diff --git a/CHANGES.rst b/CHANGES.rst index 80fe9a14..847723a6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -6,6 +6,9 @@ New Features - Added a ``disp_bounds`` argument to ``tracing.FitTrace``. The argument allows for adjusting the dispersion-axis window from which the trace peaks are estimated. +- Added a new ``specreduce.wavecal1d.WavelengthCalibration1D`` class for one-dimensional wavelength + calibration. The old ``specreduce.wavelength_calibration.WavelengthCalibration1D`` is + deprecated and will be removed in v. 2.0. 1.6.0 (2025-06-18) ------------------ diff --git a/docs/api.rst b/docs/api.rst index e4b4de6b..3f3937f9 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -21,6 +21,9 @@ API Index .. automodapi:: specreduce.extract :no-inheritance-diagram: +.. automodapi:: specreduce.wavecal1d + :no-inheritance-diagram: + .. automodapi:: specreduce.calibration_data :no-inheritance-diagram: :include-all-objects: diff --git a/docs/conf.py b/docs/conf.py index cf78023f..efc1a09f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -156,6 +156,7 @@ man_pages = [('index', project.lower(), project + u' Documentation', [author], 1)] +extensions.append('nbsphinx') # -- Options for the edit_on_github extension --------------------------------- diff --git a/docs/index.rst b/docs/index.rst index 40727b1a..e1422330 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -50,7 +50,8 @@ Calibration .. toctree:: :maxdepth: 1 - wavelength_calibration.rst + tilt_correction/tilt_correction.rst + wavelength_calibration/wavelength_calibration.rst extinction.rst specphot_standards.rst mask_treatment/mask_treatment.rst diff --git a/docs/tilt_correction/common.py b/docs/tilt_correction/common.py new file mode 100644 index 00000000..edb606ac --- /dev/null +++ b/docs/tilt_correction/common.py @@ -0,0 +1,19 @@ +from pathlib import Path + +from astropy import units as u +from astropy.io import fits as pf +from astropy.nddata import CCDData, VarianceUncertainty + + +def read_file(fname, bias): + d = pf.getdata(fname).astype('d') + return CCDData(d - bias, unit=u.dn, uncertainty=VarianceUncertainty(d)) + + +def read_data(): + bias = pf.getdata('gtc_osiris_example/osiris_bias.fits.bz2').astype('d') + obj = read_file('gtc_osiris_example/osiris_tres_3b.fits.bz2', bias) + lamps = 'HgAr', 'Ne', 'Xe' + arc_files = sorted(Path('gtc_osiris_example').glob('*arc*')) + arcs = [read_file(f, bias) for f in arc_files] + return arcs, lamps, obj diff --git a/docs/tilt_correction/gtc_osiris_example/osiris_arc_HgAr.fits.bz2 b/docs/tilt_correction/gtc_osiris_example/osiris_arc_HgAr.fits.bz2 new file mode 100644 index 00000000..6493fee9 Binary files /dev/null and b/docs/tilt_correction/gtc_osiris_example/osiris_arc_HgAr.fits.bz2 differ diff --git a/docs/tilt_correction/gtc_osiris_example/osiris_arc_Ne.fits.bz2 b/docs/tilt_correction/gtc_osiris_example/osiris_arc_Ne.fits.bz2 new file mode 100644 index 00000000..60053842 Binary files /dev/null and b/docs/tilt_correction/gtc_osiris_example/osiris_arc_Ne.fits.bz2 differ diff --git a/docs/tilt_correction/gtc_osiris_example/osiris_arc_Xe.fits.bz2 b/docs/tilt_correction/gtc_osiris_example/osiris_arc_Xe.fits.bz2 new file mode 100644 index 00000000..18d4e993 Binary files /dev/null and b/docs/tilt_correction/gtc_osiris_example/osiris_arc_Xe.fits.bz2 differ diff --git a/docs/tilt_correction/gtc_osiris_example/osiris_bias.fits.bz2 b/docs/tilt_correction/gtc_osiris_example/osiris_bias.fits.bz2 new file mode 100644 index 00000000..d6b2f284 Binary files /dev/null and b/docs/tilt_correction/gtc_osiris_example/osiris_bias.fits.bz2 differ diff --git a/docs/tilt_correction/gtc_osiris_example/osiris_tres_3b.fits.bz2 b/docs/tilt_correction/gtc_osiris_example/osiris_tres_3b.fits.bz2 new file mode 100644 index 00000000..942d5174 Binary files /dev/null and b/docs/tilt_correction/gtc_osiris_example/osiris_tres_3b.fits.bz2 differ diff --git a/docs/tilt_correction/osiris_example.ipynb b/docs/tilt_correction/osiris_example.ipynb new file mode 100644 index 00000000..6f972d57 --- /dev/null +++ b/docs/tilt_correction/osiris_example.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Tilt Correction Tutorial 1\n", + "\n", + "This tutorial demonstrates 2D tilt correction (rectification) using three arc lamp\n", + "spectra (HgAr, Ne, and Xe) and a single long-slit science frame, all observed with the\n", + "[Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/) at the\n", + "[Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/) using the R1000R grism\n", + "configuration in 2012.\n", + "\n", + "OSIRIS, which operated until 2023 before being upgraded to OSIRIS+, featured two 2048 x 4096\n", + "pixel Marconi CCD detectors. For simplicity and file size considerations, we use data from just\n", + " one CCD that has been binned to 512 x 1024 pixels." + ], + "id": "35f5a105fadc80e7" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:38.741985Z", + "start_time": "2025-05-14T11:50:37.511090Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from astropy.visualization import simple_norm\n", + "from specreduce.tilt_correction import TiltCorrection\n", + "from common import read_data\n", + "\n", + "plt.rc('figure', figsize=(6.3, 2))\n", + "plt.rc('font', size=8)" + ], + "id": "744ee1d9a9ffd1ad", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 1. Read in the Arc Frames and Object Frame\n", + "\n", + "We have hidden the data reading in `common.read_data` utility function as not to distract from\n", + "the main topic. The function returns a tuple of three arc frames as bias-subtracted `CCDData`\n", + "instances, a tuple containing the arc lamp names, and a single bias-subtracted `CCDData` instance\n", + "containing the science data. (We are cutting some corners what comes to basic data reduction and\n", + "skipping the steps not absolutely necessary for tilt correction, such as flat field correction,\n", + "but normally you would want to do these basic steps before.)\n", + "\n", + "Let's read in the data and take a look at it." + ], + "id": "7660d1df03a95655" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:39.349943Z", + "start_time": "2025-05-14T11:50:38.887669Z" + } + }, + "cell_type": "code", + "source": [ + "arcs, lamps, obj = read_data()\n", + "frames = arcs + [obj]\n", + "labels = lamps + ('Target',)" + ], + "id": "3e40fd913a645ec5", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:41.035690Z", + "start_time": "2025-05-14T11:50:40.431249Z" + } + }, + "cell_type": "code", + "source": [ + "fig, axs = plt.subplots(4, figsize=(6.3, 5), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(frames):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=250_000))\n", + " axs[i].text(0.01, 0.9, labels[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + "plt.setp(axs, ylabel='CD axis [pix]')\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "id": "f7681e5a755a40d1", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "ea64070e-7884-410e-9c43-01dcb6f101db", + "metadata": {}, + "source": [ + "We immediately see that OSIRIS spectra exhibit significant tilt distortion that must be corrected\n", + "to achieve reliable background subtraction. The example science frame comes from a time-series\n", + "of spectroscopic observations taken during an exoplanet transit (transmission spectroscopy). The\n", + "scientific importance of proper tilt correction is highlighted by the original analysis of this\n", + "dataset [(Parviainen et al. 2016)](https://ui.adsabs.harvard.edu/abs/2016A%26A...585A.114P/abstract):\n", + "insufficient correction of the tilt distortion led to residual time variations in\n", + "telluric absorption lines that compromised the scientific results.\n", + "\n", + "\n", + "## 2. Initialize the Tilt Correction Class\n", + "\n", + "The `TiltCorrection` class is initialized with the following parameters:\n", + "\n", + "- ``ref_pixel``: Reference pixel coordinate near detector center. The polynomial\n", + " fit centers around this pixel to improve numerical stability.\n", + "- ``arc_frames``: Either a single `CCDData` instance or a list of `CCDData` instances containing\n", + " the arc lamp spectra. These should be bias-subtracted and include uncertainties for the\n", + " internal line finding routine to work reliably.\n", + "- ``cd_sample_lims``: Pixel range for sampling the cross-dispersion direction.\n", + "- ``n_cd_samples``: Number of points to sample along cross-dispersion.\n", + "- ``cd_samples``: A list of cross-dispersion locations to use. Overrides ``n_cd_samples`` if provided.\n", + "\n", + "The initialization sets up the transformation model that will be used to map tilted spectral features to straight lines." + ] + }, + { + "cell_type": "code", + "id": "5e4b9637-a3b8-4805-8908-8ba6b50d713f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:44.337551Z", + "start_time": "2025-05-14T11:50:44.334742Z" + } + }, + "source": "s = TiltCorrection((500, 300), arcs, cd_sample_lims=(50, 500), n_cd_samples=7)", + "outputs": [], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 3. Find Arc Lines\n", + "\n", + "The `find_arc_lines()` method is used after initializing the `TiltCorrection` object to detect emission peaks in arc lamp spectra. The detection process employs the `specutils.fitting.find_lines_threshold` routine, which uses parameters for the expected line full-width half-maximum (FWHM) and noise threshold to identify significant emission lines above the background.\n", + "\n", + "The process begins by identifying lines at the reference row, which are stored separately. The\n", + "method then detects lines at specified cross-dispersion locations to generate a set of 2D points\n", + "in the *detector space*. These detector space points are essential for mapping the spectral tilt and curvature patterns across the detector. The points are stored in a kd-tree for each arc frame and will be used in the model fitting process.\n", + "\n", + "The *rectified space* is defined by the points found in the reference row. The 2D rectification process maps the *detector space* columns to align with the wavelengths in the reference row across all elements (rows) of the cross-dispersion axis.\n" + ], + "id": "67439310-4340-4d7f-9bea-c41dafef371b" + }, + { + "cell_type": "code", + "id": "3b1815ec-bbc6-43fb-9130-ffd8108ce003", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:49.281261Z", + "start_time": "2025-05-14T11:50:47.282875Z" + } + }, + "source": "s.find_arc_lines(fwhm=2.5, noise_factor=10)", + "outputs": [], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Let's take a look at the data to see how we track the spectral lines using both reference-row\n", + "samples and detector-space samples across our arc frames. By using kd-trees, we can match up\n", + "spectral lines successfully even if they're not found at every cross-dispersion sample point.\n", + "This approach helps us get a solid final fit, handling any outliers that pop up in the data.\n" + ], + "id": "67bf1f15916b10c1" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:50.754949Z", + "start_time": "2025-05-14T11:50:50.361300Z" + } + }, + "cell_type": "code", + "source": [ + "fig, axs = plt.subplots(3, figsize=(6.3, 4), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(arcs):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=250_000))\n", + " axs[i].text(0.01, 0.9, lamps[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + " axs[i].plot(s._samples_det_x[i], s._samples_det_y[i], 'k.', ms=3)\n", + " axs[i].plot(s._lines_ref[i], np.full_like(s._lines_ref[i], s.ref_pixel[1]), 'r.', ms=3)\n", + "plt.setp(axs, ylabel='CD axis [pix]')\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "id": "f5084cc9fef3338c", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "fbf4d669-f594-4447-a7d3-ae7f153418bd", + "metadata": {}, + "source": [ + "## 4. Fit the model\n", + "\n", + "The `fit()` method is used to calculate the geometric transformation that maps tilted spectral features to straight lines. This method:\n", + "\n", + "1. Takes a `degree` parameter (set to 4 here) that determines the polynomial order of the transformation\n", + "2. Uses the arc line positions detected earlier as reference points\n", + "3. Performs an initial optimization to find a rough transformation\n", + "4. Refines the solution through further optimization\n", + "5. Generates the final transformation model that will be used to rectify the spectra\n", + "\n", + "The fitted model captures both the tilt and any curvature present in the spectral features across the detector." + ] + }, + { + "cell_type": "code", + "id": "def37f07-78f1-43a2-b012-3a38b6ea7566", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T12:03:26.500872Z", + "start_time": "2025-05-14T12:03:25.619511Z" + } + }, + "source": [ + "s.fit(degree=4)" + ], + "outputs": [], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Let's examine how well our transformation fits the data using the `TiltCorrection.plot_fit_quality` method. This visualization displays both 2D and 1D residuals between the measured detector-space points and the transformed reference points.\n", + "\n", + "Our fit shows good accuracy across the detector. While the data contains some outlier points, they don't notably affect the overall fit quality in this example. If outliers do become problematic, we can enhance the transformation model by using `TiltCorrection.refine_fit()` with a tighter `match_distance_bound` parameter (such as 0.5 pixels). This refinement would focus the fit on only the most reliable emission line positions, ensuring better accuracy in the final transformation.