpS04k11KfLxpCjs54qxn5OjFDvecpTiH1PkKGlt2/KTdGJN9SnysaRI7Otqf18nPyl2rOUnxT+myE/S27blKB1VH+/6sq0ZRmj8+PGZA8Xx9D6m//Oorjj132kNjFP/He3JrKe3PZLvTO96TqYv/e/v/7xaEac/+8xnPhO6urpK/f/DP/zDUkI1GLGuzVjHE654ch3/4mgDL37xi8OHPvSh8P73vz/s2LGjVKEcp/YsR4xO9PMdaj2jifXA9RT5mPLNb34zbNmypTSl4Vve8pZRP9+2XXvb9nDT186YMSP80R/9UWk0iugHP/hB5n7xrr14U12KfDwpCvu52t/PyVGKH285SvGPKXKU9LZtOUpa8aa6FPl4UhT2c7W/n5OjFD/ecpTiH1PkKGlt2/KTdGJN9SnysaRI7Otqe18nPyl+rOUnxT+myE/S27blKOOrPt6KZsjdaKdVGs20T+QTpzgF3mDPiyZNmjQm6+ltj+Q707uek+nLaKcCrCbxRPoTn/hEOHDgQKivrw8f/OAHwznnnDPk48W6dmM9mKuuuiqsXLmyVLX+D//wD2H//v1jHqOx+nx71zOaWA9cT1GPKTGJ+ta3vlW6/e53v/uE+mvbLta2Hc2bN680GkG0bdu2sHv37r77xLt48aayino8KRL7udrez8lR0or3QHKUYhxT5Ci27cHIUdKKN5VV1ONJkdjP1fZ+To6SVrwHkqMU45giR7FtDyQ/SSfWVF5RjyVFY19Xu/s6+Uk6sR6M/KQYxxT5iW17MHKU/OOtaIbcxeqyadOmlW63tLQM+9h4EtC7gc2aNasi/SMc83kfL067du0a9HnRzJkz+2733+kPpvd16urqMs/rv97j9aV/f4bry/HW0//+WP1bK+Jn/PGPfzy0traWPscbbrihNL3jcMS6NmM9nN6Yx/3nww8/fFIxGuzzHc13ZrjPt7c/sZ8x+R/JeqZPn56pLi7qMeV73/teqaI6njzHPt91113H/MWRRno99thjfct7Typt28XbtqPTTjtt0HiIdzHjTeUU9XhSJPZztbufk6OkFe+hyFFq/5giR7FtD0WOMnxfihZvKqeox5MisZ+r3f2cHCWteA9FjlL7xxQ5im17MPKT4ftSpFhTWUU9lhSNfV1t7uvkJ+nEejjyk9o/pshPbNtDkaPkG+/Gsq0ZRrkjWL16dal6Lk4r2DsF1UBbt27NPIfK6f95x0rY4QwXp4HrWbhw4ZDr6X2duDMdWD0Y17Nhw4Zw8ODBsGfPntL0ZYOJScShQ4dKt0899dRjKhnjuuMOt3+fh+vLYOupVnv37i2NPLB9+/ZS+13vele4+uqrj/s8sa69WB9PTDh67dy5s+/2KaecUhqVoru7+7ixHu5z6R/r432+vffH/fz8+fOPWc99993X93rLly8fdB3xOBGPF4P1pajHlN5pDOP2/Nd//dfHffw3vvGNvtt/8zd/U9qubNvF27Z7E5fBiHcx401lFfF4UiT2c7W5n5OjpBXv4chRav+YIkexbQ9FjjJ8XwZbD4xUEY8nRWI/V5v7OTlKWvEejhyl9o8pchTb9mDkJ8P3ZbD1wEgV8VhSNPZ1tbevk5+kE+vjkZ/U/jFFfmLbHoocJd94m2mGqrBixYrSv7GqMm5oQ3niiSeOeQ6VMXfu3NDc3Fy6HU9ShtN7f6wUnDNnTua+s846a9B4DhR3tM8999yQsR7pevrf1/85A5fFnXJ8zaJ89+LB6i//8i/Ds88+W2q//e1vD9dee+2InivWtRXrkRhqKr/GxsawdOnS0u2nnnqqVOF+vM8lVvsvWbIkc19sx3X1f9xg4vrj6wx8zmhjvX79+r6RAwaLkWPK4GzbxYx3735+YIW+eBcz3lSW40l1s5+rve+dHCWteB+PHGX49zTUeorGtl3MeMtR0oo3leV4Ut3s52rveydHSSvexyNHGf49DbWeorFtFy/e8pN0Yk3lOZZUP/u62vruyU/SifVIyE+Gf09DradobNvFjLccJd94K5qhKlx66aV9t3/2s58N+phYIXvnnXeWbk+ZMiWce+65Fesfz1c49k79F6v6ek+IBorLe6v+LrnkkmMqI2PFc28l4D333NN3QjTQHXfcMej3o1f/dQ/1nem/nvjY+JyB+k9h2f81+4t9jH3traCM76Gaxf5+8pOfDBs3biy13/SmN4XrrrtuxM8X69qJ9Uj1vqfojDPOGPRziRW/vdX/A8Vq30cffbR0+7zzzitVAfcX2+eff37pdnzcUNPpxfX3VhYPFuu4X588eXLpdtzf9/T0nNB3pojHlBtvvDHceuutw/695S1v6Xv8xz72sb7l8aQ6sm0Xb9vesWNH+PWvf126Had07Z9MiXfx4k3lFfF4UiT2c7W1n5OjpBXvkZCj1P4xRY5i2x6MHCWteFN5RTyeFIn9XG3t5+QoacV7JOQotX9MkaPYtgeSn6QTa/JRxGNJ0djX1c6+Tn6STqxHSn5S+8cU+YltezBylDtyj7eiGapCrIA9++yz+zawwTb47373u30b+6te9apjKlcpv1e/+tWlKf6if/qnfwodHR2Z+2M7Lo/iNHmvec1rBl3P6173utK/+/fvD1/5yleOuT9OtXfbbbeVbsdp/QbbKcfpvq688srS7UceeSTce++9xzwm7kjjfdFVV1016BRhcd3xABTF1+ydCrC/f/7nfw4HDhwo3X79618fqlms8P7sZz8b1qxZ0xez3/qt3xr1esS6+mPdeyIxMDaD7Tsfeuih0u14Ut27r+318pe/vC+Bufnmm8O+ffuOSTr+/u//vvTvcJ9Lb6zjNJn/8A//0Pf4/tPI/su//Etf8nLNNdccs464X4/79yju77/zne8c85h4fOg9ETvnnHP6RlDozzFlaLbt2ti2owcffLC0PQ0lVt9/7nOf6xs55JWvfOUxjxHv2ok31cnxpPrZz9XGfk6Okla85ShylNGybdfGth3JUdKKN9XJ8aT62c/Vxn5OjpJWvOUocpTRsm3XxrYtP0kn1lQvx5LaYF9X/fs6+Uk6sY7kJ/KT0bJt18a2HclRrqqJeNf1DFXeBxUWq6U/+tGPljbsOK3cG9/4xlJFaGzffffd4cc//nHpcQsWLAif+tSnjqmAZXhPPvlkZocTT2x6d4hxOqt4QtXfS1/60kHXE0+2vvWtb5VuL1q0KLzhDW8o7dS2b98e/u3f/q2v6j1WvMepIgcTT7JidWzvCf9ll11Wev2pU6eGdevWhW984xuhra2tVH34x3/8x+Giiy4adD27du0q3R/fSzwIvPa1rw0vfOELS/f96le/Kp0sxQPR9OnTw6c//ekwa9asQdezatWq0v1xd9jU1BTe/OY3l07G4kHjJz/5SV9Vdpwq7M///M/7DkzVKCZS999/f1+l+Dvf+c5hHx9PIIeqzBTr6o51b1V6rOiPn2vsc4xP3H8ePnw4PP300+EXv/hF32cfYx0/wwsuuOCY9fzoRz8KX/rSl0q34zriqBVxpILW1tbwve99Lzz++OOl+1784heHP/iDPxiyP3/1V39V2l9Hcf8dT8zitIGxL9/85jdL353ove99b3jFK14x6Dri+4n97J3+Lz7uiiuuCOPHjy/1I548xfcX25/4xCfCwoULB11PiseUONrA17/+9dLtuN0NNaqCbbv6t+3e7TsmSvGzXb58eek/Q+L3Pn5WcUrKuN32/udHfF/x+x6n1R1IvGsj3lSvFI8nlSI/SWc/J0dJK95yFDlKf3KU4mzbkRwlrXhTvVI8nlSKHCWd/ZwcJa14y1HkKP3JUYq1bctP0og11S3FY0klyVHS2NfJT9KJdSQ/kZ/0Jz8pzrYdyVE+XRPxVjRD1VXbff7zn++b1m2geND7yEc+UqpuY3RuuummvinqRnpQHmqH+n/+z/8ZdsqtWFkcT5aG23nFHWmcWnL9+vWD3h8PCO9+97uPSfIGWrt2bfjMZz5TqsQcTKxe/PCHPxyWLVs27HriidU//uM/9lVyDhR30vEAEHfw1ez6668f1ePnzJlT+m4MRqyrO9a9J1s7d+487uPiCckNN9wwaCLVf5uPJ0RDnRbEk6P/+l//a+lkbigxUYkV0b0jHgwUT7TiSc/xvqfxP37id6Y3oRooJj4f+MAH+k7ChpLaMWWkyZRtu/q37dFs3zGxed/73lca2WMw4l0b8aa6pXY8qRT5STr7OTlKWvGWo8hR+pOjFGfbjuQoacWb6pba8aRS5Cjp7OfkKGnFW44iR+lPjpLeti0/qf1YU/1SO5ZUkhwljX2d/CSdWEfyE/lJf/KT4mzbkRxlWU3EW9EMVSfuOG6//fZSddnu3btLVbPxQLdy5cpw7bXXhgkTJuTdxaSTqV4xPnFHFneqsQJy2rRpYcmSJeE3fuM3hqw8HChWGcZKwV/+8pel6fNiVfHMmTNLlfNxqrHTTz99ROuJO/j4nXnggQf6DjyxUvOSSy4pVUDHvo1ErJL+/ve/Hx577LHSdy9WLZ966qnhJS95SengEKslq91YJlO9xLp6bd26tRSfOMJIrCaOJyexAjcmPPEEIlbnx4Tj8ssvH9G+M1YX//CHPwyrV68uVRPHk7MzzzyzNCJJ/GxGKsY5Tim6efPm0tR5sUI4TqMZ9+Gxknok4nck9iVO7ReTq3jCFJPC+J2L35n43R2JlI4pI02metm2q1scZSD+xWln4/YdYxT/YyC+p7gtxG0pbpsj3abEG05OSseTSpGfpLOfk6OkFW85yvGldEyRoxRn247kKGnFm+qX0vGkUuQo6ezn5ChpxVuOcnwpHVPkKMXZtuUn6cSa2pDSsaSS5Chp7OvkJ+nEOpKfHF9KxxT5SXG27UiOUhvxVjQDAAAAAAAAAAAAAABA4Qw9Nw8AAAAAAAAAAAAAAADUKEUzAAAAAAAAAAAAAAAAFI6iGQAAAAAAAAAAAAAAAApH0QwAAAAAAAAAAAAAAACFo2gGAAAAAAAAAAAAAACAwlE0AwAAAAAAAAAAAAAAQOEomgEAAAAAAAAAAAAAAKBwFM0AAAAAAAAAAAAAAABQOIpmAAAAAAAAAAAAAAAAKBxFMwAAAAAAAAAAAAAAABSOohkAAAAAAAAAAAAAAAAKR9EMAAAAAAAAAAAAAAAAhaNoBgAAAAAAAAAAAAAAgMJRNAMAAAAAAAAAAAAAAEDhKJoBAAAAAAAAAAAAAACgcBTNAAAAAAAAAAAAAAAAUDiKZgBgDC1cuDDU1dWFd77znXl3BQAAQI4CAABUFTkKAABQLeQnAOlQNAMAAAAAAAAAAAAAAEDhKJoBAAAAAAAAAAAAAACgcOp6enp68u4EAAAAAAAAAAAAAAAAjCUzzQAAAAAAAAAAAAAAAFA4imYAAAAAAAAAAAAAAAAoHEUzADCErVu3hj/+4z8OF198cWhqagrjxo0L8+bNC+eff3747d/+7fDlL3857N27N/OchQsXhrq6uvDOd74zs3zTpk2l5SP9e+lLXzpkv372s5+Fd7zjHWHx4sVh8uTJYfr06aU+ffjDHy71GQAAKCY5CgAAUE3kKAAAQLWQnwAwnMZh7wWARP3iF78Ir33ta49Jlnbs2FH6e+yxx8LXvva1MHv27NLjKuHw4cPhXe96V+l1B4r9iX9f+MIXwle/+tXwute9riJ9AgAAKkOOAgAAVBM5CgAAUC3kJwAcj6IZABigvb09/NZv/VYpkZo2bVq44YYbwste9rIwd+7c0NHRETZu3BjuvvvucNttt414naeeemp49NFHh33MrbfeGj7+8Y+Xbp955pmZ+3p6esJb3vKW8L3vfa/UjsnS9ddfXxqFoL6+Ptx///3hc5/7XHj66adLj7vrrrvCJZdcckLvHwAAqC5yFAAAoJrIUQAAgGohPwFgJOp64t4ZAOjz05/+NLz85S8v3f7Od74z5AgDnZ2d4eDBg6VpM/tP27l58+bStJpxWs+RevDBB8NVV10VDh06FM4+++xw7733Ztb7pS99Kbz3ve8tTR367W9/O1x77bXHrKO1tTVceeWV4fHHHw8vfvGLwy9/+ctRvnMAAKAayVEAAIBqIkcBAACqhfwEgJGoH9GjACAh27Zt67sdE5yhNDY2ZhKeE7V169bwhje8oZRIzZw5s5TA9V9vrG/99Kc/Xbr9gQ98YNBEKmpubg6f+cxnSrfjCARr16496b4BAAD5k6MAAADVRI4CAABUC/kJACOhaAYABliwYEHf7X/6p38q62vFBOq6664rJVQxOfv6178elixZknnME088EdavX1+6HafkHE7/5O+ee+4pU68BAIBKkqMAAADVRI4CAABUC/kJACOhaAYABnjJS14SFi9eXLr9wQ9+MFx66aXhk5/8ZKmqv6OjY0xf693vfnd44IEHSrf/9//+3+FlL3vZoFN69rr88stDXV3dkH9Tp04ddCQFAACgdslRAACAaiJHAQAAqoX8BICRUDQDAAOMGzeuNHXm2WefXWrHZOdP/uRPSknWjBkzStNm3nzzzaGrq+ukXufjH/94+NrXvla6/Z//838ON9xww6CP27Fjxwmt/+DBgyfVPwAAoDrIUQAAgGoiRwEAAKqF/ASAkWgc0aMAIDHnnHNOePTRR0tJVfz7+c9/HtatW1eaZvOHP/xh6e9//a//FW6//fYwd+7cUa//G9/4RvjYxz5Wuv3yl788/PVf//WQj+2ftMW+LFy4cESvcSL9AgAAqpMcBQAAqCZyFAAAoFrITwA4HkUzADCEhoaGcN1115X+oueeey784Ac/CDfddFP41a9+Vfr7/d///XDbbbeNar0PPfRQ+N3f/d3Q09MTli5dGm699dbQ2Dj0IXnWrFl9t+MICOedd95JvCsAAKBWyVEAAIBqIkcBAACqhfwEgOHUD3svANBnwYIF4V3vele45557wsUXX1xa9t3vfrc0KsFIbdu2LbzhDW8oTanZ1NRUGlFg5syZwz7noosu6rt91113ncQ7AAAAikSOAgAAVBM5CgAAUC3kJwD0p2gGAEZp3Lhx4eqrry7d7uzsDHv27BnR8w4fPlwazeCZZ54pjW7wta99LZx11lnHfV5M3E477bTS7S9+8Yul9QAAAPSSowAAANVEjgIAAFQL+QkAkaIZABjgF7/4RVi3bt2Q93d0dIQ777yzdHvq1Klhzpw5I1rv7/3e74X77ruvdPszn/lMuPbaa0f0vPr6+vAnf/InpdsbNmwoTfnZ3t4+5OP37t0b/uZv/mZE6wYAAKqfHAUAAKgmchQAAKBayE8AGInGET0KABLyk5/8JHz84x8PV155ZXjNa14TLrjgglLCFKfnfOqpp8Lf/d3fhVWrVpUe+5/+038KjY3HP5z+4z/+Y/iXf/mX0u1rrrkm/MZv/EZ47LHHhnz8lClTwqJFi/ra73vf+8KPfvSjcNttt4V//dd/Lb3+7//+74dLL720NP1nTKCefPLJcMcdd4Rvf/vbYeLEieH973//mHweAABAvuQoAABANZGjAAAA1UJ+AsBIKJoBgEF0d3eXRhnoHWlgMG94wxvCJz/5yRGt7+mnn+67/dOf/jScf/75wz4+TgsaE6NedXV14ZZbbgl/8Ad/UErm1q9fH/7bf/tvQz5/7ty5I+oXAABQG+QoAABANZGjAAAA1UJ+AsDxKJoBgAE+9KEPlUYd+PGPfxweeuihsHXr1rBjx47SffPnzy9V/cepM+PoBJU0bty48Ld/+7fhhhtuCF/60pdKyVZM0vbv31+aPjSOWPDCF74wvOpVrwqvfe1rK9o3AACgfOQoAABANZGjAAAA1UJ+AsBI1PX09PSM6JEAAAAAAAAAAAAAAABQI+rz7gAAAAAAAAAAAAAAAACMNUUzAAAAAAAAAAAAAAAAFI6iGQAAAAAAAAAAAAAAAApH0QwAAAAAAAAAAAAAAACFo2gGAAAAAAAAAAAAAACAwlE0AwAAAAAAAAAAAAAAQOEomgEAAAAAAAAAAAAAAKBwFM0AAAAAAAAAAAAAAABQOIpmAAAAAAAAAAAAAAAAKBxFMwAAAAAAAAAAAAAAABSOohkAAAAAAAAAAAAAAAAKR9EMAAAAAAAAAAAAAAAAhaNoBgAAAAAAAAAAAAAAgMJRNAMAAAAAAAAAAAAAAEDhKJoBAAAAAAAAAAAAAACgcBTNAAAAAAAAAAAAAAAAUDiKZgAAAAAAAAAAAAAAACgcRTMAAAAAAAAAAAAAAAAUjqIZAAAAAAAAAAAAAAAACkfRDAAAAAAAAAAAAAAAAIWjaAYAAAAAAAAAAAAAAIDCUTQDAAAAAAAAAAAAAABA4SiaAQAAAAAAAAAAAAAAoHAUzQAAAAAAAAAAAAAAAFA4imYAAAAAAAAAAAAAAAAIRfP/A+bnBeBZSHsLAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -359,20 +342,14 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "df47932f-d736-4e23-b3ee-d79a94c6b46e", - "metadata": {}, - "source": [ - "# Series Search" - ] - }, { "cell_type": "markdown", "id": "a716ea8f-9b1d-428c-8b41-0ab17af814d1", "metadata": {}, "source": [ - "## Dot products" + "## Updating the dot products used in MASS when computing matrix profiles\n", + "\n", + "This is part of the STOMP algorithm, which update the dot products of the sliding query instead of recomputing it everytime. When you compute $MASS(X,q_i)$, and $q_i$ is taken from a series $Y$ such as $q_i = Y[i:i+L]$, you can compute the dot product of $q_0$, and then only update it for subsequent $q_1, ...$" ] }, { @@ -382,8 +359,10 @@ "metadata": {}, "outputs": [], "source": [ - "from aeon.similarity_search._commons import get_ith_products\n", - "from aeon.similarity_search.matrix_profiles.stomp import _update_dot_products_one_series\n", + "from aeon.similarity_search.series._commons import (\n", + " _update_dot_products,\n", + " get_ith_products,\n", + ")\n", "\n", "\n", "def compute_all_products(X, T, L):\n", @@ -409,7 +388,7 @@ " \"\"\"\n", " prods = get_ith_products(X, T, L, 0)\n", " for i in range(T.shape[1] - L + 1):\n", - " prods = _update_dot_products_one_series(X, T, prods, L, i)\n", + " prods = _update_dot_products(X, T, prods, L, i)\n", " return prods\n", "\n", "\n", @@ -428,11 +407,12 @@ " mask = np.ones((1, search_space_size), dtype=bool)\n", " # Used for numba compilation before timings\n", " compute_all_products(X, T, query_length)\n", - " _times = %timeit -r 3 -n 7 -q -o compute_all_products(X, T, query_length)\n", - " times.loc[(size, _query_length), \"compute_all_products\"] = _times.average\n", - " # Used for numba compilation before timings\n", " update_products(X, T, query_length)\n", - " _times = %timeit -r 3 -n 7 -q -o update_products(X, T, query_length)\n", + "\n", + " _times = %timeit -r 2 -n 2 -q -o compute_all_products(X, T, query_length)\n", + " times.loc[(size, _query_length), \"compute_all_products\"] = _times.average\n", + "\n", + " _times = %timeit -r 2 -n 2 -q -o update_products(X, T, query_length)\n", " times.loc[(size, _query_length), \"update_products\"] = _times.average" ] }, @@ -444,7 +424,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -463,76 +443,10 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "d11a14ed-65f0-41d5-ad00-1e3877733d2e", - "metadata": {}, - "source": [ - "## Stomp vs naive" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "63d8fe31-86b8-408b-b5c6-6c18987fdc08", - "metadata": {}, - "outputs": [], - "source": [ - "# Sizes are limited to not time-out the CI, you can test with more sizes locally !\n", - "sizes = [500, 1000, 2500, 5000]\n", - "query_lengths = [0.05, 0.1]\n", - "times = pd.DataFrame(\n", - " index=pd.MultiIndex(levels=[[], []], codes=[[], []], names=[\"size\", \"query_length\"])\n", - ")\n", - "\n", - "for size in sizes:\n", - " for _query_length in query_lengths:\n", - " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", - " T = rng.random((1, size))\n", - " search_space_size = size - query_length + 1\n", - " mask = np.ones((1, search_space_size), dtype=bool)\n", - " # Used for numba compilation before timings\n", - " naive_squared_matrix_profile(X, T, query_length, mask)\n", - " _times = %timeit -r 1 -n 3 -q -o naive_squared_matrix_profile(X, T, query_length, mask)\n", - " times.loc[(size, _query_length), \"Naive\"] = _times.average\n", - " # Used for numba compilation before timings\n", - " stomp_squared_matrix_profile(X, T, query_length, mask)\n", - " _times = %timeit -r 1 -n 3 -q -o stomp_squared_matrix_profile(X, T, query_length, mask)\n", - " times.loc[(size, _query_length), \"Stomp\"] = _times.average" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cc4801ae-bb48-46d1-8e71-21c045c69773", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(ncols=len(query_lengths), figsize=(20, 5), dpi=200)\n", - "for j, (i, grp) in enumerate(times.groupby(\"query_length\")):\n", - " grp.droplevel(1).plot(label=i, ax=ax[j])\n", - " ax[j].set_title(f\"query length {i}\")\n", - " ax[j].set_yscale(\"log\")\n", - "ax[0].set_ylabel(\"time in seconds\")\n", - "plt.show()" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "391737ea-a185-4ac9-906d-90724a279017", + "id": "61dac86c-a1f3-4899-bcd5-33c8468e4c07", "metadata": {}, "outputs": [], "source": [] @@ -540,8 +454,8 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -554,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/examples/similarity_search/distance_profiles.ipynb b/examples/similarity_search/distance_profiles.ipynb index ec56fcc6bf..d2bf3fd87f 100644 --- a/examples/similarity_search/distance_profiles.ipynb +++ b/examples/similarity_search/distance_profiles.ipynb @@ -37,11 +37,11 @@ "We can then find the \"best match\" between $Q$ and $X$ by looking at the distance profile minimum value and extract the subsequence $W_{\\text{argmin} P(X,Q)}$ as the best match.\n", "\n", "### Trivial matches\n", - "One should be careful of what is called \"trivial matches\" in this situation. If $Q$ is extracted from $X$, it is extremely likely that it will match with itself, as $dist(Q,Q)=0$. To avoid this, it is common to set the parts of the distance profile that are neighbors to $Q$ to $\\infty$. This is the role of the `q_index` parameter in the similarity search `predict` methods. The `exclusion_factor` parameter is used to define the neighbors of $Q$ that will also get $\\infty$ value.\n", + "One should be careful of what is called \"trivial matches\" in this situation. If $Q$ is extracted from $X$, it is extremely likely that it will match with itself, as $dist(Q,Q)=0$. To avoid this, it is common to set the parts of the distance profile that are neighbors to $Q$ to $\\infty$. This is the role of the `q_index` parameter in the similarity search `predict` methods. The `exclusion_factor` parameter is used to define the neighbors of $Q$ that will also get $\\infty$ value.\n", "\n", - "For example, if $Q$ was extracted at index $i$ in $X$ (i.e. $Q = \\{x_i, \\ldots, x_{i+(l-1)}\\}$), then all points in the interval `[i - l//exclusion_factor, i + l//exclusion_factor]` will the set to $\\infty$ in the distance profile to avoid a trivial match.\n", + "For example, if $Q$ was extracted at index $i$ in $X$ (i.e. $Q = \\{x_i, \\ldots, x_{i+(l-1)}\\}$), then all points in the interval `[i - floor(l*exclusion_factor), i + floor(l*exclusion_factor)]` will the set to $\\infty$ in the distance profile to avoid a trivial match.\n", "\n", - "The same reasoning can also be applied for the best matches of $Q$. It is highly likely that the two best matches will be neighbours, as if $W_i$ and $W_{i+/-1}$ share $l-1$ values. The `apply_exclusion_to_result` boolean parameter in `predict` allows you to apply the exclusion zone defined by `[i - l//exclusion_factor, i + l//exclusion_factor]` to the output of the algorithm.\n" + "The same reasoning can also be applied for the best matches of $Q$. It is highly likely that the two best matches will be neighbours, as if $W_i$ and $W_{i+/-1}$ share $l-1$ values. The `apply_exclusion_to_result` boolean parameter in `predict` allows you to apply the exclusion zone defined by `[i - floor(l*exclusion_factor), i + floor(l*exclusion_factor)]` to the output of the algorithm.\n" ] }, { diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index cdbaa86948..6bb339f13f 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -7,12 +7,27 @@ "source": [ "# Time Series Similarity Search with aeon\n", "\n", - "The goal of Time Series Similarity Search is to asses the similarities between a time\n", - " series, denoted as a query `q` of length `l`, and a collection of time series,\n", - " denoted as `X`, with lengths greater than or equal to `l`. In this\n", - " context, the notion of similiarity between `q` and the other series in `X` is quantified by similarity functions. Those functions are most of the time defined as distance function, such as the Euclidean distance. Knowing the similarity between `q` and other admissible candidates, we can then perform many other tasks for \"free\", such as anomaly or motif detection.\n", + "\"time\n", "\n", - "\"time" + "The objectives of the similarity search module in aeon is to provide estimators with a `fit`/`predict` interface to solve the following use cases :\n", + "\n", + "- Nearest neighbors search on time series subesequences or whole series\n", + "- Motifs search on time series subsequences\n", + "\n", + "Similarly to the `transformer` module, the `similarity_search` module split estimators between `series` estimators and `collection` estimators, such as :\n", + "\n", + "- `series` estimators take as input a single time series of shape `(n_channels, n_timepoints)` during fit and predict.\n", + "- `collection` estimators take as input a time series collection of shape `(n_cases, n_channels, n_timepoints)` during fit, and a single series of shape `(n_channels, n_timepoints)` during predict.\n", + "\n", + "Note that the above is a general guideline, and that some estimators can also take `None` as input during predict, or series of length different to `n_timepoints`. We'll explore the different estimators in the next sections.\n", + "\n", + "### Other similarity search notebooks\n", + "\n", + "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are also avaiable to go more in depth with specific subject of similarity search in aeon:\n", + "\n", + "- [The theory and math behind the similarity search estimators in aeon](distance_profiles.ipynb)\n", + "- [Analysis of the performance of the estimators provided by similarity search module](code_speed.ipynb)\n", + "\n" ] }, { @@ -22,25 +37,34 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_best_matches(top_k_search, best_matches):\n", + "# Define some plotting functions we'll use later !\n", + "def plot_best_matches(\n", + " X_fit, X_predict, idx_predict, idx_matches, length, normalize=False\n", + "):\n", " \"\"\"Plot the top best matches of a query in a dataset.\"\"\"\n", - " fig, ax = plt.subplots(figsize=(20, 5), ncols=3)\n", - " for i_k, (id_sample, id_timestamp) in enumerate(best_matches):\n", + " fig, ax = plt.subplots(figsize=(20, 5), ncols=len(idx_matches))\n", + " if len(idx_matches) == 1:\n", + " ax = [ax]\n", + " for i_k, id_timestamp in enumerate(idx_matches):\n", " # plot the sample of the best match\n", - " ax[i_k].plot(top_k_search.X_[id_sample, 0], linewidth=2)\n", + " ax[i_k].plot(X_fit[0], linewidth=2)\n", " # plot the location of the best match on it\n", + " match = X_fit[0, id_timestamp : id_timestamp + length]\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " top_k_search.X_[id_sample, 0, id_timestamp : id_timestamp + q.shape[1]],\n", + " range(id_timestamp, id_timestamp + length),\n", + " match,\n", " linewidth=7,\n", " alpha=0.5,\n", " color=\"green\",\n", " label=\"best match location\",\n", " )\n", " # plot the query on the location of the best match\n", + " Q = X_predict[0, idx_predict : idx_predict + length]\n", + " if normalize:\n", + " Q = Q * np.std(match) + np.mean(match)\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " q[0],\n", + " range(id_timestamp, id_timestamp + length),\n", + " Q,\n", " linewidth=5,\n", " alpha=0.5,\n", " color=\"red\",\n", @@ -66,73 +90,30 @@ " plt.show()" ] }, - { - "cell_type": "markdown", - "id": "7e06b213-6038-4901-b98e-2433625115c4", - "metadata": {}, - "source": [ - "## Similarity search Notebooks\n", - "\n", - "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are avaiable to go more in depth with specific subject of similarity search in aeon:\n", - "\n", - "- [Deep dive in the distance profiles](distance_profiles.ipynb)\n", - "- [Analysis of the speedups provided by similarity search module](code_speed.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "ca967c08-9a05-411a-a09a-ad8a13c0adb9", - "metadata": {}, - "source": [ - "## Expected inputs and format\n", - "For both `QuerySearch` and `SeriesSearch`, the `fit` method expects a time series dataset of shape `(n_cases, n_channels, n_timepoints)`. This can be 3D numpy array or a list of 2D numpy arrays if `n_timepoints` varies between cases (i.e. unequal length dataset).\n", - "\n", - "The `predict` method expects a 2D numpy array of shape `(n_channels, query_length)` for `QuerySearch`. In `SeriesSearch`, the predict methods also expects a 2D numpy array, but of shape `(n_channels, n_timepoints)` (`n_timepoints` doesn't have to be the same as in fit) and a `query_length` parameter." - ] - }, { "cell_type": "markdown", "id": "d1fd75ae-84c2-40be-95f6-bd7de409317d", "metadata": {}, "source": [ - "## Available estimators\n", - "\n", - "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which requires the following arguments:\n", - "- `distance` : a string indicating which distance function to use as similarity function. By default this is `\"euclidean\"`, which means that the Euclidean distance is used.\n", - "- `normalise` : a boolean indicating whether this similarity function should be z-normalised. This means that the scale of the two series being compared will be ignored, and that, loosely speaking, we will only focus on their shape during the comparison. By default, this parameter is set `False`.\n", + "### A word on base clases\n", "\n", - "Another parameter, which has no effect on the output of the estimators, is a boolean named `store_distance_profile`, set to `False` by default. If set to `True`, the estimators will expose an attribute named `_distance_profile` after the `predict` function is called. This attribute will contain the computed distance profile for query given as input to the `predict` function.\n", + "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which define the some abstract methods that estimator must implement, such as `fit` and `predict` and some private function used to validate the format of the time series you will provide. Then, the two submodules `series` and `collection` also define a base class (`BaseSeriesSimilaritySearch` and `BaseCollectionSeriesSearch`) that their respective estimator will inherit from. If you ever want to extend the module or create your own estimators, these are the classes you'll want to use to define the base structure of your estimator.\n", "\n", - "To illustrate how to work with similarity search estimators in aeon, we will now present some example use cases." - ] - }, - { - "cell_type": "markdown", - "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", - "metadata": {}, - "source": [ - "### Query search" - ] - }, - { - "cell_type": "markdown", - "id": "8e99b251-d156-4989-b5a0-3a2c79cb75d4", - "metadata": {}, - "source": [ - "We will use the GunPoint dataset for this example, which can be loaded using the `load_classification` function." + "### Load a dataset\n", + "In the following, we'll use an easy dataset (`ArrowHead`) to help build intuition. Don't hesitate to swap it with other datasets to explore ! We load it using the `load_classification` function." ] }, { "cell_type": "code", "execution_count": 2, - "id": "f8a6bb7e-b219-41f1-b508-b849c45672eb", + "id": "20d3b591-f275-4548-a7d2-75b16380b055", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -146,7 +127,7 @@ "from aeon.datasets import load_classification\n", "\n", "# Load GunPoint dataset\n", - "X, y = load_classification(\"GunPoint\")\n", + "X, y = load_classification(\"ArrowHead\")\n", "\n", "classes = np.unique(y)\n", "\n", @@ -162,12 +143,43 @@ }, { "cell_type": "markdown", - "id": "5392f7f4-1825-4b15-9248-27eeecb1af3c", + "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", "metadata": {}, "source": [ - "The GunPoint dataset is composed of two classes which are discriminated by the \"bumps\" located before and after the central peak. These bumps correspond to an actor drawing a fake gun from a holster before pointing it (hence the name \"GunPoint\" !). In the second class, the actor simply points his fingers without making the motion of taking the gun out of the holster.\n", + "## 1. Series estimators\n", "\n", - "Suppose that we define our input query for the similarity search task as one of these bumps:" + "First, we'll explore estimators of the `series` module, where you must provide single series of shape `(n_channels, n_timepoints)` during fit." + ] + }, + { + "cell_type": "markdown", + "id": "78f17f93-28b3-49c0-be5f-1d430a273b0c", + "metadata": {}, + "source": [ + "### 1.1 Subsequence nearest neighbors with MASS\n", + "\n", + "To perform nearest neighbors search on subsequences on a series, we can use the `MassSNN` estimator.\n", + "\n", + "It takes as parameter during initialisation :\n", + "- `length` : an integer giving the length of the subsequences to extract from the series. It is also the expected length of the series given in `predict`\n", + "- `normalize`: a boolean indicating wheter the subsequences should be independently z-normalized (`(X-mean(X))/std(X)`) before the distance computations. This results in a scale-independent matching.\n", + " \n", + "To parameterize the search, additional parameters are available when calling the `predict` method:\n", + "\n", + "- `k` (int) : the number of nearest neighbors to return.\n", + "- `dist_threshold` (float) : the maximum allowed distance for a candidate subsequence to be considered as a neighbor.\n", + "- `allow_trivial_matches` (bool) : wheter a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False).\n", + "- `inverse_distance` (bool) : if True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones.\n", + "- `exclusion_factor` (float): A factor of the `length` used to define the exclusion zone when `allow_trivial_matches` is set to False. For a given timestamp, the exclusion zone starts from `id_timestamp - floor(length*exclusion_factor)` and end at `id_timestamp + floor(length*exclusion_factor)`.\n", + "- `X_index` (int): If series given during predict is a subsequence of series given during fit, specify its starting timestamp. If specified, neighboring subsequences of X won't be able to match as neighbors." + ] + }, + { + "cell_type": "markdown", + "id": "33105406-fc83-4143-9345-af589a06a00a", + "metadata": {}, + "source": [ + "First, we'll select a series from the dataset to use during fit. This is the series we want our neighbors to come from." ] }, { @@ -178,83 +190,108 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAAsTAAALEwEAmpwYAAAnTElEQVR4nO3de1hU17038O+eGQEVBRUGxgFEHEQYRYyDaHNiRURMc4ImUSRJDaka8jT29DTJieF588a074lHenJp89SmPSS20pNEStJGbKNUSeolRsVR1Cga0YAwCONwvwgMM7PfP7hE5KYOsIeZ7+d5fJzLYq9fdut3NmvtWUsQRVEEERE5PZnUBRAR0chg4BMRuQgGPhGRi2DgExG5CAY+EZGLUEhdQH98fHwQHBwsdRlERKNKSUkJqqqq+nzPYQM/ODgYer1e6jKIiEYVnU7X73sc0iEichEMfCIiF8HAJyJyEQx8IiIXwcAnInIRDHwiIhfBwCcichEMfCIiB/LX0wZ8rC8blmMz8ImIHITNJuJXeZex+0z5sByfgU9E5CCOF1ejrKYFSbrAYTk+A5+IyEFknyzDBA8FErT+w3J8Bj4RkQOob2nHvvOVWBWlhscY+bD0wcAnInIAfzt7HW0W27AN5wAMfCIih5CtL8Ms/wmYrZ44bH0MSeDn5uYiLCwMGo0G6enpvd4vLS1FbGws5s2bh8jISOzdu3couiUicgoXKxpwzlCPtdGBEARh2PqxO/CtVis2bdqEffv2obCwELt27UJhYWGPNq+//jqSkpJQUFCArKwsPPfcc/Z2S0TkND7WG+Aml2FVlHpY+7E78PPz86HRaBASEgI3NzckJycjJyenRxtBENDQ0AAAqK+vx9SpU+3tlojIKZgtNnxaYEB8hB8mjXcb1r7s3vGqvLwcgYHfTTIEBATgxIkTPdr8/Oc/x/Lly/Gb3/wGzc3NyMvL6/NYGRkZyMjIAACYTCZ7SyMicnh5F42ovdmONbqAYe9rRCZtd+3ahaeffhoGgwF79+7FunXrYLPZerVLTU2FXq+HXq+Hr6/vSJRGRCSpbH0ZVF4eeCB0+DPP7sBXq9UoK/tu3QeDwQC1uuc41I4dO5CUlAQAWLRoEVpbW/vdZJeIyFVU1Lfg8GUTVs8PgFw2fJO1XewO/OjoaBQVFaG4uBhmsxlZWVlITEzs0SYoKAiff/45AODixYtobW3lFTwRuby/nDLAJgJr5g/fvfe3sjvwFQoFtm/fjoSEBISHhyMpKQlarRZbtmzBnj17AABvvfUW3nvvPcydOxePP/44du7cOay3HhEROTqbTUS23oBFIVMQNGXciPQpiKIojkhPd0mn00Gv10tdBhHRsDj+bTWSM47jV2vn4pF5QzdhO1B28pu2REQSyNaXYYK7Aiu0qhHrk4FPRDTCGlrbsffrCjwcNRVj3YZnobS+MPCJiEbY389WoLXdhrXDuFBaXxj4REQjLFtfhjC/CYgM8BrRfhn4REQj6LKxEWfK6rBGFzDidysy8ImIRlD2yTKMkQt4ZN7wLpTWFwY+EdEI6VgorRzLwv0wxdN9xPtn4BMRjZAvLt1AdbN5WHe1GggDn4hohHxyqgx+E93xQKiPJP0z8ImIRkB1UxsOfmPCqnlqKOTSRC8Dn4hoBPzt7HVYbCIeHcJlFO4WA5+IaAT8taAc2qkTEeY/QbIaGPhERMPsyo0mnDPUS3Ir5q0Y+EREw+zTAgNkApAYJe1+3gx8IqJhZLOJ2F1wHYtn+kI5wUPSWoYk8HNzcxEWFgaNRoP09PQ+22RnZyMiIgJarRZPPPHEUHRLROTwThTXoLyuRfLhHABQ2HsAq9WKTZs24cCBAwgICEB0dDQSExMRERHR3aaoqAjbtm3D0aNHMWnSJNy4ccPebomIRoVPCwzwdFdgeYS/1KXYf4Wfn58PjUaDkJAQuLm5ITk5GTk5OT3avPfee9i0aRMmTZoEAFAqlfZ2S0Tk8Frbrdj7dSVWzPYf0XXv+2N34JeXlyMw8LuvCQcEBKC8vLxHm8uXL+Py5cu4//77sXDhQuTm5trbLRGRw9tfaERTmwWP3if9cA4wBEM6d8JisaCoqAgHDx6EwWDA4sWL8fXXX8Pb27tHu4yMDGRkZAAATCbTSJRGRDRsPj1twFQvDyycPkXqUgAMwRW+Wq1GWVlZ93ODwQC1uuenWUBAABITEzFmzBhMnz4dM2fORFFRUa9jpaamQq/XQ6/Xw9fX197SiIgkY2psw+GiKqycp4ZMNrLr3vfH7sCPjo5GUVERiouLYTabkZWVhcTExB5tVq1ahYMHDwIAqqqqcPnyZYSEhNjbNRGRw9pz9jqsNhGPOsDdOV3sDnyFQoHt27cjISEB4eHhSEpKglarxZYtW7Bnzx4AQEJCAqZMmYKIiAjExsbijTfewJQpjvErDhHRcPi0wIA5ai+E+km3lMLtBFEURamL6ItOp4Ner5e6DCKiu1ZkbET8rw5jy79GYP2/TB/RvgfKTn7TlohoiP21oBxymSD5Ugq3Y+ATEQ2hjqUUyvH9mb7wkWAbw4Ew8ImIhtDxb6tRUd/qEEsp3I6BT0Q0hP5aUI4J7grER/hJXUovDHwioiHSYrZi39cVeHCOPzzGSL+Uwu0Y+EREQ2R/YSWazVY8ep902xgOhIFPRDRE/nq6HGrvsVgQPFnqUvrEwCciGgI3GltxpMiEVfOmOsxSCrdj4BMRDYE9Z67DJgKPzHPM4RyAgU9ENCT+fq4Cs9UToVF6Sl1Kvxj4RER2qqhvwZmyOjw4WyV1KQNi4BMR2ekf5ysBACtmS7+N4UAY+EREdsq9UImZfp6Y4eu4wzkAA5+IyC7VTW3IL67BCq1jX90DDHwiIrscKDTCJgIJDj6cAzDwiYjsknuhEkGTxyFCNVHqUgY1JIGfm5uLsLAwaDQapKen99vuL3/5CwRB4MYmROQU6lvacfRKFVbM9ocgOOaXrW5ld+BbrVZs2rQJ+/btQ2FhIXbt2oXCwsJe7RobG/HOO+8gJibG3i6JiBzCPy/dQLtVRMIoGL8HhiDw8/PzodFoEBISAjc3NyQnJyMnJ6dXu1dffRUvv/wyPDw87O2SiMgh5J6vhN9Ed8wL9Ja6lDtid+CXl5cjMDCw+3lAQADKy8t7tDl9+jTKysrw0EMPDXisjIwM6HQ66HQ6mEwme0sjIho2N80WHLx8Awlaf4ddO+d2wz5pa7PZ8MILL+Ctt94atG1qair0ej30ej18fX2HuzQiont2+LIJre22UXE7Zhe7A1+tVqOsrKz7ucFggFr93dZejY2NOH/+PJYsWYLg4GAcP34ciYmJnLglolEt93wlJo0bgwXTHXMp5L7YHfjR0dEoKipCcXExzGYzsrKykJiY2P2+l5cXqqqqUFJSgpKSEixcuBB79uyBTqezt2siIkm0Waz4/OINxEf4QSEfPXe3212pQqHA9u3bkZCQgPDwcCQlJUGr1WLLli3Ys2fPUNRIRORQvrpajcY2i8OvnXM7QRRFUeoi+qLT6TjsQ0QOKe0v5/D3cxU49eoyuCsca+/agbJz9PwuQkTkACxWG/YXGrF0ltLhwn4wDHwiortwsqQWNc3mUTecAzDwiYjuyj8uVMJdIcP3Z46+W8cZ+EREd8hmE5F7vhLfn+mL8e4Kqcu5awx8IqI7dNZQh8qG1lE5nAMw8ImI7ljuhUooZALiZvlJXco9YeATEd0BUewYzvmexgde48ZIXc49YeATEd2BS5WNuFZ9c1StnXM7Bj4R0R3IPV8JQQDiI0bncA7AwCciuiO55ysRPW0yfCe4S13KPWPgExEN4ltTE74xNo7au3O6MPCJiAax73wlACCBgU9E5Lxumi3449FifG/GFKi9x0pdjl0Y+EREA/jj0RJUNZnx4vIwqUuxGwOfiKgf9S3t+J9DVxE3S4n50yZJXY7dhiTwc3NzERYWBo1Gg/T09F7vv/3224iIiEBkZCTi4uJw7dq1oeiWiGhYvXf4WzS0Wpzi6h4YgsC3Wq3YtGkT9u3bh8LCQuzatQuFhYU92sybNw96vR7nzp3D6tWrsXnzZnu7JSIaVqbGNvzhaDEenjsVEVMnSl3OkLA78PPz86HRaBASEgI3NzckJycjJyenR5vY2FiMGzcOALBw4UIYDAZ7uyUiGlbvHryCNosNzy8LlbqUIWN34JeXlyMwMLD7eUBAAMrLy/ttv2PHDjz44IN9vpeRkQGdTgedTgeTyWRvaURE96S8rgUfHi/F6vsCEOLrKXU5Q2ZEF3T+4IMPoNfrcejQoT7fT01NRWpqKoCOfRmJiKTwm8+LAAA/daKre2AIAl+tVqOsrKz7ucFggFqt7tUuLy8PW7duxaFDh+DuPnq/mkxEzq24qhkfnzJg3cJpo/6++9vZPaQTHR2NoqIiFBcXw2w2IysrC4mJiT3aFBQU4Nlnn8WePXugVCrt7ZKIaNj86sBluMll2BSrkbqUIWd34CsUCmzfvh0JCQkIDw9HUlIStFottmzZgj179gAAXnrpJTQ1NWHNmjWIiorq9YFAROQILlY04G/nruNH9weP6kXS+iOIoihKXURfdDod9Hq91GUQkQvZmKnHieJqfLl56ajd5GSg7OQ3bYmIABSU1iLvohHPLg4ZtWE/GAY+ERGAN/d/gynj3fCj+6dLXcqwYeATkcv76koVjl6pxnOxGox3H9G71UcUA5+IXJooinhj/zdQeXngyZggqcsZVgx8InJpX1y6gYLSOvw0LhQeY+RSlzOsGPhE5NL+cLQYau+xWD0/QOpShh0Dn4hcVklVM45eqcbjCwIxRu78cej8/4VERP3YdbIUcpmANbrAwRs7AQY+Ebkks8WGT/QGxM1Swm+ih9TljAgGPhG5pAOFRlQ3m/GEk9+ZcysGPhG5pF35pVB7j8UDob5SlzJiGPhE5HJKqprx5ZUqJEcHQi4TpC5nxDDwicjlZJ0sg1wmICnaNSZruzDwicilmC02fHKqzKUma7sw8InIpeRdNKKqyYzHXWiytgsDn4hcykcnOiZrF7vQZG2XIQn83NxchIWFQaPRID09vdf7bW1tWLt2LTQaDWJiYlBSUjIU3RIR3ZVr1R2TtWtdbLK2i92Bb7VasWnTJuzbtw+FhYXYtWsXCgsLe7TZsWMHJk2ahCtXruD555/Hyy+/bG+3RER3rXuy1kW+WXs7uwM/Pz8fGo0GISEhcHNzQ3JyMnJycnq0ycnJQUpKCgBg9erV+Pzzz+GgOysSkZMyW2z4WF+GpbOU8PdyrcnaLnYHfnl5OQIDv/u0DAgIQHl5eb9tFAoFvLy8UF1d3etYGRkZ0Ol00Ol0MJlM9pZGRNSta7L2iQWuN1nbxaEmbVNTU6HX66HX6+Hr63oTKkQ0fLq+Wbt4putmi92Br1arUVZW1v3cYDBArVb328ZisaC+vh5Tpkyxt2siojtSWn0TR4pcd7K2i92BHx0djaKiIhQXF8NsNiMrKwuJiYk92iQmJiIzMxMA8Mknn2Dp0qUQBNc96UQ0srJOlkImwGUna7vYvVuvQqHA9u3bkZCQAKvVivXr10Or1WLLli3Q6XRITEzEhg0bsG7dOmg0GkyePBlZWVlDUTsR0aDarTZk6w1YOsvPZSdruwiig94uo9PpoNfrpS6DiEa5fV9X4McfnsYfntZh6Sw/qcsZdgNlp0NN2hIRDbWP8ksx1csD35+plLoUyTHwichpldV0TdYGufRkbRcGPhE5rV35nZO10QFSl+IQGPhE5JRunaxVeY2VuhyHwMAnIqd0oNCIqqY2POmCyyD3h4FPRE7pwxPXXP6btbdj4BOR0ympasbRK9Uut2ftYBj4ROR0duWXQi4TsNbF9qwdDAOfiJxKm8WKj08ZEB/uB6WL7Vk7GAY+ETmV3POVqGk24wlO1vbCwCcip/LRiVIETR6Hf9H4SF2Kw2HgE5HTuHKjCSeKa/D4giDIOFnbCwOfiJzGRydKMUYuYI2O36ztCwOfiJxCa7sVfzltwHKtP3w83aUuxyEx8InIKez9ugL1Le140oX3rB2MXYFfU1OD+Ph4hIaGIj4+HrW1tb3anDlzBosWLYJWq0VkZCT+/Oc/29MlEVGfPjpRihCf8Vg0g9un9seuwE9PT0dcXByKiooQFxeH9PT0Xm3GjRuHP/3pT7hw4QJyc3Pxs5/9DHV1dfZ0S0TUwzeVjdBfq8XjC4K4feoA7Ar8nJwcpKSkAABSUlKwe/fuXm1mzpyJ0NBQAMDUqVOhVCphMpns6ZaIqIePTlyDm1yGx+ZzsnYgdu1pazQaoVKpAAD+/v4wGo0Dts/Pz4fZbMaMGTP6fD8jIwMZGRkAwA8FIrojLWYr/lpQjgfn+GPyeDepy3Fogwb+smXLUFlZ2ev1rVu39nguCMKAv0pVVFRg3bp1yMzMhEzW9y8WqampSE1NBdCxLyMR0WD+du46GlsteDJmmtSlOLxBAz8vL6/f9/z8/FBRUQGVSoWKigoolX3vGdnQ0ICHHnoIW7duxcKFC++9WiKi23x4ohQapSeigydJXYrDs2sMPzExEZmZmQCAzMxMrFy5slcbs9mMRx55BE899RRWr15tT3dERD1cuF6Ps2V1eIKTtXfErsBPS0vDgQMHEBoairy8PKSlpQEA9Ho9Nm7cCADIzs7G4cOHsXPnTkRFRSEqKgpnzpyxu3Aioo9OlMJdIcNj93Gy9k4IoiiKUhfRF51OB71eL3UZROSgmtosiNmahxWzVXgraa7U5TiMgbKT37QlolFp+xdX0Gy2chnku8DAJ6JR57f/vILfH7qKJF0A7gvylrqcUYOBT0SjyvtHvsUb//gGq6KmYtujkZysvQsMfCIaNTK/KsHrn13EQ3NUeHPNXG5QfpcY+EQ0Knx0ohSv7bmA+Ag//Do5Cgo54+tu8YwRkcP75JQBr+z+GrFhvtj+xDyMYdjfE541InJoOWfKsfmTs7h/hg9+98P5cFfIpS5p1GLgE5HD2vd1BV7IPovo4Ml47ykdPMYw7O3BwCcih3Sg0Ih/21WAqEBv/OHpaIx1Y9jbi4FPRA7n8GUTNn14GtqpE/HHH0VjvLtdK7lTJwY+ETmUU9dq8ez/nkKI73j8aX0MJnqMkbokp8HAJyKH8U1lI9bvPAnlRHf8acMCeI1j2A8lBj4ROYTS6ptYt+MEPMbI8MGGGCgneEhdktPhwBgRSe5GYyvW/eEEzFYbsp9dhMDJ46QuySnxCp+IJFXf0o6nduTD1NiGPz4djZl+E6QuyWkx8IlIMi1mKzbsPImrpib8z7r5mBfEbQqHk12BX1NTg/j4eISGhiI+Ph61tbX9tm1oaEBAQAB+8pOf2NMlETkJs8WGH394CqdLa/FO8jw8EOordUlOz67AT09PR1xcHIqKihAXF4f09PR+27766qtYvHixPd0RkZOw2UT8x8dncfAbE7Y+Mgc/mKOSuiSXYFfg5+TkICUlBQCQkpKC3bt399nu1KlTMBqNWL58uT3dEZETEEURr+25gD1nr+PlFbPw+ALuWDVS7Ap8o9EIlarjk9nf3x9Go7FXG5vNhhdffBFvvvnmoMfLyMiATqeDTqeDyWSypzQickA2m4hf/K0Q/3v8Gp5dHIIfL5khdUkuZdDbMpctW4bKysper2/durXHc0EQ+tx55t1338UPfvADBAQMvqt8amoqUlNTAXRsxEtEzqPdasPmT87h04JybPiX6Uh7cJbUJbmcQQM/Ly+v3/f8/PxQUVEBlUqFiooKKJXKXm2OHTuGI0eO4N1330VTUxPMZjM8PT0HHO8nIufS2m7FTz46jbyLN/BSQhieWzKDWxNKwK4vXiUmJiIzMxNpaWnIzMzEypUre7X58MMPux/v3LkTer2eYU/kQhpa27ExU4+TJTX4z1WzsW7hNKlLcll2jeGnpaXhwIEDCA0NRV5eHtLS0gAAer0eGzduHJICiWj0qmpqw+MZx3H6Wsetlwx7aQmiKIpSF9EXnU4HvV4vdRlEdI8MtTfx1I58XK9vwe9+OB+xYb2HfGnoDZSdXEuHiIbclRuNWLcjH01tFnywIQa64MlSl0Rg4BPREDtbVoen/5gPuUyGP6cuQsTUiVKXRJ0Y+EQ0JCxWG3bllyJ93yVMGu+GDzbEINhnvNRl0S0Y+ERkF1EU8c9vbuC/9l7ClRtNWBQyBb9aGwV/L65n72gY+ER0zy5WNGDrZxfx5ZUqTPcZj/ee0mFZuJL32DsoBj4R3bUbja14e/9l/FlfBq+xY/DawxF4MmYa3BRccd2RMfCJ6I61mK14/8i3+N2hq2i32rDh/un4t6Wh3Ht2lGDgE9GAbDYRBWV12F9YiZyC66hsaMUKrT/SHpzFSdlRhoFPRL20Waz46mo19l8w4kChEVVNbVDIBCyaMQXvJEchJmSK1CXSPWDgExGAjjVv/nnpBvYXGnHw0g00m60Y7ybHkjAllmv9sCRMCa+xHLoZzRj4RC6qzWLF6Wt1OHa1CkevVuNsWR0sNhE+nm5IjJqK5RH+WDRjCjzGyKUulYYIA5/IRVhtIr4ur8dXV6vw1ZVqnCypQZvFBpkAzAnwRuriEMSFKxEVOAlyGW+rdEYMfKJbtLZbIRMEp7i9sM1ixfnyeuhLanGypBYniqvR2GoBAIT5TcATMUH43gwfxIRMxkQPDtW4AgY+OT1RFFHTbEZFfStuNLaiqtEMU1MbTI23/Ol83tTWEYg+nm7wm+jR/cd/ogf8JrrDz6vjsf9ED3iPG+NQXzCqbTbj1LVanLxWg1MltThXXg+zxQYACJ4yDv8aqcKiGT5YFDIFvhPcJa6WpMDAJ6dQWn0TFysbUFnfior6VlTWt+B6fSsq61tR2dDaHXy3muihgM8Ed/h6ukM7dSJ8J7jDx9Md7VYbjA2tMDa0obK+FWfL6lDdbO718+4KGVReHvD38oDKa2zn3x0fBiqvsVBOdMeU8W5QyIf2t4U2ixVlNTdx1dSM4qpmXLnRhILSWlw1NQMAxsgFaKd6IWXRNMyfNhnzp01iwBMAOwO/pqYGa9euRUlJCYKDg5GdnY1Jkyb1aldaWoqNGzeirKwMgiBg7969CA4OtqdrcnGiKOLC9Qbsv1CJf1ww4htjY/d7bnIZ/DuDeF6Qd0cQT/SAf2cIKzuD/W4mI9ssVpga22BsaEVlfRsqGzo+VCo6P1Tyi2tgbGiFxdZzewlBAKaMd4OPpzt8J7hDOcEDvhPcu/+4dw4dCejcF7rzZ7p+VhSB63Ut+LaqGd92Bryh9iZu7cbH0w2RAd549L4A6KZNwtxAb060Up/s2gBl8+bNmDx5MtLS0pCeno7a2lr88pe/7NVuyZIleOWVVxAfH4+mpibIZDKMGzduwGNzAxS6ncVqw8mSWuwvrMT+C0aU17VAJgC64MlI0PpjQfBkqLw9MHmcG2QSTDrabCKqm82orG/F9foWmBrbcOP2YaOGVpia2tBuvbt/duPc5JjuMx7TfcYjxGc8Qnw9Md1nPIJ9xvNWSeph2DZAycnJwcGDBwEAKSkpWLJkSa/ALywshMViQXx8PADA09PTni7JBZ26Votd+aX4/KIRtTfb4aaQYXGoD/49LhRx4UpM8XSM4QqZTOi+cp8T4NVvO1EUUd/SDlNjG8xWG7ouuUQRECHe8riDyssDygnuDjVfQKOTXYFvNBqhUqkAAP7+/jAajb3aXL58Gd7e3nj00UdRXFyMZcuWIT09HXJ57185MzIykJGRAQAwmUz2lEZOoMVsxX//4xJ2flUCT3cF4mYpkaD1x+KZvhjvPnqnnwRBgPc4N3iPc5O6FHIxg/6rWbZsGSorK3u9vnXr1h7PBUHo8wrEYrHgyJEjKCgoQFBQENauXYudO3diw4YNvdqmpqYiNTUVQMevJeS69CU1+I+Pz6Kk+iaeWjQNL6+YNapDnsgRDPovKC8vr9/3/Pz8UFFRAZVKhYqKCiiVvTcpDggIQFRUFEJCQgAAq1atwvHjx/sMfKLWdive/Mc32HG0GGrvsfjomRh8b4aP1GUROQW77hdLTExEZmYmACAzMxMrV67s1SY6Ohp1dXXdQzRffPEFIiIi7OmWnNSpa7X4wTtH8P6XxXhiQRByf7aYYU80hOwK/LS0NBw4cAChoaHIy8tDWloaAECv12Pjxo0AALlcjjfffBNxcXGYM2cORFHEM888Y3/l5DRa263Ytvci1vz+K7RZbPhgQwy2PjIHnhzCIRpSdt2WOZx4W6ZrOFNWhxezz+CqqRmPLwjC//nBLEzg1/yJ7tmw3ZZJdK/MFht+80UR3j14FcoJ7vjT+gVYPNNX6rKInBoDn0bcN5WNeCH7DC5cb8Bj9wXgtcQILt5FNAIY+DRirDYR7x35Fm/vv4yJYxXIWDcfy7X+UpdF5DIY+DQirlU348Xss9Bfq8UKrT+2PjLbYb4hS+QqGPg0rERRxIcnSvFfey9CLhPwq7VzsSpKzWUCiCTAwKdhU1p9E/835zwOXzbhgVAf/PfqSKi8xkpdFpHLYuDTkGq32vD5RSM+yi/DkSITPBRy/Oeq2fhhTBCv6okkxsCnIVFWcxNZJ0uRrTfA1NgGlZcH/j0uFMnRQfD38pC6PCICA5/s0HE1fwMf5ZfiSJEJAoCls5R4fEEQloQpuRE2kYNh4NOAujb1qKhvwfW6Vlyva+l43LnL061X80m6QEz15hg9kaNi4A8hURRhtYlot4pot9nQbrF1PO7c5MImirCJHVtciKIIm4ger9tsgMVmg00UYbV13LdutYmwiiJsnY9FdLQXRbHzZzs2zeg4VsdxzBYbzBYb2jr7N1tsMFut3a+320RYrR3HvbWPW1+7abagor4VFXWtMFt77gfrrpBhqvdY3BfkjTXzA7EkzHfI920loqHndIFf02xG3FsHIQgCZAK6/5YJAmSdk4YyWcfzrgEH4ZbHENDjdVtn2NrEjgAWxa5QRPdji1WE2WrrDnZHJJcJcJPL4KaQYYxcgFwmQCGTQSYD5ELHc7ms4xwp5AI8FHJEBnhjhdYDU73HQuXV8fdU77GYNG4MJ2CJRiGnC3w3hQwPz53aeRWMHle+NhG3vN57KzlRFLsfo/PKueuDQi4TIAgd4SgTBMhkAuSdHxxj5DKMkcvgJheg6Hw8Ri50hqusI0wFATIZIKDjODLhu787hroFKLpCV9bxuCt8u/qXd/7M7T/fvQF253M3haw73Lv+5ng6ETld4Hu6K/D/Vs6WugwiIofDgVciIhdhV+DX1NQgPj4eoaGhiI+PR21tbZ/tNm/eDK1Wi/DwcPz0pz+Fgy7BT0Tk1OwK/PT0dMTFxaGoqAhxcXFIT0/v1earr77C0aNHce7cOZw/fx4nT57EoUOH7OmWiIjugV2Bn5OTg5SUFABASkoKdu/e3auNIAhobW2F2WxGW1sb2tvb4efnZ0+3RER0D+wKfKPRCJVKBQDw9/eH0Wjs1WbRokWIjY2FSqWCSqVCQkICwsPD7emWiIjuwaB36SxbtgyVlZW9Xt+6dWuP5x23Bfa+9e/KlSu4ePEiDAYDACA+Ph5HjhzBAw880KttRkYGMjIyAAAmk+nO/guIiOiODBr4eXl5/b7n5+eHiooKqFQqVFRUQKlU9mrz6aefYuHChfD09AQAPPjggzh27FifgZ+amorU1FQAHRvxEhHR0LFrSCcxMRGZmZkAgMzMTKxcubJXm6CgIBw6dAgWiwXt7e04dOgQh3SIiCQgiHbcI1ldXY2kpCSUlpZi2rRpyM7OxuTJk6HX6/H73/8e77//PqxWK5577jkcPnwYgiBgxYoVePvttwc9to+PD4KDg++1NJhMJvj6+t7zz4+00VYvwJpHymirebTVCzhXzSUlJaiqqurzZ+wKfEem0+mg1+ulLuOOjbZ6AdY8UkZbzaOtXsB1auY3bYmIXAQDn4jIRTht4Hfd7TNajLZ6AdY8UkZbzaOtXsB1anbaMXwiIurJaa/wiYioJwY+EZGLcLrAz83NRVhYGDQaTZ+rdzqi4OBgzJkzB1FRUQ77DeP169dDqVRi9uzvNpe50+WxpdJXzT//+c+hVqsRFRWFqKgo7N27V8IKeyorK0NsbCwiIiKg1WrxzjvvAHDs89xfzY58nltbW7FgwQLMnTsXWq0Wr732GgCguLgYMTEx0Gg0WLt2Lcxms8SVduiv3qeffhrTp0/vPsdnzpwZ/GCiE7FYLGJISIh49epVsa2tTYyMjBQvXLggdVmDmjZtmmgymaQuY0CHDh0ST506JWq12u7XXnrpJXHbtm2iKIritm3bxM2bN0tVXp/6qvm1114T33jjDQmr6t/169fFU6dOiaIoig0NDWJoaKh44cIFhz7P/dXsyOfZZrOJjY2NoiiKotlsFhcsWCAeO3ZMXLNmjbhr1y5RFEXx2WefFd99910py+zWX70pKSnixx9/fFfHcqor/Pz8fGg0GoSEhMDNzQ3JycnIycmRuiynsHjxYkyePLnHa3eyPLaU+qrZkalUKtx3330AgAkTJiA8PBzl5eUOfZ77q9mRCYLQvbZXe3s72tvbIQgCvvjiC6xevRqAY53n/uq9F04V+OXl5QgMDOx+HhAQ4PD/5wM6/gddvnw55s+f371a6GhwJ8tjO6Lt27cjMjIS69evd6jhkVuVlJSgoKAAMTExo+Y831oz4Njn2Wq1IioqCkqlEvHx8ZgxYwa8vb2hUHSsJ+lo2XF7vV3n+JVXXkFkZCSef/55tLW1DXocpwr80erLL7/E6dOnsW/fPvz2t7/F4cOHpS7prvW3PLaj+fGPf4yrV6/izJkzUKlUePHFF6UuqZempiY89thj+PWvf42JEyf2eM9Rz/PtNTv6eZbL5Thz5gwMBgPy8/Nx6dIlqUsa0O31nj9/Htu2bcOlS5dw8uRJ1NTU4Je//OWgx3GqwFer1SgrK+t+bjAYoFarJazoznTVqFQq8cgjjyA/P1/iiu5M1/LYAPpdHtvR+Pn5QS6XQyaT4ZlnnnG4c93e3o7HHnsMTz75JB599FEAjn+e+6vZkc9zF29vb8TGxuLYsWOoq6uDxWIB4LjZ0VVvbm4uVCoVBEGAu7s7fvSjH93ROXaqwI+OjkZRURGKi4thNpuRlZWFxMREqcsaUHNzMxobG7sf79+/v8ddJY7sTpbHdjRdwQl07NXgSOdaFEVs2LAB4eHheOGFF7pfd+Tz3F/NjnyeTSYT6urqAAAtLS04cOAAwsPDERsbi08++QSAY53nvuqdNWtW9zkWRRG7d+++s3M85FPKEvvss8/E0NBQMSQkRHz99delLmdQV69eFSMjI8XIyEgxIiLCYWtOTk4W/f39RYVCIarVavH9998Xq6qqxKVLl4oajUaMi4sTq6urpS6zh75q/uEPfyjOnj1bnDNnjvjwww+L169fl7rMbkeOHBEBiHPmzBHnzp0rzp07V/zss88c+jz3V7Mjn+ezZ8+KUVFR4pw5c0StViv+4he/EEWx499idHS0OGPGDHH16tVia2urxJV26K/e2NhYcfbs2aJWqxWffPLJ7jt5BsKlFYiIXIRTDekQEVH/GPhERC6CgU9E5CIY+ERELoKBT0TkIhj4REQugoFPROQi/j/99XdQ6wfDdwAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 251)\n" + ] } ], "source": [ - "# We will use the fourth sample an testing data\n", - "X_test = X[3]\n", - "mask = np.ones(X.shape[0], dtype=bool)\n", - "mask[3] = False\n", - "# Use this mask to exluce the sample from which we will extract the query\n", - "X_train = X[mask]\n", - "\n", - "q = X_test[:, 20:55]\n", - "plt.plot(q[0])\n", - "plt.show()" + "from aeon.similarity_search.series import MassSNN\n", + "\n", + "series_fit = X[2]\n", + "series_predict = X[3]\n", + "\n", + "length = 50\n", + "snn = MassSNN(length=length, normalize=False).fit(series_fit)\n", + "\n", + "plt.plot(series_fit[0], label=\"series fit\")\n", + "plt.plot(series_predict[0], label=\"series predict\")\n", + "plt.legend()\n", + "plt.show()\n", + "print(series_fit.shape)" ] }, { "cell_type": "markdown", - "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", + "id": "320ef728-ca92-4fd5-9686-2b9739fcab83", "metadata": {}, "source": [ - "Then, we can use the `QuerySearch` class to search for the top `k` matches of this query in a collection of series. The training data for `QuerySearch` can be seen as the database in which want to search for the query on." + "Then we'll take a subsequence of size `length` in another series of the same class to use in `predict` :" ] }, { "cell_type": "code", "execution_count": 4, - "id": "80eaab8f-204f-439f-84c8-ad3462f1575e", + "id": "98560db4-4289-4072-8662-2cde2ad5c44a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" + "match 0 : 177 with distance 2.550008590853018\n", + "match 1 : 176 with distance 2.6262080735121316\n", + "match 2 : 31 with distance 2.7331649479116393\n" ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from aeon.similarity_search import QuerySearch\n", - "\n", - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, distance=\"euclidean\")\n", - "# Call fit to store X_train as the database to search in\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "starting_timestep_predict = 30\n", + "\n", + "indexes, distances = snn.predict(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + " k=3,\n", + " allow_trivial_matches=True,\n", + ")\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + "plot_best_matches(\n", + " series_fit, series_predict, starting_timestep_predict, indexes, length\n", + ")" ] }, { "cell_type": "markdown", - "id": "3dc402cf-80b7-4d0c-b07c-2f8e7822ac97", + "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", "metadata": {}, "source": [ - "The similarity search estimators return a list of size `k`, which contains a tuple containing the location of the best matches as `(id_sample, id_timestamp)`. We can then plot the results as:" + "The `predict` method returns two lists, containing the starting timesteps of the matches in `series_fit` and the squared euclidean distance of these matches to the subsequence we gave in `predict`. Now, you can then play with the different parameters of `predict` to customize your search results to your needs!\n", + "\n", + "It is also possible to get the distance profile which is used to extract the best matches :" ] }, { "cell_type": "code", "execution_count": 5, - "id": "23efe48e-8257-4ecc-93a2-d72f19024ab5", + "id": "7d2bd3f7-7eb9-4406-be1c-b6fcd9c76730", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -262,205 +299,310 @@ } ], "source": [ - "plot_best_matches(top_k_search, best_matches)" + "distance_profile = snn.compute_distance_profile(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + ")\n", + "plt.figure(figsize=(7, 2))\n", + "plt.plot(distance_profile)\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "877b1b32-d978-4c54-a4e7-b475496f710a", + "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", "metadata": {}, "source": [ - "You may also want to search not for the top-k matches, but for all matches below a threshold on the distance from the query to a candidate. To do so, you can use the `threshold` parameter of `QuerySearch` :" + "### 1.2 Motif search with StompMotif estimator" + ] + }, + { + "attachments": { + "f492cb89-5bf3-4641-8be2-a77805f20b88.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "6aecb58e-9de9-4264-959e-4180ab3fa27a", + "metadata": {}, + "source": [ + "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] and make some adjustement to clear out some confusion due to the naming of each method:\n", + "\n", + "![image.png](attachment:f492cb89-5bf3-4641-8be2-a77805f20b88.png)\n", + "\n", + "For now, the `StompMotif` estimators supports only the following configuration, which you will have to specify using the parameters of the `predict` method :\n", + "\n", + "- for **\"Pair Motifs\"** : This is the default configuration with ```{\"motif_size\": 1}```, meaning we extract the closest match to each candidate, so we end up with the pair ```(candidate, closest match)```\n", + "\n", + "- for **\"k-motif\"**, which we define as the extension of **Pair motifs** to : ```{\"motif_size\": k}```. For ```k=2```, we would extract ```(candidate, closest match 1, closest match 2)```\n", + "\n", + "- for **\"r-motifs\"**, which we renamed from **k-motif** in the figure, because it is a range-based method : ```{\"motif_size\": np.inf, \"dist_threshold\": r, \"motif_extraction_method\": \"r_motifs\"}```\n", + "\n", + "These configuration will extract the best motif only, if you want to extract more than one motifs, you can use the `k` parameter to extract the `top-k` motifs. \n", + "\n", + "**The term `k` of `top-k` motifs, while also used in `k-motifs`, is not the same. To avoid confusion of both terms, we use `motif_size` instead of `k` to specify the size of the motifs to extract. This avoids the phrasing \"extracting the `top-k` `k-motif`\", which would be confusing and ill defined. Rather, we extract the `top-k` `motif_size-motifs`**.\n", + "\n", + "The `top-k` using `motif_extraction_method=\"r_motifs\"` will be the motifs with the highest cardinality (i.e. the more matches in range `r`), while for `motif_extraction_method=\"k_motifs\"`,which is the default value, the best motifs will be those who minimize the maximum pairwise distance." ] }, { "cell_type": "code", "execution_count": 6, - "id": "23ad7adb-2b01-4425-a2e8-c393f3721a0f", + "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n", - "match 3 : [176 25] with distance 0.21889484294879047 to q\n", - "match 4 : [23 20] with distance 0.22668346183441293 to q\n", - "match 5 : [167 23] with distance 0.24774491003815066 to q\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\antoine\\Documents\\aeon\\aeon\\similarity_search\\query_search.py:270: UserWarning: Only 6 matches are bellow the threshold of 0.25, while k=inf. The number of returned match will be 6.\n", - " return extract_top_k_and_threshold_from_distance_profiles(\n" - ] + "data": { + "text/plain": [ + "([array([[ 40, 192]]), array([[192, 40]]), array([[158, 8]])],\n", + " [array([0.21749257]), array([0.21749257]), array([0.23961497])])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=np.inf, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "from aeon.similarity_search.series import StompMotif\n", + "\n", + "motif = StompMotif(length=length, normalize=True)\n", + "motif.fit_predict(series_fit, k=3, motif_size=1)" ] }, { "cell_type": "markdown", - "id": "0efd83a5-b36f-4809-be96-94de734d931c", + "id": "ace51787-71c2-4f0e-bf37-b46b51ace354", "metadata": {}, "source": [ - "You may also combine the `k` and `threshold` parameter :" + "The above use of `fit_predict` is equivalent to the following calls, with `is_self_computation=True` indicating that the series in fit is the same that the series in predict, so it shouldn't match the same subsequences as motifs : " ] }, { "cell_type": "code", "execution_count": 7, - "id": "65db1593-3873-4a47-9e2a-d8dfcf42dd1a", + "id": "c5dde2db-178b-444c-99ab-f659137638b8", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" - ] + "data": { + "text/plain": [ + "([array([[ 40, 192]]), array([[192, 40]]), array([[158, 8]])],\n", + " [array([0.21749257]), array([0.21749257]), array([0.23961497])])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "motif = StompMotif(length=length, normalize=True)\n", + "motif.fit(series_fit)\n", + "motif.predict(series_fit, k=3, motif_size=1, is_self_computation=True)" ] }, { "cell_type": "markdown", - "id": "ff62a385-d58e-4fb1-95dd-eb0474711531", + "id": "d16036a3-f5b9-41d2-ae23-a1bcf0737c93", "metadata": {}, "source": [ - "It is also possible to return the **worst** matches (not that the title of the plots are not accurate here) to the query, by using the `inverse_distance` parameter :" + "While the above example only use `series_fit` to search motifs in the same series, we also support giving another series in `predict`, which will use this series to search for the motifs matching subsequences in the series given during `fit`. For those familiar with the matrix profile notations, this is the case of using `MP(A,B)`, while not using a series in `predict` is doing a self matrix profile `MP(A,A)`." ] }, { "cell_type": "code", "execution_count": 8, - "id": "6d6078ab-9104-462e-9856-1d0fc9594b24", + "id": "59117ea7-2cbf-49d6-829a-792805b4aaf7", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", "text/plain": [ - "
" + "([array([[149, 4]]), array([[ 62, 201]]), array([[3, 5]])],\n", + " [array([0.15686187]), array([0.27831027]), array([0.29831867])])" ] }, + "execution_count": 8, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, inverse_distance=True, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "plot_best_matches(top_k_search, best_matches)" - ] - }, - { - "cell_type": "markdown", - "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", - "metadata": {}, - "source": [ - "## Using the speed_up option for similarity search" + "from aeon.similarity_search.series import StompMotif\n", + "\n", + "motif.predict(\n", + " series_predict,\n", + " k=3,\n", + " motif_size=1,\n", + ")" ] }, { "cell_type": "markdown", - "id": "b5e13c31-2aa3-4987-8d44-8a296c81a318", + "id": "9190fdf4-db3d-4d51-b2c8-41b88a9f6f74", "metadata": {}, "source": [ - "In the similarity search module, we implement different kind of optimization to decrease the time necessary to extract the best matches to a query. You can find more information about these optimization in the other notebooks of the similarity search module. An utility function is available to list the optimizations currently implemented in aeon :" + "You can also return the matrix profile with the same parameterization as `predict` (minus `motif_extraction_method` parameter) using :" ] }, { "cell_type": "code", "execution_count": 9, - "id": "d22e2d74-f44d-4c81-ba1b-72d618bd5862", + "id": "4c36738a-e6a0-4452-aee2-ccbad99d6d8b", "metadata": {}, "outputs": [ { "data": { + "image/png": "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", "text/plain": [ - "{'normalised euclidean': ['fastest', 'Mueen'],\n", - " 'euclidean': ['fastest', 'Mueen'],\n", - " 'normalised squared': ['fastest', 'Mueen'],\n", - " 'squared': ['fastest', 'Mueen']}" + "
" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "QuerySearch.get_speedup_function_names()" + "MP, IP = motif.compute_matrix_profile(series_predict)\n", + "\n", + "plt.figure(figsize=(7, 2))\n", + "plt.plot([MP[i][0] for i in range(len(MP))])\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "bf12616c-6ace-478b-806f-5419c2c19f2b", + "id": "7d2522e0-e6f4-412e-b0cb-2945016d188a", "metadata": {}, "source": [ - "By default, the `fastest` option is used, which use the best optimisation available. You can change this behavior by using the values of t with the corresponding distance function and normalization options in the estimators, for example with a `QuerySearch` using the `normalised euclidean` distance:" + "# 2. Collection estimators\n", + "\n", + "Now, we'll explore estimators of the `collection` module, where you must provide single series of shape `(n_cases, n_channels, n_timepoints)` during fit and predict." ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "6313f26a-5788-42dc-881a-40746458414c", + "cell_type": "markdown", + "id": "5aea3e4f-e613-4646-b012-e64c5ec9586f", "metadata": {}, - "outputs": [], "source": [ - "top_k_search = QuerySearch(distance=\"euclidean\", normalise=True, speed_up=\"Mueen\")" + "## 2.1 Approximate nearest neighbors with RandomProjectionIndexANN\n", + "\n", + "This method uses a random projection locality sensitive hashing index based on cosine similarity. W we define a hash function as a boolean operatio such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time ser ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we com \n", + " ``X.V > 0`` to obtainhash of ``X``e \r\n", + " In the case where ``L 0``` instead.\n", + "\n", + "The ```RandomProjectionIndexANN``` estimators use the parameter ```n_hash_funcs``` to create that much random hash function as defined above. Each series `X` of the collection given in fit is then represented as an array of ```n_hash_funcs``` boolean, which is then hashed to a dictionnary as ``h(bool_arry): case_id_array}```.\n", + "\n", + "To compute the nearest neighbors of a series ``X`` given in predict, we first transform this series to a boolean array using our previously defined hash functions, and theusedthe resulting o `h(bool_aryy)``` to look at the bucket in which ``X`` falls, and consider the ```case_id_array``` as the indexes of its neighbors. If this bucket doesn't exists, we compute a distance matrix between the boolean array of ``X`` and every boolean array making the keys of the dictionnary to get similar buckets.\n", + "\n", + "This method will not provide exact results, but will perform approximate searchs. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance.y distance.\r\n" ] }, { - "cell_type": "markdown", - "id": "6ab51d84-7220-4333-b50e-2db695eaf45d", + "cell_type": "code", + "execution_count": 10, + "id": "cc719800-0119-42f9-9018-32288c2db69b", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 32 with distance 1.0\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 1 : 159 with distance 2.0\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 2 : 203 with distance 2.0\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "For more information on these optimizations you can refer to the [distance profile notebook](distance_profiles.ipynb) for the theory, and to the [analysis of the speedups provided by similarity search module](code_speed.ipynb) for a comparison of their performance." + "from aeon.similarity_search.collection import RandomProjectionIndexANN\n", + "\n", + "X_fit = X[:-2]\n", + "# we use a single series for this example but it will be converted into a collection\n", + "# as this is a collection estimators.\n", + "X_predict = X[-1]\n", + "index = RandomProjectionIndexANN().fit(X_fit)\n", + "indexes, distances = index.predict(X_predict, k=3)\n", + "# as X_predict is converted to a collection, we select the first returns\n", + "# to obtain its results\n", + "indexes = indexes[0]\n", + "distances = distances[0]\n", + "\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" ] }, { "cell_type": "markdown", - "id": "4149c40f", + "id": "c4c7a34a-3620-475c-96b8-a9bb605d09c3", "metadata": {}, "source": [ - "# Series search\n", - "For series search, we are not interest in exploring the relationship of the input dataset `X` (given in `fit`) and a single query, but to all queries of size `query_length` that exists in another time series `T`. For example, with using again our simple GunPoint dataset:" + "You can then play with the different parameter of the estimator to affect the speed vs accuracy of the index, for example increasing ```n_hash_funcs``` from the default 128 to 512, and considering larger vectors (``V`` of shape ``(n_channels, L)``) for the hash functions by tuning ```hash_func_coverage``` (a float between 0 and 1, with 0.25 as default) such as ```L = n_timepoints * hash_func_coverage```:" ] }, { "cell_type": "code", "execution_count": 11, - "id": "d510c4cc", + "id": "1b22b743-5710-4691-b740-8edaa3bbac2e", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 17 with distance 12.0\n" + ] + }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -470,45 +612,56 @@ "name": "stdout", "output_type": "stream", "text": [ - "Index of the 20-th query best matches : [[195 26]]\n" + "match 1 : 190 with distance 13.0\n" ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from aeon.similarity_search import SeriesSearch\n", + "index = RandomProjectionIndexANN(n_hash_funcs=512, hash_func_coverage=0.75).fit(X_fit)\n", + "indexes, distances = index.predict(X_predict, k=2)\n", "\n", - "query_length = 35\n", - "estimator = SeriesSearch(distance=\"euclidean\").fit(X_train) # X_test is a 3D array\n", - "mp, ip = estimator.predict(X_test, query_length) # X_test is a 2D array\n", - "plot_matrix_profile(X_test, mp, 0)\n", - "print(f\"Index of the 20-th query best matches : {ip[20]}\")" + "indexes = indexes[0]\n", + "distances = distances[0]\n", + "\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" ] }, { "cell_type": "markdown", - "id": "0dca5122", + "id": "7828c48c-abdb-4807-bc94-d9b8414b5282", "metadata": {}, "source": [ - "Notice that we find the same best match for the 20-ith query, which was the query that we used for `QuerySearch` !\n", - "\n", - "`SeriesSearch` returns two lists, `mp` and `ip`, which respectively contain the distances to the best matches of all queries of size `query_length` in `X_test` (the `i-th` query being `X_test[:, i : i + query_length]`) and the indexes of these best matches in `X_train` in the `(ix_case, ix_timepoint)` format, such as `X_train[ix_case, :, ix_timepoint : ix_timepoint + query_length]` will be the matching subsquence.\n", - "\n", - "Most of the options (`k`, `threshold`, `inverse_distance`, etc.) from `QuerySearch` are also available for `SeriesSearch`." + "This type of method is mostly interesting where speed of the search is paramount, or when the dataset size grows large (> 10k samples)." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", + "cell_type": "markdown", + "id": "1610adf3-5cb1-466e-9cad-fb248148fd5a", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## References\n", + "[1] Patrick SchΓ€fer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection\n", + " of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -521,7 +674,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/examples/transformations/preprocessing.ipynb b/examples/transformations/preprocessing.ipynb index 99dcd8567f..cccc7ac42f 100644 --- a/examples/transformations/preprocessing.ipynb +++ b/examples/transformations/preprocessing.ipynb @@ -797,7 +797,7 @@ }, "cell_type": "code", "source": [ - "X[5][0][55] = np.NAN\n", + "X[5][0][55] = np.nan\n", "has_missing(X)" ], "outputs": [ diff --git a/examples/transformations/sast.ipynb b/examples/transformations/sast.ipynb index f7dbd9c251..3e7072df36 100644 --- a/examples/transformations/sast.ipynb +++ b/examples/transformations/sast.ipynb @@ -35,16 +35,18 @@ }, { "cell_type": "code", - "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:46.448396Z", "iopub.status.busy": "2020-12-19T14:32:46.447602Z", "iopub.status.idle": "2020-12-19T14:32:51.904418Z", "shell.execute_reply": "2020-12-19T14:32:51.905034Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:19.000929Z", + "start_time": "2025-02-19T16:46:18.281830Z" } }, - "outputs": [], "source": [ "import numpy as np\n", "from sklearn.linear_model import RidgeClassifierCV\n", @@ -53,7 +55,9 @@ "from aeon.classification.shapelet_based import SASTClassifier\n", "from aeon.datasets import load_classification\n", "from aeon.transformations.collection.shapelet_based import SAST" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "markdown", @@ -70,19 +74,23 @@ }, { "cell_type": "code", - "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:51.908710Z", "iopub.status.busy": "2020-12-19T14:32:51.908101Z", "iopub.status.idle": "2020-12-19T14:32:51.918987Z", "shell.execute_reply": "2020-12-19T14:32:51.919508Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:19.007891Z", + "start_time": "2025-02-19T16:46:19.003992Z" } }, - "outputs": [], "source": [ "X_train, y_train = load_classification(\"UnitTest\", split=\"train\")" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "markdown", @@ -93,29 +101,25 @@ }, { "cell_type": "code", - "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:51.923023Z", "iopub.status.busy": "2020-12-19T14:32:51.922451Z", "iopub.status.idle": "2020-12-19T14:32:52.164365Z", "shell.execute_reply": "2020-12-19T14:32:52.164864Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.655540Z", + "start_time": "2025-02-19T16:46:19.139803Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - } - ], "source": [ "sast = SAST()\n", "sast.fit(X_train, y_train)\n", "X_train_transform = sast.transform(X_train)" - ] + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "markdown", @@ -133,29 +137,451 @@ }, { "cell_type": "code", - "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:52.168847Z", "iopub.status.busy": "2020-12-19T14:32:52.168155Z", "iopub.status.idle": "2020-12-19T14:32:52.284816Z", "shell.execute_reply": "2020-12-19T14:32:52.285506Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.667169Z", + "start_time": "2025-02-19T16:46:23.657545Z" } }, + "source": [ + "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n", + "classifier.fit(X_train_transform, y_train)" + ], "outputs": [ { "data": { - "text/html": [ - "
RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], "text/plain": [ "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n", " 4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n", " 2.15443469e+02, 1.00000000e+03]))" + ], + "text/html": [ + "
RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "execution_count": 4, @@ -163,10 +589,7 @@ "output_type": "execute_result" } ], - "source": [ - "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n", - "classifier.fit(X_train_transform, y_train)" - ] + "execution_count": 4 }, { "cell_type": "markdown", @@ -177,20 +600,24 @@ }, { "cell_type": "code", - "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:52.289448Z", "iopub.status.busy": "2020-12-19T14:32:52.288717Z", "iopub.status.idle": "2020-12-19T14:32:53.307829Z", "shell.execute_reply": "2020-12-19T14:32:53.308341Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.693290Z", + "start_time": "2025-02-19T16:46:23.674752Z" } }, - "outputs": [], "source": [ "X_test, y_test = load_classification(\"UnitTest\", split=\"test\")\n", "X_test_transform = sast.transform(X_test)" - ] + ], + "outputs": [], + "execution_count": 5 }, { "cell_type": "markdown", @@ -201,7 +628,6 @@ }, { "cell_type": "code", - "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:53.312125Z", @@ -209,13 +635,20 @@ "iopub.status.idle": "2020-12-19T14:32:53.409775Z", "shell.execute_reply": "2020-12-19T14:32:53.410342Z" }, - "scrolled": true + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.705394Z", + "start_time": "2025-02-19T16:46:23.700306Z" + } }, + "source": [ + "classifier.score(X_test_transform, y_test)" + ], "outputs": [ { "data": { "text/plain": [ - "0.9795918367346939" + "0.8636363636363636" ] }, "execution_count": 6, @@ -223,9 +656,7 @@ "output_type": "execute_result" } ], - "source": [ - "classifier.score(X_test_transform, y_test)" - ] + "execution_count": 6 }, { "cell_type": "markdown", @@ -242,19 +673,23 @@ }, { "cell_type": "code", - "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:56.012465Z", "iopub.status.busy": "2020-12-19T14:32:56.011939Z", "iopub.status.idle": "2020-12-19T14:32:56.013801Z", "shell.execute_reply": "2020-12-19T14:32:56.014399Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.716427Z", + "start_time": "2025-02-19T16:46:23.714150Z" } }, - "outputs": [], "source": [ "sast_pipeline = make_pipeline(SAST(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)))" - ] + ], + "outputs": [], + "execution_count": 7 }, { "cell_type": "markdown", @@ -265,37 +700,462 @@ }, { "cell_type": "code", - "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:56.017692Z", "iopub.status.busy": "2020-12-19T14:32:56.017166Z", "iopub.status.idle": "2020-12-19T14:32:56.420648Z", "shell.execute_reply": "2020-12-19T14:32:56.421247Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.752245Z", + "start_time": "2025-02-19T16:46:23.725945Z" } }, + "source": [ + "X_train, y_train = load_classification(\"UnitTest\", split=\"train\")\n", + "\n", + "# it is necessary to pass y_train to the pipeline\n", + "# y_train is not used for the transform, but it is used by the classifier\n", + "sast_pipeline.fit(X_train, y_train)" + ], "outputs": [ { "data": { + "text/plain": [ + "Pipeline(steps=[('sast', SAST()),\n", + " ('ridgeclassifiercv',\n", + " RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n", + " 4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n", + " 2.15443469e+02, 1.00000000e+03])))])" + ], "text/html": [ - "
Pipeline(steps=[('sast', SAST()),\n",
+       "
Pipeline(steps=[('sast', SAST()),\n",
        "                ('ridgeclassifiercv',\n",
        "                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03])))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SAST()
RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03])))])"
+       "       2.15443469e+02, 1.00000000e+03]))
" ] }, "execution_count": 8, @@ -303,13 +1163,7 @@ "output_type": "execute_result" } ], - "source": [ - "X_train, y_train = load_classification(\"UnitTest\", split=\"train\")\n", - "\n", - "# it is necessary to pass y_train to the pipeline\n", - "# y_train is not used for the transform, but it is used by the classifier\n", - "sast_pipeline.fit(X_train, y_train)" - ] + "execution_count": 8 }, { "cell_type": "markdown", @@ -320,20 +1174,28 @@ }, { "cell_type": "code", - "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:56.425026Z", "iopub.status.busy": "2020-12-19T14:32:56.424348Z", "iopub.status.idle": "2020-12-19T14:32:57.602704Z", "shell.execute_reply": "2020-12-19T14:32:57.603291Z" + }, + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.781486Z", + "start_time": "2025-02-19T16:46:23.762476Z" } }, + "source": [ + "X_test, y_test = load_classification(\"UnitTest\", split=\"test\")\n", + "\n", + "sast_pipeline.score(X_test, y_test)" + ], "outputs": [ { "data": { "text/plain": [ - "0.956268221574344" + "0.8636363636363636" ] }, "execution_count": 9, @@ -341,11 +1203,7 @@ "output_type": "execute_result" } ], - "source": [ - "X_test, y_test = load_classification(\"UnitTest\", split=\"test\")\n", - "\n", - "sast_pipeline.score(X_test, y_test)" - ] + "execution_count": 9 }, { "cell_type": "markdown", @@ -359,16 +1217,439 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.795361Z", + "start_time": "2025-02-19T16:46:23.791501Z" + } + }, + "source": [ + "clf = SASTClassifier(seed=42)\n", + "clf" + ], "outputs": [ { "data": { - "text/html": [ - "
SASTClassifier(seed=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], "text/plain": [ "SASTClassifier(seed=42)" + ], + "text/html": [ + "
SASTClassifier(seed=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "execution_count": 10, @@ -376,10 +1657,7 @@ "output_type": "execute_result" } ], - "source": [ - "clf = SASTClassifier(seed=42)\n", - "clf" - ] + "execution_count": 10 }, { "cell_type": "markdown", @@ -390,16 +1668,438 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.825075Z", + "start_time": "2025-02-19T16:46:23.805052Z" + } + }, + "source": [ + "clf.fit(X_train, y_train)" + ], "outputs": [ { "data": { - "text/html": [ - "
SASTClassifier(seed=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], "text/plain": [ "SASTClassifier(seed=42)" + ], + "text/html": [ + "
SASTClassifier(seed=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "execution_count": 11, @@ -407,9 +2107,7 @@ "output_type": "execute_result" } ], - "source": [ - "clf.fit(X_train, y_train)" - ] + "execution_count": 11 }, { "cell_type": "markdown", @@ -420,13 +2118,20 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-19T16:46:23.851369Z", + "start_time": "2025-02-19T16:46:23.833347Z" + } + }, + "source": [ + "clf.score(X_test, y_test)" + ], "outputs": [ { "data": { "text/plain": [ - "0.9650145772594753" + "0.8636363636363636" ] }, "execution_count": 12, @@ -434,9 +2139,7 @@ "output_type": "execute_result" } ], - "source": [ - "clf.score(X_test, y_test)" - ] + "execution_count": 12 }, { "cell_type": "markdown", @@ -453,25 +2156,30 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-19T16:46:24.377310Z", + "start_time": "2025-02-19T16:46:23.859834Z" + } + }, + "source": [ + "fig = clf.plot_most_important_feature_on_ts(\n", + " X_test[y_test == \"1\"][0, 0], clf._classifier.coef_\n", + ")" + ], "outputs": [ { "data": { - "image/png": "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", "text/plain": [ "
" - ] + ], + "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "fig = clf.plot_most_important_feature_on_ts(\n", - " X_test[y_test == \"1\"][0, 0], clf._classifier.coef_[0]\n", - ")" - ] + "execution_count": 13 }, { "cell_type": "markdown", @@ -482,25 +2190,30 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-19T16:46:24.666511Z", + "start_time": "2025-02-19T16:46:24.387533Z" + } + }, + "source": [ + "fig = clf.plot_most_important_feature_on_ts(\n", + " X_test[y_test == \"2\"][0, 0], clf._classifier.coef_\n", + ")" + ], "outputs": [ { "data": { - "image/png": "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", "text/plain": [ "
" - ] + ], + "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "fig = clf.plot_most_important_feature_on_ts(\n", - " X_test[y_test == \"2\"][0, 0], clf._classifier.coef_[0]\n", - ")" - ] + "execution_count": 14 }, { "cell_type": "markdown", diff --git a/examples/transformations/smoothing_filters.ipynb b/examples/transformations/smoothing_filters.ipynb new file mode 100644 index 0000000000..6a7776f04e --- /dev/null +++ b/examples/transformations/smoothing_filters.ipynb @@ -0,0 +1,326 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7054d3cf-79d1-4e17-95c3-6e922b293b37", + "metadata": {}, + "source": [ + "# Smoothing time series\n", + "\n", + "> In statistics and image processing, to smooth a data set is to create an\n", + "approximating function that attempts to capture important patterns in the data,\n", + "while leaving out noise or other fine-scale structures/rapid phenomena.\n", + "(https://en.wikipedia.org/wiki/Smoothing)\n", + "\n", + "In this notebook, we demonstrate the usage and results of different smoothing\n", + "transformations." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "003724f9-a594-4aca-8ee4-be8b43ac3afb", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from aeon.datasets import load_airline, load_solar" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "94c546cc-b0fc-420f-b399-a845db1da26d", + "metadata": {}, + "outputs": [], + "source": [ + "# Load time series example\n", + "x_airline = load_airline()\n", + "x_solar = load_solar()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c376920c-dbb1-422f-8b2b-1ffe0018ce8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate random serie\n", + "np.random.seed(42)\n", + "x_random = np.random.random(128) * 10\n", + "\n", + "# Generate sinus/cosinus signal with random noise\n", + "t1 = np.linspace(0, 64, 256)\n", + "t2 = np.linspace(0, 32, 256)\n", + "x_signal = np.sin(t1) + np.cos(t2) + (np.random.random(256) - 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5fb82b4d-3cc8-4139-ae87-f266b74d6758", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot functions\n", + "\n", + "\n", + "def plot_axes(axe, x1, x2, title):\n", + " \"\"\"Plot x1 and x2 on axe.\"\"\"\n", + " axe.plot(x1, label=\"Original Series\", color=\"red\")\n", + " axe.plot(x2, label=\"Smoothed Series\", color=\"blue\")\n", + " axe.set_title(title)\n", + " axe.legend()\n", + "\n", + "\n", + "def plot_transformation(transformer=None):\n", + " \"\"\"Plot transformation for each ts.\"\"\"\n", + " fig, axes = plt.subplots(2, 2, figsize=(16, 8), dpi=75)\n", + "\n", + " plot_axes(\n", + " axes[0, 0], x_airline, transformer.fit_transform(x_airline)[0], \"x_airline\"\n", + " )\n", + " plot_axes(axes[0, 1], x_solar, transformer.fit_transform(x_solar)[0], \"x_solar\")\n", + " plot_axes(axes[1, 0], x_random, transformer.fit_transform(x_random)[0], \"x_random\")\n", + " plot_axes(axes[1, 1], x_signal, transformer.fit_transform(x_signal)[0], \"x_signal\")" + ] + }, + { + "cell_type": "markdown", + "id": "4fdbdad4-5f3a-43e1-9b6a-6f6fffb8cd1b", + "metadata": {}, + "source": [ + "## GaussSeriesTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ee05e35d-3fbd-41ec-a9cb-be85ee33cf1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from aeon.transformations.series import GaussSeriesTransformer\n", + "\n", + "t = GaussSeriesTransformer()\n", + "plot_transformation(t)" + ] + }, + { + "cell_type": "markdown", + "id": "438da739-2a14-4efb-98a5-a8dd564bc84c", + "metadata": {}, + "source": [ + "## DFTSeriesTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ba95f571-fdc7-41f0-910e-034bff5ec6f6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from aeon.transformations.series import DFTSeriesTransformer\n", + "\n", + "t = DFTSeriesTransformer()\n", + "plot_transformation(t)\n", + "\n", + "t = DFTSeriesTransformer(r=0.1, sort=True)\n", + "plot_transformation(t)" + ] + }, + { + "cell_type": "markdown", + "id": "29f5265a-43e1-4f5d-a33d-23c91f30dbfe", + "metadata": {}, + "source": [ + "## SIVSeriesTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f1826953-90de-4453-9438-b320679cad14", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from aeon.transformations.series import SIVSeriesTransformer\n", + "\n", + "t = SIVSeriesTransformer()\n", + "plot_transformation(t)" + ] + }, + { + "cell_type": "markdown", + "id": "622aa446-74b5-4f36-8843-33a06731a1b2", + "metadata": {}, + "source": [ + "## SGSeriesTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "36f0ce95-fe76-4802-865e-d66a9b2d452b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from aeon.transformations.series import SGSeriesTransformer\n", + "\n", + "t = SGSeriesTransformer()\n", + "plot_transformation(t)" + ] + }, + { + "cell_type": "markdown", + "id": "95ce5b47-106c-4895-8df3-a8392c801799", + "metadata": {}, + "source": [ + "## MovingAverageSeriesTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6e665aeb-ddbb-4414-9f41-c2f644e38a95", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from aeon.transformations.series._moving_average import MovingAverageSeriesTransformer\n", + "\n", + "t = MovingAverageSeriesTransformer()\n", + "plot_transformation(t)" + ] + }, + { + "cell_type": "markdown", + "id": "fe3a3eb0-35a2-4965-90ce-9fd949c81969", + "metadata": {}, + "source": [ + "## ExpSmoothingSeriesTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d4749fd8-3fd4-43c3-81e4-2784974c0003", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer\n", + "\n", + "t = ExpSmoothingSeriesTransformer()\n", + "plot_transformation(t)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85639aea-d14f-4b41-9b1f-b72444ecc7ae", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/visualisation/plotting_results.ipynb b/examples/visualisation/plotting_results.ipynb index 535334c1d8..f8f8629922 100644 --- a/examples/visualisation/plotting_results.ipynb +++ b/examples/visualisation/plotting_results.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "initial_id", "metadata": { "ExecuteTime": { @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "bd9201e73b7ba7d7", "metadata": { "ExecuteTime": { @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "da87284606d4cfd1", "metadata": { "ExecuteTime": { @@ -89,7 +89,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "9ba66c6d0ac437a5", "metadata": { "ExecuteTime": { @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "id": "66493f5cdea9d644", "metadata": { "ExecuteTime": { @@ -144,9 +144,19 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " func(*args, **kwargs)\n", + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -157,13 +167,54 @@ ], "source": [ "_ = plot_pairwise_scatter(\n", - " classifier_accuracies[0], classifier_accuracies[1], classifiers[0], classifiers[1]\n", + " classifier_accuracies[0],\n", + " classifier_accuracies[1],\n", + " classifiers[0],\n", + " classifiers[1],\n", + " best_on_top=False,\n", ")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, + "id": "a85b3be4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " func(*args, **kwargs)\n", + "c:\\Users\\aryan\\anaconda3\\envs\\aeon\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_pairwise_scatter(\n", + " classifier_accuracies[0],\n", + " classifier_accuracies[1],\n", + " classifiers[0],\n", + " classifiers[1],\n", + " best_on_top=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "a0ba27ecd0bf0a4b", "metadata": { "ExecuteTime": { @@ -175,7 +226,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -190,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "a40d1305b6ba5e93", "metadata": { "ExecuteTime": { @@ -202,7 +253,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -233,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "c2944d477b66ab8d", "metadata": { "ExecuteTime": { @@ -249,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "cf683faa01e14340", "metadata": { "ExecuteTime": { @@ -261,7 +312,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -301,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "id": "467f2ad0789368e8", "metadata": { "ExecuteTime": { @@ -386,7 +437,7 @@ "4 0.7 0.6 0.5 0.4" ] }, - "execution_count": 2, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -400,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "id": "ac865d14b86c42a1", "metadata": { "ExecuteTime": { @@ -415,13 +466,13 @@ "
" ] }, - "execution_count": 3, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -438,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "id": "3c26d721", "metadata": {}, "outputs": [ @@ -448,13 +499,13 @@ "
" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAGGCAYAAADsPu62AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddVhUWR8H8O8QQ3ejCEiDpIFgK4qK3d269tqxdrey2Lp2rL5rdyE2oIIoCCIgId3dM/f9A706MkMpFr/P89znWU7dc8e7M+eee4LDMAwDQgghhBBCyG9P7EdXgBBCCCGEEPJ9UOOfEEIIIYSQOoIa/4QQQgghhNQR1PgnhBBCCCGkjqDGPyGEEEIIIXUENf4JIYQQQgipI6jxTwghhBBCSB1BjX9CCCGEEELqCGr8E0IIIYQQUkdQ459UCYfDwYULF2r9PPfu3QOHw0FmZiYbduHCBRgbG0NcXBwzZszA4cOHoaysXOt1IVVH9wepDN0jpDJ0jxDynTCkzktISGCmTp3KGBoaMlwul6lfvz7TrVs35s6dO2waAMz58+drvS5FRUVMQkICw+fz2TBNTU1m/vz5TFxcHJOdnc3k5+czSUlJ3/zcxcXFzLx585hGjRoxsrKyjI6ODjN8+HAmLi7um5/rV0L3xydnz55lOnbsyKiqqjIAmBcvXtTKeX41dI98smzZMsbMzIyRlZVllJWVmQ4dOjA+Pj61cq5fCd0jwv3xxx8MAGbbtm21fi5CPpL4kQ8e5MeLiopCixYtoKysjE2bNsHa2holJSW4efMmpkyZgjdv3nzX+nC5XGhra7N/5+bmIjk5Ga6urtDV1WXDZWRkvuo8JSUlkJSUFAjLz8+Hv78/lixZAltbW2RkZODPP/9Ejx498Pz5868636+K7g9BeXl5aNmyJQYMGIDx48d/1Tl+F3SPCDI1NcWOHTvQsGFDFBQUYNu2bejUqRPCw8OhoaHxVef8VdE9Itz58+fh4+MjcE5Cvosf/fRBfqwuXbow9erVY3Jzc8vFZWRksP+NL3pk5s2bx5iYmDAyMjKMoaEhs3jxYqa4uJiNDwgIYNq2bcvIy8szCgoKjIODA/Ps2TOGYRgmKiqK6datG6OsrMzIysoylpaWzNWrVxmGYRgvLy8GAJORkcH+9+eHl5cXc+jQIUZJSUmgrhcuXGDs7e0ZKSkpxtDQkFm+fDlTUlIiUP9du3Yx3bt3Z2RlZZlly5ZV6fN5+vQpA4CJjo6uUvrfDd0fwkVGRlLP/wd0j1QsKyuLASDQw13X0D1SXmxsLFOvXj0mKCiI0dfXp55/8l1Rz38dlp6ejhs3bmDNmjWQk5MrF1/ReEcFBQUcPnwYurq6CAwMxPjx46GgoIB58+YBAIYOHQp7e3vs3r0b4uLiCAgIYHtApkyZguLiYjx48ABycnIIDg6GvLx8uXM4OzsjNDQUZmZmOHv2LJydnaGqqoqoqCiBdA8fPsSIESPg4eGBVq1aISIiAhMmTAAALFu2jE23fPlyrF+/Hu7u7pCQqNqtn5WVBQ6HUyfHftL9QSpD90jFiouLsW/fPigpKcHW1rbS9L8jukfK4/P5GD58OObOnQsrK6sKPz9CasWPfvogP46vry8DgDl37lylaVHJWMxNmzYxjRs3Zv9WUFBgDh8+LDSttbU1s3z5cqFxn/fIMExZrxA+9MR89GWPTIcOHZi1a9cKlHPs2DFGR0dHoP4zZswQWX9hCgoKGAcHB2bIkCHVyve7oPtDNOr5L0P3iHCXL19m5OTkGA6Hw+jq6jJPnz6tUr7fEd0j5a1du5bp2LEjO+eAev7J90bdW3UYwzA1znv69Gl4eHggIiICubm5KC0thaKiIhs/a9YsjBs3DseOHYOLiwv69+8PIyMjAMD06dMxadIk3Lp1Cy4uLujbty9sbGxqXJeXL1/i8ePHWLNmDRvG4/FQWFiI/Px8yMrKAgCaNGlS5TJLSkowYMAAMAyD3bt317huvzK6P0hl6B4Rrl27dggICEBqair279+PAQMGwNfXF5qamjWu46+K7hFBfn5++Pvvv+Hv7w8Oh1Pj+hDyNWipzzrMxMQEHA6n2pOtvL29MXToUHTt2hVXrlzBixcvsGjRIhQXF7Npli9fjtevX8PNzQ13796FpaUlzp8/DwAYN24c3r17h+HDhyMwMBBNmjTB9u3ba3wdubm5WLFiBQICAtgjMDAQYWFhkJaWZtMJe+UszMeGf3R0NG7fvi3wY1OX0P1BKkP3iHBycnIwNjZG8+bNceDAAUhISODAgQM1rt+vjO4RQQ8fPkRycjIaNGgACQkJSEhIIDo6GrNnz4aBgUGN60dItfzQ9w7kh+vcuXO1J2Jt3ryZadiwoUDasWPHlpsc9blBgwYx3bt3Fxq3YMECxtrammGYmr2OdXZ2ZsaMGSP6IpnKXyd/VFxczPTq1YuxsrJikpOTK03/u6P7Qzga9vMJ3SOVa9iwYZUnCP+O6B75JDU1lQkMDBQ4dHV1mfnz5zNv3rypMC8h3woN+6njdu7ciRYtWqBZs2ZYuXIlbGxsUFpaitu3b2P37t0ICQkpl8fExAQxMTE4deoUmjZtiqtXr7K9LQBQUFCAuXPnol+/fjA0NERsbCyePXuGvn37AgBmzJiBLl26wNTUFBkZGfDy8oKFhUWNr2Hp0qXo1q0bGjRogH79+kFMTAwvX75EUFAQVq9eXeVySkpK0K9fP/j7++PKlSvg8XhITEwEAKiqqoLL5da4jr8quj8EpaenIyYmBvHx8QCA0NBQAIC2trbA0oF1Cd0jn+Tl5WHNmjXo0aMHdHR0kJqaip07dyIuLg79+/evcf1+dXSPfKKmpgY1NTWBMElJSWhra8PMzKzG9SOkWn700wf58eLj45kpU6Yw+vr6DJfLZerVq8f06NFDoBcEX/RozJ07l1FTU2Pk5eWZgQMHMtu2bWN7SYqKiphBgwYxenp6DJfLZXR1dZmpU6cyBQUFDMMwzNSpUxkjIyNGSkqK0dDQYIYPH86kpqYyDFOzHhmGYZgbN24wzs7OjIyMDKOoqMg0a9aM2bdvn8j6C/OxN1fY8fn56xq6Pz45dOiQ0PujLvfqMgzdIx8VFBQwvXv3ZnR1dRkul8vo6OgwPXr0qNMTfj+ie0Q0mvBLvjcOw3zFbBxCCCGEEELIL4Mm/BJCCCGEEFJHUOOfEEIIIYSQOoIa/4QQQgghhNQR1PgnhBBCCCGkjqDGPyGEEEIIIXUENf7JV9m5cycMDAwgLS0NR0dHPH36VGTatm3bgsPhlDvc3NwE0oWEhKBHjx5QUlKCnJwcmjZtipiYGDa+sLAQU6ZMgZqaGuTl5dG3b18kJSUJlBETEwM3NzfIyspCU1MTc+fORWlp6be9eFKp2rg/Ppo4cSI4HA7c3d0FwtPT0zF06FAoKipCWVkZY8eORW5urkCaV69eoVWrVpCWloaenh42btz41ddKaqY698jhw4fL3R+f764KAAzDYOnSpdDR0YGMjAxcXFwQFhYmkIbukV9Lde6R/fv3o1WrVlBRUYGKigpcXFzKpc/NzcXUqVNRv359yMjIwNLSEnv27BFIQ78z5Lf2g5caJb+wU6dOMVwulzl48CDz+vVrZvz48YyysjKTlJQkNH1aWhqTkJDAHkFBQYy4uDhz6NAhNk14eDijqqrKzJ07l/H392fCw8OZixcvCpQ5ceJERk9Pj/H09GSeP3/ONG/enHF2dmbjS0tLmUaNGjEuLi7MixcvmGvXrjHq6urMwoULa+2zIOXVxv3x0blz5xhbW1tGV1e33PrYnTt3ZmxtbRkfHx/m4cOHjLGxMTN48GA2Pisri9HS0mKGDh3KBAUFMf/++y8jIyPD7N2791tePqmC6t4jhw4dYhQVFQXuk8TERIE069evZ5SUlJgLFy4wL1++ZHr06MEYGhqy678zDN0jv5Lq3iNDhgxhdu7cybx48YIJCQlhRo0axSgpKTGxsbFsmvHjxzNGRkaMl5cXExkZyezdu5cRFxdnLl68yKah3xnyO6PGP6mxZs2aMVOmTGH/5vF4jK6uLrNu3boq5d+2bRujoKAgsOX7wIEDmWHDhonMk5mZyUhKSjL//fcfGxYSEsIAYLy9vRmGYZhr164xYmJiAo2C3bt3M4qKikxRUVGVr498ndq4PxiGYWJjY5l69eoxQUFB5TbHCQ4OZgAwz549Y8OuX7/OcDgcJi4ujmEYhtm1axejoqIicC/Mnz+fMTMzq8llkq9Q3XtE2MZLn+Pz+Yy2tjazadMmNiwzM5ORkpJi/v33X4Zh6B751Xzt90hpaSmjoKDAHDlyhA2zsrJiVq5cKZDOwcGBWbRoEcMw9DtDfn807IfUSHFxMfz8/ODi4sKGiYmJwcXFBd7e3lUq48CBAxg0aBDk5OQAAHw+H1evXoWpqSlcXV2hqakJR0dHXLhwgc3j5+eHkpISgfOam5ujQYMG7Hm9vb1hbW0NLS0tNo2rqyuys7Px+vXrr7lsUkW1cX8AZffI8OHDMXfuXFhZWZXL4+3tDWVlZTRp0oQNc3FxgZiYGHx9fdk0rVu3BpfLZdO4uroiNDQUGRkZ1b5WUjM1vUdyc3Ohr68PPT099OzZU+D/6cjISCQmJgqUqaSkBEdHR4HvB7pHfg3f4nskPz8fJSUlUFVVZcOcnZ1x6dIlxMXFgWEYeHl54e3bt+jUqRMA+p0hvz9q/JMaSU1NBY/HE/jiAwAtLS0kJiZWmv/p06cICgrCuHHj2LDk5GTk5uZi/fr16Ny5M27duoXevXujT58+uH//PgAgMTERXC4XysrKIs+bmJgotF4f40jtq437AwA2bNgACQkJTJ8+XWi+xMREaGpqCoRJSEhAVVWV7o+fTE3uETMzMxw8eBAXL17E8ePHwefz4ezsjNjYWACf/v0qKpPukV/H136PAMD8+fOhq6sr0JDfvn07LC0tUb9+fXC5XHTu3Bk7d+5E69atAdDvDPn9SfzoCpC66cCBA7C2tkazZs3YMD6fDwDo2bMnZs6cCQCws7PDkydPsGfPHrRp0+aH1JV8f8LuDz8/P/z999/w9/cHh8P5gbUjP4qTkxOcnJzYv52dnWFhYYG9e/di1apVP7Bm5Ge0fv16nDp1Cvfu3ROYGL59+3b4+Pjg0qVL0NfXx4MHDzBlypRyDwmE/K6o55/UiLq6OsTFxcutfpCUlARtbe0K8+bl5eHUqVMYO3ZsuTIlJCRgaWkpEG5hYcGu9qOtrY3i4mJkZmaKPK+2trbQen2MI7WvNu6Phw8fIjk5GQ0aNICEhAQkJCQQHR2N2bNnw8DAAEDZv29ycrJAvtLSUqSnp9P98ZP5mnvkI0lJSdjb2yM8PBzAp3+/isqke+TX8TX3yObNm7F+/XrcunULNjY2bHhBQQH++usvbN26Fd27d4eNjQ2mTp2KgQMHYvPmzQDod4b8/mql8T9q1ChwOBxMnDixXNyUKVPA4XAwatSo2jh1jbi6ukJcXBzPnj370VX5ZXC5XDRu3Bienp5sGJ/Ph6enp0DPnDD//fcfioqKMGzYsHJlNm3aFKGhoQLhb9++hb6+PgCgcePGkJSUFDhvaGgoYmJi2PM6OTkhMDBQ4Af+9u3bUFRULPdgQWpHbdwfw4cPx6tXrxAQEMAeurq6mDt3Lm7evAmg7N8+MzMTfn5+bL67d++Cz+fD0dGRTfPgwQOUlJSwaW7fvg0zMzOoqKh89bWTqvmae+QjHo+HwMBA6OjoAAAMDQ2hra0tUGZ2djZ8fX0Fvh/oHvk11PQe2bhxI1atWoUbN24IzO0AgJKSEpSUlEBMTLD5Iy4uzr59pt8Z8turjVnEI0eOZPT09BglJSUmPz+fDS8oKGCUlZWZBg0aMCNHjqyNU1dbdHQ0Iy8vz0yfPp2ZOHHij64OU1xc/KOrUGWnTp1ipKSkmMOHDzPBwcHMhAkTGGVlZXb1g+HDhzMLFiwol69ly5bMwIEDhZZ57tw5RlJSktm3bx8TFhbGbN++nREXF2cePnzIppk4cSLToEED5u7du8zz588ZJycnxsnJiY3/uARbp06dmICAAObGjRuMhoYGLcH2ndXG/fGlL1f7YZiyZRzt7e0ZX19f5tGjR4yJiYnAMo6ZmZmMlpYWM3z4cCYoKIg5deoUIysrS8s4/gDVvUdWrFjB3Lx5k4mIiGD8/PyYQYMGMdLS0szr16/ZNOvXr2eUlZWZixcvMq9evWJ69uwpdKlPukd+DdW9R9avX89wuVzmzJkzAkvC5uTksGnatGnDWFlZMV5eXsy7d++YQ4cOMdLS0syuXbvYNPQ7Q35ntdb479mzJ9OoUSPm+PHjbPiJEycYGxsbpmfPnmzjn8fjMWvXrmUMDAwYaWlpxsbGRmB5rdLSUmbMmDFsvKmpKePu7i70fJs2bWK0tbUZVVVVZvLkyVVqSC9fvpwZNGgQExISUu5hhWEYJiMjg5kwYQKjqanJSElJMVZWVszly5fZ+EePHjFt2rRhZGRkGGVlZaZTp05Meno6wzDCGya2trbMsmXL2L8BMLt27WK6d+/OyMrKMsuWLavSNTMMwxw4cICxtLRkuFwuo62tzS6HNnr0aMbNzU0gbXFxMaOhocH8888/lX4m1bF9+3amQYMGDJfLZZo1a8b4+PiwcW3atCn3kPfmzRsGAHPr1i2RZR44cIAxNjZmpKWlGVtbW+bChQsC8QUFBczkyZMZFRUVRlZWlunduzeTkJAgkCYqKorp0qULIyMjw6irqzOzZ89mSkpKvv6CSbXUxv3xOWH/j6WlpTGDBw9m5OXlGUVFRWb06NECP/wMwzAvX75kWrZsyUhJSTH16tVj1q9fX6PrI1+vOvfIjBkz2LRaWlpM165dGX9/f4Hy+Hw+s2TJEkZLS4uRkpJiOnTowISGhgqkoXvk11Kde0RfX58BUO74/Hc3ISGBGTVqFKOrq8tIS0szZmZmzJYtWxg+n8+mod8Z8jur1cb/1q1bmQ4dOrDhHTp0YLZt2ybQ+F+9ejVjbm7O3Lhxg4mIiGAOHTrESElJMffu3WMYpqzRunTpUubZs2fMu3fvmOPHjzOysrLM6dOnBc6nqKjITJw4kQkJCWEuX77MyMrKMvv27auwnnw+n9HX12euXLnCMAzDNG7cmDl69Cgbz+PxmObNmzNWVlbMrVu3mIiICOby5cvMtWvXGIZhmBcvXjBSUlLMpEmTmICAACYoKIjZvn07k5KSwjBM1Rv/mpqazMGDB5mIiAgmOjq6Ste8a9cuRlpamnF3d2dCQ0OZp0+fsud6/PgxIy4uzsTHx7Ppz507x8jJyZX7gSOEEEIIIXVHrTb+k5OTGSkpKSYqKoqJiopipKWlmZSUFLbxX1hYyMjKyjJPnjwRyD927FiBV7BfmjJlCtO3b1+B8+nr6zOlpaVsWP/+/SsdOnDr1i1GQ0ODfVLftm0b06ZNGzb+5s2bjJiYWLleo48GDx7MtGjRQmT5VW38z5gxo8J6Mkz5a9bV1WU3JBHG0tKS2bBhA/t39+7dmVGjRlV6HkIIIYQQ8vuq1dV+NDQ04ObmhsOHD+PQoUNwc3ODuro6Gx8eHo78/Hx07NgR8vLy7HH06FFERESw6Xbu3InGjRtDQ0MD8vLy2LdvH7v6y0dWVlYQFxdn/9bR0WEn4qxdu1ag/I95Dx48iIEDB0JComzF08GDB+Px48fsuQMCAlC/fn2YmpoKvb6AgAB06NDhqz+nLyckVXbNycnJiI+Pr/Dc48aNw6FDhwCUrUBw/fp1jBkz5qvrSgghhBBCfl21vs7/mDFjMHXqVABlDdrP5ebmAgCuXr2KevXqCcRJSUkBAE6dOoU5c+Zgy5YtcHJygoKCAjZt2sTuxPiRpKSkwN8cDoeduT9x4kQMGDCAjdPV1UV6ejrOnz+PkpIS7N69m43j8Xg4ePAg1qxZAxkZmQqvrbJ4MTExMAwjEPb56hEffb6DKVD5NVd2XgAYMWIEFixYAG9vbzx58gSGhoZo1apVpfkIIYQQQsjvq9Yb/507d0ZxcTE4HA5cXV0F4iwtLSElJYWYmBiRGzg9fvwYzs7OmDx5Mhv2+VuBqlBVVRXY2hsATpw4gfr16+PChQsC4bdu3cKWLVuwcuVK2NjYIDY2Fm/fvhXa+29jYwNPT0+sWLFC6Hk1NDSQkJDA/p2dnY3IyMhK61vZNSsoKMDAwACenp5o166d0DLU1NTQq1cvHDp0CN7e3hg9enSl5yWEEEIIIb+3Wm/8i4uLIyQkhP3vzykoKGDOnDmYOXMm+Hw+WrZsiaysLDx+/BiKiooYOXIkTExMcPToUdy8eROGhoY4duwYnj17BkNDw6+q14EDB9CvXz80atRIIFxPTw8LFy7EjRs34ObmhtatW6Nv377YunUrjI2N8ebNG3A4HHTu3BkLFy6EtbU1Jk+ejIkTJ4LL5cLLywv9+/eHuro62rdvj8OHD6N79+5QVlbG0qVLy30GwlTlmpcvX46JEydCU1MTXbp0QU5ODh4/foxp06axacaNG4du3bqBx+Nh5MiRX/V5EUIIIYSQX9932eFXUVERioqKQuNWrVqFJUuWYN26dbCwsEDnzp1x9epVtqH7xx9/oE+fPhg4cCAcHR2RlpYm0CNeE35+fnj58iX69u1bLk5JSQkdOnTAgQMHAABnz55F06ZNMXjwYFhaWmLevHng8XgAAFNTU9y6dQsvX75Es2bN4OTkhIsXL7JzCBYuXIg2bdqgW7ducHNzQ69evWBkZFRp/apyzSNHjoS7uzt27doFKysrdOvWDWFhYQJpXFxcoKOjA1dXV+jq6tbosyKEEEIIIb8PDvPloHTy28jNzUW9evVw6NAh9OnT50dXhxBCCCGE/GDfpeeffF98Ph/JyclYtWoVlJWV0aNHjx9dpSorKirC8uXLUVRU9KOrQn5SdI+QitD9QSpD9wip66jn/zcUFRUFQ0ND1K9fH4cPH/4my5F+L9nZ2VBSUkJWVpbIoWKkbqN7hFSE7g9SGbpHSF1X6xN+yfdnYGBQbolRQgghhBBCaNgPIYQQQgghdQQ1/gkhhBBCCKkjaNjPDxYR8e5HV+GnUlRUhGnTpiM2Ng5SUqk/ujo/nCSfJqR9qai4GDOmTkbS+0hkcrk/ujo/nIbvuR9dhZ8Kv6QUf/VuB/7ZbSiQpJ84AAg7eetHV+GnUszjY6JxA7zt7wauOPWBAoDNtfs/ugrkO6IJvz8YNf5JRajxTypDjX9SGWr8k8pQ479uoUdeQgghhBBC6ghq/BNCCCGEEFJHUOOfEEIIIYSQOoIa/4QQQgghhNQR1PgnhBBCCCGkjqDGPyGEEEIIIXUENf4JIYQQQgipI6jxTwghhBBCSB1BjX9CCCGEEELqCGr8E0IIIYQQUkdQ458QQgghhJA6ghr/hBBCCCGE1BHU+CeEEEIIIaSOoMY/IYQQQgghdQQ1/gkhhBBCCKkjqPFPCCGEEEJIHUGNf/LD+fj4wNjYCNnZ2WzY7du30L59O5iammD16lUiwwghhBBCSNVJ/OgKkOqbN28uzp07h8GDB2PVqtUCccuWLcOJE8fRp08fbNy46QfVsEybNq0RFxcHAJCSkoK6ujpsbGwxZMhgODk5s+kcHBzg7e0DBQUFNmzx4sXo27cfRo4cCTk5OZFh5Od25PhJ7DtwECkpqbAwN8OKJYtgZ2sjMv3V6zewxX07YuPiYGCgjwVzZqF92zYAgJKSEmx294DX/QeIeR8LBQV5tHRywoI5s6ClpcmWkZmZiaWr1sDz7j2IiYmhs2tHLF+0UOCeCXkTiiUrVuFVYBBUVVUxavhQTBw/tvY+CCLSnts+cL/2CElZubDW08aWEd3Q1Ki+0LQHvZ7h5KMABMcmAQDsDXWxvH+ncunfxCVj8elbePQmEqU8PszraeLf6YOhp64MACgsLsGCkzdwxvcVikp4cLE2hvuoHtBSkmfLeJ+aiemHL+FBSCTkpbgY2soeKwd0hIS4eO18EESkU9HxOBL5HqlFxTBVkMcCSyNYKytWmu96fDIWvHyDdppqcG9sxYbnl/LgHhoJr6RUZJWUop6MNAYb6GJAA102TRGPjy1vInAjIQXFfD6c1VWxyMoYalJcNk1CQSHWvA7Hs7RMyEiIo0c9LUw3NYSEGOfbfgCEfGPU8/+L0tHRwZUrV1BYWMiGFRUV4fLlS9DV1a0g5/c1Y8YMeHv74PbtO9i0aTMUFRUwYsQI7Nq1k03D5XKhoaEBDqfsCzMvLw9paWlo1aoVtLS0IC8vLzSsJoqLi7/JdZHKXb56HavXbcCfUyfjyoUzsDA3x/CxE5CaliY0/XP/F5g2ay4G9O+DqxfOopNLB0yYMg2hb8MAAAWFhQh6HYzpkyfi6vkz2LvDA+8iIzF20hSBcqbPnoewsHAcP/wPDu7dhafPnmPBkuVsfE5uLoaPGYd69XRx5fx/+GveHGzbvhMnT/2v1j4LItwZn0AsOHkdf/VuhyerJsO6gTZ6bjyM5KxcoekfhkSiv5MNrv81Fl7L/kA9VSX02HgYcemf3hq+S0qDy+r9MNNRx42/xuLp2qlY0KstpCQ/9XXNO3Ed1wLe4PjUQbi5aCwSMnMw+O+TbDyPz0efLcdQUsrD3aUTsO+Pvjj+0B8rz3rW3odBhLqRkIzNIRH4w1gfp5wdYKYoh0nPgpBWVPF3eVx+Iba+eQcHlfIPCZvfROBJajrW2prjfKsmGGpQD+uDw3Ev6dN306aQCNxPTscmewscdLRFSlERZvkHs/E8hsHU50Eo4fNxxMkOq23McCk2CbvCor7ZtRNSW6jx/4uysrKCjo4Obt68yYbdvHkTurq6sLS0ZMP4fD52796Ntm3bwMrKEt26ueH69etsPI/Hw4IFC9j4jh1dcPjwIYFzzZs3FxMn/oF//tkPJ6fmaNKkMZYtW4aSkpJK6yknJw8NDQ3o6uqiWbNmWLNmLaZMmQp3d3e8e/cOgOCwHx8fH9h+6BkePnwYjI2NRIYBwPPnzzFo0EBYWVmiZcsWWLlyBfLz89nzt2nTGjt2bMecObNha2uLxYsXVTnfrl27sGDBfNja2qBVq5Y4depfgWtLSEjAjBl/onFjB1hbN0KvXj0REBDAxt++fRs9evSApaUF2rVrCw8PD5SWllb6mf0u/jl0GIMG9MeAvn1gamyMtSuXQUZaGv87c05o+kNHjqFNq5aYOG4sTIyNMGfGdDSytMSR4ycAAIoKCjhx+AC6de0Co4aGcLCzxcqlixEY9Bpx8fEAgLDwCNx/+Agb1qyCva0tmjZpjBVLFuHy1WtISkoGAFy4dAXFJSXYtHY1TE1M0KNbV4wePgz/HDryfT4YwvK4/hij2zbBiNaNYVFPE9tH94CMlCSOPvATmv7Q5AH4w8URtvo6MNPVwO5xvcHnM7gXHMGmWf7fHbjammLN4M6wM9BFQy01dHOwgOaHXv2s/EIcue+HDUO6oK2VERwM62Hv+D7wCYvB0/D3AIA7geEIiUvGgUn9YauvA1dbUyzp64J9d3xRXIf+H/4ZHIuMQx89HfSqrw0jBTkstjKBtLgYLsQmiszDYxj89fINJpnoo76sTLn4gIxsdK+nhaZqyqgnK41+DXRgqiCPoKyyh8icklKcj03EHPOGcFRTgaWSAlZamyEgMxuvMsrSeKdm4F1uPtbamsNcUR4tNVQx2VQfp2PiUcLn186HQcg3Qo3/X1i/fv1x9uwZ9u8zZ/5D3779BNLs2bMbFy6cx8qVq3D9+g2MHj0as2fPgq+vL4CyhwNtbW1s374dN27cxNSp07BlyxZcvXpVoBwfHx/ExMTg+PET2LhxE86dO4uzZ8/WqN6jRo0CwzC4c+d2uTgHBwfcvn0HALBz5y54e/uIDIuOjsaYMaPh6toZV69ehYeHB54/98OKFcsFyvznn39gbm6BS5cuYcqUqVXOd/DgATRqZI2LFy9h6NBhWLp0KfvAkpeXhyFDhiApKQl79+7D5ctXMH78BPA/fOk/e/YMc+fOwahRI3Hjxk2sWrUa586dxa5du2r0mf1qiouLEfg6GC2dm7NhYmJiaOnsBP/PHpA+5x8QgJbOTgJhrVu2gP+LlyLPk5OTAw6HA0VFRbYMRUVF2Fg3YtO0dHaCmJgYXrx8VZbmRQAcmzQBl/vp9X3rVi0QERmJrKysal8rqZni0lK8iIpHOysjNkxMTAztrYzg+6ERXpn8ohKU8HhQkStr4PH5fNx4GQpjbXX02HgY+pPXofWyPbj0/FOP7YvIOJTweALnNdPVgJ6aEnzDYgAAvuExsNLTEhgG1NHaGNkFRQiOTf6q6yZVV8LnIyQ7B80/DNcCADEOB83VlfEqM0dkvr3h0VCRkkQfPR2h8XYqirifnIakwiIwDIOnaZmIziuAk7oKACA4OwelDAPHD38DgKG8LHSkpfAys6zx/zIjGyYKcgLDgJzVVZBbykN4Tj4I+ZlR4/8X1rNnTzx//hxxcXGIi4uDn58fevbsycYXFRVh9+7dWLduPVq3bo0GDRqgb99+6NmzF9uLLSkpiRkzZsDa2gZ6enro2bMn+vbth2vXrgmcS0lJCcuWLYeRkRHat2+Ptm3bwdv7SY3qraysDDU1NcTGxpWL43K5UFNTY8+poaEhMmzPnj3o0aMHRo8eDQMDQzg4NMbSpUtx/vx5FBUVsWU6OTlh3Lhx0NfXh76+fpXztWnTFsOGDYOBgQH++OMPqKiosG8cLl++hPT0dOzevQdNmjSBgYEB3Nzc4ODgAADYvt0Df/wxEX369EWDBg3QsmVLzJgxs9zbg99VRkYmeDwe1NXVBcLV1dWQkpIqNE9KairU1dW+SK+OlFTh6QuLirBu81b06NYVCh+GgaWkpEJdTVUgnYSEBJSVlNhyhJ+n7O9kEXUj315qTj54fL5AAxsANBXlkZQpfNjPlxafvgkdFQW0/9CQT87OQ25hMbZcfoCO1ia4NH8UejSxwGCPf/EwJBIAkJSVC66EOJTlBHuENZXkkfRhuFFSZm75en34O0nEkCTy7WUUl4DHAGqfPagDZX+nihj245+ehfPvE7GskanIchdYGKOhvCw6efmiyc1HmPwsEH9ZGaOxqjIAIK2oBJIcDhQlBadFqkp9Om9acTFUpb6o14e/02h4KfnJ0YTfX5iamhratWuHs2fPgmEYtG3bDqqqnxo+0dHRKCgowKhRIwXylZSUCAwNOnbsGM6c+Q/x8QkoKipESUkJLCwsBPKYmJhA/LOJbpqaGggNDQUA7Nq1C3v27Gbjbty4Wem8A4Zh2DH+NfXmTQjevAnFpUuXBMrl8/l4//49jI2NAQDW1tY1ymdubsbGczgcaGhoIO3DePXg4BBYWlpCWVlZaN1CQt7Az88Pu3d/6unn8XgoKipCQUEBZGTKv4omVVdSUoIpf84CwzBYs2LZj64O+QE2X76PMz6BuPHXWEhzJQEAfIYBAHRrbIFpXVoAAGz1deAT9h7/3H2KVhaGP6y+pPbllZZi0as3WGZtCpUP94Qw/0bH4VVmDv52sIKujBT8MrKw9nU4NKS4aP5Zbz8hvytq/P/i+vXrzw5XWb58uUDcxzHs+/f/Ay0tLYG4j0Merly5jPXr12Hhwr9gb28POTk5/PPPfrx8KTjUQkJC8FbhcDjg88t+aIcMGYKuXbuycZqamqhIRkYG0tPToacnfEWPqsrPz8fgwYMwYsTIcnGfP3zIyMjWKJ+EhOCPR9k1lw3rkZaWrqRuefjzzz/RqZNruTgpKakK8/4OVFSUIS4ujtQveu1TU9OgoaEuNI+GujpSU9O+SJ8KjS/eHnxs+MfFxePfo4fYXn8A0NBQR2paukD60tJSZGZlseUIP0/Z35oi6ka+PXUFWYiLiZXrSU/OzoWWcsUT+t2vPsKWKw9xZf5oWDfQFihTQlwM5roaAunNdTXw5G00AEBLSR7FpTxk5hUI9P4nZ33q7ddSlsfzd7GC9fpQzy/fCJDao8KVhDinfE96WnEx1L/odQeA9/mFiC8ownS/IDbsw88UHG48wMVWTaEhzYXH2yhsc7BEa82yN36mivIIzc7DkchYNFdXgZqUJEoYBtklpQK9/+lFn86rxuUi6IuhRx8nIX/5poKQnw01/n9xrVu3/jDxloNWrVoLxBkbG4PL5SI+Ph6Ojo5C8/v5+cHBwQHDhg1jw2JiYqpVB2VlZZE94MIcOXIYYmJicHHpWK3zfMnKygrh4eEwMDD4Lvk+Z25uhv/97zQyMzOFXruVlRXevYv8qnP8yrhcLqytLPHY2weuHV0AlI3Hfuztg5HDhgjN42Bnh8fePhg7agQb9vCJNxzsbdm/Pzb8I6OjcerYYaioKJcrIzs7G4FBr2HdqGxpvyc+vuDz+bD/MGncwd4Om7a5o6SkBJKSZQ94jx4/gZGhIZSUlL7ZZ0AqxpWQgL2BLu4Fv0OPJmVvIvl8Prxev8PEjsK/rwBg65WH2HjpHi7NG4XGDeuVK7OxYT2EJQo+dIYlpqLBh3Hj9ob1ICkujnvB79Cradk98jYhBe/TsuBo0gAA4GjcABsv3kdyVi473MczKAKKMlKwqFdx5wb5diTFxGChqADftEy01yp7MOczDHxTMzFIv/zbZUM5WZxp2VggbOfbKOTxeJhnYQRtGSkU8fgoZRiIffHmWYzz6c2RpaICJDgcPE3LgIt22YNkVG4+EgqLYPthiVFbFUX8ExGDtKJidriPT2om5CXEYSQv2OFEyM+Gxvz/4sTFxXHjxk3cuHFDYFgOAMjLy2PcuHFYu3YNzp07i+joaAQFBeHo0SM4d65ssq6BgQECAwPx4MEDREZGYtu2rXj16tU3q19eXi5SUlIQHx+Pp0+fYtGiv7Bz507MmjX7qxvGEyb8AX9/fyxfvhzBwcGIiorE7du3y70B+Vb5PtetW3doaGhg0qSJ8PN7jpiYGNy4cQP+/v4AgKlTp+HChfPw8PDA27dvER4ejitXLmPr1i1fccW/lnGjR+HU/87gzLkLCAuPwKJlK5BfUID+fXsDAGbOXYANm7ey6UePHI77Dx9h34FDCI94h20eOxAYFISRw4YCKGv4T5o+A6+CXuPvzRvB4/GQnJKC5JQUdglXE2MjtGnVEvMXL0XAy1d45uePpStXo7tbV3YvgJ7d3cCVlMS8v5bgbVgYLl+9joNHj2Pc6PJvgkjtmt6lBQ7de47jD/3xJi4Z0w9fQn5RMYa3LmvAjdtzBktP32LTb7nyACvP3sGe8X3QQF0ZiZk5SMzMQW7hp7k6M9xa4YxPEA56PUNEUhp23/bBtRehmNChGQBASVYaI9s0xvwT13A/+B38I+Pwx75zcDTWQzNjPQCAi7UxLOppYtzeM3gVnYDbr8Kw8swdTHBxFFgylNS+4Yb1cO59Ai7FJuJdbj5Wvw5DAY+PXvXL3vgsevkGf4eWzeeQEheDiYKcwKEgKQE5cXGYKMhBUkwM8pISaKKqhK1v3uFZWiZi8wtwMTYRV+KS0eHDA4aCpAR619fG5pB3eJqWieCsHCwNfAtbZUXYfFg61EldBQ3lZbHoVShCs3PxOCUdO8KiMLCBLrji1LQiPzf6FvsNfL451pdmzpwFVVVV7NmzB+/fv4eCggKsrKwwadJkAMCgQYMRHByMP/+cDg6Hg27dumPo0GF48OD+N6mbu7s73N3dISnJhYaGOuzs7HD06DE4OTlVnrkS5ubmOHnyX2zZsgWDBw8CwzBo0KABunZ1q5V8n+NyuTh8+AjWrl2LsWPHgsfjwdjYGMuXrwBQ9kZm37792LFjO/bt2wsJCQkYGRmhf/8BX3XNv5Lubl2Qlp6OrR7bkZKSCksLcxw9sJcdfhOfkAAxsU8/kk0c7OGxZSM2u3tg01Z3GBjoY9/O7TAzNQEAJCYl47anFwCgS88+Auc6dewwnBzLGnceWzZiyco1GDJqDMQ4ZZt8rVj8F5tWUUEBxw7+gyUrVqFb7/5QUVHBn1MmYciguvNv87Po19waKTl5WHXWE0lZubBpoIMLc0eyQ2vep2UK9NDu93yK4lIehngITpz/q3c7LO7TAQDQs4klPEb3wObLDzDn2FWY6Kjj5PTBcDYzYNNvHNoFYhwOhnj8i6KSUrjYmMB9ZHc2XlxMDGdnD8P0Q5fRbuU+yElJYmhLeyzt26EWPw0iTGcdTWQUl2BXWDRSi4phpiiPXU0bsb3tiYVF5XrxK7PBzgJ/h0Zi4cs3yC4phY6MFKaaGqB/g0+rA821MIIYB5j9IvjDJl8qWGRlwsaLczjY3qQR1rwOwwjvAMiIi6N7fS1MNjH4JtdNSG3iMMyH91zfslAOB+fPn0evXr2+ddEC7t27h3bt2iEjI4MdenHhwgXMmTMHkZGRmDZtGuzs7DBjxgxkZmbWal1qKiLi3Y+uAvmJSfKLKk9E6jQNX+H7JhDyUdjJW5UnInWazbVv0+FHfg3VfjeVmJiIadOmoWHDhpCSkoKenh66d+8OT8/vv/Ohs7MzEhISBMbp/vHHH+jXrx/ev3+PVatWYeDAgXj79u03P3dJSQnmz58Pa2tryMnJQVdXFyNGjED8h82GCCGEEEII+dlUa9hPVFQUWrRoAWVlZWzatAnW1tYoKSnBzZs3MWXKFLx586a26ikUl8uFtvanlR5yc3ORnJwMV1fXL1Z7+bplFT+fGPhRfn4+/P39sWTJEtja2iIjIwN//vknevTogefPn3/V+QghhBBCCKkN1er5nzx5MjgcDp4+fYq+ffvC1NQUVlZWmDVrFrv5kTDz58+HqakpZGVl0bBhQyxZsuTDCjVlXr58iXbt2kFBQQGKiopo3Lgx24COjo5G9+7doaKiAjk5OVhZWbEbUN27dw8cDgeZmZm4d+8eO/a9ffv24HA4uHfvHg4fPlxuNZaLFy/CwcEB0tLSaNiwIVasWIHSz7Zs53A42L17N3r06AE5OTmsWbOm3DUpKSnh9u3bGDBgAMzMzNC8eXPs2LEDfn5+1V4thxBCCCGEkO+hyj3/6enpuHHjBtasWQM5Obly8RUt9aigoIDDhw9DV1cXgYGBGD9+PBQUFDBv3jwAwNChQ2Fvb4/du3dDXFwcAQEBbE/7lClTUFxcjAcPHkBOTg7BwcGQly+/zrKzszNCQ0NhZmaGs2fPwtnZGaqqqoiKihJI9/DhQ4wYMQIeHh5o1aoVIiIiMGHCBADAsmWfNgtavnw51q9fD3d393Jr3IuSlZUFDodTrWUvCSGEEEII+V6q3PgPDw8HwzAwNzev9kkWL17M/reBgQHmzJmDU6dOsY3/mJgYzJ07ly3bxOTTjPqYmBj07duX3aW1YcOGQs/B5XLZzaVUVVUFhgN9bsWKFViwYAFGjhzJlrdq1SrMmzdPoPE/ZMgQjB49usrXWFhYiPnz52Pw4MFQVFSscj5CCCGEEEK+lyo3/r9mUaDTp0/Dw8MDERERyM3NRWlpqUADedasWRg3bhyOHTsGFxcX9O/fH0ZGRgCA6dOnY9KkSbh16xZcXFzQt29f2NjY1LguL1++xOPHjwWG8vB4PBQWFiI/Px+ysmWbczRp0qTKZZaUlGDAgAFgGAa7d++ucd0IIYQQQgipTVUe829iYgIOh1PtSb3e3t4YOnQounbtiitXruDFixdYtGgRuykPUDbE5vXr13Bzc8Pdu3dhaWmJ8+fPAwDGjRuHd+/eYfjw4QgMDESTJk2wffv2atXhc7m5uVixYgUCAgLYIzAwEGFhYZCWlmbTCRvaJMzHhn90dDRu375dZ3v9fX190bJlix9dDaHatm2Dx48f/+hq1Ak79+xD9z4DYGnfBA7NW2L8pKmIeBdZpbzu23fizznzRMZfvX4D7V3dYNrIDp269cTde1Vbmu7MuQvoO2iY0Lhnz/3QZ9BQ2DZzgqm1Pdq7uuGfQ0eqVC6pmdXnPCE7fLHAYTfPvUp515y7izG7/xMZf843CHbz3KEyZjmaLtyOGwGhVSr3+EN/dFi1T2T8g5B3cFq8E8qjl6HR7K049sC/SuWS6tsdFgXb6w8Ejp4PnlUp756waCx8KbqNcishBT0fPEPTmw/R9+FzPExOr1K5l2ITMdInQGT8s7RMDHzsjyY3HqLb/ae4GJtYpXIJ+VGq3POvqqoKV1dX7Ny5E9OnTy/XOM7MzBQ61v3JkyfQ19fHokWL2LDo6Ohy6UxNTWFqaoqZM2di8ODBOHToEHr3LtsJVE9PDxMnTsTEiROxcOFC7N+/H9OmTatq1QU4ODggNDQUxsbGNcr/uY8N/7CwMHh5eUFNTe2ry/yWiouLweVyER8fL7D6UW24c+cO2rev2gY4PB4PHA5HYIOn2vLmzRtkZWWhWbNmVUovbGUnYb7HZ/or8n32HCOGDYatdSOUlvKwcas7ho8ZhzvXLrNv1US55XkXkyeMExr33P8Fps2ai3mzZ6BD27a4eOUqJkyZhqvnz7KbgFVUbscO7YTGycjKYOSwIbAwM4WMjCye+fnhr6UrICsjQ5t+1SLLepq4suDTsEqJKu6IesU/BHO6txYa5/M2BiN3/Q8rB3REFzsznPZ+hYHuJ/Fk1WRY6WlVUu4buNlbCI2LSk5Hn83HMK5DMxya1B9ewe8w+cAFaCsroKNNxfceqRkjeVnsa/bpDb94FTfx8kpOw5iGekLjAjKysOBlCKabGqK1phquxSdjhv9rnGrhABOFijv7vJLT0FZT+O97bH4BpvoFob+eDtbZmsM3LQMrgt5CXYqLFhqqVao3Id9btVpfO3fuBI/HQ7NmzXD27FmEhYUhJCQEHh4eIndsNTExQUxMDE6dOoWIiAh4eHiwvfoAUFBQgKlTp+LevXuIjo7G48eP8ezZM1hYlH0Rz5gxAzdv3kRkZCT8/f3h5eXFxtXE0qVLcfToUaxYsQKvX79GSEgITp06JTAvoSpKSkrQr18/PH/+HCdOnACPx0NiYiISExMF3mp8T0OGDMHy5cuxevUqNG3aBKNHjwIAzJ07B126dMb+/fuQnJxcaTl+fs8xZMgQNGpkBQcHe4waNQpZWVkV5vH0vIMOHYQ3/s+ePQN7ezvcuXMHrq6usLS0qNF+CNnZ2Vi8eBEcHZvB0tICXbp0xt27dyvMc+fObbRu3Vpkg97Y2AgnTpzAhAkTYG3dCLt27RJZVnx8PHbt2gkXlw5YtWqlQNx///2Hzp07w8LCAk5OzbF8+fJqX9/v4OiBfejfpzdMTUxgaWGOLRvWIi4+AYGvgyvMF5+QgLCwcLRp3Upo/KEjx9CmVUtMHDcWJsZGmDNjOhpZWuLI8RMVlltYVISHjx/Dpb3wxn8jS0v07OYGUxMT6NWvhz49e6B1yxZ4+tyvahdMakRcXAzaygrsoV5J4wsAYtMyERKXLLLBvfPWE3S0McFMt1Ywr6eJZf1cYGeggz13RK9EBwCFxSXwDAyHm4Pw+Wz/3H0GAw0VrB/SBeb1NDGpY3P0bmqF7TeeVH6hpEYkOByoS3HZQ4VbeYdMYkEhInLy0EJdRWj8iah4OKurYlRDPTSUl8VUUwNYKMrjVHTFv0VFPD68UzNENv7/i0lAPRlpzLEwQkN5WQzWrwcXbQ0cj4qr/EIJ+UGqtc5/w4YN4e/vjzVr1mD27NlISEiAhoYGGjduLHKse48ePTBz5kxMnToVRUVFcHNzw5IlS9jGkbi4ONLS0jBixAgkJSVBXV0dffr0wYoVKwCU9RJPmTIFsbGxUFRUROfOnbFt27YaX7CrqyuuXLmClStXYsOGDZCUlIS5uTnGjRPe4yhKXFwcLl26BACws7MTiPPy8kLbtm1rXMevcf78OQwZMgSnT/+PDfPw2I4rVy7j/Pnz2Lx5M1q2bIk+ffrAxaUjpKSkBPIHBwdj+PDh6NevP5YsWQJxcXH4+PiAx+OJPOfbt2+RlpYu8gEQKJsQvW/fXqxbtxbKyipQU1PDxYsXsWRJxQ9dBw4cRNOmTcHn8zFmzBjk5eViy5ataNCgAcLDwyFeSY+hp6cnxowZU2EaD4+/MXfuPCxevLjcyk75+fm4efMmzp8/Bx8fH9jZ2WHs2LHo2tWNTXPixAmsXbsGc+fORZs2bZGTkwM/P2o8AkBOTg4AQPmzjfiEue3pheaOzaAgZCUvAPAPCMC4Dw+zH7Vu2QK37lT88Pf4iQ+0tbRgbCR8oYAvBQUHw//FC8ye8WeV0pOaiUhMQ8NpGyAtKQFHYz2sHNAJeurKFea54v8GrS0MoSgjLTTeN/w9pncWHHroYm2CK34hFZbrFfwOuiqKMNPVEFFuDNo1MhIs18YY845fq7BcUnPR+QVwuesDrpgYbJUVMN3MEDoi/t0/upecjiZqypCXFN6seZWZjeEG9QTCnDVU4JWUVmG5vmkZ0JSWgqG88DeXrzKz0VxN8IHDWV0Fm0IiKiyXkB+pWo1/ANDR0cGOHTuwY8cOkWm+nBy8ceNGbNy4USBsxowZAMpW6fn3339FllXR+P62bdsKnEtZWbncuUeNGoVRo0YJhLm6usLV1bXK9RfGwMCg2pOgi4qKUFRUVC7sywb419DXN8D8+QsEwtTU1DBy5CiMHDkK4eHhOHfuHNatW4clS5bAzc0Nffr0hb29PQBg//59sLa2xsqVn3q2TU1NKzznnTt30KpVK3C5XJFpSkpKsGLFSoG3Nh06dICtrW2FZX9ctenx48d49eolbt68BUNDQwBAgwYNKsybmJiIN29C0aZN2wrTde/eA/369RMI8/X1xfnz53D9+nWoqqqhZ8+eWLVqNfT19cvl37VrJ8aOHYtRoz4NY/iaSem/Cz6fjxVr1qOJg0OlQ3Nue95Fxw7tRcanpKZCXV2w501dXR0pqamVliuq1/9zjq3aIT09HaU8HmZMm4LBA/pVmofUTFMjPeyb0BcmOupIzMzB2vN34bJ6P56vmw4FGdHfhVf9Q+DmIPqtb1JmLjSVBN8gaCrJIykrp8L6XPELEdnrDwBJWbnQVBR8KNVUlEd2QREKiksgU4VeaVJ11sqKWGVtBgM5GaQUFWNveAxG+7zE2VaNIVfBstteSalop6UuMj61qBhqUoK/UWpcLlKLKn5Tf6+CIT9l5ZZATUrwHlDjcpFbykMhjwdpcfEKyyfkR6h245/U3Lp169g3Gh9NmzYdf/757XoZGzWyqjDe2NgY8+bNw5w5c7B//364u2/7MBE7AAAQHByCLl26VOucnp53MGzY8ArTSEpyyy0TKy8vL3TPBmFCQoKhra3NNvyrVi9PNGnSuNJJ2B+Xkf3c0KFDIC0tjUWLFmHw4CEi86alpSIpKQlOTs5VrlddsWTFKrwNC8OZf49XmC4nNxe+T59h49pV3/T8DMPgjpcXdrlvrTTtfyePIT8/Hy8CXmL9lq0w0G+Ant3cKs1Hqs/V9lNngnUDbTQ1qg/zmZtx1jcQo9oKX2Utu6AQD99EYfe43t+0LgzD4NqLNzg+bdA3LZfUXMvPxsmbouxhoMs9X9xMSEEfPR2heXJLSuGXnoUV1hV3VFUXwzC4n5yOTXY1H2pMyM+IGv/f0cKFCzFr1iyBsNjYbzsusLJJlfHx8bh06RIuXLiA2Nj36NKlC/r2/dTLKS1dvbcQycnJCA4ORrt2FfeuSktLgfPFpK3qDPv5fCWmqqpoHsLnZGVlyoXt27cf586dw6pVq/Dvv6fQq1cvdO/eHRoagkMDpKSqX6+6YMmK1fD0uo//nTgKHRF7bnx07/5DGBsbQVdH+A87AGioqyM1VfD1fGpqKjTURff0Bbx6BV4pD40d7CutbwO9+gAAczNTpKSlwX37Tmr8fyfKcjIw1lbHuyTRK6/cehkG83qaqK+mLDKNlrI8krPyBMKSs3KhpaQgMs+ziFjw+Hw0NxH9FlFLSR7J2bmC5WbnQlFGinr9vwNFSQnoy8ngfX6hyDSPUtPRUF4W2hUMDVKX4iLti17+tOJiqEuJfmMdmJUDHp+BrYroDiR1KUmkFZWUK1deQpx6/clPixr/35GUlFS5IT5SUhUPW/gWcnNzcfPmDZw/fx5Pnz6Fg4MDxowZgy5dukBBQfCH0dzcHN7eT9hhWZW5e9cT9vYONdrVuDrDfszMzJGYmIjIyMgq9f7n5eXBx8cHK1fWrDe5ffv2aN++PbKysnD58mWcP38OGzasR4sWLdCrV2907NgRMjIykJeXR/369eHt/aTCOQ91BcMwWLpyDW7evoPTxw+zjeqK3PK8i04VDPkBAAc7Ozz29sHYUSPYsIdPvOFgL/r+uX3nLtq3bQPxav4AM3z+D5u0XxflFhYhMjkd2i3sRKa54h+CbhUMzQEAR2M9eL2OwNTOn97C3Q0KRzMT4au/fCy3s60ZxCtYeczRuAFuvnwrEHY3KALNjCsedki+jfxSHt7nF8JNV3Qj/V5SGtppVbzano2yInzTMjHM8NN3kk9qJmyURTfs7yWloZWmaoWrDdkoK+JRiuCDq09qRoXlEvKj1f5ai+SHmzRpIrZv344mTZrg9u07OHXqNAYMGFCu4Q8AEydOwqtXgVi6dCnevHmDiIgInDhxAunpwnvlPD09q9S7Loy8vDwMDAwqPD72+Ds6OqJp06aYMmUyHj16hPfv3+P+/Xu4f1/4Wu8PHjyAoaEh6tevvPFZESUlJQwbNgxnz57DtWvXYW5ugQ0bNmDOnNlsmunTp+PAgQM4cuQwoqIiERQUhKNH6+Za8YtXrMKFS5fhsXUT5OTkkJySguSUFBQWCu+1Ky0txb0HD+FSSeN/9MjhuP/wEfYdOITwiHfY5rEDgUFBGDlsqMg8t+96VTre/8jxk7hz1wuRUVGIjIrCqf/OYt+BQ+jVo3vlF0tqZOHJ63gYEonolAz4vI3BIPeTEBfjoL+T8HkypTwebr18W+F4fwCY0skZtwPD8Pe1RwiNT8Hqc57wj4zHRJfmIvNc83+DrpU8VIxr3xSRyelY9O8NhManYO8dX5z1DcK0zjTUrzZsefMOz9MyEZdfiICMLMz0fw1xcNBFR/iE7FI+g0cpolfj+WiogS6epGbgSGQsInPzsTssCq+zcjBIX/SSzfeS09CmknL7N9BBbEEhtr15h8jcfJyOjsetxBQM+2JyMSE/E+r5rwNWrFgJQ0PDcsNuhDE0NMThw4exZctm9OnTG9LS0rC1tUX37uUbQ/n5+Xjy5AkWLareMqk1tXPnLqxfvw4zZ85Afn4+9PX1MXeu8E2h7typ2pCf6jAyMmLnS3y+V0WfPn1RVFSEQ4cOYf369VBRUUHnzp2/6bl/FcdPngIADBw2UiB88/o16N+n/Hhtn6fPICcrC2srywrLbeJgD48tG7HZ3QObtrrDwEAf+3ZuFzmRODomBtHRMWjTquKN5/h8PjZs2Yb3sXGQEBdHgwZ6WDB3NobSGv+1Ji49GyN3/Q/puflQV5CDs6k+7i37AxqKwpf7fPgmCnLSXNgbVLyvRnPTBjg8aQBWnLmDZf/dhrGWGk7PGCJyjf93SWmISE5HR+uKJ6MbaKri3JzhmHfiOnbe8kY9VUXsGtuL1vivJUmFRVjw8g0yi0ugwpWEvaoSjjnZQVXE8By/9EzISojBooLhXQBgp6KEdbbm2BEWhe2hkWggJwN3ByuRa/y/zyvA+/wCOItYOvSj+rIy2NG4ETaFROBEVBy0pKWwrJEprfFPfmocprpL1vwAO3fuxKZNm5CYmAhbW1ts375d5KZNbdu2Fdob3LVrV1y9epX9OyQkBPPnz8f9+/dRWloKS0tLnD17ll1BprCwELNnz8apU6dQVFQEV1dX7Nq1C1pan35IYmJiMGnSJHh5eUFeXh4jR47EunXryi0XWZGIiHdVTvuzuXnzJrZu3YqbN2/+6KoIKC0tRfPmjjhw4GClw4p+dpL8osoT/cKWrVqDUh4Pa5Yv/abl7j94GI+eeOPIP3u/abk/Iw3fcz+6CrVq9tErKOXz8feoHt+0XI/rj3E3KAIX5o6oPPEvLuzkrR9dhVqzPjgcPIbBIqtv+zB2NDIWvmkZ2Nmk/IIQvyOba1XbMZ38Hn76nv/Tp09j1qxZ2LNnDxwdHeHu7g5XV1eEhoZCU1OzXPpz584JjNdNS0uDra0t+vfvz4ZFRESgZcuWGDt2LFasWAFFRUW8fv1aYFLpzJkzcfXqVfz3339QUlLC1KlT0adPHzx+/BhA2f4Dbm5u0NbWxpMnT5CQkIARI0ZAUlISa9eurcVP5OchKyuLefOE97z/SFlZmRg9ejQtt/kLMDMxgYO93TcvV0dbC1P+GP/NyyXfn2V9LThWMG6/puqpKmJuD+G7BZNfh7G8XIUTcmtKS1oKYxvSvA7ye/rpe/4/jvX+uK8An8+Hnp4epk2bhgULFlSSG3B3d8fSpUuRkJAAObmy13uDBg2CpKQkjh07JjRPVlYWNDQ0cPLkSXb99zdv3sDCwgLe3t5o3rw5rl+/jm7duiE+Pp59G7Bnzx7Mnz8fKSkpFa55/7lfueef1L7fveeffL3fveeffL3fueeffBvU81+3/NQTfouLi+Hn5wcXFxc2TExMDC4uLvD29q5SGQcOHMCgQYPYhj+fz8fVq1dhamoKV1dXaGpqwtHRERcuXGDz+Pn5oaSkROC85ubmaNCgAXteb29vWFtbCwwDcnV1RXZ2Nl6/fv01l00IIYQQQkit+Kkb/6mpqeDxeAINbADQ0tJCYmJipfmfPn2KoKAgjBs3jg1LTk5Gbm4u1q9fj86dO+PWrVvo3bs3+vTpw84VSExMBJfLLbd85efnTUxMFFqvj3E/Cx8fHxgbGyE7OxsAcPbsGdjXwjCLusLY2Ai3b1MvGiGEEEJ+TT/9mP+vceDAAVhbWwtMDubz+QCAnj17YubMmQAAOzs7PHnyBHv27EGbNm1+SF0rc/LkSWzYsB5+fv7shOK8vDw0buwAB4fGOHnyJJvWx8cHw4YNhafnXTg4OMDb20fosp6E1LYjx09i34GDSElJhYW5GVYsWQQ7W9FzMQ4cPorj/55CXHwCVFVU0LVzJ8ybPRPSH/bH8H32HHv/OYjA16+RnJyCfTs94Nrx0xu6kpISbHb3gNf9B4h5HwsFBXm0dHLCgjmzoKX1aY5Qi3YuiI2LFzj3/NkzMZnmCXx3e277wP3aIyRl5cJaTxtbRnRDUyPRS/Se8w3CyrN3EJ2aCWMtNawa2Amd7cwE0ryJS8bi07fw6E0kSnl8mNfTxL/TB0NPXRnpuflYfe4uPAPD8T4tE+qKcujuYIGl/VygJPtp3pfs8PKrmB2ZPEDkkqSk9pyKjseRyPdILSqGqYI8FlgawbqCdfRvJaRgZ1gU4gsK0UBWBjPMGqKVpuDqO+9y8+Ee+g5+6VkoZRgYyctii70ldGSkkVVcgl3h0fBOzUBiQRFUuJJop6WGKSYGUJD81Gyyvf6g3LnX25qji275+YiE/Ex+6sa/uro6xMXFkZSUJBCelJTEbv4kSl5eHk6dOoWVK1eWK1NCQgKWloJLC1pYWODRo0cAyjaWKi4uRmZmpkDv/+fn1dbWxtOnT8vV62Pct9a8eXPk5eUhMDAQ9vZlO5Y+f/4M6urqePkyAEVFRewGYj4+PtDV1YW+vj4AlNuVlpDv4fLV61i9bgPWrFwGO1sbHDx8DMPHToDXzatQVyu/dvaFy1ewYfNWbFy3Go3t7REZFYXZC/4CwMHSv+YDKFte1sLcDAP69sEfU6eXK6OgsBBBr4MxffJEWJibIys7GytWr8XYSVNw5dx/Amln/TkNgwd82t1aXk74kn+k9pzxCcSCk9fhMboHmhrpYceNJ+i58TACNs6AppJ8ufQ+b2Mwctf/sHJAR3SxM8Np71cY6H4ST1ZNZpf0fJeUBpfV+zGydWMs7tMeijJSCI5LhtSHRltCRg4SMrKxdnBnWNTTQExqJqYfvoSEzBycnD5Y4Hx7x/cRWNJTWZZ29P7ebiQkY3NIBBY3MoG1kgJORMdh0rMgXGzdBGpClv8MyMjCgpchmG5qiNaaargWn4wZ/q9xqoUDu6zn+7wCjPIJQO/62phkbAB5CXFE5OaD+2Gzt+SiYqQUFmOWWUMYycsivrAQq4PCkVJYjC0Ogm2HldaCy3oqVGO1P0J+lJ962A+Xy0Xjxo3h6enJhvH5fHh6ela6m+p///2HoqIiDBs2rFyZTZs2RWhoqED427dv2cZy48aNISkpKXDe0NBQxMTEsOd1cnJCYGAgkpOT2TS3b9+GoqJiuQeLb6Fhw4bQ1NSEr68vG+br6wsXl46oX18PL168EAhv3rxsY5svh/0I4+npid69e8HS0gJNmzbBpEkT2bisrCzMmTMbDg72aNTICmPGjEZUVCSAspWUmjd3xK5du9j0/v5+sLAwx5MnjyvND3wahvTgwQO4unaCjY01Ro8eJfC5funjNXl5ecHNrSssLS3Qt29fvH0bKjLP5s2b0bdvn3Lh3bq5Yfv27QCAV69eYeTIEWjatAns7GwxePBgBAUFVVqPzz/b4OBgGBsbITY2lg17/vw5Bg0aCCsrS7Rs2QIrV65Afn6+yHJ/F/8cOoxBA/pjQN8+MDU2xtqVyyAjLY3/nRE+QdXPPwCNHezRq3s36NWvh9YtW6CHW1e8fBXIpmnXpjXmzvwTnTu5CC1DUUEBJw4fQLeuXWDU0BAOdrZYuXQxAoNeIy5esKdfXk4Omhoa7CErK/vtLp5Uicf1xxjdtglGtG4Mi3qa2D66B2SkJHH0gZ/Q9DtvPUFHGxPMdGsF83qaWNbPBXYGOthzx4dNs/y/O3C1NcWawZ1hZ6CLhlpq6OZgwT5MWOlp4d8/h8DNwRwNtdTQ1soIy/t1xLUXb1DK4wmcT0lWGtrKCuwhzZWsvQ+DCHUsMg599HTQq742jBTksNjKBNLiYrgQK3x47YmoeDirq2JUQz00lJfFVFMDWCjK41T0p///t4dFoaWGKmaaN4SFkjz05GTQVkuNfZgwUZDDVgdLtNVSg56cDBzVVDDN1AD3k9NQyhdcI0VBUgLqUlz2kBL/qZtVhAD4yRv/ADBr1izs378fR44cQUhICCZNmoS8vDyMHj0aADBixAgsXLiwXL4DBw6gV69eUBPSwzh37lycPn0a+/fvR3h4OHbs2IHLly9j8uTJAMp2dR07dixmzZoFLy8v+Pn5YfTo0XBycmIb1Z06dYKlpSWGDx+Oly9f4ubNm1i8eDGmTJnC9sB/a82bN4ePz6cfOR8fHzg6OsLRsRkbXlhYiJcvA9h6VsbLywuTJ09CmzZtcenSZRw9egw2Np/Wxp83bx4CA4Owd+8+/PffGTAMg7Fjx6KkpARqampYv349tm/3QGDgK+Tm5mL27DkYNmw4nJ1bVJr/o8LCQhw48A82b96Mkyf/RXx8AtavX1dp3devX4+FC//C+fMXoKqqigkTJgiU+7kePXrg5cuXAptzvX37Fm/evGE3MMvLy0Xv3n1w6tRpnDlzFgYGBhg3bixyc3Or9FkKEx0djTFjRsPVtTOuXr0KDw8PPH/uhxUrlte4zF9BcXExAl8Ho6Xzp/tQTEwMLZ2d4B8QIDRPYwc7BL0ORsDLVwCAmJj38Lr/EO3atPqquuTk5IDD4UBRUXCYwO59+2HbzAldevbBnn8OoLS09KvOQ6qnuLQUL6Li0c7KiA0TExNDeysj+Ia/F5rHN/w92n+WHgBcrE3wNKwsPZ/Px42XoTDWVkePjYehP3kdWi/bg0vPgyusS1ZBIRRlpCAhLi4QPvPoZehNWotWy3bjyH0//OSL4/12Svh8hGTnoLm6MhsmxuGguboyXmXmCM3zKjMbzdWUBcKcNVTwKrOsk4bPMHiYnA59ORlMfBaItp7eGPrkBe4mpVZYl9zSUshLSEBCTHCzzLWvw9HmzhMMefIC598n0j1Cfgk//fupgQMHIiUlBUuXLkViYiLs7Oxw48YNdnJtTEwMxMQEn2FCQ0Px6NEj3LolfGJm7969sWfPHqxbtw7Tp0+HmZkZzp49i5YtW7Jptm3bBjExMfTt21dgk6+PxMXFceXKFUyaNAlOTk6Qk5PDyJEjyw0z+pYcHZtjzZrVKC0tRWFhIYKDg9GsWTOUlpbg5Ml/AQAvXvijuLgYzZtX/Gbko127dsLNrRtmzJjBhllYWAAAoqIi4el5B//73//g4NAYALB16za0atUSt2/fRteuXdG2bTsMGDAQs2bNgrW1NWRlZTBnzpwq5wfKxmmvXLmKffMyfPhw7NixvdK6T58+jf0327RpE1q2bIFbt27Bzc2tXFpTU1NYWFjg8uVLmDp1GgDg0qVLsLW1g4GBAQDAyclZIM+aNWtgb2+Pp0+fon379lX6PL+0Z88e9OjRg31YNTAwxNKlSzFkyGCsXLmq1h4Uf7SMjEzweDyoq6sLhKurqyHinfDlbXt174aMjAz0GzIMDFO2WduwwQMxddIfNa5HYVER1m3eih7dukJB/tMwklHDh6GRlSWUlZTg9+IFNmxxR3JyKju8iNS+1Jx88Ph8aH0xvEdTUR6h8cIbYkmZudBUEhyepakkj6SssoZgcnYecguLseXyAyzr54JVA11x+9VbDPb4FzcWjkErC0Mh9cjD+gteGN2uqUD4kr4d0NayIWS4kvAMCseMI5eRV1iMya5V+24lXy+juAQ8BlD7YulsNS4XkblZQvOkFhWXGw6kxuUitahs/5/04hLk83g4+O49ppoYYIaZIR6npGOWfzD+aWaDJl88OHysx77wGPRtIDikd7KJPpqpKUNaTBzeqRlYGxyGfB4PQw3qfcVVE1L7fvrGPwBMnToVU6dOFRp37969cmFmZmaVPn2PGTMGY8aMERkvLS2NnTt3YufOnSLT6Ovr49q1axWe51tydHREfn4+Xr16hezsLBgYGEJNTQ3Nmjli/vz5KCoqgq+vL/T0GkBXV7dKZYaEhGDgwEFC48LDIyAhIQFbWzs2TEVFBYaGDREREcGGLVy4EF26dMH169dx4cJFtkFb1fwyMjJswx8ANDU1kJaWVmnd7e0d2P9WVlYWKNfG5tOujD179sSqVavRo0cPnDlzBlOnTgPDMLhy5bLAPZCamoqtW7fA19cXaWlp4PP5KCgoQPwXw0Wq482bELx5E4pLly6xYQzDgM/n4/379zA2Nq5x2b8bb9+n2LlnH1YtWwp7WxtERcdgxZq1+Hvnbvw5ZVK1yyspKcGUP2eBYRisWbFMIG78mFHsf1uYm0FSUhJ/LV2B+XNmQqqKe3SQnw//w/d+t8YWmNal7O2jrb4OfMLe45+7T8s1/rMLCtFn8zGY19PE4t6CD/gLe7Vj/9vOQBd5RcXYdu0hNf5/cR/vkXaaahhuWDax3FxRHi8zs/Hf+4Ryjf/cklJMfR6EhvKymGisLxD3x2d/WyjJo4DHw5HI99T4Jz+9X6LxT8oYGBhAW1sbPj4+yM7OYlcx0tLSgo6ODvz9/eDj4wMnp6oN+QEgsKtxTcXERCM5OQl8Ph+xsbEwMzOrPNNnJL6YIMXhcL761emlS5fZ/1ZQKOtZ7NatOzZu3IigoCAUFRUiISFB4C3B3LlzkJmZiSVLlkBXtx64XC769+8vcijRxzdOn9f1y6Ej+fn5GDx4EEaMGFkuf1Uf0H5FKirKEBcXR2qqYA9uamoaNDTUhebZ4u6B3j17sJNwzc1MkV+Qj4VLlmPapD/KveGryMeGf1xcPP49ekig118Ye1sblJaWIjY2DkYNy/cOk29PXUEW4mJiSMoSHFaXnJ0LLWXh/15ayvJIzsoTTJ+VCy0lBbZMCXExmOsKLnJgrquBJ2+jBcJyCorQc+MRKMhwcfrPIZCUEBzy86WmRnpYf+EeikpK2cnDpHapcCUhzgHSiosFwtOKi6EuZLIvAKhLcZFWJDq9ClcSEhwOGsoLzvExlJNFQIbg3Li80lJMfh4EOQlxbHOwgmQl30HWygrYFxGDYh4fXBr7T35idHf+Ypo3bw5fX1/4+vrC0dGRDW/atBnu37+Ply9fVXnID1D2luTJkydC44yNjVBaWoqXLwPYsIyMDERGvmN7rIuLizF79my4ublhxoyZ+OuvhUhLS61y/q/x+STnrKwsREVFwsiobDywgYEBe6iplTU2dXR00KxZM1y6dAkXL15CixYt2DgA8Pf3x4gRI9G2bTuYmpqCy+UiIyNd5PlVVctWePh8cnJwsODYYisrK4SHhwvU5+NR1V2gf0VcLhfWVpZ47P1pjgqfz8djbx842NkJzVNQWFiugS/+YQx2dR4GPzb8I6OjceLIAaioKFea53XIG4iJiUFdTbXStOTb4EpIwN5AF/eCPw0D4/P58Hr9Do7GekLzOBrrwet1hEDY3aBwNDPRY8tsbFgPYYmCD51hialo8Nm48eyCQnTfeBhcCXH8N3NYlSbyvopOgIqcDDX8vyNJMTFYKCrANy2TDeMzDHxTM2GjLHz5ahtlRYH0AOCTmgmbD0uDSoqJwUpJAVF5BQJpovMLoCPzaRhmbkkpJj4LhKQYB383tqrSRN7Q7DwoSkpQw5/89OgO/cU0b+4EP7/nCAkJEWj8Ozo2w7//nkJJSXGVJ/sCwLRp03HlymW4u7sjPDwcoaGh2Lt3L4Cy8ekuLi74669FeP687JyzZ8+ClpYWu/vx1q1bkJOTgyVLluKPP/6AoaEhFixYUOX8X2PHju148uQx3r4Nxbx586CiooKOHTtWmKdHj564evUKrl+/jh49egrE6esb4MKFCwgPD0dAQABmz55V4ZsRfX196OjowMPDA1FRkfDy8sLBgwcE0kyY8Af8/f2xfPlyBAcHIyoqErdv38by5ctrfN2/inGjR+HU/87gzLkLCAuPwKJlK5BfUID+fXsDAGbOXYANm7ey6V3atcXxk6dw6co1xLyPxcPHT7DF3QMu7dqyDwF5eXl4HRyC18EhAID3sXF4HRzCruRTUlKCSdNn4FXQa/y9eSN4PB6SU1KQnJKC4g+9h34vAnDg8FEEh7xBTMx7nL90GavWbkDvHt2hpKT0/T4gguldWuDQvec4/tAfb+KSMf3wJeQXFWN467I5QuP2nMHS05/mbk3p5IzbgWH4+9ojhManYPU5T/hHxmOiy6fvvBlurXDGJwgHvZ4hIikNu2/74NqLUEzoUPamNLugEN03HEZ+UTF2j+uN7IIiJGbmIDEzB7wP+8Bc9X+DQ/ee4/X7JEQkpWHfHV9sunQfEztW/buVfBvDDevh3PsEXIpNxLvcfKx+HYYCHh+96peNv1/08g3+Dv20gtxQA108Sc3AkchYRObmY3dYFF5n5WCQ/qc3rSMN6+NmQgrOvk9ATF4B/o2Ow4PkNAxoUJbmY8O/gMfH8kamyCvlIbWoGKlFxeB96Ii4l5SGc+8TEJaTh5i8AvwvOh7/vIvBYP3f940u+X1QF8Yvpnnz5igsLISRkZHAZMpmzRyRl5fLLglanfK2b9+BHTt2YO/evZCXl0ezZp8mvm3YsBGrVq3E+PHjUFJSgqZNm+LAgQOQlJSEj48PDh8+jOPHT7CbiG3evAXdurnhxIkTGDp0aIX5v9bcufOwatUqREVFw9LSAvv27a+0N71Lly5YsWI5xMXFyz0orFu3DosXL0LPnj2go6OD2bPnVLjqkKSkJNzd3bF06VK4ubnBxsYGM2fOwrRpn+anmJub4+TJf7FlyxYMHjwIDMOgQYMG6Nq1/KTk3013ty5IS0/HVo/tSElJhaWFOY4e2AuND/dtfEKCQE//tMkTweFwsNn9byQmJUNNVQUd2rXD3Fl/smleBb3GoOGj2L9XrdsAAOjXuxe2bFiLxKRk3Pb0AgB06Sm4tOupY4fh5NgMXC4Xl69eg/v2nSgqLoZe/XoYO2oExn02D4B8H/2aWyMlJw+rznoiKSsXNg10cGHuSHYS8Pu0TIhxPq2u0ty0AQ5PGoAVZ+5g2X+3YaylhtMzhrBr/ANAzyaW8BjdA5svP8CcY1dhoqOOk9MHw9nMAAAQEBWPZxFlS/E2mrNNoD4hW2dDX0MFkhJi2HvHF/NPXAPDAA21VLF+aBeMaduklj8R8qXOOprIKC7BrrBopBYVw0xRHruaNmIn9SYWFgncI3YqSlhna44dYVHYHhqJBnIycHewYtf4B4AO2upYbGWCg+9isCE4AgZyMthibwkH1bKH/5DsXAR+mETe7cEzgfpca9MM9WSlISnGwanoeGwKeQcGDBrIymCOeUP01dOp7Y+EkK/GYWhdqh8qIkL4yidEtI87GPv7vyi3fOPvRpJf9KOrQH5yGr7C900g5KOwk8JXviPkI5tr9390Fch3RMN+CCGEEEIIqSOo8U8IIYQQQkgdQWP+yS+nefPmCA+PqDwhIYQQQggRQD3/hBBCCCGE1BHU+CeEEEIIIaSOoMY/IYQQQgghdQQ1/gkhhBBCCKkjqPFPCCGEEEJIHUGNf0IIIYQQQuoIavwTQgghhBBSR1DjnxBCCCGEkDqCGv+EEEIIIYTUEdT4J4QQQgghpI6gxj8hhBBCCCF1BDX+CSGEEEIIqSOo8U8IIYQQQkgdQY1/QgghhBBC6ghq/BNCCCGEEFJHUOOfEEIIIYSQOkLiR1eAEEIIIYSQwsJCFBcXV5iGy+VCWlr6O9Xo90SN/x+slKF/AlIBejdHKsPl/ugakJ+cpIzkj64CIZUqLCyErow8MsCrMJ22tjYiIyPpAeArUMuTEEIIIYT8UMXFxcgAD0ekG0JWRM9XPvgYmfgOxcXF1Pj/CtT4J4QQQgghPwV5GUnIccSFxokxPKDwO1foN0SNf0IIIYQQ8lMQE+dATIwjPI4vPJxUDzX+CSGEEELIT0FcRgziYsKH/Yjzme9cm98TNf4JIYQQQshPgSPJAUdEzz+Hev6/CWr8E0IIIYSQn4I4Vwzi4iJ6/nnU8/8t0EKChBBCCCHkpyAmzqnwqK6dO3fCwMAA0tLScHR0xNOnT0WmPXz4MDgcjsDx5apCDMNg6dKl0NHRgYyMDFxcXBAWFlbtev1I1PgnhBBCCCE/BTFJ8QqP6jh9+jRmzZqFZcuWwd/fH7a2tnB1dUVycrLIPIqKikhISGCP6OhogfiNGzfCw8MDe/bsga+vL+Tk5ODq6orCwl9nGSJq/BNCCCGEkJ+CuIQYxCVFHBLVa7Zu3boV48ePx+jRo2FpaYk9e/ZAVlYWBw8eFJmHw+FAW1ubPbS0tNg4hmHg7u6OxYsXo2fPnrCxscHRo0cRHx+PCxcu1PSSvztq/BNCCCGEkJ8CR1z00J+Py/9nZ2cLHEVFReXKKS4uhp+fH1xcXNgwMTExuLi4wNvbW+T5c3Nzoa+vDz09PfTs2ROvX79m4yIjI5GYmChQppKSEhwdHSss82dDjX9CCCGEEPJTqMqwHz09PSgpKbHHunXrypWTmpoKHo8n0HMPAFpaWkhMTBR6bjMzMxw8eBAXL17E8ePHwefz4ezsjNjYWABg81WnzJ8RrfZDCCGEEEJ+ChyxCpb6/BD+/v17KCoqsuFSUlLf5NxOTk5wcnJi/3Z2doaFhQX27t2LVatWfZNz/Ayo8U8IIYQQQn4KFY3tF+eUhSsqKgo0/oVRV1eHuLg4kpKSBMKTkpKgra1dpbpISkrC3t4e4eHhAMDmS0pKgo6OjkCZdnZ2VSrzZ0DDfgghhBBCyE+BwxEDR0zEwal6s5XL5aJx48bw9PRkw/h8Pjw9PQV69yvC4/EQGBjINvQNDQ2hra0tUGZ2djZ8fX2rXObPgHr+CSGEEELIT+Hjyj5C46rZZz1r1iyMHDkSTZo0QbNmzeDu7o68vDyMHj0aADBixAjUq1ePnTOwcuVKNG/eHMbGxsjMzMSmTZsQHR2NcePGAShbCWjGjBlYvXo1TExMYGhoiCVLlkBXVxe9evWq+UV/Z9T4J4QQQgghP4WqjPmvqoEDByIlJQVLly5FYmIi7OzscOPGDXbCbkxMDMTEPj1QZGRkYPz48UhMTISKigoaN26MJ0+ewNLSkk0zb9485OXlYcKECcjMzETLli1x48aNcpuB/cw4DMPQXsk/UGh4zI+uAvmJSXBKf3QVyE9O1+/sj64C+clFnb75o6tAfnIWZ+/86CogOzsbSkpKeNazHeQlhfdN55aUoulFL2RlZVU65p+IRj3/hBBCCCHkpyAmIQYxERN+xRiaqvotUOOfEEIIIYT8FMqG/Qhv5Fd32A8Rjhr/5KswDIPtf2/Ff//7F9nZ2XBo3ATLVqyBgYFhhflOHD+CA//sQ2pKCszNLbB46QrY2NoBADIzM7HdYyseP3qIhPg4qKqqoYNLJ/w5czYUFD695ouPj8OKpYvg6+sNWVk59OrdF7PmzIeExKfb2tfXGxvWrkJYWBh0dHQwcfI09Onbv1Y+C0JI9e25+QTul+8jKTMH1vo62DK6J5oaNxCa9qCnL04+8EPw+7Kl++wN62H54M4i00/bfxYH7vhi44jumOrWig1Pz83H7IMXcM0/BGIcDno6WmPzqB6Ql/60VnhgdAJmHjwPv4hYqCvKYZJrC8zq2fbbXTipspMR73EwLAaphcUwU5LHIltT2KgqVZrv2vtEzHn2Gu111LHDyVYgLiI7D1uDwvEsNQM8hoGRghzcm9tAV7Zs3HYRj4eNgWG4FpuEYh6DllqqWGJnBvXP7pH4/EKsfPEGT1MzICshjp4NdDDTyggSIhqupGrEJERP+KWe/2+DPkXyVf7ZtwfHjh7G8pVr8b8zFyEjI4txo4ejqKhQZJ5rVy9j/drVmDL1T5y7cAVmFhYYN2Y40tJSAQDJyUlITkrCvPmLcPnqbazbsBkPH97HooXz2DJ4PB7+GD8aJSUl+Pf0OazfuAXnz52Bx99b2TSx72MwcfxoNGvuhAuXrmHEqDFYsmg+Hj68X3sfCCGkys48CcCCo5fxV18XPFn/J6z1ddBz7QEkZ+UKTf/wdQT6O9vh+tI/4LVqCuqpKaPHmn8Ql55VLu3Fp0F4GhYDHZXy44JHb/8XwbFJuLxoPM7OH43HIe8wdd+nuRPZ+YXovmY/Gqir4PG66Vg71A1rztzGgTs+3+7iSZVcj03ChsAwTDY3xJn2TWGuJI8JjwOQVlhcYb64vAJsCgxHYzXlcnExufkY9uA5DBVkcbh1Y5zv4IiJ5oaQ+qzRvv5VGLwSUrGtmTWOtnZAcmER/vQJZON5DINJTwJQwjA40aYJ1ja2xIXoBGwPfvfNrr2uErnM54eDfD36FEmNMQyDo0cOYOLkqejg0glm5hbYsGkrkpOTcef2LZH5Dh/8B/0HDkLffgNgbGKKFSvXQlpGBmfP/A8AYGpqhu0796J9Bxc00NdHc6cWmDlrLrzueqK0tGwC7ONHDxARHoaNW9xhYWmF1m3a4c8Zs3Hy+FEUF5f9KJz69wTq19fDgoVLYGRsgmHDR8G1c1ccOXSg9j8cQkilPK4+xOgOjhjRriks6mth+7g+kOFK4qjXM6HpD00fgj9cnWFroAuzeprYPbEf+AyDe4HhAuni0rMw+9BFHJo2GJIS4gJxb2KTcDsgFLv+6IdmJg3gbG6ILaN74b8nLxH/4SHi1KMXKCnlYc+k/rDU00b/FnaY3KUFtl99WDsfBBHpcFgM+hvUQx8DXRgrymOZvTmkxcVxLjpeZB4ew2Des9eYatkQenIy5eL/Do5Aay11zLE2gaWyAhrIy6K9rgbUpLkAgJySUpyNisd8GxM011SFlYoi1jS2xIv0LLz8cI88TkpDRHYeNjSxhIWyAlprq2OaZUP8+y4WxXx+7XwYdcTHMf+iDvL16FMkNRb7/j1SUlLg7NySDVNQUISNrR0CXvgLzVNcXIzXrwMF8oiJicHJuaXIPACQk5MNeXl5dkhPwAt/mJqaQ11dg03TslVr5ObmIDzsLZvG6bPzAECLlq0rPA8h5PsoLi3Fi3dxaGdtzIaJiYmhvbUJfMOiq1RGflExSkp5UJH/1MDj8/kYt+MUZnZvA0u98rt4+obFQFlOBo2N9Niw9tbGEONw8Cz8PQDg6dtotLBoCO5nQwhdbEzxNj4FGbn51b5WUjPFfD6CM3PQXFOVDRPjcOCkqYIAIW97PtoVEglVKS76GuiWi+MzDO4npsFAXhbjH71Ay6sPMNDrGe7Ep7BpXmdko5Rh4KTx6bwNFeSgIyONgLSy875Mz4KJkrzAMKCWWmrILeUhPDvvq667rqOe/9pHnyKpsZTUZACAmrq6QLi6ujpSU1OEZUFGRgZ4PF75PGrqSE0RkSc9Hbt3bseAQYM/O3dKuTLUPjwIfDy3sDTq6urIzc1BYaHoYUmEkNqXmp0HHp8PLSUFgXBNJXkkZeZUqYzFJ65DR1UR7a1N2LAtF+9BQlwMk7u0EJonKTMHGopyAmES4uJQlZdhz5uUlQNNJXnBeikrsPnJ95FZVAIew0BdiisQribFRaqIYT9+qZk4FxWPlQ7mQuPTioqRX8rDP2+j0FJLDftb2MNFVwN/+rzCs5QMAEBqUTEkxThQ5EoK5FWX5iK1qOy8qYXFQutVFldU/YslLDEJ8QoP8vWo8U+q7PLF83CwtWCP0pLaX4M+NycHf4wfDSNjY0ydNrPWz0cI+TVsvuCFM08CcGr2SEh/aKT5v4vFzuuPsHfSAHA4tCpIXZNXUooFz19jhYM5VL5omH/0cWej9joaGGnSABbKChhvZoC22uo4HRn3HWtLROJwKj7IV6PVfkiVtevQETZ29uzfH8fWp6WmQlNTiw1PTU2FhYVlufwAoKKiAnFxcaSlpgqEp6alQl1DQyAsNzcX48aOgJy8HHbs2gdJyU+9MBrqGgh8+VIgfdqHHv+PQ4E01DXKnyc1FfLyCr/UTnyE/I7UFeUgLiaGpCzBnvTkrFxoKSuIyFXG/fJ9bLnohSuLx8NaX4cNfxISiZTsPJhNWceG8fh8LDh2BTuuP8KbHQuhpayAlC+GZZTyeEjPLWDPq6WkUG7ScfKHHv/K6ka+HWUpSYhzOGxv+0dpRcVQly7fuI/JK0BcfiGmeL9iw/gfWvvW5+/iasfm0JaVhgSHA6Mv3v40VJSDf2omAEBdiosSPoPs4hKB3v/Pe/vVpbl4lZFdrl5lcVIgNScmLrqHX4xH8ym+Ber5J1UmLy8PfX0D9jA2NoGGhga8vR+zaXJzcvDqZQDs7B2ElsHlcmFlZS2Qh8/nw+fJY4E8uTk5GDt6GCQludi15wCkpAQb63b2Dnj79g27QhAAPH78CPLyCjA2NmHTfH4eAHjy+KHIuhFCvh+uhATsG9YTmKzL5/PhFRQORxN9kfm2XryH9Wc9cXHhWIFx+wAwuLUDnm6cCZ8NM9hDR0URM3u0waW/xgIAHE0aIDOvAP7vYtl894IiwGcYNDUuK6+ZqT4eh7xDSSmPTeMZGAZTXQ2oyMt+k+snleOKicFSWQE+yelsGJ9h4JOcATshS302VJDFxQ6OONe+GXu001FHMw0VnGvfDNqy0uCKiaGRiiIicwTnbkTl5LPLfFqpKEKCw4HPh2FAABCZk4eEgkLYqZWd11ZVCWFZuQKrDj1JToe8hDiMFQQfLEj1lK3zL/ogX48a/6TGOBwORowciz27tuOu522Ehr7B/HmzoKmpCZeOndh0o0YMxvFjhz/9PWYc/jt9CufPnUFEeBiWL12EgoJ8dv39sob/cBQUFGDN2o3Izc1BSkoyUlKSweOV/Ri3aNkaRsYmmDdnJt6EBOPhw/v4e9tmDBk2Alypsl6XQYOHIvZ9DDZtWIt3EeE4eeIobly/ipGjx36/D4kQItJ0t1Y4dPcpjt9/jjexSZj+z3nkFxVjeNsmAIBxO05h6cnrbPotF72w8n83sWdSfzTQVEViZg4SM3OQ+2GMtZqCHKwaaAsckhLi0FJSgKmuJgDAvL4WOtqZYcreM3gWHgPvN1GYdegC+jvbQvdDg3JgSztISohj0p7/EPw+EWeeBGDX9UeY9tleAeT7GGXSAGei4nEhOgER2XlY8eINCng89P7wxmfB89fYGlT2ACklLg4TJXmBQ1FSEnISZeHcD5NFx5g0wPXYJPwXGYfo3HyciHiPe4mpGNSwPgBAQVICfQ10seFVGHxT0vE6IxuL/EJgp6oE2w/3SAstNRgpymHB89d4k5mDR0lp8AiOwOCG9cEVp6bV1yhb1UfUmH/6bL/FnMVaGfbD4XBw/vx59OrVqzaKZ927dw/t2rVDRkYGlJWVAQAXLlzAnDlzEBkZiWnTpsHOzg4zZsxAZmZmrdalrho3YSIKCvKxdPFCZGdno3GTJth/8KhAT31MTAwyMj71oHR164709DRs/3srUlJSYGFhif0HjrLDdV4HB+HlyxcAgE4urQXOd8frEerX14O4uDj27DuIFcsWYdCA3pCRkUWvPn0x/c9ZbNr6eg2wZ/8hrF+zEkePHIK2tjZWrdmAVq3a1OZHQgipon7OdkjJzsOq/91CUmYObAx0cWHhWHZozfu0TIh91tO3/7YPikt5GLL1mEA5f/VzweL+nVBVh6YNxqyDF+C2ah/EOGLo6dgIW0b3ZOOVZGVwedF4zDx4Hi0WekBNQQ4L+7pgrEvzr7xiUl1d6mshvagY24PfIbWoCOZKCtjbwo4dWpOQXwgxVK832KWeJpbZm2N/aBTWvnwLAwVZuDtao7G6MptmgY0JxDjAnz6BKOHz0UJLDUvszNh4cQ4Hu5xtsfJFKIbcfw4ZcXH01NfBNMuG3+S66zKOhDg4Iob9cHh1c8Ivn8/HmjVrsGfPHiQlJeHt27do2LAhlixZAgMDA4wdW71OTQ7DfJz+UjWJiYlYs2YNrl69iri4OGhqarIN7A4dOpQV+p0a/8XFxUhPT4eWlhY7uUtLSwujR4/G9OnToaCgAAkJCeTk5EBTU/Obn//cuXPYs2cP/Pz8kJ6ejhcvXsDOzq5aZYSGx3zzepHfhwSn9idVk1+brt/ZyhOROi3q9M0fXQXyk7M4e+dHVwHZ2dlQUlJCxJwhUBAxYTunqBhGm08iKysLiorlN/D7Xa1cuRJHjhzBypUrMX78eAQFBaFhw4Y4ffo03N3d4e3tXa3yqvX+JCoqCo0bN8bdu3exadMmBAYG4saNG2jXrh2mTJlSrRN/C1wuF9ra2mzDPzc3F8nJyXB1dYWuri4UFBQgIyPz1Q3/kpISoeF5eXlo2bIlNmzY8FXlE0IIIYQQWupTmKNHj2Lfvn0YOnQoxMU/fQa2trZ48+ZNtcurVuN/8uTJ4HA4ePr0Kfr27QtTU1NYWVlh1qxZ8PERve35/PnzYWpqCllZWfY1xecN6pcvX6Jdu3ZQUFCAoqIiGjdujOfPnwMAoqOj0b17d6ioqEBOTg5WVla4du0agLJhPxwOB5mZmbh37x4UFMpeFbdv3x4cDgf37t3D4cOH2SFBH128eBEODg6QlpZGw4YNsWLFCnbnWKDszcXu3bvRo0cPyMnJYc2aNUKva/jw4Vi6dClcXFyq8zESQgghhBBhOGKAmIiDUzfH/MfFxcHY2LhcOJ/PF9lBXZEqj/lPT0/HjRs3sGbNGsjJlZ/J/mUD+3MKCgo4fPgwdHV1ERgYiPHjx0NBQQHz5s0DAAwdOhT29vbYvXs3xMXFERAQwC7rOGXKFBQXF+PBgweQk5NDcHAw5OXly53D2dkZoaGhMDMzw9mzZ+Hs7AxVVVVERUUJpHv48CFGjBgBDw8PtGrVChEREZgwYQIAYNmyZWy65cuXY/369XB3d2d3lSWEEEIIIbWHIy4OMXERY/5FhP/uLC0t8fDhQ+jrC66EdubMGdjb24vIJVqVW7Xh4eFgGAbm5sJ3zavI4sWL2f82MDDAnDlzcOrUKbbxHxMTg7lz57Jlm5h82q0xJiYGffv2hbW1NQCgYUPhk2m4XC47vEdVVRXa2uW3dQeAFStWYMGCBRg5ciRb3qpVqzBv3jyBxv+QIUMwevToal8rIYQQQgipITFO2SEqrg5aunQpRo4cibi4OPD5fJw7dw6hoaE4evQorly5Uu3yqtz4r+a8YAGnT5+Gh4cHIiIikJubi9LSUoGJGrNmzcK4ceNw7NgxuLi4oH///jAyMgIATJ8+HZMmTcKtW7fg4uKCvn37wsbGpsZ1efnyJR4/fiwwlIfH46GwsBD5+fmQlS1bw7lJkyY1PgchhBBCCKm+isb219Ux/z179sTly5excuVKyMnJYenSpXBwcMDly5fRsWPHapdX5cFTJiYm4HA41Z5Y4O3tjaFDh6Jr1664cuUKXrx4gUWLFrG7wwJlQ2xev34NNzc33L17F5aWljh//jwAYNy4cXj37h2GDx+OwMBANGnSBNu3b69WHT6Xm5uLFStWICAggD0CAwMRFhYmsOursKFNdd2/J46hRzdXNLazQmM7Kwzs3wsP7ntVKe+O7e6YO/tPkfE3rl9FF9f2sLEyRXe3Trh/726Vyj1/7gyGDOorNO7WzesYM3IonJrZs/V9+PB+lcolhNSO1f/dguzAeQKH3cxNVcq75r/bGLP9X5Hx57xfwW7mJqgM+wtN52zFjRchVSr3+P3n6LB0l8j4B68j4DTfHcpDF6LR9A04du95lcol1bcj+B0sz3kKHG63qraSyc6Qd5j37LXI+BuxSXC75Q27C17oeccH9xNTRab93IXoBAy7L/rf/GlKBvp6PoXthbtwvfkE56Pjq1QuEYHDKRvbL/Somz3/ANCqVSvcvn0bycnJyM/Px6NHj9CpU9WXOP5clRv/qqqqcHV1xc6dO5GXl1cuXtQ6+k+ePIG+vj4WLVqEJk2awMTEBNHR0eXSmZqaYubMmbh16xb69OmDQ4cOsXF6enqYOHEizp07h9mzZ2P//v1VrXY5Dg4OCA0NhbGxcblDTKxuTiSpKi1tHcyeMx9nL1zBmfOX0dzJGVMmjUdY2NtK8969cwvtOwh/OvX3f47ZM6ehX78BOH/xKlxcOmHq5Al4+za0auW2F17u82dP4dyiFfb9cxhnL1yBY3NnTP5jLIJfB1VaLiGk9ljW18K7vUvY486KyVXKd+X5a7g1sRQa5xMahZEeJzGyXVN4r/8T3ZpaYeCmo3gdk1h5uc+C4dZYeLlRyenos+Eg2lgZwWfDDEzp2hKT957B7YDKv59IzRgryuF+15bscbxN4yrluxufinY66kLjXqRlYu6z1+hjoIuz7Zuhg44Gpnm/QlhWbuXlJqSgnY6G0LjYvAJMehLwYRdhR4ww1sNS/zd4lJRWpTqT8j6u8y/qqIuePXsGX1/fcuG+vr7sAjnVUa3W7s6dO8Hj8dCsWTOcPXsWYWFhCAkJgYeHB5ycnITmMTExQUxMDE6dOoWIiAh4eHiwvfoAUFBQgKlTp+LevXuIjo7G48eP8ezZM1hYWAAAZsyYgZs3byIyMhL+/v7w8vJi42pi6dKlOHr0KFasWIHXr18jJCQEp06dEpiXUFXp6ekICAhAcHAwACA0NBQBAQFITKz8x+ZX1L6DC9q0bQ8DA0MYGjbEzFnzICsri5cB/hXmS0iIR1hYmMjNtY4dOYSWrdpg7PiJMDI2wZ8z58DSshFOHDtSYblFRYV4/Pgh2ncQvtrSX4uXYdyEibC2sYWBgSFmzZ4HfX0DeN31rNoFE0Jqhbi4GLSVFdhDXbHyN62xqZkIiU1CR1szofE7rz9CRztTzOzRFub1tbBsoCvsDOthz83HFZZbWFwCz1dvRT5U/HPbBwYaqlg/ojvM62thUucW6O1oje3XHlZ+oaRGxDkcaEhLsYeKiDXfP5eQX4jwnFy00lITGn8s/D1aaqlirKk+jBTlMN3KCJbKCjjxLrbCcot4PDxOShf5UHE6Mg715GQw38YERopyGGqkh071NHA0jPbwqSmOmDg44iIOsbrZ+J8yZQrev39fLjwuLq5GS+1Xq/HfsGFD+Pv7o127dpg9ezYaNWqEjh07wtPTE7t37xaap0ePHpg5cyamTp0KOzs7PHnyBEuWLGHjxcXFkZaWhhEjRsDU1BQDBgxAly5dsGLFCgBl4/GnTJkCCwsLdO7cGaampti1S/Tr2cq4urriypUruHXrFpo2bYrmzZtj27Zt5WZQV8WlS5dgb28PNzc3AMCgQYNgb2+PPXv21Lh+vwoej4erVy4hP78AdnYOFaa963kbzRybQ/7DUqxfCnjhD2fnlgJhLVq1RkAlDxXeTx5DU0sbDY3KL38lDJ/PR15eHpSUlaqUnhBSOyISU9Fw4ipYTluP0R4n8T41o9I8V/yC0drSCIqy0kLjfd/GoH0jE4EwF1tTPH1bcSPMKygcuqqKMKsnfD8Y37fRaGdd/XJJzcXk5qPNtYfodOMx5j4LQnx+YaV5vBJS0ExdBfKSwqcyBqRnwUlTVSCshZYaXqZnVViuT3IGtGSk0FBB+ANqQFoWnDS+KFdTDQGVlEtE44hxKjzqouDgYDg4lG9r2dvbsx3Q1VHtNSx1dHSwY8cO7NixQ2SaLycHb9y4ERs3bhQImzFjBoCyVXr+/Vf0GM6Kxve3bdtW4FzKysrlzj1q1CiMGjVKIMzV1RWurq5Vrr8owsr+3YWGvsHgAb1RVFQEWVk57Ni1F8YmphXm8bxzGx1cRE9ISU1NgZq6YK+Kuro6UlNSKi7X8zbat6/6HgsH/9mH/Pw8dOnarcp5yM/l4sWLWLLk01u6AwcOomnTpj+wRqS6mho3wL5JA2Giq4HEjGysPXsHLst24/nmWVCQEd6wB4Crz1/DrYmVyPikzBxoKgsuA62pJI+krJwK61PRUCIASMrKgabSF+UqyyO7oBAFxSWQ4UpWWD6pHhtVRaxpbAlDBVmkFBZjV0gkht/3wyUXR8iJaNgDwN2EVLQX0TsPAKmFxVD74g2CuhQXqYVFFdanbMhPBeUWFUFNWvBtg5o0F7mlPBTyeJCuo0tTfhVx8bJDVFwdJCUlhaSkpHIrXiYkJNRoOXoa5P4dFRUVITs7W+AoLqr4i+dnY2jYEOcvXcfpMxcxaMgwLJg3G+EVjPnPzcnBs6e+Isf71xTDMLh3906Vy7186QJ27nDHtr93QU1N9Bc5+bl16NABly5dZo+PSwCTX4ervTn6ONnAWl8HHe3McH7BGGTlFeKs9yuRebLzC/Ew+B26iRiXX1MMw+CaXwjcGot+qCDfV2ttdXSurwUzJQW01FLDHmdb5JSU4EZcssg8uSWleJaaIXJcfk0xDAOvBNHzCEjt4HDEKjzqok6dOmHhwoXIyvr0RikzMxN//fVX7a72Q77eunXroKSkJHDs3VvzIUw/ApfLhb6+ARo1ssbsOfNhbmGBo0cOiUz/4ME9GBsbQ0dHV2QadXUNpKUKrrqQmpoKdQ3RX+SvXgagtJQHe4fKJ4JdvXIJSxbNx7a/d8K5RctK05Ofl7y8PAwMDNjj8xW6yK9JWU4GxjrqeJcoeoLkrYA3MK+vhfrqyiLTaCkrIDlTcPJmclYutJSEDzcEgGfh78Hj8dHcTPSwTy0lBSR/MSk0OTMXijLS1Ov/HShyJWEgL4vo3HyRaR4mpcFIQQ46IoaEAYC6NBdpRcUCYalFxVCXlhKZ51VGNngMA3s1ZdHlSkkhrVCw3LTCYshLiFOvfw3RhN/yNm/ejPfv30NfXx/t2rVDu3btYGhoiMTERGzZsqXa5VHj/zv6+NT2+fHHH1Vb5eJnxefzBZZt/ZLnnVto71LxUlR29g7w9haclPfk8cMK5xJ4et5Gm7btIV7Jl+uVyxfx14I52LJtO9q261BhWkLI95dbWITIpDRoq4hupF95HoxuFQzNAQBH0wbwCgoXCLsbGIZmpg0qKPc1OjuYQ7yCld4cTfVxr5rlkm8nr7QUMXkF0KigkX43PgXtK+n1t1NVgk+y4NwS7+R02KqKngN2Nz4FbbTVIV7B8pJ2akrwSUkvV65dBeWSSnA4FR/VtHPnTrazyNHREU+fPhWZdv/+/WjVqhVUVFSgoqICFxeXculHjRoFDocjcHTu3Lna9aqOevXq4dWrV9i4cSMsLS3RuHFj/P333wgMDISenl61y6PG/3ckJSUFRUVFgYMrJfoL7WezZfMGPHvqi9jY9wgNfYMtmzfgqa8PuvfoJTR9aWkpHj64J3I1no+GjxyNRw/v4+CBfXgXEY7tHtvwOigQQ4ePFJnHy/N2peVevnQBC+bNwvyFi2Fja4eUlGSkpCQjJye70mslhNSOhceu4GFwBKKT0+ETGoVBm49CXEwM/VvYCU1fyuPhVkCoyKU4P5rSpSVuvwzF35fvIzQuGav/uwX/iFhMdG0hMs+158HoWkm54zo2R2RyGhYdv4rQuGTsvfkEZ71fYVrXVpVeK6m+jYFheJaSgbi8ArxIy8R0n0CIczhw09MSmr6Uz8fDpLQKx/sDwHBjPTxKSsOhsGi8y8nDjuB3CMrIxtCG9UXm8UqsfMjPQMN6iM0rwObAMLzLycO/EbG4EZeMESb0cFhj4mKfxv2XO6rXbD19+jRmzZqFZcuWwd/fH7a2tnB1dUVysvBhZPfu3cPgwYPh5eUFb29v6OnpoVOnToiLixNI17lzZyQkJLBHRXNXvxU5OTlMmDABO3fuxObNmzFixAhIStbs7WP1ZwmQOis9LRXz581CSnIyFBQUYGZujn8OHkOLlsJ/BJ899YGsrBysrCoel+3g0ASbt3rAfdtmbNuyCQYGBtixax9MTYUv6RcTHY3o6Gi0FLF06Ef/O/0vSktLsXL5Eqxc/mmFqV69+2H9xuq/JiOEfL24tCyM9DiJ9Jx8qCvKw9nMAPdWT4WGorzQ9A+D30FOmgv7ChppANDczACHpw3BitM3sOzUDRhrq+P03BGwaqAtNP27xDREJKWJXDr0IwNNVZybPwbzjl7GzuuPUE9NCbv+6IeOdhXnIzWTVFCIOc+CkFlcAlUuFw7qSvi3bROoilju81lqJmQlxGGpolhhufZqytjY1Aoewe/g/joC+vKy2O5kAxMl4fddTG4+YnIL0ELE0qEf1ZeTwW5nO6x/9RbHIt5DW0YaKx3M0bKSfEQ0jpgYOCLexokKF2Xr1q0YP348Ro8eDQDYs2cPrl69ioMHD2LBggXl0p84cULg73/++Qdnz56Fp6cnRowYwYZLSUlBW1v4d0ttCQsLg5eXF5KTk8Hn8wXili5dWq2yfpnG/86dO7Fp0yYkJibC1tYW27dvR7NmzYSmbdu2Le7fL7+Ta9euXXH16tVy4RMnTsTevXuxbds2dhUioGwd/2nTpuHy5csQExND37598ffff0Ne/tOXxatXrzBlyhQ8e/YMGhoamDZtGubNm/f1F/wTWrOuartwfuR55zbata/aUJvOXdzQuYtb1cr1vIXmTk6QlZWtMN2xE6erVB4h5Ps5OmNotdJfeR6Mrg5Vm+jbx8kGfZxsqljua7SxMoKcdOVryLf+sMEXqX1bmlVvEv/dhBS01a7ahNzO9bXQub7wNwjly02Fo4YKZKswxryZhgrOdXCsUrmkCsQqWO3nwzr/2dmCb/ClpKQg9cVIiuLiYvj5+WHhwoWfsouJwcXFBd7eVds1Oj8/HyUlJVBVFVzO9d69e9DU1ISKigrat2+P1atXQ02t9h749u/fj0mTJkFdXR3a2trgfDb8icPh/J6N/4+vbfbs2QNHR0e4u7vD1dUVoaGh0NQsvzbzuXPnBMahp6WlwdbWFv379y+X9vz58/Dx8YGubvkJqUOHDkVCQgJu376NkpISjB49GhMmTMDJkycBlN18nTp1gouLC/bs2YPAwECMGTMGysrKmDBhwjf8BH5NJqZmsLOveA+AmtDW1sGEP6q/qQUh5NdjqacFR9Pq78NSmXpqSpjbq/03L5d8XyaK8rUyvl5LRgrjK5gITmpPRav6fAz/cpz7smXLsHz5coGw1NRU8Hg8aGkJPvBpaWnhzZs3VarL/PnzoaurCxeXT8OMO3fujD59+sDQ0BARERH466+/0KVLF3h7e1c6D7GmVq9ejTVr1mD+/PnfpLxfovFf3dc2Xz6hnTp1CrKysuUa/3FxcZg2bRpu3rzJbtT1UUhICG7cuIFnz56hSZMmAMr2HOjatSs2b94MXV1dnDjxf/buOq6q8w/g+OcC0i0CgiIpZaDYOhPFjpmznTGd3fqz2xmza/Y2a5tdiJ1YICoGKqIYdIfkvb8/0KtXuAiok+nzfr3Oa7vnPOd7nnN4wOc+54ltpKens2nTJtTV1XF1dcXf359ff/1VVP6Bzl26fpa4Yp5+Qfh29PWo8Vnitq9Z8bPEFf5dnWwsP0vcZvl8QyB8BvmY5//Zs2fo67/t6vV+q/+nMH/+fHbu3MmZM2cUZpbr0qWL/P/Lly9PhQoVsLOz48yZMzRq9HkmFomNjc21AbuwivyA3zevbd791lXQ1zYbN26kS5cu6Oi8XaFPKpXSo0cPxo4di6trzjmefXx8MDQ0lFf8ATw8PFBRUeHKlSvyNHXr1kVd/e1r4zdvJGJjP7xipSAIgiAIgvAOpYN9334peH/ylNwq/yYmJqiqqhIeHq6wPzw8/IP99RctWsT8+fPx9vamQoW8uxLa2tpiYmLCo0eP8kz3MTp27Ii3t/cni1fkW/4/9rXN1atXCQgIYOPGjQr7f/nlF9TU1Bg2bFiu54WFheXoUqSmpoaxsTFhYWHyNDY2Njny9eaYkZHRB/MnCIIgCIIgvCZRyd6UHcsndXV13N3dOXnyJG3btgWyG35PnjzJkCFDlJ63YMEC5syZw7FjxxQagJV5/vw50dHRlCxZMt95Kyh7e3umTJnC5cuXKV++fI5ZfpTVZZUp8pX/j7Vx40bKly+vMDjY19eXZcuW4efnpzBoQhAEQRAEQfiC3kz1qexYAYwaNYpevXpRpUoVqlWrxtKlS0lOTpZ3I+/ZsyeWlpbMmzcPyG4Ynjp1Ktu3b8fa2lre2Kurq4uuri5JSUnMmDGD9u3bY25uTlBQEOPGjcPe3h5PT8/C3/MH/Pbbb+jq6nL27NkcE9pIJJKvr/L/Ma9tkpOT2blzJzNnzlTYf/78eSIiIrCyejsPb1ZWFqNHj2bp0qU8efIEc3PzHPPAZmZmEhMTI7+uubl5rvl6c+xbsO3PrWzc8BtRkZE4OTkzeeoMKlR0U5p+6+aN7NjxJ6EvX2BkZIxn0+aMGjMODY3s/nTXrl5h44Z13Llzm8iICFau/g2Pxm9/oTIyMli2ZBFnz57m+bMQdPX0qFWrDqPGTFB4O9Swfm1evniucO1RY8Yz4D++qJogfG3WHrvE0oNnCY9LpHyZkizu04aq9srnSN/jc4uZfx3jaWQs9uYmzOrWjKaVnBXS3H8ezuTtR7hwN5hMaRZOlmbsGN2D0iZGxCSlMPsvb07eesCzqDhM9HVpVdWVqZ2bYKCtJY+h3TnnrG1bh3VVuh6B8PlsD3rGpochRKWm42igy6SKZamQxyBfr+fhrLj7mBcpqZTR1WJUOXvqvTcjUFBCMr8GPOJaVCxZMhl2ejosrVEBC21N4tIzWHn3MZciYghNScVIoxiNLEowzMUOvWJvq00ue07muPaiqq40L/1t/Pv/2eS1mFcBG2w7d+5MZGQkU6dOJSwsDDc3N7y8vOT1hZCQEFTemT50zZo1pKen06FDB4U4bwYUq6qqcuvWLbZu3UpcXBwWFhY0adKEWbNmfZZxB28EBwd/0nhFvvJf2Nc2AH///TdpaWl0795dYX+PHj0UxhBAdl/9Hj16yL8N1qxZk7i4OHx9fXF3dwfg1KlTSKVSqlevLk8zadIkMjIy5K9gjh8/jqOj4zfR5efI4YPMnzub6TPnULGiG1u3bqLfjz046n2a4sVzTr128MA+Fi/6hTnzFlCpsjtPgoOZOGE0SGDi/7KnqXr1KgUnJ2fad+jE0ME/5YiRmvqKu3cC+HnwMBydnEmIj2fu7Bn8PLAvu/ceUkg7bPgoOnb+Qf5ZRyf3+ZwFQfgy/rnkz4TfD7K83/dUdbBi5ZHztJm7Ef8lYzHNZf71y4FP6LV8OzN/aEqzys7suuhP54W/c2n+cPl8/o/DovGYtoZeDaoyuWMT9LU0ufs8DI3Xf6NDYxIIjU1gbo+WOFuaERIVy7ANewiNTWD7qB4K11s3qJPCfP6G2poI/66jz8P55fZDprk5UcFYnz8ePWPARX8ON65J8Vymab0RHcfYa3cY4WpHfXMTDj8LY6jPLXY3rCaf0z8kKYXu567TvowFg11s0VVT5VFCMhqvK4GRr9KITE1jbHl77PR0eJmSygz/+0S+SmNpDcX+33PcnRXm9NcvVuSrVUVfPgb8FsSQIUOU1hfPnDmj8PnJkyd5xtLS0uLYsWMFzsOnkp6eTnBwMHZ2dqipFb6s/SdKaUFf27yxceNG2rZtm2Pu1eLFi+fYV6xYMczNzXF0zP5D7+zsTNOmTenfvz9r164lIyODIUOG0KVLF/m0oF27dmXGjBn07duX8ePHExAQwLJly1iyZMnnehRFypZNG+jYuQvtO3QCYMbMuZw9c4rd//yVawv7jRu+VHZ3l68IXKpUaVq0bM2tm/7yNHXrNaBuvQZKr6mnp8+mrYqLcEyZNpOO7Vvz8uULLCzezvygo6NLiRI5p4IVBKFoWH74PH0aVadng6oArOj3PV5+9/n99DXGtM35d2DV0Qs0divLyNb1AZjW2ZNTtx6y9thFVvRvD8D0nV54VnJiTve3M7jZmr/9e+9qZc6O0T0Vjk3v3JQfV+4gMysLtXcqFwY6mpgb6n3SexYKZsvDEDpaW/K9dfa/u9MqOXE2LJo9T1/S39E6R/o/Hj2jjpkxfV9PDzvM1Y5LETFse/yc6ZWcAFh2N4i6ZiaMKe8gP89K9+26MQ4Guix7p5JvpavNcBc7xl+/Q6ZUito7LcV6xYpRQvPztfh+kz5Rn/+vSUpKCkOHDmXr1q0APHjwAFtbW4YOHYqlpWWuM1/m5T/xFDt37syiRYuYOnUqbm5u+Pv753htExoaqnBOYGAgFy5coG/fvoW+7rZt23BycqJRo0Y0b96cOnXq8Ntvv8mPGxgY4O3tTXBwMO7u7owePZqpU6d+E9N8pqenc+fObWrVqiPfp6KiQs1adfC/4ZfrOZUquXMnIEBe2X8WEsK5M6fzrOznR2JiIhKJBH09xRUe1/+2hupVK9KudTM2rl9LZmbmR11HEIRPJz0zkxuPX9CgvL18n4qKCg3LO3Dl4dNcz7nyIISG5RwU9nlULMvVByFA9lthrxv3sC9pQus5GyjTfwZ1J63gwLWAPPMSn/IKfS1NhYo/wMiN+yjdbzrf/W8FW09fQyaTFeZWhUJKl0q5G5dIDdO303erSCTUNDXCPyY+13P8Y+Kpaao43Xdts+LcfJ1eKpNxNiwaa11t+l+4QZ3D5+h8+honXkbmmZekjEx01dQUKv4As/0DqXUoO8buJy9FGfkUVPKY6Ufl88yjX9RNnDiRmzdv5ph21MPDg127Cr6g6X+i5R8K9toGwNHRsUC/hLm96jE2NpYv6KVMhQoVOH/+fL6v87WIjY0lKyuL4iaK3XtMipsQHBSU6zmtWrclNjaWbj90QCaTkZmZSZcfujNwUN7dt/KSlpbKooXzaNGyNbp6b1voevTsjYtrOQwNDLnh58uvi38hIjJC3r1IEIQvKyohmSypFDMDxZZ1UwNdAl9G5HpOeFwipoa6OdKHxycCEJGQTFJqOov3n2ZaZ09mdWvOcf9Aflj8B15TB/Cdi12u+Zi/5yR9PBRXaJ3SqQn1Xe3R0ijGyVsPGLFxL8mpafzcrE6OGMLnEZeWQZZMhomGYvee4hrqPE5MyfWcqNR0ir+X3kRDnajUNACi09JJycxiw4MnDHOxY1Q5ey6ERzP88i22fFeZqiVydtmNTUtnzf0ndHxvTYGhzrZUNzVCU1WVS+HRzPIPJCUzix72pXPEEApAtPznsG/fPnbt2kWNGjUUJqpxdXUlSEmdKy//mcq/8N935YoPv61dxdTps6hQsRIhT58wd/YMVq9cxs9Dhhc4XkZGBiOGDQaZjOkz5igc6/Njf/n/Ozo5U6xYMaZN/R+jR49H/TMOyhEE4cuRSqUAtKziytAWdQGoaG3B5QdP2HD8co7Kf0JKKt//sgmnUmZM7tBY4djE9m/HhbnZWJKcls6Sg2dF5f8/7k2bYMOSJejlkD2w3NlQD//oeHYFv8hR+U/KyGTgpZvY6esw2Flxau9B73x2MdTjVVYWmx8+FZX/j/WJ+/x/DSIjI3NMPw/ZE9sUZtbKb/MrlPDRjIyMUFVVJToqSmF/VHQUJiVK5HrO8qWLad2mHR07/YCjoxONmzRl5Oix/LZutfwf7fzKyMhg5PDBvHz5go1btim0+uemglslMjMzef7eDECCIHwZJvo6qKqoyFvt34iIT8JMST97M0M9IuKScqZ//fbARF8HNVUVnCwV14VxsjTjWVScwr7EV6m0mbcRPU0Ndo3uSTG1vCsVVe2teBEdT1qG6D74bzHUKIaqREJUWrrC/ui0dExyGewLYKKpTvR76aPS0jF53S/fUKMYahIJdvo6Cmls9XUITUlV2JeckcmAi/7oqKmyokZ5iqnkXWWqYGRA2Ks00rMK9u+ZoEimooJMRVXJ9m1WW6tUqcLhw4fln99U+Dds2EDNmjULHO/bfIrCR1NXV8fVtTw+Phfl+6RSKZcvXcStUuVcz3n16pXClFoAKq/77xWki9abiv/TJ8Fs3rItXzMr3b97BxUVlVxnIRIE4d+nrqZGJVtLztx+uyqmVCrldMAjqjuUyfWc6mWtOB2guIrmqdsPqVbWSh7T3a40D0MV+28/DI3E6p0W3YSUVFrN2YC6mip/j+uNprrigjm5ufXkJUY6WmiI2Vz+NeoqKrgY6nE5Ika+TyqTcTkiFjclU326GRtwOSJWYZ9PRAwVX6dXV1GhnJE+we91G3qSmILFO7M5JWVk0u/iDYqpSFhVsyIa+WhxvhefiH4xNdQLOBe98J433X6Ubd+guXPn8r///Y9BgwaRmZnJsmXLaNKkCZs3b2bOnDkfDvAe8VdMKLTeP/ZjwrjRlCtXgQoVKrJ1yyZevUrh+/YdARg/diSmZuaMHjMegAYNPdiyaQPOLq5UrOjG06dPWb50MQ0aeqD6+g9rcnIyIU+fyK/x/Pkz7t29g4GhIRYWlmRkZDB86CDu3glg7W+byJJmERmZ3T/YwMAQdXV1btzw5Za/P9Vr1ERHRxf/G77MmzuLVm3aYWCgfG5oQRD+XcNafEf/1X9R2a4UVexKs/LIBVLS0ulRP3tVzX4rd2JhbMDMrs0AGNysDk1mrGXZwbM0rezM35f88Qt6zsrXM/0AjGhVj55Lt1Hb2YZ6rnZ4+wdyxPcex6ZlTx38puL/Kj2dTUN+IOFVGgmvsvuDl3j9NuKw710i4hKp5lAGTXU1Tt56yMJ9pxjest6//ISE3g5WTLx+l3JG+pQ30uf3RyG8ysqiXZns1VQnXL+DqaYGo8plDxzvYV+aXuf82PzwKfXMTTjyLJyA2ARmvJ7pB+BHBytGXQ2giokh1UoYcSE8mjNhUWz5LrvhKikjk34XbpCaJeWXGq4kZWaS9HrCCGMNdVQlEk6HRhKdmk5FYwPUVVXwiYhhfeATeiv54irk35tWfmXHvkV16tTh5s2bzJs3j/Lly+Pt7U3lypXx8fGhfPnyBY4nKv9CoTVv0YqYmGhWLPuVyMhInJ1dWL/xd0xMsrv9vHz5Esk739IH/TwUiUTCsiWLCA8Pw9i4OA0aNmLEqLHyNAEBt+jVvYv88/y5swBo264D8xcsJjw8jFMnj2fva91MIT9b/9xJ9eo1UVdX58jhg6xcsZT09DRKlSpNrz596dOn32d7FoIgFFyHWm5EJiQz6y9vwuMSqWBtwb6JfeXdfp5Fx6Gi8rY/aw1Ha7YM7cqMXV5M2+mFvbkJu8b2lM/xD9CmWjmW9/+eRftOMWbzfhwsSrB9VA9qOWX3z/YPfsG1R9mzA5Ub/otCfu6tmEAZU2OKqaqyztuH8b8fRCbLng50fo9W/NioGsK/q1kpM2LS0llx9zFRaWk4GeixrrabvBtPaEoqKrwtI5WKG7KgqivL7z5m6Z0gyuhqs6JmBfkc/wAelqZMq+TE+sAnzL35AGs9bZZWL4+7iSEAd+MSuRWbAEBTbx+F/Bz3rIWljhZqEhW2P37O/NsPkcnASleLceUdcgwKFgrhEy7y9TXIyMjgp59+YsqUKaxfv/6TxJTIxLxUX1Tg63+EBCE3ahLRv1jIm4Xv7i+dBaGIe7Lryy1KJPw3OO8+8aWzQEJCAgYGBrz0/gN9He3c0ySnYNGkB/Hx8ejr6+ea5mtkYGCAv78/NjY2H06cD99m5ylBEARBEASh6BF9/nNo27Yt+/bt+2TxRLcfQRAEQRAEoUgQff5zcnBwYObMmVy8eBF3d3d0dBRnqxo2bFiB4onKvyAIgiAIglAkyFBBpqSFX/aNdljZuHEjhoaG+Pr64uvrq3BMIpGIyr8gCIIgCILwH6WiAspa+L/Ref6Dg4M/abxv8ykKgiAIgiAIRY7yBb6Udwf6VqSnpxMYGEhm5sdNBiIq/4IgCIIgCEKRIEOS5/YtSklJoW/fvmhra+Pq6kpISPZMkUOHDmX+/PkFjicq/4IgCIIgCEKRIFNRy3P7Fk2cOJGbN29y5swZNDXfrkTt4eHBrl27Chzv23yKgiAIgiAIQpEjk0iQKVnMS9n+r92+ffvYtWsXNWrUQPLOM3B1dSUoKKjA8UTlXxAEQRAEQSgSxFSfOUVGRmJqappjf3JyssKXgfwS3X4EQRAEQRCEokGSPdVnbtu3ushXlSpVOHz4sPzzmwr/hg0bqFmzZoHjiZZ/QRAEQRAEoUiQSlSRSnJv4Ve2/2s3d+5cmjVrxt27d8nMzGTZsmXcvXuXS5cucfbs2QLH+za/QgmCIAiCIAhFjwSQSJRsXzpzX0adOnXw9/cnMzOT8uXL4+3tjampKT4+Pri7uxc4nmj5FwRBEARBEIoE0fKfbdSoUcyaNQsdHR3OnTtHrVq1WL9+/SeJLVr+BUEQBEEQhCJBWX9/eb//b8SKFStISkoCoEGDBsTExHyy2KLlXxAEQRAEQSgSRMt/Nmtra5YvX06TJk2QyWT4+PhgZGSUa9q6desWKPa38xVKEARBEARBKNKkEpU8t4JatWoV1tbWaGpqUr16da5evZpn+r///hsnJyc0NTUpX748R44cUTguk8mYOnUqJUuWREtLCw8PDx4+fFjgfH3IwoUL2bhxIw0aNEAikdCuXTvq16+fY2vQoEGBY4vKvyAIgiAIglAkfMpuP7t27WLUqFFMmzYNPz8/KlasiKenJxEREbmmv3TpEj/88AN9+/blxo0btG3blrZt2xIQECBPs2DBApYvX87atWu5cuUKOjo6eHp6kpqa+lH3/b62bdsSFhZGQkICMpmMwMBAYmNjc2yF6Q4kKv+CIAiCIAhCkSBFVd71J8dGwbr9/Prrr/Tv358+ffrg4uLC2rVr0dbWZtOmTbmmX7ZsGU2bNmXs2LE4Ozsza9YsKleuzMqVK4HsVv+lS5cyefJk2rRpQ4UKFfj99995+fIl+/bt+9hbVzBq1CiSk5PR1dXl9OnT2NjYYGBgkOtWUKLyLwiCIAiCIBQJMiR5bvmVnp6Or68vHh4e8n0qKip4eHjg4+OT6zk+Pj4K6QE8PT3l6YODgwkLC1NIY2BgQPXq1ZXGLKx3B/w2bNhQDPj9mpx8VPpLZ0EowtTFb6jwAQ3c23/pLAhFnI2G5pfOgiDkW3bffmUDfrPbrBMSEhT2a2hooKGhobAvKiqKrKwszMzMFPabmZlx//79XOOHhYXlmj4sLEx+/M0+ZWk+lc854FdULQRBEARBEIQiQSaRIJPk3sL/Zn/p0ooNp9OmTWP69OmfO2v/qoULFzJw4EDmzZsnH/CbG4lEQlZWVoFii8q/IAiCIAiCUCTIZKpIZbm3/Mte73/27Bn6+vry/e+3+gOYmJigqqpKeHi4wv7w8HDMzc1zjW9ubp5n+jf/DQ8Pp2TJkgpp3NzcPnBnBfNmsHFSUhL6+voEBgZiamr6SWKLPv+CIAiCIAhCkZCfPv/6+voKW26Vf3V1ddzd3Tl58qR8n1Qq5eTJk9SsWTPXa9esWVMhPcDx48fl6W1sbDA3N1dIk5CQwJUrV5TG/FifY8CvaPkXBEEQBEEQigQpKkiVtE0r26/MqFGj6NWrF1WqVKFatWosXbqU5ORk+vTpA0DPnj2xtLRk3rx5AAwfPpx69eqxePFiWrRowc6dO7l+/Tq//fYbkN3FZsSIEcyePRsHBwdsbGyYMmUKFhYWtG3btvA3nYuEhAT5241KlSqRkpKiNO27b0HyQ1T+BUEQBEEQhCJBKlNBKlNS+VeyX5nOnTsTGRnJ1KlTCQsLw83NDS8vL/mA3ZCQEFRU3sasVasW27dvZ/Lkyfzvf//DwcGBffv2Ua5cOXmacePGkZyczIABA4iLi6NOnTp4eXmhqflpB9YbGRkRGhqKqakphoaGSHIZByGTyQrV518ik8lknyqjQsGt9hKPX1BOzPYjfEgDm+AvnQWhiLMMOPylsyAUcZpthn7pLJCQkICBgQGX/QLR1dPLNU1SYiI1KjsSHx9f4Nbu/5qzZ89Su3Zt1NTUOHv2bJ5p69WrV6DYomohCIIgCIIgFAmfstvPf9m7FfqCVu4/RFT+BUEQBEEQhCJBhgSZTMlUnwVY5Otr8vDhQ/bv38+TJ0+QSCTY2trSpk0bbG1tCxVPVP4FQRAEQRCEIiELFbKUtPAr2/81mzdvHlOnTkUqlWJqaopMJiMyMpLx48czd+5cxowZU+CY395TFARBEARBEIokmUyS5/YtOX36NJMnT2bSpElERUURGhpKWFgYkZGRTJgwgQkTJnDu3LkCxxUt/4IgCIIgCEKRIJVJ8pjt59uq/K9du5Z+/frlWL3Y2NiYmTNnEhYWxpo1a6hbt26B4oqWf0EQBEEQBKFIyM8iX9+Kq1ev0qNHD6XHe/ToweXLlwscV7T8C4IgCIIgCEWCVCYhS0kL/7fW8h8eHo61tbXS4zY2NoSFhRU4rqj8C4IgCIIgCEXCp1zk678uNTUVdXV1pceLFStGenp6geOKyr8gCIIgCIJQJOQ1sPdbG/ALsGHDBnR1dXM9lpiYWKiYovIvCIIgCIIgFAlZeXT7Ubb/a2VlZcX69es/mKagROVfEARBEARBKBLyGtj7rQ34ffLkyWeJ+211nhIEQRAEQRCKLKlUkuf2rXv+/DlSqfSjYojKvyAIgiAIglAkSJHkuX3rXFxcPvqNgOj2I3wUmUzG4T+mcdFrA6+S47B1qU2XIasxtXRQes6xXfPwv7iX8Of3Kaauha1LLdr+OB+zUo4K6R7f8+Hg1sk8uX8FFRVVLO3cGDLbC3UNLQCSE2P4a/UwAq4cRKKiglvt7+kwcBmaWm8HxrwIvsWuVUN4+uAaugYlqN96CI07jvs8D0PIlUwm48DWaZw/uoGUpDjsXWvTbdhqzEopLyNHdszD78Jewp7dR11DCzuXWrTvNx/z0o450spkMpZPakHANS9+nr6HSrXbyo9FR4SwbdnPBN48jYaWLjUb9+T7vvNQVX37py/w5hn+Wjual0/vYFSiNC26TqK2Z+9P+QgEQfgIa4+cY8neU4THJVDe2pJf+3egatkyuabd5H2JbaevcjckFIBKdqWZ0b2VPH1GZhbTtx3imO9dgsOj0dfWpGFFR2b1bI2FsYE8TkxiMqPW/8ORawGoSFRoW7Mii/q1R1dLQ57m9pMXjFj3N76PQjDR12VQi7qM/t7jMz6Jb0NeLfyi5T/737yPJVr+hY9y/O8FnDmwgi5D1zB26WXUNXVYObkpGempSs95ePscdVv9zJglPgyd601WZgYrJnmSlposT/P4ng+rJjfDuXJjxi67wrjlV6nXajASydsiu2VBd0JD7jBkrjcDpx/kUcB5diz/SX78VXICKyZ5YmxahvErrtOu7wIOb5vBhSO/fZ6HIeTKa9cCTu5bQffha/jfiuwysnRi3mXkwa1zNGj9MxOX+zByfnYZWTLBk7RXyTnSntizFHJpDZJmZbFiUksyM9MZv/QifcZu4ZL3VvZvmSpPExkazPLJLXGsWJ+pa27g0W44v//an4Brxz7FrQuC8JH+vuDH+E17mdSlKT6/jqWCtSWtZ6wmIi73WU7OBTyk03fueM0ayplfRlHKxIhW01fzIjoOgJS0dPwfP2dCJ098fh3Lzgl9efAigo5zFP9d6LPkd+6FhHFoxmB2Tx7AhbtBDF69U348IeUVraavxsrUmEuLxzK3dxvm7DzKxmMXP9uz+FaIRb4+P1H5FwpNJpNxet8ymnaZRMWabbC0qUCvMVuJj37JzUv7lJ43ZPZRajbujUUZV0rZVqTHqM3ERoQQ8tBXnmb3ulHUbzOUJp0mYFHGFbNSjrjX7UQx9exWl7CQe9y97kW34euxcaqOfbk6dBy0HN+zO4mLfgnAtdPbyMpIp/vIjViUcaVK/S7Ubz2UU3uXfNbnIrwlk8k4uXcZLbpNwq1WG0rZVuDH8VuJi37JjYv7lJ43Yt5Ranv2xtLaldJ2FekzdjMxESE8faeMAIQ88sf7n1/pPWZjjhh3fL15GXKXfhP+wMrejfLVmtGm10zOHFhNZkb2vMhnD63FxNyGTgMXU7KMMw3bDsG9bofXXygEQfjSlu8/TZ8mtejZqAbOpUuyYlAntDTU2Xoy91VNt4zqxU/Nv6OibSkcS5mxZvAPSGVSztx6AICBjhaHZwymQ53KlLU0o7qjDUsGdMAv6BkhkTEA3H8WhrffPVYP+YFqZa2p7WLHr/3b8/cFP17GxAOw8+x10jOzWDekKy5WJen0nTs/t6zH8gOn/50H8xWTSiVkKdlEyz/873//w9jY+KNiiMq/UGjRYcEkxIbhWOnta04tHQOsHasTfN8n33FepWT/MdXRyy7MiXERPAm8gp6BKYtG1WbCD+YsGVufRwEX5Oc8vueDlq4hZcpWke9zquSBRKLCk/tXAAi+fxn78nVRK/Z2gQwXd0/CnweSkhhbuJsWCiQqLJj4mDCc3ykj2joG2DpV5/HdApSRZMUyApCWmsKGed3oNnQlBsbmOc55fNcHS+vy6BuZyfe5VvHkVUoCL5/eyU5z7zLOlRopnOfq3qRAeRME4fNIz8jkRtAzGlZ4291PRUWFhhUduRoYnK8YKenpZGRJMdLVVpomISUViUSCoU52l9IrgcEY6mjhbv92CsWGFR1RkUi49uDJ6zRPqO1ih3qxt10IG1dy4sGLCGKTUgpym8J7ZLK8t2/dxIkTMTQ0/KgYos+/UGgJsdlLSr9buQLQMzIjITY8XzGkUim7143E1qU2FtblAIgKfQzAkW0zaNdvIaVs3bhy8ndWTPRg0trbmFo6kBAbhp6BqUIsVVU1tPWM5flKiAmjuLm1Yt4MzeR519YzKtgNCwUWH6O8jMQXoIzsXDMSe9faWNqUk+//a+1I7Fxq4larTe7Xjg3Lcd03n9/kKz4m9zSvUhJIT3slH18iCMK/LyoxmSypFFNDPYX9pgZ6BD7P39+PyVsPUNJIn4YVc44XAkhNz2Dy1v10+q4y+trZv+/hsYmUMFC8ppqqKsZ62oTHJrxOk4C1WXHFfL3OZ3hsQp5fNoS8iXn+c8rKymLLli2cPHmSiIiIHLP9nDp1qkDxROVfyLerp7axY8VA+eefZxz66Ji7Vg3m5ZMARi06L98nk2UX6trNB1CzSR8ASttXItD/FD7em2jTZ95HX1f4PC6f3MafS9+WkaGzP76MbF+RXUbGLXlbRvwvHeD+jdNMWev30fEFQfg6Ldx9nL8v+HFs9lA01YvlOJ6RmUX3hZuRAcsHdvr3Myjk6k0XH2XHvkXDhw9ny5YttGjRgnLlyiGRfNxzEJV/Id8q1GiNtVN1+efMjDQAEmLDMTAuKd+fGBtOKbuKH4y3a/UQAq4eZuTCsxiVKCXfr/86VkkrF4X05lbOxEQ8y05jZE5ifITC8aysTFISY9A3Mn8dx5zEOMU0iXHh8vOFT8+tZmts3ykjGe+UEcPiimWkdD7KyPYVQ7h15TBjF5/F+J0yct//FJGhQQxvq/j2Zs3MDjiU+46xi09jYGRO8P1rCsffvJF6003IwNg8x1uqhNhwtLT1Rau/IHxhJno6qKqo5BjcGxGfiLmRnpKzsi3Zd5LFu09weOZgyltb5jiekZlFt4WbCYmM4ejMofJWfwAzIz0i4xWvmZmVRUxiCmZG+q/T6BP+fr5ef36TRiicvLr3fKvdfnbu3Mlff/1F8+bNP0k80edfyDdNbT1MLezlW0krF/SNzAn0PylP8yo5gSeBV7Bxqqk0jkwmY9fqIdy8tI/h809iYm6jcLy4mTUGxS0Ifx6osD/i+QOMzbL7YNo61+RVUpzCIOEH/qeQyaTyLyg2TjV4dPscWZkZ8jT3bhzHrJSj6PLzmWhq62FqaS/fLMq4YGBszv0bimXk8f0r2LrkXUa2rxjCjYv7GL3gJCVKKpaRZl0mMG3dTaauvSHfADoP/JXeYzYBYOtSkxdPbpMQ+/YL4D2/42hp68u/WNo61+D+DcXXpXf9TuSZN0EQ/h3qxdSoZFea068H60J2N8DTtwKp5mij9LzFe04w/69j7J82UKHf/htvKv5BoZEcnjGY4vo6CserO9oQl/wKv0ch8n1nbj1AKpNRtaz16zTWXLwbREZmljzNSf9Aylqaii4/HykLibzrT47tG53tR11dHXt7+08WT1T+hUKTSCQ0aDscr51zuHX5AC+Cb/P74l4YFLegYq228nTLJnhw5sBK+eddqwZz7dQ2+ozbhoaWHvExYcTHhJGe9koe16P9GM7sX4Hf+X+IePmIg79PIfz5fWo16QtkvwVwqdKU7csG8CTwKkF3LvLXmqG41+uCYXELAKo26IpqMXX+XNqPl0/v4Ht2F2f2Ladhu5H/3kP6xkkkEhq1G87h7XPwv3SA58G32bSgF4bFLRTm41881oNT+96Wke0rBnP55Db6TdyGpnbOMmJgbI6lTTmFDcDY1Er+RcHVvQkWVi5s/KUnz4JuEnDtGPu2TKF+65/ls0bVazmQyLDH/LN+HKEh9zl9YDXXz/6Fx/cj/p0HJAhCnoa1acDm45f489QV7j8LY9jav0hJTadno+xGnr5L/2DKHwfk6RftOc7M7YdZO6QrZUyLExabQFhsAkmvst9CZmRm0XXBRvwehbB5ZE+ypDJ5mvSMTACcSpvTpLIzg1fv5NqDp1y695iR6/+hY53K8rUAOtetgrqaKgNXbuduSCh/X/Bj1aGzDGvd4F9+Ql8fMeA3p9GjR7Ns2bJPMsc/iG4/wkdq3HEc6anJbF/+E6+S4rBzrcPgWUcppq4pTxMVGkRyQpT88/nDawFYOl7xj2T3UZuo2bg3AA3bjSAzI5Xdv40iJTEGS9uKDJnjTQkLO3n63uP+5K/VQ1k+MXuWH7fa39Nx0HL5cS0dA4bOOcauVUP4ZWgVdPVNaNZ1CnWaD/gcj0JQomnn7DLyx9KfSEmKw6FcHYbPUywjkaFBJL1TRs4czC4ji8YolpHeYzblewEuFVVVhs4+yJ/Lfmb+8Fqoa+pQq3FP2vSeKU9ToqQNw2YfYteaUZzcuxwjk1L0HLWeclU9P+KOBUH4VDrWqUxUfBIzdxwhPDaBCjal2D9tEGaG2V1rnkXGovJO/+f1Ry+SnplF1wWbFOJM6tyUyT8052V0HIeuBgBQfeQvCmmOzRpK3fLZiw9uHtmTkb/9Q/OpK1FRkdC2ZkUW9+sgT2ugo8XB6T8zYt3f1Bq9kOL6Okzs7Elfz9qf5Tl8S0Sf/5wuXLjA6dOnOXr0KK6urhQrpjiGZc+ePQWKJ5F9qq8R7waVSNi7dy9t27b91KEVnDlzhgYNGhAbGyuf9mjfvn2MGTOG4OBghg4dipubGyNGjCAuLu6z5qWwVnt9o19jhXxRF1/PhQ9oYJO/KQ+Fb5dlwOEvnQWhiNNsM/RLZ4GEhAQMDAzYcCwObZ3cx02kJCfQz9OQ+Ph49PW/nbEVffr0yfP45s2bCxSvwFWLsLAw5syZw+HDh3nx4gWmpqbyCnajRo0+HOATqlWrFqGhoRgYvF2S+6effqJPnz4MGzYMPT091NTUPtkAifdNnz6dnTt38uzZM9TV1XF3d2fOnDlUr179wycLgiAIgiAICqRSyJIqP/YtKmjl/kMK1Of/yZMnuLu7c+rUKRYuXMjt27fx8vKiQYMGDB48+JNmLD/U1dUxNzeXT3mUlJREREQEnp6eWFhYoKenh5aWFqamph+IlLeMjIxc95ctW5aVK1dy+/ZtLly4gLW1NU2aNCEyMvKjricIgiAIgvAtkskkeW6fS0xMDN26dUNfXx9DQ0P69u1LUlJSnumHDh2Ko6MjWlpaWFlZMWzYMOLj4xXSSSSSHNvOnTs/233kR4Eq/z///DMSiYSrV6/Svn17ypYti6urK6NGjeLy5dyX2gYYP348ZcuWRVtbG1tbW6ZMmaJQob558yYNGjRAT08PfX193N3duX79OgBPnz6lVatWGBkZoaOjg6urK0eOHAGyu/1IJBLi4uI4c+YMenrZU381bNgQiUTCmTNn2LJlS46V0Pbv30/lypXR1NTE1taWGTNmkJmZKT8ukUhYs2YNrVu3RkdHhzlz5uR6X127dsXDwwNbW1tcXV359ddfSUhI4NatWwV5rIIgCIIgCALZrf55bZ9Lt27duHPnDsePH+fQoUOcO3eOAQOUjxF8+fIlL1++ZNGiRQQEBLBlyxa8vLzo27dvjrSbN28mNDRUvhWmW/w///xDp06dqFGjBpUrV1bYCirf3X5iYmLw8vJizpw56Ojo5Die11LDenp6bNmyBQsLC27fvk3//v3R09Nj3LhxQPYDr1SpEmvWrEFVVRV/f3/5YIbBgweTnp7OuXPn0NHR4e7du+jq6ua4Rq1atQgMDMTR0ZHdu3dTq1YtjI2NefLkiUK68+fP07NnT5YvX853331HUFCQ/Ic7bdo0ebrp06czf/58li5diprahx9Teno6v/32GwYGBlSs+OH5ywVBEARBEARFUqny7j2fq9vPvXv38PLy4tq1a1SpUgWAFStW0Lx5cxYtWoSFhUWOc8qVK8fu3bvln+3s7JgzZw7du3cnMzNToe5oaGiIuXnh1xdavnw5kyZNonfv3uzfv58+ffoQFBTEtWvXCtXzJt8t/48ePUImk+Hk5FTgi0yePJlatWphbW1Nq1atGDNmDH/99Zf8eEhICB4eHjg5OeHg4EDHjh3lFeiQkBBq165N+fLlsbW1pWXLltStWzfHNdTV1eXde4yNjTE3N0ddXT1HuhkzZjBhwgR69eqFra0tjRs3ZtasWaxbt04hXdeuXenTpw+2trZYWeWcJ/iNQ4cOoauri6amJkuWLOH48eOYmJgU+BkJgiAIgiB866SyvLfPwcfHB0NDQ3nFH8DDwwMVFRWuXLmS7zhvBiK/32g8ePBgTExMqFatGps2bSrwlJ2rV6/mt99+Y8WKFairqzNu3DiOHz+eazej/Mh3y//HTAq0a9culi9fTlBQEElJSWRmZiqM0h41ahT9+vXjjz/+wMPDg44dO2Jnlz2l47Bhwxg0aBDe3t54eHjQvn17KlSoUOi83Lx5k4sXLyp05cnKyiI1NZWUlBS0tbMX53i3AOSlQYMG+Pv7ExUVxfr16+nUqRNXrlz56HEGgiAIgiAI35qsrOxN2THInhnoXRoaGmhoaBT6mmFhYTnqbWpqahgbGxMWFpavGFFRUcyaNStHV6GZM2fSsGFDtLW18fb25ueffyYpKYlhw4blO38hISHUqlULAC0tLRITs1eT7tGjBzVq1GDlypV5nZ5Dvlv+HRwckEgk3L9/v0AX8PHxoVu3bjRv3pxDhw5x48YNJk2aRHp6ujzN9OnTuXPnDi1atODUqVO4uLiwd+9eAPr168fjx4/p0aMHt2/fpkqVKqxYsaJAeXhXUlISM2bMwN/fX77dvn2bhw8foqn5dt7x3Lo25UZHRwd7e3tq1KjBxo0bUVNTY+PGjYXOX1F39uAqpvSyYXhrLRaMqMGTwKv5Ou/wthlsWdBD6XG/838zs78zw1trMWdQBQKuHslX3MvHt7J49He5Hntw6wyDm6nk2OJj8veLLBTO6f2rmNDdhkHNtZg7tAbB9/NXRg78MYMN85WXketn/2bKj84Maq7F9P4VuH0lf2XkkvdWfhmRexkJvHmG/o1VcmyijAjClzF7xxG02g5T2CoOnp2vc+fsPEqfJb8rPb774g0qDp6NYcdRVBk2D6/rd/IV989TV2g4canS4+duP6TmqAUYdBiJ68CZ/HEy/y3FQk75WeSrdOnSGBgYyLd58+blGmvChAm5Drh9dytovTY3CQkJtGjRAhcXF6ZPn65wbMqUKdSuXZtKlSoxfvx4xo0bx8KFCwsU39zcnJiYGACsrKzk42yDg4ML1Tif75Z/Y2NjPD09WbVqFcOGDctROY6Li8u13/+lS5coU6YMkyZNku97+vRpjnRly5albNmyjBw5kh9++IHNmzfTrl07IPuHPHDgQAYOHMjEiRNZv349Q4cWbk7aypUrExgY+EmXSX6XVColLS3ts8T+0nzP7mLPb6PpMnQN1o7VOb1vKSsnN2Xa+vvoGeb9puO2zwEadxqf67HHdy+xeX5XWveZS/lqLbl2Zju/zWrHhBW+WFiXyzPurcsHqFCjVZ5ppq6/j6b22zdNH8qrUHjXzuzir3Wj6T5sDTbO1TmxZylLJzZl1qb76Bvl/dxvXjpA0y65l5FHdy6xfm5Xvu87lwrVW3Ll9HZWTW/HlNW+8tV9lfG/dICKNfMuI7M230dLlBFBKBJcrEpyeMbbfsxqqvlrpzx09Tajv/fI9ZjP/cf0WryVmT1a0byKK7vO+dJp/gZ8Fo/FtUzO/tzvOnj1Ni2q5v535kl4NO1mr6OfZ202j+zJ6VsPGLRqB+bG+jSu5JyvfAuKsmTKB/Zmva7nPnv2TKEHibJW/9GjR9O7d+88r2dra4u5uTkREREK+zMzM4mJiflgX/3ExESaNm2Knp4ee/fuzbEA1/uqV6/OrFmzSEtLy/fbioYNG3LgwAEqVapEnz59GDlyJP/88w/Xr1/n+++/z1eMdxVonv9Vq1ZRu3ZtqlWrxsyZM6lQoQKZmZkcP36cNWvWcO/evRznODg4EBISws6dO6latSqHDx+Wt+oDvHr1irFjx9KhQwdsbGx4/vw5165do3379gCMGDGCZs2aUbZsWWJjYzl9+jTOzoX/hZo6dSotW7bEysqKDh06oKKiws2bNwkICGD27Py1LgAkJyczZ84cWrduTcmSJYmKimLVqlW8ePGCjh07Fjp/RdnJvUuo1awfNZtkLzbRZehaAq4dwcd7E006TVB6XmzkM0Kf3sHFvWmux0/vX45LlaY07jAWgFY9Z3Hf7wRnD67kh6FrlcbNSE/lnp83rXvnPhvTG3qGpmjrGn7g7oRP4fjuJXzXrB+1m2aXke7D13L7yhEuHttEsy7Ky0hMxDNePr1DuSq5l5GTe5fjWrUpnp2yy0jb3rO463uCU/tX0mNE3mXkrq837X7Mu4zoizIiCEWGmooK5kYFW8DpWWQsd0NCaVI59/rBqoNnaVLZmVHtstcjmtatBSdv3mftkfOsGNRZadzU9AxO+t9nZvfcGxDWe13A2qw4v/yY3VjpVNqcS/ces+LAaVH5LySZTKa0NfvNfn19/Xwt8lWiRAlKlCjxwXQ1a9YkLi4OX19f3N3dATh16hRSqTTPtZsSEhLw9PREQ0ODAwcOKPQgUcbf3x8jI6MCdVP67bffkL4e7Tx48GCKFy/OpUuXaN26NT/99FO+47xRoKk+bW1t8fPzo0GDBowePZpy5crRuHFjTp48yZo1a3I9p3Xr1owcOZIhQ4bg5ubGpUuXmDJlivy4qqoq0dHR9OzZk7Jly9KpUyeaNWvGjBkzgOz++IMHD8bZ2ZmmTZtStmxZVq9eXeAbfcPT05NDhw7h7e1N1apVqVGjBkuWLKFMmTIFiqOqqsr9+/flU562atWK6Ohozp8/j6ura6HzV1RlZqTz7KEvTm5vW1VUVFRwcvPg8T3l07xCduu8Q4X6aClZsS/4ng+ObooLxDm7NyH4A3ED/U9iWNwS89J5D0KfN7gSE7tasOJ/TQi6czHPtELhZWak8/SBL86VFcuIc2UPgu7m/bO86XOAsnmUkcd3fXCprFhGXKs0+WDZu3fjJIYmlpS0yruMzBxYiTGdLfh1fBMeBYgy8l+1f/9+KlQoL9+uXbv2pbMkFMKj0Ehs+kzG+acZ9P51KyGRMR885/C129Qt54C+tlaux68EPqFBhbIK+xpXcuZKYN4rZJ++9QALY0McS5kVIK4TVwKffDDPQu6kWW/7/b+/SZWMBfhYb+qY/fv35+rVq1y8eJEhQ4bQpUsX+Uw/L168wMnJiatXs7uyJiQk0KRJE5KTk9m4cSMJCQmEhYURFhZG1uvBCQcPHmTDhg0EBATw6NEj1qxZw9y5cwvce0VFRUVhEHGXLl1Yvnw5Q4cOzXVymw8p8Aq/JUuWZOXKlXkOLnj/G9uCBQtYsGCBwr4RI0YA2bP07NixQ2msvPr3169fX+FahoaGOa7du3fvHK98PD098fT0zHf+c6OpqcmePXs+mO5daWlpOboEZaSrU0y98INU/i1JCVFIpVnoGSn+AdQzMiXsed795W75HKBCzdZKjyfEhqH/Xlx9IzMSYvPud33LZz/l8+jyY2Bcki5D11DGoQqZGWlc9NrA0vENGLv0Mlb2BZ8XV8hbUnx2Gcn5szQl7FneZcT/0gHcaikvI/GxYegZ5iwjH+qb739pf55dfgyMS9J9+Bqsy1YhIyONC0c3sGhMAyauuEwZB1FG/msaNWqkMNXyx0ytJ3wZVcta89uwbpS1NCUsNoE5O4/i8b9l+C6fiJ6W8lbVQ1du07J6eaXHw+MSMDVUbFwwNdAjPDYxz/wcunKbFtWUdy0Mj0vAzFAvR9yElFRepaWjpVHwitm37t2+/bkd+1y2bdvGkCFDaNSoESoqKrRv357ly5fLj2dkZBAYGEhKSgoAfn5+8pmA3u9KHhwcjLW1NcWKFWPVqlWMHDkSmUyGvb09v/76K/379y9w/s6fP8+6desICgrin3/+wdLSkj/++AMbGxvq1KlToFgFrvwLhTdv3jz5G403mnebSovu079Mhv4Fr5ITeHT7LN1HbvikcWUyGbevHKLv/3YpTWNWyhGzUo7yz7YutYgKfcypvUvpPVb5oDDh3/UqOYEHt8/Sa/SnLyO3Lh9iwGTlZcS8tCPmpd+WEXvXWkS+fMyJ3UvpO0GUkf8aXV3dXNeBEf47PN1d5P9f3tqSqg5lcBwwnd0XbtC7cc1cz0lIecX5O49YM6TrJ82LTCbjyPUA/hjT55PGFfKW12Jen3ORL2NjY7Zv3670uLW1tULj8PsN0Llp2rQpTZvm3p21IHbv3k2PHj3o1q0bN27ckDckx8fHM3fuXPnit/lVoG4/wseZOHEi8fHxCluTThO/dLbyRVffBBUVVRJjwxX2J8ZGoG+kvHXt7vWjmFu5YFSitNI0+kbmJLwXNyE2PM+4TwKvIs3KxNa5Vj7vIFsZx6pEvXxUoHOE/NE1yC4jOX+WeZeRgGtHKWnlgrGp8jJiYGROYlzOMmJgrDxu8P2rZGVlYu9SsDJi7VSVCFFGBKFIMNTVxt7ClKCwSKVpjvndw7m0OaVLGClNY2aoT0Sc4vSQEfGJmBnpKTkDrj18SmZWFjWdbPKMGx6n+PYgIj4RfW1N0epfSDKpLM/tWzR79mzWrl3L+vXrFQYU165dGz8/vwLHE5X/f5GGhoZ8kMqb7b/Q5QdArZg6pR3cCfQ/Kd8nlUoJ9D+JrXMNpefdupx3lx8AG+eaBPqfUth3/8YJbPKMux/Xai1QUVXN5x1ke/H4JvrGJQt0jpA/asXUKVPWnXs3FMvIvRsnsXNR/rP8UJcfAFuXmty7oVhG7vmdyLPs+V/aT/lClJFnQTcxEGVEEIqEpFdpBIdFYW5koDTNoSu3aVlNeZcfgOqO1py59UBh30n/+1R3VF6xP3TlNk3dXVHNY7ah3OMGUt3ROs/8CMq9aflXtn2LAgMDc13g1sDAgLi4uALHE5V/Id8atRvJRa8NXD6+lbCQe+xcOYi0tGRqNM79lWhWViZ3rh+lfI28K3YN2gzjrq8XJ3YvJuzZfQ7/OZ2Qh9ep12qI0nNuXz74wSk+T+1dyk2f/US8fMTLJwH8s3YEgTdPUbflzx++WaFQGrcfyfkjG7jkvZXQp/fYtnwQ6anJ1PZUXkYCrh2l4ge+IDZqN4w717zw/nsxoSH3OfD7dJ48uE7DNsrLyM3LB3H7wBSfJ/Ysxf/SfiJePOJFcAA7V4/gvv8pGrQWZUQQvoQJm/dxPuAhT8Oj8bn/mM7zN6CqIqHTd7mPwcnMysLb7y4tPlD5H9yqHt437rF03ykCn4cze8cR/IKeMbB57muAABy+FvDBuP2b1iE4PJr/bdlP4PNw1h05z+6LNxjausGHb1bIVVaWLM/tW2Rubs6jRznfSF+4cAFbW9sCxxN9/oV8c6/XmcT4SA79OY3EmDAs7dwYPOtojgGebzy8dRYNTd0PDq61dalFn/HbOLh1Cge3TKKEpQMDpuxVOsd/5MsgIl8+wtld+aBtgKzMdPasH0N89AvUNbSxsKnAsLnHKVtR/FH+XKrW70xiXCT7t04jITaM0nZuDJ+rvIw8uHkWDS3dDw6utXetRb+J29i3ZQp7N0/C1NKBwdP3Kp3jP+JlEBEvHuFaJe8ykpmRzl/rxhAXlV1GStlWYNQvx3FyE2VEEL6EF9Fx9Fy8lZjEZEwMdKnlbMfZX0ZRwiD37jnnAx6ho6lBJTvl3QYBajrZsmVUL2ZsO8y0Pw9ib2HKXxP6KZ3j/3FoJEGhkTSulPdMYdZmxdk7+SfGbdrDqkNnsCxuyJrBP4hpPj/ClxrwW5T179+f4cOHs2nTJiQSCS9fvsTHx4cxY8YozKCZXxJZYZYG+wJWrVrFwoULCQsLo2LFiqxYsYJq1arlmnbLli306aPY0qihoUFqaqr8s0wmY9q0aaxfv564uDhq167NmjVrcHBwkKeJiYlh6NChHDx4UD7ye9myZQoDym7dusXgwYO5du0aJUqUYOjQoYwbNy7f97Xa6z/x+AvlrzXDkGZl0mVI4admzc3JPb9y/8ZJBs86/EnjFkXqX/nX8x2rsstIt2Gftox4//Mr9/xOMnzu119GGtjkPVWhIFgGfL2/B6PW/0NWlpRlAzt90rjL9p/i9M0H7Js68JPGLao02xRu4dRPKSEhAQMDAyb+Fq2wMOe7UlMSmDegOPHx8fma5/9rIZPJmDt3LvPmzZPPNqShocGYMWOYNWtWgeP9J7r97Nq1i1GjRjFt2jT8/PyoWLEinp6eOVZje5e+vj6hoaHy7f1VhRcsWMDy5ctZu3YtV65cQUdHB09PT4UvCN26dePOnTscP36cQ4cOce7cOQYMGCA//maO1zJlyuDr68vChQuZPn06v/3226d/CP9BFmXK8V2LQZ88rqFJKTw7K18wSvjvsLQuR71Wn76MGJUoRbMfRBkRhK+dq1VJ+jct2DSH+WFZ3JAx7Rt/8rjCh8mQyRf6yrHx9TaY5kUikTBp0iRiYmIICAjg8uXLREZGFqriD/+Rlv/q1atTtWpV+doCUqmU0qVLM3ToUCZMyPkP/JYtWxgxYoTSQRAymQwLCwtGjx7NmDFjgOzpkszMzNiyZQtdunTh3r17uLi4cO3aNapUqQKAl5cXzZs35/nz51hYWLBmzRomTZpEWFiYfJGFCRMmsG/fPu7fz3te8ze+5pZ/4eN97S3/wscTLf/Ch3zNLf/Cp1GUWv7HrYlEQyv3Vv20VwksGFTim2n5//HHH/OVbtOmTQWKW+Rb/tPT0/H19cXDQ3HVUA8PD3x8fJSel5SURJkyZShdujRt2rThzp078mPBwcGEhYUpxDQwMKB69erymD4+PhgaGsor/gAeHh6oqKjIF3Xw8fGhbt26CqureXp6EhgYSGxs7MffvCAIgiAIwjdEKst7+5Zs2bKF06dPExcXR2xsrNKtoIp8u2JUVBRZWVmYmSkOGDQzM1Pauu7o6MimTZuoUKEC8fHxLFq0iFq1anHnzh1KlSpFWFiYPMb7Md8cCwsLw9TUVOG4mpoaxsbGCmlsbGxyxHhzzMhI+ZzDgiAIgiAIgiJplgypkll9lO3/Wg0aNIgdO3YQHBxMnz596N69O8bGxh8dt8hX/gujZs2a1Kz5diXAWrVq4ezszLp16wrdP0rI3dmDqzjxzyISYsOwtK1Ip0HLsXbMfSA2gN/5vzn0+1Siw59gaulAmz7zKVetuUKasJB77Ns0gYe3zyLNysTcyoX+k//B2NSK5MQYDv8xjXt+x4mNDEHXoAQVarahVc9ZaOm8nQd6cLOcL7X6jN9OlfpdPt3NCx90ev8qjv29iPiYMErbVeSHwcuxcVJePq6f/Zv9W6cSFfYEM0sH2vebT/nqiuUj9Ok9dm+YwINbZ8mSZlLSyoVB0/6huKkVyQkx7P99Gnd9jxMTEYKeQQncarehTe9ZaL9TPvo3zlk++v9vO9UaiPIhCEXN2iPnWLL3FOFxCZS3tuTX/h2oWraM0vS7L95g5vbDPI2Iwb5kCWb3bE3TKq4Kae4/C2Py7wc4f+cRmVlSnEqbs2P8j1iVMCYmMZlZO45y0v8+z6JiMdHXpVX18kzr2gIDHS15DK22w3Jce+voXnT6zv3T3fw3SCqVIVXSxK9s/9dq1apV/Prrr+zZs4dNmzYxceJEWrRoQd++fWnSpAkSiaRQcYt85d/ExARVVVXCwxVX9wwPD8fcXPnqnu8qVqwYlSpVks+R+ua88PBwSpZ8u5hPeHg4bm5u8jTvDyjOzMwkJiZGfr65uXmu+Xr3Gl8z37O72PPbaLoMXYO1Y3VO71vKyslNmbb+PnqGpjnSP757ic3zu9K6z1zKV2vJtTPb+W1WOyas8JVP6xn5Mohfx3xHTc8fadF9Opra+oSG3KGYuiYA8dEviY8J5ft+CzG3ciEm4ik7Vw4iPjqU/pP/Vrhe91GbcHF/u6y2tq7h53sYQg7Xzuzir3Wj6T5sDTbO1TmxZylLJzZl1qb76BvlLB+P7lxi/dyufN93LhWqt+TK6e2smt6OKat95VN6RrwM4peR31Gn2Y+07pVdPl4+uUOxYtnlIy76JfHRoXQcsJCSZVyIDn/Kn8sGERcdyqCpiuWj95hNlKsqyocgFGV/X/Bj/Ka9rBjUmaply7DywFlaz1jNzVWTMTXMOf2nz/3H9Fq8lZk9WtG8iiu7zvnSaf4GfBaPlU/r+Tg0kkb/W0qvRjWZ/EMz9LU0ufssDM3XK6eGxsQTGhPPvN5tcC5tTkhkLEPX7iI0Jp4d4/sqXO+3od1oXPnttJ6G73w5EApHtPwr0tDQ4IcffuCHH37g6dOnbNmyhZ9//pnMzEzu3LmjMANlfhX5Pv/q6uq4u7tz8qTiqqEnT55UaN3PS1ZWFrdv35ZX9G1sbDA3N1eImZCQwJUrV+Qxa9asSVxcHL6+vvI0p06dQiqVUr16dXmac+fOkZGRIU9z/PhxHB0dv4kuPyf3LqFWs37UbNKHkmVc6DJ0Leoa2vh45z7w5PT+5bhUaUrjDmMxt3KmVc9ZlLarzNmDK+VpDm6djEvV5rTru4DS9pUoYWFHhRqt5V8mLKzL0X/yP5Sv0YoSFnY4ujWkVa/ZBFw5SFZWpsL1tHUMMTA2l29vvkAI/47ju5fwXbN+1G7aB4syLnQfnl0+Lh7LvXyc3Lsc16pN8ew0lpJlnGnbexZW9pU5tf9t+di3eTLlqzWnQ/8FWNlXwtTCDrdareVfJixtyjFo2j9UrNkKUws7nCs1pF2f2dy6nEv50BXlQxCKuuX7T9OnSS16NqqBc+mSrBjUCS0NdbaevJxr+lUHz9KksjOj2jXCqbQ507q1wM22FGuPnJenmbbtMJ6VXZjbuw1utqWxLVmCltXKy79MuJaxYOeEvrSoVh7bkiWoX6Es07u15Mi1ADKzshSuZ6CjhbmRvnzTVC/2+R7GNyJLKs1z+5apqKggkUiQyWRkvVcWCxTnE+bpsxk1ahTr169n69at3Lt3j0GDBpGcnCyfy79nz55MnDhRnn7mzJl4e3vz+PFj/Pz86N69O0+fPqVfv35A9pRJI0aMYPbs2Rw4cIDbt2/Ts2dPLCwsaNu2LQDOzs40bdqU/v37c/XqVS5evMiQIUPo0qULFhbZrQddu3ZFXV2dvn37cufOHXbt2sWyZcsYNWrUv/uAvoDMjHSePfTFyU1xILaTmweP7+X+Rzn4ng+Obo0U9jm7NyH4dXqpVErAtcOYWTqwclJTxncxY8GIGty8tC/PvLxKjkdTWx9VVcUXWbtWD2Fc5xIsGF6dS8c28R+Y2OqrkZmRztMHvjhXViwfzpU9CLqbe/l4fNcHl8qK5cO1ShN5eZJKpdy6chizUg4smdCUUR3NmDu0Bjcu7sszL8rKx/YVQxjZvgRzhlTngpcoH4JQ1KRnZHIj6BkNKzjK96moqNCwoiNXA3Of5epK4BMaVCirsK9xJWeuvE4vlUrxun4HBwtTWk1fjVWv//Hd2MUcuHwrz7wkpLxCX1sTNVVVhf0jfvubUj0mUmfsIrae8BF/Rz6BN91+lG3fmrS0NHbs2EHjxo0pW7Yst2/fZuXKlYSEhBSq1R/+A91+ADp37kxkZCRTp04lLCwMNzc3vLy85INrQ0JCUFF5+z0mNjaW/v37ywfduru7c+nSJVxcXORpxo0bR3JyMgMGDCAuLo46derg5eWFpubb1r9t27YxZMgQGjVqJF/ka/ny5fLjBgYGeHt7M3jwYNzd3TExMWHq1KkKawF8rZISopBKs9B7b+VWPSNTwp7nPhA7ITYsx0qv+kZmJMRmD6BOjIsg7VUS3n/9Qqtes2jz43zu+XqxfnZ7hs8/hUOFejnzER/F0R2zqd2sv8L+lj1mULZiQ9Q1tLnn582uVYNJS02iQZucfTSFTy8pPrt85Px5mxL2LPfyER8bhp5hzvIRH6NYPo7u+oW2vWfRvt987lz3Ys2M9oxeeArHijnLR2J8FIe2zaZuc8Xy0abXDJzcGqKuqc2d695sWz6YtFdJNGonyocgFBVRiclkSaU5uveYGugR+Dw813PC4xIwNdTPkT48NhGAiPgkklLTWLTnBNO6tWB2z9Z437hHl182cmzWEL4r55AjZlRCEvP+OsaPTWor7J/6Q3PqVSiLtkYxTvjfZ/i6v0lKTWdwy5x/i4T8k0rz6PbzjVX+f/75Z3bu3Enp0qX58ccf2bFjByYmJh8d9z9R+QcYMmQIQ4YMyfXYmTNnFD4vWbKEJUuW5BlPIpEwc+ZMZs6cqTSNsbEx27dvzzNOhQoVOH/+fJ5phPyRybJf51Wo2YaG7UYCUNrOjcd3fTh/ZF2Oyv+r5ARWT2tJSSsXWnSfrnCsWde3y12Xtq9EemoyJ/5ZJCr//2Gy16973Wq2oXH77PJhZe9G0B0fzh5al6Py/yo5gRWTW2JRxoVWPacrHGvZ/W35sHpdPo79vUhU/gXhKyd93TLfslp5hrVuAEBF21JcuR/M+mMXc1T+E1Je0W7WOpxLmzO5SzOFYxM7vx0z5GZbmpTUdJbsPSkq/x9JJpPJf065HfuWrF27FisrK2xtbTl79ixnz57NNd2ePXsKFPc/0e1HKHp09U1QUVElMVax9SUxNgJ9o9wHO+sbmZPwXvqE2HB5el19E1RU1TC3clZIY17aidjIEIV9qSmJrJrSDE0tPQZM2YOqWt79LK2dqhMX9ZyM9LR83Z/wcXQNsstHzp+38vJhYGROYlzO8mFgbC6PqaqqRsky75UPKydiInKWj2X/yy4fP0/fg9oHyoeNc3ViI0X5EISixERPB1UVFSLiEhX2R8QnYm6Uc7AvgJmhPhFxCTnSm71Ob6Kng5qqCs6lFf8OOZYy41mk4nzpia9SaT1jDXpaGuya0I9iaopdft5Xtaw1L6LjSHtnHKBQcNJMaZ7bt6Rnz540aNAAQ0NDDAwMlG4F9Z9p+ReKFrVi6pR2cCfQ/yQVa7UFsvtSBvqfpF7rwbmeY+Nck0D/UzRsN0K+7/6NE9g415DHLFO2KuHPHyicF/HiIcamb6d1e5WcwKrJTVErpsHAafvzNVDzeZA/2rpGFFPXKOCdCoWR/bN0596Nk1Sq3RbILh/3bpykYZvcy4etS03u3TiFx/cj5Pvu+Z3A9p3yYe1YlfBniuUj/MVDipsplo+lE7PLx+CZ+Ssfzx75o60nyocgFCXqxdSoZFea07ce0LpGBSD778jpW4EMbF4313OqO1pz5tYDhr5u1Qc46X+f6o428pju9lY8eKHY0PDwZSRWJd7On56Q8opWM9agoabGP5MG5Gsg763g5xjpaqNRTAz6/Rh5Leb1jfX6YcuWLZ8lrqj8C4XWqN1Ifl/cGyuHKlg7VuPUvqWkpSVTo3H2QOyti3phWNyCNn3mAdCgzTCWjKvPid2LKVetBb5ndxLy8Dpdh62Tx/RoP4ZN87vgUO47HCo24O51L25fOcjwX04D2RW7lZM8SU9LodfYP3iVksCrlOxWHj2DEqioqnL78kES4sKxcaqBmrom9/2Oc2zXPBq1H/0vP6FvW+P2I9m0oDfWZatg41iNE3uXkp6aTG3P7PKx8ZdeGJlY8H3f7PLRqN0wFo2uj/ffiylfvQXXzuzkyYPr9Bjxtnw06TiG3+Z0waHCdzhVbEDANS9u+RxkzOK35WPJhOzy0XfCH6SmJJD6Xvm46XOQhNhwbJ1rUExdk7t+xzmycx5NOojyIQhFzbA2Dei/7E/c7UtTxaEMKw+eISU1nZ6Nsmfd67v0DyyKGzCrR2sABreqR5NJy1m67xTNqrjy93lf/IKesernt2t4jGzXiB6LtlDH1Z565R3w9rvHkWsBHJs9FMiu+LecvppXaRlsntCDhJRUElJSASihr4uqqgqHr94mIj6RamWt0VQvxkn/+yz45zgj2jb8dx/QV0iaJUWalXsLv7L9QsGIyr9QaO71OpMYH8mhP6eRGBOGpZ0bg2cdlQ/yjI0IQSJ527PM1qUWfcZv4+DWKRzcMokSlg4MmLJXPsc/gFvtdnQZsgbvv+bz99rhmJZypN/kf7AvVweAZ0F+PAm8AsD0vop9M2dueUxxM2tU1Ipx7uBqdv82CplMRgkLe74fsJjaTRUHfQqfV9X6nUmMi2T/1mkkxIZR2s6N4XPflo+Y98qHvWst+k3cxr4tU9i7eRKmlg4Mnr5XPsc/QOU67eg+fA1Hd8xn56rhmJVyZNC0f3B4XT5CHvkRfD+7fEzqpVg+5v3xGBNza1TVinH6wGp2rR0Fr8tHp58W811zUT4EoajpWKcyUfFJzNxxhPDYBCrYlGL/tEGYvR7U+ywyFpV3Fjqq6WTLllG9mLHtMNP+PIi9hSl/Tegnn+MfoE2NiqwY2ImFu08wesNuylqYsmP8j9R2sQPAP+g51x48BcB1kOLCoPfXTaOMWXGKqamy7sh5xm3ciwwZduYl+OXHdvzYOH9TkAvKSaXKB/Z+4zN9fjIS2bc2eqKIWe0lHr+gnLr4ei58QAOb3Kc8FIQ3LAMOf+ksCEWcZpuhXzoLJCQkYGBgQPf/BaGumfuYjvTURP6ca0d8fDz6+vq5phE+TFQtBEEQBEEQhCIhSyolS0n3nm99ka9PRVT+BUEQBEEQhCJBJpUhU9LtR9l+oWBE5V8QBEEQBEEoEqRZylv+xYDfT0NU/gVBEARBEIQiQbT8f36i8i8IgiAIgiAUCdKsLKRZWUqPCR9PrPArCIIgCIIgFAlSqSzP7XOJiYmhW7du6OvrY2hoSN++fUlKSsrznPr16yORSBS2gQMHKqQJCQmhRYsWaGtrY2pqytixY8nMzPxs95EfouVfEARBEARBKBK+1CJf3bp1IzQ0lOPHj5ORkUGfPn0YMGAA27dvz/O8/v37M3PmTPlnbW1t+f9nZWXRokULzM3NuXTpEqGhofTs2ZNixYoxd+7cz3YvHyIq/4IgCIIgCEKR8CX6/N+7dw8vLy+uXbtGlSpVAFixYgXNmzdn0aJFWFhYKD1XW1sbc3PzXI95e3tz9+5dTpw4gZmZGW5ubsyaNYvx48czffp01NXVP8v9fIjo9iMIgiAIgiAUCdnz/Gflvn2mef59fHwwNDSUV/wBPDw8UFFR4cqVK3meu23bNkxMTChXrhwTJ04kJSVFIW758uUxMzOT7/P09CQhIYE7d+58+hvJJ9HyLwiCIAiCIBQJ+Wn5T0hIUNivoaGBhoZGoa8ZFhaGqampwj41NTWMjY0JCwtTel7Xrl0pU6YMFhYW3Lp1i/HjxxMYGMiePXvkcd+t+APyz3nF/dxE5V8QBEEQBEEoErKyslBRMqtP1uv9pUuXVtg/bdo0pk+fniP9hAkT+OWXX/K83r179wqXUWDAgAHy/y9fvjwlS5akUaNGBAUFYWdnV+i4n5uo/AuCIAiCIAhFQn6m+nz27Bn6+vry/cpa/UePHk3v3r3zvJ6trS3m5uZEREQo7M/MzCQmJkZpf/7cVK9eHYBHjx5hZ2eHubk5V69eVUgTHh4OUKC4n5qo/AuCIAiCIAhFQn66/ejr6ytU/pUpUaIEJUqU+GC6mjVrEhcXh6+vL+7u7gCcOnUKqVQqr9Dnh7+/PwAlS5aUx50zZw4RERHybkXHjx9HX18fFxeXfMf91MSAX0EQBEEQBKFIkCob7JvHG4GP5ezsTNOmTenfvz9Xr17l4sWLDBkyhC5dushn+nnx4gVOTk7ylvygoCBmzZqFr68vT5484cCBA/Ts2ZO6detSoUIFAJo0aYKLiws9evTg5s2bHDt2jMmTJzN48OCPGqPwsUTlXxAEQRAEQSgS3rT8K9s+l23btuHk5ESjRo1o3rw5derU4bfffpMfz8jIIDAwUD6bj7q6OidOnKBJkyY4OTkxevRo2rdvz8GDB+XnqKqqcujQIVRVValZsybdu3enZ8+eCusCfAmi248gCIIgCIJQJEizspBm5t3n/3MwNjbOc0Eva2trZLK3Xz5Kly7N2bNnPxi3TJkyHDly5JPk8VMRlX9BEARBEAShSJDJpMhkuc/nr2y/UDCi8i8IgiAIgiAUCVmZUiQSJVN9ZorK/6cgKv+CIAiCIAhCkZCf2X6EjyMq/4IgCIIgCEKRkJGWoLRvf1Zm8r+cm6+TqPx/YaeOBX/pLAhFWDH1Yl86C0IRp9Hc5ktnQSjiapVr8aWzIBRxRWEtWnV1dczNzbl2vEOe6czNzVFXV/+XcvV1EpV/QRAEQRAE4YvS1NQkODiY9PT0PNOpq6ujqan5L+Xq6yQq/4IgCIIgCMIXp6mpKSr2/wKxyJcgCIIgCIIgfCNE5V8QBEEQBEEQvhGi8i8IgiAIgiAI3whR+RcEQRAEQRCEb4So/AuCIAiCIAjCN0JU/gVBEARBEAThGyEq/4IgCIIgCILwjRCVf0EQBEEQBEH4RojKvyAIgiAIgiB8I0TlXxAEQRAEQRC+EaLyLwiCIAiCIAjfCFH5FwRBEARBEIRvhKj8C4IgCIIgCMI3QlT+BUEQBEEQBOEbISr/giAIgiAIgvCNEJV/QRAEQRAEQfhGiMq/IAiCIAiCIHwj1L50BoT/NplMxt3LS3lyexfpaQkUt3CnUsOZ6BnZ5Ov8wGtrCbi4EHu33lSsP0W+PzU5ktvn5xMecoHM9GT0jGxxqvYzlg5N5WnSU+PwPz2D0OBTSJBg6dCUivWmoKauI08TH3mfG6enERt+Cw0tY+zceuJY5adP9wCED5LJZARc+JWgW9vJSEvAxLIKVRrPRc84f2Xk7uVV3Dr3C2Xdf6Ryo+kKx6Je+HLr/EKiQ28gkahiZOpCvY5/olZME4C0V3H4nZjKi6ATSCQqlCrbjMqNplPsnTISF3GP6ycmExN6Cw1tY8pW7o1z9UGf7P6FvMlkMvZsnsaZQxtISYrDoVxteo9ajXkpB6XnHNw2j+vn9hIacp9iGlo4uNai80/zKWnlCEBSQgx7Nk8j4PpxosND0DMsgXudNrT/cRbaugbyOFHhIWxd8jP3bpxGQ0uXOp496dR/Hqpqb/9pvHfjDNtXj+bFkzsYlyhNmx6T+K5Z78/2PARBED430fIvfJQH138j6MZWKjWaRcMue1Arps2FvX3Iykz74LkxYbd4fHsHBiZOOY5dOzaGxNjH1Gr9Gx49jmBh34TLR4YSF3FHnubq0ZEkRD/ku3ZbqdVmA5EvruJ3cpL8eEZaIuf39kJb35JGXfdT/rsJ3Lu8nMe3d3yamxfy5f7VNTzw20yVJvNo3P0AasW0OfN3d7IyUz94bnToTYJubsewhHOOY1EvfDn7d0/Mrb+jSY8DNOlxEIfKvZBIJPI0lw8NIz76AQ06baNu+01EPrvCtWMT5Mcz0hI583d3dPRL4dnrEG71JxFwcQmP/Ld9mpsXPujwjgUc372C3qPWMG3NZTS0dFg4tinpacrLx33/c3i0/Zmpq30Yv8ibrKwMFoz1JO1VMgBxUS+Jiw7lh0ELmbv5NgMmbObW1WNsXNBPHkOalcWvE1qSmZHOlJUXGTBxCxe8trJn81R5msjQYBZPbIlzpfrM2nADzw7D2biwP7euHvtsz0MQBOFzE5V/odBkMhmPbmzGqfpgLOwaY1DCiaqei0hNDudlkHee52amJ3PNaySVPeZSTMMgx/HoUD/s3HpibF4RXQMrnKsPQV1Dn9iIAAASYh4R/vQc7o3nYlzSDRPLKrjVn8azwEO8SgoHIOT+AaRZGVRpPB/94mUp7dgKO7dePPTb9OkfhpArmUxG4PWNuNYcSimHJhiaOlO9xRJeJUXw/GHeZSQjPZnLh4ZR1XM+xTRzlpEbp2bi4N4HlxqDMTBxRL+4HVZOrVBV0wAgPvohocFnqOb5C8UtKlGiVDXcPWYScu8ArxLDAHhydx/SrHSqNVuIgYkjZZxbU9a9D4HXN3z6hyHkIJPJOPbPMlr3mIR7nTZY2VXgp4lbiYt6id+FfUrPG7vwKN81600pG1es7CvSf8JmosNDCH7gC0Ap23IMm/kPlWq1wszSDpfKDenYbzY3fA6SlZkJwO3r3rx4epeBk/6gjIMbFas3o/2PMzmxbzWZGekAnDqwlhLmNnT9eTGWZZxp/P0QqtbrwLG/l37uRyMIgvDZiMq/UGjJCc9ITYnEtHRt+b5iGnoYm7sRHXojz3NvnJ6GuU0DzKxq53q8eMnKPH9wmPTUOGQyKc8CD5KVmUaJUtUBiAm9QTENfYzMKsjPMbWqjUSiQkyYf3aaMD9MLKuioqouT2NW5juSYh+Tnhpf2NsWCiA5PoTU5EjMytSR71PX0Kd4STeiX/rmea7v8cmUtG2IufV3OY6lJkcRHXoDTe3iHP+zHXtXVubk9o5EPr8qTxP9wo9iGvoYl6wo32dmXQeJRIXoUP/sNC99KVGqOqrvlBFz63okxgSRnhpXyLsW8isyNJj4mDBc3T3k+7R1DbB1qc6juz75jvMqKfv3WVfPWGmalKR4tLT15V16Ht3xobRNeQyMzeRpylfz5FVyAs+f3Hmd5jKu7o0U4pSv1qRAeRMEQShqROVfKLS05EgANHRMFPZraJvIj+XmWeBB4iLuUK72WKVpqjdfgTQrk4Nr3dm7whm/k5Op2WoNuobWQPaYAA3t4grnqKiooa5pQGpy1Os0UWhqK+btzefUFOX5Ez6d1NflQPO9MqKpY8KrJOU/g6f3DhAbHkDFeuNzPZ4UHwJAwMUl2FX8gfodf8fIrBynd3UlMSYYgFfJkTl+/ioqaqhrGfLqdb5eJUfmmrc3x4TPKz4m+w3MuxVwAAMjM+JiwvMVQyqV8ufKkTiUq00p23K5pkmMi2L/H7Op36q/wrX137uuvpGZQr7iYsJyzdur5ATS017lK3+CIAhFjRjwK+RbyP39+J2cLP9cu03Bu0akJL7k5tlZfNfud3n3jNzc9fmVjLQEvvv+d9S1jHkZdJwrh4dSr9MuDEwcC5V/4fN7cmcv170nyj/Xbb+lwDGSE17id3I6DTptQ1VNM/dEMikAdm7dsC3fCQAjs3KEh1zk8e1dVKw3IffzhC/q0vFtbF48UP559PxDHx3z96WDeREcwOQV53M9/io5gcUTW2JZxoV2vad/9PUEQRD+60TlX8i3kraN8DB/24VCmpXdLzYtOQotHVP5/rSUKAxyGaAJEBseQFpKNCe3t5bvk8myiHpxlaCbf9Bu6D2SE54TdPMPGvc4in7xsgAYlnAm6sU1gm7+QeVGs9HUKUFaSrRCbKk0k/TUeHnLraaOCakpUQpp3nzW1C5R2Mcg5MHSvjHFLSrJP0uzsgd+pyZHoaX7tgU1NTkKIzOXXGPEht8mLSWKY1uby/fJZFlEPrvCQ7+tdBz9CM3X5c2guOKMMPrG9qQkvARAS6dEjp+/VJpJ+qs4tHRKvE2T/F4Zef35TRrh06lUuzV2ztXlnzMysstHfEw4hsVLyvfHx4ZTxr5ijvPf9/vSIfj7HGbS8rMYm5bKcfxVSiILxzVDU0uPYbP2oKZWTH7MwNicx/euKaRPiA2XHwMwNDYn/r03EPGx4Wjp6KOuofXB/AmCIBRFotuPkG/F1HXRNbSWb3rGDmhqlyDi2SV5moy0RGLC/CleslKuMUytauHR/QiNuh2Ub0Zm5bFyakOjbgeRqKi+nQVGolg8JRJVZK9bfI1LViIjLYHY8Nvy45HPfJDJpBibu2WnMa9M1ItrSLMy5GkiQi6ia2SLei4DSIWPV0xDFz0ja/mmX7wsmjolCH96UZ4mIy2R6FB/ilu45xrDzKo2Tfscx7O3l3wzNq9AGZe2ePb2QkVFFR2D0mjpmpEQ81jh3MTYYLQNLAEoblmZjLQEYsJuyY+HP72ETCaleEm37DQW7kQ+v6JQRsKenkfP2A51TcNP9FSEN7S09TArZS/fLK1dMDA2567fSXmaV8kJPL57BXuXmkrjyGQyfl86BN8L+5iw5CQlSuacNvZVcgILxniipqbOyLn7UddQfItk71qTZ8G3SYiNkO8LuH4cLR19LMu4vE5Tg7t+pxTOC7h+Is+8fc02bFhP3brfUbasA/b2dly+fDnXdPXq1cXe3o5ly5Z99DWXLVuGvb0d9erV/ehYAPb2dtjb27F79z9K01y+fFmeTtk9FkbXrl2xt7dj3DjlXV4F4d8gKv9CoUkkEuwr9eH+1VW8DDpBfFQg146NQVPHDAu7JvJ053Z355H/70D2FwgDE0eFTVVNG3VNQ3l3Hj0jW3QMy3Dj5GRiwm6SFPeUB74biAi5gIVdYyC7hdesTF38TkwiJuwmUS+v4396OqUdW8pbmK2cWqOiWgzfExNIiH7As8BDPLqxBYfKP/7LT+rbJZFIcKzSlzs+y3nx0Ju4yPtcPjwSLV1TSjm8LSOndnbhgd8WIPsLhGEJR4VNtZg2GlpGGJZwlMd1qvYTD3038yzwMImxT7h1fhGJMY+wLd8ZyH4rUNKmPte8JhAd6k/k82v4nZiClXNrtPSyW3bLuLRBRVWdq15jiY8KJOTeAR74bsKxSj+Ez08ikeDZYTj7/5iD38UDPHt8m3Vze2FoYkHlOm3l6eaP8uD4npXyz1uXDubS8W0MmrwNTS094qLDiIsOk/fDf1PxT09Npu+4DbxKTpCnkWZlAVC+ShMsy7iwdm5PQh7d5NbVY/yzcQoebX+mmHp2l8SGrQcSEfqYnWvH8fLpfU7sW83V03/h2XHEv/aMvoQ3ldR3K9wBAQHMnz+fly9fYmlZiooV3dDV1c31fBcXFypWdMPc3PzfynKR8fz588/yxUEQPqXP0u1HIpGwd+9e2rZt+znCy505c4YGDRoQGxuLoaEhAPv27WPMmDEEBwczdOhQ3NzcGDFiBHFxcZ81L9+qslUGkJmZgt/JSWSkJVDcogp12m1W6M+fHBdC+qvYfMdUUS1G7TYbCbi4kEsH+pOZnoKuYRmqeC6kpE0DebpqzZbgf3o653f3AIkES/umuNV/O0d3MQ09vmu3lRunp3Fyexs0tIxxrj4U2/I/fJqbF/LFqdogMtNfcc17IumpCZQoVYV6Hf9Q6M+fFBdCWkpMgeI6VulHVmYafqdmkp4ah2EJF+p32oaekbU8TY2Wy/E9MYXTO3/IXuTLsRmVG82QH1fX0Kd+xz+5fmIyx7a2REPLiHK1hmPv1u2j71vInxY/jCMtNZnNi37KXuSrfB3GLDiq0FIf8SKIxPi33bNO7V8LwNwRDRRi9R+/ie+a9ebJAz+C7l0BYGw3xa5hi3c8pkRJa1RUVRk17yBblvzMzMG10NDUoY5nT77vM1OetkRJG0bPO8S2VaPw3r0c4xKl6Dt2PRWqeX7y51DUPXr0SP7/Xl5eaGgoH7O1Zs3afyNLgiAUkkQmk8kKckJYWBhz5szh8OHDvHjxAlNTU3kFu1Gj7CnR/q3Kf3p6OjExMZiZmckX9jEzM6NPnz4MGzYMPT091NTUSExMxNTU9APRPs7AgQNZt24dS5YsYcSIEfk+r8PIxx9OJHyziqkX+3Ai4ZvWvHnOvu6C8K5apYILlL5r165cvXoFS0tLzp49x7hxY9mzZ0+OdGfOnKVUqZzlr169urx48YKhQ4cxfPhwLl++TPfu2V+o16xZy6ZNG7l16xaWlpZMmDCRhg0bKs3LsmXLWLFiOZaWlkyZMpWFCxfw7NkzXF1dmT17Do6ObyeAOHHiBOvX/8a9e/fIzMzEzs6eHj160KlTJ3kae3s7AH755Rfat+8AwOHDh1m0aCERERHUqFGD1q3bMHr0KAD+/HMbNWrUyNdz2737H8aPzzlDWbVq1dm+fbv8ubZr1w4bG1v+/PMPUlNTqV+/ATNnzpS/SZFKpfz++1Z27drF06dP0dTUpFat2owfP57SpUvnuNYff/zJvHnzCAp6hJ2dPTNmzKBSpdy73ipjZ2dboPTCf1uBuv08efIEd3d3Tp06xcKFC7l9+zZeXl40aNCAwYMHf648KqWuro65ubm84p+UlERERASenp5YWFigp6eHlpbWR1f8MzIy8jy+d+9eLl++jIWFxUddRxAEQRCKGisrK0qXtpJ/rljRjYoV3VBXV8/jrNwNGzaUyMhIJBIJjx8/ZuTIkfl6Mx8ZGcmIEcORSFSQyWTcuHGDH3/8kVevsrt67du3j4EDf8LX1xdtbW1KlCjBvXt3+d//JrJ69Sqlce/evcvIkSN49uwZ6urqBAcHM2XKZKXp82JsXBxn57cTGdjZ2VOxohv29vYK6Y4ePcq6devQ0NAgISGBAwf2s27d27cl06dPZ/bs2Tx8+JAyZcqgqqqKl9dROnXqSHS04gQFAH37/khq6iuysrK4e/cOI0YMJ/P1YnaCkJsCVf5//vlnJBIJV69epX379pQtWxZXV1dGjRqVZ9+28ePHU7ZsWbS1tbG1tWXKlCkKFeqbN2/SoEED9PT00NfXx93dnevXrwPw9OlTWrVqhZGRETo6Ori6unLkyBEgu9uPRCIhLi6OM2fOoKenB0DDhg2RSCScOXOGLVu2yLsEvbF//34qV66MpqYmtra2zJgxQ+EXRSKRsGbNGlq3bo2Ojg5z5sxRem/ZrRtD2bZtG8WKiVZaQRAE4esyZMhQhgx528C3e/dudu/eXaiGtR49enLixEmWLs0eDJycnMTNmzc/eF56ejqrV6/By8uLdet+AyA8PIy9e/cC8Ouvi4HsLyZnz57jzJmzNGmSPa5o9erV8i8J79u4cSNSqRQ9PT2OHz/BqVOn5ecVVIMGDVizZo3884wZM9i9ezczZ85USKeqqsaxY8c4efIU5cplr01x6VL2wnHPnj1jx47tACxcuJCjR704c+Ys5ubmREZG8vvvv+e47oQJE/D2Ps7EidnTLL948YKnT58W6h6Eb0O+K/8xMTF4eXkxePBgdHR0chx/v4L9Lj09PbZs2cLdu3dZtmwZ69evZ8mSJfLj3bp1o1SpUly7dg1fX18mTJggr0gPHjyYtLQ0zp07x+3bt/nll19yHWRUq1YtAgMDgew/TKGhodSqVStHuvPnz9OzZ0+GDx/O3bt3WbduHVu2bMlRwZ8+fTrt2rXj9u3b/Phj7gNEpVIpPXr0YOzYsbi6uiq9f0EQBEEQkHcHfrc1PLfW7PcZGBhQt272AOS6detiYJA9Y1tgYCDR0VG8fJk9xa+nZxM0NDSQSCS0aNESgNTUVB4+fJhr3Df73d3dMTHJnia6WbPmuab9VGrWrIG5uTkqKirY2mZ3Q3rzDG7fvs2b3thjx47F3t6OihUrEBaWvfCcv79/jnht27YDwN7+7fiWqKgPP1Ph25XvAb+PHj1CJpPh5ORU4ItMnvz2FZq1tTVjxoxh586djBs3DoCQkBDGjh0rj+3g8LYAh4SE0L59e8qXLw+ArW3u/dLU1dXlrRDGxsZKZxmYMWMGEyZMoFevXvJ4s2bNYty4cUybNk2ermvXrvTp0yfP+/rll19QU1Nj2LBheaYTBEEQBAH09fUBUFN7W/0o4NDD/7w3zwBATU0VyP0ZODu75OhaZWFhqTSeqqqqfN+39kyFgsl35f9jCtKuXbtYvnw5QUFBJCUlkZmZqVD4R40aRb9+/fjjjz/w8PCgY8eO2NllfxseNmwYgwYNwtvbGw8PD9q3b0+FChUKnZebN29y8eJFhZb+rKwsUlNTSUlJQVtbG4AqVarkGcfX15dly5bh5+cnH3MgCIIgCMKnFx8fz4ULF6hTpw4XLlwgPj4eAEdHR4oXN8HCwoKXL19y7Jg3vXr1Rl1dncOHs1eQ1tTUVGhUfJeDgwN3797B19eX6Ogoihc3wcvraI507w5azmsQsJbW21mqlHU1yku5cuWQSCTIZDLat/+e3r2zGyFlMhnXr1+Xd28WhI+R724/Dg4OSCQS7t+/X6AL+Pj40K1bN5o3b86hQ4e4ceMGkyZNIj09XZ5m+vTp3LlzhxYtWnDq1ClcXFzk/fj69evH48eP6dGjB7dv36ZKlSqsWLGiQHl4V1JSEjNmzMDf31++3b59m4cPH6Kp+faXNreuTe86f/48ERERWFlZoaamhpqaGk+fPmX06NFYW1sXOn9FXdDNPzi6sS57Vzhzasf3xIR9uK8mwN3Ly7nqNUrp8ecPjnBsa2P2rnDm+B/NCA0+na+4T+/u5sxfnXI9FvnsMruX2uXYUpMj8xVbKJyHfls5sLYWfy12wPuP1kSH+ufrvICLS/A5NFzp8ZD7hzi8oQF/LXbg6KbGvAw6pTTtu4ID/ubEtu9zPRYe4sPOBVY5tldJEbmmFz6NE3tXMaqzDX0bazF9UA2C7l3N13l7t8xg7eweSo9fPfM343s407exFv/rU4Gbl4/kK+55r63MGvJdrsfu3ThDz/oqOba46LB8xRY+DXV1dQYO/IlmzZrSv39/AExNTWnXLrvLy6hRowG4edOfevXqUr9+Pby9vYHs8YpaWrmvyPzjjz8ikUhITEzEw8ODRo0acvjw4ULn09i4OEZGRgCMGTOa9u2/5/fft+b7fCsrKzp3zl6rZPbs2TRoUJ8WLZpTqZIbP/zQhTt3AgqdN0F4I9+Vf2NjYzw9PVm1ahXJyck5jisbrX/p0iXKlCnDpEmTqFKlCg4ODrkORClbtiwjR47E29ub77//ns2bN8uPlS5dmoEDB7Jnzx5Gjx7N+vXr85vtHCpXrkxgYCD29vY5NhWV/I9/7tGjB7du3VL4EmFhYcHYsWM5duxYofNXlD0LPMStc3NxrjGMRl0PYFDCiQt7e5Oa8uG+haFBJ7CwbZTrseiXvlw9OgJr14406nYQC7vG+BwcRHxU4Afjvgw6QUklcd9o0usELfpflm8a2sU/GFconJB7B7hxehblao/As9dhDEs4c+av7qQmf7iMvHh4HEt7j1yPRb24js/BodiW74xn7yNYOnhyYW9/4iI/XEay4zbOM03zfmdo8/N1+aapY/LBuELhXD61i+2rR9O291RmrvfFyq4CC8c2VVhpVxm/iweoVLtVrsceBlxi9cyu1G3xIzM3+FG5ThuWTm7H88cfriz5XTxAZSVx3/jlj/ss3/1Svukbfd7powVFJUqUYOnSZUil2au8u7lVYuPGTfJKfdu2bVm7dh3u7u4kJycTGRmJs7MLc+fO4+eflc9G6OrqypIlSyhVqhRpaWlYWloyc+asHOkSErLfNGhqalKmTBml8SQSCXPmzKFMmTIkJWUPZn7x4kWB7nXmzFlMmjQZR0dHwsPDefHiBaVKleLHH/tSvXr+ph0VhLwUaJGvVatWUbt2bapVq8bMmTOpUKECmZmZHD9+nDVr1nDv3r0c5zg4OBASEsLOnTupWrUqhw8flrfqQ/ZrsbFjx9KhQwdsbGx4/vw5165do3379gCMGDGCZs2aUbZsWWJjYzl9+jTOzs6FvuGpU6fSsmVLrKys6NChAyoqKty8eZOAgABmz56d7zjFixeneHHFSmSxYsUwNzdXmHf4a/LQbxPW5Tpj7Zo9N3LlRrMJCz7D0zv/4Fh1oNLzUhJfkhDzELMyuS/P/ujGFsys6+JYZQAArrVGERFykaCbf1C5kfKfSVZmGuEhF3CtPSbPfGtoFUddUz/PNMKncf/6Buwq/IBt+ey3MVU95xH6+BSPb+/CpYbyf4CTE14SH/2Akjb1cz0eeH0TJW3q4Vw9u5xV+G4M4U/O89BvC1U95ymNm5WZStiTc1SoOy7PfGtqF0dd0yDvmxM+Ca+/l1C/RT/qNsvuztB71FpuXj7C2SObaNVtgtLzoiOe8eLJHSpUa5rr8WO7l1O+WlNadBkLQIe+s7hz/QTH966kz2jli06lp6UScM2bjv2Uz+oGoG9oio6e4Qfu7uuwffv2HPvat+8gnxf/Q86ePafwuUaNGjx6FKSwr1SpUjn25Wb48OEMH/72jaCHR+4NBG+O5XUcyPWaLVu2omVLxS9/HToo3uu1a9kzEA4ZMpSSJUvmeY0mTTxp0iTnQnC5PdcFCxayYMFChX0qKir06dMnz3GHuf08cnvOgpCbAk31aWtri5+fHw0aNGD06NGUK1eOxo0bc/LkSYXprd7VunVrRo4cyZAhQ3Bzc+PSpUtMmTJFflxVVZXo6Gh69uxJ2bJl6dSpE82aNWPGjOxVOLOyshg8eDDOzs40bdqUsmXLsnr16kLfsKenJ4cOHcLb25uqVatSo0YNlixZkuc3eQGkWenERQRgWvrtDEoSiQqmVrWIDr2R57mhj09iYlmdYhq591WMDruBaenaCvvMynxHzAfiRjy7hJauGfrGdnmmO7mtJYd/q8H5PT2Jenk9z7RC4WVlpRMbdhsz6zryfRKJCmZl6hD90i/Pc18+Oo5p6RrKy8hLP4W4AOY2dT8YN/zpRbR0zdEvbp9nOq8tzdi3yp3Tu7oS+fxanmmFwsvMSOdJoC+u7m8raCoqKri4e/DorvLpogFuXDyAk1t9tHRy/yL/6I4Pru6KbwHLV2vywbh3/U5iVMISizJ5T2YxpV8lhn5vwS+jm/Dg9sU80wpfn6tXr1C2bFn69u37pbMiCB+tQC3/ACVLlmTlypWsXLlSaZr3BwcvWLCABQsWKOx7swquuro6O3bsUBorr/799evXV7iWoaFhjmv37t2b3r17K+zz9PTE01P58uyFHdz85MmTQp33X5D2KhaZLAtNbcXuEJraJiTG5L1K8cugE1jYKW+NSU2OQvO9rjga2iakpuTdN/9l0PE8u/xo6phSqeEsjMzKI81KJzjgL879040GXXZjZFouz9hCwaWnxOReRnRMSIjJuzXq+SNvStkrn1s7NTkSTe0S78UtwasPjN94/tA7zy4/WjqmVGkyF2PzCkiz0gm6tZNTOzvTuPt+jM3L5xlbKLjE+Cik0iz0jc0U9hsYmRIakvd4suyuOa2VHo+PCcPgvbj6RmbEx+TdN9/v4n4q1VLe5ceweEl6j1qDjWMVMjPSOHN4A/NGNGDamstYl62cZ2zh67F//4EvnQVB+GQKXPkXCi8tLY20tDSFfVmZaaiqaXyhHH1+GWmJRL24intj5V0zCkMmkxH2+BTVWyj/cqhnbIue8dupYYtbuJMcH8Ijv81Ubbr4k+ZHKLyMtEQin12hWtOFH05cADKZjJdBJ6jVWvmbQv3idugXf/vmyMSyCkmxTwm8voGaLZd90vwIhfcqOYH7N8/Sd9yGTxpXJpNx49IhhkzfpTRNSStHSlq97crpUK4WES8f4/X3UgZOyrngkiAIQlFXoG4/wseZN28eBgYGClvgNeV9UYsSDS0jJBLVHIN7U1Oi0NQpoeQsCHtyFj1je7T1LJSm0dQxITUlWmFfWkpUjpbed8WG3UQqy6J4yYK1vBmZVyApTqx8+DmoaxvnXkaSo9DKo4yEPj6NfnEHdPTzKiMlcrwJSk2OzDNuTKg/MmkWJpZ5T9v7vuIl3UQZ+Uz0DExQUVElISZcYX98bAQGxrmvzQJw88pRLMq4UNy0tNI0BsbmxL8XNyE2PM+4j+9dRZqViYNrzgUh82LrVJXwF48KdI4gCEJRISr//6KJEycSHx+vsOU1ULYoUVFVx9C0HJHPLsn3yWRSIp/5ULxkJaXnhT4+gYVt3gOwiptXIuKduADhIRcwziPuy8cnKGldH4mKqtI0uYmPvJfnlxWh8FRV1TEyL0/407f9oWUyKeFPL1LcQvmXtOePPjwbT3GLygpxAcKeXPhAXG9K2jVEpYBlJDbiDlo6YiaXz0GtmDrWju7c8Tsp3yeVSrnrexJ7F+WzmHyoyw+AvWtN7vopTv8acP3EB+Lup2KNFqioFqyMhDy6iWHxvAd9Cp9G165dsbe3Y9y4sV86K4Lw1RCV/3+RhoYG+vr6Ctt/qcuPQ+UfCQ7YxdO7u0mIecSNk1PIzEihjEvuM0BIpZmEPTlLSbu8p+K0r9Sb8KfneOC7gYSYIO76LCM2PAC7isrn8w59fIKSeYwjAHjot5mXQcdJintCfFQgN8/MIuKZD3YVu3/4ZoVCcarSj6CbOwgO+Jv46Idc9/4fmRkp8tl/3ieVZhL6+PQHK/+OVX4kNPgs96/+RkL0I25f+JXYsFs4VO6t9JyX+fhSEXh9A88fepMY+4S4yED8Tk4nIuQS9pV7fvBehcJp2nEkZw9t4LzXVl48vcfWJYNIS02Wz/7zvqzMTG5dOfrByr9n+2HcvurF0V2Lefn0Pns2Tyc48DqN2w1Reo7fxYNKpw59w+vvpfhe2E/480c8fxzAnytGcPfGKTza/vzhmxW+WVFRUUyYMJ5q1ari7OyMp6cnv/+ev25iT548YfDgwbi7V8bV1YXWrVvLFyx7Y9myZdjb2+W6ZWZmfo5bEr4ios+/kG+lHVuS9iqGuz5LSU2JwsDEmTptNyudEz3q+RXUiml/cHBtcQt3qjVdwh2fX7lzaTG6hmWo2WoNBia5T5maFPeUpLinmJXJfVGeN6TSdG6dm8urpHDUimlhYOLId9//jmnpmvm7YaHArJxbk/oqhtsXfiU1ORJDUxfqd/xD6duWiGeXUVPX+eDgWhPLKtRsuZzb5xdx6/wC9IysqdNuPYYlci8jibFPSIx9SknrennGbJqNAQAAG1FJREFUlWZl4H96Fq+SwlBV08KwhDP1O23HrEzBuoEI+VejYWcS4yLZs3ka8TFhWNm7MXbB0RyDdd+4f/Msmlq6Hxxc61CuFoOmbOOfjVP4e8MkzCwdGDF7L6Vsc//7E/4iiIgXj6hQVfnkDwBZmensWD2G2KgXqGtqY2VbgfGLj+NSqUH+blj4qkilUi5evIizszMmJrn/25eSkkLXrj/w+PFjNDU1sbS0ICjoETNnziAmJpoRI0YqjR8REUHnzp2Ijo5GV1cXU1NT7t69w/Dhw0lJeUXHjh0V0hsZGWNlZaWwTyKRfPyNCl+1/0Tlf9WqVSxcuJCwsDAqVqzIihUrqFatWq5p169fz++//05AQPbCLu7u7sydO1chfVJSEhMmTGDfvn1ER0djY2PDsGHDGDjwbRec1NRURo8ezc6dO0lLS8PT05PVq1djZvb2H6iQkBAGDRrE6dOn0dXVpVevXsybNw81tf/EYy0Ue7ee2Lvlr1X05eMPL8D1RqmyzSlVtnm+45YoXRO1Ytp5pnOs8hOOVX7KV0zh0ylbuTdl82iRf9eLh95YfuANzhtWTi2xcmqZv7iPjmNWphZq6nmXEefqg3CuPihfMYVPp/H3Q2j8vfIW+Xf5XdyPW638/dyr1e9ItfodP5zwdVznyg3R0Mp7NfcWP4yjxQ95rxPxNapXry4vXrxgwICfSElJ4eDBA2RlZdG6dRsmTZqEhkbub63T0tKoUaM6iYmJTJ48md69s9/oPH36lEaNGgKwceMmqlevzogRw7l//z7R0dFkZmZhYVGSVq1a8fPPg1FXV881/vPnz6lfP/tL/Z9/bqNGjRoK+R06dJh8XYDw8HCWLPmVc+fOERsbi7m5Oe3bt2fgwEEF/nc6KCiIPXv2sG/fPsLDwzhw4KDSyv+OHTt4/PgxEomEf/7ZjZOTE3PnzmXTpo2sW7eO7t17KD137do1REdHo6Ojy7Fj3piZmTF48GCOHfNiwYIFtGnTRuHZNGhQP8c6AYLwIUW+28+uXbsYNWoU06ZNw8/Pj4oVK+Lp6UlERO6rQZ45c4YffviB06dP4+PjQ+nSpWnSpInCCnujRo3Cy8uLP//8k3v37jFixAiGDBnCgQNvp/IaOXIkBw8e5O+//+bs2bO8fPmS77//Xn48KyuLFi1akJ6ezqVLl9i6dStbtmxh6tSpn+9h/MfoFy+LbYVunzyutq45TlVFhe1rYGDiiH0l5d27Cktbzxzn6qJbxteglE05GrX59L/vxiVK5bmomJBty5bNHD58CH19fZKSkti+fRuLFi1Sml5DQ4MWLVoAcOjQYfn+w4ez/9/MzIw6deqQnp7OiRMnSE1NxdrahuLFjXn69CkrV67k118/fja22NhYOnRozz///ENycgp2dnaEhoaydOlSJk+elK8Y8fHxbNu2jfbt2+Pp2YR169ZiZGTIuHHj8lwb6Ny5swBYW1vj5JS9fkTTptlvmDIyMrh06ZLSc8+ezT63UqVK8sbGN1OTx8bGEBBwWyH9sWPHcHV1oWbNGvTv3487d+7k696Eb1uRr/z/+uuv9O/fnz59+uDi4sLatWvR1tZm06ZNuabftm0bP//8M25ubjg5ObFhwwakUiknT74dYHbp0iV69epF/fr1sba2ZsCAAVSsWJGrV68C2b/wGzdu5Ndff6Vhw4a4u7uzefNmLl26xOXL2QvGeHt7c/fuXf7880/c3Nxo1qwZs2bNYtWqVaSnp3/+B/MfYFv+B6Vddz5GqbItMLGs+snjCv8+e7duGJbIe3GlwrByaoVp6eqfPK7w72vQagClbT/9mgvVG3TCsULeXQcFsLCw4PTpM5w+fYZWrbLHR/z5558kJiYqPedNQ5m//w15w9uRI9mV/zZt2qCqqoqWlhZHj3px+fIVDh48yIULF2nTpi0Ahw4dyjVuQfzxxx+EhoZiYmLCqVOnOHToMCtWZK9PtHv37jzX5blz5w5DhgyhZs0aTJs2lejoKAYOHMSRI0c5dOgwAwb8hI6O8jdGoaGhABQv/nb9muLF37b0v3z5skDnmpi8/f93z1VVVcXExIRSpUoRGRnJ6dOn6dixg/gCIHxQka78p6en4+vrq7Bct4qKCh4eHvj4+OQrRkpKChkZGRgbG8v31apViwMHDvDixQtkMhmnT5/mwYMHNGmSvciQr68vGRkZCtd1cnLCyspKfl0fHx/Kly+v0A3I09OThIQE8YsnCIIgfBUaNGiIrq4uAC1aZHe/yshIJzg4mNOnT9O+fXv5NmhQdtfZypXdsba2BuDw4UMEBQVx/372Im7ff98eyP63fP/+/Xh4NMLZ2Rl7ezv2798HoPTNfkHcunUTyB54W716Nezt7eT5k8lk3Lx5U+m5J06cwMvrKKqqqsyaNYvTp88wZswYypYtW+j8FHbx0Oxzc+5r3bo1V65c5eTJUxw75s2mTZuB7HrTn3/+WehrCd+GIt05PSoqiqysLIUKNmS/Nnzzh+RDxo8fj4WFhUJFfsWKFQwYMIBSpUqhpqaGiooK69evp27dugCEhYWhrq6OoaFhjuuGhYXJ0+SWrzfHBEEQBOFrFhMTzc2b/vLPlpaW8v9v164dS5Ys4fDhw6SmZi9uWaFCBezt7QFYt24ta9eukZ9nYlKCsLAwwsPDkEqlSq/57mBWqTRL/v/K3kTo6OjKr/kuLS1NpdeoW7cu/v43uHTpElOmTGHbtm20atWaVq1aYWGhfD2SN0qWLMnjx4+Jjn67fk1MzNv/zytGyZIlefr0qcK57/7/m3NtbGxy5NnIyIjY2Ng83ywIAhTxlv+PNX/+fHbu3MnevXvR1Hz7i75ixQouX77MgQMH8PX1ZfHixQwePJgTJ058wdwKgiAIQtFy5sxpkpOTAThy5AgAxYqpY2NjQ/v2HXj0KEi+nT17Tn5e27btkEgk3Llzhx07tgNvW/0BbtzwB7IrsWfPnuOvv/7C2fnDXQDf7Q4THPwEgIsXL5KQkKCQrnz5CgCoqamybNkydu/eze7du9m6dSvdu3ejSRPlszxVqlSJzZu3cO7cecaNG0dmZhYLFy6gXr26dOnSmW3btpGamqr0/O++y25IfPLkibyh0svrGADFihWjVq3s2cR+//13mjRpTJMmb6ckftMIeePGDcLDsxetO3Ys+1wjI2PKlcvuArdu3TqFSv6FCxeIjY0FoFSpt1/CBCE3Rbrl38TEBFVVVfkvwBvh4eGYmytftRFg0aJFzJ8/nxMnTlChQgX5/levXvG///2PvXv3ygclVahQAX9/fxYtWoSHhwfm5uakp6cTFxen0Pr/7nXNzc3lYwTePf7m2Lci6OYfPLi+ntSUSAxMnHFrMA1j84pK0z9/cIQ7PktISXiOrqE15eqMo6SN4pR5CTGPCLiwgMjnV5BJs9Avbk+NFqvR1rcgPTWOuz5LCQ+5QErCSzS0jbGwa4xrzVEU09CTx9i91C7Htas1W0ppx7zn9BY+rYd+W7l3dd3raT+dcfeYSfGSbkrTh9w/xO0Li0mOf46ekTUV603Ewq6hQpr46IfcPDOPyGdXkMoyMSjuQO2269DRtyTtVRwBF38lLPgcKYkv0NAqjqVDE8p/NwZ1DX15jJ0LrN6/NDVbraSMc95zyQuf3om9qziycxHxMWGUtq9Ij2HLsXPOfTY3gKtn/mb3xqlEhT3BrJQDnX+aT8UaijOFvXh6j7/WTeD+zbNkZWViWcaFoTP/wcTMiqSEGPZsnkbA9eNEh4egZ1gC9zptaP/jLLR1DeQxetbP2Tb285Tt1GjU5dPd/H9AeHg49evXR1dXl2fPQgDo1q0renp6eZ5naWlJ9erVuXz5MpGRkairq9Oy5dtZm5ycHDl9+hTBwcHUr1+PjIxM0tKUV6jf0NTUpFKlSty4cYN58+Zy9OgRbt68iYqKisIbg+7du/PXX38RHh5G48aNsbe3IykpmbCwUDIyMmjX7vs8rpLNzMyMAQN+YsCAn7h16xZ79uzh0KGDTJs2lUqVKuHi4pLreT/88AM7d+7gyZMndOjQnpIlSxIcHAxAv3795DP9xMbG8vjxY4Vzf/ppIIcOHSY2NgZPzyYYGf2/vXsPizHv/wD+bqZG50yppkKollJKB4pYh11ZWjm0zhI/PHL2tJ7n51mPK6xTjrs/1lq7y7V+i2KxJIceKeuQQ1jH0CaFSik6TdOM5vfHcDPboOXnkHm/rmuuq77zue+57/Ftro+57+/nI0Vubi4AIDo6Wqj0o1l4vQQODg4wMTFFVtYfAABTU1OhwhLRs7zT3/xLJBL4+flpLdZ9vHg3KOjZtdpjY2Mxb9487Nu3D/7+/lrPKZVKKJVKiETapy4Wi4UPDj8/PxgZGWm97tWrV5GTkyO8blBQEC5cuKB1b2JSUhIsLS2f+YHwvsm9moDzhxfAPXAKug/dBSvbVjiyIxJVlUU64+/dScfJvdPQrPVn6D5sNxxdPsbx3VF4UHRViCm/fxOp8YNgIW2BD8M34aPhe9Cq3SSIDDUfePLyAsgr7sKr00x8PGIv/HvEoiD7MNKTalft8Pt4MXqPTRMeji49Xs8bQTrlXNmFs4fmwbPjNISM3IOGtu5IiR+Oqgrd86Po9mkc3z0ZLbwGISQyEU5uITiyYyzuFz6ZH2Ul2Tj48wBY2rig25A49Izcj9ZBUyAWa8oOyssLIC8vgE/XL9BzVBLa91qG/BupOLm3dnfQdp8sQ9iE08KjsRvnx5uWlhyHTd9Eo2/kbMxdl46mLm2wZEZPlJbovuf7+sVj+GbuUHTuPRpzvz8D3+AwrJzVD7eyLgoxBbf/wJeTO8GhaUvMXHkI83/4HWERsyCRaK7+3i+6g/v38jAkagkWrL+Acf+9HudP7scPsWNqvd7Yf/6Ir3+5Izx8g/u+lvfhXTZy5EiEhYWhtPQBzMzMMWTIEMyYUbfSp09XyOvWrbvWl2lRURPQv39/oYpQaGhvDBtWt+pwsbFLEBCgKfqQn5+POXPmwMFBu+OyjY0Ntm3bhvDwcEilDXH9+nUoFFXw9/fHF1/MqtPrPK1NmzaIiYnBsWPHsWrVaq0rEH9mZmaGTZs2o3///jAxMcWtW7fg4uKCWbNmITr68+e+jkwmQ3x8PEJCQmBgYICCggK4u3tg+fIVGDz4yX88o6Ki0KFDB6hUKuTm5sDJyQl9+oRh585f4ebm9pfPj/SLgfpVVqG8AXFxcRg5ciTWrl2Ldu3aYeXKlYiPj0dGRgbs7e0REREBJycnLFy4EACwePFizJ49G5s2bULHjh2F/ZibmwuLlrp06YKioiKsWrUKzs7OSE1NRVRUFJYvX46oKE1JuaioKCQmJmLDhg2wtLTE5MmTAUAo0fXw4UP4+PjA0dERsbGxyM/Px4gRIzBmzBgsWLCgzucXPj3rxUHvqOTN/SGVtUHbrjEAALW6BonfB8PVJwItA8bXij+xZzJUKjk6hn0vjB3aMgBWtu7w7f6lJiZxCkQiIwT0rHupt1vXEnFqfzTCJl6ASKS5mPXLShcEhq6Bk2v9TuiMJEZv+xBe2oGNfWAj84bfx/MAaObHrjXt4eYbCY/AibXij/46AQ+VlegcvkEYS9oYhoZ2HggI0fx9H9s1EQYiQwSFflXn48jJSEDanmkIn54hzI8tsU0R3G8dGrs9v8FTfdCrV+O3fQgvLSYqEC1a+iNimqYKS01NDaYPbIqP+k3SWYZz1ZzBUMgrEL1otzA2JyoITV29MSr6WwDA6jlDIDY0wvgv6tZNFdBcTfh2/gis21sO8aP67xFdRJg6bzv8OvV9hTN8N3RofOMvb6Orbj69v1xcWrztQ6A36J3+5h8ABg0ahKVLl2L27Nnw8fHBuXPnsG/fPmFxbU5OjlAaCwDWrFmD6upqhIeHw8HBQXg8XZd4y5YtCAgIwLBhw+Dh4YFFixZh/vz5Wk2+VqxYgdDQUAwYMACdO3eGTCbD9u3bhefFYjESEhIgFosRFBSE4cOHIyIiAnPnzn0D78rbV/OwGvfvXoRdkyedUA0MRLBr2gH38s7q3OZe/lnYNemoNWbv3AnFj+LV6hrk30iBubQZftseiYS1AUje3B+3Mw8891iU1WUwlJgLid1j5w7FYPe3/kje3A/Zl7a+UrUF+msePqxGSf4F2DcLFsYMDESwdw7GvTtndG5z784ZrXgAkDXvLMSr1TW480cyLKxbICV+OHasaosDG/vg1vX9zz0WpaIMRjrmR3rSLGz/H28c+OlTZJ2P4/x4w1TKamRfTUdrP+1qbh5+HyHzcprObTIvHUdrP+3GgV7tegjxNTU1+D1tD2RN3BA7oycm9rVHTFQg0n/b+dxjqSx/ABNTSyHxf+ynryZhQh9bxIxvj9TEHzlHiOi98E7f8//YpEmTMGmS7m6QKSkpWr8/r3bvYzKZDOvXr39ujLGxMVavXo3Vq1c/M8bZ2VlYAKVvFPISqNUPYWyq3aXQ2LQRyop1X82oqiiCsan2pdIGpo1QVVmo2WflPaiUFbh6ai1ad/g7vIL/gYKbh5GWMAGdw3+GbePaddsV8mJknFiF5p6DtMY9gqZpugAbmqDg5hGcTZ4NVXUFXNtGvsJZU11VVxbrnh9mjVBa/IfObaoqCmFsavuneFvIKwofPV8ElbICV058gzbBM+D94Uzk3UjBkR3j0G1wHOyaBtbap6KyGJeOfw0X76Fa457B0bBv2gGGRibIzz6M00mzoFJW4AO/0a9y2vQXlD0oQk3NQ1haa1dNs5LaIS9HdzW3B8X5sPpTvKXUHg+KNRXWSkvuokpejoRNixH+X/MwaNwinD+5D1/PHoCZK5LRyufD2sdxvwi/bvwSXT4dqzXef/QceLTtBomxKS6eOoCfVkyEQl6OHgOmvMppExG9dfUi+Sf9oFZr1lw4unwEN19NEtbQzgP38s4g6/ymWsm/UlGGozvHwMLaFR6B2pel3dtPFn5uaNcaKlUlrqWvY/Jfnz2aH06uPdAyQHN/ttS+NYpupyPz3P/WSv6VijKk/hIJKxs3eHacrvWcZ4cn80Vq7wlVtRxXTq5l8l/PPf4M8e0Yhp6faf7Nnd18kHnpOJJ3ra2V/MsrSrFsZiicnD3QLzJG67m+Ef8Wfm7m1haKqgokblmqN8n/05V7iOj98s7f9kPvpgYmUhgYiGst7q2qLIKxma3ObYzNGqGq8p7WmKKySPi2t4GJFAYiQ1hYa9dktpC6QF6mXbdYWV2OIztHwVBihqBPv4VI/Px7461lPpCX5+OhSlGn86NXIzG11j0/Kopg8sz5YStcBXoSXyjES0ytYSAyhJWN9mI2SxtXVJbd1hpTKsqRsjUCRhIzBPf77oXzw8bRB/KyPM6PN8jCqhFEIjFKi7WruT0ouQsra90V06ysZXjwp/jSkgIh3sKqEcRiQzg5u2vFODq3wr27OVpj8soyLPnHJzA2scCUedthaPj8OeLi3h7FhbegrOYcIaL6jck/vRSRWIKGdp4ozD0mjKnVNSjMPQ4bh7Y6t7GRtcXdp+IBoCDnCKwfxYvEEkjtvVBeor04rfz+DZhaPqlbrFSU4cj2SIhEEnTo8x3Ehg1eeLwPCi/DqIFVnWLp1YnFEkhlXii4eVQYU6trUHDzKGwcfXVuY+PoqxUPAPnZR4R4sVgCa5l3rduGykpuwNTyyaJXpaIMKVuHQyQ2Qqf+P0Js+OxmPo+V3L0MiTHnx5tkaCRBs5Z+uHRGu5rb5fSDcPWofQsXALi2DsLlM8laYxdP/0eINzSSoHmrAOTlXtOKyc+9Dht7Z+F3eUUpYj8PgaGhBNMX/ApJgxfPkZzMczCzkMJIwjlCRPUbb/uhl+bmOxqnD8yA1N4LUpk3Ms+sh0pZCWePcADAqf3RMDGTwTNYU2bRtW0kUrcNxbX07yFr3hW3riagpOAifLvPF/b5gd9YnEicikZOAbBtEoj87MPIy0pG53BNkxilogxHdkRCpZIjqOcyqKrLoaouBwA0MLGGgUiMO1kHoagsgrXMB2LDBii4eRQZJ9fgA7/apfzo9WnlPwZpidGwlnnB2sEH107/AJWyEi28BgIA0vZMg4m5DN4faqq6tPQfjYObByLj5HdwdOmGm1d2oST/PAJCFgn7dG/3NxzbNRF2TdrDrmkH5N1IwZ3M/6DbkDgAjxL/+OFQqeQI7r0SSkUZlApN588GpjYQicS4nZmEqooi2Dj6QmzYAPnZv+Fy2iq0Chj3ht8h6vnZdKxbGInmLf3Rwr0dDmxbCUVVBTp/oqlTvnbBSEgbOWLgOE21p5ABU7BgahfsjVsG78DeSEveghtXT2N09Fphn70Gf47VcwajpXcnePh0xfmT+3D22G7MXHkIwJPEv1pRifFfbIS8ohTyCk2DKMuGthCJxTh7bDceFBfA1SMQRhJjXExPwq6fF6LXoOg3/A4REf3/e+dLfb7v6nOpTwDIPPcTrqevQ1VlkabJV5fZsH7UxCl161CYWTrBP2SJEK9p8rUclaW3Yd7QGZ7B/6zV5Cv70lZknFoDeVk+LKQt4BE0FY4umg6IhblpOPyL7lrQPUelwsyqMfKzU3Hx6FJU3L8JNdQwt3JGizZD0dxrMAwM6tfFrvpc6hMArp3ZgAyhyZcH/LrPgY2j5krPwc0DYWbVGIG9lgvxORkJuPDbUlSUPm7y9a9aTb6yzsfhctpqyMvzYGHtAs+Ofxdq9BfkHMehLdqLvx8L/dtRmFs1QV5WCn4/vBjlJdkA1DCXNoOrz3C4eA+td/MDqN+lPgEgafsqJMZpmnw1dfXBiMlfwcVDs75nwdSuaCRrhnEznxRoOJmyFdt++LemyZeTGwaPX1yryVdq4o9I+HkRigtvwaFJS/QbFQO/4DAAwJWzKVg4XXtOPbZscxZsHZrh/Il9iF/3L9y9nQm1Wg17J1d0CxuPLqFja/WIqQ9eptQn6ReW+tQvTP7fsvqe/NPrVd+Tf3r96nvyT68fk396ESb/+qX+fYVBREREREQvhck/EREREZGeYPJPRERERKQnmPwTEREREekJJv9ERERERHqCyT8RERERkZ5g8k9EREREpCeY/BMRERER6Qkm/0REREREeoLJPxERERGRnmDyT0RERESkJ5j8ExERERHpCSb/RERERER6gsk/EREREZGeYPJPRERERKQnmPwTEREREekJJv9ERERERHqCyT8RERERkZ5g8k9EREREpCeY/BMRERER6QkDtVqtftsHQURERERErx+/+SciIiIi0hNM/omIiIiI9ASTfyIiIiIiPcHkn4iIiIhITzD5JyIiIiLSE0z+iYiIiIj0BJN/IiIiIiI9weSfiIiIiEhPMPknIiIiItIT/wcgCBRh9/3S5gAAAABJRU5ErkJggg==", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAGGCAYAAADsPu62AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1gURx/A8e/RpfemqEhTsAH23o3d2HuJJbYYLLHE2Hs3xm40aizR2Fti1/iq2FFsKAhiASmCiJSj7PsHenpyB4ddmc/z3BNvdtoeE5ibnf2tTJIkCUEQBEEQBEEQvnpan7oDgiAIgiAIgiB8HGLyLwiCIAiCIAj5hJj8C4IgCIIgCEI+ISb/giAIgiAIgpBPiMm/IAiCIAiCIOQTYvIvCIIgCIIgCPmEmPwLgiAIgiAIQj4hJv+CIAiCIAiCkE+Iyb8gCIIgCIIg5BNi8i/kSiaTsXPnzg/ezvHjx5HJZMTHxyvSdu7ciaurK9ra2vj5+bFmzRrMzc0/eF+EvBFjRMiNGCNCbsQYEYSPRBLytYiICGnQoEGSs7OzpKenJxUqVEhq2rSpdPjwYUUeQNqxY8cH70tqaqoUEREhZWZmKtJsbW2lkSNHSg8fPpQSEhKkpKQk6fHjx++9bblcLo0YMUIqWbKkZGhoKDk4OEhdu3aVHj58+N7b+tKIMfLKtm3bpAYNGkhWVlYSIF2+fPmDtPOlEWPklfHjx0seHh6SoaGhZG5uLtWtW1fy9/f/IG19ScQYUa1v374SIM2fP/+DtyUIL+l8yi8ewqcVFhZG1apVMTc3Z9asWZQuXZq0tDQOHDjAwIEDuXXr1kftj56eHvb29or3iYmJREVF0bBhQxwdHRXpBQoUeKd20tLS0NXVVUpLSkri0qVLjB07ljJlyhAXF4efnx/NmzfnwoUL79Tel0yMEWXPnz+natWqtG3blj59+rxTG18LMUaUubu7s2jRIooVK0ZycjLz58+nQYMGBAcHY2Nj805tfqnEGFFt586dnD17VqlNQfgoPvW3D+HTadSokVSwYEEpMTEx27G4uDjFv3ljNWbEiBGSm5ubVKBAAcnZ2Vn65ZdfJLlcrjgeEBAg1apVSzI2NpZMTEwkHx8f6fz585IkSVJYWJjUtGlTydzcXDI0NJQ8PT2lffv2SZIkSceOHZMAKS4uTvHv11/Hjh2T/vjjD8nMzEypr7t375Z8fHwkfX19ydnZWZowYYKUlpam1P+lS5dKzZs3lwwNDaVx48Zp9PmcO3dOAqR79+5plP9rJMaIaqGhoWLl/wUxRnL29OlTCVBa4c5vxBjJ7sGDB1LBggWla9euSUWKFBEr/8JHJVb+86knT57w77//MnXqVIyMjLIdz2mvo4mJCWvWrMHR0ZHAwED69OmDiYkJI0aMAKBz5854e3uzdOlStLW1CQgIUKx+DBw4ELlczn///YeRkRE3btzA2Ng4WxtVqlQhKCgIDw8Ptm3bRpUqVbC0tCQsLEwp34EDB+jSpQsLFy6kevXqhISE0LdvXwDGjx+vyDd+/HimT5/O/Pnz0dbW1ugzevr0KTKZLN/u+xRjRMiNGCM5k8vlrFixAjMzM8qUKZNr/q+RGCPZZWZm0rVrV3766Se8vLxy/PwE4YP41N8+hE/j7NmzEiBt374917zksg9z1qxZkq+vr+K9iYmJtGbNGpV5S5UqJU2YMEHlsddXYyQpa0WIF6swL725GlO9enVp2rRpSvX8+eefkoODg1L//fz81PZfleTkZMnX11fq3Llznsp9TcQYUU+s/GcRY0S1PXv2SEZGRpJMJpMcHR2lc+fOaVTuayTGSHbTpk2T6tevr7jnQKz8Cx+bWPnPpyRJArKiK+TV1q1bWbBgAcHBwSQmJpKeno6pqani+NChQ+nduzd//vkn9erVo23btri4uAAwePBg+vfvz8GDB6lXrx6tW7emdOnSb30eFy9e5Pz580ydOlWRlpGRQUpKCklJSRgaGgJQrlw5jetMS0ujQ4cOZGZmsmTJkrfu25dOjBEhN2KMqFa7dm0CAgKIiYlh5cqVtGvXjrNnz2Jra/vWffxSiTGSvZ5ff/2VS5cuvdVnIgjvgwj1mU+5ubkhk8m4efNmnsr5+/vToUMHGjVqxN69e7l8+TJjxoxBLpcr8kyYMIHr16/TpEkTjh49iqenJzt27ACgd+/e3L17l65duxIYGEi5cuX47bff3vo8MjMzmThxIgEBAYpXYGAgd+7cwcDAQJFP1eVmVdLS0mjXrh2hoaEcOnRI6Q9NfiPGiJAbMUZUMzIywtXVlUqVKrFq1Sp0dHRYtWrVW/fvSybGiLKTJ08SFRVF4cKF0dHRQUdHh3v37jFs2DCKFi361v0ThDz5tBcehE/pm2++yfNNWHPmzJGKFSumlLdXr17Zbox6XYcOHaRmzZqpPDZq1CipVKlSkiS93aXYKlWqSN999536k5Ryv5T8klwul1q2bCl5eXlJUVFRuebPD8QYUU1s+3lFjJHcubi4SOPHj3+rsl8DMUZeiYmJkQIDA5Vejo6O0siRI6Vbt27lWFYQ3hex7ScfW7JkCVWqVKFChQpMmjSJ0qVLk56ezqFDh1i6dKnKlRpXV1fCw8P566+/KF++PPv27VOstAAkJyfz008/0aZNG5ydnXnw4AHnz5+ndevWAPj5+dGoUSPc3d2Ji4vj6NGjlChR4q3PYdy4cTRt2hQnJyfatm2LlpYWV69eJTAwkClTpmhcT3p6Om3atOHSpUvs3buXjIwMIiMjAbC0tERPT++t+/glE2NE2ZMnTwgPD+fRo0cABAUFAWBvb68UOjA/EWPklefPnzN16lSaN2+Og4MDsbGxLFmyhAcPHtC2bdu37t+XToyRV6ysrLCyslJK09XVxd7eHg8Pj7funyDkyaf+9iF8Wo8ePZIGDhwoFSlSRNLT05MKFiwoNW/eXGkFhDdWM3766SfJyspKMjY2ltq3by/Nnz9fsUKSmpoqdejQQXJycpL09PQkR0dHadCgQVJycrIkSZI0aNAgycXFRdLX15dsbGykrl27SjExMZIkvd1qjCRJ0r///itVqVJFKlCggGRqaipVqFBBWrFihdr+q/JyJVfV6/X28yMxRl75448/VI6R/LyqK0lijLyUnJwsffvtt5Kjo6Okp6cnOTg4SM2bN8/XN/y+JMaIeuKGX+Fjk0nSi7txBEEQBEEQBEH4qokbfgVBEARBEAQhnxCTf0EQBEEQBEHIJ8TkXxAEQRAEQRDyCTH5FwRBEARBEIR8Qkz+BUEQBEEQBCGfEJN/4a0tWbIEZ2dnDAwM8PX15eTJk2rz9ujRA5lMlu3l5eWllC8+Pp6BAwfi4OCAgYEBJUqUYP/+/XlqV5IkJkyYgKOjIwUKFKBWrVpcv379/Z24oLEPMUZe+uuvv5DJZLRs2TLP7Yox8vnIyxg5fvy4yjFy69YtpXzbtm3D09MTfX19pae+5qVdMUY+D3kZH9u3b6d+/frY2NhgampK5cqVOXDgQLZ8CxYswMPDgwIFCuDk5MSQIUNISUnJU7tifAhftE8ZZ1T4cv3111+Srq6utHLlSunGjRvSjz/+KBkZGUn37t1TmT8+Pl6KiIhQvO7fvy9ZWloqxUdPTU2VypUrJzVu3Fj63//+J4WFhUknT56UAgIC8tTujBkzJBMTE2nbtm1SYGCg1L59e8nBwUFKSEj4YJ+HkN2HGCMvhYWFSQULFpSqV68utWjRIs/tijHyecjrGHkZnz0oKEhprKSnpyvynD59WtLW1pamTZsm3bx5U5o2bZqko6Mj+fv756ldMUY+vbyOjx9//FGaOXOmdO7cOen27dvS6NGjJV1dXenSpUuKPOvXr5f09fWlDRs2SKGhodKBAwckBwcHyc/PL0/tivEhfMnE5F94KxUqVJD69eunlFa8eHFp1KhRGpXfsWOHJJPJpLCwMEXa0qVLpWLFiklyufyt283MzJTs7e2lGTNmKI6npKRIZmZm0rJlyzTqm/B+fIgxIkmSlJ6eLlWtWlX6/fffpe7du2eb/Isx8uXI6xh58+FMqrRr10765ptvlNIaNmwodejQQeN2xRj5PLzr7xBJkiRPT09p4sSJivcDBw6U6tSpo5Rn6NChUrVq1TRuV4wP4Usntv0IeSaXy7l48SINGjRQSm/QoAGnT5/WqI5Vq1ZRr149ihQpokjbvXs3lStXZuDAgdjZ2VGyZEmmTZtGRkaGxu2GhoYSGRmplEdfX5+aNWtq3Dfh3X2oMQIwadIkbGxs6NWr11u1K8bI5+Fdxoi3tzcODg7UrVuXY8eOKR07c+ZMtjobNmyoqFOMkS/D+/gdkpmZybNnz7C0tFSkVatWjYsXL3Lu3DkA7t69y/79+2nSpInG7YrxIXzpdD51B4QvT0xMDBkZGdjZ2Sml29nZERkZmWv5iIgI/vnnHzZu3KiUfvfuXY4ePUrnzp3Zv38/d+7cYeDAgaSnpzNu3DiN2n35X1V57t27l+dzFd7Ohxojp06dYtWqVQQEBLx1u2KMfB7eZow4ODiwYsUKfH19SU1N5c8//6Ru3bocP36cGjVqAFk/35zqFGPky/Cuv0MA5s6dy/Pnz2nXrp0irUOHDkRHR1OtWjUkSSI9PZ3+/fszatQojdsV40P40onJv/DWZDKZ0ntJkrKlqbJmzRrMzc2z3aiZmZmJra0tK1asQFtbG19fXx49esTs2bMZN25cntp9274J79f7HCPPnj2jS5curFy5Emtr63duV4yRz0Nefg4eHh54eHgo3leuXJn79+8zZ84cxeRf0zrFGPkyvO3PYNOmTUyYMIFdu3Zha2urSD9+/DhTp05lyZIlVKxYkeDgYH788UccHBwYO3ZsntoV40P4UonJv5Bn1tbWaGtrZ1t9iYqKyrYS8iZJkli9ejVdu3ZFT09P6ZiDgwO6urpoa2sr0kqUKEFkZCRyuVyjdu3t7YGslRkHB4c89U14fz7EGAkJCSEsLIxmzZop0jIzMwHQ0dEhKCgIJycnMUa+EO8yRl5XqVIl1q9fr3hvb2+fY53i98iX4V3Gx+bNm+nVqxd///039erVUzo2duxYunbtSu/evQEoVaoUz58/p2/fvowZM0aMDyFf+CB7/l+G7OvXr1+2YwMGDEAmk9GjR48P0fRbadCgAdra2vj7+3/qrnwR9PT08PX15dChQ0rphw4dokqVKjmWPXHiBMHBwSr3a1etWpXg4GDFhA7g9u3bODg4oKenp1G7zs7O2NvbK+WRy+WcOHEi174J78+HGCPFixcnMDCQgIAAxat58+bUrl2bgIAAnJycxBj5grzLGHnd5cuXlSZglStXzlbnwYMHFXWKMfJleNvxsWnTJnr06MHGjRsV+/hfl5SUhJaW8tRHW1sbKSsAihgfQv7wIe4i7t69u+Tk5CSZmZlJSUlJivTk5GTJ3NxcKly4sNS9e/cP0XSe3bt3TzI2NpYGDx4s9e7d+1N3J8dIN5+Tl6HQVq1aJd24cUPy8/OTjIyMFJFZRo0aJXXt2jVbuS5dukgVK1ZUWWd4eLhkbGwsDRo0SAoKCpL27t0r2draSlOmTNG4XUnKCsFmZmYmbd++XQoMDJQ6duwoQrB9Ah9ijLxJVbQfMUa+HHkdI/Pnz5d27Ngh3b59W7p27Zo0atQoCZC2bdumyHPq1ClJW1tbmjFjhnTz5k1pxowZakN9ijHyecvr+Ni4caOko6MjLV68WCkUbHx8vCLP+PHjJRMTE2nTpk3S3bt3pYMHD0ouLi5Su3btNG5XksT4EL5sH2zy36JFC6lUqVLS+vXrFekbNmyQSpUqJbVo0UIx+c/MzJRmzpwpOTs7SwYGBlLp0qWlv//+W1EmPT1d+u6776SiRYtKBgYGkru7u7RgwQKV7c2ePVuyt7eXLC0tpQEDBmg0kZ4wYYLUoUMH6ebNm5KJiYmUmJiodDwuLk7q06ePZGtrK+nr60teXl7Snj17FMf/97//STVq1JAKFCggmZubSw0aNJCePHkiSZIkFSlSRJo/f75SfWXKlFGKWw5IS5culZo3by4ZGhpK48aN0+icJUmSVq1aJXl6ekp6enqSvb29NHDgQEmSJKlnz55SkyZNlPKmpaVJdnZ20qpVq3L9TDS1ePFiqUiRIpKenp7k4+MjnThxQnGse/fuUs2aNZXyx8fHSwUKFJBWrFihts7Tp09LFStWlPT19aVixYpJU6dOVYrhnVu7kpQ1psaPHy/Z29tL+vr6Uo0aNaTAwMB3P2Ehzz7EGHmdqsl/bu1Kkhgjn5O8jJGZM2dKLi4ukoGBgWRhYSFVq1ZN2rdvX7Y6//77b8nDw0PS1dWVihcvrvTlQJN2JUmMkc9FXsZHzZo1JSDb6/XFxrS0NGnChAmKceTk5CQNGDAgW/hYMT6Er9kHnfzPmzdPqlu3riK9bt260vz585Um/z///LNUvHhx6d9//5VCQkKkP/74Q9LX15eOHz8uSVLWSvi4ceOkc+fOSXfv3pXWr18vGRoaSps3b1Zqz9TUVOrXr5908+ZNac+ePZKhoWGuE4jMzEypSJEi0t69eyVJkiRfX19p9erViuMZGRlSpUqVJC8vL+ngwYNSSEiItGfPHmn//v2SJEnS5cuXJX19fal///5SQECAdO3aNem3336ToqOjJUnSfPJva2srrVq1SgoJCZHCwsI0OuclS5ZIBgYG0oIFC6SgoCDp3LlzirZernw9evRIkX/Xrl2SkZGR9OzZsxw/E0EQBEEQBOHr9UEn/9HR0ZK+vr4UGhoqhYWFSQYGBlJ0dLRi8p+YmCgZGBhIp0+fVirfq1cvqWPHjmrrHzBggNS6dWul9ooUKaK0Qty2bVupffv2Ofbz4MGDko2NjZSWliZJUtYl5apVqyqOHzhwQNLS0pKCgoJUlu/YsaNS/jdpOvl//cmC6rx5zo6OjtKYMWPU5vf09JRmzpypeN+yZUupR48eubYjCIIgCIIgfL0+6EO+rK2tadKkCWvXruWPP/6gSZMmSiH6bty4QUpKCvXr18fY2FjxWrduHSEhIYp8y5Yto1y5ctjY2GBsbMzKlSsJDw9XasvLy0spSoyDgwNRUVEATJs2Tan+l2VXrVpF+/bt0dHJCnrUsWNHzp49S1BQEAABAQEUKlQId3d3lecXEBBA3bp13/lzKleuXLa0nM45KiqKR48e5dh27969+eOPPxT59+3bx3fffffOfRUEQRAEQRC+XB881Od3333HoEGDAFi8eLHSsZdRXfbt20fBggWVjunr6wOwZcsWhgwZwty5c6lcuTImJibMnj2bs2fPKuXX1dVVei+TyRT19+vXT+khH46Ojjx58oSdO3eSlpbG0qVLFccyMjJYvXo1M2fOpECBAjmeW27HtbS0kCRJKS0tLS1bPiMjI6X3uZ1zbu0CdOvWjVGjRnHmzBnOnDlD0aJFqV69eq7lBEEQBEEQhK/XB5/8f/PNN8jlciDrEeuv8/T0RF9fn/DwcGrWrKmy/MmTJ6lSpQoDBgxQpL1+VUATlpaWSo/3BtiwYQOFChVi586dSulHjhxh+vTpTJ06ldKlS/PgwQNu376tcvW/dOnSHDlyhIkTJ6ps18bGhoiICMX7hIQEQkNDc+1vbudsYmJC0aJFOXLkCLVr11ZZh5WVFS1btuSPP/7gzJkz9OzZM9d2BUEQBEEQhK/bB5/8a2trc/PmTcW/X2diYsLw4cMZMmQImZmZVKtWjYSEBE6fPo2xsTHdu3fH1dWVdevWceDAAZydnfnzzz85f/48zs7O79SvVatW0aZNG0qWLKmUXqRIEUaOHMm+ffto0aIFNWrUoHXr1sybNw9XV1du3bqFTCbjm2++YfTo0ZQqVYoBAwbQr18/9PT0OHbsGG3btsXa2po6deqwZs0amjVrhoWFBWPHjs32GaiiyTlPmDCBfv36YWtrS6NGjXj27BmnTp3ihx9+UOTp3bs3TZs2JSMjg+7du7/T5yUIgiAIgiB8+T7onv+XTE1NMTU1VXls8uTJjBs3junTp1OiRAkaNmzInj17FBPdfv360apVK9q3b0/FihWJjY1VWhF/GxcvXuTKlSu0bt062zETExMaNGjAqlWrANi2bRvly5enY8eOeHp6MmLECDIyMgBwd3fn4MGDXLlyhQoVKlC5cmV27dqluIdg9OjR1KhRg6ZNm9K4cWNatmyJi4tLrv3T5Jy7d+/OggULWLJkCV5eXjRt2pQ7d+4o5alXrx4ODg40bNgQR0fHt/qsBEEQBEEQhK+HTHpzU7rw1UhKSsLR0ZHVq1fTqlWrT90dQRAEQRAE4RP7KCv/wseVmZnJo0ePGDt2LGZmZjRv3vxTd0ljqampTJgwgdTU1E/dFeEzJcaIkBMxPoTciDEi5Hdi5f8rFBYWhrOzM4UKFWLNmjXvJRzpx5KQkICZmRlPnz5Vu1VMyN/EGBFyIsaHkBsxRoT87oPf8Ct8fEWLFs0WYlQQBEEQBEEQxLYfQRAEQRAEQcgnxORfEARBEARBEPIJse3nEwsJufupu/BZSU1N5YcfBvPgwUP09WM+dXc+Od1McUPam1LlcvwGDeDx/VDi9fQ+dXc+OZuz2z91Fz4rmWnp/PxtbTK3zSdZV/yJA7iz8eCn7sJnRZ6RST/Xwtxu2wQ9bbEGClB6/4lP3QXhIxI3/H5iYvIv5ERM/oXciMm/kBsx+RdyIyb/+Yv4yisIgiAIgiAI+YSY/AuCIAiCIAhCPiEm/4IgCIIgCIKQT4jJvyAIgiAIgiDkE2LyLwiCIAiCIAj5hJj8C4IgCIIgCEI+ISb/giAIgiAIgpBPiMm/IAiCIAiCIOQTYvIvCIIgCIIgCPmEmPwLgiAIgiAIQj4hJv+CIAiCIAiCkE+Iyb8gCIIgCIIg5BNi8i8IgiAIgiAI+YSY/AuCIAiCIAhCPiEm/4IgCIIgCIKQT4jJvyAIgiAIgiDkE2LyL3xy/v7+uLq6kJCQoEg7dOggderUxt3djSlTJqtNEwRBEARBEDSn86k7IOTdiBE/sX37djp27MjkyVOUjo0bN46NGzfQqlUrZs2a/Yl6mKVmzRo8fPgQAH19faytrSldugydOnWkcuUqinw+Pj6cOeOPiYmJIu2XX36hdes2dO/eHSMjI7Vpwudt3YZNLF+1muioaNzcXBn/8ygqlC+nNr//ufNMnj6TO3eCsbW1pV+f7+jSsYPi+KbNf7Nt5y6C7gQDUMrLkxFD/ShbpnSe2pUkiQW/LWbjlr95+jQB7zKlmTz+F9zd3N7zJyDkZvnhsyzYd5LIp4mUKGjL7C6NqepRVGXeneev8/uRc1wNjyA1LYMShWwZ820d6pdW/rnFP09mwt+H2XXhOvFJKRS1sWB6x2/4pqyHxu1KksTUHUdZfewC8c+TKe9SiPndm+FZyO5DfAyCGpvvPWJN6H1iUuW4GBsxooQLPpZmuZa7HPeUXmev4GpsxJZqvkrH1oc+YMv9CCKTUzHX06G+vQ2D3Z3R1361Hppbu5IksSz4HtvuR5KQlk4pcxNGe7riaiL+NgmfP7Hy/4VycHBg7969pKSkKNJSU1PZu3cPjo6On7Bnyvz8/Dhzxp9Dhw4ze/YcTE1N6NatG0uWLFbk0dPTw8bGBplMBsDz58+JjY2levXq2NnZYWxsrDLtbcjl8vdyXkLu9uz7h0nTpjOo3/fs27mNCuV86d7nex4+eqQyf/j9B/To048K5XzZt3MbA/v1ZcKUaew/cFCR58y5czRv2oS/1v3Bjs0bcXR0oOt3fYiMfJyndpetXMXvf6xl0thf2LNtCzbW1nTu2ZvExOcf7gMRstnqH8iI9fsZ0aIWZyYPoKpHEVrOXsf9mHiV+U8FhVGnpCvbh3fj1OT+1CjhTJt56wkIe/Wzlaen03TmGu7FxLFxcEeuzPqRxd+1wNHCNE/tztt3kt/+Oc28bk05ObE/dmYmNJ25hmfJqR/q4xDe8G9EFLNuhtDHpTCbq/riY2HGgAuBRCSn5FjuWVo6v1wJooKVRbZj+x4+5tfbofRzLcKO6uWYUNKdAxHRLLwdmqd2/7j7gD9DHzLK05UNVbyx0tej3/lAnqenv78PQBA+EDH5/0J5eXnh6OjIgQMHFGkHDhzAwcEBT09PRZokSaxYsZzatWvh5eVJ06ZN+OeffxTHMzIyGDVqFLVq1cTLy5P69euxZs0fSm2NGPET/fp9z++/r6Ry5UqUK+fL+PHjSUtLy7WfRkbG2NjY4OjoSIUKFZg6dRoDBw5iwYIF3L17F1De9uPv70+ZF6u4Xbt2wdXVRW0awKVLF+nYsQNeXp5Uq1aVSZMmkpSUpGi/Zs0aLF68iBEjfqJs2TKMGfOzxuWWLFnCqFEjKVOmNNWrV+OvvzYpnVtERAQ//jgYX18fSpUqScuWLQgICFAcP3LkCC1aNMfTswS1a9di4cKFpOejPwy//7GG9m1a07FdG9xcXRg/ZjQO9g6s3/iXyvwb/tqMo4MD48eMxs3VhY7t2tCudStWrHo1HhfOnU23zh3x8iyBq0sxZk6ZRGZmJqfO+GvcriRJrFq7jkH9v6dRw/p4uLsxd9Z0UpJT2LV374f9UAQlC/85RfeavvSsVY7iBW2Z3aUJhazMWHnknMr8s7s0YWjT6pQrVghXe2smtWuAq70V+y/fUuRZe+IScc+T2OLXmcruRShsbUEVj6KULuKgcbuSJLHo39OMaFGTluW98HKyY+X3rUmWp7H5zJUP+6EICn+GPuTbQva0cnKgmLEhIzxdsDfQZ0t4RI7lJl+/QyNHW8qYm2Q7diX+GWUtzGjsaEtBQwOq2FjyjYMN158+07hdSZLYcO8hvV0KU8/eGjcTI6aU8iAlI4P9j6Le74cgCB+AmPx/wVq3bsO2bVsV77du/Zs2bdoq5Zk3by7btm1j4sRJ/PPPv/Ts2ZNhw4Zy9uxZADIzM7G3t2fhwt/4998DDBr0A3PnzmXfvn1K9fj7+xMeHs769RuYNWs227dvY9u2bW/V7x49eiBJEocPH8p2zMfHh0OHDgOwePESzpzxV5sWFBREz549adCgIfv27WPhwoVcuHCRiRMnKNW5cuVK3N3d2blzFwMHDtK43OrVqyhZshS7du2mc+cujBs3jpCQECDr6kSnTp2Iiopi+fIV7Nmzlz59+pKZmQnAf//9x7BhQ+nevTv//nuAyZOnsH37NpYsWfJWn9mXRi6XE3j9BtWrVlVKr1GtChcvB6gsc+lyADWqVXkjfzUCr11X+0UzOTmFtPR0zM3NNG73/v0HREfHUP21tvT19KhYoRwXL6num/D+ydPTuRz2iLqlXJXS65Z0xf9OuEZ1ZGZm8iwlFQtjQ0Xavku3qOhaGL+1eyg6cDrlRi1k1u7jZLz4f1OTdsOi43j8NJG6JV/l0dfVoVrxopzVsG/Cu0nLzORmwjMqWyuv3le2tuBKXIKaUrDzQSQPkpLp51pE5XFvC1NuPn1GYHxWHQ+Skvlf9BOq21hq3O7D5BRiUuVKefS0tfC1NOdKvPq+CcLnQuz5/4K1bNmSOXNm8+DBA2QyGRcvXmTBgl85ezZrFTQpKYnVq1fz55/r8fHxAaBw4cJcuHCRv/7aRMWKFdHV1cXPz09Rp5OTE5cuXWL//v00adJEkW5mZsb48RPQ1tbGxcWFWrVqc+bMaTp06EBemZubY2VlxYMHD7Md09PTw8rKStGmjY0NgMq0lStX0KxZM3r27AlA0aLOjBs3jk6dOjJp0mT09fUBqFy5Mr1791G0MXz4MI3K1axZiy5dugDw/fff88cfqzl79iwuLi7s2bObJ0+esGPHDszNzV/UU1TRxtKlS/j++360atVa8bn7+Q1h1qyZDB48OM+f2ZcmLi6ejIwMrK2tlNKtrayIjolRWSY6JgZrqzfyW1uRnp7Ok7h47GxtspWZMWce9na2VK1SWeN2o17818bK+o081mq3JAnvX8yzJDIyM7EzVd7CZ2tmxOOniRrV8es/p0hKldO6QklFWlj0E07cjKd95dJsH96NkMhYhqzbQ3pGJj9/W0ejdh/HJ75IeyOPqTH3Y+PzeqrCW4iTp5EhgZW+rlK6lb4eMfI4lWXuPU/m16BQ/qhUBh0tmco8jRxtiZOn0cM/6wpOuiTRrrADvVwKa9xuTKr8RdqbeXR5JLaFCV8AMfn/gllaWlK7dm22b9+OJEnUqlUbS0tLxfHg4GBSU1Pp0aO7Urm0tDSlrUEbN25ky5bNPHz4iNTUFNLS0ihRooRSGTc3N7S1tRXvbW1tCAoKAmDJkiUsW7ZUcezffw/ket+BJEmKPf5v69q169y7d4/du3cr1ZuZmcn9+/dxdc1atStVqtRblSte/NXNgTKZDBsbG2JjYwG4ceMmnp6eiol/9r5d4+rVqyxd+mqlPyMjg9TUVJKTkylQoMA7nfuX4s2fsYSEDPU/92z5JelFeva8y1auYve+fWz+cy0GL76w5aldFW2965gU8u7Nj1ySVP+837TlzBWmbj/KliGdlSbpmZKEjakRi3u1RFtLCx/ngkTEP2P+vpP8/G2dPLWrahyRw/gV3r83/7+VJNU/gQxJYvSVm/R3K0JRI0MVObKcj43n95Bwxni5UsrclPDnycy6GYK1/j2+f+1qgSbtvvleXd8E4XMjJv9fuDZt2iq2q0yYMEHp2MstKCtX/o6dnXKECj09PQD27dvH1KlTGD36Z7y9vTEyMuL331dy5YryvlYdHeWhIpPJyMzMmph16tSJxo0bK47Z2trm2Oe4uDiePHmCk1MhzU5SDUnKpGPHDnTr1j3bsde/fBQooPyHQNNyOjrKqzpZ55z1mRoYGOTYt8zMTH788UcaNGiY7Zj+GxPVr5GFhTna2tpERyuv8sfGPsm2Kv+SjbV1tqsCsbFP0NHRweKNL1nLV61m8bIVbFizihKvfUnTpF1b66wV/+iYaKWrCbFPYrNdeRA+HGsTQ7S1tIh8Y5U/OuE5tqY539C/1T+Q/r/vZP0PHahTUnn7jr2ZCbo6WmhrvdrV6uFow+OnicjT0zVq184867+P45/h8Nq+8eiE59iZiWguH4OFni7asler7C89kcuxevH363XP0zO4/jSRWwnBzLiRFQ0sUwIJ8Pn3P5aWL0VFKwsW3wmjaUE7Wjll3QPiZmJEckYGk6/doY9LYY3atdbP+m9Maho2Bvqv5UnLdjVAED5HYvL/hatRo4ZiP3T16jWUjrm6uqKnp8ejR4+oWLGiyvIXLpzHx8dHsb0FIDw8b3tazc3N1a6Aq7J27Rq0tLSoV69+ntp5k5eXF3fu3FHabvMhy72ueHEPtmzZTHx8vMpz9/Ly4u7d0Hdq40ump6dHKS9PTp4+zTcN6inST546TYO6dVSW8fEuy+Gjx5TSTp46RamSXujqvvqDuuz3VSxaspx1q1dSulRJpfyatOvkVAgbG2v+d+oMJV9cAZPL5Zw9d4FRPw19txMXNKano4N3UUeOXgumRblXVyKPXgumqU8JteW2nLlCv5U7WDOgHY1eC935UiX3wmw5c5XMzEy0XnwBCI6Mwd7cBL0Xixi5tVvUxgI7M2OOXguhbNGsBQF5ejr/uxXG5PYN3v3khVzpamlRwtQE/9g46tq/2qLnHxNPLbvsX9KNdbTZ+kZIzy3hjzgXG88cb08KFshasEnJyMy2Oq8tkyGR9UVBk3YLFjDAWl8P/9g4Sry46pSWmcnFJ/H86OH87icvCB+YmPx/4bS1tfn33wOKf7/O2NiY3r17M23aVCQpE1/fciQmJnLp0iWMjAxp1ao1RYoUYceOHfz33384OTmxc+cOrl69ipOT03vp3/PniURHR5OWlsaDBw/YtWsnW7ZsYfjwn955Yty37/e0adOa8ePH0759ewwNCxAcHMKpU/9j/PgJ773c65o2bcbSpUvp378fw4cPx8bGlhs3bmBra4uPjw+DBv1A3759cHBwoFGjRmhpaREUdIugoCCGDh32Tuf9pejdswdDRoykdEkvfMqWZdOWv3kUEUHnju0BmDlnHpGPo5g/ewYAnTu0Z+36jUyaNpOO7dpwKSCAzVu3sXDeHEWdy1auYu6Chfw6bzaFCjoSFR0NgJGhoeLZD7m1K5PJ6NW9G4uXraBokSI4Fy3ComUrMChgQIumTT/mR5TvDW5UlV7LtuLjXJCKrk6sPnaB+7FP6V23PADjNh/kUVwCv/drA2RN/Hsv38bsLk2o4OpEZHxWhJYCerqYGWZN7vrWrcCyQ/4MX7+f/vUrEfw4ltm7T9C/QWWN25XJZAz6pgqz95zAxd4KVzsrZu85QQE9XdpXLvMxP6J8ratzQcZcCcLT1IQyFqZsux9BREoKbQtnrdr/GhRKVEoqU8sUR0smw+2NGPuWerroa2kppde0teTP0IcUNzWmlLkJ95NSWHwnjJq2Vmi/2OaVW7symYzORQqyKiScwoYFKGxUgFUh4Rhoa9PYMecr34LwORCT/6/A6w/HetOQIUOxsrJi2bJl3L9/HxMTE7y8vOjffwAAHTt24ubNm/z442BkMhlNmzajc+cu/PffiffStwULFrBgwQJ0dfWwsbGmbNmyrFv3J5UrV869cC6KFy/Oxo2bmDt3Lh07dkCSJAoXLkzjxk0+SLnX6enpsWbNWqZNm0avXr3IyMjA1dWVCRMmAllXZFasWMmiRb+xcuUKdHR0cHFxoW3bdu90zl+SZk0aERcfz8LFS4mKisbd3Y01K5dTqGBBAKKiY3gU8SpkX2GnQqxZuYxJ02bw54aN2NrZMuGXn2nc8NVK658bNyFPS6P/D35KbfkNGsCQwYM0ahegX59epKSk8MvESSQ8TaBsmdKsX/07xsZiS8fH1KZSKWITk5i+8xiR8c/wLGTHjuFdKfwiikpk/DOlG2xXHT1PekYmQ9buYcjaPYr0LtW8WfF91s31hazM2TOiByM27KfCmEU4WpgwoGFlhjWtoXG7AEObVCdZnobfmt3EJ6VQvlgh9ozogUmBr3/b3ufiGwdbnsrTWRFyj+gUOa4mRiwuVxLHF6v4MalyIlPydoNtH5ciyJCx+E4YUSlyLPR0qWlrySD3Vyv2ubUL0LNYIVIzM5h2I5iEtDRKmZmytHwpjHTEtEr4/Mmkl3fUvc9KZTJ27NhBy5Yt33fVSo4fP07t2rWJi4tTbL3YuXMnw4cPJzQ0lB9++IGyZcvi5+dHfHz8B+3L2woJufupuyB8xnQzReQIIWc2Z7d/6i4In7k7Gw/mnknI10rvfz8LfsKXIc9x/iMjI/nhhx8oVqwY+vr6ODk50axZM44cOfIh+pejKlWqEBERgZnZq0duf//997Rp04b79+8zefJk2rdvz+3bt99722lpaYwcOZJSpUphZGSEo6Mj3bp145EIFSgIgiAIgiB8pvJ0fSosLIyqVatibm7OrFmzKF26NGlpaRw4cICBAwdy69at3Ct5j/T09LC3t1e8T0xMJCoqioYNG74R7eXdwiqmpaUp3XAIWTH0L126xNixYylTpgxxcXH4+fnRvHlzLly48E7tCYIgCIIgCMKHkKeV/wEDBiCTyTh37hxt2rTB3d0dLy8vhg4dir+/v9pyI0eOxN3dHUNDQ4oVK8bYsWOVnth55coVateujYmJCaampvj6+iom0Pfu3aNZs2ZYWFhgZGSEl5cX+/fvB7K2/chkMuLj4zl+/Lhi73udOnWQyWQcP36cNWvWZIvGsmfPHnx9fTEwMKBYsWJMnDiR9PR0xXGZTMayZcto0aIFRkZGTJkyJds5mZmZcejQIdq1a4eHhweVKlXit99+4+LFi3mOliMIgiAIgiAIH4PGK/9Pnjzh33//ZerUqYqoGq/LKdSjiYkJa9aswdHRkcDAQPr06YOJiQkjRowAoHPnznh7e7N06VK0tbUJCAhQrLQPHDgQuVzOf//9h5GRETdu3MDYOHsM6CpVqhAUFISHhwfbtm2jSpUqWFpaEhYWppTvwIEDdOnShYULF1K9enVCQkLo27cvAOPHj1fkGz9+PNOnT2f+/PnZouio8/TpU2QyWZ7CXgqCIAiCIAjCx6Lx5D84OBhJkihevHieG/nll18U/y5atCjDhg1j8+bNisl/eHg4P/30k6JuNzc3Rf7w8HBat26teEprsWLFVLahp6eneLiUpaWl0nag102dOpVRo0bRvXt3RX2TJ09mxIgRSpP/Tp068d1332l8jikpKYwaNYpOnTphamqqcTlBEARBEARB+Fg0nvy/DAr05uPONbF161YWLFhAcHAwiYmJpKenK02Qhw4dSu/evfnzzz+pV68ebdu2xcXFBYDBgwfTv39/Dh48SL169WjdujWlS5fOcx9eunjxIufPn2fq1KmKtIyMDFJSUkhKSsLQMOtpsOXKldO4zrS0NDp06EBmZiZLlix5674JgiAIgiAIwoek8Z5/Nzc3ZDIZN2/ezFMD/v7+dOjQgUaNGrF3714uX77MmDFjkMtfPTp7woQJXL9+nSZNmnD06FE8PT3ZsWMHAL179+bu3bt07dqVwMBAypUrx2+//ZanPrwuMzOTiRMnEhAQoHgFBgZy584dDAxexfBVtbVJlbS0NNq1a0doaCiHDh3Kt6v+/v7+VK5ciQ8QOfadderUiY0bN37qbuQLi5etoFmrdnh6l8OnUjX69B9EyN1Qjcr+vX0HLdt2UHvc/9x5mnzbBveSZalWpwHrN/2lUb0PHj7EzasMzxITsx2Li4unW6++lK9WEzevMlSqUYexE6eozCu8H1O2H8Gw6y9Kr6KDZmhU9s//LlFzwjK1x0/eDKXK2CVYfDcBz6FzWXnknEb1hsfEYd5zPAnJKSqP34+Jp/XcP7HuNRGn/tMYtm4v8tfuExPen6V3wijzz39KrzpHzmhUdteDSLqcvqz2+IXYeDqcukT5AydpfPwcW8I1i873KDmFcv+eJDFN9c88IjmFHy5co+LB/1Hz8Glm3AgmLTNTo7oF4VPQeOXf0tKShg0bsnjxYgYPHpxtchwfH69yr/upU6coUqQIY8aMUaTdu3cvWz53d3fc3d0ZMmQIHTt25I8//uDbb78FwMnJiX79+tGvXz9Gjx7NypUr+eGHHzTtuhIfHx+CgoJwdXV9q/Kveznxv3PnDseOHcPKKvsjxz8luVyOnp4ejx49Uop+9CEcOXKEunXranRlKCMjA5lMhpZWniPN5ll8fDyXL19i3rx5GuV/+Znl5mN8pl+is+cv0K1LR8qUKkl6egaz5/9K1+96c3j/HsVVNXUOHTlG/bp1VB4Lv/+AHn360bFdGxbMnsmFS5cZO3ESlpaWSg8BU13vUSpVrICJinuFtLRk1K9bh+F+g7G0tCDsXjjjJk7h56dP+W3ebM1PXMgTz4K27B3VU/FeW8PfBfsu36KpTwmVx8KinvDtnHX0rF2O1f3acOZOOH5r9mBjakTL8l451rv34i1qlHDG9LWHOL2UkZlJq7l/Ym1iyOGxfXiSmESfFduQgHndxBOhPwQXY0NWVHh1hV/TvxTHo2Kpbaf67/CDpGQGXrxG60IOTCtTnIC4p0y9Hoylni717G1yrPfY41jKWZljrJt9ypQhSQy6cA0LPV3WVCzL07Q0frkahASM9nz3eYYgfAh5mn0tWbKEjIwMKlSowLZt27hz5w43b95k4cKFap/Y6urqSnh4OH/99RchISEsXLhQsaoPkJyczKBBgzh+/Dj37t3j1KlTnD9/nhIlsn7B+/n5ceDAAUJDQ7l06RJHjx5VHHsb48aNY926dYqrDTdv3mTz5s1K9yVoIj09nTZt2nDhwgU2bNhARkYGkZGRREZGKl3V+Jg6derEhAkTmDp1KuXLl1Pc1/DTT8Np1OgbVq5cQVRUVK71XLx4gY4dO1KypBc+Pt706NGDp0+f5ljmyJHD1K1bT+Wxbdu24u1dlqNHj9KwYUM8PUvw8OHDPJ9famoqM2fOoFq1qpQoUYK6deuwZcuWHMscO3aM4sWLq70HpGbNGixevIgRI36ibNkyjBnzs9q6YmNjWbPmD5o3b87333+vdOzw4cO0bNkCT88SlC9fjgED+uf5/L4G61atoG2rb3F3c8OzRHHmzJjKw0cRBF6/kWO5lNRUTp46Rf26tVUe3/DXZhwdHBg/ZjRuri50bNeGdq1bsWLVH7n26eDho9Sro7peMzMzunbqQOlSJSlUsCDVqlSma+cOnL9wMfeTFd6atrYW9uYmipeNae5XWlPkaRwJDKaJj+r7zn4/eh4na3Nmd2lC8YK29KxVjm41fViw/3+51r330k0ae6uu93BgMDcfRrGqf1vKFnWkTklXZnRsxB/HL6i9UiC8Gx2ZDGt9PcXLUj/3BZnUjEzOxMRR01b15P/v8AgcDPQZ4elCMWNDWjk50LKQPWtDH+Ra9/GoWGqpqfdMTBx3E5OYVqY4JcyMqWRtwbDixdh+P0LtlQJB+NTyFOff2dmZS5cuMXXqVIYNG0ZERAQ2Njb4+vqydOlSlWVatGjBkCFDGDRoEKmpqTRp0oSxY8cyYcIEALS1tYmNjaVbt248fvwYa2trWrVqxcSJE4GsVeKBAwfy4MEDTE1N+eabb5g/f/5bn3DDhg3Zu3cvkyZNYtasWejq6lK8eHF69+6dp3oePHjA7t27AShbtqzSsWPHjlGrVq237uO72LFjO506dWLz5i2KLTgLF/7G3r172LFjB3PmzKFatWq0atWKevXqo6+v/Kj6Gzdu0LVrV9q0acu4cePQ1tbG39+fjIwMtW3evn2bmJgYqlSpojZPSkoKy5YtZfr0aZibW2BlZcWuXbsYOzbnL12TJ0+hRYsWQNaXmMuXLzNu3DiKFy/Bgwf3iYuLy7H8kSNHqFdP9ZeSl1auXMmgQYMYMGBgtmNyuZzjx4+xfft2Tpw4gYuLCy1bfkvz5s0VeY4dO8bAgQPo338Ac+bMJS0tjWPHjuXYZn7x7NkzAMxfexCfKqdO+2NjbY37azf7v+7S5QBqVFMeXzWqVWPz1u0qn8Px0tOEBM5fvMjcmdM06u/jx1H8e/AwFStofs+PkHchkbEU+2Em+jralHdxYmK7+jjbWuZY5tiNu9iZGeNZyE7l8bPB4dQtqbzSWq+UG2tPXCQtPQNdHdVR2+KfJ3Mq6B4r+rZSW69XITscLV5t6axX2o3UtHQuhz6ipqfqIBTC27uXlEy9o/7oaskoZW7KYPeiFDLM+Xk9Z2PjsNbXw9VE9RfJq/EJVLa2UEqrYm3BzgeRpGVmoqvm6lNCWjqXnjxlcil3lcevxCXgamKErcGrv6VVbSyQZ0rcSEikgpV5jv0WhE8hT5N/AAcHBxYtWsSiRYvU5nlz3/esWbOYNWuWUpqfnx+QFaVn06ZNauvKaX9/rVq1lNoyNzfP1naPHj3o0aOHUlrDhg1p2LChxv1XpWjRonne356amkpqamq2tDcn4O+icOEijBw5SinNysqK7t170L17D4KDg9m+fTvTp09n7NixNGnShFatWuPt7Q3AypUrKFWqFJMmTVKUd3dX/UvvpcOHD1O9evUczyMtLY2JEycpXbWpW7cuZcqUybFua2trAEJDQ9m/fz9r166jatWqL861cI5lU1NTOXnyZK5bxCpXrkzv3n2U0q5du8b27dvYs2cPurq6NG3ajB07dqqMdrVkyWKaNGmqGNPAO12d+lpIksTk6bMo7+uDh7vqSf1Lh44cVbvlByA6JgbrN7bVWVtbkZ6ezpO4eOxsVV+2P3biPzzc3XF0cMix/R+GDOfgkaOkpKRQr05tZk6dnGN+4e2Vd3Hi935tcLW3IuppIjN3Haf2pBVcnD4YKxP1W8P2XrxJU1/1/189fpqIrZnyxM/O1Jj0jExiEpNwMDdRWe7Aldt4OdlRSM0k7XF89notjAqgp6PN46fP1PZHeDulzE2ZWtqDIkaGxKbKWRkSTrczAWyvXg5zPdVf8iHn1XmAmNQ0rN64gmClr0u6JBEvT8PGQPXfr/9FP8HNxAh7FVvCAGLlcizf6Jepri66MhmxqZ9mF4Ag5ObDb7oWFKZPn46ZmZnSa9ky9TevvY1SpUrmeNzV1ZURI0bw338n6devH1u3buW7717tvb1x4yaVK6tfwVcla8tP3Rzz6OrqZZs4GxsbU7Ro0RxfL5/pcOPGDbS1talQoYLG/fL3P4OFhTkeHh455nsZRvZ1AwcOYMOGDXTt2o2TJ//Hzz//rDbM7c2bN3O86pFfjZ04hVtBQfw2f06O+SRJ4vAx9fv9X3rzfpJXEcjUlzl0+Cj11Wz5UerrzyPZt2MrK5f8xr3wcCZPn5lrGeHtNCzjTsvyXpR0sqdOSVe2D+sGwIb/qb9RU5Ik9l++RRM1W3NekvHGGOHFGMmhzN5LN3OvV8Ugk6Ts7QnvrpqNJfXsbXAzMaKStQW/+Wb9Tdv98LHaMpIkcSLqSY6Tf8g+Dl4u3+V0r9qxxzl/qVBX/vMLfSEIr+R55V94e6NHj2bo0KFKaQ8e5H3ve05yu6ny0aNH7N69m507d/LgwX0aNWpE69ZtFMcN1Kx+qBMdHc3169epVSvnCZaBgX62X5B52fbzeiQmTWmy5QegQIHsn9mcOXPZuvVvVq36nd27d9GyZUtatGiJk5NTtrxv07ev3bhJUzh89BhbNqzDQc39Fi8FXL1KWloa5X191OaxsbYmOiZGKS029gk6OjpYqHmoXlpaGidO/o8B3/dRefx1tjY22NrY4OpSDAtzc9p06srgAf3VXlEQ3h8jAz1KFrIjODJWbZ7zIQ9Iy8igikcRtXnszIx5/FQ5SlNUwnN0tLWwMlb9ezEtPYNDV+8wvFlN9fWaG3MhRHlfeNzzZNIyMrA1y34TufB+Gepo42ZiRPjzZLV5Ap8+Iy0zE29L9dsLrfV1iXljJf5Jaho6MhlmKm7kBUjLzOR0zBN6uWT/vf+SlZ4egfHKV4AS0tJIl6RsVxoE4XMhJv8fkb6+fratMfr6MWpyvz+JiYkcOPAvO3fu5OzZs/j4+NCzZ08aN26MiYnypfDixYtz5sxppS0sOTly5DDe3t5YWua8X1eVvGz78fDwIDMzk3Pnzim2/eREkiSOHDnKnDlvF7GlfPnylC9fnvHjJ3DgwL9s376d3377DR8fH1q0aEnjxo0VYV09PDw4ffo0bdq0yaXWr58kSYybNJUDhw6zef0aCjsVyrXMocNHqVOzZo5P0vbxLsvho8r3UZw8dYpSJb3U7vc/c/YcpqYmeHnmbQvWyysKn+rG/fwmNS2dW4+ic5zY7710k2/KeOQYFaiia2H2X76llHYkMBgf54Jq9/ufuHkXM0MDyhRRvy2somthZu06QUT8M8XWoSOBwejr6uDtLCJ+fWjyjEzuJibhbaF+Yn/8cSzVbSzRzmEFv7S5Kf9FPVFKOxMTh6eZsdr9/udj4zHR0aG4qfoveWUsTPk9JJzolFTF1qHTMXHoacnwzKGcIHxKYvKfD/Tv34/79+/TsmVLpkyZSpEi6v/I9uvXn8aNGzNu3Dg6deqErq4u/v7+NGrUSOUEPyvEZ+6r66oYGxsrtvXkplChQrRq1YpRo0Yqbvh9+PAhsbGxNGnSJFv+a9cCSU5Oonx5zbcJqWJoaMi337bi229b8ejRI3bs2M7vv69k06aN7NqVdcP3Dz8Mplu3rhQuXJimTZuSkZHBiRPH6dv3+1xq//r8MnEyu/fsY+XSRRgZGREVHQ2AqYmJ2iskh44eY8jgQTnW27lDe9au38ikaTPp2K4NlwIC2Lx1Gwvnqd9SdOjIMbVRfl46evwEMbGxlClVCkNDQ+4EBzN99lzK+fjgVKhgLmcrvI3RG/+hsXdxnKzMiEp4zsxdx3mWnEqX6t5qy+y/dItfWue8tbB3nfIsO+TPyA376VmrHGeD77P2xEXWDmyntsy+S7dooiZ06Ev1SrlSoqAtvZf9zdQO3xD3PJnRm/6hZ61yKkODCu9m7q271LSxxL6APk/kaawMDud5egbN1dzoDVn7/Qe4qf+7BtC2sAN/hT9i9s0QWjs5cCUugR0PIplZVv2Wr+NRT6ipJnToS5WtLShmbMiYq0EM8ShGQloa827dpZWTg8rQoILwORAjMx+YOHESzs7OGsXgd3Z2Zs2aNcydO4dWrb7FwMCAMmXK0KxZs2x5k5KSOH36tNIzHD6kSZMmM2fOHMaPH09cXDyOjg707z9AZd7Dhw9Tq1ZtdHTe3xB3dHRk4MBBDBw4iJCQEEV6pUqV+O23rJvgly9fjrGxMRUqlH9v7X5J1m/MevBW+y7dldLnzJhK21bfZst/Lzyce/fCqVkt56s5hZ0KsWblMiZNm8GfGzZia2fLhF9+zjHG/+GjR5k9bUqO9RoYGLBpy1YmT5tJqlyOo4M939SvT//v8xb9S9DcwycJdF+yhdhnSVibGlLBxYnjE76n8BuRWF66+ziWkKgn1CuVc8z0oraW7BjejREb9rP88FkczE2Y07VJjjH+912+xbLe2cfl67S1tNg+rCs/rt1D3ckrKaCnQ7vKZZje8ZvcT1bIs8cpqYy6cos4eRoWerqUNjflz8plcVTzRev+82TuJyVTxTrnq8+FDAuw2Lcks2/dZfO9R9gY6DHS0yXHGP8nomKZqCbKz0vaMhmLypVk6vVgevgHoK+tRSNHW4Z5iChQwudLJn2Oj2R9w5IlS5g9ezYRERF4eXmxYMECqlevrjJvjx49WLt2bbZ0T09Prl+/rngfHx/PmDFj2L59O3FxcTg7OzN37lwaN26scbuSJDFx4kRWrFhBXFwcFStWZPHixXh55fxAmdeFhNzVOO/n5sCBA8ybN48DBw586q5k06RJYwYMGKjyqsCXRDczNfdMX7CVq9fwv9NnWPv78vdab+D1G3Tq1pNL/v9Tuy3oa2Fzdvun7sIHtfCfUxy9FsLOn7q913ovhz2i8fTVhC8erXZb0NfizsaDn7oLH8y60AecjY1jcbnsgRvexc2nz+hz7irH6lZWuy3oa1J6/4lP3QXhI/rsR/TmzZvx8/NjzJgxXL58merVq9OoUSPCw8NV5v/111+JiIhQvO7fv4+lpSVt27ZV5JHL5dSvX5+wsDC2bt1KUFAQK1eupGDBV5f5NWl31qxZzJs3j0WLFnH+/Hns7e2pX7++Irb5187Q0JARI0Z86m5kI5fLadjwG2rWVH8Tn/B5cLC3Y6AGN+TmVUZ6OhPHjvnqJ/75QUFLU35qXuO915uekcncrk2++on/187OQJ9exXIO+/w20iWJkZ6u+WLiL+Q/n/3Kf8WKFfHx8VF6iFiJEiVo2bIl06dPz7X8zp07adWqFaGhoYq97suWLWP27NncunVL7eQgt3YlScLR0RE/Pz9GjhwJZMWVt7OzY+bMmdmeAKvOl7zyL3x4X/vKv/DuvvaVf+Hdfc0r/8L7IVb+85fP+iutXC7n4sWLNGigvK+3QYMGnD59WqM6Vq1aRb169ZRuct29ezeVK1dm4MCB2NnZUbJkSaZNm6Z4iq0m7YaGhhIZGamUR19fn5o1a2rcN0EQBEEQBEH4mD7ryX9MTAwZGRnY2Snf5W9nZ0dkZGSu5SMiIvjnn3/o3Vv55r27d++ydetWMjIy2L9/P7/88gtz585l6tSpGrf78r9v27ePxd/fH1dXFxISEgDYtm0r3t5lP22nvmCuri4cOiRW0QRBEARB+DJ9EdF+VD3ZU5PINWvWrMHc3JyWLVsqpWdmZmJra8uKFSvQ1tbG19eXR48eMXv2bMaNG5endt+2b3m1ceNGZs6cwcWLlxQRbJ4/f46vrw9ly5blr782K/KeP3+ejh07cOjQYXx8fDhzxj9bPH9B+BjWbdjE8lWriY6Kxs3NlfE/j6JC+XJq8+/YvYflK1cTeu8eJibG1KpejTEjR2BhYQ7A7Tt3mPvrIq5dv86Dh48Y9/MoevVQvhF08bIV/HvwMCGhdzHQN8DXuyyjfhqGSzFnRZ5hI39m646dSuW8y5Rm599/vbdzFzSz/PBZFuw7SeTTREoUtGV2l8ZU9SiqNv/Jm6GM3PgPNx9G4WBuwpAm1elTVzmkb/zzZCb8fZhdF64Tn5RCURsLpnf8hm/KZj3te/buE+y6cIPbEdEU0NWlolthpnRogLvDq8gvfZdvY/0bTx0u71KIExP6vb+TF3K1+d4j1oTeJyZVjouxESNKuOCTw8O8LsTGM+fWXUISn2Ojr0+PYoVoV1j5eQwJaeksuh3KkcexJKSlUbCAAcOKu1DdNiti0KqQcI48jiE0MRl9bS3Kmpvi5+FM0dceFjf2alC2pw6XMjNhfRX1IWsF4XPxWU/+ra2t0dbWzraSHhUVlW3F/U2SJLF69Wq6du2Knp7yU/YcHBzQ1dVVeqhQiRIliIyMRC6Xa9Su/YunlkZGRuLg4KAyz/tUqVIlnj9/TmBgIN7eWb9cLlw4j7W1NVevXiU5OZkCBQoAcPasP3Z2djg7Z012bGzEU0qFj2/Pvn+YNG06k8ePo5yPNxs3b6F7n+85vH8PBR2zPxzp/IWLDB0xmnE/j6Ru7do8fvyYn8dPZOSYsaxY8hsAyckpFHYqRJNvGjJp+gyV7Z49f4FuXTpSplRJ0tMzmD3/V7p+15vD+/coPQG7ZvVqzJkxVfFeT9wc/NFt9Q9kxPr9LOjRjMpuhVl17DwtZ6/j0ozBOFmbZ8sfFvWEb+eso2ftcqzu14Yzd8LxW7MHG1MjRUhPeXo6TWeuwcbUiI2DO1LQ0pQHsU8xfu3p5SdvhfF9vYr4FitIekYmE7YeptnMNVya8SNGBq/+XtQv7cbyPq0U7/XEzcEf1b8RUcy6GcIYL1fKWpixNTyCARcC2VG9HA4qQn8+SEpm4MVrtC7kwLQyxQmIe8rU68FY6ukqQnqmZWbS7/xVLPX0mONdAjsDfSKTUzF67Wd74clT2hd2xMvMhAxJ4rfbYfQ7H8j26uUwfC1fVWsLJpX2ULzX/QALf4LwIXzW23709PTw9fXl0KFDSumHDh2iSpUqOZY9ceIEwcHB9OrVK9uxqlWrEhwcTGZmpiLt9u3bODg4oKenp1G7zs7O2NvbK+WRy+WcOHEi1769jWLFimFnZ8fZs2cVaWfPnqVevfoULlyES5cuKaVXrFgJyL7tR5XDhw/TsmULPD1LUL58OQYM6K849vTpU4YPH4aPjzclS3rx3Xc9CQsLBSA2NpZKlSqyZMkSRf6AgABKlCjOyZMncy0Pr7Yh/ffffzRs2IDSpUvRs2cPoqKi1Pb35TkdO3aMpk2b4OlZgtatWxEUFKS2zOzZs2ndunW29CZNGrNgwQIArl69Svfu3Shfvhxly5ahY8eOXLt2Ldd+vP7Z3rhxA1dXFx48eKBIu3TpIh07dsDLy5Nq1aoyadJEkpKS1Nb7tfj9jzW0b9Oaju3a4Obqwvgxo3Gwd1A8C+BNl65coVDBgvTs1pXCToUoX86XTu3bcfXaqxC9ZUqXYszIn2jetDH6b3ypf2ndqhW0bfUt7m5ueJYozpwZU3n4KILA6zeU8unr6WFrY6N4mZubv7dzFzSz8J9TdK/pS89a5She0JbZXZpQyMqMlUfOqcz/+9HzOFmbM7tLE4oXtKVnrXJ0q+nDgv3/U+RZe+IScc+T2OLXmcruRShsbUEVj6KUfu0pvrtHdKdrDR88C9lRuogDy/u04n7sUy6HPVRqT19HB3tzE8XL8rWVX+HD+zP0Id8WsqeVkwPFjA0Z4emCvYE+W8IjVOb/OzwCBwN9Rni6UMzYkFZODrQsZM/a0Fe/j3c8iOSpPJ35Pp54W5jhWMAAH0szPF57Gu/S8qVoUcgeVxMjPEyNmVTKnYiUVG4mKEfy09PSwlpfT/Ey0xMLCMKX4bOe/AMMHTqU33//ndWrV3Pz5k2GDBlCeHg4/fplXXodPXo03bplj/+8atUqKlasSMmSJbMd69+/P7Gxsfz444/cvn2bffv2MW3aNAYOHKhxuzKZDD8/P6ZNm8aOHTu4du0aPXr0wNDQkE6dOn2Qz6JixYr4+/sr3vv7+1OxYkUqVKiAv/8ZIOsLyOXLl6lUqZJGdR47doyBAwdQq1Ztdu/ew7p1f1Ky5Kt4ySNGjCAw8BrLl6/g77+3IkkSvXr1Ii0tDSsrK2bMmMFvvy0kMPAqz58/Z9iwoXTu3FnxPIScyr+UkpLCqlW/M2fOHDZu3MSjRxHMmJF7JKcZM2YwatRoduzYiaWlFd9/31ep3te1aNGcK1cCuHfvniLt9u3bBAUF0aJFCwCeP0/k229bsWnTX2zduo2iRYvSu3cvEhMTNfosVQkKCqJnz540aNCQffv2sXDhQi5cuMjEiRPeus4vgVwuJ/D6DapXVX54V41qVbh4OUBlGV9vbyIjIzl6/ASSJBEdE8M/Bw5Sp9a7hXl8GXrX3Ex5q4D/ufP4VKpGrQaNGDlmHDGxse/UjpA38vR0Loc9ou4bD++qW9IV/zuqQzmfDQ6nbknl/PVKuXEp9CFp6VkBG/ZdukVF18L4rd1D0YHTKTdqIbN2HyfjtcWeNyUkpwBgYaQ8uT95K5QiA6ZT+qf5DFi1g6inb/+7QMibtMxMbiY8o/IbD3+rbG3BlTjVi1lX4xOy5a9ibcGNp4mkvfj5n4iKpbSFKdNvBFP7yBlanbzA7yHhZOQQ+DDxxdgyfePq4IUn8dQ6coZmJ84zMfA2sanyPJ+nIHwKn/W2H4D27dsTGxvLpEmTiIiIoGTJkuzfv18RvSciIiJbzP+nT5+ybds2fv31V5V1Ojk5cfDgQYYMGULp0qUpWLAgP/74oyJkpybtQtbENjk5mQEDBige8nXw4MEPtr++QoWKTJs2lfT0dFJSUrhx4wYVKpQnMzND8WCzgIAAUlJSNJ78L1mymCZNmuLn56dIK1Ei63H3YWGhHDlymC1btuDj4wvAvHnzqV69GocOHaJx48bUqlWbdu3aM3ToUEqVKo2+vj4//TRC4/IAaWlpTJo0WfHZdu3alUWLfsu174MH/0C1atWArJX9atWqcvDgQZUP9nJ396B48eLs2bObQYN+ALKiPpUuXVqxPapyZeUrNlOmTMHHx4dz585Rp04djT7PN61cuYJmzZrRs2dPAIoWdWbcuHF06tSRSZMmo6+vn0sNX6a4uHgyMjKwtrZSSre2siI6JkZlmXI+3iyYO4tBQ4aRmionPT2d+nVrM3Hs2z9BWpIkJk+fRXlfHzzc3RTptWpUp/E3DSlU0JH7Dx4wd8FCOnbryd4dW9VeURDer5hnSWRkZmL32oorgK2ZEY/VTLIfP03E1sxIKc3O1Jj0jExiEpNwMDchLPoJJ27G075yabYP70ZIZCxD1u0hPSOTn7/N/v+xJEmM3PAPVdyL4OX0astmgzLufFuxJIWtzAmLjmPStsM0nr6aU5MHoK/72f/p/OLFydPIkMBKX3nCbaWvR4w8TmWZmNQ0rPT13sivS7okES9Pw8ZAnwdJKTxKjqexoy2Ly5Xk3vNkpt8IJj1Top9bkWx1SpLEnFsheFuY4mbyauxVtbGgvr01DgUMeJiUwpI7YfQ5d5W/qvigp/3Zr6sK+dwX8RtswIABDBgwQOWxNWvWZEszMzPLdVtF5cqVlVbR89ouZK3+T5gwgQkTJuRYz/tSqVIlkpKSuHr1KgkJTyla1BkrK2sqVKjI8OHDSUpK4uzZszg6OlK4sGYPPbl58ybt23dQeSw4OAQdHR3KlCmrSLOwsMDZuRghISGKtNGjR9OoUSP++Wc/O3bsVExoNS1foEABpS9VtrY2xGqwCuvt7aP4t7m5uVK9pUu/unrRokULJk+eQvPmzdm6dSuDBv2AJEns3buHHj16KPLFxsawYMECzpw5Q0xMDJmZmSQnJ/Po0aNc+6LOtWvXuXfvHrt371akSZJEZmYm9+/fx9XVNYfSX75sN8QjIUP1vtjbwcFMmDKNwQP7U7NaNaKio5k2aw4/j5/I7GlT3qr9sROncCsoiK2b1iulN2vSSPFvD3c3SpUsSdXadTl67ASNGtZ/q7aEt/PmNmlJyp6mlJ/sYyorPUumJGFjasTiXi3R1tLCx7kgEfHPmL/vpMrJ/5C1e7l2P5LDY5UfNtem0qvfIV5OdvgUK0hxvzn8ExCkuL9A+PCy/bwl1PwGeZlf2cv1/Je/izIlsNTTY1xJd7RlMjzNTIhOlbM29IHKyf/0G8HcefacNRXLKqV/42Cr+LebiRFeZsZ8c/wc/0U/oZ69tYZnJwifxhcx+ReyFC1aFHt7e/z9/UlIeEqFClkRLmxsbChUqBAXL17E3/8MlStX1rhOA4PsN029pP75b5LSH+fw8HCioh6TmZnJw4cPKV68eJ7Kv4xe9JJMJsuhbM5e/oLfvXuPIs3EJGtlsVmz5syePZtr166RmppCREQETZs2U+QbMWIET5484ZdffsHRsSB6enq0bdtW7VYirRdPfny9r+np6Up5JCmTjh070K1b92zlHVXc9Pq1sLAwR1tbm+ho5VX+2Ngn2a4GvLRk2UrK+XjTr3fWfTolintgWKAAbTp1Zbjfj9jZ5u3G9XGTpnD46DG2bFiHw4sb9NWxs7WhoKMjYa9tCxM+LGsTQ7S1tIh8Y5U/OuE5tm9cDXjJzsw421WBqITn6GhrYfViP769mQm6Olpov/ZkVg9HGx4/TUSeno7ea79vhq7by77LNzk0pjeFcoggA+BgbkJha3NCHovtYR+DhZ4u2jKIeWMrzRO5HCs1V+es9XWz509NQ0cmw+zF1RobfT10tGRov/ZHqJixITGpctIyM5We6Dv9RjDHo2JZXbEMdgVyvkprY6CPYwF9wp8n5+k8BeFTENemvjCVKlXi7NmzL27qrahIr1ChAidPniQgIEDjLT8AHh4eah9K5ubmSnp6OleuBCjS4uLiCA0NxcUla8VaLpczbNhQmjRpwpAhQxk9ehQxL7Z1aFL+XVy+/CoM39OnTwkLC6VYsWJA1helly8rq6xVGAcHB8qXr8Du3bvZtWs3VapUwdr61QrNhQsX6NatO7Vq1cbd3R09PT3i4p6obd/SMiss3Os3J9+4oXxTqZeXF3fu3FHqz8vXm1GoviZ6enqU8vLk5Btj6+Sp0/iqec5EckoKWjLlX0laLyNy5eHLoCRJjJ04hX8PHmbTutUUdiqUa5m4uHgiIiKxFZGxPho9HR28izpy9FqwUvrRa8FUclN95bKia+Fs+Y8EBuPjXBDdF1FYKrkXJuTxE6WADsGRMdibmygm/pIkMWTtHnZduM4/o7+j6IsQjzmJfZbEgydPsTcXYZM/Bl0tLUqYmuAfq7zFxz8mnjIWpirLlDY3xT8mXintTEwcnmbGikl9WQtT7iclk/na75R7z5Ox0ddT5JEkiWnXgzkSGcPKCmUoZFgg1/7Gy9OITEnFxuDr/b0ufD3E5P8LU6lSZS5evMDNmzffmPxXZPPmzaSmpioi/Wjihx8Gs3fvHhYsWEBwcDBBQUGsWLEcyNqfXq9ePX7+eQwXLmS1OWzYUOzs7KhXrx4A8+bN5dmzZ4wdO46+ffvi4uLK6NGjNC7/LhYt+o3Tp09x+3YQI0aMwMLCgvr1c96y0bx5c/bt28s///xDixYtlY4VLlyEnTt3EhwcTEBAAMOGDc3xykiRIkVwcHBg4cKFhIaGcuzYMVavXqWUp2/f77l8+TLjx4/nxo0bhIWFcvjw4a/+hl+A3j17sPnvrWzeuo07wSFMmjaDRxERdO7YHoCZc+Yx5KdRivz16tTi30OH+XPjX4SH3+f8xUtMmDyNsqVLYWeXdYldLpdz/cZNrt+4iTwtjcjHj7l+46bSiv0vEyezc/ceFs6bjZGREVHR0URFR5OSknVT5/Pnz5kyYxYXLwdw/8FDzpw9x3f9BmBhYUHD+u8+LgXNDW5UlTXHL7L2xEVuPYxixPr93I99Su+65QEYt/kgvZdtVeTvXac84THxjNywn1sPo1h7IqusX+Nqijx961bgSWISw9fv505EDP8EBDF79wm+r/fq96Xf2j38dfoKa/q3w9hAn8j4Z0TGPyNZnnWVLzElldEb/+HsnXDuRcfx3827tJn3J1bGhjT39fxIn47Q1bkg2+9HsuN+JHcTk5h9M4SIlBTaFs6K3PRrUChjrtxS5G9b2IFHKSnMvhnC3cQkdtyPZMeDSLo7v1oAaFfYgXh5OjNvhhD2PIn/omL5PSSc9q89C2DajWD2P3rMjDLFMdLRJiZVTkyqnJSMrBt/k9IzmHvrLlfiEniYlML52HgGX7yOua4udexUX9kUhM+J2PbzhalUqRIpKSm4uLgorVpXqFCB588TKVy4cJ62k1SqVInfflvEokWLWL58OcbGxlSoUF5xfObMWUyePIk+fXqTlpZG+fLlWbVqFbq6uvj7+7NmzRrWr9+guMl57ty5NGnSmA0bNtC5c+ccy7+rn34aweTJkwkLu0eJEsVZvnxFrqvpjRs3ZtKkiWhra2f7ojBjxgx++WUMzZs3w9HRkWHDhucYdUhXV5cFCxYwbtw4mjZtQunSpRkyZCg//DBIkad48eJs3LiJuXPn0rFjByRJonDhwjRunP2m5K9NsyaNiIuPZ+HipURFRePu7saalcspVLAgAFHRMTyKeBWyr22rb0lMfM7a9RuYMmMWpqYmVKlUkdHDhynyPI6KpnHLVyFbV6z6gxWr/qBShfJsXp910/vLUKLtuyhvtZozYyptW32LtrY2QbfvsH3nbhKeJWBrY0PlihVZvGAuxsbKN5MKH1abSqWITUxi+s5jRMY/w7OQHTuGd6Xwi4gtkfHPuB8br8hf1NaSHcO7MWLDfpYfPouDuQlzujZR2oNfyMqcPSN6MGLDfiqMWYSjhQkDGlZmWNNXUaNehhJtOE35y/ryPq3oWsMHbS0trj94zMb/BRCflIK9uTE1SxTjz0HtMcll+4fw/nzjYMtTeTorQu4RnSLH1cSIxeVK4vgixn9MqpzIlFRF/kKGBVjsW5LZt+6y+d4jbAz0GOnpoojxD2BfwIBl5Usx+2YIbe9fxFZfn85FC9KzmJMiz8tQor3OXVXqz6RS7rQoZI+WDO48e86eh495lpaOjb4e5a3MmVW2OEY6YlolfP5k0tturhbei5CQu5+6C18cf39/unTpzKVLlzE1VX3592uhm5maeyYhX7M5u/1Td0H4zN3ZePBTd0H4zJXef+JTd0H4iMS2H0EQBEEQBEHIJ8TkXxAEQRAEQRDyCbE5TfjiVKpUieDgkNwzCoIgCIIgCErEyr8gCIIgCIIg5BNi8i8IgiAIgiAI+YSY/AuCIAiCIAhCPiEm/4IgCIIgCIKQT4jJvyAIgiAIgiDkE2LyLwiCIAiCIAj5hJj8C4IgCIIgCEI+ISb/giAIgiAIgpBPiMm/IAiCIAiCIOQTYvIvCIIgCIIgCPmEmPwLgiAIgiAIQj4hJv+CIAiCIAiCkE+Iyb8gCIIgCIIg5BNi8i8IgiAIgiAI+YSY/AuCIAiCIAhCPiEm/4IgCIIgCIKQT+h86g4IgiAIgiAIQkpKCnK5PMc8enp6GBgYfKQefZ3E5P8TS5fEj0DIgbg2J+RGT+9T90D4zOkW0P3UXRCEXKWkpOBYwJg4MnLMZ29vT2hoqPgC8A7EzFMQBEEQBEH4pORyOXFksNagGIZqVr6SyKR75F3kcrmY/L8DMfkXBEEQBEEQPgvGBXQxkmmrPKYlZUDKR+7QV0hM/gVBEARBEITPgpa2DC0tmepjmarThbwRk39BEARBEAThs6BdQAttLdXbfrQzpY/cm6+TmPwLgiAIgiAInwWZrgyZmpV/mVj5fy/E5F8QBEEQBEH4LGjraaGtrWblP0Os/L8PIpCgIAiCIAiC8FnQ0pbl+MqrJUuW4OzsjIGBAb6+vpw8eVJt3uPHjyOTybK9bt26pZRv27ZteHp6oq+vj6enJzt27Mhzvz4lMfkXBEEQBEEQPgtauto5vvJi8+bN+Pn5MWbMGC5fvkz16tVp1KgR4eHhOZYLCgoiIiJC8XJzc1McO3PmDO3bt6dr165cuXKFrl270q5dO86ePftW5/spiMm/IAiCIAiC8FnQ1tFCW1fNSydv09Z58+bRq1cvevfuTYkSJViwYAFOTk4sXbo0x3K2trbY29srXtrar750LFiwgPr16zN69GiKFy/O6NGjqVu3LgsWLHib0/0kxORfEARBEARB+CzItNVv/XkZ/j8hIUHplZqamq0euVzOxYsXadCggVJ6gwYNOH36dI598Pb2xsHBgbp163Ls2DGlY2fOnMlWZ8OGDXOt83MiJv+CIAiCIAjCZ0GTbT9OTk6YmZkpXtOnT89WT0xMDBkZGdjZ2Sml29nZERkZqbJtBwcHVqxYwbZt29i+fTseHh7UrVuX//77T5EnMjIyT3V+jkS0H0EQBEEQBOGzINPKIdTni/T79+9jamqqSNfX11dfn0y5LkmSsqW95OHhgYeHh+J95cqVuX//PnPmzKFGjRpvVefnSKz8C4IgCIIgCJ8Ftfv9X7wATE1NlV6qJv/W1tZoa2tnW5GPiorKtnKfk0qVKnHnzh3Fe3t7+3eu81MTk39BEARBEAThsyCTaSHTUvOSaT5t1dPTw9fXl0OHDimlHzp0iCpVqmhcz+XLl3FwcFC8r1y5crY6Dx48mKc6PzWx7UcQBEEQBEH4LLy+wp/tWB7XrIcOHUrXrl0pV64clStXZsWKFYSHh9OvXz8ARo8ezcOHD1m3bh2QFcmnaNGieHl5IZfLWb9+Pdu2bWPbtm2KOn/88Udq1KjBzJkzadGiBbt27eLw4cP873//e8sz/vjE5F8QBEEQBEH4LGiy519T7du3JzY2lkmTJhEREUHJkiXZv38/RYoUASAiIkIp5r9cLmf48OE8fPiQAgUK4OXlxb59+2jcuLEiT5UqVfjrr7/45ZdfGDt2LC4uLmzevJmKFSu+xdl+GjJJksSzkj+hoOCcHzQh5G86svRP3QXhM+d4cVvumYR8LWzzgU/dBeEzV2Lb4U/dBRISEjAzM+N8i9oY66pem05MS6f8rmM8ffpU6YZfIW/Eyr8gCIIgCILwWdDS0UJLzcO8tCRxq+r7ICb/giAIgiAIwmcha9uP6kl+Xrf9CKqJyb/wTiRJYtFvC9iyeSMJT59Suow34yZMxs3NPcdyB/7dz8IFcwkPD6dw4cL4Df2J+g2+URxfvmwxhw7+y927IRjoG+Dt48uwn0ZRrJhLntqWp6Yyc+ZU9u3dTWpKCpUqV2X8hCnYv3bnviAIn87yA6dZsOcEkfHPKFHIjtndm1O1hLPKvDvPBvL7IX+uhj0iNT2dEoXsGNOmPvXLeqjM//epALov3EjTcl5s+al7ntqVJImpWw+x+shZ4hOTKe9WmPnftcTTyf79nbyQq00hD1h95x7RKXJcTY0YVdqNctYWuZa7FBtP9/8u4WpqxI66ynuxE+Rp/HojhEMPo0lIS6eQkQE/lXKjpr21xu1KksTim6H8HfaQBHk6pS1N+aWsB26mxu/v5PMpLR31N/yKlf/3Q3yKwjv5fcUy1qz+nbHjJvH39j3Y2NjwXY/OJCYmqi1z+fJFhvoNonnLVuza8w/NW7ZiyI8DuRJwWZHn/LmzdOrcjc1/72T1mvWkp6fTu2dXkpKS8tT2tKmTOHzwAPPmL2LDpq0kJT2nX9/vyMjI+DAfiCAIGtt6OoARa/cw4ts6nJnxI1WLO9Ny+irux8SpzH/qZih1SrmxfdR3nJo+mBpeLrSZtYaA0IfZ8oZHxzF6/T6qFs/+RUKTduftPs5v+04yr2dLTk4bjJ2ZCU2nruRZcsr7+wCEHP3z4DHTr97me4+ibKtTAV8rc74/dYVHSTn/DJ6lpTP6wg0q2WT/kiDPzKT3/y7z8HkKCyqVYl+DSkz0LoGdwas48Zq0u+r2PdYGh/NLGQ+21C6PtYEevf93medp4j6td6U2zOeLl/DuxKcovDVJkli3dhX9+g+iQcNGuLt7MGPmXFKSU9i7Z5facuvWrKZK1Wp8328gxVxc+b7fQCpVrsraNasVeX5fvY5Wrdvi5uZO8RKeTJ8xh0ePHnL9WqDGbT97lsC2rZsZOfoXqlSthqdXSWbN+ZXbt29x+vSXE5JLEL5WC/edpHud8vSsW5HiheyY3aM5hazMWXnQX2X+2T2aM7RFLcq5OuHqYMOkjo1wdbBm/8UbSvkyMjPp+dsmfmlbH2c7yzy3K0kSi/b/jxHf1qFlxVJ4FbZn5cD2JKemsfl/Ae/9cxBUW3MnnNZFHWnjXBAXUyNGl3HHwVCfv+4+yLHchMs3aVLIjjKWZtmObQ97xNO0dH6rXBofK3MKGhbA19qc4uYmGrcrSRLrgu/zvUdR6he0xc3MmOm+XqRkZLL3fmS2NoW8ebnnX91LeHfiUxTe2oP794mOjqZqteqKND19fcpXqMjlyxfVlgu4fImq1WoopVWrXoOAHMo8S3wGgJm5ucZtX78WSFpamlJbdnZ2uLl7cPmS+rYEQfjw5OnpXL77kLqllbcI1i3jhv/tMI3qyMzM5FlyKhbGhkrp07YextrUiB51KrxVu2FRT3gc/0wpj76uDtU8i3H29j2N+ia8G3lmJjfin1HVVvnLWxVbSwKePFVbbnvYI+4nJjNAzdaxYxExlLE0Y0pAENX3/Ufzw/4svxVGxovAh5q0+yAphZhUOVXsrBTH9bS1KGdtnmPfBM2Ilf8PT3yKwluLjokCwMraRindytqamOhoteViYqKxsrJWSrOysiZaTRlJkpgxbTK+5crj7u6hcdvR0dHo6uphZqa8+mNllXP/BEH48GISnpORmYmdmfIeaVszEx7HP9Oojl/3/kdSqpzWlcso0s7cCmPtsfMs7tvmrdt9+V/bbHmMNe6b8G7iU9PIkCSsDPSU0q309YlJkassE5aYxPzrwcwqXxIdNZPEB8+TOfgwigxJYlmVsvTzKMqa4HssvxWqcbsxKakAWOsr57HW11PbN0FzWjraOb6Edycm/4LG9uzagU+ZEopX+ou9jbI3b76XJGTZEpVlO55DmckTxxIUdIu5835TUU/e29YojyAIH8Wb/y9KGv7/ueXUZaZuPcS6HzsrJunPklP4btEmFvdtjbWp0Tu3mz0PIH51fFSyNz5wCUnljyBDkhhx7hoDSxSjqImhihxZMpGw1Ndlok8JvCxMaexkz/cezvx1V/m+EU3azfbnR0Wa8BZkspxfwjsT0X4EjdWuW5/SZb0V7+XyF6sg0dHY2top0mNjY7Gyts5W/iVraxtiYpRX3mOfxGKtoszkSeM4euQw6zduUYrQY2Ntm2vbNjY2pKXJefr0qdLqf+yTWMr6+Gp0zoIgfBjWpkZoa2kR+cZKenRCYrYV9zdtPR1A/2VbWT+kC3VKuynS7z5+wr3oONrMWqNIy3yxncOk4yiuzP+JQtZmubZr92L/9+P4ZzhYmCrlsTMzQfjwzPV10ZbJFKvsLz1JlWdblQd4npbOtfhn3Lxym6lXbgNZP3sJKLXjKCurlqWSrSU2BvroyGRovzaJLGZiSEyqHHlmpkbtWr+4OTg6VY5NgVc3Cseq6ZuQN1ra6lf4tTIyP3Jvvk5i5V/QmLGxMUWKFFW8XF3dsLGx4fSpVzfPyuVyzp87i7e3+sl1WW8fTp86qZR26n//Ufa1MpIkMWniWA4d/Jc1f26ikFNhpfyFnJxybdurZCl0dXWV2oqKesyd20F4i8m/IHxSejo6eBcryNGrd5TSj169QyX3omrLbTl1mb5LtvDH4I408imhdMzD0Ybzs4fiP9NP8Wri60lNLxf8Z/pRyNpMo3aL2lpiZ26ilEeens7/btylonuRdztxQSN6Wlp4mptwOuqJUvrpqCeUVXEjr7GuDrvqVmR7nQqKV3vngjgbG7K9TgVKvyjjbWlG+PNkxZdCgHuJSdgY6KGnpaVRu4UMDbDW1+PMa3nkmZlciIlX2Tchb7Li/Kt/Ce9OrPwLb00mk9Gtey+WL1tMkaJFKVLUmeVLF2FQwICmzVoo8o38aQi2dvYMGz4SgK7de9K1UztWLl9K3Xr1OXL4EGdOn2LDpq2KMpMm/MLePbtZvHQlRkZGREdn7fE3MTHFwMBAo7ZNTExp3aY9M6dPwdzcHDMzc2bNnIq7e3GqVKn2ET8pQRBUGdykOr0WbcbHpRAV3Qqz+shZ7sfE07t+JQDGbfyHR0+e8vugDkDWxL/34s3M7t6cCm5FFKv3BfR0MDMsgIGeLl6FlePwmxsZACil59auTCZjUONqzN55FBcHa1ztrZm98ygF9HVpX63sh/5YhBd6uBVm5PnreFmYUtbSjL/DHhKRlEr7YgUBmHctmKiUVGaU80JLJsPtjStGlvp66GlrKaV3KFaIDXcfMO3Kbbq4OHEvMYkVQWF0dnHSuF2ZTEY3VydWBIVRxKgARYwNWREUhoG2Fk3FcyDeWVZUH3Ur/yJMd0pKCgYGBu9UxweZ/MtkMnbs2EHLli0/RPUKx48fp3bt2sTFxWH+IgrMzp07GT58OKGhofzwww+ULVsWPz8/4uPjP2hf8qveffuRkprCpAm/8PRpAqXLlGXVH+sxNn71y/bRo0fIZK8uMvn4lGPu/N/4dcFcFv46FyenwsxbsIgyr20p2rRxPQDdurRXam/ajDm0at1W47ZHjxmLto42fj8OVDzka+mKuWhri5uGBOFTa1OlLLHPkpi+7TCRcQl4OtmzY9R3FH4Rnz0yPoH7sfGK/KsOnyU9I5Mhq3cyZPVORXqXmr6sGNAeTeXWLsDQ5rVIlqfht2oH8c+TKe/qxJ6f+2BS4N3+6Aqaa1TIjvjUNJbeCiU6JRU3U2OWVy1DQcMCAMSkyInIJeb/mxwMDfi9qjczrt6m5ZGz2BXQp4tLYXp7vLqik1u7AL3ci5CSkcmkgCAS0rIe8vV7VW+MdMWa6ruS6WgjUzP5l2Xkz7/dmZmZTJ06lWXLlvH48WNu375NsWLFGDt2LEWLFqVXr155qk8mSa9d+9JAZGQkU6dOZd++fTx8+BBbW1vFBLtu3bpZlX6kyb9cLufJkyfY2dkpbsyys7OjZ8+eDB48GBMTE3R0dHj27Bm2trbvvf3t27ezfPlyLl68SGxsLJcvX6Zs2bJ5qiMoOPy990v4eujIxANjhJw5Xtz2qbsgfObCNh/41F0QPnMlth3+1F0gISEBMzMzQoZ3wkRf9b0Tz1LluMzZyNOnTzE1NVWZ52s0adIk1q5dy6RJk+jTpw/Xrl2jWLFibNmyhfnz53PmzJk81ZenPf9hYWH4+vpy9OhRZs2aRWBgIP/++y+1a9dm4MCBeWr4fdDT08Pe3l4x8U9MTCQqKoqGDRvi6OiIiYkJBQoUeOeJf1pamsr058+fU7VqVWbMmPFO9QuCIAiCIAgi1Kcq69atY8WKFXTu3Flp50Lp0qW5detWnuvL0+R/wIAByGQyzp07R5s2bXB3d8fLy4uhQ4fi76/6iYwAI0eOxN3dHUNDQ8Vlitcn1FeuXKF27dqYmJhgamqKr68vFy5cAODevXs0a9YMCwsLjIyM8PLyYv/+/UDWth+ZTEZ8fDzHjx/HxCQrCkOdOnWQyWQcP36cNWvWKLYEvbRnzx58fX0xMDCgWLFiTJw4kfT0VyusMpmMZcuW0aJFC4yMjJgyZYrK8+ratSvjxo2jXr16efkYBUEQBEEQBFVkWqCl5iXLn3FqHj58iKura7b0zMxMtQvUOdF4c9qTJ0/4999/mTp1KkZG2eMnvznBfp2JiQlr1qzB0dGRwMBA+vTpg4mJCSNGjACgc+fOeHt7s3TpUrS1tQkICEBXVxeAgQMHIpfL+e+//zAyMuLGjRtKe7pfqlKlCkFBQXh4eLBt2zaqVKmCpaUlYWFhSvkOHDhAly5dWLhwIdWrVyckJIS+ffsCMH78eEW+8ePHM336dObPny/2hwuCIAiCIHwEMm1ttNTMu2T5dD7m5eXFyZMnKVJEOdrY33//jbe3t5pS6mk8+Q8ODkaSJIoXL57nRn755RfFv4sWLcqwYcPYvHmzYvIfHh7OTz/9pKjbze1V3Obw8HBat25NqVKlAChWrJjKNvT09BTbeywtLbG3V33H/dSpUxk1ahTdu3dX1Dd58mRGjBihNPnv1KkT3333XZ7PVRAEQRAEQXhLWrKsl7pj+dD48ePp2rUrDx8+JDMzk+3btxMUFMS6devYu3dvnuvTePL/8r7gt3ky6tatW1mwYAHBwcEkJiaSnp6udKPG0KFD6d27N3/++Sf16tWjbdu2uLi4ADB48GD69+/PwYMHqVevHq1bt6Z06dJ57sNLFy9e5Pz580ydOlWRlpGRQUpKCklJSRgaZj0ZsFy5cm/dhiAIgiAIgpB3Oe3tz697/ps1a8bmzZuZNm0aMpmMcePG4ePjw549e6hfv36e69N485SbmxsymYybN2/mqQF/f386dOhAo0aN2Lt3L5cvX2bMmDGKp8MCTJgwgevXr9OkSROOHj2Kp6cnO3bsAKB3797cvXuXrl27EhgYSLly5fjtt9/y1IfXZWZmMnHiRAICAhSvwMBA7ty5oxQ3VdXWpvxu04Y/ad60Ib5lvfAt60X7ti3578Qxjcpu3/Y37du0VHv83Fl/WrVsQmkvd+rVrsZfL0J95ubhwweU8nQj8dmzbMdu3bzBUL8fqFW9EmVKutO4YR3WrVmtUb2CIHwYU/4+iGH7EUqvon0naVT2z+MXqDlmkdrjJ2+EUGXUr1h0+RnPH2aw8pBmETDCo+Mw7zyaBDVhI+/HxNF65h9YdxuDU+8JDPtjF/J0EYnrQ1h04y6e248ovarvO5l7QWDHvUd0OHZe7fHz0XG0OXqOsjuP0eDfU/x194FG9T5MSqbMzqMkpqn+mT9KSmHA6Sv47jpGlb3/MfVKEPJM8STatyaTZe3tV/nKnyv/AA0bNuTEiRMkJiaSlJTE//73Pxo0aPBWdWm88m9paUnDhg1ZvHgxgwcPzjY5jo+PV7nv/9SpUxQpUoQxY8Yo0u7du5ctn7u7O+7u7gwZMoSOHTvyxx9/8O233wLg5OREv3796NevH6NHj2blypX88MMPmnZdiY+PD0FBQSpvnBByZmfvwLDhIylcpCgAO3dsZWD/PmzftR83N/ccyx47cog69VR/O31wP5zv+/SgbbuOzJ6zgEuXLjBpwlgsLC1p+E3jHOs9cvgQFSpWwvjFzd6vu34tEEtLS2bNWYCDgyOXL19g3C+j0dLWokvXHhqdsyAI759nITv2ju2reK+t4aX8fRdu0LScp8pjYVFP+HbGanrWqcjqQR04ExSG36qd2Jga07JiqRzr3XvhOjU8XTA1zB7DPyMzk1Yz/sDa1IjDEwfw5FkSfZZsRpIk5n3XUqN+C3njamrEqmqv9jFrazjhOxYRQx1HG5XHHjxPpt/pANoULcjMcl5cjo1nUkAQlvp6NCiYc0TAo49iqGBtgbGKGP4ZkkT/0wFY6uvxZ81yxMvT+PnCDSQJfinroVG/BWU5xvnPpyv/58+fJzMzk4oVKyqlnz17Fm1t7TzvVsnT0yiWLFlClSpVqFChApMmTaJ06dKkp6dz6NAhli5dqvKqgKurK+Hh4fz111+UL1+effv2KVb1AZKTk/npp59o06YNzs7OPHjwgPPnz9O6dWsA/Pz8aNSoEe7u7sTFxXH06FFKlCiRrR1NjRs3jqZNm+Lk5ETbtm3R0tLi6tWrBAYGqo3qo86TJ08IDw/n0aNHAAQFBQFgb2+v9p6DL1mduspRjYYMHcFfG9dzJeBSjpP/1NQUTp06yeAhw1Ue/2vTBhwcHPn5l6x7Llxc3bgWGMjqVStynfwfPXKQ+g2+UXmsdVvlh/44FS5MwOVLHDr4r5j8C8InpK2thb159i/sOUmRp3Hk6m3GtVe90vX7IX+crCyY3aM5AMUL2XHp7gMW7DmhweT/Bs0rlFR57PCV29x88JjbS37G0dIMgBldm9J36RYmdPhG5RcG4d1oy2TYGOjnqUxqRganHj/hB0/V9wVuDn2Ig6EBo8tk/a1yMTXiWvwz/rhzL/fJf0Q09dV8qTj1OJaQhOesbOSNbYGsPo8o5crPF2/i5+Wi8guDkDOZlrbaG3tlWvlz8j9w4EBGjBiRbfL/8OFDZs6cydmzZ/NUX55iJjk7O3Pp0iVq167NsGHDKFmyJPXr1+fIkSMsXbpUZZkWLVowZMgQBg0aRNmyZTl9+jRjx45VHNfW1iY2NpZu3brh7u5Ou3btaNSoERMnTgSy9uMPHDiQEiVK8M033+Dh4cGSJUvydJKva9iwIXv37uXQoUOUL1+eSpUqMW/evGx3UGti9+7deHt706RJEwA6dOiAt7c3y5Yte+v+fSkyMjLYt3c3SUnJlC3rk2PeM6dPYW1to/YLQsDlS1StVkMprVr1Gly/FphjCKuEhKdcvHCeOnU13+/27NkzzMzMNc4vCML7FxIZQ7F+kykxaDrdFmwg9HFsrmWOXQvGztwETyfVCytnb9+jbhk3pbR6ZTy4dPcBaekZauuNf57MqZuhNPVVfUXh7J17eDnZKyb+WfW6k5qWzmUNt40IeROemETN/Sep/+8php0L5P7z5FzL+EfFYW2gh5tp9miAAAGxT6lia6mUVs3Wkutxz0jLYYtOgjyNizHx1HZQPfm/8uQpbmbGiok/QFU7K+SZmVyPT8i130J2Mi1Zjq/86MaNG/j4ZJ9reXt7c+PGjTzXl+evpA4ODixatIhFi9Tvu3zzocGzZs1i1qxZSml+fn5AVpSeTZs2qa0rp/39tWrVUmrL3Nw8W9s9evSgR48eSmkNGzakYcOGGvdfHVV1f+2Cgm7Rsd23pKamYmhoxKIly3HNZcvPkRy2/ABEx0RTzdpaKc3K2pr09HTi4p5ga2unstyJ48dwc/fAwcFRo75fvnyRf//Zx7IVf2iUX/j87Nq1i7FjX0UPW7VqNeXLl/+EPRLyqrxrYX4f2AFXB2ui4hOZueMItccu5uLcYViZqL/Xau+F62q3/AA8fvoMWzPlqwl2ZsakZ2QS8+w5DhaqnwZ64PItvArbU8jaXHW98c+wNVeeUFoYG6Kno83jp9nvNRLeTWlLU6aX86KosSExqXKW3wql0/EL7KlXCXN9XbXljkZEU0fNBB0gJjUVK30rpTQrAz3SJYn41DRsCqi+0vDf41jczYxxUHOFJyZFjtUbT6M109NFV0tGTIpcZRkhF9raWS91x/IhfX19Hj9+nC3iZUREBDo6eb+6JK5HfUSpqamkpqYqpclTU9HTz9vlzU/J2bkYO3b/Q0JCAgcP/MOoEcP4c8NmtV8AJEni+NHDzJ2v/ssiqIgipUF0qaNHDmm86n/nzm0G9uvDgIGDqVqtukZlhM9P3bp1KVOmjOL917i97mvX0Pu1cNGFoaJ7EbwGz2DDiYsMblpDZRlJkth/8SbrfuyUY91v/rZ4uY6T0++RvReu00TNqv+rerOXlyTV6cK7qWH/aiHIHShraUbDA6fZGR5BD7fCKstIksSxiBjmqtm69VK2PzOKA+rLHH0UTW0Ha/UZ1BTPGh/C25DJtJCpeZiXuvSvXf369Rk9ejS7du3CzCzrKmR8fDw///zzh432I7y76dOnY2ZmpvRavvzttzB9Cnp6ehQpUpRSpUozbPhIipcowbq16lfSr14JQJ6Whm859auzNtY2xERHK6XFxsaio6ODubmFyjJpaWmc/O8EdTWY/AffuU2Prh1p274D/QcOzjW/8PkyNjamaNGiitfrEbqEL5ORgR4lCzsQHBmjNs/54PukpWdQpbiz2jx2ZibZVuKjEhLR0dbCythQZZm09AwOBdymaXkv9fWam/A4XrneuMQk0jIysDVTvcVEeH8MdbRxNzPmXmKS2jxX4xJIkzLxUXP1BsBaX5+YFOXFtycpcnRkMsz1VF9RSMvM5H+PY3O8omBtoEdMqvIK/1N5GumShJWBnppSQk5e3vCr7pUfzZ07l/v371OkSBFq165N7dq1cXZ2JjIykrlz5+a5PjH5/4hGjx7N06dPlV7ffz/gU3frnUiSpBS29U1HjhyiVq06OT4luay3D6dPKYdyO/W/k3iVLKV40vObzvqfwdTUlBKe6v9oQ9aKf/euHWn5bWuGDB2RY15BED6+1LR0bj2MyvEG4L0XrvONd3G0tdT/yaroXoSjV+8opR25ehufYoXQVTNhOHE9BDMjA8oUVb91sKJbEa7fjyQi7tX+7SNXb6Ovq4N3sUJqywnvhzwjk7sJz7HJYSJ99FE0Ne2tc4wKVNbKjNNRT5TSTkU9wcvCBF014+pcdBwmurqUyGFslrE0487TRKKTX32xOB31BD0tLbzMVW81E3Ihk+X8yqMlS5bg7OyMgYEBvr6+nDypPnTs9u3bqV+/PjY2NpiamlK5cmUOHDiglGfNmjXIZLJsr5QU1aGC34eCBQty9epVZs2ahaenJ76+vvz6668EBgbi5OSU5/rE5P8j0tfXx9TUVOn1JW35mTd3FhfOn+PBg/sEBd1i/rxZnDvrT7PmLdWWOabB1pwOHTvz6NFDpk+bREjwHbb9vZltWzfzXa++asscPXqI2m9EH3rTnTu36d6lA1WqVqPHd72Jjo4iOjqKJ7G531woCMKHMfrPvZy8EUJY1BPO3Qmn07w/eZacQpea6kPV7b9wI8fVeYDe9SsRHhPHyHV7uPXgMWuPnWft0fP4Nauptsy+izdy3fJTr4w7JQrZ0XvRXwSEPuRY4B1Gr99HzzoVRKSfD2BW4B3OR8fx4HkyV548xe9sIInp6bQo4qC2zLHImBxX5wHaOxckIimFmVdvE5LwnG1hj9gW9oiebuqDfRyLiMl1y09VOytcTI0YeeE6N+KfcSbqCbMD79CmqKOI9PO2tLVe7fvP9srbtHXz5s34+fkxZswYLl++TPXq1WnUqBHh4eEq8//333/Ur1+f/fv3c/HiRWrXrk2zZs24fPmyUj5TU1MiIiKUXh/6SrSRkRF9+/Zl8eLFzJkzh27duqldIM2NGJmCxmJjohnx0xCio6IwMTHBo3hxVq5ap3YPffi9e9y7d49q1dX/8QUo5FSY5SvXMGPaJDau/xNbO1vG/DIhxzCfx44cZur02TnW++8/+3jyJJY9u3eyZ/dORbpjwUIcPX4qx7KCIHwYD2Of0n3hRmITkrA2NaKCW2GOTxlEYRvVW/zuRsYS8jiWemVyDixQ1NaSHaO+Y8TaPSw/cBoHC1Pm9GyeY5jPfRdusKxf2xzr1dbSYvuonvy4agd1xy2hgJ4u7aqWZXrXprmfrJBnj5NTGH7+GnGpaVjq61HG0pRNtcpR0LCAyvzhiUmEJyZT1c5S5fGXChkVYFmVssy4eoeNdx9ga6DPz2XccwzzeTQimim5fDnUlslYWqUskwNu0eXEBfS1tWhSyJ4RpdxyLCeoJ9PSQqbmaoy6dHXmzZtHr1696N27NwALFizgwIEDLF26lOnTp2fLv2DBAqX306ZNY9euXezZswdv71fPnpDJZB/9nrPbt29z/PhxoqKiyHwjQtW4cePyVNcXMflfsmQJs2fPJiIiAi8vLxYsWED16qonnD169GDt2rXZ0j09Pbl+/Xq29L/++ouOHTvSokULdu7cmad2JUli4sSJrFixgri4OCpWrMjixYvx8sp5hepLldtk+01HjhykUuXKGj0tuULFSmzftV+jeq9fDyQxMZHyFSrmmO+HwUP4YfAQjeoUBOHjWOfXOU/59164Tk0vF4w1iPte3dOFMzP9NKr38t0HPEtOobqauPCvc7K2YPvI7zSqV3g3cyvk/EyGNx2NiKGijQVGGkQ8KW9jwba6FTSq90ZcAs/TMyifw30ELzkaGrC0SlmN6hU0oJVDtJ8Xcf4TEpTDqOrr66P/xk4KuVzOxYsXGTVqlFJ6gwYNOH36tEZdyczM5NmzZ1haKn+5TExMpEiRImRkZFC2bFkmT56s9OXgfVu5ciX9+/fH2toae3t7pSAGMpksz5P/z37bT14v2fz6669Kl2Hu37+PpaUlbdtmX925d+8ew4cPV/lFQpN2Z82axbx581i0aBHnz5/H3t6e+vXr8+yZCP8GYG/vQN/vB773ejPSM/hl3MS3vtwlCMKXo6CVGT+1rPPe603PzGRuzxZq7wcQvgx2BfTp45H35/TkJl2SGFPGXe39AMKH8zLaj7oXgJOTk1LwFFWr+DExMWRkZGBnpxwu3M7OjsjISI36MnfuXJ4/f067du0UacWLF2fNmjXs3r2bTZs2YWBgQNWqVblz504ONb2bKVOmMHXqVCIjIwkICODy5cuK16VLl/Jcn0zSNKj9J1KxYkV8fHyUHiJWokQJWrZsqfKH/aadO3fSqlUrQkNDlR7klZGRQc2aNenZsycnT54kPj5eaeU/t3YlScLR0RE/Pz9GjhwJZIXytLOzY+bMmXz//fcanV9QsOovMYIAoCNL/9RdED5zjhe3feouCJ+5sM0Hcs8k5Gslth3+1F0gISEBMzMzHv8xWe39NAlJKdj1HMv9+/cxNX11Q7Wqlf9Hjx5RsGBBTp8+TeXKlRXpU6dO5c8//+TWrVs59mfTpk307t2bXbt2Ua+e+nsMMzMz8fHxoUaNGixcuFCTU80zU1NTAgICssX5f1uf9Vfal5dsGjRQfpx7Xi7ZrFq1inr16mV7gu+kSZOwsbGhV69eb9VuaGgokZGRSnn09fWpWbOmxn0TBEEQBEEQXqP2Zt9X24HeDJ7y5sQfwNraGm1t7Wyr/FFRUdmuBrxp8+bN9OrViy1btuQ48QfQ0tKifPnyH3Tlv23bthw8ePC91fdZ7/l/10s2ERER/PPPP2zcuFEp/dSpU6xatYqAgIC3bvflf1XluXfvXq59EwRBEARBEN4g08p6qTumIT09PXx9fTl06BDffvutIv3QoUO0aNFCbblNmzbx3XffsWnTJpo0aZJrO5IkERAQQKlSebtfJS9cXV0ZO3Ys/v7+lCqVPQz64MF5e4bRZz35f+nNpzNKkpTjExtfWrNmDebm5rRs2VKR9uzZM7p06cLKlSuxts7lqX0atPu2fRMEQRAEQRDe8DLUp7pjeTB06FC6du1KuXLlqFy5MitWrCA8PJx+/foBWc9fevjwIevWrQOyJv7dunXj119/pVKlSoqF3gIFCiierDtx4kQqVaqEm5sbCQkJLFy4kICAABYvXvyWJ5y7FStWYGxszIkTJzhx4oTSMZlM9nVN/t/lko0kSaxevZquXbuip/fq4SAhISGEhYXRrFkzRdrLkEk6OjoEBQXh5OSUa7svQzxFRkbi4OCgMs/XbuOGdaz6fTnRUdG4urnx85jxlCuvPpLCnl07+P335dwLC8XExIRq1WsxYtQYLCyyQvzduXObhQvmcv36NR49fMDon8fRvafytqzlyxZz6OC/3L0bgoG+Ad4+vgz7aRTFirko8owaMYydO7YqlStTxpvNW3e+v5MXBOG9WH7gNAv2nCAy/hklCtkxu3tzqpZQ/yTfkzdCGLluLzcfPMbBwpQhzWvSp35lpTzxz5OZ8Ne/7Dp3jfjnyRS1sWR6tyZ8410CgNk7jrLr3DVuP4qigJ4uFd2LMqVzI9wdX4V97LtkM+tPXFSqt7xrYU5MHfQez17IzaaQB6y+c4/oFDmupkaMKu1GOWvVYWEBzkfHMTPwDsEJz7E10OM79yJ0eONhbAnyNH69EcKhh9EkpKVTyMiAn0q5UdM+a0FwRVAYhx9GcTcxCQNtLcpamjGspCvOJq8i1/184QY7wyOU6i1tYcpftdU/zV7QUE4P88rj4mr79u2JjY1l0qRJREREULJkSfbv36/YCh4REaEUyGX58uWkp6czcOBABg58FbCke/furFmzBoD4+Hj69u1LZGQkZmZmeHt7899//1GhgmaRpN5GaGjoe63vs578v+0lG4ATJ04QHBycbU9/8eLFCQwMVEr75ZdfePbsGb/++itOTk4atevs7Iy9vT2HDh1ShHeSy+WcOHGCmTNnvtN5fwn279vD9KmTGDdhMj4+5dj810b69u7O3n8O4+hYMFv+ixfOM3LEUEb9PI46dery+PFjJoz7mbFjRrJoyQoAUpKTcXIqzDeNmjBj2iSV7Z4/d5ZOnbtRqnQZMtLTmT9vNr17dmXvP4cxNDRU5KteoybTZsxRvNfVFY9ZF4TPzdbTAYxYu4cFvVpS2aMoqw6fpeX0VVyaNwwnFRO8sKgnfDtjNT3rVGT1oA6cCQrDb9VObEyNFfH85enpNJ2yEhszYzYO6UpBKzMexMYrhQo9efMu3zesgq9LIdIzMpmw+V+aTf2dS3OHY/Tak2Trl/Vgef9XUT70RGSgj+qfB4+ZfvU248p64G1lzpbQh3x/6gp76lfCUcUNoQ+eJ9PvdABtihZkZjkvLsfGMykgCEt9PUU8f/n/2bvPqKiOPgDjD733jtKLInas2Hsvid0EjUYTo7EbS9TYNZZEjV2jr0aNEnsUGxbsWBDsXRQLSO9Sd98PxMWVXVhQI8b5nXNPwty5M3d3B5ydO/MfiYQBp0Mx19FmUZ1K2OjpEJWeicFrn+2lmAR6uZWlopkxuRIpi28+YMDpMPa2qIP+a/nq21gwy8dL9rOIDPSOaBQS6lNZeiEGDx7M4MGDFZ571aF/JSgoqMjyFi5cyMKFC4t9H+9CVlYW4eHhuLm5oalCeFtlSnXnH4r/yOaVtWvXUrt2bSpWrCiXrqurWyDN1NQUQC69qHrV1NQYMWIEs2fPxsPDAw8PD2bPno2+vj69e/d+129DqbN+3e906dqDbt17AfDjpCmcPnWCLX9uYvSYcQXyh4VdpkyZsvTp2w/I29ire8/erF2zSpanUuUqVKpcBYBfFij+AvX7OvnPec7PC/CtU50b16/Jxf3X1tbBykr55i2CIHx4vwWcom/TmvRrlve7O/+rjhy5cpc1h4OZ3rtNgfy/BwbjYGHG/K86AlC+rA2XHz5l0d4Tss7/huMXSUhL5/iMIbIwnm9uIPb3jwPkfl71XXecBk4n9OFT6r8W919HUxNbU6N394KFYll/L4IuzvZ0dckbUJpQxZMz0XFsffiUURXdC+T3D3+Gnb4uE/7ZEM7N2IDriSn8795jWed/56PnJGXnsLlxDVln/c0NxFbXl4/XPsunAvUDTnEzMVnuqYO2uhpWKuw/IRTTO5rz/1+Snp7O0KFDZftY3b17F1dXV4YNG4a9vX2BvQyKUuo7/8V9ZAOQlJTEjh07WLx48XurF2Ds2LG8fPmSwYMHyzb5Onz4MEZG/+1/LLKysrhx4xoDv/1