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Refactor steps in blending code (#453)
* Refactored all names in the steps blending code from old to new * Made some name changes but test still do not pass * Fixed naming changes, now the tests pass * Built the rough scaffolding for the blending class * Refactored untill no rain case * Added code to estimation of ar parameters of radar * Next go, start with forecast loop #7 * Added some uniformity between nowcast and blending steps. Now at # 8.4 for the refactoring * Small changes since prev commit * All code is tranfered. Last part of the main loop needs to be refactored * Everything is refactored, no test ran as of yet * Old forecast function is updated to fit newly refactored code * Removed old code which is no longer used * 6 more tests that fail * All tests pass, still need to fix TODOs * Updated gitignore * Cleanup of params and state dataclasses, next step: better typing * Cleanup of params and state dataclasses, now all tests pass * Added correct typing to all parts of params and state * Ready for pull request * Made changes for Codacy review * Added aditional tests which currently fail in master branch * Update .gitignore Co-authored-by: mats-knmi <145579783+mats-knmi@users.noreply.github.com> * Used the __zero_precip_time in __zero_precipitation_forecast() * Changed typing hints to python 3.10+ version * Added comments back to the State dataclass * Changed the self.__state.velocity_perturbations = [] to self.__params.velocity_perturbations = [] in __initialize_random_generators * Added code changes as suggested by Ruben, comments and documentation to come later * Added frozen functionality to dataclasses, removed reset_state and fixed seed assingments * Added frozen dataclass to nowcast * The needed checks are done for this TODO so it can be removed * Use the seed in all rng in blending code (#449) * Use seed for all rng to make a test run completely deterministic * fix probmatching test and some copy paste oversights * Add test for vel_pert_method * Change the test so that it actually runs the lines that need to be covered * Removed deepcopy of worker_state. The state is now accessable to all workers at the same time * Update to probmatching comments to keep in track with main * Fix for multithreading issue, this produces exactly the same results as the master * Added additional documentation * Bump version * Updates some files that do not pass the new black version * Updated examples to work with new black version --------- Co-authored-by: mats-knmi <145579783+mats-knmi@users.noreply.github.com>
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‎.gitignore

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# Mac OS Stuff
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.DS_Store
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# Running local tests
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/tmp
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/pysteps/tests/tmp/

‎PKG-INFO

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Metadata-Version: 1.2
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Name: pysteps
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Version: 1.13.0
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Version: 1.14.0
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Summary: Python framework for short-term ensemble prediction systems
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Home-page: http://pypi.python.org/pypi/pysteps/
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License: LICENSE

‎examples/advection_correction.py

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Advection correction is a temporal interpolation procedure that is often used
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when estimating rainfall accumulations to correct for the shift of rainfall patterns
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between consecutive radar rainfall maps. This shift becomes particularly
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between consecutive radar rainfall maps. This shift becomes particularly
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significant for long radar scanning cycles and in presence of fast moving
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precipitation features.
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‎examples/plot_cascade_decomposition.py

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Cascade decomposition
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=====================
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This example script shows how to compute and plot the cascade decompositon of
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This example script shows how to compute and plot the cascade decompositon of
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a single radar precipitation field in pysteps.
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"""

‎examples/plot_ensemble_verification.py

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Ensemble verification
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=====================
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In this tutorial we perform a verification of a probabilistic extrapolation nowcast
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In this tutorial we perform a verification of a probabilistic extrapolation nowcast
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using MeteoSwiss radar data.
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"""

‎examples/plot_extrapolation_nowcast.py

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Extrapolation nowcast
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=====================
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This tutorial shows how to compute and plot an extrapolation nowcast using
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This tutorial shows how to compute and plot an extrapolation nowcast using
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Finnish radar data.
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"""

‎examples/plot_optical_flow.py

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Optical flow
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============
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This tutorial offers a short overview of the optical flow routines available in
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pysteps and it will cover how to compute and plot the motion field from a
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This tutorial offers a short overview of the optical flow routines available in
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pysteps and it will cover how to compute and plot the motion field from a
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sequence of radar images.
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"""
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