\n" + ], + "id": "e4dd47b844051180" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T12:03:26.758088Z", + "start_time": "2025-05-14T12:03:26.523009Z" + } + }, + "cell_type": "code", + "source": "s.plot_fit_quality(figsize=(6.3, 5), rlim=(-0.5, 0.5))", + "id": "8e7b1520eec71b8d", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "You can see the transform directly in detector space with the `TiltCorrection.plot_wavelength_contours` method. This method creates an informative visualization by overlaying contour lines showing where wavelengths remain constant across the detector surface.\n", + "id": "a3e158126376faeb" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:47:15.893633Z", + "start_time": "2025-05-13T09:47:14.900989Z" + } + }, + "cell_type": "code", + "source": [ + "fig, axs = plt.subplots(4, figsize=(6.3, 5), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(frames):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=250_000))\n", + " axs[i].text(0.01, 0.9, labels[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + " s.plot_wavelength_contours(ax=axs[i])\n", + "plt.setp(axs, xlim=(0, arcs[0].shape[1]), ylabel='CD axis [pix]');\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "id": "9fd5b71f830d5fdf", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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q68eHD9UVnqlAx5jQdn300UErjSk7nX9ocBBXei5ZbfR1lcrbc/YEipJf0zy0nVSA/OjQhxaevnwKZmZmcFQewc4Hzp87q02mKJ4+pQkAHPxgn5WuHh4K9u/d4/RHeu3yPZsLfmqEwOGhIa8A46NVXfsbT/4kSXD44IdEuCCTU2Qi+YGhgQFcudyTUmnXg1PHjiCqVmViz+Lr4PTZJ4es3woHuvNSMDU9hfPnzpiyAD9B5LOLF89jcnJiTkGUwrHPPkm3T3JNV9iLowinTxw3OCM9GeiOc2hwEMODgxbD8eGlFqnTp06iJjWwhnmqhSq9Mz5+9IhdN8FB/VbZZmZntIDhpnebDgBXrlzGzOyM0zhSPv2TuGvkGLyFa4E0jnH+7FmvAOjjLeOjo8IW6Cp9yvUYuIBarQZ6ZE/BCKPi99kzp633ipnTYyJVdalYRH9f2jaN1s/J76HBAU1H5qRz83LO0UPsjID6u2WhyebouXSx7qbHrW96agpjY2OkDFUHrGfqZ19fHyK5EWAynSuc0qYoniEEJ3vcWpoYxlCrVjGkhBGCb52hg/HxMczOmo2AOjakbad1F8cG0sd8zqcCxhj6ei9LPLmVngq66lWpWMTs1KSsH2YDCn9/jY6MoFarGk2gKj+FiPgY6O/Vm4xECo1U40kbEEURhgeHrLp9msBE8vyZqSkhkDLaNr+wwxgwODiAJEnq8i86hhiAwYH+VPut9KaZqFYqmJgYt94pETqwlAri2cTEuNgok3rVxQ3LNEZ+GZTjy4eDO/aTJMHoyIh3s0PbqtIXS7OYlfNal0XSuhuDmYkJ8NisjXSTz3QbTJ6R4eHUOK3XB7VaDZOTk+l21uGX09PTqFarc/IW2vbR0WGndTa4ZhpjY6P6u5uunr371eCnRggEzK0kCnds24QTx49az/71v/wDvPbKi/Mq83/5B38Hv/m1v4NisZjambldweB0nt5VpoUMX36Q/CpPPYnfK8vNo/z5FEQnFH1bb2OtFji3PIsp0PTXgNdcAqlNav/tXiNw2u9cOxAfE6Kdpo5M1J9PCNMaMR+ybgX1CEEKZxQHjYr/ZnvqCZdt9qSvx6wpk6k77py8YRh626HrdN754lzSxZRqMBhj2u6JzgVXIFFpzY1m0hZaj4N7NpuzNJg+wdW0gyGTIWYbMMw+IGWrOZIJMwJ3Op/J4kVv/QOwjuAjTg3JlXBi51cnHy6+LnAOZDIhQnkkRenInIRK+Ayz+VR/qO9Gsy6ehYyhUGiY96IbBAGCTNY8ryPUK8jlcvo0Q+FOb0u6Q6+xqRlgRngD6mjg5GdzcxMZA9zqL5WZyd+5XA6FQt7bLlfrBABNjU16TPrwsDRaTJhLUPx89SQSR8YYGhubrLYoTWDoMeIu5AtivJN+dvlLQNrQ1t5ubYiVxpOOW93HANra2uz5RdrlnuJkMlmN+1ygzI4amhoRZjNX1X4p/Duk1wdVP8h3tw+CIEC756SvnpzV0NiIQqH+SZ+CRPLfRYs6rUlkrc+OYKfT67T2jWB6afVa4KfGJrCru9vatamdB0e6Q9va2uuWQ4k9PTWFN159Cddv2YofPfd9/Mwv/E1fBu1ChgNYuXqNsMEiwOQWPcUoAdx29z04NllNFat2hy7c/9AjpkyCg6uRBIBFi7twc0urliuo8GIJMrK922+5HVlyld0FyiQA4OFHnpz3kVQul8eDn3sklb5e/o2br9ft8DFY9VzB5x55HNlczu532Ece1Pbliae+aAs9dfDgALqXLkPXkm7djz6g9L17xz22QFpPEpHwzJe/ar1XZXFP+kI+jye+8HRaI0PzyAecc1y/5Uav8J7CX46BJ77wNFkI587IGMMXv/Kz1rOUrRaBJd3dWNy1RP+ut5FRL++8eyemyxGmSpG3bxhgMcmnv/SVVLl0LNCNSBiG+MLTX/Rotkwa+m7jxk3avZNopwcfguT9D31O40Xbpo5S9TyUyD3+hWdIGrXQO0NH/mjv6MDu+x5I1W/kKhu5rTfdklq4fRccFKy98yGERICleHDuHDsy4NEnnvKWQ7X4CpavWImlmwAc6BUaK9V/MHUEpBPu2nGPNa4p3d0jbQB45LEndOJ4jrEIDjQ0NGD3/cYWc67NFWPC1tcHXP6j7WUMuO+BBxGGYV06O2wbD3/uUateyvc0feQ8XdzVhUWdnTot51zcluUAFY0VubZtvxkcQtDwHgc7E+WBBx92cBEvxDE5s3ALw9Aej6RdSkika8eqVastOrjKFdrXnAPX33Q7mhob01paS4tmnu/cfR9coDIBhdbWVmy/+ZZUeh9wLuyUE7h4GHwp/gzA3ffsnqM8sv7IOb9z127rmfqu0tfTWM8FPzWawFKxaAl8utM9actl4ZKlv68XX/nCI7j3ru34xZ95Br/4M0/jT/6vf6+J/+z3/hK77nsAjz35NP7iz/6zVcaKjjz+v3/8f+ArX/gcvvFPf18/HxsdwcjIkG2r4Fvs5MMLZ0/pYyOBt7ldrNOSfMeOfGr9duULyoimpyZx6cI5zTzqyiKysvNnT2vbIZqungbw44/SriEod6AMtFqt4OOPDnq1pz643HMRV3p6KHpzwoEP9iGq1TTDv5pt43vvvGVNJOZ8AoYnDg8P4eiRT21Bfo7iPz18CGOjo9ZibxXqwOuvvFw/Cbc/y5UK3nn7zXkJ3hziqPncmdN18XVvab/+6suoVCr+tEgX89ILz9l1clVuOv/g4AAOfLhf/zYaCabLp3gd/HA/Bvr7rWemnjQhX3juWVM20n1K8a9Wq3jpRy9IPMRfoLQ+sAUqRcfjx44Qhm/qV8IIRen1V1/SR7AUU2Wnpo7sVHkv/OB7+nfsCLECD/NseHhIu1vyCsdSq6CyfHTwQ1y+dNHgQLSRCjip4/y+ly3tLE2jvxMB67nvfzv1XuHsal8uXjiHyyeNGwxfH5mNAPDmG69hYnxcl6cEUC3IOnX+4HsGF/dY3QIGTE5O4o3XXiG82mx+1NxXF304Bz795GOcO2vMa9wh6I7953/wfcRxfX9uLlrPfu87lqBCy6M4MsZw5fJlHDjwgZVfXeAJA2nKQTY0+/a8h+Eh4W7J0EWVn6bPc89+L82qHKFL9W25VMJrr7zkbZ9Cg9Lq5Inj2gRGzRuttYXbVuCj99/UPEnNTR/OqoqXfvicxvFqa8fQ4CA+3L9P4uIp05mLBw98gOHBAatNrnYP3OD+ssSFllFPG1itVjUd69m0L2gC54AisXmhA5QD+Dt/8+es45Ozp08BAH7/t/8R7rn3Pvzm/+t30XvlMu7fcQseePgRPRj+4s/+M/7xb/8eZmdncO7saZw7exrrN5jdYLVSxfd++LrVs7MzM6hUq761HgBS2qSR4SGEbctTHUs3ZhSGBo1hskgo03AOrjJJKJfLwsYPMDd3YZervjMAU5MT6FycvjTDHaahvo8QH24kcWp3zzmQxEnqQoPSgvgWslKxqI+w6mkCocoHMDE+Zj33abPoTmpqciKl7QH5TXGKajWUiiVvQr3xIBuO2WIRcRIbDSMtVFXKzNfZ2RmrUyy8yOrIIGwCle8xUpQX74AxVGtVZMKMTpheqO0jB2H24F/8ufOMASiVS1bfpIzTSb1xLUKs7DYhxwXM7U06FjnnKFequlYxBkyfMsbEYkYqr1XTFzHcca4+kyTRjt19TJXmA4A4jlJaPbNIp0dwJO0qXaACmKIBY0AUxyavtCOjQhotil7cqcdn9AaEiTFj36z2lMmFw2iRKLEWJ6tcGFs7AAjAEQShpZX1gZrDcZIg4aJsqsnz8TqBSmK5iHEXZV8Bus9k4lDTmutFGkyUHabcg/C6bY/jGAHR7Gk6Om1UkCQJwjCth/FppER+5uV1ljZQZoyT2MKdk/Zqp9jcrCNxHAutJIA4MZseynNoWYDR3lnsS2uiDF6qbLctFHeVJ+EiPT1xovzF4mnqRZLYx+qwN2HzlYl82kDVp6Icu0x3AwMACUlPyzMCoXPRTNHLKdfUY1ILOppb+XMJewsXQzwQyMnmm2D/13/5S7y555D+Ux7l977/Ln7ub/wyGGNYsXKVVmlzznH86BEMDQ7g3gceQlNzM778s7+Av/yv/z+r7J/9xV+2BB4ACMMMcjlj36M0U26XqU7OFxpAu8kStEh61efNLS0W03EzchjBJJPJoKGxyTvBfUOoobFJRzig6S2tJFe+shjaOxZZuLnIWt3AGDo6Fs1LgyVwaURTU5O/fPJMaQM6FnWaxWKO+aEmT9eSbu33a87JBiCbz6Oto13U57zXgg6BjkUdyOfy1kJsFeiMie6ly1J10h80exiG6O7utut3qjCLFEdLSytaWlq96dVYoYLQsmXLjP8xJy1Duv0rVq6ynmmbuEBqosjLQkODvpnvE0Zpcg6gq6sLhYZGa0GgF1bMPBDPVq9Zowqzxiltgyo7CAKsWr1av1eO0NV44uQPAFrb2tHRscgsfK4m0GoLsHLVau0aQi+knKc0gQrWSZ4ktCJG8LUEQdnWQr6A5StWWPnrzSvOga6uJdplETgHh2mrSce15qRt+br6F0M4t04GsgHD+o2b9DiaCxgD2lrb0NAujjFp9Adr/JIGrV6zRm/gqT2bsgFmViZg02bj/svVeFpCKhfzes3adQQ/sxlSfUaF5yXdS9EhI8a464xPeNt83Q0AbN9/Ki0FVZ6KFDEXqKxtrW1YtmKlPa7lmM+GzBrrDMDqNWvR2NRkCfFUIaHWDIXflhu3pvg+/aSQyWYtuoOlx4lqJwPQ3b0Ui6WygQrS7hxNuIiqtW7jdcbe10MLl543bt3mCFr2J4WW1laskW7UUhsMJz8ArF67Di2trXXfi99q/jJsvWm7VWZ94Z8hk8nghi03WuOUHv9e64UQBT81msDly1cB8BPZBartStkVyA788//yJ5idmcHuO24CAERRDUmS4Ld/759qDVVTU7Ml8HAA62WonXrClutja8fuB7D3yphOqBYHzgHOzORQaN73oPHJJbQF5Add8DiwqLMLHcTQVNfrwQsAtm7brnc5PnAXpLt33ivb4U9Pd3YNhTxulr756OSt110rV61JLZRWG4igwyH8sgVhqG1CXRxMPvHr1tvusCaY11ieAYwLG1IrZNxVYN26jWbhghDx51oet920vT4hnLy5bBabNl/vLc/Xr11dSxAE4vKGK8j5qtywcbPWGHDYfvV02dwwus2brxeP5O+YG3s2nUl+b2xqwtLQ+AfTizSI83CCY/fSZYiCPGqJYqBzXFjhHGvWrpflKk2Hn0aAEAJXrFyt0wP2hs2lZWtrm9C0k/p0mWSCqmnYvXSZ8FvJGBiIU2yZJ3QM2pYuX6HfKY0ctdtTQkkAjmwuh85Ow8Oo9sFHo9b2djQ0Nur6aXuNVtXcvG1s76xrTqFsGlUdYcDQ1bUkpQmkQhGlZT5fQKbQBGDaciOSmrMyQ2trmxamlY3YXC5XOhZ16vGj6KjTk/IBIBOGaG1rJ+PadtpNgUHYEObzhbSmymmD+t7W3q61X5SHu8IgY0AcJ2hta/OW49KRc44gDNEkfRAqiGKxydAXlQh98oWCpqOxlbQJqNvDgUbpd9fa3MBZL2HmamNDo60Rg1FtuAJyGIYipKbbIbDHiprHuXxOhmF1LrnVWT8K0o2aW2+9dUpdyqr3nj4PgkCHkE2lk4mVGMEAy3ettV4ToAK20pC6Qneq7dcAPzWawMFB43pC7azq7bxGh4cBADt33Ytv//l/AQD0XrmMve+/A0DYXT373b/ED994D+8d/Az/2zf+CIdPXMDSZcvx1msv+48t5OeJY0dx6uRx/ZxOXPXb4MnxxksvoFqtQrmHqSfxq7a8+Lxtq1FPuGAM6O+7jI8PHfAejym86KTbv/c94cfNAcU0qPqbMeDVl54XmgVO2uXBnUFcrd/z7lspnOvhf/zoEVy6eKHOWzs/A/D6Ky9ajJbWbaWXbXn15R+ly5KLqf6TyPX1XhE2gfXAWcAOfLAP05OT6QWF7gxIw19/9WX9O0U9wug4BB0PEN9TKg9NQxnxqVMntHsbl+e6nwDwzltvWOWmzBQc9N5+63XnmEo816GrSIbB/n6cOHE8VQhTwohT/icff4QpaaOqcHHnBxV63n/37RQNFHCnoaViEYcPiTBdSvPDGEtp9RRO586d0a5QAGG3Z2manAr3731fH9vSuREnJA9BSYWw4wAiKWSpcHoujI4Ma/92Lk18QvKJo0c0HZNECgoyDzVKV3kHT35UV9NBNYYAEPAIHx8+mKo7LbiIv94rlzE22AvOhVPj1PyEHA/y90eHDqJWq8n8XPMizuWxJ7PrPfDBfj0eY6k5tMY9M306MT6OUyePk422LeQz2Fre06dOYnxszMv/lbBHhTg1T12BUfFPqqGK4xiHDx1MCYqWUKjzMwz09+Hy5R6Ne5JwRHLyhYFRQihcj3z6CSrlikirxq3HlYzKdOjDD7xrhTs3FE86deoEnCJI2TZfvnDhHMalKxSqFHFHbiLnwNmjn+px7lvLjMDHEScJPv34I/1c0MuWBygMDQ3qkLNzgcp77NhRVJV9IlkLTTqqRQY+/fiw1Rd19rAARFzt06dPWppEd27/VS6GzEsTWCwWr5omCIJ5XY3+cYEaoC6N9UC2Jp348r//4b/Gr//9X8HzP/gu1m/YhDvu2oGW1ja8+uILWLlqNTZvvh6zxEbqyz/7C/hvf/an+PzjXzBly4JV3UmSIAgN2ev1ucIh4QkYV9JV4NV+WTsyDi8Tou1V6X0hcVLlke8qfZrpq4XLPNcLn7OYMR/3gmI8TNc3H5uH+Qx0ijvVxgSBHXybtpkK5HPMSd0ur+2Qqp8WwgDGKR39bmtSHehwV7poOcVrGylXoNM2cm413ITdojTnkK5NHAHGx2jm6id6fAoY7YvPTUXC5XiUDdOLJuyLDCqnsjWyNg0OLrZGzq6TbgoYAM7sucFkH2mBBiaDVSfkGCDhoqgWk/q7EziZfrJshLg5Dlb0YeSdGgb6lidLC0mAPa/nM0domC59O5lRjZqKmqKEHxHK0u11tZDGiTluzyBBwILUfE7F65UQxTE4Cy0aUBBjluJu7M2ok2rOOQk759ASSmBMJC50lTYVRWR8MVUmmUfuhiMmuLhaL19jr9Y3VBPottMVolT7uGqbZ26osgpZluIhSZzo9DE3F0gUrebC1Z0LSphRvI/a1VmJnDaox3EkcSft9PI6lyGki7XKB6T9q54baeHbBZ8941ygcKflzSXY0bXGxYcukYwxxFGE0Ikept65vPhabALnJQQ2NzfXXZTV82XLlqG3t3feFf/PhoaGJi/TOXT0TGoX8P/59/8Jt95+J0qlEr7z3MvIZDIY6O/DYw/eg//tG9/Eho2b8fSXvgoOcWSwas06AMDf/Qe/jr/z938dAHBlXN6gdGi2aFEnQsfNCh3cLo5r1m3EJbK4ZOY4AgWE6xS9K7MkOfNDTaCWljZxKaBO3W5Nq9euR4Oj1gfIrh9KsArAGHDdlm1+JD2zrVAoYNN11xk8OLcmgQvLV6xAodCQel5vUt/kXPNXGgBjDG4mVJIk2H7zraZPyDurGfKzs3Ox5cPLqt/lkBC2PQXXpcEc3Xrb7Xfa7WNkU+Mw1MamJtxw49ZUsfWYw+rVa5HLG5sqS3gFLE0JANxx192mKR7hkDttuVOGL9NaVJlFGePTBaGrqxstre1WmRQXl0TbbtoOlm9GxMlCz4xwr3BX7+7asVMvkuLCib0YapoxhsamJmy/5VZNgwS21sft2nXrNyLIZIhWwbwNlQaRzJu779lthEAiLNDQXrS9O3YZ0wp6XOfeIgaAxUu60dbW7h23Pj6+dfvNaJEh7JSbG1frRd3frNp2l9Bw6ELVhte4r1FVNDQWcMe2HSlBQo8FIuhwDqxbvwFNg1lgfBJZn48XB3becy+y2azuV9o0dQGClnDfAw/pvouo1tWphkHYnLa0tuoxSjci9Laqgu0334oGjyKEOZ+Krz348Oe9AoKroQKED8J773/AEoxSfFg/Z1i3foM1PsXRqeiDXBiYASyF8V2770OhgZioECHQ2sjJQj//6GMaBxrPnuKlni3q7MStt5lQlvVA1bH9lluQyWQtWTHFSjlQSxLECXDPfZ8z45rg6l6c4lwcvz748CN1NdkurFu3fl4ClSr/nt33aVt1F+h6oubDI489Yd451bgaxEWdnbhrx07y3vYL+FfRAgLzPA7evn074jhGkiSpP/V8yZIlVy/oxwiFxrTAAPgHwPIVKwEAF86dwSP3340Hd96Grz79KP7x//v37DjBAFgQoEu23ZoI8K/rhcbGqzrCpOW0tLUj4QzqODjjuU1m8nFhw0K1C0gv2ArCTCjC0nlwdlNzALlcFmEmY4qSTFcdYSmVu9AisKtqhmldnHNkM1nruVdJpndXQoM113j3MUhVhqnDX4ArNFkLKey+jSL7ZqhaFH07VQAoV8qemJbmTwtH8rPk0cSr5LRsDqBaq2kXR/S5Bw0AIjxeHKvwe0Tw4A7zlzBJjl9VHvpJK4zj2KSX7+NEMD+fJqlYnNVh4ABbYFRA2zA6MmKF/vLZcKqxBQjXKYDjbqKOlqhcLmOSuB5R5erxR9IziNvn1XLZaByIQOIuigDQ12c2zNQmTQuB9GYn5xjs79OCUy3mOp9vTzg9NYnJyYl5HxUN9PXqeOZGi2dfmEi4wW16uFe7GVE4EZZgCcFBXLPc+Ch6qE8X/eHhQZSKIlqEYnV0zlPgAM6dNRFpFO4KtCZL/lPpVYp6tm8qz+TEhAgBxuxx5BOkOYCLF84bVyVz8B71/uSJ44IGzB4j1t5QviuVyrhw/lwqvQ845xjo78O4jGDDJW0UfTKELqqqY8eO6LyuL8+UEMSAYySUmhoHPs0XY+JYvb/3isqaxpfQiAE4e+aM8EIAmxZuftV/504eteev/EeDQyicKqUSTp08bs3NuaD3yhUMDfrDwPngyGefIOEeP8AEEm5o//Hhj+rKCi6MjY7iwvlzBA97baInW9cC8xIC/+2//bf/XdL8OGFmehpAepL5YP/e9wAAW7beJG4M7z2E9z78FL/4y387dQQwMzONo5994i0nJYgBuHT+LEaGB+uOInfgf/rRh2LHGscAY8hm5hYCPz180F7MgfQEkXUMDQyg98plrwbQR6LjR48YewdSHifMlCvhgQGffmyHsKvHABhE2C0V63KuIazIduniBUxNTabIqBik+q5eH/30k1RZroZE/Y6iCCdOHEs9pzhTGBoexODgQIrhMOe3YnanT57Qi67eAboyFJHMTxw/lpLkdB85G4+ZmWlcuWxs0yw8nDYDQE9PD2ZnZ1JCEED99BlGf/qkHQd2LoYTx5HFtABhzwYAoWcwjI2OiBB28p0VucABDhHvNI6iukICxR0Azp05o7W/nBs3LKo8lZ5DuJTq7+/T75QNnvqtylHQ39+H2eKsxkXZPjImhJHAGZcXz9txXRUoO8Fsxmxw4jjGpUsXNa41ZeAfUCHPlDE9OYnJCSXAphcKVyt35XIPkjgG5+RyBWxn0VQ4mB7qdeaO6aOEc8tGOIgrgt+RtC6+tH9Hhof1RiDrDBKfsHblymVrg6G92PD0DWtwrucGNY/wjUVwEVZzcmLCKcIg7ioqBwf6EcdRqih3I6V4VO+VyymBgo4R1Q4AqFQqGJa26vQ5nLIVjI+NoUzi6XIANRkPMBeajY8ay329vRBXlIQLooSry0nORkn2tTr1owI/1VJSmJ2d0aHXFG8zG5L0+jQ8PKRdRdGoLi4dAbFRGBtOh7BzWKqGSq2KSekujAqC9WSCyckJlCv2prpe+oRzDA70Q5lPuEB5jZJDhoZMeMe58ACEWd60lGMo1LOFni/MSwjctWtX3XdV6XtrrjQ/CaDO0tUO6ho1phqo+pWD2g6RNHPkj5R9hG/3yc0AoYVFMYfSBGbDAPUWXmrvQMsREy9dX+L4V6L4+wTYOI4RBMTORO461I1ba4dP6kth68E/SWJjx+TLQ4AxhUuQeu5+r1eWWthdjZ+yp8k4thfKEFyVaeFO7EYo8+Ge9AzSzsQKMeYksCRsD/L0lSM8Jo4tEL184tsYaNs01U6k+07tLutpTHzt1GUz5ZbH1hpnlN0iyaRtqpx2+4RwBtsnl0rnE3LcZ0rYSdGDCO90btBwW5bbEdJ+zqHtmABzCxowTFbPRw5xI5uUoT7pcbCmi/SbptLVCD56TBKkEp4glLcO3SMjH7Ag0HSME659M9L+VsIhIFx+2JsG+zuldz5kyOXycIVVrfmCPcSDIEAibQKzQQB12gAnHSD6KS/dbRkB1qTKBPbCGMUxGrVbKW7sU5nhO7Rwxlgq/B4jPM/FJZvNIpNJR1SyeAI3f+pGtnpeb01SNG1qavJqoXx8KQhDZDVtxKZBzeXGHOGbsg8am5oszX82ZJZNIKfpOUezNB+g/KIe/kEQoLGx0SrDwp8IeAziNm5WuiIzmwt78yWEVVFQS2urJVhadVPBios0La1tfkQd4FyGAsz7TxF90NTUXPd2sCmX66gs7TI6GR0fPmBMhL1saWlJafR1v5F5dy3awGu6Hfzkk09iXB6RAMC5c+ewY8eOaykCAPBP/+k/BWMMR4+KmL1DQ0N49NFHsWnTJmzduhV79uzRaYvFIn7+538eGzduxObNm/Hss8/WK3ZOaHTO6eeadDvuudci4lzaoJaWVmy/5TZbCII9zun3zddvQdeSpfOWRO/adT8qcSLTB8hlg7pSfhiG2Ln7fo2Dxt+TlnNg1Zq1WLdhk8bRHTbu4r5z933IFwrkyFCkoMcHSlgIGPCADHPE4B/cRlABFnctwc233mHhbCaGnZlz4Jbb7kDXku7Uc7fdinE8Ii/ruOlSdn6MoZDP44HPPWLVW68/OYS/r02brtN10vrpD/X7848+gVw2l05HEVafDHjy6S+mBpVVNhFeli5bjjvv3pGinftbFbXjnl1Y0r3UK2yqPqB0UKHX1JwImCMYEUEnXyjg0Se+oNMDQkPGQI54SV9s2bIN199wo67cdc1i4cUYnnzqGb1YqLZZRyMqvXz2xa/8jNYAJY5QqwRq1dJly5djp7TDi+WiH9prp0WjnffswrLlxjefOrJljKUuDQHAM1/+GUNHGBpEkciXyygHyyIU4ONPPm1pArlc0Hz++jZfdwO23Lit7gUGmpYxEVKxsVHY+kaxcEQdBmlNoBIFbtj1qHEf45SpfMwp9rZkSTd27PKHxvKxsVtuuxOFdjGvC1l1+9HQSeGt4MlnvmgEhYRb/hld05lsJoPHnjB8QI0Br501B9Zv2Igbt27zCC0mPR1DDzz4MJqbm1Ntc0tXa8WTX3jaSwO6WVDpOzo6sPOe3an0Pr4NANtvvgUrV62y2ppwoT1uypoNq+JLjzz2hEbUmPc4Qgap81Fpy0bXAndzpNqwctVqbLlxa+q0QW2edDtlvh07d2kbVR9oXsKFTeCu+z9nbT5UWUojR8O1NTc148670zKLu5ER9QDXXX8DVq5aWRcXd1174KHPpeJ+q7rV8TTdQNz/4MPWRtI3HvS9i+XLcf0NW1KyCeA6x56/AAhcoxB433334fbbb8f+/fvxne98B/fffz9++7d/+5oqPHz4MD744AOsXm3iA/7O7/wO7r77bpw5cwZ/+qd/ir/xN/6GPi775je/iXw+j7Nnz+LVV1/F1772NUsQnS8oBubrbBf6+sxxRz1BQEGxOIvRkeE5y6P9OtDXh0rFb7Plg0sXzqIacYCL260NOfsuDx2wtWoVPRfPW2Wq3bYPucGBPkzIq/gKT3dxo3D86Gc2tkzYCtEoEErAYoxZR7DW4CbHC0pAHB0ZweWeS/XIkIKTx4+hODvrnWwKDyVHAcDBD/fr+ozGMh1/ERARPY58+nFqx0XkMotO58+dwcBA/9waTG7e7Xn/Ha15S815l5Ny4P133k41kHvycC5CHZ4mLogo46ZCt6rmwIf7MT01pR+4C62rUXvrjVf1d3MJgAiWRJCanZ3B/n1mQwcYLVaG3LJQY+b4sSPouXRRP3fDeqk2qN8vv2Tc+NRzl6JwZYzhpR+9oLU4nM/tJ/ByzyXtvkFdiqBHpGqMK7z2vP+utsEC7KPJ0IPbSz/8gcaN9mWSGA2aos309DTefut1nSaKDQ1dm0YG4OiRT3H+3FnrnRn7joNsDrz4wg/0cXAsXcQoAZ+2R83zs/teBUg5lI6qDDUmpof6cejAB96FieKlYM/772BmQrihyoXkZEAJDbod4ssPvvcdc1IhLygojSC1nGFMuNh45UUzZpTA6BUCGXD0s09x8rhxWeQ9UidZn//B91GrVbWWU9OE4EDhe9/5S++aYfiTedbX24t9e9/XGjGCpuY79Pme99+1bDFVvwJAIWPfXgWAZ79rwulFUpCnx+SUj5TLZbz4w+c9eJsMtKknjh8TJi1uelImnVcv/egFlIhdM+W7gOGbNWk68caLPzAbBVqQLJd279DgAPbvfd/Z4DhCO/n+wf596O8z7uXSbSblAHj+ue+l0tA1UY9lDsS1Kl556UdXDWGq5s7p06dw/NhR63l6g3HtTqOvSQj8+te/jj/5kz/BAw88gH/0j/4R3nnnHfzsz/7s1TNKqFQq+LVf+zX8u3/37yxEv/Od7+DXfu3XAAB33HEHuru7tTbw29/+tn63bt063HvvvXj++fQAvBq4N5jmAmqv49Ms6HcQtoajwyPWkYWuxqPBGhzoE+GrXE5By6WLeu9lVGKzXSpk7WNKmrZarWB0ZFjjWm8sqEk3OTGRCjHmfqeMd6C/D+rmryo6IYuG1tzIY6rhQdsg3KULbX2xOINicSatRa3TZ+NjI+COAW494ICwNVPtUX2l8HCEmWqlkrK98JFSoTY9NYXqHKEA3cwT4+PaD1uqMEcK5+D6ckVdBNR3BpRKJZTLlZQW12aiRpiamZ72to0yXArTU4Iurt1pKh+AuFbTNqSA7TokZMzi7hwyvjepz+fMVzFRQIbTgxHEfPiqX0mS6LGu3bCwwEqjBHUOYebCuQgFH+lLWXZdFEqlksUragT3jEcTqC7vUEGSXr4oZE2eWlQDTxJdb03SkWrr6IanWqnqDQ7TZSvNixSsCS7VakVfshLCq4oYYtLQ42Ce1Lx9D1UPN5rKLJL0KQpPzyf1u1atiotwAPIZ6eevTl0AdGg/QIwX9zhY1wkgjiITOQrGbjOglchBxmR6bbbBCZ9A2pwAEJeygiBM8ayrLckurTXOpJwojpAJM6mNrGqLW1cURdr5M2PC/2Qci7lXCE2bUtVydXnLtqmk6RRdzNrD0+yLtIniwmXnuwIjVWZEUYRsJpMSbK3NO4QvTsak7SI3azTXmg+bhgnngo6ZjDdaCdVK0rYq3K8GdZVAzhhW6eIkRiaTmbfmrlatagfzuqwUDtceO/iahMCLFy/i61//On75l38Za9euxR/8wR+kbiLOBf/r//q/4hd/8Rexbt06/Wx0dBRJkqCry3i4X7t2LXp6hIPGnp4erFHhnpx3PqhUKpiamrL+AHEUMF8IQ7tj6i0sHABPEuQLeXjBM7uDIDBev+EfOPRZPl9ANYqBJAZYgOZcUDct51yEgWPEK3kdWZNz0U6FuysAUuFBvW9qUq6CTDl00VC7zTBg4DxBq7R3sOol32nZYZhBY2NzanLWg4bGJuFG4Cr0U9AhQ9gBfoZLd1BBEKCjo2NeeHAADU1N2tyAtkn/cTt95+IuvSBztzAncxIn4rjWl462Q34vFApoa2+3kqWyMSNMtXd06CNVl+nqogltupcu82pQffnCMNQ35yHrU8ekIZOh52SlDCL6Aw176GoCrbZwjhUrVmlhlkYuoXkY+b5KHo9pzZ5vHMjPxsYmHU1HWGNwfYRNBWRVdteSJZb9mMoD2OHPFKxavTZVp7BHFZJJQ9b4Mstlsli2bIURlNTtYGITSDeh7R0daG1t03zAoodnAVm9Zh3UFlf5IFT2jwoScru0c/kaS/ikfU9voTIGtDW3WGYbLkt0x0330uVAKKJF5IgqKjUGZMXrN2w0R2KJOooWpSohUOUMMhmsXr3G0FFppelmQJbLOdDR2Sn4Bld4M0ub46K0YcNGZDJpezC6UaewcdNm/dzm43Y+QBxjLlu+om5Zqp1qPixfsdKyf7Q1gXZMYQ4RTk8LkEl9R+SAcLOyYcOmtLDL7I2m0lp2Le7Cos7FRovP7TwKD0Dk2bR5M7LZjG6TaheR7bSwkw0ZNl13va6Yjkd3bgBAc3MLVq1arQVpSmsfbVeuXq3tH68GnHNs2XKj88zGhUIuk8HGTZv1mKqntFHjbsmS7pQJlJvmrwLXJATu3r0bv/Vbv4X/8B/+A9577z10dHTgzjvvvHpGAPv378fBgwfxta99LfUuvVPkdd9fTcr9xje+gba2Nv2nGH9IAntfDZ7+0le9C51vR7tqzVpsv+V28Ru+nZXte+2eex9AW3sHQLR1zErOddkA8MAjX0CllkAdB7cWQgsvenTQ0tqGu3buTmktqYac7iRv3LoNK1asSh3JqElHfzMAD37uUXvScuk/TDKX2MKLYfcDD1ukcOuhk3X5ilXYuHmzd4HwDe5bb78zZecpUbK+q5933+O3S1LlU61gU3Mztmy9yTvWfILS+g2b0NW1xHpP6UwFagbg9jvuMnWnkHF+Bgy33Ha7XcAcsLhrCVZKJqfwSeGvGR/DdddvQaFBGD4r2z61W9d5yO5y203bLeHHErx0+eJZQ2Mj1q3fqN/HXAgJQjsmBRjSpmUrV1phuowLDz9tqH0MFfZ8htFJkuC667dorQggNYEeQVoJpEr4pk6FfXRlAFauXK3DSwG2TWA2UJczTJ616zfo72q8cC41gQxoJJrAbC6n3VYxpjSB8nINETBVOYsWdRrDfUfzR/FX31evMUJdNUmg7HptTaCc5zxB59L6NlJq4VenA+3NzejoMKEp3Q2R+33x4sVIpO/SgiNQ+YY/tcOMEm5tHHIZO3UYhujq6tZ4KI1XNkjbcDEmBIampmYoTTU1e9Dt4GbOd3cv1e3Xfz4ewiHdqi3VdannauNO8eBcjIG29rZUWT66cM7R2tJqjUchBCZgDGiUl4boeF/U2anbpvxEutFxDE4mLnyqbR688vm8sDl10lDBjo5PcXHDFozc9jHGEMkx2drWLgV1SjgPXlxs8Juk3aZLa/WbPsvn8sgRu+N6oLq5ubnFr9Uln9R+r6W1lWjp/cK9arMKyDHnJa+/giB4TULgG2+8oY9/wzDEN7/5TXzjG9+YV953330XJ0+exLp167B27VpcuXIFjzzyCA4cEKGZ6PX3S5cuaZvB1atX4+LFi953Pvjd3/1dTE5O6r/L0iXAXASmUKvV8MqLLwBAaqFLMVQm3H2cPH7Mqz2jFar6X3/lRZSl3zc6MCjQ56/+8HtCE8g5EIRob8h4F2DOxVHzgf176g4EKsBwDnyw730MDPTPiQuFH/7gu6lnUcylh3mmDdYzAUOlXMabr71Ut37AnqenThzDmVOnrOdmJ5fG6rWXf4RarZZ6TiexEsAYgJd++JwsS032+huLwYF+fHJYuLdRx1tzwYEP9mFsdMQSsk3Z8hOm7a+89MO6QpoLxdmiCHdGO2iOeX765AlcunDeekbrpvVyzvHm669qP4GKGc3FSF5/7eVU2bRcWvbQYD+OHf1UP0+01thvjP/xoYOYkO4bwEGOJenOw8Cb0j5xPlAul7F/3x6tFRHHqfXl6nPnTuNKz0UA6pgxfdvUtgl8R9MRsH1nZmTEDDU2GQPef+dNkZcIFkrQYoyhKR9oDcHw0CBOHD8q0xsBMxPawov6evTIpykTAnvhJYI7E+H0FB2qsXEPQiN2qFvDca2KvlOfpI9zJSJGEyg0lcP9PVY4PRcft5wDH+xDrSrswXPEW4Bdmfm69/13dXtqSaJvwQJp/5JjoyM4edLY+NFb35wmhKDz8WNHMCFd7Vg4SMGFOplmAPbueU9/pwIU5QGq/6vVKg4d+lC/p2Mj1VwGXLl8CZd7eozQRNL7ePehgwcEf+SmrXEsLobkM+nLhR/u36fHRpxwZBhSF6cUnXU4PUbfpeeSwu3UqRPGZyFRUDCkprTGRQG1gaSsT5wqJIijGo4f+djqB6/gKvMP9Peh90qPhXc94Bz45JPD2gOK+y71DMChgx+m3nNujwfluHt6agJnTp00G2/4+1+9O3v2jAhLyJjXjvCvchQMXKMQeJ2M6EDhiSeemFfe3/md30FfXx8uXryIixcvYuXKlXj11Vfx2GOP4atf/Sq+9a1vAQAOHjyIgYEB7XKGvrtw4QLeffddPPXUU3XryefzaG1ttf4AWzCYC0QcSkfo8zB+ml4dy9AdoLutUF8rlTKCaziaThKOcjWG0AQGaMnZLjEoniJkjf/iiPpBsa9Wa157B2uy0TbBPgrmMAtkEDBz1BcwxFEN2Uw2vVg49ajioqiGTNbgoo6ynNMDDVez1VA7aFUH1ZgAjgDt5K1K2wvA8TpP8tF2xFFkRV6x8CDlu213n/mQiSQd9burjN8oihBmzFHKVYq33NvQPGajYwQf6nxVC4xIC/e0bEoXE43CHMFRvKo10lbYmsC0pkYMDMVg9SLgLFzqd1QzY12ZHGTUkbTTZg5FR+GWRfk2zLqhAQnTpeOR5qkn8PoEbXopoyFjbnDGcWRpI1QM2FzGHBnTcuMoQjY09mBu++wNkP1MGdsr9zMqqbJX5EmMfC6bKlct7hFP7JMKnkj7MXv+peghP2u1GrgUmnPEfo+7jMfTlsixEaZ+BlWfZjMZOW64jjSTCQNbeFBC4hzzWh0HpwQf/T6NH01Xq9WQzWZTWkC7Dpo+QjaX1XVqwZKbOaj6QY2ZTCajK1X9JzSk0s2YU58qJ06Mvak7rznnqEmeVFdr5eBfiyJtAkXfqzrTGwp/uW7ehHPwOEJOmVcRjZpK69Yl+IATjaSObMAYUKuzRroaRFVh4Ly3+lenT/OM+WjwIjlmLBtMpxP+KprAeUkjDz30EN588010dXWlNCiMMQwNDV1zxRT+8A//EH/zb/5NbNq0CblcDn/2Z3+mifP1r38dv/Irv4KNGzciCAJ861vfwqJFflX0fOBqgjJjDCtXrdbfRR4iMHDbjqu1tRXZXF4LHaQg/YDB7GhWrFxtT4ir4LNq7Xrs6ZOawDBEUzZjH7sx47m/obERS5ctd9pj/1DVBUwcwTY1NXsXcN/iuOm661PpanGimYsyzM6EAbLZHNaSo0BajhaMYSbq4q5ux28Wt5ibC9dtuVEbs7u7LrftnHPh6gF+etskYli0qDPtUkjh4kwyBmGX1NTcbAlFbtm0LTdtvyWNQJ1xUGhowPVbttQX/hwCrVy12orUYi0QIHsT+f7mW28zx8AEDY0OIVjAGG6XIezmYjUqS0fnYixZ1KGfKyHHcstBpOEbttyoj2oAc2wibExFGg5o+7877ro7NT4UT2LOwGhobMTWm7ZrPFR7dF4OcGbKXrtmPbLyOC2SglGGpS/DKG3dnXfv1Jhwbm7wBswWHgVKXKaHhafQ1oiLFI0ZE7O2c/EStLa167Tqwkw+TOMDAFu23YRmEsaQCrcu7uAcO+7ZrUPs1eRxsI4IIjMJ4Q7I5BuwftNNnlqlIC9dzIjjK4b16zdgcVujlW6ueX3njl14ff8ogoAhFzg+ID157rn3Pv29Fpsb2YwZIVnV2bWkG22t7QAT/DiKjWlCCpiYp03zsAdTdL3/gYd03R4ZyxIIGhoacNfdO1NHkr70ALBh40Z96USX71RAnavfs/s+bQdPL4YAwnUOtZkFYFyVcEGbguNBW/cZY+juXoqO9g6r7T6ep77fcsttlp/Aq4kpDz70+Tnfi7EgTqBy+bw29dG8mfA3KuhxAOs2bEJzQfnQlLh7+kA9u//Bh5DN5lLvADsPl5U99PlHrfLstYnrjSsALOnuxvqV3ZqH1Gurgltuux0N0nRH8TlX+/c/TBP4X//rfwUAHDp0CAcPHtR/6vdfBS5evIitW0WM0+7ubrz22ms4c+YMjh07hvvuMxO7qakJ3/72t3H27FmcPn0aX/nKV/5K9QFz7zAUhGGIlavXOPnsHSUtprWtHe0dHXpHZhJyO48cmMuWSwPveSCTJAmWLF1uNIFBqDUEzMGLQdmNtKcEVRfUTrK5pdnys+amoYsHB1JONhmAUhRrhq81gaFgGS0tLemdJJDiXgyC7oV83qtZ8rWlkPfE6KxD0iSOvXGGXdA7tDhGLptL0dcV6BTExLFwPQGQfs4piToctFargSnph5Mkarw5ba6Uy97A8fWgUqkYAbceSkqbFkX+I/g6ZVfLZSQ80RuWWszTNw8VYbkIdxaGodw0cSusl73zFuN7emrKuxOmR6y6neUyKvISm3FYbR8DMpjNx+TkBBJ5vKvw0MKcTKu0xBzm9rnCUwlqDAxZJ7xhFEWpSBQA8f8XGE0gAMzOTKNaqeh268gPmbSLGECYM1DSGtqYZ4rGIv2ATleNRX+FzL44I+K0csTVMqpFc3Oe8iHOTTp1OjA1MYpqzT5Os+YIoSEA9PVdQSQF4fwcITLBBB37e3vNaYLUQnLdX/bt6YnxcUxPT+nf2lm0z9kigJ6eSzqCCxVg1HhQgrKi5wXHDMPXXtFm4a5maDAd6aIe9Pf3idvz8G90VT+ov7NnT+uXnHPtxSEMmdGQyo84jnH+3BldZgIS25u0QbV1dGQEY2Ojhmdwez5QvDiA8+fOapdv84GTJ0+YcoAUf1H9m3AgqZTQd+Wyfm5poQkuQrjl6L1y2YoeVk+oVs+OHT2SjjxD28cJTpzjxPGjjmbe0IHJCajm3fjYiDUGUookp02nT53UYQkpHWy85iHkODAvIXDZsmUAgDVr1mDJkiXo7+/HwMAAlixZYt3c/UmG+QrIY6MjVlxEkZd7icu58J037vja00Keh0GrkHRXQ4hDuCo58vFBVCoRwDlYGFqLA8UPAHp7LqGv94phTOnZY5V/+OABy46J4uoKYgDw0YEP9Dv1KYRAwfCV9iMXBhgfH8MFT2isekP0zOmTmJmZ1mksBuQh1ceH05sP346OQdiDndRh4OoLi0pY6b3Sg+HhQe97t2wOFS/StnVJkZ58/8wNM+hKvKQTJsbGcOVyj/XM3X3Tui6cP4fZ2VlvcUC67crv1FysQ42vcrmMs2fPeMdGOo8QRibGxnTZkdQaB0H6iJQx4NTJExZjSzhHAOZnwhDpRd70Ttq9HDU5OYHBgX593CVuB6cXOgWXey6hJMOX1dTxqxJKJFFp+rOnT1kMP9IRGsSxJF1wqtWq8IdI6+UclVqsteraeB/AyPAQJiYmdDqlmaS3POmpxYVzZ+0jI08D1bgImHCJpV7VEuEWJ58JACJ8KwE+Ls+iNGn4nVt/JTaCWBgyDA30oUp9vjk4uAvZxQvn9UaB+glUn7Qt1VoVfVeu6Ly1xHbGm6cXQ7jg7VoI5NxcUnId4klhuefSJbt+dZLCiQNigj/tU9PetGsxzoGZ6RmM1AkDR5+p50MDA6hWK5ZgY6V18l5WHjS4GY9q7rlCb7VWQ7/0KRglZKNGBSmYzdfExHjKhZZbv9aWQYXH4/XTOr/7ZJxhH7+lmWIuNiWTE2O2EKzSO8IY58DY6DBqclPCyXOf5o4xggvpC7dcpWWs1WoYcU5FKR1cmJ2ewszMjJW27trEOfr7+2zFBBH61e+/CszfOA3A22+/jV/4hV/AsmXLwDnH4OAg/uIv/sLS3P2kgk/K9kGtVvPeBqo3iKvSxsB65/RmMkfn+kAxjmq1gkwmi3I5ArjQNmljaU951VoVLS2tpBwnnaPZiqKatqfwtc0FpnbWnAgGsVq4zLFZGAaoVqvI5XLp3aTEwxKYGBDVqtoGy0IZ8M+gq4C1IBNbM2vC6/qZxayjKEKzPAJQjE8xQd9xsLLtcOQ3jTvVEtMyUrfNke7Xaq2GbC5rKnPbCXth1LZGTrluHve5hXudTU8cRciTPtV0cQpWYyOq1ZCRuOsjKS5crdCFSJVRc+z2Ir0Y2eXbt7ndDYN/BEdRpMOdRTxBwGxNF6ULIHzz5bI5qd2yhRLdeAdUOzjnkMo664KF1hLW4THFWoxEhvGjGv8oitHQaFi18PcGNOVcn6GCLnEc67nnpQYDGDcbPbpwKkFKXZpR5FTHxAwJCuTWqeZ3EqpJokPmBfJWdEh4TCr+MtxhHVgbBTcNTRvHMQoNDRaOuomMIUePy5kIp5fPFnS7fGNRISddFepwfXRu2Bpqk1eZ+VAfgtbtbdIZSZIgX2gwmjwihLjP1A/3WNJKTzeHnKOQL1j9r/ovkwnsuccYkjhGU3Oz4GVSi5ubQwvLGNMmJ0Lo5lbj3D4NMxnNk9L9nQZ15Hm1dLWYgzEubnCDzoH6wl0QBMjl8nXxcNkeNU/x9ZECzgGeJPoOAq0TsHm0ErLDIERjQ0HjfTUZIZ/Paz+Bf9VLID64JiHwN37jN/Dcc8/hrruEi4sDBw7gb//tv40jR478d0HmfzT4pH0XFi3qFPZdRK3sLQuiY2/cug0tre11K3IX9h333Jsqy3d8xQEUGhqx9eY7ULkgNGpBGJB4jpRZiC/rN26ua8icygThroa58XcdPFTqhHPsvu8hMdjJ+7I8Ds4EgaUJXLZsGZYtXQoOJ6IAAMY5ONGScg7cfucOEb+SpiNoc5iJwjnw0Oce0+9coAyAAWhubSV2I77yOcnLsGXrTZag5h4xaGYrPz//2JPWO0bKVkclFB6VIew001J5PNLZ6jVrwVetNs+cDuJOY3bddz9yZOFy+9MaNwCeeOoZG7k6c4MxhpbWVuy+7wF/AoKHEi623XwLGkmEG+GDjiMIA1v7IuGpL9qmHjHnWoiiQoDqjy8882WMzMZp7a9ikqrBANauW481a9cJYZNcfvCgDwC4/0ER/inm5tgw53H6LOoDvviVn9WkE/Zmid5guPW0tbfjgYcfSRUyVa2BJxxBhlmOqbffcquVVF0MUW5k3EXpmS//jPFN6OCaEtwhcFfvqlKTlwsDfSFHCMIJEs7RtHg5rr9pOXzAAVSi2MQ/zgTYfd8DqQssPhzU788/+SW88P3PUhqr1GhhQEtzCx783CP6eF/ZJKtLG+5Fnm3bbtbfE85FCEMm4rH7+PxTz3zR305ubq5TeEaFVKyTBzA0WLV6FVatXmU9V999PO2eXfa6YQmUlB/oufFFi74VFQ4wtIXAhHM0NjXhwYeFHV4UJ+ZUw+Hbqvzrb9jiXxeZn308+tgT3s2z7zvnHE/IcHouHdQGS+HNObCoqxu3bFku1xlm2UWK8mz8b73jbrQ12IoPr9At4bHHn7Te1RMAAREm8/4HjVs0V96wN64MGzZtQnMh66elBx586HMyPnm9k0myTl2D1umabgc3NTVpARAA7rzzTjR5fLX9JAKdYHPRZ3p6CtVKpa6a1QhFopyB/n7vcZUSAF3o672SQsBn18M5R3F2GuPjo6hUhE1gmAn9cQll4/p7r6BardhaS2cS0Kznz52pKxBTeYQDqFSq6Ll0IdWmcmQMyZUmMJdhGBocwMT4qMZhLrU4Y8CJ40eRkKgIagFV7932HHGPVD1lKhgbG8XFi+clE5kLDyE8nD51AkVypHq16fSRdAug0rrCPP1eKpfx2Scf2xpB9ZIj1VGXLp5Hf1+vgyj8XJQBH+7biyiOjVaKJtcLjsm89/1352wjvRk8PDykXZV48zD769HPPsHE+Lhm3rU40TcP6UKkmvz2G69Zv8VxsOPCQ+JUKpWwR+Feh+kx8u7M6VO4cP6c0Kwl6bBoLrzz5uva/rESSbc2HsFVPXnt5RcF7ty4wgGATJCOwzs0OICPPzLmDFwOzKlqDUmSIAyZleejgx9ieGhQ00bhrzSBtGzGGF7+0Qt1x6AaB4o2lUoFb7xq3P5U5UYuG9o0FUIgUBy8iKGLtpkHPYYryyN/cRwc4K3XX9EBBdwNpDuMAeClF36QOra05hTJNDg4gP179+j2KxwFj2fIhsaGExDuZ/r7e6UQJzRJARHSOSmfAXju2e9ZU1KBEpLo+KnWavjRC88RmqTbRvvp5MkTOHrkM5EutYmxBRPGgFde/hGKxVn9jG5EKR3VpvLZ731HC3IMQCVWWub0XBkeHMR7MjSlOPaHRRNVPpflf7h/H6709KROFBjSfcohwuPNBziAJInxg+9/t+66pGiuXBGNXD6vw5PqG8/u+qg+Ocfbr7+C6RkTJUmtTfXq+74Mp6fAp0hSz8ZHR/Du22/aGkBPuYo3fPzRQfT0XPSu1/PBRbXJdznENfOaC67ZWbS6JAIAf/7nf47HHnvsWor4sUK9nRYl4NDgIKanp1JEdftGdfzZM6fBmIeMzH/jR8X2rVe/2RkyTE9NYWJiXPvNoqF67KrEw4G+XkRRpBkBxVOXT773Xq4fecVl1tVqGaMjI6k2FWtihxkETN8+EzaBoyiXK7pBdTaOus19vVcQBLa9o6uhozA8NOBNR/tY8YTizAyKs0ULD7c/laDCGMPw0FBK1T7H3MTI8JBX40LbqKBWqWB6ZhoupMqXD6anpryXMeoVMDIyrC+GzCWkKZhw43Az6AUdILtLiLBuPp9ZFAcGo5mYnpoQJgQQZQpHxGKsZKh9nYSZmWmr7pjXF/CiWk1cauHCrutqN+RmZ2f0PHYvBFDjfgWTU5MIZUgnZTuWOjYk7RYh7MT4onF26XGjQqtUKukLByoPAExWIqhjVHUjmkm6BOriERcXQxiDCf9F2s0518KCKLyOZkqmr1Wr0omwSFWNhCBFYxcDRkOY1Kqm75x6AaBc45YmsFScJW4wNEqEP9nYFctlIUAypi9e0LbQzVKtWrW0xBpHiUvB0fCVy2WEYUZqmUQfZUNb2NTEgXBtoqsm5ShfhJSXVFUoUE0TpIAKGzXihsrUYedV2jDOgXKprM2O6MZeRkxL8Wu9oZb5i1Gk/QS6w7hKzDDUuM0GgSX8UfrUohpyOXJMTolhf/VvLslDl0yVak0fwbvvKS5xIlz88DhCLpfVPMbHp+ncVmZK1nuPIH41oOnFxoNbbn8SHyJQ807kj2pV64j/agoqagNoNN7+DNF81gwJ13Qc/J//83/G6Ogo/u7f/bsAxM3Czs5O/PEf/zEY+7/vKuZ/Bvi0StSoularojXbqgkNpDcX1s4U8N4M9XGBOEmQyWbrivupHWcSI8zlUavOAJwjk81cNdh0LmWvQ3alTt5CoZBSc5tdk83k4ihCs7KP0Im51gRmAoZqJBaTQpYJu6aGgs0cYHaTqlz1trGxUbh8Ifgxghwd8Elih6Sre2RvtsdoaW3RAgptl06i283R0NCAbC6n66TpqLCooL1jkZf5+AQxzjk6Fy1KLW6sDrfL5/Noamm2F0CaTiWX3xct6kQQGP9qc40WEa5xiZ1GMjTubGI4hM1TO3ENoRlvnfHc0tKKfKGgUVaLdCZI31BkgBUSSYcjhO1oleLjC6FE5zJtf1NTs4iiwaWGkQlhVMwRnqJVd/dShEGAOE5Q0xcIApNO9ocSOJYtX6m7iLpNouHXVJcXCgV0dqajaEyUYq1Bo5rAzs7FaGho1GMvioVGp5H4g9R2rcy4uOJwxhXSPIYxhmUrVug+rKiNXMYW9Kry5nKuoRntbR0mv1NeOUpMbOaQYfnyFZYfSoqDoj2FzmWrkPQKN1OhtCnUG2pn7OcLBXR1L9WPyzWTIAgY8jRGLhOh/dTJlbZPDaDrsez3AKxdu04PdPpeksg6cs+EgUjvtNMrgHOgtbUd+Xw+tfD7N/nAqtVrbGGam7LssgWe60lEGgCYqQrNUC7j+ESEsMFbLiOviJvZ6ma1IxxL6O5eqm0IaTuVuQ61iQSADRs32bTggl+5eDAAYRBgw8ZN1hrr0lFt5KKYo6W1HYuXLLHGkptWlZVwYM3adcgR/4wqmxK41aeCTZvTvpF9wBhDY1MTVq1eIza6pDyDCAOTm5QADCtWrEBLa6s17uqVzTnHddffUJfXejLNLx2uUQg8dOjQtST/iQKfGjedhuG2O+6ydumALQDQ3wDwzJe/OmeZqk7GhFHqE099KT3SXDzlQrxqzQY0dFUQvfAWACCTzVjHBFYdAHbd/7De5dKFp95wePTJZzR+XjwI7m3ti3DXzl3mJReLQllqAjNhICKbQGgCt2zdbo5jCI6+xZwBePBzj2nmy8hzdzFXTO4+JyQdxdttz4qVqxAEQWoBrAe33n7nnKGCqKDBGMM9u+8z72C3gbYTAJpbWrBt+y1Gw0Ylbw+s37BJXAzxCH8+uOPuHakk1o7dgdvvTPvas2/YmUsznYu70KVClXHlmJUUqpoin11//RYdLorDuA/JELskKttuv9nYvikD/EIYpDY+nHMUCgVs3HQdZuO0Q28VlUThwTnHsuUrkM/nRRB5bi6c1CPljTdu07t2YYBuLipookLUHccxtty41Qi7UUKiUSBlLtLS2pa2IwYwVY4Bbuy2uOyYFdL3IwO0jSJjQDNxrk77YdNm259nSvCTnwmATDaLtWvX63fVSKRuIGHruNzsxQlHobVdbwRU2cq3IofgB0oIzIYB1q3fYAlLahPm8lRAbEoWLV8FfmVMaqyMIGKNUTloGhobkSeun4yPQlEH9aTAIUIqqksH4uJR2m6Q1rVy1erUADFaGEehAGaFsHMFGJefNre0oKmp0Xpm6I0Un1+xYgXCMLC0iZQctM4kSTQu6tlkRfDmQja0NrYMQDab02HjlJui0OPMXZXV2tqGQqGBKAyM8KW017TuJUu6vf3tguInXV1LLEEXMHNa8SLhE5Kj0NCAluYWw0ccYiphTEHHog5xiW+ONc9oYDk6OxfPgbHd/jAI0NrWll6buWmD4imMCR+wKgzcXDZ8qvw2ovhw39H83DdI5oBrOg5es2bNnH8/yTBfmux9/11MTU563zFIhkd+v0TsQK4Gk5MT2PPuW3oBBdQGgSyIBM/jRz4WLhOkcKVvidbB7cXnv29pr1JANJucc28YOJqUMq9LF8/jk8OHUrt3tfPPhMJFDGPCYP39d97ApAy5RHF0Z4j69cPnvud9nvrOGCYnJvDu22/Uxd1V7x/59BNcPH/O20ZLtpKLzks/fE5/94GaeGrxe/GFH6RwTwlW8q+n5xKOfPpxusw6P/bteQ+T4xPpwklaOh5fefGHqSQpwZsbP3vKJtDFwaULAJw8cQyXey5ZTD+NvPn55msv67oAaZwOWEdwNOsbJCSdiTOswq0RYYAx9Pf14thRcSEtdfGICCUK/0MHPsCkdLOScGML5s4TtdipkHQJFxqHkMnjYNWRpM7i7Cw+3L9X5JdCY8zNEbK7EJ05dQpXLl8i9BV1zlTkJauMrQl8/5239MZUO9wOGApZo/FV7QRgzQ262PsW4qHBASskXUVeOimE9mUOpSGc7DmJ6qxxs6I+FV+oxuY4OJcJsOe9t21cmC34U7xKxSI+/uhDJEmCTJB2DcSdLxfPn0PvFWObJmw3BT4hI74WZYL9e99HHMdCkyS10tkwbWOt5tSe999Nj21ujvotP5EjIzgh3VC57VK0tXj70c9kCLD0ht4VCDkXuMy5Wde/GcrlMj46eMBKN1MWYyubCVJ19vRcFG6oJF3ihFsh9yh/AYCDBz6Q/kvNe582U9Fg/749pEHioW8sMgDjY2M4efK4xZvVRoPJ9qn+A4D+C6cxNTGuaa5sHo2ASqrmHAc/2GfhOBdEUQ2HCB3tspwmMaCv9zJ6r5gQiZZQSeihTjg+/fijuc1rHDjw4f451yS6EZysI8P4YF5C4DPPPPPfJc1PMigCTk1O6NAsLsE5Sad+V6rGeaOVmgh2amdRKZe9t3HrQblUQhQEQghkDNmsbePi5k2SOI0zT/ExsZOq1RCEoZexaLU4SV+tEPsFMsnKkdg5ZsPAHFPlAtSqVeRyeatMLUDKSWAJK57Ni+/WNADUalXkSdmp9joZqtWKdUyu2hQ4zEhNJKoJVjtP+p0KJHGdkEjWAkD+omoVGallZIRb1hsH1WpV4O5KlKpAp+0UB08Sq17XjU9KaCUaTys9c46KmD0vlD1MwhN9NA0YNxVZdURKKoxje+wmxJ2Mb0NQrVaRycrQVRQXT1qVPl8ogHPhysgIaPZxv7t4R7HQHGqhhHSmEn50H6l2RomcQwwZxlIXQ2q1KnLEFkgLQVVhE5jNBnKDYWijXBBFcYI4FnRVR7Zqbrljt95CS6Hm2EipG/5Ui8aYMPXgnANxhKa80b6pdopxwFCuJdo2WJwEpDdTvjEp6FJDzEJ9sqDoZtlskqKqVcGTDC8yjp2DgOmjT1q+clUSSb+HGTfqippDalGlPEJroxRdDE+gfWoJR04buaRZpZq2B5sLOOcpoULlc4XOmnIrxQ3NpqUmMKtootvAUKtWddAApWVW5hpU+6bqcceM5rlOY13h0AV3Q6Bxz6Zdhbm8TEeHiWONuy+PqsfUqXhaGh/6jDE1r3P6na8teixAubiyo4Gl1jRA+yiNomjOEycfXO04WL2vXYNwOa/j4P379+O3f/u350xz7NixeVf6kwhqcHQt6UbBE5pFAe0CzkUYOPcdA9kdE8hms1i6bIVJo8uxhR01eNo6OjEYFBDVIoAFyOUyKaZCYY20SbGEK/LbZcXrN2ySbZ+bCXGI8HhUgFVlViXjzYQBIqmhaMyGWLx6LfLyCEvVocryLU4bPSHpXBwg+yOfL2DVmrVzpyeTtnvpcusI62pww41bZRmmX+oG7Aaw5cZt+pmP6dPnizoXW9oq6AXUk4EDGzdtFj65eJ0CKTDgpu03e3FxxwODOE7bKG1e6DjX48cZFKtWr0FLS2uaEXFYC71asG6+5Tar4kosLh2ISBewtAIMwM233q5xVv7KlFNpd1PW1bUEhRbRpz7jeNpW0Udb0dDQgFrCEXNzYcNd4BTcKsPjqUse1o1mZtrMADQ2N+kxA9i+MzMh0wKdQn/NunVoaGjS7VI3WkvSbisMmdRoiPS333m3oaE8bg3DwBs7mHMTkk6h6qOJgsVd3ejsXKyPzysylFpBX6AQZapj4s5116G1pVnWy2DpUmV+fRycCXDnrTtRD1zcGhob0b1pC/iRWUk3JeSQeUI6au26DXpDRXEUFz4CY44iBaWdu3YjCAIkXNxU59y2fbOQ4sBuaeahhSyZTtmBUp7QtaQbHR2LJF3S/FQ9Uzm233wL2trbpEBA+CMRMGne3fc+YDZ5PM3TaXUNDQ24/Y67zHMOzFaFU3/X/x/nYh1g0n5SHakHmh+khfjd996vLyqptY4SigqmAHDv/Q965yZNp+Zv15Il6FjUUXfcqnIiiefGLTehvaNDatgMvei6o8YwB7D7vgc9JafxYkzY7u6QJlAuf7BIIl+uW78RGW8wB5NM/Ba8Yffu+43fP5mgntDMGMMDD33O+5zyY/V7cVfXVdupYF6awK997Wtoamqa8+/v//2/P+9Kf1KBc4616zdoQ2Z38NPdFiBsLzZs3GS9B2RnEw2G6qNcLp+K7TuXXN/esQhlVhDXvVmAfD60jsZcWErsQKxJoH47mpYl3Uut3eVckC/k0dralhIsyjW5wwwDGfcUaMpm0NramjIIp/SjskySJOhob7cERVdoETia2ludEHY6rdMOBiCTCZF34umqelzjZ8YYGhoaU/X56xK7ucampro7NLfNURwhL7VGWoPCkW60Zqjc3CJk5M+7k+XWuHOKSoHvhiIn6eltNEBoplX6ekKXwEN8RlFNV8w5RyUykS7ccVSpVi0a1qTAmJU2iG5fqGgeVFPkxUXWPT01JcYj54gTMU5p1BJ3jBXlbV/h1gZSe+mvozRbtG7jFWuxPnLOSmGGoj85MaGPOikNyzV5ZJcN5eIqnk9NTmgcyzKqiOtGRhVfq9X0TWV3OLnPGIDp6UnLnYS63NWYDa3FSx3lVyfHkJW2iFqAlgljbqLCMMaQy4QYHRkGBVWmsVk2hCnOzmJiUuCedfzZaYTJo9HhIUBGOOEQAozCKxMyO3YwAwb6+zSuUczBwZHPmGNGCtVqFSMjw9DaNKcNjMHqw4mxMVTKpbr81NUkXbncU3fcUiFS5blyJe3JgcEWeBTMTE9jfHxM/0441xsMcRxsm0H09/eiVhGaI+VDM8MCSwtI4cKF82mtOzN/FGq1Gvp6L6fWTnfcqs/RkRFMTEwCSO931VwGN7eYR67Y5imUHiK9qZNz4MI5496oHs4q7fT0tA7BWG/uq3oCxtDbexnFYjHVd9Zax01861OnTljrz1zLTa1Ww9kzpz14yrZrDaf4PToyUr8wB+YlBP6Tf/JPrvr3W7/1W/Ou9CcJXCn6vbff1N/pObu7TnMOVMolHPpwf/3F0Pl98cI5bTOgdy2kPIOH+Pzow70Ym6kAUQ1gDPl8aOVx4cO973ufuwIgIHwanT1zyuA6hzTKOXDi2FFMT0+m3lVrgmnks4F0QRCgNZfB4YMmxJzejbkNlFCpVPDZp59YO2WV18ZDPOm90oOBgb66uFJIuLC9iOPYWtT0DtSzi6I+3FxG7eI0OTEu/M9Rod/FiTw7d/ZsygXRXBLMZ59+bJdHkXegVqvh5PFjukgfE6UwONCv/c9RUIuiGv9KC3fi+DEtMLjCrXVsLj9PHFO2ZiK/cCfEUfCYNcxMTeFyzyWdP5KG3+6lD1Vez8ULOnQVXcQZqY/C8WNHwZhw/qycUFObMz2/OUcUxzh9WsyNGjk2DFyuLmF0dETHDgaEJlB1b5bUo2o7d/aM0O47NCzLhTqTCSzcTp86qb9PVyNpg5sWkhhjmJmZRi8JpUbHgCsUcgD9fX1WKLUoFv3YkAn1Aplwc2Fkuv8Cshn/JjlOuL4ZDQiN77mzZ6w0RuOVvlg0Pj6K0TEhvChhxQJu/13quWTFpK1Kf46JtCXNZgIz9zhwjggApSgWWunQHgNK6CsVi+jv77PmmRpbShMo4iuLBIODA5iemdGnOD6NHhUEz509I7whEIGPpnfzXawTl9hdRwBganoKY2OjehMpaCOcqhey6TWk78oVYePHGCpyI58L0xs1NYDckIc+dygqb7lcxoAMSUff0fWAjtHR0RG9AasrEzHoOPUj/VeMnb5Du5TwDqC314Swo5pVq3g57mdnZjDuutDylKnqGezv15tBNQZ8Qrria9R+0IcHhWq1iuHh+t5XXGFwYmJuvClc08WQv47gStCUVXpvphEolUvWkacsCFALJ2wJv1Iu65BLKcHIKQIQvn4makwIgUGIQiFrYUgHe5IkKXtDb6Eyf7lc1qF/HNS9UKlUkMsX9DGhYuAVyVxyIZO2SgwtxM+TKtdWn9u4VCpl5PN5qz0M6bBMWiNVrmhtmgvuuhEwibvrGwq2cAbZHt9tK/UuVRcEHV1c3LKpwFStlJHPF/RzUbiTmaXzpypwygWASsng4o5ma8GV5VeqFWG36VbAjCBF6VCt2HSvJ2S6tFICZUluGJpzthNfQI0vgQuDiv5gH1/RUsvlirapci+GmKOstECYJNIZLjPxi+niL8oua/uuWpSIW7RKmPMwgmrVjC/GhGNezoVpBHW6LPpA2lTl82SsQR5nCiEwnwnTIe3kQjdVrSGOudaUucPS7SNGvqjxQrNUqxV9Q1E4UE4QBgz5rLmJGidcXwxhga1h4zBzJYoTIbwrTSBL6s5TylONjWoNMRM3N7NhgJCpBdkxmZCfUa2mbTHFbd9EHyFnM4GgPcmWCULd1yV12S4IbOFBzo1arZbij0r40xdDyMWiKIq8dsou7zNaxMCyl/XSSNI/SWId6sxHQ8bsPo2jCA2FBjGmGUPCzQYjG6Q3tZxz6a5GCLghdeGE9BpFbaBV/YpXpMZjtYZG6ZZHCWqKtzHnk0O4Ucs3NKTqBMhc5kZjmc2E4kgVpp8Tj4DJAfAkEeH0SIf4BHAFcRyjsbHRg4kDzPRpjq5jHl7BudiEMgY0qhOnOvVTiKIILc0tMl39hPXWr7ngmlzE/DTA7XcKFxuWhkh+0gnNmPA9duO27TodVy8AMKpBlINy3YaN+qjRBXdHCAC33X0PTp+rAHEEBCFaGvy3g7nMv+Oee+t3vqOpWrpsGZZ0d5vdCjNt9MHNt96OZnIVX+3mqlGibyqqY6qGXIhd9xrbC9W2lCAoHzQ1NRu6gzAFRxOkYPP1N6SOMWmxqk4GwRAefPgRhNJFDNUGAvaaroSVhz//mGY4SmPhoyoHsHzFSnQvXWaVqcqlDEgxqTvv3mnF3dQ4yAxKG6qeff7RJ+wK6wAD0NzSjF0yrJsPX2X3BUn667ds1SnrCU10Hnz+sSf0pSlahxIEaJsDxvDYk09bOJdrKtyZOO5UhOEAlq1Yga7upQK3gOnbfz4XHowx7Ny1G5PlBOWacXTsCqO6zQCe+uKXhSZQXjjJBOSom3PtZxEQPis/9+jjAKCjnOTksa41QCXcsGWrDmMFGC0TY9CCCJ1njz35FBg5blOCVk1r1W0ziqe/9FW9qZyu1qCOO31zfemy5Vi6bLlBka6w8jtFf8c9uy0Nq7rclcsYLZUQ7gRum3c9oW3tXGE/ijkiefknCBhaGgt47OGnTV8A3nmnfm++/ga0DuaQHB0UgqaPl5FHj8iQXuJCjNRiSppm3FBwDPjiV35GpGdANTHurKxiJZ26lixBV9cS+Ca/8oMXkDF/944dFu+h9mmU96k0X5K4+NK4z4MgxNNf/FJaY+XUpWD9ho1WXYAYx0HA0JCz7boB4MGHPy9scAHj0F3NKZY+sn76S1+xlBiuIEV/L1q0CPfdL9aCgAFJAoueFv8FcMutt3k1eK59voq/+8AjT+kNkqiD+ceZXCSfeOoZh+dL+nnov2btWqxZuxZzAT2t2HXvfYYX0TZqvsEQk5CVj33hKY2aSwsXWlpacN8DdE2t758VAK67YcuceFP4qdcEKlD2Xco9jI/BuqrdyYkJ/xVvIgDqvIC2L6gHbqcO9vdhulQDYqEJbGvMeSclAzA7O2sdSTmoQGvvNC4DKJVKXuHT3bkCwLkzp1JGr3HCdfSCDBMRQzIyLuy5s6etMjQT9LR7fGwUIyO2qjtgzNLoiHK4xOV03dtPlNkq4fj4sSMpZuUDzjlmi0Wv7QWtHzDj4PKlixiTdGfkz8VJ0f7wRwf9k547zyTjOHzIdvVAJR1usoEDGBoeslzhcDhpiOYJAI4d/Qwz09NW+noCIOcc+/ftsYRE7011Lo8PK2WNuyqnrOzN6M1T+Xfh3DntpkIY7os8Wc9lJADYt+d91KpVa5NA20qBc45333pDlx1JFxhU20b7YmR4WLvx0XiE6RvNql2ffPyRdVRTlkenAZgWHum4fOO1l1P8JU44okjckC7kQr3AR7Ua3nnrdaMJrMRaU0bvqSi4cP4czpw+aWQXTmhCiKNwf++dtyQfEAJyJC/BZKRPOkBs9FSUlSuH3/GGzxO0EjeDlSaQladwQLrOodWnFnnJmz45/BFGBgWPbMymnRpb+AN48YXn9Ps44Tpes0sfVQcN61aKYgQMyNOLEqT8SxfO4zM5BjTesg9U+9SFJAB48803MDExkeKn7nfV7c8967jE8vBh9XtycgJvvfG6tVm3yoRNp08+PozzhA8kXPFpJujq8OLnf/B9caucE5vAOn0MJkLScTKu9P5CbmhoMy5duoiDBz4UdCM8zreGMQa8+85bGBkZsXiXeGdPVoXney8/Z/mh9AmQCmZnZvD6Ky/qdUEVaY8T8/2zTz/BKWmK4etTcrACQNDRF93JCMvmpClgDM9+99sW359rferr7cW+vXtkebY7GB98sHfPnO8pXJMQePnyZS307N27F3/8x3+s7XL+OkClXEbvlZ4UkZmz0CgYGhq0FlGSwbsYnTt72l44mX/QKqHh8qXzmClHQBIDYRaLm3Op9ApmpqcwQYyBdfkMXlwG+nsR1WqpHaglRJGMYoFmJhFjiKQNkL41J2/l8aiGEWlr5pvstCIGEbqsWCxaNEg8A1396u+7Mqe6223DQH+f9dyHlyq7VJy1bKRU2tTxsPwcHR1BImP1uu+Y80BtBLwRZuhvmTFJEiHYM/t5CmTGmelplCvlFA712jo2MqKPV/S7OuMeEMI6FQDr35rnKJdLKOvLG2I+lCNxXNeUNRddFKOfmpoEJ7Gjq9I2ybV7UxuDkZFhBNIdg28oKIFW4Tg5OQnOubztC2SCQJdlbdYYQ7E4K21Iubjpy50bzdykBYCpyUmEYSgXci4dqAtNRS4MdTpO+sldiKOYo1qVmtK80XKXy2XUyKWZybLUYGVD7xywQsbB5ieK5hT3yYkJrVWvxkKQygSBdRxYIzd+k2rJ6hOyJxHCor4YAuR5Fe4orzuEOTAzO4MoEcesDVJYUYun1SAJFTLWK0RQ5Zwjn7WdjKsQgyqDukSSo6EqyWQpFot1zWt0JBti6zk7MyO15J62edqq1lHKZ11tofpdKZeFs3uS1hVk6EavVCoizJjLO7VY8WlxKYsrpCVUq1XtTF9p2EJi+kAFsjiOoRQKhr9xudEx9DBlVzQuXlqQz4TLy2fSjQ/lXeo7l/RRQiB4ol/4zLfo+KxUKtpFEBXk6slTlUoFWQ+PcWmuftdk+D23bFp8rOuTFGVGAJ5jSUuZNF3tuLdSm7+LmGsSAp9++mkkSYLe3l783M/9HPbu3Ytf+ZVfuZYifmKBc45SuYQC8UBPJ5Y16CVUK2UUGgo6DR1N7uLLIWw1Uv6VvLiI8sIwg1JFCoHZHFZ3EFsfRxtTq9bQQOwX1GRRuLgjLI5jFOrYO/gmhbJjUwk456jUYtTkcXBFOl/NZQJEcQ1t7e3+sr31JWhubjJtU3+Opkn9CsJQu/ER+R1hkZtJxRjQJMPd1VlLrO9JHHs9swN+NzGZTAYNjQL3pM44oYxOlS2OIu20br5arYbFXUvSiLiVqB1tEKC9rd0SAF160zobGhoFHS0GV5+5dHYu1nZRPq2oQk29WtzVReYQ9NFtE4l0IfkgCoUCmltbdT/XyLEJbYuqd/HiLouGPo2x0lrGcYzupcu0totz4atQC0ROO7PZLBbJSAEq1F2BGsrTfEy4TyoQMw/lr44xZmuaIOi/dNnylBahFieIpLampZCxNknLlq/UNBSuPsRmiy5wChobm6zxqxY8RldR8q67uxs5GSKxIm8eZ8jNY8aMcAcAi7qXW7eqaVnqEo1yZt3eXEBXtx3azzv/JV3a2zuAjOAzTfm0qw23kI3r1+lHCncl+IvIGESA4RyrSUCDcpyAwdi+cVIuuOAZizsXp4RmcENP6ix66dJlXg8E9LvS9jFG3Hk5i79P45TN5bB8xcrUJtYSIAEtiHV2LkZba5uuuFKLzdjKE1MM+bFm3TpZn/GJ6YYNVJDEiQ7rBhg7SdpO2sfNTc3oluEdXb6u2BhNv2z5CjQqF21OeRyG5pEM5bh+w0Yobbe7oQOoYM0RZrPWGKC0Zsz+BAS/a+/oqKvZdUF4CmHpskleEXdavNy0+Trr3Vxlt7S06NB+qj1zwTLHC8lccM02gYVCAS+++CL+3t/7e/j93/99bN++/VqL+IkExhgWL+7C7vsf1IzEO5FhBqUIMcd1fioIpgYjgC988atz7oRdePiJL+J7f35YCIGZLFa2FoxwQ/MCWL12LVZjrVWfTkcWFFWNsh1TKNfTBqrfjz75NBizVfqlaow4ThCGAWalFqOQC9Hc1Ix77n3A1MtM3T46brruBq+RdD27vAcffsRyP1PP0FeV8fDnH0uVTYHSsrNrCbqWdHvr9fXp1ptuNs5Nmf8mMW3rvdI+Rowvl6vblWUyGdy94x67QHd3QWDFylXGvRHS9Bb1mvQ333a7bfzODc0VjvoVN/7nfBpaV0hrbm5Gd6fxt6i0xgFjKGQc7QuA1WvX64shaqevono4ScEYwy233o4SV8J0+gKRe2x9y223AzBOZnOBOnIVt0nVjhwAOjo60blY+k1LjH81Rgay7lsObNi4GY2NjeBc7PTVTdowYMh5xvWWG29KPYtieRwcMCxqMIbu2VwOGzdthrJd1PMsG3p5wRLq5xTOUOH2s4QDm6+7QdOrVI2Fj72McVbNmLgcE3MOnsRYtn5zKpKHgkocyws9wo9hV3s7li3vlH1goaFpT8dS99LlSM4NAKihLW/Pb9/matPmzabuWqIFfEAcJ3OZiUGE9HLD4zEmNYHuJGdis1ZoaEhPNQZ9eUIJXpwDa9asRUZqg93YuUr4U9+TJNGxfV3e64NcLmdvHBz+rNBP5PxdtKhTb3w1baRNYFPONsUAgFWr1oh2JWIchgEsu0/KDzg4VpHY1Lr+Oo0oNDSKS38kPZzvlPSLF3dp58++dMLFCtebxKXLVhCNnFnnKF3U9zAIsaSr2zuWRPtUOeKzpaUFzc3NFr2pIO/ywW7ick2Ba6+pLhWBJ1gmcTfzof54yGSzYjw6c6aebWBrS6unhX64Jk1gpVJBpVLB66+/jgceeOBasv5EgyLi2TOnLLcpdMeiPim533nrdZRL5hiTbuvcfuQAXvnR8/PGaXxsFAf3vYtisQokMTLZDBYX6tyIBfDp4YO4Il1suPVqzQjB64UffE8f29ByUmXLwf6j575nBAiZabxSFZqDTICivH3WkMvg8qWL+OSwcbPi03hSXN5/503Lrk69d9uo4Plnv+PB1EJN4zo7M4PXX30ptTPzyVKMMZw4dsQaA3MBB/DKSz/URzup8pxnDMAPn39W500VRj+ZCOl18MAHqcLqrRkHP/wAg447Bu780Spe/tELdnlzlF+r1fDGa6/U1XK69V26cB6nThzX6WN5czQT2jcPVZ49776NcrEIcGgmHzDiy48R/DnHq9K2R9nbpW4kwzwbHxvDgQ/2QV18AMyxGOdmxVYM9diRT9HfewUcQmMEyGNd0mA6Tt94/RXNxJNExNnl8reKvGGOiDjefvPVVPupJrCrOaPp2N93BSdPHNNtUX4589Jmzp1bBw98gOmpqRQt3I5Sx9Vvv/maflasiZBqSgBQc0k5v44rJQyd+jg1R6nNZ0I0gdO9F9En/dvRuaAEQAs/AO+/+xaqtQiMAa35jDVP3TEKAC+9/IrGoRTF+nID50BTjtywZsDw0KAOjxdz5RSbiYshFmKSn35yGONjY6k2Kk2goo+6ifreu2/BB1QAVFCpVLCf2Er68tDvFy9cwKWLF1LCgdZegbANxvDB/n2WrXq5FiOWl2aaqSmGxH3Pe++QeSfjZKub1WRjyxjDxNgYjh39zNp8UGFU4aN+nzxxDCPDwzIdEV5gdaVeD/btec/LR3UadfKVJEjiGJ8d2p+iBQVdJRcmUBcv2j4OXQGPwkcfHUSpVEqltYR6kn7vnvfsOkk7VduV5rQ0O40jn31i06COAMiYcLU0NDToOfVKC4AA8OGH+7zPfXBNQuDP//zPY+nSpejp6cHOnTvR398/vyvUP+GgBtb01LS0vZBCk/x0F3LxDpgYn0Aub7sRqKvT5VzbsNiP053KIWzTgkwWxWIN4AmyuSwasvWPSGZnZ5HL+20GfTaNcRSl7K0Mw1P5xGe5XEYmQ9y+SBzHylVtB1iqCn9djfkMyuUi8vlC3cnsjvRyuaQDu7tp3duq8wF6xFIql9DY0JhS99crs1QSJgFXm2wqtxtCSaeHs3ngykmxEDS8EUicH6VyCQ1utBC16DuMBhA2UgXnKMVdRO2jJ26l9eGuwOdSyC1HfBef1UoZDSTIfCVKtPuRrHtECtFWsdOVQhGxTXK1SKId6R2xCyqNGl+MMaEJBJALAs+CIcZ1uVLRR3vKdqxAcWY2fTnh3vRSSxgwNEp7KDUmy+WKHU5RllGRQmAQBOhsMPO4Uq6gUDAagEqNGvgzjYN+L/vJEtTmOX3UzWN1M1jQRAh3ccKRRFU0NTakaK00dZU41pEcwjBAJolRyJub8HRMufaknAO1KELEmXYzZW3CHSkySeyQhLM1EXJPCbfuLVjtgghCUK/KC23ZkKUGO+ciPR3v9GTA579S1av2FC4vFWWIzwpx42PPRyNg0HwVOR7d9+5So/BQbn8UEkV5HBwEDK25rBHsiCAHuYGpxRwZJjZfvpWsQkJwmjHCvadTiu6FQmFefGcuoMJ4Ii8w8biGQkPB4nH1yuQQ7pMKjosrSkNXCKtU6rsiA4DRmSr6J8okr5yPdfgVoLTEDFGtauEyF3DOBS51QqX+34VrOg7+/d//ffzDf/gPRQgxxtDS0oLvfe97V8/4EwBXkyM452hobER7x6LUAqMGtAvLli1PG/mTbQIdlEmSYM2adfXr19lF/lwuj0WLl6JSEk5Cc/lcXVscQNgvtLS0kXLIezK6Vfq16zZoNxb1dpgU1m/abNrDGHjCMVYSjmvz2QAVqQlsK4RoaW1DIZ+z1fEKBY+mdMWqNfroReVxaU77ZNPmq4SYI5M5n8tjzbr1njR+dtHdvRSLFqkjLBJKzSdkwISY8zFA9V3lj+MY26RLIeuYBWSNI41va2tHc3OLZtacwcvo1O+169ajpaXFws9NQ9fSm2662TyjbaVtlM9zuRy2kNBo3mMIbna6y5YtQzsJL1aqCm1SLhPom4e6qYxh27btOj6uYPKJDu1GIWAMSZLgpu23AIA+lrPQ4PZRcGtbOzZsFAy0EpsINxSIfIH16zego2MRODcXVBpp1BaHrirEHGD71AsYQ54IVICIXrP9ltusehMA5UiYVgRhgEUFE4O2e+kyvTEVR80Cf3Wsp8ij2rz1pu16QzUXy1P9d/udd+v+n6lFSDhQyDBLyKkmou+yhSas31Q/HFU5jsWxMRenA5s2bUBnV4fEDylBwYVbbt+BVw9MiD7LKYN81UDSICloUVOJYhRLAU3kackHFi/pWtKNrq4lYExdaOPIhvaGxPA3MTeam1tEXdSpOBfmBIFyMQTRrp333Ovd/7tCHiBcEN166+3etFTQU7B+wwYrzrAPFO4BY9ixc5d2iQUuBGTlvqs5lxHCLFOuizjuufc+AMKUIU6EM/cgMO9VIzjn6FzchY72dkvDBma7H6M8Zuu27WhtbdVCEge3b/M6bd197/1WOT5Qgnghl8PdO3Zaaevm4Ryr16xFW5Px46e1+u6cls937txl/K46aRkToVKp1ly5wqGgQx8qTaC0CezoWIRVXe0WznPJKDdu3ZZSlIg8/uPgXbvvr1+Yi+N8El24cAEAcPz4cfT19eHkyZM4fvw4enp6MCldqvw/HRhjWNLdrQUA9Qyos5gyYMOmzUhBHeEximOs96RP3TqV+XP5PBo6OlEtVwEWIN+Q9w4SVVv7okVaC8RsNKzRpR4vXW4MR+spL/X7JEF391IHb2BCCoG5TIgKMTzOZbO2cToVND27wPa29pRNoFqYbHsU4TRTGe274FPX8yRBhxM3uO5ukXOEYYiGxkZv3/vGQUNDo9aE+MqmzKlaraJFbqBoWuYWLH9HUST8SnKky/eMhSRJtD2ND1dRl6Eno46YrzIIKuWy1gDUTU/oXqmUkc2buK7K3ky4EHIWXs4RxZHGjUb1oK4q1LdqtQowRnyx2WlcxlguFhGGodAKRgkCoK6fPQCYnp7SDnHVJY9GjxZeLXbUJES5TQLEIpHPUhqL24+JutkI0zfFKEIi7Ws7CmbBn5qc0HMjTpQmEGjJ+1n35OSEvtWqKUC1URoXYYM4Mzuj7ayKUsAsOOHWKrG4eRtXZpG3FaLWPKnE6jhYtKM4Pa5x9/IuZwyNjo4IbXEY6Ish2jZbSjlczoVSqaTD4wHiONhoAo0bItVHExPjqNVqcqxxVOXxaDaTdpnCIDwKZDLmxjdkWxOuNIGGJyVcRAyR8pBX8AN5NzU1hdnZWb82j9mfgCybJ1pzxZHmM5Q/9PVeseouReI4OJMJ0JCTt8plOdVqFSPSvVGSiOPgMLD9BNJ92MTYGEqOJwdwbt2UVu1kDLjcc8mMR4k8xV0LV/L35cvp8HguiM0ZR608i+mJ8bqaRZcHDg30o1qp2PU6fUUVCOdlJCiVzk3b3phFR5PgE3Ec4xKJpGKVCTlPmNkkT02Oibnq0R764NLFCxbfMOX7+faFOhFmfDAvIfDXf/3XAQBPPPFE6u/JJ5+cd2U/TphrjVPM4/133rLiaNLBRRmEKk+FmLPAFerk5+jwEE5Km5T5wKnjR9E3PIyqjOnY2JRLRdCg+Hy4931bszTHoKrVajj44f56KKegr/eKFSoIcjc8pd1VBKjWYgQBQ3M+wJFPD2v3IBRPXqeygwf2m8micXLaKXebM9PT2ranHtCJfPHiBQwN26HR6glIAHDowAepieUKbRRcP35q0fGlnRgbxcULF/RCpfGwpDTz9czpU5iZnZ5bfULgk48/0t99RdIdabVa1SHmaCaqQaPCVH9/H4YGB7z16gsTMOWfPH5Mh/RijIlIF9J0IBsyPacUnseOfKZxVcc9ShPIJG5clj87M4NLFy8KoT2wL3H56H758iVMTYnNajUR/uHMDj6tSTx65DOtfVOhyITz4nQlnHOcIHSMpRaTAchIA3u6uIyPjabpyDkmKzXEcYxMJkBj3pwuXLhwTrv9ieJEhv9iaGswMZxVOzjnOH3yhH5uoes0kjGGSrmEy5cu6k3MrNTmN+YcwVXa20UzY0iKM1Y5lEcWpe0ZOJDLBbh41rat9Q1jqk07e+Y04lhorFyH2a7WZmp6EgMDA3rDW5YCbCztEZUtpqq09/JlTUflADxkMGPRka7Onj0DsPSRqDLsD4kmMI4iXJTxdKlgZ887U8bY2CjGx8e8pzA0vXp34fx5xLG6cW5r2qQ8Z8F5KQCo/MqmUwmBChiEGdHggBiPCRe3u+lNeLE2QvOswcEBzEohUAvInkao9p4/d8Y46if1ArB4gHqnwuMx553Fs7kQVqvFGUxOjlvrok9AVkqBfukWTdOR2f3i9kePR6jTOMAWwCqVCgYcP8DuOACkCx4wTI6PY2ZmRm8krqaI8QmYc5lJ9XpiTdeDeR0H/+hHPwJgNIJ/3UBrIOJI+vlJ94galOoItVarIZN1wrghPQDVHCkWizpMDE1Lj+D0bo5zlIqzQGcGtaqIG9zc7NcECvxNOyjjoe9oHaVSEU1NTZgLKD6p9HL2CHcVIsxVLU6QyTC05ELMlEpobLTL13RxmMZcAqhrD8gBzBZnU2XTol38i8VZLFmyxHpu6ra5gDqydaORUE0ApUstinTIonq7ULpDLxaLaPDYVFkIEyjRMUM4YT0hc05ZUWZQO/xyqWTdflRHNUymc2lfLpfQ2NTsPaYRQph4qm5GVsolEbpKgrLZymcDEa3DU4ZCU0X1yMvQaEIgJeOxXNK2uL6NERWyxXgXbeUQx7tKw6jS+dqk6FGOEmQCE3PVhapjExrLyBUALFcbCkXhhoq4lZIvh2Zr4AlHNhuaPFChJkX6Wiy0jGFojktdnFU+63kqoSBMWYYwVHjom8eOexCl4eNRFa115h4AzFQTLaxksyHiqGYfp5Hxq/Clx2RxzBHHCQr5DPI+FyUSb0WXhoaCGRMy2grn0EIgnYOVqrGpiuVxcC7DtKsd6gi83txSY5Nz2wyhUqlYR3WKragmuxqkqkyfcHNcSFmRyx6iqIaGhoLDu0x5Lj+gR8EcQkBOkgSZTGBcC8n8tWpVuzcSLn5EfiEQ2bwXAOJazdh5ko1ZPd4TBKHRBmNuvhVFUSrspZsuke+qUQIkERobmlJpXEFQCbE8SZBvMLi7QJcmxuzweN62EfrUajU0NxlXZD4hU2myxfckNWbMd/8Rr2ufONfpTfYa7AevySZw37592Llzp/Xsu9/9Lr761a9eSzE/sXDHXXbb9CQmz1RnBQHDHXftgNPHqe9qICzpXgp0L9UPUgPVweX6rTdj70gMVKtAkEFzc/qYT+flwN277k0tavVkjUKhAbfcdqfeVc61CWEQ9oOZbNYa1EkijvgYE9oOZXjclM1gy87dc08ggic4cN+DD/vr9jRA+8Cqh6/DgLduu1n7cnSL813+eOhzj+rv1jEj1bLKzyAI8NDDj