OLr1e/YaEXg5ReE216j4s+nUBJ4KO0bBRE+LiYjl08ACNGr9d/O6U1Lw9FUz++QL3yoXzwfjWro6RsTG1atVmxKgfsLAofI2HIAj/nqycHEIfPmN0pyZy6c2qeBB895HCa87ffUyzKvI7pzavUo4Nxy+SnZOLlqYGAZduUtvDiRHrdhFw6SaWRgZ0r1+N0Z0ao6FkZDY5PQMAM0N9ufRTNx/gNHAaJgZ61PdyZWrP1libGJbwFQvFkSWRcDMxhYGe8pH6fK3NCYtPUnhNWFwSvtbymzHVtzZn56PnZEskaKmrczwylirmJswMu8OxyBjMdLRpV9aWAeWc0FAypSQlOy/ksskbiywvxiZSP+AkRlqa1LQ0Y3gFNyx0xVPmt6bCJl+fmgkTJnDlyhWCgoJo3bq1LL158+ZMmTLlv9f5h+I9sgEwMTEhPT1d5fIVlVFUvZA3+j916lSmTp2qcl3/BQkJCeTm5mLxxoJpC0tLYmNjFF5TvXoN5v+yiJEjvicrM5OcnByaNmvBpJ+mlfg+pFIpP8+egU+Nmnh6lpOlN2zUmNZt2mJfpixPnzzht0W/8JVfL3bs2oe2gnBggiD8+2KT08iVSLB5ozNtbWLEi0TFGyW+SErB2kR+cMXGxJCcXAmxKWnYmRnzKDqeEzce0KN+NXaO78+DyFhGrttNTm4uP3ZtUaBMqVTKuD/24lveGW9HW1l6y6rl+KxOZRwtzXgUE890/0O0nb6KMz8PR0fro/in86OWmJlNrlRaoDNtoaNDbEa8wmtiMzOx0LGQz6+rTY5USmJmNlZ6OjxNe8n5mATaO9iw0rcqj1PTmXHlDrlSCYO9CsZQl0qlzLt6j+oWJni81lYb2FrQqow19vq6PE1/yW83H9Lv9GW2N6mFdjEXpQpvECP/BezevRt/f3/q1KkjF1SmQoUKPHjwoNjlib9gQokViGpUSKSj+/fuMmvmVIYMGUb9Bo2Ijolm/tzZTP3pR2bNmV+i+mdMm8ydO7f5c4v84t627fIXc3t6lqNipUo0a1yPoKBjtGxVcCqBIAgfTnEjpr155tU2la+ukUilWBkbsuybLmioq1PdtSyRCcks3HtCYed/5LrdXI+I4sg0+SeZXX2ryv7f29GW6q5lKT9kDgcu35JNMRLeP7U3PnEp0gJtQC7/m/8s5RcEgAQp5jpaTKvuhYaaGt5mxkRnZLHu7mOFnf+ZV+5wJzmVTQ195NLblM0P7OFhYkhFU2OaHTzDiahYWpQRU07fyjue8/9fEBMTg7V1wXaVlpZWogiTn+ZXKOGtmJmZoaGhQWyM/Ch/XFyc0qk1q1ctp3r1Gnw9cBDlynvRoEEjpkydyY7tfxEd/aLY9zBj+k8cO3qEPzZuwfa1aEuKWFvbYG9fhsePHhW7HkEQ3g9LYwM01NWJemOUPyY5VenUGhsTI14kyeePTk5FU0Mdi3+m7NiaGuFhZyk3xadcGWteJKaQlSO/Y/aodbsJCLnJwZ++payFaaH3a2dmjKOVKQ+iYlV9icJbMNXRQkNNjdiMTLn0+MwspVNrLHV0CubPyEJTTQ1T7bwpO1a6Ojgb6stN8XE10ic2M4usfyL/vTIz7A7HI2NZ36A6tkp2nH3FSk8He31dHqeqPutAUEyqro5UXUPJ8Wl2W2vWrElAQIDs51cd/jVr1sjtXqyqT/NdFN6KtrY23t6VOHvmlFz62TOnqFbdR+E1L1++RP2NX1r1fx6NSqWKrlBMKpUyfdpkAg8fZP3GLZR1cCzymoSEBCIjI7FS8K1ZEIQPQ1tTk2quZTh2VX5XzGNX71HH01nhNbU9nQrkP3r1LtVdy8oW99Yp58yDF3GyEM4A9yNjsTUzQvuf6BhSqZSR63az58J1Dkz+Buc35okrEpeSxtO4JGxNjYvzMoUS0lZXp4KpEWej5af4nI2Op6q5icJrqlqYFMh/JjoebzMj2eLeauYmRKS9RPLaPzyPU9Ox0tVGW/3Vv0lSZobd4cjzGNY1qE5ZA/kFwYokZmYT9TJTLAB+F15N+1F2fILmzJnDxIkT+e6778jJyWHx4sW0aNGC9evXM2vWrGKX92m+i8Jb+6r/ALZv82fHNn8e3L/HnFnTiYx8Ts9eXwB50XrG/TBSlr9J0+YEHj7Ils0beRIRweWQi8yaMZXKlavK9kXIysri1s0b3Lp5g+zsLF68iOLWzRs8fvxIVs70qZPYu2c3C375DQMDA2JioomJiSYjI2/BXlpaGnN/nkloaAhPnz7h/PlzfPdtf8zMzGjeotW/9wYJglCkYe0asP7YBTYcv8jtpy8Yu+FvnsQmMqBFHQB++vMAA5ZuleUf0KIOEbEJjPtjL7efvmDD8YtsOHaRER0ayfJ806Iu8SlpjFn/N/eex3Dg8i3m7z7Gty19ZXlGrN3N1lOXWT+sF4Z6ukQlphCVmMLLrGwAUjMymbBxH+fvPuZxdDwnbzyg67z1WBgZ0LGW97/07ghfeTiy/dFzdjx6zoPkNH6+epfI9Ex6uOZF//n1+n3GX7ohy9/DpQyR6RnMvXqXB8lp7Pjn2n4e+YuGe7qWJTErm9lX7vIoJZ0TkbGsvvOIXq/tBTAj7A57n0Qxv6Y3BpoaxGRkEpORSUZuLgBpOTnMu3aPsLgknqW95EJMAoPPXcFMW4vm9lb/0rvz36V81D/v+BT5+vpy9uxZ0tPTcXNz4/Dhw9jY2HDu3Dl8fBQPuhZGzPkXSqRtuw4kJiawbNlvxERH4+Hpyao16ylTJu8PaEx0NM+fP5fl/7xLN9LSUtm8aQNzf56JkbExder4MuaHCbI80dEv+KxTW9nP69auZt3a1dSsVYeNm/0B2PLnJgD6fNlD7n5m/7yAz7t0Q0NDg7t37rBn105SUpKxsrKmVu26LFy8DENDEaVDEEqTrr5ViUtJZ86OI0QlJFPBwZZd4/vLQnNGJSbzJC5Rlt/Z2pxd4/szdsNeVh06i52ZMQv6dZSbg1/W0pS9EwcydsNeao1diL25MYPb1Gd0p8ayPGsCzwHQalp+qGHIC/np17gGGurq3IiI4s+TISSmZWBrZkQjbzc2Dv8CI73Cp38I706bsjYkZmaz4nY4MRmZeBgbsqpeFVloztiMLCL/idQEUNZAj5W+Vfn56j3+fPgUa10dfqziKQvzCWCnr8vv9arx89W7dD56Hhs9Hb50c2RAufwvCFvDnwHQ99RlufuZ5ePFZ072aKipcS8plb8jIknOysFKV4faVmb8UqsiBmIx+Nt7h5t8/RdkZ2fzzTffMHnyZFmoz7elJpUWZ9KF8K7duR9RdCbhk6WpllN0JuGTZh+y40PfglDKPfI/9KFvQSjlvHYc+dC3QHJyMiYmJjw/vBFjA33FedLSsW/pR1JSEsbGn84UPFNTUy5fvoyra8FF6SUhpv0IgiAIgiAIpYOY81/AZ599xu7du99ZeeL5lCAIgiAIglAqFDa3/1Od8+/u7s6MGTM4e/YsPj4+GBgYyJ0fNmxYscoTnX9BEARBEAShVJCijlTJCL/0E52w8vvvv2NqakpISAghISFy59TU1ETnXxAEQRAEQfhIqauDshH+TzTOf3h4+Dst79N8FwVBEARBEIRSR4T6VC4rK4s7d+6Qk/N2wUBE518QBEEQBEEoFaSoFXp8itLT0/n666/R19fH29ubiIi8SJHDhg3j559/LnZ5ovMvCIIgCIIglApSdc1Cj0/RhAkTuHLlCkFBQejq5u810rx5c/z9/Ytd3qf5LgqCIAiCIAiljlRNDamSzbyUpf/X7d69G39/f+rUqYPaa+9BhQoVePDgQbHLE51/QRAEQRAEoVQQoT4LiomJwdraukB6Wlqa3JcBVYlpP4IgCIIgCELpoJYX6lPR8alu8lWzZk0CAgJkP7/q8K9Zs4a6desWuzwx8i8IgiAIgiCUChI1DSRqikf4laX/182ZM4fWrVtz8+ZNcnJyWLx4MTdu3ODcuXOcOHGi2OV9ml+hBEEQBEEQhNJHDVBTU3J86Jv7MHx9fTlz5gzp6em4ublx+PBhbGxsOHfuHD4+PsUuT4z8C4IgCIIgCKWCGPnPM2rUKGbMmIGBgQEnT57E19eXDRs2vJOyxci/IAiCIAiCUCoom+8vm/f/iViyZAmpqakANGnShPj4+HdWthj5FwRBEARBEEoFMfKfx9nZmd9++42WLVsilUo5d+4cZmZmCvM2bNiwWGV/Ol+hBEEQBEEQhFJNoqZe6FFcy5cvx8XFBV1dXXx8fDh16lSh+U+cOIGPjw+6urq4urqycuXKAnl27NhBhQoV0NHRoUKFCuzatavY91WU+fPns3btWpo0aYKamhqfffYZjRs3LnA0adKk2GWLzr8gCIIgCIJQKrzLaT/+/v6MGDGCiRMnEhoaSoMGDWjTpg0REREK84eHh9O2bVsaNGhAaGgoP/74I8OGDWPHjh2yPOfOnaNHjx74+flx5coV/Pz86N69O+fPn3+r1/2mzp07ExUVRXJyMlKplDt37pCQkFDgKMl0IDWpVCp9p3crFMud+4oboCAAaKrlfOhbEEo5+5AdRWcSPmmP/A996FsQSjmvHUc+9C2QnJyMiYkJ1y5fwsjIUGGelJRUKlWvQVJSEsbGxkWWWbt2bapXr86KFStkaV5eXnTu3Jk5c+YUyD9u3Dj+/vtvbt26JUsbNGgQV65c4dy5cwD06NGD5ORkDhw4IMvTunVrzMzM2LJli8qvtyivL/g9ceIE9erVQ1Pz3czWFyP/giAIgiAIQqkgRa3QQ1VZWVmEhITQsmVLufSWLVty9uxZhdecO3euQP5WrVpx6dIlsrOzC82jrMySen3Bb9OmTcWC3/+So/cdPvQtCKWYtvgNFYrQxKfLh74FoZRz0dH90LcgCCrLm9uvbMFv3ph1cnKyXLqOjg46OjpyabGxseTm5mJjYyOXbmNjQ1RUlMLyo6KiFObPyckhNjYWOzs7pXmUlVlS73PBr+haCIIgCIIgCKWCVE0NqZriEf5X6Q4O8gOnU6ZMYerUqQqvUXujLKlUWiCtqPxvphe3zJKYP38+gwYNYs6cObIFv8ruNzc3t1hli86/IAiCIAiCUCpIpRpIpIpH/qX/pD958kRuzv+bo/4AlpaWaGhoFBiRj46OLjBy/4qtra3C/JqamlhYWBSaR1mZJdW5c2c6d+5MamoqxsbG3LlzB2tr63dStpjzLwiCIAiCIJQKqsz5NzY2ljsUdf61tbXx8fEhMDBQLj0wMBBfX1+FddetW7dA/sOHD1OjRg20tLQKzaOszLdlaGjI8ePHcXFxwcTEROFRXGLkXxAEQRAEQSgVJKgjUTI2rSxdmVGjRuHn50eNGjWoW7cuq1evJiIigkGDBgEwYcIEnj17xh9//AHkRfZZunQpo0aNYuDAgZw7d461a9fKRfEZPnw4DRs2ZO7cuXTq1Ik9e/Zw5MgRTp8+XcJXrFhycrLs6Ua1atVIT09XmleVyEevE51/QRAEQRAEoVSQSNWRSJV0/pWkK9OjRw/i4uKYPn06kZGRVKxYkf379+Pk5ARAZGSkXMx/FxcX9u/fz8iRI1m2bBn29vb89ttvdOmSH1jB19eXrVu3MmnSJCZPnoybmxv+/v7Url27BK9WOTMzMyIjI7G2tsbU1FThmoJXaw2KO+dfxPn/wJYfFG+/oJyI9iMUpYlL+Ie+BaGUK3M94EPfglDK6XYa+qFvQRbnP/jyHQyNjBTmSU1JoU71cirH+f+YvR7b/8SJE4XmbdSoUbHKFl0LQRAEQRAEoVR4l9N+Pmavd+iL27kviuj8C4IgCIIgCKWCFDWkUiWhPouxydd/yb1799izZw+PHj1CTU0NV1dXOnXqhKura4nKE51/QRAEQRAEoVTIRZ1cJSP8ytL/y+bMmcNPP/2ERCLB2toaqVRKTEwM48aNY/bs2YwZM6bYZX5676IgCIIgCIJQKkmlaoUen5Ljx48zadIkJk6cSGxsLJGRkURFRRETE8P48eMZP348J0+eLHa5YuRfEARBEARBKBUkUrVCov18Wp3/lStXMmDAgAK7F5ubmzN9+nSioqJYsWIFDRs2LFa5YuRfEARBEARBKBVU2eTrU3HhwgX8/PyUnvfz8yM4OLjY5YqRf0EQBEEQBKFUkEjVyFUywv+pjfy/ePECZ2dnpeddXFyIiooqdrmi8y8IgiAIgiCUCu9yk6+PXUZGBtra2krPa2lpkZWVVexyRedfEARBEARBKBUKW9j7qS34Bfj9998xNDRUeC4lJaVEZYrOvyAIgiAIglAq5BYy7UdZ+n+Vo6Mja9asKTJPcYnOvyAIgiAIglAqFLaw91Nb8Pvo0aP3Uu6nNXlKEARBEARBKLUkErVCj0/d06dPkUgkb1WG6PwLgiAIgiAIpYIEtUKPT12FChXe+omAmPYjvBWpVMr+zdM4c2AN6akJOJerTfchS7F38lZ6zZkDazh/dCPPH18HwNHdh45fzcK5XC25fImxz9i9bjw3Lx0gK+sl1mU8+XLE7zh6+Khcd3ZWJrt+H8OlE1vJznxJuarN6DFkGWZWZd/DuyG8SSqVsnfjNE4G5H1GLuVr03voUso4K28fJ/ev4VzgRp4/ymsfTh4+fNZ/Fi7laynMv3/LHHatm0izz4bRc/CiYtWdnZXJttVjuHh8K1lZL/Gq2ozew5ZhLtqHIJQKq/afYuHuo0QlJFPBwZZ5X3ehvrebwry7z11hzcHTXA1/SmZ2Dl6Odkzq2YYW1bxkedYdPsvm4xe4GREJQDU3B6Z92YGank7FqlcqlTJr6wHWHj5LYtpLano4sejbblRwtHsP78KnpbARfjHyn9f23pYY+RfeSuC2eRzbuZDug5cwdvEFjM1sWfpjSzLSla9Av3v1BDUa92T4z8cY8+tZzKwcWDqxFYmxz2R50lMS+GV0fTQ0tRg8Yz+TV93g8wEL0DMwLVbd21eN4MrZ3fQfv4VRC06RmZHKiqkdkOTmvpf3Q5B30H8egTsW0vv7JUxcegETc1sWjiu8fdy5coJaTXoyev4xxi8+i7m1AwvHtyLhtfbxSvidi5zcv4ayrpVLVLf/ihGEntnNwIlbGLfwFBkZqSyZJNqHIJQG205f5od1OxnXrSXBv47Ft4IbnWesICImXmH+0zfu07RKOXZNHsTZX36gUUUPusxaTdjDJ7I8J6/fo3sDHw7OGErQ3FE4WJnRYepynsUlFqveX3Yd4be/j7Pwm26cnj8aGzNj2k1ZRsrLjPf2fnwqxCZf75/o/AslJpVKOb57Ma16/kjVep9j71wRv9HrycpM52LQn0qv6zduEw3bD8bBrSq2DuX5YvgapBIJd8KOyvIc3jYXMysH/Eatw7lcLSxsnClfrRlW9m4q1/0yLYlzh9fx+cAFlK/WHAf3avT9YSPPH13jdtiR9/vmCEilUo7uWkzbXj9SvcHnlHGpSL8f8j6j88eUt4+BEzbRpONgHN2rYudYnj4j1yCVSrgVelQuX8bLVH6f8yV9Rq5G39Cs2HWnpyVx+uA6un+7gArVm+PoXo0B4zby7NE1bl4W7UMQPrTf9hznq+Z16NfCl/IOtiwY0IWylmasOXhaYf4FA7ow+vPm1PBwwt3emul+HXC3s2L/xeuyPOtH9eXbtg2o4lqWcmVtWD64FxKphKCrd1WuVyqVsmzvCcZ2a0nnulXwdrLn9+Ff8DIzG/+TIe/3TfkESCRq5Co5xMg//Pjjj5ibm79VGaLzL5RYXFQ4yQlReFVvKUvT0tbBvVIjwm+eU7mcrMx0cnOz0TfKb8zXgvfi6OHD77O6M66nDXOGVOfMgfxwV6rUHXEvhNycbLk8phb22DtV5OHNsyV6zYLqYqPCSYqPwruG/GfkWbkRD4rbPnKyMTCS/2P355LvqVy7LRWqNy9R3Y/v5rWPCj6vtQ9Le8o4V+SBaB+C8EFlZecQ+uAJzaqWl0tvVrU8wbfDVSpDIpGQ8jITM0MDpXnSs7LIzpVgZqivcr2PXsQRlZBM89fy6Ghp0aCim8r3JignlRZ+fOomTJiAqanpW5Uh5vwLJZackLeltJGZjVy6sak18dERKpez53/jMbEoQ/lq+Z242KiHnApYSdPPR9KqxwQe3b3AtpXD0dTSoXbzPirVnZwQhaamNvpG8qPCRqY2suuF9ycpPu89NjZ94zMysybuhertY8fv4zG1LCPXyb9wfCsR9y4zcdmFEtednBCFppY2Bm+0D2PRPgThg4tNSSNXIsHa1Egu3cbEiBcJqm1stGjPcdIzM+lSr5rSPJP/+Bt7cxOaVimncr1RickAWJsay+WxNjFWOiVJUJ2I819Qbm4u69ev5+jRo0RHRxeI9nPs2LFilSc6/4LKLhzbzJYlg2Q/D562DwA1NflfRilSUFPtFzRw2zwuBW1lxLzjaGnr5pchleDoUYNOX80GwMG9GpGPb3AqYCW1m/eR5StZ3dIC1wlvL/joZjYtym8fQ2fmtY8Cn4dU9ff/oP88LgRt5YcF+e0jPvoJW5ePYOTPh+TajEIlqFuKFMS8UkEoFdQo+DdelT8f/idDmLX1ANt+HFigI//KLzuP8NepyxyaORRdba1i1/vmbah6b0LhXk3xUXbuUzR8+HDWr19Pu3btqFix4lv3YUTnX1BZ5TodcS5fW/ZzTnYmAMnxUZiY50c4SEmMKTDiqsiR7Qs45D+HobMDKeMiv2DT2NwOO0cvuTRbBy/CzuzMO29mW2Tdxma25ORkkZ6SIDf6n5IYjYtXXZVes6C6qnU74vpa+8h+1T4SojC1yP+MkhNjMDYrun0c2raA/VvmMGpuoNyC3sf3QkhJjGbm4BqyNIkkl3vXTnJ8zzJW7M/AxNy2yLqNzWzJyc4iLSVBbvQ/JTEatwqifQjCh2RpZICGujov/hllfyU6KVVpZ/6Vbacv893SP9k8tr9sRP9NC3cfZf72QAKmD6GSc5li1Wv7z4j/i8Rk7MxNZHliklIKPA0Qiq+w6T2f6rSfrVu38tdff9G2bdt3Up6Y8y+oTFffCGt7d9lh51gBYzNbbocGyvLkZGdx/9oJXIroPAVun8+BLTMZMuMATp41Cpx3q1CPF0/vyqVFP7uLuXVeODYLW5ci63b08EFDU4tbr+VJio/k+ePruFbwLf4bIBRKV98I6zLussPeqQIm5rbcDJH/jO5ePVFk5/rQX/MJ2DST4bMP4FxOvn14VWvG1NVX+WllqOxw8qxB7aZf8NPKUNQ1NLC0dSmybifPvPZx83J+nsS4SJ49uo6baB+C8EFpa2lSzc2BY2F35NKPhd2mTnkXpdf5nwzhm982s35UX9rUUBxS+NddR/n5r0PsmTIIH3fHYtfrbGOBrZkxR1/Lk5Wdw6nrDwq9N0E1uajJpv4UOD7Rp7La2tq4u7u/s/LEyL9QYmpqajTpPJxD/nOwsvfAuowHh/znoK2jT83GvWX5Nizoi6mFPZ36zQHypvrs++Mnvhq3GXMbZ9n8bB09Q3T1DAFo2nkEC0bX4+DW2VRv2J3Hdy5w5sAaeg1bpXLdegYm1G3Zn51rxmBgZIGBkTk7f/8Be+dKlK9acJGo8G6pqanR7LPh7N8yB+syHtiU8WD/lrzPqHbT/Paxdm5fzCzt+fzrvPZx0H8eezb8xIAJm7G0Ldg+dPWNKONSUa4uHV0DDIzNZemq1K1vYEL91v3ZtmoMhkYWGBibs23VD5RxrqRwEbEgCP+uYZ2a8PWijVR3d6B2ORfWHj7Lk9gEBrSqD8DkjX/zPC6JtSP8gLyO/4DFG1nwdRdqlXMmKiFv9F5PWwsTAz0gb6rP9D8DWD+qL07WFrI8hro6GOrpqFSvmpoaQzo0Yv72QNztrXC3s2Le9kD0dLTo0dDnX32P/ovEyH9Bo0ePZvHixSxduvSdTFsWnX/hrbToNpbsrJf4Lxsi22jr+1mH0NXPfyybEB2Bmlr+Q6aT+1aQk5PF77O6yZXV9oufaPflVACcytXkm8k7+Xv9jxz4cwYWti50/XYhtZp+Uay6u367EA0NTdbN6UFW1kvKVWlGn9H/Q11D4z29I8LrWvfI+4z+XDKEtJQEXMvXZuTP8p9R/BvtI2jvCnKys1g5Xb59dPD7iY59pr7Tunt8txB1DU1WzexBdtZLyldrxtAfRPsQhNKgW/3qxCenMdv/EFEJSXg72rF78iCcrPMif0XFJ/MkJkGWf+2hM+TkShixehsjVm+TpX/ZpBZrhn8JwOoDp8nKyaX3vHVydU3s0ZpJvdqqVC/A6M+ak5GZzYhV20hITaempxP7pg7GSK+IdUhCkcSc/4JOnz7N8ePHOXDgAN7e3mhpya9R2blzZ7HKU5O+i63C3ixUTY1du3bRuXPnd120nKCgIJo0aUJCQoIs7NHu3bsZM2YM4eHhDB06lKpVqzJixAgSExPf672U1PKDn+jXWEEl2uLruVCEJi4itKBQuDLXAz70LQilnG6noR/6FkhOTsbExITfDyWib6B47UR6WjIDWpmSlJSEsfGns76iX79+hZ7/3//+V6zyit21iIqKYtasWQQEBPDs2TOsra1lHexmzZoVt7i34uvrS2RkJCYm+Qtuvv32W/r168ewYcMwMjJCU1PznS2QeNPUqVPZunUrT548QVtbGx8fH2bNmkXt2rWLvlgQBEEQBEGQI5FArkT5uU9RcTv3RSnWgt9Hjx7h4+PDsWPHmDdvHteuXePgwYM0adKEIUOGvNMbU4W2tja2tray+U+pqalER0fTqlUr7O3tMTIyQk9PD2tr67eqJzs7W2G6p6cnS5cu5dq1a5w+fRpnZ2datmxJTEzMW9UnCIIgCILwKZJK1Qo93peEhAT8/PwwMTHBxMQEPz+/QmeNZGdnM27cOCpVqoSBgQH29vb06dOH58+fy+Vr3LgxampqckfPnj3f2+tQRbE6/4MHD0ZNTY0LFy7QtWtXPD098fb2ZtSoUQQHByu9bty4cXh6eqKvr4+rqyuTJ0+W61BfuXKFJk2aYGRkhLGxMT4+Ply6dAmAx48f06FDB8zMzDAwMMDb25v9+/cDedN+1NTUSExMJCgoCCOjvLm8TZs2RU1NjaCgINavX19gJ7S9e/fi4+ODrq4urq6uTJs2jZycHNl5NTU1Vq5cSadOnTAwMGDmzJkKX1fv3r1p3rw5rq6ueHt78+uvv5KcnMzVq1eL87YKgiAIgiAI5I36F3a8L7179yYsLIyDBw9y8OBBwsLC8PPzU5o/PT2dy5cvM3nyZC5fvszOnTu5e/cuHTt2LJB34MCBREZGyo5Vq1YV+/62b99O9+7dqVOnDtWrV5c7ikvlaT/x8fEcPHiQWbNmYWBQcKvswrYaNjIyYv369djb23Pt2jUGDhyIkZERY8eOBeCLL76gWrVqrFixAg0NDcLCwmSLGYYMGUJWVhYnT57EwMCAmzdvYmhoWKAOX19f7ty5Q7ly5dixYwe+vr6Ym5vz6NEjuXyHDh3iyy+/5LfffqNBgwY8ePCAb775BoApU6bI8k2ZMoU5c+awcOFCNFRY/JeVlcXq1asxMTGhSpUqReYXBEEQBEEQ5Ekkyqf3vK9pP7du3eLgwYMEBwfLpm6vWbOGunXryvqWbzIxMSEwMFAubcmSJdSqVYuIiAgcHfPDyOrr62Nra1vi+/vtt9+YOHEiffv2Zc+ePfTr148HDx5w8eLFEs28UXnk//79+0ilUsqXL1/sSiZNmoSvry/Ozs506NCB0aNH89dff8nOR0RE0Lx5c8qXL4+HhwfdunWTdaAjIiKoV68elSpVwtXVlfbt29OwYcMCdWhra8um95ibm2Nra4u2tnaBfLNmzWL8+PH07dsXV1dXWrRowYwZMwp8C+vduzf9+/fH1dUVJycnpa9t3759GBoaoqury8KFCwkMDMTS0rLY75EgCIIgCMKnTiIt/Hgfzp07h4mJidyazTp16mBiYsLZs2dVLicpKQk1NbUCA+KbN2/G0tISb29vxowZQ0pKSrHub/ny5axevZqlS5eira3N2LFjCQwMZNiwYSQlJRWrLCjGyP+roEAliS+6fft2Fi1axP3790lNTSUnJ0dulfaoUaMYMGAAGzdupHnz5nTr1g03NzcAhg0bxnfffcfhw4dp3rw5Xbp0oXLlysqqKlJISAgXL15k1qxZsrTc3FwyMjJIT09HX18fgBo1Cm48pUiTJk0ICwsjNjaWNWvW0L17d86fP//W6wwEQRAEQRA+Nbm5eYeyc5AXGeh1Ojo66OjolLjOqKgohf02a2troqKiVCojIyOD8ePH07t3b7k+7hdffIGLiwu2trZcv36dCRMmcOXKlQJPDQoTERGBr2/e5pN6enqyLw9+fn7UqVOHpUuXqlwWFGPk38PDAzU1NW7dulWsCoKDg+nZsydt2rRh3759hIaGMnHiRLKysmR5pk6dyo0bN2jXrh3Hjh2jQoUK7Nq1C4ABAwbw8OFD/Pz8uHbtGjVq1GDJkiXFuofXSSQSpk2bRlhYmOy4du0a9+7dQ1c3Pz6voqlNihgYGODu7k6dOnVYu3YtmpqarF27tsT3V9qd3Lecn75yZXhHPX4eWoP710+pdN25wPXMH6F8V9d7V0/w89AaDO+ox0/93DgVsFKlcuNePGZ4B11epiUrPD+kjXqBQ9WyhZI5/vdyxvu58l1bPWYMrsHda6q1kTOH1jN7qPI2cufKCWYMrsF3bfWY4OdG0F7V28h3bZW3kYEt1AscqpYtCMK7NXPLfvQ6D5M7nL+aqNK1G4+ep+HYX5SeP3X9Hr6j5mHabRRe305jzcHTKpX7ODoek64jSU5/qfB8REw8XWauwqLHGMr6TWDUmu1kZecozCsU7dUmX8oOAAcHB9nCXBMTE+bMmaOwrKlTpxZYbPvm8WqNqaLBbalUqtKgd3Z2Nj179kQikbB8+XK5cwMHDqR58+ZUrFiRnj17sn37do4cOcLly5dVfk9sbW2Ji4sDwMnJSbbONjw8nJJE7Fd55N/c3JxWrVqxbNkyhg0bVqBznJiYqHDe/5kzZ3BycmLixPxf3sePHxfI5+npiaenJyNHjqRXr17873//47PPPgPyPuRBgwYxaNAgJkyYwJo1axg6tGQxaatXr86dO3fe6TbJr5NKpWRmZr6Xsj+0kBP+bF81kh5DluFWoR6n969i2eS2TF51A3Nrx0KvvRa8l8p1Cy6CAYiNCmf5T+2o13oAX/2wkQc3z+C/bAiGJlZUq9+l0HKvBu/Bo3Jj9JTEBAb4ctQ6Kvi0lv2sZ2CiNK/wdi4G+eO/YiRfDF2Gu3c9TgSs4rcf2zJt7Q0simgjV87tpaqv4jYSExnOb5Pa0aDNAAaM28j9G2fYvGQIRqZW+DQovI2End2DZxFt5Ksx66hYU7QRQSgNKjjaETAtfx6zhrpqMw72XbxGh1qVFJ579CKOzjNW0a9FXdaN7MO52w8ZvmoblsaGfOZbtfByL1yjYUUPjPX1CpzLzZXw+YxVWJoYcnTOcOJT0hmweBNSKSz8pqtK9y3Iy5UqX9ib+08/98mTJ3Kj68pG/b///vsiI+s4Oztz9epVXrx4UeBcTEwMNjY2hV6fnZ1N9+7dCQ8P59ixY0XuP1C9enW0tLS4d++eyot1mzZtyt69e6levTpff/01I0eOZPv27Vy6dInPP/9cpTJeV6w4/8uXL8fX15datWoxffp0KleuTE5ODoGBgaxYsULhUwF3d3ciIiLYunUrNWvWJCAgQDaqD/Dy5Ut++OEHunbtiouLC0+fPuXixYt06ZL3D/qIESNo06YNnp6eJCQkcOzYMby8vIr9Ql/56aefaN++PQ4ODnTr1g11dXWuXr3KtWvXlEb1USQtLY1Zs2bRsWNH7OzsiIuLY/ny5Tx9+pRu3boVXcBH6OiuhdRt2Z96rQcA0HXQIm5ePsypgBV06qf4WzdAdlYGty4fpn2f6QrPnw5YiZm1I10HLQLA1tGLiHuXOLrjl6I7/+f+pmq9zwrNo29giol5yRfaCKoL3LGQ+q3706BtXhvpOXgRNy4d5sTeFXz+deFt5GbIYTp9pbiNnNi3EnMrR3oOXgSAnZMXj+5e4vC2X1To/P9N9fpFtBFD0UYEobTQVFfH1qx4GzhlZGVzNOw2U3q3U3h+zcHTOFiZsWBA3t+L8g62XL7/hEV7jhXd+T9/jU51FU83PhJ2m1tPo7g3dTr25nmDBj/368w3v21m2pftFH5hEAonlUqVjma/Sjc2NlZpky9LS0uV1mHWrVuXpKQkLly4QK1atQA4f/48SUlJsuk2irzq+N+7d4/jx49jYWFRZF03btwgOzsbOzu7IvO+snr1aiT/rHYeNGgQ5ubmnD59mg4dOjBo0CCVy3mlWKE+XVxcuHz5Mk2aNGH06NFUrFiRFi1acPToUVasWKHwmk6dOjFy5Ei+//57qlatytmzZ5k8ebLsvIaGBnFxcfTp0wdPT0+6d+9OmzZtmDZtGpA3H3/IkCF4eXnRunVrypUrV+CRSnG0atWKffv2ERgYSM2aNalTpw6//vproYt6FdHQ0OD27dt06dIFT09P2rdvT0xMDKdOncLb27vE91da5WRn8eReCF7VW8qle1VvwcOb5wq99k7YUYzNbLF3Uvy+PLwdjFf1Fm+U24rH9y6Rm6N4jwWA9NREHtw4ReU6ikeLX/lr+VDG9rBi7rBanApYKfsFEt6tnOwsHt8NoYKPfBvx9mnBgxuFt5FboUcxNreljLOSNnIrGG8f+TbiXaMVj+9eIqeINnLv+imqKHnq9MqfS4cysosVM4fUImivaCMfqz179lC5ciXZcfHixQ99S0IJ3I+MwaXfJMp/MxW/BesJj4ot8prjV+9iY2pMBUfFHarzdx7RrKp8wJLm1cpz+X4E2TlKJpgDianpnLn1gHY1FT9ROH8nHG9HO1nHH6BFNS8ys3MIffCkyPsWCpLk5s/7f/OQKP+o3sqrPubAgQMJDg4mODiYgQMH0r59e7lIP+XLl5cNYOfk5NC1a1cuXbrE5s2byc3NJSoqiqioKNnU9gcPHjB9+nQuXbrEo0eP2L9/P926daNatWrUq1dP5ftTV1dHUzN/vL579+789ttvDBs2TGFwm6IUe4dfOzs7li5dWujigje/sc2bN4958+bJpY0YMQLIi9KzZcsWpWUVNr+/cePGcnWZmpoWqPurr77iq6++kktr1aoVrVq1Uvn+FdHV1WXnzp1F5ntdZmZmgSlB2VnaaGmXfJHKvyU1ORaJJBdjM/nHX0amNiQnFL4Y5uq5PUqn/ACkJERhZCpfrrGZDZLcHFKTYzExV/zH/MbF/dg7V8LMykFp2e37TKdc1WZoaetxJ+woO9eMITU5lja9JhV6z0LxpSYpaSNmNiQV0UbCzu6haiFtJCk+CqMaBdtIbm4OqUmxmFoobiPXLuynjEslzK2Vt5FOX03Hq1peG7kdepRtq/PaSPsvRBv52DRr1kwu1PLbhNYTPoyans78PvxLPOytiU5K4ee/DtFk/EJCfvsRC2Pla/H2nb9GeyVTfgBeJCZjY2Ikl2ZtakROroTY5FTszBVP9TsYcpOKjnY4WJkpLjchBes3yjUz1EdbU4OohOJFdBHyvD63X9G592Xz5s0MGzaMli3zBrA6duxYoK97584dWXSdp0+f8vfffwNQtWpVuXzHjx+ncePGaGtrc/ToURYvXkxqaioODg60a9eOKVOmqBRG/nWnTp1i1apVPHjwgO3bt1OmTBk2btyIi4sL9evXL1ZZxe78CyU3Z84c2RONV9p+8RPtvpz6YW6oJN5c+FLEYhipVMq18/voP175F7y8YuXLyP8Cprzsq8F/U6lOh0LLfb2T7+BWFYADf84Qnf/3qEB7UKGNXA3ex8Afi9dGUCECWdjZv6lSt/A28non39G9KgD7Ns0Qnf+PkKGhocJ9YISPRyufCnI/1y7njPeg6Ww6fp7hnZoqvEYqlbL/0nX+GN230LKV/Akp9G/IvgvXaFfIlwpl10sV1CeoprDNvN7nJl/m5uZs2rSp0DyvDw47OzsXOVjs4ODAiRMn3vreduzYgZ+fH1988QWhoaGygeSUlBRmz54t2/xWVcWa9iO8nQkTJpCUlCR3tOw+4UPflkoMjS1RV9cgOV5+BDclKbrAqP3rHt25QG5OFm7eyr+VGpnZFnh6kJIYjbqGJobGiufP5eZkc/PSQSrX6VSMVwEu5euQkZ5MckLBhT3C2zE0yWsjSW+2kcRojAtpI+G3L5CTnYVHReVtxMTctkDbS06MRkNDEwMlbSQnJ5sbFw9StW7x2oirVx1eijYiCKWCga4O3k72PIiMUZrn4r3HZOXk4OvlpjSPjakxUYnyI/ExSSloaqhjYaT4iUJ2Ti6BobcKfaJgY2bEi0T5SGIJqelk5+RiY2qk5CqhMFKJtNDjUzRz5kxWrlzJmjVrZJvgQt4Gt8WJGvSK6Pz/i3R0dGSLVF4dH8OUHwBNLW0cPHy4HSofl/b25SO4VlAenvFq8B68a7VDvZDHW67l63D78hG5tFuXD+PkUQMNTS2F19y9chw9Q1PZaL6qnjwIRUtbFz0D02JdJxRNU0sbJ08fbl2WbyM3Lx/BzVt5Gwk7u4dKtYtoI151uPlGG7kZchgnzxpoKmkjd8Ly2sir0XxVRdwXbUQQSovM7GxuP40qdAHwvvPXaO3jjYaG8i5N7XLOHAu7LZd2NOw21d0d0dJU/LfnxLW7mBjoUcW1bCHlunAjIpLI+PyNlo6E3UZHS5NqbsqnGwrKvRr5V3Z8iu7cuaNwg1tjY2MSExOLXZ7o/Asqa/bZSM4eWsvZQ+uIirjF9lUjiY+JoH5b5SvNrwXvLXJBbv12g4iPfsyO1aOIirjF2UPrOHd4Hc26jFZ6zdXzf1O5duHTOa4F7+XMgTU8f3SdmOcPOHPwd/ZumES9NgM/mi9dH5sWXUZy6sBaTh9cR+TjW/ivGEl8dASN2itvI1eClYf4fKVR+0HERT/Gf+UoIh/f4vTBdZw+uI6W3ZS3kSvnip7yc+XcXk7uX8Oz8OtEP3/Aqf2/s/t/k2jYVrQRQfgQxv9vN6eu3+PRizgu3H1E77nrSEnP4IsmtZVeE3DxeqGj8wADW9cnIiaBset2cvtJFBuOnGP9kWBGKJlKBLDv4nWlC31faV61PF5lbfl60UbCHj7h+JU7TPjfbvq18BWRfkooN1da6PEpsrOz4/79+wXST58+jaura7HLE3P+BZX5NOpBWkocB/6cQXJ8JHbOFRk8PQALG8WRkmKePyDm+X28fJQvrgawtHVh8PQAdqwexcm9yzGxsKfboMWFhvm8FryXL0cWvpmauqYWJ/etYMea0UglEiztXGnvN42GHYYUep1QcjUb9yA1OY59m2aQFB+JvXNFhs1S3kainz8g+tl9vGsU3kas7FwYNjOAv1aOIujvvDbSc/DiQsN8Xjm3l75jCm8jGppaBP29gr9WjkYqlWBl60rHvtNo0km0EUH4EJ7FJdLnlw3EpaRhaWxILU9nTswbhZO1ucL8DyNjeBAZQ4tqhYcAd7axYPfkbxm7bher9p/CztyEXwZ0KTTMZ8CF66wa2rvQcjU01Nk5+VtGrNpG0/GL0NPRonuDGvzcr3jTDYV8H2rBb2n27bffMnz4cNatW4eamhrPnz/n3LlzjBkzhp9++qnY5alJS7I12L9s+fLlzJ8/n8jISLy9vVm0aBENGjRQmDcoKIgmTZoUSL916xbly+eH+dqxYweTJ0/mwYMHuLm5MWvWLNmmYqrWK5VKmTZtGqtXryYhIYHatWuzbNmyYoX6XH6w1L/9JXZ056/cDj3KkBkB77TciPuX+W18M+ZujVY6Lei/Qvs//vX88PZfuXX5KMNnv9s28vjeZX75oRm/bo9WOi3ov6KJS/iHvgWhlCtz/d3+fpUmi/cc4/iVu+z+qfixzgsT+uAJbSYv5ckfs5VOC/ov0e1Uso1T36Xk5GRMTEyYsDoOXX3F07wy0pOZ840FSUlJKsX5/y+ZOHEiCxcuJCMjA8ibSj5mzBhmzJhR7LJK/bQff39/RowYwcSJEwkNDaVBgwa0adOGiIiIQq+7c+cOkZGRssPDw0N27ty5c/To0QM/Pz+uXLmCn58f3bt35/z588Wqd968efz6668sXbqUixcvYmtrS4sWLUhJEeG9AEwty9Kqx/h3Xq4kN4du3/32n+/4fwrMrMrSptf7aSO9hvz2n+/4C8KnroyFKWO6tCg6YzHl5Er4dWCXT6LjX9pIkco2+ipw8N8dMC3KrFmziI2N5cKFCwQHBxMTE1Oijj98BCP/tWvXpnr16nKbiHl5edG5c2fmzCm4Y+irkf+EhARMTU0VltmjRw+Sk5M5cOCALK1169aYmZnJ9hwoql6pVIq9vT0jRoxg3LhxQF4cfxsbG+bOncu3336r0uv7L4/8C2/vvz7yL7w9MfIvFOW/PPIvvBulaeR/7IoYdPQUj+pnvkxm3ndWn8zIf//+/VXKt27dumKVW6pH/rOysggJCZFtuPBKy5YtOXv2bKHXVqtWDTs7O5o1a8bx48flzp07d65Ama1atZKVqUq94eHhREVFyeXR0dGhUaNGRd6bIAiCIAiCUJBEWvjxKVm/fj3Hjx8nMTGRhIQEpUdxlepxxdjYWHJzc7GxkY8RbmNjQ1SU4h1D7ezsWL16NT4+PmRmZrJx40aaNWtGUFCQLExSVFRUoWWqUu+r/yrK8/jx4xK+YkEQBEEQhE+XJFeKRElUH2Xp/1WDBg1i69atPHz4kP79+/Pll19ibq548XtxlOrO/yuKdn9VtiNfuXLlKFeunOznunXr8uTJExYsWCAXI1WVMt9Vnv+qk/uWc2T7ApLiI7Fz8qbrtwtxr6h4ITbAvasn2LFmNJGPb2BiYU+Lrj/QoJ38Iq301ET2bphI2JldpKcmYGHrwucDFlCxVlsADvnPIezMLl48vY2Wth6uFXzp3P9nbMrmf+Z//NKP80c2yJXrXK42Pyw69w5fvaCK438v59C2BSTFRWLv7E2P7xbiWUl5G7lz5QR/rRrN80c3MLWwp1X3H2jcoWAb2bVuIqFndpGWkoClrQvdv11Apdp5bWT/ljlcPr2LqCe30dbRw62CL10G/IytQ34bWTevH+cC5duIS/na/LhEtBFBKE1W7T/Fwt1HiUpIpoKDLfO+7kJ9b+WbeZ26fo9x63Zx80kUduYmjPqsGQNby28gmJiaztTN+9gTfJWE1HScbSz4+avOtK6RF6xj/vbD7A6+yt2nL9DT0aJ2ORdm9e2IZ5n8wb6Bizex6fgFuXJrejpxcp7y8MOCaiQSKRIlQ/zK0v+rli9fzsKFC9m5cyfr1q1jwoQJtGvXjq+//pqWLVuWuL9Zqjv/lpaWaGhoFBjlj46OLjDiXpg6derIbdlsa2tbaJmq1GtrawvkPQGws7Mr8b19rEJO+LN91Uh6DFmGW4V6nN6/imWT2zJ51Q3MrR0L5I+NCmf5T+2o13oAX/2wkQc3z+C/bAiGJlaykJ452Vks+bElRqbWDJi4DVPLsiTEPEFXP3+XxHvXTtKww2CcPGsiyc1h74ZJLJnYismrbqCjm79LY4UarflyZP4cOE0t7ff4bgiKXAzyx3/FSL4Yugx373qcCFjFbz+2ZdraG1goaCMxkeH8NqkdDdoMYMC4jdy/cYbNS4ZgZGolC+mZk53Fr+NaYmxqzaDJ2zCzKkt8zBN09fLbyN2rJ2nScTDO5fLayK7/TWLh+FZM//0GOnr5baRizdZ8Nea1NqIp2ogglCbbTl/mh3U7WfxtN+qWd+X3Q2foPGMFl5f8iKNVwdHPRy/i6DxjFf1a1GXdyD6cu/2Q4au2YWlsKAvpmZWdQ7upy7E2MWTz2P6UsTDlaWwCRnq6snJO3bjPoDYN8PFwJCdXwtTN+2g/dTmhS37EQDd//4+W1b1YNfQL2c/aYnHwOyFG/uXp6OjQq1cvevXqxePHj1m/fj2DBw8mOzubmzdvYmhoWOwyS3XnX1tbGx8fHwIDA+XCcAYGBtKpk+oxdENDQ+U66HXr1iUwMJCRI0fK0g4fPoyvr6/K9bq4uGBra0tgYCDVqlUD8tYKnDhxgrlz55bsBX9Eju5aSN2W/anXegAAXQct4ublw5wKWEGnfgUXYp8OWImZtSNdBy0CwNbRi4h7lzi64xdZ5//c4XWkp8Qz5tczskg+b8aH/37mAbmfvxy5jvG9bIi4F4JHpfwnO5paOpiY276z1ysUX+COhdRv3Z8GbfPaSM/Bi7hx6TAn9q7g868LtpET+1ZibuVIz8GLALBz8uLR3Usc3vaLrPN/+mBeGxm/+Iwsks+bbWTEHPk20m/MOkZ1s+HxvRA8K4s2Iggfi9/2HOer5nXo1yLv3+YFA7pwJOw2aw6eZoZfwY0B1xw8jYOVGQsG5P29KO9gy+X7T1i055is87/haDAJKWkE/TxSFsnnzT0E/p4yWO7nVUN749h3IqEPnlDf212Wrq2pWejOw0LJ5Eok5EoUb+WrLP1ToaamhpqaGlKpFMlbvBeluvMPMGrUKPz8/KhRowZ169Zl9erVREREMGhQ3lSACRMm8OzZM/744w8AFi1ahLOzM97e3mRlZbFp0yZ27NjBjh07ZGUOHz6chg0bMnfuXDp16sSePXs4cuQIp0+fVrleNTU1RowYwezZs/Hw8MDDw4PZs2ejr69P796FbwryscvJzuLJvRBadhsnl+5VvQUPbyqeNvHwdjBe1Vu8kb8VZw+tIzcnGw1NLa4G78XFqy7+y4ZwNfhvDE2sqNG4Fy27jUNdQ/GIysv0vC3VDYzk/3jfuxrEuJ426Bua4l6pIR37zsLI1LqkL1koppzsLB7fDaF1D/k24u3Tggc3lLSRW8F4+8i3Ee8arThzcB05Odloampx5dxeXCvU5c8lQwg7+zdGplbUatKLNj0KaSNpitvInStBjOpmg56BKeUqN6Rzv1kYm4k2IgilQVZ2DqEPnjCmS3O59GZVyxN8W3GEq/N3HtGsanm5tObVyrP+yDmyc3LR0tQg4MJ1apd3YcSqbey7cA1LE0N6NPBh9OfN0dBQHAMlOT0vrrqZob5c+qnr93Hs+yMmBno08HZn6hftsTY1UlSEUAxi2o+8zMxM2bSf06dP0759e5YuXUrr1q1RVy9Z3J5S3/nv0aMHcXFxTJ8+ncjISCpWrMj+/ftxcsob7YuMjJSLvZ+VlcWYMWN49uwZenp6eHt7ExAQQNu2bWV5fH192bp1K5MmTWLy5Mm4ubnh7+9P7dq1Va4XYOzYsbx8+ZLBgwfLNvk6fPgwRkb/7V/+1ORYJJJcjM3kpzcZmdqQnKB4IXZKQhRGpvL5jc1skOTmkJoci4m5HXFRD7l75Rg1m/Rm8PQAop/d46/l3yPJzaHtFwV3sJNKpexcPRo37/rYO1eUpXvXaE31Bl0xt3YiLiqcvRt/YvH4Zoz77RJa2joFyhHevdQkJW3EzIYkJW0kKT4KoxoF20hubg6pSbGYWtgRG/WQ22HHqN2sN8NnBfDi2T3+XJLXRjr4KW4jf60cjXvF+pRxyW8jlWq1pkajrlhYOxEbFc7u9T/xy9hmTFom2ogglAaxKWnkSiQFOtM2Jka8SFC8l86LxGRsTOTzW5sakZMrITY5FTtzE8JfxBJ0LZ6eDWuwa/K33I+MYeTqbeRIcvmxR5sCZUqlUsat24WvlyveTvay9JY+Ffi8XjUcrcx49CKO6X/up81PSzn7yxh0tMT+Im9DIilk2s8n1vkfPHgwW7duxdHRkX79+rF161YsLCzeutxS3/mHvBc/ePBghefWr18v9/PYsWMZO3ZskWV27dqVrl27lrheyBv9nzp1KlOnTi2yvv+kNxeaFLHYWdHi6H/O/POzBCNTa3oPW426hgaOHj4kxT/nyPYFCjv/fy3/nmfhVxm14JRcuk+jHrL/t3euiKNnDSb3debGxQCq1vu8GC9QeFsF2kMx28irvdxfpUskEoxNrekzIq+NOHn6kBj3nMPbFijs/P+55Huehl9l7EL5NlKzcX4bKeNSESfPGoz/0plr5wOo3kC0EUEoLdR4498NpAX+6ZHLr/hPSP7fEKkUKxMjlg3uiYaGOtXdHYmMT2LR7mMKO/8jV2/j2qPnHJ0zXC69W/3qsv/3drKnursj5b6ZyoFLN+lct0oxXqHwJqlUikTJFlSlfGuqd27lypU4Ojri4uLCiRMnOHHihMJ8O3fuLFa5H0XnXyhdDI0tUVfXIDlefgQ3JSm6wOj+K0ZmtgWeCqQkRqOuoYmhcd63WGMzOzQ0teSmb9g6eJGcEEVOdpbcot2/lg/lavBeRs4/gZlV2ULv18TcDnNrJ6Kf3SvW6xRKztAkr40kvdlGEqMxVtJGTMxtC7Sp5MRoNDQ0MfinjZiaF2wjdo5eJMUXbCN/Lh3KleC9/PDLCcyLaCOmFnZYWDvxQrQRQSgVLI0M0FBX50Vislx6dFKq0qk1NqbGRCXKPxWISUpBU0MdC6O8xf62ZsZoaWjITfEpX9aWqIRksrJz0NbK7xaNXL2dfReuc2T2cMpamhV6v3bmJjhamXM/MrpYr1MoSJIjQZKjeD67svT/qj59+ryXCJKi8y8Um6aWNg4ePtwODaRqvfwF0bcvH6Fy3YKLsABcy9fh2vl9cmm3Lh/GyaOGbHGvq7cvl45vQSKRyOaxRT+7i4m5naxTJ5VK+WvFUK6c3c2IucextHUp8n5Tk+NIiHmCibldkXmFd0NTSxsnTx9uXQ6kev38NnLz8hGq+ippI151uBos30ZuhhzGybOGbHGvm7cvF95oIy+eFmwjW5YOJfTMbsYsOI6VnWptJD7mCSYWoo0IQmmgraVJNTcHjoXdoVOd/JH0Y2G3aV+7ksJrapdzZv/F63JpR8NuU93dUba4t255V/xPhsj9Dbn3PBpbM2NZx18qlTJyzXb+Dr7K4ZlDcbYpeppFXHIaT2MTsDMzKdHrFfIVtpnXJzbrp8DslnelVO/wK5RezT4bydlDazl7aB1REbfYvmok8TER1G+btyB6z/8msGFBX1n++u0GER/9mB2rRxEVcYuzh9Zx7vA6mnXJj4ncsN13pKXEsX3lcF48vcv1CwEc8p9Dw/b5U6/8lw3h4rHN9Bu7GR09I5Lio0iKjyIr8yUAGS9T2blmDA9vnSPuxSPuXg1i5dSOGBpbUsU3vxMqvH8tuozk1IG1nD64jsjHt/BfMZL46Agatc9rIzvXTmDt3Pw20qj9IOKiH+O/chSRj29x+uA6Th9cR8tu+W2kcYfvSE2OY+vy4UQ9vcvV8wHs3zKHJh3z28ifS4YQfHQzAyZsRldfcRvZtmoMD26eIzbqEXeuBLF0ckcMTSypXk+0EUEoLYZ1asL/jpxjw5Fz3H4SxQ9rd/IkNoEBrfLi9k/e+DdfL9ooyz+wdX0iYhIYu24nt59EseHIOdYfCWZEp6ZyeeJT0hj9+07uPYvmwKUbzN8eyKC2+fuPjFi1ja1Bl9gwqg+GerpEJSQTlZDMy8wsAFJfZjL+f7sJvh3O4xdxnLx2jy6zVmFhbEDHOpX/pXfnv0uSKyn0EN6eGPkXSsSnUQ/SUuI48OcMkuMjsXOuyODpAbKwi0nxUSRE5y/EtrR1YfD0AHasHsXJvcsxsbCn26DFsjCfAGZWDnw/6xA7Vo1i9uAqmFqUoXGnYXJRhU4FrARg0bgmcvfz5ah11G3xFerqGjx/dJ3zRzfyMi0RY3M7PCs34esJW+X2CxDev5qNe5CaHMe+TTNIio/E3rkiw2blt5HEuCjiX2sjVnYuDJsZwF8rRxH0d14b6Tl4sSzMJ4C5tQMjfz6E/4pRTPumCmaWZWj22TDavBZVKGhvXhtZMEa+jXw1Zh31WuW1kafh1zl3ZCPpqYmYmNtRrkoTvpko2ogglCbd6lcnPjmN2f6HiEpIwtvRjt2TB8lCc0bFJ/MkJkGW39nGgt2Tv2Xsul2s2n8KO3MTfhnQRRbmE8DByoy9Uwczdt1Oao74GXtzE4a0b8Toz/OjCq0+mBf5r+WkJXL3s3roF/g1q42Guho3Hj/nz6ALJKa9xNbMmEYVPdg4pp/cfgFCyUgkyhf2fuKRPt8ZNemntnqilFl+ULz9gnLa4uu5UIQmLorDHgrCK2WuB3zoWxBKOd1OQz/0LZCcnIyJiQlf/vgAbV3FAzFZGSlsmu1GUlISxsZij4WSEl0LQRAEQRAEoVTIlUjIVTK951Pf5OtdEZ1/QRAEQRAEoVSQSqRIlUz7UZYuFI/o/AuCIAiCIAilgiRX+ci/WPD7bojOvyAIgiAIglAqiJH/9090/gVBEARBEIRSQZKbiyQ3V+k54e2JOP+CIAiCIAhCqSCRSAs93peEhAT8/PwwMTHBxMQEPz8/EhMTC73mq6++Qk1NTe6oU6eOXJ7MzEyGDh2KpaUlBgYGdOzYkadPn76316EK0fkXBEEQBEEQSoUPtclX7969CQsL4+DBgxw8eJCwsDD8/PyKvK5169ZERkbKjv3798udHzFiBLt27WLr1q2cPn2a1NRU2rdvT+4HfIohpv0IgiAIgiAIpcKHmPN/69YtDh48SHBwMLVr1wZgzZo11K1blzt37lCuXDml1+ro6GBra6vwXFJSEmvXrmXjxo00b563kdymTZtwcHDgyJEjtGrV6t2/GBWIkX9BEARBEAShVMiL85+r+HhPcf7PnTuHiYmJrOMPUKdOHUxMTDh79myh1wYFBWFtbY2npycDBw4kOjpadi4kJITs7GxatmwpS7O3t6dixYpFlvs+iZF/QRAEQRAEoVRQZeQ/OTlZLl1HRwcdHZ0S1xkVFYW1tXWBdGtra6KiopRe16ZNG7p164aTkxPh4eFMnjyZpk2bEhISgo6ODlFRUWhra2NmZiZ3nY2NTaHlvm9i5F8QBEEQBEEoFZSO+v9zADg4OMgW5pqYmDBnzhyFZU2dOrXAgtw3j0uXLgGgpqZW4HqpVKow/ZUePXrQrl07KlasSIcOHThw4AB3794lICCg0NdYVLnvmxj5FwRBEARBEEoFVUJ9PnnyBGNjY1m6slH/77//np49exZan7OzM1evXuXFixcFzsXExGBjY6PqrWNnZ4eTkxP37t0DwNbWlqysLBISEuRG/6Ojo/H19VW53HdNdP4FQRAEQRCEUkGVaT/GxsZynX9lLC0tsbS0LDJf3bp1SUpK4sKFC9SqVQuA8+fPk5SUVKxOelxcHE+ePMHOzg4AHx8ftLS0CAwMpHv37gBERkZy/fp15s2bp3K575qY9iMIgiAIgiCUCpJCpvy8r02+vLy8aN26NQMHDiQ4OJjg4GAGDhxI+/bt5SL9lC9fnl27dgGQmprKmDFjOHfuHI8ePSIoKIgOHTpgaWnJZ599BoCJiQlff/01o0eP5ujRo4SGhvLll19SqVIlWfSfD0GM/AuCIAiCIAilwocI9QmwefNmhg0bJovM07FjR5YuXSqX586dOyQlJQGgoaHBtWvX+OOPP0hMTMTOzo4mTZrg7++PkZGR7JqFCxeiqalJ9+7defnyJc2aNWP9+vVoaGi8t9dSFNH5FwRBEARBEEoFSW4ukpzC5/y/D+bm5mzatKnQPFJp/pcPPT09Dh06VGS5urq6LFmyhCVLlrz1Pb4rovMvCIIgCIIglApSqQSpVHE8f2XpQvGIzr8gCIIgCIJQKuTmSFBTUzzCn5sjOv/vguj8C4IgCIIgCKXCh5rz/ykRnX9BEARBEAShVMjOTFY6tz83J+1fvpv/JtH5/8COHQr/0LcglGJa2lof+haEUk6nrcuHvgWhlPOt2O5D34JQyrl96BsAtLW1sbW15WJg10Lz2draoq2t/S/d1X+T6PwLgiAIgiAIH5Suri7h4eFkZWUVmk9bWxtdXd1/6a7+m0TnXxAEQRAEQfjgdHV1Rcf+XyB2+BUEQRAEQRCET4To/AuCIAiCIAjCJ0J0/gVBEARBEAThEyE6/4IgCIIgCILwiRCdf0EQBEEQBEH4RIjOvyAIgiAIgiB8IkTnXxAEQRAEQRA+EaLzLwiCIAiCIAifCNH5FwRBEARBEIRPhOj8C4IgCIIgCMInQnT+BUEQBEEQBOETITr/giAIgiAIgvCJEJ1/QRAEQRAEQfhEiM6/IAiCIAiCIHwiROdfEARBEARBED4RovMvCIIgCIIgCJ8I0fkXBEEQBEEQhE+E5oe+AeHjJpVKuRX8G+HXt5KVkYS5bVWqNZ2KsYWnStc/ubOXCwdGYOfaHN+Oq2TpEkkOt4IXE3H7bzLSYtA1sMa5QhfK1x6Cmpq6ynXn5mRy7dQcntzZR25OBtaOvlRtMg19I7t39yYISkmlUq6fWciDK3+SnZmEuV01arSYgYllOZWuf3zrb87t/Z4y7i1p8PnvcufSU6K4cmIOkQ+Pk5uTgZG5K7Vaz8PctrLKdefmZBIWNIvHt/aQm5OBjWM9arScJdrHv0gqlbJr/TSC9q0hLSUBN6/a9BmxlLIu3kqvOb5vDWcObeRp+HUAnD196DZwFm5etWR59m6ew6WTu4iMuI2Wjh4e3r70+PZn7BzzP39V6s7OymTLijEEH91KVtZLvKs3o++IZZhbl30P74YgCML7J0b+hbdy99Jq7oWuo2qTqTTttQtdA0tO7exLdlZqkdemJT/j2qmfsSxTU0G5q3h4dQtVm0ylZZ/DVGowjrsha7gftqFYdV85MZPnDwKp1XYxjbv7k5OVxtk9A5FKct/NGyAU6vaFFdy59Ds+LWbQwm8fegZWHPf/guxMFdpH0lPCjs/EqmytAueyMhI5svlz1NU1adTtD9p8fZSqTSahpWNcrLpDj03j6d2D+HZYSvPeO8jJTufkjn5IRPv41wRsmcfBbQvxG76EaSsvYGJuy7wxLXmZnqL0mtthJ6jTrCcTFh7jp2VnsbBxYP6YVsTHPHstz0madx7MT8vPMW7BYXJzc5j3QysyX6YVq+7NS0cQcmo3g3/awqQlp8h4mcqvEzogyRVtRBCEj5Po/AslJpVKuR/6P8rXHEwZ91aYWJajRsv55Ga/5Mntvwu/VpLLxYMj8aozHANjhwLn4yJDsXdrjp1LEwxMylLWow3WTvVJfHFd5bqzM1N4dGMblRpMwMaxHqbW3tRs/StJcXd4EXHm3b8hghypVMqdS2vxrvs9Dp5tMLUqR+22v5Kbk8HjW7sLvVYiyeXcvmFUrD8KA1PHAudvnV+BvrEdtdv+goVdVQxNHLB1qo+RmbPKdWdlJvPwqj/VmkzC1rkBZjYVqdtuEUkxt3nx+PQ7fjcERaRSKYe2L6bjlz9Ss+HnlHWtyDcT1pOVkc65I38qve67SZto3nkwTh5VsXcqz9dj1iCRSrh5+agszw/zD9CgzVeUdfHG0b0KA8evI+5FBOF3Q1SuOz01iRP719Fr8AIq1miOs0c1Bk3cyJPwa1wPOfJ+3xxBEIT3RHT+hRJLS35CRnoMNk71ZWkamjpYlq1NXOTlQq+9dX4JOnrmuFTsrvC8pX0NoiPOkpIQDkBizC3inl/C1rmRynUnRF9DKsnGxqmBLI+eoQ0mFp7EF3F/wttLS4ogIy0GW+eGsjQNTR2sHWoT+yyk0GtvnF2Ejr4FbpV7Kjz/7H4g5jaVObNnELuWVuPg+jY8uJLfWVSl7oSoa0gk2di65OfRM7LFxLIcsc8uleg1C8UTExlOUnwUFWu2lKVpaetQrmoj7t04p3I5mZnp5OZkY2BkrjTPy9QkAAz/yaNK3Y/uhpCbk02l1/KYWdpT1qUi92+cVfn+BEEQShMx518oscy0GAB09C3l0nX0LUhPfq70utjnl3h0YxvNvtirNI9njW/Jzkzh8IYWqKlrIJXk4u07GofyHVWuOyMtFnUNbbR1Td7IY0nGP9cL78+r91i3wGdkSXryM0WXABDz9CIPr/rT+quDSvOkJj7hftgmytUcQIU63xMXGcblo1NQ19DGpWJXlep+mRbzT/swlc9jINrHvyUpPgoAEzMbuXQTM2tiX0SoXM5fq8djZlkGb5/mCs9LpVL+XD4az0r1KetaUeW6E+Oj0NTSxsDI7I08NrLrBUEQPjai8y+oLOL2Hi4fnST7uV6nfxZgqqm9kVNaMOkf2VmpXDw4murNZqGjp3yU7undfUTc3k2tNgsxtvAkMeYmV0/MRM/QGqcKXfIzFqPu1/OokEkopkc3dnHp8ATZzw27rM/7HwWfkbL3PzszleCAEdRsPRcdfeXtA6kEM9vKVGk4DgAzm4okxd7lftgmXCp2zc9XjLrzs0gB0T7eh7OBm/nfL4NkP4/+eR8Aam98JlKpFDUVP4OALfMIPrqVCYuOo62jqzDPH4u/58mDq0xacqrAuZLULRVtRBCEj5jo/Asqs3NtRnPbKrKfJblZQN4ovJ6BtSw9Mz2+wIj8K2mJEaQnP+Xs39/I0qRSCQA7F3vSsm8ghqZOXDv1M+VqDsKhXAcATCzLkZ78jNsXV+JUoQs6BlZF1q1rYIkkN4usjCS50f/M9Dgs7Kq/1XshFFTGvQUW9tVkP0tyM4G8JwB6hvmjq5npcQVG5F9JTXxMWtITTu3oL0t71T7857vQdsBxjMyc0TW0xsTCQ+5aYwsPnt49AIDuP+2jsLr1DKz+aR+JcqP/melxWJbxKfbrF4pWrV5H3Lxqy37Ozs5rI4nxUZha5EdYSk6MwdjcpsD1b9q/dQF7N81h7C+BOLpVVpjnj8VDCT2zl4m/nZCL0GNibltk3abmtuRkZ5GWkiA3+p+cGI1HxbqqvGRBEIRSR8z5F1SmpW2Ioamz7DAy90BX34oXEfmLIyW5WcQ+Pa+0c21k7kbzL/fT7Iu9ssPOtRlWDnVo9sVeWYjF3JwM3myeamoa8E9H0MDYoci6zawroaauRfRreV6mRZMUdxdz0fl/57R0DDEyc5Ydxhae6BpYEfUof7Q1NzeL6CfnlXaujS3caN0vkFZfHZQdZdxbYO1Yl1ZfHUTf2B4AyzI1SE54IHdtSvxD9I3zOncGJo5F1m1mWwl1dS25PC9TX5AUewfLMjXezZsiyNHTN8KmrLvsKONcARNzW25cCpTlycnO4k7YCTy8C+9cB2ydz56NMxkz7wCu5Qt+XlKplD8WfU/IqV2MX3gUKzsXufNWdi5F1u3s6YOGphbXX8uTGBfJ0/DruHv7lug9+Jj9/vsaGjZsgKenB+7ubgQHByvM16hRQ9zd3Vi8ePFb17l48WLc3d1o1Khh0ZlV4O7uhru7Gzt2bFeaJzg4WJZP2Wssid69e+Pu7sbYsT+8szIFoSTEyL9QYmpqarhX68edCytkXwjuXFyBhpaebG4+wMVDo9EzsKVi/R/Q0NQpEONd+5/wjK+n27k05c7F5egb22Ns7kFizA3uha7DuUJXlevW0jHC2bsbV0/ORlvXFG1dU66emoOJRTlsHOu977fnk6empka5Gl9zM3gZRmYuGJq5cDN4KRqaujh5dZblCw4YgZ6hLVUajUdDUxdTK/n2oaWb1z5eTy9XYwBHNn/GjXNLcSzfnrjIMB5c/ZOaLX9WuW5tHWNcK/cg9PhMtPXM0NE1JfT4TEysysstJBfeHzU1NVp1Hc7eTXOwKeuBbRkP/t48B21dfeo27y3Lt2p2X8ws7en+zRwgb6rPjnU/8d2kzVjaOpMYlzf/XlfPEF19QwA2LBpC8JEtjJi1G109I1kefUMTtHX0VKpb39CERm37s2X5GAyNLTAwNmfrih9wcKlERSXrC/4LevfuzYUL5ylTpgwnTpwE4MaNG/z8c97vl4ODI+bm5hgaGiq8vkKFClhaWmFra/uv3XNp8fTpUxo3zgtMsWnTZurUqfOB70gQCnovnX81NTV27dpF586d30fxMkFBQTRp0oSEhARMTU0B2L17N2PGjCE8PJyhQ4dStWpVRowYQWJi4nu9l0+VZ41vyM3JIOzYFLIy8zbaqv/ZerS08/9RSE+ORK2YD5mqNJnCzbMLCTv2ExnpcegZ2uBaqSdetYcWq+4qjSahrq7B+f3DyM3JwMrBF99O81BT13j7Fy8UqXyt78jJzuBS4ESyMpKxsKtK4+6b0dLJ/4zSkp+DWvHah4VdFep3Xs3Vk3O5cXYxhiYOVG86BWfvz4pVd7WmP6GmrsnZPYPzNvlyqkfttr+iLtrHv6Zdr7FkZb5kw8IhpKck4FqhNmPnH0JP30iWJ+5FhGxzP4Cju1eQk53Fkind5Mrq3PcnPu83FYBje1YCMHtEE7k8A8eto0Gbr1Suu/eQhahraLJ0Wg+yM19SoXozRs75H+oan1YbuXfvnuz/9+3bh4GBgdK8K1as/DduSRCEElKT5q1cUllUVBSzZs0iICCAZ8+eYW1tLetgN2vWLK/Qf6nzn5WVRXx8PDY2NrJFWzY2NvTr149hw4ZhZGSEpqYmKSkpWFtbF1Ha2/n2229ZvXo1CxcuZMSIESpf13Xkw/d3U8JHT0tb60PfglDKtW0rdpoVCudbNrxY+d8c+R879gd27txZIF9Q0AnKli3Y/ho1asizZ88YOnQYw4cPJzg4mC+//AKAFStWsG7dOq5evUqZMmUYP34CTZs2VXovixcvZsmS3yhTpgyTJk1i/vz5PH36FG9vb2bOnEW5cvlPBI8cOcKaNau5desWubm5uLm54+fnR7du+V8S3d3dAJg7dy5duuQ9SQ4ICGDBgvlER0dTp04dOnbsxOjRo4Dijd7v2LGdcePGFUivVas2f/75p+x9/eyzz3BxcWHTpk1kZGTQuHETpk+fLnuSIpFI+OOPDfj7+/P48WN0dXXx9a3HuHHjcHBwKFDXxo2bmDNnDg8e3MfNzZ1p06ZRrVq1AvdRGDc312LlFz5uxRpue/ToET4+Phw7dox58+Zx7do1Dh48SJMmTRgyZMj7ukeltLW1sbW1lXX8U1NTiY6OplWrVtjb22NkZISent5bd/yzs7MLPb97927Onz+Pvb39W9UjCIIgCKWNo6MjDg75m+1VqVKVKlWqoq2tXeyyhg0bRkxMDGpqajx8+JCRI0eq9GQ+JiaGESNGoK6ugVQqJTQ0lP79+/Py5Usg79/hQYO+JSQkBH19fSwtLbl58wYTJoxn+fJlSsu9efMmI0eO4MmTJ2hraxMeHs7kyZOU5i+MubkFXl4VZD+7ublTpUpV3N3d5fIdOHCAVatWo6OjQ3JyMn//vYdVq/KflkydOpWZM2dy7949nJyc0NDQ4ODBA3Tv3o24uNgC9X79dX8yMl6Sm5vLzZs3GDFiODk5OSV6DcKnoVid/8GDB6OmpsaFCxfo2rUrnp6eeHt7M2rUqEIXxYwbNw5PT0/09fVxdXVl8uTJch3qK1eu0KRJE4yMjDA2NsbHx4dLl/I22Xn8+DEdOnTAzMwMAwMDvL292b9/P5A37UdNTY3ExESCgoIwMsp7VNu0aVPU1NQICgpi/fr1silBr+zduxcfHx90dXVxdXVl2rRpcr8oampqrFy5kk6dOmFgYMDMmTOVvrZnz57x/fffs3nzZrS0xCitIAiC8N/y/fdD+f77/AG+HTt2sGPHjhINrPn59eHIkaMsWpS3GDgtLZUrV64UeV1WVhYrVqzk4MGDrFq1GoAXL6LYtWsXAL/++guQ98XkxImTBAWdoGXLvM3Zli9fLvuS8Ka1a9cikUgwMjIiMPAIx44dl11XXE2aNGHFihWyn6dNm8aOHTuYPn26XD4NDU0OHTrE0aPHqFSpEgBnz+ZtLPfkyRO2bMnbsHD+/PkcOHCQoKAT2NraEhMTwx9//FGg3vHjx3P4cCATJuSFWn727BmPHz8u0WsQPg0qd/7j4+M5ePAgQ4YMUTjX780O9uuMjIxYv349N2/eZPHixaxZs4aFCxfKzn/xxReULVuWixcvEhISwvjx42Ud6SFDhpCZmcnJkye5du0ac+fOVbjIyNfXlzt37gB5f5giIyPx9S0YjeHQoUN8+eWXDBs2jJs3b7Jq1SrWr1/PrFmz5PJNmTKFTp06ce3aNfr371+gHMh7NOfn58cPP/yAt7e30tcvCIIgCAKy6cCvj4YrGs1+k4mJCQ0b5kX8adiwISYmeeGb79y5Q1xcLM+f523u2KpVS3R0dFBTU6Ndu/YAZGRkyK1ZeN2rdB8fHywt88IAt2nTtgSvTHV169bB1tYWdXV1XFzyptu8eg+uXbvGq9nYP/zwA+7ublSpUpmoqLwF62FhYQXK69w5b62Tu3t++OPY2KLfU+HTpfKC3/v37yOVSilfvnyxK5k0Kf8RmrOzM6NHj8bf35+xY8cCEBERwQ8//CAr28MjvwFHRETQpUsX2bdjV1fF89K0tbVloxDm5uZKowzMmjWL8ePH07dvX1l5M2bMYOzYsUyZMkWWr3fv3ko7/a/MnTsXTU1Nhg0bVmg+QRAEQRDA2DgvepemZn73Q5Wlh29uxva2+d6s+/XrirkUsthevQcAmpoacnW+XreXV4UCU6vs7csoLU/jtUXo7/s1CB83lTv/in5BVLV9+3YWLVrE/fv3SU1NJScnR67xjxo1igEDBrBx40aaN29Ot27dcHPLW5QzbNgwvvvuOw4fPkzz5s3p0qULlSsr3sxFFSEhIVy8eFFupD83N5eMjAzS09PR19cHoEaNwuN8h4SEsHjxYi5fvlyi90QQBEEQBNUkJiZy8uRJGjZsyOnTp0lKSgKgXLlyWFhYYm9vz/Pnzzl48BB9+vRFW1ubgIC8HaR1dXXlBhVf5+npya1bNwkJCSEuLhYLC0sOHTpYIN/ri5YLWwSsp5e/y7SyqUaFqVSpEmpqakilUrp0+ZyvvuoH5PXBQkJClIZXFYTiUHnaj4eHB2pqaty6datYFQQHB9OzZ0/atGnDvn37CA0NZeLEiWRlZcnyTJ06lRs3btCuXTuOHTtGhQoVZPP4BgwYwMOHD/Hz8+PatWvUqFGDJUuWFOseXieRSJg2bRphYWGy49q1a9y7dw9d3fxf2sLCmAGcOnWK6OhoHB0d0dTURFNTk8ePHzN69GicnZ1LfH+l3YMrmziwrhG7lnhx9M+OxD67qNJ1j25s5/jWLkrPxzw9z9E/O7JriRcH1jXm4dU/VSo3LfkZu5Z4kZ2ZovD8jkVuBQ5VyxZK5l7oH+xdVY+/fvHg0Ia2RD85r9J1D69tI3BjJ6XnoyOCObShLX/94sHeVfW4H7pRpXLTkp7y1y/uStvI1nmOBQ5VyxZK5sju5Yzq6crXLfT46Zsa3Ll6quiLgFMH1jPtO+Wbf90OO8FP39Tg6xZ6jO7lJgv3WZTYqMf0b6HLy7Rkhef7NFYvcKhatvBuaGtr8913g2jdujUDBw4EwNrams8+y5vyMmrUaACuXAmjUaOGNG7ciMOHDwN56xX19PQUltu/f3/U1NRITk6mefPmNGvWlICAgBLfp7m5BWZmebtBjxkzmi5dPuePPzaofL2joyM9evQAYObMmTRp0ph27dpSrVpVevbswY0b10t8b4Lwisoj/+bm5rRq1Yply5YxbNiwAp3jxMREhfP+z5w5g5OTExMnTpSlKVqI4unpiaenJyNHjqRXr17873//k/1SOzg4MGjQIAYNGsSECRNYs2YNQ4cOLVCGKqpXr86dO3cKrL4vLj8/P5o3l9/kpVWrVvj5+dGvX7+3Kru0enJnH1dOzKRa02lY2PsQfnULp3f3p6XfIdnOq8pEPjyKnaviTXHSkp5wZvfXuFTsQc3WvxL3PITQY1PQ0TOnjEfrwst9EIhlmdpo6RgpzePTYi62zo1kPxeWV3g7Ebf+JvToNHxazMSybA0ehG3m5Pa+tPn6KAbGBR9Xv+75/UDKeLRQeC41MYITO/riVrkXddovJvbpJUICJ6Gjb4FDucLn5z67fxhrhzqFfu612vyCncvrbcRYaV7h7QQf82fz0pH0HbEMj0r1OP73KhaMbcucDTewtHEs9NrLZ/dSvX5HhediIsNZML4djdsN4NuJG7l37QwbFg3ByNSKmo2UDzwAXD6zh/JVG6NnoPxzHzhuHZVq5f890jc0KbRM4d2ysrKShfoEqFq1GjNmzJB16jt37oyBgYEs1GdycjJeXhXo06ePXKjPN3l7e7Nw4UIWLFhAdHQ0ZcqU4bvvBjN+vHzIzuTkvCcNurq6ODk5KS1PTU2NWbNmMXfuXJ4+fcqVK1eKnEnwpunTZ+Dm5s727dsIDw9HW1ubsmXL4utbj9q1xaZhwtsr1iZfy5cvx9fXl1q1ajF9+nQqV65MTk4OgYGBrFixQuFTAXd3dyIiIti6dSs1a9YkICBANqoPeY/FfvjhB7p27YqLiwtPnz7l4sWLdOmS98d6xIgRtGnTBk9PTxISEjh27BheXl4lfsE//fQT7du3x8HBgW7duqGurs7Vq1e5du1aoVF93mRhYYGFhYVcmpaWFra2tnJxh/9L7l1eh7N3N1wq5o1KVGk8mRePT/Hw6mYq1le+XXluTiYvIk7j7TtS4fmHV/9E38ieKo0nA2Bs7k7Ci2vcDfm9yM7/84dHKOPeqtA8WjrG6BpYFZpHeDduX/od18o9cKvSC4DqzaYSFX6S+6EbqdJovNLrcnMyiHp0kkoNRis8fz9sEwZGZajebCoAJhYexEdd5fbF1UV2/p/eO4yDZ5tC82jrGqNn+H73AhHyHNy2kEZt+9O4/QAAvhy6iGsXD3NszwrZDr6KZGVmcP3iYbr0n67w/LG/V2Jh7ciXQxcBUMbJi/A7l9jv/4sKnf+/qdHgs0Lz6BuaYmrxaexY++efBZ+OdunSVRYXvyivdgV+pU6dOty//0AurWzZsgXSFBk+fDjDhw+X/dyihfJIPC1atKBFC8UDCK8oqrN9+w60b99BLq1rV/nXevFiXgTCoUOHYWdnV2gdLVu2omXLgv8uKXpf582bz7x58+XS1NXV6devX6EDiYo+D0XvsyAoUqxQny4uLly+fJkmTZowevRoKlasSIsWLTh69KhceKvXderUiZEjR/L9999TtWpVzp49y+TJk2XnNTQ0iIuLo0+fPnh6etK9e3fatGnDtGnTgLz5+EOGDMHLy4vWrVtTrlw5li9fXuIX3KpVK/bt20dgYCA1a9akTp06/Prrr4V+kxdAkptFYvR1bJzqy6VbO9UnLvJyoddGPzmLrr4lxhaeCs/HR4Vi/Ua5Nk4NSIi+hiRX+R4LWRnJxD67hJ1rs0LrvxI0jb0ra3BsS2ceXv0TqVRSaH6hZHJzs0iIuoatc0O5dFuXBsQ+Cyn02hePz6BrYIWJpeIvznHPL2Pr0kAuzc6lEfFRV4toI0nEPr2IvXvhHYKQwMnsXFKFw3+0537oRtFG3pOc7Cwe3QmhYk35Dlylmi24d+NcodfevHwUE3Nbyroojqx2/0YwlWrKf86VarXi0Z1L5OQobyNpKYncuXqKavUUP1F45Y/FQxnc0Yop39bi2J6VSCSijXxKLlw4j6enJ19//fWHvhVBeGvFGvkHsLOzY+nSpSxdulRpnjdXmc+bN4958+bJpb3aBVdbW5stW7YoLauw+f2NGzeWq8vU1LRA3V999RVfffWVXFqrVq1o1Ur5aHFJV8k/evSoRNd9DDJfJiCV5qKrbymXrqtvyYv0mEKvff4gEHs35Z2vjLQYbJwKliuV5JCZkYCegeIR2ahHQZhYeqJvpHzKUYW6I7F28EVDU5foJ2e5enI2mS/j8ar9faH3LBRfVnp8XhsxkP8sdfStyEgrvI08vXeYMu7KR/Qy0mLQ0Zd/eqNr8E8beRmPnqGNwuuePzyOiVU5DAqZllap/hhsnOqhoanLi8dnCA2aSebLBLx9RRSvdy0lKRaJJBcTM/nPy9jMhqT4qEKvvXxmj9IpPwCJ8VFUeqNcEzMbcnNzSE2KxdRC8WjtlfP7cXCthIW1g9Kyu/SfTgWfZmhr63Hz8lH+XDGGlKRYOvUp2WZQwsdnz56/P/QtCMI7U+zOv1BymZmZZGZmyqXl5mSioanzge6oJN6MbCRVkPbaWamUqIfHqNV2cbHKlfJPdKlCyo58EKh0HcErr3fyTa3zdl68dX6J6Py/VwraSCERsaRSKc8fHMG3g/JdOKFgEflf0pWX/ezeYcoUMer/eiffzCZvVPnG2cWi8/8+FfwwC/1dl0qlhJ7dx5CflA8U5RX7xt8RFdrI5TN/U823g9LzgFwn38mjKgC7/5ghOv+CIHyUijXtR3g7c+bMwcTERO64c/HjiBiho2eGmpoGGW+M8mekxxV4GvC6hKgrSCTZWNorX/Cka2BVoNzM9DjU1DXR1jVVeI0kN5uoxyexL6Lz/yZzu6rkZKWSkSY2QHnXtPXN89pI2pufZWyhbSQ+MgxJbjaWZWsqzaNrUPDpwas2oqNnpvAaSW42UeEnCn2ioIiFfTWys1KKfFohFJ+RiSXq6hoFRvmTE6MxNlf89Abg4a0L5ORk4VmpvtI8pua2CsvV0NDE0MRC4TU5Odlcu3CQ6vWUR5lSxK1CHV6mJZMU/6JY1wmCIJQGovP/L5owYQJJSUlyR7magz70balEXUMbU+uKREeckUuPjjiDhV11pdc9f3gEW5fGqKlrKM1jblutQLkvHp/GzLoS6hpaCq+JeRqMto6xbDRfVYnRN1HX0BERf94DDQ1tzGwrEfVIPmxj1KNTWJbxUXrd0/uHsXdrinohbcTCvrqCck9ibltZaRt5EXEOLV1j2Wi+qhKib6ChqSMi/rwHmlraOJfz4fqlQLn065eO4OGtPITn5TN7qFqnHeoaytuIu3cdrl86Il/uxcM4l6uBpqbiNnIr9Dj6hqay0XxVPb4Xipa2LvqGpsW6Tii+3r174+7uxtixyoNKCIJQPGLaz79IR0cHHR35KT4amh/PCLRH9f5cPDQGM5tKmNtVI/zaVtJTnuNSubfSayIfHqFCnRGFlutauTcPrmzkyolZuFTqQXxkKI9ubKN2m0VKr3n+8GiRC32fPzxKZloM5nbV0NDUJeZpMDfO/oJLpZ4f2VSrj0f5GgMIDhiJuW1lLMpU50HYn6QnP8e96pdKr3l+P5CK9UYVWq571S+5F7qB0GPTca3Si7hnl3l41Z+6HZSvCXp+P7DIKT/P7geSkRaDhb0PGpo6REec49rJ+bhV6S3ayHvSuttIVs3ug0u5Grh71yVo72riXkTQtKPygZDLZ/byeb+phZbbtOMgAnctY/OyUTRuP5D7N85xYv86Bk9Wvq9HqApTfkLP7iUxPgqPCnXR0tHjVuhxtq+dRJMOA9HSFm1EUCwmJoYFC+Zz/PhxUlJScXR0pHfvXvTt+1WR1z56FM78+fMJDg4mIyMDNzd3Bg4cQIcO+WteFi9ezJIlvym8/vbtO3I7KAvCm0TrEFTmUK49WRmJ3ApeQkZ6DMYWHtTrtFZp/PbUxMekJj7GxqmBwvOvGJg4UK/zWq6emMXDq5vQNbCmauOfCg3zGfnwCD4t5hZarrq6Jg+ububqydlIpRIMTByoUHcEblX8in6xQok4enUkMyOR62cXk5EWjYmlJw27bsDApKzC/CkJj0hJeIztazH2FTE0daRRl7zO/73QP9AztKF6s6mFhvl8dj+QWm3mKz0PoK6uxb3QjYQem4EUCYYmjlSsPwqP6n2LfrFCidRp2oPU5Dj2bJhBYnwkZV0qMnpuAJa2iiOuvXj2gOhn96lUs/CQvlZ2Loz5OYDNy0ZxdPdyTC3s8Ru6uNAwn5fP7mXA2LWFlquhocXR3SvYsmw0EqkEaztXPu83jeadhxT9YoX/HIlEwpkzZ/Dy8sLSUvF0xrS0NHr16smjR4/Q1dWlTBl7Hjy4z4wZM4iNjWX06DFKy4+KiqJbt+4kJMRjaGiItbU1N2/eYOTIkaSlpdOzZ0+5/GZm5jg6yu+P8ebaF0F400fR+V++fDnz588nMjISb29vFi1aRIMGijuUO3fuZMWKFYSFhZGZmYm3tzdTp04tEN1n0aJFrFixgoiICCwtLenatStz5syR2+W3qHqlUinTpk1j9erVJCQkULt2bZYtW4a3d/GmGXxM3Kp8iVsV5aO4r3v+8AhWDnXR1C58t2QAq7K1afaFatEUEqKvk5OZilWZWoXms3VuJLe5l/Dv8KjWB49qfVTK++x+IDZOvmip0EasHevQ6qv9KpUbH3WN7KxUrB0K3xDHzrUxdq6NVSpTeHeadx5M886DVcp7+cwevKo3RVffsMi85as2YsaawsPKvvLo7mUy0pIpX7XwvxGVa7emcu3C9xv5L2rUqCHPnj3jm2++JTU1lYCAfeTm5tKxYycmTpxY4Cn2K5mZmdSpU5uUlBQmTZrEV1/lxap/+vQpjRvnvdfr1v2PGjVqMGrUSG7fvk1cXBw5ObnY29vRoUMHBg8egra2tsLyXy9n06bN1KlTR+5+hw4dJtsX4MWLFyxc+CsnT54kISEBW1tbunTpwqBB3xV7ZPzBgwfs3LmT3bt38+JFFH//vVdp53/Lli08evQINTU1tm/fQfny5Zk9ezbr1q1lzZo19OnTFysrxXvPrFq1koSEeAwMDDl06DA2NjYMGTKEQ4cO8ssvC/j888/l3psmTRoX2CdAEIpS6uf8+/v7M2LECCZOnEhoaCgNGjSgTZs2REREKMx/8uRJWrRowf79+wkJCaFJkyZ06NCB0NBQWZ7Nmzczfvx4pkyZwq1bt1i7di3+/v5MmDChWPXOmzePX3/9laVLl3Lx4kVsbW1p0aIFKSkp7+8N+YjoG9pSvuZ377xcqSSXKk2mKJ3rLXw89I1s8aqtWiewOKTSXKo3nybayH+AuVVZOnyhfIO4ksrNzcFv+G9K1wMIedav/x8HDuzH2NiY1NRU/vxzMwsWLFCaX0dHh7Zt857I7dsXIEvft28fADY2NtSrV4+MjAyOHDlCRkYGzs4uWFiY8/jxY5YuXcqvv/7y1vcdHx9P165d2L59O2lp6bi5uREZGcmiRYuYNGmiSmUkJSWxefNmunTpQqtWLVm1aiVmZqaMHTu20L2BTp3K2+TM2dmZ8uXLA9C6dd4AZE5ODufOKd/T4uTJvGurVauGjU3eIvhXg5cJCQlcv35NLv+hQ4fw9q5A3bp1GDDga27cuKHSaxM+baW+8//rr7/y9ddfM2DAALy8vFi0aBEODg5KNxVbtGgRY8eOpWbNmnh4eDB79mw8PDzYu3evLM+5c+eoV68evXv3xtnZmZYtW9KrVy8uXbqkcr1SqZRFixYxceJEPv/8cypWrMiGDRtIT09XuIvfp6isZzssyyiP4FJS5rZVcPIqfDdO4ePgWL4D1g6133m5FnZVcfEufFdX4eNQu0l3ylUufOpgSbh51aJeSzEFsCj29vYcPx7E8eNBdOiQtz5i06ZNhQ5yff755wCEhYXy7NkzAAIC8r4IdOrUGQ0NDQwNDTlw4CDBwefZu3cvp0+foVOnzkD+F4W3sWnTJiIjI7G0tOTYsWPs2xfAkiV5+xPt2LGj0H15bty4wdChQ6lbtw5TpvxEXFwsgwZ9x/79B9i3L4BvvvkWAwPlTysjIyMBsLDIjzJlYZH/lOD58+fFutbSMv//X79WS0sLKysrypYtS0xMDEFBQXTr1lV8ARCKVKo7/1lZWYSEhNCypXyovpYtW3L27FmVypBIJKSkpGBubi5Lq1+/PiEhIVy4cAGAhw8fsn//ftq1a6dyveHh4URFRcnl0dHRoVGjRirfmyAIgiCUZk2aNMXQMG/KVbt27QHIzs4iPDyc48eP06VLF9nx3Xd5i7Z9fGrIRsYDAvYRHh7OrVs3gfwvBhoaGuzZs4fmzZvh5eWFu7sbe/bsBiA6Ovqt7/vq1SsAxMbGUrt2Ldzd3WT3J5VKuXLlitJrjxw5woED+9HQ0GDGjBkcPx7EmDFj8PRUvEv9mxRtFPp6WnHn5L9e3KtrO3XqSHDweY4cOcqhQ4dZt+5/QF7/ZdOmTcUqX/j0lOo5/7GxseTm5soefb1iY2NDVFThu0G+8ssvv5CWlkb37t1laT179iQmJob69esjlUrJycnhu+++Y/z48SrX++q/ivI8fvy4eC9UEARBEEqhwjqq8fFxXLkSJvu5TJn84A+fffYZixYtIiAggMzMLACqVKmCu7s7kDe3feXKFbLrLC2tiIqK4sWLKCQSiUr3I5Hkyv7/zScRrzrbBgaGsjpfp6enWyDtlYYNG3LlShhnzpxh8uTJbN68mQ4dOtKhQwfs7ZXvFv6Kvb094eHhxMbmR/OLj4+T/b+dneLdpl+de/z4sdy1cXEFr3V2dilwz2ZmZiQkJBT6ZEEQoJSP/L+iaNdGVb45b9myhalTp+Lv74+1tbUsPSgoiFmzZrF8+XIuX77Mzp072bdvHzNmzCh2vSW9N0EQBEEo7Y4dO0pqaioA+/fnLbjX0tLGxcWFLl26cv/+A9lx4sRJ2XWfffY5ampq3Lhxgz//3CxLeyU0NAwAFxcXTpw4ybZtaay4BAAAEm5JREFU2/DyKl/k/bw+HSY8/BEAZ86cITk5WS5f5cpVANDU1GDx4sXs2LGDHTt2sGHDBr788gtatlQePapatWqsW/c/Tp48xdixY8nJyWX+/Hk0atSQnj17sHnzZjIyMpRe36BBQwAeP37MzZt5Tzz27z/wz/1o4uvrC8Aff/xBy5YtaNkyPyRxw4Z514aFhckGGQ8ezLvWzMyMihUrAbBq1Sq5Tv7p06dJSEgAoGxZxRH4BOGVUj3yb2lpiYaGRoFR/ujo6AIj7m/y9/fn66+/Ztu2bTRvLr8L7OTJk/Hz82PAgAEAVKpUibS0NL755hsmTpyoUr22trZA3hOA17/Fq3Jv/xUPrmzibsgaMtKiMbbwoEqjyYXO8Y95ep6rJ2eRHHcPXQMbytX4Btc39gjIykjmxtlfeH7/EFmZSRgYO1Cp4QTsXJoAcPvCCp4/OERK/EM0NHUwt6tOpfrjMDJ3lZVx6dAPPL61U65cc9uqNOm54x2+ekEV90L/4PaFVbxMjcbE0oNqTacUOsc/OiKY0OPTSYq9h56hNV61BuFeTX5edlZGEldPzefp3QNkZSRjaOJA1SaTsHdrCsDN4KU8vXuQ5LgHaGjpYmnvQ5VGEzC2cJOVEbx/FI+ub5cr18KuGi389rzDVy+o4sju5ezfuoCkuEjKuHjzxfcLC53jfzvsBH8uH82z8BuYWtrTrucPNO0kv0dAWkoi29dO5NLJXaSnJGBp50LvwQuoUidvIerezXO4dHIXkRG30dLRw8Pblx7f/oydYzlZGavn9OP0oQ1y5bp51WbKCuWLNf+LoqOjadKkMYaGRjx5khfw4osvemNkVPhGiWXKlKFWrVqcP3+emJgYtLW1ad++vex8+fLlOH78GOHh4TRu3Ijs7BwyM5V3qF/R1dWlWrVqhIaG8vPPczhwYD9XrlxBXV1d7onBl19+yV9//cWLF1G0aNECd3c3UlPTiIqKJDs7W+6LiDI2NjZ88823fPPNt1y9evWfgcK9TJnyE9WqVaNCBcWbTPbq1YutW/Mi/nTv3g1bW1vZGoOBAwfKogQlJCTw8OFDuWu//XYQ+/YFkJAQT+vWrTA1NeXp06cAjB49RhbpJ2/h9Xzs7e3R1dXj4cMHAOjr68siLAmCMqW686+trY2Pjw+BgYF89ln+As/AwEA6dVK+HfuWLVvo378/W7Zskc3jf116ejrq6vIPPTQ0NJBKpUilUpXqdXFxwdbWlsDAQKpVqwbkzbU7ceIEc+cWHn/+v+DJnX1cOTGTak2nYWHvQ/jVLZze3Z+WfofQNy74WDQt6Qlndn+NS8Ue1Gz9K3HPQwg9NgUdPXNZPH9Jbhand/VBR8+C2u2Xomdox8uUSLlQobHPzuNa+UvMbSsjkeRy4+wvnN7VlxZ9DqGppS/LZ+PUkBot58l+FlFf/n0Rt/4m9Og0fFrMxLJsDR6Ebebk9r60+fqowr0hUhMjOLGjL26Ve1Gn/WJin14iJHASOvoWsnj+ublZBP31BTr6ltTrtBJ9IzvSU56jqZ0fBjL6yXncq/XFwi6vjVw7OY+gbV/Stv9RNLXz24idS2NqtcmPWqKuoTi0oPD+BB/zZ/PSkfQdsQyPSvU4/vcqFoxty5wNN7C0cSyQPyYynAXj29G43QC+nbiRe9fOsGHREIxMrWTx/HOys5g3piXGZtYMnbYNc6uyxEU/QU8/v7N6O+wkzTsPxqV8TSS5OWz7fRLzfmjFz+tvoKOX//emcq3WDBi3Tvazptan10b69OnLy5fp7N69GwMDQzp27MAPP4xV6drPP/+c8+fPA9C0aTNMTU1l5777bjBRUVEcPZr3ZKFLly7o6uqybNmyIsudN28+P/44gWvXrhEVFcW0adNYvHixbHEx5D0h2L59O4sXL+LkyZPcu3cPc3NzatSoQdOmhW8QqUjlypWpXLkyP/74I8eOHZN7AvEmAwMD/vxzyz+bfAXx7Nkz3Nzc6NWrV5Edc1tbW/766y8WLJjP2bNniY6OxsurAgMHDqRjx/xNvr777jv279/P/fv3iYmJoUyZMlSv7sP333+Pq6trITUIAqhJFa1MKUX8/f3x8/Nj5cqV1K1bl9WrV7NmzRpu3LiBk5MTEyZM4NmzZ/zxxx9AXse/T58+LF68WLawCEBPTw8TExMApk6dyq+//srq1aupXbs29+/f57vvvsPHxwd/f3+V6gWYO3cuc+bM4X//+58sslBQUBB37twpclTkla4jHxadqRQ6tuVzTK29qd4sf6rU4Q0tsXdrQcX6Bbdhv3ZqLpEPj9Ky72FZ2uWjk0iKuU2TnnkjsA+v/sndkDW07HNY5c56Znoc+1bXomHXLViVzYv7f+nQD2RlJuPbcdXbvMRSQUv74/3ScnhjR8xtKlKj5WxZ2v7fm1LGoyVVGhUM3RgWNJvn94/QdsAxWdrFQxNIjLlFiy93A3A/dCO3Lq6i3dfHVW4jGelx7F5ajaa9tsmeOgTvH0V2RjINPv/9LV5h6dC2reIN1D4GU7+rg7NHNb4alR+9bVyfCvjU70T3b+YUyO+/ahyXz+xl7h83ZWn/+2UQEQ+uMmV5XqCFY3tWst9/AT//cUvlMJ7JiTF839mGHxcHUb5K3rSL1XP6kZ6ayIhZu97mJZYKvmXDi32Norj5wn+Xm5v4wvApKdUj/wA9evQgLi6O6dOnExkZScWKFdm/f7+sAx4ZGSkXe3/VqlXk5OQwZMgQhgzJ34Gxb9++rF+/HoBJkyahpqbGpEmTePbsGVZWVnTo0IFZs2apXC/A2LFjefnyJYMHD5Zt8nX48GGVO/4fK0luFonR1ylX81u5dGun+sRFXlZ4TXxUKNZO9eXSbJwa8OjGNiS52ahraPH84RHM7aoRenwKkQ+PoKNnjkO5jpSr8S1q6hoKy83Oylvkpa1rIpce+/Q8+1bVREvHGMuytfD2HY2uvuINWYR3Lzc3i4Soa1R4I4a/rUsDYp8p3oQp7vllbF3kp3vYuTTi4TV/WRt59uAIlvY+XAqcxLP7gejqm+Po1Rmv2t+hrqyNZL5qI6Zy6dFPgtm1tBraOsZYOdSmcoOx6BqINvJvycnO4tGdENr3HieXXqlmC+7dUDy15v6NYCrVbCGfv1YrTu5fR05ONpqaWlw+uxf3CnX5Y9EQLp/5GyMTK+o270X7XuNQ11DcRl6mJgFgaGQul347LIghnW3QNzSlfJWGdBswC2Mza0VFCIIgfDRKfecfYPDgwQwerHgjoFcd+leCgoKKLE9TU5MpU6YwZcqUEtcLeYt9p06dytSpU4us878k82UCUmlugc60rr4lL9JjFF6TkRaDjVPB/FJJDpkZCegZWJOW9ISYJ+dwLN+Jep3Wkpr4iLDjU5FKcvGqM7RAmVKplKsnZ2NhXwMTy/y5ujbOjSjj2RZ9I3vSkp9y8+xCTu34kqa99qChqXhXSuHdykqPz2sjb3SmdfStyEhT3kZ09OV3vdQ1+KeNvIxHz9CG1MQI0pLO4lShM426ricl4REhgZOQSnKoWG9EgTKlUimhx6ZjWbYmplb5bcTepTGO5dqhb1yWtKQIrp3+heP+PWnZJ0C0kX9JSlIsEkkuJmbya6SMzWxIilcczS0xPopKb+Q3MbMhNzeH1KRYTC3siHn+kFtRx6jbojejfw7gxdN7bFj8PZLcHDr3/alAmVKplD+Xj8azUn3KulaUpVeu3ZpajbtiaeNETFQ4O9b+xJyRzZi++hJa2qKNCILw8fooOv9CafVmVCOpgjTl+aVI/0n9J10qQUffgurNZqGmroGZTSVepkVz79IahZ3/sONTSYq5TaPu/nLpDuXyF5WZWJbDzKYSB9Y2JOpREGXclUd4EN4HBW2kkGhYb57Kn5WY30Z09S2o2epn1NU1MLetzMvUF9y+sFJh5z/kyGQSY27T/Av5xd6OXvlzZ02tymFuW5m9K315/vAYDp5tVHxtwjtR8EPP/5ugMHvBCGv/nAFAIpVgZGZN/9GrUdfQwKWcDwlxz9m/dYHCzv8fi7/nyYOrTFpySi69TtMesv8v61oRl3I1GNnDmbDgAGo2LHqx6Mfu9cg9giD8t4jOv1BsOnpmqKlpkPHGKH9GepzSqTW6BlYF8memx6GmrimbjqFrYI2auqbcFB9jMzcy0mOQ5GbJLcgMOz6VyIdHaNRtK/pGymMmA+gZWKNvbE9qwqNivErhbWjrm+e1kbQ3P/PYwttIgfx5bURHz+yfPNaoa2jKTfExtnAnIy2G3NwsNF5rIyFHfuLZ/UCa9dpWdBsxtEHfuAypCcWfGy2UjJGJJerqGgVG+ZMTozE2VxwxzdTcVmF+DQ1NDE3yFmCaWtihoaElN8XH3smLpPgocrKz5Bbt/rF4KKFn9jLxtxOYWxe+dsLUwg5LGydePL1XrNcpCIJQ2nwUcf6F0kVdQxtT64pER5yRS4+OOIOFXXWF15jbViuQ/8Xj05hZV5It3LSw9yEt8TFSaX64tpTE8H86fHn/YEulUkKPT+XZ/cM06LIJAxOHIu8382UCL1Mi0TWwKjKv8G5oaGhjZluJqEfyo6lRj05hWcZH4TUW9tUV5D+JuW1lWRuxLFuDlIQ32kj8Q3QNrGUdf6lUSkjgZJ7ePUDTHlsxNC0YNeZNmS8TSE+JRNdAzOf+t2hqaeNczofrlwLl0q9fOoKHd12F17h71+H6pSPy+S8exrlcDdniXs+KvkQ/uy8X9jHqyV1MLexkHX+pVMofi74n5NQuxi88ipWd/IZJiqQkxREf/QRTi8K/SAqCIJR2ovMvlIhH9f6EX/+LRze2kRx/nysnZpKe8hyXf+L2Xz89n4uHRsvyu1buTXryM66cmEVy/H0e3djGoxvb8PQZIJcnKyORK0HTSUkIJzL8OHcursCtypeyPGHHp/Dk1m5qtVmIlrYhGWkxeaO+OXnxoXOy0rh6cjZxzy+TlvSUmCfBnP17IDp65ti7t/yX3h0BoHyNATy8upWHV/1JirvH5aPTSE9+jnvVvM/zyomfCQ74f3v3Ftr0GcZx/CeINms7ldp6WBket3moyDwwZbqtKqhIdWzFAx6Guk3xQltEUXohZW7o2NbhARUsCMYTiKDYG1EXEVvBmlJIK2aWthrbJk2bxqRJ00q9aEkXDBQGnc3e7+f6vQgkhC/vP3mePdHzU2ZvVNDvkv1Oodq8TtVUXlZN5WV9Mu+Hf5zZpEioVY9vH5K/pUYvn91WVdkJTf10S/RM+a0C1VZd04JVxzR0WLJCAbdCAbe6Ons+I52RoOx3f1Kzq1yBtudqqi/VvatbNdwySpm9Y2fx31iemyfbzbOylRTLVVct6/E8eZvqlZ3TM7f/ypkDOv1z33ubnbNDzU11sp7Il6uuWraSYtlKirVybd93TfbqnQr4vTp/bLcanj9VRelN3bD+oqVr+v6/da5olx7csmpngVVJllT5vI3yeRsV6QhJksLtAV08uVdOR6k8DbWqtv+lPw7mKGXEaM1Z1Df+GQAS0aAf9fl/l6ijPqXeJV+Pzijc7tH7aVM1a3FBzLjNoN+lL3IvRM97XjxUpe2w/C1OJSVn6OO5P7615Mv78rEq7x2Wz1MlS8pYTZiRGzPt52rRZMUzZ9kRTZjxrV53hfXg+g61eRyKdLySJTld6ZmfafrCPL2X2v9a9sEmkUd9Sj1LvqofnlI46NaI0R/FLPkqK8lXsO2Flqy/Ej3vri/rjf+nsqSMibvkq9lVLvudQrW6q2RJHaNJWetipv1cOhr/pn/+it80KStXXZ1h3b+2Xa1uhzrDfiWlZCjjwwXK+nyvkuPsqBjsEnnUp9S75Ovir/K1NChz4kxt2PV7zLjN5sZaHfzzbvT8kwpbT/zXOjQybbxWrd/31pIvp6NUF47nq/7vCo1K/0CLV26Nmfaz+cv4917f7y/WohXfKdIRUlHB16pz2tUe8Glk2jhNm/2VvtlWqLSM/p82Djb/ZtQnzMKoT7MQ/+9YIsc/Bl6ixz8GXqLHPwYe8Y/+EP9m4Wc/AAAAgCGIfwAAAMAQxD8AAABgCOIfAAAAMATxDwAAABiC+AcAAAAMQfwDAAAAhiD+AQAAAEMQ/wAAAIAhiH8AAADAEMQ/AAAAYAjiHwAAADAE8Q8AAAAYgvgHAAAADEH8AwAAAIYg/gEAAABDEP8AAACAIYh/AAAAwBDEPwAAAGAI4h8AAAAwxJDu7u7ud/0iAAAAAAw8bv4BAAAAQxD/AAAAgCGIfwAAAMAQxD8AAABgCOIfAAAAMATxDwAAABiC+AcAAAAMQfwDAAAAhiD+AQAAAEO8AUcYazZrnELQAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -473,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "id": "0be31bf2", "metadata": {}, "outputs": [ @@ -483,13 +534,13 @@ "
" ] }, - "execution_count": 5, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -511,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "id": "4f1e9e75", "metadata": {}, "outputs": [ @@ -521,13 +572,13 @@ "
" ] }, - "execution_count": 24, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -549,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "a9919cda13fadd3", "metadata": { "ExecuteTime": { @@ -564,13 +615,13 @@ "
" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEhCAYAAAAQxzQXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADmu0lEQVR4nOzddVhVSR/A8e+lBWmQUFIMQJSwsAPFjrVb11hdYxX7de2OtbtdN3R3bV0TMVbBAEEUExUsRFq6zvsH68UrIaKuNZ/nOc/jnfidOXfPXubOnTMjkyRJQhAEQRAEQRCEz5LSx26AIAiCIAiCIAjFJzr0giAIgiAIgvAZEx16QRAEQRAEQfiMiQ69IAiCIAiCIHzGRIdeEARBEARBED5jokMvCIIgCIIgCJ8x0aEXBEEQBEEQhM+Y6NALgiAIgiAIwmdMdOgFQRAEQRAE4TMmOvTCRyWTydi7d+8HP8+pU6eQyWTExcXJ0/bu3YudnR3KysqMHDmSrVu3oqen98HbInwdxL0tfInEfS0InybRoRc+mIiICIYPH46trS3q6upYWFjQunVrvL29//O21KpVi6dPn6KrqytP++677+jYsSMPHz5k5syZdOnShdu3b7/3c2dkZDB+/HicnJzQ0tLC3Nyc3r178+TJk/d+LuG/Ie7tXNOmTaNixYpoaWmhr6+Ph4cHFy5c+CDnEj4scV/nb/DgwchkMpYuXfrBzyUIxaXysRsgfJkePHhA7dq10dPTY+HChTg5OZGRkcHRo0cZOnQoN2/e/E/bo6amhqmpqfx1YmIikZGReHp6Ym5uLk8vUaLEO50nIyMDVVVVhbTk5GQCAgKYPHkyVapUITY2lh9++IE2bdpw+fLldzqf8N8T97ai8uXLs3LlSmxtbUlJSWHJkiU0bdqUu3fvYmxs/E7nFP474r7O3549e/Dz81M4pyB8kiRB+ACaN28ulS5dWkpMTMyTFxsbK/83IO3Zs0f+ety4cVK5cuWkEiVKSDY2NtKPP/4opaeny/MDAwOlBg0aSCVLlpS0tbUlV1dX6dKlS5IkSdKDBw+kVq1aSXp6epKmpqbk4OAgHTp0SJIkSfLx8ZEAKTY2Vv7vVw8fHx9py5Ytkq6urkJb9+7dK7m4uEjq6uqSjY2NNG3aNCkjI0Oh/atXr5Zat24taWpqSlOnTi3S+3Px4kUJkMLCwopUXvh0iHu7cPHx8RIgnThxokjlhU+DuK/zevTokVS6dGnp2rVrkpWVlbRkyZKiv6GC8B8TI/TCexcTE8ORI0eYPXs2WlpaefILm/Oora3N1q1bMTc3Jzg4mIEDB6Ktrc24ceMA6NGjBy4uLqxZswZlZWUCAwPloytDhw4lPT2dM2fOoKWlRUhICCVLlsxzjlq1anHr1i0qVKjArl27qFWrFgYGBjx48ECh3NmzZ+nduzfLly+nbt26hIaGMmjQIACmTp0qLzdt2jTmzZvH0qVLUVEp2v9S8fHxyGQyMf/zMyPu7cKlp6ezfv16dHV1qVKlyhvLC58GcV/nlZ2dTa9evRg7diyOjo6Fvn+C8En42N8ohC/PhQsXJEDavXv3G8vy2mjP6xYuXCi5ubnJX2tra0tbt27Nt6yTk5M0bdq0fPNeHe2RpJwRJ/4d5Xnp9dGexo0bS3PmzFGIs337dsnMzEyh/SNHjiyw/flJSUmRXF1dpe7du79VPeHjE/d2/g4cOCBpaWlJMplMMjc3ly5evFikesKnQdzXec2ZM0dq0qSJlJ2dLUmSJEbohU+eGKEX3jtJkopdd+fOnSxfvpzQ0FASExPJzMxER0dHnu/l5cWAAQPYvn07Hh4edOrUibJlywIwYsQIhgwZwrFjx/Dw8KBDhw5Urly52G0JCgri3LlzzJ49W56WlZVFamoqycnJaGpqAlC1atUix8zIyKBz585IksSaNWuK3Tbh4xD3dv4aNmxIYGAgUVFRbNiwgc6dO3PhwgVKlSpV7DYK/x1xXyvy9/dn2bJlBAQEIJPJit0eQfgviVVuhPeuXLlyyGSyt36IytfXlx49etCiRQsOHjzIlStXmDRpEunp6fIy06ZN4/r167Rs2ZKTJ0/i4ODAnj17ABgwYAD37t2jV69eBAcHU7VqVVasWFHs60hMTGT69OkEBgbKj+DgYO7cuYOGhoa8XH4/UefnZWc+LCyM48ePK/zREz4P4t7On5aWFnZ2dtSsWZNNmzahoqLCpk2bit0+4b8l7mtFZ8+eJTIyEktLS1RUVFBRUSEsLIzRo0djbW1d7PYJwgf1cX8gEL5UzZo1e+sHrBYtWiTZ2toqlO3fv3+eh55e1bVrV6l169b55k2YMEFycnKSJKl4P9/WqlVL+vbbbwu+SOnNPz+/lJ6eLrVr105ydHSUIiMj31he+HSJe/vNbG1ti/wQrfBpEPd1rqioKCk4OFjhMDc3l8aPHy/dvHmz0LqC8LGIKTfCB7Fq1Spq165N9erVmTFjBpUrVyYzM5Pjx4+zZs0abty4kadOuXLlCA8PZ8eOHVSrVo1Dhw7JR3IAUlJSGDt2LB07dsTGxoZHjx5x6dIlOnToAMDIkSNp3rw55cuXJzY2Fh8fH+zt7Yt9DVOmTKFVq1ZYWlrSsWNHlJSUCAoK4tq1a8yaNavIcTIyMujYsSMBAQEcPHiQrKwsIiIiADAwMEBNTa3YbRT+e+LezpWUlMTs2bNp06YNZmZmREVFsWrVKh4/fkynTp2K3T7hvyfu61yGhoYYGhoqpKmqqmJqakqFChWK3T5B+KA+9jcK4cv15MkTaejQoZKVlZWkpqYmlS5dWmrTpo3CCAuvjZaMHTtWMjQ0lEqWLCl16dJFWrJkiXwEJi0tTeratatkYWEhqampSebm5tKwYcOklJQUSZIkadiwYVLZsmUldXV1ydjYWOrVq5cUFRUlSVLxRnskSZKOHDki1apVSypRooSko6MjVa9eXVq/fn2B7c/P/fv38yy59vJ49fzC50Pc2zlSUlKk9u3bS+bm5pKamppkZmYmtWnTRjwU+5kS93XBxEOxwqdOJknv8DSMIAiCIAiCIAgflXgoVhAEQRAEQRA+Y6JDLwiCIAiCIAifMdGhFwRBEARBEITPmOjQC4IgCIIgCMJnTHToBUEQBEEQBOEzJjr0widp1apVWFtbo6GhQY0aNbh48WKBZTds2EDdunXR19dHX18fDw+PQssPHjwYmUzG0qVLFdJjYmLo0aMHOjo66Onp0b9/fxITExXKXL16lbp166KhoYGFhQULFix4p+sUvj5vc29v3boVmUymcLy64yWAJElMmTIFMzMzSpQogYeHB3fu3FEoI+5t4UN7m/u6QYMGee5rmUxGy5YtgZy9O8aPH4+TkxNaWlqYm5vTu3dvnjx5ohBH3NeC8IqPvGymIOSxY8cOSU1NTdq8ebN0/fp1aeDAgZKenp707NmzfMt3795dWrVqlXTlyhXpxo0bUt++fSVdXV3p0aNHecru3r1bqlKlimRubp5nTeFmzZpJVapUkfz8/KSzZ89KdnZ2Urdu3eT58fHxkomJidSjRw/p2rVr0u+//y6VKFFCWrdu3Xu9fuHL9bb39pYtWyQdHR3p6dOn8iMiIkKhzLx58yRdXV1p7969UlBQkNSmTRvJxsZGvta3JIl7W/iw3va+jo6OVrinr127JikrK0tbtmyRJEmS4uLiJA8PD2nnzp3SzZs3JV9fX6l69eqSm5ubQhxxXwtCLtGhFz451atXl4YOHSp/nZWVJZmbm0tz584tUv3MzExJW1tb2rZtm0L6o0ePpNKlS0vXrl3Ls0lISEiIBEiXLl2Spx0+fFiSyWTS48ePJUmSpNWrV0v6+vpSWlqavMz48eOlChUqFOcyha/Q297b+W2c86rs7GzJ1NRUWrhwoTwtLi5OUldXl37//XdJksS9LXx47/qZvWTJEklbW1tKTEwssMzFixclQAoLC5MkSdzXgvA6MeVG+KSkp6fj7++Ph4eHPE1JSQkPDw98fX2LFCM5OZmMjAwMDAzkadnZ2fTq1YuxY8fi6OiYp46vry96enpUrVpVnubh4YGSkhIXLlyQl6lXrx5qamryMp6enty6dYvY2Ni3vlbh61LcezsxMRErKyssLCxo27Yt169fl+fdv3+fiIgIhZi6urrUqFFDHlPc28KH9D4+szdt2kTXrl3R0tIqsEx8fDwymQw9PT1A3NeC8DrRoRc+KVFRUWRlZWFiYqKQbmJiQkRERJFijB8/HnNzc4U/MPPnz0dFRYURI0bkWyciIoJSpUoppKmoqGBgYCA/b0RERL7tepknCIUpzr1doUIFNm/ezL59+/jll1/Izs6mVq1aPHr0CMi97wqLKe5t4UN618/sixcvcu3aNQYMGFBgmdTUVMaPH0+3bt3Q0dEBxH0tCK9T+dgNEIT3ad68eezYsYNTp07JHx709/dn2bJlBAQEIJPJPnILBaHo3N3dcXd3l7+uVasW9vb2rFu3jpkzZ37ElgnC+7Fp0yacnJyoXr16vvkZGRl07twZSZJYs2bNf9w6Qfh8iBF64ZNiZGSEsrIyz549U0h/9uwZpqamhdZdtGgR8+bN49ixY1SuXFmefvbsWSIjI7G0tERFRQUVFRXCwsIYPXo01tbWAJiamhIZGakQLzMzk5iYGPl5TU1N823XyzxBKMy73Nsvqaqq4uLiwt27d4Hc+66wmOLeFj6kd7mvk5KS2LFjB/379883/2VnPiwsjOPHj8tH50Hc14LwOtGhFz4pampquLm54e3tLU/Lzs7G29tbYaTydQsWLGDmzJkcOXJEYU4lQK9evbh69SqBgYHyw9zcnLFjx3L06FEgZyQ0Li4Of39/eb2TJ0+SnZ1NjRo15GXOnDlDRkaGvMzx48epUKEC+vr67+X6hS9Xce/tV2VlZREcHIyZmRkANjY2mJqaKsRMSEjgwoUL8pji3hY+pHe5r//880/S0tLo2bNnnryXnfk7d+5w4sQJDA0NFfLFfS0Ir/nYT+UKwut27NghqaurS1u3bpVCQkKkQYMGSXp6evLl+nr16iVNmDBBXn7evHmSmpqa9NdffykshfbixYsCz/H6KjeSlLMEmouLi3ThwgXpn3/+kcqVK6ewBFpcXJxkYmIi9erVS7p27Zq0Y8cOSVNTUyyBJhTZ297b06dPl44ePSqFhoZK/v7+UteuXSUNDQ3p+vXr8jLz5s2T9PT0pH379klXr16V2rZtm++yleLeFj6Ut72vX6pTp47UpUuXPOnp6elSmzZtpDJlykiBgYEKn+uvrlgj7mtByCU69MInacWKFZKlpaWkpqYmVa9eXfLz85Pn1a9fX+rTp4/8tZWVlQTkOaZOnVpg/Pw69NHR0VK3bt2kkiVLSjo6OlK/fv3yfCkICgqS6tSpI6mrq0ulS5eW5s2b9z4uV/iKvM29PXLkSHlZExMTqUWLFlJAQIBCvOzsbGny5MmSiYmJpK6uLjVu3Fi6deuWQhlxbwsf2tvc15IkSTdv3pQA6dixY3li3b9/P9/PdEDy8fGRlxP3tSDkkkmSJP2Xvwj07duXbdu28d1337F27VqFvKFDh7J69Wr69OnD1q1b/8tmCYIgCIIgCMJn6aPMobewsGDHjh2kpKTI01JTU/ntt9+wtLT8GE16Z+np6fmmvzp3720Ut54gCIIgCILwdfkoHXpXV1csLCzYvXu3PG337t1YWlri4uIiT8vOzmbu3LnY2NhQokQJqlSpwl9//SXPz8rKon///vL8ChUqsGzZMoVz9e3bl3bt2rFo0SLMzMwwNDRk6NChb+wwx8XFMWDAAIyNjdHR0aFRo0YEBQXJ86dNm4azszMbN27ExsZGvkSiTCZjzZo1tGnTBi0tLWbPng3AmjVrKFu2LGpqalSoUIHt27crnK+geoIgCIIgCIJQmI+2ys23337Lli1b5K83b95Mv379FMrMnTuXn3/+mbVr13L9+nVGjRpFz549OX36NJDT4S9Tpgx//vknISEhTJkyhf/973/88ccfCnF8fHwIDQ3Fx8eHbdu2sXXr1jdO6enUqRORkZEcPnwYf39/XF1dady4MTExMfIyd+/eZdeuXezevZvAwEB5+rRp02jfvj3BwcF8++237Nmzhx9++IHRo0dz7do1vvvuO/r164ePj4/COV+vJwiCIAiCIAhv8lHm0MfFxbFhwwYsLCy4desWABUrVuThw4cMGDAAPT091q1bh4GBASdOnFBY+mrAgAEkJyfz22+/5Rt/2LBhREREyEfy+/bty6lTpwgNDUVZWRmAzp07o6SkxI4dO/KN8c8//9CyZUsiIyNRV1eXp9vZ2TFu3DgGDRrEtGnTmDNnDo8fP8bY2FheRiaTMXLkSJYsWSJPq127No6Ojqxfv16e1rlzZ5KSkjh06FCB9QRBEARBEAThTT7aTrHGxsa0bNmSrVu3IkkSLVu2xMjISJ5/9+5dkpOTadKkiUK99PR0hWk5q1atYvPmzYSHh5OSkkJ6ejrOzs4KdRwdHeWdeQAzMzOCg4MBmDNnDnPmzJHnhYSEEBQURGJiYp51b1NSUggNDZW/trKyUujMv/T6Oug3btxg0KBBCmm1a9fOMz3o9XqCIAiCIAiC8CYfrUMPOdNuhg0bBuR0zF+VmJgIwKFDhyhdurRC3stR8x07djBmzBh++ukn3N3d0dbWZuHChVy4cEGhvKqqqsJrmUxGdnY2AIMHD6Zz587yPHNzcxITEzEzM+PUqVN52qynpyf/t5aWVr7XVVD6mxS3niAIgiAIgvD1+qg7xTZr1oz09HQyMjLw9PRUyHNwcEBdXZ3w8HDs7OwUDgsLCwDOnTtHrVq1+P7773FxccHOzk5hBL0oDAwMFGKrqKjg6upKREQEKioqec796q8IRWVvb8+5c+cU0s6dO4eDg8NbxxKKLy0tjWnTppGWlvaxmyII7424r4UvkbivBeHtfNQOvbKyMjdu3CAkJERhSgyAtrY2Y8aMYdSoUWzbto3Q0FACAgJYsWIF27ZtA6BcuXJcvnyZo0ePcvv2bSZPnsylS5feuV0eHh64u7vTrl07jh07xoMHDzh//jyTJk3i8uXLbx1v7NixbN26lTVr1nDnzh0WL17M7t27GTNmzDu3VSi6tLQ0pk+fLv5ACF8UcV8LXyJxXwvC2/moU24AdHR0CsybOXMmxsbGzJ07l3v37qGnp4erqyv/+9//APjuu++4cuUKXbp0QSaT0a1bN77//nsOHz78Tm2SyWT8/fffTJo0iX79+vH8+XNMTU2pV68eJiYmbx2vXbt2LFu2jEWLFvHDDz9gY2PDli1baNCgwTu1UxAEQRAEQRD+81VuBOFjSUhIQFdXl/j4+EK/SArC50Tc18KXSNzXgvB2PuqUG0EQBEEQBEEQ3s1Hn3LztbsXevdjN+GrkZaWxojhw3n86CFRr+wvILx/R+6U/dhN+GpkpKvRoscUtpxSQ1VN/OD6IZ08ev9jN+GrkZWZhn2NEfSZ9BhllaiP3Zwv3l9LbD92E4R3JKbcfGSiQy98iUSHXvgSiQ698KUSHfrPn5hyIwiCIAiCIAifMdGhFwRBEARBEITPmOjQC4IgCIIgCMJnTHToBUEQBEEQBOEzJjr0whfNz+8CZe3KkZCQIE87dvw4DRs1plz5CsycNavANEEQBEEQhM+BWLZSUDB23Dh2795Dt25dmTVzpkLe1KnT+OXXX/nmm/YsXLDgI7UwR736DXj8+DEA6urqGBkZUaVyZbp170Ytd3d5OVdXF/x8z6OtrS1P+/HHyXTs0IE+fXqjpaVVYJogfAiSJHFo+1TOHdlISlIctg616TpsNaVKlyuwztGdcwk8t4dnj26iqlYCW4datPt2HiZlKiiUu3fDlwPbfuTBzQsoKSlTuqwzw2YdQU29BABJL2L4Y/UIrl04gExJCefa39Bx8DI0SpSUx3h8/yo7Vw0j7PYlSuoa06DNMJp0Gvdh3gzhiyFJEiF+S3kQvJP0tAQMzd1waTQDbX2bItW/dWkt184txM65L1UaTJanpyY9J/jsPJ6F/0NmehLa+rZUrP49pcs1k5dJT40j0Gc6T++fRIaM0uWaUaX+ZFTUcj/L45/f5IrPVGKfXUW9hAFlnXtToep37+8NEISPTIzQC3mYmZlx8OAhUlNT5WlpaWnsP3AAc3Pzj9gyRSNH/oCf73lOHD/GooUL0NbRoXfvPqxavVpeRk1NDWNjY2QyGQBJSUlER0dTt25dTExMKFmyZL5pxZGenv5erkv4sh3/cwGn9q+g6/A1jF3qh5qGFit/bEZGemqBde4En6Fe6+8Zs8SX4XOOkZWZwYpJnqSlJsnL3Lvhy6ofm2Pv2oSxyy4wbvlF6rceikyW+zG/dUFPnoZfZ9icYwyedoC7187y+/LcTk1KUgIrJnliUMqK8Ssu077/Ag79Op1//l7/Yd4M4Ytx+/J6Qq9sw6XxTBp13Y2Kqib/7OlHVmbaG+vGRFzlXvDv6BpVzJN36egYXsTeo1ab9Xj0+htzu6b4/T2cuMjr8jIXD48iIfoOddtvo1bbjTx/fJEA70ny/Iy0F5zd0wdNndI07r4Pp7oTuOG3nHvBv7+fixeET4Do0At5ODo6YmZmxtGjR+VpR48exdzcHAcHB3ladnY2a9aspX6Dhjg4VqJlq9YcPnxYnp+VlcWECRPl+R5NmrJl61aFc40dN47vBg9hw8aN1HSvhVvVakydOo2MjIw3trOklhbGxsaYm5tTvXp15syexbChQ1m6dBn37t0DFKfc+PldoHIVZwB69upFWbtyBaYBXL58mS5du+HgWInadeoyfcYMkpOT5eevV78BK1auZPSYsVSp4sykH38scr3Vq9cwfsIEKldxpk7devy+Y4fCtT19+pQfRo7E1a0qlZwq07ZdewIDA+X5x4+foE2bttg7ONKgYUOWL19BZmbmG98z4eOSJAmfvcto1nUSVdzbUtqmMn3GbCM++glB5/cWWG/YrMO4N+mLuZUjZWyr0MtrC7GR4YTf8ZeX2bXOiwZth9O08wTMrRwxKVMBt3qdUVXL2UQtIvwGIZeP0OOHDdhUrIFdpTp0GrIc/9M7iIt+AsAln1/Jykin56hNmFs5UrVBVxq0Gc7JPUs+6PsifN4kSeLulS1UrDEU87JN0DWuSDXPRaQmPeNJ6LFC62amJ3HpyChcPeagqq6bJz/6aQBlnXtjYFqFkrqW2NcYhpq6DrGR1wBIiLnLs7AzuDWZg4GZM0alq+LcYCoPbx0kJfEZAOE395OdlUHVJvPQMSyPRYXWlHXuw52Aze//zRCEj0R06IV8derYkb927Za//vOvXXTs8I1CmTVr17Jn7x5mzpjBkcN/069fX7xGj+HChZwOcXZ2NqampqxYsZyjRw4zfNhQfvppMYcO/a0Qx8/Pj/DwcH79ZTsLF8xn1+7d7Hrl3G+jb98+SJLE8RMn8uS5urpw4njOH5fVq1bi53u+wLSwsDD6fdufZp5NOXToIMuXL8P/sj/Tpk9XiLlx4ybsK1Zk//59DBs6tMj1Nm3ejFMlJ/bv20fPHj2YMmWq/EtIUlIS3bv34NmzZ6xft5aDBw4waOBAsrNz9oC7dOkSY8aOpU/fPhw9cphZM2eya/duVq9eU6z3TPjvREfcJyE2ggouHvK0Elq6WFeowf2bvkWOk5IcD4CWtgEAL+IieXDrAtq6pVjkVZsJ3UxZMrYBd6/9I69z74YvJUrqYVW+qjytoosHMpkSD27m/D97/6Yfdk71UFFVk5dxcPPk2aNbJL+ILd5FC1+8pISHpCY/p5RFbXmaqro2BqbORD+9UmjdKz5TMbVpiIll7XzzDc1ceXT7EOmpcUhSNg9vHSArMw3jMjUAiHl6BVV1HfRNKsvrlLKsjUymRExEYE6ZiACMSldDSTn3vjaxqkti7D3SU+OLe9mC8EkRHXohX23btuHy5cs8fvyYx48f4+/vT9u2beX5aWlprFmzlnlz51GvXl0sLS3p2KED7dq2lY82q6qqMnLkD1R2csLCwoK2bdvSsUMH/v5bsUOvq6vLtKlTKVu2LI0aNaJhgwac9y165+ZVenp6GBoa8vjR4zx5ampqGBoa/ntOPYyNjQtMW7t2HW3atKZfv37YWFvj5urKlCmT2bNnL2lpuT8hu7u7M2BAf6ysrLCysipyvQb169OzZw+sra347rtB6Ovr4+fnB8D+AweIiYlh7Zo1VK1aFWtrK1q2bIGrqwsAy1esZPB339Hhm2+wtLSkTp06jBo5Ms8ov/DpSYiNAEBH30QhXVvfhITYZ0WKkZ2dza51o7B1qI25dSUAop7mfBn8+9fp1G42gKEzD2Nh58KKiR5EPr4jP7e2bimFWMrKKmhqG8jblRATgbaeYhltPROFtgvC69KSngOgrmWkkK6uaSTPy8/DWweIi7xOpdpjCyxTo8UKsrMyObDWjT0r7Anw/hH31msoqWcN5MyxV9c0VKijpKSCmoYuqUlR/5aJQkNTsW0vX6cmF9w+QficiIdihXwZGhrSsGEDdu3ajSRJNGzQAAMDA3l+WFg4KSkp9OnbV6FeRkYGDg728tfbt//Cn3/9xdMnT0hNSyMjIwN7e3uFOuXK2aGsrCx/bVzKmFu3bgOwevUa1qxdK887euTwG+fxS5IknzNfXDdu3uTWzZvs339AIW52djYPHz7Ezs4OACenSsWqV6Fi7sOMMpkMY2NjoqOjc2KE3MDBwQE9Pb1823bzxg38/f1ZvSZ3RD4rK4u0tDRSUlIoUaLEO1278P5cPPkrv68YLH/9/fSD7xxz56qhPHlwDa9FZ+VpkpQNQO0Wg3Bv2g8ACzsXbgWexPfYZtr2m/vO5xWEl8Jv7iPA+0f569ptN751jOQXTwg6PZO67X9GWUW9wHIhvovJSEug7jc/o1bCgCehx7lwaDj1O+9E16hCgfUE4WsjOvRCgTp17Mi06TMAmDZtqkJecnLOw3gbN6zHxMRUIU9NLednzQMHDzJ33jz+N3EiLi7OaGmVZMPGDQQFBSmUV1FRVXgtk8mQsnM6KN27d6NFixbyvFKlFEcPXxcbG0tMTAxlLMoU9TLzlZycRNduXenTu0+ePHNzM/m/NV/rPBe1nqqK4v96MhnyKTUaGhqFti0pOZkffhiBZ1PPPHnq6gX/YRT+e5VrtsG6Yg3568yMnF9pEmKfoWuQez+8iH1GmbJV3hhv5+phXLt4iFELT6NvnHuP6/wby8zSQaG8qaU9MZEPc8rom/IiPlIhPysrk+QXMejom/4bx5QXcYplXsQ9k9cXBAAz28Z4mOber9lZOQsCpCVFUUIr9zM6LTkKXWP7PPUBYp9dIy05Gu/f2sjTJCmLqMcXCQ3aTvvhN0hKeERo0Haa9DqMjmF5APSM7Yl6fInQoO24Np6FhpYxacnRCrGzszNJT41H499fDDS0jEhNjlIo8/K1hqZxcd8GQfikiA69UKB69eqRkZGBDKhXt65Cnp2dHWpqajx58pQaNWrkW9/fPwBXV1d69uwhTwsPf/hWbdDT0ytwpDo/W7dtQ0lJiSYeTd7qPK9zdHTk7t1QrK2t/pN6r6pQsQI7//iDuLi4fK/d0dGR+/fuv9M5hP+GhqY2Gpq5S6ZKkoSOvim3Ar2xKOsM5Kws8+DWBeq2HFxAlJx6f6wZTtD5vYyc74ORqeJSgIYm1ugamvPs0S2F9MhHt3GolrO8n629OymJcYTf8ceynBsAtwNPIknZ8i8dNhVrcmDbj2RlZqD87xftG1eOY1KmApra+u/2ZghfDFW1kqiq5a4GJkkSGprGRD48j16pnC+VGWkviIkIxLZy93xjlLKshUdPxemX/sfHo61flvJVByFTUiYr89+Vn2SKs4NlMmX5r1IGZi5kpCUQ+ywYfRMnAJ4/9EWSsjEwdc4pY+rK9fM/kZ2VgZJyzn0dGX6Okvq2qGnkfRBXED5HYg69UCBlZWWOHjnMkSOHFabEAJQsWZIBA/oze84cdu3eTVhYGNeuXWfbzz+za3fOA63W1lYEBwdz5sxZ7t+/z+IlS7h69ep7a19iUhLPnz/nyZOnXLx4kf9N+pFVq1Yz2mvUO3d2vxs0iICAAKZNm05ISAj3Hzzg+PETTJs2/YPUe1XrVq0wNjZm8JAhXPb3Jzw8nCNHjhAQkPNw2fBhw9izdy/Ll6/g9u073L17lwMHD/LT4sXvdM3ChyeTyWjY7geO7JjNVb/9PL4fzM8/9UHX0JwqtdrJyy2b4MGp/Svlr3euGsqlk7/Sb9yvqJfQJj4mgviYCNLTUuRxPTqM4dS+FQSc/YvIJ3c58PNknj26Sa2m/YGc0XqHqs34bdkgHty6SOj1c/yxZjhu9buiZ5gzja1aw+4oq6rxy9IBPAm7jv/pnZzau5xG7Uf9d2+S8NmRyWTYufTj5sVVPAk9QXzULS4dHYOGlgnmZZvKy53Z1ZO7gT8DOV8KdI0qKBzKKpqoaejJp9Jo69uipWfFFe8fiYkIIjEujNv+G4kM/wfzsjmDNjoGdphY1SPgxCRiIoKIenKZQJ9pWFRoRYmSOc9/WFZsg5KyKv4nJpAQfZuHtw5y98pWyrl++x+/U4Lw4YgReqFQr27I9DqvUaMwMDBg7dp1PHz4EG1tbRwdHfl+SM5IY7euXQkJCWHEDz8gk8lo3aoVPXv04PSZ0++lbUuXLmPp0mWoqapiZGyMs7Mz23/+GXf3mu8cu2LFivz+26/89NNiunbrjiRJWFpa0vKV6T/vs96r1NTU2LZ1C3PmzKV//wFkZWVhZ2fH9H+nPdWrV5cN69ezYuVK1q1fj4qKCmXL2tK5U+d3umbhv9Gk0zjSU5P4bfl3pCTGUdaxDkNnHkZVLXeqVdTTUJIScqcInD2U8xzJ0vENFWL19NqMe5O+ADRqP5LMjFR2rfci+UUMpW2rMGz2MYzNy8rL9x33C3+sHs7yiTmr2zjX/oZOQ5bL80to6TJ89lF2rhrG/OFVKaljRPPuk6nTYtCHeCuEL0j5qoPIzEwmwHsSGWkJGJpXpU77LQrz45PiwklPKfpqSUrKqtRuu4lr5xZyfv9AMtOTKalnRVXPhZjZ5P6/UL35EgJ9pnF2Vy+QySht1wznBlPk+arq2tRtv40rPlPx/q0t6iUMsK8xHFunbu/n4gXhEyCTJEl670FlMvbs2UO7du3ed2gFp06domHDhsTGxsqnJuzdu5cxY8Zw//59hg8fjrOzMyNHjiQuLu6DtqW47oXe/dhNEIT37sidsm8uJAifmZNH73/sJgjCB/HXEtuP3QThHb31lJuIiAiGDx+Ora0t6urqWFhY0Lp1a7y9vT9E+wpVq1Ytnj59iq5u7hy47777jo4dO/Lw4UNmzpxJly5duH379ns/d0ZGBuPHj8fJyQktLS3Mzc3p3bs3T548ee/nEgRBEARBEISCvNWUmwcPHlC7dm309PRYuHAhTk5OZGRkcPToUYYOHcrNmzc/VDvzpaamhqlp7soLiYmJREZG4unpqbC04bsu45eRkYGqquJKLMnJyQQEBDB58mSqVKlCbGwsP/zwA23a5KzfLgiCIAiCIAj/hbcaof/++++RyWRcvHiRDh06UL58eRwdHfHy8pJvipOf8ePHU758eTQ1NbG1tWXy5MlkZGTI84OCgmjYsCHa2tro6Ojg5uYm7xSHhYXRunVr9PX10dLSwtHRUb4x0alTp5DJZMTFxXHq1Cn5fO9GjRohk8k4deoUW7duzbNSyL59+3B1dUVDQwNbW1umT59OZmamPF8mk7FmzRratGmDlpYWs2fPznNNurq6HD9+nM6dO1OhQgVq1qzJypUr8f/3IUZBEARBEARB+C8UeYQ+JiaGI0eOMHv2bLS0tPLkF7a0oLa2Nlu3bsXc3Jzg4GAGDhyItrY248aNA6BHjx64uLiwZs0alJWVCQwMlI+IDx06lPT0dM6cOYOWlhYhISGULFkyzzlq1arFrVu3qFChArt27aJWrVoYGBjw4MEDhXJnz56ld+/eLF++nLp16xIaGsqgQTkPfE2dmrvW+rRp05g3bx5Lly5FRaVob1N8fDwymeytllkUBEEQBEEQhHdR5A793bt3kSSJihUrvvVJfvwxd0c5a2trxowZw44dO+Qd+vDwcMaOHSuPXa5cOXn58PBwOnTogJNTzvqytrb5P7ihpqYm33TIwMBAYSrOq6ZPn86ECRPo06ePPN7MmTMZN26cQoe+e/fu9OvXr8jXmJqayvjx4+nWrRs6OjpFricIgiAIgiAI76LIHfp3WQxn586dLF++nNDQUBITE8nMzFTo9Hp5eTFgwAC2b9+Oh4cHnTp1omzZnFUyRowYwZAhQzh27BgeHh506NCBypUrF7stQUFBnDt3TmEaTVZWFqmpqSQnJ6OpqQlA1apVixwzIyODzp07I0kSa9asKXbbBEEQBEEQBOFtFXkOfbly5ZDJZG/94Kuvry89evSgRYsWHDx4kCtXrjBp0iTS09PlZaZNm8b169dp2bIlJ0+exMHBgT179gAwYMAA7t27R69evQgODqZq1aqsWLHirdrwqsTERKZPn05gYKD8CA4O5s6dO2ho5K4Dnd+0ovy87MyHhYVx/PhxMTr/AVy4cIHadeq+ueBHUL9BQ86dO/exmyF8og79Mo2hzZUUjhkD7YtW99fpbF3Qq8D8gLN/MmOgPT+0KcHsIZW5dvHvAsu+yu/4Nn4aXfD/T7evnmLeMDd+aK3B1G/L4Xt8a5HiCl+XEN9l7FpaVuE4uq1oO3SH+C3n4hGvAvMf3f6bo9uasGeFPce3N+fpfZ8ixQ0L2cWpPwrej+P5Qz+8f23DnhX2HNnSkAfX/ypSXEH4HBR5hN7AwABPT09WrVrFiBEj8nR4C9qm/vz581hZWTFp0iR5WlhYWJ5y5cuXp3z58owaNYpu3bqxZcsW2rdvD4CFhQWDBw9m8ODBTJw4kQ0bNjB8+PCiNl2Bq6srt27dws7Orlj1X/WyM3/nzh18fHwwNDR855ifi/T0dNTU1N5Y7smTJworDhXHiRPeNG7UqEhls7KykMlkKCl9+E2Qb968SXx8PNWrVy9S+ZerJb2P90T4fJhZOTJ8znH5a2Xlon3sBvvup0nn8fnm3Qs5z5Z53WnTbw5O1Vtx6dRvrJ/Zngkr/DG3rlRo3Kt++6lcs3W+eVER91kzpRV1Wn5H33G/cDPQm9+WDkTXwAwHN88itVv4eugYlqPuN9vlr2VKyoWUzvU09AQVqn2Xb170E38uHh6JY+0xmNk24uHN/fgeGELj7vvkO8gW5EnoCcxsG+eblxT/kHP7BmBbuTvVmi8mMvw8ASf+h4ZWKUyt6xWp3YLwKXurXs+qVavIysqievXq7Nq1izt37nDjxg2WL1+Ou7t7vnXKlStHeHg4O3bsIDQ0lOXLl8tH3wFSUlIYNmwYp06dIiwsjHPnznHp0iXs7XNGsUaOHMnRo0e5f/8+AQEB+Pj4yPOKY8qUKfz8889Mnz6d69evc+PGDXbs2KEwz78oMjIy6NixI5cvX+bXX38lKyuLiIgIIiIiFH59+FJ0796DadOmM3PWLKpWq07ffgVvmf3ixQv++ONPunbrRqvWbRTyLvv70717DxwrOeHi6kbfvv2Ij48v9NwnvL1p3Dj/D+m/du3C2cWVEye88fRshr2DY7H2AkhISGDSjz9SvUZN7B0cada8BSdPniy0zvETJ6hXr16eJU1fKmtXjl9//ZVBg76jklNlVq/OmY5Vv0FDevXuw549e0lJSXlj2/7880+aNWuOvb0DNd1rMW3a9Le+PuHjUVJWQdfAVH6U1DV6Y53Y5w95GnYdB7dm+eb77FuOQ9VmNOk4FlNLe1r3nolFWVdOH1hZaNyM9FRuBBzDqWabfPP/ObQWQ1MbOgz8CVNLexq0GYZLnY6c3LP0jW0Wvj4ymQoaWsbyQ72EwRvrJL94QkLMHUys8u9E372yFRPrelSoOggdAzsca3mhX8qR0KDt+ZZ/KSszjWfh/2Bm65Fv/r2rv6GlW4bK9f6HjoEdds69KV2uGXevbH7zhQrCZ+Ct1qG3tbUlICCA2bNnM3r0aJ4+fYqxsTFubm4Fzh1v06YNo0aNYtiwYaSlpdGyZUsmT57MtGnTAFBWViY6OprevXvz7NkzjIyM+Oabb5g+PafTkpWVxdChQ3n06BE6Ojo0a9aMJUuWFPuCPT09OXjwIDNmzGD+/PmoqqpSsWJFBgwY8FZxHj9+zP79+wFwdnZWyPPx8aFBgwbFbuOnaveePXTv3o0/du7Ik5ednc358+fZvXsPR48dw8TEhHZt27Jg/nx5mZCQEHr16k2njh2ZPPlHlJVV8PPzIysrq8Bz3r59h5joaNzdaxZYJjU1lXXr1zNn7hz09fQwNDRk3759/Dh5SoF1ADZv2ki1atXIzs7m22/7k5iUxOKfFmFpacndu3dRUi58tMnb+yTfflv4g9PLlq9g3Ngx/PjjJPlqSUePHGb37j0sXrKEqdOm0bx5M75p357q1asjk8kU6v/666/MnjOXsWPH0KB+fV68eIG/v3+h5xQ+Lc8f3+F/PUqjoqaBTUV32vabg0Epy0LrXPXbT7nKDSihlf8Uvvs3fGnUfpRCmr1bU6767is07q1Ab/QMS2Nqkf/iBvdu+lHRWfHLs71bU/5aNyrf8sLXLTHuAYc2uKOkrI6hmQuVao9FU6fwXx+f3vPGqHQNVNW1882PjrhCOZf+CmkmVnV5Eno83/IvRT48T4mSJugY5L9LdUzEFUpZ1H4tbj2CTs8sNK4gfC7eqkMPYGZmxsqVK1m5suCRoNcfoF2wYAELFixQSBs5ciSQszrN77//XmCswubLN2jQQOFcenp6ec7dt29f+vbtq5Dm6emJp2fBPx8X5QFga2vrt35QOC0tjbS0tDxp6urqbxXnY7G2smLCeMUpAPfv32fXrt3s2Zsz2tyiRXN+/nkbbq6ueeqv37ABJycnZszIHWEuX75cnnKvOnHiBHXr1i10ek9GRgYzpk9T+OWmcePGVKniXGhsU1MTAM6dO0fQ1ascO3oEGxsbACwtC+9wRUREcOvmTRrUr19ouTatW9OxY0eFNFtbW8aMGc3o0V74+V1gz949DBr0HfoG+rRv155vvmmPhYUFAKtWr6F//2/p98o9/C4PhQv/LesKNeg1egsmZSoQH/OUv3+dweKx9fhxTTAamvl3aACu+u6nsnv+o+gACbER6OibKKTp6JuQEBtRaHuu+u7DqYDpNgAvYiPQfi2utp4JqckJpKeloKb+bpv0CV8OA9MqVG26AG19W1KSIrlxYTmn/+yCR6/DqKrlXVr6pSehJzAvm/8oOkBqUhQamorTV9U1jUhNfl5oe56EHi9wuk1O3OeoWyn+OqauaUhmeiJZmakoq2gUUFMQPg8ffqKxIDd37lx0dXUVjrVr133sZhWZY6W8c3MnTfqRNWvX0qhRQ86f+4dZM2fm25kHuBFyg1oFTM0qyAnvEwVOt3lJ7d9fWV5VsmRJrK2tCj1ePgQdcuMGpqam8s58UXh7e+NWteobH4J2cip4PrNMJsPdvSYL5s/nn3/O4uLiwvIVK5g1ew4AUdHRPHv2jFrutYrcLuHT4litOa51O1HapjIObp58P+MQKYlxBJz9o8A6KUkJ3A0+TeUCpsUUlyRJBF84+N7jCl8nU5sGlCnfAl3jipha16N2282kpyXw6HbBD2dnpL0g6vHFQjvexSFJEhH3TmJewHQbQfgavPUIvVB8EydOxMtL8cn+x48efqTWvD1Nzbyjcz/+OIk//vyLgwcPcvKkD23btKFdu3b5jryra7zdCEhkZCQhITdo2LBBoeXUNTTyTFV5myk3Gm/ZLoAT3idp3PjND+qW+HcZ1IJcu3adPXv3cODAQWQyGf2//ZbOnTsBoPGZ/HIjFJ1mST1KlS7P8yd3CywTcvkwppYO6BtbFFhGR9+UhNhnCmkJsc/Q0c9//w2AB7cukp2Via19wV8QtfVNefFa3Bdxz9DQ1BGj80Kh1DR00Na3ISku76IXL0U8OI22gR2a2gVPy9HQMiI1OVohLS05Cg1N4wLrxEYEkS1lYWiW/2BSTlxj0pKjXosbjYpaSTE6L3wRRIf+P6Surp5nek3UZ95pc3BwYNrUKfxv4gR8fHzYvXsPbdq2pXy5crRr147WrVthbJzzQVyxYgXO+/oycuQPRYrtffIkri4uxdp5922m3FSsUIGIiAju379fpFH6pKQk/Pz8mDmjeA+nPn36lH379rN3717CwsNp3KgR8+bOoV69egq7EpcsWZIyZcpw3vd8oc8QCJ+P1JREop6GotO4Z4FlrvoVPt0GwMbenVuBJ2nUfqQ87eaVE9jYF3yfXPXbh2P1loU+G2JbsSbXLx9WSMuJ+3a/rAlfn8z0JBLjwrGs2K7AMk/vnXjjKLqhqQuRD89TzjX3+aRn4f9gYOZSYJ0n905gZt2g0FV2DExdiHhwSiEtMvwfDAuJKwifEzHlRngv1NTU8PT0ZN26tZz75yzt27djz549NGveQl5myODBBF+9ypQpU7l58yahoaH8+uuvxMTE5BvTu5DVbd7kbabc1KhRg2rVqvH90GH8888/PHz4kFOnT3P69Jl8Y585cwYbGxvKlClTrLbVq9+AY8eP0bNnT/x8z7Ny5QoaNWqk0Jl/acSI4WzatJmt27Zx/8EDrl27zraffy7WeYX/3u4NY7hz9TTRzx5wL+Q8G2Z+g5KSMlXrd8u3fFZWJtcvHy5wFZqXGrYdQYj/EU7s+omIhzc59Ms0wu9cpn7rYQXWCfY7UOBylS/VaTmYqKf32LNpHBEPb3Lm4GoCzvyh8MVBEACunpnD80cXSIp/RPQTf3wPDkGmpIxFhfzvsezsTCIenMasbOGf6XYufXkWdobb/htJiAklxHcZsc+uUbZKwXsyPL13ArNC5uUD2FbuTlL8Q4LPziMhJpTQoF94dPtv7FwKXrFNED4nYoReeO8MDQ3p168f/fr1IzQ0VJ5uY2PD1q1bWPTTYtp/0wENDQ2qVKlC69Z5/wAkJydz/rwvP056u+VEi2v1qpXMnTePkaO8SE5OxsrKinFjx+Rb9sQJ7yJNtynIkcN/y3dCfpMO33xDWloaW7ZsZd68+ejr69OsmVgP/HMRF/WYLfO7k5QQTUldY8o61mHMEl+09fKfPnDn6mnUNUpiaVfw1AEAW4da9Bv/Kwe2TebA1kkYly7HoMl7ClyD/vmTUJ4/uYv9G9aSNzK1YciMg+xa58WpvcvRMypD95EbxBr0Qh4piRFcPDyS9NQ41EsYYGjuRsMuf6Gumf9+LFGPLqCiqol+qcL3STA0d6N6syVc913M9fM/UVLPCvfWawpcgz4xLozEuDBMrArffFBL14LabTcSdGY2dwO3UaKkKa4ec8Qa9MIXQya97VItH8mqVatYuHAhERERVKlShRUrVhS4oc+GDRv4+eefuXbtGgBubm7MmTOnwPKDBw9m3bp1LFmyRL76DkBMTAzDhw/nwIEDKCkp0aFDB5YtW0bJkrlP8F+9epWhQ4dy6dIljI2NGT58OOPGjSvydd0LLXgu7dfs6NGjLF68hKNHj3zspijIzMykRk13Nm/aSJUqVT52cz5ZR+4U7QuLoOiPNSPIzsqk67DV7zWu9+7F3LzizdCZh95r3K/NyaP3P3YTPluBp6YjZWfh0mjGe417O2ATkeHnqNNOrCf/Lv5aYvuxmyC8o89iys3OnTvx8vJi6tSpBAQEUKVKFTw9PYmMjMy3/KlTp+jWrRs+Pj74+vpiYWFB06ZNefz4cZ6ye/bswc/PL9+dO3v06MH169c5fvw4Bw8e5MyZMwwaNEien5CQQNOmTbGyssLf35+FCxcybdo01q9f//4u/iulqanJuHFjP3Yz8oiLj6dfv75i6UjhgzC3qkTdlkPee1w9ozJ4dpnw3uMKQlHpGJbHtnKP9x5Xs6QpFau9//9nBOFz81mM0L+c4/xy7fvs7GwsLCwYPnw4Eya8+Y9UVlYW+vr6rFy5kt69e8vTHz9+TI0aNTh69CgtW7Zk5MiR8hH6Gzdu4ODgwKVLl6hatSoAR44coUWLFjx69Ahzc3PWrFnDpEmTiIiIkK+TPmHCBPbu3cvNmzeLdG1ihF74EokReuFLJEbohS+VGKH//H3yI/Tp6en4+/vj4ZH7wIuSkhIeHh74+voWKUZycjIZGRkYGORuS52dnU2vXr0YO3Ysjo6Oeer4+vqip6cn78wDeHh4oKSkxIULF+Rl6tWrp7DpkaenJ7du3SI2Nvatr1UQBEEQBEEQ3tYn36GPiooiKysLExPF3QtNTEyIiCh8V8SXxo8fj7m5ucKXgvnz56OiosKIESPyrRMREUGpUqUU0lRUVDAwMJCfNyIiIt92vcwTBEEQBEEQhA/tk+/Qv6t58+axY8cO9uzZI1+m0N/fn2XLlrF169Y8GxIJRefnd4GyduVISEgA4K9du3B2KXx1DqFgZe3Kcez48Y/dDEEQBEEQPjOffIfeyMgIZWVlnj1T3L3w2bNnmJoWvCsiwKJFi5g3bx7Hjh1TeIjx7NmzREZGYmlpiYqKCioqKoSFhTF69Gisra0BMDU1zfPQbWZmJjExMfLzmpqa5tuul3mfi99++43KVZzJzMyUpyUlJVGhoj3duys+xPSyEx8WFoarqwt+vufR1tb+r5ssCJ+dO8FnWDO1Df/rUZqhzZUIOr/3jXVuXz3FvGFu/NBag6nflsP3+NY8ZU4fWMXkPjb80KYEC0bW5MGtiwr5Gemp7Fw1lHGdjRjVXpsNszrm2WU2JjKc1VNaMbKdFuO7mrB741iysjIRhKJ4/ugi5/YN5NAGd3YtLcvju8feXOehH96/tmHPCnuObGnIg+t/5SkTGrSdw5vqsWeFPSd//4aYiCCF/KzMNK6cnMqBtW7sXeWE78HvSU1S3A02OeEJ5/b2Z+9KRw6uq8bVs3PJzhb3tvDl+eQ79Gpqari5ueHt7S1Py87OxtvbG3f3gncvXLBgATNnzuTIkSMK8+ABevXqxdWrVwkMDJQf5ubmjB07lqNHjwLg7u5OXFwc/v7+8nonT54kOzubGjVqyMucOXOGjIwMeZnjx49ToUIF9PX138v1/xdq1qxJUlISwcHB8rRLly9jZGREYFAQaWlp8vSXKwJZWVmhpqaGsbGx+JVDEIogPTWJMraV6fz9yiKVj4q4z5oprShXpQETV12hYbsf+G3pQEL8j8rL+J/eye71o2nRYwoTVvhTxqYyK39sxou43MGIv9aNIvjCQfr/7w9GLThFfPQTNszqIM/PzspizdRWZGWmM/qnc/QevZULx7dxcPuU93fxwhctKyMZPeOKODecVqTySfEPObdvAMYWNWnc4wB2Lv0IOPE/Ih7kbub38NZBrp6Zg33NETTuvh9d44r8s6cvqcm5Hfag07N4et+bGi1XUL/jb6QmRuJ3MHfFGyk7i3P7+pOdnUGDzn9StelCwkJ2E+K79H1duiB8Mj75Dj2Al5cXGzZsYNu2bdy4cYMhQ4aQlJREv345W0P37t2biRMnysvPnz+fyZMns3nzZqytrYmIiCAiIoLExEQgZ+OjSpUqKRyqqqqYmppSoULO5hX29vY0a9aMgQMHcvHiRc6dO8ewYcPo2rWrfInL7t27o6amRv/+/bl+/To7d+5k2bJleHl5/cfv0LuxtbWlVKlSXLiQO7J34cIFmnh4YFGmDFeuBCqk16yZ84Xm9Sk3+fH29qZd+2+wd3CkarXqDB7yvTwvPj6e0WPG4uLqhmMlJ/p925/7Dx4AEB0dTY2a7qxevUZe3j8ggIr2Dpw7f/6N9SF3CtCZM2dp6umJU+Uq9O33bYHLnb56TT4+PrRo2Qp7B0c6dOjIrdu3C6yzaNFPfNOhQ570lq1as2LFCiBnv4LeffpQtVp1qji70K1bd65du/7Gdrz63oaEhFDWrhyPHj2Sp12+fJkuXbvh4FiJ2nXqMn3GDJKTkwuMK3w8jtWa07rPLJxrty9S+X8OrcXQ1IYOA3/C1NKeBm2G4VKnIyf3LJWX8d6zhFrNB+DetB9mVg50Hb4WNXVNfI/lrMmdkhSP77HNfDPwJyo4N8KynBs9vTZzL+Q892/4AXAj4BhPw0PoM3Y7FmWdcazWnFa9Z3DmwGoyM9Lf+/sgfHlMbRrgWGs0pe2KtgHZvau/oaVbhsr1/oeOgR12zr0pXa4Zd6/kriV/J2Az1pW6YO3YER3Dcrg2noWySgnC/h3Jz0h7wYPrf1K53iRKWdRC38QJt6bziX4aQPTTKwA8CztLQsxdqnkuRq+UQ0473UcRGrSd7Cxxbwtfls+iQ9+lSxcWLVrElClTcHZ2JjAwkCNHjsgfQA0PD+fp06fy8mvWrCE9PZ2OHTtiZmYmPxYtWvRW5/3111+pWLEijRs3pkWLFtSpU0dhjXldXV2OHTvG/fv3cXNzY/To0UyZMkVhrfrPRc2aNfDz85O/9vO7QI0a1aleo7o8PTU1lcCgIGrWrFmkmD4+Pgz5figN6tfnwP59bP95G1Vemfo0btx4rgUHs37dWv768w8kSaJ//wFkZGRgaGjIvHlzWb5iBVeDg0lMTGTM6DH06tmT2rVqvbH+S6mpqWzctJFFixbx+2+/8fTJE+bOm/fGts+bN5//TZzA3j27MTAwYNCg7xTivqpNmzYEBV0lLCxMnnb79h1u3rxJ69ZtAEhMSuKb9t+wc8fv7PrrT6ytrek/YID8S2ZxhIWF0e/b/jTzbMqhQwdZvnwZ/pf9mTZ9erFjCp+Oezf9qOjcWCHN3q0p92/krO6VmZHOwzv+VHRWXAGsorMH9/7trIff8ScrM4OKLrllTC0qol/Kkvs3c+Lcv+GLubUTOvomr5zHk9TkBJ6GFfylUxCKKybiCqUsaiukmVjVk3fEs7PSiYu8RimLWvJ8mUyJUpa15GViI4ORsjMU4ugYlEVT25yYf8tER1xB17ACGlpGr5ynLpnpiSRE3/lg1ycIH4PKx25AUQ0bNoxhw4blm3fq1CmF1w9eGaUtqvzqGBgY8NtvvxVar3Llypw9e/atz/epqVmjJrNmzyYzM5PU1FRCQkKoXr06GZmZ/P7b7wAEXLlCeno67kXs0K9avYZWLVsycuQP8jR7e3sA7j94wAlvb/74YydurjkP0i5Z/BN16tbj+PETtGjRnIYNGtClc2e8vEbj5FSJEpqajBkzusj1ATIyMpg5YwZWVlZAznSrFSvfPOVh+Ijh1KlTB4CFCxdQu05djh07TsuWLfKULV++HPb2Fdl/4ADD/71H9+/fj3OVKlhb55y31mvTw2bPnoWLiysXL16kUaNGRXo/X7d27TratGkt/6XKxtqaKVMm0617D2bOmIG6unqx4gqfhhexEWjrK66ipa1nQmpyAulpKSQnxpKdnZW3jH4pIh7l7IOREBuBiooamiX1FMro6JmQEBMhL6OjZ5In/2WeILxvqUnPUbcyUkhT1zQkMz2RrMxU0lPjkaQsNDQVy2hoGvEi5t6/MaJQUlZDTUPntThGpCY9zz2P5uvnMZLnCcKX5LPp0AsfVo0aNUhOTubq1WDiE+KxsbbG0NCQGtWrM378BNLS0rhw4QKWFhb57qqbnxs3btC1S+d880LvhqKiooJzlSryNH19fWxtbAh9ZbOtiRMn0Lx5Cw4fPsK+vXvkndSi1i9RooS8Mw9gXMqY6OjoN7bd1cVF/m89Pb1/44YC4FQ595xt27Zh1syZtGnThr/++ovhw4YhSRIHDh7k22/7yctFRUXx0+IlXLhwgejoaLKzs0lJSeHJk9xflt7WjZs3uXXzJvv3H5CnSZJEdnY2Dx8+xM7OrtixBUEQBEH4fIgOvQCAtbUVpqam+Pn5EZ8QT/Xq1YGcdfXNzMzwDwjAz+8CNQt5EPl1L5cJfRdh4eE8i4wkOzubR48eyZ9xKCoVFcVbXCaT8a6bIx/Yv1/+75LaJQFo3aoVCxYs5Nq166SmpfL06VNatmwpLzdm7Dji4uKYPPlHSpuXRk1NjU6dOpFRwBxlJaWcB41fbeurqxABJCcn0bVbV/r07pOnvrm5WfEvUPgkaOub8uK11WhexD1DQ1MHNfUSKCkpo6SknLdMbCQ6+jmrbOnom5KZmU5yYpzCKH1C3DN0DHLLPLh9SSFGQtwzeZ4gvG8aWsakJSuuRpOWHI2KWkmUVTRQL6GETKas8AAsQGpyFBpaxv/GMCI7K5301ASFUfo0hTLGxEZcfe08UfI8QfiSfBZz6IX/Rs2aNblw4QIXLlyUr+QDUL1aNU6fPs3VoCDca9YoJIKiChUqcP58/rv5lrUrS2ZmJoFBucuQxcbGcu/+ffnIcnp6OqNHj6ZlyxaMGjmSif+bRNS/o+tFqf8uXn0QOD4+nvsPHlC2bFkg58vPy8PI0BAAMzMzqlevzv79+9m/bz+1a9eW5wEEBATQp3dvGjZoQPny5VBTUyOmkN2EX+5q/OoDvCEhNxTKODo6cvduqEJ7Xh6v7l4sfJ5sK9bkVtBJhbSbV05gY5/zpVpFVQ2Lcm7cClRcAexWoDe29jnT4izLuaGsoqpQ5tmjW8RGhmNTMSeOjb07Tx4EK6yMczPgOBqaOphaOnyw6xO+XgamLkQ+PK+QFhn+D4ZmOb+MKimroVeqEs9fKSNJ2Tx/6Csvo1/KCZmSqkKZFzH3SH7xBIN/yxiauhAffUvhi8Gz8H9QUSuJtoH4BVP4sogOvSDnXrMGl/39uXHjBjVqVJenV69RnR2/7yA9I6PID8QCjBg+nAMHD7J06TLu3r3LrVu3WLduHZAz39vDw4NJ/5vE5cuXuXHjBl6jx2BiYiLf0fenxYt58SKRKZMn8913g7CxsWHChAlFrv8uVqxcybnz57l1+zbjxo1HX1+fJk0Kj9u2TRsOHjrE4cOHadumjUKetZUVe/fu5e7duwQGBuI12qvQXzCsrKwwMzNj+fIV3H/wAB8fHzZt3qxQ5rtBgwgICGDatOmEhIRw/8EDjh8/wbRp4qHYT1FqSiIPQwN5GBoIQPSz+zwMDSQmMhyAfVsmsm1R7q8tdVoOJurpPfZsGkfEw5ucObiagDN/0Kj9SHmZxu1Hce7IRvyObyMi/AY7Vg4hLS2Jmk1ypnuV0NLFvem37NowmttBPoTf8Wf74m+xsXfH5t9Ov71rU8wsHdi2sDeP7gUR4n+UAz9Ppl7r71FVE89hCG+WmZ5EXGQIcZEhACQnPCIuMoTkhCcAXPtnIZeOjpaXt63cnaT4hwSfnUdCTCihQb/w6Pbf2Ll8Ky9TzvVb7l/bSVjILhJi7nLFezKZGclYOXQEQFVdG2vHTlw9M5vIh77EPgvm8vFxGJi5yDv9JlZ10TGw49KRMcQ9v0HEgzOEnF9M2Sq9UFYR97bwZRFTbgS5mjVrkpqaStmythgZ5T5IVKN6dRKTkuTLWxY9Xg1WrljOypWrWLduHSVLlqRa9Wry/AXz5zFj5iwGDBxERkYG1apVY9OmjaiqquLnd4GtW7fx6y/b5RtX/bRoIS1btebXX3+lR48ehdZ/V+PGjmHmzFmEPXiAvYM9G9ave+Ood/PmzZg2fTrKysp5Ov9z585h0o+TadO2HWZmZowZ7cXcefMLjKWqqsrSpUuYMmUqLVu2onJlJ7xGjWTY8BHyMhUrVuT3337lp58W07VbdyRJwtLSkpYt8j64K3x84Xcus2x87gPQu9bndHBqePSh9+gtxMdEEPtv5x7AyNSGITMOsmudF6f2LkfPqAzdR27AwS13aUC3+l14Ef+cg79M5UVMBKXLOjN05mGFFWs6frcEJSUlNszqSGZGGvZunnQZukqer6SszOBpB9ix8nsWedVCXV2LGh69adVrxod8O4QvSOyzYM7syt2E8OqZ2QBY2X9DVc+FpCZFkpyQ+7yQlq4FtdtuJOjMbO4GbqNESVNcPeZgal1PXsaiQivSUmII8V1KanIUukb21Gm3RWHFmir1f+SqTAm/g0PJzkrHxKouLo1y71uZkjK12m7kysnJnNrZEWVVTazs2+PgPvIDvhuC8HHIpHedUCy8k3uvPMApfHx+fhfo0bMnVwL80dHReXMFIV9H7pT92E0QhPfu5NH7H7sJgvBB/LXE9mM3QXhHYsqNIAiCIAiCIHzGRIdeEARBEARBED5jYg69ILyiZs0ahN4VOwgKgiAIgvD5ECP0giAIgiAIgvAZEx16QRAEQRAEQfiMiSk3giAIgiAIwkeXmppKenreHdTV1NTey+7zXzLRof/IVLIzPnYTBOG9UxOfLMIXSFXt3fe4EAQhf6mpqZiXKEksWXnyTE1NuX//vujUF0L82RUEQRAEQRA+qvT0dGLJ4metsmjKcmeEJ0vZ9I4IJT09XXToCyE69IIgCIIgCMInQbuEKlpKyvLXytlZkPQRG/SZEB16QRAEQRAE4ZOgXEIJZaXcEXrlbOkjtubzITr0giAIgiAIwidBRUsZFeXcEXqVvFPqvyipqanvZSqRWLZSEARBEARB+CQoqyrlOYpj1apVWFtbo6GhQY0aNbh48WKBZbdu3YpMJlM4PuR8/ezsbGbOnEnp0qUpWbIk9+7dA2Dy5Mls2rSpWDFFh14QBEEQBEH4JCirKec53tbOnTvx8vJi6tSpBAQEUKVKFTw9PYmMjCywjo6ODk+fPpUfYWFh73IZhZo1axZbt25lwYIFqKmpydMrVarExo0bixVTdOgFQRAEQRCET4KSikxhdF5JRQZAQkKCwpGWllZgjMWLFzNw4ED69euHg4MDa9euRVNTk82bNxdYRyaTYWpqKj9MTEze+7W99PPPP7N+/Xp69OiB8ivTi6pUqcLNmzeLFVN06IVP0rZffqN2Qw/KV3KmbccuBAZdLbT8ocNHaOTZkvKVnGnaqi0nT52W52VkZDB34U80bdWWilXcqFanPqPGTuDZM8Vv6nFxcYwYPRZHl2o4udVg7P9+JClJ8dH6Gzdv0bFbT8pXcqZmvUas3VC8n8aEr5ckSezbOoUxXcz5vqUmi8c14dmjO4XW+fv3ucwaWp1hbXTw6mTCqqntiXh4q8D4y/7XgoFNlLhybq9CXnRkOMsntWJoKy28Opnw5/qxZGVlKpS5FXSKmUPcGNJCg//1Kce5o1vf5XKFr4QkSQSf/Ym9q9z4c3E5fHZ240XM/SLXD/FbxY4FlgR4T8uTF/XYn5M7uvLnkgr8tdQB7986kpmRKs9PS4nD98AI/lrqwK5llbhweCwZ6Yqf3XGRNzjxWwf++Kkc+9bU4MaFNcW+VuHDUlJVznMAWFhYoKurKz/mzp2bb/309HT8/f3x8PDIjamkhIeHB76+vgWeNzExESsrKywsLGjbti3Xr19/vxf2isePH2NnZ5cnPTs7m4yM4u1PJDr0wifnwKHDzJo7nx+Gfc/BvX9hX7EivfoPIio6Ot/ylwOuMNxrLJ07fcOhvbto6tGYQUOHc+t2TicpJTWVa9dDGPH9YA7t+Yt1K5dz7/59+g8ZqhBnxOhx3Llzl1+2bmTzutVcvHSZCZOnyfNfJCbS69sBlC5tzsE9f/K/cWNYsmIVv+3444O9F8KX58jOBXjvXUHPH9bwvxV+qGlosXRiMzLSUwusc/vqGRq2+Z6Jy30ZNe8YWZkZLJngSVpK3rXcTuxeCsjypGdnZbFiUisyM9MZv/Qc/cZu5fyxbezbOkVe5vnT+yz/sRUVqjRgyporeLT/gZ8XD+TapaPv49KFL9jNi2u4HbCFqk3n0qTnflRUNTn1Z0+yMgu+r1+KfhpEaNBv6Bnb58mLeuzP6T97Y2pdl6a99tO01wHKufZBJsu9x/0OjiA++jYNO/9KvQ6bef7wApeOTpDnZ6S94NSfPdHSKYNnn4M4N5jEtXNLuBv46/u5eOG9UlFXznMAPHz4kPj4ePkxceLEfOtHRUWRlZWVZ4TdxMSEiIiIfOtUqFCBzZs3s2/fPn755Reys7OpVasWjx49er8X9y8HBwfOnj2bJ/2vv/7CxcWlWDFFh1745GzcspWunTvRucM3lLezY86MqZTQ0OCPv3bnW37Ltu3Ur1uHwQP6U86uLGNGjqCSgwPbfsn5sNbR1ubXrZto1aI5ZW1tcHWuwowpPxJ87TqPnzwB4M7dUE6f/Yf5s2fiUqUK1aq6MX3yJA4c+ls+kr93/0HSMzJYOGcW5cuVo02rFvTr1ZONW7b9N2+M8NmTJAnvPcto2WMSzrXaUsa2Mt+O30Zc9JM8o+mvGjn3MLU9+1La2hGLslXoN3YLMZHhhN3xVygXfjeQY38tpu+YvL8cXfc/xpPwEAZM2I6lnTNO1ZvTts8MTu1fTWZGzlbrpw+uxcjUhs6Df8LMyp5G7YbhVq/jv18SBCF/kiRx6/ImHN2HU6ZcU/RK2VOj5RJSEiN5dOdYoXUz0pPwOziCap7zUNXQzZN/5eQMyrn1w6HmUHSNKqBjWBbLiq1RVlEHID76Dk/vn6K653wMzV0wLlMdN48ZhN/YT8qLnM7bg5C9ZGelU735QnSNKmBl34bybv24dbl4c5WFD0tJWRkllVeOf6ek6OjoKBzq6urv7Zzu7u707t0bZ2dn6tevz+7duzE2NmbdunXv7RyvmjJlCsOGDWP+/PlkZ2eze/duBg4cyOzZs5kyZcqbA+RDdOiFT0p6ejrB10OoU6umPE1JSYk6tdwJCAzMt05AYCB1arkrpNWrU5uAK0EFnufFixfIZDJ0dHTkMXR0dKjsVElepk4td5SUlLjy73SfgCuB1KhaVeEBlnp1axN6/z7x8fFvfa3C1ycq4j7xMRHYu+T+FKyppYttxRrcCyn4p+DXpSTl3G9a2gbytLTUZDbO7UGP4SvRNTDNU+deiC+lrZ3Q0c8dtXKs6klKcgJPwnJ+Wr53ww97l8YK9Rzdmr5V24SvT1J8OKlJzzGxqiNPU1PXwdDMmegn/oXUBP/jP2Jm2whT67p58lKTooh+egUNTUOO/9KePStd8f6tE88f5a5WEv04AFV1HQzMqsjTTKzrIJMpEf00MKfME3+My9RAWTn3s9vUuj4vYkJJT40r5lULH8q7rnJjZGSEsrIyz549U0h/9uwZpqZ5Pxvzo6qqiouLC3fv3n2rcxdV27ZtOXDgACdOnEBLS4spU6Zw48YNDhw4QJMmTYoVU3TohU9KbGwcWVlZGBkZKaQbGRny/HlUvnWeR0VhZGT4WnkjnkflXz41LY25ixbTplULtEuWzInxPAojQwOFcioqKujp6srj5H+enNeRBbRNEF4VH5MzYvhqpxpAW9+E+Nhn+VXJIzs7mx1rRmHnWJvSNrlfQP9YO4qyDu4412qb/7ljI/Kc9+Xrl+2Kj8m/TEpyAulpKUVqn/D1SU16DoCGluLntoaWESmJzwusF3ZjP7HPrlGl/vh88xPjwwG4dm4JZat0o0Gnn9E3qYTPzu7y+fkpSc/R0FQ8r5KSCmol9Ej5t10pSc/zbdvLPOHTojA6/+/xNtTU1HBzc8Pb21uelp2djbe3N+7u7oXUzJWVlUVwcDBmZmZvde63UbduXY4fP05kZCTJycn8888/NG3atNjxxMZSwlclIyODoT94IUkSs6dP/djNEb5wft6/8svSwfLXw2cdfOeYv60YypMH1xi3JHf+ZeD5/dy84sPktQHvHF8Q3uTB9T1cPpY7f7leh61vHSMp4QkB3tNo2PlXlFUKWO9bygagrHMPbJ06A6BvUoln4ee4F7yTKvUn5F9P+KwpqymjrJrbPVWWvf1OsV5eXvTp04eqVatSvXp1li5dSlJSEv369QOgd+/elC5dWv5g7YwZM6hZsyZ2dnbExcWxcOFCwsLCGDBgwPu5qNdcunSJ7OxsatSooZB+4cIFlJWVqVq16lvHFB164ZOir6+HsrIyUa+NrkdFRWNsbJRvHWMjI6Kiol8rH4Xxa6P8Lzvzjx8/4feft8hH5wGMjY2Iio5RKJ+ZmUlcfLw8Tv7nyXldqoC2CV83Z/c22FbM/cDOyMhZZi0h9hl6hrkjPy9in2FRtkqe+q/7bcUwrl44xNifTmNgXEaefjPwJM+fhvJDO32F8mtmdKRcpbqM/ckHXX1T7t+8pJCf8O+vAi+n6OgamMrTXi1TQlMHNfUSRblk4StQ2q4Jhua5D+5lZ+Xc16lJUZQomfsLT2pSFPomDvnGiH0WTFpyFEe3tZCnSVIWzx9e4E7ANjqNvouGVikAdA3LKdTVMbAjOSHn+acSWsakJiv+vcjOziQ9JY4SWsa5ZZIUy7x8/bKM8OlQUlGcZqMkvf1kki5duvD8+XOmTJlCREQEzs7OHDlyRP6gbHh4OEpKuXFjY2MZOHAgERER6Ovr4+bmxvnz53FwyP/+fVdDhw5l3LhxeTr0jx8/Zv78+Vy4cOGtY4oOvfBJUVNTw8nRgXO+fng2yZlnnJ2dzTlfP/r07J5vHVdnZ875+tG/b2952tnzvri65HaQXnbm74eFsWP7VvT19fLESEhIIPjadZwqOQJw3u8C2dnZuFSpnFPGxZmFS5aSkZGBqqoqAP+cO09ZGxt0dfM+zCUIGpraaGhqy19LkoSugSk3r3hjaecMQEpSAvduXqB+68EFRMmp9/vK4Vw5t5cxi3wwNrNRyG/edQJ1myuOJE0bVJkugxdTuWZrAGwd3Dn0+xwSYiPR0c/pKN0IOE4JTR3MLHP+aNna1+TaxcMKcUICTmDrULSfqYWvg6p6SVTVcwdEJElCQ8uYZ2Hn0DfJ+fzMSHtB9NNA7Fx65RvDxLI2zfodV0i7eHg02gZlsa/xPUpKymjpWlCipAkJMfcUyr2IvY+ZbQMADEu7kpGWQEzEVQxMcz6rn4WdR5KyMTRzzilj7kbw2QVkZ2WgpJzz2R0RdhZtg7Koaei969shvGevT7NRys4uVpxhw4YxbNiwfPNOnTql8HrJkiUsWbKkWOcpjpCQEFxdXfOku7i4EBISUqyYYg698MkZ0K8vO/74i7927+XO3VAmTZ1OckoKnTq0B2DU2AnMX7RYXr5fn16cPvsP6zdt4W7oPZYsX0nwtWv06dkDyOnMDxkxkqvXrrNs0QKysrKIfP6cyOfPSU/PWd2jnF1Z6tetw/gfpxAYdJVL/gFMmTGL1i1bYGKS0/lp27olaqqqjPvfZG7fucOBQ4fZ/PMvDOjX5z9+h4TPlUwmo3H7Hzj022wCz+/n0f1gNi/og56hOS6128nL/TTWg5N7V8pf/7ZiKH7evzJg4q9oaGoTHxNBfEyEfF67roEppW0qKRwABqUs5Z1/R7emmFs6sGl+bx6GBnHt0lH2bp1Mgzbfo6qWs1pE/VaDeR5xj782jONp+E189q/m8uk/8Phm5H/zBgmfJZlMRoWq/bnuu5zHd44R9/wmfodGUaJkKcqUy50TfHJHV24HbAVyvhToGVdQOJRVNVEvoY+ecQV53IrVv+OO/xYe3jrEi9gHXD27iBcxd7F16gLkjN6b2TTg0pEJRD8N5PmjSwScmIylfRtKaOf88mTl0BYlZTUuHhlLfNQtwm/s57b/ZipU/TDTKYR3865z6D8H6urqeR7aBXj69CkqKsUbaxcj9MInp3XL5kTHxLB4+QqeP4/Cwb4iP29aJ5/68uTpU4Wfyqq6urD8pwUsWrqchYuXYm1txfpVK6hQPudn2ohnkRz39gGgedtvFM61Y/tW3GtUB2D5TwuYPGM23ft+i5JMiWaeTZj+4//kZXW0tdm+eSOTp8+kVftO6Ovr88PQIXTv2vmDvh/Cl6VZl3Gkpyaxfel3JCfGUa5SHX6YexhVtdx5xM+fhpKYkDtF4NSBtQAsGtNQIVbfMZup7dm3SOdVUlZm+KwD/LLse+b9UAs1DS1qNelN274z5GWMzWwYMesgO9d44b1nOfpGZejttYFK1Tzf4YqFr0HF6kPITE/h0rGJpKcmYFymKvU7bVeYH58YF05ackwhUfKqUHUAWZlpBJycQXpqHHrGDjTo/Cva+tbyMjVbLcf/xGR8dnRDJlOiTIXmuDaeLs9XU9ehQadfuHziR45ua4V6CX0q1foBO+ce73zdwvv3vkboP2VNmzZl4sSJ7Nu3T/4Lf1xcHP/73/+KvcqNTJKkt3/a4E1BZTL27NlDu3bt3ndoBadOnaJhw4bExsaip6cHwN69exkzZgz3799n+PDhODs7M3LkSOLi4j5oW4or/M6Nj90EQXjvjoVV/NhNEIT3zvv4h9lkRhA+tt/nW3zsJpCQkICuri43h3RAW11Vnv4iLYOKa3YRHx8vX2r6c/f48WPq1atHdHS0fCOpwMBATExMOH78OBYWb//f462n3ERERDB8+HBsbW1RV1fHwsKC1q1bKywP9F+pVasWT58+VZi//N1339GxY0cePnzIzJkz6dKlC7dv3/4g59+9ezdNmzbF0NAQmUxGYAHrpAuCIAiCIAhv9jVMuSldujRXr15lwYIFODg44ObmxrJlywgODi5WZx7ecsrNgwcPqF27Nnp6eixcuBAnJycyMjI4evQoQ4cO5ebNm8VqRHGpqakpbBKQmJhIZGQknp6emJuby9NLlHi31RlefQjyVUlJSdSpU4fOnTszcODAdzqHIAiCIAjC105JRYaSyiur3GTJPmJrPhwtLS0GDRr03uK91Qj9999/j0wm4+LFi3To0IHy5cvj6OiIl5cXfn5+BdYbP3485cuXR1NTE1tbWyZPnkxGRoY8PygoiIYNG6KtrY2Ojg5ubm5cvnwZgLCwMFq3bo2+vj5aWlo4Ojry999/AzlTbmQyGXFxcZw6dQpt7ZzVJBo1aoRMJuPUqVNs3bpVPh3npX379uHq6oqGhga2trZMnz6dzMxMeb5MJmPNmjW0adMGLS0tZs+ene919erViylTpuDh4ZFvviAIgiAIglB0MmXlPMeX6M6dO6xfv55Zs2YxY8YMhaM4ijxCHxMTw5EjR5g9ezZaWlp58l/vNL9KW1ubrVu3Ym5uTnBwMAMHDkRbW5tx48YB0KNHD1xcXFizZg3KysoEBgbKR8SHDh1Keno6Z86cQUtLi5CQEEq+sn74S7Vq1eLWrVtUqFCBXbt2UatWLQwMDHjw4IFCubNnz9K7d2+WL19O3bp1CQ0NlX9Dmjo1d6OhadOmMW/ePJYuXVrsJ44FQRAEQRCEolNSVUVJLXdWhFL2e3/U86PbsGEDQ4YMwcjICFNTU2Sy3F8hZDIZU6ZMeeuYRe6p3r17F0mSqFjx7R92+/HHH+X/tra2ZsyYMezYsUPeoQ8PD2fs2LHy2OXK5W4iER4eTocOHXBycgLA1tY233OoqalRqlTO8oIGBgYKU3FeNX36dCZMmECfPn3k8WbOnMm4ceMUOvTdu3eX7ygmCIIgCIIgfHgyZWWUXhmV/xJH6GfNmsXs2bMZP378e4tZ5A79uyyGs3PnTpYvX05oaCiJiYlkZmYqPKns5eXFgAED2L59Ox4eHnTq1ImyZcsCMGLECIYMGcKxY8fw8PCgQ4cOVK5cudhtCQoK4ty5cwrTaLKyskhNTSU5ORlNTU2AYm27KwiCIAiCIBSfTEUZ2SsPwsq+wIdiY2Nj6dSp03uNWeQ59OXKlUMmk731g6++vr706NGDFi1acPDgQa5cucKkSZPkG/pAzvSW69ev07JlS06ePImDgwN79uwBYMCAAdy7d49evXoRHBxM1apVWbFixVu14VWJiYlMnz6dwMBA+REcHMydO3fQ0MhdLze/aUXCh7P9tx14tm6Ho0s1HF2q0a5zN3xOnylS3aUrVvHDmHEF5h86fIRGni0pX8mZpq3acvLU6SLF/Wv3Xjp07Zlv3uGjx+nRtz8uNWrL23v67D9Fiit8Pfb/PI2BTZQUjsnf2het7vbpbJyX/y6bAJdP/8nkb+0Z0qIE0wZWJvjC30WKe/7YNuaPrFtg/q2gU8wc4saQFhr8r085zh3dWqS4wtcl+J/F7FhgqXAc2tjwzRWBa+eW4HvwhwLzw28e5NDGhvzxUzkOb27Ck9CTRYp7/9qfnPj1mwLzn4X7cnRrC/74yY6D6+tyL/jPIsUV/lsyZaXX5tB/eXugdurUiWPHjr3XmEUeoTcwMMDT05NVq1YxYsSIPB3euLi4fOfRnz9/HisrKyZNmiRPCwsLy1OufPnylC9fnlGjRtGtWze2bNlC+/Y5O4NaWFgwePBgBg8ezMSJE9mwYQPDhw8vatMVuLq6cuvWLezs7IpVX/gwzExNGD96FDbWVkgS/LVnLwO/H8bfe3dR/pUpWPk55n2S7wflv+Pf5YArDPcay7jRI2ncoAH7Dh5i0NDhHNqzS77xVGFxmzTO/w/UxcuXqVu7FuO8RqKjo82fu/bQf/D37P1zB5UcHIp20cJXwdzaEa/5uVvcKykX7WM36Px+mnXN/+fYu9fPs2FOd77pP4fKNVpxwec3Vk1rz+TV/vJdYgsSeH4/Vdxb55v3/Ol9lv/Yivotv2PAhF+4ccWbnxcPRNfATGwuJeSha1SeBp1/k79WUiravf34znHsaw7JNy/q8WV8Dwyncr3xmNs1JixkH//sGUjTPn/Ld5AtLG5pu/w35UmMC+fMrr7YVelJzdbLeBZ2jktHxlGiZCnMbOoXqd3Cf+NrGKG3s7Nj8uTJ+Pn54eTklGclxREjRrx1zLd62nPVqlXUrl2b6tWrM2PGDCpXrkxmZibHjx9nzZo13LiRd5OkcuXKER4ezo4dO6hWrRqHDh2Sj74DpKSkMHbsWDp27IiNjQ2PHj3i0qVLdOjQAYCRI0fSvHlzypcvT2xsLD4+PtjbF22EKz9TpkyhVatWWFpa0rFjR5SUlAgKCuLatWvMmjXrrWLFxMQQHh7OkydPALh16xYApqamBc7hF/Ln0Uix4zzOayS//L6DgMCrhXbonzx9yp07d6lfL/8Rxy3btlO/bh0GD+gPwJiRI/jn3Hm2/fIrc2ZMKzBualoaZ8+dY5zXyHzzp06aqNje0aM45n0S75OnRIdeUKCkpIKuwdt9HsREPuRJ2HUqVW2Wb773nuU4VmuGZ+exALTrO5MQ/xOc3LeSXiPXFhg3Iz2VEP9jtP82/5W7Th9ci5GpDZ0H/wSAmZU9d6+f48TupaJDL+QhU1KhRMlSb1UnKeEJ8dG3MbNpkG/+rcubMbOpj32NwQBUrjuGZw/OcidgK9U85xYYNyszlYgHZ6hcL/9fa+8G/kJJXQtcGk0GQNewHFGPLnHr8kbRof/EKKmooKSa2z1Vysz6iK35MNavX0/JkiU5ffo0p08rzhqQyWTF6tC/1e8Ytra2BAQE0LBhQ0aPHk2lSpVo0qQJ3t7erFmzJt86bdq0YdSoUQwbNgxnZ2fOnz/P5MmT5fnKyspER0fTu3dvypcvT+fOnWnevDnTp+ds25yVlcXQoUOxt7enWbNmlC9fntWrV7/1hb7k6enJwYMHOXbsGNWqVaNmzZosWbIEKyurt461f/9+XFxcaNmyJQBdu3bFxcWFtWvz/4OalpZGQkKCwpH2ytQjIUdWVhb7D/5NSnIKri5VCi173NuHmjWqo53PykcAAYGB1KnlrpBWr05tAq4EFRr33Hk/TE1MsCub/0PYr8vOziYpKUlhkzNBAIh8cocxXUozsVdZNsztSXRk+BvrBPnup3zlBpTQyn9XxHshvji4NlZIc6zalHs3Cl4+GODGFW/0jEpjZpn/4gb3bvhh7/JaXLem3AvxfWObha/Pi9j77F1VlQPrauN7YARJCY/fWOfJ3eOUsqiJqrp2vvnRTwIwsa6jkGZqU4/oJwGFxn0Wdo4SJU3RMcz/1/foJwGYWL0etz7RjwuPK3wEysp5jy/M/fv3Czzu3btXrJhvvR6jmZkZK1euZOXKlQWWef0B2gULFrBgwQKFtJEjRwI5q9P8/vvvBcYqbL58gwYNFM6lp6eX59x9+/alb9++Cmmenp54ehY82lTUB4Dzi12YuXPnyr+ovDRy2PeMGjGsyDG+ZDdv3aZ9l26kpaWjpanJulXLKf+GqVHHvU/SpHGjAvOfR0VhZGSokGZkZMTzqKg3xn39V4PCrN+0haTkZFq1yH9EVfg62VSsQb8xWzC1qEBc9FMO/jKDBaPqMX1DMBqa+XdoIGdajHOtNgXmx8dGoK1nopCmo29CfExEoe0JPL+vwOk2APExEejo542bkpxAeloKaurvtkmf8OUwNHehRvOf0DEoS0pSJNfOLcX7t44073ccVfX8B1gAHt09Rhm7pgXmpyY9R0PTWCFNQ8uYlKTnhbbn0Z1jBU63kcfVMlKMq2lERvoLMjNSUVHVKKCm8F97fe35L3GVm5fS09O5f/8+ZcuWfecl0r+8Jw0+YRMnTiQ+Pl7h+H7w+9sl7HNna2PN4X272ffnDnp268Lo8f/j9t27BZZ/kZjIhYuXCpznXlySJHHCx4cmRezQ7z1wkKUrV7N66WKMDA3fXEH4ajhVb07V+p0oY1uZStU8GTH7ECmJcVw6/UeBdVKSErgdfJoq7gV36ItDkiSu+h1873GFr5O5bUMsK7ZCr5Q9Zjb1qd9xKxmpCYTfOlhgnYy0Fzx/eAHzQjrexSFJEk9CT2BuJzZ5/BJ8DRtLJScn079/fzQ1NXF0dCQ8POeX2+HDhzNv3rxixRQd+v+Quro6Ojo6Coe6mtrHbtYnQ01NDWsrK5wqOTJ+jBf2FSuwZdv2AsufOn0WO7uymJuZFVjG2MiIqKhohbSoqCiMjYwKqAGBV6+SlZmFm6vLG9u8/+DfjJ80hVVLF1Ondq03lhe+bpol9ShVpjzPnxT8RfXapcOYWTpgUMqiwDK6+qa8iHumkJYQ+6zQufr3b14kKysTO4eC71NdA1MSYvPGLaGpI0bnhUKpaeiibWBDYuyDAss8veeDjmE5tHTMCyyjoWVMarLiaHxq0nNKaBkXUANingYiZWdhVLrg5aY1tIxJTVL8ZTY1OQpVNW0xOv+JkamqIFNVfeX48jb3nDhxIkFBQZw6dUphhUUPDw927txZrJiiQy98srIlifT0jALzj3mfpGkh020AXJ2dOeerOK/47HnfQufmHz9xkkYN6qP8hlGBfQcPMWbiJFYsXkjjhuKhKuHNUlMSef40FF2Dgr+Evmm6DYCtgzs3rigu5Xcj4AS29jULibsPp+otFTZsyRPXviY3X4sbEnACWwf3AmoIQo6M9CQS48IKfUj20d2CV6F5ydDclWdh5xTSIh78g6G5ayFxj2FWthFKSgXf2/nHPYth6YLjCh+J0mvz5wv57/q52rt3LytXrqROnToKu8Q6OjoSGhparJiiQy98EuYvWsyFS5d5+OgxN2/dZv6ixfhduEi7Nq3yLZ+ZmcmpM2fxeEOHvl+fXpw++w/rN23hbug9lixfSfC1a/Tp2aPAOsdP+rxx/vzeAwfxGjeRHyeMw7lKZSKfPyfy+XMSXrx488UKX40/143hVtBpoiIecPf6eVZP+wYlJWWqN+yWb/msrEyuXTr8xmkxjduP4PqlIxz78yeeht9k/8/TeHD7Mo3aFvw8TpDfAZwLmT8PUL/VYJ5H3OOvDeN4Gn4Tn/2ruXz6Dzy+GfmmSxW+Mld8ZhEZ7kdi/EOiHl/mnz0DkcmUsbRvm2/57OxMnt7zeWOHvkLVb3l6/zQ3L64nIfouwf8sJjbiKuVc+xZY50kRvijYOfckMT6cwFOzSYi+y50rP/Pw5kEqVM1/yWPh4/kaptw8f/6cUqXyfvlNSkpS6OC/jS/vdwzhsxQVE4PXuAlERj5HW1ubihXKs33zBuoWMI3F7+IltDQ1cXIsfInIqq4uLP9pAYuWLmfh4qVYW1uxftWKAtegDwsPJywsnPp1axca9/edf5KZmcnk6TOZPH2mPL1j+3b8NH/OG65W+FrERj1mw5zuJL2IpqSuMeUq1WHicl+09fKfPnA76DTqJUpiVa7wUUM7x1oMmPgre7dOZs+WSZQqXY6h0/YUuAZ95JNQIh/fxbFq4UtPGpvZMGLWQXau8cJ7z3L0jcrQ22uDWLJSyCPlxVPOHxhGemoc6iUMMC5TDY+ee9HQzP85osiHfqioaWFg6lRoXKPSVXFvtZzgs4u4enYB2vrW1Gm/ocA16F/EPuBFbBhm1oX/SlpSz5J6HbZy5eQMbvtvoYS2KdWaLRBLVn6KlF4blf8CR+irVq3KoUOH5HsqvezEb9y4EXf34v0i+ll06FetWsXChQuJiIigSpUqrFixgurVq+dbdsOGDfz8889cu3YNADc3N+bMmZOn/I0bNxg/fjynT58mMzMTBwcHdu3ahaWlJQCpqamMHj2aHTt2kJaWhqenJ6tXr8bEJHcFiPDwcIYMGYKPjw8lS5akT58+zJ07952fVP4aLZzzdnsAHPc+SeNGDYpUtmXzZrRsXrTVZ46dOIl7zRpoamoWWm7nL9uKFE/4ug2aVPAKXvkJ9N1HlZr5/yr1uqr1O1G1ftG2Dg88v4+Kzo1QL/HmHbArVGnAlLViKT+hcLXarHqr8o/vHKN02aI9tGpZsRWWFYv2/8Hju8cxsaqFilrhn9kAJpbuNOt7uEhxhY9IVTXnkL/+8tahnzNnDs2bNyckJITMzEyWLVtGSEgI58+fz7MufVF98lNudu7ciZeXF1OnTiUgIIAqVarg6elJZGRkvuVPnTpFt27d8PHxwdfXFwsLC5o2bcrjx7nr44aGhlKnTh0qVqzIqVOnuHr1KpMnT1Z4MGHUqFEcOHCAP//8k9OnT/PkyRO++SZ3S+msrCxatmxJeno658+fZ9u2bWzdupUpU6Z8uDdDkKtQrhy9unV973HNTE0Y+t3A9x5XEIqitHUl6rfOfwfNd6FvXIbm3Sa897iCUFS6RhWwc+n13uNqaptiX+P79x5X+IiUlHJH6ZWUc15/YerUqUNQUBCZmZk4OTlx7NgxSpUqha+vL25ubsWKKZOKuuj6R1KjRg2qVasmX/c+OzsbCwsLhg8fzoQJb/4DlZWVhb6+PitXrqR3795AzgZQqqqqbN+e/woq8fHxGBsb89tvv9GxY0cAbt68ib29Pb6+vtSsWZPDhw/TqlUrnjx5Ih+1X7t2LePHj+f58+eoFXH1mvA7eXfXFYTP3bGw/DcuEoTPmffxRx+7CYLwQfw+v+BVtf4rCQkJ6Orq8uyPxeho5q6qlZCcgklnL+Lj49HRyX+zvc9JRkYG3333HZMnT8bGxua9xf2kv/akp6fj7++Ph0fuz3RKSkp4eHjg61u0nQuTk5PJyMjAwMAAyPlCcOjQIcqXL4+npyelSpWiRo0a7N27V17H39+fjIwMhfNWrFgRS0tL+Xl9fX1xcnJSmILj6elJQkIC169ff5fLFgRBEARB+DopqYDyK4fSlzWNWVVVlV27dr33uJ90hz4qKoqsrCyFTjOAiYkJERGF74j40vjx4zE3N5d3ziMjI0lMTGTevHk0a9aMY8eO0b59e7755hv5vKWIiAjU1NTQ09Mr8LwRERH5tutlniAIgiAIgvCWFKbbfJnLVrZr105hIPl9+LK+9rxm3rx57NixQ2Hh/uzsbADatm3LqFGjAHB2dub8+fOsXbuW+vXFE++fgm2//Mb6TZt5/jwK+4oVmD55Es5VKr+x3v6DfzPcawxNGzdiw5qV8nRJkli8fCW///EnCQkvqOrqwuzpU7Cxtlao7+1zmuWrVnPj1m3U1dWpWa2qPE7IjZusWb+RS/4BxMTGUqZ0aXp268K3fd7/vFDhy3P76hmO/rmIsNv+xMc85ftpu3Gp3a7QOreCTvHH2tE8CbuOvrEFLbtPorZnX4UyPvtWcfTPRcTHRGBRtgrdhi7HpmLuIgAZ6an8sXY0l07tJDMjDceqnvQYsQod/dwBiejIcH5d9j23gnxQL1ES9ya9+ab/XJSVv+g/EcJ7EvnwAjcvriUmIpjUpEjqtN9AmXKFr4z0LNyXwJMziY++jaa2GQ7uI7B1UnzI+07ANm5cXEdq0nP0Stnj5jEDQzNneX5WZipXfGYRfmM/2VnpmNrUp2qTWWi8sglVUsJjLh+bRGT4eVTUtLBx7Ejl+uNR+sJGfb8kkooK0iuLi0hf4EIj5cqVY8aMGZw7dw43Nze0tBQXLBgxYsRbx/ykR+iNjIxQVlbm2TPFnQufPXuGqWnBOyICLFq0iHnz5nHs2DEqV87tCBoZGaGiooKDg+Jyh/b29vKtd01NTUlPTycuLq7A85qamubbrpd5QvEdOHSYWXPn88Ow7zm49y/sK1akV/9BREVHF1rv4aPHzJ6/kOpV8z5QsnbDJrb+/Atzpk9l35870NQsQa9vB5GaliYv8/fRY4waN55OHdpzZP8edu34hbatW8rzg69fx9DQgKWL5nPi0H6GDRnE/J+WsHX7r+/v4oUvVlpqEmVsK9N9+Mo3FwaeP73P8h9b5aw6s+YKHu1/4OfFA7l26ai8zKVTO/lj3Wha95zC5DX+lLGtzNKJzUiIzV00YOeaUVz1O8h3k/9g7E+niIt+wuppHeT52VlZrJjUiszMdMYvPUe/sVs5f2wb+7aKB/yFosnMSEavlANVmxRttbLEuHDO7OpLKUt3PPsepnzV/lw6Mo6n93NX9wi/sZ8rPjOpVHsknn0OoWdsz6k/eirs9nrl5Aye3D1B7bZraNTtD1ISn/HP3kHy/OzsLM781ZfsrHQ8euyhRovF3L/2J8H//PT+Ll54/5SUc6bZyI8vb4R+06ZN6Onp4e/vz/r161myZIn8WLp0abFiftIdejU1Ndzc3PD29panZWdn4+3tXeg6nQsWLGDmzJkcOXKEqlUVt4JWU1OjWrVq3Lp1SyH99u3bWFlZATlLXaqqqiqc99atW4SHh8vP6+7uTnBwsMJqO8ePH0dHRyfPlwXh7WzcspWunTvRucM3lLezY86MqZTQ0OCPv3YXWCcrK4sfxoxj1IhhWFooPtwjSRKbtv3MsO+/o6lHY+wrVmDxgnlERkZy7HjOf+PMzEymz5rL/8aNpWe3rtjaWFPezo5WLZrL43Tp2IFpP/6PmtWrYWlpwTdt29Dpm/YcOX7iw7wRwhfFqXpz2vebhWud9kUqf/rgWoxMbeg8+CfMrOxp1G4YbvU6cmL3UnmZ47uWULf5AGo364e5lQM9f1iLmrom545uBiA5KZ5/jmym8+CfsHdphFV5N/qO2UxoyHlCQ3J2UL7uf4wn4SEMmLAdSztnnKo3p22fGZzav5rMjPT3/j4IXx5z24ZUrjuWMuWLtjzw3cBfKKlrgUujyegalqO8a18sKrTg1uWN8jI3L2+kbOVu2Dp1RteoPNU856KiWoJ7wTsBSE9L4N7Vnbg0moyJVW0MTCtTo/kioh77E/UkZ9nViAdnSIi+g3urZeibOGJu2xCnOqO5G/AzWVni3v5USUrKeY4vzf379ws87t27V6yYn3SHHsDLy4sNGzawbds2bty4wZAhQ0hKSqJfv34A9O7dm4kTJ8rLz58/n8mTJ7N582asra2JiIggIiKCxMREeZmxY8eyc+dONmzYwN27d1m5ciUHDhzg++9zlr7S1dWlf//+eHl54ePjg7+/P/369cPd3Z2aNXO2Vm/atCkODg706tWLoKAgjh49yo8//sjQoUNRV1f/D9+hL0t6ejrB10OoUyt3C3slJSXq1HInIDCwwHrLVq7GyMCArp065Ml7+PARz59HUeeVL4E62to4V6ksj3nteggRz56hpCSjedtvqFq7Hr37D+LW7TuFtvdF4gv0dHXf7iIFoQju3fDD3qWxQpqjW1PuheQ8mJ+ZkU7YbX/sXRUXDbB39ZB31sNu+5OVmaFQxsyyIgalLLl3IyfOvRBfSls7KUzBcazqSUpyAk/CxAP+wvsX/SQAE6s6CmmmNvWJfpzTEc/KSic2IhgT69wyMpkSJlZ1iP63sx4bEUx2doZCHB1DOzR1SsvjRD8OQNe4osIUHFOb+mSkvyAh6vYHuz7hHSkr5z2+UOnp6dy6dYvMzMx3jvXJd+i7dOnCokWLmDJlCs7OzgQGBnLkyBH5A6jh4eE8ffpUXn7NmjWkp6fTsWNHzMzM5MeiRYvkZdq3b8/atWtZsGABTk5ObNy4kV27dlGnTu4Hw5IlS2jVqhUdOnSgXr16mJqasnt37gixsrIyBw8eRFlZGXd3d3r27Env3r2ZMWPGf/CufLliY+PIysrCyMhIId3IyJDnz6PyrXPpsj87/9rNvFn5v/eRUVH/xig4ZvjDnOXolq5YxfDvB7Nl3Rp0dXXp0rNPnqlXL10OuMLBv4/QvUvRNvcRhLcRHxOh0MkG0NE3ISU5gfS0FBLjo8jOzsqnTCkSYnMezE+IjUBFVQ3Nknp54sTH5JSJj83/PC/bIAjvW2rSczS0FD+PNTSNyEh/QWZGKunJMUhSFhqar5XRMiIl6TkAKUnPUVJWQ01DN0+clKTIf8tE5hPDWF5f+DRlK6vmOb40ycnJ9O/fH01NTRwdHeVTvocPH868efOKFfOzeNJg2LBhDBs2LN+8U6dOKbx+8OBBkWJ+++23fPvttwXma2hosGrVKlatKng3PCsrK/7+++8inU/4MBITkxg5bgLzZk3HwEC/2HGypZyHpYcN/o4Wnk0BWDRvNjXrNuTQkaP06NpFofyt23cYOGQYPwz7nnp1ahf/AgRBEARBkJNkitNsJNmXN0I/ceJEgoKCOHXqFM2a5U5V8/DwYNq0aUXaZ+l1n0WHXvh66OvroaysTFSU4mh8VFQ0xsZGecqHhYfz6NFj+g8eKk97uZKRrb0TPkcPUerfkfmoqChMSuX+9BoVFY2Dfc4GSKWMc9LL2ZWV56urqWFpUYbHT3J/AQK4ffcu3ft8S7cunRjx/eB3uVxBKJCugSkJsYoP3ifEPqOEpg5q6iVQUlJGSUk5nzKR6OjnPJivo29KZkY6yYlxCqP0CbHP0DXIKaOrb8r9m5fynOdlGwThfdPQMlZ4uBUgNTkKVTVtVFQ1kCkZIJMpk5r8WpmkKEr8O8JeQsuY7Kx00lPjFUbpU5OjKKFV6t8ypYiJCHotxnN5feET9XKn2Fdff2H27t3Lzp07qVmzJjKZTJ7u6OhIaGhosWJ+ee+S8FlTU1PDydGBc75+8rTs7GzO+frh6uycp3zZsrYcO7iPw/t2y48mjRriXqM6h/ftxszUFAuLMhgbGynEfJGYSGDQVXlMp0qOqKupEXr/gbxMRkYGjx4/oYy5uTzt9p07dOvVjw7t2zLOa+T7vnxBkLO1r8nNKycV0kICTmDrkPMsiIqqGlbl3bhxRXHRgBtXvCnrkPMMilV5N5RVVBXKRDy8RUxkOLb2OXFsHdx5/CBYYWWcGwHHKaGpg5mleMBfeP8MzV15FnZOIS3iwVkMS7sCoKyshr6pk0IZScrmWdg5DM1zyuibOqGkpKpQJiE6lOSEx/I4hqVdiX9+U+HLQ8SDs6iqaaNjWO6DXZ/wbr6Gh2KfP39OqVKl8qQnJSUpdPDfhhihFz45A/r1ZfT4iVSuVIkqlZ3YvO1nklNS6NQhZ3WQUWMnYGpSivFjvNBQV6dCecUP5pdbQ7+a3r9Pb1asWYeNtRUWZcrw09LllCpViqZNch461C5Zkh7durBk+UrMzUwpbW7Ouo05K4W0bJ6znvKt23fo1rsf9erUZkC/PkQ+zxnpUVZWxvDfnYgFoSCpKYlEPr4rfx0VcZ/wu4Fo6RhgWMqS3ZsmEhv1hP7jtwFQv9VgfPav4q8N46jt+S03A09y+fQfDJ91UB6jSYdRbF7QF+vyVbGpUJ0Te5aSnppEbc+cRQM0tXSp0+xb/lg7Gi1tA0po6vD7qhGUdXCXd/od3ZpibunApvm96ThwPvExEezdOpkGbb5HVU084C+8WUZ6EomxD+Svk+IeEvvsOmol9NDSKU3Q6XmkJEZQs+VSAOyce3LnyjYCT83G1qkLz8LP8/DmQep13CqPUbHqAPz+Ho2BqRMGZs7cvryJzIxkbJ06A6CmroNt5S5c8ZmJmoYequol8T8xFUNzN4z+7fSbWtdDx7AcfodGUqXB/0hNek7wP4uwc+2Nsoq4tz9V2TJlsl+ZZpP9BU65qVq1KocOHWL48OEA8k78xo0bC13FsTCiQy98clq3bE50TAyLl6/g+fMoHOwr8vOmdRj/O3XmydOnKL3lT3CDB/YnOSWFiZOn5mws5ebKz5vWo/HKikT/GzcGZWVlRo2dQGpqKs5VKvP7z5vR/XcVm7+PHCU6JoY9+w+wZ/8Beb0ypc055yOWrhQKF3b7MovGNJK//mPtaADcm/Th23FbiIuOICYyXJ5vbGbDiFkH2bnGC+89y9E3KkNvrw1Uqpa7YU+1Bl14EfecfdumkhAbgUVZZ36Yc1jhIdcuQ5YgkymxZkbHnI2l3HI2lnpJSVmZ4bMO8Muy75n3Qy3UNLSo1aQ3bfuKB/yFoomJuIrPjtznjK745Nw71pU6UrPFYlKSIklKeCLPL6lnSb0OW7lycga3/bdQQtuUas0WYGaTu7GjpX0bUlNiCP5n8b8bSznQoNN2hRVrXBpNAZkS5/Z9R1ZWOmbW9XF7ZS18JSVl6nXYwuXjkzjxSztUVDWxrtQRpzqjP+TbIbyj1x+E/RIfip0zZw7NmzcnJCSEzMxMli1bRkhICOfPn+f06dNvDpAPmSRJ0ntup/AWwu/c+NhNEIT37lhYxY/dBEF477yPP/rYTRCED+L3+RZvLvSBJSQkoKury33fY2iXzN059UViEjbuTYmPj5f/Av8lCA0NZd68eQQFBZGYmIirqyvjx4/HycmpWPHECL0gCIIgCILwSfhSp9x4eXkxc+ZMtLS0OHPmDLVq1WLDhg3vLb54KFYQBEEQBEH4JEgypZylK+XHl9FVXbFihXyT04YNGxITE/Ne44sRekEQBEEQBOGTkKWkQpaSisLrL4G1tTXLly+nadOmSJKEr68v+vr5759Tr169t47/ZXztEQRBEARBED572UrKeY7iWLVqFdbW1mhoaFCjRg0uXrxYaPk///yTihUroqGhgZOT03vfOHThwoVs2rSJhg0bIpPJaN++PQ0aNMhzNGzYsFjxRYdeEARBEARB+CS8nEP/6vG2du7ciZeXF1OnTiUgIIAqVarg6elJZGRkvuXPnz9Pt27d6N+/P1euXKFdu3a0a9eOa9euvevlyLVr146IiAgSEhKQJIlbt24RGxub5yjuVBzRoRcEQRAEQRA+Cdm81qHn7Tv0ixcvZuDAgfTr1w8HBwfWrl2LpqYmmzdvzrf8smXLaNasGWPHjsXe3p6ZM2fi6urKypUr3/Vy5Ly8vEhKSqJkyZL4+PhgY2ODrq5uvkdxfBkTkz5jxhd2f+wmCMJ718i9y5sLCcJnRr1F2Y/dBEH44mXLlF5b5SZn7DkhIUGhnLq6OurqeTcIS09Px9/fn4kTJ8rTlJSU8PDwwNfXN99z+vr64uXlpZDm6enJ3r17i3sZeaxYsYLx48ejpaVFo0aNePr0ab67xRaX6NALgiAIgiAIn4QslMl6ZVT+5b8tLBTXyp86dSrTpk3LUz8qKoqsrCxMTEwU0k1MTLh582a+54yIiMi3fERERHEuIV8f+qFY0aEXBEEQBEEQPgkSitNspH///fDhQ4WNpfIbnf+ULVy4kMGDBzN37lz5Q7H5kclkZGVlvXV80aEXBEEQBEH4f3v3HV/j9Qdw/HOzh0zZCCIJSZAQW+0Re9Sm1VpFbWr97FUt1aJWqdIqpa0dM1aN2jEiCCERiey9k5t7f3/cuMmV3CzUOu/X677ce59zznPulfB9znPO9whvBRkayPIt8Xz+3NjYuEQ7xVpYWKCpqUlkZKTK+5GRkdjY2BRax8bGplTly+L5QtuUlBSMjY0JCAh4pVNuxKJYQRAEQRAE4a0gk2sUeJSGjo4Onp6enDx5Mq9NmYyTJ0/SuHHjQus0btxYpTyAj4+P2vIvQyyKFQRBEARBEN5r6ubQl8bkyZP57LPPqFevHg0aNGDlypWkpqYyZMgQAAYPHkyFChVYunQpABMmTKBFixasWLGCzp07s3PnTq5du8bGjRtfzYdCsaj3+R2GOnXqkJaWprZsSe5EvEgE9IIgCIIgCMJbIQcNcvKNyueUYTJJv379iI6OZu7cuURERODh4cHRo0eVC19DQkLQ0Mhrt0mTJuzYsYPZs2fzv//9DycnJ/bt20fNmjVf/gPlMjMzU2a2MTU1RSKRFCgjl8vFHHpBEARBEATh3fbiNJvSTrl5buzYsYwdO7bQY2fOnCnwXp8+fejTp0+ZzlUSp06dwtzcHIDTp0+/8vZFQC8IgiAIgiC8FWRyyQsBfcGR7HdRixYtCn3+qoiAXnhr/HXxNt8fOkfAs2j0dbRp4erA4n7tcbAur7bOnF3HOHc/mKDIOJIyMrE1NaKDR3VmdG+JlUk5lbLn7wex7MBZrj16Snq2FGuTcnSp68J3n3ZWlvENCmP+Xye4/DAEqUyGRxVbZvVsTeuajiptrT9+kU0nr/A4Kg4TfT061KnOwr7tsX7hnIIgCB8CuVzOni3zOOP9M2kpCTjVbMrnk9dhU9FJbZ2D25dy7exewkPuo62rj5NbE/qN/AZb++oApCTFsWfLPO5c8yE2MgQjU0s8P+pOr6GLMCiXt3AwJjKEX3/4kns3TqOrX46PvAbTd8RSNLXyQpx7N86wY90UwoL9MbesRPdPZ9Gs4+ev7fsQyi5HroE0/5SbMo7Qv80ePnzI/v37CQ4ORiKR4ODgQPfu3XFwcChzm+/ftyS8k7aeucZn6/7k1pNwbEyNyJHJ2HfVn9YLNxKRkKy23grvc1x/HIaVSTnMy+kTHB3PBp9LdP5mCzKZTFlu92U/Oi7dwgm/h2hqaFDDzhIJcOzWA2UZv5AI2i/+mRN+D9HV1sTMUJ+LD0Lovvw3Tvg9VJZb8PcJpmw7xP1n0dhbmJKSmcW2s750+HozaZlZr+X7EQRBeJsd+mMZPrt/5PPJ65m3/hK6+oYsn9qBrMwMtXXu3zxL2x5fMnfdRaZ/d5ycnGyWTfUiMz0VgISYZyTEhjNg9HK+3uLHFzO2cPvKMTYvG65sQ5aTw/czuiDNzmLOmgt8MXMr54/+yp4tc5VlosODWDGzCy51WrLo5xt49Z7A5uUjuH3l2Gv7PoSye9ksN2+7pUuX4urqyvTp09m9ezd//fUXX331FTVq1OC7774rc7vv17ckvJOypFLm/nkcgB713bj7/RR8v52AkZ4uUUmpLD/wj9q607q14PGP07m6dBwPVk6lR303APxDI7kdotjhLTUjiwlbD5AjkzG5czOC1kzn4uIx3PvhK/5d9KWyrQV/nyAtK5vKFqb4r5jC/R+mUL9aRXJkMv73x1EAIhNT+N77HAATOjbl9vJJnJk3EolEQsCzaH4+dfW1fEeCIAhvK7lczrG/V9Ht01l4ftQd+2q1GTnzVxJinuF7fp/aelOXH6FZx8+pWNUNe0d3RszYQmxkCEEPrgNQ0aEm4xf+TZ0mXbGuUA3Xuq3pM3wxNy4eJEcqBcDv2nHCntxl1KxtVHbywL1hR3oNXciJfeuQZisGWE4d2IClTVUGfrmCCpVdaPfxWOq36M2xv1a+7q9GKIP3OaA/ffo0s2fPZtasWcTExBAeHk5ERATR0dHMmDGDGTNmcPbs2TK1/f58S8I76/rjMGKSFembetR3BcDOzJgGjhUB8Mk3Ov6i+X3aYWlsCICmhgaNnOyVx3S1FbdbT/k/Ii4lHYCoxBQcJyyn4ugl9P7+d6KSUgCQ5uRw2v8RAG1qOWKkr4uWpiad69YA4M7TSJ7FJ3H6TiDZuavPn1881LK3oZq1YqGLz+28EX9BEIQPQXR4EIlxEbh5tlW+Z1DOBAfXhgTevVjidtJTEgEoZ2SutkxaSiL6BsbK6TSB/hepVLUWJubWyjK1GniRnppEaLB/bplLuHm2UWmnVoP2peqb8N/JkUsKPN4XGzZsYPjw4cyfPx8zMzPl++bm5ixcuJChQ4eyfv36MrUtAnrhjQuNTVQ+tzTOm4P+fA7803zHi5KakcX28zcAaOxsj0sFxQ5sD8NjlGW2X7iJRTkD0rOkHL5xH68lm0lMyyAmOY30rOyCfcj3PDQ2kdC4/H01LFCupH0VBEF4XyTGKe6G5g+qAUzMrEmIiyysSgEymYzf10zCqWZTKjoUniowOSGG/dsW07LrCJVzG79wXmMza5V+JcRFFNq39NQksjLTS9Q/4b8jk0kKPN4XV65c4dNPP1V7/NNPP+XSpUtlalsE9MJbSy4vednopFQ6ffMLfiERVLez5PdxA5THpLK8fK5zPm7NtW/Gc2DaZwA8i0/iwLW76vtQ0r6WuKQgCMK77V+f7YzoYKR85EizX7rN31aOISzoDmPm/lHo8fTUJFbM7EKFyq70/Hz+S59PeHtJZRoFHu+LyMhIqlSpovZ41apViYiIKFPbIsuN8MZVLJ+XrSA6dwpM/ueVyhe9DfKD8Gh6fvcbQVHxNHCsxN+TP8HCKG/03M4sb8c1TwfFNJ56uX8CPImJx8LIAH0dbdKzsgvtw/N+VjTP39dUZQae6KTUEvVVEAThXVenaTequTRUvs7OzgQgMS4S0/K2yvcT4yOp7OhebHu/rRzLzYuHmLX6H8ytKhY4np6WzPJpHdHTN2L8oj1oaWkrj5mY2/D4nurapaT4SOUxAFNzGxJfuFOQGB+JvqExOrr6xfZP+G+9OM3mfZpyk5GRgY6Ojtrj2traZGWVLbmGCOiFN87ToQLlyxkQm5LGvqt36dvYnWfxSVwJDAWgXS1F2jOPaSsBGNmuEaPbNQIUqSj7r9pBXEo6Peu7sXlUb/R0tFXab+nqgIZEgkwuxzcojHa1nfANClMed7Quj5amJi1dHThyM4CTfoEkp2eir6PFId/7ANSsZI2dmTEt3aqhpamBNEeRhaehkz1+IRE8ioxT9LW282v9rgRBEN40fQMj9A2MlK/lcjkm5jbc9T1JZScPQDGi/vjuZdp0G6W2HblczrZV47h+fh8zV57G0rZqgTLpqUksm9oBbW1dJn29Hx1dPZXjjm6NOfD71yTFR2FspphmeeeaD/qGxlSo7JpbphG3Lh1RqXfn2gkcXRuX6fMLr5f8hWk28vdoyg3Azz//TLlyhae4Tk5Wn9WvOCKgF944HS0t5vdpx7gt+9l31R/XySuIS0kjOSMTCyMDpnRtDsCD3Lnwscmpyrpdvt1KljQHiUTC09hEvL7erDw2o0crOnpUp2J5U0a1a8S64xdZuPskf1/yIyhKEYC7VLCiZwPFfM15vdty5u5jnsQk4DZlBTpaWjyLT0JTQ4PF/b0AsDE1YmKnj/ju4FlWHbnA4RsBhMYlIpfLcbQpz7BW9f+T70wQBOFtIZFI8Oo9gf3blmBd0QlL26rs3jwXUws76n7UQ1num8lt8fyoB+0+Vuze+evKMVw68QcTl+xDT9+IhFjFVAODcibo6OorgvmvvMjKTGPUrG2kpyaRnpoEgLGpJRqamtSq154KlV3Z8PVg+o/8loS4CP7ePIe2Pb5EW0cXgNbdRuGzdy07N0yjeceh3L1xiiun/2TyN97/7RcllMj7PEJvb2/Ppk2bii1TFq8loJdIJOzdu5cePXq8juaVzpw5Q6tWrYiPj8fU1BSAffv28dVXXxEUFMS4cePw8PBg4sSJJCQkvNa+CC9nWOv6GOpqs/LIBQKeRaOnrUX3eq4s6tdeZcrMi7Kkivnxcrmca49DVY7FJOUF/ssGdcTWzIitZ67zMCIGOzNjOnhUZ06vNspsOLUr23Js1jAW/OXD5cCnpGRk0cjJnv/1bEXbWnmboyzo0w5rk3L8fOoqjyPjMDHQpVeDmizs1x5DPfW30gRBEN5XnQdMIzMjlS3fjVRsLFXrI75adkRlRD0q7BHJiXlJCk7t3wDA1xNbqbQ1YvovNOv4OcEPfHl07zIAUwepblC14o/HWNpWQUNTk8lLD7L1hy9ZOKYJunqGfOQ1mI+HLFSWtbStypSl3mxfO5nju1djblmRYVM3UbuB1yv/HoSXJ82RIM2RqLx+XwQHB7+2tiVyeWmWHkJERARLlizh0KFDhIWFYWVlpQya27RRpIX6rwL6rKws4uLisLa2RiJR/IVbW1szZMgQxo8fj5GREVpaWiQnJ2NlZfXKzz9//nx27tzJ06dP0dHRwdPTkyVLltCwYcPiK+dK/33JK++XILxp4Y37vekuCMIrdyG02pvugiC8Fp+2ePNBc1JSEiYmJvx6Mh4Dw7yBvLTUJD5rY0ZiYiLGxuoH+N5VoaGh2NnZoaHxcot/S1U7ODgYT09PTp06xfLly/Hz8+Po0aO0atWKMWPGvFRHykJHRwcbGxtlMJ+SkkJUVBReXl7Y2dlhZGSEvr7+Swfz2dmFr+B3dnZmzZo1+Pn5cf78eapUqUL79u2Jjo5+qfMJgiAIgiB8iGSygo/3maur6ysZuS9VQP/ll18ikUi4cuUKvXr1wtnZGTc3NyZPnlxk3szp06fj7OyMgYEBDg4OzJkzRyVIvnXrFq1atcLIyAhjY2M8PT25du0aAE+ePKFr166YmZlhaGiIm5sbhw8fBhRTbiQSCQkJCZw5cwYjI8UindatWyORSDhz5gxbt25VTsd5bv/+/dStWxc9PT0cHBxYsGAB0txd50Bxh2H9+vV069YNQ0NDliwpfBR94MCBtG3bFgcHB9zc3Pj+++9JSkri9u3bpflaBUEQBEEQBCBHJinweJ+VcqKMWiWeQx8XF8fRo0dZsmQJhoaGBY6/GDTnZ2RkxNatW7Gzs8PPz48RI0ZgZGTEtGnTABg0aBB16tRh/fr1aGpqcvPmTbS1FZlKxowZQ1ZWFmfPnsXQ0JC7d+8Wujq4SZMmBAQEUL16dXbv3k2TJk0wNzcvcNVz7tw5Bg8ezOrVq2nWrBmPHj3iiy++AGDevHnKcvPnz+ebb75h5cqVaGkV/zVlZWWxceNGTExMcHcvPk2XIAiCIAiCoEomgxyZ6muheCUO6AMDA5HL5dSoUaPUJ5k9e7byeZUqVfjqq6/YuXOnMqAPCQlh6tSpyradnPIWv4SEhNCrVy9q1aoFgIODQ6Hn0NHRUU6tMTc3x8bGptByCxYsYMaMGXz22WfK9hYtWsS0adNUAvqBAwcyZMiQYj+bt7c3/fv3Jy0tDVtbW3x8fLCwsCi2niAIgiAIgqDqfV4UW5j//e9/mJubv3Q7JQ7oX+aWwK5du1i9ejWPHj0iJSUFqVSqsrBh8uTJDB8+nG3bttG2bVv69OlDtWqKxUfjx49n9OjRHD9+nLZt29KrVy9q165d5r7cunWLCxcuqEyjycnJISMjg7S0NAwMDACoV69eidpr1aoVN2/eJCYmhk2bNtG3b18uX778WhbhCoIgCIIgvM9enDf/vo/Qz5w585W0U+KA3snJCYlEwv3790t1gosXLzJo0CAWLFiAl5cXJiYm7Ny5kxUrVijLzJ8/n4EDB3Lo0CGOHDnCvHnz2LlzJz179mT48OF4eXlx6NAhjh8/ztKlS1mxYgXjxo0rVT+eS0lJYcGCBXz88ccFjunp5aXXKmxaUWEMDQ1xdHTE0dGRRo0a4eTkxObNm1/ZX9D77q+Lt/n+0DkCnkWjr6NNC1cHFvdrr9yBtSjhCck4jl/Gys+6MqJNg0LLRCamMPfP4xy9EUBiegYOVuaMaNtQuTFVccZt2c8h3/s8Wj1Nufg6v8V7TvL13tOF1k3augAtTc0SnUcQBOFdsmfLfPb9ulDlPdtK1fl2271i6+7duoDI0EBGzd5W6PErZ/5i9+a5xEQEY13RiX4jv8G9Uadi2z139FfOeP/MnDXnCj1+78YZdqybQliwP+aWlej+6Syadfy82HaF/1bOC1Nuct7DgD4nJ4etW7dy8uRJoqKikL1w1XLq1KlSt1nigN7c3BwvLy/Wrl3L+PHjCwS8CQkJhc6j//fff6lcuTKzZs1SvvfkyZMC5ZydnXF2dmbSpEkMGDCALVu20LNnTwAqVarEqFGjGDVqFDNnzmTTpk1lDujr1q1LQEAAjo6OZapfHJlMRmZm5mtp+32z9cw1vty8D4AqlmbEpaSx76o//wYEc2nJWGxMjYqsf8hX8R9H57qFTwNLzcjCa8nPPAiPQV9HG3sLU+4/i2bKb95EJ6Uwt1fbItuXy+Uc8r1P57o1Cg3m87MwMqCqleots+LqCIIgvMsqVHFj+gof5WtNzZKFFL4XDtBl4PRCjz288y/rFg6kzxdf49G4CxdP7GDl7J4s2nidig41i223btOuhR6LDg9ixcwutO42klGzf+fu9ZNsXj4Ck/K2Ih/9WyYnR/HI//p9M2HCBLZu3Urnzp2pWbPmK4kXSrWx1Nq1a2natCkNGjRg4cKF1K5dG6lUio+PD+vXr+fevYJX5k5OToSEhLBz507q16/PoUOH2Lt3r/J4eno6U6dOpXfv3lStWpXQ0FCuXr1Kr169AJg4cSIdO3bE2dmZ+Ph4Tp8+jYuLS5k/8Ny5c+nSpQv29vb07t0bDQ0Nbt26xZ07d1i8eHGJ20lNTWXJkiV069YNW1tbYmJiWLt2LWFhYfTp06fM/ftQZEmlzP3zOAA96ruxY/wAnsUnUWfaKqKSUll+4B9WDO5SZBve1+/jWbWC2o2nNp++yoPwGEXGo3kjqWVvw4wdR1h95AIrDp5jZNtGWJsUvv0ywLXHYUQkJNOlbvE/bx3cq7NxZK9iywmCILwvNDW1MC1f+Ho1dWKjnhIW7E/tBh0KPX5s92pqNehA5/5TAeg9bBH+107gs3cNQ6ZsUNtuVmYGd64ep8/wwrPSnTqwAUubqgz8UjE7oEJlFx74XeDYXytFQP+WkcrkSHPkKq/fNzt37uTPP/+kU6fi7zyVVKnSVjo4OODr60urVq2YMmUKNWvWpF27dpw8eZL169cXWqdbt25MmjSJsWPH4uHhwb///sucOXOUxzU1NYmNjWXw4ME4OzvTt29fOnbsyIIFCwDFbYkxY8bg4uJChw4dcHZ2Zt26dWX+wF5eXnh7e3P8+HHq169Po0aN+OGHH6hcuXKp2tHU1OT+/fvK9J1du3YlNjaWc+fO4ebmVub+fSiuPw4jJjkNgB71XQGwMzOmgWNFAHz8HhZZPzk9k3/uPaaLp/pF2sdvPQDA0bo8tewV/+n0qKf4u8nOyeGM/6Miz3Hw+l2M9HRp6Vr4Quz89l3zx3zofKqO/YaPV2zjZvCzYusIgiC8yyLCHjK+VwWmDKjG+sWfEBMZUmydGxcOUMOjJfqGhQ/EBPpfxM2zjcp7tRq0J/Cu+tTYAHd9T2JmWQG7yoX/nxDof0lNuxeL7bPw35Ll5I3S5+QoXr9vdHR0XvlMkVKN0APY2tqyZs0a1qxZo7bMiwtoly1bxrJly1TemzhxIqD4UH/88Yfatn788Ue1x1q2bKlyLlNT0wLn/vzzz/n8889V3vPy8sLLS/0VeUkWAOvp6bFnz55iy+WXmZlZYDqOLFuKrnap/xreeaGxicrnlsZ5o+RWuSPmT/MdL4zP7YdkZkvp6umq/hxxibnt500PszLJe/40NqHIcxzyvU+72k7F/v1oamhgbWKElqYGAc+iOXozgNP+jzg99ws8qtgVWVcQXsb+/fuZPWeu8vUvm3+mfv36b7BHwoeimmtDvpixBZtK1UmIDWffrwtZMr45X2/xQ99A/XRJxbSYbmqPJ8ZFYGJurfKesZk1iXERRfbH98J+6jQpfLoNQEJcBLVfaNfEzJr01CSyMtPR0dUvsn3hv/MhzKGfMmUKq1atYs2aNa9seu6HF0m+QUuXLlXeeXjufz1bMfvjNmpqfHhKmkzJ2/ce1azL41KhdNmESnrj7lFkLPfCopjatXmR5fo1dufL9o0xL6fIjuRz+yHdl/9KZraUn05cZv3wnqXqnyCURps2bXB391C+trGxVl9YEF4h94Ydlc/tq9WmmktDJvevwpXTf9Ki87BC66SnJnH/1j8Mm/bzK+2LXC7nxr/ejJ2/65W2K7wZshw5snxTbvI/f1+cP3+e06dPc+TIEdzc3JR7Lz1X2gFjEAH9f2rmzJlMnjxZ5T3Z7h/eUG/erIrlTZTPo5NSCjyvlO/4i6Q5ORy9GcBnLTyLPoe5CQ/CY4hOSs1rPzHveaXypmrrHrx2Dy1NDTp4VC/yHE62qnsOtKvtRPlyBsSmpBFazB0AQXhZ5cqVK3SjPUH4rxkamWJT0ZnIsEC1ZW5dPoJdZVfKW1VSW8bE3IbEuEiV95LiIzExVz9X//G9K8hypDi5NVFbxrSQdhPjI9E3NBaj82+ZD2GE3tTUVJn45VURAf1/SFdXF11dXZX30j/A6TYAng4VlIHvvqt36dvYnWfxSVwJDAWgXS0ntXXP3Q8mIS2Drp5FL1ZtV9uJU/6PCIyMxS8kglr2Nuy75g+AtqYmLd2qqa3r7XuPZjWqYGpY9D/0K7zP0rdRbSpZmAJw0i+Q2BTF2gB7C7Mi6wqCILwvMtJSiHr2iKbtP1FbprjpNgCObo2563uKDn0mKt+7c+0Ejq7qUw37XtiPe6POaBSRJtjRrRG3Lh1ReU/RbuMi+yP896RSOVKpXOX1+2bLli2vvM0PM5oU3jgdLS3m92nHuC372XfVH9fJK4hLSSM5IxMLIwOmFDHVxfv6PSyNDGnkZF/kOYa1rs/m01cJjIil5YKfqGhuwsOIGAAmdv5IbYab6KRULj0MYfknxa8+33TyCnP/9KGiuTGGujoEhCvaN9TVYWwH9aNFgiAI77I/1n1FnSZdKW9dmYTYZ+zZMh8NDU0atRlQaPkcqZTbl4/Qqd+UItv16jWerye05MiuFbg36sylUzsJCrjG0Ck/qa3je+EgHw9doPY4QOtuo/DZu5adG6bRvONQ7t44xZXTfzL5G+/iP6zwn8rJkZOTb5pNzns45eZ1EAG98MYMa10fQ11tVh65QMCzaPS0tehez5VF/dqrTUUJitHzDnWqo6FRdJKmcnq6HJs1nHm7jnP0VgDB0fFUt7NkeOv6jPFSH2wfvnEfmVxeonSVU7u2YO+VO9wNiyIoOh778iY0dq7MjB4tcba1LLa+IAjCuyguOox1iwaSkhSLkYklzrU+Yu66ixibFv7v3v1b/6CnX44qznWLbNepZhNGz9nO35vn8NfPs7Cu4MTExXvV5qCPDHtEVFggtesXnXrS0rYqU5Z6s33tZI7vXo25ZUWGTd0kUla+hWQy1YBe9h6mrQT4+++/+fPPPwkJCSErK0vlmK+vb6nbe2cC+rVr17J8+XIiIiJwd3fnxx9/pEGDwncH3bp1K0OGDFF5T1dXl4yMDOVruVzOvHnz2LRpEwkJCTRt2pT169fj5JQ31SMuLo5x48Zx8OBBNDQ06NWrF6tWrVKZs3r79m3GjBnD1atXsbS0ZNy4cUybNu0Vf/r3V/+mHvRv6lHi8jeDn/E0NrHY6TbP2ZoalTo/vLfvPdwr2yqn0RRlWOv6DGstsooIgvBhGTNPfXa6wvhe2I9Hk6L3FnmuQcs+NGhZsv1cfC/sx6Vua3T1i9/d3aVOSxb/XPpASfhv5cjk5OQL4nPew4B+9erVzJo1i88//5z9+/czZMgQHj16xNWrVxkzZkyZ2ixVHvo3ZdeuXUyePJl58+bh6+uLu7s7Xl5eREVFqa1jbGxMeHi48vHi7rTLli1j9erVbNiwgcuXL2NoaIiXl5dK0D9o0CD8/f3x8fHB29ubs2fP8sUXXyiPJyUl0b59eypXrsz169dZvnw58+fPZ+PGja/+SxAAyM6R8b+erWhT8/Xs9AvQ2Kky8/u0e23tC4IgfGgqVq1Jm+6jX3m75pYV6TpoxitvV3hzpNmyAo/3zbp169i4cSM//vgjOjo6TJs2DR8fH8aPH09iYtFpu9WRyEuSdP0Na9iwIfXr11fmvpfJZFSqVIlx48YxY0bBX+StW7cyceJEEhISCm1PLpdjZ2fHlClT+OqrrwBITEzE2tqarVu30r9/f+7du4erqytXr16lXr16ABw9epROnToRGhqKnZ0d69evZ9asWURERKCjowPAjBkz2LdvH/fv3y/RZ0v/vfBd7QThXRbeuN+b7oIgvHIXQtUvpBeEd9mnLV5NLvSXkZSUhImJCeO/j0BXP2/abWZ6Eqsn25CYmIixsfrpuO8SAwMD7t27R+XKlbGyssLHxwd3d3cePnxIo0aNiI2NLXWbb/0IfVZWFtevX6dt27bK9zQ0NGjbti0XL6rf4S0lJYXKlStTqVIlunfvjr+/v/JYUFAQERERKm2amJjQsGFDZZsXL17E1NRUGcwDtG3bFg0NDS5fvqws07x5c2UwD4pNqwICAoiPj3/5Dy8IgiAIgvAByZHJCjzeNzY2NsTFxQFgb2/PpUuKnZCDgoJKtLlpYd76gD4mJoacnBysrVU3TLG2tiYiovCd46pXr84vv/zC/v37+f3335HJZDRp0oTQUEVKxOf1imozIiICKyvVTYu0tLQwNzdXKVNYG/nPIQiCIAiCIJTM8yw3+R/vm9atW3PgwAEAhgwZwqRJk2jXrh39+vUrc376d2ZRbGk0btyYxo3zcss2adIEFxcXfvrpJxYtWvQGeyYIgiAIgiCoI5PJyMm3m5TsPRyh37hxo/JzjRkzhvLly/Pvv//SrVs3Ro4cWaY23/qA3sLCAk1NTSIjVXd4i4yMxMZG/c5x+Wlra1OnTh0CAxU72D2vFxkZia2trUqbHh4eyjIvLrqVSqXExcUp69vY2BTar/znEErur4u3+f7QOQKeRaOvo00LVwcW92uPg3X5IuutP36RTSev8DgqDhN9PTrUqc7Cvu2VeebD4pIY+8s+/EIiiElORV9HG/vypvRv6s6Ejk2V6S/vhkay4O8TXHscSnh8MgBTujRjUT/VtGZzdh3j3P1ggiLjSMrIxNbUiA4e1ZnRvSVWanLbC4IgvK/u3zrL4Z3fEfzgOgmx4UxYtAfPZj2KrHPvxhl2rJtCWLA/5paV6P7pLJp1/FylzIm9azm88zsS4yKo5OjOp+NXU80lL7tdVmYGf6yfwqVTu5BmZVKrgRefTVyLiXnenfOYyBB+/eFL7t04ja5+OT7yGkzfEUvR1Hrrw58PljRbhoamTOX1+0ZDQ0Ml9Xb//v3p37//y7X5sp163XR0dPD09OTkyZPK92QyGSdPnlQZhS9KTk4Ofn5+yuC9atWq2NjYqLSZlJTE5cuXlW02btyYhIQErl+/rixz6tQpZDIZDRs2VJY5e/Ys2dnZyjI+Pj5Ur14dMzOxS2hpbD1zjc/W/cmtJ+HYmBqRI5Ox76o/rRduJCIhWW29BX+fYMq2Q9x/Fo29hSkpmVlsO+tLh683k5apyOsak5zKP3cfU05PB7dK1mhpaOD3NIJZO4/x/aFzyrYCI2Lx9r2Psb5ekX1d4X2O64/DsDIph3k5fYKj49ngc4nO32x5L0cSBEEQipKZkYp9tdoMnrimROWjw4NYMbMLLnVasujnG3j1nsDm5SO4feWYssylU7vYsW4KPT6fy8JN17GvVpvlUzuQFJ830LZj7SRu/OvNuPl/8r9VZ4iPecbquXlpimU5OXw/owvS7CzmrLnAFzO3cv7or+zZMveVfXbh1ZPlyAo83kfnzp3jk08+oXHjxoSFhQGwbds2zp8/X6b23vqAHmDy5Mls2rSJX3/9lXv37jF69GhSU1OVueYHDx7MzJkzleUXLlzI8ePHefz4Mb6+vnzyySc8efKE4cOHAyCRSJg4cSKLFy/mwIED+Pn5MXjwYOzs7OjRowcALi4udOjQgREjRnDlyhUuXLjA2LFj6d+/P3Z2dgAMHDgQHR0dhg0bhr+/P7t27WLVqlVMnjz5v/2C3nFZUilz/zwOQI/6btz9fgq+307ASE+XqKRUlh/4p9B6kYkpfO+tCMgndGzK7eWTODNvJBKJhIBn0fx86ioAbhWtiNo0h5vLJnJh4Zfc/X4KBjraAFx8EKJsr4WrA+E/zeLGtxOK7O+0bi14/ON0ri4dx4OVU+lR3w0A/9BIboeItROCIHxY3Bt2pPfwxdRrVrK5v6cObMDSpioDv1xBhcoutPt4LPVb9ObYXyuVZY7+9QMtOw+necchVKjiyueTN6CrZ8A/h38BIC0lkX8O/8LAL1fgWrc1Vat7MmL6Lzy88y+B/ooFhn7XjhP25C6jZm2jspMH7g070mvoQk7sW4c0O6uwrglvAcW8eVm+x/s3h3737t14eXmhr6/PjRs3yMzMBBQZF7/++usytflOBPT9+vXju+++Y+7cuXh4eHDz5k2OHj2qXIAaEhJCeHi4snx8fDwjRozAxcWFTp06kZSUxL///ourq6uyzLRp0xg3bhxffPEF9evXJyUlhaNHj6Knlzc6u337dmrUqEGbNm3o1KkTH330kUqOeRMTE44fP05QUBCenp5MmTKFuXPnquSqF4p3/XEYMclpAPSor/g7sjMzpoFjRQB8/B4WWu/0nUCyc3Jy6ymC6lr2NlSzNlfUu/0AAC1NTbQ0Nen53W80nbsO18krSMtS3FVpUr2ysj0TA71iR+cB5vdph6WxYhMTTQ0NGjnZK4/paovbuIIgCEUJ9L+Em2cblfdqNWhP4F1FljlpdhbBAddx81TNbufq2ZbAu4pgPfjBdXKk2Spl7CrXoLy1vbKdQP+LVKpaS2UKTq0GXqSnJhEanJf5Tni7yKSyAo/3zeLFi9mwYQObNm1CW1tb+X7Tpk3LtEssvANz6J8bO3YsY8eOLfTYmTNnVF7/8MMP/PDDD0W2J5FIWLhwIQsXLlRbxtzcnB07dhTZTu3atTl37lyRZYSihcbmbaJgaZw3B/35fPSnsYVvshAal79e3i6BVsblCIyILVDvRvAzohJTlK8nd27G5M7NXqrvqRlZbD9/A4DGzva4VLAqpoYgCMKHLSEugtrmqhniTMysSU9NIiszndTkeGSyHIwLlLEiPOS+sg0tbR0MjUwLtJMYp7hTmhgXUaANYzNr5THh7STNyUFDmqPy+n0TEBBA8+bNC7xvYmKidg+l4rwTI/TCh6msW57JKbxi8JoZxPw8l91TPqWcng4rD59n6z/XCy1bEtFJqXT65hf8QiKobmfJ7+MGlLktQRAEQRAUI/Q5+R7v4wi9jY2NMlFLfufPn8fBwaFMbYqAXnjjKpY3UT6PTkop8LxSvuMq9czz10st8Lywega6OnT0qE7rmo7I5HIW7T5ZoExJPAiPpuWCDVx9FEoDx0r4zB6OralRmdoSBEH4kJia25AYp5ohLjE+En1DY3R09TEysUBDQ5OkAmWiMDG3UbYhzc4iNTmhQDvPy5iY2xRoIyk+UnlMeDvJcnIKPF6XuLg4Bg0ahLGxMaampgwbNoyUlJQi67Rs2RKJRKLyGDVqVKnOO2LECCZMmMDly5eRSCQ8e/aM7du389VXXzF69OgyfRYR0AtvnKdDBcqXMwBg39W7ADyLT+JKoGIjsHa1nADwmLYSj2krWe+jmEPZ0q0aWpoaufUU8yH9QiJ4FKnYfa1dbWcADly7y8PwGOX5ohJT8A1SrCh/ngmnNM7fD6L1wo0ERcXTs74bR2cOxcLIsPiKgiAIAo5ujbjre0rlvTvXTuDoqsgyp6WtQ5Xqnvj7qma3u3v9JI6ujQCo4uyJppY2d/OVCQ8JIDYyRNmOo1tjngb5qWTGuXPNB31DYypUzltTJ7xdVBfEquakf9UGDRqEv78/Pj4+eHt7c/bs2RKtgxwxYgTh4eHKx7Jly0p13hkzZjBw4EDatGlDSkoKzZs3Z/jw4YwcOZJx48aV6bO8M3PohfeXjpYW8/u0Y9yW/ey76o/r5BXEpaSRnJGJhZEBU7oq5pk9yA3KY5MVI/A2pkZM7PQR3x08y6ojFzh8I4DQuETkcjmONuUZ1qo+AN7X79F/1Q5szYywKGfIw4gYMrKlAAz6qI6yH1cCnzJ0/V8qfdt86ip7r/hTwdyYY7MUWZK6fLuVLGkOEomEp7GJeH29WVl+Ro9WdPSo/pq+KUEQhLdPRloKkWF50weiI4J48vAmhsbmWFjb8+fGmcTHPGPk/34FoHW3UfjsXcvODdNo3nEod2+c4srpP5n8jbeyjQ59JrFp6edUrV4PB5cGHP97JZkZqTTvqMhuZ1DOhBadhrJj3RQMjc3RNzBm2+rxOLo1xtFNEfTXqteeCpVd2fD1YPqP/JaEuAj+3jyHtj2+RFtH9z/8hoTSkEqlSDSkKq9fh3v37nH06FGuXr1KvXr1APjxxx/p1KkT3333nTKjYWEMDAxear8hiUTCrFmzmDp1KoGBgaSkpODq6kq5cmXfy0YE9MJbYVjr+hjqarPyyAUCnkWjp61F93quLOrXHjszY7X1FvRph7VJOX4+dZXHkXGYGOjSq0FNFvZrj6GeDgCtalYjMDKWh+Ex3A2LwkBHm5qVbOjfxJ1R7Roq28rIzuZxVJxK+wlpGSSkZSDNl18+K3exjlwu59rjUJXyMfmm/giCIHwIggKusXRSa+XrHWunAPCR12d8MXMLCbERxEbmpQi2tK3KlKXebF87meO7V2NuWZFhUzdRu0HeJn6NWvcjOSGaPVvmkRgXgb2jB1OXHVHJWDNwzA9INDT4cW5vsrMzqVVfsbHUcxqamkxeepCtP3zJwjFN0NUz5COvwXw8RH0yDOHNk0nlyDRkKq9BsV9Qfrq6uujqlv3C7OLFi5iamiqDeYC2bduioaHB5cuX6dlTfRrW7du38/vvv2NjY0PXrl2ZM2cOBgYGxZ5z6NChJerbL7/8UqJy+Unk8rIuPRRehfTfl7zpLgjCKxfeuN+b7oIgvHIXQqu96S4IwmvxaQvJm+4CSUlJmJiY4PX5FbR18kaqs7NSOLa1QYHy8+bNY/78+WU+39dff82vv/5KQECAyvtWVlYsWLBA7Vz2jRs3UrlyZezs7Lh9+zbTp0+nQYMG7Nmzp9hzamhoULlyZerUqUNR4ffevXtL92EQI/SCIAiCIAjCW+LFhbDPnz99+hRj47w79upG52fMmMG3335b5Dnu3btX5v7ln2Nfq1YtbG1tadOmDY8ePaJataIv+kePHs0ff/xBUFAQQ4YM4ZNPPsHc3LzMfclPLIoVBEEQBEEQ3gryFzaVkuemrTQ2NlZ5qAvop0yZwr1794p8ODg4YGNjQ1RUlEpdqVRKXFxcqebHN2yomLpbWBrKF61du5bw8HCmTZvGwYMHqVSpEn379uXYsWNFjtiXhBihFwRBEARBEN4KUmk2SLJVX5eCpaUllpaWxZZr3LgxCQkJXL9+HU9PTwBOnTqFTCZTBuklcfPmTQBsbW1LVF5XV5cBAwYwYMAAnjx5wtatW/nyyy+RSqX4+/uXeWGsGKEXBEEQBEEQ3gr/VR56FxcXOnTowIgRI7hy5QoXLlxg7Nix9O/fX5nhJiwsjBo1anDlyhUAHj16xKJFi7h+/TrBwcEcOHCAwYMH07x5c2rXrl3qPmhoaCCRSJDL5eS85OcUAb0gCIIgCILwVlDsEJuT7/H68tBv376dGjVq0KZNGzp16sRHH33Exo0blcezs7MJCAggLS0NAB0dHU6cOEH79u2pUaMGU6ZMoVevXhw8eLDE58zMzOSPP/6gXbt2ODs74+fnx5o1awgJCRFpKwVBEARBEIR3X3ZmksqofI709aWDNjc3Z8eOHWqPV6lSRWVue6VKlfjnn3/KfL4vv/ySnTt3UqlSJYYOHcoff/yBhYVFmdvLT6StfMNud2rxprsgCK+c88jeb7oLgvDKPavZ8U13QRBeC4dqjm+6C2RkZFC1alUiIiIKHLOxsSEoKAg9Pb030LNXR0NDA3t7e+rUqYNEoj5VaElSYL5IjNALgiAIgiAIb5Senh5BQUFkZWUVOKajo/POB/MAgwcPLjKQfxkioBcEQRAEQRDeOD09vfcicFdn69atr61tsShWEARBEARBEN5hIqAXBEEQBEEQhHeYCOgFQRAEQRAE4R0mAnpBEARBEARBeIeJgF4QBEEQBEEQ3mEiy43w1jjyLIqtQaEEpaShq6lBA3NTJlavSiVD/WLr5sjlDLl0i1sJSQAMcajExOpVlcfvJSbzU2AIdxKTScjKxkhbCxfjcgyvZk9dcxNluZDUdFYFBHElLoHMHBlVyxkwxKEiHWytXllfhQ/Pn+eu88Pek9wPjURfR5sWtZxYMrgbDraWauvM/u0A5/wDCYqIISktA1tzEzp4ujGzrxdWpkYAhMbE8+1fx7l47zFhsQlk5+RQ2ao8n7RuwNguLdHW0lS29yg8mtm/HeAfv4ekZ2VTo6I1kz9uS5+P6r50XwVBEIQ3S2ws9YaJjaUU9jwNZ8GdhwBU0NcjMTubFGkO5jra/PWRJxa6OkXWX/cwmJ8CQ5Sv8wf0SdlSOp25QrJUioGmJvaG+gSnppGRI0NHQ8Kxlg0x19UhOiOTvhd8icvKppyWJiba2oSlZwAwv6YzPSvZvJK+fgjExlJ5tvpcZPTaPwCoYl2euORUktIysDIx4vLK6diYGRdaT7/HeDQ1NKhR0Zq4lDTC4xIBcLO35crK6WhoaHDW7yFec36knJ4u1WwtCIqMJSlN8TP7RYePWDWqLwDhcYk0mrSMqMRkjA30MDcyJDgyFoANYwfwWdvGL9XXD4XYWEp4X70NG0sJL0dMuRHeuGyZjNUBwQC0tbbgcMsG7G1WD0NNTeKysvn5UUiR9W/GJ/LzoxDa2xQ+ghiYnEqyVArAvFpO7Gpal5muin+8smRyYrOyAdj8+ClxWdkYamqyt1k9DrdsQFtrxZbMqwKCyJbJXrqvwoclK1vKnG0HAOjR2J17P83jxppZGOnrEpWYzLK/j6utO71Pe4K2LOLa6pk83LSAHo3dAfAPCed2cBgAZkYGrBvTn9BtX3Pph+nc3zifKtblAdh59pqyreW7fYhKTMZIX5cba2Zx76d5yvZm/3aArGzpS/VVEARBeLNEQC+8cXcSk4nPVgTVbW0UAbSVni61cqcVXIiOV1s3JVvK/24FYKmry5yaToWWcTQywFhbMbtsgd9D+l/wZendQPQ0NBherRJORoa554kDoLaZMVZ6ugC0ye1PfHY2/onJL9VX4cNzLTCEmKRUAHo09gDAztyEBs5VAPC5cU9t3fmDumBpovi50tTUoFGNvClkurk/z7WqVGBIuyboamsDYFbOADd7W5UyAMd9FedpWL0qdrlTzLo3UgT0MUmpXH8U8lJ9FQRBEN4sEdALb1xEeqbyubmOtvJ5+dypKxEZGWrrfn03kPD0DL52r64M2l9krK3NlobuVNTXIy0nh3tJKWTkyDDX1aa6Ubm8fmRkFuxDvufh6Zkv1VfhwxMak3eBZ2WS97P2fA780xJeAKZmZLLj9FUAGrs44FLJttByD8IiOeP3AIAh7ZoU6Idlvj5Y5/bheT9eVV8FQRCE/54I6IW3VnGLO05GxHDoWRTDqtnjaW6qtlyaNIe5fgGEpmcwuYYDF9s1ZUoNB56lZzLt5j3uJaaUuQ+lLScIULqfl+jEZDrOXcPt4DCqV7Rm+9QhhZa79vAJ7WetJjUji+6N3JkzoOj53uJnWxAE4f0hAnrhjbPR11U+j8udzw4Ql5mlOK6nV2i9B8mKQHxbcCiNjp+n0fHzymPbgkJpd+oSAEfCo/DPDdp7VrTGQEuTHhWtAUWwciU2Pvc8ugX7kO+5rb5umfsqfJgqWpgpn0flu3CMTkgGoJKlWYE6+T0Ii6TFtO+5+uAJDapX4cTXE7DNl5XpuYOXb+M1+0ciE5IZ1r4J26cOQUszL8PN835E5+tDVG4fnvfjZfsqCIIgvDkioBfeuJomRpjmTpc5EREDQFRGJn65gUTT3ECi+9mrdD97lT+ehKnUz8iRkZ77eE4ql5OekwMo5tk/9zyw988XsOjnBj5NLM0BuB2fRFTu9JuTuf0x09bGzcSoxH0VBIB6jvaUz12jse/iTQCexSVy5UEwAO3quADgPmYx7mMWs/7QWWXd8/6BtJrxA0GRsfRs4sGxReOwMC7Hi9YcPEP/bzeTnpXN4sHdWPNlfzQ1Vf9pf36eywFBPMvNlrP/0i0ALIwN8axmX+K+Cm+3TT//TLPmLXByrk41RycuXbpcaLnmLVpSzdGJVatWv/Q5V61aTTVHJ5q3aPnSbQFUc3SimqMTf+/erbbMpUuXleXUfcayGDhwENUcnZg6bdora1MQ/gsiD73wxmlraDDOuSqL/B9yIjKGTmeukJidTWpODmba2gx1qARAcGo6AAm5I+Ojnaow2qmKSlvuRxQBUf60lc2tyvPjg2Cy5XLGXbtDlXIGPElNA8BIS5NWuVlBhjpU4tizaOKzs+l57ppK2spx1augraEIkkrSV0EA0NHWYsEnXRi7fhf7Lt7CZeQC4pJTSU7PxMLYkK96tQPgQVgUALFJeReaneetJUuag0Qi4Wl0PO1n5wVeM/t2oGM9Ny7dD2Lq5j0AGOnrsv/SLWWgDrBrxnBszU34qldb/j5/nZikVOqMXaKStnLBJ13Reb5ovAR9Fd4OAwcO4vKVK1SoUIGz/5wB4M4df7755lsA7CtVwtzcnHLlCl4EAri6umJpYYGNjc1/1eW3RmhoKC1atgJg+++/06hRwzfcI0F4eSKgF94Kve1t0dfU4NegUIJS09DV0KCNtQUTqldVZpwpq6rlDPiloTu/PH7KncRkglPSMNfVxsPMhJHV7LHMbd9aT5etjd1ZHRDMldh4ojMzqW5kyOcOlehkl7ex1Ovsq/D+GebVFAM9HVbtO8X90Ej0tLXp3sidxYO7KjPOFCZLqrjDJJfLufbwicqxmNw7TFn57j4lp2dy9YFquefHK5Q35dTSSczZdpAztx8QHpeIe9UKTOzRhv4t6r10X4W3Q2BgoPL50aNH0NVV/+/RhvXr/osuCYLwH3ktAb1EImHv3r306NHjdTSvdObMGVq1akV8fDympqYA7Nu3j6+++oqgoCDGjRuHh4cHEydOJCEh4bX2RXh5nStY07mCtdrjtzo2L7YNdWVqmxmz0tOt2PpVDA34vq5rseWK66sg5DegRX0GtKiv9nj6voLTHgp770XNazmVqByAUwUrds4YVmy54voqvJ2mTpvGnj17la9d3WoC8M+Z01SsWLFA+eYtWhIWFsb4ceOYMGE8ly5dZtAnnwCwYf16Nv+ymdu3/ahQoQIzZ0yndevWJerHiRMnWbZ8GU+fhuLm5saSxYuoXr26yvGNmzZx7949pFIpjtWq8emnn9K3b58i2z106DDLv1tOVFQ0jRo1onu3riXqz4v+3r2b6dNnKF8//8wNGzRgx47tyvflcli3bj3bfv+djIwMWrVsycKFC5R3PGQyGb/+9hu7dv3JkydP0NPTo2mTJkyfPo1KlSoVONfv27axdOlSAh89wrFaNRYsmE+dOnXK9BkEoTClnkMfERHBuHHjcHBwQFdXl0qVKtG1a1dOnjz5OvpXpCZNmhAeHo6JSd7I0ciRI+nduzdPnz5l0aJF9OvXjwcPHrz2vowaNQqJRMLKlStf+7kEQRAEIT97e3vsK+VN+fNwd8fD3R0dndLvXD1u/Hiio2OQSCQ8fvyYSZMml2hQLDo6mgkTJ6Ih0UAul3Pjxg2GDh1GerpiuuS+ffsZOWoU169fx8DAAEtLS+7eu8fM//2PtevU3zG4e/cuEydN4unTUHR0dAgKCmL2nLml/lwA5c3NcXXJWw/iWK0aHu7uODqq7pR65MgRfvrpJ3R1dUhKSmL/gQNs+Gmj8vj8+QtYvHgJDx8+pHLlymhqanLk6FH69O1HTGxsgfMOHTaM9IwMcnJy8L97lwkTJyGVSguUE4SyKlVAHxwcjKenJ6dOnWL58uX4+flx9OhRWrVqxZgxY15XH9XS0dHBxsYGiUQCQEpKClFRUXh5eWFnZ4eRkRH6+vpYWVkV01LRsrOzizy+d+9eLl26hJ2d3UudRxAEQRDKYtzYsYwZm/f/8O7df7N7999l+v9v8KefcvKED6tyB6hSUlO5det2sfWysrJYv24dR48eYeNPPwEQERnJ3r2KOwcrvv8eUFxsnP3nDP+cOU379oq1GevWrVcG/i/avPkXZDIZRkZGnPA5zulTJ2nfvn2pPxdAq1atWJ9vutGCBQvYvftvFi5coFJOS1OTY8eOcurkSWrWVNztuPjvvwA8ffqUHX/8AcDy5cs4euQw/5w5jY2NDdHR0fz2228FzjtjxnR8jh9j5kzFiH1YWBhPnjwpUE4QyqpUAf2XX36JRCLhypUr9OrVC2dnZ9zc3Jg8eTKXLl1SW2/69Ok4OztjYGCAg4MDc+bMUQmSb926RatWrTAyMsLY2BhPT0+uXVNsW/7kyRO6du2KmZkZhoaGuLm5cfjwYUAx5UYikZCQkMCZM2cwMlJsgNK6dWskEglnzpxh69atyuk4z+3fv5+6deuip6eHg4MDCxYsULlSlkgkrF+/nm7dumFoaMiSJUvUfrawsDDGjRvH9u3b0dbWVltOEARBEN4FPXp0B1AZtY6JjSm2nomJCc2bNwOgefNmyrvnAQEPiImN5dmzZwC092qPrq4uEomELp27AJCRkcHDhw8Lbff5+56edbGwUOzQ3aljh7J8tBJr1LgxNjY2aGhoUM3BAUA58u7ndwe5XLFDw9Sp06jm6ERtdw8iIiIAuHnzZoH2euZOQXbK/53GFP+dCkJJlXgOfVxcHEePHmXJkiUYGhoWOP5i0JyfkZERW7duxc7ODj8/P0aMGIGRkRHTctNCDRo0iDp16rB+/Xo0NTW5efOmMjgeM2YMWVlZnD17FkNDQ+7evVvoqv0mTZoQEBBA9erV2b17N02aNMHc3Jzg4GCVcufOnWPw4MGsXr2aZs2a8ejRI7744gsA5s2bpyw3f/58vvnmG1auXImWVuFfk0wm49NPP2Xq1Km4uRU/P1sQBEEQ3nbGxsYAaGnl7WXwPID9UBgb5+2krJn7PRT2Hbi6uBSY1lTBrkIh7Sm+U03NvHjiA/tKhdesxAF9YGAgcrmcGjVqlPoks2fPVj6vUqUKX331FTt37lQG9CEhIUydOlXZtpOTk7J8SEgIvXr1olatWgA45F4pv0hHR0d5a9Hc3FxtKq4FCxYwY8YMPvvsM2V7ixYtYtq0aSoB/cCBAxkypPAdGZ/79ttv0dLSYvz48UWWEwRBEIT3XWJiIufPn+ejjz7i/PnzJCYq9jyoXt0Zi/LlsbOz49mzZxw/dpzPP/sMHR0dvA95A6Cnp6fyf39+Tk5O+N+9y/XrvsTExmJRvjxHjh4rUC7/wt6i0lHq6esrn6enp5X6c9as6YZEIkEul/Nxr48Z8vnnQG5GqmvXlLMFBOG/VOKA/mWuznft2sXq1at59OgRKSkpSKVS5dUqwOTJkxk+fDjbtm2jbdu29OnTh2rVqgEwfvx4Ro8ezfHjx2nbti29evWidu3aZe7LrVu3uHDhgso0mpycHDIyMkhLS8PAwACAevXqqWsCgOvXr7Nq1Sp8fX2Vc/gFQRAE4UOlo6PDyFGjqVSpEk9y745bWVnRs2dPAKZMnsyUr77i5q1bNG/REl1dXcLCFBsFfvnlaPTzBdr5DR06hP0HDpCcnEzbtu0wNzcnPDy8zP0sb26OmZkZ8fHxTPlqKlWqVKZ79+58Nnhwierb29vTr19fdu7cxeLFS/j1118xMDAkLCyMlJQUvv32mzINfgrCyyjxHHonJyckEgn3798v1QkuXrzIoEGD6NSpE97e3ty4cYNZs2aRlZWlLDN//nz8/f3p3Lkzp06dwtXVVbmIZvjw4Tx+/JhPP/0UPz8/6tWrx48//liqPuSXkpLCggULuHnzpvLh5+fHw4cP0dPTU5YrbFpRfufOnSMqKgp7e3u0tLTQ0tLiyZMnTJkyhSpVqpS5fx+aI8+i6HfBlwbHztPsxL9M8b3L09TCF0a9KDojE48jZ/kz5JnaMrGZWcy9HUDLkxepd/QcPc9eK7DTbFEW3XlI21OX1F7Qrn8YjPuRs4U+pDJxP/VD9ue56zSevAyzvlOw+2QGA77dzOPw6BLVDY9LxKDnBDYdPa+2TGRCEiN/3I79Z//DpPck6oxdorLTbHHGrd9F1SGz1f5sL/7jMPo9xhf6kObuwiwI+VlaWrJq5UpkMsXPRx0PD37Z/LMyUO/Rozs/bdiAp6cnqampREdH4+riwtKvv2bMl1+qbdfNzY0ffvieihUrkpmZSYUKFVj0wiJWgMQkxR0BPT09Kle2V9ueRCJhyZLFVK5cmZSUFG7duk1YmPr/RwqzaOFCZs/6H9WrVycyMoqwsDAqVqzIsKFDadRQbFQl/Pck8lIMvXfs2BE/Pz8CAgIKBLwJCQnKefT589CvWLGCdevW8ejRI2XZ4cOH8/fff6tNgzVgwABSU1M5cOBAgWMzZ87k0KFD3L59u0Ae+oSEBMzMzDh9+jQtW7YEYOvWrSp56Js2bUqNGjXYvHmz+i+lBHn0Y2NjC4wQeHl58emnnzJkyBCVvLtFud2pRYnKvY/2PA1nwR3FYqcK+nokZmeTIs3BXEebvz7yxEK36HRrf4U8Y4l/IMdbNSx0Q6c0aQ4D/vUlODUdPQ0NrPR0CUlTXCx8Uc2eMc5VimxfLpfT7vRlWliVZ07Nwm8Fr38YzIbAEMy0talooKdy7NfGHmh+oHdvnEf2ftNdeKO2+lxk9FpFFowq1uWJS04lKS0DKxMjLq+cjo2ZcZH1fz52gfEb/iRw88JCN3RKzcikyZTlPAiLQl9HmwoWpgQ+U1wszOzrxdyBnYtsXy6X4zB0Dl0a1OLH0f0KLbP4j8Ms2XUUC2NDqtpYqBw7vXQSmpqlznr8zntWs+Ob7oJQhCVLvuaXLVuY+tVXjBo18k13553iUM2x+ELCW61UG0utXbuWpk2b0qBBAxYuXEjt2rWRSqX4+Piwfv167t27V6COk5MTISEh7Ny5k/r163Po0CHl6DtAeno6U6dOpXfv3lStWpXQ0FCuXr1Kr169AJg4cSIdO3bE2dmZ+Ph4Tp8+jUu+HLKlNXfuXLp06YK9vT29e/dGQ0ODW7ducefOHRYvXlzidsqXL0/58uVV3tPW1sbGxqbEwfyHLFsmY3VAMABtrS1YUdeVqIxMepy9RlxWNj8/CmGGa9H/wJyJisXNxEjt7qx/Pw0nODUdCbCtsQfOxuX47t4jtgWH8cvjp/SvbEf5Ii4a7iQmE52ZRSvr8mrLPNfMypxFtcXfu6DYnXXONsVgRI/G7vwxfRjP4hLxGLOYqMRklv19nO9HFH3B433ZD09He7W7s/587AIPwqKQSCT8s2wytapUYPove1l94DTf7TnByE7NsDZVf9Fw7WEIEfFJdGlQq9jP08HTjU0TPim2nCC8aZevXMHZ2Zlhw4a+6a4Iwn+uVEMsDg4O+Pr60qpVK6ZMmULNmjVp164dJ0+eZP369YXW6datG5MmTWLs2LF4eHjw77//MmfOHOVxTU1NYmNjGTx4MM7OzvTt25eOHTuyYIHidlpOTg5jxozBxcWFDh064OzszLoiNqAojpeXF97e3hw/fpz69evTqFEjfvjhBypXrlzmNksqMzOTpKQklUdWjuy1n/dtdCcxmfjc1KVtc0f/rPR0qWWqWEx0ITq+yPqpUilXYhOKDLYvRMcBYG+oj7NxOZVzSeVyLscmFHmO05GxGGpq0sDctNjPcyIihgbHztPm1CXGXrvDvcSUYusI76drgSHEJKUC0KOxBwB25iY0yL0j5HOj4MBHfsnpGZzxe0DXIoLt476KNhxtLalVRZFRo0cTdwCypTmcvlX0ZnoHL9/GSF+XlrULv/OU376LtzDrO4WqQ2bz8eKfuPn4abF1BOFNOLB/H0cOHxIppIUPUqlG6AFsbW1Zs2YNa9asUVvmxVk8y5YtY9myZSrvTZw4EVAsovkjd4OGwhQ1X75ly5Yq5zI1NS1w7s8//5zPc1egP+fl5YWXl1eJ+19SL6bIfNHSpUuVFyrPjXK0Z7RTlTKd710WkZ6pfG6uk/eP7/MR84iMjCLrX4iOJ0smp5WV+oA+IiOzYPv50ouFpxd9jtNRsTS1NEOnmKkFmhKw0NVGUyIhKDWdc9FxXI6N57dGdXAxKZhiVXi/hcbkXYxa5fv7t8q9WH1azMXqcd97ZGZL6dpIfUD//ByW+dq3NsnLrPE0puhzeF/xo10dF3SLCXw0NTSwNjNGS1ODgNBIjlzz59StAM58OwkPh0pF1hUEQRD+Ox/eJMg3aObMmSQmJqo8hjmoX7jzISrppdTpqFjsDfSoZlT04uWyth+Sms7jlDRaFnHBANDRzorTrRtzsEUD9jWvz7p6ih0Fs2RydhWxWFf48JT0Z8/7ih/VbC1xqWT7Wtp/FB7NvacRdGlY9HSbfs3rEfLrEu6sn8PNNbM4MG80AJnZUn46fK5UfRMEQRBeLxHQ/4d0dXUxNjZWeRQ3+vu+stHPm/cel5W3a3BcpiL7kY2eXoE6z0llcs5FxdHS2kJtGUUbugXbz5ddyVZf/TlOR8agJZHQrJiAvoqhASb57gA0tTTHVFtx46u4OwDC+6mihZnyeVS+qVfRCckAVLI0K1DnOWlODkev+Rc7t/35OaLztR+VmKx8XslC/TkOXL6NlqYGHT2L3gzPqYIV5vkumNvVcaF87uvi7jIIH5aBAwdRzdGJqbl7ywiC8N/7MKNJ4Y2raWKkDHxPRCi2v47KyMQvN+hpWkTQcz0ugWSptMjpNgBNLM0BxWj7g6QUlXNpSSQ0LG+qtu7pqFg8zU0w1i56Vtovj56qBO4XY+JJyJYCUMFA/QWD8P6q52ivDHz3XbwJwLO4RK48CAYUgbE65+4EkpCaTtdiRs+ftxEYHo1fsCIN675/bwGgraVJK3dntXW9L/vRzM0R03IGRZ7juz0+hOSuQwE4efM+scmKtQGVi/ndE4R3QUxMDNNnzKB+g4a4uLji5dWB337bVqK6wcFPGDNmLHU96+HqVpNu3brjfeiQSplVq1ZTzdGp0IdUKn0dH0n4gJV6Dr0gvAraGhqMc67KIv+HnIiModOZKyRmZ5Oak4OZtjZDi5ifezoqFjMdbTyKSf3Xp5Itf4eEE5KWzqcXb2Ktr8uT3Bz3nztUVJvhJi4zi1vxSUx1qVbs5/jr6TNWPwjCRk8XfU1NglIVuw7qa2owqHLB7b+F95+OthYLPunC2PW72HfxFi4jFxCXnEpyeiYWxoZ81aud2roHr/hhaVKOxjWqFnmO4V5N2Xz8AoHPomkx7XsqWpjx8FkUAJN6tFGb4SY6MZlLAUEsH/pxsZ9j05ELzN3mTUULUwx1dQgIU7RvqKfD2G4fbrpd4e0nk8m4cOECLi4uWFgUfic3LS2NAQMH8fjxY/T09LCrUIHAR49YsHAhsXGxTMpd51eYqKgo+vbrR2xsLOXKlcPKyhL/u3eZMGEi6Wlp9OnTR6W8uZkZ9vaq02vFhpTCqyZG6IU3pre9LV/Xrk51I0OiMzORAG2sLfi1sYfaVJQAZyJjaW5pjkYx/yAaaGnyS8PadKtgjb6mJmFpGVQ11GeaSzXGOasPmP6JikMGJUpXOczBnoblTZHK5YSmp2Orr0snOyt2Nqlb6vn9wvtjmFdTfpn0Ke5VKxAel4gECd0buXP6m0lqU1GCYv58x3puaGgU/U9zOX1dji8ezyetGmCop0NwVCzVK1qzfNjHLPiki9p6h6/eQSaTFzt/HmBa73a0qu1MtjSHoMhY7C3N6N+iHv+umFrq+f3C69G8RUuqOTqxbNly5s9fQF3Perh71GHu3HlkZmaqrZeZmYlHnbpUc3Riy9atyvefPHmiHEH+55+zZGRkMHLUaFq0bEXNWrVxcXGldZu2/LBypcrmkC8KDQ1VtnPp0uUC/V21arXyvcjISKbPmEHjJk2p4eJKy1at+HHNmjKNYD969Ijly7+jWbPmfD5kKFFRUWrL/vHHTh4/foxEImH3339x8oQPw4Yq0l3+9NNGYmJi1NZdv2GDIpg3NOT4saOcOX2aDrmJNpYtW17gu2nZqiW7d/+t8tDU1Cz15xOEorwzI/Rr165l+fLlRERE4O7uzo8//kiDBg0KLduyZUv++eefAu936tSJQ4cOkZ2dzezZszl8+DCPHz/GxMSEtm3b8s0332BnZ6csHxcXx7hx4zh48CAaGhr06tWLVatWUa5cXmaJ27dvM2bMGK5evYqlpSXjxo1jmphHWGKdK1jTuYJ1icvfS0whPCOT1sXMn3/OUk+31Pnhz0TFUsO4XJFz7J/rbW9Lb3sR3AgFDWhRnwEt6pe4/M3HT3kaHU/XhrVLVN7W3KTU+eG9r/jh4VAR+9zpaEUZ5tWUYV5NS9W+8GZs2boVQ0NDjI2NePo0lO07dqCrq8usWf8rtLyuri6dO3di585dHPI+xJDcTHCHDh0GwNramo8+akpqaionTpzAwsKCqlWqEBcfz5MnT1izZi0ZGRnMnDHjpfodHx9Pr959CA8Pp5yhIdWqVSMwMJCVK1cRGhrKt998U2wbiYmJeHt7s2fPXm7eUkw7q1GjBoM/G1xkOup/zipihCpVqlCjRg0AvDp4sfmXX8jOzubff/+lW7duhdf9R7Ejc506dbC2Vvz/5eXVnqPHjhEXH4/fnTt41q2rLH/s2HEOHTqMsbExNWu6MWniRNzcil7DIgil9U6M0O/atYvJkyczb948fH19cXd3x8vLS+3V9549ewgPD1c+7ty5g6ampvI2WFpaGr6+vsyZMwdfX1/27NlDQEBAgV/eQYMG4e/vj4+PD97e3pw9e5YvvvhCeTwpKYn27dtTuXJlrl+/zvLly5k/fz4bN258fV/GB04qlzHK0Z5GFqav7RweZsaMK2YXWUF41bKlMmb160Ab99e3QVmjGg7MH6R+BF94N9nZ2XHm9CnOnD5N166Kv9/ff/+d5ORktXU+/lgx7erGzZuEhSnWYRw6rAjou3fvhqamJvr6+hw9cpjLly5y8OABLpw/R4/u3QHw9j5UeMOlsG3b74SHh2NhYcGpUyc55H2QNbmpqnfv3kNw8BO1df39/Rk7dhyNGjdh7rz5xMTGMnrUKI4cPswh74OM/OKLAjva5xceHgGgskGkRb7nz56FF6iTVze8QN3y+ab2PHuWl+FMU1MTCwsLKlasQHR0NKdPn6F3n774+/urbV8QyuKdGKH//vvvGTFiBEOGDAFgw4YNHDp0iF9++YUZhYwQmJurjj7t3LkTAwMDZUBvYmKCj4+PSpk1a9bQoEEDQkJCsLe35969exw9epSrV69Sr149QJETv1OnTnz33XfY2dmxfft2srKy+OWXX9DR0cHNzY2bN2/y/fffqwT+wqtTy9SYWkXsgPkqDBH5tYU3oL5zZeo7v94N7qZ83Pa1ti+8Ga1btVLeOe7SuQsHD3qTlZ1NUFAQsbGxrFmzVlnW0sqKDevX4Vm3LlWqVCE4OBjvQ4do26YN9+/fB6BXbrCvoaHB/v0HOHL0KM/CwsjKzssYVtR0lpK6dVsxoh4TE0ODho1Ujsnlcm7dukmVKoX/Tpw4cZIjR4+ir6/PokULGdC//0vPSy/jFjRqK3fr1pXPPhuMqakpAGfPnmPI0KFkZWXx++/bWbr065c4oSCoeutH6LOysrh+/Tpt2+b9R6ShoUHbtm25ePFiidrYvHkz/fv3L/JqPTExEYlEovzFu3jxIqampspgHqBt27ZoaGhw+fJlZZnmzZujk2+zIi8vLwICAoiPF2ndBEEQhDcrNi6Om7duKR93795VHuvZswegmGrzfLpN7dq1cXR0BGDDTz+xfsMGgoODsbSywsPdHZvcKSYymfpdzvMH1jmyHOVzdXcMyhka4uHuXuChp6+v9hzNmzejWbNmZGVlMWfOXLp07cZPP/2kMjpeFFtbGwBiY2OV78XG5T23s1M/ldLW1rZg3dj8dRVTd6tWraqMKZ732cxMkcGtpP0UhJJ66wP6mJgYcnJylPPUnrO2tiYiIqLY+leuXOHOnTsMHz5cbZmMjAymT5/OgAEDMDZWjP5GRERgZWWlUk5LSwtzc3PleSMiIgrt1/NjgiAIgvC6nT5zhtRURUrRw7nTZnS0talatSq9e/XiUeBD5ePsP2eU9Xr26IFEIsHf358duTu2Px+dB7h54yagCEzP/nOGP//cRQ2XGsX2J/9UlOCgIAAuXLhAUlKSSrnatRTrRTS1tFi1aqVyweivv25l0CeD8GrfXu056tSpw9Ytv3Du7D9MmzaVHKmUZcu/o3mLlvTrP4Dt27eTUcSO482bNVf0LzhYeWfi2NFjAGhra9OkSRMAfvttG+3ae9Gufd7u8s2bK+reuHGDyMhIRd1jxwFFRptaNRUbDL54gXH+/HnlYF+FihXV9k0QyuKdmHLzMjZv3kytWrXULqDNzs6mb9++yOVy1q9f/x/3TsjvyLMotgaFEpSShq6mBg3MTZlYvSqVDNWP0gDsCA7jr5BwnqalU05bi+aW5kyoXlWZljIyI5NFdx4SkJRCfFY2epoa2Orr0dnOisFVKyqz5QQmp7LmYTB3EpKJzt3gaohDJSZWV82IszIgiOtxCTxNyyBVKsVSV5dmVuZ8Uc1ebSpM4cP257nr/LD3JPdDI9HX0aZFLSeWDO6Gg61lkfXWef/DpqPneRwRg4mhPh3rubHw067KtJRhsQmMXbeT28FhxCSmoK+jg72VGf1b1Gdi91bKbDl3Q8KZv/0Q1x4+ITwuEVBMv1k8WHXd0OzfDnDOP5CgiBiS0jKwNTehg6cbM/t6YWVq9Bq+GeFViIyMpGVLxbSbkKdPARg4aCBGRkX/nVWoUIGGDRty6dIloqOj0dHRoUuXzsrj1WvU4NTp0wQFBdGiZSuk2dlkFJE95zk9PT3q1KnDjRs3+HrpNxw+cpRbt26hoaGhMrL/ySeD+PPPP4mIjKRdu/ZUc6xGakoq4RERZGdn83HPnsWey9rampFffMHIL77g9u3b7Nmzl4Pe3sydN586derg6upaaL0BA/rzx86dBAcH06t3H2xtbQnKvfgYPny4Mt1lfHw8jx8/Vqk7auQXHPL2Ji4+nvZeHTAzM+Xp01AApkyZorxrv33HHyz/bgW2trYY6OvzKLcdAwMDhnz+WbGfTRBK460fobewsEBTU1N5FfxcZGQkNjY2RdZNTU1l586dDBs2rNDjz4P5J0+e4OPjoxydB7CxsSkwR1AqlRIXF6c8r42NTaH9en5MKLk9T8OZces+95NSsNDVQSaXcyIyhsGXbhKTqT492poHwXx77xGPU9Ow1dcjXZrD/rBIhl2+RXqO4lZvfFY2V2ITMNDSxMnIEE2JhAfJqfwQEMSWx0+VbYWkpXMmMpZyWkWnE9vy+Cn+icmU19HGRFubsPQMdj55xhdXbiN7qUmYwvtoq89FPlvxKzcfh2JjZkyOTMa+i7doNWMlEfFJaust2H6IKT/v5n5oJPZW5qSkZ/Lbyct4zf6RtNzfiZjEFM74PaScni41K9uhpamBX/AzZv26nxV7TyrbCnwWjfcVP4yL2exsxZ4TXH8YgpWJEeZGhgRHxrLh8Fk6zV1T5BQL4c367LPP6N69O4lJSZQzNGTAgP5Mmzq1RHU//jgvaG7TurXKFJEvR4/i4497YmxsTEpKCp27dGHQoIElanf5sm+pX18xZTUiIoIFC+Yrp6o8V758ef7++y969+6FqZkZDx8GkpGZSb169ZitJkNPUWrXrs38+fO4+O8F1q75UeVOwYsMDQ35Y8d2Pv64Jwb6+opUm9UcmD17Fl9NmVzkeWxsbPjzz114ebVHIpEQGRmFq4sLP3y/gv79+ynLjR49iiZNGiOVSgl5+pQKFSrQvVs39u/bi5OTU6k/nyAURSKXv/0RSMOGDWnQoAE/5q5+l8lk2NvbM3bs2EIXxT63detWRo0aRVhYWIFf7OfB/MOHDzl9+jSWlqojZffu3cPV1ZVr167h6ekJwPHjx+nQoQOhoaHY2dmxfv16Zs2aRWRkJNra2gD873//Y8+ePcpbeMW53Uls0JItk9Hu1GXis7Npa23BirquRGVk0uPsNVJzchhQ2Y4Zro4F6sVmZtH+9GWkcjmDq1ZkSg0HHiSl0PeCL3JgSg0HBletiFSm+BHX0lCMxKdKpbQ+dYmMHBnNLc35sZ7i9mhythQJUE5bC/cjirRkhY3Qr3kQxMDKFTDX1SFHLmfajXuciFTkLN7ZpC4uJuX40DmP7P2mu/BWyMqWUm3YHGKSUunR2J0/pg/jWVwiHmMWk5yeyejOzfl+RMHvKjIhCafh88iW5jChe2u+GdIDv+AwGk5ahlwu55shPZjQvTXS3ItWrdyc1snpGVT5fDZpmVl0qufG7tkjAUhMTUciAWMDffR7jAcKH6Gfv92bMV1aYGliRE6OjE++28K+i4qFixe/n4rHB75g/FnNjm+6Cyqat2hJWFgY48eNY8KE8W+6O8I7zKFawf9jhXfLWz9CDzB58mQ2bdrEr7/+yr179xg9ejSpqanKrDeDBw9m5syZBept3ryZHj16FBrM9+7dm2vXrrF9+3ZycnKIiIggIiJCuSGEi4sLHTp0YMSIEVy5coULFy4wduxY+vfvr1zwMnDgQHR0dBg2bBj+/v7s2rWLVatWMXly0Vf3gqo7icnE52ZPaGujuM1ppadLrdxb/BeiC19gfCkmHmnu9Wjb3Lz0zsblqGSgn1tPsW29loYELQ0JY6/dYcAFXzqduUJGjmK0sY5Z3iY/RtpalNMufhbaWOeqmOdOrdGUSHDPt2OtjobY/U/Icy0whJgkxdzmHo09ALAzN6FBblpUnxv3Cq136lYA2dKc3HruANSqUoFqtoqfcx9fRT0tTU20NDXpuWgDTacsx+WLBcrR+8auDsr2TAz1MTYoeuoawPxBXbA0UfzeaWpq0CjfjrW6JfjdEARBEN6Md+Jf6H79+hEdHc3cuXOJiIjAw8ODo0ePKheghoSEFNhZMSAggPPnz3P8+PEC7YWFhXHgwAEAPDw8VI6dPn2ali1bArB9+3bGjh1LmzZtlBtLrV6dt8OdiYkJx48fZ8yYMXh6emJhYcHcuXNFyspSikjPm5NprqOtfP58PnqEmoVNERnq6mkTkpauchzgbmIysVl5adc+r1qRIQ4vtzApTZqDd5himpWHmbHYHVZQERqTdzFqle/OzfP56E/VXKyGxiQUXs/EiMBn0TyNUa1349FTIhPyMohM7tmGKT1fLkVlakYmO05fBaCxi4PYHVYQBOEt9k4E9ABjx45l7NixhR47c+ZMgfeqV6+OutlEVapUUXssP3Nzc3bs2FFkmdq1a3Pu3Lli2xJKr6xzwdTVO9WmMek5OVyNTWD6zfv8FhRKZUN9Pi5joBKXmcX46/4EJKdS1VCf7zxcythj4UPzqn+2g7cuIS0zi3/8HjL4u62s3H8KRztLhrRrUqbzRCcm02vJRm4Hh1G9ojXbpw4pY4+F1yl/xhpBED5s78SUG+H9ZqOvq3wel28EPS536oCNXuEL+Wz01NXLLnD8OX1NTZpblaeRhSkyYN1D9TsRFiU4JY1PL97ELzGZ2qZGbGnkgWUh5xM+bBUtzJTPoxJTlM+jc0fTK1maFaijqGdaeL3E3HoWBesZ6OrQsZ4brT2qI5PJWbjjcJn6/CAskhbTvufqgyc0qF6FE19PwNbcpPiKgiAIwhsjAnrhjatpYoRp7vzcExGKxaVRGZn45QY9TXODnu5nr9L97FX+eKLYpryhhRlauSknny9KfZCUwtO09Nx6ih2DT0XGEJyapjxfbGYWd3ODpOeZcErjelwCgy/dJDQ9g3Y2FvzcwB2zfFN+BOG5eo72lM+dhrXv4k0AnsUlcuVBMADt6iju6riPWYz7mMWsP6RYjN2qdnW0NDVy6ykWpfoFh/EoXPFz3q6uot6BS7d5GJaXjSsqIRnfQEXmprQiskOpc94/kFYzfiAoMpaeTTw4tmgcFsZikbcgCMLb7p2ZciO8v7Q1NBjnXJVF/g85ERlDpzNXSMzOJjUnBzNtbYbmZtYITlUE6gm5o/EWujp8VrUimx8/5begUP6JiiUyPRM5YG+gT+/cqTSnI2OZ5HsXS10dzHS0eZKaTmZuCr6uFfI2BrudkMT/bqlmJ/o7JJwTEdFY6+myuaFiceLIK35ky+VIgPD0TIZdvqUs/4WjPc2t1KdKEz4sOtpaLPikC2PX72LfxVu4jFxAXHIqyemZWBgb8lWvdgA8yA3KY5MUF5o2ZsZM6tGG5bt9WLX/FIev3iE0Jh65XI6jnSXDvZoCcPDybfp98zO25iZYGBvy8Fk0Gbm/H4Na5e29ceVBMEO+/02lb5uPXWDvvzepUN6U40sUGVI6z1tLljQHiUTC0+h42s/OWzM0s28HOtZze03flCAIgvAyREAvvBV629uir6nBr0GhBKWmoauhQRtrCyZUr4pVEVNZxjlXobyuDn+FPONpWgZGWlq0t7VkQvWqGOTmk29Y3pSQ1HSCU9N4lJKKnoYmTiZGdLKzYkBlO2VbmTkynqapLsBNlkpJlkrJybfmIjv3uRxFhp784vNN/REEgGFeTTHQ02HVvlPcD41ET1ub7o3cWTy4K3ZFTGVZ8EkXrEyN+PnYBcXGUgZ69Gpah0WDu2GY+zvR2r06geHRPAyL4m5IBAa62tR0qkz/FvUY3amZsq2MzGwe5979ei4hNZ2E1HSkOXn55bNyM+vI5XKuvTAdLSbf1B9BEATh7fJO5KF/n4k89ML7SOShF95Hb1seekF4VUQe+nefmEMvCIIgCIIgCO8wEdALgiAIgiAIwjtMBPSCIAiCIAiC8A4TAb0gCIIgCIIgvMNEQC8IgiAIgiAI7zAR0AuCIAiCIAjCO0wE9IIgCIIgCILwDhN56AVBEARBEAThHSZG6AVBEARBEAThHSYCekEQBEEQBEF4h4mAXhAEQRAEQRDeYSKgFwRBEARBEIR3mAjoBUEQBEEQBOEdJgJ6QRAEQRAEQXiHiYBeEARBEARBEN5hIqAXBEEQBEEQhHeYCOgFQRAEQRAE4R32f6gmOVIvtNm6AAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -598,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "aeon", "language": "python", "name": "python3" }, @@ -612,7 +663,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 0ccbd341d9..06408e7b9d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "aeon" -version = "1.0.0" +version = "1.1.0" description = "A toolkit for machine learning from time series" authors = [ {name = "aeon developers", email = "contact@aeon-toolkit.org"}, @@ -42,47 +42,50 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] -requires-python = ">=3.9,<3.13" +requires-python = ">=3.9,<3.14" dependencies = [ "deprecated>=1.2.13", - "numba>=0.55,<0.61.0", - "numpy>=1.21.0,<2.1.0", + "numba>=0.55,<0.62.0", + "numpy>=1.21.0,<2.3.0", "packaging>=20.0", "pandas>=2.0.0,<2.3.0", - "scikit-learn>=1.0.0,<1.6.0", - "scipy>=1.9.0,<1.15.0", + "scikit-learn>=1.0.0,<1.7.0", + "scipy>=1.9.0,<1.16.0", "typing-extensions>=4.6.0", ] -[project.optional-dependencies] # soft dependencies +[project.optional-dependencies] all_extras = [ - # Upper bound set as <1.0.0 as 1.0 dropped support for python 3.9. We will remove - # the upper bound once we also drop support for python 3.9 later in 2025. - "esig>=0.9.7,<1.0.0; platform_system != 'Darwin' and python_version < '3.11'", "imbalanced-learn", - "matplotlib>=3.3.2", # Remove upper bound + "matplotlib>=3.3.2", "pycatch22>=0.4.5", "pyod>=1.1.3", - "prts>=1.0.0.0", "pydot>=2.0.0", "ruptures>=1.1.9", "seaborn>=0.11.0", + "sparse", "statsmodels>=0.12.1", "stumpy>=1.5.1", - "tensorflow>=2.14", + "tensorflow>=2.14; python_version < '3.13'", "torch>=1.13.1", "tsfresh>=0.20.0", "tslearn>=0.5.2", - "sparse" ] dl = [ - "tensorflow>=2.14", + "tensorflow>=2.14; python_version < '3.13'", ] unstable_extras = [ - "mrsqm>=0.0.7,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) - "mrseql>=0.0.4,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) + # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html) + "mrsqm>=0.0.7,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", + "mrseql>=0.0.4,<0.1.0; platform_system != 'Windows' and python_version < '3.12'", + # very outdated and used code is deprecated + "prts>=1.0.0.0", + # Upper bound set as <1.0.0 as 1.0 dropped support for python 3.9. We will remove + # the upper bound once we also drop support for python 3.9 later in 2025. + "esig>=0.9.7,<1.0.0; platform_system != 'Darwin' and python_version < '3.11'", ] # development dependencies @@ -104,7 +107,7 @@ binder = [ "jupyterlab", ] docs = [ - "sphinx<8.2.0", + "sphinx<8.3.0", "sphinx-design", "sphinx-version-warning", "sphinx_issues", @@ -176,6 +179,8 @@ addopts = ''' --dist worksteal --reruns 2 --only-rerun "crashed while running" + --only-rerun "zipfile.BadZipFile" + --only-rerun "accessible `.keras` zip file." ''' filterwarnings = ''' ignore::UserWarning From 8c9de78ef85f4aafdb6f468abfdbef8767133f7c Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 16 May 2025 19:37:36 +0100 Subject: [PATCH 02/70] Fix bug in AutoARIMA algorithm --- aeon/forecasting/_arima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 4de0fee3d3..e6f0e66cc6 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -378,7 +378,7 @@ def auto_arima(data): points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) p.append(aic) model_points.append(points) - current_model = max(model_parameters, key=lambda item: item[5]) + current_model = min(model_parameters, key=lambda item: item[5]) current_points = model_points[model_parameters.index(current_model)] while True: better_model = False From e1ea7d7b4e785d0ac3107f45953a296ea8f5e882 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 19 May 2025 18:51:08 +0100 Subject: [PATCH 03/70] Fix test issues --- aeon/forecasting/_autoets.py | 2 +- aeon/forecasting/_ets.py | 6 +++--- aeon/forecasting/_ets_fast.py | 2 +- aeon/forecasting/_naive.py | 2 +- aeon/testing/testing_data.py | 2 ++ aeon/transformations/format/_sliding_window.py | 3 ++- aeon/utils/base/_register.py | 2 ++ 7 files changed, 12 insertions(+), 7 deletions(-) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py index 7501bee0e2..e019646d82 100644 --- a/aeon/forecasting/_autoets.py +++ b/aeon/forecasting/_autoets.py @@ -46,7 +46,7 @@ class AutoETSForecaster(BaseForecaster): >>> forecaster.fit(y) AutoETSForecaster() >>> forecaster.predict() - 366.90200486015596 + array([407.74740434]) """ def __init__( diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index fb29ce4e47..faf7b9a352 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -58,16 +58,16 @@ class ETSForecaster(BaseForecaster): Examples -------- - >>> from aeon.forecasting import ETSForecaster + >>> from aeon.forecasting._ets import ETSForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4,\ seasonality_type=2, trend_type=2) >>> forecaster.predict() - 366.90200486015596 + array([366.90200486]) """ def __init__( diff --git a/aeon/forecasting/_ets_fast.py b/aeon/forecasting/_ets_fast.py index fdbd9c005a..3322206aaa 100644 --- a/aeon/forecasting/_ets_fast.py +++ b/aeon/forecasting/_ets_fast.py @@ -71,7 +71,7 @@ class ETSForecaster(BaseForecaster): ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, seasonality_type=2, trend_type=2) >>> forecaster.predict() - 366.90200486015596 + array([366.90200486]) """ def __init__( diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py index 9bdfa82fb9..30fa10638c 100644 --- a/aeon/forecasting/_naive.py +++ b/aeon/forecasting/_naive.py @@ -41,7 +41,7 @@ class NaiveForecaster(BaseForecaster): >>> forecaster.fit(y) NaiveForecaster() >>> forecaster.predict() - 366.90200486015596 + array([432.]) """ def __init__( diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index 3337f83b0c..ef4e192afb 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -23,6 +23,7 @@ make_example_multi_index_dataframe, ) from aeon.transformations.collection import BaseCollectionTransformer +from aeon.transformations.format import BaseFormatTransformer from aeon.transformations.series import BaseSeriesTransformer from aeon.utils.conversion import convert_collection @@ -869,6 +870,7 @@ def _get_task_for_estimator(estimator): or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) or isinstance(estimator, BaseSeriesSimilaritySearch) + or isinstance(estimator, BaseFormatTransformer) ): data_label = "None" else: diff --git a/aeon/transformations/format/_sliding_window.py b/aeon/transformations/format/_sliding_window.py index 899eaaf44a..b173cb9ad2 100644 --- a/aeon/transformations/format/_sliding_window.py +++ b/aeon/transformations/format/_sliding_window.py @@ -33,7 +33,8 @@ class SlidingWindowTransformer(BaseFormatTransformer): >>> transformer = SlidingWindowTransformer(3) >>> Xt = transformer.fit_transform(X) >>> print(Xt) - ([[1, 2], [2, 3], [3, 4], [4, 5]], [3, 4, 5, 6], [0, 1, 2, 3]) + (array([[1., 2.], [2., 3.], [3., 4.], [4., 5.]]), + array([3., 4., 5., 6.]), array([0., 1., 2., 3.])) Returns diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 5e81e29b33..321b787389 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -29,6 +29,7 @@ from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer +from aeon.transformations.format import BaseFormatTransformer from aeon.transformations.series import BaseSeriesTransformer # all base classes @@ -48,6 +49,7 @@ "regressor": BaseRegressor, "segmenter": BaseSegmenter, "series-transformer": BaseSeriesTransformer, + "format-transformer": BaseFormatTransformer, "forecaster": BaseForecaster, "series-similarity-search": BaseSeriesSimilaritySearch, "collection-similarity-search": BaseCollectionSimilaritySearch, From 9694bfd59a3e90ecc1fffcd3ee106aea67536511 Mon Sep 17 00:00:00 2001 From: alexbanwell1 <31886108+alexbanwell1@users.noreply.github.com> Date: Tue, 13 May 2025 09:46:04 +0100 Subject: [PATCH 04/70] [ENH] Add ETS/ARIMA Stuff (#2536) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * forecaster base and dummy * forecasting tests * forecasting tests * forecasting tests * forecasting tests * regression * notebook * regressor * regressor * regressor * tags * tags * requires_y * forecasting notebook * forecasting notebook * remove tags * fix forecasting testing (they still fail though) * _is_fitted -> is_fitted * _is_fitted -> is_fitted * _forecast * notebook * is_fitted * y_fitted * ETS forecaster * add y checks and conversion * add tag * tidy * _check_is_fitted() * _check_is_fitted() * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. (#2318) Co-authored-by: Alex Banwell * Add faster numba version of ETS forecaster * Seperate out predict code, and add test to test without creating a class - significantly faster! * Modify _verify_ets.py to allow easy switching between statsforecast versions. This confirms that my algorithms without class overheads is significantly faster than nixtla statsforecast, and with class overheads, it is faster than their current algorithm * Add basic gradient decent optimization algorithm for smoothing parameters * Ajb/forecasting (#2357) * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. * Add faster numba version of ETS forecaster * Seperate out predict code, and add test to test without creating a class - significantly faster! * Modify _verify_ets.py to allow easy switching between statsforecast versions. This confirms that my algorithms without class overheads is significantly faster than nixtla statsforecast, and with class overheads, it is faster than their current algorithm * Add basic gradient decent optimization algorithm for smoothing parameters --------- Co-authored-by: Alex Banwell * Add additional AutoETS algorithms, and comparison scripts * Add ARIMA model in * [MNT] Testing fixes (#2531) * adjust test for non numpy output * test list output * test dataframe output * change pickle test * equal nans * test scalar output * fix lists output * allow arrays of objects * allow arrays of objects * test for boolean elements (MERLIN) * switch to deep equals * switch to deep equals * switch to deep equals * message * testing fixes --------- Co-authored-by: Tony Bagnall * Automated `pre-commit` hook update (#2533) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [DOC] Improve type hint guide and add link to the page. (#2532) * type hints * bad change * text * Add new datasets to tsf_datasets.py * Add functions for writing out .tsf files, as well as functions for manipulating the train/test split and windowing * Fix issues causing tests to fail * [DOC] Add 'Raises' section to docstring (#1766) (#2484) * Fix line endings * Moved test_cboss.py to testing/tests directory * Updated docstring comments and made methods protected * Fix line endings * Moved test_cboss.py to testing/tests directory * Updated docstring comments and made methods protected * Updated * Updated * Removed test_cboss.py * Updated * Updated * Add files for generating the datasets, and the CSV for the chosen datasets * Add windowed series train/test files * Automated `pre-commit` hook update (#2541) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * fix test (#2528) * [BUG] add ExpSmoothingSeriesTransformer and MovingAverageSeriesTransformer to __init__ (#2550) * update docs to fix 2548 docs * update init to fix 2548 bug * Automated `pre-commit` hook update (#2567) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump ossf/scorecard-action in the github-actions group (#2569) Bumps the github-actions group with 1 update: [ossf/scorecard-action](https://github.com/ossf/scorecard-action). Updates `ossf/scorecard-action` from 2.4.0 to 2.4.1 - [Release notes](https://github.com/ossf/scorecard-action/releases) - [Changelog](https://github.com/ossf/scorecard-action/blob/main/RELEASE.md) - [Commits](https://github.com/ossf/scorecard-action/compare/v2.4.0...v2.4.1) --- updated-dependencies: - dependency-name: ossf/scorecard-action dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [ENH] Added class weights to feature based classifiers (#2512) * class weights added to classification/feature based * Automatic `pre-commit` fixes * Test function for Catch22Classifier added * Test function for SummaryClassifier added * Test for tsfreshClassifier added * Soft dependecy check added for tsfresh * Test signature test case added * added test_mlp.py (#2537) * test file for FCNNetwork added (#2559) * Documentation improvement of certain BaseClasses (#2516) Co-authored-by: Antoine Guillaume * [ENH] Test coverage for AEFCNNetwork Improved (#2558) * test file added for aefcn * Test file for aefcn added * Test file reforammted * soft dependency added * name issues resolved * [ENH] Test coverage for TimeCNNNetwork Improved (#2534) * Test coverage improved for cnn network * assertion changed for test_cnn * coverage improved along with naming * [ENH] Test coverage for Resnet Network (#2553) * Resnet pytest * Resnet pytest * Fixed tensorflow failing * Added Resnet in function name * πŸ“ Add shinymack as a contributor for code (#2577) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * πŸ“ Add kevinzb56 as a contributor for doc (#2588) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * [MNT] Raise version bound for `scikit-learn` 1.6 (#2486) * update ver and new tags * default tags * toml * Update _shapelets.py Fix linear estimator coefs issue * expected results * Change expected results * update * only linux * remove mixins just to see test * revert --------- Co-authored-by: Antoine Guillaume * [MNT] Bump the python-packages group across 1 directory with 2 updates (#2598) Updates the requirements on [scipy](https://github.com/scipy/scipy) and [sphinx](https://github.com/sphinx-doc/sphinx) to permit the latest version. Updates `scipy` to 1.15.2 - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.9.0...v1.15.2) Updates `sphinx` to 8.2.3 - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/master/CHANGES.rst) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v0.1.61611...v8.2.3) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production dependency-group: python-packages - dependency-name: sphinx dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Automated `pre-commit` hook update (#2581) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * Automated `pre-commit` hook update (#2603) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Adds support for distances that are asymmetric but supports unequal length (#2613) * Adds support for distances that are asymmetric but supports unequal length * Added name to contributors * create smoothing filters notebook (#2547) * Remove datasets added * Reorganise code for generating train/test cluster files, including adding sliding window and train/test transformers * Add NaiveForecaster * Fix Bug in NaiveForecaster * Fix dataset generate script stuff * [DOC] Notebook on Feature-based Clustering (#2579) * Feature-based clustering * Feature-based clustering update * Update clustering overview * formatting * Automated `CONTRIBUTORS.md` update (#2614) Co-authored-by: chrisholder <4674372+chrisholder@users.noreply.github.com> * Updated Interval Based Notebook (#2620) * [DOC] Added Docstring for regression forecasting (#2564) * Added Docstring for Regression * Added Docstring for Regression * exog fix * GSoC announcement (#2629) * Automated `pre-commit` hook update (#2632) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump tj-actions/changed-files from 45 to 46 in the github-actions group (#2637) * [MNT] Bump tj-actions/changed-files in the github-actions group Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 45 to 46 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v45...v46) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] * Update pr_precommit.yml --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthew Middlehurst * [MNT] Update numpy requirement in the python-packages group (#2643) Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version. Updates `numpy` to 2.2.4 - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.21.0...v2.2.4) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [MNT,DEP] _binary.py metrics deprecated (#2600) * functions deprecated * Empty-Commit * version changed * Support for unequal length timeseries in itakura parallelogram (#2647) * [ENH] Implement DTW with Global alignment (#2565) * Implements Dynamic Time Warping with Global Invariances * Adds Numba JIT compilation support * Adds docs and numba support for dtw_gi and test_distance fixed * Fixes doctests * Automatic `pre-commit` fixes * Minor changes * Minor changes * Remove dtw_gi function and combine with private method _dtw_gi * Adds parameter tests * Fixes doctests * Minor changes * [ENH] Adds kdtw kernel support for kernelkmeans (#2645) * Adds kdtw kernel support for kernelkmeans * Code refactor * Adds tests for kdtw clustering * minor changes * minor changes * [MNT] Skip some excected results tests when numba is disabled (#2639) * skip some numba tests * Empty commit for CI * Update testing_config.py --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Remove REDCOMETs from testing exclusion list (#2630) * remove excluded estimators * redcomets fix * Ensure ETS algorithms are behaving correctly, and do more testing on AutoETS, along with AutoETS forecaster class * Fix a couple of bugs in the forecasters, add Sktime and StatsForecast wrappers for their AutoETS implementations * [ENH] Replace `prts` metrics (#2400) * Pre-commit fixes * Position parameter in calculate_bias * Added recall metric * merged into into one file * test added * Changes in test and range_metrics * list of list running but error! * flattening lists, all cases passed * Empty-Commit * changes * Protected functions * Changes in documentation * Changed test cases into seperate functions * test cases added and added range recall * udf_gamma removed from precision * changes * more changes * recommended changes * changes * Added Parameters * removed udf_gamma from precision * Added binary to range * error fixing * test comparing prts and range_metrics * Beta parameter added in fscore * Added udf_gamma function * f-score failing when comparing against prts * fixed f-score output * alpha usage * Empty-Commit * added test case to use range-based input for metrics * soft dependency added * doc update --------- Co-authored-by: Matthew Middlehurst Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> * Clarify documentation regarding unequal length series limitation (#2589) Co-authored-by: Matthew Middlehurst * Automated `pre-commit` hook update (#2683) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump tj-actions/changed-files in the github-actions group (#2686) Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 46.0.1 to 46.0.3 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.1...v46.0.3) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [ENH] Set `outlier_norm` default to True for Catch22 estimators (#2659) * sets outlier_norm=True by deafault * Minor changes * Docs improvement * [MNT] Use MacOS for examples/ workflow (#2668) * update bash to 5.x for lastpipe support * added esig installation * install boost before esig * fixed examples path issue for excluded notebooks * switched to fixed version of macos * added signature_method.ipynb to excluded list * removed symlink for /bin/bash * Correct AutoETS algorithms to not use multiplicative error models for data which is not strictly positive. Add check to ets for this * Reject multiplicative components for data not strictly positive * Update dependencies.md (#2717) Correct typo in dependencies.md * Automated `pre-commit` hook update (#2708) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Test Coverage for Pairwise Distance (#2590) * Pairwise distance matrix test * Empty commit for CI --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * re-running notebook for fixing cell output error (#2597) * Docstring (#2609) * [DOC] Add 'Raises' section to docstring #1766 (#2617) * [DOC] Add 'Raises' section to docstring #1766 * Automatic `pre-commit` fixes * Update _base.py * Automatic `pre-commit` fixes --------- Co-authored-by: ayushsingh9720 <199482418+ayushsingh9720@users.noreply.github.com> * [DOC] Contributor docs update (#2554) * contributing docs update * contributing docs update 2 * typos * Update contributing.md new section * Update testing.md testing update * Update contributing.md dont steal code * Automatic `pre-commit` fixes * Update contributing.md if --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> Co-authored-by: Antoine Guillaume * prevent assignment on PRs (#2703) * Update run_examples.sh (#2701) * [BUG] SevenNumberSummary bugfix and input rename (#2555) * summary bugfix * maintainer * test * readme (#2556) * remove MutilROCKETRegressor from alias mapping (#2623) Co-authored-by: Matthew Middlehurst * Automated `pre-commit` hook update (#2731) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump the github-actions group with 2 updates (#2733) Bumps the github-actions group with 2 updates: [actions/create-github-app-token](https://github.com/actions/create-github-app-token) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `actions/create-github-app-token` from 1 to 2 - [Release notes](https://github.com/actions/create-github-app-token/releases) - [Commits](https://github.com/actions/create-github-app-token/compare/v1...v2) Updates `tj-actions/changed-files` from 46.0.3 to 46.0.4 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.3...v46.0.4) --- updated-dependencies: - dependency-name: actions/create-github-app-token dependency-version: '2' dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.4 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Fixed a few spelling/grammar mistakes on TSC docs examples (#2738) * Fix docstring inconsistencies in benchmarking module (resolves #809) (#2735) * issue#809 Fix docstrings for benchmarking functions * Fixed docstrings in results_loaders.py * Fix docstring inconsistencies in benchmarking module - resolves #809 * Fix docstring inconsistencies in benchmarking module - resolves #809 * [ENH] `best_on_top` addition in `plot_pairwise_scatter` (#2655) * Empty-Commit * best_on_top parameter added * changes * [ENH] Add dummy clusterer tags (#2551) * dummy clusterer tags * len * [ENH] Collection conversion cleanup and `df-list` fix (#2654) * collection conversion cleanup * notebook * fixes --------- Co-authored-by: Tony Bagnall * [MNT] Updated the release workflows (#2638) * edit release workflows to use trusted publishing * docs * [MNT,ENH] Update to allow Python 3.13 (#2608) * python 3.13 * tensorflow * esig * tensorflow * tensorflow * esig and matrix profile * signature notebook * remove prts * fix * remove annoying deps from all_extras * Update pyproject.toml * [ENH] Hard-Coded Tests for `test_metrics.py` (#2672) * Empty-Commit * hard-coded tests * changes * Changed single ticks to double (#2640) Co-authored-by: Matthew Middlehurst * πŸ“ Add HaroonAzamFiza as a contributor for doc (#2740) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * [ENH,MNT] Assign Bot (assigned issues>2) (#2702) * Empty-Commit * point 2 working * changes * changes in comment message * [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) (#2704) * added extra features * added comments * optimized code * optimized code * made changes requested by moderators * fixed conflicts * fixed conflicts * fixed conflicts --------- Co-authored-by: Ramana-Raja * πŸ“ Add Ramana-Raja as a contributor for code (#2741) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * Release v1.1.0 (#2696) * v1.1.0 draft * finish * Automated `pre-commit` hook update (#2743) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump the github-actions group with 2 updates (#2744) Bumps the github-actions group with 2 updates: [crs-k/stale-branches](https://github.com/crs-k/stale-branches) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `crs-k/stale-branches` from 7.0.0 to 7.0.1 - [Release notes](https://github.com/crs-k/stale-branches/releases) - [Commits](https://github.com/crs-k/stale-branches/compare/v7.0.0...v7.0.1) Updates `tj-actions/changed-files` from 46.0.4 to 46.0.5 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.4...v46.0.5) --- updated-dependencies: - dependency-name: crs-k/stale-branches dependency-version: 7.0.1 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.5 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [DOC] Add implementation references (#2748) * implementation references * better attribution * use gpu installs for periodic tests (#2747) * Use shape calculation in _fit to optimize QUANTTransformer (#2727) * [REF] Refactor Anomaly Detection Module into Submodules by Algorithm Family (#2694) * Refactor Anomaly Detection Module into Submodules by Algorithm Family * updated documentation and references * implemented suggested changes * minor changes * added headers for remaining algorithm family * removing tree-based header * Automated `pre-commit` hook update (#2756) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH]Type hints/forecasting (#2737) * Type hints for primitive data types in base module * Type hints for primitive data types and strings in forecating module * type hints for primitives in foreacasting module * Revert "type hints for primitives in foreacasting module" This reverts commit 575122d14b28742140ef1e16a3a351dd5db5072b. * type hints for primitives in forecasting module * Automated `pre-commit` hook update (#2766) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Implement `load_model` function for ensemble classifiers (#2631) * feat: implement `load_model` function for LITETimeClassifier Implement separate `load_model` function for LITETimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * feat: implement `load_model` function for InceptionTimeClassifier Implement separate `load_model` function for InceptionTimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * fix: typo in load model function * feat: convert load_model functions to classmethods * test: implement test for save load for LITETIME and Inception classification models * Automatic `pre-commit` fixes * refactor: move loading tests to separate files * Update _ae_abgru.py (#2771) * Automated `pre-commit` hook update (#2779) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [DOC] Fix Broken [Source] Link and Improve Documentation for suppress_output() (#2677) * Fix Broken [Source] Link and Improve Documentation for suppress_output() Function * modified docstring and added tests * modified docstring example * modifying docstring examples * modifying docstring examples * updating conf file * updated docstring * base transform tidy (#2773) * DOC: Add Raises section for invalid weights in KNeighborsTimeSeriesClassifier (#1766) (#2764) Document the ValueError raised during initialization when an unsupported value is passed to the 'weights' parameter. Clarifies expected exceptions for users and improves API documentation consistency. Co-authored-by: Matthew Middlehurst * [ENH] Fixes Issue Improve `_check_params` method in `kmeans.py` and `kmedoids.py` (#2682) * Improves _check_params * removes function and adds a var * minor changes * minor changes * minor changes * line endings to LF * use variable instead of duplicating strings * weird file change * weird file change --------- Co-authored-by: Matthew Middlehurst * [ENH] Add type hints for deep learning regression classes (#2644) * type hints for cnn for regrssion * editing import modules Model & Optim * type hints for disjoint_cnn for regrssion * FIX type hints _get_test_params * ENH Change linie of importing typing * type hints for _encoder for regrssion * type hints for _fcn for regrssion * type hints for _inception_time for regrssion * type hints for _lite_time for regrssion * type hints for _mlp for regrssion * type hints for _resnet for regrssion * type hints for _base for regrssion * FIX: mypy errors in _disjoint_cnn.py file * FIX: mypy typing errors * Fix: Delete variable types, back old-verbose * FIX: add model._save in save_last_model_to_file function * FIX: Put TYPE_CHECKING downside * Fix: Put Any at the top * [DOC] Add RotationForest Classifier Notebook for Time Series Classification (#2592) * Add RotationForest Classifier Notebook for Time Series Classification * Added references and modified doc * minor modifications to notebook description * Update rotation_forest.ipynb --------- Co-authored-by: Matthew Middlehurst * fix: Codeowners for benchmarking metrics AD (#2784) * [GOV] Supporting Developer role (#2775) * supporting dev role * pr req * Update governance.md * typo * Automatic `pre-commit` fixes * aeon --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT, ENH, DOC] Rework similarity search (#2473) * WIP remake module structure * Update _brute_force.py * Update test__commons.py * WIP mock and test * Add test for base subsequence * Fix subsequence_search tests * debug brute force mp * more debug of subsequence tests * more debug of subsequence tests * Add functional LSH neighbors * add notebook for sim search tasks * Updated series similarity search * Fix mistake addition in transformers and fix base classes * Fix registry and api reference * Update documentation and fix some leftover bugs * Update documentation and add default test params * Fix identifiers and test data shape for all_estimators tests * Fix missing params * Fix n_jobs params and tags, add some docs * Fix numba test bug and update testing data for sim search * Fix imports, testing data tests, and impose predict/_predict interface to all sim search estimators * Fix args * Fix extract test * update docs api and notebooks * remove notes * Patrick comments * Adress comments and clean index code * Fix Patrick comments * Fix variable suppression mistake * Divide base class into task specific * Fix typo in imports * Empty commit for CI * Fix typo again * Add check_inheritance exception for similarity search * Revert back to non per type base classes * Factor check index and typo in test --------- Co-authored-by: Patrick SchΓ€fer Co-authored-by: Matthew Middlehurst Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> * [ENH] Adapt the DCNN Networks to use Weight Norm Wrappers (#2628) * adapt the dcnn networks to use weight norm wrappers and remove l2 regularization * Automatic `pre-commit` fixes * add custom object * Automatic `pre-commit` fixes * fix trial --------- Co-authored-by: Matthew Middlehurst * [GOV] Remove inactive developers (#2776) * inactive devs * logo fix * Automated `pre-commit` hook update (#2792) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * Code to generate differenced datasets * Add AutoARIMA algorithm into Aeon * Add ArimaForecaster to forecasting list * Fix predict method to return the prediction in the correct format --------- Signed-off-by: dependabot[bot] Co-authored-by: Tony Bagnall Co-authored-by: Tony Bagnall Co-authored-by: MatthewMiddlehurst Co-authored-by: Alex Banwell Co-authored-by: Matthew Middlehurst Co-authored-by: aeon-actions-bot[bot] <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> Co-authored-by: Nikita Singh Co-authored-by: Ali El Hadi ISMAIL FAWAZ <54309336+hadifawaz1999@users.noreply.github.com> Co-authored-by: Cyril Meyer <69190238+Cyril-Meyer@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Co-authored-by: Akash Kawle <128881349+shinymack@users.noreply.github.com> Co-authored-by: Kevin Shah <161136814+kevinzb56@users.noreply.github.com> Co-authored-by: Antoine Guillaume Co-authored-by: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> Co-authored-by: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Co-authored-by: Divya Tiwari <108270861+itsdivya1309@users.noreply.github.com> Co-authored-by: chrisholder <4674372+chrisholder@users.noreply.github.com> Co-authored-by: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> Co-authored-by: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Co-authored-by: TinaJin0228 <60577222+TinaJin0228@users.noreply.github.com> Co-authored-by: Ayush Singh Co-authored-by: ayushsingh9720 <199482418+ayushsingh9720@users.noreply.github.com> Co-authored-by: HaroonAzamFiza Co-authored-by: adityagh006 <142653450+adityagh006@users.noreply.github.com> Co-authored-by: V_26@ Co-authored-by: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Co-authored-by: Ramana-Raja Co-authored-by: Ahmed Zahran <136983104+Ahmed-Zahran02@users.noreply.github.com> Co-authored-by: Adarsh Dubey Co-authored-by: Somto Onyekwelu <117727947+SomtoOnyekwelu@users.noreply.github.com> Co-authored-by: Saad Al-Tohamy <92796871+saadaltohamy@users.noreply.github.com> Co-authored-by: Patrick SchΓ€fer Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> Co-authored-by: Aadya Chinubhai <77720426+aadya940@users.noreply.github.com> --- .../workflows/periodic_github_maintenace.yml | 35 ++ .github/workflows/precommit_autoupdate.yml | 39 ++ .github/workflows/scorecard.yml | 46 ++ CONTRIBUTORS.md | 14 + .../metrics/anomaly_detection/__init__.py | 5 + .../anomaly_detection/_range_metrics.py | 22 + .../anomaly_detection/range_metrics.py | 521 ++++++++++++++++ .../anomaly_detection/tests/test_metrics.py | 572 ++++++++++++++++++ .../distance_based/_time_series_neighbors.py | 6 + aeon/datasets/Final Dataset Selection.csv | 101 ++++ aeon/datasets/__init__.py | 11 +- aeon/datasets/_data_writers.py | 301 ++++++++- aeon/datasets/dataset_generation.py | 218 +++++++ aeon/datasets/tests/test_data_writers.py | 1 - .../tests/test_dataset_collections.py | 2 +- aeon/datasets/tsad_datasets.py | 2 +- aeon/datasets/tsf_datasets.py | 13 + aeon/forecasting/__init__.py | 8 +- aeon/forecasting/_arima.py | 421 +++++++++++++ aeon/forecasting/_autoets.py | 457 ++++++++++++++ aeon/forecasting/_autoets_gradient_params.py | 297 +++++++++ aeon/forecasting/_compare_external_autoets.py | 207 +++++++ aeon/forecasting/_ets.py | 565 ++++++++--------- aeon/forecasting/_ets_fast.py | 476 +++++++++++++++ aeon/forecasting/_naive.py | 94 +++ .../_plot_autoets_gradient_method.py | 66 ++ aeon/forecasting/_sktime_autoets.py | 78 +++ aeon/forecasting/_statsforecast_autoets.py | 78 +++ aeon/forecasting/_time_autoets.py | 37 ++ aeon/forecasting/_utils.py | 115 ++++ aeon/forecasting/_verify_arima.py | 31 + aeon/forecasting/_verify_ets.py | 345 +++++++++++ aeon/forecasting/tests/test_ets.py | 113 +++- aeon/transformations/format/__init__.py | 11 + .../transformations/format/_sliding_window.py | 92 +++ aeon/transformations/format/_train_test.py | 93 +++ aeon/transformations/format/base.py | 301 +++++++++ aeon/transformations/series/__init__.py | 2 + aeon/transformations/series/_difference.py | 52 ++ 39 files changed, 5469 insertions(+), 379 deletions(-) create mode 100644 .github/workflows/periodic_github_maintenace.yml create mode 100644 .github/workflows/precommit_autoupdate.yml create mode 100644 .github/workflows/scorecard.yml create mode 100644 aeon/benchmarking/metrics/anomaly_detection/range_metrics.py create mode 100644 aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py create mode 100644 aeon/datasets/Final Dataset Selection.csv create mode 100644 aeon/datasets/dataset_generation.py create mode 100644 aeon/forecasting/_arima.py create mode 100644 aeon/forecasting/_autoets.py create mode 100644 aeon/forecasting/_autoets_gradient_params.py create mode 100644 aeon/forecasting/_compare_external_autoets.py create mode 100644 aeon/forecasting/_ets_fast.py create mode 100644 aeon/forecasting/_naive.py create mode 100644 aeon/forecasting/_plot_autoets_gradient_method.py create mode 100644 aeon/forecasting/_sktime_autoets.py create mode 100644 aeon/forecasting/_statsforecast_autoets.py create mode 100644 aeon/forecasting/_time_autoets.py create mode 100644 aeon/forecasting/_utils.py create mode 100644 aeon/forecasting/_verify_arima.py create mode 100644 aeon/forecasting/_verify_ets.py create mode 100644 aeon/transformations/format/__init__.py create mode 100644 aeon/transformations/format/_sliding_window.py create mode 100644 aeon/transformations/format/_train_test.py create mode 100644 aeon/transformations/format/base.py create mode 100644 aeon/transformations/series/_difference.py diff --git a/.github/workflows/periodic_github_maintenace.yml b/.github/workflows/periodic_github_maintenace.yml new file mode 100644 index 0000000000..952150313b --- /dev/null +++ b/.github/workflows/periodic_github_maintenace.yml @@ -0,0 +1,35 @@ +name: GitHub Maintenance + +on: + schedule: + # every Monday at 01:00 AM UTC + - cron: "0 1 * * 1" + workflow_dispatch: + +permissions: + issues: write + contents: write + +jobs: + stale_branches: + runs-on: ubuntu-24.04 + + steps: + - name: Create app token + uses: actions/create-github-app-token@v2 + id: app-token + with: + app-id: ${{ vars.PR_APP_ID }} + private-key: ${{ secrets.PR_APP_KEY }} + + - name: Stale Branches + uses: crs-k/stale-branches@v7.0.1 + with: + repo-token: ${{ steps.app-token.outputs.token }} + days-before-stale: 140 + days-before-delete: 175 + comment-updates: true + tag-committer: true + stale-branch-label: "stale branch" + compare-branches: "info" + pr-check: true diff --git a/.github/workflows/precommit_autoupdate.yml b/.github/workflows/precommit_autoupdate.yml new file mode 100644 index 0000000000..a670feaf2f --- /dev/null +++ b/.github/workflows/precommit_autoupdate.yml @@ -0,0 +1,39 @@ +name: Update pre-commit Hooks + +on: + schedule: + # every Monday at 12:30 AM UTC + - cron: "30 0 * * 1" + workflow_dispatch: + +jobs: + pre-commit-auto-update: + runs-on: ubuntu-24.04 + + steps: + - uses: actions/checkout@v4 + + - name: Setup Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - uses: browniebroke/pre-commit-autoupdate-action@v1.0.0 + + - if: always() + name: Create app token + uses: actions/create-github-app-token@v2 + id: app-token + with: + app-id: ${{ vars.PR_APP_ID }} + private-key: ${{ secrets.PR_APP_KEY }} + + - if: always() + uses: peter-evans/create-pull-request@v7 + with: + token: ${{ steps.app-token.outputs.token }} + commit-message: "Automated `pre-commit` hook update" + branch: pre-commit-hooks-update + title: "[MNT] Automated `pre-commit` hook update" + body: "Automated weekly update to `.pre-commit-config.yaml` hook versions." + labels: maintenance, full pre-commit, no changelog diff --git a/.github/workflows/scorecard.yml b/.github/workflows/scorecard.yml new file mode 100644 index 0000000000..3c57528fc5 --- /dev/null +++ b/.github/workflows/scorecard.yml @@ -0,0 +1,46 @@ +name: Scorecard supply-chain security + +on: + branch_protection_rule: + schedule: + - cron: '30 1 * * 6' + push: + branches: + - main + +permissions: read-all + +jobs: + analysis: + name: Scorecard analysis + runs-on: ubuntu-24.04 + permissions: + # Needed to upload the results to code-scanning dashboard. + security-events: write + # Needed to publish results and get a badge (see publish_results below). + id-token: write + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Run analysis + uses: ossf/scorecard-action@v2.4.1 + with: + results_file: results.sarif + results_format: sarif + publish_results: true + + - name: Upload artifact + uses: actions/upload-artifact@v4 + with: + name: SARIF file + path: results.sarif + retention-days: 5 + + - name: Upload to code-scanning + uses: github/codeql-action/upload-sarif@v3 + with: + sarif_file: results.sarif diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 2103194799..a236c509fc 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -275,6 +275,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Thach Le Nguyen
Thach Le Nguyen
πŸ’» ⚠️ + + TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
πŸ“– Thomas Buckley-Houston
Thomas Buckley-Houston
πŸ› Tom Xu
Tom Xu
πŸ’» πŸ“– @@ -285,6 +287,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Utsav Kumar Tiwari
Utsav Kumar Tiwari
πŸ’» πŸ“– + + Vedant
Vedant
πŸ“– Viktor Dremov
Viktor Dremov
πŸ’» ViktorKaz
ViktorKaz
πŸ’» πŸ“– 🎨 @@ -295,6 +299,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d William Zheng
William Zheng
πŸ’» ⚠️ + + Yair Beer
Yair Beer
πŸ’» Yash Lamba
Yash Lamba
πŸ’» Yi-Xuan Xu
Yi-Xuan Xu
πŸ’» ⚠️ 🚧 πŸ“– @@ -305,6 +311,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d alexbanwell1
alexbanwell1
πŸ’» 🎨 πŸ“– + + bethrice44
bethrice44
πŸ› πŸ’» πŸ‘€ ⚠️ big-o
big-o
πŸ’» ⚠️ 🎨 πŸ€” πŸ‘€ βœ… πŸ§‘β€πŸ« bobbys
bobbys
πŸ’» @@ -315,6 +323,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d danbartl
danbartl
πŸ› πŸ’» πŸ‘€ πŸ“’ ⚠️ βœ… πŸ“Ή + + hamzahiqb
hamzahiqb
πŸš‡ hiqbal2
hiqbal2
πŸ“– jesellier
jesellier
πŸ’» @@ -325,6 +335,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d neuron283
neuron283
πŸ’» + + nileenagp
nileenagp
πŸ’» oleskiewicz
oleskiewicz
πŸ’» πŸ“– ⚠️ pabworks
pabworks
πŸ’» ⚠️ @@ -335,6 +347,8 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d sri1419
sri1419
πŸ’» + + tensorflow-as-tf
tensorflow-as-tf
πŸ’» vNtzYy
vNtzYy
πŸ› ved pawar
ved pawar
πŸ“– diff --git a/aeon/benchmarking/metrics/anomaly_detection/__init__.py b/aeon/benchmarking/metrics/anomaly_detection/__init__.py index 9e1f2c3e57..817d52b7ec 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/__init__.py +++ b/aeon/benchmarking/metrics/anomaly_detection/__init__.py @@ -43,3 +43,8 @@ range_roc_auc_score, range_roc_vus_score, ) +from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( + ts_fscore, + ts_precision, + ts_recall, +) diff --git a/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py index 7143100c04..da8c8ae1ad 100644 --- a/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py +++ b/aeon/benchmarking/metrics/anomaly_detection/_range_metrics.py @@ -6,6 +6,7 @@ import warnings import numpy as np +from deprecated.sphinx import deprecated from aeon.benchmarking.metrics.anomaly_detection._range_ts_metrics import ( _binary_to_ranges, @@ -15,6 +16,13 @@ from aeon.benchmarking.metrics.anomaly_detection._util import check_y +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_precision is deprecated and will be removed in v1.2.0. " + "Please use ts_precision from the range_metrics module instead.", + category=FutureWarning, +) def range_precision( y_true: np.ndarray, y_pred: np.ndarray, @@ -82,6 +90,13 @@ def range_precision( ) +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_recall is deprecated and will be removed in v1.2.0. " + "Please use ts_recall from the range_metrics module instead.", + category=FutureWarning, +) def range_recall( y_true: np.ndarray, y_pred: np.ndarray, @@ -142,6 +157,13 @@ def range_recall( ) +# TODO: Remove in v1.2.0 +@deprecated( + version="1.1.0", + reason="range_f_score is deprecated and will be removed in v1.2.0. " + "Please use ts_fscore from the range_metrics module instead.", + category=FutureWarning, +) def range_f_score( y_true: np.ndarray, y_pred: np.ndarray, diff --git a/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py new file mode 100644 index 0000000000..9084188f59 --- /dev/null +++ b/aeon/benchmarking/metrics/anomaly_detection/range_metrics.py @@ -0,0 +1,521 @@ +"""Calculate Precision, Recall, and F1-Score for time series anomaly detection.""" + +__maintainer__ = [] +__all__ = ["ts_precision", "ts_recall", "ts_fscore"] + +import numpy as np + + +def _flatten_ranges(ranges): + """ + If the input is a list of lists, it flattens it into a single list. + + Parameters + ---------- + ranges : list of tuples or list of lists of tuples + The ranges to flatten. each tuple shoulod be in the format of (start, end). + + Returns + ------- + list of tuples + A flattened list of ranges. + + Examples + -------- + >>> _flatten_ranges([[(1, 5), (10, 15)], [(20, 25)]]) + [(1, 5), (10, 15), (20, 25)] + """ + if not ranges: + return [] + if isinstance(ranges[0], list): + flat = [] + for sublist in ranges: + for pred in sublist: + flat.append(pred) + return flat + return ranges + + +def udf_gamma_def(overlap_count): + """User-defined gamma function. Should return a gamma value > 1. + + Parameters + ---------- + overlap_count : int + The number of overlapping ranges. + + Returns + ------- + float + The user-defined gamma value (>1). + """ + return_val = 1 + 0.1 * overlap_count # modify this function as needed + + return return_val + + +def _calculate_bias(position, length, bias_type="flat"): + """Calculate bias value based on position and length. + + Parameters + ---------- + position : int + Current position in the range + length : int + Total length of the range + bias_type : str, default="flat" + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + """ + if bias_type == "flat": + return 1.0 + elif bias_type == "front": + return 1.0 - (position - 1) / length + elif bias_type == "middle": + if length / 2 == 0: + return 1.0 + if position <= length / 2: + return position / (length / 2) + else: + return (length - position + 1) / (length / 2) + elif bias_type == "back": + return position / length + else: + raise ValueError(f"Invalid bias type: {bias_type}") + + +def _gamma_select(cardinality, gamma): + """Select a gamma value based on the cardinality type. + + Parameters + ---------- + cardinality : int + The number of overlapping ranges. + gamma : str + Gamma to use. Should be one of ["one", "reciprocal", "udf_gamma"]. + + Returns + ------- + float + The selected gamma value. + + Raises + ------ + ValueError + If an invalid `gamma` type is provided or if `udf_gamma` is required + but not provided. + """ + if gamma == "one": + return 1.0 + elif gamma == "reciprocal": + return 1 / cardinality if cardinality > 1 else 1.0 + elif gamma == "udf_gamma": + if udf_gamma_def(cardinality) is not None: + return 1.0 / udf_gamma_def(cardinality) + else: + raise ValueError("udf_gamma must be provided for 'udf_gamma' gamma type.") + else: + raise ValueError( + "Invalid gamma type. Choose from ['one', 'reciprocal', 'udf_gamma']." + ) + + +def _calculate_overlap_reward_precision(pred_range, overlap_set, bias_type): + """Overlap Reward for y_pred. + + Parameters + ---------- + pred_range : tuple + The predicted range. + overlap_set : set + The set of overlapping positions. + bias_type : str + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + + Returns + ------- + float + The weighted value for overlapping positions only. + """ + start, end = pred_range + length = end - start + 1 + + max_value = 0 # Total possible weighted value for all positions. + my_value = 0 # Weighted value for overlapping positions only. + + for i in range(1, length + 1): + global_position = start + i - 1 + bias_value = _calculate_bias(i, length, bias_type) + max_value += bias_value + + if global_position in overlap_set: + my_value += bias_value + + return my_value / max_value if max_value > 0 else 0.0 + + +def _calculate_overlap_reward_recall(real_range, overlap_set, bias_type): + """Overlap Reward for y_real. + + Parameters + ---------- + real_range : tuple + The real range. + overlap_set : set + The set of overlapping positions. + bias_type : str + Type of bias to apply, Should be one of ["flat", "front", "middle", "back"]. + + Returns + ------- + float + The weighted value for overlapping positions only. + """ + start, end = real_range + length = end - start + 1 + + max_value = 0.0 # Total possible weighted value for all positions. + my_value = 0.0 # Weighted value for overlapping positions only. + + for i in range(1, length + 1): + global_position = start + i - 1 + bias_value = _calculate_bias(i, length, bias_type) + max_value += bias_value + + if global_position in overlap_set: + my_value += bias_value + + return my_value / max_value if max_value > 0 else 0.0 + + +def _binary_to_ranges(binary_sequence): + """ + Convert a binary sequence to a list of anomaly ranges. + + Parameters + ---------- + binary_sequence : list + Binary sequence where 1 indicates anomaly and 0 indicates normal. + + Returns + ------- + list of tuples + List of anomaly ranges as (start, end) tuples. + + """ + ranges = [] + start = None + + for i, val in enumerate(binary_sequence): + if val and start is None: + start = i + elif not val and start is not None: + ranges.append((start, i - 1)) + start = None + + if start is not None: + ranges.append((start, len(binary_sequence) - 1)) + + return ranges + + +def ts_precision(y_pred, y_real, gamma="one", bias_type="flat"): + """ + Calculate Precision for time series anomaly detection. + + Precision measures the proportion of correctly predicted anomaly positions + out of all all the predicted anomaly positions, aggregated across the entire time + series. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one"]. + + Returns + ------- + float + Precision + + Raises + ------ + ValueError + If an invalid `gamma` type is provided. + ValueError + If input sequence is binary and y_real and y_pred are of different lengths. + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), MontrΓ©al, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + # Check if inputs are binary or range-based + is_binary = False + if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( + y_pred[0], (int, np.integer) + ): + is_binary = True + elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, np.integer) + ): + is_binary = True + + if is_binary: + if not isinstance(y_pred, (list, tuple, np.ndarray)) or not isinstance( + y_real, (list, tuple, np.ndarray) + ): + raise ValueError( + "For binary inputs, y_pred and y_real should be list or tuple, " + "or numpy array of integers." + ) + if len(y_pred) != len(y_real): + raise ValueError( + "For binary inputs, y_pred and y_real must be of the same length." + ) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_real_ranges = _binary_to_ranges(y_real) + else: + y_pred_ranges = y_pred + y_real_ranges = y_real + + if gamma not in ["reciprocal", "one"]: + raise ValueError("Invalid gamma type for precision. Use 'reciprocal' or 'one'.") + + # Flattening y_pred and y_real to resolve nested lists + flat_y_pred = _flatten_ranges(y_pred_ranges) + flat_y_real = _flatten_ranges(y_real_ranges) + + total_overlap_reward = 0.0 + total_cardinality = 0 + + for pred_range in flat_y_pred: + overlap_set = set() + cardinality = 0 + + for real_start, real_end in flat_y_real: + overlap_start = max(pred_range[0], real_start) + overlap_end = min(pred_range[1], real_end) + + if overlap_start <= overlap_end: + overlap_set.update(range(overlap_start, overlap_end + 1)) + cardinality += 1 + + overlap_reward = _calculate_overlap_reward_precision( + pred_range, overlap_set, bias_type + ) + gamma_value = _gamma_select(cardinality, gamma) + total_overlap_reward += gamma_value * overlap_reward + total_cardinality += 1 + + precision = ( + total_overlap_reward / total_cardinality if total_cardinality > 0 else 0.0 + ) + return precision + + +def ts_recall(y_pred, y_real, gamma="one", bias_type="flat", alpha=0.0): + """ + Calculate Recall for time series anomaly detection. + + Recall measures the proportion of correctly predicted anomaly positions + out of all the real/actual (ground truth) anomaly positions, aggregated across the + entire time series. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one", "udf_gamma"]. + bias_type : str, default="flat" + Type of bias to apply. Should be one of ["flat", "front", "middle", "back"]. + alpha : float, default: 0.0 + Weight for existence reward in recall calculation. + + Returns + ------- + float + Recall + + Raises + ------ + ValueError + If input sequence is binary and y_real and y_pred are of different lengths. + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), MontrΓ©al, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + is_binary = False + if isinstance(y_pred, (list, tuple, np.ndarray)) and isinstance( + y_pred[0], (int, np.integer) + ): + is_binary = True + elif isinstance(y_real, (list, tuple, np.ndarray)) and isinstance( + y_real[0], (int, np.integer) + ): + is_binary = True + + if is_binary: + if not isinstance(y_pred, (list, tuple, np.ndarray)) or not isinstance( + y_real, (list, tuple, np.ndarray) + ): + raise ValueError( + "For binary inputs, y_pred and y_real should be list or tuple, " + "or numpy array of integers." + ) + if len(y_pred) != len(y_real): + raise ValueError( + "For binary inputs, y_pred and y_real must be of the same length." + ) + + y_pred_ranges = _binary_to_ranges(y_pred) + y_real_ranges = _binary_to_ranges(y_real) + else: + y_pred_ranges = y_pred + y_real_ranges = y_real + + # Flattening y_pred and y_real to resolve nested lists + flat_y_pred = _flatten_ranges(y_pred_ranges) + flat_y_real = _flatten_ranges(y_real_ranges) + + total_overlap_reward = 0.0 + + for real_range in flat_y_real: + overlap_set = set() + cardinality = 0 + + for pred_range in flat_y_pred: + overlap_start = max(real_range[0], pred_range[0]) + overlap_end = min(real_range[1], pred_range[1]) + + if overlap_start <= overlap_end: + overlap_set.update(range(overlap_start, overlap_end + 1)) + cardinality += 1 + + existence_reward = 1.0 if overlap_set else 0.0 + + if overlap_set: + overlap_reward = _calculate_overlap_reward_recall( + real_range, overlap_set, bias_type + ) + gamma_value = _gamma_select(cardinality, gamma) + overlap_reward *= gamma_value + else: + overlap_reward = 0.0 + + recall_score = alpha * existence_reward + (1 - alpha) * overlap_reward + total_overlap_reward += recall_score + + recall = total_overlap_reward / len(flat_y_real) if flat_y_real else 0.0 + return recall + + +def ts_fscore( + y_pred, + y_real, + gamma="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + beta=1.0, +): + """ + Calculate F1-Score for time series anomaly detection. + + F-1 Score is the harmonic mean of Precision and Recall, providing + a single metric to evaluate the performance of an anomaly detection model. + + Parameters + ---------- + y_pred : list of tuples or binary sequence + The predicted anomaly ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + y_real : list of tuples, list of lists of tuples or binary sequence + The real/actual (ground truth) ranges. + - For range-based input, each tuple represents a range (start, end) of the + anomaly where start is starting index (inclusive) and end is ending index + (inclusive). + - If y_real is in the format of list of lists, they will be flattened into a + single list of tuples bringing it to the above format. + - For binary inputs, the sequence should contain integers (0 or 1), where 1 + indicates an anomaly. In this case, y_pred and y_real must be of same length. + gamma : str, default="one" + Cardinality type. Should be one of ["reciprocal", "one", "udf_gamma"]. + p_bias : str, default="flat" + Type of bias to apply for precision. + Should be one of ["flat", "front", "middle", "back"]. + r_bias : str, default="flat" + Type of bias to apply for recall. + Should be one of ["flat", "front", "middle", "back"]. + p_alpha : float, default=0.0 + Weight for existence reward in Precision calculation. + r_alpha : float, default=0.0 + Weight for existence reward in Recall calculation. + beta : float, default=1.0 + F-score beta determines the weight of recall in the combined score. + beta < 1 lends more weight to precision, while beta > 1 favors recall. + + Returns + ------- + float + F1-Score + + References + ---------- + .. [1] Tatbul, Nesime, Tae Jun Lee, Stan Zdonik, Mejbah Alam,and Justin Gottschlich. + "Precision and Recall for Time Series." 32nd Conference on Neural Information + Processing Systems (NeurIPS 2018), MontrΓ©al, Canada. + http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf + """ + precision = ts_precision(y_pred, y_real, gamma, p_bias) + recall = ts_recall(y_pred, y_real, gamma, r_bias, r_alpha) + + if precision + recall > 0: + fscore = ((1 + beta**2) * (precision * recall)) / (beta**2 * precision + recall) + else: + fscore = 0.0 + + return fscore diff --git a/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py new file mode 100644 index 0000000000..0fbbe16fa3 --- /dev/null +++ b/aeon/benchmarking/metrics/anomaly_detection/tests/test_metrics.py @@ -0,0 +1,572 @@ +"""Test cases for the range-based anomaly detection metrics.""" + +import numpy as np + +from aeon.benchmarking.metrics.anomaly_detection.range_metrics import ( + ts_fscore, + ts_precision, + ts_recall, +) + + +def test_single_overlapping_range(): + """Test for single overlapping range.""" + y_pred = np.array([0, 1, 1, 1, 1, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.600000 + expected_f1 = 0.666667 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for single overlapping range! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for single overlapping range! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for single overlapping range! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_non_overlapping_ranges(): + """Test for multiple non-overlapping ranges.""" + y_pred = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]) + y_real = np.array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]) + + expected_precision = 0.000000 + expected_recall = 0.000000 + expected_f1 = 0.000000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple non-overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple non-overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple non-overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_overlapping_ranges(): + """Test for multiple overlapping ranges.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + + expected_precision = 0.666667 + expected_recall = 0.400000 + expected_f1 = 0.500000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_nested_lists_of_predictions(): + """Test for nested lists of predictions.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0]) + + expected_precision = 0.555556 + expected_recall = 0.566667 + expected_f1 = 0.561056 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for nested lists of predictions! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for nested lists of predictions! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for nested lists of predictions! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_all_encompassing_range(): + """Test for all encompassing range.""" + y_pred = np.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + y_real = np.array([0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0]) + + expected_precision = 0.600000 + expected_recall = 1.000000 + expected_f1 = 0.750000 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for all encompassing range! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for all encompassing range! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for all encompassing range! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_range_based_input(): + """Test with input being range-based or bianry-based.""" + y_pred_range = [(1, 2)] + y_true_range = [(1, 1)] + y_pred_binary = np.array([0, 1, 1, 0]) + y_true_binary = np.array([0, 1, 0, 0]) + + expected_precision = 0.5 + expected_recall = 1.000000 + expected_f1 = 0.666667 + + # for range-based input + precision_range = ts_precision( + y_pred_range, y_true_range, gamma="reciprocal", bias_type="flat" + ) + recall_range = ts_recall( + y_pred_range, + y_true_range, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score_range = ts_fscore( + y_pred_range, + y_true_range, + gamma="reciprocal", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision_range, + expected_precision, + decimal=6, + err_msg=( + f"Precision mismatch: " + f"ts_precision={precision_range} vs" + f"expected_precision_range={expected_precision}" + ), + ) + np.testing.assert_almost_equal( + recall_range, + expected_recall, + decimal=6, + err_msg=( + f"Recall mismatch: " + f"ts_recall={recall_range} vs expected_recall_range={expected_recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score_range, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score mismatch: " + f"ts_fscore={f1_score_range} vs expected_f_score_range={expected_f1}" + ), + ) + + # for binary input + precision_binary = ts_precision( + y_pred_binary, y_true_binary, gamma="reciprocal", bias_type="flat" + ) + recall_binary = ts_recall( + y_pred_binary, + y_true_binary, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score_binary = ts_fscore( + y_pred_binary, + y_true_binary, + gamma="reciprocal", + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision_binary, + expected_precision, + decimal=6, + err_msg=( + f"Precision mismatch: " + f"ts_precision={precision_range} vs " + f"expected_precision_binary={expected_precision}" + ), + ) + np.testing.assert_almost_equal( + recall_binary, + expected_recall, + decimal=6, + err_msg=( + f"Recall mismatch: " + f"ts_recall={recall_range} vs expected_recall_binary={expected_recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score_binary, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score mismatch: " + f"ts_fscore={f1_score_range} vs expected_f_score_binary={expected_f1}" + ), + ) + + +def test_multiple_overlapping_ranges_with_gamma_reciprocal(): + """Test for multiple overlapping ranges with gamma=reciprocal.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.666667 + expected_recall = 0.200000 + expected_f1 = 0.307692 + + precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="flat") + recall = ts_recall( + y_pred, + y_real, + gamma="reciprocal", + bias_type="flat", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="reciprocal", + beta=1, + p_bias="flat", + r_bias="flat", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_overlapping_ranges_with_bias_middle(): + """Test for multiple overlapping ranges with bias_type=middle.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.333333 + expected_f1 = 0.461538 + + precision = ts_precision(y_pred, y_real, gamma="one", bias_type="middle") + recall = ts_recall( + y_pred, + y_real, + gamma="one", + bias_type="middle", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="one", + beta=1, + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) + + +def test_multiple_overlapping_ranges_with_bias_middle_gamma_reciprocal(): + """Test for multiple overlapping ranges with bias_type=middle, gamma=reciprocal.""" + y_pred = np.array([0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0]) + y_real = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1]) + expected_precision = 0.750000 + expected_recall = 0.166667 + expected_f1 = 0.272727 + + precision = ts_precision(y_pred, y_real, gamma="reciprocal", bias_type="middle") + recall = ts_recall( + y_pred, + y_real, + gamma="reciprocal", + bias_type="middle", + alpha=0.0, + ) + f1_score = ts_fscore( + y_pred, + y_real, + gamma="reciprocal", + beta=1, + p_bias="middle", + r_bias="middle", + p_alpha=0.0, + r_alpha=0.0, + ) + + np.testing.assert_almost_equal( + precision, + expected_precision, + decimal=6, + err_msg=( + f"Precision failed for multiple overlapping ranges! " + f"Expected={expected_precision}, Got={precision}" + ), + ) + np.testing.assert_almost_equal( + recall, + expected_recall, + decimal=6, + err_msg=( + f"Recall failed for multiple overlapping ranges! " + f"Expected={expected_recall}, Got={recall}" + ), + ) + np.testing.assert_almost_equal( + f1_score, + expected_f1, + decimal=6, + err_msg=( + f"F1-Score failed for multiple overlapping ranges! " + f"Expected={expected_f1}, Got={f1_score}" + ), + ) diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index fdf588b118..18afc29d5f 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -64,6 +64,12 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier): If ``weights`` is not among the supported values. See the ``weights`` parameter description for valid options. + Raises + ------ + ValueError + If ``weights`` is not among the supported values. + See the ``weights`` parameter description for valid options. + Examples -------- >>> from aeon.datasets import load_unit_test diff --git a/aeon/datasets/Final Dataset Selection.csv b/aeon/datasets/Final Dataset Selection.csv new file mode 100644 index 0000000000..c336db5a22 --- /dev/null +++ b/aeon/datasets/Final Dataset Selection.csv @@ -0,0 +1,101 @@ +Dataset,Series,Category +weather_dataset,T1,Weather +weather_dataset,T2,Weather +weather_dataset,T3,Weather +weather_dataset,T4,Weather +weather_dataset,T5,Weather +solar_10_minutes_dataset,T1,Energy Production +solar_10_minutes_dataset,T2,Energy Production +solar_10_minutes_dataset,T3,Energy Production +solar_10_minutes_dataset,T4,Energy Production +solar_10_minutes_dataset,T5,Energy Production +sunspot_dataset_without_missing_values,T1,Other +wind_farms_minutely_dataset_without_missing_values,T1,Energy Production +wind_farms_minutely_dataset_without_missing_values,T2,Energy Production +wind_farms_minutely_dataset_without_missing_values,T3,Energy Production +wind_farms_minutely_dataset_without_missing_values,T4,Energy Production +wind_farms_minutely_dataset_without_missing_values,T5,Energy Production +elecdemand_dataset,T1,Energy Demand +us_births_dataset,T1,Demographic +saugeenday_dataset,T1,Weather +london_smart_meters_dataset_without_missing_values,T1,Energy Demand +london_smart_meters_dataset_without_missing_values,T2,Energy Demand +london_smart_meters_dataset_without_missing_values,T3,Energy Demand +traffic_hourly_dataset,T1,Transportation +traffic_hourly_dataset,T2,Transportation +traffic_hourly_dataset,T3,Transportation +traffic_hourly_dataset,T4,Transportation +traffic_hourly_dataset,T5,Transportation +electricity_hourly_dataset,T1,Energy Demand +electricity_hourly_dataset,T2,Energy Demand +electricity_hourly_dataset,T3,Energy Demand +pedestrian_counts_dataset,T1,Transportation +pedestrian_counts_dataset,T2,Transportation +pedestrian_counts_dataset,T3,Transportation +pedestrian_counts_dataset,T4,Transportation +pedestrian_counts_dataset,T5,Transportation +kdd_cup_2018_dataset_without_missing_values,T1,Other +australian_electricity_demand_dataset,T1,Energy Demand +australian_electricity_demand_dataset,T2,Energy Demand +australian_electricity_demand_dataset,T3,Energy Demand +oikolab_weather_dataset,T1,Weather +oikolab_weather_dataset,T2,Weather +oikolab_weather_dataset,T3,Weather +oikolab_weather_dataset,T4,Weather +m4_monthly_dataset,T122,Macro +m4_monthly_dataset,T145,Macro +m4_monthly_dataset,T180,Macro +m4_monthly_dataset,T186,Macro +m4_monthly_dataset,T17051,Micro +m4_monthly_dataset,T17088,Micro +m4_monthly_dataset,T17132,Micro +m4_monthly_dataset,T17146,Micro +m4_monthly_dataset,T26710,Demographic +m4_monthly_dataset,T27138,Industry +m4_monthly_dataset,T27170,Industry +m4_monthly_dataset,T27175,Industry +m4_monthly_dataset,T27186,Industry +m4_monthly_dataset,T37009,Finance +m4_monthly_dataset,T37070,Finance +m4_monthly_dataset,T37238,Finance +m4_monthly_dataset,T37248,Finance +m4_monthly_dataset,T47915,Other +m4_weekly_dataset,T1,Other +m4_weekly_dataset,T2,Other +m4_weekly_dataset,T19,Macro +m4_weekly_dataset,T20,Macro +m4_weekly_dataset,T21,Macro +m4_weekly_dataset,T55,Industry +m4_weekly_dataset,T56,Industry +m4_weekly_dataset,T60,Finance +m4_weekly_dataset,T61,Finance +m4_weekly_dataset,T62,Finance +m4_weekly_dataset,T224,Demographic +m4_weekly_dataset,T225,Demographic +m4_weekly_dataset,T226,Demographic +m4_weekly_dataset,T227,Demographic +m4_weekly_dataset,T248,Micro +m4_weekly_dataset,T249,Micro +m4_weekly_dataset,T250,Micro +m4_daily_dataset,T1,Macro +m4_daily_dataset,T2,Macro +m4_daily_dataset,T6,Macro +m4_daily_dataset,T130,Micro +m4_daily_dataset,T131,Micro +m4_daily_dataset,T145,Micro +m4_daily_dataset,T1604,Demographic +m4_daily_dataset,T1605,Demographic +m4_daily_dataset,T1606,Demographic +m4_daily_dataset,T1607,Demographic +m4_daily_dataset,T1614,Industry +m4_daily_dataset,T1615,Industry +m4_daily_dataset,T1634,Industry +m4_daily_dataset,T1650,Industry +m4_daily_dataset,T2036,Finance +m4_daily_dataset,T2037,Finance +m4_daily_dataset,T2041,Finance +m4_daily_dataset,T3595,Other +m4_daily_dataset,T3597,Other +m4_hourly_dataset,T170,Other +m4_hourly_dataset,T171,Other +m4_hourly_dataset,T172,Other diff --git a/aeon/datasets/__init__.py b/aeon/datasets/__init__.py index 4185769f6f..5ca365c171 100644 --- a/aeon/datasets/__init__.py +++ b/aeon/datasets/__init__.py @@ -16,7 +16,10 @@ "load_human_activity_segmentation_datasets", # Write functions "write_to_ts_file", + "write_to_tsf_file", "write_to_arff_file", + "write_regression_dataset", + "write_forecasting_dataset", # Single problem loaders "load_airline", "load_arrow_head", @@ -57,7 +60,13 @@ load_from_tsv_file, load_regression, ) -from aeon.datasets._data_writers import write_to_arff_file, write_to_ts_file +from aeon.datasets._data_writers import ( + write_forecasting_dataset, + write_regression_dataset, + write_to_arff_file, + write_to_ts_file, + write_to_tsf_file, +) from aeon.datasets._single_problem_loaders import ( load_acsf1, load_airline, diff --git a/aeon/datasets/_data_writers.py b/aeon/datasets/_data_writers.py index 29ec83e648..0f2ea35f90 100644 --- a/aeon/datasets/_data_writers.py +++ b/aeon/datasets/_data_writers.py @@ -1,9 +1,20 @@ +"""Dataset wrting functions.""" + import os import textwrap +from datetime import datetime import numpy as np +import pandas as pd + +from aeon.transformations.format import SlidingWindowTransformer, TrainTestTransformer +from aeon.transformations.series._difference import DifferencingSeriesTransformer -__all__ = ["write_to_ts_file", "write_to_arff_file"] +__all__ = [ + "write_to_ts_file", + "write_to_tsf_file", + "write_to_arff_file", +] def write_to_ts_file( @@ -83,7 +94,6 @@ def write_to_ts_file( class_labels=class_labels, comment=header, regression=regression, - extension=None, ) missing_values = "NaN" for i in range(n_cases): @@ -99,6 +109,186 @@ def write_to_ts_file( file.close() +def write_to_tsf_file( + df, + full_file_path, + metadata, + value_column_name="series_value", + attributes_types=None, + missing_val_symbol="?", +): + """ + Save a pandas DataFrame in TSF format. + + Parameters + ---------- + df : pandas.DataFrame + The DataFrame to be saved. It is assumed that one column contains the series + (by default, named "series_value") and all other columns are series attributes. + full_file_path : str + The full path (including file name) where the TSF file will be saved. + metadata : dict + A dictionary containing metadata for the forecasting problem. It must + include the following keys: + - "frequency" (str) + - "forecast_horizon" (int) + - "contain_missing_values" (bool) + - "contain_equal_length" (bool) + value_column_name : str, optional (default="series_value") + The name of the column that contains the time series values. + attributes_types : dict, optional + A dictionary mapping attribute column names to their TSF type + (one of "numeric", "string", "date"). + If not provided, the type is inferred from the DataFrame dtypes as follows: + - numeric dtypes -> "numeric" + - datetime dtypes -> "date" + - all others -> "string" + missing_val_symbol : str, optional (default="?") + The symbol to be used in the file to represent missing values in the series. + + Raises + ------ + Exception + If any required metadata or a series or attribute value is missing. + """ + # Validate metadata keys + required_meta = [ + "frequency", + "forecast_horizon", + "contain_missing_values", + "contain_equal_length", + ] + for key in required_meta: + if key not in metadata: + raise AttributeError(f"Missing metadata entry: {key}") + + # Determine attribute columns (all columns except the series column) + attribute_columns = [col for col in df.columns if col != value_column_name] + + # If no attributes are present, warn the user. + if not attribute_columns: + raise AttributeError( + "The DataFrame must contain at least one \ + attribute column besides the series column." + ) + + # Determine attribute types if not provided. + # For each attribute, assign a type: + # - numeric dtypes -> "numeric" + # - datetime dtypes -> "date" (and will be formatted as "%Y-%m-%d %H-%M-%S") + # - all others -> "string" + if attributes_types is None: + attributes_types = {} + for col in attribute_columns: + if pd.api.types.is_numeric_dtype(df[col]): + attributes_types[col] = "numeric" + elif pd.api.types.is_datetime64_any_dtype(df[col]): + attributes_types[col] = "date" + else: + attributes_types[col] = "string" + else: + # Ensure that a type is provided for each attribute column + for col in attribute_columns: + if col not in attributes_types: + raise ValueError( + f"Attribute type for column '{col}' is \ + missing in attributes_types." + ) + + # Build header lines for the TSF file. + header_lines = [] + # First, write the attribute lines (order matters!) + for col in attribute_columns: + att_type = attributes_types[col] + if att_type not in {"numeric", "string", "date"}: + raise ValueError( + f"Unsupported attribute type '{att_type}' for column '{col}'." + ) + header_lines.append(f"@attribute {col} {att_type}") + + # Now add the metadata lines. (The order here is flexible, + # but must appear before @data.) + header_lines.append(f"@frequency {metadata['frequency']}") + header_lines.append(f"@horizon {metadata['forecast_horizon']}") + header_lines.append( + f"@missing {'true' if metadata['contain_missing_values'] else 'false'}" + ) + header_lines.append( + f"@equallength {'true' if metadata['contain_equal_length'] else 'false'}" + ) + + # Add the data section tag. + header_lines.append("@data") + # Open file for writing using the same encoding as the loader. + with open(full_file_path, "w", encoding="cp1252") as f: + # Write header lines. + for line in header_lines: + f.write(line + "\n") + + # Process each row to write the data lines. + for idx, row in df.iterrows(): + parts = [] + # Process each attribute value. + for col in attribute_columns: + val = row[col] + col_type = attributes_types[col] + if pd.isna(val): + raise ValueError( + f"Missing value in attribute column '{col}' at row {idx}." + ) + if col_type == "numeric": + try: + val_str = str(int(val)) + except Exception as e: + raise ValueError( + f"Error converting value in column '{col}' \ + at row {idx} to integer: {e}" + ) from e + elif col_type == "date": + # Ensure val is a datetime; if not, attempt conversion. + if not isinstance(val, datetime): + try: + val = pd.to_datetime(val) + except Exception as e: + raise ValueError( + f"Error converting value in column '{col}' \ + at row {idx} to datetime: {e}" + ) from e + val_str = val.strftime("%Y-%m-%d %H-%M-%S") + elif col_type == "string": + val_str = str(val) + else: + # Should not get here because we validated types earlier. + raise ValueError( + f"Unsupported attribute type '{col_type}' for column '{col}'." + ) + parts.append(val_str) + + # Process the series data from value_column_name. + series_val = row[value_column_name] + if not hasattr(series_val, "__iter__"): + raise ValueError( + f"The series in column '{value_column_name}' \ + at row {idx} is not iterable." + ) + + series_str_parts = [] + for s in series_val: + # Check for missing values in the series. + if pd.isna(s): + series_str_parts.append(missing_val_symbol) + else: + series_str_parts.append(str(s).removesuffix(".0")) + # Join series values with commas. + series_str = ",".join(series_str_parts) + parts.append(series_str) + + # The data line consists of the attribute values and + # then the series, separated by colons. + line_data = ":".join(parts) + f.write(line_data + "\n") + + def _write_header( path, problem_name, @@ -108,25 +298,24 @@ def _write_header( comment=None, regression=False, class_labels=None, - extension=None, ): if class_labels is not None and regression: raise ValueError("Cannot have class_labels true for a regression problem") # create path if it does not exist - dir = os.path.join(path, "") + dir_path = os.path.join(path, "") try: - os.makedirs(dir, exist_ok=True) - except OSError: - raise ValueError(f"Error trying to access {dir} in _write_header") + os.makedirs(dir_path, exist_ok=True) + except OSError as exc: + raise ValueError(f"Error trying to access {dir_path} in _write_header") from exc # create ts file in the path - load_path = os.path.join(dir, problem_name) - file = open(load_path, "w") + load_path = os.path.join(dir_path, problem_name) + file = open(load_path, "w", encoding="utf-8") # write comment if any as a block at start of file if comment is not None: file.write("\n# ".join(textwrap.wrap("# " + comment))) file.write("\n") - """ Writes the header info for a ts file""" + # Writes the header info for a ts file file.write(f"@problemName {problem_name}\n") file.write("@timestamps false\n") file.write(f"@univariate {str(univariate).lower()}\n") @@ -175,7 +364,7 @@ def write_to_arff_file( ------- None """ - if not (isinstance(X, np.ndarray)): + if not isinstance(X, np.ndarray): raise TypeError( f" Wrong input data type {type(X)}. Convert to np.ndarray (n_cases, " f"n_channels, n_timepoints) if possible." @@ -187,31 +376,77 @@ def write_to_arff_file( f"received {X.shape}" ) - file = open(f"{path}/{problem_name}.arff", "w") + with open(f"{path}/{problem_name}.arff", "w", encoding="utf-8") as file: - # write comment if any as a block at start of file - if header is not None: - file.write("\n% ".join(textwrap.wrap("% " + header))) - file.write("\n") + # write comment if any as a block at start of file + if header is not None: + file.write("\n% ".join(textwrap.wrap("% " + header))) + file.write("\n") - # begin writing header information - file.write(f"@Relation {problem_name}\n") + # begin writing header information + file.write(f"@Relation {problem_name}\n") - # write each attribute - for i in range(X.shape[2]): - file.write(f"@attribute att{str(i)} numeric\n") + # write each attribute + for i in range(X.shape[2]): + file.write(f"@attribute att{str(i)} numeric\n") - # lass attribute if it exists - comma_separated_class_label = ",".join(str(label) for label in np.unique(y)) - file.write(f"@attribute target {{{comma_separated_class_label}}}\n") + # lass attribute if it exists + comma_separated_class_label = ",".join(str(label) for label in np.unique(y)) + file.write(f"@attribute target {{{comma_separated_class_label}}}\n") - # write data - file.write("@data\n") - for case, target in zip(X, y): - # turn attributes into comma-separated row - atts = ",".join([str(num) if not np.isnan(num) else "?" for num in case[0]]) - file.write(str(atts)) - file.write(f",{target}") - file.write("\n") # open a new line + # write data + file.write("@data\n") + for case, target in zip(X, y): + # turn attributes into comma-separated row + atts = ",".join([str(num) if not np.isnan(num) else "?" for num in case[0]]) + file.write(str(atts)) + file.write(f",{target}") + file.write("\n") # open a new line - file.close() + +def write_regression_dataset(series, full_file_path, dataset_name): + """Write a regression dataset to file.""" + train_series, test_series = TrainTestTransformer().fit_transform(series) + differenced_train_series = DifferencingSeriesTransformer().fit_transform( + train_series + ) + X_train, Y_train, train_indices = SlidingWindowTransformer().fit_transform( + differenced_train_series + ) + differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) + X_test, Y_test, test_indices = SlidingWindowTransformer().fit_transform( + differenced_test_series + ) + write_to_ts_file( + [[item] for item in X_train], + full_file_path, + Y_train, + f"{dataset_name}_TRAIN", + None, + True, + ) + write_to_ts_file( + [[item] for item in X_test], + full_file_path, + Y_test, + f"{dataset_name}_TEST", + None, + True, + ) + + +def write_forecasting_dataset(series, full_file_path, dataset_name): + """Write a regression dataset to file.""" + train_series, test_series = TrainTestTransformer().fit_transform(series) + differenced_train_series = DifferencingSeriesTransformer().fit_transform( + train_series + ) + differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) + train_df = pd.DataFrame(differenced_train_series) + train_df.to_csv( + f"{full_file_path}/{dataset_name}_TRAIN.csv", index=False, header=False + ) + test_df = pd.DataFrame(differenced_test_series) + test_df.to_csv( + f"{full_file_path}/{dataset_name}_TEST.csv", index=False, header=False + ) diff --git a/aeon/datasets/dataset_generation.py b/aeon/datasets/dataset_generation.py new file mode 100644 index 0000000000..674c7501f3 --- /dev/null +++ b/aeon/datasets/dataset_generation.py @@ -0,0 +1,218 @@ +"""Code to select datasets for regression-based forecasting experiments.""" + +import gc +import os +import tempfile +import time + +import pandas as pd + +from aeon.datasets import load_forecasting +from aeon.datasets._data_writers import ( + write_forecasting_dataset, + write_regression_dataset, +) + +filtered_datasets = [ + "nn5_daily_dataset_without_missing_values", + "nn5_weekly_dataset", + "m1_yearly_dataset", + "m1_quarterly_dataset", + "m1_monthly_dataset", + "m3_yearly_dataset", + "m3_quarterly_dataset", + "m3_monthly_dataset", + "m3_other_dataset", + "m4_yearly_dataset", + "m4_quarterly_dataset", + "m4_monthly_dataset", + "m4_weekly_dataset", + "m4_daily_dataset", + "m4_hourly_dataset", + "tourism_yearly_dataset", + "tourism_quarterly_dataset", + "tourism_monthly_dataset", + "car_parts_dataset_without_missing_values", + "hospital_dataset", + "weather_dataset", + "dominick_dataset", + "fred_md_dataset", + "solar_10_minutes_dataset", + "solar_weekly_dataset", + "solar_4_seconds_dataset", + "wind_4_seconds_dataset", + "sunspot_dataset_without_missing_values", + "wind_farms_minutely_dataset_without_missing_values", + "elecdemand_dataset", + "us_births_dataset", + "saugeenday_dataset", + "covid_deaths_dataset", + "cif_2016_dataset", + "london_smart_meters_dataset_without_missing_values", + "kaggle_web_traffic_dataset_without_missing_values", + "kaggle_web_traffic_weekly_dataset", + "traffic_hourly_dataset", + "traffic_weekly_dataset", + "electricity_hourly_dataset", + "electricity_weekly_dataset", + "pedestrian_counts_dataset", + "kdd_cup_2018_dataset_without_missing_values", + "australian_electricity_demand_dataset", + "covid_mobility_dataset_without_missing_values", + "rideshare_dataset_without_missing_values", + "vehicle_trips_dataset_without_missing_values", + "temperature_rain_dataset_without_missing_values", + "oikolab_weather_dataset", +] + + +def filter_datasets(): + """ + Filter datasets to identify and print time series with more than 1000 data points. + + This function iterates over a list of datasets, loads each dataset, + and checks each time series within it. If a series contains more than 1000 + data points, it is counted as a "hit." The function prints up to 10 matches + per dataset in the format: `,`. + + Returns + ------- + None + The function does not return anything but prints matching dataset + and series names to the console. + + Notes + ----- + - The function introduces a 1-second delay (`time.sleep(1)`) between processing + datasets to control HTTP request frequency. + - Uses `gc.collect()` to explicitly trigger garbage collection, to avoid + running out of memory + """ + num_hits = 0 + for dataset_name in filtered_datasets: + # print(f"{dataset_name}") + time.sleep(1) + dataset_counter = 0 + dataset = load_forecasting(dataset_name) + for index, row in enumerate(dataset["series_value"]): + if len(row) > 1000: + num_hits += 1 + dataset_counter += 1 + if dataset_counter <= 10: + print(f"{dataset_name},{dataset['series_name'][index]}") # noqa + # if dataset_counter > 0: + # print(f"{dataset_name}: Hits: {dataset_counter}") + del dataset + gc.collect() + # print(f"Num hits in datasets: {num_hits}") + + +# filter_datasets() + + +def filter_and_categorise_m4(frequency_type): + """ + Filter and categorize M4 dataset time series. + + Parameters + ---------- + frequency_type : str + The frequency type of the M4 dataset to process. + Accepted values: 'yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly'. + + Returns + ------- + None + The function does not return any values but prints categorized series + information. + + Notes + ----- + - The function constructs an appropriate prefix ('Y', 'Q', 'M', 'W', 'D', 'H') + based on the dataset type to match metadata identifiers. + - Limits printed results to 10 per category. + """ + metadata = pd.read_csv("C:/Users/alexb/Downloads/M4-info.csv") + m4daily = load_forecasting(f"m4_{frequency_type}_dataset") + categories = {} + prefix = "" + if frequency_type == "yearly": + prefix = "Y" + elif frequency_type == "quarterly": + prefix = "Q" + elif frequency_type == "monthly": + prefix = "M" + elif frequency_type == "weekly": + prefix = "W" + elif frequency_type == "daily": + prefix = "D" + elif frequency_type == "hourly": + prefix = "H" + for index, row in enumerate(m4daily["series_value"]): + if len(row) > 1000: + category = metadata.loc[ + metadata["M4id"] == f"{prefix}{m4daily['series_name'][index][1:]}", + "category", + ].values[0] + if category not in categories: + categories[category] = 1 + else: + categories[category] += 1 + if categories[category] <= 10: + print( # noqa + f"m4_{frequency_type}_dataset,\ + {m4daily['series_name'][index]},{category}" + ) + + +# filter_and_categorise_m4('monthly') +# filter_and_categorise_m4('weekly') +# filter_and_categorise_m4('daily') +# filter_and_categorise_m4('hourly') + + +def gen_datasets(problem_type, dataset_folder=None): + """ + Generate windowed train/test split of datasets. + + Returns + ------- + None + The function does not return anything but writes out the train and test + files to the specified directory. + + Notes + ----- + - Requires a CSV file containing a list of the series to process. + """ + final_series_selection = pd.read_csv("./aeon/datasets/Final Dataset Selection.csv") + current_dataset = "" + dataset = pd.DataFrame() + tmpdir = tempfile.mkdtemp() + folder = problem_type if dataset_folder is None else dataset_folder + location_of_datasets = f"./aeon/datasets/local_data/{folder}" + if not os.path.exists(location_of_datasets): + os.makedirs(location_of_datasets) + with open(f"{location_of_datasets}/windowed_series.txt", "w") as f: + for item in final_series_selection.to_records(index=False): + if current_dataset != item[0]: + dataset = load_forecasting(item[0], tmpdir) + current_dataset = item[0] + print(f"Current Dataset: {current_dataset}") # noqa + f.write(f"{item[0]}_{item[1]}\n") + series = ( + dataset[dataset["series_name"] == item[1]]["series_value"] + .iloc[0] + .to_numpy() + ) + dataset_name = f"{item[0]}_{item[1]}" + full_file_path = f"{location_of_datasets}/{dataset_name}" + if not os.path.exists(full_file_path): + os.makedirs(full_file_path) + if problem_type == "regression": + write_regression_dataset(series, full_file_path, dataset_name) + elif problem_type == "forecasting": + write_forecasting_dataset(series, full_file_path, dataset_name) + + +gen_datasets("forecasting", "differenced_forecasting") diff --git a/aeon/datasets/tests/test_data_writers.py b/aeon/datasets/tests/test_data_writers.py index d31700ac2b..e7428a39fc 100644 --- a/aeon/datasets/tests/test_data_writers.py +++ b/aeon/datasets/tests/test_data_writers.py @@ -128,7 +128,6 @@ def test_write_header(): _write_header( tmp, problem_name, - extension=".csv", comment="Hello", regression=True, ) diff --git a/aeon/datasets/tests/test_dataset_collections.py b/aeon/datasets/tests/test_dataset_collections.py index 624870ab5e..bb185fac14 100644 --- a/aeon/datasets/tests/test_dataset_collections.py +++ b/aeon/datasets/tests/test_dataset_collections.py @@ -69,7 +69,7 @@ def test_list_available_tser_datasets(): def test_list_available_tsf_datasets(): """Test recovering lists of available data sets.""" res = get_available_tsf_datasets() - assert len(res) == 53 + assert len(res) == 62 res = get_available_tsf_datasets("FOO") assert not res res = get_available_tsf_datasets("m1_monthly_dataset") diff --git a/aeon/datasets/tsad_datasets.py b/aeon/datasets/tsad_datasets.py index 4372772dc5..8f10af3eaf 100644 --- a/aeon/datasets/tsad_datasets.py +++ b/aeon/datasets/tsad_datasets.py @@ -67,7 +67,7 @@ def tsad_collections() -> dict[str, list[str]]: df = _load_indexfile() return ( df.groupby("collection_name") - .apply(lambda x: x["dataset_name"].to_list(), include_groups=False) + .apply(lambda x: x["dataset_name"].to_list()) .to_dict() ) diff --git a/aeon/datasets/tsf_datasets.py b/aeon/datasets/tsf_datasets.py index b5c008c3dd..562f9ad5ae 100644 --- a/aeon/datasets/tsf_datasets.py +++ b/aeon/datasets/tsf_datasets.py @@ -54,4 +54,17 @@ "australian_electricity_demand_dataset": 4659727, "covid_mobility_dataset_with_missing_values": 4663762, "covid_mobility_dataset_without_missing_values": 4663809, + "bitcoin_dataset_with_missing_values": 5121965, + "bitcoin_dataset_without_missing_values": 5122101, + "rideshare_dataset_with_missing_values": 5122114, + "rideshare_dataset_without_missing_values": 5122232, + "vehicle_trips_dataset_with_missing_values": 5122535, + "vehicle_trips_dataset_without_missing_values": 5122537, + "temperature_rain_dataset_with_missing_values": 5129073, + "temperature_rain_dataset_without_missing_values": 5129091, + "oikolab_weather_dataset": 5184708, + # These datasets generate HTTP Error 404: NOT FOUND errors + # "extended_wikipedia_web_traffic_daily_dataset_with_missing_values": 7370977, + # "extended_wikipedia_web_traffic_daily_dataset_without_missing_values": 7371038, + # "residential_power_and_battery_data": 8219786, } diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index de203a0bcd..7d39be08e3 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -1,13 +1,19 @@ """Forecasters.""" __all__ = [ + "ARIMAForecaster", "DummyForecaster", "BaseForecaster", "RegressionForecaster", "ETSForecaster", + "AutoETSForecaster", + "NaiveForecaster", ] +from aeon.forecasting._arima import ARIMAForecaster +from aeon.forecasting._autoets import AutoETSForecaster from aeon.forecasting._dummy import DummyForecaster -from aeon.forecasting._ets import ETSForecaster +from aeon.forecasting._ets_fast import ETSForecaster +from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster from aeon.forecasting.base import BaseForecaster diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py new file mode 100644 index 0000000000..4de0fee3d3 --- /dev/null +++ b/aeon/forecasting/_arima.py @@ -0,0 +1,421 @@ +"""ARIMAForecaster class. + +An implementation of the arima statistics forecasting algorithm. + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["ARIMAForecaster"] + +from math import comb + +import numpy as np + +from aeon.forecasting._utils import calc_seasonal_period, kpss_test +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + + +class ARIMAForecaster(BaseForecaster): + """ARIMA forecaster. + + An implementation of the Hyndman-Khandakar Auto ARIMA forecasting algorithm[1]_. + Adjusted to add basic seasonal ARIMA. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + """ + + def __init__(self, horizon=1): + super().__init__(horizon=horizon, axis=1) + self.data_ = [] + self.differenced_data_ = [] + self.residuals_ = [] + self.aic_ = 0 + self.p_ = 0 + self.d_ = 0 + self.q_ = 0 + self.ps_ = 0 + self.ds_ = 0 + self.qs_ = 0 + self.seasonal_period_ = 0 + self.constant_term_ = 0 + self.c_ = 0 + self.phi_ = 0 + self.phi_s_ = 0 + self.theta_ = 0 + self.theta_s_ = 0 + + def _fit(self, y, exog=None): + """Fit AutoARIMA forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ARIMAForecaster. + """ + self.data_ = np.array(y.squeeze(), dtype=np.float64) + ( + self.differenced_data_, + self.aic_, + self.p_, + self.d_, + self.q_, + self.ps_, + self.ds_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + parameters, + ) = auto_arima(self.data_) + (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = extract_params( + parameters, self.p_, self.q_, self.ps_, self.qs_, self.constant_term_ + ) + ( + self.aic_, + self.residuals_, + ) = arima_log_likelihood( + parameters, + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + y = np.array(y, dtype=np.float64) + value = calc_arima( + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + len(self.differenced_data_), + self.c_, + self.phi_, + self.phi_s_, + self.theta_, + self.theta_s_, + self.residuals_, + ) + history = self.data_[::-1] + differenced_history = np.diff(self.data_, n=self.d_)[::-1] + # Step 1: undo seasonal differencing on y^(d) + for k in range(1, self.ds_ + 1): + lag = k * self.seasonal_period_ + value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] + + # Step 2: undo ordinary differencing + for k in range(1, self.d_ + 1): + value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] + + if y is None: + return np.array([value]) + else: + return np.insert(y, 0, value)[:-1] + + +# Define the ARIMA(p, d, q) likelihood function +def arima_log_likelihood( + params, data, p, q, ps, qs, seasonal_period, include_constant_term +): + """Calculate the log-likelihood of an ARIMA model given the parameters.""" + c, phi, phi_s, theta, theta_s = extract_params( + params, p, q, ps, qs, include_constant_term + ) # Extract parameters + + # Initialize residuals + n = len(data) + residuals = np.zeros(n) + for t in range(n): + y_hat = calc_arima( + data, + p, + q, + ps, + qs, + seasonal_period, + t, + c, + phi, + phi_s, + theta, + theta_s, + residuals, + ) + residuals[t] = data[t] - y_hat + # Calculate the log-likelihood + variance = np.mean(residuals**2) + liklihood = n * (np.log(2 * np.pi) + np.log(variance) + 1) + k = len(params) + aic = liklihood + 2 * k + return ( + aic, + residuals, + ) # Return negative log-likelihood for minimization + + +def extract_params(params, p, q, ps, qs, include_constant_term): + """Extract ARIMA parameters from the parameter vector.""" + # Extract parameters + c = params[0] if include_constant_term else 0 # Constant term + # AR coefficients + phi = params[include_constant_term : p + include_constant_term] + # Seasonal AR coefficients + phi_s = params[include_constant_term + p : p + ps + include_constant_term] + # MA coefficients + theta = params[include_constant_term + p + ps : p + ps + q + include_constant_term] + # Seasonal MA coefficents + theta_s = params[ + include_constant_term + p + ps + q : include_constant_term + p + ps + q + qs + ] + return c, phi, phi_s, theta, theta_s + + +def calc_arima( + data, p, q, ps, qs, seasonal_period, t, c, phi, phi_s, theta, theta_s, residuals +): + """Calculate the ARIMA forecast for time t.""" + # AR part + ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) + # Seasonal AR part + ars_term = ( + 0 + if (t - seasonal_period * ps) < 0 + else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) + ) + # MA part + ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) + # Seasonal MA part + mas_term = ( + 0 + if (t - seasonal_period * qs) < 0 + else np.dot( + theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] + ) + ) + y_hat = c + ar_term + ma_term + ars_term + mas_term + return y_hat + + +def nelder_mead( + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + tol=1e-6, + max_iter=500, +): + """Implement the nelder-mead optimisation algorithm.""" + num_params = include_constant_term + p + ps + q + qs + points = np.full((num_params + 1, num_params), 0.5) + for i in range(num_params): + points[i + 1][i] = 0.6 + values = np.array( + [ + arima_log_likelihood( + v, data, p, q, ps, qs, seasonal_period, include_constant_term + )[0] + for v in points + ] + ) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = arima_log_likelihood( + reflected_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if between best and second best, use reflected value + if len(values) > 1 and values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = arima_log_likelihood( + expanded_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = arima_log_likelihood( + contracted_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = arima_log_likelihood( + points[i], + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] + + +# def calc_moving_variance(data, window): +# X = np.lib.stride_tricks.sliding_window_view(data, window_shape=window) +# return X.var() + + +def auto_arima(data): + """ + Implement the Hyndman-Khandakar algorithm. + + For automatic ARIMA model selection. + """ + seasonal_period = calc_seasonal_period(data) + difference = 0 + while not kpss_test(data)[1]: + data = np.diff(data, n=1) + difference += 1 + seasonal_difference = 1 if seasonal_period > 1 else 0 + if seasonal_difference: + data = data[seasonal_period:] - data[:-seasonal_period] + include_c = 1 if difference == 0 else 0 + model_parameters = [ + [2, 2, 0, 0, include_c], + [0, 0, 0, 0, include_c], + [1, 0, 0, 0, include_c], + [0, 1, 0, 0, include_c], + ] + model_points = [] + for p in model_parameters: + points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) + p.append(aic) + model_points.append(points) + current_model = max(model_parameters, key=lambda item: item[5]) + current_points = model_points[model_parameters.index(current_model)] + while True: + better_model = False + for param_no in range(4): + for adjustment in [-1, 1]: + if (current_model[param_no] + adjustment) < 0: + continue + model = current_model.copy() + model[param_no] += adjustment + for constant_term in [0, 1]: + points, aic = nelder_mead( + data, + model[0], + model[1], + model[2], + model[3], + seasonal_period, + constant_term, + ) + if aic < current_model[5]: + current_model = model + current_points = points + current_model[5] = aic + current_model[4] = constant_term + better_model = True + if not better_model: + break + return ( + data, + current_model[5], + current_model[0], + difference, + current_model[1], + current_model[2], + seasonal_difference, + current_model[3], + seasonal_period, + current_model[4], + current_points, + ) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py new file mode 100644 index 0000000000..7501bee0e2 --- /dev/null +++ b/aeon/forecasting/_autoets.py @@ -0,0 +1,457 @@ +"""AutoETS class. + +Extends the ETSForecaster to automatically calculate the smoothing parameters + +""" + +__maintainer__ = [] +__all__ = ["AutoETSForecaster"] +import numpy as np +from numba import njit +from scipy.optimize import minimize + +from aeon.forecasting._autoets_gradient_params import _calc_model_liklihood +from aeon.forecasting._ets_fast import _fit, _predict +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + + +class AutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster. + + An implementation of the exponential smoothing statistics forecasting algorithm. + Chooses betweek additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + + Examples + -------- + >>> from aeon.forecasting import AutoETSForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = AutoETSForecaster() + >>> forecaster.fit(y) + AutoETSForecaster() + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + method="internal_nelder_mead", + horizon=1, + ): + self.method = method + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self.alpha_ = 0 + self.beta_ = 0 + self.gamma_ = 0 + self.phi_ = 0 + self.error_type_ = 0 + self.trend_type_ = 0 + self.seasonality_type_ = 0 + self.seasonal_period_ = 0 + self.n_timepoints_ = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 + self.residuals_ = [] + self.fitted_values_ = [] + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + ( + self.error_type_, + self.trend_type_, + self.seasonality_type_, + self.seasonal_period_, + self.alpha_, + self.beta_, + self.gamma_, + self.phi_, + ) = auto_ets(data, self.method) + ( + self.level_, + self.trend_, + self.seasonality_, + self.n_timepoints_, + self.residuals_, + self.fitted_values_, + self.avg_mean_sq_err_, + self.liklihood_, + self.k_, + self.aic_, + ) = _fit( + data, + self.error_type_, + self.trend_type_, + self.seasonality_type_, + self.seasonal_period_, + self.alpha_, + self.beta_, + self.gamma_, + self.phi_, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = _predict( + self.trend_type_, + self.seasonality_type_, + self.level_, + self.trend_, + self.seasonality_, + self.phi_, + self.horizon, + self.n_timepoints_, + self.seasonal_period_, + ) + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + +def auto_ets(data, method="internal_nelder_mead"): + """Return the best ETS model based on the supplied data, and optimisation method.""" + if method == "internal_nelder_mead": + return auto_ets_nelder_mead(data) + elif method == "internal_gradient": + return auto_ets_gradient(data) + else: + return auto_ets_scipy(data, method) + + +def auto_ets_scipy(data, method): + """Calculate ETS model parameters based on scipy optimisation functions.""" + seasonal_period = calc_seasonal_period(data) + lowest_liklihood = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + optimise_result = optimise_params_scipy( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + method, + ) + alpha, beta, gamma = optimise_result.x + liklihood_ = optimise_result.fun + phi = 0.98 + if lowest_liklihood == -1 or lowest_liklihood > liklihood_: + lowest_liklihood = liklihood_ + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +def auto_ets_gradient(data): + """ + Calc model params using pytorch. + + Calculate ETS model parameters based on the + internal gradient-based approach using pytorch. + """ + seasonal_period = calc_seasonal_period(data) + lowest_liklihood = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + (alpha, beta, gamma, phi, _residuals, liklihood_) = ( + _calc_model_liklihood( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + ) + if lowest_liklihood == -1 or lowest_liklihood > liklihood_: + lowest_liklihood = liklihood_ + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +@njit(nogil=NOGIL, cache=CACHE) +def auto_ets_nelder_mead(data): + """Calculate model parameters based on the internal nelder-mead implementation.""" + seasonal_period = calc_seasonal_period(data) + lowest_aic = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + ([alpha, beta, gamma, phi], aic) = nelder_mead( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + if trend_type == 0: + phi = 1 + if lowest_aic == -1 or lowest_aic > aic: + lowest_aic = aic + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +def optimise_params_scipy( + data, error_type, trend_type, seasonality_type, seasonal_period, method +): + """Optimise the ETS model parameters using the scipy algorithms.""" + + def run_ets_scipy(parameters): + alpha, beta, gamma, phi = parameters + if not ( + 0 <= alpha <= 1 and 0 <= beta <= 1 and 0 <= gamma <= 1 and 0 <= phi <= 1 + ): + return float("inf") + ( + _level, + _trend, + _seasonality, + _n_timepoints, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + _k, + aic_, + ) = _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return aic_ + + initial_points = [0.5, 0.5, 0.5, 0.5] + return minimize( + run_ets_scipy, initial_points, bounds=[[0, 1] for i in range(3)], method=method + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def run_ets( + parameters, data, error_type, trend_type, seasonality_type, seasonal_period +): + """Create and fit an ETS model and return the liklihood.""" + alpha, beta, gamma, phi = parameters + if not ( + 0 <= alpha <= 1 + and 0 <= beta <= 1 + and 0 <= gamma <= 1 + and 0.8 <= phi <= 1 + and ( + data.min() > 0 + or (error_type != 2 and trend_type != 2 and seasonality_type != 2) + ) + ): + return np.finfo(np.float64).max + ( + _level, + _trend, + _seasonality, + _n_timepoints, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + _k, + aic_, + ) = _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return aic_ + + +@njit(nogil=NOGIL, cache=CACHE) +def nelder_mead( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + tol=1e-6, + max_iter=500, +): + """Implement the nelder-mead optimisation algorithm.""" + points = np.array( + [ + [0.5, 0.5, 0.5, 0.9], + [0.6, 0.5, 0.5, 0.9], + [0.5, 0.6, 0.5, 0.9], + [0.5, 0.5, 0.6, 0.9], + [0.5, 0.5, 0.5, 0.95], + ] + ) + values = np.array( + [ + run_ets(v, data, error_type, trend_type, seasonality_type, seasonal_period) + for v in points + ] + ) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = run_ets( + reflected_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # if between best and second best, use reflected value + if values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = run_ets( + expanded_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = run_ets( + contracted_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = run_ets( + points[i], + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] diff --git a/aeon/forecasting/_autoets_gradient_params.py b/aeon/forecasting/_autoets_gradient_params.py new file mode 100644 index 0000000000..119211a29a --- /dev/null +++ b/aeon/forecasting/_autoets_gradient_params.py @@ -0,0 +1,297 @@ +"""AutoETSForecaster class. + +Extends the ETSForecaster to automatically calculate the smoothing parameters + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = [] + +import torch + +from aeon.forecasting._ets_fast import ADDITIVE, MULTIPLICATIVE, NONE, ETSForecaster + + +def _calc_model_liklihood( + data, error_type, trend_type, seasonality_type, seasonal_period +): + alpha, beta, gamma, phi = _optimise_parameters( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + forecaster = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + 1, + ) + forecaster.fit(data) + return alpha, beta, gamma, phi, forecaster.residuals_, forecaster.liklihood_ + + +def _optimise_parameters( + data, error_type, trend_type, seasonality_type, seasonal_period +): + torch.autograd.set_detect_anomaly(True) + data = torch.tensor(data) + n_timepoints = len(data) + if seasonality_type == 0: + seasonal_period = 1 + level, trend, seasonality = _initialise( + trend_type, seasonality_type, seasonal_period, data + ) + alpha = torch.tensor(0.1, requires_grad=True) # Level smoothing + parameters = [alpha] + if trend_type == NONE: + beta = torch.tensor(0) # Trend smoothing + else: + beta = torch.tensor(0.05, requires_grad=True) # Trend smoothing + parameters.append(beta) + if seasonality_type == NONE: + gamma = torch.tensor(0) # Trend smoothing + else: + gamma = torch.tensor(0.05, requires_grad=True) # Seasonality smoothing + parameters.append(gamma) + phi = torch.tensor(0.98, requires_grad=True) # Damping factor + batch_size = len(data) # seasonal_period * 2 + num_batches = len(data) // batch_size + # residuals_ = torch.zeros(n_timepoints) # 1 Less residual than data points + optimizer = torch.optim.SGD([alpha, beta, gamma, phi], lr=0.01) + for _epoch in range(10): # number of epochs + for i in range(0, num_batches): + batch_of_data = data[i * batch_size : (i + 1) * batch_size] + liklihood_ = torch.tensor(0, dtype=torch.float64) + mul_liklihood_pt2 = torch.tensor(0, dtype=torch.float64) + for t, data_item in enumerate(batch_of_data): + # Calculate level, trend, and seasonal components + fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( + _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[t % seasonal_period], + data_item, + alpha, + beta, + gamma, + phi, + ) + ) + liklihood_ += error * error + mul_liklihood_pt2 += torch.log(torch.abs(fitted_value)) + liklihood_ = (n_timepoints - seasonal_period) * torch.log(liklihood_) + if error_type == MULTIPLICATIVE: + liklihood_ += 2 * mul_liklihood_pt2 + liklihood_.backward() + optimizer.step() + optimizer.zero_grad() + # Impose sensible parameter limits + alpha = alpha.clone().detach().requires_grad_().clamp(0, 1) + if trend_type != NONE: + # Impose sensible parameter limits + beta = beta.clone().detach().requires_grad_().clamp(0, 1) + if seasonality_type != NONE: + # Impose sensible parameter limits + gamma = gamma.clone().detach().requires_grad_().clamp(0, 1) + # Impose sensible parameter limits + phi = phi.clone().detach().requires_grad_().clamp(0.1, 0.98) + level = level.clone().detach() + trend = trend.clone().detach() + seasonality = seasonality.clone().detach() + return alpha.item(), beta.item(), gamma.item(), phi.item() + + +def _predict( + trend_type, + seasonality_type, + level, + trend, + seasonality, + phi, + horizon, + n_timepoints, + seasonal_period, +): + # Generate forecasts based on the final values of level, trend, and seasonals + if phi == 1: # No damping case + phi_h = float(horizon) + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = phi * (1 - phi**horizon) / (1 - phi) + seasonal_index = (n_timepoints + horizon) % seasonal_period + return _predict_value( + trend_type, seasonality_type, level, trend, seasonality[seasonal_index], phi_h + )[0] + + +def _initialise(trend_type, seasonality_type, seasonal_period, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + level = torch.mean(data[:seasonal_period]) + # Initial Trend + if trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + trend = torch.mean( + data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] + ) + elif trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + trend = torch.mean( + data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] + ) + else: + # No trend + trend = torch.tensor(0) + # Initial Seasonality + if seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + seasonality = data[:seasonal_period] - level + elif seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + seasonality = data[:seasonal_period] / level + else: + # No seasonality + seasonality = torch.zeros(1) + return level, trend, seasonality + + +def _update_states( + error_type, + trend_type, + seasonality_type, + curr_level, + curr_trend, + curr_seasonality, + data_item: int, + alpha, + beta, + gamma, + phi, +): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + fitted_value, damped_trend, trend_level_combination = _predict_value( + trend_type, seasonality_type, curr_level, curr_trend, curr_seasonality, phi + ) + # Calculate the error term (observed value - fitted value) + if error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if error_type == MULTIPLICATIVE: + level = trend_level_combination.clone() * (1 + alpha.clone() * error.clone()) + trend = damped_trend.clone() * (1 + beta.clone() * error.clone()) + seasonality = curr_seasonality.clone() * (1 + gamma.clone() * error.clone()) + if seasonality_type == ADDITIVE: + # Add seasonality correction + level += alpha.clone() * error.clone() * curr_seasonality.clone() + seasonality += ( + gamma.clone() * error.clone() * trend_level_combination.clone() + ) + if trend_type == ADDITIVE: + trend += ( + (curr_level.clone() + curr_seasonality.clone()) + * beta.clone() + * error.clone() + ) + else: + trend += ( + (curr_seasonality.clone() / curr_level.clone()) + * beta.clone() + * error.clone() + ) + elif trend_type == ADDITIVE: + trend += curr_level.clone() * beta.clone() * error.clone() + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= curr_seasonality.clone() + trend_correction *= curr_seasonality.clone() + seasonality_correction *= trend_level_combination.clone() + if trend_type == MULTIPLICATIVE: + trend_correction *= curr_level.clone() + level = ( + trend_level_combination.clone() + + alpha.clone() * error.clone() / level_correction + ) + trend = damped_trend.clone() + beta.clone() * error.clone() / trend_correction + seasonality = ( + curr_seasonality.clone() + + gamma.clone() * error.clone() / seasonality_correction + ) + return (fitted_value, error, level, trend, seasonality) + + +def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): + """ + + Generate various useful values, including the next fitted value. + + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model + + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if trend_type == MULTIPLICATIVE: + damped_trend = trend.clone() ** phi.clone() + trend_level_combination = level.clone() * damped_trend.clone() + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend.clone() * phi.clone() + trend_level_combination = level.clone() + damped_trend.clone() + # Calculate forecast (fitted value) based on the current components + if seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination.clone() * seasonality.clone() + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination.clone() + seasonality.clone() + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_compare_external_autoets.py b/aeon/forecasting/_compare_external_autoets.py new file mode 100644 index 0000000000..b57f67a874 --- /dev/null +++ b/aeon/forecasting/_compare_external_autoets.py @@ -0,0 +1,207 @@ +"""Test Other Packages AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import math +import time + +import matplotlib.pyplot as plt +from sktime.forecasting.ets import AutoETS as sktime_AutoETS +from statsforecast.models import AutoETS as sf_AutoETS +from statsforecast.utils import AirPassengers as ap +from statsforecast.utils import AirPassengersDF +from statsmodels.tsa.exponential_smoothing.ets import ETSModel + +from aeon.forecasting._autoets import auto_ets +from aeon.forecasting._ets_fast import ETSForecaster + +plt.rcParams["figure.figsize"] = (12, 8) + + +def test_other_forecasters(): + """TestOtherForecasters.""" + plt.plot(AirPassengersDF.ds, AirPassengersDF.y, label="Actual Values", color="blue") + # Statsmodels + start = time.perf_counter() + statsmodels_model = ETSModel( + ap, + error="mul", + trend=None, + damped_trend=False, + seasonal="mul", + seasonal_periods=12, + ) + statsmodels_fit = statsmodels_model.fit(maxiter=10000) + end = time.perf_counter() + statsmodels_time = end - start + print( # noqa + f"Statsmodels: Alpha: {statsmodels_fit.alpha}, \ + Beta: statsmodels_fit.beta, gamma: {statsmodels_fit.gamma}, \ + phi: statsmodels_fit.phi" + ) + print(f"Statsmodels AIC: {statsmodels_fit.aic}") # noqa + sm_internal_model = ETSForecaster( + 2, 0, 2, 12, statsmodels_fit.alpha, 0, statsmodels_fit.gamma, 1 + ) + sm_internal_model.fit(ap) + print(f"Statsmodels AIC: {sm_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + statsmodels_fit.fittedvalues, + label="statsmodels fit", + color="green", + ) + # Sktime + start = time.perf_counter() + sktime_model = sktime_AutoETS(auto=True, sp=12) + sktime_model.fit(ap) + end = time.perf_counter() + sktime_time = end - start + # pylint: disable=W0212 + print( # noqa + f"Sktime: Alpha: {sktime_model._fitted_forecaster.alpha}, \ + Beta: {sktime_model._fitted_forecaster.beta}, \ + gamma: {sktime_model._fitted_forecaster.gamma}, \ + phi: sktime_model._fitted_forecaster.phi" + ) + + if sktime_model._fitted_forecaster.error == "add": + sk_error = 1 + elif sktime_model._fitted_forecaster.error == "mul": + sk_error = 2 + else: + sk_error = 0 + if sktime_model._fitted_forecaster.trend == "add": + sk_trend = 1 + elif sktime_model._fitted_forecaster.trend == "mul": + sk_trend = 2 + else: + sk_trend = 0 + if sktime_model._fitted_forecaster.seasonal == "add": + sk_seasonal = 1 + elif sktime_model._fitted_forecaster.seasonal == "mul": + sk_seasonal = 2 + else: + sk_seasonal = 0 + print( # noqa + f"Error Type: {sk_error}, Trend Type: {sk_trend}, \ + Seasonality Type: {sk_seasonal}, Seasonal Period: {12}" + ) + print(f"Sktime AIC: {sktime_model._fitted_forecaster.aic}") # noqa + sk_internal_model = ETSForecaster( + sk_error, + sk_trend, + sk_seasonal, + 12, + sktime_model._fitted_forecaster.alpha, + sktime_model._fitted_forecaster.beta, + sktime_model._fitted_forecaster.gamma, + 1, + ) + sk_internal_model.fit(ap) + print(f"Sktime AIC: {sk_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + sktime_model._fitted_forecaster.fittedvalues, + label="sktime fitted values", + color="red", + ) + # pylint: enable=W0212 + # internal + start = time.perf_counter() + ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) = auto_ets(ap) + internal_model = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + internal_model.fit(ap) + end = time.perf_counter() + internal_time = end - start + print( # noqa + f"Internal: Alpha: {internal_model.alpha}, Beta: {internal_model.beta}, \ + gamma: {internal_model.gamma}, phi: {internal_model.phi}" + ) + print( # noqa + f"Error Type: {internal_model.error_type}, \ + Trend Type: {internal_model.trend_type}, \ + Seasonality Type: {internal_model.seasonality_type}, \ + Seasonal Period: {internal_model.seasonal_period}" + ) + print(f"Internal AIC: {internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds[seasonal_period:], + internal_model.fitted_values_, + label="Internal fitted values", + color="black", + ) + # statsforecast + start = time.perf_counter() + sf_model = sf_AutoETS(season_length=12) + sf_model.fit(ap) + end = time.perf_counter() + statsforecast_time = end - start + print( # noqa + f"Statsforecast: Alpha: {sf_model.model_['par'][0]}, \ + Beta: {sf_model.model_['par'][1]}, gamma: {sf_model.model_['par'][2]}, \ + phi: {sf_model.model_['par'][3]}" + ) + print( # noqa + f"Statsforecast Model Type: {sf_model.model_['method']}, \ + AIC: {sf_model.model_['aic']}" + ) + sf_internal_model = ETSForecaster( + 2 if sf_model.model_["components"][0] == "M" else 1, + ( + 2 + if sf_model.model_["components"][1] == "M" + else 1 if sf_model.model_["components"][1] == "A" else 0 + ), + ( + 2 + if sf_model.model_["components"][2] == "M" + else 1 if sf_model.model_["components"][2] == "A" else 0 + ), + 12, + 0 if math.isnan(sf_model.model_["par"][0]) else sf_model.model_["par"][0], + 0 if math.isnan(sf_model.model_["par"][1]) else sf_model.model_["par"][1], + 0 if math.isnan(sf_model.model_["par"][2]) else sf_model.model_["par"][2], + 0 if math.isnan(sf_model.model_["par"][3]) else sf_model.model_["par"][3], + ) + sf_internal_model.fit(ap) + print(f"Statsforecast AIC: {sf_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + sf_model.model_["fitted"], + label="statsforecast fitted values", + color="orange", + ) + print( # noqa + f"Statsmodels Time: {statsmodels_time}\ + Sktime Time: {sktime_time}\ + Internal Time: {internal_time}\ + Statsforecast Time: {statsforecast_time}" + ) # noqa + plt.ylabel("Air Passenger Numbers") + plt.grid() + plt.legend() + plt.show() + + +if __name__ == "__main__": + test_other_forecasters() diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index efc99d6d47..ac7f31a58d 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -3,20 +3,20 @@ An implementation of the exponential smoothing statistics forecasting algorithm. Implements additive and multiplicative error models, None, additive and multiplicative (including damped) trend and -None, additive and multiplicative seasonality +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + """ __maintainer__ = [] -__all__ = ["ETSForecaster", "NONE", "ADDITIVE", "MULTIPLICATIVE"] +__all__ = ["ETSForecaster"] import numpy as np -from numba import njit from aeon.forecasting.base import BaseForecaster -NOGIL = False -CACHE = True - NONE = 0 ADDITIVE = 1 MULTIPLICATIVE = 2 @@ -25,44 +25,31 @@ class ETSForecaster(BaseForecaster): """Exponential Smoothing forecaster. - An implementation of the exponential smoothing forecasting algorithm. - Implements additive and multiplicative error models, None, additive and - multiplicative (including damped) trend and None, additive and mutliplicative - seasonality. See [1]_ for a description. + An implementation of the exponential smoothing statistics forecasting algorithm. + Implements additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. Parameters ---------- - error_type : int, default = 1 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - trend_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - seasonality_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - seasonal_period : int, default=1 - Length of seasonality period. If seasonality_type is NONE, this is assumed to - be 1 alpha : float, default = 0.1 Level smoothing parameter. beta : float, default = 0.01 - Trend smoothing parameter. If trend_type is NONE, this is assumed to be 0.0. + Trend smoothing parameter. gamma : float, default = 0.01 - Seasonal smoothing parameter. If seasonality is NONE, this is assumed to be - 0.0. + Seasonal smoothing parameter. phi : float, default = 0.99 Trend damping smoothing parameters horizon : int, default = 1 The horizon to forecast to. - - Attributes - ---------- - mean_sq_err_ : float - Mean squared error. - likelihood_ : float - Likelihood of the fitted model based on residuals. - residuals_ : arraylike - List of train set differences between fitted and actual values. - n_timpoints_ : int - Length of the series passed to fit. + error_type : int + The type of error model; either Additive(1) or Multiplicative(2) + trend_type : int + The type of trend model; one of None(0), additive(1) or multiplicative(2). + seasonality_type : int + The type of seasonality model; one of None(0), additive(1) or multiplicative(2). + seasonal_period : int + The period of the seasonality (m) (e.g., for quaterly data seasonal_period = 4). References ---------- @@ -74,11 +61,13 @@ class ETSForecaster(BaseForecaster): >>> from aeon.forecasting import ETSForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1) + >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8) + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + seasonality_type=2, trend_type=2) >>> forecaster.predict() - 449.9435566831507 + 366.90200486015596 """ def __init__( @@ -92,19 +81,37 @@ def __init__( gamma: float = 0.01, phi: float = 0.99, horizon: int = 1, + error_type: int = ADDITIVE, + trend_type: int = NONE, + seasonality_type: int = NONE, + seasonal_period: int = 1, + alpha: float = 0.1, + beta: float = 0.01, + gamma: float = 0.01, + phi: float = 0.99, + horizon: int = 1, ): - self.error_type = error_type - self.trend_type = trend_type - self.seasonality_type = seasonality_type - self.seasonal_period = seasonal_period self.alpha = alpha self.beta = beta self.gamma = gamma self.phi = phi - self.mean_sq_err_ = 0 - self.likelihood_ = 0 + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self._beta = beta + self._gamma = gamma + self.error_type = error_type + self.trend_type = trend_type + self.seasonality_type = seasonality_type + self.seasonal_period = seasonal_period + self._seasonal_period = seasonal_period + self.n_timepoints = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 self.residuals_ = [] - self.n_timpoints_ = 0 super().__init__(horizon=horizon, axis=1) def _fit(self, y, exog=None): @@ -124,39 +131,153 @@ def _fit(self, y, exog=None): self Fitted BaseForecaster. """ - self.n_timepoints_ = len(y) - if self.error_type != MULTIPLICATIVE and self.error_type != ADDITIVE: - raise ValueError("Error must be either additive or multiplicative") - self._seasonal_period = self.seasonal_period - if self.seasonal_period < 1 or self.seasonality_type == NONE: + assert ( + self.error_type != NONE + ), "Error must be either additive or multiplicative" + if self._seasonal_period < 1 or self.seasonality_type == NONE: self._seasonal_period = 1 - self._beta = self.beta - if self.trend_type == NONE or self.trend_type is None: - self._beta = 0 - self._gamma = self.gamma - if self.seasonality_type == NONE or self.trend_type is None: - self._gamma = 0 - data = np.array(y.squeeze(), dtype=np.float64) - ( - self._level, - self._trend, - self._seasonality, - self.residuals_, - self.mean_sq_err_, - self.likelihood_, - ) = _fit_numba( - data, - self.error_type, - self.trend_type, - self.seasonality_type, - self._seasonal_period, - self.alpha, - self._beta, - self._gamma, - self.phi, + if self.trend_type == NONE: + self._beta = ( + 0 # Required for the equations in _update_states to work correctly + ) + if self.seasonality_type == NONE: + self._gamma = ( + 0 # Required for the equations in _update_states to work correctly + ) + data = y.squeeze() + self.n_timepoints = len(data) + self._initialise(data) + num_vals = self.n_timepoints - self._seasonal_period + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + # 1 Less residual than data points + self.residuals_ = np.zeros(num_vals) + for t, data_item in enumerate(data[self._seasonal_period :]): + # Calculate level, trend, and seasonal components + fitted_value, error = self._update_states( + data_item, t % self._seasonal_period + ) + self.residuals_[t] = error + self.avg_mean_sq_err_ += (data_item - fitted_value) ** 2 + liklihood_error = error + if self.error_type == MULTIPLICATIVE: + liklihood_error *= fitted_value + self.liklihood_ += liklihood_error**2 + self.avg_mean_sq_err_ /= num_vals + self.liklihood_ = num_vals * np.log(self.liklihood_) + self.k_ = ( + self.seasonal_period * (self.seasonality_type != 0) + + 2 * (self.trend_type != 0) + + 2 + + 1 * (self.phi != 1) ) + self.aic_ = self.liklihood_ + 2 * self.k_ - num_vals * np.log(num_vals) return self + def _update_states(self, data_item, seasonal_index): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + level = self.level_ + trend = self.trend_ + seasonality = self.seasonality_[seasonal_index] + fitted_value, damped_trend, trend_level_combination = self._predict_value( + level, trend, seasonality, self.phi + ) + # Calculate the error term (observed value - fitted value) + if self.error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if self.error_type == MULTIPLICATIVE: + self.level_ = trend_level_combination * (1 + self.alpha * error) + self.trend_ = damped_trend * (1 + self._beta * error) + self.seasonality_[seasonal_index] = seasonality * (1 + self._gamma * error) + if self.seasonality_type == ADDITIVE: + self.level_ += ( + self.alpha * error * seasonality + ) # Add seasonality correction + self.seasonality_[seasonal_index] += ( + self._gamma * error * trend_level_combination + ) + if self.trend_type == ADDITIVE: + self.trend_ += (level + seasonality) * self._beta * error + else: + self.trend_ += seasonality / level * self._beta * error + elif self.trend_type == ADDITIVE: + self.trend_ += level * self._beta * error + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if self.seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= seasonality + trend_correction *= seasonality + seasonality_correction *= trend_level_combination + if self.trend_type == MULTIPLICATIVE: + trend_correction *= level + self.level_ = ( + trend_level_combination + self.alpha * error / level_correction + ) + self.trend_ = damped_trend + self._beta * error / trend_correction + self.seasonality_[seasonal_index] = ( + seasonality + self._gamma * error / seasonality_correction + ) + return (fitted_value, error) + + def _initialise(self, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + self.level_ = np.mean(data[: self._seasonal_period]) + # Initial Trend + if self.trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + self.trend_ = np.mean( + data[self._seasonal_period : 2 * self._seasonal_period] + - data[: self._seasonal_period] + ) + elif self.trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + self.trend_ = np.mean( + data[self._seasonal_period : 2 * self._seasonal_period] + / data[: self._seasonal_period] + ) + else: + # No trend + self.trend_ = 0 + # Initial Seasonality + if self.seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + self.seasonality_ = data[: self._seasonal_period] - self.level_ + elif self.seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + self.seasonality_ = data[: self._seasonal_period] / self.level_ + else: + # No seasonality + self.seasonality_ = [0] + def _predict(self, y=None, exog=None): """ Predict the next horizon steps ahead. @@ -166,7 +287,7 @@ def _predict(self, y=None, exog=None): y : np.ndarray, default = None A time series to predict the next horizon value for. If None, predict the next horizon value after series seen in fit. - exog : np.ndarray, default = None + exog : np.ndarray, default =None Optional exogenous time series data assumed to be aligned with y Returns @@ -174,250 +295,60 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - return _predict_numba( - self.trend_type, - self.seasonality_type, - self._level, - self._trend, - self._seasonality, - self.phi, - self.horizon, - self.n_timepoints_, - self.seasonal_period, - ) - - -@njit(nogil=NOGIL, cache=CACHE) -def _fit_numba( - data, - error_type: int, - trend_type: int, - seasonality_type: int, - seasonal_period: int, - alpha: float, - beta: float, - gamma: float, - phi: float, -): - n_timepoints = len(data) - level, trend, seasonality = _initialise( - trend_type, seasonality_type, seasonal_period, data - ) - mse = 0 - lhood = 0 - mul_likelihood_pt2 = 0 - res = np.zeros(n_timepoints) # 1 Less residual than data points - for t, data_item in enumerate(data[seasonal_period:]): - # Calculate level, trend, and seasonal components - fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( - _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality[t % seasonal_period], - data_item, - alpha, - beta, - gamma, - phi, - ) - ) - res[t] = error - mse += (data_item - fitted_value) ** 2 - lhood += error * error - mul_likelihood_pt2 += np.log(np.fabs(fitted_value)) - mse /= n_timepoints - seasonal_period - lhood = (n_timepoints - seasonal_period) * np.log(lhood) - if error_type == MULTIPLICATIVE: - lhood += 2 * mul_likelihood_pt2 - return level, trend, seasonality, res, mse, lhood - - -def _predict_numba( - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - phi: float, - horizon: int, - n_timepoints: int, - seasonal_period: int, -): - # Generate forecasts based on the final values of level, trend, and seasonals - if phi == 1: # No damping case - phi_h = float(horizon) - else: - # Geometric series formula for calculating phi + phi^2 + ... + phi^h - phi_h = phi * (1 - phi**horizon) / (1 - phi) - seasonal_index = (n_timepoints + horizon) % seasonal_period - return _predict_value( - trend_type, - seasonality_type, - level, - trend, - seasonality[seasonal_index], - phi_h, - )[0] - - -@njit(nogil=NOGIL, cache=CACHE) -def _initialise(trend_type: int, seasonality_type: int, seasonal_period: int, data): - """ - Initialize level, trend, and seasonality values for the ETS model. - - Parameters - ---------- - data : array-like - The time series data - (should contain at least two full seasons if seasonality is specified) - """ - # Initial Level: Mean of the first season - level = np.mean(data[:seasonal_period]) - # Initial Trend - if trend_type == ADDITIVE: - # Average difference between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] - ) - elif trend_type == MULTIPLICATIVE: - # Average ratio between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] - ) - else: - # No trend - trend = 0 - # Initial Seasonality - if seasonality_type == ADDITIVE: - # Seasonal component is the difference - # from the initial level for each point in the first season - seasonality = data[:seasonal_period] - level - elif seasonality_type == MULTIPLICATIVE: - # Seasonal component is the ratio of each point in the first season - # to the initial level - seasonality = data[:seasonal_period] / level - else: - # No seasonality - seasonality = np.zeros(1) - return level, trend, seasonality - - -@njit(nogil=NOGIL, cache=CACHE) -def _update_states( - error_type: int, - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - data_item: int, - alpha: float, - beta: float, - gamma: float, - phi: float, -): - """ - Update level, trend, and seasonality components. - - Using state space equations for an ETS model. - - Parameters - ---------- - data_item: float - The current value of the time series. - seasonal_index: int - The index to update the seasonal component. - """ - # Retrieve the current state values - curr_level = level - curr_seasonality = seasonality - fitted_value, damped_trend, trend_level_combination = _predict_value( - trend_type, seasonality_type, level, trend, seasonality, phi - ) - # Calculate the error term (observed value - fitted value) - if error_type == MULTIPLICATIVE: - error = data_item / fitted_value - 1 # Multiplicative error - else: - error = data_item - fitted_value # Additive error - # Update level - if error_type == MULTIPLICATIVE: - level = trend_level_combination * (1 + alpha * error) - trend = damped_trend * (1 + beta * error) - seasonality = curr_seasonality * (1 + gamma * error) - if seasonality_type == ADDITIVE: - level += alpha * error * curr_seasonality # Add seasonality correction - seasonality += gamma * error * trend_level_combination - if trend_type == ADDITIVE: - trend += (curr_level + curr_seasonality) * beta * error - else: - trend += curr_seasonality / curr_level * beta * error - elif trend_type == ADDITIVE: - trend += curr_level * beta * error - else: - level_correction = 1 - trend_correction = 1 - seasonality_correction = 1 - if seasonality_type == MULTIPLICATIVE: - # Add seasonality correction - level_correction *= curr_seasonality - trend_correction *= curr_seasonality - seasonality_correction *= trend_level_combination - if trend_type == MULTIPLICATIVE: - trend_correction *= curr_level - level = trend_level_combination + alpha * error / level_correction - trend = damped_trend + beta * error / trend_correction - seasonality = curr_seasonality + gamma * error / seasonality_correction - return (fitted_value, error, level, trend, seasonality) - - -@njit(nogil=NOGIL, cache=CACHE) -def _predict_value( - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - phi: float, -): - """ + # Generate forecasts based on the final values of level, trend, and seasonals + if self.phi == 1: # No damping case + phi_h = 1 + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = self.phi * (1 - self.phi**self.horizon) / (1 - self.phi) + seasonality = self.seasonality_[ + (self.n_timepoints + self.horizon) % self._seasonal_period + ] + fitted_value = self._predict_value( + self.level_, self.trend_, seasonality, phi_h + )[0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + def _predict_value(self, level, trend, seasonality, phi): + """ - Generate various useful values, including the next fitted value. + Generate various useful values, including the next fitted value. - Parameters - ---------- - trend : float - The current trend value for the model - level : float - The current level value for the model - seasonality : float - The current seasonality value for the model - phi : float - The damping parameter for the model - - Returns - ------- - fitted_value : float - single prediction based on the current state variables. - damped_trend : float - The damping parameter combined with the trend dependant on the model type - trend_level_combination : float - Combination of the trend and level based on the model type. - """ - # Apply damping parameter and - # calculate commonly used combination of trend and level components - if trend_type == MULTIPLICATIVE: - damped_trend = trend**phi - trend_level_combination = level * damped_trend - else: # Additive trend, if no trend, then trend = 0 - damped_trend = trend * phi - trend_level_combination = level + damped_trend + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model - # Calculate forecast (fitted value) based on the current components - if seasonality_type == MULTIPLICATIVE: - fitted_value = trend_level_combination * seasonality - else: # Additive seasonality, if no seasonality, then seasonality = 0 - fitted_value = trend_level_combination + seasonality - return fitted_value, damped_trend, trend_level_combination + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if self.trend_type == MULTIPLICATIVE: + damped_trend = trend**phi + trend_level_combination = level * damped_trend + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend * phi + trend_level_combination = level + damped_trend + + # Calculate forecast (fitted value) based on the current components + if self.seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination * seasonality + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination + seasonality + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_ets_fast.py b/aeon/forecasting/_ets_fast.py new file mode 100644 index 0000000000..fdbd9c005a --- /dev/null +++ b/aeon/forecasting/_ets_fast.py @@ -0,0 +1,476 @@ +"""ETSForecaster class. + +An implementation of the exponential smoothing statistics forecasting algorithm. +Implements additive and multiplicative error models, +None, additive and multiplicative (including damped) trend and +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["ETSForecaster"] + +import numpy as np +from numba import njit + +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + +NONE = 0 +ADDITIVE = 1 +MULTIPLICATIVE = 2 + + +class ETSForecaster(BaseForecaster): + """Exponential Smoothing forecaster. + + An implementation of the exponential smoothing statistics forecasting algorithm. + Implements additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. + + Parameters + ---------- + alpha : float, default = 0.1 + Level smoothing parameter. + beta : float, default = 0.01 + Trend smoothing parameter. + gamma : float, default = 0.01 + Seasonal smoothing parameter. + phi : float, default = 0.99 + Trend damping smoothing parameters + horizon : int, default = 1 + The horizon to forecast to. + error_type : int + The type of error model; either Additive(1) or Multiplicative(2) + trend_type : int + The type of trend model; one of None(0), additive(1) or multiplicative(2). + seasonality_type : int + The type of seasonality model; one of None(0), additive(1) or multiplicative(2). + seasonal_period : int + The period of the seasonality (m) (e.g., for quaterly data seasonal_period = 4). + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + + Examples + -------- + >>> from aeon.forecasting import ETSForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) + >>> forecaster.fit(y) + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + seasonality_type=2, trend_type=2) + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + error_type=ADDITIVE, + trend_type=NONE, + seasonality_type=NONE, + seasonal_period=1, + alpha=0.1, + beta=0.01, + gamma=0.01, + phi=0.99, + horizon=1, + ): + self.alpha = alpha + self.beta = beta + self.gamma = gamma + self.phi = phi + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self._beta = beta + self._gamma = gamma + self.error_type = error_type + self.trend_type = trend_type + self.seasonality_type = seasonality_type + self.seasonal_period = seasonal_period + self._seasonal_period = seasonal_period + self.n_timepoints_ = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 + self.residuals_ = [] + self.fitted_values_ = [] + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ETSForecaster. + """ + assert ( + self.error_type != NONE + ), "Error must be either additive or multiplicative" + if self._seasonal_period < 1 or self.seasonality_type == NONE: + self._seasonal_period = 1 + + if self.trend_type == NONE: + # Required for the equations in _update_states to work correctly + self._beta = 0 + if self.seasonality_type == NONE: + # Required for the equations in _update_states to work correctly + self._gamma = 0 + data = y.squeeze() + ( + self.level_, + self.trend_, + self.seasonality_, + self.n_timepoints_, + self.residuals_, + self.fitted_values_, + self.avg_mean_sq_err_, + self.liklihood_, + self.k_, + self.aic_, + ) = _fit( + data, + self.error_type, + self.trend_type, + self.seasonality_type, + self._seasonal_period, + self.alpha, + self._beta, + self._gamma, + self.phi, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = _predict( + self.trend_type, + self.seasonality_type, + self.level_, + self.trend_, + self.seasonality_, + self.phi, + self.horizon, + self.n_timepoints_, + self._seasonal_period, + ) + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + def _initialise(self, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + self.level_, self.trend_, self.seasonality_ = _initialise( + self.trend_type, self.seasonality_type, self._seasonal_period, data + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, +): + assert error_type != NONE, "Error must be either additive or multiplicative" + assert ( + error_type != MULTIPLICATIVE + and trend_type != MULTIPLICATIVE + and seasonality_type != MULTIPLICATIVE + or data.min() > 0 + ), "Data must be positive with multiplicative components" + if seasonal_period < 1 or seasonality_type == NONE: + seasonal_period = 1 + if trend_type == NONE: + # Required for the equations in _update_states to work correctly + beta = 0 + if seasonality_type == NONE: + # Required for the equations in _update_states to work correctly + gamma = 0 + n_timepoints = len(data) - seasonal_period + level, trend, seasonality = _initialise( + trend_type, seasonality_type, seasonal_period, data + ) + avg_mean_sq_err_ = 0 + liklihood_ = 0 + residuals_ = np.zeros(n_timepoints) # 1 Less residual than data points + fitted_values_ = np.zeros(n_timepoints) + for t, data_item in enumerate(data[seasonal_period:]): + # Calculate level, trend, and seasonal components + fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( + _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[t % seasonal_period], + data_item, + alpha, + beta, + gamma, + phi, + ) + ) + residuals_[t] = error + fitted_values_[t] = fitted_value + avg_mean_sq_err_ += (data_item - fitted_value) ** 2 + liklihood_error = error + if error_type == MULTIPLICATIVE: + liklihood_error *= fitted_value + liklihood_ += liklihood_error**2 + avg_mean_sq_err_ /= n_timepoints + liklihood_ = n_timepoints * np.log(liklihood_) + k_ = ( + seasonal_period * (seasonality_type != 0) + + 2 * (trend_type != 0) + + 2 + + 1 * (phi != 1) + ) + aic_ = liklihood_ + 2 * k_ - n_timepoints * np.log(n_timepoints) + return ( + level, + trend, + seasonality, + n_timepoints, + residuals_, + fitted_values_, + avg_mean_sq_err_, + liklihood_, + k_, + aic_, + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def _predict( + trend_type, + seasonality_type, + level, + trend, + seasonality, + phi, + horizon, + n_timepoints, + seasonal_period, +): + # Generate forecasts based on the final values of level, trend, and seasonals + if phi == 1: # No damping case + phi_h = 1 + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = phi * (1 - phi**horizon) / (1 - phi) + seasonal_index = (n_timepoints + horizon) % seasonal_period + return _predict_value( + trend_type, + seasonality_type, + level, + trend, + seasonality[seasonal_index], + phi_h, + )[0] + + +@njit(nogil=NOGIL, cache=CACHE) +def _initialise(trend_type, seasonality_type, seasonal_period, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + level = np.mean(data[:seasonal_period]) + # Initial Trend + if trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + trend = np.mean( + data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] + ) + elif trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + trend = np.mean( + data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] + ) + else: + # No trend + trend = 0 + # Initial Seasonality + if seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + seasonality = data[:seasonal_period] - level + elif seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + seasonality = data[:seasonal_period] / level + else: + # No seasonality + seasonality = np.zeros(1, dtype=np.float64) + return level, trend, seasonality + + +@njit(nogil=NOGIL, cache=CACHE) +def _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality, + data_item: int, + alpha, + beta, + gamma, + phi, +): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + curr_level = level + curr_seasonality = seasonality + fitted_value, damped_trend, trend_level_combination = _predict_value( + trend_type, seasonality_type, level, trend, seasonality, phi + ) + # Calculate the error term (observed value - fitted value) + if error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if error_type == MULTIPLICATIVE: + level = trend_level_combination * (1 + alpha * error) + trend = damped_trend * (1 + beta * error) + seasonality = curr_seasonality * (1 + gamma * error) + if seasonality_type == ADDITIVE: + level += alpha * error * curr_seasonality # Add seasonality correction + seasonality += gamma * error * trend_level_combination + if trend_type == ADDITIVE: + trend += (curr_level + curr_seasonality) * beta * error + else: + trend += curr_seasonality / curr_level * beta * error + elif trend_type == ADDITIVE: + trend += curr_level * beta * error + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= curr_seasonality + trend_correction *= curr_seasonality + seasonality_correction *= trend_level_combination + if trend_type == MULTIPLICATIVE: + trend_correction *= curr_level + level = trend_level_combination + alpha * error / level_correction + trend = damped_trend + beta * error / trend_correction + seasonality = curr_seasonality + gamma * error / seasonality_correction + return (fitted_value, error, level, trend, seasonality) + + +@njit(nogil=NOGIL, cache=CACHE) +def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): + """ + + Generate various useful values, including the next fitted value. + + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model + + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if trend_type == MULTIPLICATIVE: + damped_trend = trend**phi + trend_level_combination = level * damped_trend + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend * phi + trend_level_combination = level + damped_trend + + # Calculate forecast (fitted value) based on the current components + if seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination * seasonality + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination + seasonality + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py new file mode 100644 index 0000000000..9bdfa82fb9 --- /dev/null +++ b/aeon/forecasting/_naive.py @@ -0,0 +1,94 @@ +"""ETSForecaster class. + +An implementation of the exponential smoothing statistics forecasting algorithm. +Implements additive and multiplicative error models, +None, additive and multiplicative (including damped) trend and +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["NaiveForecaster"] + +import numpy as np + +from aeon.forecasting.base import BaseForecaster + +NONE = 0 +ADDITIVE = 1 +MULTIPLICATIVE = 2 + + +class NaiveForecaster(BaseForecaster): + """Naive forecaster. + + Forecasts future values as the last observed value. + + Parameters + ---------- + horizon : int, default = 1 + The number of steps ahead to forecast. + + Examples + -------- + >>> from aeon.forecasting import NaiveForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = NaiveForecaster() + >>> forecaster.fit(y) + NaiveForecaster() + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + horizon=1, + ): + self.last_value_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Naive forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted NaiveForecaster. + """ + self.last_value_ = y[0][-1] + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + if y is None: + return np.array([self.last_value_]) + else: + return np.insert(y, 0, self.last_value_)[:-1] diff --git a/aeon/forecasting/_plot_autoets_gradient_method.py b/aeon/forecasting/_plot_autoets_gradient_method.py new file mode 100644 index 0000000000..a84a41baa1 --- /dev/null +++ b/aeon/forecasting/_plot_autoets_gradient_method.py @@ -0,0 +1,66 @@ +"""Test AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import matplotlib.pyplot as plt +from statsforecast.utils import AirPassengers as ap +from statsforecast.utils import AirPassengersDF + +from aeon.forecasting._autoets import auto_ets +from aeon.forecasting._ets_fast import ETSForecaster + +plt.rcParams["figure.figsize"] = (12, 8) + + +def test_autoets_forecaster(): + """TestETSForecaster.""" + ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) = auto_ets(ap, method="internal_gradient") + print( # noqa + f"Error Type: {error_type}, Trend Type: {trend_type}, \ + Seasonality Type: {seasonality_type}, Seasonal Period: {seasonal_period}, \ + Alpha: {alpha}, Beta: {beta}, Gamma: {gamma}, Phi: {phi}" + ) # noqa + etsforecaster = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + 1, + ) + etsforecaster.fit(ap) + print(f"liklihood: {etsforecaster.liklihood_}") # noqa + + # assert np.allclose([parameter.item() for parameter in parameters], + # [0.1,0.05,0.05,0.98]) + plt.plot(AirPassengersDF.ds, AirPassengersDF.y, label="Actual Values", color="blue") + plt.plot( + AirPassengersDF.ds, + etsforecaster.fitted_values_, + label="Predicted Values", + color="green", + ) + plt.plot( + AirPassengersDF.ds, etsforecaster.residuals_, label="Residuals", color="red" + ) + plt.ylabel("Air Passenger Numbers") + plt.grid() + plt.legend() + plt.show() + + +if __name__ == "__main__": + test_autoets_forecaster() diff --git a/aeon/forecasting/_sktime_autoets.py b/aeon/forecasting/_sktime_autoets.py new file mode 100644 index 0000000000..127d93040b --- /dev/null +++ b/aeon/forecasting/_sktime_autoets.py @@ -0,0 +1,78 @@ +"""SktimeAutoETS class. + +Wraps sktime AutoETS model for forecasting. + +""" + +__maintainer__ = [] +__all__ = ["SktimeAutoETSForecaster"] + + +import numpy as np +from sktime.forecasting.ets import AutoETS + +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + + +class SktimeAutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster from sktime. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + """ + + def __init__( + self, + horizon=1, + ): + self.model_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + season_length = calc_seasonal_period(data) + self.model_ = AutoETS(auto=True, sp=season_length) + self.model_.fit(data) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = self.model_.predict(self.horizon, exog)[0][0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] diff --git a/aeon/forecasting/_statsforecast_autoets.py b/aeon/forecasting/_statsforecast_autoets.py new file mode 100644 index 0000000000..8ce77d257d --- /dev/null +++ b/aeon/forecasting/_statsforecast_autoets.py @@ -0,0 +1,78 @@ +"""StatsforecastAutoETS class. + +Wraps statsforecast AutoETS model for forecasting. + +""" + +__maintainer__ = [] +__all__ = ["StatsForecastAutoETSForecaster"] + + +import numpy as np +from statsforecast.models import AutoETS + +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + + +class StatsForecastAutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster from statsforecast. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + """ + + def __init__( + self, + horizon=1, + ): + self.model_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + season_length = calc_seasonal_period(data) + self.model_ = AutoETS(season_length=season_length) + self.model_.fit(data) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = self.model_.predict(self.horizon, exog)["mean"][0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] diff --git a/aeon/forecasting/_time_autoets.py b/aeon/forecasting/_time_autoets.py new file mode 100644 index 0000000000..3d9e263e15 --- /dev/null +++ b/aeon/forecasting/_time_autoets.py @@ -0,0 +1,37 @@ +"""Test AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import timeit + +from statsforecast.utils import AirPassengers as ap + +from aeon.forecasting._autoets import nelder_mead, optimise_params_scipy + + +def test_optimise_params(): + nelder_mead(ap, 2, 2, 2, 12) + + +def test_optimise_params_scipy(): + optimise_params_scipy(ap, 2, 2, 2, 12, method="L-BFGS-B") + + +def test_autoets_forecaster(): + """TestETSForecaster.""" + for _i in range(20): + test_optimise_params() + test_optimise_params_scipy() + optim_ets_time = timeit.timeit(test_optimise_params, globals={}, number=1000) + print(f"Execution time Optimise params: {optim_ets_time} seconds") # noqa + optim_ets_scipy_time = timeit.timeit( + test_optimise_params_scipy, globals={}, number=1000 + ) + print( # noqa + f"Execution time Optimise params Scipy: {optim_ets_scipy_time} seconds" + ) + + +if __name__ == "__main__": + test_autoets_forecaster() diff --git a/aeon/forecasting/_utils.py b/aeon/forecasting/_utils.py new file mode 100644 index 0000000000..aeee0db3ae --- /dev/null +++ b/aeon/forecasting/_utils.py @@ -0,0 +1,115 @@ +""" +Forecasting utilities class. + +Contains useful utility methods for forecasting time series data. + +""" + +import numpy as np +from numba import njit + + +@njit(cache=True, fastmath=True) +def calc_seasonal_period(data): + """Estimate the seasonal period based on the autocorrelation of the series.""" + lags = _acf(data, 24) + lags = np.concatenate((np.array([1.0]), lags)) + peaks = [] + mean_lags = np.mean(lags) + for i in range(1, len(lags) - 1): # Skip the first (lag 0) and last elements + if lags[i] >= lags[i - 1] and lags[i] >= lags[i + 1] and lags[i] > mean_lags: + peaks.append(i) + if not peaks: + return 1 + else: + return peaks[0] + + +@njit(cache=True, fastmath=True) +def _acf(X, max_lag): + length = len(X) + X_t = np.zeros(max_lag, dtype=float) + for lag in range(1, max_lag + 1): + lag_length = length - lag + x1 = X[:-lag] + x2 = X[lag:] + s1 = np.sum(x1) + s2 = np.sum(x2) + m1 = s1 / lag_length + m2 = s2 / lag_length + ss1 = np.sum(x1 * x1) + ss2 = np.sum(x2 * x2) + v1 = ss1 - s1 * m1 + v2 = ss2 - s2 * m2 + v1_is_zero, v2_is_zero = v1 <= 1e-9, v2 <= 1e-9 + if v1_is_zero and v2_is_zero: # Both zero variance, + # so must be 100% correlated + X_t[lag - 1] = 1 + elif v1_is_zero or v2_is_zero: # One zero variance + # the other not + X_t[lag - 1] = 0 + else: + X_t[lag - 1] = np.sum((x1 - m1) * (x2 - m2)) / np.sqrt(v1 * v2) + return X_t + + +def kpss_test(y, regression="c", lags=None): # Test if time series is stationary + """ + Implement the KPSS test for stationarity. + + Parameters + ---------- + y (array-like): Time series data + regression (str): 'c' for constant, 'ct' for constant + trend + lags (int): Number of lags for HAC variance estimation (default: sqrt(n)) + + Returns + ------- + kpss_stat (float): KPSS test statistic + stationary (bool): Whether the series is stationary according to the test + """ + y = np.asarray(y) + n = len(y) + + # Step 1: Fit regression model to estimate residuals + if regression == "c": # Constant + X = np.ones((n, 1)) + elif regression == "ct": # Constant + Trend + X = np.column_stack((np.ones(n), np.arange(1, n + 1))) + else: + raise ValueError("regression must be 'c' or 'ct'") + + beta = np.linalg.lstsq(X, y, rcond=None)[0] # Estimate regression coefficients + residuals = y - X @ beta # Get residuals (u_t) + + # Step 2: Compute cumulative sum of residuals (S_t) + S_t = np.cumsum(residuals) + + # Step 3: Estimate long-run variance (HAC variance) + if lags is None: + # lags = int(12 * (n / 100)**(1/4)) # Default statsmodels lag length + lags = int(np.sqrt(n)) # Default lag length + + gamma_0 = np.sum(residuals**2) / (n - X.shape[1]) # Lag-0 autocovariance + gamma = [np.sum(residuals[k:] * residuals[:-k]) / n for k in range(1, lags + 1)] + + # Bartlett weights + weights = [1 - (k / (lags + 1)) for k in range(1, lags + 1)] + + # Long-run variance + sigma_squared = gamma_0 + 2 * np.sum([w * g for w, g in zip(weights, gamma)]) + + # Step 4: Calculate the KPSS statistic + kpss_stat = np.sum(S_t**2) / (n**2 * sigma_squared) + + if regression == "ct": + # p. 162 Kwiatkowski et al. (1992): y_t = beta * t + r_t + e_t, + # where beta is the trend, r_t a random walk and e_t a stationary + # error term. + crit = 0.146 + else: # hypo == "c" + # special case of the model above, where beta = 0 (so the null + # hypothesis is that the data is stationary around r_0). + crit = 0.463 + + return kpss_stat, kpss_stat < crit diff --git a/aeon/forecasting/_verify_arima.py b/aeon/forecasting/_verify_arima.py new file mode 100644 index 0000000000..34758eb6eb --- /dev/null +++ b/aeon/forecasting/_verify_arima.py @@ -0,0 +1,31 @@ +from pmdarima import auto_arima as pmd_auto_arima +from statsforecast.utils import AirPassengers as ap +from statsmodels.tsa.stattools import kpss + +from aeon.forecasting._arima import ARIMAForecaster, auto_arima, nelder_mead +from aeon.forecasting._utils import kpss_test + + +def test_arima(): + model = pmd_auto_arima( + ap, + seasonal=True, + m=12, + trace=True, + error_action="ignore", + suppress_warnings=True, + ) + print(model.summary()) # noqa + print(f"Optimal Model: {nelder_mead(ap, 2, 1, 1, 0, 1, 0, 12, True)}") # noqa + print(model.predict(n_periods=1)) # noqa + print(kpss_test(ap)) # noqa + print(kpss(ap, regression="c", nlags=12)) # noqa + print(auto_arima(ap)) # noqa + forecaster = ARIMAForecaster() + forecaster.fit(ap) + print(forecaster.predict()) # noqa + + +if __name__ == "__main__": + test_arima() +# Fit Auto-ARIMA model diff --git a/aeon/forecasting/_verify_ets.py b/aeon/forecasting/_verify_ets.py new file mode 100644 index 0000000000..65d3ca0faf --- /dev/null +++ b/aeon/forecasting/_verify_ets.py @@ -0,0 +1,345 @@ +"""Script to test ETS implementation against ETS implementations from other modules.""" + +import random +import time +import timeit + +import numpy as np +from statsforecast.utils import AirPassengers as ap + +import aeon.forecasting._ets as ets +import aeon.forecasting._ets_fast as etsfast +from aeon.forecasting import ETSForecaster + +NA = -99999.0 +MAX_NMSE = 30 +MAX_SEASONAL_PERIOD = 24 + + +def setup(): + """Generate parameters required for ETS algorithms.""" + y = ap + m = random.randint(2, 24) + error = random.randint(1, 2) + trend = random.randint(0, 2) + season = random.randint(0, 2) + alpha = round(random.random(), 4) + if alpha == 0: + alpha = round(random.random(), 4) + beta = round(random.random() * alpha, 4) # 0 < beta < alpha + if beta == 0: + beta = round(random.random() * alpha, 4) + gamma = round(random.random() * (1 - alpha), 4) # 0 < beta < alpha + if gamma == 0: + gamma = round(random.random() * (1 - alpha), 4) + phi = round( + random.random() * 0.18 + 0.8, 4 + ) # Common constraint for phi is 0.8 < phi < 0.98 + return (y, m, error, trend, season, alpha, beta, gamma, phi) + + +def statsmodels_version( + y: np.ndarray, + m: int, + f1: ETSForecaster, + errortype: int, + trendtype: int, + seasontype: int, + alpha: float, + beta: float, + gamma: float, + phi: float, +): + """Hide the differences between different statsforecast versions.""" + from statsmodels.tsa.holtwinters import ExponentialSmoothing + + ets_model = ExponentialSmoothing( + y[m:], + trend="add" if trendtype == 1 else "mul" if trendtype == 2 else None, + damped_trend=(phi != 1 and trendtype != 0), + seasonal="add" if seasontype == 1 else "mul" if seasontype == 2 else None, + seasonal_periods=m if seasontype != 0 else None, + initialization_method="known", + initial_level=f1.level_, + initial_trend=f1.trend_ if trendtype != 0 else None, + initial_seasonal=f1.seasonality_ if seasontype != 0 else None, + ) + results = ets_model.fit( + smoothing_level=alpha, + smoothing_trend=( + beta / alpha if trendtype != 0 else None + ), # statsmodels uses beta*=beta/alpha + smoothing_seasonal=gamma if seasontype != 0 else None, + damping_trend=phi if trendtype != 0 else None, + optimized=False, + ) + avg_mean_sq_err = results.sse / (len(y) - m) + # Back-calculate our log-likelihood proxy from AIC + if errortype == 1: + residuals = y[m:] - results.fittedvalues + assert np.allclose(residuals, results.resid) + else: + residuals = y[m:] / results.fittedvalues - 1 + return ( + (np.array([avg_mean_sq_err])), + residuals, + (results.aic - 2 * results.k + (len(y) - m) * np.log(len(y) - m)), + ) + + +def obscure_statsforecast_version( + y: np.ndarray, + m: int, + f1: ETSForecaster, + errortype: int, + trendtype: int, + seasontype: int, + alpha: float, + beta: float, + gamma: float, + phi: float, +): + """Hide the differences between different statsforecast versions.""" + init_state = np.zeros(len(y) * (1 + (trendtype > 0) + m * (seasontype > 0) + 1)) + init_state[0] = f1.level_ + init_state[1] = f1.trend_ + init_state[1 + (trendtype != 0) : m + 1 + (trendtype != 0)] = f1.seasonality_[::-1] + # from statsforecast.ets import pegelsresid_C + # amse, e, _x, lik = pegelsresid_C( + # y[m:], + # m, + # init_state, + # "A" if errortype == 1 else "M", + # "A" if trendtype == 1 else "M" if trendtype == 2 else "N", + # "A" if seasontype == 1 else "M" if seasontype == 2 else "N", + # phi != 1, + # alpha, + # beta, + # gamma, + # phi, + # nmse, + # ) + from statsforecast.ets import etscalc + + e = np.zeros(len(y)) + amse = np.zeros(MAX_NMSE) + lik = etscalc( + y[m:], + len(y) - m, + init_state, + m, + errortype, + trendtype, + seasontype, + alpha, + beta, + gamma, + phi, + e, + amse, + 1, + ) + return amse, e[:-m], lik + + +def test_ets_comparison(setup_func, random_seed, catch_errors): + """Run both our statsforecast and our implementation and crosschecks.""" + random.seed(random_seed) + ( + y, + m, + error, + trend, + season, + alpha, + beta, + gamma, + phi, + ) = setup_func() + # tsml-eval implementation + start = time.perf_counter() + f1 = ETSForecaster( + error, + trend, + season, + m, + alpha, + beta, + gamma, + phi, + 1, + ) + f1.fit(y) + end = time.perf_counter() + time_fitets = end - start + e_fitets = f1.residuals_ + amse_fitets = f1.avg_mean_sq_err_ + lik_fitets = f1.liklihood_ + f1 = ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + # pylint: disable=W0212 + f1._fit(y)._initialise(y) + # pylint: enable=W0212 + if season == 0: + m = 1 + # Nixtla/statsforcast implementation + start = time.perf_counter() + amse_etscalc, e_etscalc, lik_etscalc = statsmodels_version( + y, m, f1, error, trend, season, alpha, beta, gamma, phi + ) + end = time.perf_counter() + time_etscalc = end - start + amse_etscalc = amse_etscalc[0] + + if catch_errors: + try: + # Comparing outputs and runtime + assert np.allclose(e_fitets, e_etscalc), "Residuals Compare failed" + assert np.allclose(amse_fitets, amse_etscalc), "AMSE Compare failed" + assert np.isclose(lik_fitets, lik_etscalc), "Liklihood Compare failed" + return True + except AssertionError as e: + print(e) # noqa + print( # noqa + f"Seed: {random_seed}, Model: Error={error}, Trend={trend},\ + Seasonality={season}, seasonal period={m},\ + alpha={alpha}, beta={beta}, gamma={gamma}, phi={phi}" + ) + return False + else: + print( # noqa + f"Seed: {random_seed}, Model: Error={error}, Trend={trend},\ + Seasonality={season}, seasonal period={m}, alpha={alpha},\ + beta={beta}, gamma={gamma}, phi={phi}" + ) + diff_indices = np.where( + np.abs(e_fitets - e_etscalc) > 1e-3 * np.abs(e_etscalc) + 1e-2 + )[0] + for index in diff_indices: + print( # noqa + f"Index {index}: e_fitets = {e_fitets[index]},\ + e_etscalc = {e_etscalc[index]}" + ) + print(amse_fitets) # noqa + print(amse_etscalc) # noqa + print(lik_fitets) # noqa + print(lik_etscalc) # noqa + assert np.allclose(e_fitets, e_etscalc) + assert np.allclose(amse_fitets, amse_etscalc) + # assert np.isclose(lik_fitets, lik_etscalc) + print(f"Time for ETS: {time_fitets:0.20f}") # noqa + print(f"Time for statsforecast ETS: {time_etscalc}") # noqa + return True + + +def time_etsfast(): + """Test function for optimised numba ets algorithm.""" + etsfast.ETSForecaster(2, 2, 2, 4).fit(ap).predict() + + +def time_etsnoopt(): + """Test function for non-optimised ets algorithm.""" + ets.ETSForecaster(2, 2, 2, 4).fit(ap).predict() + + +def time_etsfast_noclass(): + """Test function for optimised ets algorithm without the class based structure.""" + data = np.array(ap.squeeze(), dtype=np.float64) + # pylint: disable=W0212 + ( + level, + trend, + seasonality, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + ) = etsfast._fit(data, 2, 2, 2, 4, 0.1, 0.01, 0.01, 0.99) + etsfast._predict(2, 2, level, trend, seasonality, 0.99, 1, 144, 4) + # pylint: enable=W0212 + + +def time_sf(): + """Test function for statsforecast ets algorithm.""" + x = np.zeros(144 * 7) + x[0:6] = [122.75, 1.123230970596215, 0.91242363, 0.96130346, 1.07535642, 1.0509165] + obscure_statsforecast_version( + ap[4:], + 4, + x, + 2, + 2, + 2, + 0.1, + 0.01, + 0.01, + 0.99, + ) + + +def time_compare(random_seed): + """Compare timings of different ets algorithms.""" + random.seed(random_seed) + (y, m, error, trend, season, alpha, beta, gamma, phi) = setup() + # etsnoopt_time = timeit.timeit(time_etsnoopt, globals={}, number=10000) + # print (f"Execution time ETS No-opt: {etsnoopt_time} seconds") + # Do a few iterations to remove background/overheads. Makes comparison more reliable + for _i in range(10): + time_etsfast() + time_sf() + time_etsfast_noclass() + etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) + print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa + etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) + print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) + print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa + etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) + print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa + etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) + print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) + print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa + # _ets_fast_nostruct implementation + start = time.perf_counter() + f3 = etsfast.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + f3.fit(y) + end = time.perf_counter() + etsfast_time = end - start + # _ets_fast implementation + # _ets implementation + start = time.perf_counter() + f1 = ets.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + f1.fit(y) + end = time.perf_counter() + etsnoopt_time = end - start + assert np.allclose(f1.residuals_, f3.residuals_) + assert np.allclose(f1.avg_mean_sq_err_, f3.avg_mean_sq_err_) + assert np.isclose(f1.liklihood_, f3.liklihood_) + print( # noqa + f"ETS No-optimisation Time: {etsnoopt_time},\ + Fast time: {etsfast_time}" + ) + return etsnoopt_time, etsfast_time + + +if __name__ == "__main__": + np.set_printoptions(threshold=np.inf) + test_ets_comparison(setup, 300, False) + SUCCESSES = True + for i in range(0, 300): + SUCCESSES &= test_ets_comparison(setup, i, True) + if SUCCESSES: + print("Test Completed Successfully with no errors") # noqa + # time_compare(300) + # avg_ets = 0 + # avg_etsfast = 0 + # avg_etsfast_ns = 0 + # iterations = 100 + # for i in range (iterations): + # time_ets, etsfast_time = time_compare(300) + # avg_ets += time_ets + # avg_etsfast += etsfast_time + # avg_ets/= iterations + # avg_etsfast/= iterations + # avg_etsfast_ns /= iterations + # print(f"Avg ETS Time: {avg_ets}, Avg Fast ETS time: {avg_etsfast},\ diff --git a/aeon/forecasting/tests/test_ets.py b/aeon/forecasting/tests/test_ets.py index ce7513a965..c5c5118b60 100644 --- a/aeon/forecasting/tests/test_ets.py +++ b/aeon/forecasting/tests/test_ets.py @@ -1,27 +1,92 @@ -"""Test ETS forecaster.""" +"""Test ETS.""" -import pytest +__maintainer__ = [] +__all__ = [] + +import numpy as np from aeon.forecasting import ETSForecaster -from aeon.testing.data_generation import make_example_1d_numpy - - -def test_ets_params(): - """Test ETS forecaster.""" - y = make_example_1d_numpy(n_timepoints=100) - forecaster = ETSForecaster(error_type=3) - with pytest.raises( - ValueError, match="Error must be either additive or " "multiplicative" - ): - forecaster.fit(y) - forecaster = ETSForecaster(seasonality_type=-3) - forecaster.fit(y) - assert forecaster._seasonal_period == 1 - forecaster = ETSForecaster(trend_type=None, seasonality_type=0, beta=1.0, gamma=1.0) - forecaster.fit(y) - assert forecaster._beta == 0 - assert forecaster._gamma == 0 - - forecaster = ETSForecaster(error_type=2, phi=1.0) - pred = forecaster.forecast(y) - assert isinstance(pred, float) + + +def test_ets_forecaster_additive(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.5, + beta=0.3, + gamma=0.4, + phi=1, + horizon=1, + error_type=1, + trend_type=1, + seasonality_type=1, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 9.191190608800001) + + +def test_ets_forecaster_mult_error(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.6, + gamma=0.1, + phi=0.97, + horizon=1, + error_type=2, + trend_type=1, + seasonality_type=1, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.20176819429869) + + +def test_ets_forecaster_mult_compnents(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.4, + beta=0.2, + gamma=0.5, + phi=0.8, + horizon=1, + error_type=1, + trend_type=2, + seasonality_type=2, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 12.301259229712382) + + +def test_ets_forecaster_multiplicative(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.5, + gamma=0.2, + phi=0.85, + horizon=1, + error_type=2, + trend_type=2, + seasonality_type=2, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.811888294476528) diff --git a/aeon/transformations/format/__init__.py b/aeon/transformations/format/__init__.py new file mode 100644 index 0000000000..9409e0c3a4 --- /dev/null +++ b/aeon/transformations/format/__init__.py @@ -0,0 +1,11 @@ +"""Format transformations.""" + +__all__ = [ + "SlidingWindowTransformer", + "TrainTestTransformer", + "BaseFormatTransformer", +] + +from aeon.transformations.format._sliding_window import SlidingWindowTransformer +from aeon.transformations.format._train_test import TrainTestTransformer +from aeon.transformations.format.base import BaseFormatTransformer diff --git a/aeon/transformations/format/_sliding_window.py b/aeon/transformations/format/_sliding_window.py new file mode 100644 index 0000000000..899eaaf44a --- /dev/null +++ b/aeon/transformations/format/_sliding_window.py @@ -0,0 +1,92 @@ +"""Sliding Window transformation.""" + +__maintainer__ = [] +__all__ = ["SlidingWindowTransformer"] + +import numpy as np + +from aeon.transformations.format.base import BaseFormatTransformer + + +class SlidingWindowTransformer(BaseFormatTransformer): + """ + Create windowed views of a series by extracting fixed-length overlapping segments. + + This transformer generates multiple subsequences (windows) of a specified width from + the input time series. Each window represents a shifted view of the series, moving + forward by one time step. + + Parameters + ---------- + window_size : int, optional (default=100) + The number of consecutive time points in each window. + + Notes + ----- + - The function assumes that `window_width` is smaller than the length of `series`. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.format import SlidingWindowTransformer + >>> X = np.array([1, 2, 3, 4, 5, 6]) + >>> transformer = SlidingWindowTransformer(3) + >>> Xt = transformer.fit_transform(X) + >>> print(Xt) + ([[1, 2], [2, 3], [3, 4], [4, 5]], [3, 4, 5, 6], [0, 1, 2, 3]) + + + Returns + ------- + X : np.ndarray (2D) + A numpy array where each element is a window (subsequence) of length + `window_width - 1` from the original series. + Y : np.ndarray (1D) + A numpy array containing the next value in the series for each window. + indices : list of int + A list of starting indices corresponding to each extracted window. + + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + "output_data_type": "Tuple", + } + + def __init__(self, window_size: int = 100): + super().__init__(axis=1) + if window_size <= 1: + raise ValueError(f"window_size must be > 1, got {window_size}") + self.window_size = window_size + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + The input time series from which windows will be created. + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + X = X[0] + # Generate windowed versions of train and test sets + X_t = np.zeros((len(X) - self.window_size + 1, self.window_size - 1)) + Y_t = np.zeros(len(X) - self.window_size + 1) + indices = np.zeros(len(X) - self.window_size + 1) + for i in range(len(X) - self.window_size + 1): + X_t[i] = X[ + i : i + self.window_size - 1 + ] # Create a view from current index onward + Y_t[i] = X[i + self.window_size - 1] # Next value + indices[i] = i + return X_t, Y_t, indices diff --git a/aeon/transformations/format/_train_test.py b/aeon/transformations/format/_train_test.py new file mode 100644 index 0000000000..0d31d48aa9 --- /dev/null +++ b/aeon/transformations/format/_train_test.py @@ -0,0 +1,93 @@ +"""Sliding Window transformation.""" + +__maintainer__ = [] +__all__ = ["TrainTestTransformer"] + +import math + +from aeon.transformations.format.base import BaseFormatTransformer + + +class TrainTestTransformer(BaseFormatTransformer): + """ + Convert a single time series into train/test sets. + + This function assumes that the input DataFrame contains only one time series. + It splits the series into training and testing sets based on + the specified proportion. + + Parameters + ---------- + train_proportion : float, optional (default=0.7) + The proportion of the time series to use for training, + with the remaining used for test. + max_series_length : int, optional (default=10000) + The maximum length of the series to consider. If the series is longer + than this value, it will be truncated. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.format import TrainTestTransformer + >>> X = np.array([-3, -2, -1, 0, 1, 2, 3, 4]) + >>> transformer = TrainTestTransformer(0.75) + >>> Xt = transformer.fit_transform(X) + >>> print(Xt) + (array([-3, -2, -1, 0, 1, 2]), array([3, 4])) + + Returns + ------- + None + A tuple containing the training and testing sets. + + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + "output_data_type": "Tuple", + } + + def __init__( + self, train_proportion: float = 0.7, max_series_length: int = 10000 + ) -> None: + super().__init__(axis=1) + if train_proportion <= 0 or train_proportion >= 1: + raise ValueError( + f"train_proportion must be between 0 and 1, got {train_proportion}" + ) + self.train_proportion = train_proportion + self.max_series_length = max_series_length + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + X = X[0] + # Compute split index + if len(X) < self.max_series_length or self.max_series_length == -1: + end_location = len(X) + else: + end_location = self.max_series_length + train_test_split_location = math.ceil(end_location * self.train_proportion) + + # Split into train and test sets + train_series = X[:train_test_split_location] + test_series = X[train_test_split_location:end_location] + + # Generate windowed versions of train and test sets + return train_series, test_series diff --git a/aeon/transformations/format/base.py b/aeon/transformations/format/base.py new file mode 100644 index 0000000000..9047c667e1 --- /dev/null +++ b/aeon/transformations/format/base.py @@ -0,0 +1,301 @@ +"""Base class for Series transformers. + +class name: BaseSeriesTransformer + +Defining methods: +fitting - fit(self, X, y=None) +transform - transform(self, X, y=None) +fit & transform - fit_transform(self, X, y=None) +""" + +from abc import abstractmethod +from typing import final + +import numpy as np +import pandas as pd + +from aeon.base import BaseSeriesEstimator +from aeon.transformations.base import BaseTransformer + + +class BaseFormatTransformer(BaseSeriesEstimator, BaseTransformer): + """Transformer base class for collections.""" + + # tag values specific to SeriesTransformers + _tags = { + "input_data_type": "Series", + "output_data_type": "Tuple", + } + + @abstractmethod + def __init__(self, axis): + super().__init__(axis=axis) + + @final + def fit(self, X, y=None, axis=1): + """Fit transformer to X, optionally using y if supervised. + + State change: + Changes state to "fitted". + + Parameters + ---------- + X : Input data + Time series to fit transform to, of type ``np.ndarray``, ``pd.Series`` + ``pd.DataFrame``. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + self : a fitted instance of the estimator + """ + # skip the rest if fit_is_empty is True + if self.get_tag("fit_is_empty"): + self.is_fitted = True + return self + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + # reset estimator at the start of fit + self.reset() + X = self._preprocess_series(X, axis=axis, store_metadata=True) + if y is not None: + self._check_y(y) + self._fit(X=X, y=y) + self.is_fitted = True + return self + + @final + def transform(self, X, y=None, axis=1): + """Transform X and return a transformed version. + + State required: + Requires state to be "fitted". + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + transformed version of X with the same axis as passed by the user, if axis + not None. + """ + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + Xt = self._transform(X, y) + return Xt + + @final + def fit_transform(self, X, y=None, axis=1): + """ + Fit to data, then transform it. + + Fits the transformer to X and y and returns a transformed version of X. + + Changes state to "fitted". Model attributes (ending in "_") : dependent on + estimator. + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + transformed version of X with the same axis as passed by the user, if axis + not None. + """ + # input checks and datatype conversion, to avoid doing in both fit and transform + self.reset() + X = self._preprocess_series(X, axis=axis, store_metadata=True) + Xt = self._fit_transform(X=X, y=y) + self.is_fitted = True + return Xt + + @final + def inverse_transform(self, X, y=None, axis=1): + """Inverse transform X and return an inverse transformed version. + + State required: + Requires state to be "fitted". + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + inverse transformed version of X + of the same type as X + """ + if not self.get_tag("capability:inverse_transform"): + raise NotImplementedError( + f"{type(self)} does not implement inverse_transform" + ) + + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + Xt = self._inverse_transform(X=X, y=y) + return Xt + + @final + def update(self, X, y=None, update_params=True, axis=1): + """Update transformer with X, optionally y. + + Parameters + ---------- + X : data to update of valid series type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + update_params : bool, default=True + whether the model is updated. Yes if true, if false, simply skips call. + argument exists for compatibility with forecasting module. + axis : int, default=None + axis along which to update. If None, uses self.axis. + + Returns + ------- + self : a fitted instance of the estimator + """ + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis, False) + return self._update(X=X, y=y, update_params=update_params) + + def _fit(self, X, y=None): + """Fit transformer to X and y. + + private _fit containing the core logic, called from fit + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + self: a fitted instance of the estimator + """ + # default fit is "no fitting happens" + return self + + @abstractmethod + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing the core logic, called from transform + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + transformed version of X + """ + + def _fit_transform(self, X, y=None): + """Fit to data, then transform it. + + Fits the transformer to X and y and returns a transformed version of X. + + private _fit_transform containing the core logic, called from fit_transform. + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation. + + Returns + ------- + transformed version of X. + """ + # Non-optimized default implementation; override when a better + # method is possible for a given algorithm. + self._fit(X, y) + return self._transform(X, y) + + def _inverse_transform(self, X, y=None): + """Inverse transform X and return an inverse transformed version. + + private _inverse_transform containing core logic, called from inverse_transform. + + Parameters + ---------- + X : Input data + Time series to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + inverse transformed version of X + of the same type as X. + """ + raise NotImplementedError( + f"{self.__class__.__name__} does not support inverse_transform" + ) + + def _update(self, X, y=None, update_params=True): + # standard behaviour: no update takes place, new data is ignored + return self + + def _check_y(self, y): + # Check y valid input for supervised transform + if not isinstance(y, (pd.Series, np.ndarray)): + raise TypeError( + f"y must be a np.array or a pd.Series, but found type: {type(y)}" + ) + if isinstance(y, np.ndarray) and y.ndim > 1: + raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py index 8b71ba9fc8..677f48db01 100644 --- a/aeon/transformations/series/__init__.py +++ b/aeon/transformations/series/__init__.py @@ -5,6 +5,7 @@ "BaseSeriesTransformer", "ClaSPTransformer", "DFTSeriesTransformer", + "DifferencingSeriesTransformer", "Dobin", "ExpSmoothingSeriesTransformer", "GaussSeriesTransformer", @@ -32,6 +33,7 @@ from aeon.transformations.series._boxcox import BoxCoxTransformer from aeon.transformations.series._clasp import ClaSPTransformer from aeon.transformations.series._dft import DFTSeriesTransformer +from aeon.transformations.series._difference import DifferencingSeriesTransformer from aeon.transformations.series._dobin import Dobin from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer from aeon.transformations.series._gauss import GaussSeriesTransformer diff --git a/aeon/transformations/series/_difference.py b/aeon/transformations/series/_difference.py new file mode 100644 index 0000000000..42addd377b --- /dev/null +++ b/aeon/transformations/series/_difference.py @@ -0,0 +1,52 @@ +"""Differencing transformations.""" + +__maintainer__ = ["TonyBagnall"] +__all__ = ["DifferencingSeriesTransformer"] + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class DifferencingSeriesTransformer(BaseSeriesTransformer): + """Differencing transformations. + + This transformer returns the differenced series of the input time series. + The differenced series is obtained by subtracting the previous value + from the current value. + + Examples + -------- + >>> from aeon.transformations.series import DifferencingSeriesTransformer + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> transformer = DifferencingSeriesTransformer() + >>> y_hat = transformer.fit_transform(y) + """ + + _tags = { + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__( + self, + ): + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing the core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed, shape (n_channels, n_timepoints) + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + transformed version of X + """ + X = X[0] + return X[1:] - X[:-1] From 55e99b8ddf6f1cbc980261b102c7c8d73e4e74d3 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 16 May 2025 19:37:36 +0100 Subject: [PATCH 05/70] Fix bug in AutoARIMA algorithm --- aeon/forecasting/_arima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 4de0fee3d3..e6f0e66cc6 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -378,7 +378,7 @@ def auto_arima(data): points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) p.append(aic) model_points.append(points) - current_model = max(model_parameters, key=lambda item: item[5]) + current_model = min(model_parameters, key=lambda item: item[5]) current_points = model_points[model_parameters.index(current_model)] while True: better_model = False From 85a83d9a035f4fd245a87e638145e1a82babf646 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 19 May 2025 18:51:08 +0100 Subject: [PATCH 06/70] Fix test issues --- aeon/forecasting/_autoets.py | 2 +- aeon/forecasting/_ets.py | 6 +++--- aeon/forecasting/_ets_fast.py | 2 +- aeon/forecasting/_naive.py | 2 +- aeon/testing/testing_data.py | 2 ++ aeon/transformations/format/_sliding_window.py | 3 ++- aeon/utils/base/_register.py | 2 ++ 7 files changed, 12 insertions(+), 7 deletions(-) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py index 7501bee0e2..e019646d82 100644 --- a/aeon/forecasting/_autoets.py +++ b/aeon/forecasting/_autoets.py @@ -46,7 +46,7 @@ class AutoETSForecaster(BaseForecaster): >>> forecaster.fit(y) AutoETSForecaster() >>> forecaster.predict() - 366.90200486015596 + array([407.74740434]) """ def __init__( diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index ac7f31a58d..86f7429dde 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -58,16 +58,16 @@ class ETSForecaster(BaseForecaster): Examples -------- - >>> from aeon.forecasting import ETSForecaster + >>> from aeon.forecasting._ets import ETSForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4,\ seasonality_type=2, trend_type=2) >>> forecaster.predict() - 366.90200486015596 + array([366.90200486]) """ def __init__( diff --git a/aeon/forecasting/_ets_fast.py b/aeon/forecasting/_ets_fast.py index fdbd9c005a..3322206aaa 100644 --- a/aeon/forecasting/_ets_fast.py +++ b/aeon/forecasting/_ets_fast.py @@ -71,7 +71,7 @@ class ETSForecaster(BaseForecaster): ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, seasonality_type=2, trend_type=2) >>> forecaster.predict() - 366.90200486015596 + array([366.90200486]) """ def __init__( diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py index 9bdfa82fb9..30fa10638c 100644 --- a/aeon/forecasting/_naive.py +++ b/aeon/forecasting/_naive.py @@ -41,7 +41,7 @@ class NaiveForecaster(BaseForecaster): >>> forecaster.fit(y) NaiveForecaster() >>> forecaster.predict() - 366.90200486015596 + array([432.]) """ def __init__( diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index f3360d93cb..ae4c2733a8 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -22,6 +22,7 @@ make_example_multi_index_dataframe, ) from aeon.transformations.collection import BaseCollectionTransformer +from aeon.transformations.format import BaseFormatTransformer from aeon.transformations.series import BaseSeriesTransformer from aeon.utils.conversion import convert_collection @@ -874,6 +875,7 @@ def _get_task_for_estimator(estimator): or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) or isinstance(estimator, BaseSeriesSimilaritySearch) + or isinstance(estimator, BaseFormatTransformer) ): data_label = "None" else: diff --git a/aeon/transformations/format/_sliding_window.py b/aeon/transformations/format/_sliding_window.py index 899eaaf44a..b173cb9ad2 100644 --- a/aeon/transformations/format/_sliding_window.py +++ b/aeon/transformations/format/_sliding_window.py @@ -33,7 +33,8 @@ class SlidingWindowTransformer(BaseFormatTransformer): >>> transformer = SlidingWindowTransformer(3) >>> Xt = transformer.fit_transform(X) >>> print(Xt) - ([[1, 2], [2, 3], [3, 4], [4, 5]], [3, 4, 5, 6], [0, 1, 2, 3]) + (array([[1., 2.], [2., 3.], [3., 4.], [4., 5.]]), + array([3., 4., 5., 6.]), array([0., 1., 2., 3.])) Returns diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 5e81e29b33..321b787389 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -29,6 +29,7 @@ from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer +from aeon.transformations.format import BaseFormatTransformer from aeon.transformations.series import BaseSeriesTransformer # all base classes @@ -48,6 +49,7 @@ "regressor": BaseRegressor, "segmenter": BaseSegmenter, "series-transformer": BaseSeriesTransformer, + "format-transformer": BaseFormatTransformer, "forecaster": BaseForecaster, "series-similarity-search": BaseSeriesSimilaritySearch, "collection-similarity-search": BaseCollectionSimilaritySearch, From bf8e535ed653f96661e2e770ae3671836c50d4cc Mon Sep 17 00:00:00 2001 From: alexbanwell1 <31886108+alexbanwell1@users.noreply.github.com> Date: Tue, 13 May 2025 09:46:04 +0100 Subject: [PATCH 07/70] [ENH] Add ETS/ARIMA Stuff (#2536) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * forecaster base and dummy * forecasting tests * forecasting tests * forecasting tests * forecasting tests * regression * notebook * regressor * regressor * regressor * tags * tags * requires_y * forecasting notebook * forecasting notebook * remove tags * fix forecasting testing (they still fail though) * _is_fitted -> is_fitted * _is_fitted -> is_fitted * _forecast * notebook * is_fitted * y_fitted * ETS forecaster * add y checks and conversion * add tag * tidy * _check_is_fitted() * _check_is_fitted() * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. (#2318) Co-authored-by: Alex Banwell * Add faster numba version of ETS forecaster * Seperate out predict code, and add test to test without creating a class - significantly faster! * Modify _verify_ets.py to allow easy switching between statsforecast versions. This confirms that my algorithms without class overheads is significantly faster than nixtla statsforecast, and with class overheads, it is faster than their current algorithm * Add basic gradient decent optimization algorithm for smoothing parameters * Ajb/forecasting (#2357) * Add fully functional ETS Forecaster. Modify base to not set default y in forecast. Update tests for ETS Forecaster. Add script to verify ETS Forecaster against statsforecast module using a large number of random parameter inputs. * Add faster numba version of ETS forecaster * Seperate out predict code, and add test to test without creating a class - significantly faster! * Modify _verify_ets.py to allow easy switching between statsforecast versions. This confirms that my algorithms without class overheads is significantly faster than nixtla statsforecast, and with class overheads, it is faster than their current algorithm * Add basic gradient decent optimization algorithm for smoothing parameters --------- Co-authored-by: Alex Banwell * Add additional AutoETS algorithms, and comparison scripts * Add ARIMA model in * [MNT] Testing fixes (#2531) * adjust test for non numpy output * test list output * test dataframe output * change pickle test * equal nans * test scalar output * fix lists output * allow arrays of objects * allow arrays of objects * test for boolean elements (MERLIN) * switch to deep equals * switch to deep equals * switch to deep equals * message * testing fixes --------- Co-authored-by: Tony Bagnall * Automated `pre-commit` hook update (#2533) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [DOC] Improve type hint guide and add link to the page. (#2532) * type hints * bad change * text * Add new datasets to tsf_datasets.py * Add functions for writing out .tsf files, as well as functions for manipulating the train/test split and windowing * Fix issues causing tests to fail * [DOC] Add 'Raises' section to docstring (#1766) (#2484) * Fix line endings * Moved test_cboss.py to testing/tests directory * Updated docstring comments and made methods protected * Fix line endings * Moved test_cboss.py to testing/tests directory * Updated docstring comments and made methods protected * Updated * Updated * Removed test_cboss.py * Updated * Updated * Add files for generating the datasets, and the CSV for the chosen datasets * Add windowed series train/test files * Automated `pre-commit` hook update (#2541) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * fix test (#2528) * [BUG] add ExpSmoothingSeriesTransformer and MovingAverageSeriesTransformer to __init__ (#2550) * update docs to fix 2548 docs * update init to fix 2548 bug * Automated `pre-commit` hook update (#2567) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump ossf/scorecard-action in the github-actions group (#2569) Bumps the github-actions group with 1 update: [ossf/scorecard-action](https://github.com/ossf/scorecard-action). Updates `ossf/scorecard-action` from 2.4.0 to 2.4.1 - [Release notes](https://github.com/ossf/scorecard-action/releases) - [Changelog](https://github.com/ossf/scorecard-action/blob/main/RELEASE.md) - [Commits](https://github.com/ossf/scorecard-action/compare/v2.4.0...v2.4.1) --- updated-dependencies: - dependency-name: ossf/scorecard-action dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [ENH] Added class weights to feature based classifiers (#2512) * class weights added to classification/feature based * Automatic `pre-commit` fixes * Test function for Catch22Classifier added * Test function for SummaryClassifier added * Test for tsfreshClassifier added * Soft dependecy check added for tsfresh * Test signature test case added * added test_mlp.py (#2537) * test file for FCNNetwork added (#2559) * Documentation improvement of certain BaseClasses (#2516) Co-authored-by: Antoine Guillaume * [ENH] Test coverage for AEFCNNetwork Improved (#2558) * test file added for aefcn * Test file for aefcn added * Test file reforammted * soft dependency added * name issues resolved * [ENH] Test coverage for TimeCNNNetwork Improved (#2534) * Test coverage improved for cnn network * assertion changed for test_cnn * coverage improved along with naming * [ENH] Test coverage for Resnet Network (#2553) * Resnet pytest * Resnet pytest * Fixed tensorflow failing * Added Resnet in function name * πŸ“ Add shinymack as a contributor for code (#2577) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * πŸ“ Add kevinzb56 as a contributor for doc (#2588) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * [MNT] Raise version bound for `scikit-learn` 1.6 (#2486) * update ver and new tags * default tags * toml * Update _shapelets.py Fix linear estimator coefs issue * expected results * Change expected results * update * only linux * remove mixins just to see test * revert --------- Co-authored-by: Antoine Guillaume * [MNT] Bump the python-packages group across 1 directory with 2 updates (#2598) Updates the requirements on [scipy](https://github.com/scipy/scipy) and [sphinx](https://github.com/sphinx-doc/sphinx) to permit the latest version. Updates `scipy` to 1.15.2 - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.9.0...v1.15.2) Updates `sphinx` to 8.2.3 - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/master/CHANGES.rst) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v0.1.61611...v8.2.3) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production dependency-group: python-packages - dependency-name: sphinx dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Automated `pre-commit` hook update (#2581) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * Automated `pre-commit` hook update (#2603) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Adds support for distances that are asymmetric but supports unequal length (#2613) * Adds support for distances that are asymmetric but supports unequal length * Added name to contributors * create smoothing filters notebook (#2547) * Remove datasets added * Reorganise code for generating train/test cluster files, including adding sliding window and train/test transformers * Add NaiveForecaster * Fix Bug in NaiveForecaster * Fix dataset generate script stuff * [DOC] Notebook on Feature-based Clustering (#2579) * Feature-based clustering * Feature-based clustering update * Update clustering overview * formatting * Automated `CONTRIBUTORS.md` update (#2614) Co-authored-by: chrisholder <4674372+chrisholder@users.noreply.github.com> * Updated Interval Based Notebook (#2620) * [DOC] Added Docstring for regression forecasting (#2564) * Added Docstring for Regression * Added Docstring for Regression * exog fix * GSoC announcement (#2629) * Automated `pre-commit` hook update (#2632) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump tj-actions/changed-files from 45 to 46 in the github-actions group (#2637) * [MNT] Bump tj-actions/changed-files in the github-actions group Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 45 to 46 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v45...v46) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions ... Signed-off-by: dependabot[bot] * Update pr_precommit.yml --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matthew Middlehurst * [MNT] Update numpy requirement in the python-packages group (#2643) Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version. Updates `numpy` to 2.2.4 - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.21.0...v2.2.4) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production dependency-group: python-packages ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [MNT,DEP] _binary.py metrics deprecated (#2600) * functions deprecated * Empty-Commit * version changed * Support for unequal length timeseries in itakura parallelogram (#2647) * [ENH] Implement DTW with Global alignment (#2565) * Implements Dynamic Time Warping with Global Invariances * Adds Numba JIT compilation support * Adds docs and numba support for dtw_gi and test_distance fixed * Fixes doctests * Automatic `pre-commit` fixes * Minor changes * Minor changes * Remove dtw_gi function and combine with private method _dtw_gi * Adds parameter tests * Fixes doctests * Minor changes * [ENH] Adds kdtw kernel support for kernelkmeans (#2645) * Adds kdtw kernel support for kernelkmeans * Code refactor * Adds tests for kdtw clustering * minor changes * minor changes * [MNT] Skip some excected results tests when numba is disabled (#2639) * skip some numba tests * Empty commit for CI * Update testing_config.py --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Remove REDCOMETs from testing exclusion list (#2630) * remove excluded estimators * redcomets fix * Ensure ETS algorithms are behaving correctly, and do more testing on AutoETS, along with AutoETS forecaster class * Fix a couple of bugs in the forecasters, add Sktime and StatsForecast wrappers for their AutoETS implementations * [ENH] Replace `prts` metrics (#2400) * Pre-commit fixes * Position parameter in calculate_bias * Added recall metric * merged into into one file * test added * Changes in test and range_metrics * list of list running but error! * flattening lists, all cases passed * Empty-Commit * changes * Protected functions * Changes in documentation * Changed test cases into seperate functions * test cases added and added range recall * udf_gamma removed from precision * changes * more changes * recommended changes * changes * Added Parameters * removed udf_gamma from precision * Added binary to range * error fixing * test comparing prts and range_metrics * Beta parameter added in fscore * Added udf_gamma function * f-score failing when comparing against prts * fixed f-score output * alpha usage * Empty-Commit * added test case to use range-based input for metrics * soft dependency added * doc update --------- Co-authored-by: Matthew Middlehurst Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> * Clarify documentation regarding unequal length series limitation (#2589) Co-authored-by: Matthew Middlehurst * Automated `pre-commit` hook update (#2683) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump tj-actions/changed-files in the github-actions group (#2686) Bumps the github-actions group with 1 update: [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `tj-actions/changed-files` from 46.0.1 to 46.0.3 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.1...v46.0.3) --- updated-dependencies: - dependency-name: tj-actions/changed-files dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [ENH] Set `outlier_norm` default to True for Catch22 estimators (#2659) * sets outlier_norm=True by deafault * Minor changes * Docs improvement * [MNT] Use MacOS for examples/ workflow (#2668) * update bash to 5.x for lastpipe support * added esig installation * install boost before esig * fixed examples path issue for excluded notebooks * switched to fixed version of macos * added signature_method.ipynb to excluded list * removed symlink for /bin/bash * Correct AutoETS algorithms to not use multiplicative error models for data which is not strictly positive. Add check to ets for this * Reject multiplicative components for data not strictly positive * Update dependencies.md (#2717) Correct typo in dependencies.md * Automated `pre-commit` hook update (#2708) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Test Coverage for Pairwise Distance (#2590) * Pairwise distance matrix test * Empty commit for CI --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * re-running notebook for fixing cell output error (#2597) * Docstring (#2609) * [DOC] Add 'Raises' section to docstring #1766 (#2617) * [DOC] Add 'Raises' section to docstring #1766 * Automatic `pre-commit` fixes * Update _base.py * Automatic `pre-commit` fixes --------- Co-authored-by: ayushsingh9720 <199482418+ayushsingh9720@users.noreply.github.com> * [DOC] Contributor docs update (#2554) * contributing docs update * contributing docs update 2 * typos * Update contributing.md new section * Update testing.md testing update * Update contributing.md dont steal code * Automatic `pre-commit` fixes * Update contributing.md if --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> Co-authored-by: Antoine Guillaume * prevent assignment on PRs (#2703) * Update run_examples.sh (#2701) * [BUG] SevenNumberSummary bugfix and input rename (#2555) * summary bugfix * maintainer * test * readme (#2556) * remove MutilROCKETRegressor from alias mapping (#2623) Co-authored-by: Matthew Middlehurst * Automated `pre-commit` hook update (#2731) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump the github-actions group with 2 updates (#2733) Bumps the github-actions group with 2 updates: [actions/create-github-app-token](https://github.com/actions/create-github-app-token) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `actions/create-github-app-token` from 1 to 2 - [Release notes](https://github.com/actions/create-github-app-token/releases) - [Commits](https://github.com/actions/create-github-app-token/compare/v1...v2) Updates `tj-actions/changed-files` from 46.0.3 to 46.0.4 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.3...v46.0.4) --- updated-dependencies: - dependency-name: actions/create-github-app-token dependency-version: '2' dependency-type: direct:production update-type: version-update:semver-major dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.4 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Fixed a few spelling/grammar mistakes on TSC docs examples (#2738) * Fix docstring inconsistencies in benchmarking module (resolves #809) (#2735) * issue#809 Fix docstrings for benchmarking functions * Fixed docstrings in results_loaders.py * Fix docstring inconsistencies in benchmarking module - resolves #809 * Fix docstring inconsistencies in benchmarking module - resolves #809 * [ENH] `best_on_top` addition in `plot_pairwise_scatter` (#2655) * Empty-Commit * best_on_top parameter added * changes * [ENH] Add dummy clusterer tags (#2551) * dummy clusterer tags * len * [ENH] Collection conversion cleanup and `df-list` fix (#2654) * collection conversion cleanup * notebook * fixes --------- Co-authored-by: Tony Bagnall * [MNT] Updated the release workflows (#2638) * edit release workflows to use trusted publishing * docs * [MNT,ENH] Update to allow Python 3.13 (#2608) * python 3.13 * tensorflow * esig * tensorflow * tensorflow * esig and matrix profile * signature notebook * remove prts * fix * remove annoying deps from all_extras * Update pyproject.toml * [ENH] Hard-Coded Tests for `test_metrics.py` (#2672) * Empty-Commit * hard-coded tests * changes * Changed single ticks to double (#2640) Co-authored-by: Matthew Middlehurst * πŸ“ Add HaroonAzamFiza as a contributor for doc (#2740) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * [ENH,MNT] Assign Bot (assigned issues>2) (#2702) * Empty-Commit * point 2 working * changes * changes in comment message * [MNT,ENH] Assign-bot (Allow users to type alternative phrases for assingment) (#2704) * added extra features * added comments * optimized code * optimized code * made changes requested by moderators * fixed conflicts * fixed conflicts * fixed conflicts --------- Co-authored-by: Ramana-Raja * πŸ“ Add Ramana-Raja as a contributor for code (#2741) * πŸ“ Update CONTRIBUTORS.md [skip ci] * πŸ“ Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * Release v1.1.0 (#2696) * v1.1.0 draft * finish * Automated `pre-commit` hook update (#2743) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT] Bump the github-actions group with 2 updates (#2744) Bumps the github-actions group with 2 updates: [crs-k/stale-branches](https://github.com/crs-k/stale-branches) and [tj-actions/changed-files](https://github.com/tj-actions/changed-files). Updates `crs-k/stale-branches` from 7.0.0 to 7.0.1 - [Release notes](https://github.com/crs-k/stale-branches/releases) - [Commits](https://github.com/crs-k/stale-branches/compare/v7.0.0...v7.0.1) Updates `tj-actions/changed-files` from 46.0.4 to 46.0.5 - [Release notes](https://github.com/tj-actions/changed-files/releases) - [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md) - [Commits](https://github.com/tj-actions/changed-files/compare/v46.0.4...v46.0.5) --- updated-dependencies: - dependency-name: crs-k/stale-branches dependency-version: 7.0.1 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions - dependency-name: tj-actions/changed-files dependency-version: 46.0.5 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [DOC] Add implementation references (#2748) * implementation references * better attribution * use gpu installs for periodic tests (#2747) * Use shape calculation in _fit to optimize QUANTTransformer (#2727) * [REF] Refactor Anomaly Detection Module into Submodules by Algorithm Family (#2694) * Refactor Anomaly Detection Module into Submodules by Algorithm Family * updated documentation and references * implemented suggested changes * minor changes * added headers for remaining algorithm family * removing tree-based header * Automated `pre-commit` hook update (#2756) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH]Type hints/forecasting (#2737) * Type hints for primitive data types in base module * Type hints for primitive data types and strings in forecating module * type hints for primitives in foreacasting module * Revert "type hints for primitives in foreacasting module" This reverts commit 575122d14b28742140ef1e16a3a351dd5db5072b. * type hints for primitives in forecasting module * Automated `pre-commit` hook update (#2766) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [ENH] Implement `load_model` function for ensemble classifiers (#2631) * feat: implement `load_model` function for LITETimeClassifier Implement separate `load_model` function for LITETimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * feat: implement `load_model` function for InceptionTimeClassifier Implement separate `load_model` function for InceptionTimeClassifier, which takes in `model_path` as list of strings and `classes` and loads all the models separately and stores them in `self.classifiers_` * fix: typo in load model function * feat: convert load_model functions to classmethods * test: implement test for save load for LITETIME and Inception classification models * Automatic `pre-commit` fixes * refactor: move loading tests to separate files * Update _ae_abgru.py (#2771) * Automated `pre-commit` hook update (#2779) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [DOC] Fix Broken [Source] Link and Improve Documentation for suppress_output() (#2677) * Fix Broken [Source] Link and Improve Documentation for suppress_output() Function * modified docstring and added tests * modified docstring example * modifying docstring examples * modifying docstring examples * updating conf file * updated docstring * base transform tidy (#2773) * DOC: Add Raises section for invalid weights in KNeighborsTimeSeriesClassifier (#1766) (#2764) Document the ValueError raised during initialization when an unsupported value is passed to the 'weights' parameter. Clarifies expected exceptions for users and improves API documentation consistency. Co-authored-by: Matthew Middlehurst * [ENH] Fixes Issue Improve `_check_params` method in `kmeans.py` and `kmedoids.py` (#2682) * Improves _check_params * removes function and adds a var * minor changes * minor changes * minor changes * line endings to LF * use variable instead of duplicating strings * weird file change * weird file change --------- Co-authored-by: Matthew Middlehurst * [ENH] Add type hints for deep learning regression classes (#2644) * type hints for cnn for regrssion * editing import modules Model & Optim * type hints for disjoint_cnn for regrssion * FIX type hints _get_test_params * ENH Change linie of importing typing * type hints for _encoder for regrssion * type hints for _fcn for regrssion * type hints for _inception_time for regrssion * type hints for _lite_time for regrssion * type hints for _mlp for regrssion * type hints for _resnet for regrssion * type hints for _base