1iCnk7v+b16zVpL6K9LfPlsx67d4uIVrZTZPyk88tiTVt2WcOO0vbmlBffsvs9qm1o0tP8zbvxubblxmzwaMu8s2uhFSTx/5PEntY0fYGy2CpnAckSr0Hrsyaf170Qe2TU7Dn/Vt2XLlqO5fTGmytzYLpG2aSYq89559055HCxuFYaBsTVMjQEATzz1RQAy8kkkNJK5TJBWSUC471B0B1R0DWEakVfxa0lbN2++Xh8J0U3I4HRNHOdnQxnSTuR56POP6UU0ihMd/qs9b88v1Y6nvvgVG8E6qzPnwpXIvdIllpjPQpDSjpolaUrS/cri9duwjoTRcslRkkIg5xyFXAZPf+mr3uMufcmCCP6MMex+5Gm8+MPjOi7yXLB27XqsWbNOl1GOEnNUGzDkHVdJ997/oP5ei8UmQ2mlKanUaPjyV382RT4uNWUJ7IshTU1NePTxJ0m6+hsyxoCt225y5g+83xV84alnUmXQYevygS995WeM9pcDxVqiNYEuXZcsXYrOJd0ImAjVGCdi86X6iG4sOIA7d+zUgittqyUwkndf/PJXUzi666r6nclk8NQzX/LyN72ewhxbr1izHtu2LE8Rm7BKnZcDuPeBh9HSkLGE7vSaYJ498yVnLjnto3nb2trwwEMPW89d4Z7DHAdv236ztut1u9wn3D319Be9uNSDJ558at5pr+l28C/90i/hj/7ojwAIyfdrX/sa/sW/+BfXhNyPC+qp3hVwzjE2Omov/FAaAfNbpBURDqrViq8gWzMof+gjoDnUv/TZQN9l9M9EQFQBwgxaG7NGwGPKkavAKI4jDPT3pQTA1OCSn2OjI9qepi4upL0XL5wXeJN0ccL1jeBsJkAcJ8hkQrTmsrh0/qxFrxQQGhdnZ9F7+bKZOPALf6q8vr4rOqLHHMVqOH7sMyPAcUMDtx71Xbg/sAuqp2GZGB/HpZ6LFn6pTQP5fe7sacxMTxvhiiPFwCnhDx88gIAFdoHcLwAmSYKPPzrkrZcjnaG/vw+9PeLIQBi7E+EUhhGpcj795DDKZXO7ndprUtwEXWGHC+NcuB/hHI3Z9EI0MzuLT+VRNofwVRZz4dA5IONCwbmzZ7Tdk09WcJnovj3vIUkSEhbNHOepNup5BeGuBhCLTSVOkA2ZsO1TdZG5NTIyjKNHPtV1VaWblIABWSlsqoUy4SIU1eiIcZkh+oZjvFgDOJDPkwgjAN56/RU9r2sxRxQJm7k2KQTS5idJgrfffN3elLjCgmwsYww9ly7i9KkTGhcV0aUxG1pzvSgjoIyf+xhcmnn4LkzNVmNE0kdiQy7EG6++pHmVAkbaTcfQ9NQU3t/7jriAkiEh8Sw1mink2LHP9NEhABSriR7HmUDYBApBRtT52ssvIpHH9FGcIEqE70c6Fqlw8qMXnreqVECPgwV+wn7w0EHbLISTvEITZd7t37cXgwO2iYoqCySPguefe9Yu20GK8p1qtYqXX/yhNS5mq+LGdkMuk5ovZ0+fwvGjRwBI7RrsUHoWDwHwxmuvYGZmxua1LK3ZV+1R4fE0z/IkVOvV2NgY3nn7zTonDQZU1J/eU0fQc+mCv24Hb86BHz3/fYt4VFhTv9WzJOF49ntzuCJzfvdcuoQDH35gp2Hpvoqlmcv+99/B8NCQtTbVrYsxfP+7366fwElbLBbxyssvzis9cI1C4IEDB/D+++/j8ccfx44dOwAI7eD/E0BNRJ+gIK7VVy0bAMuAHvakZgwYGRrScYZ1HvnSFi7E56UL50D9mc8lkALA5YvnMTRZBuIIyOTQKt1G+LIVZ2cwSmKXehpo8IPwmVWxFnSDv7vDTDjQc/E8QqXVkYSMEhFhIAiE3zdleNyWz+LKlR6A2fY0GndHyJ6ZmcL01CRdX+dUcw8ODCCKojr9SCa0fHblco/lWFq1i9bD5XfOuY7v7DtitBZXCCP8csm2faTgojg4MFBXoEztNABjy+i843YyMIjj3emZ6RTzq4fU5MQ4ojgSAg1j1hj30X9wYMDchFd0czYGgHGOOkbC3THGMFsVmpqGXKDrU3UWZ2dRqVQ07jV5ESEb2G5QVFUTE+NIEjGUApa221Kg8BwdGUEmk0GUiIgIWR0b1/Q/FX4nJyeEMEqODa2b+UR4n52dQRQZO+LpaoRIXjooSDcu9IhpYnwMTIYqo2NsqihslfL5jDW2aVjOWpRI5+xMGvibdMLGr4yoVrPmkm6gM3g555iZMWXH3MR2bspkrAWxVBWXEuLiNAoNyj0Is2iWcKNxYoyhIZ+Rlx8M7/FpdRV/LZWKmKqqi2bydqo7yAnMTE9bodHUBR7lozCXkf0r883OzOjxq+IM5zNM3/Kl5EmSRG/wGYwiIGBMh6YLpAuVhItNjNmYwwtUIJiZmRbuvOoIfW76aqWieVu9DTsgtEylUsnwXvliqixsAvOSrhSKxSIyoTCBqkYJEnDtSJ2WrX7PzMwgm82ZEyGX4VK8OFCj4cvkOHSTqraWSyVkM8bWVaWjc5xz5fCdI66VkVMmWWQu0+qsMRpH1gVEn0ZPPY+iqK6ihju/AWHmQU+/3HVUmz1IGaRcLiGXn/vGtymLW3cVrpbWDTF3NbgmIXDRokX4pV/6Jezbtw8DAwP4jd/4jTn96Pwkgq9jGWOYmZlGS0tLXQHEHbizs7NobmlOvbc+1WLHBJNrbmq2dk1zyYFBGGJ8pgLENSBbQGeLPA72ZCqXS2hpaTM7QroDIZxJrQW1atXyKK+AO+WbSRIgl8tZQlwUJ6jUxNFUyIA4Fscr+QyzbR+J4EJppX5XK1W0klBnc4FaeJqbW7xMky626nVTc7OOflC3XFl2uVxG5+IuW8iRadwFjEEsFu3tHWmtm/NdfYaZDBqlbSV30uqCycO21nbzXGXy0JNDaOa7uvzuO1xBSSxogb7B7Qv95GpJm5qa0NDQ4KUNzayOkDoXd1kMX106UGGrVPsZgIQnWCxxZxBMnnMgHzqCjvzM5/JolOPXpwlUGibVhs7Fi8EYQy2SDquDtDZSHX/VajUsX74SDDLcVsJNtBC305iIQtC5eLFeoKekr70wYMiRo2DV1qbmlpQ9bsKB6ZLIl8+FFn9YKsPdAeKWZ62WpAz8VX/EcYzlK1bo+tz+cUlVyBe0N4Q4TkRYPwDNOdtKSBy1cjS2daC50e+mAjCRKcCEv9Dly1dobZxvc0EX7iAMETQvQpLIkG8efGkjWlpadVQEDnFkrfpA3MoOLUF1xcqVugg1vgoZ29xAESyKIqxeszZVNYe8eALB8xSODYUGLFnSnRIqzFyyFRCLF3el/Ov6BBH1bNXqNV4BUz2jc5YxhlWr11hIq3jTTYVsau1paW1FR2en2ETERhPIYfMAJU+vWLECuVzW0qLVW8c4RHg8zY8lwawpRDLn83ksXb7CEt5SZTN1sQdY1GFMg3wXvNzfq4mLNkpf31oCcGzctKlOy9J90dLcgiVLuq33brmcCx+HDMDy5cutMK9XUwqJkHQuDv5MmUzGHgNXgWuyCfyN3/gN7N27Fx999BFOnDiBRx99FP/sn/0z/OIv/uK1FPMTCYsWdeLeBx6aM41YsESH3XzrbSmtk+5zj6bk8ae+JNJxcxxs7yjM84QDn3/yS/jun30kNIHZHFa05eoOlCXdy7CkexnRcHkYhvpkwK133JUS9gJ3csq0CRch43QoJNmgaiRuKqqdZZJwFHIhCrkMPv/4F6w6faDKX7FqFVaSAUsZgA/uuGtHSrNH0NLfVfs+98jjqXb5gHOOQqGAXffer3+r9O4NOFXemrXrhK3ZPMoHhF+zrIzTStOm2sxFnffe/6ARDNWKQxYaWm9TczNuue0OLx7m6M48X7dhIzLZHMBhtAN1JGXOOe7acU+K6VDUFN4JB3iSYMfOXbptnJtwZ635jIUch4gZ296+SD+rSvuugrTjc8fy+o2bUOZZlOUmxIcvxfW2O+4CIBcPeVzo+tzUNAoCbL/lVqFVqcaIEgiNEZFKKJk6F3chlJozzoGZWoQ44chlpSbQwe26664Xbn9InRxAqSJc5DTmDUtmjOFG6VeSAZisVqVGx1we0UODMeTyeWy67gaTn1bsbDAAYaesBNJIOlAOAxJVQuJYlJcuutZuRla7TbHLEg6YTWSKxgxww9ZtmAvoaUtjYxPC9mVAzwgKudDudM+kWrZ8BVrb2sAYQ8K5jEstysqGgXB4TdLT8HjFKELMubB9pKRhhpabNl/nxVlFWaKbiJbWVjvUmaSNJcwQZFauXFXX8brKT2m8YcNGQjM7nf4uP10BgAOYLYsNhnYPQ6C9vQMNTc3iNCGKEYDJTY+DlCSS8HXLNC5irjmbZPk1jmKsW7/R4OkwOrpecQi3aEuXLrUUGT6OVJWbko5Fi9DUnL4YYnAz/IcDWCHD3VH6WcIso58cK1etTj2nddC2NjQ2ouA49KZ4qItmytF4V9eSa9LW+WIBU5MMusEKggBdvpjzdeCaNIHlchn79u3Dhg0b8OSTT+K9997Dv//3//5aivixwVySNuccp04exwVpy6aezVXOm6+9grI8wrJ23XXyXUvIuMnxUXy4913MzlaBJEIml8Pa9vQOXMGRTw/jsnOFnClkU7tv4MUXnrXUy65A4Q7yH/7gu+aZJECxZrQS5YhrJjMy1I8P9+1J40Lw0TyBAwc/3I/eKz3z0o4CwHPf/07dvqHVMAhN3fPPfjf13pdb2Uh9/NEhewcM/46LQYRbGh0Z9uKcoicDXnju+7qu+siLv3K5jDdefZkUAFBhkDuVnD19SoQXg18LpF09yHdvv/m68PfF1Dt3nNi/X/KMX187OOeYmhzH/n3vW8+LNWFv1pbLphA8duQz6apE/Na2Sc7NWlXn66+8hEjGIQ7moVl/9aUfgXNx2UQdM4twc8zKxxjD4EA/Pvn4MBgTDq65FBZE45x6OHD4o4Paxg8AZqriAkzImLxgYTvSfe3Vl1L0ixOOSkUczTdIAYhB2He9986behEbr9T0cam6nUrb3HPxAs6ePmk33tk80AZ8uG8PSrOzAIQdZk1e7mrIhVrDIxxUi/7o/2yfdZHCzGMRF7oSqRi1AXJJGR8dMCG9PDKozssAnD51AlcuXwYArQk0PNXNBLz79ptIEmEHCFm/0jrmMoEeOyrr22++rse0sU8NNS1oHw0NDgrbYKTHuLIJpM8/+/RjjI2MGPJ6hAUK77z9ptZa2rSwfzMm7L33vP9uKl09Fnjlcg8unD1D0qoLfAythYzmRQoOHvgAxZlZcAhXMoAJ1egbOu+9+7YRsLSgQ53qm7TT09M4/NFBW1sJ+ztVpJw9cxp9vb3etYjSshqJuX/64wPWyYRLIwoJBz6QYd20BpUIdy4rGx0ZwYljR63Tpbng2NEj8k6BSU+XX8V/lRJpn8SlHr50fEVRhA+kjbW5aEXmoZO5t/cKLl387+wnUMF//I//0Tr+Xb16Nd577705chgol8t45plnsHnzZtx888149NFHcfHiRQDA0NAQHn30UWzatAlbt27Fnj1GiCgWi/j5n/95bNy4EZs3b8azzz5bp4a5od6kUVqDifFxK5wT4GjqQJkeMDU5aWjhmb3cfLVsTEgSw3ic7FNTU8jm8iJuMOfIFXJY3kSukzttmJ6aSu0suSrYoyavVatebZrbVg5xzOizqRsvV+VlkADFirCfaCpkUS7Oau2Cd+Ko3b/8OTszjabmZueIQKaZQ9jzlW3tkiHd8jQ12VoxuEKuqWtmZhpNzU2WhsYqn/xxAKXZWTQ1EZMAwuGsBZpwUoupyURUqFB/xdlZNDY32QOPOX1PfhSLs2iS7jtc0tCxqN4LdzX+kI/upQ8hyPgFPl89wj1Mo0aEA3ohWtyQT7W7WCyigRyRluJIXKxQC7nTEVESI1BuLEi9qb6FGL9ZGYJMHSPlAnMM6PZxqVhEozzynKmJca1DtHnoXyoVLc3edCWWNDOh5lLjiI5TLo6dKxVBH3pkV5J9pATJoZkaOAcaZFxdCpxzFEtFEfIQ9li0KzcIlcol5OURfyT9G2a1Q2GRplKLtRCYDe0ILgxqsWMo1xJE0pVMEDDkedUyC0nTwIRh4wCKs0VUIGyqGrOOJsojxEbKPRNjloNuzjny2dAWVh3NcFH6r1QOpV0ky2UROhIQAqXSuiqn2jSSDWPCMXqDHDPzEe4MDa7+vFQqIZ9PuzVx86ruLpVEnyqSibkXiZBxhdCak6KtJtSkcp+UJePWmlMeYcnwFsVHzZ8KNelrv8uTAEFHb1tJenARghIAGE/03DaXJu06NB2SRHsSoHhSTSCts1wu69CRc4E2hZDpU30PNU9EuoRzy8ekXg9cupKCROi9htTzelCtVDQd5wPXdBwMAM8++yw++eQT66bgv/yX/3JeeX/1V38Vjz32GBhj+OM//mP86q/+Kl577TX8zu/8Du6++2688sorOHjwIL7yla/g3LlzyGQy+OY3v4l8Po+zZ8/iwoUL2LFjBx544AF0dHRcK+peUJ3T3NyMdieyhBpAvrm6cvVqowJP96A1cKMowvoNm+hrBweTmAPI5fJY3L0MldIpgCfIFXJoyKZV+YoJL1rUida29tQCSD913RBHgW456p2r9o7jGBtpmDaJ/Gi5oo+mihVxUaO9IYPGpgLa2trq4mELYMDSZStFeCaKo8O0KWy+7nqKhlWW+mTk4WaCu57wMBOTTqqOjkV6QWeOFtVdhwBgzfr1digfbn9Vgp4qZsuNW/XxBBjTPvmsCuRnLp/Hxo2bUwXWy7J06TK0SPsYnzDk4n7j1m3CHxgpnnNuhchSkCQJtm2/+arCubog0NLSgrbmDQCH1MiKS0SZgKG5kLHawwCsWbMWHR3mOLhM4rpagrWsf/vNt0ofa65UZqdTWN5yqwjTFsVC45kLzU1fRvIknGNR52J0LFqsjw0BoJAxNmruwrh58/WWfa1ysxISNyUgeW+51RzZK5itxKhWhdF6S8Gw5Fw+jxu33qR/jxcj61iP4sEYw4oVq5DP58Uz+tLdzcqPm7bfoo3ZozhBLUrQVMhajppL1VhrXVdvuTUVbk8NhVI1Ri0yQuCSznZsXrPGSkvnnJ7jsoB1GzYiGB4Axotp1xlkE6Tgzrt3yPq5vMAjNM0qjGVItFlxkuCunffoosrS4XYhTAvTgAgxp6ISVWqx4UVcOvtlNFwfcOON22QoVZsmgF/Q27Fz11U3sQry+Txuv+NO/Z7yZ0oeNb5Wr1lrLvFJXMrSlVdL3h6PAHDb7XciJ01UqomYd66gr8oJGLDznt1GuwXjF9RtA+fCbcpWFSYz3TTCh0X6TZuvR5Ozaddthpl7SmN52533eDZDpmxaUcK5dolFy03VIwvsXroUnYvnjkwl6hOF3HTzLWhra0tt8DTecgMRy/QqVF89+YJCoVDQ492dP74j4TVr12F2ZqZueS5ckybwN3/zN/Gnf/qn+E//6T8hjmP85V/+JUZHR+eVt1Ao4PHHH9cT6u6778b580Jl+Z3vfAe/9mu/BgC444470N3drbWB3/72t/W7devW4d5778Xzzz9ft55KpYKpqSnrbz7QvWw5mmncVaV2hW8Ac2HrAC9/kinkJxeC1Jr1G/TocdXPVGUMiDjBhbZOlItlgAVobG7wGrKrMjo6O7UzXFU+xcMWHDi6ly7z4qomJIU4itC9bHkK376pGuJYLEiz0p6puyWLTJixhGldv0fzwjnQ2tqiFyKXhqmjmDhGBwnrZ9LZZao641oNHYs6U4ITnZhuXc0tLXqXNteei8GEjFO/3XoUPoAMGdfSqhfC1MR3KqtWKtZ4pBno2q6+V6pVoTUiz1IaPfIu8GiC6wnepVJJXwpRzMeXVt0MrlbKlqPoKOGoxgkyJByYpe0oFo0DZQDVJEGG+PKjNakLEMoWiZHnBg/zvVQq6VuktVjeUM7YFw/UWAgYw+zsjLbVKclbv3k3Pjj5XizOauficSJuqXIuBKGCq0FPEtRqlbTmv1xDFCVgAUNnoxECi8VZ6zbjREnMsyZpN+gK+pMT42Iu+YQ/Zj7VZm9mZlofqVdq4kZ2Pit9Gyr6VWNBt1oVIa+KW7E8vVGbrUYinYxRm60VwZ0wV4Y/pcfP8PAwqjEQhiLub2qeOON/YmJc/xZaSLMoNuZCYSvKFB2LqJTLus7ZmnGFQwtV+I2Pj6V3mRLUGFe3gzmA/v5+hKG6qewX5jQNOMfwXJ4cHJienkaRhGlTdZijWPty1MjwkDUXYuLFQdMVpq2DgwNggQiHpwRpGqoRMHymVqthZGSECHoCEePOx+AHiPHoek+g/JLJtIqm/X29uj71SYU6ZTpQkTaB40MDFh93hz39XS6VMDZmZJV6wrSC4aGhVIi5uZRwly5eQBCE3jTuWpAJA1zpuWTeX0UKnJycFGMS9TfgFPr6elGVkVHmA9ckBL755pt4/vnn0dXVhX/1r/4VDh48iKGh+Q9oCv/m3/wbfOELX8Do6CiSJLFuNq5duxY90odZT08P1pAdJX3ng2984xtoa2vTf6tWrZoXPm4IOEtAcNKWyxUc+GCfHsiWAEAGufrZe/kSLilfe7r8+kLGkY8PoX9iQsYNZmhvb9ALHhX+FHy4930vni4ugFhwP/vkcKpOV1hUzy73XMJA3xULXw5gZFYMssZ8RjOZ7uYcPv34EGrVqp7gerEl9KTC6gESvu5qMDM9jZMnjqWe15t4ly5dwPDw4JwTk8Lhjw7q+Ixz7dBUf/tCxqm87rOpyQmcO3umvmBpSW/AxYvnMDE+7khA5sOt6+OPDtnuDwAttAE2jTjn+PSTw/L4pH4MXQXj42O4cvmylc5cZLKFL845Lpw/a8d1lVoiZbAPbo+3zz77RJcdJxw1IgRSgRYQWsljR4+Ac1hCIhXcCRkxMjSowzlV4gQJoEOKpRYLznHm9ClUq1UkXAiBjHmODUmeY9LHGiDimRZrwjYtEwRozIbWAl0qlWQ8UlMfY8B4pYpaLUEYhljaYm5eDvb3Y3xsVAi+HChWhGaSanQonD55wo4v6hk7tN7TJ09IbQ7HTFVcaClkQ+uofEa6vEmqJVTGB1MXahRMV2siXcIRhgEqY8NaG2HxSe6xPwVw7NgxVGMxFlVIPF87VB+fPmVsH4uVSNvqAUBrIbSaPjU5Qfy0KvtUhoLTrwrHnksXrZMuBQmX7QuMLSoDcPbMKeuCEhXQXIhkiLn5wsjIsCXwKgWCe4ypxv35c2eRxLHu72qUoFYT5hN03CvUzp0R9oNxIuIpZ0OWulSjvheLRfT391r40TnkHrEODAxYfEDgm+57Vf6ZM6eFmYdTL82RcI5yHCMMGAauXPQOcYa014DpqSmMaf+cSIHSdCro6+sV7naQVqr44ML5cwhDvzileF0ko80wcPRIUzhFt7lgYnxcrAUe8J3O9F6+PG+XMsA1CoGFQkHa4jDUajV0d3ejt7f36hkd+IM/+AOcOXMG//yf/3MAaQ2EO1DmMoJ04Xd/93cxOTmp/y5LY+O5wFffXAvjzPQkWlqNewIuClGFmedyUkxPTaG1tfXqIr/EZXZmBmNxFrxSAYIMlixulOGx3LTp/FerYnp6Ci0tLZbMYeUnzxmAmekpNDW3GK2L3G1PlcSC1JDLoFqLkckwtOWyKBZntRsUCxdX/w61k7R3eyKpvxEzM9Nobm6xtVtztHdmZkamp2jUzyBsKWy70HqatCQRBvBUMKbpXJidnUFzc7PezXKa2Hogvs/OzIpjRleq8dQnxpqNj5c5yrZUq1Xkc/m6/Q8YTQ9jDDPT02hqakodQbiZhNEzk3ahzXpuTJeEIJGTIePAUHf8RbE42ssGtv2ZEh5mZ2fR0NCIhOCnSOQKpYBw5dTUJOgubj8KOy+vNhZAcWYWjY2NULdixU3XTApZTgkrQdyYN8fBjdmM1X1FGfLQFsiByaqIGxyEAZY05rVQWyoVdYhEZeAvNDp+zWSpLDS2dXcw1qAwwhhjDJPVqtCiZe0lYVoKh0mtjNZmZSrh0IJzTNciRPKiRiYTIJfEIiwhqz+vGXlWjWLU5FFyWy5rp+X2NIlqNWQzWT2+Jis1xOqSCID2gi1EKvtXdSRXrinbtzSRlNaI2svSjZTZOFGBzFOOs/lSnyVS9jyUOiJsYEODX3Ah3xVtdAg7idJ0OZIbDDmOQZUJDExFo5GO0RmTDs5JHeqvWqloO0/Vr5wT7Z5DhlqtKuzkKJ6EdhRvxoAkjq8aqo1DRv1hQFbeYvfT36RPOEe1VkVTY6PVb65GlUIcRbapjwRXuNTavUwm1UfWugNhqsDBEdVqlquwq63ZtaiGhoYGr1zik4eSJEF+nj4IgWu0CWxpaUGxWMSuXbvwy7/8y1i6dOlVO82Fb37zm3j22WfxxhtvoLGxUftLGh4e1trAS5cuYfVqcZ179erVuHjxovXu8ccfr1t+Pp+/Zt+FnHPcvXOXJVX7GJYa9E1NLdp1g0mQ7kk10Nas34CGxibAFQDqwC133oPnr0RAtQhkcljRqRYCexCK8jl27r6ftGXuQdXW3oGt22/R64RH/rC+b7r+BmFszs27KOZaK5HPipBx2WyI5lwGu+59EKHj360eYpwDDzz8Oc/E8tsELu5aoo+ajUBiF0+zbbvpZhQKBd3GwCfAkDoffPiRukKimytgDA9//jFL++Rbe1W+5ctXorvb+Hy72nbgtjvvEkekpOC5xs7nH3vS+95dKBhjyGazePDhR8R7VwKHTX/OOdZLG9KUAEhWCsYEkwOAXfc+gKZGEx93tFRBwjkachlTLkx7VMg4QIZd4+IGbyYMrP5gjKG5uRm7H3gYUxWkoj2oNPTZDTdu1b9LUYyAmTjANA+DWCw+9+jjyOVyqEQJypE46mrIpjVG6suTT39JP6vUxNEpII7UCo5A1dm5GPfsutdqDzjHZClCHMXINWawRMbDZQBuvvV2na4axSjXhGaytWBs5pQPSAbgqWe+bLTBcwxK1Y/PfPlnRFLOMVkRl06a86EluM1GkfARuGgptt+yxLeXAwAUaxEieRSezYa4Z9dudLcVbHoh3T8KHnr8i3jzpZNCCMyn1xTdDA5ksll84RlD98lKTR8pMsbQ0WjGGWPC3Ysa01GSWBcg1IUfkVbU87lHHrNMChSNEy7CxuUc/4Jf+Zmfk3SU84DDy5sAoLW1FY88Wn/9cuGm7Td7eZY6ZbGfcTz51DPWHJssCrOdMAzQTOafgi99RYyBWF6ayocmnjLlJwzAku5uLF1qfOFB09yP+5133a1pkBCElR2hppVsjw7Vx2xbQ4oH59CbhSee/rJnbBNhneC5avUaNG/ekFqGrL0Gebf7vvv9jXJA1afD4zldRYVppQlsbCjgiSefMqcX3JTjo+V1112f4oM+jbqChz//yLzN4IBr1AT+xV/8BTKZDP7oj/4IN954I4IgwHe/m3bBUQ/+9b/+1/iLv/gLvP7662hvb9fPv/rVr+Jb3/oWAODgwYMYGBjArl27Uu8uXLiAd999F089Nf+4eFcDzjmmp6ZQqZStnXHdG6IMGB4emI9ST0PPhfMpn0D1BC8A6O/tweWRGekjMI/l0kegu7MAgOmpSX1coHc56gfSjKLvyhVt66DqZk46OvnOnDyRstmrRAlK1QhhKPynxbEQApsyGVw4d9rfTlejxoDJyXEMDgzYi/Ecu51LF8+jWCpag9+nHVWPPvv0Y8tIOqkzaRQcP2aO9ly6Mdh0Gh0ZwZXLl6y0eqMAm3kyAGdOn7SNdV2iw8700YEPEQbpW9kKqGsUzjkOHzrgFUKZmxHA0NAgei5dgA9crTtjDEePfKrD3XnTM6OFCxhwYP8efazDGMNYWWiZmnMkHJjMH0URDn64X6etxcK+K0uOg+mYuHK5Rx+neR1FO5+HDx1AUbpBqcq4wfmse7nBmGm8/+7bYEzcOFUao4ZcaAvJ8rNUKuHgh/v171os3NAwALmQpYTNC+fOCtshZuoFhDPfRM6hFnIUun/Pe6jIuVqqxlLjHqA1b8J/JYRPvfXGa2mCWA01yExNTeHghx9oOo1LzX5zzo6oUIpixAnH7MB5lMeG4QMOEZosSeTFm1yAg3vetI+m66Ek8X/79ZdRi+2QeBqcfh4eHMThQwek4C6OogETLaQtlzUad87x8eFDGJTHwUJbq6LGqI6wBY0Xf/iCV1hNuBnjCpKE48UfviDQJGPbFnzN955Ll/DpJx+nnqfpIj73vP8eRonrkXosjEPMExrujnOO0XJF8ucALbms1Z5yuYzXX31JaNdiEY83lHRRQ4W25czpUzh+7JjGQ6VxT6nUu9dfexXTTog52mTu0P25H3zfem61TX6P4gS1JEG1OIODe9+xaEAFKpcnH/vsU5yzXOdgTpr+8IXnUCN2dXMpVxgj4fFcoZKkUyYLoyOD2Lsn7VWlXvkHD3yIyz09Vz0FVfCD73933mmBaxQCu7u7kcvl0NDQgN/7vd/DN7/5Ta2xuxpcuXIFv/Vbv4WJiQk88MADuPnmm3HXXcKJ6x/+4R9i37592LRpE/7W3/pb+LM/+zNtbP31r38dpVIJGzduxCOPPIJvfetbWLRo0VxVXRMwxjA6OozJiUlL+6Hfe/JcvHBe2F1A7cbt9+7kuXjhnNihO4KQ73utVkN/72UMjRWBJAYKTVjakkvtBBSMjY5idmZ67gWRmb++K5cs3LQgQX8TAePy5UviFikpv1SNUa6KBSnh0BdEMizBkLS/UmXRSQ6HVuNjoyiVilfViqn2DvT1egVFqt6nePb39eodu2IMPk0fYwzVahWjo8bXF8XZZUiAsJOrVklIJJJWCbk048BAf323PKQylWVoyMQWdZmoWtzU71KxiNmZmbrt02UwkW9ibAxxQv1E+jcMCoYGB5Ahrhg0/R28rJBxBCYqgpk25217M0C42alUKrqsKOZIwFEIQ4Qs3Q8T4+OIYvHU9ZXnjjUG4fMtn8+Lo7I4QS7DUjdc1Y6cMYaJiXG9a6/GCfIhQ6Mbok3+K87MII4iXW8tShBJQbOQDbSwqQWtiQmEYQhivgYOIYBxzpHPZ9BI3L+MDA/pCzPqgkYYMnTks7qd1DbNF1ObUaIQYs3O2EL9dCXRbkQoTWelr8SkNI0Oqd0V842Ew4MRAgGgUMiiNDstXLgQGlt4kedJkmBmdhZxLLRs1GG2gzbAgJnZGaLpA6YqQsObcK6FSFrf5MQEshkhAJXlBZhC1rh+caFYnJWCjXHlAZjNQoaMn0qljIT7hV134w4IsxCXD/h5kvicmpq0TrZcPqe0l6qEcrlkt70qNLyZTKCdl6u3pVIJjIlnkRTgG+TNamuTqXCfmdHrsoWrSkfxgnRdls/r8Z7S0JH8jAFRreoVhCgeMZe3/KtlfRzsgiv/iHB6syg4dHS/02flUslq69wCO7cExnogYooz1CoVoZzQSqe5883OzGhXOHNpABWIsKpXW1UNXLOLmL8qrFy5si7y3d3deO01/y62qakJ3/72/IInzwfUJKIwPTWNtvY2ksYmoIt3cWYWrTLklq8CRjQLjInjC1mQnRRpLWBxdgatbR2Y6qsASYxMPofuJmMn5PZtuVxCm+Paxk1E7R6q1ap2JeLinMoAWLc8FaibgGEYyN2xsA1EXMGiznToMkuIItXUqjV0dHTUNWFymxTHCVpUqChyHOybxAD07VpK53pH/ZVSCd0y7I9K77MbU7+TJDZuTUgFiimLykymMAzR3NpiF6LSMPsnALR3dADMxj21nkumUKtWsaS7WyyoUgNC2ygubCj8xMrU0bGoLtHpURogwos1e8IMKg2KwltpBToXd1m4TlfEItmSD3WbVPlxHKO7e6kog3N9ZCJCxqnjEtNnmWwWjU1i/NJLCvYYE65uOBfRHHL5PKKEI04S5MIgdftR4885lizpFmMt4ahGHJlQaA5VN9H5mnCOJUuX6t+lKNaxcwuZQB+rKSgU8iLMoMODxmdr4AlHQ0NGt4lzjq7ubn28O1uN9bHeooKJ36txSRIsX25Co/l2mbSvwjBEd3e3/j1bFYJUUzZjtbH8/2fvv8MsSa77UPAXmXl9+aou095Oj+np8X4GMwPMwBIAQQCCKJIfQfJJ3KWetLufHimKkqiP2n3E6olvRS2X/Jba9yRSwhNFEGYGwGC8N93T3dPd09Ped1d3eX/9vZkZ+0eYPBGZt+oWJAoYTp3vq7r3ZoY54U6cOOfEOb5QgWbyBQxZbrloGyrNQISMgwwZR8LXKcZD23FKZJhkCprNJnqHNyC4HiKdSsuYy6QegjsDkE6lsG5wSJvnqIgm6mZyZzplzLGenh7txqfhhwi4kNTqwwDpHw4RYo4TXFX9am565MKS7wfYtGkLbGi1B2ezuZi5ku2lgP5uJ8Qc/bl58xYhHZZFLNVlPOa0a4QaVHVs3CguTgYhl+pgB67CgdI/Juipcs0m5jAn3+Of69dv0FokLtPZpA+Adu2zafPWSIUun5OmiDkUCjcr6bSHjYPRpU+7u+mc4Rzo6e3TrsuWk+opEOHu2mOkgiDQJjPLppPccD6XRX/H+vg+xJJdvqwbHERnV1dLTYxtq7112/a28Fbw340J/GmAJAaQc47b7rgz1sEtjeABfFrq8/UEbcGYKwLyM1/8cmISTj5VNZ1dPbjnocdQfPd1IPSRyWUwlGvt+PHGm/eY8S+BZWf5409+2rAjUun1b9JJnHN8+nNfMPAEgLl6A41GgEzGQ60pNr6OrIfuri48QGyekkAtbgDYdcNufSqmk7kVPPLYx/XpbDnGUW3oTzz56eR+t+rhAPIdHbjn/geNdlJCSJkABmDr9p06eLnKEJsLKgMHHnjwEW3MblRMv0uUwjDEI48+biZJYhblOBU6OnD7nXdrFZhNGKjUkHOOLVu3IUUcoysJh+oSmpcxhnutkHGtxknUJULGKbxDzrFUU7da42rYXKEg7PY4wBwmbAJDjqwVFk2NxZat21ALXVQaMkIEzHGBbr/4ftfd94IxhnozQC0IkfeINNJa22EYihBzXKjHan6IgUzKcJ5Lx6yjs9OIe73UaOqoG9mUGZ+YMYYtW7bpmMekE7BQroNzjnw+RdxtMOy59XadbLHRQLMZIJv1tJsdCkEQYO/td8QWhW4ii/DnnKOjs0v7QQtCjlIjugSjGZyQo1QT67t7aCMG+nrMgwihp0u1AI1GCHCgM5fCTTdHtpiq24w1TvqRMYbezTvgXxlHxnO0pI0yfroMCLvmtPSHyHnkoLsZhEgnhNTbunW7dnFUawjn1xkvlXgYYBAh5gy8VR9LBoSOq+d52HXDDW0xFgBEqLNCoWV6e05u276jpQaB9j+HmL87d90g+kUO1EJVMOdpzzX8PwLCfn7D5i1gTMTVbgYcaSfOgKsLfH19/eghwpKIiSNSYZJ32/YdURkJ+Gsmj0t/tDt3GYcGW3LIIeZkrRkim8lg/fr1LfcAykByztHX1x8JBTSdaz1mWxJiR7eCMAxjjBdlhhXyynQjl89joCsfY7Tp2NsHAXUxRL1rxZswxrBpU3vaWQWrUgd/2IFKxBQwxvDsD5/WvsdEOvNTp5Wfz3z/e6ZNAa2ADiSEyvPN119pG7+Tx9/HxQvnhY9AANl8FnnbeapsA2MML/7o+6gTdwZJJy0KT3/3W/HnanJZeRcWF/AqsTNiEsnFekMSFidyW5FL4czJD3Du7Ol4v9CqEHXR88/+EGXpA2slETfnwPe/F9mfRhIiM42C4uIiXnv5RWMjsSVctN5TJ4/jnAy5lbi4aL0AXnr+WVRsH1ggbbYkMT94+rtxCWEsk4ClhUW8+fqr8XQtfp488QEuSnsXRfTstlI1/EsvPKfd+LRSj2tUOcePfvC0QYCSEOFcqIOXFheMkHFByFGuCwZD2SSpOhhjOHniA1y9fEkwKVzEr+VcxA1mDAZTxAC8+PyzqDeaYMxyIdPi87kf/RAAUGsGIlqIK0PGJbR9dmYGhw7sByAuefghDGaObkaAsDMau35NPy/KuMHiZnBEWlU9L73wrO4rCksVoUrqzKbEpiDTv/ziszpNWV68SHsuClJlTM4YmJ6exPtHD5vMlfpnzUUA+OD9I5ienJRtlXHAmRk3OOSCEQ44x9TxfVoiSotS/Fy1IULGgQGdWQ9vvPIibGBAzBwAAMauX8eJk8cRhuLykMsI7nHUcfjQQczPzWn1oHLQzbmIMqNu/SqV5suEhjUCOb+8ZJVnvdHAW2+8lrguhC9C4cScy/pGR6/g7JkzbTGAgHCJVSwWV1AvRt9ffTnej0nAIG5Bv7s/crnFAZTqYl/Lpt3YbegL589h9PIlMCgfmjySwLN4WML977yFer0emTPwaJwozur766+9Ykj/7CarPZQxYcpw5MihmM9BOj6C0eXwOTB3/RKmJ8da9gfFJ+TA/nfeNPxWrrDd4I3XX10+AYH5uTmc+OCY8cwWOHFEjsYvnD2FmekpoumIl0nnn8IliUmMCTQ4x5tvvNY27sAqJIHFYhHf/OY3cUIahu7Zswe/8Au/gM7OzhVy/vSB3enVasXS/5vTVanYOIQ61b7+vxwsLCygQ4UWY8kXTujgLi0uoDDSox1Fd3Tm4LlxNbDKV61VdXgbnUQxAFb6IBABwhP4jugZj1TZxaVF4dpG1Sc/p0o+fJ+jkPFQrjXBGDDcmUKxuITBwegGFlO4KObYakRNOiFuB8IwJIwNOQEnnOYYBFHpsHCnJy6bSSouFbF12zb9TKtVSV56Sq1WK8grVwxaNIIoCgglAKGpWjIQsjlvhXtHR3wAraSa+SiWsI6osm2GjZPnDECtXtN2cvYkTiIqrexROWR7JQPHOUdFusJR0Ay4VDUy9Eq3BRT3UrGE4ZENGj8VpUPZ03GYjIPfbML1UoAkqLQs0oUAEwHslRSl3gyFHzw3LglkEGu8WCoKNz4AKjJSRkfatKMC+V6ulLFZtpVzYKHWBAfgOQ46LDtCznnkVkjNXQgmplxuCLVlIa3LbjQaSKWiy2RLDR9BIDbzbMqUdAHKFU6hbWakUoncz9SaAerNEJ7LUEgTJjDkWtWa8hjSys8cwV8x/7WmUFczxtCd8rUtoz02SQxBpVJGmacRhlzYA7KIyVKHXUojq9UoHKQfhKgSdXDGa23rB4iQcUHIUUi7MQf8gAobmDfmvaq5GYRwwOCq27NMpi+Y6lp7TiaXL/shIeFKY5hE87gsO5czXbIU60KSm03J9pJDVa1aRbd0vt+QzL6WwHPlginqgGpVhNNTjEtA1o+iyYqpEyQ/ilfeqn9UP9Rk2ZSu2zQbXEgChd/KuqC/iPd1IgMW+JFqeoV9ezWXKgAZqi8hxBztB3Wz3AFDoxb1I01n0wvxTnSooBsRH9JKEqjCKa4G2pIEXr9+HXv27ME3v/lNeJ4H13XxH//jf8SePXt+LD+BP20gbBHim6GhRpOffrOJXbt263TGBpGwetPpNEY2bEyUVKhFAkQLpae3D7VUFs1aHXBc9PfnYupe+msbDUcXIa/RUX+AmCA33HRLnP8g7aRtymSyWC9Fy7R7JoqCES5kU9p32eaeLNYNDqGnt9/sBkLtbJ5n1+6bjLYtpwoOggA3yxBaYsHE0yrGkwPI5vPYsnVb9A6UsJlMAAAMj4yI6CJy3CkzBcQJ+y17bo2ptZJEn1zivnfv7XEpoUWo1LtCoSMW2s/OSmHTpk3okfaJHJHT5hguksHfu/d2zRDSuu02K5u922XYtVag+pxz4QJj2/adutCGH6LeFM5d+3LpKL3Eb8vWrYbbn5p00Kxu1trtve2OO7ULDps5o/go3ecdd90NxgQeIReSwKRhY4yhr7cPW6UKa1FdZkkLH4Ec8eHdvmMnurq6I3VwTUhdHIdpB9OUWN9x1z3yWVRGEHIRN9hh6M5HjqIZYzo9AMxXAs0kUcfEqgMGBtZh69btGk/1F+sjiezuG2/Svk5rzRBNGdEll3Z1e4TqTahaN9x4RyLTBAjmq+EH8H3hlHioM4vb77zbaItVfZSXcwwPrwc6xSFGXB4y8bYPzntu3StUqhCHjLof6Ju76ZRUBxNU7yMht6p+AA7B7LB4NyKbzWLv7Xca9SqgIeNU3g0bN2u7OpEHid8V3HnXPSK0H0PSdhGDBx58OFaO1rQQxoFBmCfcuvf2aP5zoFQT8a87cinpIisqZ9v2HTp6VEX2YU6G0lOmFjo9A+5/4CHBACfUH28zx4MPPQIgfnGSMj8Kunt6ccueW6NyEDGXFPxAmIts23kj1g0O6rTLsm2c4/4HH9FM7Up9zznHwyTE3EqwbnAQu2+8yXim5B6KtgDKdRBw85696FG28HqNtC7/kUceNQ/0VmKDV2HMcEPVDrTFBP7Lf/kv8Wu/9mt4++238Yd/+If4t//23+Kdd97Br/7qr+L3fu/3VlXhTwpaDXyz2cT2nbuMZ0liV7XR+b6PzVu2Jkqfkla956UwsG6wBU7W1XoIJvBqkQO1MuCmsGW4yyRW5EcQBBgcGo63i4iZ1R8gnH0Or19v4rwccI7+fjN+oh9yzJXErdjOXAp1eUt4U0ce2WxWByNvVbYioJxzQ8q4khFuvVYVFw4YXThx5lgRj2ajoS/vRHxoch1qjJVUMqlcZqVPpzMm4VGMBU0sN7NqpYKunh6zT6xCaZW1elU4uU6oG9ZvDhkyjvS7/Z4yzbYUdMVLUNLRro2DTkc2BA6gXinrOQCIG7N1P0TKc9CVNw32ATNkHCAuV6TkZYyIueS6zmazKRgt+8RC0qoWlIslpNJpcA6pZuYiZFzCwYNB3MRMZzLgnGNJ3vbrIHZUjHxhEBFs0ulIeqds0zw3sq2juNdqVd1XCko1H/W6D8d10Evcw5RLJQPPmbJod2c2pW+u6kYzYHFxAV7adK2ScNbQbV5aXNASg3oziuiSS0fRNvxA2NDxMEQqqBnqdwpKkigurjDkeN044FJc7PwOY5idm0HZF/NS306WJzo6X9R8mJuf0xdm/CBEoxmp+bIp17hc4/u+4TNNMTuFlJeIU3FpSUdbsA8agbzdSQ+uMzNTWrpraECQvN/Mzs6sSOsUNBoNLC0ttmQQbAZiYX4evpSkK3qkzHW6s56RlnOOickJ3Y/KhyZloG0mbGpq0thP7EtoVApYrdb0bfXQor+UhisGbnZ2Fo163NuCQbsZtLnI0vSE9EcbrV+7b2gdkxPjiWmSoFKpYHFxIfllAsxMT6NpeYrQzCYiKaa6HXz92lW4llQ9CVTbrl+/pml1zESNmR4bSqUS5mSIuXahLSbwzTffxO/8zu/Env/O7/wO3nzzzYQcP31AJy+FmekpnDl1MrYxJG2QAHDuzCnMzEzFyhIMQLQqVc53970VvV8RR45D+9/GhekK0KwDXhoPbOsmeJmfxaVFnD11MnpPXybkuXzpAmYmJ5eVXNLnR947iEASFTWh/YCjVPN1LMpmM0Aq5aIr6+G9A/tWZCzVOJRLJR0Crh2iODp6FVOT44nvVG7tigDA8fePImg2jdOWOjEZonb5eeS9g23hwiHE/6dPnZD9Hb1gapeiiSFc1UxOjGuiSd/ZTCMAnDxxPIpbSdJzK6uC948eTiBwycSxXq/jNJkzOn3siYDJiXFMTU4Y6CblUzffzp09JQiifNHwQzSaATKek3ih4YNj7+vvnAtffimH6Y2c1lev13HmzGmEXLqHUSI9wo/S8ZucnMCMDGupgs7H4gaTE/aZ06fhS59zlaYPh0VRFpLgxIkPdH1+yLVaUoWMAyJaUq1WceniBfksKmOyWEOzKdTW/YWorslJETJOb/DSj59whBzH5eyZ03qtqr6g0kDAbPOpkyf0g6ovVN+ZlHQjIiuoSLc08Ouozo4luqECREQYFTfY8xwESyLMldgIme5f+qm+h5zj3JkzKNeacF2G7ixh0rmZnkkG7OzpU/pZww91zFvOIVxVyYslIhZ0Gdevj+rNWDE7XYphZmQpMoaJiXEsLS0ahw/VZ4IJFHNPvTp75rQp6eTx9UnxP3f2TIu3cSgWixgfb233ZsP4+BhKpaKBc6XugzGgv2DOG8YYzp45DdcVkt9aEMBlMoqKvbfJiXTh/Nn4c2YeDFS/lIpLmJgYN5hi24xKO42GoJHlShlqSVMGVI8FVxJ9jumrF+HFXO1QWq/+xGHkyqULbe3BgDgITK8iHC4NMUeBzoVQqoQdBly6cM4I8ZkEiuELwxAXL16I7Vn0cEn3tOLSEuZmZ5MLbQFtMYGe5yXqmVOp1Kr1zz8paLW3L8zPCTclVmcCFhGAmJgL83Po7ulLlgRGGfWzICA6eotzNNTBXLj68FIpXJkqAkETyBZwY3+HwZDSehYXFtDV3Y2VQC3EpcUFdBEn3S1BIlWtVpAlIY44xMZQqQtH0ZwDvh8im3bhOQkMlD4OxTt/cXEB3eRm5UqwtLiIzs44QwwkSDogQ8xJe1VDImEQbG58JoF+Q07HxdJS5GZHU6wWBTBgqbiIrq5ufZpdqbLi0pJwCYBoM4++tAe2Ck1VK0IYWnOGECubyIgwg12x8ihzQdNXyiUh4ZUJy3UfvjT4t/3zUSk7YwyNIJQh4xxD5akkMuViER0dApck1WSsraWitlmu+oIJzCREC1FQKZfQ0dUpHBBLv3lKYkR4fZMhl22oNwNU5Q1bz2U6TqsuW4bSE3mi59dLFfh+ANdzMZCPpIrlclnbVnLOUaqKyzCUUaQHimq5rN2gUKaPTpuki1GAvHQScuTTrjFGpZqPph+CN2vo6ixYB+Uo/1y1gaYvmMBUyoXbrMdcCjHE8VHPS6USmkgLR8+5SGKVzECYi0A46I4kgZ0Z12AeKpUyCtL2ERCucBgYOmX0DHvpVsoi7KUhbYUYs5DHHSPXqjURlYjFcbahHefZFISNX37ZNLQ7hI11lF7ZajqOmDd6XqiDi+9rDwc1eTHIkAQyxegBHDzGuFAaEEnsxF+tVkOhUDDSJ2pYZPpGQ4Sk4zz5oKk1BxLPlOtEniVa9InC3w8CpMit95WgVqsZYQNXEgw0VKg+G28WzfdQzh/hjNuN/Hsy2GyBbIN46Pu+NB+IPD3YZk00T71Rj/X7StAWB7cco7fasHE/KWi1z2/YuFk7wm0FVAy79467tC1N9J6UT48iECG0ADkZFBFWM92SHjHHwV0PfAz//i/PAGGAVC6L7kw80ojCdGh4RPtYS8LebtKe2+5AR0dnhLDdKRR3xvDYJz4p2x4lWag20PADeJ6LknQQW8gKNxqPy1BkSZUz8phzYHBwCH29ffqEvtJCu3XvbUhL/1qtxpIS4I8/+WkdS1dJJBJF6RB9+qlPf06W3eJiBMna3zcgXIlQSGIGZeF7br09cvPQimkk3NWTn/4sUjIWJSfMJ2VEKcPz2c+ZEXQSjYaZ2MD6+vtx3wMPQanakrqS5r117+06XngsHcEhkFP6k5/+HFKplB6jqWoNQchFODKCnwIaMq7eDNEIOHqyXiKR6+3vxz0PPoJiPZkJVLirfLfddofui6ofgDFm+Eqz4VOf+zxSXgq1ZoBqM4TnABnPMdacGgeHMXzhZ7+s6yrXhdSMAVKdbdbT3z+Ah6SdEV1+48UmwiBEJpMyXEHddvudOl3ABTPtOAz9+Wgz03gx4LNf+FmkVGxYtvIFtJ/9ua/q70uNpnDzpELGyXRz9bpw19PVj9v37jYPXjxa4rO1OppBFDLu7rvvwqb+vNlnBOhzDuDxz/wsnv2hCBnXqW4ny7mvQj3SNn1JhrsDhBNtn3jf7slFTDsDMExoJIdwaq2iwKj6KX73P/AgArLRckA7olYRSVzSEV/5W1+LMYutgDGGr37t51u+s9fsxk2bsGHjxlha1feRAEG044GHHjbKCDlHoxHAdR0M5KyY6AC+/JWvIYQUQISRI3Vm1YNQjMOXv/o1PeYhFwmYkthbg7xp8xZs3LQ5koaRjla40zY88rFHDdxpGqrhqUqb4c98/suxPcXAGdEz13HwxZ/7SktBkA3btm/Htu2Ry5eVLoo8+vjHjd8UB0TNlpJABz/3la/p9oDH+45CKpXCF774pZZ7pD1vdkhb8v/mYeM++OADDA4Oxv7WrVuH48ePt13ZTxIYMz8VnD93pq1Yw4pgXL50UZ+I7Mmmf2ipSBmTMoIGp+9hTloFUxNjWFgsYmmhAvAQuUIOKdcUt9PvF86djjUyib9QcPrEBzERugEEmSAIcO7MKeO0BgCTlRqazRDptItqQxgdd+ZSmJ+dwsz0tFnWMovn3JnTqDfqiZs9aY6u9/B7B9uWOnPOcezo4aicWLnmJjk3O4srMoyajUvMByOA82dPo1RcijNQtohD/j7w7j6hdpHMP92kNH7kx6F390fSvwT8KSzMz+PM6ZOJuNs4hZzj3NkzmJ2dTkRXfyfl7H/nLaiLMrpIoudQ/aOM5ve9/WZUIAMmK8I+rCfnGpVwzrG0uIjjx47q59VGgIBz6bA23u5zZ89gfExcRNP2aVYiivvbb76Opu8LW8VA2BrSiw/URgkA3nhNuHKq+yFqzRCeI0K/JfX/4uIi3jt4QP+u1IXUjDHhKNqOG3z+7BlcG70aK2uyJJjAdNpFbz468L39xms6CkG9GYjLNS7Th0I9f2SBNGQcDY9ozzVASNXf3f+O/j1fE/GBNQMl1+RSQ9ykrU1dRmVaqCXtsy4gIsKouMGZjIuj776RGE3HRkqV9dyzz4iLKZ6K9mElJw+KS0vY97Y0seEyLFrINaMwUIgOEBzAqZMncOWyWNvgwp2M57LYDWKFy0svPo9GvW4ccJQUUKnzQOjSj575gYVr62bPz8/jzddfi7XN3shVe49/cAyXLl6MlUP3Mip9fOG5HwmbWdmghh+iXvdFFJV0yrgVH3KOZ37wNBik7acfhYxT7abM1dzsLN5+0wx1JhhkVaaJ47H3j+LK5UvLSkYpPPOD70f2jARodB0GsY4dBrz50jO64GRJmsQfwMzMNPYlhGlrBUcOv4crly+3nf4H339K25EC5hxQqClH447DTHdhLUDNi+mpKbz9VmRyR7WVVCqo4L1DB1eFO9CmJPD8+fOrKvSnEexJreDypQu48+57k/MQESwABL6P8evXrDSteZ25uRlUKuXYc97i+9j1awg7+lAtC/uCfGcenryNRU89CrcrFy/g5j17zQ2atBUwTyVqAzWQ1xnNo9nSwjzKpTjus9UmgiBENpWOjI7zKczKEFcaVyLuoBdgOBcn66tXLyd6Nrf7U6E1NTkBm5y06vtqtRoLoWUvFvp9fnYGvh8YJzYlJUyCsbExrBscNtRxLRMzYGpqQkslVfIIsUjaBybE/zoWtExLNyQb5ufnNAFaTr3NIAj29NRksklAi413anIyxnyrdcFkXVzVyTkWJe6qbbNlMUf68lEZaiwWFxcM3111eYM3qwy+LWI6OzON/hFxE9OQBBKGm3zF1NQUMuk0Ai78w6UchoznxMYZPGJKweTmGYTIeE5ccigzF5cWwUNii9Pw4YfCNk0wgWa++blZrCe3SBUsVpoIwxCZjIeObNRHs7PT+nC6VPVRbwRIpRx0pYmzdNlFIectQsYlu5Eoy7Brqq8WqsL+sZ+MUci5jjbhV0vozkUuX+wSlUNicWHKRXlpQauwWgKPcJ9fXELDC2TIt0glnnR4K5aKRrkiLBqX74GBXNrIt7i4oDU3fij8HqaciAmkdTEIn28Z6YiaQiA3cRVSDRBzRsWlbgfKpRKYrVJNGB/1bHFxUUaYSR7HKL1oe3FpSceo5wAWyoI59zwHnWnPUt9ybcfWkI6iMy6T+w0zyuUQlyUcEstcRSGiN6UNXIpLGJDOyJPoF6XdjAmTAE+G9mtF10POUZPS9tBvxKRodAuLJJYctWpFa1bsfdGW2gFinJT0uB2oVirykJ+AD4AQ4lJICHGIUD5alwM13lUSvs42VbP3MzUf0+s3LL/2LGiLCezt7cXc3By2bt1qPL906RL6+/vbruwnCWKjij9Pp9uQAsoOLpWK6B+IwqIpgqgnE6BVvuAc5XIF/TKMmtowwUSItpCUoU47jUYDNTeHerUOOB66urKG80x7YDPZrFYBKSmggbf+J25Bd3f3msdI3Tk89rxaqWBwKPI9p3CcLgnCks94qEgV1bpCCkHg65jOHJAq7/hEVI88z0M+wXYhiQHknKO7u0cYf8dyROlUbdVqRbidUOWxZImeAj8MdPQEgePyC8h13cgW0+bAdSHR176+fjMNI5/kO4NY9Crkll0UJeKUkVm3btDYKGxJJyWIqVRK9mW0kUUHB1P1xhhD/8CAlqrEVBIsyhNyoNmoYyNRXwkGQ7iH6c7EbXI4gHWE2Cqj7yyxp6O2sJlMBvmOLjDGIiaQGx9G3wwOiX7xfREtJOe58KSj36h8kbnZbGrVW70ZouZz9OVcZNORDSGTX5joLAyRkHGlZlNcHJDRQtKeoyPzAEA2l9OucCgUqyJkXD6fMi5lDA2PQJlizJTr8H15ASsdN10Jg8BwU0IhllaO4eDQkD5gLFSFiq07TeOTSh9znCOdyWLjwKBBB9R88cMQxXoA3xcULZfxsGHDemPcYgOjvnOhcehYN4JghiOXIUx3Ar0TtqAuRqSHAw5gTjra9mVIvYFs1qino7NTu/GpNALUA458yonZp6ryNmzcZPhkU7RdRQvxpLqUS9w3W5ElluHV4Hmexn0lxg4Q7pa6urtbptN0T+K4Qc4BDjHOU+UagoAjn3djEu0gCLB1mwiN1vBDNEOOzowDz4lCxqnLNgwiVN/wyIjRWfaeREl+T09PPDwpkhk8zoWrKPpbpyfpQg5pdhBgy9btmkE1SLC1f4ADqVQaQyMjxnaUtA0q6O3ri5l8LQfKFdly24aSBPIwwPadO4UUdYU8jDFkMhkMDQ3HaLNoX1xF3D8wgK7u7lXZn7bFBP7Wb/0WnnzyyRgT+NZbb2Hfvn34kz/5k7Yr/ElCXMrE8bkvfCmWjnYu7fyurm489LHHrLSt69t1w27j5MchEKB8gN6kOXD3/Q/jL09MIqxWANfDwEBei4UBwgDIhnziU581T3eAJlyUz+AAHNfFY098Uq9U9TwROMfg8AhGNmyMymUADzmmy9LvVNbD6GwdnsewuSeNWzbuNU/Q1jHL5gkfffyJljek6GkNEAbVj338iRi+rfq+s7MLt91xZ1QekheMerdt2w7hGoSkFXgkr9B7738wsoVN4swIhEGIBx/6WIQ7Q5xyEX4mk8lo/3B0/GjxjEXOrEdG1hsuVuh8BWBI2hhj2LP3duSyufhJkqTRdYQh7rnvASO/MW8BzTAIezBPhK+T6Rt+qB1Fd6VTWn2k+re/fwCpdDRnavLyRtYzHS2rr7t234Q6Mmj4YWSXpT5sLpADd9x5DziEH7yGz9GTiRw/c9VWHvXv3tvvhFKjNQOOXNpBmqjH6Jeenl6sWzcUHRCbAfyAI5NyUEiLKCN0mLdu26Hjf9J5u1gRPje7CmlDurnn1ttknwMztTqCgKOQd7W0UDGkYGJ97L3tjlh/AYhtHAwwQsaFnKNUD+C5jmAwZbF+yLWj4a6hDRge6NX46HXJxY3ZEmECO3Mp6UMTJrD4Tw6Be9/WXQgmp5FNucvabDLGUOjoQK88bALAYk3MGT8M4boMXTnz4syGDRvR1S029HLNRzPgyOZk/GgWlav6Z/eNN5kHKFmWkgR6TqTVCMMQN918S0t8l8M9UVpvMYYj6zcsG4zB7uMbbtit9wbGGMbLNRk32BPO16327rrhRgBivvsBR9p19K17e/hyhQJ6+6JDjBKqGGSM/BgaHkFnZ0c01wlNt+k7Y8D27Ttj5XCyNgHBANaDEB44du4y/ePGDpjkcJgvFNDXGV1wXElINjg4lBwrvQVsWyZWr8ItCDk4OBg4tm7dHtH1BHwiSSxHNpdb9vKnvVepWNPFYrFlHhvasgl844038OUvfzn2/Jd+6Zfwxhvt69p/2mD06hW8d/Dd2HN781e/Dx3Yj+ujVwGYE1RPWqucZ57+rrZzEDfLonc0rdhEged/8B2MzleBehlIZbFzuEtvWjYUlxbx+ssvGAjQk6vNm5w/cxqnT3wQMWVmA+0OwJuvvyLUkoQghByYLwvXJT2FtDY63trVgR99/7txJDVDYVYRBAGe/cFTCa0y+0PB2PVreO9QZH9FT3Hm6U78OPzeQYzF1PaW3Q1596MfPq2ZpeXP5uL9Mz8kuFMO3M7MhLpWhVGzGYmkn6dOHsdl6UqE4mNv7ApeeekF1Gq1xNMhlfIpePaZ7y9bP51v5XJZhK+jdVv1A5EN2pVLF3D+3BmdsNIIUG0IBqM7Hdm7KfzefusNlEslLZWsNAXDqOzp7Ln/wrPPSEkWotvDpN85aUy9Xsdrr7wk2lEXtm3U3Yu90V+9clnYVjJhfB6GHB1pcUs5yW71wLv7tNoeAJbqPjgXksAu4vBYgQoZZ/dluSJs5wa6Ikbe93289nJk4zdXbSIIOHJp1/DjB8HDYuz6NZw6edzoW9VG4zArnx89fAhzc8KNRDMQDqE9R4SMU8xx0w9RbYTgAKZP7EdBXtaxwQ85qo1QM4E9uRTeevUlM1ECQ69wGR8fw5lTJ+D7IXIZD2nPMZgQ+/PY0cOYnZ0BIFRsxbqQNAcBR0aG1KOgQsYxiPkoQtM5ybfLOcerL7+YQP9FO0NwIUmWyE9OTODE8Q9i5SQB5xzvHz1i2k0npKHQbsg4dQh75ZWX9JoHgMlSA2EYIiUl00oFDwgzj5MnPgBDFDIuK0MqqjZTOHHsfQN3TVsshk6BWntiPyJ016DX0fvXXqXpzT1MZWkGXMy3xTmcP5Pg5oqZf4CgTadPfICZmenEdiXBa6++vHKiVaTngL7BvjA3i5Mn5FptgQ81QTt54jimp6YShRj0Brt6//pr7YWoNcppJ1GrANZAa2nJTxuoiUUnyNzsLHp6e1fIF22u83Oz6JWRGSgjok80Vl6fuIdhMFUpNg2q1+vwvDTGZiuA3wAyOTy6oztRhA0Aiwvz6OntTTwB2RsQGMP8/FyiOkpnsmBpcTHmSqThhyhVhT+vnqyLRiNAOu1iXaelUqdMaULxi4sL6G6FC+ILY3FhHj22s+XEZnCdno4T3QgjhjZCKgxDOK6pLkmc10y4MsikM3EOxd7d5PeF+Xl0k35slU3B4sJios1eAn8JAKjVqolOrulmoJgYoSJgsTJoXsrLLllhA2la2g61tspF6X5GFlCu+aj7gXAUnYvsWtQ4lYrChYtiLGuBD9eByegYfcB1bF6mGkD/CAjXNsLhdrUhoyF4ka2hPb4iVF8nOBfRQoKQJ6qwVfuLS8J1jnq/UBWXKzzHQaetsrUOKtHGyVEuN8EchvU9Gc0Ml0tFcYtf9u1cxddxdbMpV7dX0BEm3CEVOkzJbgLjyiCGplQsamfkJTJGWSKFa/ghalI9n/YcI345LVYxkb4vQuL1p33DvUZLlYPc4cvlEmosq7ULnuWQ2j7ElEol7fLFDzkq0i1PGAo87Qs5AgVRQrHRRMgFc++qOUxoQ61aRTYnpOT0wgPnYhN3IG/PSoTK5VKCG5SEtsryK5XIjY96ttz+uZwds11nvV7XNqRM5p2rCK1NNiVd/6g9C8J1Tj5fEDTNDxGEQMZ1xaGH4iA/y5WyMa5KCujo/muN+3KSOkCYQdmXM+0pw7lwBdQMOODX0EFduJD6DVWy3JurtaoRqq8daCUIsqHZbMY8pFAJp2qM8InI0KhXkc/nVuSb1HsaWpXSTsCMDEXvL6zkg9CGttTByuu6vSEsLi7qG2w/7RCdOqJn3d3dLaN5RPkiCdKGjZv0IjYmNpmAlObdsNsMJcMAYTOYIJEKwgC7btqDyRemgaCJVD6PnT2dBHeRUi26bC6HTVu2xdqYCJxjcGgIfesGkzdXwrSptt1w481wrQsB5bqPsvQRGHAO3w+FexgHuPnW2xLr18wy6RfP9bBr940t09rf+/sHUOjojNtywV70Yqy2btuOjs4ovSJIFAdKoPaqUEu0fBtpVV/IheqNTgB6ZOUw3nX39BgHjRhxI8VzAJu3bIlc51jvkqoUrkTMG4aaACvVo2QYwjDEnXer0GUsJry0+6Cj0KFVRrRe+yZjyMUpd+OmTRhYN6ALqzYCNJohOrIeunIpc9MFsPe2O4TXfwjj6Zq8vJFNuTEiyTnHHXfdA87NSyG6n/Rgi4fZbA433bIHYEId7DASJSLh0DayfgPyhQ4wAMWmiIetpJdJp/A9e29DNpvVfaiihaTUDVcLwTvvvjc2Dxt+iFrNh+u52NQTbYKZbBa33na7noMLVcEEduRSou2czn+OkZEN2n2SGhO7fdG8B2659Vbk5SZarDbR9EPJYEaXZhp+qEPGbdlzp3bAbM8XPxBhAYNA+FXcNtiB2wbuMvlyFj/4cNkJw8Prwbt8YKaMvnzKOBwn0apb9tyqaXC1EaAuTQjCkCOX8bTNnuq7++5/AJAkt9wUTFE+5Rr9p/rL8zzcffe9EWND+kzdfk+5EVbDI+sT5mkyHeac49Zbb9PqXWq2QaU5FB548OGYBqMVuK6Le+97AMpUhANYrIowhvmMJ+LPI5obQ8MjGAhCKPOHkItoOtqOlav9Umxqt966F91ELamlgIlthQ4Z1woo/Ra4P2j0m7JHpDRPmWkMrBvEjh1DifXST0AwSjffcit62vCPq8YuCfdWY+A4Dh548OHEtlFQ82dwaAjd2Q0t09mw59a9ev9Isgu08Xv4kUfbmi9GG9pJ9PM///P4pV/6JczPz+tn8/Pz+JVf+RX87b/9t1dV4U8LMAa4nteWAahaOF3dPYkElsFiRiAM/NcNDhmEhO5T1GaBQzqq7e7HwlwJgLgZnE05BkFSUgTGhIPK3r5+wJLKGPOKiCuZ4xi2YwosHkc8C0Pk8nHnlxMl5R7Gw0xJENSufAqVSgm9knGB1T+0nQyCia1UylpSR9GkfUi/lytldBCpi2K2gYS4lIxFLnzIM9pW9Y5BOAbN5nKJ/WBL9YDIebJ+liSiI4UUi0vaaXWLJEZV9VpNhDpDfI7R366U7IVh2FICRDd+MCEByuVyyXMF5vwEhCQwn1/eWa1gLkWO4tKi4TS12GzCDwSDkUu7MeLVaERRUTgH6n6AtOtGNzdJu0rlEsAchJzHLoUoRNTGDy6kzZlMBpwDpaYPxsTNYCoNodnnZ2eRl/NgqS5uy/Zm0zFpjfpWlbf+1dov1lW0EKFWpZVUq1WEQaDtIVW9i5UmGo0AnudhqCClOEw4dXeI9kVFCxkoSCbQ2CwZZmdntD0u/aNdpBkA2TeuXCOL9aa2ZaRSRhHpJUToN5EO61r6ykhZipEVTKCIFtLpN4xDK2w8KH1gwn1HqSHe9kv3LrEJKitlTIyT0kypQ4YCMceiOsIg0P7SGETIOADIk9CBymkvIIQafhAkLXs0AyEdpAeQ6emptrRk6nNqatIMdUb6wrbtYoBW2avn8Y0/+l4sLqEu1xOTiJdqTSlhTUlbxugAMjM9pQlvxQ/AuZKUq/E12zI+Pk5uqUYh45Iv3HFMT0/ptJT5pHirz6WlJVSrlcS20XkchBzNkKO2NA+GULcn6qPoU+23nAuvGCkvOdIOBfV+cnJi+YQEisUiiqW4/Z3WPErcw5DDZQxLc7M6ss9KvBpjDKOjV/Uca2XXTmE1EWYUtMUE/tN/+k/R09ODTZs24Y477sAdd9yBTZs2obOzE//8n//zVVf6k4CIeEXPDh3Y1xY3Dgiff6dOfJDIZetTEfkcuz6KMcslix0YneJ2/vQpXJ2dQ6VUAZiD7t4OaaQbMX8UDh/cr6Uoql7NhKmNSx1tOMfR9w6BvI7+AMMOhDGxSVw6f07jpuBasYogEDeDi1UhLenvyGD82lXMSTsdmoex5DafPPGBIUG21fS0rwDg2JHDUZgw0sdUFaGGxfd9nPjgWCKBtfEDgNnpKVy/di22acZ2VJng0oXzKJaWEt/FphID3j9yGA5z9KZnMx92dR8cOxpjXg11NmHwSsUiRq9cjsqwDgTqNK82hevXRjE3Oxsjnjb+6vPUyeMIgyBirmDirxiBQNZz5uRxYxNfrAv1W3fO1f7H1JjVGw0ZckskrzcDNENhl0TnpoKZ6SlMTU2BMeHGQjVU96XFEF68cA71uvBRWA8CuA6EcTyifLpdDDhx/AM4jmAySw3hOLelWppznD55UpdVbwZo+JE9I73cwBiwsDCnNxbKdFyYLyHwA6TSHvqIU/jRK1dQrUSbYrnWhOMwbOxJmwc+2Zcnjq/g/9PIBJw9fUrnLTWELWMh40X9A+Gup+4HQKMKXpwx5wCZZ5W6cNYcBCFSKRfB0gzKalMka58yC3Qenz55EtVmAM9jGChYPgJt0TcHzpAwbfVmCJ9cfCpkvKh/GVAslTB2bVTPWeVouDujXJEQnABMTYqQcXbXcQ4dMo5Gsjl39kyCypagb9GdMyTcHVXtaZdUZJ2XKxWMXr26rFSH1jU1OYlFIqQJeBQyrivnGQcQhwEXL5zXzIjqFx1S0Zr0DIhsfWHS26T10Ww2I9+MCfjazNrc3CzmZbxbu7n0ZzMQmo3FiesIg7hPQbtPRHkcly6cj79oAUEQ6PCO7cD83NyyYdo4aMg4hvGxUeEfNwFXBXQ+XTh/Th8cbIZbzRkqSb544bz83nYT2lMHu66LP//zP8fv/u7v4vBh4YT3zjvvxI4dO9qv6acEFIFoEBuKJMECYHbkzMw0+gfWJXLiitHQr5g4sQ6vXy8YMltMCHH9HmGUb2F+Fs2+7WjUGkAqi8HBjrhaQmcXC1qpaNSzaHNQn+JLpVpFIZ+L2QqKTZzrjVvb1M3Por+/n0jZxKqfKArGracjjWrDRzrtYlNPGovzc9i56wbNrNp1GH3FhKSju6db+piKO0qlarNASrpUXxibiW4DZDuA4uKisJUku5buD5mP7i2Li/Po6e0xmBy9ySWcvhcW5nHTzbfETrcarE0hDAKk5c1YWrZpdyS+C/uStLHJGu2Wu5kqf3FhHt09PaJ9lihTERkHTOddXFrA5s1bo/Fh0LdqaR+poorFIjq7u8wJqKSwLPoEIv9pDmPa3nKp0YTrONjQnTY2ZwdinLp7ejRuVamy7ZJObdVzVfXSwgI6OrvgMMQcqHOKj0q/uITu7m5hz8U5CmkXeWrjZ6UHhGqn0Qx0hJNc2jX6SK2xkrIFkwWUpSo4m3bRlZXuOEjhpWIRXV2dWjqhOvjqYg3MYSgU0ujJpeShSdj47eq6AY7D4AccTT9ENutiY2fk/oSR/gyDAF4qmZQ7MOla4PvwvJSmGbVAXKzoz3s6WoSQnoq17gZVDPZ3adzpAYIxhoW6TOc6yOdTCJtVw1aSHsaMdSElSdVaDdxNI50OMZDN6LG3x4ZJ+qQjVEBcaACAlOvA85womorMXCmVIvMdztEIQmRT0hchqUAxRqVSCUMj640NOpJaCXqrJIGMiXjQhUKh9cGOVKKM+O2DXStQYQPpBk8leTZdqlYrxi3SMORoBiFc18FgR0oLE1SbKpWK7puAC5+YSWYYCtek53ZoR4VTpVIx3H/ZWg0barIfRTnWS8Js+kEo2uE3W6eneMsELrnwYrfLtqmzQ8bZ6WyoN+qJ2hLKKENKBR0m+A47Pd3zKO4cMLyLGP5rSVqV1282haaHMWNsVgLGV6tA/pDB0pLYDC6NzWo1HocgnJVyGR1EHWzvpbRj6vU6giAw1F08IZ3qzXK5jEwmE4lyyTsuEwrfU+KUOTc3j39/fB5//hf7gTDE3/rqffinH98pbDmYYBod+QnGUJLxZW0Gh0rLVH1BEKBWq6Gjo7VfPrqYGo0GwpBrJplzgecfvX0JBy7M4o6tfbg4VUK57uPvPbQZtw/mkc1mSNgqsw6KG5N9ow2q9WyPoaaZpXKlgo5CoaX9mtpYGRNtDXwfmWzWsJ9gCYsGAHy52XmplImKjZesvFqrSqfYDDbjlQTValW4ZKH9IsuNzx2Oer2ObCYbKzOpCj8I5O2/lPFetTnkoky1cTWbTTDmwCPqsFBGcaebNIMgOPV6XZsQ0D6jeQCgWPPBAKSdEGmpygYH/vPRUey/tIgv3zqER3cJf5laIsE5mk0f6bSQyowv1HBkfB7buzuwa7gDjmOefMMgQLHmo9rk6OtIC9Wu7FPVTsqgNJtNeJ6HRsDx9sUZ+Jzj4W0DWkpnz4l6o4FUKoWlqo9vfyAk+F/Zsx7d+cjoW693zuEHATxX3Ka9PF3G//beNTSDELcMF/Clm0fQlSP5ONeXj5SLmIBz/Mk7l/GfX76A4eEO/O+/cCd6C2l58AngOMIUpBlw/I/f+QCLlQb+75++UfSNtejDIGipllRMubIVAxdrxJW+GJ87PYFnTszg0V09+PKtkY/H/Zdm8RdHx8E5x8/tWYdHdw3JtpgM0lsXZ/BnB67h3OV5DA934P/3tdvQmU1Ft6opMtai5QDmS3X8zrNnUKo18U8/cQNuGOnQ8XltxhGANn8IOXDy+hL+3cFRhCHHXKmOz982iK/dtsn0wiDbHoQc3z1+HVcX6vjanvU6rB2z0nKO2AYacjHGjDFsHcjr92EYwiVO4Gl9xs1sXQ45+CG+/h1mRsjgVnobzDUZeZ7lEPbbv/FXxzA6UcSvP7EdX71toxGvNpR+Dx3G8Pr5aVxZrOLJHYPY0JszGEYlAQUsRq8hVMiFTERLYhoXQofsvl6uLUkQcuDcRAlvjc5i77pO3L6pBykS9cXuc4VLseYLe2Bya7xVPba9XdKeQZlwm0FPqp9zcVi5OluFH4TY1J9HxmORAIexGO1K2g7t8W918FCwtLSE9et6sLiYfLmPwuqukXyIwZHcsThtA5cunkelWpYSDGhRv/rOGIx37x85JN4TiYdLPtWf5wi7kSOH3kU65Yky5DP9J+tSv8FDnDp+FAvlBjzPQ2dfNx7e3g3PFe89JyrXcRgW5mZw7eolLX2hZTsMseeXL5xDeWnBxNeJ8PWc6LfLGI4eOghXluPqNovbeN35NLb2ZZDyHAx0ZbGtpwOH3n0bGdlWXQ75pG0NAh+HD71r9iWpm+LhMIbpyUlcuXghlsYhfy5jcF2R/sypE1hYmDfG05MnQfVHx/Wdt17X+OnxdyJiqcdZzp133nwdruPoZ8vNh1q1isOHDug5p9KpPIyZbb165TKuXb1itlX+KWmoGleHMRw5dBCVUsl4r2y31Eaq6nIdhtdeeUnPSYaIMKv6RT61XoA3X3/FkEiq97TNTJa9MD+L48fe13iI9QP0daQx0pGL2iHLOnXiOCYnxyOpH4QUsCPr6fbp+phww8DDAJmUo321MYKrOiCp5y+/9Lwex5znoi8jGEclDY8OVsLUY9/bbwqGKeTozLgY7kwjm3ajMSPtvXTxAi6ePycYYSYIfU/OQ38hhaGOtIgbTHB55503USoVddtp/Nl8PoWB7hzyaVfj/9Lzz2qbPT8UrmH6OzPozqciCalsMw9DvPzi8zE8ab9SjcLY9Ws4cfyY7l+PMXTlUxjKZ3V/M4g50ZFNoTl+Bl6lGM1bY+xF2R3ZFDo60ljXlcUbLz1L5jfMP8DKC7zw7A+Rz3jo68igkHH1ONI5qf5mp6dxcP8+3RaHMXTmUujMpdBdSGMwn9EMDGMMx4+9j2ujV3WdWc9FX07Yp6q+VsAYwwvPPwvfb+rf9BDiuY505h3h88wPvh9J+Ek5CjeQ50tLS3jjtVe1GbFOT/60/RyA06dO4vzZsxF+Vh5aFwPw6ssvoVwuG/2cy3jo6BD9QhlAAHjmh0/r+ZzzXHRnPS3Bpu12mLCbfvH558i6EX9KQBHREvH74oULOP7BMWOd0XGx/9568w0sLixofGypqWqz5zB0ZV0cePU5uISJSpKuRgw4xws/+oGxfmmfR3NJtHv06lUcOfyeMfdoX3NE/csA7N/3DiYnJoxnKg9dIykZqvC5HzxlSJPpXIE9Nxjw9FPfjc0TzZST3wzCZdH+d95eVuqaBO0FY/0bAGqDV6tg9Mol3HXPfcYzIPquuGw1QGPXRvHAgw+Zp4gWrHjIOWZnpgwjYiUdkWcMfSpkIcf80gJ44KPhh8h15DC0vg/3ru/TUQf0xiyJ6+z0JFKeC4/OBoKrfQoduz6K2++4K4YP09QHRjnjY9fw0MMP675gUpydTzsY6slhc1cOXfky1nWkMNiVxqH5uWQno3RFSCguLoCHgSQM5kv1i5PHszNTSKejzY+ejmlG1Z7xsevYsmVrRITpikqA2ZlpZNJU2iM3TieOWxCGKC4twup2A5SkCwAW5+cEw+gk1y3QikKwzU5PYWBgHZKSMysPY8Dk5DjuvOuuGOGGVPFHql7xbmlxgRBj+Y4njAMDSqWylBzGT8QUPwYg7TooLc4jlfL0Ow6G7kwKW3tD9Nq2XgCmpyYxPDwMxxHtT7kO+rIZdGQ9TTwp7vNzs8hnMwg4DPtQ2mYK5VJJt7MrnRLMo9q0GIzT/tLiAlKuqxnc/lwavZm09FkXH4P5uVkMDKyD5wiJlOcyDHeKSxsj+RwynhM5swYwMzWFQqGg2wqIerqyLvr6cti2riAYTtl55XJJOJuW6oP+zgxSDhN9ozYQWXalXDIYYhuU5I7JMSkuLSCdimIEd6VT2NCdxkhHVjPNIYRPxaHOFJphCQM9HbpeTvrZZQz5lIv+zjTmenLY1JdHbbqi09qMkLnBMdQbDfh+EwP9abgO5NhHmyeVOgLAUnEJXkrMjxAcGc/BcGdKXGpIORgp5Ax6ubgwj6GhIX0Y6s2ICxJKKkRpJIdwi5VOR+YYKg3jXEeBoZKearVirkudJ2Lo1O9SqSgk8DS91Ub1PeTiktXg4LCRhpbpMKJuBDC/MI98Pi/x4/Achp58Go1mgOFC1lizDmOoViq6nztTKTSyob68ZY9ZpVKBq1zjSFySYgyrOkqlIrKZrE7PYdMnExbn55HNZqJ5ys15omh7JuWgN5PCpN/Q80RBojKJMTRqVbiOY/Qf7VPdTvm7XC4Zl0gUfdYaM2vsiktLyGbjUcei/YfBdYSbHj/kaDYbEQNMykJC+UEQIAgCXZdOa7VBvavXqtJ+MHlvagUfHSaQma4lXNfBQH9fwmYiwCZAfX298Oy4j5q4mrxUrVLFxo0bI0Ju7s8AlApL1OE3ati4aRMy54GBoR7csrMfA51pHTc4OtFLwhUEGFm/IXZTUH+lpyMA6ZSH/v4+wweUoem02jGwbgCeG6k5mFRZr+9KY7AjheFCFtv7s9jVn4fHfWzavMm8uIE4YVPgNxvYtHnzsoyRYSvnMAwNDcm+5EbbKCj8c9ksenuFrdlyYnX1fHBo2CQmzHxPoVKrYdu2bQaDSQkxZTjFBh5iw/oNMQIYI/6ytnQ6hXXr1sGVzIVNcGgdnAN9vb3IGq5BVLnMSKy27/Xr1xsMFk1rMDuModlotGSmaZs5hFuUdMpFf0/UlyE4RvI5dKdT6MqlYn1QKBTQK/1cMiYuYfR3pLUk0B67kZH1SKdcfTs4tjkSPGu1OrZu3SqkB66DnkJKSn+YkV7PBcYwskHE28x4DjZ25rXPOt0eorLJ5XLo6+8XBJ4JVdOOngJSLsNgZ0bjp2BocBBZdfGDMe0uYlN3FjtHunD3xg5dV8g5Nm7cKDZ5CLXRjYNZdKYjCRaFIAiwafMWvVnZYB5mODKZLLqIGUl/PoPdAwUMdGaiwyZE5I0b1+VRWD+E9YN9BnNPoTebxg3rcgg5cMf6Anqym+CStZfERGvc/SZ27tiBbC6LfMqNHQA04yn7PuV5GB4W4b+EpNLDrn5halFp+hjoTBv909XdHdmdgmOokEV/wHV4PoMhALBp06Y4XZLq4YJ0s6Je+76P7du3JzLf9KCh5qXneVi/fkMsPf2tDgkOAzo6OnWEDms5J9QHbFi/AZ7rartN12HY1p9BPu2gvzOj54f63Lxlq26rWnMqrraBE0RZmzdvMXBUAohofkWHqo5CQdoqy7EMoQ+Ttv03IMI7RgysWb86ICs813fkEeyI0yVGytMHPAjp4fbt24wxUSp/Q1giy+koFOBJ3OlBMbnvOQbWDaCjoyNWHsUBjqBvQcixZcsWIiU39zm7b8Qc26EZe/qO3kFwHKHBSKXTGB4eXgbfZPjI2ASOTy+gp6db2yNVKtKBJMxTlcEcyc/A9xGEoQ7ObYNtS1Cv1+G6rr5SL9KQ9DKPPOijVCojlcng/7PvCi5OV/DFPevwxA1DWrxMOTSHMWHL2FFItOdLglKplBgGJ4mZajabaBIDU9WmZsBxZqyItOegK5/C9fkqRrqz6MsJg/q0FRGCMpUGLuUy8rmsEYzcZoooFItFdJFwWzRt0sZn+7NMSqPa7fu+tJWMjMdt2w4K1ao4aaXTaYNAtEpflC5ZPM9b0eYFEC4qulcIEUTLWFhYaNP/FYfv+2g0GtoWcyV8SqUS0ul0bM6HkoGxid3CwiK6ujoNR6XTRXFiV7ZulNAtLCygV/q/4lzYx/oh15cTKGphGMp13GOc0Gk76BjXajWEYagdxNb9UJg9uMntXVpaQjabQyqVEreDaz6yKReZlIMk6jg/P2c4I681QyxWmnAdhs6sp13cqPk/NzeHvr4+w0E5AFybq2J0oYIdAx0Y7BLMfLPZRLVa1XM4CDmuzFSQz3gY7lYX2SImq1wuw3FdYadK6kz6FLjPo6e7WxucL1aaqDYCfehUUKr5WKg0UVmax47NI6b0l/T3UtXH5GINC9Um1ndnkENNxxCn6WHhE3JhhF9v+JipMmTTLtb3ZGOSTkqf5xcWxCYtnfPWGgGmi3WEXMzLjX05pL3o8Do7M4MBEhN8XsYZ7rH8ESqYsdLTdgrGnenDaL1eR71elwx18ryic7NYLCKdTgvfktbaS7JFm5mZQX9/v4FHK0afMYapqSmsW7dO08Yg5Dh4aR5VP8CDO/qRlod6BiAIfBSLRb3+FirCTVB/R9pY2wpKpRIcxzEuNCTZzSmYnZ1FT09P265NpqamMDi4vL9eQNzCny/VEdSK2DAS9xOYBKVSGUEQoLu7vVjA8/PzKBQKLfd6GyYnJzE01BoX1fZaU9hQLs7NYHh4KPY+Cer1OqrVqqbxK/XjwoJwi5XL5bC0tISh/u41m0AKmrgwhlKphFdeflGcKJ1IRaRUxkz+KZuhq1cu49jRI/o3AwxdfGRvIco6/N4hTIyNgdo3aHtEJ7LbU/Z4r770HBwe4O/s3YB//OgOPL5zEBnP0RuX50R2RA4DnpEh11R51L6C2lMwJm4MvfjCc4ZNBLUds/8mxsdxlNhEqHxpz8HNG7uwa7gDw90Z3LGlB+t7szjxwTFcGx3V6ajtCe1L9ffqyy+iVqtp/BXRoZs7tfH40TM/1N+jMTSJj26r7+PFF55PLIf+KRgbG8PRo0eMcuinDcfefx/XRkcTy6egCORLL76gwwbaTFMSPPujZ1q+s+up1Wp47dX2QgQxJmxdThw/nliWjTcAHD1yGFNTU7E0ar7RchhjePF5MywaYwzrOtMYsCRjKv3zzz1r1CdsrlzLLZL4W1pawoF398dCuJkn+ejzwvnzuHD+vJ5XubSLlApHltDufe+8jXJZ+Od0HYbufEq7S6G2p2q+vvj8c7IukT/jORjsymCgM20aq0Nc3lGhy2x7vc39eTy4YwBD3VmN28T4uHZxJGyJHOwY6sBIT1bno3Ds/aOYnpzQay7pU+HCINZfGIb6d28hhZGebMR8y/QdWQ+b+nI4uv+1yEYzYU315FPYNdyBe7b1ouA08N7BA3F6hAgfPY8Yw8Xz53B99DJ2DXdgU1/OoElROjVvgP3vvC3s3uTzfMbF5v48tgzksW2wgIxls/fySy8YtKevkEJfIc4AKtrxxmuvGm3TBvuMxW4Gj4+N4fSpkzFmzi5XfR49chiz0pWIPQftugDg5RdfiJVB09v07JWXRdg1RSNdh+Gurb14ZOeAkPARejw7O4ujRw4bYzjQmdbri7aDc47Tp05h7Lrp7qzVWgKAN15/zXABttLhV+G+kjwqk3KRc3wcfe/AsukoXL1yOdFdTSs4eOBdlEqlttO/+sryIeNU27MpFx4L8eYbryW+T4KJ8XGcOnkyJnBoBcc/OKbn2GrgI8MEUpiempJqBXNh0e90kk9Nmdy+Iir0u15kED7N1g0OxgiwkpDRhe04DH6zgUI+h839OewYKiAvbTMos6OIaBAE8FIpTZTVO/XJrTpnZ2Yw0E9OtzCJsT2lZmensW7duhjz5jBoptR1hKGrwxhmZqYxOLjOKGO5NV+tVqXrA7PfKeFVvwPlo86CVgzM7KxwbbMS0VEwNzuL/r7+lu/tBTc3N4u+/tbpbfzo7Vr7nQ1hGK4q3M/c3Jw+yf+3Sk9xW235Ief65E83NSB+gm00GkiTUEs2cbP7aGF+Ht3dPVqK34ooqnwLiwvo7evT7+xPe84p6bE9NLR8e5OmzLBBD6x+KRLJtL2m1Lqih8ul4hK6uruNdaqYHsMlh/xcWlrSboKSTBNsWhX4fiwSUAwvRAys60YqaJueqefq0pcIG9hpMHK0bqV2VVAqFdHV2RXD0QbVrkq5hK6uzth7auoTaQlarzWbgeGcY0l7W4jm7HJQLEah/Wi59lyL2iq0Ma3KtevlCc/sOhSo9WTP67Tn6MMPrUNokjqNMlvthYCwk8sX4p4lWgENYbcaaIduV6uVWKi+5aBSqazo8P6/Jv1qoFar/Vi4LDc2FKokxNxq4CNjE0ihs6sLvZbKAkjeLBhj2LBhI4aGh9su/+ab90h1qqXmJGuXnujvuuserS4zbcEYIQYicxgEuPee+0AeibRA7BkA5At53HTzLQltVXiYOA4Pj2iVZNKmYsPu3TcKv2AJ75OI2D333BsjYq3UI2EY4v4HHjSe2YwFJXD5fB4337Jn2bIpDI+MJKrJbfwV3HjjTcuqa2247777205L22pDUhs6Ojp0W9tRNW+Uzt1bgV3G3r23rYpg3X//AwBaSy5suPf+BxLTJKnke/v6tI1RO7B9+47Ew0Cr/Pfdd3/MxUqrtJxzPPDgQwhDbmgXIpph2irmclncceddyx6MRLkiz4b16+GlIj92NJ9Sxav0ALBnzx4SiizOgEfqe5H+/gcejKSECQyF0QaItlL6QJthm2f09fejo7NzWYaUwvYdO4311IqGKBzvTRynOO4KHpDrKekwYqtXc7kcbr/jzhhdSQLOOTZu2hSLGUvrsvPvve12Ect6hYmg3j/40MOxZ61+O46D+x98qK1yGWMYWLcudphtRUMYY7jxpptbmp0k5Xvo4UfaXqucczz08PIh5ih0dXVhz617206/bfv2VTFGd919z6oY2NXgnslkcPc997adfr20U24Xbt1724qq3yT4SEoCZ6anE6UcSWJ2QJzilG+45RakICohgsCPET5uMWwKFhcXZNnit62qoHc/GGOYmZ5ODHVnqkqjuqcmJ9HT022c9JPSKZibnTUYo6R0VAJSrVZbOqZUUhvVb9Vq1XhPCVOSjcnc3ByyLRZw0uKYnpqKMXXLLaKpyclVMTrFYrHtRVmtVlcVV3t2djZxUwFat1URt3ZwmpyYWLatdhmLi4ttt7VSqaDRaLSVFhBt9bzk82fSJjo1OSlDwMUPD/Sz3fS0fM455ufnE9ZxXGoIiDnQbDZjhtxmG6LvM9MzcJxIFd2qT9UaGh8f15uQfchRjButZ3pm2rCvpW218fN9HwuLi7FyTNyjtb2wsCAixlhMXVI7AdHvlEmjdMuugzGGsbHrhqTclqxqmij7Zm5uLhHXpGflchnVanXZgwbFZW5uzgjBuJwkkDGhDm61XpNganKybUl/EAQto1Ak4bW4uIhmi/VH14j6m04w81hurV+/dq2ljVwr2tQuVCoVVMrlttPPzc1pE5t2YGJ8fFUalqnJybbpnu/7mJ2ZWTmhhMXFRdSsPXA5mJ6aWlEiTeH6tWst6epy8JFkAt9//+iqBvrkyRPGImrFCHLOMT09jYmJcf2cEjb1Sf+ujY5iUYYqsk/PmoEkdZ05cwpB4MfKskERzqNHDq9qYhz/4Bhh3Mx39qZUqVRw6dLFxL6g6dVEnhgfx8zMTGwztPtUfb9w/pxeNHb6JDh27P1lY3nacPLkibYJRKPRwLlzZ1dOKGF6ehpT0ysTQ9WWK5cvG6HCVoLjMsxZu3DKsl9aDprNJs7LsIHtwPT0NGZWQQyvXL68KmJ4/PgH8DyvJTNhPz9NwnPZ6W2o1Wq4IkPvJZVl20dNTU5ibn6uLYkRAFy+fBHNZjOmyk7CizGGk8c/QDqlYrTGD0ZU4ggAZ8+cMdapXT6VVhaLRUxOjC97kKUwPjYWxd5dppnq3blzZ5e1i7MZrLNnzsCGVn0ahqFefzYzl5R+fm4OMzKU5UobKecc10ZHjTm50riePn1qVbTm7Nl4W1tBuVyOhRxdDq+pyUksLCwsm56ulYsXL6yKkTp79kxbzLF6vxo6OT8/r+MMtwPXRkdXRSdXO06rwb1SqbQcpySYmZ5uOU5JcOnSxVWN0/nz59qm8RQ+ckxgQ0YGaBempqYwMDAQ23BabUiTExMYGhrWzE8YUtVyPN/k5IS2N6SMolE2IumEsnujdVJGy8aJ2mrZ7+xPFTJnuRM2AONEuW7doJFmOVXv9PS0vMGWvFHYMDs7i35yW2+59JzzRBu8VhAEIipDuyetubk53e9JcHWmgnoziNLPzhq2mK1AtaVde0MF1Wq1bSlmO+piCvPz86uyB1xYJn1S/y4szKNnFeU3m822VDRivUW2la3mCcWp1Q1rW0qt+nBxcRG9Pb0t1z/9rqRpPdpdRnxDTtpcKXO/nK1ks9nUtmCtyqf5hH1isjlDUl+VSkXi6kPhGW+z+isVi4ZqulU9SYzhSvOzXC4b6tQkukfflUoldHZ0Gs9XamvHMuYSNgRB0PY+wjlveWCzDxlAFDKuXVhNes75qstPOqC3gtXQJQCoVirI59u38avVqquyT1zNOAHtaVUUrLqtK6Rv+iGWqpH2qFarrUqV/eMwgAA+PC5izp07h1/+5V/GzMwMenp68Gd/9me4+eabV8ynXMRMzi5qVyO+769qAQdBYLl7Sd5U6Wk/ifFKsn0Llgn5RIlcIJlJHkaTOgkP26bK9/2WksCk/EnpkyQS6nkYhiu2ldr4AViVWsSVobbsspZL3y6sJv1ybQXE+BjOuOU8aLetShXV7kJe7UWSv870Py1tpfNsufT2fF4NPqtpq20OYeOZBFQlqYDSgaTDmI3LSvRJHX5a4WSr2VX5yzGkSWVwHvf1t1z6lfq