for regrssion * FIX: mypy errors in _disjoint_cnn.py file * FIX: mypy typing errors * Fix: Delete variable types, back old-verbose * FIX: add model._save in save_last_model_to_file function * FIX: Put TYPE_CHECKING downside * Fix: Put Any at the top * [DOC] Add RotationForest Classifier Notebook for Time Series Classification (#2592) * Add RotationForest Classifier Notebook for Time Series Classification * Added references and modified doc * minor modifications to notebook description * Update rotation_forest.ipynb --------- Co-authored-by: Matthew Middlehurst * fix: Codeowners for benchmarking metrics AD (#2784) * [GOV] Supporting Developer role (#2775) * supporting dev role * pr req * Update governance.md * typo * Automatic `pre-commit` fixes * aeon --------- Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * [MNT, ENH, DOC] Rework similarity search (#2473) * WIP remake module structure * Update _brute_force.py * Update test__commons.py * WIP mock and test * Add test for base subsequence * Fix subsequence_search tests * debug brute force mp * more debug of subsequence tests * more debug of subsequence tests * Add functional LSH neighbors * add notebook for sim search tasks * Updated series similarity search * Fix mistake addition in transformers and fix base classes * Fix registry and api reference * Update documentation and fix some leftover bugs * Update documentation and add default test params * Fix identifiers and test data shape for all_estimators tests * Fix missing params * Fix n_jobs params and tags, add some docs * Fix numba test bug and update testing data for sim search * Fix imports, testing data tests, and impose predict/_predict interface to all sim search estimators * Fix args * Fix extract test * update docs api and notebooks * remove notes * Patrick comments * Adress comments and clean index code * Fix Patrick comments * Fix variable suppression mistake * Divide base class into task specific * Fix typo in imports * Empty commit for CI * Fix typo again * Add check_inheritance exception for similarity search * Revert back to non per type base classes * Factor check index and typo in test --------- Co-authored-by: Patrick SchΓ€fer Co-authored-by: Matthew Middlehurst Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> * [ENH] Adapt the DCNN Networks to use Weight Norm Wrappers (#2628) * adapt the dcnn networks to use weight norm wrappers and remove l2 regularization * Automatic `pre-commit` fixes * add custom object * Automatic `pre-commit` fixes * fix trial --------- Co-authored-by: Matthew Middlehurst * [GOV] Remove inactive developers (#2776) * inactive devs * logo fix * Automated `pre-commit` hook update (#2792) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> * Code to generate differenced datasets * Add AutoARIMA algorithm into Aeon * Add ArimaForecaster to forecasting list * Fix predict method to return the prediction in the correct format --------- Signed-off-by: dependabot[bot] Co-authored-by: Tony Bagnall Co-authored-by: Tony Bagnall Co-authored-by: MatthewMiddlehurst Co-authored-by: Alex Banwell Co-authored-by: Matthew Middlehurst Co-authored-by: aeon-actions-bot[bot] <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> Co-authored-by: Nikita Singh Co-authored-by: Ali El Hadi ISMAIL FAWAZ <54309336+hadifawaz1999@users.noreply.github.com> Co-authored-by: Cyril Meyer <69190238+Cyril-Meyer@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Co-authored-by: Akash Kawle <128881349+shinymack@users.noreply.github.com> Co-authored-by: Kevin Shah <161136814+kevinzb56@users.noreply.github.com> Co-authored-by: Antoine Guillaume Co-authored-by: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> Co-authored-by: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com> Co-authored-by: Divya Tiwari <108270861+itsdivya1309@users.noreply.github.com> Co-authored-by: chrisholder <4674372+chrisholder@users.noreply.github.com> Co-authored-by: Aryan Pola <98093778+aryanpola@users.noreply.github.com> Co-authored-by: Sebastian Schmidl <10573700+SebastianSchmidl@users.noreply.github.com> Co-authored-by: Kaustubh <97254178+Kaustbh@users.noreply.github.com> Co-authored-by: TinaJin0228 <60577222+TinaJin0228@users.noreply.github.com> Co-authored-by: Ayush Singh Co-authored-by: ayushsingh9720 <199482418+ayushsingh9720@users.noreply.github.com> Co-authored-by: HaroonAzamFiza Co-authored-by: adityagh006 <142653450+adityagh006@users.noreply.github.com> Co-authored-by: V_26@ Co-authored-by: Ramana Raja <83065061+Ramana-Raja@users.noreply.github.com> Co-authored-by: Ramana-Raja Co-authored-by: Ahmed Zahran <136983104+Ahmed-Zahran02@users.noreply.github.com> Co-authored-by: Adarsh Dubey Co-authored-by: Somto Onyekwelu <117727947+SomtoOnyekwelu@users.noreply.github.com> Co-authored-by: Saad Al-Tohamy <92796871+saadaltohamy@users.noreply.github.com> Co-authored-by: Patrick SchΓ€fer Co-authored-by: baraline <10759117+baraline@users.noreply.github.com> Co-authored-by: Aadya Chinubhai <77720426+aadya940@users.noreply.github.com> --- aeon/datasets/Final Dataset Selection.csv | 101 ++++ aeon/datasets/__init__.py | 11 +- aeon/datasets/_data_writers.py | 301 +++++++++- aeon/datasets/dataset_generation.py | 218 +++++++ aeon/datasets/tests/test_data_writers.py | 1 - .../tests/test_dataset_collections.py | 2 +- aeon/datasets/tsad_datasets.py | 2 +- aeon/datasets/tsf_datasets.py | 13 + aeon/forecasting/__init__.py | 8 +- aeon/forecasting/_arima.py | 421 +++++++++++++ aeon/forecasting/_autoets.py | 457 ++++++++++++++ aeon/forecasting/_autoets_gradient_params.py | 297 +++++++++ aeon/forecasting/_compare_external_autoets.py | 207 +++++++ aeon/forecasting/_ets.py | 565 ++++++++---------- aeon/forecasting/_ets_fast.py | 476 +++++++++++++++ aeon/forecasting/_naive.py | 94 +++ .../_plot_autoets_gradient_method.py | 66 ++ aeon/forecasting/_sktime_autoets.py | 78 +++ aeon/forecasting/_statsforecast_autoets.py | 78 +++ aeon/forecasting/_time_autoets.py | 37 ++ aeon/forecasting/_utils.py | 115 ++++ aeon/forecasting/_verify_arima.py | 31 + aeon/forecasting/_verify_ets.py | 345 +++++++++++ aeon/forecasting/tests/test_ets.py | 113 +++- aeon/transformations/format/__init__.py | 11 + .../transformations/format/_sliding_window.py | 92 +++ aeon/transformations/format/_train_test.py | 93 +++ aeon/transformations/format/base.py | 301 ++++++++++ aeon/transformations/series/__init__.py | 2 + aeon/transformations/series/_difference.py | 52 ++ 30 files changed, 4209 insertions(+), 379 deletions(-) create mode 100644 aeon/datasets/Final Dataset Selection.csv create mode 100644 aeon/datasets/dataset_generation.py create mode 100644 aeon/forecasting/_arima.py create mode 100644 aeon/forecasting/_autoets.py create mode 100644 aeon/forecasting/_autoets_gradient_params.py create mode 100644 aeon/forecasting/_compare_external_autoets.py create mode 100644 aeon/forecasting/_ets_fast.py create mode 100644 aeon/forecasting/_naive.py create mode 100644 aeon/forecasting/_plot_autoets_gradient_method.py create mode 100644 aeon/forecasting/_sktime_autoets.py create mode 100644 aeon/forecasting/_statsforecast_autoets.py create mode 100644 aeon/forecasting/_time_autoets.py create mode 100644 aeon/forecasting/_utils.py create mode 100644 aeon/forecasting/_verify_arima.py create mode 100644 aeon/forecasting/_verify_ets.py create mode 100644 aeon/transformations/format/__init__.py create mode 100644 aeon/transformations/format/_sliding_window.py create mode 100644 aeon/transformations/format/_train_test.py create mode 100644 aeon/transformations/format/base.py create mode 100644 aeon/transformations/series/_difference.py diff --git a/aeon/datasets/Final Dataset Selection.csv b/aeon/datasets/Final Dataset Selection.csv new file mode 100644 index 0000000000..c336db5a22 --- /dev/null +++ b/aeon/datasets/Final Dataset Selection.csv @@ -0,0 +1,101 @@ +Dataset,Series,Category +weather_dataset,T1,Weather +weather_dataset,T2,Weather +weather_dataset,T3,Weather +weather_dataset,T4,Weather +weather_dataset,T5,Weather +solar_10_minutes_dataset,T1,Energy Production +solar_10_minutes_dataset,T2,Energy Production +solar_10_minutes_dataset,T3,Energy Production +solar_10_minutes_dataset,T4,Energy Production +solar_10_minutes_dataset,T5,Energy Production +sunspot_dataset_without_missing_values,T1,Other +wind_farms_minutely_dataset_without_missing_values,T1,Energy Production +wind_farms_minutely_dataset_without_missing_values,T2,Energy Production +wind_farms_minutely_dataset_without_missing_values,T3,Energy Production +wind_farms_minutely_dataset_without_missing_values,T4,Energy Production +wind_farms_minutely_dataset_without_missing_values,T5,Energy Production +elecdemand_dataset,T1,Energy Demand +us_births_dataset,T1,Demographic +saugeenday_dataset,T1,Weather +london_smart_meters_dataset_without_missing_values,T1,Energy Demand +london_smart_meters_dataset_without_missing_values,T2,Energy Demand +london_smart_meters_dataset_without_missing_values,T3,Energy Demand +traffic_hourly_dataset,T1,Transportation +traffic_hourly_dataset,T2,Transportation +traffic_hourly_dataset,T3,Transportation +traffic_hourly_dataset,T4,Transportation +traffic_hourly_dataset,T5,Transportation +electricity_hourly_dataset,T1,Energy Demand +electricity_hourly_dataset,T2,Energy Demand +electricity_hourly_dataset,T3,Energy Demand +pedestrian_counts_dataset,T1,Transportation +pedestrian_counts_dataset,T2,Transportation +pedestrian_counts_dataset,T3,Transportation +pedestrian_counts_dataset,T4,Transportation +pedestrian_counts_dataset,T5,Transportation +kdd_cup_2018_dataset_without_missing_values,T1,Other +australian_electricity_demand_dataset,T1,Energy Demand +australian_electricity_demand_dataset,T2,Energy Demand +australian_electricity_demand_dataset,T3,Energy Demand +oikolab_weather_dataset,T1,Weather +oikolab_weather_dataset,T2,Weather +oikolab_weather_dataset,T3,Weather +oikolab_weather_dataset,T4,Weather +m4_monthly_dataset,T122,Macro +m4_monthly_dataset,T145,Macro +m4_monthly_dataset,T180,Macro +m4_monthly_dataset,T186,Macro +m4_monthly_dataset,T17051,Micro +m4_monthly_dataset,T17088,Micro +m4_monthly_dataset,T17132,Micro +m4_monthly_dataset,T17146,Micro +m4_monthly_dataset,T26710,Demographic +m4_monthly_dataset,T27138,Industry +m4_monthly_dataset,T27170,Industry +m4_monthly_dataset,T27175,Industry +m4_monthly_dataset,T27186,Industry +m4_monthly_dataset,T37009,Finance +m4_monthly_dataset,T37070,Finance +m4_monthly_dataset,T37238,Finance +m4_monthly_dataset,T37248,Finance +m4_monthly_dataset,T47915,Other +m4_weekly_dataset,T1,Other +m4_weekly_dataset,T2,Other +m4_weekly_dataset,T19,Macro +m4_weekly_dataset,T20,Macro +m4_weekly_dataset,T21,Macro +m4_weekly_dataset,T55,Industry +m4_weekly_dataset,T56,Industry +m4_weekly_dataset,T60,Finance +m4_weekly_dataset,T61,Finance +m4_weekly_dataset,T62,Finance +m4_weekly_dataset,T224,Demographic +m4_weekly_dataset,T225,Demographic +m4_weekly_dataset,T226,Demographic +m4_weekly_dataset,T227,Demographic +m4_weekly_dataset,T248,Micro +m4_weekly_dataset,T249,Micro +m4_weekly_dataset,T250,Micro +m4_daily_dataset,T1,Macro +m4_daily_dataset,T2,Macro +m4_daily_dataset,T6,Macro +m4_daily_dataset,T130,Micro +m4_daily_dataset,T131,Micro +m4_daily_dataset,T145,Micro +m4_daily_dataset,T1604,Demographic +m4_daily_dataset,T1605,Demographic +m4_daily_dataset,T1606,Demographic +m4_daily_dataset,T1607,Demographic +m4_daily_dataset,T1614,Industry +m4_daily_dataset,T1615,Industry +m4_daily_dataset,T1634,Industry +m4_daily_dataset,T1650,Industry +m4_daily_dataset,T2036,Finance +m4_daily_dataset,T2037,Finance +m4_daily_dataset,T2041,Finance +m4_daily_dataset,T3595,Other +m4_daily_dataset,T3597,Other +m4_hourly_dataset,T170,Other +m4_hourly_dataset,T171,Other +m4_hourly_dataset,T172,Other diff --git a/aeon/datasets/__init__.py b/aeon/datasets/__init__.py index 4185769f6f..5ca365c171 100644 --- a/aeon/datasets/__init__.py +++ b/aeon/datasets/__init__.py @@ -16,7 +16,10 @@ "load_human_activity_segmentation_datasets", # Write functions "write_to_ts_file", + "write_to_tsf_file", "write_to_arff_file", + "write_regression_dataset", + "write_forecasting_dataset", # Single problem loaders "load_airline", "load_arrow_head", @@ -57,7 +60,13 @@ load_from_tsv_file, load_regression, ) -from aeon.datasets._data_writers import write_to_arff_file, write_to_ts_file +from aeon.datasets._data_writers import ( + write_forecasting_dataset, + write_regression_dataset, + write_to_arff_file, + write_to_ts_file, + write_to_tsf_file, +) from aeon.datasets._single_problem_loaders import ( load_acsf1, load_airline, diff --git a/aeon/datasets/_data_writers.py b/aeon/datasets/_data_writers.py index 29ec83e648..0f2ea35f90 100644 --- a/aeon/datasets/_data_writers.py +++ b/aeon/datasets/_data_writers.py @@ -1,9 +1,20 @@ +"""Dataset wrting functions.""" + import os import textwrap +from datetime import datetime import numpy as np +import pandas as pd + +from aeon.transformations.format import SlidingWindowTransformer, TrainTestTransformer +from aeon.transformations.series._difference import DifferencingSeriesTransformer -__all__ = ["write_to_ts_file", "write_to_arff_file"] +__all__ = [ + "write_to_ts_file", + "write_to_tsf_file", + "write_to_arff_file", +] def write_to_ts_file( @@ -83,7 +94,6 @@ def write_to_ts_file( class_labels=class_labels, comment=header, regression=regression, - extension=None, ) missing_values = "NaN" for i in range(n_cases): @@ -99,6 +109,186 @@ def write_to_ts_file( file.close() +def write_to_tsf_file( + df, + full_file_path, + metadata, + value_column_name="series_value", + attributes_types=None, + missing_val_symbol="?", +): + """ + Save a pandas DataFrame in TSF format. + + Parameters + ---------- + df : pandas.DataFrame + The DataFrame to be saved. It is assumed that one column contains the series + (by default, named "series_value") and all other columns are series attributes. + full_file_path : str + The full path (including file name) where the TSF file will be saved. + metadata : dict + A dictionary containing metadata for the forecasting problem. It must + include the following keys: + - "frequency" (str) + - "forecast_horizon" (int) + - "contain_missing_values" (bool) + - "contain_equal_length" (bool) + value_column_name : str, optional (default="series_value") + The name of the column that contains the time series values. + attributes_types : dict, optional + A dictionary mapping attribute column names to their TSF type + (one of "numeric", "string", "date"). + If not provided, the type is inferred from the DataFrame dtypes as follows: + - numeric dtypes -> "numeric" + - datetime dtypes -> "date" + - all others -> "string" + missing_val_symbol : str, optional (default="?") + The symbol to be used in the file to represent missing values in the series. + + Raises + ------ + Exception + If any required metadata or a series or attribute value is missing. + """ + # Validate metadata keys + required_meta = [ + "frequency", + "forecast_horizon", + "contain_missing_values", + "contain_equal_length", + ] + for key in required_meta: + if key not in metadata: + raise AttributeError(f"Missing metadata entry: {key}") + + # Determine attribute columns (all columns except the series column) + attribute_columns = [col for col in df.columns if col != value_column_name] + + # If no attributes are present, warn the user. + if not attribute_columns: + raise AttributeError( + "The DataFrame must contain at least one \ + attribute column besides the series column." + ) + + # Determine attribute types if not provided. + # For each attribute, assign a type: + # - numeric dtypes -> "numeric" + # - datetime dtypes -> "date" (and will be formatted as "%Y-%m-%d %H-%M-%S") + # - all others -> "string" + if attributes_types is None: + attributes_types = {} + for col in attribute_columns: + if pd.api.types.is_numeric_dtype(df[col]): + attributes_types[col] = "numeric" + elif pd.api.types.is_datetime64_any_dtype(df[col]): + attributes_types[col] = "date" + else: + attributes_types[col] = "string" + else: + # Ensure that a type is provided for each attribute column + for col in attribute_columns: + if col not in attributes_types: + raise ValueError( + f"Attribute type for column '{col}' is \ + missing in attributes_types." + ) + + # Build header lines for the TSF file. + header_lines = [] + # First, write the attribute lines (order matters!) + for col in attribute_columns: + att_type = attributes_types[col] + if att_type not in {"numeric", "string", "date"}: + raise ValueError( + f"Unsupported attribute type '{att_type}' for column '{col}'." + ) + header_lines.append(f"@attribute {col} {att_type}") + + # Now add the metadata lines. (The order here is flexible, + # but must appear before @data.) + header_lines.append(f"@frequency {metadata['frequency']}") + header_lines.append(f"@horizon {metadata['forecast_horizon']}") + header_lines.append( + f"@missing {'true' if metadata['contain_missing_values'] else 'false'}" + ) + header_lines.append( + f"@equallength {'true' if metadata['contain_equal_length'] else 'false'}" + ) + + # Add the data section tag. + header_lines.append("@data") + # Open file for writing using the same encoding as the loader. + with open(full_file_path, "w", encoding="cp1252") as f: + # Write header lines. + for line in header_lines: + f.write(line + "\n") + + # Process each row to write the data lines. + for idx, row in df.iterrows(): + parts = [] + # Process each attribute value. + for col in attribute_columns: + val = row[col] + col_type = attributes_types[col] + if pd.isna(val): + raise ValueError( + f"Missing value in attribute column '{col}' at row {idx}." + ) + if col_type == "numeric": + try: + val_str = str(int(val)) + except Exception as e: + raise ValueError( + f"Error converting value in column '{col}' \ + at row {idx} to integer: {e}" + ) from e + elif col_type == "date": + # Ensure val is a datetime; if not, attempt conversion. + if not isinstance(val, datetime): + try: + val = pd.to_datetime(val) + except Exception as e: + raise ValueError( + f"Error converting value in column '{col}' \ + at row {idx} to datetime: {e}" + ) from e + val_str = val.strftime("%Y-%m-%d %H-%M-%S") + elif col_type == "string": + val_str = str(val) + else: + # Should not get here because we validated types earlier. + raise ValueError( + f"Unsupported attribute type '{col_type}' for column '{col}'." + ) + parts.append(val_str) + + # Process the series data from value_column_name. + series_val = row[value_column_name] + if not hasattr(series_val, "__iter__"): + raise ValueError( + f"The series in column '{value_column_name}' \ + at row {idx} is not iterable." + ) + + series_str_parts = [] + for s in series_val: + # Check for missing values in the series. + if pd.isna(s): + series_str_parts.append(missing_val_symbol) + else: + series_str_parts.append(str(s).removesuffix(".0")) + # Join series values with commas. + series_str = ",".join(series_str_parts) + parts.append(series_str) + + # The data line consists of the attribute values and + # then the series, separated by colons. + line_data = ":".join(parts) + f.write(line_data + "\n") + + def _write_header( path, problem_name, @@ -108,25 +298,24 @@ def _write_header( comment=None, regression=False, class_labels=None, - extension=None, ): if class_labels is not None and regression: raise ValueError("Cannot have class_labels true for a regression problem") # create path if it does not exist - dir = os.path.join(path, "") + dir_path = os.path.join(path, "") try: - os.makedirs(dir, exist_ok=True) - except OSError: - raise ValueError(f"Error trying to access {dir} in _write_header") + os.makedirs(dir_path, exist_ok=True) + except OSError as exc: + raise ValueError(f"Error trying to access {dir_path} in _write_header") from exc # create ts file in the path - load_path = os.path.join(dir, problem_name) - file = open(load_path, "w") + load_path = os.path.join(dir_path, problem_name) + file = open(load_path, "w", encoding="utf-8") # write comment if any as a block at start of file if comment is not None: file.write("\n# ".join(textwrap.wrap("# " + comment))) file.write("\n") - """ Writes the header info for a ts file""" + # Writes the header info for a ts file file.write(f"@problemName {problem_name}\n") file.write("@timestamps false\n") file.write(f"@univariate {str(univariate).lower()}\n") @@ -175,7 +364,7 @@ def write_to_arff_file( ------- None """ - if not (isinstance(X, np.ndarray)): + if not isinstance(X, np.ndarray): raise TypeError( f" Wrong input data type {type(X)}. Convert to np.ndarray (n_cases, " f"n_channels, n_timepoints) if possible." @@ -187,31 +376,77 @@ def write_to_arff_file( f"received {X.shape}" ) - file = open(f"{path}/{problem_name}.arff", "w") + with open(f"{path}/{problem_name}.arff", "w", encoding="utf-8") as file: - # write comment if any as a block at start of file - if header is not None: - file.write("\n% ".join(textwrap.wrap("% " + header))) - file.write("\n") + # write comment if any as a block at start of file + if header is not None: + file.write("\n% ".join(textwrap.wrap("% " + header))) + file.write("\n") - # begin writing header information - file.write(f"@Relation {problem_name}\n") + # begin writing header information + file.write(f"@Relation {problem_name}\n") - # write each attribute - for i in range(X.shape[2]): - file.write(f"@attribute att{str(i)} numeric\n") + # write each attribute + for i in range(X.shape[2]): + file.write(f"@attribute att{str(i)} numeric\n") - # lass attribute if it exists - comma_separated_class_label = ",".join(str(label) for label in np.unique(y)) - file.write(f"@attribute target {{{comma_separated_class_label}}}\n") + # lass attribute if it exists + comma_separated_class_label = ",".join(str(label) for label in np.unique(y)) + file.write(f"@attribute target {{{comma_separated_class_label}}}\n") - # write data - file.write("@data\n") - for case, target in zip(X, y): - # turn attributes into comma-separated row - atts = ",".join([str(num) if not np.isnan(num) else "?" for num in case[0]]) - file.write(str(atts)) - file.write(f",{target}") - file.write("\n") # open a new line + # write data + file.write("@data\n") + for case, target in zip(X, y): + # turn attributes into comma-separated row + atts = ",".join([str(num) if not np.isnan(num) else "?" for num in case[0]]) + file.write(str(atts)) + file.write(f",{target}") + file.write("\n") # open a new line - file.close() + +def write_regression_dataset(series, full_file_path, dataset_name): + """Write a regression dataset to file.""" + train_series, test_series = TrainTestTransformer().fit_transform(series) + differenced_train_series = DifferencingSeriesTransformer().fit_transform( + train_series + ) + X_train, Y_train, train_indices = SlidingWindowTransformer().fit_transform( + differenced_train_series + ) + differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) + X_test, Y_test, test_indices = SlidingWindowTransformer().fit_transform( + differenced_test_series + ) + write_to_ts_file( + [[item] for item in X_train], + full_file_path, + Y_train, + f"{dataset_name}_TRAIN", + None, + True, + ) + write_to_ts_file( + [[item] for item in X_test], + full_file_path, + Y_test, + f"{dataset_name}_TEST", + None, + True, + ) + + +def write_forecasting_dataset(series, full_file_path, dataset_name): + """Write a regression dataset to file.""" + train_series, test_series = TrainTestTransformer().fit_transform(series) + differenced_train_series = DifferencingSeriesTransformer().fit_transform( + train_series + ) + differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) + train_df = pd.DataFrame(differenced_train_series) + train_df.to_csv( + f"{full_file_path}/{dataset_name}_TRAIN.csv", index=False, header=False + ) + test_df = pd.DataFrame(differenced_test_series) + test_df.to_csv( + f"{full_file_path}/{dataset_name}_TEST.csv", index=False, header=False + ) diff --git a/aeon/datasets/dataset_generation.py b/aeon/datasets/dataset_generation.py new file mode 100644 index 0000000000..674c7501f3 --- /dev/null +++ b/aeon/datasets/dataset_generation.py @@ -0,0 +1,218 @@ +"""Code to select datasets for regression-based forecasting experiments.""" + +import gc +import os +import tempfile +import time + +import pandas as pd + +from aeon.datasets import load_forecasting +from aeon.datasets._data_writers import ( + write_forecasting_dataset, + write_regression_dataset, +) + +filtered_datasets = [ + "nn5_daily_dataset_without_missing_values", + "nn5_weekly_dataset", + "m1_yearly_dataset", + "m1_quarterly_dataset", + "m1_monthly_dataset", + "m3_yearly_dataset", + "m3_quarterly_dataset", + "m3_monthly_dataset", + "m3_other_dataset", + "m4_yearly_dataset", + "m4_quarterly_dataset", + "m4_monthly_dataset", + "m4_weekly_dataset", + "m4_daily_dataset", + "m4_hourly_dataset", + "tourism_yearly_dataset", + "tourism_quarterly_dataset", + "tourism_monthly_dataset", + "car_parts_dataset_without_missing_values", + "hospital_dataset", + "weather_dataset", + "dominick_dataset", + "fred_md_dataset", + "solar_10_minutes_dataset", + "solar_weekly_dataset", + "solar_4_seconds_dataset", + "wind_4_seconds_dataset", + "sunspot_dataset_without_missing_values", + "wind_farms_minutely_dataset_without_missing_values", + "elecdemand_dataset", + "us_births_dataset", + "saugeenday_dataset", + "covid_deaths_dataset", + "cif_2016_dataset", + "london_smart_meters_dataset_without_missing_values", + "kaggle_web_traffic_dataset_without_missing_values", + "kaggle_web_traffic_weekly_dataset", + "traffic_hourly_dataset", + "traffic_weekly_dataset", + "electricity_hourly_dataset", + "electricity_weekly_dataset", + "pedestrian_counts_dataset", + "kdd_cup_2018_dataset_without_missing_values", + "australian_electricity_demand_dataset", + "covid_mobility_dataset_without_missing_values", + "rideshare_dataset_without_missing_values", + "vehicle_trips_dataset_without_missing_values", + "temperature_rain_dataset_without_missing_values", + "oikolab_weather_dataset", +] + + +def filter_datasets(): + """ + Filter datasets to identify and print time series with more than 1000 data points. + + This function iterates over a list of datasets, loads each dataset, + and checks each time series within it. If a series contains more than 1000 + data points, it is counted as a "hit." The function prints up to 10 matches + per dataset in the format: `,`. + + Returns + ------- + None + The function does not return anything but prints matching dataset + and series names to the console. + + Notes + ----- + - The function introduces a 1-second delay (`time.sleep(1)`) between processing + datasets to control HTTP request frequency. + - Uses `gc.collect()` to explicitly trigger garbage collection, to avoid + running out of memory + """ + num_hits = 0 + for dataset_name in filtered_datasets: + # print(f"{dataset_name}") + time.sleep(1) + dataset_counter = 0 + dataset = load_forecasting(dataset_name) + for index, row in enumerate(dataset["series_value"]): + if len(row) > 1000: + num_hits += 1 + dataset_counter += 1 + if dataset_counter <= 10: + print(f"{dataset_name},{dataset['series_name'][index]}") # noqa + # if dataset_counter > 0: + # print(f"{dataset_name}: Hits: {dataset_counter}") + del dataset + gc.collect() + # print(f"Num hits in datasets: {num_hits}") + + +# filter_datasets() + + +def filter_and_categorise_m4(frequency_type): + """ + Filter and categorize M4 dataset time series. + + Parameters + ---------- + frequency_type : str + The frequency type of the M4 dataset to process. + Accepted values: 'yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly'. + + Returns + ------- + None + The function does not return any values but prints categorized series + information. + + Notes + ----- + - The function constructs an appropriate prefix ('Y', 'Q', 'M', 'W', 'D', 'H') + based on the dataset type to match metadata identifiers. + - Limits printed results to 10 per category. + """ + metadata = pd.read_csv("C:/Users/alexb/Downloads/M4-info.csv") + m4daily = load_forecasting(f"m4_{frequency_type}_dataset") + categories = {} + prefix = "" + if frequency_type == "yearly": + prefix = "Y" + elif frequency_type == "quarterly": + prefix = "Q" + elif frequency_type == "monthly": + prefix = "M" + elif frequency_type == "weekly": + prefix = "W" + elif frequency_type == "daily": + prefix = "D" + elif frequency_type == "hourly": + prefix = "H" + for index, row in enumerate(m4daily["series_value"]): + if len(row) > 1000: + category = metadata.loc[ + metadata["M4id"] == f"{prefix}{m4daily['series_name'][index][1:]}", + "category", + ].values[0] + if category not in categories: + categories[category] = 1 + else: + categories[category] += 1 + if categories[category] <= 10: + print( # noqa + f"m4_{frequency_type}_dataset,\ + {m4daily['series_name'][index]},{category}" + ) + + +# filter_and_categorise_m4('monthly') +# filter_and_categorise_m4('weekly') +# filter_and_categorise_m4('daily') +# filter_and_categorise_m4('hourly') + + +def gen_datasets(problem_type, dataset_folder=None): + """ + Generate windowed train/test split of datasets. + + Returns + ------- + None + The function does not return anything but writes out the train and test + files to the specified directory. + + Notes + ----- + - Requires a CSV file containing a list of the series to process. + """ + final_series_selection = pd.read_csv("./aeon/datasets/Final Dataset Selection.csv") + current_dataset = "" + dataset = pd.DataFrame() + tmpdir = tempfile.mkdtemp() + folder = problem_type if dataset_folder is None else dataset_folder + location_of_datasets = f"./aeon/datasets/local_data/{folder}" + if not os.path.exists(location_of_datasets): + os.makedirs(location_of_datasets) + with open(f"{location_of_datasets}/windowed_series.txt", "w") as f: + for item in final_series_selection.to_records(index=False): + if current_dataset != item[0]: + dataset = load_forecasting(item[0], tmpdir) + current_dataset = item[0] + print(f"Current Dataset: {current_dataset}") # noqa + f.write(f"{item[0]}_{item[1]}\n") + series = ( + dataset[dataset["series_name"] == item[1]]["series_value"] + .iloc[0] + .to_numpy() + ) + dataset_name = f"{item[0]}_{item[1]}" + full_file_path = f"{location_of_datasets}/{dataset_name}" + if not os.path.exists(full_file_path): + os.makedirs(full_file_path) + if problem_type == "regression": + write_regression_dataset(series, full_file_path, dataset_name) + elif problem_type == "forecasting": + write_forecasting_dataset(series, full_file_path, dataset_name) + + +gen_datasets("forecasting", "differenced_forecasting") diff --git a/aeon/datasets/tests/test_data_writers.py b/aeon/datasets/tests/test_data_writers.py index d31700ac2b..e7428a39fc 100644 --- a/aeon/datasets/tests/test_data_writers.py +++ b/aeon/datasets/tests/test_data_writers.py @@ -128,7 +128,6 @@ def test_write_header(): _write_header( tmp, problem_name, - extension=".csv", comment="Hello", regression=True, ) diff --git a/aeon/datasets/tests/test_dataset_collections.py b/aeon/datasets/tests/test_dataset_collections.py index 624870ab5e..bb185fac14 100644 --- a/aeon/datasets/tests/test_dataset_collections.py +++ b/aeon/datasets/tests/test_dataset_collections.py @@ -69,7 +69,7 @@ def test_list_available_tser_datasets(): def test_list_available_tsf_datasets(): """Test recovering lists of available data sets.""" res = get_available_tsf_datasets() - assert len(res) == 53 + assert len(res) == 62 res = get_available_tsf_datasets("FOO") assert not res res = get_available_tsf_datasets("m1_monthly_dataset") diff --git a/aeon/datasets/tsad_datasets.py b/aeon/datasets/tsad_datasets.py index 4372772dc5..8f10af3eaf 100644 --- a/aeon/datasets/tsad_datasets.py +++ b/aeon/datasets/tsad_datasets.py @@ -67,7 +67,7 @@ def tsad_collections() -> dict[str, list[str]]: df = _load_indexfile() return ( df.groupby("collection_name") - .apply(lambda x: x["dataset_name"].to_list(), include_groups=False) + .apply(lambda x: x["dataset_name"].to_list()) .to_dict() ) diff --git a/aeon/datasets/tsf_datasets.py b/aeon/datasets/tsf_datasets.py index b5c008c3dd..562f9ad5ae 100644 --- a/aeon/datasets/tsf_datasets.py +++ b/aeon/datasets/tsf_datasets.py @@ -54,4 +54,17 @@ "australian_electricity_demand_dataset": 4659727, "covid_mobility_dataset_with_missing_values": 4663762, "covid_mobility_dataset_without_missing_values": 4663809, + "bitcoin_dataset_with_missing_values": 5121965, + "bitcoin_dataset_without_missing_values": 5122101, + "rideshare_dataset_with_missing_values": 5122114, + "rideshare_dataset_without_missing_values": 5122232, + "vehicle_trips_dataset_with_missing_values": 5122535, + "vehicle_trips_dataset_without_missing_values": 5122537, + "temperature_rain_dataset_with_missing_values": 5129073, + "temperature_rain_dataset_without_missing_values": 5129091, + "oikolab_weather_dataset": 5184708, + # These datasets generate HTTP Error 404: NOT FOUND errors + # "extended_wikipedia_web_traffic_daily_dataset_with_missing_values": 7370977, + # "extended_wikipedia_web_traffic_daily_dataset_without_missing_values": 7371038, + # "residential_power_and_battery_data": 8219786, } diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index de203a0bcd..7d39be08e3 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -1,13 +1,19 @@ """Forecasters.""" __all__ = [ + "ARIMAForecaster", "DummyForecaster", "BaseForecaster", "RegressionForecaster", "ETSForecaster", + "AutoETSForecaster", + "NaiveForecaster", ] +from aeon.forecasting._arima import ARIMAForecaster +from aeon.forecasting._autoets import AutoETSForecaster from aeon.forecasting._dummy import DummyForecaster -from aeon.forecasting._ets import ETSForecaster +from aeon.forecasting._ets_fast import ETSForecaster +from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster from aeon.forecasting.base import BaseForecaster diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py new file mode 100644 index 0000000000..4de0fee3d3 --- /dev/null +++ b/aeon/forecasting/_arima.py @@ -0,0 +1,421 @@ +"""ARIMAForecaster class. + +An implementation of the arima statistics forecasting algorithm. + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["ARIMAForecaster"] + +from math import comb + +import numpy as np + +from aeon.forecasting._utils import calc_seasonal_period, kpss_test +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + + +class ARIMAForecaster(BaseForecaster): + """ARIMA forecaster. + + An implementation of the Hyndman-Khandakar Auto ARIMA forecasting algorithm[1]_. + Adjusted to add basic seasonal ARIMA. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + """ + + def __init__(self, horizon=1): + super().__init__(horizon=horizon, axis=1) + self.data_ = [] + self.differenced_data_ = [] + self.residuals_ = [] + self.aic_ = 0 + self.p_ = 0 + self.d_ = 0 + self.q_ = 0 + self.ps_ = 0 + self.ds_ = 0 + self.qs_ = 0 + self.seasonal_period_ = 0 + self.constant_term_ = 0 + self.c_ = 0 + self.phi_ = 0 + self.phi_s_ = 0 + self.theta_ = 0 + self.theta_s_ = 0 + + def _fit(self, y, exog=None): + """Fit AutoARIMA forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ARIMAForecaster. + """ + self.data_ = np.array(y.squeeze(), dtype=np.float64) + ( + self.differenced_data_, + self.aic_, + self.p_, + self.d_, + self.q_, + self.ps_, + self.ds_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + parameters, + ) = auto_arima(self.data_) + (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = extract_params( + parameters, self.p_, self.q_, self.ps_, self.qs_, self.constant_term_ + ) + ( + self.aic_, + self.residuals_, + ) = arima_log_likelihood( + parameters, + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + y = np.array(y, dtype=np.float64) + value = calc_arima( + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + len(self.differenced_data_), + self.c_, + self.phi_, + self.phi_s_, + self.theta_, + self.theta_s_, + self.residuals_, + ) + history = self.data_[::-1] + differenced_history = np.diff(self.data_, n=self.d_)[::-1] + # Step 1: undo seasonal differencing on y^(d) + for k in range(1, self.ds_ + 1): + lag = k * self.seasonal_period_ + value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] + + # Step 2: undo ordinary differencing + for k in range(1, self.d_ + 1): + value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] + + if y is None: + return np.array([value]) + else: + return np.insert(y, 0, value)[:-1] + + +# Define the ARIMA(p, d, q) likelihood function +def arima_log_likelihood( + params, data, p, q, ps, qs, seasonal_period, include_constant_term +): + """Calculate the log-likelihood of an ARIMA model given the parameters.""" + c, phi, phi_s, theta, theta_s = extract_params( + params, p, q, ps, qs, include_constant_term + ) # Extract parameters + + # Initialize residuals + n = len(data) + residuals = np.zeros(n) + for t in range(n): + y_hat = calc_arima( + data, + p, + q, + ps, + qs, + seasonal_period, + t, + c, + phi, + phi_s, + theta, + theta_s, + residuals, + ) + residuals[t] = data[t] - y_hat + # Calculate the log-likelihood + variance = np.mean(residuals**2) + liklihood = n * (np.log(2 * np.pi) + np.log(variance) + 1) + k = len(params) + aic = liklihood + 2 * k + return ( + aic, + residuals, + ) # Return negative log-likelihood for minimization + + +def extract_params(params, p, q, ps, qs, include_constant_term): + """Extract ARIMA parameters from the parameter vector.""" + # Extract parameters + c = params[0] if include_constant_term else 0 # Constant term + # AR coefficients + phi = params[include_constant_term : p + include_constant_term] + # Seasonal AR coefficients + phi_s = params[include_constant_term + p : p + ps + include_constant_term] + # MA coefficients + theta = params[include_constant_term + p + ps : p + ps + q + include_constant_term] + # Seasonal MA coefficents + theta_s = params[ + include_constant_term + p + ps + q : include_constant_term + p + ps + q + qs + ] + return c, phi, phi_s, theta, theta_s + + +def calc_arima( + data, p, q, ps, qs, seasonal_period, t, c, phi, phi_s, theta, theta_s, residuals +): + """Calculate the ARIMA forecast for time t.""" + # AR part + ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) + # Seasonal AR part + ars_term = ( + 0 + if (t - seasonal_period * ps) < 0 + else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) + ) + # MA part + ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) + # Seasonal MA part + mas_term = ( + 0 + if (t - seasonal_period * qs) < 0 + else np.dot( + theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] + ) + ) + y_hat = c + ar_term + ma_term + ars_term + mas_term + return y_hat + + +def nelder_mead( + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + tol=1e-6, + max_iter=500, +): + """Implement the nelder-mead optimisation algorithm.""" + num_params = include_constant_term + p + ps + q + qs + points = np.full((num_params + 1, num_params), 0.5) + for i in range(num_params): + points[i + 1][i] = 0.6 + values = np.array( + [ + arima_log_likelihood( + v, data, p, q, ps, qs, seasonal_period, include_constant_term + )[0] + for v in points + ] + ) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = arima_log_likelihood( + reflected_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if between best and second best, use reflected value + if len(values) > 1 and values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = arima_log_likelihood( + expanded_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = arima_log_likelihood( + contracted_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = arima_log_likelihood( + points[i], + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] + + +# def calc_moving_variance(data, window): +# X = np.lib.stride_tricks.sliding_window_view(data, window_shape=window) +# return X.var() + + +def auto_arima(data): + """ + Implement the Hyndman-Khandakar algorithm. + + For automatic ARIMA model selection. + """ + seasonal_period = calc_seasonal_period(data) + difference = 0 + while not kpss_test(data)[1]: + data = np.diff(data, n=1) + difference += 1 + seasonal_difference = 1 if seasonal_period > 1 else 0 + if seasonal_difference: + data = data[seasonal_period:] - data[:-seasonal_period] + include_c = 1 if difference == 0 else 0 + model_parameters = [ + [2, 2, 0, 0, include_c], + [0, 0, 0, 0, include_c], + [1, 0, 0, 0, include_c], + [0, 1, 0, 0, include_c], + ] + model_points = [] + for p in model_parameters: + points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) + p.append(aic) + model_points.append(points) + current_model = max(model_parameters, key=lambda item: item[5]) + current_points = model_points[model_parameters.index(current_model)] + while True: + better_model = False + for param_no in range(4): + for adjustment in [-1, 1]: + if (current_model[param_no] + adjustment) < 0: + continue + model = current_model.copy() + model[param_no] += adjustment + for constant_term in [0, 1]: + points, aic = nelder_mead( + data, + model[0], + model[1], + model[2], + model[3], + seasonal_period, + constant_term, + ) + if aic < current_model[5]: + current_model = model + current_points = points + current_model[5] = aic + current_model[4] = constant_term + better_model = True + if not better_model: + break + return ( + data, + current_model[5], + current_model[0], + difference, + current_model[1], + current_model[2], + seasonal_difference, + current_model[3], + seasonal_period, + current_model[4], + current_points, + ) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py new file mode 100644 index 0000000000..7501bee0e2 --- /dev/null +++ b/aeon/forecasting/_autoets.py @@ -0,0 +1,457 @@ +"""AutoETS class. + +Extends the ETSForecaster to automatically calculate the smoothing parameters + +""" + +__maintainer__ = [] +__all__ = ["AutoETSForecaster"] +import numpy as np +from numba import njit +from scipy.optimize import minimize + +from aeon.forecasting._autoets_gradient_params import _calc_model_liklihood +from aeon.forecasting._ets_fast import _fit, _predict +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + + +class AutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster. + + An implementation of the exponential smoothing statistics forecasting algorithm. + Chooses betweek additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + + Examples + -------- + >>> from aeon.forecasting import AutoETSForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = AutoETSForecaster() + >>> forecaster.fit(y) + AutoETSForecaster() + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + method="internal_nelder_mead", + horizon=1, + ): + self.method = method + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self.alpha_ = 0 + self.beta_ = 0 + self.gamma_ = 0 + self.phi_ = 0 + self.error_type_ = 0 + self.trend_type_ = 0 + self.seasonality_type_ = 0 + self.seasonal_period_ = 0 + self.n_timepoints_ = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 + self.residuals_ = [] + self.fitted_values_ = [] + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + ( + self.error_type_, + self.trend_type_, + self.seasonality_type_, + self.seasonal_period_, + self.alpha_, + self.beta_, + self.gamma_, + self.phi_, + ) = auto_ets(data, self.method) + ( + self.level_, + self.trend_, + self.seasonality_, + self.n_timepoints_, + self.residuals_, + self.fitted_values_, + self.avg_mean_sq_err_, + self.liklihood_, + self.k_, + self.aic_, + ) = _fit( + data, + self.error_type_, + self.trend_type_, + self.seasonality_type_, + self.seasonal_period_, + self.alpha_, + self.beta_, + self.gamma_, + self.phi_, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = _predict( + self.trend_type_, + self.seasonality_type_, + self.level_, + self.trend_, + self.seasonality_, + self.phi_, + self.horizon, + self.n_timepoints_, + self.seasonal_period_, + ) + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + +def auto_ets(data, method="internal_nelder_mead"): + """Return the best ETS model based on the supplied data, and optimisation method.""" + if method == "internal_nelder_mead": + return auto_ets_nelder_mead(data) + elif method == "internal_gradient": + return auto_ets_gradient(data) + else: + return auto_ets_scipy(data, method) + + +def auto_ets_scipy(data, method): + """Calculate ETS model parameters based on scipy optimisation functions.""" + seasonal_period = calc_seasonal_period(data) + lowest_liklihood = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + optimise_result = optimise_params_scipy( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + method, + ) + alpha, beta, gamma = optimise_result.x + liklihood_ = optimise_result.fun + phi = 0.98 + if lowest_liklihood == -1 or lowest_liklihood > liklihood_: + lowest_liklihood = liklihood_ + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +def auto_ets_gradient(data): + """ + Calc model params using pytorch. + + Calculate ETS model parameters based on the + internal gradient-based approach using pytorch. + """ + seasonal_period = calc_seasonal_period(data) + lowest_liklihood = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + (alpha, beta, gamma, phi, _residuals, liklihood_) = ( + _calc_model_liklihood( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + ) + if lowest_liklihood == -1 or lowest_liklihood > liklihood_: + lowest_liklihood = liklihood_ + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +@njit(nogil=NOGIL, cache=CACHE) +def auto_ets_nelder_mead(data): + """Calculate model parameters based on the internal nelder-mead implementation.""" + seasonal_period = calc_seasonal_period(data) + lowest_aic = -1 + best_model = None + for error_type in range(1, 3): + for trend_type in range(0, 3): + for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): + ([alpha, beta, gamma, phi], aic) = nelder_mead( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + if trend_type == 0: + phi = 1 + if lowest_aic == -1 or lowest_aic > aic: + lowest_aic = aic + best_model = ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return best_model + + +def optimise_params_scipy( + data, error_type, trend_type, seasonality_type, seasonal_period, method +): + """Optimise the ETS model parameters using the scipy algorithms.""" + + def run_ets_scipy(parameters): + alpha, beta, gamma, phi = parameters + if not ( + 0 <= alpha <= 1 and 0 <= beta <= 1 and 0 <= gamma <= 1 and 0 <= phi <= 1 + ): + return float("inf") + ( + _level, + _trend, + _seasonality, + _n_timepoints, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + _k, + aic_, + ) = _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return aic_ + + initial_points = [0.5, 0.5, 0.5, 0.5] + return minimize( + run_ets_scipy, initial_points, bounds=[[0, 1] for i in range(3)], method=method + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def run_ets( + parameters, data, error_type, trend_type, seasonality_type, seasonal_period +): + """Create and fit an ETS model and return the liklihood.""" + alpha, beta, gamma, phi = parameters + if not ( + 0 <= alpha <= 1 + and 0 <= beta <= 1 + and 0 <= gamma <= 1 + and 0.8 <= phi <= 1 + and ( + data.min() > 0 + or (error_type != 2 and trend_type != 2 and seasonality_type != 2) + ) + ): + return np.finfo(np.float64).max + ( + _level, + _trend, + _seasonality, + _n_timepoints, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + _k, + aic_, + ) = _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + return aic_ + + +@njit(nogil=NOGIL, cache=CACHE) +def nelder_mead( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + tol=1e-6, + max_iter=500, +): + """Implement the nelder-mead optimisation algorithm.""" + points = np.array( + [ + [0.5, 0.5, 0.5, 0.9], + [0.6, 0.5, 0.5, 0.9], + [0.5, 0.6, 0.5, 0.9], + [0.5, 0.5, 0.6, 0.9], + [0.5, 0.5, 0.5, 0.95], + ] + ) + values = np.array( + [ + run_ets(v, data, error_type, trend_type, seasonality_type, seasonal_period) + for v in points + ] + ) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = run_ets( + reflected_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # if between best and second best, use reflected value + if values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = run_ets( + expanded_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = run_ets( + contracted_point, + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = run_ets( + points[i], + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + ) + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] diff --git a/aeon/forecasting/_autoets_gradient_params.py b/aeon/forecasting/_autoets_gradient_params.py new file mode 100644 index 0000000000..119211a29a --- /dev/null +++ b/aeon/forecasting/_autoets_gradient_params.py @@ -0,0 +1,297 @@ +"""AutoETSForecaster class. + +Extends the ETSForecaster to automatically calculate the smoothing parameters + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = [] + +import torch + +from aeon.forecasting._ets_fast import ADDITIVE, MULTIPLICATIVE, NONE, ETSForecaster + + +def _calc_model_liklihood( + data, error_type, trend_type, seasonality_type, seasonal_period +): + alpha, beta, gamma, phi = _optimise_parameters( + data, error_type, trend_type, seasonality_type, seasonal_period + ) + forecaster = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + 1, + ) + forecaster.fit(data) + return alpha, beta, gamma, phi, forecaster.residuals_, forecaster.liklihood_ + + +def _optimise_parameters( + data, error_type, trend_type, seasonality_type, seasonal_period +): + torch.autograd.set_detect_anomaly(True) + data = torch.tensor(data) + n_timepoints = len(data) + if seasonality_type == 0: + seasonal_period = 1 + level, trend, seasonality = _initialise( + trend_type, seasonality_type, seasonal_period, data + ) + alpha = torch.tensor(0.1, requires_grad=True) # Level smoothing + parameters = [alpha] + if trend_type == NONE: + beta = torch.tensor(0) # Trend smoothing + else: + beta = torch.tensor(0.05, requires_grad=True) # Trend smoothing + parameters.append(beta) + if seasonality_type == NONE: + gamma = torch.tensor(0) # Trend smoothing + else: + gamma = torch.tensor(0.05, requires_grad=True) # Seasonality smoothing + parameters.append(gamma) + phi = torch.tensor(0.98, requires_grad=True) # Damping factor + batch_size = len(data) # seasonal_period * 2 + num_batches = len(data) // batch_size + # residuals_ = torch.zeros(n_timepoints) # 1 Less residual than data points + optimizer = torch.optim.SGD([alpha, beta, gamma, phi], lr=0.01) + for _epoch in range(10): # number of epochs + for i in range(0, num_batches): + batch_of_data = data[i * batch_size : (i + 1) * batch_size] + liklihood_ = torch.tensor(0, dtype=torch.float64) + mul_liklihood_pt2 = torch.tensor(0, dtype=torch.float64) + for t, data_item in enumerate(batch_of_data): + # Calculate level, trend, and seasonal components + fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( + _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[t % seasonal_period], + data_item, + alpha, + beta, + gamma, + phi, + ) + ) + liklihood_ += error * error + mul_liklihood_pt2 += torch.log(torch.abs(fitted_value)) + liklihood_ = (n_timepoints - seasonal_period) * torch.log(liklihood_) + if error_type == MULTIPLICATIVE: + liklihood_ += 2 * mul_liklihood_pt2 + liklihood_.backward() + optimizer.step() + optimizer.zero_grad() + # Impose sensible parameter limits + alpha = alpha.clone().detach().requires_grad_().clamp(0, 1) + if trend_type != NONE: + # Impose sensible parameter limits + beta = beta.clone().detach().requires_grad_().clamp(0, 1) + if seasonality_type != NONE: + # Impose sensible parameter limits + gamma = gamma.clone().detach().requires_grad_().clamp(0, 1) + # Impose sensible parameter limits + phi = phi.clone().detach().requires_grad_().clamp(0.1, 0.98) + level = level.clone().detach() + trend = trend.clone().detach() + seasonality = seasonality.clone().detach() + return alpha.item(), beta.item(), gamma.item(), phi.item() + + +def _predict( + trend_type, + seasonality_type, + level, + trend, + seasonality, + phi, + horizon, + n_timepoints, + seasonal_period, +): + # Generate forecasts based on the final values of level, trend, and seasonals + if phi == 1: # No damping case + phi_h = float(horizon) + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = phi * (1 - phi**horizon) / (1 - phi) + seasonal_index = (n_timepoints + horizon) % seasonal_period + return _predict_value( + trend_type, seasonality_type, level, trend, seasonality[seasonal_index], phi_h + )[0] + + +def _initialise(trend_type, seasonality_type, seasonal_period, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + level = torch.mean(data[:seasonal_period]) + # Initial Trend + if trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + trend = torch.mean( + data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] + ) + elif trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + trend = torch.mean( + data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] + ) + else: + # No trend + trend = torch.tensor(0) + # Initial Seasonality + if seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + seasonality = data[:seasonal_period] - level + elif seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + seasonality = data[:seasonal_period] / level + else: + # No seasonality + seasonality = torch.zeros(1) + return level, trend, seasonality + + +def _update_states( + error_type, + trend_type, + seasonality_type, + curr_level, + curr_trend, + curr_seasonality, + data_item: int, + alpha, + beta, + gamma, + phi, +): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + fitted_value, damped_trend, trend_level_combination = _predict_value( + trend_type, seasonality_type, curr_level, curr_trend, curr_seasonality, phi + ) + # Calculate the error term (observed value - fitted value) + if error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if error_type == MULTIPLICATIVE: + level = trend_level_combination.clone() * (1 + alpha.clone() * error.clone()) + trend = damped_trend.clone() * (1 + beta.clone() * error.clone()) + seasonality = curr_seasonality.clone() * (1 + gamma.clone() * error.clone()) + if seasonality_type == ADDITIVE: + # Add seasonality correction + level += alpha.clone() * error.clone() * curr_seasonality.clone() + seasonality += ( + gamma.clone() * error.clone() * trend_level_combination.clone() + ) + if trend_type == ADDITIVE: + trend += ( + (curr_level.clone() + curr_seasonality.clone()) + * beta.clone() + * error.clone() + ) + else: + trend += ( + (curr_seasonality.clone() / curr_level.clone()) + * beta.clone() + * error.clone() + ) + elif trend_type == ADDITIVE: + trend += curr_level.clone() * beta.clone() * error.clone() + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= curr_seasonality.clone() + trend_correction *= curr_seasonality.clone() + seasonality_correction *= trend_level_combination.clone() + if trend_type == MULTIPLICATIVE: + trend_correction *= curr_level.clone() + level = ( + trend_level_combination.clone() + + alpha.clone() * error.clone() / level_correction + ) + trend = damped_trend.clone() + beta.clone() * error.clone() / trend_correction + seasonality = ( + curr_seasonality.clone() + + gamma.clone() * error.clone() / seasonality_correction + ) + return (fitted_value, error, level, trend, seasonality) + + +def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): + """ + + Generate various useful values, including the next fitted value. + + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model + + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if trend_type == MULTIPLICATIVE: + damped_trend = trend.clone() ** phi.clone() + trend_level_combination = level.clone() * damped_trend.clone() + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend.clone() * phi.clone() + trend_level_combination = level.clone() + damped_trend.clone() + # Calculate forecast (fitted value) based on the current components + if seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination.clone() * seasonality.clone() + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination.clone() + seasonality.clone() + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_compare_external_autoets.py b/aeon/forecasting/_compare_external_autoets.py new file mode 100644 index 0000000000..b57f67a874 --- /dev/null +++ b/aeon/forecasting/_compare_external_autoets.py @@ -0,0 +1,207 @@ +"""Test Other Packages AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import math +import time + +import matplotlib.pyplot as plt +from sktime.forecasting.ets import AutoETS as sktime_AutoETS +from statsforecast.models import AutoETS as sf_AutoETS +from statsforecast.utils import AirPassengers as ap +from statsforecast.utils import AirPassengersDF +from statsmodels.tsa.exponential_smoothing.ets import ETSModel + +from aeon.forecasting._autoets import auto_ets +from aeon.forecasting._ets_fast import ETSForecaster + +plt.rcParams["figure.figsize"] = (12, 8) + + +def test_other_forecasters(): + """TestOtherForecasters.""" + plt.plot(AirPassengersDF.ds, AirPassengersDF.y, label="Actual Values", color="blue") + # Statsmodels + start = time.perf_counter() + statsmodels_model = ETSModel( + ap, + error="mul", + trend=None, + damped_trend=False, + seasonal="mul", + seasonal_periods=12, + ) + statsmodels_fit = statsmodels_model.fit(maxiter=10000) + end = time.perf_counter() + statsmodels_time = end - start + print( # noqa + f"Statsmodels: Alpha: {statsmodels_fit.alpha}, \ + Beta: statsmodels_fit.beta, gamma: {statsmodels_fit.gamma}, \ + phi: statsmodels_fit.phi" + ) + print(f"Statsmodels AIC: {statsmodels_fit.aic}") # noqa + sm_internal_model = ETSForecaster( + 2, 0, 2, 12, statsmodels_fit.alpha, 0, statsmodels_fit.gamma, 1 + ) + sm_internal_model.fit(ap) + print(f"Statsmodels AIC: {sm_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + statsmodels_fit.fittedvalues, + label="statsmodels fit", + color="green", + ) + # Sktime + start = time.perf_counter() + sktime_model = sktime_AutoETS(auto=True, sp=12) + sktime_model.fit(ap) + end = time.perf_counter() + sktime_time = end - start + # pylint: disable=W0212 + print( # noqa + f"Sktime: Alpha: {sktime_model._fitted_forecaster.alpha}, \ + Beta: {sktime_model._fitted_forecaster.beta}, \ + gamma: {sktime_model._fitted_forecaster.gamma}, \ + phi: sktime_model._fitted_forecaster.phi" + ) + + if sktime_model._fitted_forecaster.error == "add": + sk_error = 1 + elif sktime_model._fitted_forecaster.error == "mul": + sk_error = 2 + else: + sk_error = 0 + if sktime_model._fitted_forecaster.trend == "add": + sk_trend = 1 + elif sktime_model._fitted_forecaster.trend == "mul": + sk_trend = 2 + else: + sk_trend = 0 + if sktime_model._fitted_forecaster.seasonal == "add": + sk_seasonal = 1 + elif sktime_model._fitted_forecaster.seasonal == "mul": + sk_seasonal = 2 + else: + sk_seasonal = 0 + print( # noqa + f"Error Type: {sk_error}, Trend Type: {sk_trend}, \ + Seasonality Type: {sk_seasonal}, Seasonal Period: {12}" + ) + print(f"Sktime AIC: {sktime_model._fitted_forecaster.aic}") # noqa + sk_internal_model = ETSForecaster( + sk_error, + sk_trend, + sk_seasonal, + 12, + sktime_model._fitted_forecaster.alpha, + sktime_model._fitted_forecaster.beta, + sktime_model._fitted_forecaster.gamma, + 1, + ) + sk_internal_model.fit(ap) + print(f"Sktime AIC: {sk_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + sktime_model._fitted_forecaster.fittedvalues, + label="sktime fitted values", + color="red", + ) + # pylint: enable=W0212 + # internal + start = time.perf_counter() + ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) = auto_ets(ap) + internal_model = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) + internal_model.fit(ap) + end = time.perf_counter() + internal_time = end - start + print( # noqa + f"Internal: Alpha: {internal_model.alpha}, Beta: {internal_model.beta}, \ + gamma: {internal_model.gamma}, phi: {internal_model.phi}" + ) + print( # noqa + f"Error Type: {internal_model.error_type}, \ + Trend Type: {internal_model.trend_type}, \ + Seasonality Type: {internal_model.seasonality_type}, \ + Seasonal Period: {internal_model.seasonal_period}" + ) + print(f"Internal AIC: {internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds[seasonal_period:], + internal_model.fitted_values_, + label="Internal fitted values", + color="black", + ) + # statsforecast + start = time.perf_counter() + sf_model = sf_AutoETS(season_length=12) + sf_model.fit(ap) + end = time.perf_counter() + statsforecast_time = end - start + print( # noqa + f"Statsforecast: Alpha: {sf_model.model_['par'][0]}, \ + Beta: {sf_model.model_['par'][1]}, gamma: {sf_model.model_['par'][2]}, \ + phi: {sf_model.model_['par'][3]}" + ) + print( # noqa + f"Statsforecast Model Type: {sf_model.model_['method']}, \ + AIC: {sf_model.model_['aic']}" + ) + sf_internal_model = ETSForecaster( + 2 if sf_model.model_["components"][0] == "M" else 1, + ( + 2 + if sf_model.model_["components"][1] == "M" + else 1 if sf_model.model_["components"][1] == "A" else 0 + ), + ( + 2 + if sf_model.model_["components"][2] == "M" + else 1 if sf_model.model_["components"][2] == "A" else 0 + ), + 12, + 0 if math.isnan(sf_model.model_["par"][0]) else sf_model.model_["par"][0], + 0 if math.isnan(sf_model.model_["par"][1]) else sf_model.model_["par"][1], + 0 if math.isnan(sf_model.model_["par"][2]) else sf_model.model_["par"][2], + 0 if math.isnan(sf_model.model_["par"][3]) else sf_model.model_["par"][3], + ) + sf_internal_model.fit(ap) + print(f"Statsforecast AIC: {sf_internal_model.aic_}") # noqa + plt.plot( + AirPassengersDF.ds, + sf_model.model_["fitted"], + label="statsforecast fitted values", + color="orange", + ) + print( # noqa + f"Statsmodels Time: {statsmodels_time}\ + Sktime Time: {sktime_time}\ + Internal Time: {internal_time}\ + Statsforecast Time: {statsforecast_time}" + ) # noqa + plt.ylabel("Air Passenger Numbers") + plt.grid() + plt.legend() + plt.show() + + +if __name__ == "__main__": + test_other_forecasters() diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index efc99d6d47..ac7f31a58d 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -3,20 +3,20 @@ An implementation of the exponential smoothing statistics forecasting algorithm. Implements additive and multiplicative error models, None, additive and multiplicative (including damped) trend and -None, additive and multiplicative seasonality +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + """ __maintainer__ = [] -__all__ = ["ETSForecaster", "NONE", "ADDITIVE", "MULTIPLICATIVE"] +__all__ = ["ETSForecaster"] import numpy as np -from numba import njit from aeon.forecasting.base import BaseForecaster -NOGIL = False -CACHE = True - NONE = 0 ADDITIVE = 1 MULTIPLICATIVE = 2 @@ -25,44 +25,31 @@ class ETSForecaster(BaseForecaster): """Exponential Smoothing forecaster. - An implementation of the exponential smoothing forecasting algorithm. - Implements additive and multiplicative error models, None, additive and - multiplicative (including damped) trend and None, additive and mutliplicative - seasonality. See [1]_ for a description. + An implementation of the exponential smoothing statistics forecasting algorithm. + Implements additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. Parameters ---------- - error_type : int, default = 1 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - trend_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - seasonality_type : int, default = 0 - Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2). - seasonal_period : int, default=1 - Length of seasonality period. If seasonality_type is NONE, this is assumed to - be 1 alpha : float, default = 0.1 Level smoothing parameter. beta : float, default = 0.01 - Trend smoothing parameter. If trend_type is NONE, this is assumed to be 0.0. + Trend smoothing parameter. gamma : float, default = 0.01 - Seasonal smoothing parameter. If seasonality is NONE, this is assumed to be - 0.0. + Seasonal smoothing parameter. phi : float, default = 0.99 Trend damping smoothing parameters horizon : int, default = 1 The horizon to forecast to. - - Attributes - ---------- - mean_sq_err_ : float - Mean squared error. - likelihood_ : float - Likelihood of the fitted model based on residuals. - residuals_ : arraylike - List of train set differences between fitted and actual values. - n_timpoints_ : int - Length of the series passed to fit. + error_type : int + The type of error model; either Additive(1) or Multiplicative(2) + trend_type : int + The type of trend model; one of None(0), additive(1) or multiplicative(2). + seasonality_type : int + The type of seasonality model; one of None(0), additive(1) or multiplicative(2). + seasonal_period : int + The period of the seasonality (m) (e.g., for quaterly data seasonal_period = 4). References ---------- @@ -74,11 +61,13 @@ class ETSForecaster(BaseForecaster): >>> from aeon.forecasting import ETSForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1) + >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8) + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + seasonality_type=2, trend_type=2) >>> forecaster.predict() - 449.9435566831507 + 366.90200486015596 """ def __init__( @@ -92,19 +81,37 @@ def __init__( gamma: float = 0.01, phi: float = 0.99, horizon: int = 1, + error_type: int = ADDITIVE, + trend_type: int = NONE, + seasonality_type: int = NONE, + seasonal_period: int = 1, + alpha: float = 0.1, + beta: float = 0.01, + gamma: float = 0.01, + phi: float = 0.99, + horizon: int = 1, ): - self.error_type = error_type - self.trend_type = trend_type - self.seasonality_type = seasonality_type - self.seasonal_period = seasonal_period self.alpha = alpha self.beta = beta self.gamma = gamma self.phi = phi - self.mean_sq_err_ = 0 - self.likelihood_ = 0 + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self._beta = beta + self._gamma = gamma + self.error_type = error_type + self.trend_type = trend_type + self.seasonality_type = seasonality_type + self.seasonal_period = seasonal_period + self._seasonal_period = seasonal_period + self.n_timepoints = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 self.residuals_ = [] - self.n_timpoints_ = 0 super().__init__(horizon=horizon, axis=1) def _fit(self, y, exog=None): @@ -124,39 +131,153 @@ def _fit(self, y, exog=None): self Fitted BaseForecaster. """ - self.n_timepoints_ = len(y) - if self.error_type != MULTIPLICATIVE and self.error_type != ADDITIVE: - raise ValueError("Error must be either additive or multiplicative") - self._seasonal_period = self.seasonal_period - if self.seasonal_period < 1 or self.seasonality_type == NONE: + assert ( + self.error_type != NONE + ), "Error must be either additive or multiplicative" + if self._seasonal_period < 1 or self.seasonality_type == NONE: self._seasonal_period = 1 - self._beta = self.beta - if self.trend_type == NONE or self.trend_type is None: - self._beta = 0 - self._gamma = self.gamma - if self.seasonality_type == NONE or self.trend_type is None: - self._gamma = 0 - data = np.array(y.squeeze(), dtype=np.float64) - ( - self._level, - self._trend, - self._seasonality, - self.residuals_, - self.mean_sq_err_, - self.likelihood_, - ) = _fit_numba( - data, - self.error_type, - self.trend_type, - self.seasonality_type, - self._seasonal_period, - self.alpha, - self._beta, - self._gamma, - self.phi, + if self.trend_type == NONE: + self._beta = ( + 0 # Required for the equations in _update_states to work correctly + ) + if self.seasonality_type == NONE: + self._gamma = ( + 0 # Required for the equations in _update_states to work correctly + ) + data = y.squeeze() + self.n_timepoints = len(data) + self._initialise(data) + num_vals = self.n_timepoints - self._seasonal_period + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + # 1 Less residual than data points + self.residuals_ = np.zeros(num_vals) + for t, data_item in enumerate(data[self._seasonal_period :]): + # Calculate level, trend, and seasonal components + fitted_value, error = self._update_states( + data_item, t % self._seasonal_period + ) + self.residuals_[t] = error + self.avg_mean_sq_err_ += (data_item - fitted_value) ** 2 + liklihood_error = error + if self.error_type == MULTIPLICATIVE: + liklihood_error *= fitted_value + self.liklihood_ += liklihood_error**2 + self.avg_mean_sq_err_ /= num_vals + self.liklihood_ = num_vals * np.log(self.liklihood_) + self.k_ = ( + self.seasonal_period * (self.seasonality_type != 0) + + 2 * (self.trend_type != 0) + + 2 + + 1 * (self.phi != 1) ) + self.aic_ = self.liklihood_ + 2 * self.k_ - num_vals * np.log(num_vals) return self + def _update_states(self, data_item, seasonal_index): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + level = self.level_ + trend = self.trend_ + seasonality = self.seasonality_[seasonal_index] + fitted_value, damped_trend, trend_level_combination = self._predict_value( + level, trend, seasonality, self.phi + ) + # Calculate the error term (observed value - fitted value) + if self.error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if self.error_type == MULTIPLICATIVE: + self.level_ = trend_level_combination * (1 + self.alpha * error) + self.trend_ = damped_trend * (1 + self._beta * error) + self.seasonality_[seasonal_index] = seasonality * (1 + self._gamma * error) + if self.seasonality_type == ADDITIVE: + self.level_ += ( + self.alpha * error * seasonality + ) # Add seasonality correction + self.seasonality_[seasonal_index] += ( + self._gamma * error * trend_level_combination + ) + if self.trend_type == ADDITIVE: + self.trend_ += (level + seasonality) * self._beta * error + else: + self.trend_ += seasonality / level * self._beta * error + elif self.trend_type == ADDITIVE: + self.trend_ += level * self._beta * error + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if self.seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= seasonality + trend_correction *= seasonality + seasonality_correction *= trend_level_combination + if self.trend_type == MULTIPLICATIVE: + trend_correction *= level + self.level_ = ( + trend_level_combination + self.alpha * error / level_correction + ) + self.trend_ = damped_trend + self._beta * error / trend_correction + self.seasonality_[seasonal_index] = ( + seasonality + self._gamma * error / seasonality_correction + ) + return (fitted_value, error) + + def _initialise(self, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + self.level_ = np.mean(data[: self._seasonal_period]) + # Initial Trend + if self.trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + self.trend_ = np.mean( + data[self._seasonal_period : 2 * self._seasonal_period] + - data[: self._seasonal_period] + ) + elif self.trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + self.trend_ = np.mean( + data[self._seasonal_period : 2 * self._seasonal_period] + / data[: self._seasonal_period] + ) + else: + # No trend + self.trend_ = 0 + # Initial Seasonality + if self.seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + self.seasonality_ = data[: self._seasonal_period] - self.level_ + elif self.seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + self.seasonality_ = data[: self._seasonal_period] / self.level_ + else: + # No seasonality + self.seasonality_ = [0] + def _predict(self, y=None, exog=None): """ Predict the next horizon steps ahead. @@ -166,7 +287,7 @@ def _predict(self, y=None, exog=None): y : np.ndarray, default = None A time series to predict the next horizon value for. If None, predict the next horizon value after series seen in fit. - exog : np.ndarray, default = None + exog : np.ndarray, default =None Optional exogenous time series data assumed to be aligned with y Returns @@ -174,250 +295,60 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - return _predict_numba( - self.trend_type, - self.seasonality_type, - self._level, - self._trend, - self._seasonality, - self.phi, - self.horizon, - self.n_timepoints_, - self.seasonal_period, - ) - - -@njit(nogil=NOGIL, cache=CACHE) -def _fit_numba( - data, - error_type: int, - trend_type: int, - seasonality_type: int, - seasonal_period: int, - alpha: float, - beta: float, - gamma: float, - phi: float, -): - n_timepoints = len(data) - level, trend, seasonality = _initialise( - trend_type, seasonality_type, seasonal_period, data - ) - mse = 0 - lhood = 0 - mul_likelihood_pt2 = 0 - res = np.zeros(n_timepoints) # 1 Less residual than data points - for t, data_item in enumerate(data[seasonal_period:]): - # Calculate level, trend, and seasonal components - fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( - _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality[t % seasonal_period], - data_item, - alpha, - beta, - gamma, - phi, - ) - ) - res[t] = error - mse += (data_item - fitted_value) ** 2 - lhood += error * error - mul_likelihood_pt2 += np.log(np.fabs(fitted_value)) - mse /= n_timepoints - seasonal_period - lhood = (n_timepoints - seasonal_period) * np.log(lhood) - if error_type == MULTIPLICATIVE: - lhood += 2 * mul_likelihood_pt2 - return level, trend, seasonality, res, mse, lhood - - -def _predict_numba( - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - phi: float, - horizon: int, - n_timepoints: int, - seasonal_period: int, -): - # Generate forecasts based on the final values of level, trend, and seasonals - if phi == 1: # No damping case - phi_h = float(horizon) - else: - # Geometric series formula for calculating phi + phi^2 + ... + phi^h - phi_h = phi * (1 - phi**horizon) / (1 - phi) - seasonal_index = (n_timepoints + horizon) % seasonal_period - return _predict_value( - trend_type, - seasonality_type, - level, - trend, - seasonality[seasonal_index], - phi_h, - )[0] - - -@njit(nogil=NOGIL, cache=CACHE) -def _initialise(trend_type: int, seasonality_type: int, seasonal_period: int, data): - """ - Initialize level, trend, and seasonality values for the ETS model. - - Parameters - ---------- - data : array-like - The time series data - (should contain at least two full seasons if seasonality is specified) - """ - # Initial Level: Mean of the first season - level = np.mean(data[:seasonal_period]) - # Initial Trend - if trend_type == ADDITIVE: - # Average difference between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] - ) - elif trend_type == MULTIPLICATIVE: - # Average ratio between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] - ) - else: - # No trend - trend = 0 - # Initial Seasonality - if seasonality_type == ADDITIVE: - # Seasonal component is the difference - # from the initial level for each point in the first season - seasonality = data[:seasonal_period] - level - elif seasonality_type == MULTIPLICATIVE: - # Seasonal component is the ratio of each point in the first season - # to the initial level - seasonality = data[:seasonal_period] / level - else: - # No seasonality - seasonality = np.zeros(1) - return level, trend, seasonality - - -@njit(nogil=NOGIL, cache=CACHE) -def _update_states( - error_type: int, - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - data_item: int, - alpha: float, - beta: float, - gamma: float, - phi: float, -): - """ - Update level, trend, and seasonality components. - - Using state space equations for an ETS model. - - Parameters - ---------- - data_item: float - The current value of the time series. - seasonal_index: int - The index to update the seasonal component. - """ - # Retrieve the current state values - curr_level = level - curr_seasonality = seasonality - fitted_value, damped_trend, trend_level_combination = _predict_value( - trend_type, seasonality_type, level, trend, seasonality, phi - ) - # Calculate the error term (observed value - fitted value) - if error_type == MULTIPLICATIVE: - error = data_item / fitted_value - 1 # Multiplicative error - else: - error = data_item - fitted_value # Additive error - # Update level - if error_type == MULTIPLICATIVE: - level = trend_level_combination * (1 + alpha * error) - trend = damped_trend * (1 + beta * error) - seasonality = curr_seasonality * (1 + gamma * error) - if seasonality_type == ADDITIVE: - level += alpha * error * curr_seasonality # Add seasonality correction - seasonality += gamma * error * trend_level_combination - if trend_type == ADDITIVE: - trend += (curr_level + curr_seasonality) * beta * error - else: - trend += curr_seasonality / curr_level * beta * error - elif trend_type == ADDITIVE: - trend += curr_level * beta * error - else: - level_correction = 1 - trend_correction = 1 - seasonality_correction = 1 - if seasonality_type == MULTIPLICATIVE: - # Add seasonality correction - level_correction *= curr_seasonality - trend_correction *= curr_seasonality - seasonality_correction *= trend_level_combination - if trend_type == MULTIPLICATIVE: - trend_correction *= curr_level - level = trend_level_combination + alpha * error / level_correction - trend = damped_trend + beta * error / trend_correction - seasonality = curr_seasonality + gamma * error / seasonality_correction - return (fitted_value, error, level, trend, seasonality) - - -@njit(nogil=NOGIL, cache=CACHE) -def _predict_value( - trend_type: int, - seasonality_type: int, - level: float, - trend: float, - seasonality: float, - phi: float, -): - """ + # Generate forecasts based on the final values of level, trend, and seasonals + if self.phi == 1: # No damping case + phi_h = 1 + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = self.phi * (1 - self.phi**self.horizon) / (1 - self.phi) + seasonality = self.seasonality_[ + (self.n_timepoints + self.horizon) % self._seasonal_period + ] + fitted_value = self._predict_value( + self.level_, self.trend_, seasonality, phi_h + )[0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + def _predict_value(self, level, trend, seasonality, phi): + """ - Generate various useful values, including the next fitted value. + Generate various useful values, including the next fitted value. - Parameters - ---------- - trend : float - The current trend value for the model - level : float - The current level value for the model - seasonality : float - The current seasonality value for the model - phi : float - The damping parameter for the model - - Returns - ------- - fitted_value : float - single prediction based on the current state variables. - damped_trend : float - The damping parameter combined with the trend dependant on the model type - trend_level_combination : float - Combination of the trend and level based on the model type. - """ - # Apply damping parameter and - # calculate commonly used combination of trend and level components - if trend_type == MULTIPLICATIVE: - damped_trend = trend**phi - trend_level_combination = level * damped_trend - else: # Additive trend, if no trend, then trend = 0 - damped_trend = trend * phi - trend_level_combination = level + damped_trend + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model - # Calculate forecast (fitted value) based on the current components - if seasonality_type == MULTIPLICATIVE: - fitted_value = trend_level_combination * seasonality - else: # Additive seasonality, if no seasonality, then seasonality = 0 - fitted_value = trend_level_combination + seasonality - return fitted_value, damped_trend, trend_level_combination + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if self.trend_type == MULTIPLICATIVE: + damped_trend = trend**phi + trend_level_combination = level * damped_trend + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend * phi + trend_level_combination = level + damped_trend + + # Calculate forecast (fitted value) based on the current components + if self.seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination * seasonality + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination + seasonality + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_ets_fast.py b/aeon/forecasting/_ets_fast.py new file mode 100644 index 0000000000..fdbd9c005a --- /dev/null +++ b/aeon/forecasting/_ets_fast.py @@ -0,0 +1,476 @@ +"""ETSForecaster class. + +An implementation of the exponential smoothing statistics forecasting algorithm. +Implements additive and multiplicative error models, +None, additive and multiplicative (including damped) trend and +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["ETSForecaster"] + +import numpy as np +from numba import njit + +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + +NONE = 0 +ADDITIVE = 1 +MULTIPLICATIVE = 2 + + +class ETSForecaster(BaseForecaster): + """Exponential Smoothing forecaster. + + An implementation of the exponential smoothing statistics forecasting algorithm. + Implements additive and multiplicative error models, + None, additive and multiplicative (including damped) trend and + None, additive and mutliplicative seasonality[1]_. + + Parameters + ---------- + alpha : float, default = 0.1 + Level smoothing parameter. + beta : float, default = 0.01 + Trend smoothing parameter. + gamma : float, default = 0.01 + Seasonal smoothing parameter. + phi : float, default = 0.99 + Trend damping smoothing parameters + horizon : int, default = 1 + The horizon to forecast to. + error_type : int + The type of error model; either Additive(1) or Multiplicative(2) + trend_type : int + The type of trend model; one of None(0), additive(1) or multiplicative(2). + seasonality_type : int + The type of seasonality model; one of None(0), additive(1) or multiplicative(2). + seasonal_period : int + The period of the seasonality (m) (e.g., for quaterly data seasonal_period = 4). + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. + + Examples + -------- + >>> from aeon.forecasting import ETSForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, + error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) + >>> forecaster.fit(y) + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + seasonality_type=2, trend_type=2) + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + error_type=ADDITIVE, + trend_type=NONE, + seasonality_type=NONE, + seasonal_period=1, + alpha=0.1, + beta=0.01, + gamma=0.01, + phi=0.99, + horizon=1, + ): + self.alpha = alpha + self.beta = beta + self.gamma = gamma + self.phi = phi + self.forecast_val_ = 0.0 + self.level_ = 0.0 + self.trend_ = 0.0 + self.seasonality_ = None + self._beta = beta + self._gamma = gamma + self.error_type = error_type + self.trend_type = trend_type + self.seasonality_type = seasonality_type + self.seasonal_period = seasonal_period + self._seasonal_period = seasonal_period + self.n_timepoints_ = 0 + self.avg_mean_sq_err_ = 0 + self.liklihood_ = 0 + self.k_ = 0 + self.aic_ = 0 + self.residuals_ = [] + self.fitted_values_ = [] + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ETSForecaster. + """ + assert ( + self.error_type != NONE + ), "Error must be either additive or multiplicative" + if self._seasonal_period < 1 or self.seasonality_type == NONE: + self._seasonal_period = 1 + + if self.trend_type == NONE: + # Required for the equations in _update_states to work correctly + self._beta = 0 + if self.seasonality_type == NONE: + # Required for the equations in _update_states to work correctly + self._gamma = 0 + data = y.squeeze() + ( + self.level_, + self.trend_, + self.seasonality_, + self.n_timepoints_, + self.residuals_, + self.fitted_values_, + self.avg_mean_sq_err_, + self.liklihood_, + self.k_, + self.aic_, + ) = _fit( + data, + self.error_type, + self.trend_type, + self.seasonality_type, + self._seasonal_period, + self.alpha, + self._beta, + self._gamma, + self.phi, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = _predict( + self.trend_type, + self.seasonality_type, + self.level_, + self.trend_, + self.seasonality_, + self.phi, + self.horizon, + self.n_timepoints_, + self._seasonal_period, + ) + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] + + def _initialise(self, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + self.level_, self.trend_, self.seasonality_ = _initialise( + self.trend_type, self.seasonality_type, self._seasonal_period, data + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def _fit( + data, + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, +): + assert error_type != NONE, "Error must be either additive or multiplicative" + assert ( + error_type != MULTIPLICATIVE + and trend_type != MULTIPLICATIVE + and seasonality_type != MULTIPLICATIVE + or data.min() > 0 + ), "Data must be positive with multiplicative components" + if seasonal_period < 1 or seasonality_type == NONE: + seasonal_period = 1 + if trend_type == NONE: + # Required for the equations in _update_states to work correctly + beta = 0 + if seasonality_type == NONE: + # Required for the equations in _update_states to work correctly + gamma = 0 + n_timepoints = len(data) - seasonal_period + level, trend, seasonality = _initialise( + trend_type, seasonality_type, seasonal_period, data + ) + avg_mean_sq_err_ = 0 + liklihood_ = 0 + residuals_ = np.zeros(n_timepoints) # 1 Less residual than data points + fitted_values_ = np.zeros(n_timepoints) + for t, data_item in enumerate(data[seasonal_period:]): + # Calculate level, trend, and seasonal components + fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( + _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[t % seasonal_period], + data_item, + alpha, + beta, + gamma, + phi, + ) + ) + residuals_[t] = error + fitted_values_[t] = fitted_value + avg_mean_sq_err_ += (data_item - fitted_value) ** 2 + liklihood_error = error + if error_type == MULTIPLICATIVE: + liklihood_error *= fitted_value + liklihood_ += liklihood_error**2 + avg_mean_sq_err_ /= n_timepoints + liklihood_ = n_timepoints * np.log(liklihood_) + k_ = ( + seasonal_period * (seasonality_type != 0) + + 2 * (trend_type != 0) + + 2 + + 1 * (phi != 1) + ) + aic_ = liklihood_ + 2 * k_ - n_timepoints * np.log(n_timepoints) + return ( + level, + trend, + seasonality, + n_timepoints, + residuals_, + fitted_values_, + avg_mean_sq_err_, + liklihood_, + k_, + aic_, + ) + + +@njit(nogil=NOGIL, cache=CACHE) +def _predict( + trend_type, + seasonality_type, + level, + trend, + seasonality, + phi, + horizon, + n_timepoints, + seasonal_period, +): + # Generate forecasts based on the final values of level, trend, and seasonals + if phi == 1: # No damping case + phi_h = 1 + else: + # Geometric series formula for calculating phi + phi^2 + ... + phi^h + phi_h = phi * (1 - phi**horizon) / (1 - phi) + seasonal_index = (n_timepoints + horizon) % seasonal_period + return _predict_value( + trend_type, + seasonality_type, + level, + trend, + seasonality[seasonal_index], + phi_h, + )[0] + + +@njit(nogil=NOGIL, cache=CACHE) +def _initialise(trend_type, seasonality_type, seasonal_period, data): + """ + Initialize level, trend, and seasonality values for the ETS model. + + Parameters + ---------- + data : array-like + The time series data + (should contain at least two full seasons if seasonality is specified) + """ + # Initial Level: Mean of the first season + level = np.mean(data[:seasonal_period]) + # Initial Trend + if trend_type == ADDITIVE: + # Average difference between corresponding points in the first two seasons + trend = np.mean( + data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] + ) + elif trend_type == MULTIPLICATIVE: + # Average ratio between corresponding points in the first two seasons + trend = np.mean( + data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] + ) + else: + # No trend + trend = 0 + # Initial Seasonality + if seasonality_type == ADDITIVE: + # Seasonal component is the difference + # from the initial level for each point in the first season + seasonality = data[:seasonal_period] - level + elif seasonality_type == MULTIPLICATIVE: + # Seasonal component is the ratio of each point in the first season + # to the initial level + seasonality = data[:seasonal_period] / level + else: + # No seasonality + seasonality = np.zeros(1, dtype=np.float64) + return level, trend, seasonality + + +@njit(nogil=NOGIL, cache=CACHE) +def _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality, + data_item: int, + alpha, + beta, + gamma, + phi, +): + """ + Update level, trend, and seasonality components. + + Using state space equations for an ETS model. + + Parameters + ---------- + data_item: float + The current value of the time series. + seasonal_index: int + The index to update the seasonal component. + """ + # Retrieve the current state values + curr_level = level + curr_seasonality = seasonality + fitted_value, damped_trend, trend_level_combination = _predict_value( + trend_type, seasonality_type, level, trend, seasonality, phi + ) + # Calculate the error term (observed value - fitted value) + if error_type == MULTIPLICATIVE: + error = data_item / fitted_value - 1 # Multiplicative error + else: + error = data_item - fitted_value # Additive error + # Update level + if error_type == MULTIPLICATIVE: + level = trend_level_combination * (1 + alpha * error) + trend = damped_trend * (1 + beta * error) + seasonality = curr_seasonality * (1 + gamma * error) + if seasonality_type == ADDITIVE: + level += alpha * error * curr_seasonality # Add seasonality correction + seasonality += gamma * error * trend_level_combination + if trend_type == ADDITIVE: + trend += (curr_level + curr_seasonality) * beta * error + else: + trend += curr_seasonality / curr_level * beta * error + elif trend_type == ADDITIVE: + trend += curr_level * beta * error + else: + level_correction = 1 + trend_correction = 1 + seasonality_correction = 1 + if seasonality_type == MULTIPLICATIVE: + # Add seasonality correction + level_correction *= curr_seasonality + trend_correction *= curr_seasonality + seasonality_correction *= trend_level_combination + if trend_type == MULTIPLICATIVE: + trend_correction *= curr_level + level = trend_level_combination + alpha * error / level_correction + trend = damped_trend + beta * error / trend_correction + seasonality = curr_seasonality + gamma * error / seasonality_correction + return (fitted_value, error, level, trend, seasonality) + + +@njit(nogil=NOGIL, cache=CACHE) +def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): + """ + + Generate various useful values, including the next fitted value. + + Parameters + ---------- + trend : float + The current trend value for the model + level : float + The current level value for the model + seasonality : float + The current seasonality value for the model + phi : float + The damping parameter for the model + + Returns + ------- + fitted_value : float + single prediction based on the current state variables. + damped_trend : float + The damping parameter combined with the trend dependant on the model type + trend_level_combination : float + Combination of the trend and level based on the model type. + """ + # Apply damping parameter and + # calculate commonly used combination of trend and level components + if trend_type == MULTIPLICATIVE: + damped_trend = trend**phi + trend_level_combination = level * damped_trend + else: # Additive trend, if no trend, then trend = 0 + damped_trend = trend * phi + trend_level_combination = level + damped_trend + + # Calculate forecast (fitted value) based on the current components + if seasonality_type == MULTIPLICATIVE: + fitted_value = trend_level_combination * seasonality + else: # Additive seasonality, if no seasonality, then seasonality = 0 + fitted_value = trend_level_combination + seasonality + return fitted_value, damped_trend, trend_level_combination diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py new file mode 100644 index 0000000000..9bdfa82fb9 --- /dev/null +++ b/aeon/forecasting/_naive.py @@ -0,0 +1,94 @@ +"""ETSForecaster class. + +An implementation of the exponential smoothing statistics forecasting algorithm. +Implements additive and multiplicative error models, +None, additive and multiplicative (including damped) trend and +None, additive and mutliplicative seasonality + +aeon enhancement proposal +https://github.com/aeon-toolkit/aeon/pull/2244/ + +""" + +__maintainer__ = [] +__all__ = ["NaiveForecaster"] + +import numpy as np + +from aeon.forecasting.base import BaseForecaster + +NONE = 0 +ADDITIVE = 1 +MULTIPLICATIVE = 2 + + +class NaiveForecaster(BaseForecaster): + """Naive forecaster. + + Forecasts future values as the last observed value. + + Parameters + ---------- + horizon : int, default = 1 + The number of steps ahead to forecast. + + Examples + -------- + >>> from aeon.forecasting import NaiveForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = NaiveForecaster() + >>> forecaster.fit(y) + NaiveForecaster() + >>> forecaster.predict() + 366.90200486015596 + """ + + def __init__( + self, + horizon=1, + ): + self.last_value_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Naive forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted NaiveForecaster. + """ + self.last_value_ = y[0][-1] + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + if y is None: + return np.array([self.last_value_]) + else: + return np.insert(y, 0, self.last_value_)[:-1] diff --git a/aeon/forecasting/_plot_autoets_gradient_method.py b/aeon/forecasting/_plot_autoets_gradient_method.py new file mode 100644 index 0000000000..a84a41baa1 --- /dev/null +++ b/aeon/forecasting/_plot_autoets_gradient_method.py @@ -0,0 +1,66 @@ +"""Test AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import matplotlib.pyplot as plt +from statsforecast.utils import AirPassengers as ap +from statsforecast.utils import AirPassengersDF + +from aeon.forecasting._autoets import auto_ets +from aeon.forecasting._ets_fast import ETSForecaster + +plt.rcParams["figure.figsize"] = (12, 8) + + +def test_autoets_forecaster(): + """TestETSForecaster.""" + ( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + ) = auto_ets(ap, method="internal_gradient") + print( # noqa + f"Error Type: {error_type}, Trend Type: {trend_type}, \ + Seasonality Type: {seasonality_type}, Seasonal Period: {seasonal_period}, \ + Alpha: {alpha}, Beta: {beta}, Gamma: {gamma}, Phi: {phi}" + ) # noqa + etsforecaster = ETSForecaster( + error_type, + trend_type, + seasonality_type, + seasonal_period, + alpha, + beta, + gamma, + phi, + 1, + ) + etsforecaster.fit(ap) + print(f"liklihood: {etsforecaster.liklihood_}") # noqa + + # assert np.allclose([parameter.item() for parameter in parameters], + # [0.1,0.05,0.05,0.98]) + plt.plot(AirPassengersDF.ds, AirPassengersDF.y, label="Actual Values", color="blue") + plt.plot( + AirPassengersDF.ds, + etsforecaster.fitted_values_, + label="Predicted Values", + color="green", + ) + plt.plot( + AirPassengersDF.ds, etsforecaster.residuals_, label="Residuals", color="red" + ) + plt.ylabel("Air Passenger Numbers") + plt.grid() + plt.legend() + plt.show() + + +if __name__ == "__main__": + test_autoets_forecaster() diff --git a/aeon/forecasting/_sktime_autoets.py b/aeon/forecasting/_sktime_autoets.py new file mode 100644 index 0000000000..127d93040b --- /dev/null +++ b/aeon/forecasting/_sktime_autoets.py @@ -0,0 +1,78 @@ +"""SktimeAutoETS class. + +Wraps sktime AutoETS model for forecasting. + +""" + +__maintainer__ = [] +__all__ = ["SktimeAutoETSForecaster"] + + +import numpy as np +from sktime.forecasting.ets import AutoETS + +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + + +class SktimeAutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster from sktime. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + """ + + def __init__( + self, + horizon=1, + ): + self.model_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + season_length = calc_seasonal_period(data) + self.model_ = AutoETS(auto=True, sp=season_length) + self.model_.fit(data) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = self.model_.predict(self.horizon, exog)[0][0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] diff --git a/aeon/forecasting/_statsforecast_autoets.py b/aeon/forecasting/_statsforecast_autoets.py new file mode 100644 index 0000000000..8ce77d257d --- /dev/null +++ b/aeon/forecasting/_statsforecast_autoets.py @@ -0,0 +1,78 @@ +"""StatsforecastAutoETS class. + +Wraps statsforecast AutoETS model for forecasting. + +""" + +__maintainer__ = [] +__all__ = ["StatsForecastAutoETSForecaster"] + + +import numpy as np +from statsforecast.models import AutoETS + +from aeon.forecasting._utils import calc_seasonal_period +from aeon.forecasting.base import BaseForecaster + + +class StatsForecastAutoETSForecaster(BaseForecaster): + """Automatic Exponential Smoothing forecaster from statsforecast. + + Parameters + ---------- + horizon : int, default = 1 + The horizon to forecast to. + """ + + def __init__( + self, + horizon=1, + ): + self.model_ = None + super().__init__(horizon=horizon, axis=1) + + def _fit(self, y, exog=None): + """Fit Auto Exponential Smoothing forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted AutoETSForecaster. + """ + data = y.squeeze() + season_length = calc_seasonal_period(data) + self.model_ = AutoETS(season_length=season_length) + self.model_.fit(data) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + fitted_value = self.model_.predict(self.horizon, exog)["mean"][0] + if y is None: + return np.array([fitted_value]) + else: + return np.insert(y, 0, fitted_value)[:-1] diff --git a/aeon/forecasting/_time_autoets.py b/aeon/forecasting/_time_autoets.py new file mode 100644 index 0000000000..3d9e263e15 --- /dev/null +++ b/aeon/forecasting/_time_autoets.py @@ -0,0 +1,37 @@ +"""Test AutoETS.""" + +# __maintainer__ = [] +# __all__ = [] + +import timeit + +from statsforecast.utils import AirPassengers as ap + +from aeon.forecasting._autoets import nelder_mead, optimise_params_scipy + + +def test_optimise_params(): + nelder_mead(ap, 2, 2, 2, 12) + + +def test_optimise_params_scipy(): + optimise_params_scipy(ap, 2, 2, 2, 12, method="L-BFGS-B") + + +def test_autoets_forecaster(): + """TestETSForecaster.""" + for _i in range(20): + test_optimise_params() + test_optimise_params_scipy() + optim_ets_time = timeit.timeit(test_optimise_params, globals={}, number=1000) + print(f"Execution time Optimise params: {optim_ets_time} seconds") # noqa + optim_ets_scipy_time = timeit.timeit( + test_optimise_params_scipy, globals={}, number=1000 + ) + print( # noqa + f"Execution time Optimise params Scipy: {optim_ets_scipy_time} seconds" + ) + + +if __name__ == "__main__": + test_autoets_forecaster() diff --git a/aeon/forecasting/_utils.py b/aeon/forecasting/_utils.py new file mode 100644 index 0000000000..aeee0db3ae --- /dev/null +++ b/aeon/forecasting/_utils.py @@ -0,0 +1,115 @@ +""" +Forecasting utilities class. + +Contains useful utility methods for forecasting time series data. + +""" + +import numpy as np +from numba import njit + + +@njit(cache=True, fastmath=True) +def calc_seasonal_period(data): + """Estimate the seasonal period based on the autocorrelation of the series.""" + lags = _acf(data, 24) + lags = np.concatenate((np.array([1.0]), lags)) + peaks = [] + mean_lags = np.mean(lags) + for i in range(1, len(lags) - 1): # Skip the first (lag 0) and last elements + if lags[i] >= lags[i - 1] and lags[i] >= lags[i + 1] and lags[i] > mean_lags: + peaks.append(i) + if not peaks: + return 1 + else: + return peaks[0] + + +@njit(cache=True, fastmath=True) +def _acf(X, max_lag): + length = len(X) + X_t = np.zeros(max_lag, dtype=float) + for lag in range(1, max_lag + 1): + lag_length = length - lag + x1 = X[:-lag] + x2 = X[lag:] + s1 = np.sum(x1) + s2 = np.sum(x2) + m1 = s1 / lag_length + m2 = s2 / lag_length + ss1 = np.sum(x1 * x1) + ss2 = np.sum(x2 * x2) + v1 = ss1 - s1 * m1 + v2 = ss2 - s2 * m2 + v1_is_zero, v2_is_zero = v1 <= 1e-9, v2 <= 1e-9 + if v1_is_zero and v2_is_zero: # Both zero variance, + # so must be 100% correlated + X_t[lag - 1] = 1 + elif v1_is_zero or v2_is_zero: # One zero variance + # the other not + X_t[lag - 1] = 0 + else: + X_t[lag - 1] = np.sum((x1 - m1) * (x2 - m2)) / np.sqrt(v1 * v2) + return X_t + + +def kpss_test(y, regression="c", lags=None): # Test if time series is stationary + """ + Implement the KPSS test for stationarity. + + Parameters + ---------- + y (array-like): Time series data + regression (str): 'c' for constant, 'ct' for constant + trend + lags (int): Number of lags for HAC variance estimation (default: sqrt(n)) + + Returns + ------- + kpss_stat (float): KPSS test statistic + stationary (bool): Whether the series is stationary according to the test + """ + y = np.asarray(y) + n = len(y) + + # Step 1: Fit regression model to estimate residuals + if regression == "c": # Constant + X = np.ones((n, 1)) + elif regression == "ct": # Constant + Trend + X = np.column_stack((np.ones(n), np.arange(1, n + 1))) + else: + raise ValueError("regression must be 'c' or 'ct'") + + beta = np.linalg.lstsq(X, y, rcond=None)[0] # Estimate regression coefficients + residuals = y - X @ beta # Get residuals (u_t) + + # Step 2: Compute cumulative sum of residuals (S_t) + S_t = np.cumsum(residuals) + + # Step 3: Estimate long-run variance (HAC variance) + if lags is None: + # lags = int(12 * (n / 100)**(1/4)) # Default statsmodels lag length + lags = int(np.sqrt(n)) # Default lag length + + gamma_0 = np.sum(residuals**2) / (n - X.shape[1]) # Lag-0 autocovariance + gamma = [np.sum(residuals[k:] * residuals[:-k]) / n for k in range(1, lags + 1)] + + # Bartlett weights + weights = [1 - (k / (lags + 1)) for k in range(1, lags + 1)] + + # Long-run variance + sigma_squared = gamma_0 + 2 * np.sum([w * g for w, g in zip(weights, gamma)]) + + # Step 4: Calculate the KPSS statistic + kpss_stat = np.sum(S_t**2) / (n**2 * sigma_squared) + + if regression == "ct": + # p. 162 Kwiatkowski et al. (1992): y_t = beta * t + r_t + e_t, + # where beta is the trend, r_t a random walk and e_t a stationary + # error term. + crit = 0.146 + else: # hypo == "c" + # special case of the model above, where beta = 0 (so the null + # hypothesis is that the data is stationary around r_0). + crit = 0.463 + + return kpss_stat, kpss_stat < crit diff --git a/aeon/forecasting/_verify_arima.py b/aeon/forecasting/_verify_arima.py new file mode 100644 index 0000000000..34758eb6eb --- /dev/null +++ b/aeon/forecasting/_verify_arima.py @@ -0,0 +1,31 @@ +from pmdarima import auto_arima as pmd_auto_arima +from statsforecast.utils import AirPassengers as ap +from statsmodels.tsa.stattools import kpss + +from aeon.forecasting._arima import ARIMAForecaster, auto_arima, nelder_mead +from aeon.forecasting._utils import kpss_test + + +def test_arima(): + model = pmd_auto_arima( + ap, + seasonal=True, + m=12, + trace=True, + error_action="ignore", + suppress_warnings=True, + ) + print(model.summary()) # noqa + print(f"Optimal Model: {nelder_mead(ap, 2, 1, 1, 0, 1, 0, 12, True)}") # noqa + print(model.predict(n_periods=1)) # noqa + print(kpss_test(ap)) # noqa + print(kpss(ap, regression="c", nlags=12)) # noqa + print(auto_arima(ap)) # noqa + forecaster = ARIMAForecaster() + forecaster.fit(ap) + print(forecaster.predict()) # noqa + + +if __name__ == "__main__": + test_arima() +# Fit Auto-ARIMA model diff --git a/aeon/forecasting/_verify_ets.py b/aeon/forecasting/_verify_ets.py new file mode 100644 index 0000000000..65d3ca0faf --- /dev/null +++ b/aeon/forecasting/_verify_ets.py @@ -0,0 +1,345 @@ +"""Script to test ETS implementation against ETS implementations from other modules.""" + +import random +import time +import timeit + +import numpy as np +from statsforecast.utils import AirPassengers as ap + +import aeon.forecasting._ets as ets +import aeon.forecasting._ets_fast as etsfast +from aeon.forecasting import ETSForecaster + +NA = -99999.0 +MAX_NMSE = 30 +MAX_SEASONAL_PERIOD = 24 + + +def setup(): + """Generate parameters required for ETS algorithms.""" + y = ap + m = random.randint(2, 24) + error = random.randint(1, 2) + trend = random.randint(0, 2) + season = random.randint(0, 2) + alpha = round(random.random(), 4) + if alpha == 0: + alpha = round(random.random(), 4) + beta = round(random.random() * alpha, 4) # 0 < beta < alpha + if beta == 0: + beta = round(random.random() * alpha, 4) + gamma = round(random.random() * (1 - alpha), 4) # 0 < beta < alpha + if gamma == 0: + gamma = round(random.random() * (1 - alpha), 4) + phi = round( + random.random() * 0.18 + 0.8, 4 + ) # Common constraint for phi is 0.8 < phi < 0.98 + return (y, m, error, trend, season, alpha, beta, gamma, phi) + + +def statsmodels_version( + y: np.ndarray, + m: int, + f1: ETSForecaster, + errortype: int, + trendtype: int, + seasontype: int, + alpha: float, + beta: float, + gamma: float, + phi: float, +): + """Hide the differences between different statsforecast versions.""" + from statsmodels.tsa.holtwinters import ExponentialSmoothing + + ets_model = ExponentialSmoothing( + y[m:], + trend="add" if trendtype == 1 else "mul" if trendtype == 2 else None, + damped_trend=(phi != 1 and trendtype != 0), + seasonal="add" if seasontype == 1 else "mul" if seasontype == 2 else None, + seasonal_periods=m if seasontype != 0 else None, + initialization_method="known", + initial_level=f1.level_, + initial_trend=f1.trend_ if trendtype != 0 else None, + initial_seasonal=f1.seasonality_ if seasontype != 0 else None, + ) + results = ets_model.fit( + smoothing_level=alpha, + smoothing_trend=( + beta / alpha if trendtype != 0 else None + ), # statsmodels uses beta*=beta/alpha + smoothing_seasonal=gamma if seasontype != 0 else None, + damping_trend=phi if trendtype != 0 else None, + optimized=False, + ) + avg_mean_sq_err = results.sse / (len(y) - m) + # Back-calculate our log-likelihood proxy from AIC + if errortype == 1: + residuals = y[m:] - results.fittedvalues + assert np.allclose(residuals, results.resid) + else: + residuals = y[m:] / results.fittedvalues - 1 + return ( + (np.array([avg_mean_sq_err])), + residuals, + (results.aic - 2 * results.k + (len(y) - m) * np.log(len(y) - m)), + ) + + +def obscure_statsforecast_version( + y: np.ndarray, + m: int, + f1: ETSForecaster, + errortype: int, + trendtype: int, + seasontype: int, + alpha: float, + beta: float, + gamma: float, + phi: float, +): + """Hide the differences between different statsforecast versions.""" + init_state = np.zeros(len(y) * (1 + (trendtype > 0) + m * (seasontype > 0) + 1)) + init_state[0] = f1.level_ + init_state[1] = f1.trend_ + init_state[1 + (trendtype != 0) : m + 1 + (trendtype != 0)] = f1.seasonality_[::-1] + # from statsforecast.ets import pegelsresid_C + # amse, e, _x, lik = pegelsresid_C( + # y[m:], + # m, + # init_state, + # "A" if errortype == 1 else "M", + # "A" if trendtype == 1 else "M" if trendtype == 2 else "N", + # "A" if seasontype == 1 else "M" if seasontype == 2 else "N", + # phi != 1, + # alpha, + # beta, + # gamma, + # phi, + # nmse, + # ) + from statsforecast.ets import etscalc + + e = np.zeros(len(y)) + amse = np.zeros(MAX_NMSE) + lik = etscalc( + y[m:], + len(y) - m, + init_state, + m, + errortype, + trendtype, + seasontype, + alpha, + beta, + gamma, + phi, + e, + amse, + 1, + ) + return amse, e[:-m], lik + + +def test_ets_comparison(setup_func, random_seed, catch_errors): + """Run both our statsforecast and our implementation and crosschecks.""" + random.seed(random_seed) + ( + y, + m, + error, + trend, + season, + alpha, + beta, + gamma, + phi, + ) = setup_func() + # tsml-eval implementation + start = time.perf_counter() + f1 = ETSForecaster( + error, + trend, + season, + m, + alpha, + beta, + gamma, + phi, + 1, + ) + f1.fit(y) + end = time.perf_counter() + time_fitets = end - start + e_fitets = f1.residuals_ + amse_fitets = f1.avg_mean_sq_err_ + lik_fitets = f1.liklihood_ + f1 = ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + # pylint: disable=W0212 + f1._fit(y)._initialise(y) + # pylint: enable=W0212 + if season == 0: + m = 1 + # Nixtla/statsforcast implementation + start = time.perf_counter() + amse_etscalc, e_etscalc, lik_etscalc = statsmodels_version( + y, m, f1, error, trend, season, alpha, beta, gamma, phi + ) + end = time.perf_counter() + time_etscalc = end - start + amse_etscalc = amse_etscalc[0] + + if catch_errors: + try: + # Comparing outputs and runtime + assert np.allclose(e_fitets, e_etscalc), "Residuals Compare failed" + assert np.allclose(amse_fitets, amse_etscalc), "AMSE Compare failed" + assert np.isclose(lik_fitets, lik_etscalc), "Liklihood Compare failed" + return True + except AssertionError as e: + print(e) # noqa + print( # noqa + f"Seed: {random_seed}, Model: Error={error}, Trend={trend},\ + Seasonality={season}, seasonal period={m},\ + alpha={alpha}, beta={beta}, gamma={gamma}, phi={phi}" + ) + return False + else: + print( # noqa + f"Seed: {random_seed}, Model: Error={error}, Trend={trend},\ + Seasonality={season}, seasonal period={m}, alpha={alpha},\ + beta={beta}, gamma={gamma}, phi={phi}" + ) + diff_indices = np.where( + np.abs(e_fitets - e_etscalc) > 1e-3 * np.abs(e_etscalc) + 1e-2 + )[0] + for index in diff_indices: + print( # noqa + f"Index {index}: e_fitets = {e_fitets[index]},\ + e_etscalc = {e_etscalc[index]}" + ) + print(amse_fitets) # noqa + print(amse_etscalc) # noqa + print(lik_fitets) # noqa + print(lik_etscalc) # noqa + assert np.allclose(e_fitets, e_etscalc) + assert np.allclose(amse_fitets, amse_etscalc) + # assert np.isclose(lik_fitets, lik_etscalc) + print(f"Time for ETS: {time_fitets:0.20f}") # noqa + print(f"Time for statsforecast ETS: {time_etscalc}") # noqa + return True + + +def time_etsfast(): + """Test function for optimised numba ets algorithm.""" + etsfast.ETSForecaster(2, 2, 2, 4).fit(ap).predict() + + +def time_etsnoopt(): + """Test function for non-optimised ets algorithm.""" + ets.ETSForecaster(2, 2, 2, 4).fit(ap).predict() + + +def time_etsfast_noclass(): + """Test function for optimised ets algorithm without the class based structure.""" + data = np.array(ap.squeeze(), dtype=np.float64) + # pylint: disable=W0212 + ( + level, + trend, + seasonality, + _residuals, + _fitted_values, + _avg_mean_sq_err, + _liklihood, + ) = etsfast._fit(data, 2, 2, 2, 4, 0.1, 0.01, 0.01, 0.99) + etsfast._predict(2, 2, level, trend, seasonality, 0.99, 1, 144, 4) + # pylint: enable=W0212 + + +def time_sf(): + """Test function for statsforecast ets algorithm.""" + x = np.zeros(144 * 7) + x[0:6] = [122.75, 1.123230970596215, 0.91242363, 0.96130346, 1.07535642, 1.0509165] + obscure_statsforecast_version( + ap[4:], + 4, + x, + 2, + 2, + 2, + 0.1, + 0.01, + 0.01, + 0.99, + ) + + +def time_compare(random_seed): + """Compare timings of different ets algorithms.""" + random.seed(random_seed) + (y, m, error, trend, season, alpha, beta, gamma, phi) = setup() + # etsnoopt_time = timeit.timeit(time_etsnoopt, globals={}, number=10000) + # print (f"Execution time ETS No-opt: {etsnoopt_time} seconds") + # Do a few iterations to remove background/overheads. Makes comparison more reliable + for _i in range(10): + time_etsfast() + time_sf() + time_etsfast_noclass() + etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) + print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa + etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) + print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) + print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa + etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) + print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa + etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) + print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) + print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa + # _ets_fast_nostruct implementation + start = time.perf_counter() + f3 = etsfast.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + f3.fit(y) + end = time.perf_counter() + etsfast_time = end - start + # _ets_fast implementation + # _ets implementation + start = time.perf_counter() + f1 = ets.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) + f1.fit(y) + end = time.perf_counter() + etsnoopt_time = end - start + assert np.allclose(f1.residuals_, f3.residuals_) + assert np.allclose(f1.avg_mean_sq_err_, f3.avg_mean_sq_err_) + assert np.isclose(f1.liklihood_, f3.liklihood_) + print( # noqa + f"ETS No-optimisation Time: {etsnoopt_time},\ + Fast time: {etsfast_time}" + ) + return etsnoopt_time, etsfast_time + + +if __name__ == "__main__": + np.set_printoptions(threshold=np.inf) + test_ets_comparison(setup, 300, False) + SUCCESSES = True + for i in range(0, 300): + SUCCESSES &= test_ets_comparison(setup, i, True) + if SUCCESSES: + print("Test Completed Successfully with no errors") # noqa + # time_compare(300) + # avg_ets = 0 + # avg_etsfast = 0 + # avg_etsfast_ns = 0 + # iterations = 100 + # for i in range (iterations): + # time_ets, etsfast_time = time_compare(300) + # avg_ets += time_ets + # avg_etsfast += etsfast_time + # avg_ets/= iterations + # avg_etsfast/= iterations + # avg_etsfast_ns /= iterations + # print(f"Avg ETS Time: {avg_ets}, Avg Fast ETS time: {avg_etsfast},\ diff --git a/aeon/forecasting/tests/test_ets.py b/aeon/forecasting/tests/test_ets.py index ce7513a965..c5c5118b60 100644 --- a/aeon/forecasting/tests/test_ets.py +++ b/aeon/forecasting/tests/test_ets.py @@ -1,27 +1,92 @@ -"""Test ETS forecaster.""" +"""Test ETS.""" -import pytest +__maintainer__ = [] +__all__ = [] + +import numpy as np from aeon.forecasting import ETSForecaster -from aeon.testing.data_generation import make_example_1d_numpy - - -def test_ets_params(): - """Test ETS forecaster.""" - y = make_example_1d_numpy(n_timepoints=100) - forecaster = ETSForecaster(error_type=3) - with pytest.raises( - ValueError, match="Error must be either additive or " "multiplicative" - ): - forecaster.fit(y) - forecaster = ETSForecaster(seasonality_type=-3) - forecaster.fit(y) - assert forecaster._seasonal_period == 1 - forecaster = ETSForecaster(trend_type=None, seasonality_type=0, beta=1.0, gamma=1.0) - forecaster.fit(y) - assert forecaster._beta == 0 - assert forecaster._gamma == 0 - - forecaster = ETSForecaster(error_type=2, phi=1.0) - pred = forecaster.forecast(y) - assert isinstance(pred, float) + + +def test_ets_forecaster_additive(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.5, + beta=0.3, + gamma=0.4, + phi=1, + horizon=1, + error_type=1, + trend_type=1, + seasonality_type=1, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 9.191190608800001) + + +def test_ets_forecaster_mult_error(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.6, + gamma=0.1, + phi=0.97, + horizon=1, + error_type=2, + trend_type=1, + seasonality_type=1, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.20176819429869) + + +def test_ets_forecaster_mult_compnents(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.4, + beta=0.2, + gamma=0.5, + phi=0.8, + horizon=1, + error_type=1, + trend_type=2, + seasonality_type=2, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 12.301259229712382) + + +def test_ets_forecaster_multiplicative(): + """TestETSForecaster.""" + data = np.array( + [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12] + ) # Sample seasonal data + forecaster = ETSForecaster( + alpha=0.7, + beta=0.5, + gamma=0.2, + phi=0.85, + horizon=1, + error_type=2, + trend_type=2, + seasonality_type=2, + seasonal_period=4, + ) + forecaster.fit(data) + p = forecaster.predict() + assert np.isclose(p, 16.811888294476528) diff --git a/aeon/transformations/format/__init__.py b/aeon/transformations/format/__init__.py new file mode 100644 index 0000000000..9409e0c3a4 --- /dev/null +++ b/aeon/transformations/format/__init__.py @@ -0,0 +1,11 @@ +"""Format transformations.""" + +__all__ = [ + "SlidingWindowTransformer", + "TrainTestTransformer", + "BaseFormatTransformer", +] + +from aeon.transformations.format._sliding_window import SlidingWindowTransformer +from aeon.transformations.format._train_test import TrainTestTransformer +from aeon.transformations.format.base import BaseFormatTransformer diff --git a/aeon/transformations/format/_sliding_window.py b/aeon/transformations/format/_sliding_window.py new file mode 100644 index 0000000000..899eaaf44a --- /dev/null +++ b/aeon/transformations/format/_sliding_window.py @@ -0,0 +1,92 @@ +"""Sliding Window transformation.""" + +__maintainer__ = [] +__all__ = ["SlidingWindowTransformer"] + +import numpy as np + +from aeon.transformations.format.base import BaseFormatTransformer + + +class SlidingWindowTransformer(BaseFormatTransformer): + """ + Create windowed views of a series by extracting fixed-length overlapping segments. + + This transformer generates multiple subsequences (windows) of a specified width from + the input time series. Each window represents a shifted view of the series, moving + forward by one time step. + + Parameters + ---------- + window_size : int, optional (default=100) + The number of consecutive time points in each window. + + Notes + ----- + - The function assumes that `window_width` is smaller than the length of `series`. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.format import SlidingWindowTransformer + >>> X = np.array([1, 2, 3, 4, 5, 6]) + >>> transformer = SlidingWindowTransformer(3) + >>> Xt = transformer.fit_transform(X) + >>> print(Xt) + ([[1, 2], [2, 3], [3, 4], [4, 5]], [3, 4, 5, 6], [0, 1, 2, 3]) + + + Returns + ------- + X : np.ndarray (2D) + A numpy array where each element is a window (subsequence) of length + `window_width - 1` from the original series. + Y : np.ndarray (1D) + A numpy array containing the next value in the series for each window. + indices : list of int + A list of starting indices corresponding to each extracted window. + + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + "output_data_type": "Tuple", + } + + def __init__(self, window_size: int = 100): + super().__init__(axis=1) + if window_size <= 1: + raise ValueError(f"window_size must be > 1, got {window_size}") + self.window_size = window_size + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + The input time series from which windows will be created. + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + X = X[0] + # Generate windowed versions of train and test sets + X_t = np.zeros((len(X) - self.window_size + 1, self.window_size - 1)) + Y_t = np.zeros(len(X) - self.window_size + 1) + indices = np.zeros(len(X) - self.window_size + 1) + for i in range(len(X) - self.window_size + 1): + X_t[i] = X[ + i : i + self.window_size - 1 + ] # Create a view from current index onward + Y_t[i] = X[i + self.window_size - 1] # Next value + indices[i] = i + return X_t, Y_t, indices diff --git a/aeon/transformations/format/_train_test.py b/aeon/transformations/format/_train_test.py new file mode 100644 index 0000000000..0d31d48aa9 --- /dev/null +++ b/aeon/transformations/format/_train_test.py @@ -0,0 +1,93 @@ +"""Sliding Window transformation.""" + +__maintainer__ = [] +__all__ = ["TrainTestTransformer"] + +import math + +from aeon.transformations.format.base import BaseFormatTransformer + + +class TrainTestTransformer(BaseFormatTransformer): + """ + Convert a single time series into train/test sets. + + This function assumes that the input DataFrame contains only one time series. + It splits the series into training and testing sets based on + the specified proportion. + + Parameters + ---------- + train_proportion : float, optional (default=0.7) + The proportion of the time series to use for training, + with the remaining used for test. + max_series_length : int, optional (default=10000) + The maximum length of the series to consider. If the series is longer + than this value, it will be truncated. + + Examples + -------- + >>> import numpy as np + >>> from aeon.transformations.format import TrainTestTransformer + >>> X = np.array([-3, -2, -1, 0, 1, 2, 3, 4]) + >>> transformer = TrainTestTransformer(0.75) + >>> Xt = transformer.fit_transform(X) + >>> print(Xt) + (array([-3, -2, -1, 0, 1, 2]), array([3, 4])) + + Returns + ------- + None + A tuple containing the training and testing sets. + + """ + + _tags = { + "capability:multivariate": True, + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + "output_data_type": "Tuple", + } + + def __init__( + self, train_proportion: float = 0.7, max_series_length: int = 10000 + ) -> None: + super().__init__(axis=1) + if train_proportion <= 0 or train_proportion >= 1: + raise ValueError( + f"train_proportion must be between 0 and 1, got {train_proportion}" + ) + self.train_proportion = train_proportion + self.max_series_length = max_series_length + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + Xt: 2D np.ndarray + transformed version of X + """ + X = X[0] + # Compute split index + if len(X) < self.max_series_length or self.max_series_length == -1: + end_location = len(X) + else: + end_location = self.max_series_length + train_test_split_location = math.ceil(end_location * self.train_proportion) + + # Split into train and test sets + train_series = X[:train_test_split_location] + test_series = X[train_test_split_location:end_location] + + # Generate windowed versions of train and test sets + return train_series, test_series diff --git a/aeon/transformations/format/base.py b/aeon/transformations/format/base.py new file mode 100644 index 0000000000..9047c667e1 --- /dev/null +++ b/aeon/transformations/format/base.py @@ -0,0 +1,301 @@ +"""Base class for Series transformers. + +class name: BaseSeriesTransformer + +Defining methods: +fitting - fit(self, X, y=None) +transform - transform(self, X, y=None) +fit & transform - fit_transform(self, X, y=None) +""" + +from abc import abstractmethod +from typing import final + +import numpy as np +import pandas as pd + +from aeon.base import BaseSeriesEstimator +from aeon.transformations.base import BaseTransformer + + +class BaseFormatTransformer(BaseSeriesEstimator, BaseTransformer): + """Transformer base class for collections.""" + + # tag values specific to SeriesTransformers + _tags = { + "input_data_type": "Series", + "output_data_type": "Tuple", + } + + @abstractmethod + def __init__(self, axis): + super().__init__(axis=axis) + + @final + def fit(self, X, y=None, axis=1): + """Fit transformer to X, optionally using y if supervised. + + State change: + Changes state to "fitted". + + Parameters + ---------- + X : Input data + Time series to fit transform to, of type ``np.ndarray``, ``pd.Series`` + ``pd.DataFrame``. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + self : a fitted instance of the estimator + """ + # skip the rest if fit_is_empty is True + if self.get_tag("fit_is_empty"): + self.is_fitted = True + return self + if self.get_tag("requires_y"): + if y is None: + raise ValueError("Tag requires_y is true, but fit called with y=None") + # reset estimator at the start of fit + self.reset() + X = self._preprocess_series(X, axis=axis, store_metadata=True) + if y is not None: + self._check_y(y) + self._fit(X=X, y=y) + self.is_fitted = True + return self + + @final + def transform(self, X, y=None, axis=1): + """Transform X and return a transformed version. + + State required: + Requires state to be "fitted". + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + transformed version of X with the same axis as passed by the user, if axis + not None. + """ + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + Xt = self._transform(X, y) + return Xt + + @final + def fit_transform(self, X, y=None, axis=1): + """ + Fit to data, then transform it. + + Fits the transformer to X and y and returns a transformed version of X. + + Changes state to "fitted". Model attributes (ending in "_") : dependent on + estimator. + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + transformed version of X with the same axis as passed by the user, if axis + not None. + """ + # input checks and datatype conversion, to avoid doing in both fit and transform + self.reset() + X = self._preprocess_series(X, axis=axis, store_metadata=True) + Xt = self._fit_transform(X=X, y=y) + self.is_fitted = True + return Xt + + @final + def inverse_transform(self, X, y=None, axis=1): + """Inverse transform X and return an inverse transformed version. + + State required: + Requires state to be "fitted". + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + axis : int, default = 1 + Axis of time in the input series. + If ``axis == 0``, it is assumed each column is a time series and each row is + a time point. i.e. the shape of the data is ``(n_timepoints, + n_channels)``. + ``axis == 1`` indicates the time series are in rows, i.e. the shape of + the data is ``(n_channels, n_timepoints)`.``axis is None`` indicates + that the axis of X is the same as ``self.axis``. + + Returns + ------- + inverse transformed version of X + of the same type as X + """ + if not self.get_tag("capability:inverse_transform"): + raise NotImplementedError( + f"{type(self)} does not implement inverse_transform" + ) + + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis=axis, store_metadata=False) + Xt = self._inverse_transform(X=X, y=y) + return Xt + + @final + def update(self, X, y=None, update_params=True, axis=1): + """Update transformer with X, optionally y. + + Parameters + ---------- + X : data to update of valid series type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + update_params : bool, default=True + whether the model is updated. Yes if true, if false, simply skips call. + argument exists for compatibility with forecasting module. + axis : int, default=None + axis along which to update. If None, uses self.axis. + + Returns + ------- + self : a fitted instance of the estimator + """ + # check whether is fitted + self._check_is_fitted() + X = self._preprocess_series(X, axis, False) + return self._update(X=X, y=y, update_params=update_params) + + def _fit(self, X, y=None): + """Fit transformer to X and y. + + private _fit containing the core logic, called from fit + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + self: a fitted instance of the estimator + """ + # default fit is "no fitting happens" + return self + + @abstractmethod + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing the core logic, called from transform + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + transformed version of X + """ + + def _fit_transform(self, X, y=None): + """Fit to data, then transform it. + + Fits the transformer to X and y and returns a transformed version of X. + + private _fit_transform containing the core logic, called from fit_transform. + + Parameters + ---------- + X : Input data + Data to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation. + + Returns + ------- + transformed version of X. + """ + # Non-optimized default implementation; override when a better + # method is possible for a given algorithm. + self._fit(X, y) + return self._transform(X, y) + + def _inverse_transform(self, X, y=None): + """Inverse transform X and return an inverse transformed version. + + private _inverse_transform containing core logic, called from inverse_transform. + + Parameters + ---------- + X : Input data + Time series to fit transform to, of valid collection type. + y : Target variable, default=None + Additional data, e.g., labels for transformation + + Returns + ------- + inverse transformed version of X + of the same type as X. + """ + raise NotImplementedError( + f"{self.__class__.__name__} does not support inverse_transform" + ) + + def _update(self, X, y=None, update_params=True): + # standard behaviour: no update takes place, new data is ignored + return self + + def _check_y(self, y): + # Check y valid input for supervised transform + if not isinstance(y, (pd.Series, np.ndarray)): + raise TypeError( + f"y must be a np.array or a pd.Series, but found type: {type(y)}" + ) + if isinstance(y, np.ndarray) and y.ndim > 1: + raise TypeError(f"y must be 1-dimensional, found {y.ndim} dimensions") diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py index 8b71ba9fc8..677f48db01 100644 --- a/aeon/transformations/series/__init__.py +++ b/aeon/transformations/series/__init__.py @@ -5,6 +5,7 @@ "BaseSeriesTransformer", "ClaSPTransformer", "DFTSeriesTransformer", + "DifferencingSeriesTransformer", "Dobin", "ExpSmoothingSeriesTransformer", "GaussSeriesTransformer", @@ -32,6 +33,7 @@ from aeon.transformations.series._boxcox import BoxCoxTransformer from aeon.transformations.series._clasp import ClaSPTransformer from aeon.transformations.series._dft import DFTSeriesTransformer +from aeon.transformations.series._difference import DifferencingSeriesTransformer from aeon.transformations.series._dobin import Dobin from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer from aeon.transformations.series._gauss import GaussSeriesTransformer diff --git a/aeon/transformations/series/_difference.py b/aeon/transformations/series/_difference.py new file mode 100644 index 0000000000..42addd377b --- /dev/null +++ b/aeon/transformations/series/_difference.py @@ -0,0 +1,52 @@ +"""Differencing transformations.""" + +__maintainer__ = ["TonyBagnall"] +__all__ = ["DifferencingSeriesTransformer"] + +from aeon.transformations.series.base import BaseSeriesTransformer + + +class DifferencingSeriesTransformer(BaseSeriesTransformer): + """Differencing transformations. + + This transformer returns the differenced series of the input time series. + The differenced series is obtained by subtracting the previous value + from the current value. + + Examples + -------- + >>> from aeon.transformations.series import DifferencingSeriesTransformer + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> transformer = DifferencingSeriesTransformer() + >>> y_hat = transformer.fit_transform(y) + """ + + _tags = { + "X_inner_type": "np.ndarray", + "fit_is_empty": True, + } + + def __init__( + self, + ): + super().__init__(axis=1) + + def _transform(self, X, y=None): + """Transform X and return a transformed version. + + private _transform containing the core logic, called from transform + + Parameters + ---------- + X : np.ndarray + Data to be transformed, shape (n_channels, n_timepoints) + y : ignored argument for interface compatibility + Additional data, e.g., labels for transformation + + Returns + ------- + transformed version of X + """ + X = X[0] + return X[1:] - X[:-1] From fb7afd668f2c7ef7ca32304fd4c47cf3506369e2 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 16 May 2025 19:37:36 +0100 Subject: [PATCH 08/70] Fix bug in AutoARIMA algorithm --- aeon/forecasting/_arima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 4de0fee3d3..e6f0e66cc6 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -378,7 +378,7 @@ def auto_arima(data): points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) p.append(aic) model_points.append(points) - current_model = max(model_parameters, key=lambda item: item[5]) + current_model = min(model_parameters, key=lambda item: item[5]) current_points = model_points[model_parameters.index(current_model)] while True: better_model = False From 237bb91be26e76e58f03b5c585f02ff0a71b63e0 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 19 May 2025 18:51:08 +0100 Subject: [PATCH 09/70] Fix test issues --- aeon/forecasting/_autoets.py | 2 +- aeon/forecasting/_ets.py | 6 +++--- aeon/forecasting/_ets_fast.py | 2 +- aeon/forecasting/_naive.py | 2 +- aeon/testing/testing_data.py | 2 ++ aeon/transformations/format/_sliding_window.py | 3 ++- aeon/utils/base/_register.py | 2 ++ 7 files changed, 12 insertions(+), 7 deletions(-) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py index 7501bee0e2..e019646d82 100644 --- a/aeon/forecasting/_autoets.py +++ b/aeon/forecasting/_autoets.py @@ -46,7 +46,7 @@ class AutoETSForecaster(BaseForecaster): >>> forecaster.fit(y) AutoETSForecaster() >>> forecaster.predict() - 366.90200486015596 + array([407.74740434]) """ def __init__( diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index ac7f31a58d..86f7429dde 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -58,16 +58,16 @@ class ETSForecaster(BaseForecaster): Examples -------- - >>> from aeon.forecasting import ETSForecaster + >>> from aeon.forecasting._ets import ETSForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, + ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4,\ seasonality_type=2, trend_type=2) >>> forecaster.predict() - 366.90200486015596 + array([366.90200486]) """ def __init__( diff --git a/aeon/forecasting/_ets_fast.py b/aeon/forecasting/_ets_fast.py index fdbd9c005a..3322206aaa 100644 --- a/aeon/forecasting/_ets_fast.py +++ b/aeon/forecasting/_ets_fast.py @@ -71,7 +71,7 @@ class ETSForecaster(BaseForecaster): ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, seasonality_type=2, trend_type=2) >>> forecaster.predict() - 366.90200486015596 + array([366.90200486]) """ def __init__( diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py index 9bdfa82fb9..30fa10638c 100644 --- a/aeon/forecasting/_naive.py +++ b/aeon/forecasting/_naive.py @@ -41,7 +41,7 @@ class NaiveForecaster(BaseForecaster): >>> forecaster.fit(y) NaiveForecaster() >>> forecaster.predict() - 366.90200486015596 + array([432.]) """ def __init__( diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index f3360d93cb..ae4c2733a8 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -22,6 +22,7 @@ make_example_multi_index_dataframe, ) from aeon.transformations.collection import BaseCollectionTransformer +from aeon.transformations.format import BaseFormatTransformer from aeon.transformations.series import BaseSeriesTransformer from aeon.utils.conversion import convert_collection @@ -874,6 +875,7 @@ def _get_task_for_estimator(estimator): or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) or isinstance(estimator, BaseSeriesSimilaritySearch) + or isinstance(estimator, BaseFormatTransformer) ): data_label = "None" else: diff --git a/aeon/transformations/format/_sliding_window.py b/aeon/transformations/format/_sliding_window.py index 899eaaf44a..b173cb9ad2 100644 --- a/aeon/transformations/format/_sliding_window.py +++ b/aeon/transformations/format/_sliding_window.py @@ -33,7 +33,8 @@ class SlidingWindowTransformer(BaseFormatTransformer): >>> transformer = SlidingWindowTransformer(3) >>> Xt = transformer.fit_transform(X) >>> print(Xt) - ([[1, 2], [2, 3], [3, 4], [4, 5]], [3, 4, 5, 6], [0, 1, 2, 3]) + (array([[1., 2.], [2., 3.], [3., 4.], [4., 5.]]), + array([3., 4., 5., 6.]), array([0., 1., 2., 3.])) Returns diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 5e81e29b33..321b787389 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -29,6 +29,7 @@ from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer +from aeon.transformations.format import BaseFormatTransformer from aeon.transformations.series import BaseSeriesTransformer # all base classes @@ -48,6 +49,7 @@ "regressor": BaseRegressor, "segmenter": BaseSegmenter, "series-transformer": BaseSeriesTransformer, + "format-transformer": BaseFormatTransformer, "forecaster": BaseForecaster, "series-similarity-search": BaseSeriesSimilaritySearch, "collection-similarity-search": BaseCollectionSimilaritySearch, From b2fe31f9f8cb82bc6e9f18cc4214f332398fde1a Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Thu, 22 May 2025 17:30:09 +0100 Subject: [PATCH 10/70] remove dataset lists --- aeon/datasets/Final Dataset Selection.csv | 101 ---------- aeon/datasets/_data_loaders.py | 6 +- aeon/datasets/dataset_collections.py | 6 +- aeon/datasets/dataset_generation.py | 218 ---------------------- aeon/datasets/tsf_datasets.py | 2 +- 5 files changed, 7 insertions(+), 326 deletions(-) delete mode 100644 aeon/datasets/Final Dataset Selection.csv delete mode 100644 aeon/datasets/dataset_generation.py diff --git a/aeon/datasets/Final Dataset Selection.csv b/aeon/datasets/Final Dataset Selection.csv deleted file mode 100644 index c336db5a22..0000000000 --- a/aeon/datasets/Final Dataset Selection.csv +++ /dev/null @@ -1,101 +0,0 @@ -Dataset,Series,Category -weather_dataset,T1,Weather -weather_dataset,T2,Weather -weather_dataset,T3,Weather -weather_dataset,T4,Weather -weather_dataset,T5,Weather -solar_10_minutes_dataset,T1,Energy Production -solar_10_minutes_dataset,T2,Energy Production -solar_10_minutes_dataset,T3,Energy Production -solar_10_minutes_dataset,T4,Energy Production -solar_10_minutes_dataset,T5,Energy Production -sunspot_dataset_without_missing_values,T1,Other -wind_farms_minutely_dataset_without_missing_values,T1,Energy Production -wind_farms_minutely_dataset_without_missing_values,T2,Energy Production -wind_farms_minutely_dataset_without_missing_values,T3,Energy Production -wind_farms_minutely_dataset_without_missing_values,T4,Energy Production -wind_farms_minutely_dataset_without_missing_values,T5,Energy Production -elecdemand_dataset,T1,Energy Demand -us_births_dataset,T1,Demographic -saugeenday_dataset,T1,Weather -london_smart_meters_dataset_without_missing_values,T1,Energy Demand -london_smart_meters_dataset_without_missing_values,T2,Energy Demand -london_smart_meters_dataset_without_missing_values,T3,Energy Demand -traffic_hourly_dataset,T1,Transportation -traffic_hourly_dataset,T2,Transportation -traffic_hourly_dataset,T3,Transportation -traffic_hourly_dataset,T4,Transportation -traffic_hourly_dataset,T5,Transportation -electricity_hourly_dataset,T1,Energy Demand -electricity_hourly_dataset,T2,Energy Demand -electricity_hourly_dataset,T3,Energy Demand -pedestrian_counts_dataset,T1,Transportation -pedestrian_counts_dataset,T2,Transportation -pedestrian_counts_dataset,T3,Transportation -pedestrian_counts_dataset,T4,Transportation -pedestrian_counts_dataset,T5,Transportation -kdd_cup_2018_dataset_without_missing_values,T1,Other -australian_electricity_demand_dataset,T1,Energy Demand -australian_electricity_demand_dataset,T2,Energy Demand -australian_electricity_demand_dataset,T3,Energy Demand -oikolab_weather_dataset,T1,Weather -oikolab_weather_dataset,T2,Weather -oikolab_weather_dataset,T3,Weather -oikolab_weather_dataset,T4,Weather -m4_monthly_dataset,T122,Macro -m4_monthly_dataset,T145,Macro -m4_monthly_dataset,T180,Macro -m4_monthly_dataset,T186,Macro -m4_monthly_dataset,T17051,Micro -m4_monthly_dataset,T17088,Micro -m4_monthly_dataset,T17132,Micro -m4_monthly_dataset,T17146,Micro -m4_monthly_dataset,T26710,Demographic -m4_monthly_dataset,T27138,Industry -m4_monthly_dataset,T27170,Industry -m4_monthly_dataset,T27175,Industry -m4_monthly_dataset,T27186,Industry -m4_monthly_dataset,T37009,Finance -m4_monthly_dataset,T37070,Finance -m4_monthly_dataset,T37238,Finance -m4_monthly_dataset,T37248,Finance -m4_monthly_dataset,T47915,Other -m4_weekly_dataset,T1,Other -m4_weekly_dataset,T2,Other -m4_weekly_dataset,T19,Macro -m4_weekly_dataset,T20,Macro -m4_weekly_dataset,T21,Macro -m4_weekly_dataset,T55,Industry -m4_weekly_dataset,T56,Industry -m4_weekly_dataset,T60,Finance -m4_weekly_dataset,T61,Finance -m4_weekly_dataset,T62,Finance -m4_weekly_dataset,T224,Demographic -m4_weekly_dataset,T225,Demographic -m4_weekly_dataset,T226,Demographic -m4_weekly_dataset,T227,Demographic -m4_weekly_dataset,T248,Micro -m4_weekly_dataset,T249,Micro -m4_weekly_dataset,T250,Micro -m4_daily_dataset,T1,Macro -m4_daily_dataset,T2,Macro -m4_daily_dataset,T6,Macro -m4_daily_dataset,T130,Micro -m4_daily_dataset,T131,Micro -m4_daily_dataset,T145,Micro -m4_daily_dataset,T1604,Demographic -m4_daily_dataset,T1605,Demographic -m4_daily_dataset,T1606,Demographic -m4_daily_dataset,T1607,Demographic -m4_daily_dataset,T1614,Industry -m4_daily_dataset,T1615,Industry -m4_daily_dataset,T1634,Industry -m4_daily_dataset,T1650,Industry -m4_daily_dataset,T2036,Finance -m4_daily_dataset,T2037,Finance -m4_daily_dataset,T2041,Finance -m4_daily_dataset,T3595,Other -m4_daily_dataset,T3597,Other -m4_hourly_dataset,T170,Other -m4_hourly_dataset,T171,Other -m4_hourly_dataset,T172,Other diff --git a/aeon/datasets/_data_loaders.py b/aeon/datasets/_data_loaders.py index 4bbd6f1739..34e6d8b513 100644 --- a/aeon/datasets/_data_loaders.py +++ b/aeon/datasets/_data_loaders.py @@ -977,7 +977,7 @@ def load_forecasting(name, extract_path=None, return_metadata=False): >>> X=load_forecasting("m1_yearly_dataset") # doctest: +SKIP """ # Allow user to have non standard extract path - from aeon.datasets.tsf_datasets import tsf_all + from aeon.datasets.tsf_datasets import tsf_monash if extract_path is not None: local_module = extract_path @@ -993,8 +993,8 @@ def load_forecasting(name, extract_path=None, return_metadata=False): if name not in get_downloaded_tsf_datasets(extract_path): # Dataset is not already present in the datasets directory provided. # If it is not there, download and install it. - if name in tsf_all.keys(): - id = tsf_all[name] + if name in tsf_monash.keys(): + id = tsf_monash[name] if extract_path is None: local_dirname = "local_data" if not os.path.exists(os.path.join(local_module, local_dirname)): diff --git a/aeon/datasets/dataset_collections.py b/aeon/datasets/dataset_collections.py index f47dac5cc4..9e163b5229 100644 --- a/aeon/datasets/dataset_collections.py +++ b/aeon/datasets/dataset_collections.py @@ -38,7 +38,7 @@ import aeon from aeon.datasets.tsc_datasets import multivariate, univariate from aeon.datasets.tser_datasets import tser_monash, tser_soton -from aeon.datasets.tsf_datasets import tsf_all +from aeon.datasets.tsf_datasets import tsf_monash MODULE = os.path.join(os.path.dirname(aeon.__file__), "datasets") @@ -75,8 +75,8 @@ def get_available_tser_datasets(name="tser_soton", return_list=True): def get_available_tsf_datasets(name=None): """List available tsf data.""" if name is None: # List them all - return sorted(list(tsf_all)) - return name in tsf_all + return sorted(list(tsf_monash)) + return name in tsf_monash def get_available_tsc_datasets(name=None): diff --git a/aeon/datasets/dataset_generation.py b/aeon/datasets/dataset_generation.py deleted file mode 100644 index 674c7501f3..0000000000 --- a/aeon/datasets/dataset_generation.py +++ /dev/null @@ -1,218 +0,0 @@ -"""Code to select datasets for regression-based forecasting experiments.""" - -import gc -import os -import tempfile -import time - -import pandas as pd - -from aeon.datasets import load_forecasting -from aeon.datasets._data_writers import ( - write_forecasting_dataset, - write_regression_dataset, -) - -filtered_datasets = [ - "nn5_daily_dataset_without_missing_values", - "nn5_weekly_dataset", - "m1_yearly_dataset", - "m1_quarterly_dataset", - "m1_monthly_dataset", - "m3_yearly_dataset", - "m3_quarterly_dataset", - "m3_monthly_dataset", - "m3_other_dataset", - "m4_yearly_dataset", - "m4_quarterly_dataset", - "m4_monthly_dataset", - "m4_weekly_dataset", - "m4_daily_dataset", - "m4_hourly_dataset", - "tourism_yearly_dataset", - "tourism_quarterly_dataset", - "tourism_monthly_dataset", - "car_parts_dataset_without_missing_values", - "hospital_dataset", - "weather_dataset", - "dominick_dataset", - "fred_md_dataset", - "solar_10_minutes_dataset", - "solar_weekly_dataset", - "solar_4_seconds_dataset", - "wind_4_seconds_dataset", - "sunspot_dataset_without_missing_values", - "wind_farms_minutely_dataset_without_missing_values", - "elecdemand_dataset", - "us_births_dataset", - "saugeenday_dataset", - "covid_deaths_dataset", - "cif_2016_dataset", - "london_smart_meters_dataset_without_missing_values", - "kaggle_web_traffic_dataset_without_missing_values", - "kaggle_web_traffic_weekly_dataset", - "traffic_hourly_dataset", - "traffic_weekly_dataset", - "electricity_hourly_dataset", - "electricity_weekly_dataset", - "pedestrian_counts_dataset", - "kdd_cup_2018_dataset_without_missing_values", - "australian_electricity_demand_dataset", - "covid_mobility_dataset_without_missing_values", - "rideshare_dataset_without_missing_values", - "vehicle_trips_dataset_without_missing_values", - "temperature_rain_dataset_without_missing_values", - "oikolab_weather_dataset", -] - - -def filter_datasets(): - """ - Filter datasets to identify and print time series with more than 1000 data points. - - This function iterates over a list of datasets, loads each dataset, - and checks each time series within it. If a series contains more than 1000 - data points, it is counted as a "hit." The function prints up to 10 matches - per dataset in the format: `,`. - - Returns - ------- - None - The function does not return anything but prints matching dataset - and series names to the console. - - Notes - ----- - - The function introduces a 1-second delay (`time.sleep(1)`) between processing - datasets to control HTTP request frequency. - - Uses `gc.collect()` to explicitly trigger garbage collection, to avoid - running out of memory - """ - num_hits = 0 - for dataset_name in filtered_datasets: - # print(f"{dataset_name}") - time.sleep(1) - dataset_counter = 0 - dataset = load_forecasting(dataset_name) - for index, row in enumerate(dataset["series_value"]): - if len(row) > 1000: - num_hits += 1 - dataset_counter += 1 - if dataset_counter <= 10: - print(f"{dataset_name},{dataset['series_name'][index]}") # noqa - # if dataset_counter > 0: - # print(f"{dataset_name}: Hits: {dataset_counter}") - del dataset - gc.collect() - # print(f"Num hits in datasets: {num_hits}") - - -# filter_datasets() - - -def filter_and_categorise_m4(frequency_type): - """ - Filter and categorize M4 dataset time series. - - Parameters - ---------- - frequency_type : str - The frequency type of the M4 dataset to process. - Accepted values: 'yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly'. - - Returns - ------- - None - The function does not return any values but prints categorized series - information. - - Notes - ----- - - The function constructs an appropriate prefix ('Y', 'Q', 'M', 'W', 'D', 'H') - based on the dataset type to match metadata identifiers. - - Limits printed results to 10 per category. - """ - metadata = pd.read_csv("C:/Users/alexb/Downloads/M4-info.csv") - m4daily = load_forecasting(f"m4_{frequency_type}_dataset") - categories = {} - prefix = "" - if frequency_type == "yearly": - prefix = "Y" - elif frequency_type == "quarterly": - prefix = "Q" - elif frequency_type == "monthly": - prefix = "M" - elif frequency_type == "weekly": - prefix = "W" - elif frequency_type == "daily": - prefix = "D" - elif frequency_type == "hourly": - prefix = "H" - for index, row in enumerate(m4daily["series_value"]): - if len(row) > 1000: - category = metadata.loc[ - metadata["M4id"] == f"{prefix}{m4daily['series_name'][index][1:]}", - "category", - ].values[0] - if category not in categories: - categories[category] = 1 - else: - categories[category] += 1 - if categories[category] <= 10: - print( # noqa - f"m4_{frequency_type}_dataset,\ - {m4daily['series_name'][index]},{category}" - ) - - -# filter_and_categorise_m4('monthly') -# filter_and_categorise_m4('weekly') -# filter_and_categorise_m4('daily') -# filter_and_categorise_m4('hourly') - - -def gen_datasets(problem_type, dataset_folder=None): - """ - Generate windowed train/test split of datasets. - - Returns - ------- - None - The function does not return anything but writes out the train and test - files to the specified directory. - - Notes - ----- - - Requires a CSV file containing a list of the series to process. - """ - final_series_selection = pd.read_csv("./aeon/datasets/Final Dataset Selection.csv") - current_dataset = "" - dataset = pd.DataFrame() - tmpdir = tempfile.mkdtemp() - folder = problem_type if dataset_folder is None else dataset_folder - location_of_datasets = f"./aeon/datasets/local_data/{folder}" - if not os.path.exists(location_of_datasets): - os.makedirs(location_of_datasets) - with open(f"{location_of_datasets}/windowed_series.txt", "w") as f: - for item in final_series_selection.to_records(index=False): - if current_dataset != item[0]: - dataset = load_forecasting(item[0], tmpdir) - current_dataset = item[0] - print(f"Current Dataset: {current_dataset}") # noqa - f.write(f"{item[0]}_{item[1]}\n") - series = ( - dataset[dataset["series_name"] == item[1]]["series_value"] - .iloc[0] - .to_numpy() - ) - dataset_name = f"{item[0]}_{item[1]}" - full_file_path = f"{location_of_datasets}/{dataset_name}" - if not os.path.exists(full_file_path): - os.makedirs(full_file_path) - if problem_type == "regression": - write_regression_dataset(series, full_file_path, dataset_name) - elif problem_type == "forecasting": - write_forecasting_dataset(series, full_file_path, dataset_name) - - -gen_datasets("forecasting", "differenced_forecasting") diff --git a/aeon/datasets/tsf_datasets.py b/aeon/datasets/tsf_datasets.py index 562f9ad5ae..ce3248e90e 100644 --- a/aeon/datasets/tsf_datasets.py +++ b/aeon/datasets/tsf_datasets.py @@ -1,6 +1,6 @@ """Datasets in the Monash tser data archives.""" -tsf_all = { +tsf_monash = { "nn5_daily_dataset_with_missing_values": 4656110, "nn5_daily_dataset_without_missing_values": 4656117, "nn5_weekly_dataset": 4656125, From d381d5ee7e9d38bbea58a911d6cbd9c1303e6505 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Sat, 24 May 2025 14:43:02 +0100 Subject: [PATCH 11/70] arima first --- aeon/forecasting/_arima.py | 456 +++++++++++++++++++++++++++++++++++++ 1 file changed, 456 insertions(+) create mode 100644 aeon/forecasting/_arima.py diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py new file mode 100644 index 0000000000..54ebc47fe3 --- /dev/null +++ b/aeon/forecasting/_arima.py @@ -0,0 +1,456 @@ +"""ARIMAForecaster. + +An implementation of the ARIMA forecasting algorithm. +""" + +__maintainer__ = ["alexbanwell1", "TonyBagnall"] +__all__ = ["ARIMAForecaster"] + +from math import comb + +import numpy as np + +from aeon.forecasting._utils import calc_seasonal_period, kpss_test +from aeon.forecasting.base import BaseForecaster + +NOGIL = False +CACHE = True + + +class ARIMAForecaster(BaseForecaster): + """AutoRegressive Integrated Moving Average (ARIMA) forecaster. + + Implements the Hyndman-Khandakar automatic ARIMA algorithm for time series + forecasting with optional seasonal components. The model automatically selects + the orders of the non-seasonal (p, d, q) and seasonal (P, D, Q, m) components + based on information criteria, such as AIC. + + Parameters + ---------- + horizon : int, default=1 + The forecasting horizon, i.e., the number of steps ahead to predict. + + Attributes + ---------- + data_ : list of float + Original training series values. + differenced_data_ : list of float + Differenced version of the training data used for stationarity. + residuals_ : list of float + Residual errors from the fitted model. + aic_ : float + Akaike Information Criterion for the selected model. + p_, d_, q_ : int + Orders of the ARIMA model: autoregressive (p), differencing (d), + and moving average (q) terms. + ps_, ds_, qs_ : int + Orders of the seasonal ARIMA model: seasonal AR (P), seasonal differencing (D), + and seasonal MA (Q) terms. + seasonal_period_ : int + Length of the seasonal cycle. + constant_term_ : float + Constant/intercept term in the model. + c_ : float + Estimated constant term (internal use). + phi_ : array-like + Coefficients for the non-seasonal autoregressive terms. + phi_s_ : array-like + Coefficients for the seasonal autoregressive terms. + theta_ : array-like + Coefficients for the non-seasonal moving average terms. + theta_s_ : array-like + Coefficients for the seasonal moving average terms. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. OTexts, 2014. + https://otexts.com/fpp3/ + """ + + def __init__(self, horizon=1): + super().__init__(horizon=horizon, axis=1) + self.data_ = [] + self.differenced_data_ = [] + self.residuals_ = [] + self.aic_ = 0 + self.p_ = 0 + self.d_ = 0 + self.q_ = 0 + self.ps_ = 0 + self.ds_ = 0 + self.qs_ = 0 + self.seasonal_period_ = 0 + self.constant_term_ = 0 + self.c_ = 0 + self.phi_ = 0 + self.phi_s_ = 0 + self.theta_ = 0 + self.theta_s_ = 0 + + def _fit(self, y, exog=None): + """Fit AutoARIMA forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ARIMAForecaster. + """ + self.data_ = np.array(y.squeeze(), dtype=np.float64) + ( + self.differenced_data_, + self.aic_, + self.p_, + self.d_, + self.q_, + self.ps_, + self.ds_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + parameters, + ) = auto_arima(self.data_) + (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = extract_params( + parameters, self.p_, self.q_, self.ps_, self.qs_, self.constant_term_ + ) + ( + self.aic_, + self.residuals_, + ) = arima_log_likelihood( + parameters, + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + self.constant_term_, + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + y = np.array(y, dtype=np.float64) + value = calc_arima( + self.differenced_data_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + len(self.differenced_data_), + self.c_, + self.phi_, + self.phi_s_, + self.theta_, + self.theta_s_, + self.residuals_, + ) + history = self.data_[::-1] + differenced_history = np.diff(self.data_, n=self.d_)[::-1] + # Step 1: undo seasonal differencing on y^(d) + for k in range(1, self.ds_ + 1): + lag = k * self.seasonal_period_ + value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] + + # Step 2: undo ordinary differencing + for k in range(1, self.d_ + 1): + value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] + + if y is None: + return np.array([value]) + else: + return np.insert(y, 0, value)[:-1] + + +# Define the ARIMA(p, d, q) likelihood function +def arima_log_likelihood( + params, data, p, q, ps, qs, seasonal_period, include_constant_term +): + """Calculate the log-likelihood of an ARIMA model given the parameters.""" + c, phi, phi_s, theta, theta_s = extract_params( + params, p, q, ps, qs, include_constant_term + ) # Extract parameters + + # Initialize residuals + n = len(data) + residuals = np.zeros(n) + for t in range(n): + y_hat = calc_arima( + data, + p, + q, + ps, + qs, + seasonal_period, + t, + c, + phi, + phi_s, + theta, + theta_s, + residuals, + ) + residuals[t] = data[t] - y_hat + # Calculate the log-likelihood + variance = np.mean(residuals**2) + liklihood = n * (np.log(2 * np.pi) + np.log(variance) + 1) + k = len(params) + aic = liklihood + 2 * k + return ( + aic, + residuals, + ) # Return negative log-likelihood for minimization + + +def extract_params(params, p, q, ps, qs, include_constant_term): + """Extract ARIMA parameters from the parameter vector.""" + # Extract parameters + c = params[0] if include_constant_term else 0 # Constant term + # AR coefficients + phi = params[include_constant_term : p + include_constant_term] + # Seasonal AR coefficients + phi_s = params[include_constant_term + p : p + ps + include_constant_term] + # MA coefficients + theta = params[include_constant_term + p + ps : p + ps + q + include_constant_term] + # Seasonal MA coefficents + theta_s = params[ + include_constant_term + p + ps + q : include_constant_term + p + ps + q + qs + ] + return c, phi, phi_s, theta, theta_s + + +def calc_arima( + data, p, q, ps, qs, seasonal_period, t, c, phi, phi_s, theta, theta_s, residuals +): + """Calculate the ARIMA forecast for time t.""" + # AR part + ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) + # Seasonal AR part + ars_term = ( + 0 + if (t - seasonal_period * ps) < 0 + else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) + ) + # MA part + ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) + # Seasonal MA part + mas_term = ( + 0 + if (t - seasonal_period * qs) < 0 + else np.dot( + theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] + ) + ) + y_hat = c + ar_term + ma_term + ars_term + mas_term + return y_hat + + +def nelder_mead( + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + tol=1e-6, + max_iter=500, +): + """Implement the nelder-mead optimisation algorithm.""" + num_params = include_constant_term + p + ps + q + qs + points = np.full((num_params + 1, num_params), 0.5) + for i in range(num_params): + points[i + 1][i] = 0.6 + values = np.array( + [ + arima_log_likelihood( + v, data, p, q, ps, qs, seasonal_period, include_constant_term + )[0] + for v in points + ] + ) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = arima_log_likelihood( + reflected_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if between best and second best, use reflected value + if len(values) > 1 and values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = arima_log_likelihood( + expanded_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = arima_log_likelihood( + contracted_point, + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = arima_log_likelihood( + points[i], + data, + p, + q, + ps, + qs, + seasonal_period, + include_constant_term, + )[0] + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] + + +# def calc_moving_variance(data, window): +# X = np.lib.stride_tricks.sliding_window_view(data, window_shape=window) +# return X.var() + + +def auto_arima(data): + """ + Implement the Hyndman-Khandakar algorithm. + + For automatic ARIMA model selection. + """ + seasonal_period = calc_seasonal_period(data) + difference = 0 + while not kpss_test(data)[1]: + data = np.diff(data, n=1) + difference += 1 + seasonal_difference = 1 if seasonal_period > 1 else 0 + if seasonal_difference: + data = data[seasonal_period:] - data[:-seasonal_period] + include_c = 1 if difference == 0 else 0 + model_parameters = [ + [2, 2, 0, 0, include_c], + [0, 0, 0, 0, include_c], + [1, 0, 0, 0, include_c], + [0, 1, 0, 0, include_c], + ] + model_points = [] + for p in model_parameters: + points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) + p.append(aic) + model_points.append(points) + current_model = min(model_parameters, key=lambda item: item[5]) + current_points = model_points[model_parameters.index(current_model)] + while True: + better_model = False + for param_no in range(4): + for adjustment in [-1, 1]: + if (current_model[param_no] + adjustment) < 0: + continue + model = current_model.copy() + model[param_no] += adjustment + for constant_term in [0, 1]: + points, aic = nelder_mead( + data, + model[0], + model[1], + model[2], + model[3], + seasonal_period, + constant_term, + ) + if aic < current_model[5]: + current_model = model + current_points = points + current_model[5] = aic + current_model[4] = constant_term + better_model = True + if not better_model: + break + return ( + data, + current_model[5], + current_model[0], + difference, + current_model[1], + current_model[2], + seasonal_difference, + current_model[3], + seasonal_period, + current_model[4], + current_points, + ) From 3a0552b469de39ff3f0dada1696c20081a17aa27 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Sat, 24 May 2025 17:29:04 +0100 Subject: [PATCH 12/70] move utils --- aeon/forecasting/_arima.py | 49 ++++++++++++++++++++- aeon/utils/forecasting/__init__.py | 1 + aeon/utils/forecasting/_hypo_tests.py | 63 +++++++++++++++++++++++++++ 3 files changed, 111 insertions(+), 2 deletions(-) create mode 100644 aeon/utils/forecasting/__init__.py create mode 100644 aeon/utils/forecasting/_hypo_tests.py diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 54ebc47fe3..76f4859557 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -9,9 +9,10 @@ from math import comb import numpy as np +from numba import njit -from aeon.forecasting._utils import calc_seasonal_period, kpss_test from aeon.forecasting.base import BaseForecaster +from aeon.utils.forecasting._hypo_tests import kpss_test NOGIL = False CACHE = True @@ -393,7 +394,7 @@ def auto_arima(data): For automatic ARIMA model selection. """ - seasonal_period = calc_seasonal_period(data) + seasonal_period = _calc_seasonal_period(data) difference = 0 while not kpss_test(data)[1]: data = np.diff(data, n=1) @@ -454,3 +455,47 @@ def auto_arima(data): current_model[4], current_points, ) + + +@njit(cache=True, fastmath=True) +def _acf(X, max_lag): + length = len(X) + X_t = np.zeros(max_lag, dtype=float) + for lag in range(1, max_lag + 1): + lag_length = length - lag + x1 = X[:-lag] + x2 = X[lag:] + s1 = np.sum(x1) + s2 = np.sum(x2) + m1 = s1 / lag_length + m2 = s2 / lag_length + ss1 = np.sum(x1 * x1) + ss2 = np.sum(x2 * x2) + v1 = ss1 - s1 * m1 + v2 = ss2 - s2 * m2 + v1_is_zero, v2_is_zero = v1 <= 1e-9, v2 <= 1e-9 + if v1_is_zero and v2_is_zero: # Both zero variance, + # so must be 100% correlated + X_t[lag - 1] = 1 + elif v1_is_zero or v2_is_zero: # One zero variance + # the other not + X_t[lag - 1] = 0 + else: + X_t[lag - 1] = np.sum((x1 - m1) * (x2 - m2)) / np.sqrt(v1 * v2) + return X_t + + +@njit(cache=True, fastmath=True) +def _calc_seasonal_period(data): + """Estimate the seasonal period based on the autocorrelation of the series.""" + lags = _acf(data, 24) + lags = np.concatenate((np.array([1.0]), lags)) + peaks = [] + mean_lags = np.mean(lags) + for i in range(1, len(lags) - 1): # Skip the first (lag 0) and last elements + if lags[i] >= lags[i - 1] and lags[i] >= lags[i + 1] and lags[i] > mean_lags: + peaks.append(i) + if not peaks: + return 1 + else: + return peaks[0] diff --git a/aeon/utils/forecasting/__init__.py b/aeon/utils/forecasting/__init__.py new file mode 100644 index 0000000000..a168fa0f11 --- /dev/null +++ b/aeon/utils/forecasting/__init__.py @@ -0,0 +1 @@ +"""Forecasting utils.""" diff --git a/aeon/utils/forecasting/_hypo_tests.py b/aeon/utils/forecasting/_hypo_tests.py new file mode 100644 index 0000000000..73d4521e5e --- /dev/null +++ b/aeon/utils/forecasting/_hypo_tests.py @@ -0,0 +1,63 @@ +import numpy as np + + +def kpss_test(y, regression="c", lags=None): # Test if time series is stationary + """ + Implement the KPSS test for stationarity. + + Parameters + ---------- + y (array-like): Time series data + regression (str): 'c' for constant, 'ct' for constant + trend + lags (int): Number of lags for HAC variance estimation (default: sqrt(n)) + + Returns + ------- + kpss_stat (float): KPSS test statistic + stationary (bool): Whether the series is stationary according to the test + """ + y = np.asarray(y) + n = len(y) + + # Step 1: Fit regression model to estimate residuals + if regression == "c": # Constant + X = np.ones((n, 1)) + elif regression == "ct": # Constant + Trend + X = np.column_stack((np.ones(n), np.arange(1, n + 1))) + else: + raise ValueError("regression must be 'c' or 'ct'") + + beta = np.linalg.lstsq(X, y, rcond=None)[0] # Estimate regression coefficients + residuals = y - X @ beta # Get residuals (u_t) + + # Step 2: Compute cumulative sum of residuals (S_t) + S_t = np.cumsum(residuals) + + # Step 3: Estimate long-run variance (HAC variance) + if lags is None: + # lags = int(12 * (n / 100)**(1/4)) # Default statsmodels lag length + lags = int(np.sqrt(n)) # Default lag length + + gamma_0 = np.sum(residuals**2) / (n - X.shape[1]) # Lag-0 autocovariance + gamma = [np.sum(residuals[k:] * residuals[:-k]) / n for k in range(1, lags + 1)] + + # Bartlett weights + weights = [1 - (k / (lags + 1)) for k in range(1, lags + 1)] + + # Long-run variance + sigma_squared = gamma_0 + 2 * np.sum([w * g for w, g in zip(weights, gamma)]) + + # Step 4: Calculate the KPSS statistic + kpss_stat = np.sum(S_t**2) / (n**2 * sigma_squared) + + if regression == "ct": + # p. 162 Kwiatkowski et al. (1992): y_t = beta * t + r_t + e_t, + # where beta is the trend, r_t a random walk and e_t a stationary + # error term. + crit = 0.146 + else: # hypo == "c" + # special case of the model above, where beta = 0 (so the null + # hypothesis is that the data is stationary around r_0). + crit = 0.463 + + return kpss_stat, kpss_stat < crit From 0ac5380b4e6a14be8df57c103ca6eabe2a3b7cd1 Mon Sep 17 00:00:00 2001 From: Tony Bagnall Date: Sat, 24 May 2025 17:38:05 +0100 Subject: [PATCH 13/70] make functions private --- aeon/forecasting/_arima.py | 47 +++++++++++++++----------------------- 1 file changed, 19 insertions(+), 28 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 76f4859557..337444f827 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -119,14 +119,14 @@ def _fit(self, y, exog=None): self.seasonal_period_, self.constant_term_, parameters, - ) = auto_arima(self.data_) - (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = extract_params( + ) = _auto_arima(self.data_) + (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = _extract_params( parameters, self.p_, self.q_, self.ps_, self.qs_, self.constant_term_ ) ( self.aic_, self.residuals_, - ) = arima_log_likelihood( + ) = _arima_log_likelihood( parameters, self.differenced_data_, self.p_, @@ -156,7 +156,7 @@ def _predict(self, y=None, exog=None): single prediction self.horizon steps ahead of y. """ y = np.array(y, dtype=np.float64) - value = calc_arima( + value = _calc_arima( self.differenced_data_, self.p_, self.q_, @@ -181,19 +181,15 @@ def _predict(self, y=None, exog=None): # Step 2: undo ordinary differencing for k in range(1, self.d_ + 1): value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - - if y is None: - return np.array([value]) - else: - return np.insert(y, 0, value)[:-1] + return value # Define the ARIMA(p, d, q) likelihood function -def arima_log_likelihood( +def _arima_log_likelihood( params, data, p, q, ps, qs, seasonal_period, include_constant_term ): """Calculate the log-likelihood of an ARIMA model given the parameters.""" - c, phi, phi_s, theta, theta_s = extract_params( + c, phi, phi_s, theta, theta_s = _extract_params( params, p, q, ps, qs, include_constant_term ) # Extract parameters @@ -201,7 +197,7 @@ def arima_log_likelihood( n = len(data) residuals = np.zeros(n) for t in range(n): - y_hat = calc_arima( + y_hat = _calc_arima( data, p, q, @@ -228,7 +224,7 @@ def arima_log_likelihood( ) # Return negative log-likelihood for minimization -def extract_params(params, p, q, ps, qs, include_constant_term): +def _extract_params(params, p, q, ps, qs, include_constant_term): """Extract ARIMA parameters from the parameter vector.""" # Extract parameters c = params[0] if include_constant_term else 0 # Constant term @@ -245,7 +241,7 @@ def extract_params(params, p, q, ps, qs, include_constant_term): return c, phi, phi_s, theta, theta_s -def calc_arima( +def _calc_arima( data, p, q, ps, qs, seasonal_period, t, c, phi, phi_s, theta, theta_s, residuals ): """Calculate the ARIMA forecast for time t.""" @@ -271,7 +267,7 @@ def calc_arima( return y_hat -def nelder_mead( +def _nelder_mead( data, p, q, @@ -289,7 +285,7 @@ def nelder_mead( points[i + 1][i] = 0.6 values = np.array( [ - arima_log_likelihood( + _arima_log_likelihood( v, data, p, q, ps, qs, seasonal_period, include_constant_term )[0] for v in points @@ -307,7 +303,7 @@ def nelder_mead( # Reflection # centre + distance between centre and largest value reflected_point = centre_point + (centre_point - points[-1]) - reflected_value = arima_log_likelihood( + reflected_value = _arima_log_likelihood( reflected_point, data, p, @@ -326,7 +322,7 @@ def nelder_mead( # Otherwise if it is better than the best value if reflected_value < values[0]: expanded_point = centre_point + 2 * (reflected_point - centre_point) - expanded_value = arima_log_likelihood( + expanded_value = _arima_log_likelihood( expanded_point, data, p, @@ -347,7 +343,7 @@ def nelder_mead( # Contraction # Otherwise if reflection is worse than all current values contracted_point = centre_point - 0.5 * (centre_point - points[-1]) - contracted_value = arima_log_likelihood( + contracted_value = _arima_log_likelihood( contracted_point, data, p, @@ -366,7 +362,7 @@ def nelder_mead( # Shrinkage for i in range(1, len(points)): points[i] = points[0] - 0.5 * (points[0] - points[i]) - values[i] = arima_log_likelihood( + values[i] = _arima_log_likelihood( points[i], data, p, @@ -383,12 +379,7 @@ def nelder_mead( return points[0], values[0] -# def calc_moving_variance(data, window): -# X = np.lib.stride_tricks.sliding_window_view(data, window_shape=window) -# return X.var() - - -def auto_arima(data): +def _auto_arima(data): """ Implement the Hyndman-Khandakar algorithm. @@ -411,7 +402,7 @@ def auto_arima(data): ] model_points = [] for p in model_parameters: - points, aic = nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) + points, aic = _nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) p.append(aic) model_points.append(points) current_model = min(model_parameters, key=lambda item: item[5]) @@ -425,7 +416,7 @@ def auto_arima(data): model = current_model.copy() model[param_no] += adjustment for constant_term in [0, 1]: - points, aic = nelder_mead( + points, aic = _nelder_mead( data, model[0], model[1], From 44b36a7b2d34c6b3452fdef97446a4ee83fe5789 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 13:49:51 +0100 Subject: [PATCH 14/70] Modularise SARIMA model --- aeon/forecasting/_arima.py | 363 +++++++----------------- aeon/utils/forecasting/_seasonality.py | 101 +++++++ aeon/utils/optimisation/__init__.py | 1 + aeon/utils/optimisation/_nelder_mead.py | 106 +++++++ 4 files changed, 313 insertions(+), 258 deletions(-) create mode 100644 aeon/utils/forecasting/_seasonality.py create mode 100644 aeon/utils/optimisation/__init__.py create mode 100644 aeon/utils/optimisation/_nelder_mead.py diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 337444f827..00d35ec55c 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -9,10 +9,11 @@ from math import comb import numpy as np -from numba import njit from aeon.forecasting.base import BaseForecaster from aeon.utils.forecasting._hypo_tests import kpss_test +from aeon.utils.forecasting._seasonality import calc_seasonal_period +from aeon.utils.optimisation._nelder_mead import nelder_mead NOGIL = False CACHE = True @@ -83,11 +84,13 @@ def __init__(self, horizon=1): self.qs_ = 0 self.seasonal_period_ = 0 self.constant_term_ = 0 + self.model_ = [] self.c_ = 0 self.phi_ = 0 self.phi_s_ = 0 self.theta_ = 0 self.theta_s_ = 0 + self.parameters_ = [] def _fit(self, y, exog=None): """Fit AutoARIMA forecaster to series y. @@ -109,32 +112,28 @@ def _fit(self, y, exog=None): self.data_ = np.array(y.squeeze(), dtype=np.float64) ( self.differenced_data_, + self.d_, + self.ds_, + self.model_, + self.parameters_, self.aic_, + ) = _auto_arima(self.data_) + ( + self.constant_term_, self.p_, - self.d_, self.q_, self.ps_, - self.ds_, self.qs_, self.seasonal_period_, - self.constant_term_, - parameters, - ) = _auto_arima(self.data_) + ) = self.model_ (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = _extract_params( - parameters, self.p_, self.q_, self.ps_, self.qs_, self.constant_term_ + self.parameters_, self.model_ ) ( self.aic_, self.residuals_, - ) = _arima_log_likelihood( - parameters, - self.differenced_data_, - self.p_, - self.q_, - self.ps_, - self.qs_, - self.seasonal_period_, - self.constant_term_, + ) = _arima_model( + self.parameters_, _calc_sarima, self.differenced_data_, self.model_ ) return self @@ -156,19 +155,11 @@ def _predict(self, y=None, exog=None): single prediction self.horizon steps ahead of y. """ y = np.array(y, dtype=np.float64) - value = _calc_arima( + value = _calc_sarima( self.differenced_data_, - self.p_, - self.q_, - self.ps_, - self.qs_, - self.seasonal_period_, + self.model_, len(self.differenced_data_), - self.c_, - self.phi_, - self.phi_s_, - self.theta_, - self.theta_s_, + _extract_params(self.parameters_, self.model_), self.residuals_, ) history = self.data_[::-1] @@ -184,78 +175,86 @@ def _predict(self, y=None, exog=None): return value +def _aic(residuals, num_params): + """Calculate the log-likelihood of a model.""" + variance = np.mean(residuals**2) + liklihood = len(residuals) * (np.log(2 * np.pi) + np.log(variance) + 1) + return liklihood + 2 * num_params + + # Define the ARIMA(p, d, q) likelihood function -def _arima_log_likelihood( - params, data, p, q, ps, qs, seasonal_period, include_constant_term -): +def _arima_model(params, base_function, data, model): """Calculate the log-likelihood of an ARIMA model given the parameters.""" - c, phi, phi_s, theta, theta_s = _extract_params( - params, p, q, ps, qs, include_constant_term - ) # Extract parameters + formatted_params = _extract_params(params, model) # Extract parameters # Initialize residuals n = len(data) residuals = np.zeros(n) for t in range(n): - y_hat = _calc_arima( + y_hat = base_function( data, - p, - q, - ps, - qs, - seasonal_period, + model, t, - c, - phi, - phi_s, - theta, - theta_s, + formatted_params, residuals, ) residuals[t] = data[t] - y_hat - # Calculate the log-likelihood - variance = np.mean(residuals**2) - liklihood = n * (np.log(2 * np.pi) + np.log(variance) + 1) - k = len(params) - aic = liklihood + 2 * k - return ( - aic, - residuals, - ) # Return negative log-likelihood for minimization + return _aic(residuals, len(params)), residuals -def _extract_params(params, p, q, ps, qs, include_constant_term): - """Extract ARIMA parameters from the parameter vector.""" - # Extract parameters - c = params[0] if include_constant_term else 0 # Constant term - # AR coefficients - phi = params[include_constant_term : p + include_constant_term] - # Seasonal AR coefficients - phi_s = params[include_constant_term + p : p + ps + include_constant_term] - # MA coefficients - theta = params[include_constant_term + p + ps : p + ps + q + include_constant_term] - # Seasonal MA coefficents - theta_s = params[ - include_constant_term + p + ps + q : include_constant_term + p + ps + q + qs - ] - return c, phi, phi_s, theta, theta_s +# Define the SARIMA(p, d, q)(P, D, Q) likelihood function -def _calc_arima( - data, p, q, ps, qs, seasonal_period, t, c, phi, phi_s, theta, theta_s, residuals -): +def _extract_params(params, model): + """Extract ARIMA parameters from the parameter vector.""" + if len(params) != np.sum(model): + previous_length = np.sum(model) + model = model[:-1] # Remove the seasonal period + if len(params) != np.sum(model): + raise ValueError( + f"Expected {previous_length} parameters for a non-seasonal model or \ + {np.sum(model)} parameters for a seasonal model, got {len(params)}" + ) + starts = np.cumsum([0] + model[:-1]) + return [params[s : s + l].tolist() for s, l in zip(starts, model)] + + +def _calc_arima(data, model, t, formatted_params, residuals): """Calculate the ARIMA forecast for time t.""" + if len(model) != 3: + raise ValueError("Model must be of the form (c, p, q)") # AR part + p = model[1] + phi = formatted_params[1] ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) + + # MA part + q = model[2] + theta = formatted_params[2] + ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) + + c = formatted_params[0][0] if model[0] else 0 + y_hat = c + ar_term + ma_term + return y_hat + + +def _calc_sarima(data, model, t, formatted_params, residuals): + """Calculate the SARIMA forecast for time t.""" + if len(model) != 6: + raise ValueError("Model must be of the form (c, p, q, ps, qs, seasonal_period)") + arima_forecast = _calc_arima(data, model[:3], t, formatted_params, residuals) + seasonal_period = model[5] # Seasonal AR part + ps = model[3] + phi_s = formatted_params[3] ars_term = ( 0 if (t - seasonal_period * ps) < 0 else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) ) - # MA part - ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) # Seasonal MA part + qs = model[4] + theta_s = formatted_params[4] mas_term = ( 0 if (t - seasonal_period * qs) < 0 @@ -263,120 +262,20 @@ def _calc_arima( theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] ) ) - y_hat = c + ar_term + ma_term + ars_term + mas_term - return y_hat + return arima_forecast + ars_term + mas_term -def _nelder_mead( - data, - p, - q, - ps, - qs, - seasonal_period, - include_constant_term, - tol=1e-6, - max_iter=500, -): - """Implement the nelder-mead optimisation algorithm.""" - num_params = include_constant_term + p + ps + q + qs - points = np.full((num_params + 1, num_params), 0.5) - for i in range(num_params): - points[i + 1][i] = 0.6 - values = np.array( - [ - _arima_log_likelihood( - v, data, p, q, ps, qs, seasonal_period, include_constant_term - )[0] - for v in points - ] - ) - for _iteration in range(max_iter): - # Order simplex by function values - order = np.argsort(values) - points = points[order] - values = values[order] - - # Centroid of the best n points - centre_point = points[:-1].sum(axis=0) / len(points[:-1]) - - # Reflection - # centre + distance between centre and largest value - reflected_point = centre_point + (centre_point - points[-1]) - reflected_value = _arima_log_likelihood( - reflected_point, - data, - p, - q, - ps, - qs, - seasonal_period, - include_constant_term, - )[0] - # if between best and second best, use reflected value - if len(values) > 1 and values[0] <= reflected_value < values[-2]: - points[-1] = reflected_point - values[-1] = reflected_value - continue - # Expansion - # Otherwise if it is better than the best value - if reflected_value < values[0]: - expanded_point = centre_point + 2 * (reflected_point - centre_point) - expanded_value = _arima_log_likelihood( - expanded_point, - data, - p, - q, - ps, - qs, - seasonal_period, - include_constant_term, - )[0] - # if less than reflected value use expanded, otherwise go back to reflected - if expanded_value < reflected_value: - points[-1] = expanded_point - values[-1] = expanded_value - else: - points[-1] = reflected_point - values[-1] = reflected_value - continue - # Contraction - # Otherwise if reflection is worse than all current values - contracted_point = centre_point - 0.5 * (centre_point - points[-1]) - contracted_value = _arima_log_likelihood( - contracted_point, - data, - p, - q, - ps, - qs, - seasonal_period, - include_constant_term, - )[0] - # If contraction is better use that otherwise move to shrinkage - if contracted_value < values[-1]: - points[-1] = contracted_point - values[-1] = contracted_value - continue - - # Shrinkage - for i in range(1, len(points)): - points[i] = points[0] - 0.5 * (points[0] - points[i]) - values[i] = _arima_log_likelihood( - points[i], - data, - p, - q, - ps, - qs, - seasonal_period, - include_constant_term, - )[0] - - # Convergence check - if np.max(np.abs(values - values[0])) < tol: - break - return points[0], values[0] +def make_arima_llf(base_function, data, model): + """ + Return a parameterized log-likelihood function for ARIMA. + + This can then be used with an optimization algorithm. + """ + + def loss_fn(v): + return _arima_model(v, base_function, data, model)[0] + + return loss_fn def _auto_arima(data): @@ -385,7 +284,7 @@ def _auto_arima(data): For automatic ARIMA model selection. """ - seasonal_period = _calc_seasonal_period(data) + seasonal_period = calc_seasonal_period(data) difference = 0 while not kpss_test(data)[1]: data = np.diff(data, n=1) @@ -395,98 +294,46 @@ def _auto_arima(data): data = data[seasonal_period:] - data[:-seasonal_period] include_c = 1 if difference == 0 else 0 model_parameters = [ - [2, 2, 0, 0, include_c], - [0, 0, 0, 0, include_c], - [1, 0, 0, 0, include_c], - [0, 1, 0, 0, include_c], + [include_c, 2, 2, 0, 0, seasonal_period], + [include_c, 0, 0, 0, 0, seasonal_period], + [include_c, 1, 0, 0, 0, seasonal_period], + [include_c, 0, 1, 0, 0, seasonal_period], ] model_points = [] + model_scores = [] for p in model_parameters: - points, aic = _nelder_mead(data, p[0], p[1], p[2], p[3], seasonal_period, p[4]) - p.append(aic) + points, aic = nelder_mead(make_arima_llf(_calc_sarima, data, p), np.sum(p[:5])) model_points.append(points) - current_model = min(model_parameters, key=lambda item: item[5]) - current_points = model_points[model_parameters.index(current_model)] + model_scores.append(aic) + best_score = min(model_scores) + best_index = model_scores.index(best_score) + current_model = model_parameters[best_index] + current_points = model_points[best_index] while True: better_model = False - for param_no in range(4): + for param_no in range(1, 5): for adjustment in [-1, 1]: if (current_model[param_no] + adjustment) < 0: continue model = current_model.copy() model[param_no] += adjustment for constant_term in [0, 1]: - points, aic = _nelder_mead( - data, - model[0], - model[1], - model[2], - model[3], - seasonal_period, - constant_term, + model[0] = constant_term + points, aic = nelder_mead( + make_arima_llf(_calc_sarima, data, model), np.sum(model[:5]) ) - if aic < current_model[5]: - current_model = model + if aic < best_score: + current_model = model.copy() current_points = points - current_model[5] = aic - current_model[4] = constant_term + best_score = aic better_model = True if not better_model: break return ( data, - current_model[5], - current_model[0], difference, - current_model[1], - current_model[2], seasonal_difference, - current_model[3], - seasonal_period, - current_model[4], + current_model, current_points, + best_score, ) - - -@njit(cache=True, fastmath=True) -def _acf(X, max_lag): - length = len(X) - X_t = np.zeros(max_lag, dtype=float) - for lag in range(1, max_lag + 1): - lag_length = length - lag - x1 = X[:-lag] - x2 = X[lag:] - s1 = np.sum(x1) - s2 = np.sum(x2) - m1 = s1 / lag_length - m2 = s2 / lag_length - ss1 = np.sum(x1 * x1) - ss2 = np.sum(x2 * x2) - v1 = ss1 - s1 * m1 - v2 = ss2 - s2 * m2 - v1_is_zero, v2_is_zero = v1 <= 1e-9, v2 <= 1e-9 - if v1_is_zero and v2_is_zero: # Both zero variance, - # so must be 100% correlated - X_t[lag - 1] = 1 - elif v1_is_zero or v2_is_zero: # One zero variance - # the other not - X_t[lag - 1] = 0 - else: - X_t[lag - 1] = np.sum((x1 - m1) * (x2 - m2)) / np.sqrt(v1 * v2) - return X_t - - -@njit(cache=True, fastmath=True) -def _calc_seasonal_period(data): - """Estimate the seasonal period based on the autocorrelation of the series.""" - lags = _acf(data, 24) - lags = np.concatenate((np.array([1.0]), lags)) - peaks = [] - mean_lags = np.mean(lags) - for i in range(1, len(lags) - 1): # Skip the first (lag 0) and last elements - if lags[i] >= lags[i - 1] and lags[i] >= lags[i + 1] and lags[i] > mean_lags: - peaks.append(i) - if not peaks: - return 1 - else: - return peaks[0] diff --git a/aeon/utils/forecasting/_seasonality.py b/aeon/utils/forecasting/_seasonality.py new file mode 100644 index 0000000000..356b1a40d2 --- /dev/null +++ b/aeon/utils/forecasting/_seasonality.py @@ -0,0 +1,101 @@ +"""Seasonality Tools. + +Includes autocorrelation function (ACF) and seasonal period estimation. +""" + +import numpy as np +from numba import njit + + +@njit(cache=True, fastmath=True) +def acf(X, max_lag): + """ + Compute the sample autocorrelation function (ACF) of a time series. + + Up to a specified maximum lag. + + The autocorrelation at lag k is defined as the Pearson correlation + coefficient between the series and a lagged version of itself. + If both segments at a given lag have zero variance, the function + returns 1 for that lag. If only one segment has zero variance, + the function returns 0. + + Parameters + ---------- + X : array-like, shape (n_samples,) + The input time series data. + max_lag : int + The maximum lag (number of steps) for which to + compute the autocorrelation. + + Returns + ------- + acf_values : np.ndarray, shape (max_lag,) + The autocorrelation values for lags 1 through `max_lag`. + + Notes + ----- + The function handles cases where the lagged segments have zero + variance to avoid division by zero. + The returned values correspond to + lags 1, 2, ..., `max_lag` (not including lag 0). + """ + length = len(X) + X_t = np.zeros(max_lag, dtype=float) + for lag in range(1, max_lag + 1): + lag_length = length - lag + x1 = X[:-lag] + x2 = X[lag:] + s1 = np.sum(x1) + s2 = np.sum(x2) + m1 = s1 / lag_length + m2 = s2 / lag_length + ss1 = np.sum(x1 * x1) + ss2 = np.sum(x2 * x2) + v1 = ss1 - s1 * m1 + v2 = ss2 - s2 * m2 + v1_is_zero, v2_is_zero = v1 <= 1e-9, v2 <= 1e-9 + if v1_is_zero and v2_is_zero: # Both zero variance, + # so must be 100% correlated + X_t[lag - 1] = 1 + elif v1_is_zero or v2_is_zero: # One zero variance + # the other not + X_t[lag - 1] = 0 + else: + X_t[lag - 1] = np.sum((x1 - m1) * (x2 - m2)) / np.sqrt(v1 * v2) + return X_t + + +@njit(cache=True, fastmath=True) +def calc_seasonal_period(data): + """ + Estimate the seasonal period of a time series using autocorrelation analysis. + + This function computes the autocorrelation function (ACF) of + the input series up to lag 24. It then identifies peaks in the + ACF above the mean value, treating the first such peak + as the estimated seasonal period. If no peak is found, + a period of 1 is returned. + + Parameters + ---------- + data : array-like, shape (n_samples,) + The input time series data. + + Returns + ------- + period : int + The estimated seasonal period (lag) of the series. Returns 1 if no significant + peak is detected in the autocorrelation. + """ + lags = acf(data, 24) + lags = np.concatenate((np.array([1.0]), lags)) + peaks = [] + mean_lags = np.mean(lags) + for i in range(1, len(lags) - 1): # Skip the first (lag 0) and last elements + if lags[i] >= lags[i - 1] and lags[i] >= lags[i + 1] and lags[i] > mean_lags: + peaks.append(i) + if not peaks: + return 1 + else: + return peaks[0] diff --git a/aeon/utils/optimisation/__init__.py b/aeon/utils/optimisation/__init__.py new file mode 100644 index 0000000000..11eddea791 --- /dev/null +++ b/aeon/utils/optimisation/__init__.py @@ -0,0 +1 @@ +"""Optimisation utils.""" diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py new file mode 100644 index 0000000000..36dfe732ab --- /dev/null +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -0,0 +1,106 @@ +"""Optimisation algorithms for automatic parameter tuning.""" + +import numpy as np + + +def nelder_mead( + loss_function, + num_params, + tol=1e-6, + max_iter=500, +): + """ + Perform optimisation using the Nelder–Mead simplex algorithm. + + This function minimises a given loss (objective) function using the Nelder–Mead + algorithm, a derivative-free method that iteratively refines a simplex of candidate + solutions. The implementation supports unconstrained minimisation of functions + with a fixed number of parameters. + + Parameters + ---------- + loss_function : callable + The objective function to minimise. Should accept a 1D NumPy array of length + `num_params` and return a scalar value. + num_params : int + The number of parameters (dimensions) in the optimisation problem. + tol : float, optional (default=1e-6) + Tolerance for convergence. The algorithm stops when the maximum difference + between function values at simplex vertices is less than `tol`. + max_iter : int, optional (default=500) + Maximum number of iterations to perform. + + Returns + ------- + best_params : np.ndarray, shape (`num_params`,) + The parameter vector that minimises the loss function. + best_value : float + The value of the loss function at the optimal parameter vector. + + Notes + ----- + - The initial simplex is constructed by setting each parameter to 0.5, + with one additional point per dimension at 0.6 for that dimension. + - This implementation does not support constraints or bounds on the parameters. + - The algorithm does not guarantee finding a global minimum. + + Examples + -------- + >>> def sphere(x): + ... return np.sum(x**2) + >>> x_opt, val = nelder_mead(sphere, num_params=2) + """ + points = np.full((num_params + 1, num_params), 0.5) + for i in range(num_params): + points[i + 1][i] = 0.6 + values = np.array([loss_function(v) for v in points]) + for _iteration in range(max_iter): + # Order simplex by function values + order = np.argsort(values) + points = points[order] + values = values[order] + + # Centroid of the best n points + centre_point = points[:-1].sum(axis=0) / len(points[:-1]) + + # Reflection + # centre + distance between centre and largest value + reflected_point = centre_point + (centre_point - points[-1]) + reflected_value = loss_function(reflected_point) + # if between best and second best, use reflected value + if len(values) > 1 and values[0] <= reflected_value < values[-2]: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Expansion + # Otherwise if it is better than the best value + if reflected_value < values[0]: + expanded_point = centre_point + 2 * (reflected_point - centre_point) + expanded_value = loss_function(expanded_point) + # if less than reflected value use expanded, otherwise go back to reflected + if expanded_value < reflected_value: + points[-1] = expanded_point + values[-1] = expanded_value + else: + points[-1] = reflected_point + values[-1] = reflected_value + continue + # Contraction + # Otherwise if reflection is worse than all current values + contracted_point = centre_point - 0.5 * (centre_point - points[-1]) + contracted_value = loss_function(contracted_point) + # If contraction is better use that otherwise move to shrinkage + if contracted_value < values[-1]: + points[-1] = contracted_point + values[-1] = contracted_value + continue + + # Shrinkage + for i in range(1, len(points)): + points[i] = points[0] - 0.5 * (points[0] - points[i]) + values[i] = loss_function(points[i]) + + # Convergence check + if np.max(np.abs(values - values[0])) < tol: + break + return points[0], values[0] From 6d18de9c7c7dea345e2accedd7ef16be65e83ac7 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 14:05:57 +0100 Subject: [PATCH 15/70] Add ARIMA forecaster to forecasting package --- aeon/forecasting/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index 7a331f69e6..f6983cb89c 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -5,8 +5,10 @@ "BaseForecaster", "RegressionForecaster", "ETSForecaster", + "ARIMAForecaster", ] +from aeon.forecasting._arima import ARIMAForecaster from aeon.forecasting._ets import ETSForecaster from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster From b7e642432fc931d09a3f1b35b77e4f74c9a63f3b Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 14:06:31 +0100 Subject: [PATCH 16/70] Add example to ARIMA forecaster, this also tests the forecaster is producing the expected results --- aeon/forecasting/_arima.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 00d35ec55c..4c0e383140 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -68,6 +68,17 @@ class ARIMAForecaster(BaseForecaster): .. [1] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice. OTexts, 2014. https://otexts.com/fpp3/ + + Examples + -------- + >>> from aeon.forecasting import ARIMAForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = ARIMAForecaster() + >>> forecaster.fit(y) + ARIMAForecaster() + >>> forecaster.predict() + 450.74890401954826 """ def __init__(self, horizon=1): From e33fa4d3d33121b30f30ca903c49ac996c6dd5b8 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 18:24:08 +0100 Subject: [PATCH 17/70] Basic ARIMA model --- aeon/forecasting/_arima.py | 168 +++++-------------------------------- 1 file changed, 21 insertions(+), 147 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 4c0e383140..29c42bffe5 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -11,8 +11,6 @@ import numpy as np from aeon.forecasting.base import BaseForecaster -from aeon.utils.forecasting._hypo_tests import kpss_test -from aeon.utils.forecasting._seasonality import calc_seasonal_period from aeon.utils.optimisation._nelder_mead import nelder_mead NOGIL = False @@ -22,10 +20,8 @@ class ARIMAForecaster(BaseForecaster): """AutoRegressive Integrated Moving Average (ARIMA) forecaster. - Implements the Hyndman-Khandakar automatic ARIMA algorithm for time series - forecasting with optional seasonal components. The model automatically selects - the orders of the non-seasonal (p, d, q) and seasonal (P, D, Q, m) components - based on information criteria, such as AIC. + The model automatically selects the parameters of the model based + on information criteria, such as AIC. Parameters ---------- @@ -45,23 +41,14 @@ class ARIMAForecaster(BaseForecaster): p_, d_, q_ : int Orders of the ARIMA model: autoregressive (p), differencing (d), and moving average (q) terms. - ps_, ds_, qs_ : int - Orders of the seasonal ARIMA model: seasonal AR (P), seasonal differencing (D), - and seasonal MA (Q) terms. - seasonal_period_ : int - Length of the seasonal cycle. constant_term_ : float Constant/intercept term in the model. c_ : float Estimated constant term (internal use). phi_ : array-like Coefficients for the non-seasonal autoregressive terms. - phi_s_ : array-like - Coefficients for the seasonal autoregressive terms. theta_ : array-like Coefficients for the non-seasonal moving average terms. - theta_s_ : array-like - Coefficients for the seasonal moving average terms. References ---------- @@ -74,33 +61,27 @@ class ARIMAForecaster(BaseForecaster): >>> from aeon.forecasting import ARIMAForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = ARIMAForecaster() + >>> forecaster = ARIMAForecaster(2,1,1,0) >>> forecaster.fit(y) ARIMAForecaster() >>> forecaster.predict() - 450.74890401954826 + 550.9147246631134 """ - def __init__(self, horizon=1): + def __init__(self, p=1, d=0, q=1, constant_term=0, horizon=1): super().__init__(horizon=horizon, axis=1) self.data_ = [] self.differenced_data_ = [] self.residuals_ = [] self.aic_ = 0 - self.p_ = 0 - self.d_ = 0 - self.q_ = 0 - self.ps_ = 0 - self.ds_ = 0 - self.qs_ = 0 - self.seasonal_period_ = 0 - self.constant_term_ = 0 + self.p = p + self.d = d + self.q = q + self.constant_term = constant_term self.model_ = [] self.c_ = 0 self.phi_ = 0 - self.phi_s_ = 0 self.theta_ = 0 - self.theta_s_ = 0 self.parameters_ = [] def _fit(self, y, exog=None): @@ -121,30 +102,17 @@ def _fit(self, y, exog=None): Fitted ARIMAForecaster. """ self.data_ = np.array(y.squeeze(), dtype=np.float64) - ( - self.differenced_data_, - self.d_, - self.ds_, - self.model_, - self.parameters_, - self.aic_, - ) = _auto_arima(self.data_) - ( - self.constant_term_, - self.p_, - self.q_, - self.ps_, - self.qs_, - self.seasonal_period_, - ) = self.model_ - (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = _extract_params( + self.model_ = [self.constant_term, self.p, self.q] + self.differenced_data_ = np.diff(self.data_, n=self.d) + (self.parameters_, self.aic_) = nelder_mead( + make_arima_llf(_calc_arima, self.data_, self.model_), + np.sum(self.model_[:3]), + ) + (self.c_, self.phi_, self.theta_) = _extract_params( self.parameters_, self.model_ ) - ( - self.aic_, - self.residuals_, - ) = _arima_model( - self.parameters_, _calc_sarima, self.differenced_data_, self.model_ + (self.aic_, self.residuals_) = _arima_model( + self.parameters_, _calc_arima, self.differenced_data_, self.model_ ) return self @@ -166,7 +134,7 @@ def _predict(self, y=None, exog=None): single prediction self.horizon steps ahead of y. """ y = np.array(y, dtype=np.float64) - value = _calc_sarima( + value = _calc_arima( self.differenced_data_, self.model_, len(self.differenced_data_), @@ -174,15 +142,9 @@ def _predict(self, y=None, exog=None): self.residuals_, ) history = self.data_[::-1] - differenced_history = np.diff(self.data_, n=self.d_)[::-1] - # Step 1: undo seasonal differencing on y^(d) - for k in range(1, self.ds_ + 1): - lag = k * self.seasonal_period_ - value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] - # Step 2: undo ordinary differencing - for k in range(1, self.d_ + 1): - value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] + for k in range(1, self.d + 1): + value += (-1) ** (k + 1) * comb(self.d, k) * history[k - 1] return value @@ -249,33 +211,6 @@ def _calc_arima(data, model, t, formatted_params, residuals): return y_hat -def _calc_sarima(data, model, t, formatted_params, residuals): - """Calculate the SARIMA forecast for time t.""" - if len(model) != 6: - raise ValueError("Model must be of the form (c, p, q, ps, qs, seasonal_period)") - arima_forecast = _calc_arima(data, model[:3], t, formatted_params, residuals) - seasonal_period = model[5] - # Seasonal AR part - ps = model[3] - phi_s = formatted_params[3] - ars_term = ( - 0 - if (t - seasonal_period * ps) < 0 - else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) - ) - # Seasonal MA part - qs = model[4] - theta_s = formatted_params[4] - mas_term = ( - 0 - if (t - seasonal_period * qs) < 0 - else np.dot( - theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] - ) - ) - return arima_forecast + ars_term + mas_term - - def make_arima_llf(base_function, data, model): """ Return a parameterized log-likelihood function for ARIMA. @@ -287,64 +222,3 @@ def loss_fn(v): return _arima_model(v, base_function, data, model)[0] return loss_fn - - -def _auto_arima(data): - """ - Implement the Hyndman-Khandakar algorithm. - - For automatic ARIMA model selection. - """ - seasonal_period = calc_seasonal_period(data) - difference = 0 - while not kpss_test(data)[1]: - data = np.diff(data, n=1) - difference += 1 - seasonal_difference = 1 if seasonal_period > 1 else 0 - if seasonal_difference: - data = data[seasonal_period:] - data[:-seasonal_period] - include_c = 1 if difference == 0 else 0 - model_parameters = [ - [include_c, 2, 2, 0, 0, seasonal_period], - [include_c, 0, 0, 0, 0, seasonal_period], - [include_c, 1, 0, 0, 0, seasonal_period], - [include_c, 0, 1, 0, 0, seasonal_period], - ] - model_points = [] - model_scores = [] - for p in model_parameters: - points, aic = nelder_mead(make_arima_llf(_calc_sarima, data, p), np.sum(p[:5])) - model_points.append(points) - model_scores.append(aic) - best_score = min(model_scores) - best_index = model_scores.index(best_score) - current_model = model_parameters[best_index] - current_points = model_points[best_index] - while True: - better_model = False - for param_no in range(1, 5): - for adjustment in [-1, 1]: - if (current_model[param_no] + adjustment) < 0: - continue - model = current_model.copy() - model[param_no] += adjustment - for constant_term in [0, 1]: - model[0] = constant_term - points, aic = nelder_mead( - make_arima_llf(_calc_sarima, data, model), np.sum(model[:5]) - ) - if aic < best_score: - current_model = model.copy() - current_points = points - best_score = aic - better_model = True - if not better_model: - break - return ( - data, - difference, - seasonal_difference, - current_model, - current_points, - best_score, - ) From f613f7e4cd40990f577c3e7ce286bf14c59abdd8 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 18:42:25 +0100 Subject: [PATCH 18/70] Convert ARIMA to numba version --- aeon/forecasting/_arima.py | 53 +++++++++++++------------ aeon/utils/optimisation/_nelder_mead.py | 14 ++++--- 2 files changed, 37 insertions(+), 30 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 29c42bffe5..4ca197f3f0 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -9,6 +9,7 @@ from math import comb import numpy as np +from numba import njit from aeon.forecasting.base import BaseForecaster from aeon.utils.optimisation._nelder_mead import nelder_mead @@ -65,7 +66,7 @@ class ARIMAForecaster(BaseForecaster): >>> forecaster.fit(y) ARIMAForecaster() >>> forecaster.predict() - 550.9147246631134 + 550.9147246631135 """ def __init__(self, p=1, d=0, q=1, constant_term=0, horizon=1): @@ -102,11 +103,13 @@ def _fit(self, y, exog=None): Fitted ARIMAForecaster. """ self.data_ = np.array(y.squeeze(), dtype=np.float64) - self.model_ = [self.constant_term, self.p, self.q] + self.model_ = np.array((self.constant_term, self.p, self.q), dtype=np.int32) self.differenced_data_ = np.diff(self.data_, n=self.d) (self.parameters_, self.aic_) = nelder_mead( - make_arima_llf(_calc_arima, self.data_, self.model_), + _arima_model_wrapper, np.sum(self.model_[:3]), + self.data_, + self.model_, ) (self.c_, self.phi_, self.theta_) = _extract_params( self.parameters_, self.model_ @@ -148,6 +151,7 @@ def _predict(self, y=None, exog=None): return value +@njit(cache=True, fastmath=True) def _aic(residuals, num_params): """Calculate the log-likelihood of a model.""" variance = np.mean(residuals**2) @@ -155,7 +159,13 @@ def _aic(residuals, num_params): return liklihood + 2 * num_params +@njit(fastmath=True) +def _arima_model_wrapper(params, data, model): + return _arima_model(params, _calc_arima, data, model)[0] + + # Define the ARIMA(p, d, q) likelihood function +@njit(cache=True, fastmath=True) def _arima_model(params, base_function, data, model): """Calculate the log-likelihood of an ARIMA model given the parameters.""" formatted_params = _extract_params(params, model) # Extract parameters @@ -175,9 +185,7 @@ def _arima_model(params, base_function, data, model): return _aic(residuals, len(params)), residuals -# Define the SARIMA(p, d, q)(P, D, Q) likelihood function - - +@njit(cache=True, fastmath=True) def _extract_params(params, model): """Extract ARIMA parameters from the parameter vector.""" if len(params) != np.sum(model): @@ -188,37 +196,32 @@ def _extract_params(params, model): f"Expected {previous_length} parameters for a non-seasonal model or \ {np.sum(model)} parameters for a seasonal model, got {len(params)}" ) - starts = np.cumsum([0] + model[:-1]) - return [params[s : s + l].tolist() for s, l in zip(starts, model)] - - + starts = np.cumsum(np.concatenate((np.zeros(1, dtype=np.int32), model[:-1]))) + n = len(starts) + max_len = np.max(model) + result = np.full((n, max_len), np.nan, dtype=params.dtype) + for i in range(n): + length = model[i] + start = starts[i] + result[i, :length] = params[start : start + length] + return result + + +@njit(cache=True, fastmath=True) def _calc_arima(data, model, t, formatted_params, residuals): """Calculate the ARIMA forecast for time t.""" if len(model) != 3: raise ValueError("Model must be of the form (c, p, q)") # AR part p = model[1] - phi = formatted_params[1] + phi = formatted_params[1][:p] ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) # MA part q = model[2] - theta = formatted_params[2] + theta = formatted_params[2][:q] ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) c = formatted_params[0][0] if model[0] else 0 y_hat = c + ar_term + ma_term return y_hat - - -def make_arima_llf(base_function, data, model): - """ - Return a parameterized log-likelihood function for ARIMA. - - This can then be used with an optimization algorithm. - """ - - def loss_fn(v): - return _arima_model(v, base_function, data, model)[0] - - return loss_fn diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py index 36dfe732ab..749187541d 100644 --- a/aeon/utils/optimisation/_nelder_mead.py +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -1,11 +1,15 @@ """Optimisation algorithms for automatic parameter tuning.""" import numpy as np +from numba import njit +@njit(fastmath=True) def nelder_mead( loss_function, num_params, + data, + model, tol=1e-6, max_iter=500, ): @@ -53,7 +57,7 @@ def nelder_mead( points = np.full((num_params + 1, num_params), 0.5) for i in range(num_params): points[i + 1][i] = 0.6 - values = np.array([loss_function(v) for v in points]) + values = np.array([loss_function(v, data, model) for v in points]) for _iteration in range(max_iter): # Order simplex by function values order = np.argsort(values) @@ -66,7 +70,7 @@ def nelder_mead( # Reflection # centre + distance between centre and largest value reflected_point = centre_point + (centre_point - points[-1]) - reflected_value = loss_function(reflected_point) + reflected_value = loss_function(reflected_point, data, model) # if between best and second best, use reflected value if len(values) > 1 and values[0] <= reflected_value < values[-2]: points[-1] = reflected_point @@ -76,7 +80,7 @@ def nelder_mead( # Otherwise if it is better than the best value if reflected_value < values[0]: expanded_point = centre_point + 2 * (reflected_point - centre_point) - expanded_value = loss_function(expanded_point) + expanded_value = loss_function(expanded_point, data, model) # if less than reflected value use expanded, otherwise go back to reflected if expanded_value < reflected_value: points[-1] = expanded_point @@ -88,7 +92,7 @@ def nelder_mead( # Contraction # Otherwise if reflection is worse than all current values contracted_point = centre_point - 0.5 * (centre_point - points[-1]) - contracted_value = loss_function(contracted_point) + contracted_value = loss_function(contracted_point, data, model) # If contraction is better use that otherwise move to shrinkage if contracted_value < values[-1]: points[-1] = contracted_point @@ -98,7 +102,7 @@ def nelder_mead( # Shrinkage for i in range(1, len(points)): points[i] = points[0] - 0.5 * (points[0] - points[i]) - values[i] = loss_function(points[i]) + values[i] = loss_function(points[i], data, model) # Convergence check if np.max(np.abs(values - values[0])) < tol: From 24ab43332c05af9e8011f438338c2d3ec3d32fbe Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 19:04:29 +0100 Subject: [PATCH 19/70] Add Auto ARIMA starting point --- aeon/forecasting/_auto_arima.py | 350 ++++++++++++++++++++++++++++++++ 1 file changed, 350 insertions(+) create mode 100644 aeon/forecasting/_auto_arima.py diff --git a/aeon/forecasting/_auto_arima.py b/aeon/forecasting/_auto_arima.py new file mode 100644 index 0000000000..4c0e383140 --- /dev/null +++ b/aeon/forecasting/_auto_arima.py @@ -0,0 +1,350 @@ +"""ARIMAForecaster. + +An implementation of the ARIMA forecasting algorithm. +""" + +__maintainer__ = ["alexbanwell1", "TonyBagnall"] +__all__ = ["ARIMAForecaster"] + +from math import comb + +import numpy as np + +from aeon.forecasting.base import BaseForecaster +from aeon.utils.forecasting._hypo_tests import kpss_test +from aeon.utils.forecasting._seasonality import calc_seasonal_period +from aeon.utils.optimisation._nelder_mead import nelder_mead + +NOGIL = False +CACHE = True + + +class ARIMAForecaster(BaseForecaster): + """AutoRegressive Integrated Moving Average (ARIMA) forecaster. + + Implements the Hyndman-Khandakar automatic ARIMA algorithm for time series + forecasting with optional seasonal components. The model automatically selects + the orders of the non-seasonal (p, d, q) and seasonal (P, D, Q, m) components + based on information criteria, such as AIC. + + Parameters + ---------- + horizon : int, default=1 + The forecasting horizon, i.e., the number of steps ahead to predict. + + Attributes + ---------- + data_ : list of float + Original training series values. + differenced_data_ : list of float + Differenced version of the training data used for stationarity. + residuals_ : list of float + Residual errors from the fitted model. + aic_ : float + Akaike Information Criterion for the selected model. + p_, d_, q_ : int + Orders of the ARIMA model: autoregressive (p), differencing (d), + and moving average (q) terms. + ps_, ds_, qs_ : int + Orders of the seasonal ARIMA model: seasonal AR (P), seasonal differencing (D), + and seasonal MA (Q) terms. + seasonal_period_ : int + Length of the seasonal cycle. + constant_term_ : float + Constant/intercept term in the model. + c_ : float + Estimated constant term (internal use). + phi_ : array-like + Coefficients for the non-seasonal autoregressive terms. + phi_s_ : array-like + Coefficients for the seasonal autoregressive terms. + theta_ : array-like + Coefficients for the non-seasonal moving average terms. + theta_s_ : array-like + Coefficients for the seasonal moving average terms. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. OTexts, 2014. + https://otexts.com/fpp3/ + + Examples + -------- + >>> from aeon.forecasting import ARIMAForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = ARIMAForecaster() + >>> forecaster.fit(y) + ARIMAForecaster() + >>> forecaster.predict() + 450.74890401954826 + """ + + def __init__(self, horizon=1): + super().__init__(horizon=horizon, axis=1) + self.data_ = [] + self.differenced_data_ = [] + self.residuals_ = [] + self.aic_ = 0 + self.p_ = 0 + self.d_ = 0 + self.q_ = 0 + self.ps_ = 0 + self.ds_ = 0 + self.qs_ = 0 + self.seasonal_period_ = 0 + self.constant_term_ = 0 + self.model_ = [] + self.c_ = 0 + self.phi_ = 0 + self.phi_s_ = 0 + self.theta_ = 0 + self.theta_s_ = 0 + self.parameters_ = [] + + def _fit(self, y, exog=None): + """Fit AutoARIMA forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ARIMAForecaster. + """ + self.data_ = np.array(y.squeeze(), dtype=np.float64) + ( + self.differenced_data_, + self.d_, + self.ds_, + self.model_, + self.parameters_, + self.aic_, + ) = _auto_arima(self.data_) + ( + self.constant_term_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + ) = self.model_ + (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = _extract_params( + self.parameters_, self.model_ + ) + ( + self.aic_, + self.residuals_, + ) = _arima_model( + self.parameters_, _calc_sarima, self.differenced_data_, self.model_ + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + y = np.array(y, dtype=np.float64) + value = _calc_sarima( + self.differenced_data_, + self.model_, + len(self.differenced_data_), + _extract_params(self.parameters_, self.model_), + self.residuals_, + ) + history = self.data_[::-1] + differenced_history = np.diff(self.data_, n=self.d_)[::-1] + # Step 1: undo seasonal differencing on y^(d) + for k in range(1, self.ds_ + 1): + lag = k * self.seasonal_period_ + value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] + + # Step 2: undo ordinary differencing + for k in range(1, self.d_ + 1): + value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] + return value + + +def _aic(residuals, num_params): + """Calculate the log-likelihood of a model.""" + variance = np.mean(residuals**2) + liklihood = len(residuals) * (np.log(2 * np.pi) + np.log(variance) + 1) + return liklihood + 2 * num_params + + +# Define the ARIMA(p, d, q) likelihood function +def _arima_model(params, base_function, data, model): + """Calculate the log-likelihood of an ARIMA model given the parameters.""" + formatted_params = _extract_params(params, model) # Extract parameters + + # Initialize residuals + n = len(data) + residuals = np.zeros(n) + for t in range(n): + y_hat = base_function( + data, + model, + t, + formatted_params, + residuals, + ) + residuals[t] = data[t] - y_hat + return _aic(residuals, len(params)), residuals + + +# Define the SARIMA(p, d, q)(P, D, Q) likelihood function + + +def _extract_params(params, model): + """Extract ARIMA parameters from the parameter vector.""" + if len(params) != np.sum(model): + previous_length = np.sum(model) + model = model[:-1] # Remove the seasonal period + if len(params) != np.sum(model): + raise ValueError( + f"Expected {previous_length} parameters for a non-seasonal model or \ + {np.sum(model)} parameters for a seasonal model, got {len(params)}" + ) + starts = np.cumsum([0] + model[:-1]) + return [params[s : s + l].tolist() for s, l in zip(starts, model)] + + +def _calc_arima(data, model, t, formatted_params, residuals): + """Calculate the ARIMA forecast for time t.""" + if len(model) != 3: + raise ValueError("Model must be of the form (c, p, q)") + # AR part + p = model[1] + phi = formatted_params[1] + ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) + + # MA part + q = model[2] + theta = formatted_params[2] + ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) + + c = formatted_params[0][0] if model[0] else 0 + y_hat = c + ar_term + ma_term + return y_hat + + +def _calc_sarima(data, model, t, formatted_params, residuals): + """Calculate the SARIMA forecast for time t.""" + if len(model) != 6: + raise ValueError("Model must be of the form (c, p, q, ps, qs, seasonal_period)") + arima_forecast = _calc_arima(data, model[:3], t, formatted_params, residuals) + seasonal_period = model[5] + # Seasonal AR part + ps = model[3] + phi_s = formatted_params[3] + ars_term = ( + 0 + if (t - seasonal_period * ps) < 0 + else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) + ) + # Seasonal MA part + qs = model[4] + theta_s = formatted_params[4] + mas_term = ( + 0 + if (t - seasonal_period * qs) < 0 + else np.dot( + theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] + ) + ) + return arima_forecast + ars_term + mas_term + + +def make_arima_llf(base_function, data, model): + """ + Return a parameterized log-likelihood function for ARIMA. + + This can then be used with an optimization algorithm. + """ + + def loss_fn(v): + return _arima_model(v, base_function, data, model)[0] + + return loss_fn + + +def _auto_arima(data): + """ + Implement the Hyndman-Khandakar algorithm. + + For automatic ARIMA model selection. + """ + seasonal_period = calc_seasonal_period(data) + difference = 0 + while not kpss_test(data)[1]: + data = np.diff(data, n=1) + difference += 1 + seasonal_difference = 1 if seasonal_period > 1 else 0 + if seasonal_difference: + data = data[seasonal_period:] - data[:-seasonal_period] + include_c = 1 if difference == 0 else 0 + model_parameters = [ + [include_c, 2, 2, 0, 0, seasonal_period], + [include_c, 0, 0, 0, 0, seasonal_period], + [include_c, 1, 0, 0, 0, seasonal_period], + [include_c, 0, 1, 0, 0, seasonal_period], + ] + model_points = [] + model_scores = [] + for p in model_parameters: + points, aic = nelder_mead(make_arima_llf(_calc_sarima, data, p), np.sum(p[:5])) + model_points.append(points) + model_scores.append(aic) + best_score = min(model_scores) + best_index = model_scores.index(best_score) + current_model = model_parameters[best_index] + current_points = model_points[best_index] + while True: + better_model = False + for param_no in range(1, 5): + for adjustment in [-1, 1]: + if (current_model[param_no] + adjustment) < 0: + continue + model = current_model.copy() + model[param_no] += adjustment + for constant_term in [0, 1]: + model[0] = constant_term + points, aic = nelder_mead( + make_arima_llf(_calc_sarima, data, model), np.sum(model[:5]) + ) + if aic < best_score: + current_model = model.copy() + current_points = points + best_score = aic + better_model = True + if not better_model: + break + return ( + data, + difference, + seasonal_difference, + current_model, + current_points, + best_score, + ) From 9eb00f69f2d98640ea5765ff062feff57aaf1211 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 19:21:07 +0100 Subject: [PATCH 20/70] Adjust parameters to allow modification in fit --- aeon/forecasting/_arima.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 4ca197f3f0..412efde4f3 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -64,7 +64,7 @@ class ARIMAForecaster(BaseForecaster): >>> y = load_airline() >>> forecaster = ARIMAForecaster(2,1,1,0) >>> forecaster.fit(y) - ARIMAForecaster() + ARIMAForecaster(d=1, p=2) >>> forecaster.predict() 550.9147246631135 """ @@ -79,6 +79,10 @@ def __init__(self, p=1, d=0, q=1, constant_term=0, horizon=1): self.d = d self.q = q self.constant_term = constant_term + self.p_ = 0 + self.d_ = 0 + self.q_ = 0 + self.constant_term_ = 0 self.model_ = [] self.c_ = 0 self.phi_ = 0 @@ -102,6 +106,10 @@ def _fit(self, y, exog=None): self Fitted ARIMAForecaster. """ + self.p_ = self.p + self.d_ = self.d + self.q_ = self.q + self.constant_term_ = self.constant_term self.data_ = np.array(y.squeeze(), dtype=np.float64) self.model_ = np.array((self.constant_term, self.p, self.q), dtype=np.int32) self.differenced_data_ = np.diff(self.data_, n=self.d) @@ -146,8 +154,8 @@ def _predict(self, y=None, exog=None): ) history = self.data_[::-1] # Step 2: undo ordinary differencing - for k in range(1, self.d + 1): - value += (-1) ** (k + 1) * comb(self.d, k) * history[k - 1] + for k in range(1, self.d_ + 1): + value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] return value From f0c0443884e1bdd2d6b1137e17a017fe5649f3e9 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 19:59:54 +0100 Subject: [PATCH 21/70] Non-seasonal AutoARIMA Forecaster --- aeon/forecasting/__init__.py | 2 + aeon/forecasting/_auto_arima.py | 323 ++++++++------------------------ 2 files changed, 79 insertions(+), 246 deletions(-) diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index f6983cb89c..3761394bf6 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -6,9 +6,11 @@ "RegressionForecaster", "ETSForecaster", "ARIMAForecaster", + "AutoARIMAForecaster", ] from aeon.forecasting._arima import ARIMAForecaster +from aeon.forecasting._auto_arima import AutoARIMAForecaster from aeon.forecasting._ets import ETSForecaster from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster diff --git a/aeon/forecasting/_auto_arima.py b/aeon/forecasting/_auto_arima.py index 4c0e383140..7397349c06 100644 --- a/aeon/forecasting/_auto_arima.py +++ b/aeon/forecasting/_auto_arima.py @@ -1,68 +1,36 @@ -"""ARIMAForecaster. +"""AutoARIMAForecaster. -An implementation of the ARIMA forecasting algorithm. +An implementation of the AutoARIMA forecasting algorithm. """ __maintainer__ = ["alexbanwell1", "TonyBagnall"] -__all__ = ["ARIMAForecaster"] - -from math import comb +__all__ = ["AutoARIMAForecaster"] import numpy as np -from aeon.forecasting.base import BaseForecaster +from aeon.forecasting import ARIMAForecaster +from aeon.forecasting._arima import ( + _arima_model, + _arima_model_wrapper, + _calc_arima, + _extract_params, +) from aeon.utils.forecasting._hypo_tests import kpss_test -from aeon.utils.forecasting._seasonality import calc_seasonal_period from aeon.utils.optimisation._nelder_mead import nelder_mead -NOGIL = False -CACHE = True - -class ARIMAForecaster(BaseForecaster): +class AutoARIMAForecaster(ARIMAForecaster): """AutoRegressive Integrated Moving Average (ARIMA) forecaster. Implements the Hyndman-Khandakar automatic ARIMA algorithm for time series forecasting with optional seasonal components. The model automatically selects - the orders of the non-seasonal (p, d, q) and seasonal (P, D, Q, m) components - based on information criteria, such as AIC. + the orders of the (p, d, q) components based on information criteria, such as AIC. Parameters ---------- horizon : int, default=1 The forecasting horizon, i.e., the number of steps ahead to predict. - Attributes - ---------- - data_ : list of float - Original training series values. - differenced_data_ : list of float - Differenced version of the training data used for stationarity. - residuals_ : list of float - Residual errors from the fitted model. - aic_ : float - Akaike Information Criterion for the selected model. - p_, d_, q_ : int - Orders of the ARIMA model: autoregressive (p), differencing (d), - and moving average (q) terms. - ps_, ds_, qs_ : int - Orders of the seasonal ARIMA model: seasonal AR (P), seasonal differencing (D), - and seasonal MA (Q) terms. - seasonal_period_ : int - Length of the seasonal cycle. - constant_term_ : float - Constant/intercept term in the model. - c_ : float - Estimated constant term (internal use). - phi_ : array-like - Coefficients for the non-seasonal autoregressive terms. - phi_s_ : array-like - Coefficients for the seasonal autoregressive terms. - theta_ : array-like - Coefficients for the non-seasonal moving average terms. - theta_s_ : array-like - Coefficients for the seasonal moving average terms. - References ---------- .. [1] R. J. Hyndman and G. Athanasopoulos, @@ -71,37 +39,18 @@ class ARIMAForecaster(BaseForecaster): Examples -------- - >>> from aeon.forecasting import ARIMAForecaster + >>> from aeon.forecasting import AutoARIMAForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = ARIMAForecaster() + >>> forecaster = AutoARIMAForecaster() >>> forecaster.fit(y) - ARIMAForecaster() + AutoARIMAForecaster() >>> forecaster.predict() - 450.74890401954826 + 476.5824781648738 """ def __init__(self, horizon=1): - super().__init__(horizon=horizon, axis=1) - self.data_ = [] - self.differenced_data_ = [] - self.residuals_ = [] - self.aic_ = 0 - self.p_ = 0 - self.d_ = 0 - self.q_ = 0 - self.ps_ = 0 - self.ds_ = 0 - self.qs_ = 0 - self.seasonal_period_ = 0 - self.constant_term_ = 0 - self.model_ = [] - self.c_ = 0 - self.phi_ = 0 - self.phi_s_ = 0 - self.theta_ = 0 - self.theta_s_ = 0 - self.parameters_ = [] + super().__init__(horizon=horizon) def _fit(self, y, exog=None): """Fit AutoARIMA forecaster to series y. @@ -124,7 +73,6 @@ def _fit(self, y, exog=None): ( self.differenced_data_, self.d_, - self.ds_, self.model_, self.parameters_, self.aic_, @@ -133,218 +81,101 @@ def _fit(self, y, exog=None): self.constant_term_, self.p_, self.q_, - self.ps_, - self.qs_, - self.seasonal_period_, ) = self.model_ - (self.c_, self.phi_, self.phi_s_, self.theta_, self.theta_s_) = _extract_params( + (self.c_, self.phi_, self.theta_) = _extract_params( self.parameters_, self.model_ ) ( self.aic_, self.residuals_, ) = _arima_model( - self.parameters_, _calc_sarima, self.differenced_data_, self.model_ + self.parameters_, _calc_arima, self.differenced_data_, self.model_ ) return self - def _predict(self, y=None, exog=None): - """ - Predict the next horizon steps ahead. - - Parameters - ---------- - y : np.ndarray, default = None - A time series to predict the next horizon value for. If None, - predict the next horizon value after series seen in fit. - exog : np.ndarray, default =None - Optional exogenous time series data assumed to be aligned with y - - Returns - ------- - float - single prediction self.horizon steps ahead of y. - """ - y = np.array(y, dtype=np.float64) - value = _calc_sarima( - self.differenced_data_, - self.model_, - len(self.differenced_data_), - _extract_params(self.parameters_, self.model_), - self.residuals_, - ) - history = self.data_[::-1] - differenced_history = np.diff(self.data_, n=self.d_)[::-1] - # Step 1: undo seasonal differencing on y^(d) - for k in range(1, self.ds_ + 1): - lag = k * self.seasonal_period_ - value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] - - # Step 2: undo ordinary differencing - for k in range(1, self.d_ + 1): - value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - return value - - -def _aic(residuals, num_params): - """Calculate the log-likelihood of a model.""" - variance = np.mean(residuals**2) - liklihood = len(residuals) * (np.log(2 * np.pi) + np.log(variance) + 1) - return liklihood + 2 * num_params - - -# Define the ARIMA(p, d, q) likelihood function -def _arima_model(params, base_function, data, model): - """Calculate the log-likelihood of an ARIMA model given the parameters.""" - formatted_params = _extract_params(params, model) # Extract parameters - - # Initialize residuals - n = len(data) - residuals = np.zeros(n) - for t in range(n): - y_hat = base_function( - data, - model, - t, - formatted_params, - residuals, - ) - residuals[t] = data[t] - y_hat - return _aic(residuals, len(params)), residuals - - -# Define the SARIMA(p, d, q)(P, D, Q) likelihood function - - -def _extract_params(params, model): - """Extract ARIMA parameters from the parameter vector.""" - if len(params) != np.sum(model): - previous_length = np.sum(model) - model = model[:-1] # Remove the seasonal period - if len(params) != np.sum(model): - raise ValueError( - f"Expected {previous_length} parameters for a non-seasonal model or \ - {np.sum(model)} parameters for a seasonal model, got {len(params)}" - ) - starts = np.cumsum([0] + model[:-1]) - return [params[s : s + l].tolist() for s, l in zip(starts, model)] - - -def _calc_arima(data, model, t, formatted_params, residuals): - """Calculate the ARIMA forecast for time t.""" - if len(model) != 3: - raise ValueError("Model must be of the form (c, p, q)") - # AR part - p = model[1] - phi = formatted_params[1] - ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) - # MA part - q = model[2] - theta = formatted_params[2] - ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) - - c = formatted_params[0][0] if model[0] else 0 - y_hat = c + ar_term + ma_term - return y_hat - - -def _calc_sarima(data, model, t, formatted_params, residuals): - """Calculate the SARIMA forecast for time t.""" - if len(model) != 6: - raise ValueError("Model must be of the form (c, p, q, ps, qs, seasonal_period)") - arima_forecast = _calc_arima(data, model[:3], t, formatted_params, residuals) - seasonal_period = model[5] - # Seasonal AR part - ps = model[3] - phi_s = formatted_params[3] - ars_term = ( - 0 - if (t - seasonal_period * ps) < 0 - else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) - ) - # Seasonal MA part - qs = model[4] - theta_s = formatted_params[4] - mas_term = ( - 0 - if (t - seasonal_period * qs) < 0 - else np.dot( - theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] - ) - ) - return arima_forecast + ars_term + mas_term - - -def make_arima_llf(base_function, data, model): +def _auto_arima(data): """ - Return a parameterized log-likelihood function for ARIMA. + Prepare data for the AutoARIMA algorithm. - This can then be used with an optimization algorithm. + This function checks if the data is stationary + and applies differencing if necessary. """ - - def loss_fn(v): - return _arima_model(v, base_function, data, model)[0] - - return loss_fn + difference = 0 + while not kpss_test(data)[1]: + data = np.diff(data, n=1) + difference += 1 + include_c = 1 if difference == 0 else 0 + model_parameters = np.array( + [ + [include_c, 2, 2], + [include_c, 0, 0], + [include_c, 1, 0], + [include_c, 0, 1], + ] + ) + ( + differenced_data, + best_model, + best_points, + best_score, + ) = _auto_arma(data, model_parameters, 3) + return ( + differenced_data, + difference, + best_model, + best_points, + best_score, + ) -def _auto_arima(data): +def _auto_arma(differenced_data, inital_model_parameters, num_model_params=3): """ Implement the Hyndman-Khandakar algorithm. For automatic ARIMA model selection. """ - seasonal_period = calc_seasonal_period(data) - difference = 0 - while not kpss_test(data)[1]: - data = np.diff(data, n=1) - difference += 1 - seasonal_difference = 1 if seasonal_period > 1 else 0 - if seasonal_difference: - data = data[seasonal_period:] - data[:-seasonal_period] - include_c = 1 if difference == 0 else 0 - model_parameters = [ - [include_c, 2, 2, 0, 0, seasonal_period], - [include_c, 0, 0, 0, 0, seasonal_period], - [include_c, 1, 0, 0, 0, seasonal_period], - [include_c, 0, 1, 0, 0, seasonal_period], - ] - model_points = [] - model_scores = [] - for p in model_parameters: - points, aic = nelder_mead(make_arima_llf(_calc_sarima, data, p), np.sum(p[:5])) - model_points.append(points) - model_scores.append(aic) - best_score = min(model_scores) - best_index = model_scores.index(best_score) - current_model = model_parameters[best_index] - current_points = model_points[best_index] + best_score = -1 + best_model = inital_model_parameters[0] + best_points = None + for i in range(len(inital_model_parameters)): + points, aic = nelder_mead( + _arima_model_wrapper, + np.sum(inital_model_parameters[i][:num_model_params]), + differenced_data, + inital_model_parameters[i], + ) + if (aic < best_score) or (best_score == -1): + best_score = aic + best_model = inital_model_parameters[i] + best_points = points + while True: better_model = False - for param_no in range(1, 5): + for param_no in range(1, num_model_params): for adjustment in [-1, 1]: - if (current_model[param_no] + adjustment) < 0: + if (best_model[param_no] + adjustment) < 0: continue - model = current_model.copy() + model = best_model.copy() model[param_no] += adjustment for constant_term in [0, 1]: model[0] = constant_term points, aic = nelder_mead( - make_arima_llf(_calc_sarima, data, model), np.sum(model[:5]) + _arima_model_wrapper, + np.sum(model[:num_model_params]), + differenced_data, + model, ) if aic < best_score: - current_model = model.copy() - current_points = points + best_model = model.copy() + best_points = points best_score = aic better_model = True if not better_model: break return ( - data, - difference, - seasonal_difference, - current_model, - current_points, + differenced_data, + best_model, + best_points, best_score, ) From 5f2d80f6ee9b6f584974d2739cf11d2e8a7917d3 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 20:12:09 +0100 Subject: [PATCH 22/70] Numbafy AutoARIMA code --- aeon/forecasting/_auto_arima.py | 66 +++++++++++---------------------- 1 file changed, 22 insertions(+), 44 deletions(-) diff --git a/aeon/forecasting/_auto_arima.py b/aeon/forecasting/_auto_arima.py index 7397349c06..d6bb553a5b 100644 --- a/aeon/forecasting/_auto_arima.py +++ b/aeon/forecasting/_auto_arima.py @@ -7,6 +7,7 @@ __all__ = ["AutoARIMAForecaster"] import numpy as np +from numba import njit from aeon.forecasting import ARIMAForecaster from aeon.forecasting._arima import ( @@ -70,13 +71,25 @@ def _fit(self, y, exog=None): Fitted ARIMAForecaster. """ self.data_ = np.array(y.squeeze(), dtype=np.float64) + self.differenced_data_ = self.data_.copy() + self.d_ = 0 + while not kpss_test(self.differenced_data_)[1]: + self.differenced_data_ = np.diff(self.differenced_data_, n=1) + self.d_ += 1 + include_c = 1 if self.d_ == 0 else 0 + model_parameters = np.array( + [ + [include_c, 2, 2], + [include_c, 0, 0], + [include_c, 1, 0], + [include_c, 0, 1], + ] + ) ( - self.differenced_data_, - self.d_, self.model_, self.parameters_, self.aic_, - ) = _auto_arima(self.data_) + ) = _auto_arima(self.differenced_data_, model_parameters, 3) ( self.constant_term_, self.p_, @@ -94,42 +107,8 @@ def _fit(self, y, exog=None): return self -def _auto_arima(data): - """ - Prepare data for the AutoARIMA algorithm. - - This function checks if the data is stationary - and applies differencing if necessary. - """ - difference = 0 - while not kpss_test(data)[1]: - data = np.diff(data, n=1) - difference += 1 - include_c = 1 if difference == 0 else 0 - model_parameters = np.array( - [ - [include_c, 2, 2], - [include_c, 0, 0], - [include_c, 1, 0], - [include_c, 0, 1], - ] - ) - ( - differenced_data, - best_model, - best_points, - best_score, - ) = _auto_arma(data, model_parameters, 3) - return ( - differenced_data, - difference, - best_model, - best_points, - best_score, - ) - - -def _auto_arma(differenced_data, inital_model_parameters, num_model_params=3): +@njit(cache=True, fastmath=True) +def _auto_arima(differenced_data, inital_model_parameters, num_model_params=3): """ Implement the Hyndman-Khandakar algorithm. @@ -138,16 +117,16 @@ def _auto_arma(differenced_data, inital_model_parameters, num_model_params=3): best_score = -1 best_model = inital_model_parameters[0] best_points = None - for i in range(len(inital_model_parameters)): + for model in inital_model_parameters: points, aic = nelder_mead( _arima_model_wrapper, - np.sum(inital_model_parameters[i][:num_model_params]), + np.sum(model[:num_model_params]), differenced_data, - inital_model_parameters[i], + model, ) if (aic < best_score) or (best_score == -1): best_score = aic - best_model = inital_model_parameters[i] + best_model = model best_points = points while True: @@ -174,7 +153,6 @@ def _auto_arma(differenced_data, inital_model_parameters, num_model_params=3): if not better_model: break return ( - differenced_data, best_model, best_points, best_score, From d4ed4b1fc3845d6c9c23c7788527a91e6f1f4431 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 20:19:15 +0100 Subject: [PATCH 23/70] Update example and return native python type --- aeon/forecasting/_arima.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 412efde4f3..5c1933def8 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -62,7 +62,7 @@ class ARIMAForecaster(BaseForecaster): >>> from aeon.forecasting import ARIMAForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = ARIMAForecaster(2,1,1,0) + >>> forecaster = ARIMAForecaster(p=2,d=1) >>> forecaster.fit(y) ARIMAForecaster(d=1, p=2) >>> forecaster.predict() @@ -156,7 +156,7 @@ def _predict(self, y=None, exog=None): # Step 2: undo ordinary differencing for k in range(1, self.d_ + 1): value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - return value + return value.item() @njit(cache=True, fastmath=True) From a7295e88b487f716b4e41646e0f311a5dab51a98 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 21:10:51 +0100 Subject: [PATCH 24/70] Add SARIMA model --- aeon/forecasting/__init__.py | 2 + aeon/forecasting/_sarima.py | 211 +++++++++++++++++++++++++++++++++++ 2 files changed, 213 insertions(+) create mode 100644 aeon/forecasting/_sarima.py diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index f6983cb89c..6929600269 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -6,10 +6,12 @@ "RegressionForecaster", "ETSForecaster", "ARIMAForecaster", + "SARIMAForecaster", ] from aeon.forecasting._arima import ARIMAForecaster from aeon.forecasting._ets import ETSForecaster from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster +from aeon.forecasting._sarima import SARIMAForecaster from aeon.forecasting.base import BaseForecaster diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py new file mode 100644 index 0000000000..d9a0e485a9 --- /dev/null +++ b/aeon/forecasting/_sarima.py @@ -0,0 +1,211 @@ +"""SARIMAForecaster. + +An implementation of the Seasonal ARIMA forecasting algorithm. +""" + +__maintainer__ = ["alexbanwell1", "TonyBagnall"] +__all__ = ["SARIMAForecaster"] + +from math import comb + +import numpy as np +from numba import njit + +from aeon.forecasting import ARIMAForecaster +from aeon.forecasting._arima import _arima_model, _calc_arima, _extract_params +from aeon.utils.optimisation._nelder_mead import nelder_mead + +NOGIL = False +CACHE = True + + +class SARIMAForecaster(ARIMAForecaster): + """Seasonal AutoRegressive Integrated Moving Average (SARIMA) forecaster. + + Parameters + ---------- + horizon : int, default=1 + The forecasting horizon, i.e., the number of steps ahead to predict. + + Attributes + ---------- + ps_, ds_, qs_ : int + Orders of the seasonal ARIMA model: seasonal AR (P), seasonal differencing (D), + and seasonal MA (Q) terms. + seasonal_period_ : int + Length of the seasonal cycle. + phi_s_ : array-like + Coefficients for the seasonal autoregressive terms. + theta_s_ : array-like + Coefficients for the seasonal moving average terms. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. OTexts, 2014. + https://otexts.com/fpp3/ + + Examples + -------- + >>> from aeon.forecasting import SARIMAForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = SARIMAForecaster(1,1,2,0,1,0,12,0) + >>> forecaster.fit(y) + SARIMAForecaster(d=1, ds=1, q=2) + >>> forecaster.predict() + 450.7487685084027 + """ + + def __init__( + self, + p=1, + d=0, + q=1, + ps=0, + ds=0, + qs=0, + seasonal_period=12, + constant_term=0, + horizon=1, + ): + super().__init__(p=p, d=d, q=q, constant_term=constant_term, horizon=horizon) + self.ps = ps + self.ds = ds + self.qs = qs + self.seasonal_period = seasonal_period + self.ps_ = 0 + self.ds_ = 0 + self.qs_ = 0 + self.seasonal_period_ = 0 + self.phi_s_ = 0 + self.theta_s_ = 0 + + def _fit(self, y, exog=None): + """Fit AutoARIMA forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ARIMAForecaster. + """ + self.p_ = self.p + self.d_ = self.d + self.q_ = self.q + self.ps_ = self.ps + self.ds_ = self.ds + self.qs_ = self.qs + self.seasonal_period_ = self.seasonal_period + if self.seasonal_period_ == 1: + raise ValueError("Seasonal period must be greater than 1.") + self.constant_term_ = self.constant_term + self.data_ = np.array(y.squeeze(), dtype=np.float64) + self.model_ = np.array( + ( + self.constant_term, + self.p, + self.q, + self.ps, + self.qs, + self.seasonal_period, + ), + dtype=np.int32, + ) + self.differenced_data_ = np.diff(self.data_, n=self.d) + for _ds in range(self.ds_): + self.differenced_data_ = ( + self.differenced_data_[self.seasonal_period_ :] + - self.differenced_data_[: -self.seasonal_period_] + ) + (self.parameters_, self.aic_) = nelder_mead( + _sarima_model_wrapper, + np.sum(self.model_[:5]), + self.differenced_data_, + self.model_, + ) + (self.c_, self.phi_, self.theta_, self.phi_s_, self.theta_s_) = _extract_params( + self.parameters_, self.model_ + ) + (self.aic_, self.residuals_) = _arima_model( + self.parameters_, _calc_sarima, self.differenced_data_, self.model_ + ) + return self + + def _predict(self, y=None, exog=None): + """ + Predict the next horizon steps ahead. + + Parameters + ---------- + y : np.ndarray, default = None + A time series to predict the next horizon value for. If None, + predict the next horizon value after series seen in fit. + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + float + single prediction self.horizon steps ahead of y. + """ + y = np.array(y, dtype=np.float64) + value = _calc_sarima( + self.differenced_data_, + self.model_, + len(self.differenced_data_), + _extract_params(self.parameters_, self.model_), + self.residuals_, + ) + history = self.data_[::-1] + differenced_history = np.diff(self.data_, n=self.d_)[::-1] + # Step 1: undo seasonal differencing on y^(d) + for k in range(1, self.ds_ + 1): + lag = k * self.seasonal_period_ + value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] + + # Step 2: undo ordinary differencing + for k in range(1, self.d_ + 1): + value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] + return value + + +@njit(fastmath=True) +def _sarima_model_wrapper(params, data, model): + return _arima_model(params, _calc_sarima, data, model)[0] + + +@njit(cache=True, fastmath=True) +def _calc_sarima(data, model, t, formatted_params, residuals): + """Calculate the SARIMA forecast for time t.""" + if len(model) != 6: + raise ValueError("Model must be of the form (c, p, q, ps, qs, seasonal_period)") + arima_forecast = _calc_arima(data, model[:3], t, formatted_params, residuals) + seasonal_period = model[5] + # Seasonal AR part + ps = model[3] + phi_s = formatted_params[3][:ps] + ars_term = ( + 0 + if (t - seasonal_period * ps) < 0 + else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) + ) + # Seasonal MA part + qs = model[4] + theta_s = formatted_params[4][:qs] + mas_term = ( + 0 + if (t - seasonal_period * qs) < 0 + else np.dot( + theta_s, residuals[t - seasonal_period * qs : t : seasonal_period][::-1] + ) + ) + return arima_forecast + ars_term + mas_term From 2893e1b935d612caaf41610164c22736544756ab Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 21:16:35 +0100 Subject: [PATCH 25/70] Fix examples for tests --- aeon/forecasting/_arima.py | 2 +- aeon/utils/optimisation/_nelder_mead.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 5c1933def8..94b6c51af8 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -66,7 +66,7 @@ class ARIMAForecaster(BaseForecaster): >>> forecaster.fit(y) ARIMAForecaster(d=1, p=2) >>> forecaster.predict() - 550.9147246631135 + 550.9147246631132 """ def __init__(self, p=1, d=0, q=1, constant_term=0, horizon=1): diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py index 749187541d..767fbde506 100644 --- a/aeon/utils/optimisation/_nelder_mead.py +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -50,9 +50,9 @@ def nelder_mead( Examples -------- - >>> def sphere(x): + >>> def sphere(x, data, model): ... return np.sum(x**2) - >>> x_opt, val = nelder_mead(sphere, num_params=2) + >>> x_opt, val = nelder_mead(sphere, num_params=2, data=None, model=None) """ points = np.full((num_params + 1, num_params), 0.5) for i in range(num_params): From c83052bfb26f688a8ca4d02e9c0bb6ca617f287b Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 21:44:21 +0100 Subject: [PATCH 26/70] Modify AutoARIMA function to take the model function as a parameter --- aeon/forecasting/_auto_arima.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/aeon/forecasting/_auto_arima.py b/aeon/forecasting/_auto_arima.py index d6bb553a5b..45c4cc34c4 100644 --- a/aeon/forecasting/_auto_arima.py +++ b/aeon/forecasting/_auto_arima.py @@ -89,7 +89,9 @@ def _fit(self, y, exog=None): self.model_, self.parameters_, self.aic_, - ) = _auto_arima(self.differenced_data_, model_parameters, 3) + ) = _auto_arima( + self.differenced_data_, _arima_model_wrapper, model_parameters, 3 + ) ( self.constant_term_, self.p_, @@ -108,7 +110,9 @@ def _fit(self, y, exog=None): @njit(cache=True, fastmath=True) -def _auto_arima(differenced_data, inital_model_parameters, num_model_params=3): +def _auto_arima( + differenced_data, model_function, inital_model_parameters, num_model_params=3 +): """ Implement the Hyndman-Khandakar algorithm. @@ -119,7 +123,7 @@ def _auto_arima(differenced_data, inital_model_parameters, num_model_params=3): best_points = None for model in inital_model_parameters: points, aic = nelder_mead( - _arima_model_wrapper, + model_function, np.sum(model[:num_model_params]), differenced_data, model, @@ -140,7 +144,7 @@ def _auto_arima(differenced_data, inital_model_parameters, num_model_params=3): for constant_term in [0, 1]: model[0] = constant_term points, aic = nelder_mead( - _arima_model_wrapper, + model_function, np.sum(model[:num_model_params]), differenced_data, model, From 72b90f780c40939463b37fa38c3fdbf24a7f1294 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 21:49:49 +0100 Subject: [PATCH 27/70] Add AutoSARIMA Forecaster --- aeon/forecasting/__init__.py | 2 + aeon/forecasting/_auto_sarima.py | 122 +++++++++++++++++++++++++++++++ 2 files changed, 124 insertions(+) create mode 100644 aeon/forecasting/_auto_sarima.py diff --git a/aeon/forecasting/__init__.py b/aeon/forecasting/__init__.py index 0b4c1d297f..23088a5dec 100644 --- a/aeon/forecasting/__init__.py +++ b/aeon/forecasting/__init__.py @@ -8,10 +8,12 @@ "ARIMAForecaster", "SARIMAForecaster", "AutoARIMAForecaster", + "AutoSARIMAForecaster", ] from aeon.forecasting._arima import ARIMAForecaster from aeon.forecasting._auto_arima import AutoARIMAForecaster +from aeon.forecasting._auto_sarima import AutoSARIMAForecaster from aeon.forecasting._ets import ETSForecaster from aeon.forecasting._naive import NaiveForecaster from aeon.forecasting._regression import RegressionForecaster diff --git a/aeon/forecasting/_auto_sarima.py b/aeon/forecasting/_auto_sarima.py new file mode 100644 index 0000000000..7b82892aca --- /dev/null +++ b/aeon/forecasting/_auto_sarima.py @@ -0,0 +1,122 @@ +"""AutoSARIMAForecaster. + +An implementation of the Auto SARIMA forecasting algorithm. +""" + +__maintainer__ = ["alexbanwell1", "TonyBagnall"] +__all__ = ["AutoSARIMAForecaster"] + +import numpy as np + +from aeon.forecasting._arima import _arima_model, _extract_params +from aeon.forecasting._auto_arima import _auto_arima +from aeon.forecasting._sarima import ( + SARIMAForecaster, + _calc_sarima, + _sarima_model_wrapper, +) +from aeon.utils.forecasting._hypo_tests import kpss_test +from aeon.utils.forecasting._seasonality import calc_seasonal_period + +NOGIL = False +CACHE = True + + +class AutoSARIMAForecaster(SARIMAForecaster): + """Seasonal AutoRegressive Integrated Moving Average (SARIMA) forecaster. + + Implements the Hyndman-Khandakar automatic ARIMA algorithm for time series + forecasting with optional seasonal components. The model automatically selects + the orders of the non-seasonal (p, d, q) and seasonal (P, D, Q, m) components + based on information criteria, such as AIC. + + Parameters + ---------- + horizon : int, default=1 + The forecasting horizon, i.e., the number of steps ahead to predict. + + References + ---------- + .. [1] R. J. Hyndman and G. Athanasopoulos, + Forecasting: Principles and Practice. OTexts, 2014. + https://otexts.com/fpp3/ + + Examples + -------- + >>> from aeon.forecasting import AutoSARIMAForecaster + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> forecaster = AutoSARIMAForecaster() + >>> forecaster.fit(y) + AutoSARIMAForecaster() + >>> forecaster.predict() + 450.74890401954826 + """ + + def __init__(self, horizon=1): + super().__init__(horizon=horizon) + + def _fit(self, y, exog=None): + """Fit AutoARIMA forecaster to series y. + + Fit a forecaster to predict self.horizon steps ahead using y. + + Parameters + ---------- + y : np.ndarray + A time series on which to learn a forecaster to predict horizon ahead + exog : np.ndarray, default =None + Optional exogenous time series data assumed to be aligned with y + + Returns + ------- + self + Fitted ARIMAForecaster. + """ + self.data_ = np.array(y.squeeze(), dtype=np.float64) + self.seasonal_period_ = calc_seasonal_period(self.data_) + self.differenced_data_ = self.data_.copy() + self.d_ = 0 + while not kpss_test(self.differenced_data_)[1]: + self.differenced_data_ = np.diff(self.differenced_data_, n=1) + self.d_ += 1 + self.ds_ = 1 if self.seasonal_period_ > 1 else 0 + if self.ds_: + self.differenced_data_ = ( + self.differenced_data_[self.seasonal_period_ :] + - self.differenced_data_[: -self.seasonal_period_] + ) + include_c = 1 if self.d_ == 0 else 0 + model_parameters = np.array( + [ + [include_c, 2, 2, 0, 0, self.seasonal_period_], + [include_c, 0, 0, 0, 0, self.seasonal_period_], + [include_c, 1, 0, 0, 0, self.seasonal_period_], + [include_c, 0, 1, 0, 0, self.seasonal_period_], + ] + ) + ( + self.model_, + self.parameters_, + self.aic_, + ) = _auto_arima( + self.differenced_data_, _sarima_model_wrapper, model_parameters, 5 + ) + ( + self.constant_term_, + self.p_, + self.q_, + self.ps_, + self.qs_, + self.seasonal_period_, + ) = self.model_ + (self.c_, self.phi_, self.theta_, self.phi_s_, self.theta_s_) = _extract_params( + self.parameters_, self.model_ + ) + ( + self.aic_, + self.residuals_, + ) = _arima_model( + self.parameters_, _calc_sarima, self.differenced_data_, self.model_ + ) + return self From 9801e8bdb0d7b34d7f149b116b96dd43f1b89183 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 21:55:28 +0100 Subject: [PATCH 28/70] Fix Nelder-Mead Optimisation Algorithm Example --- aeon/utils/optimisation/_nelder_mead.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py index 767fbde506..6d3058a7d1 100644 --- a/aeon/utils/optimisation/_nelder_mead.py +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -50,7 +50,9 @@ def nelder_mead( Examples -------- - >>> def sphere(x, data, model): + >>> from numba import njit + >>> @njit(fastmath=True) + ... def sphere(x, data, model): ... return np.sum(x**2) >>> x_opt, val = nelder_mead(sphere, num_params=2, data=None, model=None) """ From c40ec918c87d3b2b90281160f87d4bb77f80e560 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 22:08:21 +0100 Subject: [PATCH 29/70] Fix Nelder-Mead Optimisation Algorithm Example #2 --- aeon/utils/optimisation/_nelder_mead.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py index 6d3058a7d1..9ef5a6ad01 100644 --- a/aeon/utils/optimisation/_nelder_mead.py +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -51,7 +51,7 @@ def nelder_mead( Examples -------- >>> from numba import njit - >>> @njit(fastmath=True) + >>> @njit(cache=False, fastmath=True) ... def sphere(x, data, model): ... return np.sum(x**2) >>> x_opt, val = nelder_mead(sphere, num_params=2, data=None, model=None) From 044b992a27e55f2110b96655d0d31a44da5bc5f9 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 22:11:34 +0100 Subject: [PATCH 30/70] Fix SARIMA returning np.float rather than value --- aeon/forecasting/_sarima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index d9a0e485a9..f187e6b4a0 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -175,7 +175,7 @@ def _predict(self, y=None, exog=None): # Step 2: undo ordinary differencing for k in range(1, self.d_ + 1): value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - return value + return value.item() @njit(fastmath=True) From 2f928c7533b19299d0d39ef542afa9fc439cb117 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 22:12:44 +0100 Subject: [PATCH 31/70] Fix Nelder-Mead Optimisation Algorithm Example #2 --- aeon/utils/optimisation/_nelder_mead.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py index 6d3058a7d1..9ef5a6ad01 100644 --- a/aeon/utils/optimisation/_nelder_mead.py +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -51,7 +51,7 @@ def nelder_mead( Examples -------- >>> from numba import njit - >>> @njit(fastmath=True) + >>> @njit(cache=False, fastmath=True) ... def sphere(x, data, model): ... return np.sum(x**2) >>> x_opt, val = nelder_mead(sphere, num_params=2, data=None, model=None) From 94cd5b33a9534c90a57c2e9d7c1bd51a99822c83 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 22:22:52 +0100 Subject: [PATCH 32/70] Remove Nelder-Mead Example due to issues with numba caching functions --- aeon/utils/optimisation/_nelder_mead.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py index 9ef5a6ad01..3bc90ecb93 100644 --- a/aeon/utils/optimisation/_nelder_mead.py +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -47,14 +47,6 @@ def nelder_mead( with one additional point per dimension at 0.6 for that dimension. - This implementation does not support constraints or bounds on the parameters. - The algorithm does not guarantee finding a global minimum. - - Examples - -------- - >>> from numba import njit - >>> @njit(cache=False, fastmath=True) - ... def sphere(x, data, model): - ... return np.sum(x**2) - >>> x_opt, val = nelder_mead(sphere, num_params=2, data=None, model=None) """ points = np.full((num_params + 1, num_params), 0.5) for i in range(num_params): From 0d0d63fe106f99efb48ac10eb722d0ffd48b09aa Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 22:39:30 +0100 Subject: [PATCH 33/70] Fix return type issue --- aeon/forecasting/_arima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 94b6c51af8..4644f27ab6 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -156,7 +156,7 @@ def _predict(self, y=None, exog=None): # Step 2: undo ordinary differencing for k in range(1, self.d_ + 1): value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - return value.item() + return float(value) @njit(cache=True, fastmath=True) From 6aca9efbe61ff939bec5fa2548d5ab868a2f6d6f Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 22:40:58 +0100 Subject: [PATCH 34/70] Fix return type issue --- aeon/forecasting/_sarima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index f187e6b4a0..30fd820bb8 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -175,7 +175,7 @@ def _predict(self, y=None, exog=None): # Step 2: undo ordinary differencing for k in range(1, self.d_ + 1): value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - return value.item() + return float(value) @njit(fastmath=True) From 39a3ed205ca1dfe8b16f16d3be9705325a188a8b Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 23:21:37 +0100 Subject: [PATCH 35/70] Address PR Feedback --- aeon/forecasting/_arima.py | 17 +++++---- aeon/utils/forecasting/_hypo_tests.py | 51 ++++++++++++++++++++++--- aeon/utils/optimisation/_nelder_mead.py | 15 ++++++++ 3 files changed, 69 insertions(+), 14 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 4644f27ab6..48b27d94da 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -14,9 +14,6 @@ from aeon.forecasting.base import BaseForecaster from aeon.utils.optimisation._nelder_mead import nelder_mead -NOGIL = False -CACHE = True - class ARIMAForecaster(BaseForecaster): """AutoRegressive Integrated Moving Average (ARIMA) forecaster. @@ -31,24 +28,28 @@ class ARIMAForecaster(BaseForecaster): Attributes ---------- - data_ : list of float + data_ : np.ndarray Original training series values. - differenced_data_ : list of float + differenced_data_ : np.ndarray Differenced version of the training data used for stationarity. - residuals_ : list of float + residuals_ : np.ndarray Residual errors from the fitted model. aic_ : float Akaike Information Criterion for the selected model. + p, d, q : int + Parameters passed to the forecaster see p_, d_, q_. p_, d_, q_ : int Orders of the ARIMA model: autoregressive (p), differencing (d), and moving average (q) terms. + constant_term : int + Parameters passed to the forecaster see constant_term_. constant_term_ : float Constant/intercept term in the model. c_ : float Estimated constant term (internal use). - phi_ : array-like + phi_ : np.ndarray Coefficients for the non-seasonal autoregressive terms. - theta_ : array-like + theta_ : np.ndarray Coefficients for the non-seasonal moving average terms. References diff --git a/aeon/utils/forecasting/_hypo_tests.py b/aeon/utils/forecasting/_hypo_tests.py index 73d4521e5e..664d0c76e5 100644 --- a/aeon/utils/forecasting/_hypo_tests.py +++ b/aeon/utils/forecasting/_hypo_tests.py @@ -3,18 +3,56 @@ def kpss_test(y, regression="c", lags=None): # Test if time series is stationary """ - Implement the KPSS test for stationarity. + Perform the KPSS (Kwiatkowski-Phillips-Schmidt-Shin) test for stationarity. + + The KPSS test evaluates the null hypothesis that a time series is + (trend or level) stationary against the alternative of a unit root + (non-stationarity). It can test for either stationarity around a + constant (level stationarity) or arounda deterministic trend + (trend stationarity). Parameters ---------- - y (array-like): Time series data - regression (str): 'c' for constant, 'ct' for constant + trend - lags (int): Number of lags for HAC variance estimation (default: sqrt(n)) + y : array-like + Time series data to test for stationarity. + regression : str, default="c" + Indicates the null hypothesis for stationarity: + - "c" : Stationary around a constant (level stationarity) + - "ct" : Stationary around a constant and linear trend (trend stationarity) + lags : int or None, optional + Number of lags to use for the + HAC (heteroskedasticity and autocorrelation consistent) variance estimator. + If None, defaults to sqrt(n), where n is the sample size. Returns ------- - kpss_stat (float): KPSS test statistic - stationary (bool): Whether the series is stationary according to the test + kpss_stat : float + The KPSS test statistic. + stationary : bool + True if the series is judged stationary at the 5% significance level + (i.e., test statistic is below the critical value); False otherwise. + + Notes + ----- + - Uses asymptotic 5% critical values from Kwiatkowski et al. (1992): 0.463 for level + stationarity, 0.146 for trend stationarity. + - Returns True for stationary if the test statistic is below the 5% critical value. + + References + ---------- + Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., & Shin, Y. (1992). + "Testing the null hypothesis of stationarity against the alternative + of a unit root." + Journal of Econometrics, 54(1–3), 159–178. + https://doi.org/10.1016/0304-4076(92)90104-Y + + Examples + -------- + >>> from aeon.utils.forecasting._hypo_tests import kpss_test + >>> from aeon.datasets import load_airline + >>> y = load_airline() + >>> kpss_test(y) + (1.1966313813502716, False) """ y = np.asarray(y) n = len(y) @@ -50,6 +88,7 @@ def kpss_test(y, regression="c", lags=None): # Test if time series is stationar # Step 4: Calculate the KPSS statistic kpss_stat = np.sum(S_t**2) / (n**2 * sigma_squared) + # 5% critical values for KPSS test if regression == "ct": # p. 162 Kwiatkowski et al. (1992): y_t = beta * t + r_t + e_t, # where beta is the trend, r_t a random walk and e_t a stationary diff --git a/aeon/utils/optimisation/_nelder_mead.py b/aeon/utils/optimisation/_nelder_mead.py index 3bc90ecb93..e59a70c5dd 100644 --- a/aeon/utils/optimisation/_nelder_mead.py +++ b/aeon/utils/optimisation/_nelder_mead.py @@ -28,6 +28,14 @@ def nelder_mead( `num_params` and return a scalar value. num_params : int The number of parameters (dimensions) in the optimisation problem. + data : np.ndarray + The input data used by the loss function. The shape and content depend on the + specific loss function being minimised. + model : np.ndarray + The model or context in which the loss function operates. This could be any + other object that the `loss_function` requires to compute its value. + The exact type and structure of `model` should be compatible with the + `loss_function`. tol : float, optional (default=1e-6) Tolerance for convergence. The algorithm stops when the maximum difference between function values at simplex vertices is less than `tol`. @@ -47,6 +55,13 @@ def nelder_mead( with one additional point per dimension at 0.6 for that dimension. - This implementation does not support constraints or bounds on the parameters. - The algorithm does not guarantee finding a global minimum. + + References + ---------- + .. [1] Nelder, J. A. and Mead, R. (1965). + A Simplex Method for Function Minimization. + The Computer Journal, 7(4), 308–313. + https://doi.org/10.1093/comjnl/7.4.308 """ points = np.full((num_params + 1, num_params), 0.5) for i in range(num_params): From 05a27850a44b1a2b804ec562b72576394d4bfb78 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 23:28:55 +0100 Subject: [PATCH 36/70] Ignore small tolerances in floating point value in output of example --- aeon/forecasting/_arima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 48b27d94da..42d1dece25 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -67,7 +67,7 @@ class ARIMAForecaster(BaseForecaster): >>> forecaster.fit(y) ARIMAForecaster(d=1, p=2) >>> forecaster.predict() - 550.9147246631132 + 550.914724663113... """ def __init__(self, p=1, d=0, q=1, constant_term=0, horizon=1): From 73966ab32a8dca49a5a10cc5aac5d2111d932d2e Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 23:37:12 +0100 Subject: [PATCH 37/70] Fix kpss_test example --- aeon/utils/forecasting/_hypo_tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/utils/forecasting/_hypo_tests.py b/aeon/utils/forecasting/_hypo_tests.py index 664d0c76e5..cfa86a70fc 100644 --- a/aeon/utils/forecasting/_hypo_tests.py +++ b/aeon/utils/forecasting/_hypo_tests.py @@ -52,7 +52,7 @@ def kpss_test(y, regression="c", lags=None): # Test if time series is stationar >>> from aeon.datasets import load_airline >>> y = load_airline() >>> kpss_test(y) - (1.1966313813502716, False) + (np.float64(1.1966313813502716), np.False_) """ y = np.asarray(y) n = len(y) From d5e32f8347d8beace0a0002d67286154c4aadeac Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 23:57:14 +0100 Subject: [PATCH 38/70] Fix kpss_test example #2 --- aeon/utils/forecasting/_hypo_tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/utils/forecasting/_hypo_tests.py b/aeon/utils/forecasting/_hypo_tests.py index cfa86a70fc..2d581e971e 100644 --- a/aeon/utils/forecasting/_hypo_tests.py +++ b/aeon/utils/forecasting/_hypo_tests.py @@ -52,7 +52,7 @@ def kpss_test(y, regression="c", lags=None): # Test if time series is stationar >>> from aeon.datasets import load_airline >>> y = load_airline() >>> kpss_test(y) - (np.float64(1.1966313813502716), np.False_) + (np.float64(1.1966313813...), np.False_) """ y = np.asarray(y) n = len(y) From a0f090d48e6ae066527acd38c43b15da63f17414 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 28 May 2025 23:58:28 +0100 Subject: [PATCH 39/70] Fix kpss_test example #2 --- aeon/utils/forecasting/_hypo_tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/utils/forecasting/_hypo_tests.py b/aeon/utils/forecasting/_hypo_tests.py index cfa86a70fc..2d581e971e 100644 --- a/aeon/utils/forecasting/_hypo_tests.py +++ b/aeon/utils/forecasting/_hypo_tests.py @@ -52,7 +52,7 @@ def kpss_test(y, regression="c", lags=None): # Test if time series is stationar >>> from aeon.datasets import load_airline >>> y = load_airline() >>> kpss_test(y) - (np.float64(1.1966313813502716), np.False_) + (np.float64(1.1966313813...), np.False_) """ y = np.asarray(y) n = len(y) From 17004d917e0584274437fb2cedb9d174e2b63e74 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Thu, 29 May 2025 00:34:17 +0100 Subject: [PATCH 40/70] Fix floating point inaccuracies causing test to fail --- aeon/forecasting/_sarima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index 30fd820bb8..a8302ee362 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -54,7 +54,7 @@ class SARIMAForecaster(ARIMAForecaster): >>> forecaster.fit(y) SARIMAForecaster(d=1, ds=1, q=2) >>> forecaster.predict() - 450.7487685084027 + 450.7487685... """ def __init__( From 206f70bca30b8b554cc6900c4376f7694e7b5f2c Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Thu, 29 May 2025 00:46:20 +0100 Subject: [PATCH 41/70] Fix floating point inaccuracies causing test to fail #2 --- aeon/forecasting/_sarima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index a8302ee362..3c4d5c2c89 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -54,7 +54,7 @@ class SARIMAForecaster(ARIMAForecaster): >>> forecaster.fit(y) SARIMAForecaster(d=1, ds=1, q=2) >>> forecaster.predict() - 450.7487685... + 450.74876... """ def __init__( From e8657fecc48d7fcceaa3ddd2ca4f3afa08b0d8c0 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Thu, 29 May 2025 01:01:07 +0100 Subject: [PATCH 42/70] Fix final docstring example --- aeon/forecasting/_auto_sarima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_auto_sarima.py b/aeon/forecasting/_auto_sarima.py index 7b82892aca..dafe7f6ccc 100644 --- a/aeon/forecasting/_auto_sarima.py +++ b/aeon/forecasting/_auto_sarima.py @@ -50,7 +50,7 @@ class AutoSARIMAForecaster(SARIMAForecaster): >>> forecaster.fit(y) AutoSARIMAForecaster() >>> forecaster.predict() - 450.74890401954826 + 450.74890... """ def __init__(self, horizon=1): From cbc790bdc1b8b83c9e56a20962e8f4a197e23a49 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Thu, 29 May 2025 01:10:44 +0100 Subject: [PATCH 43/70] Fix final docstring example #2 --- aeon/forecasting/_auto_sarima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_auto_sarima.py b/aeon/forecasting/_auto_sarima.py index dafe7f6ccc..e21d7e3578 100644 --- a/aeon/forecasting/_auto_sarima.py +++ b/aeon/forecasting/_auto_sarima.py @@ -50,7 +50,7 @@ class AutoSARIMAForecaster(SARIMAForecaster): >>> forecaster.fit(y) AutoSARIMAForecaster() >>> forecaster.predict() - 450.74890... + 450.748... """ def __init__(self, horizon=1): From 68847033b6b4f0f9fdfab5c55f68292a438c9b24 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 2 Jun 2025 21:01:28 +0100 Subject: [PATCH 44/70] Update documentation for ARIMAForecaster, change constant_term to be bool, and fix bug with it not operating on differemced data --- aeon/forecasting/_arima.py | 33 +++++++++++++++++++++++++-------- 1 file changed, 25 insertions(+), 8 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 42d1dece25..103fbc6d4c 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -23,6 +23,14 @@ class ARIMAForecaster(BaseForecaster): Parameters ---------- + p : int, default=1, + Autoregressive (p) order of the ARIMA model + d : int, default=0, + Differencing (d) order of the ARIMA model + q : int, default=1, + Moving average (q) order of the ARIMA model + constant_term: bool = False, + Presence of a constant/intercept term in the model. horizon : int, default=1 The forecasting horizon, i.e., the number of steps ahead to predict. @@ -41,10 +49,10 @@ class ARIMAForecaster(BaseForecaster): p_, d_, q_ : int Orders of the ARIMA model: autoregressive (p), differencing (d), and moving average (q) terms. - constant_term : int + constant_term : bool Parameters passed to the forecaster see constant_term_. - constant_term_ : float - Constant/intercept term in the model. + constant_term_ : bool + Whether to include a constant/intercept term in the model. c_ : float Estimated constant term (internal use). phi_ : np.ndarray @@ -67,10 +75,17 @@ class ARIMAForecaster(BaseForecaster): >>> forecaster.fit(y) ARIMAForecaster(d=1, p=2) >>> forecaster.predict() - 550.914724663113... + 474.49449... """ - def __init__(self, p=1, d=0, q=1, constant_term=0, horizon=1): + def __init__( + self, + p: int = 1, + d: int = 0, + q: int = 1, + constant_term: bool = False, + horizon: int = 1, + ): super().__init__(horizon=horizon, axis=1) self.data_ = [] self.differenced_data_ = [] @@ -83,7 +98,7 @@ def __init__(self, p=1, d=0, q=1, constant_term=0, horizon=1): self.p_ = 0 self.d_ = 0 self.q_ = 0 - self.constant_term_ = 0 + self.constant_term_ = False self.model_ = [] self.c_ = 0 self.phi_ = 0 @@ -112,12 +127,14 @@ def _fit(self, y, exog=None): self.q_ = self.q self.constant_term_ = self.constant_term self.data_ = np.array(y.squeeze(), dtype=np.float64) - self.model_ = np.array((self.constant_term, self.p, self.q), dtype=np.int32) + self.model_ = np.array( + (1 if self.constant_term else 0, self.p, self.q), dtype=np.int32 + ) self.differenced_data_ = np.diff(self.data_, n=self.d) (self.parameters_, self.aic_) = nelder_mead( _arima_model_wrapper, np.sum(self.model_[:3]), - self.data_, + self.differenced_data_, self.model_, ) (self.c_, self.phi_, self.theta_) = _extract_params( From 56600f72fe042b97c5dc4389aaa5d59e90371351 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 2 Jun 2025 21:06:09 +0100 Subject: [PATCH 45/70] Add type hints, convert constant_term to bool --- aeon/forecasting/_sarima.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index 3c4d5c2c89..b2a106ef6b 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -50,7 +50,7 @@ class SARIMAForecaster(ARIMAForecaster): >>> from aeon.forecasting import SARIMAForecaster >>> from aeon.datasets import load_airline >>> y = load_airline() - >>> forecaster = SARIMAForecaster(1,1,2,0,1,0,12,0) + >>> forecaster = SARIMAForecaster(1,1,2,0,1,0,12,False) >>> forecaster.fit(y) SARIMAForecaster(d=1, ds=1, q=2) >>> forecaster.predict() @@ -59,15 +59,15 @@ class SARIMAForecaster(ARIMAForecaster): def __init__( self, - p=1, - d=0, - q=1, - ps=0, - ds=0, - qs=0, - seasonal_period=12, - constant_term=0, - horizon=1, + p: int = 1, + d: int = 0, + q: int = 1, + ps: int = 0, + ds: int = 0, + qs: int = 0, + seasonal_period: int = 12, + constant_term: bool = False, + horizon: int = 1, ): super().__init__(p=p, d=d, q=q, constant_term=constant_term, horizon=horizon) self.ps = ps @@ -111,7 +111,7 @@ def _fit(self, y, exog=None): self.data_ = np.array(y.squeeze(), dtype=np.float64) self.model_ = np.array( ( - self.constant_term, + 1 if self.constant_term else 0, self.p, self.q, self.ps, From 93b3df86ebe838116b8e15633cb6a23dfaf9622b Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 2 Jun 2025 21:10:24 +0100 Subject: [PATCH 46/70] Convert constant term to bool, add type hints --- aeon/forecasting/_auto_arima.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/aeon/forecasting/_auto_arima.py b/aeon/forecasting/_auto_arima.py index 45c4cc34c4..d38d75223d 100644 --- a/aeon/forecasting/_auto_arima.py +++ b/aeon/forecasting/_auto_arima.py @@ -93,10 +93,11 @@ def _fit(self, y, exog=None): self.differenced_data_, _arima_model_wrapper, model_parameters, 3 ) ( - self.constant_term_, + constant_term_int, self.p_, self.q_, ) = self.model_ + self.constant_term_ = constant_term_int == 1 (self.c_, self.phi_, self.theta_) = _extract_params( self.parameters_, self.model_ ) From 29cf10776c98914f04d9194c5ee5144bf5746d2f Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 2 Jun 2025 21:12:32 +0100 Subject: [PATCH 47/70] Convert constant_term to bool, add type hints --- aeon/forecasting/_auto_sarima.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/aeon/forecasting/_auto_sarima.py b/aeon/forecasting/_auto_sarima.py index e21d7e3578..dce8b84e02 100644 --- a/aeon/forecasting/_auto_sarima.py +++ b/aeon/forecasting/_auto_sarima.py @@ -53,7 +53,7 @@ class AutoSARIMAForecaster(SARIMAForecaster): 450.748... """ - def __init__(self, horizon=1): + def __init__(self, horizon: int = 1): super().__init__(horizon=horizon) def _fit(self, y, exog=None): @@ -103,13 +103,14 @@ def _fit(self, y, exog=None): self.differenced_data_, _sarima_model_wrapper, model_parameters, 5 ) ( - self.constant_term_, + constant_term_int, self.p_, self.q_, self.ps_, self.qs_, self.seasonal_period_, ) = self.model_ + self.constant_term_ = constant_term_int == 1 (self.c_, self.phi_, self.theta_, self.phi_s_, self.theta_s_) = _extract_params( self.parameters_, self.model_ ) From 02a9c49a879ee708297274a175b45a468bbe8c4e Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 2 Jun 2025 21:13:18 +0100 Subject: [PATCH 48/70] Add type hints --- aeon/forecasting/_auto_arima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_auto_arima.py b/aeon/forecasting/_auto_arima.py index d38d75223d..3f65cf253b 100644 --- a/aeon/forecasting/_auto_arima.py +++ b/aeon/forecasting/_auto_arima.py @@ -50,7 +50,7 @@ class AutoARIMAForecaster(ARIMAForecaster): 476.5824781648738 """ - def __init__(self, horizon=1): + def __init__(self, horizon: int = 1): super().__init__(horizon=horizon) def _fit(self, y, exog=None): From e642605173365cf33fbabfe1ed8aced21d1b5140 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 4 Jun 2025 21:32:16 +0100 Subject: [PATCH 49/70] Remove outdated ets_fast --- aeon/forecasting/_ets_fast.py | 476 ---------------------------------- 1 file changed, 476 deletions(-) delete mode 100644 aeon/forecasting/_ets_fast.py diff --git a/aeon/forecasting/_ets_fast.py b/aeon/forecasting/_ets_fast.py deleted file mode 100644 index 3322206aaa..0000000000 --- a/aeon/forecasting/_ets_fast.py +++ /dev/null @@ -1,476 +0,0 @@ -"""ETSForecaster class. - -An implementation of the exponential smoothing statistics forecasting algorithm. -Implements additive and multiplicative error models, -None, additive and multiplicative (including damped) trend and -None, additive and mutliplicative seasonality - -aeon enhancement proposal -https://github.com/aeon-toolkit/aeon/pull/2244/ - -""" - -__maintainer__ = [] -__all__ = ["ETSForecaster"] - -import numpy as np -from numba import njit - -from aeon.forecasting.base import BaseForecaster - -NOGIL = False -CACHE = True - -NONE = 0 -ADDITIVE = 1 -MULTIPLICATIVE = 2 - - -class ETSForecaster(BaseForecaster): - """Exponential Smoothing forecaster. - - An implementation of the exponential smoothing statistics forecasting algorithm. - Implements additive and multiplicative error models, - None, additive and multiplicative (including damped) trend and - None, additive and mutliplicative seasonality[1]_. - - Parameters - ---------- - alpha : float, default = 0.1 - Level smoothing parameter. - beta : float, default = 0.01 - Trend smoothing parameter. - gamma : float, default = 0.01 - Seasonal smoothing parameter. - phi : float, default = 0.99 - Trend damping smoothing parameters - horizon : int, default = 1 - The horizon to forecast to. - error_type : int - The type of error model; either Additive(1) or Multiplicative(2) - trend_type : int - The type of trend model; one of None(0), additive(1) or multiplicative(2). - seasonality_type : int - The type of seasonality model; one of None(0), additive(1) or multiplicative(2). - seasonal_period : int - The period of the seasonality (m) (e.g., for quaterly data seasonal_period = 4). - - References - ---------- - .. [1] R. J. Hyndman and G. Athanasopoulos, - Forecasting: Principles and Practice. Melbourne, Australia: OTexts, 2014. - - Examples - -------- - >>> from aeon.forecasting import ETSForecaster - >>> from aeon.datasets import load_airline - >>> y = load_airline() - >>> forecaster = ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1, - error_type=1, trend_type=2, seasonality_type=2, seasonal_period=4) - >>> forecaster.fit(y) - ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, seasonal_period=4, - seasonality_type=2, trend_type=2) - >>> forecaster.predict() - array([366.90200486]) - """ - - def __init__( - self, - error_type=ADDITIVE, - trend_type=NONE, - seasonality_type=NONE, - seasonal_period=1, - alpha=0.1, - beta=0.01, - gamma=0.01, - phi=0.99, - horizon=1, - ): - self.alpha = alpha - self.beta = beta - self.gamma = gamma - self.phi = phi - self.forecast_val_ = 0.0 - self.level_ = 0.0 - self.trend_ = 0.0 - self.seasonality_ = None - self._beta = beta - self._gamma = gamma - self.error_type = error_type - self.trend_type = trend_type - self.seasonality_type = seasonality_type - self.seasonal_period = seasonal_period - self._seasonal_period = seasonal_period - self.n_timepoints_ = 0 - self.avg_mean_sq_err_ = 0 - self.liklihood_ = 0 - self.k_ = 0 - self.aic_ = 0 - self.residuals_ = [] - self.fitted_values_ = [] - super().__init__(horizon=horizon, axis=1) - - def _fit(self, y, exog=None): - """Fit Exponential Smoothing forecaster to series y. - - Fit a forecaster to predict self.horizon steps ahead using y. - - Parameters - ---------- - y : np.ndarray - A time series on which to learn a forecaster to predict horizon ahead - exog : np.ndarray, default =None - Optional exogenous time series data assumed to be aligned with y - - Returns - ------- - self - Fitted ETSForecaster. - """ - assert ( - self.error_type != NONE - ), "Error must be either additive or multiplicative" - if self._seasonal_period < 1 or self.seasonality_type == NONE: - self._seasonal_period = 1 - - if self.trend_type == NONE: - # Required for the equations in _update_states to work correctly - self._beta = 0 - if self.seasonality_type == NONE: - # Required for the equations in _update_states to work correctly - self._gamma = 0 - data = y.squeeze() - ( - self.level_, - self.trend_, - self.seasonality_, - self.n_timepoints_, - self.residuals_, - self.fitted_values_, - self.avg_mean_sq_err_, - self.liklihood_, - self.k_, - self.aic_, - ) = _fit( - data, - self.error_type, - self.trend_type, - self.seasonality_type, - self._seasonal_period, - self.alpha, - self._beta, - self._gamma, - self.phi, - ) - return self - - def _predict(self, y=None, exog=None): - """ - Predict the next horizon steps ahead. - - Parameters - ---------- - y : np.ndarray, default = None - A time series to predict the next horizon value for. If None, - predict the next horizon value after series seen in fit. - exog : np.ndarray, default =None - Optional exogenous time series data assumed to be aligned with y - - Returns - ------- - float - single prediction self.horizon steps ahead of y. - """ - fitted_value = _predict( - self.trend_type, - self.seasonality_type, - self.level_, - self.trend_, - self.seasonality_, - self.phi, - self.horizon, - self.n_timepoints_, - self._seasonal_period, - ) - if y is None: - return np.array([fitted_value]) - else: - return np.insert(y, 0, fitted_value)[:-1] - - def _initialise(self, data): - """ - Initialize level, trend, and seasonality values for the ETS model. - - Parameters - ---------- - data : array-like - The time series data - (should contain at least two full seasons if seasonality is specified) - """ - self.level_, self.trend_, self.seasonality_ = _initialise( - self.trend_type, self.seasonality_type, self._seasonal_period, data - ) - - -@njit(nogil=NOGIL, cache=CACHE) -def _fit( - data, - error_type, - trend_type, - seasonality_type, - seasonal_period, - alpha, - beta, - gamma, - phi, -): - assert error_type != NONE, "Error must be either additive or multiplicative" - assert ( - error_type != MULTIPLICATIVE - and trend_type != MULTIPLICATIVE - and seasonality_type != MULTIPLICATIVE - or data.min() > 0 - ), "Data must be positive with multiplicative components" - if seasonal_period < 1 or seasonality_type == NONE: - seasonal_period = 1 - if trend_type == NONE: - # Required for the equations in _update_states to work correctly - beta = 0 - if seasonality_type == NONE: - # Required for the equations in _update_states to work correctly - gamma = 0 - n_timepoints = len(data) - seasonal_period - level, trend, seasonality = _initialise( - trend_type, seasonality_type, seasonal_period, data - ) - avg_mean_sq_err_ = 0 - liklihood_ = 0 - residuals_ = np.zeros(n_timepoints) # 1 Less residual than data points - fitted_values_ = np.zeros(n_timepoints) - for t, data_item in enumerate(data[seasonal_period:]): - # Calculate level, trend, and seasonal components - fitted_value, error, level, trend, seasonality[t % seasonal_period] = ( - _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality[t % seasonal_period], - data_item, - alpha, - beta, - gamma, - phi, - ) - ) - residuals_[t] = error - fitted_values_[t] = fitted_value - avg_mean_sq_err_ += (data_item - fitted_value) ** 2 - liklihood_error = error - if error_type == MULTIPLICATIVE: - liklihood_error *= fitted_value - liklihood_ += liklihood_error**2 - avg_mean_sq_err_ /= n_timepoints - liklihood_ = n_timepoints * np.log(liklihood_) - k_ = ( - seasonal_period * (seasonality_type != 0) - + 2 * (trend_type != 0) - + 2 - + 1 * (phi != 1) - ) - aic_ = liklihood_ + 2 * k_ - n_timepoints * np.log(n_timepoints) - return ( - level, - trend, - seasonality, - n_timepoints, - residuals_, - fitted_values_, - avg_mean_sq_err_, - liklihood_, - k_, - aic_, - ) - - -@njit(nogil=NOGIL, cache=CACHE) -def _predict( - trend_type, - seasonality_type, - level, - trend, - seasonality, - phi, - horizon, - n_timepoints, - seasonal_period, -): - # Generate forecasts based on the final values of level, trend, and seasonals - if phi == 1: # No damping case - phi_h = 1 - else: - # Geometric series formula for calculating phi + phi^2 + ... + phi^h - phi_h = phi * (1 - phi**horizon) / (1 - phi) - seasonal_index = (n_timepoints + horizon) % seasonal_period - return _predict_value( - trend_type, - seasonality_type, - level, - trend, - seasonality[seasonal_index], - phi_h, - )[0] - - -@njit(nogil=NOGIL, cache=CACHE) -def _initialise(trend_type, seasonality_type, seasonal_period, data): - """ - Initialize level, trend, and seasonality values for the ETS model. - - Parameters - ---------- - data : array-like - The time series data - (should contain at least two full seasons if seasonality is specified) - """ - # Initial Level: Mean of the first season - level = np.mean(data[:seasonal_period]) - # Initial Trend - if trend_type == ADDITIVE: - # Average difference between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] - data[:seasonal_period] - ) - elif trend_type == MULTIPLICATIVE: - # Average ratio between corresponding points in the first two seasons - trend = np.mean( - data[seasonal_period : 2 * seasonal_period] / data[:seasonal_period] - ) - else: - # No trend - trend = 0 - # Initial Seasonality - if seasonality_type == ADDITIVE: - # Seasonal component is the difference - # from the initial level for each point in the first season - seasonality = data[:seasonal_period] - level - elif seasonality_type == MULTIPLICATIVE: - # Seasonal component is the ratio of each point in the first season - # to the initial level - seasonality = data[:seasonal_period] / level - else: - # No seasonality - seasonality = np.zeros(1, dtype=np.float64) - return level, trend, seasonality - - -@njit(nogil=NOGIL, cache=CACHE) -def _update_states( - error_type, - trend_type, - seasonality_type, - level, - trend, - seasonality, - data_item: int, - alpha, - beta, - gamma, - phi, -): - """ - Update level, trend, and seasonality components. - - Using state space equations for an ETS model. - - Parameters - ---------- - data_item: float - The current value of the time series. - seasonal_index: int - The index to update the seasonal component. - """ - # Retrieve the current state values - curr_level = level - curr_seasonality = seasonality - fitted_value, damped_trend, trend_level_combination = _predict_value( - trend_type, seasonality_type, level, trend, seasonality, phi - ) - # Calculate the error term (observed value - fitted value) - if error_type == MULTIPLICATIVE: - error = data_item / fitted_value - 1 # Multiplicative error - else: - error = data_item - fitted_value # Additive error - # Update level - if error_type == MULTIPLICATIVE: - level = trend_level_combination * (1 + alpha * error) - trend = damped_trend * (1 + beta * error) - seasonality = curr_seasonality * (1 + gamma * error) - if seasonality_type == ADDITIVE: - level += alpha * error * curr_seasonality # Add seasonality correction - seasonality += gamma * error * trend_level_combination - if trend_type == ADDITIVE: - trend += (curr_level + curr_seasonality) * beta * error - else: - trend += curr_seasonality / curr_level * beta * error - elif trend_type == ADDITIVE: - trend += curr_level * beta * error - else: - level_correction = 1 - trend_correction = 1 - seasonality_correction = 1 - if seasonality_type == MULTIPLICATIVE: - # Add seasonality correction - level_correction *= curr_seasonality - trend_correction *= curr_seasonality - seasonality_correction *= trend_level_combination - if trend_type == MULTIPLICATIVE: - trend_correction *= curr_level - level = trend_level_combination + alpha * error / level_correction - trend = damped_trend + beta * error / trend_correction - seasonality = curr_seasonality + gamma * error / seasonality_correction - return (fitted_value, error, level, trend, seasonality) - - -@njit(nogil=NOGIL, cache=CACHE) -def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi): - """ - - Generate various useful values, including the next fitted value. - - Parameters - ---------- - trend : float - The current trend value for the model - level : float - The current level value for the model - seasonality : float - The current seasonality value for the model - phi : float - The damping parameter for the model - - Returns - ------- - fitted_value : float - single prediction based on the current state variables. - damped_trend : float - The damping parameter combined with the trend dependant on the model type - trend_level_combination : float - Combination of the trend and level based on the model type. - """ - # Apply damping parameter and - # calculate commonly used combination of trend and level components - if trend_type == MULTIPLICATIVE: - damped_trend = trend**phi - trend_level_combination = level * damped_trend - else: # Additive trend, if no trend, then trend = 0 - damped_trend = trend * phi - trend_level_combination = level + damped_trend - - # Calculate forecast (fitted value) based on the current components - if seasonality_type == MULTIPLICATIVE: - fitted_value = trend_level_combination * seasonality - else: # Additive seasonality, if no seasonality, then seasonality = 0 - fitted_value = trend_level_combination + seasonality - return fitted_value, damped_trend, trend_level_combination From eac89a11149433ab8db84e556aa526117c14bb14 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 4 Jun 2025 22:25:57 +0100 Subject: [PATCH 50/70] Update AutoETS to work with new ETS version rather than old _ets_fast file --- aeon/forecasting/_autoets.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py index e019646d82..ea818be48f 100644 --- a/aeon/forecasting/_autoets.py +++ b/aeon/forecasting/_autoets.py @@ -11,7 +11,7 @@ from scipy.optimize import minimize from aeon.forecasting._autoets_gradient_params import _calc_model_liklihood -from aeon.forecasting._ets_fast import _fit, _predict +from aeon.forecasting._ets import _numba_fit, _predict from aeon.forecasting._utils import calc_seasonal_period from aeon.forecasting.base import BaseForecaster @@ -115,7 +115,7 @@ def _fit(self, y, exog=None): self.liklihood_, self.k_, self.aic_, - ) = _fit( + ) = _numba_fit( data, self.error_type_, self.trend_type_, @@ -290,7 +290,7 @@ def run_ets_scipy(parameters): _liklihood, _k, aic_, - ) = _fit( + ) = _numba_fit( data, error_type, trend_type, @@ -337,7 +337,7 @@ def run_ets( _liklihood, _k, aic_, - ) = _fit( + ) = _numba_fit( data, error_type, trend_type, From 8074e46ff006093859180d91c3c5edc113ac706e Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Wed, 4 Jun 2025 23:43:15 +0100 Subject: [PATCH 51/70] Purge ETSFast from files --- aeon/forecasting/_autoets_gradient_params.py | 6 +- aeon/forecasting/_compare_external_autoets.py | 2 +- .../_plot_autoets_gradient_method.py | 2 +- aeon/forecasting/_verify_ets.py | 70 ++++--------------- 4 files changed, 19 insertions(+), 61 deletions(-) diff --git a/aeon/forecasting/_autoets_gradient_params.py b/aeon/forecasting/_autoets_gradient_params.py index 119211a29a..78b7109f7e 100644 --- a/aeon/forecasting/_autoets_gradient_params.py +++ b/aeon/forecasting/_autoets_gradient_params.py @@ -12,7 +12,11 @@ import torch -from aeon.forecasting._ets_fast import ADDITIVE, MULTIPLICATIVE, NONE, ETSForecaster +from aeon.forecasting._ets import ETSForecaster + +NONE = 0 +ADDITIVE = 1 +MULTIPLICATIVE = 2 def _calc_model_liklihood( diff --git a/aeon/forecasting/_compare_external_autoets.py b/aeon/forecasting/_compare_external_autoets.py index b57f67a874..feecab1a8f 100644 --- a/aeon/forecasting/_compare_external_autoets.py +++ b/aeon/forecasting/_compare_external_autoets.py @@ -14,7 +14,7 @@ from statsmodels.tsa.exponential_smoothing.ets import ETSModel from aeon.forecasting._autoets import auto_ets -from aeon.forecasting._ets_fast import ETSForecaster +from aeon.forecasting._ets import ETSForecaster plt.rcParams["figure.figsize"] = (12, 8) diff --git a/aeon/forecasting/_plot_autoets_gradient_method.py b/aeon/forecasting/_plot_autoets_gradient_method.py index a84a41baa1..4d405f8bc2 100644 --- a/aeon/forecasting/_plot_autoets_gradient_method.py +++ b/aeon/forecasting/_plot_autoets_gradient_method.py @@ -8,7 +8,7 @@ from statsforecast.utils import AirPassengersDF from aeon.forecasting._autoets import auto_ets -from aeon.forecasting._ets_fast import ETSForecaster +from aeon.forecasting._ets import ETSForecaster plt.rcParams["figure.figsize"] = (12, 8) diff --git a/aeon/forecasting/_verify_ets.py b/aeon/forecasting/_verify_ets.py index 65d3ca0faf..1e060143c3 100644 --- a/aeon/forecasting/_verify_ets.py +++ b/aeon/forecasting/_verify_ets.py @@ -8,7 +8,6 @@ from statsforecast.utils import AirPassengers as ap import aeon.forecasting._ets as ets -import aeon.forecasting._ets_fast as etsfast from aeon.forecasting import ETSForecaster NA = -99999.0 @@ -231,31 +230,9 @@ def test_ets_comparison(setup_func, random_seed, catch_errors): return True -def time_etsfast(): +def time_ets(): """Test function for optimised numba ets algorithm.""" - etsfast.ETSForecaster(2, 2, 2, 4).fit(ap).predict() - - -def time_etsnoopt(): - """Test function for non-optimised ets algorithm.""" - ets.ETSForecaster(2, 2, 2, 4).fit(ap).predict() - - -def time_etsfast_noclass(): - """Test function for optimised ets algorithm without the class based structure.""" - data = np.array(ap.squeeze(), dtype=np.float64) - # pylint: disable=W0212 - ( - level, - trend, - seasonality, - _residuals, - _fitted_values, - _avg_mean_sq_err, - _liklihood, - ) = etsfast._fit(data, 2, 2, 2, 4, 0.1, 0.01, 0.01, 0.99) - etsfast._predict(2, 2, level, trend, seasonality, 0.99, 1, 144, 4) - # pylint: enable=W0212 + ETSForecaster(2, 2, 2, 4).fit(ap).predict() def time_sf(): @@ -284,42 +261,24 @@ def time_compare(random_seed): # print (f"Execution time ETS No-opt: {etsnoopt_time} seconds") # Do a few iterations to remove background/overheads. Makes comparison more reliable for _i in range(10): - time_etsfast() + time_ets() time_sf() - time_etsfast_noclass() - etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) - print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa - etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) - print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + ets_time = timeit.timeit(time_ets, globals={}, number=1000) + print(f"Execution time ETS: {ets_time} seconds") # noqa statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa - etsfast_time = timeit.timeit(time_etsfast, globals={}, number=1000) - print(f"Execution time ETS Fast: {etsfast_time} seconds") # noqa - etsfast_noclass_time = timeit.timeit(time_etsfast_noclass, globals={}, number=1000) - print(f"Execution time ETS Fast NoClass: {etsfast_noclass_time} seconds") # noqa + ets_time = timeit.timeit(time_ets, globals={}, number=1000) + print(f"Execution time ETS: {ets_time} seconds") # noqa statsforecast_time = timeit.timeit(time_sf, globals={}, number=1000) print(f"Execution time StatsForecast: {statsforecast_time} seconds") # noqa - # _ets_fast_nostruct implementation - start = time.perf_counter() - f3 = etsfast.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) - f3.fit(y) - end = time.perf_counter() - etsfast_time = end - start - # _ets_fast implementation # _ets implementation start = time.perf_counter() f1 = ets.ETSForecaster(error, trend, season, m, alpha, beta, gamma, phi, 1) f1.fit(y) end = time.perf_counter() - etsnoopt_time = end - start - assert np.allclose(f1.residuals_, f3.residuals_) - assert np.allclose(f1.avg_mean_sq_err_, f3.avg_mean_sq_err_) - assert np.isclose(f1.liklihood_, f3.liklihood_) - print( # noqa - f"ETS No-optimisation Time: {etsnoopt_time},\ - Fast time: {etsfast_time}" - ) - return etsnoopt_time, etsfast_time + ets_time = end - start + print(f"ETS Time: {ets_time}") # noqa + return ets_time if __name__ == "__main__": @@ -332,14 +291,9 @@ def time_compare(random_seed): print("Test Completed Successfully with no errors") # noqa # time_compare(300) # avg_ets = 0 - # avg_etsfast = 0 - # avg_etsfast_ns = 0 # iterations = 100 # for i in range (iterations): - # time_ets, etsfast_time = time_compare(300) + # time_ets = time_compare(300) # avg_ets += time_ets - # avg_etsfast += etsfast_time # avg_ets/= iterations - # avg_etsfast/= iterations - # avg_etsfast_ns /= iterations - # print(f"Avg ETS Time: {avg_ets}, Avg Fast ETS time: {avg_etsfast},\ + # print(f"Avg ETS Time: {avg_ets},\ From f863f3aecce6399245e029aa2e0b2843017ced30 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Thu, 5 Jun 2025 19:53:37 +0100 Subject: [PATCH 52/70] Correct predict method to return fitted value rather than array --- aeon/forecasting/_autoets.py | 6 +----- aeon/forecasting/_sktime_autoets.py | 8 +------- aeon/forecasting/_statsforecast_autoets.py | 8 +------- 3 files changed, 3 insertions(+), 19 deletions(-) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py index ea818be48f..22a9a2a59f 100644 --- a/aeon/forecasting/_autoets.py +++ b/aeon/forecasting/_autoets.py @@ -145,7 +145,7 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - fitted_value = _predict( + return _predict( self.trend_type_, self.seasonality_type_, self.level_, @@ -156,10 +156,6 @@ def _predict(self, y=None, exog=None): self.n_timepoints_, self.seasonal_period_, ) - if y is None: - return np.array([fitted_value]) - else: - return np.insert(y, 0, fitted_value)[:-1] def auto_ets(data, method="internal_nelder_mead"): diff --git a/aeon/forecasting/_sktime_autoets.py b/aeon/forecasting/_sktime_autoets.py index 127d93040b..8852f9a7a9 100644 --- a/aeon/forecasting/_sktime_autoets.py +++ b/aeon/forecasting/_sktime_autoets.py @@ -7,8 +7,6 @@ __maintainer__ = [] __all__ = ["SktimeAutoETSForecaster"] - -import numpy as np from sktime.forecasting.ets import AutoETS from aeon.forecasting._utils import calc_seasonal_period @@ -71,8 +69,4 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - fitted_value = self.model_.predict(self.horizon, exog)[0][0] - if y is None: - return np.array([fitted_value]) - else: - return np.insert(y, 0, fitted_value)[:-1] + return self.model_.predict(self.horizon, exog)[0][0] diff --git a/aeon/forecasting/_statsforecast_autoets.py b/aeon/forecasting/_statsforecast_autoets.py index 8ce77d257d..d294315d44 100644 --- a/aeon/forecasting/_statsforecast_autoets.py +++ b/aeon/forecasting/_statsforecast_autoets.py @@ -7,8 +7,6 @@ __maintainer__ = [] __all__ = ["StatsForecastAutoETSForecaster"] - -import numpy as np from statsforecast.models import AutoETS from aeon.forecasting._utils import calc_seasonal_period @@ -71,8 +69,4 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - fitted_value = self.model_.predict(self.horizon, exog)["mean"][0] - if y is None: - return np.array([fitted_value]) - else: - return np.insert(y, 0, fitted_value)[:-1] + return self.model_.predict(self.horizon, exog)["mean"][0] From 2eadc806bac4cd6df701f5d1bd67a989c4b12257 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 6 Jun 2025 17:31:21 +0100 Subject: [PATCH 53/70] Update ETSForecaster to allow multiple predictions without refitting the model --- aeon/forecasting/_ets.py | 69 ++++++++++++++++++++++++++++++++++------ 1 file changed, 60 insertions(+), 9 deletions(-) diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index 2899e0d768..06825e5546 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -89,7 +89,7 @@ class ETSForecaster(BaseForecaster): >>> forecaster.fit(y) ETSForecaster(...) >>> forecaster.predict() - 366.90200486015596 + array([366.9020...) """ def __init__( @@ -200,6 +200,11 @@ def _predict(self, y=None, exog=None): """ Predict the next horizon steps ahead. + If given y, use y to update the model and continue to predict horizon + steps ahead. Assumes no gap in time between the y passed in fit and the + y passed here. Assumes that y is of the same frequency as y passed in fit. + If y is None, predict horizon steps ahead of the series seen in fit. + Parameters ---------- y : np.ndarray, default = None @@ -213,18 +218,25 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - fitted_value = _predict( + if y is not None: + y = y.squeeze() + fitted_values = _predict( + self._error_type, self._trend_type, self._seasonality_type, + self.alpha, + self._beta, + self._gamma, + self.phi, self.level_, self.trend_, self.seasonality_, - self.phi, self.horizon, self.n_timepoints_, self._seasonal_period, + y, ) - return fitted_value + return fitted_values def _initialise(self, data): """ @@ -315,31 +327,70 @@ def _numba_fit( @njit(fastmath=True, cache=True) def _predict( + error_type, trend_type, seasonality_type, + alpha, + beta, + gamma, + phi, level, trend, seasonality, - phi, horizon, n_timepoints, seasonal_period, + y=None, ): - # Generate forecasts based on the final values of level, trend, and seasonals + # Predict horizon steps ahead for each time point in y + if y is None: + y = np.zeros(shape=0, dtype=np.float64) + fitted_values_ = np.zeros(shape=(len(y) + 1), dtype=np.float64) if phi == 1: # No damping case phi_h = 1 else: # Geometric series formula for calculating phi + phi^2 + ... + phi^h phi_h = phi * (1 - phi**horizon) / (1 - phi) - seasonal_index = (n_timepoints + horizon) % seasonal_period - return _predict_value( + for t, time_point in enumerate(y): + s_index = (n_timepoints + t) % seasonal_period + # Calculate level, trend, and seasonal components + (fitted_value, _error, level, trend, seasonality[s_index]) = _update_states( + error_type, + trend_type, + seasonality_type, + level, + trend, + seasonality[s_index], + time_point, + alpha, + beta, + gamma, + phi, + ) + if horizon > 1: + forecast_s_index = (n_timepoints + t + horizon) % seasonal_period + # Generate forecasts based the horizon ahead + fitted_values_[t] = _predict_value( + trend_type, + seasonality_type, + level, + trend, + seasonality[forecast_s_index], + phi_h, + )[0] + else: + fitted_values_[t] = fitted_value + forecast_s_index = (n_timepoints + len(y) + horizon) % seasonal_period + # Generate forecasts based on the final values of level, trend, and seasonals + fitted_values_[-1] = _predict_value( trend_type, seasonality_type, level, trend, - seasonality[seasonal_index], + seasonality[forecast_s_index], phi_h, )[0] + return fitted_values_ @njit(fastmath=True, cache=True) From 9af3a56f52ecae0e3dc1294fc1aa052026f81e8d Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Sun, 8 Jun 2025 19:38:49 +0100 Subject: [PATCH 54/70] Modify ARIMA to allow predicting multiple values by updating the state without refitting the model --- aeon/forecasting/_arima.py | 78 ++++++++++++++++++++++++-------------- 1 file changed, 49 insertions(+), 29 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index 103fbc6d4c..e176e3bb8e 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -6,8 +6,6 @@ __maintainer__ = ["alexbanwell1", "TonyBagnall"] __all__ = ["ARIMAForecaster"] -from math import comb - import numpy as np from numba import njit @@ -84,12 +82,12 @@ def __init__( d: int = 0, q: int = 1, constant_term: bool = False, - horizon: int = 1, ): - super().__init__(horizon=horizon, axis=1) + super().__init__(horizon=1, axis=1) self.data_ = [] self.differenced_data_ = [] self.residuals_ = [] + self.fitted_values_ = [] self.aic_ = 0 self.p = p self.d = d @@ -140,8 +138,12 @@ def _fit(self, y, exog=None): (self.c_, self.phi_, self.theta_) = _extract_params( self.parameters_, self.model_ ) - (self.aic_, self.residuals_) = _arima_model( - self.parameters_, _calc_arima, self.differenced_data_, self.model_ + (self.aic_, self.residuals_, self.fitted_values_) = _arima_model( + self.parameters_, + _calc_arima, + self.differenced_data_, + self.model_, + np.empty(0), ) return self @@ -159,22 +161,28 @@ def _predict(self, y=None, exog=None): Returns ------- - float - single prediction self.horizon steps ahead of y. + array[float] + Predictions len(y) steps ahead of the data seen in fit. + If y is None, then predict 1 step ahead of the data seen in fit. """ - y = np.array(y, dtype=np.float64) - value = _calc_arima( - self.differenced_data_, + if y is not None: + combined_data = np.concatenate((self.data_, y.flatten())) + else: + combined_data = self.data_ + n = len(self.data_) + differenced_data = np.diff(combined_data, n=self.d) + _aic, _residuals, predicted_values = _arima_model( + self.parameters_, + _calc_arima, + differenced_data, self.model_, - len(self.differenced_data_), - _extract_params(self.parameters_, self.model_), self.residuals_, ) - history = self.data_[::-1] - # Step 2: undo ordinary differencing - for k in range(1, self.d_ + 1): - value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - return float(value) + init = combined_data[n - self.d_ : n] + x = np.concatenate((init, predicted_values)) + for _ in range(self.d_): + x = np.cumsum(x) + return x[self.d_ :] @njit(cache=True, fastmath=True) @@ -187,28 +195,35 @@ def _aic(residuals, num_params): @njit(fastmath=True) def _arima_model_wrapper(params, data, model): - return _arima_model(params, _calc_arima, data, model)[0] + return _arima_model(params, _calc_arima, data, model, np.empty(0))[0] # Define the ARIMA(p, d, q) likelihood function @njit(cache=True, fastmath=True) -def _arima_model(params, base_function, data, model): +def _arima_model(params, base_function, data, model, residuals): """Calculate the log-likelihood of an ARIMA model given the parameters.""" formatted_params = _extract_params(params, model) # Extract parameters # Initialize residuals n = len(data) - residuals = np.zeros(n) - for t in range(n): - y_hat = base_function( + m = len(residuals) + num_predictions = n - m + 1 + residuals = np.concatenate((residuals, np.zeros(num_predictions - 1))) + expect_full_history = m > 0 # I.e. we've been provided with some residuals + fitted_values = np.zeros(num_predictions) + for t in range(num_predictions): + fitted_values[t] = base_function( data, model, - t, + m + t, formatted_params, residuals, + expect_full_history, ) - residuals[t] = data[t] - y_hat - return _aic(residuals, len(params)), residuals + if t != num_predictions - 1: + # Only calculate residuals for the predictions we have data for + residuals[m + t] = data[m + t] - fitted_values[t] + return _aic(residuals, len(params)), residuals, fitted_values @njit(cache=True, fastmath=True) @@ -234,17 +249,22 @@ def _extract_params(params, model): @njit(cache=True, fastmath=True) -def _calc_arima(data, model, t, formatted_params, residuals): +def _calc_arima(data, model, t, formatted_params, residuals, expect_full_history=False): """Calculate the ARIMA forecast for time t.""" if len(model) != 3: raise ValueError("Model must be of the form (c, p, q)") - # AR part p = model[1] + q = model[2] + if expect_full_history and (t - p < 0 or t - q < 0): + raise ValueError( + f"Insufficient data for ARIMA model at time {t}. " + f"Expected at least {p} past values for AR and {q} for MA." + ) + # AR part phi = formatted_params[1][:p] ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) # MA part - q = model[2] theta = formatted_params[2][:q] ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) From 554ec4d48703a241e503d8d877e02ebbffad3d21 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 9 Jun 2025 01:48:50 +0100 Subject: [PATCH 55/70] Add ability to predict multiple values by updating the state with new data, but not refitting the model --- aeon/forecasting/_sarima.py | 83 +++++++++++++++++++++++++------------ 1 file changed, 56 insertions(+), 27 deletions(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index b2a106ef6b..15ac03f29a 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -6,8 +6,6 @@ __maintainer__ = ["alexbanwell1", "TonyBagnall"] __all__ = ["SARIMAForecaster"] -from math import comb - import numpy as np from numba import njit @@ -67,9 +65,8 @@ def __init__( qs: int = 0, seasonal_period: int = 12, constant_term: bool = False, - horizon: int = 1, ): - super().__init__(p=p, d=d, q=q, constant_term=constant_term, horizon=horizon) + super().__init__(p=p, d=d, q=q, constant_term=constant_term) self.ps = ps self.ds = ds self.qs = qs @@ -135,8 +132,12 @@ def _fit(self, y, exog=None): (self.c_, self.phi_, self.theta_, self.phi_s_, self.theta_s_) = _extract_params( self.parameters_, self.model_ ) - (self.aic_, self.residuals_) = _arima_model( - self.parameters_, _calc_sarima, self.differenced_data_, self.model_ + (self.aic_, self.residuals_, self.fitted_values_) = _arima_model( + self.parameters_, + _calc_sarima, + self.differenced_data_, + self.model_, + np.empty(0), ) return self @@ -157,41 +158,70 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - y = np.array(y, dtype=np.float64) - value = _calc_sarima( - self.differenced_data_, + if y is not None: + combined_data = np.concatenate((self.data_, y.flatten())) + else: + combined_data = self.data_ + n = len(self.data_) + differenced_data = np.diff(combined_data, n=self.d) + m = n - self.d_ + seasonal_differenced_data = differenced_data + for _ds in range(self.ds_): + seasonal_differenced_data = ( + seasonal_differenced_data[self.seasonal_period_ :] + - seasonal_differenced_data[: -self.seasonal_period_] + ) + _aic, _residuals, predicted_values = _arima_model( + self.parameters_, + _calc_sarima, + seasonal_differenced_data, self.model_, - len(self.differenced_data_), - _extract_params(self.parameters_, self.model_), self.residuals_, ) - history = self.data_[::-1] - differenced_history = np.diff(self.data_, n=self.d_)[::-1] - # Step 1: undo seasonal differencing on y^(d) - for k in range(1, self.ds_ + 1): - lag = k * self.seasonal_period_ - value += (-1) ** (k + 1) * comb(self.ds_, k) * differenced_history[lag - 1] - - # Step 2: undo ordinary differencing - for k in range(1, self.d_ + 1): - value += (-1) ** (k + 1) * comb(self.d_, k) * history[k - 1] - return float(value) + # Undo seasonal differencing + last_season = differenced_data[m - self.seasonal_period * self.ds_ : m] + values = np.concatenate((last_season, predicted_values)) + for _ in range(self.ds_): + for i in range(self.seasonal_period_, len(values)): + values[i] += values[i - self.seasonal_period_] + values = values[self.seasonal_period_ * self.ds_ :] + # Undo ordinary differencing + init = self.data_[n - self.d_ : n] + values = np.concatenate((init, values)) + for _ in range(self.d_): + values = np.cumsum(values) + return values[self.d_ :] @njit(fastmath=True) def _sarima_model_wrapper(params, data, model): - return _arima_model(params, _calc_sarima, data, model)[0] + return _arima_model(params, _calc_sarima, data, model, np.empty(0))[0] @njit(cache=True, fastmath=True) -def _calc_sarima(data, model, t, formatted_params, residuals): +def _calc_sarima( + data, model, t, formatted_params, residuals, expect_full_history=False +): """Calculate the SARIMA forecast for time t.""" if len(model) != 6: raise ValueError("Model must be of the form (c, p, q, ps, qs, seasonal_period)") - arima_forecast = _calc_arima(data, model[:3], t, formatted_params, residuals) + ps = model[3] + qs = model[4] seasonal_period = model[5] + if expect_full_history and ( + (t - seasonal_period * ps) < 0 or (t - seasonal_period * qs) < 0 + ): + raise ValueError( + f"Insufficient data for SARIMA model at time {t}. \ + Seasonal period is {seasonal_period}." + f"Expected at least {seasonal_period * ps} past \ + values for AR and {seasonal_period * qs} for MA." + ) + + arima_forecast = _calc_arima( + data, model[:3], t, formatted_params, residuals, expect_full_history + ) # Seasonal AR part - ps = model[3] phi_s = formatted_params[3][:ps] ars_term = ( 0 @@ -199,7 +229,6 @@ def _calc_sarima(data, model, t, formatted_params, residuals): else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) ) # Seasonal MA part - qs = model[4] theta_s = formatted_params[4][:qs] mas_term = ( 0 From e898f2f8a263bc43dd5d2a43e1ef41d6f05b8f0f Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 9 Jun 2025 21:35:38 +0100 Subject: [PATCH 56/70] Fix bug using self.d rather than self.d_ --- aeon/forecasting/_arima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index e176e3bb8e..a0dd2cb052 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -170,7 +170,7 @@ def _predict(self, y=None, exog=None): else: combined_data = self.data_ n = len(self.data_) - differenced_data = np.diff(combined_data, n=self.d) + differenced_data = np.diff(combined_data, n=self.d_) _aic, _residuals, predicted_values = _arima_model( self.parameters_, _calc_arima, From c4d2813cda2d92435db6f14a99ac36ce3faede05 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 9 Jun 2025 21:38:03 +0100 Subject: [PATCH 57/70] Fix bug using self.d instead of self.d_ --- aeon/forecasting/_sarima.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index 15ac03f29a..6b8548e5f2 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -117,7 +117,7 @@ def _fit(self, y, exog=None): ), dtype=np.int32, ) - self.differenced_data_ = np.diff(self.data_, n=self.d) + self.differenced_data_ = np.diff(self.data_, n=self.d_) for _ds in range(self.ds_): self.differenced_data_ = ( self.differenced_data_[self.seasonal_period_ :] @@ -163,7 +163,7 @@ def _predict(self, y=None, exog=None): else: combined_data = self.data_ n = len(self.data_) - differenced_data = np.diff(combined_data, n=self.d) + differenced_data = np.diff(combined_data, n=self.d_) m = n - self.d_ seasonal_differenced_data = differenced_data for _ds in range(self.ds_): From 64f703b88572917f32aa73714d972425647b3004 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 9 Jun 2025 21:43:20 +0100 Subject: [PATCH 58/70] Update to work with predicting multiple values without refitting the model --- aeon/forecasting/_auto_sarima.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/aeon/forecasting/_auto_sarima.py b/aeon/forecasting/_auto_sarima.py index dce8b84e02..fbf6a5d3c0 100644 --- a/aeon/forecasting/_auto_sarima.py +++ b/aeon/forecasting/_auto_sarima.py @@ -53,8 +53,8 @@ class AutoSARIMAForecaster(SARIMAForecaster): 450.748... """ - def __init__(self, horizon: int = 1): - super().__init__(horizon=horizon) + def __init__(self): + super().__init__() def _fit(self, y, exog=None): """Fit AutoARIMA forecaster to series y. @@ -114,10 +114,11 @@ def _fit(self, y, exog=None): (self.c_, self.phi_, self.theta_, self.phi_s_, self.theta_s_) = _extract_params( self.parameters_, self.model_ ) - ( - self.aic_, - self.residuals_, - ) = _arima_model( - self.parameters_, _calc_sarima, self.differenced_data_, self.model_ + (self.aic_, self.residuals_, self.fitted_values_) = _arima_model( + self.parameters_, + _calc_sarima, + self.differenced_data_, + self.model_, + np.empty(0), ) return self From 4066cb63dd030d5966c074efe647ef594fa65c05 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Mon, 9 Jun 2025 21:48:11 +0100 Subject: [PATCH 59/70] Update AutoETS to work with predicting multiple values by updating state and not refitting model --- aeon/forecasting/_autoets.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py index 22a9a2a59f..895ff9eb35 100644 --- a/aeon/forecasting/_autoets.py +++ b/aeon/forecasting/_autoets.py @@ -145,17 +145,25 @@ def _predict(self, y=None, exog=None): float single prediction self.horizon steps ahead of y. """ - return _predict( + if y is not None: + y = y.squeeze() + fitted_values = _predict( + self.error_type_, self.trend_type_, self.seasonality_type_, + self.alpha_, + self.beta_, + self.gamma_, + self.phi_, self.level_, self.trend_, self.seasonality_, - self.phi_, self.horizon, self.n_timepoints_, self.seasonal_period_, + y, ) + return fitted_values def auto_ets(data, method="internal_nelder_mead"): From 0e311bbab582aa73a21ce9e4131a2bb74b57ec3e Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Tue, 10 Jun 2025 01:42:58 +0100 Subject: [PATCH 60/70] Add back in dataset generation files --- aeon/datasets/Final Dataset Selection.csv | 101 ++++++++++ aeon/datasets/dataset_generation.py | 218 ++++++++++++++++++++++ 2 files changed, 319 insertions(+) create mode 100644 aeon/datasets/Final Dataset Selection.csv create mode 100644 aeon/datasets/dataset_generation.py diff --git a/aeon/datasets/Final Dataset Selection.csv b/aeon/datasets/Final Dataset Selection.csv new file mode 100644 index 0000000000..c336db5a22 --- /dev/null +++ b/aeon/datasets/Final Dataset Selection.csv @@ -0,0 +1,101 @@ +Dataset,Series,Category +weather_dataset,T1,Weather +weather_dataset,T2,Weather +weather_dataset,T3,Weather +weather_dataset,T4,Weather +weather_dataset,T5,Weather +solar_10_minutes_dataset,T1,Energy Production +solar_10_minutes_dataset,T2,Energy Production +solar_10_minutes_dataset,T3,Energy Production +solar_10_minutes_dataset,T4,Energy Production +solar_10_minutes_dataset,T5,Energy Production +sunspot_dataset_without_missing_values,T1,Other +wind_farms_minutely_dataset_without_missing_values,T1,Energy Production +wind_farms_minutely_dataset_without_missing_values,T2,Energy Production +wind_farms_minutely_dataset_without_missing_values,T3,Energy Production +wind_farms_minutely_dataset_without_missing_values,T4,Energy Production +wind_farms_minutely_dataset_without_missing_values,T5,Energy Production +elecdemand_dataset,T1,Energy Demand +us_births_dataset,T1,Demographic +saugeenday_dataset,T1,Weather +london_smart_meters_dataset_without_missing_values,T1,Energy Demand +london_smart_meters_dataset_without_missing_values,T2,Energy Demand +london_smart_meters_dataset_without_missing_values,T3,Energy Demand +traffic_hourly_dataset,T1,Transportation +traffic_hourly_dataset,T2,Transportation +traffic_hourly_dataset,T3,Transportation +traffic_hourly_dataset,T4,Transportation +traffic_hourly_dataset,T5,Transportation +electricity_hourly_dataset,T1,Energy Demand +electricity_hourly_dataset,T2,Energy Demand +electricity_hourly_dataset,T3,Energy Demand +pedestrian_counts_dataset,T1,Transportation +pedestrian_counts_dataset,T2,Transportation +pedestrian_counts_dataset,T3,Transportation +pedestrian_counts_dataset,T4,Transportation +pedestrian_counts_dataset,T5,Transportation +kdd_cup_2018_dataset_without_missing_values,T1,Other +australian_electricity_demand_dataset,T1,Energy Demand +australian_electricity_demand_dataset,T2,Energy Demand +australian_electricity_demand_dataset,T3,Energy Demand +oikolab_weather_dataset,T1,Weather +oikolab_weather_dataset,T2,Weather +oikolab_weather_dataset,T3,Weather +oikolab_weather_dataset,T4,Weather +m4_monthly_dataset,T122,Macro +m4_monthly_dataset,T145,Macro +m4_monthly_dataset,T180,Macro +m4_monthly_dataset,T186,Macro +m4_monthly_dataset,T17051,Micro +m4_monthly_dataset,T17088,Micro +m4_monthly_dataset,T17132,Micro +m4_monthly_dataset,T17146,Micro +m4_monthly_dataset,T26710,Demographic +m4_monthly_dataset,T27138,Industry +m4_monthly_dataset,T27170,Industry +m4_monthly_dataset,T27175,Industry +m4_monthly_dataset,T27186,Industry +m4_monthly_dataset,T37009,Finance +m4_monthly_dataset,T37070,Finance +m4_monthly_dataset,T37238,Finance +m4_monthly_dataset,T37248,Finance +m4_monthly_dataset,T47915,Other +m4_weekly_dataset,T1,Other +m4_weekly_dataset,T2,Other +m4_weekly_dataset,T19,Macro +m4_weekly_dataset,T20,Macro +m4_weekly_dataset,T21,Macro +m4_weekly_dataset,T55,Industry +m4_weekly_dataset,T56,Industry +m4_weekly_dataset,T60,Finance +m4_weekly_dataset,T61,Finance +m4_weekly_dataset,T62,Finance +m4_weekly_dataset,T224,Demographic +m4_weekly_dataset,T225,Demographic +m4_weekly_dataset,T226,Demographic +m4_weekly_dataset,T227,Demographic +m4_weekly_dataset,T248,Micro +m4_weekly_dataset,T249,Micro +m4_weekly_dataset,T250,Micro +m4_daily_dataset,T1,Macro +m4_daily_dataset,T2,Macro +m4_daily_dataset,T6,Macro +m4_daily_dataset,T130,Micro +m4_daily_dataset,T131,Micro +m4_daily_dataset,T145,Micro +m4_daily_dataset,T1604,Demographic +m4_daily_dataset,T1605,Demographic +m4_daily_dataset,T1606,Demographic +m4_daily_dataset,T1607,Demographic +m4_daily_dataset,T1614,Industry +m4_daily_dataset,T1615,Industry +m4_daily_dataset,T1634,Industry +m4_daily_dataset,T1650,Industry +m4_daily_dataset,T2036,Finance +m4_daily_dataset,T2037,Finance +m4_daily_dataset,T2041,Finance +m4_daily_dataset,T3595,Other +m4_daily_dataset,T3597,Other +m4_hourly_dataset,T170,Other +m4_hourly_dataset,T171,Other +m4_hourly_dataset,T172,Other diff --git a/aeon/datasets/dataset_generation.py b/aeon/datasets/dataset_generation.py new file mode 100644 index 0000000000..674c7501f3 --- /dev/null +++ b/aeon/datasets/dataset_generation.py @@ -0,0 +1,218 @@ +"""Code to select datasets for regression-based forecasting experiments.""" + +import gc +import os +import tempfile +import time + +import pandas as pd + +from aeon.datasets import load_forecasting +from aeon.datasets._data_writers import ( + write_forecasting_dataset, + write_regression_dataset, +) + +filtered_datasets = [ + "nn5_daily_dataset_without_missing_values", + "nn5_weekly_dataset", + "m1_yearly_dataset", + "m1_quarterly_dataset", + "m1_monthly_dataset", + "m3_yearly_dataset", + "m3_quarterly_dataset", + "m3_monthly_dataset", + "m3_other_dataset", + "m4_yearly_dataset", + "m4_quarterly_dataset", + "m4_monthly_dataset", + "m4_weekly_dataset", + "m4_daily_dataset", + "m4_hourly_dataset", + "tourism_yearly_dataset", + "tourism_quarterly_dataset", + "tourism_monthly_dataset", + "car_parts_dataset_without_missing_values", + "hospital_dataset", + "weather_dataset", + "dominick_dataset", + "fred_md_dataset", + "solar_10_minutes_dataset", + "solar_weekly_dataset", + "solar_4_seconds_dataset", + "wind_4_seconds_dataset", + "sunspot_dataset_without_missing_values", + "wind_farms_minutely_dataset_without_missing_values", + "elecdemand_dataset", + "us_births_dataset", + "saugeenday_dataset", + "covid_deaths_dataset", + "cif_2016_dataset", + "london_smart_meters_dataset_without_missing_values", + "kaggle_web_traffic_dataset_without_missing_values", + "kaggle_web_traffic_weekly_dataset", + "traffic_hourly_dataset", + "traffic_weekly_dataset", + "electricity_hourly_dataset", + "electricity_weekly_dataset", + "pedestrian_counts_dataset", + "kdd_cup_2018_dataset_without_missing_values", + "australian_electricity_demand_dataset", + "covid_mobility_dataset_without_missing_values", + "rideshare_dataset_without_missing_values", + "vehicle_trips_dataset_without_missing_values", + "temperature_rain_dataset_without_missing_values", + "oikolab_weather_dataset", +] + + +def filter_datasets(): + """ + Filter datasets to identify and print time series with more than 1000 data points. + + This function iterates over a list of datasets, loads each dataset, + and checks each time series within it. If a series contains more than 1000 + data points, it is counted as a "hit." The function prints up to 10 matches + per dataset in the format: `,`. + + Returns + ------- + None + The function does not return anything but prints matching dataset + and series names to the console. + + Notes + ----- + - The function introduces a 1-second delay (`time.sleep(1)`) between processing + datasets to control HTTP request frequency. + - Uses `gc.collect()` to explicitly trigger garbage collection, to avoid + running out of memory + """ + num_hits = 0 + for dataset_name in filtered_datasets: + # print(f"{dataset_name}") + time.sleep(1) + dataset_counter = 0 + dataset = load_forecasting(dataset_name) + for index, row in enumerate(dataset["series_value"]): + if len(row) > 1000: + num_hits += 1 + dataset_counter += 1 + if dataset_counter <= 10: + print(f"{dataset_name},{dataset['series_name'][index]}") # noqa + # if dataset_counter > 0: + # print(f"{dataset_name}: Hits: {dataset_counter}") + del dataset + gc.collect() + # print(f"Num hits in datasets: {num_hits}") + + +# filter_datasets() + + +def filter_and_categorise_m4(frequency_type): + """ + Filter and categorize M4 dataset time series. + + Parameters + ---------- + frequency_type : str + The frequency type of the M4 dataset to process. + Accepted values: 'yearly', 'quarterly', 'monthly', 'weekly', 'daily', 'hourly'. + + Returns + ------- + None + The function does not return any values but prints categorized series + information. + + Notes + ----- + - The function constructs an appropriate prefix ('Y', 'Q', 'M', 'W', 'D', 'H') + based on the dataset type to match metadata identifiers. + - Limits printed results to 10 per category. + """ + metadata = pd.read_csv("C:/Users/alexb/Downloads/M4-info.csv") + m4daily = load_forecasting(f"m4_{frequency_type}_dataset") + categories = {} + prefix = "" + if frequency_type == "yearly": + prefix = "Y" + elif frequency_type == "quarterly": + prefix = "Q" + elif frequency_type == "monthly": + prefix = "M" + elif frequency_type == "weekly": + prefix = "W" + elif frequency_type == "daily": + prefix = "D" + elif frequency_type == "hourly": + prefix = "H" + for index, row in enumerate(m4daily["series_value"]): + if len(row) > 1000: + category = metadata.loc[ + metadata["M4id"] == f"{prefix}{m4daily['series_name'][index][1:]}", + "category", + ].values[0] + if category not in categories: + categories[category] = 1 + else: + categories[category] += 1 + if categories[category] <= 10: + print( # noqa + f"m4_{frequency_type}_dataset,\ + {m4daily['series_name'][index]},{category}" + ) + + +# filter_and_categorise_m4('monthly') +# filter_and_categorise_m4('weekly') +# filter_and_categorise_m4('daily') +# filter_and_categorise_m4('hourly') + + +def gen_datasets(problem_type, dataset_folder=None): + """ + Generate windowed train/test split of datasets. + + Returns + ------- + None + The function does not return anything but writes out the train and test + files to the specified directory. + + Notes + ----- + - Requires a CSV file containing a list of the series to process. + """ + final_series_selection = pd.read_csv("./aeon/datasets/Final Dataset Selection.csv") + current_dataset = "" + dataset = pd.DataFrame() + tmpdir = tempfile.mkdtemp() + folder = problem_type if dataset_folder is None else dataset_folder + location_of_datasets = f"./aeon/datasets/local_data/{folder}" + if not os.path.exists(location_of_datasets): + os.makedirs(location_of_datasets) + with open(f"{location_of_datasets}/windowed_series.txt", "w") as f: + for item in final_series_selection.to_records(index=False): + if current_dataset != item[0]: + dataset = load_forecasting(item[0], tmpdir) + current_dataset = item[0] + print(f"Current Dataset: {current_dataset}") # noqa + f.write(f"{item[0]}_{item[1]}\n") + series = ( + dataset[dataset["series_name"] == item[1]]["series_value"] + .iloc[0] + .to_numpy() + ) + dataset_name = f"{item[0]}_{item[1]}" + full_file_path = f"{location_of_datasets}/{dataset_name}" + if not os.path.exists(full_file_path): + os.makedirs(full_file_path) + if problem_type == "regression": + write_regression_dataset(series, full_file_path, dataset_name) + elif problem_type == "forecasting": + write_forecasting_dataset(series, full_file_path, dataset_name) + + +gen_datasets("forecasting", "differenced_forecasting") From ca244badef7c4809a0f14e1a693be4c992234375 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Tue, 10 Jun 2025 02:06:48 +0100 Subject: [PATCH 61/70] Modify dataset generation scripts to handle differenced y but normal x --- aeon/datasets/_data_writers.py | 53 ++++++++++++++++++----------- aeon/datasets/dataset_generation.py | 14 ++++++-- 2 files changed, 45 insertions(+), 22 deletions(-) diff --git a/aeon/datasets/_data_writers.py b/aeon/datasets/_data_writers.py index 0f2ea35f90..fef3e86d3d 100644 --- a/aeon/datasets/_data_writers.py +++ b/aeon/datasets/_data_writers.py @@ -404,49 +404,64 @@ def write_to_arff_file( file.write("\n") # open a new line -def write_regression_dataset(series, full_file_path, dataset_name): +def write_regression_dataset( + series, full_file_path, dataset_name, difference_series=False, difference_y=False +): """Write a regression dataset to file.""" train_series, test_series = TrainTestTransformer().fit_transform(series) - differenced_train_series = DifferencingSeriesTransformer().fit_transform( + if difference_series: + train_series = DifferencingSeriesTransformer().fit_transform(train_series) + test_series = DifferencingSeriesTransformer().fit_transform(test_series) + x_train, y_train, _train_indices = SlidingWindowTransformer().fit_transform( train_series ) - X_train, Y_train, train_indices = SlidingWindowTransformer().fit_transform( - differenced_train_series - ) - differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) - X_test, Y_test, test_indices = SlidingWindowTransformer().fit_transform( - differenced_test_series + x_test, y_test, _test_indices = SlidingWindowTransformer().fit_transform( + test_series ) + if difference_y: + y_train = np.concatenate( + ( + [y_train[0] - train_series[99]], + DifferencingSeriesTransformer().fit_transform(y_train), + ) + ) + y_test = np.concatenate( + ( + [y_test[0] - test_series[99]], + DifferencingSeriesTransformer().fit_transform(y_test), + ) + ) write_to_ts_file( - [[item] for item in X_train], + [[item] for item in x_train], full_file_path, - Y_train, + y_train, f"{dataset_name}_TRAIN", None, True, ) write_to_ts_file( - [[item] for item in X_test], + [[item] for item in x_test], full_file_path, - Y_test, + y_test, f"{dataset_name}_TEST", None, True, ) -def write_forecasting_dataset(series, full_file_path, dataset_name): +def write_forecasting_dataset( + series, full_file_path, dataset_name, difference_series=False +): """Write a regression dataset to file.""" train_series, test_series = TrainTestTransformer().fit_transform(series) - differenced_train_series = DifferencingSeriesTransformer().fit_transform( - train_series - ) - differenced_test_series = DifferencingSeriesTransformer().fit_transform(test_series) - train_df = pd.DataFrame(differenced_train_series) + if difference_series: + train_series = DifferencingSeriesTransformer().fit_transform(train_series) + test_series = DifferencingSeriesTransformer().fit_transform(test_series) + train_df = pd.DataFrame(train_series) train_df.to_csv( f"{full_file_path}/{dataset_name}_TRAIN.csv", index=False, header=False ) - test_df = pd.DataFrame(differenced_test_series) + test_df = pd.DataFrame(test_series) test_df.to_csv( f"{full_file_path}/{dataset_name}_TEST.csv", index=False, header=False ) diff --git a/aeon/datasets/dataset_generation.py b/aeon/datasets/dataset_generation.py index 674c7501f3..86a3f9efef 100644 --- a/aeon/datasets/dataset_generation.py +++ b/aeon/datasets/dataset_generation.py @@ -210,9 +210,17 @@ def gen_datasets(problem_type, dataset_folder=None): if not os.path.exists(full_file_path): os.makedirs(full_file_path) if problem_type == "regression": - write_regression_dataset(series, full_file_path, dataset_name) + write_regression_dataset( + series, + full_file_path, + dataset_name, + difference_series=False, + difference_y=True, + ) elif problem_type == "forecasting": - write_forecasting_dataset(series, full_file_path, dataset_name) + write_forecasting_dataset( + series, full_file_path, dataset_name, difference_series=False + ) -gen_datasets("forecasting", "differenced_forecasting") +gen_datasets("regression", "part_diff_regression") From c0daa74f41caf692e42ccd473befef3f2466e5c3 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Tue, 10 Jun 2025 02:22:28 +0100 Subject: [PATCH 62/70] Update AutoARIMA to allow predicting multiple values without refitting the model --- aeon/forecasting/_auto_arima.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/aeon/forecasting/_auto_arima.py b/aeon/forecasting/_auto_arima.py index 3f65cf253b..44f0ee80f9 100644 --- a/aeon/forecasting/_auto_arima.py +++ b/aeon/forecasting/_auto_arima.py @@ -50,8 +50,8 @@ class AutoARIMAForecaster(ARIMAForecaster): 476.5824781648738 """ - def __init__(self, horizon: int = 1): - super().__init__(horizon=horizon) + def __init__(self): + super().__init__() def _fit(self, y, exog=None): """Fit AutoARIMA forecaster to series y. @@ -104,8 +104,13 @@ def _fit(self, y, exog=None): ( self.aic_, self.residuals_, + self.fitted_values_, ) = _arima_model( - self.parameters_, _calc_arima, self.differenced_data_, self.model_ + self.parameters_, + _calc_arima, + self.differenced_data_, + self.model_, + np.empty(0), ) return self From fed0d5d592f2a2d7cfc31bd08d239f78a1381533 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Tue, 10 Jun 2025 02:30:21 +0100 Subject: [PATCH 63/70] Update NaiveForecaster to work with multiple predictions --- aeon/forecasting/_naive.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/aeon/forecasting/_naive.py b/aeon/forecasting/_naive.py index 8b7deedeeb..6d3ee63cbd 100644 --- a/aeon/forecasting/_naive.py +++ b/aeon/forecasting/_naive.py @@ -1,5 +1,7 @@ """Naive Forecaster.""" +import numpy as np + from aeon.forecasting.base import BaseForecaster @@ -19,7 +21,10 @@ def _fit(self, y, exog=None): def _predict(self, y=None, exog=None): """Predict using Naive forecaster.""" - return self.last_value_ + if y is None: + return np.array([self.last_value_]) + else: + return np.concatenate(([self.last_value_], y.flatten())) def _forecast(self, y, exog=None): """Forecast using dummy forecaster.""" From 790cb9f9a57e853586051b34cab5798dbb0dc185 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Thu, 12 Jun 2025 22:49:57 +0100 Subject: [PATCH 64/70] Fix bug in AutoETS causing the seasonal period to be considered for cases with no seasonality --- aeon/forecasting/_autoets.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/aeon/forecasting/_autoets.py b/aeon/forecasting/_autoets.py index 895ff9eb35..4f2410cb5e 100644 --- a/aeon/forecasting/_autoets.py +++ b/aeon/forecasting/_autoets.py @@ -252,18 +252,25 @@ def auto_ets_nelder_mead(data): for error_type in range(1, 3): for trend_type in range(0, 3): for seasonality_type in range(0, 2 * (seasonal_period != 1) + 1): - ([alpha, beta, gamma, phi], aic) = nelder_mead( - data, error_type, trend_type, seasonality_type, seasonal_period - ) if trend_type == 0: phi = 1 + model_seasonal_period = seasonal_period + if seasonal_period < 1 or seasonality_type == 0: + model_seasonal_period = 1 + ([alpha, beta, gamma, phi], aic) = nelder_mead( + data, + error_type, + trend_type, + seasonality_type, + model_seasonal_period, + ) if lowest_aic == -1 or lowest_aic > aic: lowest_aic = aic best_model = ( error_type, trend_type, seasonality_type, - seasonal_period, + model_seasonal_period, alpha, beta, gamma, From ff1b186c309688bf95aaaec7bc12711d58c1c5aa Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 13 Jun 2025 02:18:45 +0100 Subject: [PATCH 65/70] Remove default args to see if numba stops crashing --- aeon/forecasting/_arima.py | 2 +- aeon/forecasting/_sarima.py | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index a0dd2cb052..e521a08d7e 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -249,7 +249,7 @@ def _extract_params(params, model): @njit(cache=True, fastmath=True) -def _calc_arima(data, model, t, formatted_params, residuals, expect_full_history=False): +def _calc_arima(data, model, t, formatted_params, residuals, expect_full_history): """Calculate the ARIMA forecast for time t.""" if len(model) != 3: raise ValueError("Model must be of the form (c, p, q)") diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index 6b8548e5f2..66f56a59d9 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -199,9 +199,7 @@ def _sarima_model_wrapper(params, data, model): @njit(cache=True, fastmath=True) -def _calc_sarima( - data, model, t, formatted_params, residuals, expect_full_history=False -): +def _calc_sarima(data, model, t, formatted_params, residuals, expect_full_history): """Calculate the SARIMA forecast for time t.""" if len(model) != 6: raise ValueError("Model must be of the form (c, p, q, ps, qs, seasonal_period)") From 49fdaa6a12383af14fb3a58f79ba775416488ded Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 13 Jun 2025 02:26:00 +0100 Subject: [PATCH 66/70] Fix numba issue --- aeon/forecasting/_arima.py | 8 ++++---- aeon/forecasting/_sarima.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index e521a08d7e..b6e72f6488 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -249,7 +249,7 @@ def _extract_params(params, model): @njit(cache=True, fastmath=True) -def _calc_arima(data, model, t, formatted_params, residuals, expect_full_history): +def _calc_arima(data, model, t, formatted_params, residuals, expect_full_history=False): """Calculate the ARIMA forecast for time t.""" if len(model) != 3: raise ValueError("Model must be of the form (c, p, q)") @@ -261,13 +261,13 @@ def _calc_arima(data, model, t, formatted_params, residuals, expect_full_history f"Expected at least {p} past values for AR and {q} for MA." ) # AR part - phi = formatted_params[1][:p] + phi = formatted_params[1, :p] ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1]) # MA part - theta = formatted_params[2][:q] + theta = formatted_params[2, :q] ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1]) - c = formatted_params[0][0] if model[0] else 0 + c = formatted_params[0, 0] if model[0] else 0 y_hat = c + ar_term + ma_term return y_hat diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index 66f56a59d9..1dab183d76 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -220,14 +220,14 @@ def _calc_sarima(data, model, t, formatted_params, residuals, expect_full_histor data, model[:3], t, formatted_params, residuals, expect_full_history ) # Seasonal AR part - phi_s = formatted_params[3][:ps] + phi_s = formatted_params[3, :ps] ars_term = ( 0 if (t - seasonal_period * ps) < 0 else np.dot(phi_s, data[t - seasonal_period * ps : t : seasonal_period][::-1]) ) # Seasonal MA part - theta_s = formatted_params[4][:qs] + theta_s = formatted_params[4, :qs] mas_term = ( 0 if (t - seasonal_period * qs) < 0 From b452dc7d82aa776d162971d2fe47a4736582f83d Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 13 Jun 2025 02:44:38 +0100 Subject: [PATCH 67/70] Fix DivZero Error --- aeon/forecasting/_ets.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index c27cee2893..df9e77a1db 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -473,6 +473,8 @@ def _update_states( ) # Calculate the error term (observed value - fitted value) if error_type == 2: + if fitted_value == 0: + fitted_value = 1e-10 # Avoid division by zero error = data_item / fitted_value - 1 # Multiplicative error else: error = data_item - fitted_value # Additive error @@ -487,6 +489,8 @@ def _update_states( if trend_type == 1: trend += (curr_level + curr_seasonality) * beta * error else: + if curr_level == 0: + curr_level = 1e-10 # Avoid division by zero trend += curr_seasonality / curr_level * beta * error elif trend_type == 1: trend += curr_level * beta * error @@ -501,8 +505,14 @@ def _update_states( seasonality_correction *= trend_level_combination if trend_type == 2: trend_correction *= curr_level + if level_correction == 0: + level_correction = 1e-10 # Avoid division by zero level = trend_level_combination + alpha * error / level_correction + if trend_correction == 0: + trend_correction = 1e-10 # Avoid division by zero trend = damped_trend + beta * error / trend_correction + if seasonality_correction == 0: + seasonality_correction = 1e-10 # Avoid division by zero seasonality = curr_seasonality + gamma * error / seasonality_correction return (fitted_value, error, level, trend, seasonality) From 84e3a4bffe0693099a54830bfb5bd39757e6a437 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 13 Jun 2025 02:59:09 +0100 Subject: [PATCH 68/70] Fix bug with SARIMA Predictor --- aeon/forecasting/_sarima.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index 1dab183d76..6ee01b7c9a 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -179,7 +179,7 @@ def _predict(self, y=None, exog=None): self.residuals_, ) # Undo seasonal differencing - last_season = differenced_data[m - self.seasonal_period * self.ds_ : m] + last_season = differenced_data[m - self.seasonal_period_ * self.ds_ : m] values = np.concatenate((last_season, predicted_values)) for _ in range(self.ds_): for i in range(self.seasonal_period_, len(values)): From 863c2b9f4480d1fa9eed337c482e3a8ea1bbf2d6 Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 13 Jun 2025 04:15:35 +0100 Subject: [PATCH 69/70] Fix instability with multiplicative damped trend potentially being negative --- aeon/forecasting/_ets.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/aeon/forecasting/_ets.py b/aeon/forecasting/_ets.py index df9e77a1db..5006e5f46e 100644 --- a/aeon/forecasting/_ets.py +++ b/aeon/forecasting/_ets.py @@ -384,6 +384,7 @@ def _predict( )[0] else: fitted_values_[t] = fitted_value + # Handle the final forecast value after the last time point in y forecast_s_index = (n_timepoints + len(y) + horizon) % seasonal_period # Generate forecasts based on the final values of level, trend, and seasonals fitted_values_[-1] = _predict_value( @@ -474,7 +475,7 @@ def _update_states( # Calculate the error term (observed value - fitted value) if error_type == 2: if fitted_value == 0: - fitted_value = 1e-10 # Avoid division by zero + error = data_item - fitted_value # Avoid division by zero error = data_item / fitted_value - 1 # Multiplicative error else: error = data_item - fitted_value # Additive error @@ -546,7 +547,11 @@ def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi) # Apply damping parameter and # calculate commonly used combination of trend and level components if trend_type == 2: # Multiplicative - damped_trend = trend**phi + if trend <= 0 and phi < 1: + # Avoid NANs + damped_trend = -(np.abs(trend) ** phi) + else: + damped_trend = trend**phi trend_level_combination = level * damped_trend else: # Additive trend, if no trend, then trend = 0 damped_trend = trend * phi From 966f7390a792de263eecb345cfeef2c4cffd32fb Mon Sep 17 00:00:00 2001 From: Alex Banwell Date: Fri, 13 Jun 2025 12:48:39 +0100 Subject: [PATCH 70/70] Change caching parameters --- aeon/forecasting/_arima.py | 2 +- aeon/forecasting/_sarima.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/aeon/forecasting/_arima.py b/aeon/forecasting/_arima.py index b6e72f6488..57af81c212 100644 --- a/aeon/forecasting/_arima.py +++ b/aeon/forecasting/_arima.py @@ -193,7 +193,7 @@ def _aic(residuals, num_params): return liklihood + 2 * num_params -@njit(fastmath=True) +@njit(cache=False, fastmath=True) def _arima_model_wrapper(params, data, model): return _arima_model(params, _calc_arima, data, model, np.empty(0))[0] diff --git a/aeon/forecasting/_sarima.py b/aeon/forecasting/_sarima.py index 6ee01b7c9a..1729407127 100644 --- a/aeon/forecasting/_sarima.py +++ b/aeon/forecasting/_sarima.py @@ -193,7 +193,7 @@ def _predict(self, y=None, exog=None): return values[self.d_ :] -@njit(fastmath=True) +@njit(cache=False, fastmath=True) def _sarima_model_wrapper(params, data, model): return _arima_model(params, _calc_sarima, data, model, np.empty(0))[0]