/3fQr4blcntWkX82m+9eZfjncW/V7q/T/tbj8daf/MOO+UvqmH6LSCHSoyv+atv6NdBHz67/+6/h7f+/v4ezZs/it3/ot/Nqv/dqPVc7RI0cwPj7edvrXXn1FuzRR0GpQGGN45oc/aLlBJOV79kfPxE7USadl12Go16p4XboxaFUezXf16lUc/+CDlm2z8x85fBhjY2OJ6ZLqeuXll2J9k1S2+r5c39jAOTfcw7TCWcH8/DzeefuttsoGgHNnzyaqo1rBvnfeidkkUbCdzD7zwx+0XTYA/PAH3297sVcqFbz80ottl33l8mV8cOxY2+kPvPsuJiYm2k7/7I+eQRAEKyeUsJq21mo17fZnJWCM4crly3j/6NEV06n6VVvbxedHz/yQxGldGZ+ktraqq1Kp4CXiGqSV1EzB5UuXEtd3q/L379uH6enpxDT2GlO0zE6zHLP8/aefMtIat5mtPIuLiyvSMgpnTp/GmdOn20rPGMObb7y+KtXb959+alWbLW3rSjA5OYn9+/a1nf79o0dx+VL7bk1efOH5tuiwgtW0tdFo4Llnf9Q2LlcuX8bRI0dWTihh/759q6I1P/j+022nBbCqcVpYWDDm5Epw+tQpY06uBK+9+goWFxdbvk95jhGrfLVzcrV9o+BDwQROTU3h8OHD+MVf/EUAwJe//GVcunQJly9fXnVZ166NxpyZLgcTExNtX0lXovZ2B241/ogYY5ianESh0L4Yf2J8fNkboTaMjV1vy/mwgunp6VWpJMurMABeXFxclS3j9NTUqtQKk5MTq1L/jI+PrepmsHKw2g40Go1VXyJZjZpgNeMECJdIq5k3S0tLbUvWwzBsuWElwfz8fNsqfkD0zWraOjMzvSqXE8Vise15udq2Kmev7dKPubm5Vc2D2dmZVbW1XC63PYd931/VHF5cXEQq1Z5DXmD182BmZmbVqrrVpF2N9Ke4ivUBiMuCmVW0dbV9s5q2lkqlVWlXVkMLgOhiZLtQqVRWpUFYzforl8urbmu7TqUBsb7/usZJOcj/ceBD4SJmdHQU69ev18SXMYbNmzfj6tWr2Lp1q5FWeXJXoDjvojRwzmSyOgJBO9DfP4BisdhW2nq9jpGRkbbLnp2dxfr1G5ZNr9QqfsgxNbeAnp5eLC0ttSWGbjYayOfzbeOTSolIGO2m7+vrR7FYBOcczYBrb/ohB1KuKTFYbd/Mzc1haGi47fTVagVd3d1tpw/DEJlMpu30nZ1dqNVqbREVzjn6+wfaLrtULGJkZP2q0vf29rWd3vd95PK5ttP/OGuk/XGqYnh4GAsLiy1vlVNQkQ3aLT8IAmRz2VXNeQBtpeeco6+vv+2yKxXR1nZvWpdKJfT2Jzl4swABAABJREFU9mJufkE4hl6mf8SaC5DJtj+HM5ksgiBou61J42qreRVUKhUMk/UahlxH+0hqe6VSQXd3V0tcklRhXsprG/fOzk4RkYTsBcvBatfrunWDbaev1+soFAptp3cdEeJwtXS4HQjDEAMD7be1WFxaFa0JwwD5Qscq5mTmr43WNBoNDA0NrYo2dXW1npM2OI4D13PbTl8odKBer7ftSWE1ba2Uy1g3sE6nV/xOO9Z+HwomEEi2R0uCb3zjG/i93/u92POd2zb9teC1BmuwBmuwBmuwBmvw0wbFYnFFDdaH4mLI1NQUdu3apf2Lcc4xMjKC/fv3rygJXFhYwJYtW3D16tVVqfPW4KcDlpaWsGnTJoyOjv5YjjDX4CcLa+P34Ya18ftww9r4fbjhxx0/zkW86vXr169o0vGhkAQODg7ijjvuwDe/+U18/etfx3e+8x1s3bo1xgACQryc5E6iu7t7bRF8iKGrq2tt/D7EsDZ+H25YG78PN6yN34cbfpzxa1fo9aFgAgHgT//0T/H1r38dv//7v4+uri78+Z//+U8apTVYgzVYgzVYgzVYgw8tfGiYwN27d2PfKq7Zr8EarMEarMEarMEarEFr+FC4iPmvgUwmg3/xL/7FqoJCr8FPD6yN34cb1sbvww1r4/fhhrXx+3DDf4/x+1BcDFmDNViDNViDNViDNViD/7bwN14SuAZrsAZrsAZrsAZrsAZxWGMC12AN1mAN1mAN1mANPoKwxgSuwRqswRqswRqswRp8BGGNCVyDNViDNViDNViDNfgIwhoTuAZrsAZrsAZrsAZr8BGENSZwDdZgDdZgDdZgDdbgIwgfGmfRPy6EYYixsTF0dnaCMfaTRmcN1mAN1mAN1mAN1uCvDf7GxQ7+r4GxsTFs2rTpJ43GGqzBGqzBGqzBGqzBfzcYHR3Fxo0bl03zN54J7OzsBCA6Yy2A9hqswRqswRqswRr8TYalpSVs2rRJ8z/Lwd94JlCpgLu6utaYwDVYgzVYgzVYgzX4SEA7JnBrF0PWYA3WYA3WYA3WYA0+gvA3XhKooFgLwdIhQs7BAbiSQ24EIQAg7TqgTDPnAAfAID4dJp4BgM1ch+p5i7obfghfJkp7DjyHGWUzAJVGgLofojPrwXWYfpcEDEDIORhjUKGfFcevfnPVBs7hOMzIS+umz/2Qg3Po+kMufjMmyg+tTqn7IYKQY6naRC7toiuXAgMQcI6mHyLkQLURIJNykHId/Kcjo/j+oevYvakX/+TxHfjXb1zE5EIVv/7AFmzvL4AxhoznwHEYzkwu4feeOY18LoX/1xf34NJ8GS9emMPt6/N4bNsgPIcBjCEMOf7TkWuYKDbw9ds3YKArA845/mT/Fbxzagp37lqHoxdmUC438c9+9ibcMtiFq/MV/Nl71/H6vksYGu7G9o3dKFab+LsPbcbNg92ibAD/26FRHDg/gwduGMAv3yHsKpoBxz/8zjFMTpbwy0/uwBd2D+PoxDz+y3vjGOnN43+4ayN+5d8fxMJ8Gb/+s7fgC7uHAAD/4ch1fOu5U8gXshi/Momg2cSvf/1h/ModG+E4DGqIHDK5OIBDY3P4p392GIEf4H/86l786Mg4Go0A/+rnbsX6npyecxzAv3nrEl47dA037xrANz53ky4z5EDTD8EY8D99/wQOHbyMG2/ZgH/383fgrasz+F+fOo1MxsMf/O292NbfAYdF84khPt/pHKJzLfoufgQhx//tqePYv/8idt+8Hv/+l+6GY5XFuZhn1UaA3/zBCbz9nZeBQi++8Vufwc0DnejOpNDXkYbDmIEHY8xYb0HI8b+8fhHnJ5bwi/dvwOPbBg183rs+j7//R28hDEP86994EI9tG4QrG6fKcazfsNput/f1i1P4X390Duv68/jjr+yFwxhKNR/7rs1isR7gC7uH0VNIwQ84njs7jqvzDdy3sRt3burVawxWXVxVkNDvSeAwlrieDfy5WJMM5vwyE4o2Veo+posNdGY99HekzUbTTqAgx/DiVBl+wLFzuAOuw7BUbaLWDDG2UMVQVxbre7LJk8csCuW6j5liA4WMi4HODE5eX8KpuSU8uGlAlCEh5BxTS3X8v9+5gomFKv4vD2/D3k3dODNWxOViGXet78VgVwacA0vVpuhzxvDv3r2CYj3A371nE0ZkeQ1JrzIpB9VGgGLVx3tjc1hfyOO2zd3gHHjrwgxKzSYW6z5qzRCfv3EEF2dL6M9lsL43i5ADnVlPjKOkmw0/xLmJEv7q5AS292fxtds2IuMJ2YsfijE5cnUBxYaP4UIW24cK+n21EWC+3ERfRxq5tAtOaH4QcvghF/TSohnWsOpnDhO0gHOO2VIDDT/EUHcWKZch5KLfSzUfac9Bww/R15FG2nV0uWHIwRhQqvloBKLuZhCiI+sh5TqC9occIedIuY7ed2qNAM0gRC7tinYHHJ7LwOW8VHsv50Cp7sNhDJ7LkCZl2PuceuYHHKW6j0LGQ8plun+S9sa/DlB1/aSBk7065EDNbz/vR0YSWKmUAbRmrCKmCbhy+YrxjiHa5EDSAUAQhBi9YqaPAZkj83NzmJ2bMxanLpqLBUSfXb58Cb7vG8VwSKYsFMSAbrwXzp/Tae2pqXg4VUapVML1a9eM+jg3e4g+vz46ilKpZOERla2eiY0dOHP6pFGWw6KyzDo4ms0mLp47owtThE0x7ZShCTkwNTWJmalJEwejLiYXhliktanLCHxf45a0bmn+UyeOxROAjIFcdABQKS6hMnM9Xh43PxWEpSnwZkUfHoy0BBcuN+7G1Hnddl0GRNs4SQsAPAywcPWMgR8FumGEnGNhZgrNpekE3JNXysnjx1Cr1yMcaR4jv8B3/uLxFctU4NfKCMtTie8oo6nKunTxAmZmps3DDEsmaiHnaE6djZVpMHYScVXeoQP7EvFIgpmpSVy6eKHle42frPH4B++jWCyuWLbaYN7d93bsGZMMoJ23Wq3i6OFDoiZybtMHRcAgDpcvXcS1a6P6HZ1XsZMiB97d/46gSXQ8SLJQrjsF7x94O8pPykliLOdmZ3HqRDRnFBiHB/L97OkTKC/MisfLrGn1+fabry37Xn3hAMIgwNEDb6Md4ADGro/i/PlzejMWOCUzCJwD7x14F8VSqWWZ9ODDOcdrr76yfJnk+9LSEg68u1/sD4o2WPRG0QLOgXNnTmPs+rXY4coYJkJn33rtZYRhGMOBMSFEUDiHcn967ZWXRNlk7qq6OSRzKn9PTIzjxAfHJGMYMXI2A8hk/7x/5D3Mz80Zz6P38b566cUX4n3XgjbVajW8+cbrie9oOxScP3cOFy9caJnehtdefQWNRqPt9C88/9wyuLRdjAEfGSZwZkZsdEnMEQXGgP373jY3Y5iLQaUDgCAIcOjQgYi+rTAQExPjuDZ6NSrHwsfOfvzY+2jKSWJPU3OBiol/5PB7xnv6Qy8o+ai4tIRLF84bDUxaNGoxXb58CcWlRRMHi3mhcPJ4xEi12hwVUxf4Ps6fOy03qNadqOqbnZnGzPSUfsZ58kJWj6ozV9H0mxIXHntPmV0O4PzZ07E0djsUg1qrVVCamTAOBzyBoddlVOcBv64Jn4mvydRzAP7CWKxNtB7zZYjy9DW7G3Q9oZW+UlxEUCu2ZBpVfQquj16F32waDG7rfDyROU7CCwCCRgO8vtRWWkDMgbLcRJPWhv07KCYwuwlpFVy7etX4bUv8KWNaKpWwuCAOd2HC/LJhanJSr2sF9uYr8otvo4RmRJs6TzjoiQPV5OSEbptxcFG/CV5Li4u6H2O0iDJu8nN8bAw85MsTUlLnzNR4PKldrvxeq9ewIPtR4yxphMKHk0YtLswj8EU/Ku0OR2vUJifG9fgsx6ABQBCGWJibiZ7LfCGHZkBEOeKzUi6jWCwaAgW6rlzHxGt6ZhphECLQjI6FB8z5MzU5oQ+2FM+kOdxsNrG4uKAZrJVgaWkJ9VqN7GMscYgUzM7OGL+NuUvommrTzMx0SwGDUQ4DatUqylJoo/Y1O2/0HFhcXEAYBKT+uHSO5p+ZmYm9azUXfN/H4uJi4rskKJfLqMtDcjswJ5nXdmF+ft74rRltTublqkr8CDGBdFNdCRiLmDO6EJI615F6Ys1HtVPBCngYdFGpYVcqLyGNjS+T+ibKONoZkhkpKVGjJao+Yma6pPrVY1WfTZQ4TPE9OIeD6BS3HHOXhGvSMwaGMAyX7XfNtC3H2awAcaaujTxE+vTjqBZom5dj5uL5TIZl+XLFp/A5xYxnQHzj5ZCEmq1MYvSGaZUS8uhdEnieC9eNyudWWxQzLQgkBxwHjLG2iB4H4KXTBo62xJnOY9d14Xkpowya3p7z6XTa9N/VQoqlsuVy+ZZ4mnWKdZPN5lqms/N46RTSqXTEJBqL2kKPA7lcDoyI5laabpl8IfG5UlXTE7fjOMhl8+ZhSzNt8crS6SyY48FhVLJlz4QIz47OrpgmgNJtO19HR6cuU+DQmh6n0inRNxJXOv6cA461bjo7O+G4rjG/TWaKG8+6e3o0DsDytNdxHHR2mhchuZWOrrZMLodMNrvsWHIyF3p6eg2Go9X8Uoxwb29fTJJH2+aw6Hs6nUZHoaMFDjzGFBYKnXBTqZiUkQJ9NjAw0PJdUr7e3r4YDq0gn88jn09eq0nQ19e3oh8/CuvWrYvhJz6VxnL1+9ZHxiZwZHgEQKSJUCA6Tn6Xp8yPP/FJAMufKPXpznXx2Mef0GWpfFR9SWHrtu3a5iypDvtU97HHPo5cziToon5zESl48tOfNcqJTQmSYWDdOkFYrE7hZMdTrxiAO+++B67naUTtfjQYOQCf+twXkzQ+iZDOZPDoxz8lT/uUWCSn33nDjbqPYyfohDxdN9yPVCYbY0xbwac+/3P4j+9PrJiOc46egSH077pj2TT09O4M7ELQjBttiCQ8Rixzux5BGMRVL0n5mZvCxrs/YTxLYuYA0W/rt+1CdjDUvykodSN9/PFPflavE10m+bQZ0pG7nsC1A5da4ksh3dELt3d7i7RcqvgjPG+7857kww9RBykIQo7M1vuN8pIkzrS9P/OFL7U+bCBqq8MYNm/dBkDYSyXjb/5++GOPJRcMJK6Zz37+i+IZYXSS1GMA0NXZicceJ3OAlJW0ad900y2kTaqvFSNP0suCnvzUZ/SBklZizwn1+/7HPhlHBPE+AYDBwSHkuvqxUPF1e5PmmtJa3LL3DrxUvoJqqRGTYlL1N+fi2cc+/kmcP3xdqFpB6BuL0gjcOFzPw90PPmrgm3TIUvVs3LQVhYwb9RUQWxMgdd13/4NgjgvGGpGpTLxLNDwu95lWc562v7u7G3fdfY9eN6pPbJqt4MbdN6LcCNEIyPyjTB6pM+TAfQ99LIYvHXOzrRwPPvKokY5ZRERJDzkH1g0OYWRkWK91e1+hbebguOXWvejpas142WvlwYcebpnWhlwuhzvvuitWbyvYvGXLqpi6e+69D67rtp3+3vvuT3wuzrhMq99XAx8ZSeDi4tKKqlo1z86ePr18QhDiyDnOnBK2b8bplccXBCBUWGPjQr2XRJBtieOpE8dRq9WScUh4dvS9g0Y5sZMlqWxpcRHnWrRVM3Xk2bmzpzE/P9eSsbNPme++82ZiG5Og2WziyKH9koAqhql1rtErlzF65XIiUU7KVbryPpqN9sX0b776olkmjzNUChbmZrFw5WTseSsI5y8DUu1JVXuamSIMIwDULh0geEjmv8Vc5kETE8feEt8N/JMlhBNXLqA2fTlWfpTHJNSvv/wCKuXKsjhQGD8c2TFFfWjipcqpF+cRLlwCmJTYMbMOG/0jh97FmLJl43xZtRcHUL+0X+MST2AeqsIgwA+e+k5i3YpZoBv85YsXcOzoe7SoRFCb/csvPo8FqtohS5OuGfXsu9/6L7JsNf6RmkzbP8m8iwsLeOG5Hxl425+UKTx65D1tS0whcXg58NR3v23MC0YS065V31979qn4XFEMoUWkrl8bxXvEFlOsC64lfTQ55xwH9r0Zswlcbh788Ht/ZR76JS5UEqiyN+s1vP3Kc8SEMWImIvvmCJ9zZ07hzOlTsX4LORAiYmgUPP/sj1CtVltLAlWb5MOnvvtts+AEuqceTU9P4y1iy6bHmzDGNN/Bgwcwdv1aLL0q05ayPf/M07CBIWL67H3nuR8+rb/T8UsaqquXL+HEsfd1WZSJsw8+nHO89fqrWFpaMttHaJe93r//9FPG7yR1s4KFhYVEm8BW6Y+9/z6urnRHgMAPf/D9ttOulL6VYGgl+MgwgeVKeUWpDiAm8bVrowbRZNZ7CpxzjI0JuydKWNWCYKoQCaVSCUU5YSkd1OVZ+MzMTAs1Zuw0yaIFSp5PT0/r9zotyUMTN+oNLC4utOTQ6KRijKFYLMJvNEwCQYinXe+CZBjbOZnwMMQSsTe01ZQ2Ya/Xa6jXa8mif8Rt34J6hdykTD5200sTlZJptA+YjL9Rtt9E2Izsu6iEyEipfgQNgIeGsXsrW44QQOi3x7xyCGIWNFsbGttMpN/0gTCU9SsCauJF21Cv13RT9Ga6DE60X1SdSgigytVG4WSeq5uMyW2QuPtBIgFPkl4EnIOH1gUr1QjCdNN1rwzfqXkIrYracoVhZJMUtTfiFGLzWfZ5K7APbLF3pA41d0Iu7FCDMARLkEbY9dF+aolLwgk1DEMxV6xl1Gqda3QNQpqQkSX3C+fJl7koEw5El8FUX2tNgZUnlJJOG2lmpfdDHklpeDQfFS6cW3OUSN1o0QE38VEg2uqAskImkxPH3y479lzOAR6GhkQqaa8Q9Ul6KdPHtAFWWr3eEjZTtZzs8QvDEA6Rdi23HwgbNzO9bRYUvyQicKeHonb2elp+0vzXuLSxlhS0St8Kll17P2baVTQdwEdIHUxth+j6v/3mnchksnj38DGkpKrz//EvfxfDIyN45GOPxRYPJ8RIfebzeUNtqspOGo10Oq1vT4GmJbhp2sg5Ch0dcBwnxlC04vqVQ2xTshTHAwBSqRQKHcT2Qp+QGaXLGvKFArx0elnGjuLV299vlNHq9MQgFmEPsb1ISkoJbj5faEsSpcAr9OjFqZil5WBgcAjjK6RRBD+VzsLLxz2zazsNAH4YPUOqALip2DwAzPkFCDWmW+iX9UVlOAlbgzIMLvQNtsSZbpycA9lCJ1g6H+G2AgyNrIfjustKgSlk+0eAOSpJiI8tU/mdFFgqDzisLVz6161DvhDZm9knf6pG8gMOp3MIjLF4vycgxRjD1m3bZbmS0SObP4cYG5W2q7MLmYxwNxJycbo2pSFRVQCwcdMmpORaMrDhcXrAGMP2nTsN3FptCIwxpDMZbNq8JanLEmFg3SAK0m4vcRQVkvJz164bDJtAGwt665RzYPP2nYb6WH+lxE7W09nZheGR9botjLG43SohssPrN8Kr1dEMGXEnEkdfVbzzhpswXiF4EuRNCTXgeR62bN2hGcektur2cKBvYABduUwCmiKvax30du3eDc/zSDnLT/pb9txqqkatQyllZvKFArZIEwXFvBkMKpHAcwCbN22Gl+s0JPViD4m7CwNjuPGWW3U/KQlt0tzhXKS/6ZY9ZM4m0wr1vre/H1KpbgozmKkWVu927LoB2WzWYBaX68rbbru99UsCjDHk83ns2nVDS1xt2LR5MwqFZBvYJLjjjjvbTpuUXu3zCVtJ2/CRkQRuTiCKqt8ajTr+45/973ri9PT0Aohon30aAiICx5iDT3/2Z9rmvrds3YYbdt+ofy+XjzGGBx96BNlsNibZamUn8ejHn4gzcIRY6Gcc6Onrxa233R6jbLZaVz276eY96OszGbtWwAHcc99DxjOlIlc+FylT56VSuPW2u0h+RQQUUxr95pxjaGQ9htdvkOXGCQbddDnnyA3tiBnuq/dJfh5vvvX22MYdEUfzebZQQH5gA6lbJFD+IOkzAGD5fsDLCj91CfhQ4BxI9W2CUI9G9lEOYzEXM4KRctE5tDlWpwLaV4wB2c5uuPluANHtyhhO5PumzVuRSqXanu9Z2S92H9Jy9SM3BZbu1O1LAlpvX18/8op5IZtXzBchgGbI4ebNuaur4OpChNnWkfUbYgcpm1FQv/OFAjq7ugxJJt1kozaLh339A0jLiyf6Pe0bggznHINDw6Te5HFV89/zvMR1qpl/8oID6Ch0IJtgd2wAqbKvnxjWxwbRfAwAnV29+iHhBRMTe6kUCtalAEUzHKtDGYBsrgDmCEaKjju1g6PoFTrEIZmp8bEZRm66XSp0dMTKoAwVleKl02mkM5mY9J+uWZDzRi6XAxhLXBNJkMvl41LvBCmWYlCy2ayRTrWLPlN1plIpMCl94/Z7ZrqrYQCymSySQPVbVAbAQ45MOhMdXigOifgwpFImrbbVu9ocAoDrOIb0rZVkT4FivJdTA9N6XS8uK2tVPud8VZLA1UCSlFxpASgtWi0/+JFhAq9cudzy3T/+nd/Fv/7G/4xyuQLOo2vbxWIR/+A3/h4ee/h+PHDP7fi//oP/M5rEPQZjwkXM098TthqUWLSgjTh75jSOf3DMSJ8EanI+/+wzqNXias9Wk/cHT33XTCf/iU2OSBMZMDkxgQP731meEyXlHHx3H6anJgkjGSdIpPiY3Uir9eYwoF6r4e3XX9bPgrB1GxljuHDuDC5dON8yTUjSAkDxwiEgaK6Ii4LXX3qe4Jc8UmrTWZiZxtLV05rhjPCM0tL6grnLQKNkXhDiyfYxAeeoXjkce24zLFpC5Tcwc+6wfGaeoike6tXUtStozI/L8pjxzq4PAA7sewvNZjNBrRRnwAFg9tTB2HudzvpdLy6Al8YB5rR1sj198jjm52dJvaa0ho5FM+BojJ9I3iAoIZUNaDYaOPjuvlgf06zNMGL4xq6P4trVK0JyxU37RCFJMas8fOiAdiVhYMMSv2L/228RPCKJh20zxTlHcXHRcM9E8+lDLdkwzp07g1npQiuRI7G6691978i05nMDD/ks5Bwnjh40Lj7Qg6hRFxNuUK5cvmRUSw+AlLByAGdOHkO9VgVA/NNpdTCLzbHDh/abY2G1jd4wrteqOH/mhGxHxPyF6iCripB5rl65jClCHxXTFZkNmJUdOniAHNjj5iC2ZPu9QwesBIiNv0o/PzeHixcjf3VJy4muj9OnT6FcKupbupTh0+tCQsg5jhw+aAtxyeHKZEzqjTpOnvggXj/MchU+4+PXMTExntgPSXTk+LFjCKSLmJWYOgB4771DpOx4/fT3wsICLl2M+/1rVc/FC+extLTUFh4AcORInLa3gmaziRMnTL+rxsEOybR7JfjIMIEU7OG59fY78ODDj+CP/+gPjef/7Lf/Jzz48CN45c19eOfAEfi+j//vn/wxACrZ4FoKEy1o6DSxMUkglMZpXeeNb+CqviRIsgG0vydCAkHkaPGp9QG6UqMNrSSICqgUSj11JPMRWoSMSj4VQVHpZcEx9Y/Rj0Qio967TmRnYuDCk6MpRIxkRLC0xA3ELxkPASmpUxcgGGNIOWZ7DHUIc2NqyThjJeyS1ERyHXXSZNKFDsGVS/UkB5wEtyxqfoaKcZF1h4GwSwKgmVJ6qoxwl9+XsWWjkgNdL2mLgU8Mv6gfwZiUosbnuj3GdC1piW7CgalJjMo8yhDr3U5JIQh+xhqM94tyxBvVF/kuo1FXBG7cYCJXsgmUs2RFTUHSsyDBLslenwovrnDRphKkbJsAEJoXK9z+SeaLmmtG36oHVkOVJMVQV+q1H+eQVT+6jvgzma44XsIvqWDm7PVuSynDkBs0QzO2VtmKIbSlQKp5au3bkn/Rj8zEN2FKtGQo9NSN037f941bpyazFT8EB0Eg3NUgNtyJc5DucUl1qHeMAaEfxPqxxfkCnHMEQQDP8wg9MQ89ZgYgCAOzrS2Y7lgbmElDk6RsQRDAceK3d1uVHQQCl3bt/NpNR8tOyku2xVUzgh8Zm8COjq4EcXj0/Z/9i3+JTzz6IH7t7/46shlh1/HDHzyNgwfexR/94b8BY8ITfyqdNogTYww7d92gTz8KuP5nMkQ9/f16EzLSKpwQ0UUA2LFzl2E3osqxT2FqVd94080r9oWCjo5ObNq61awQ5gKlBGHr1m3o6Ogk6qvlTzu37L3dmKi2lxNOJCapVBq7boxwj4yp1UZq1jU0sh6eJxZECHODBWzVM0d+eCdSnkeY9ohYUIZUwa133IWrUzz2XDGCQj0rnuW6elAY3KQlgZpJNCR9URmsYxhI5QwmUZVtMjmiz9KDcZsUxdyaJ3YO5nro23JTDF/dL8SODQC6hzbA6ahBqZuTgDJ3d95zb2w+2qAPRRzo3LoXU6ONiBFgVl/IByHnYOkusPw63b54ueZB4YYbb9aqQ9XvDmOaOae2cw0/hDe425AmMHW6SJAMplIp3HvfA0abDKaE88gmEMDGjZt1CMqYU2my6SpbznvvfxCZTGQ/Zq9BNRdUGQ8/+tiy/UG/9/T24tbb7ohpJOxNXf2++ZZbTVyAOC9CMj/2ceF+xuYT1XpT6QWPzXDH/R+Lb7C0ueTHho2b0Nk3hBpltnkUHcKGvXfdh31HZhBw0xYxsQ0AHnr0E/j2hQoYowdKK5Ec21yhA9vuvAdAdOlGXT6JUI9q2b37JnQWsrF1nCQJ5ACeeOJTCRjCSEPzPfHJT5PfUZmK0aSH6w0bNxomBNb0Qsg5PMKw3nf/A6hzL7avqLbQQznnwMc/+RldrqmGj/pfrZl8oYCHP/aYgYO959Hfu2+8GWkv+TJGZM8s5zsDnnjy03Bd12D+bEaQro/P/cznY2W3gpGRkZhvvuXgvvsfiKmyl4PV4JLL5fCJJ540nnGevG+sBj4yksB8LmcwN4DZWVu3bcdXvvq38b9843+GJ22eOOf4P771Xbx94DDeevcwDh87hX/zb/84ton1SxsZOsHpAZcOUC6TRY44k4xwIYuM4l0oGDefVJ20TErLOjqSnWzSdBT3TDqTeNRLwst1XXHS5XFJCYVICucY+QMihaISE0VY6eINeCiIiKwmUIRdpgm1+D+hflKXfhY04TlJp8UoDbW5q9drejN3CKlPYvL8ZhOBH/mH05JAcvSnp30e1AFwpL3lj2wcQuXIGxUh7dBSxrjUkss6OA8R1Kv6ueq/Vu2uVstA4OtNMUmaR9s1Oz0Tf2GVTXFqFOdleeYGTUFV5der4H4NYI7ByNllKpifm9VRYGjZ9tzlnKMZcISVeYABLouXa0Oz0cDU5KTEwSxf4aDUwa7DsLg4j0q5pOujTAsHlVKKTxpqktNCaRt4REOutjBnSZJ6VMplTKtoOnYaZjKXADA+dh21alVLsfUY0YTk2YUL54xH4FSyDmNXbzaaGB+9EtvojTYTmJ2ZxuzsjMGs63E1Tjzi65WLFxDI+es6En9IuzNLgsQh3LiEIHNdEmhj7sp81UoJ49dHo36Rr8XFH2WTFtkHXrt2FaWlpZgwQPWNbXN74vgxw5VYXIIdNTcIApyk4fQ0oxXvRM45pqemMD4+1pL5tp+fOnUyinShxhKtmYvj7x+J7yeA0U8qT7lUwjkSgUnna8GsXL18CYvSfVISY0c/OQeOvHfQvAmdsCYoM3jooKVWXwampqZw5fLl2PNW0tkPjh1DtVpNfJcEB97d33bacrmMD44lmHnQ72z1ksCPDBM4Nyd9SYGesKL3HMA//if/DH/5X/4PHUfzM5/7PP7wD/4VfN8HgwjZcuHCeaPcRqOB90jYOFUHEJdmMMYwOnrF8MdE0ydNq8OHDmqVh32SVGWqTwbg4IF3jTScVGCXPz8/i8uXL0bv7AUMcyGdOXMa1XI5xk22EkEfP3bE+O1r5lDli37Xa1VcULGDIdWgRh0yrcRpfPw65mbFmNpqQE6kNIpxqk5ejNSd4MbNTrVpu2TjPnPyRCKjqMpXmw4AlJYWUV+YNuoHYKqDaQzP0jQQNBNPuzb4AUdzbtQoR+Fk4xWGHNxvoDIdhWozCTLX+dWGtDA9jaBWNCSBrYgIY8CF82cMiYNdh52/PHkZ4FFf2XirzZlzoFEpAQ0xv1pKJcn3sWujaMiNKwh5THpM527NDxEsTchxJocTwuHRKpuNBsauKx+EFF9oBqMZyIOMwzA3O6t9lSmbMUYyKSmlOk5cvXKp5UZCG6s24qtkIzLtTpnxnAEol0uYnZlONgvh9gNhh1dv1PVYaCkZSUv7ZtQKp5dIXyTufuBjdmrCkFgzKw09qCwuLqBcKkVrDwkuVgjDPDl+HTwU65eahyhJPe0Dh4mQd4Bg4hyLYaXAOdCo1VBaWjAP4IjTO/V9bnY2FjKM8/i8VOWMT4xrRtLqSlK2eBIEAaanJnWZNCGl04qWFYtLUShAFjl9V5+qLpVvcnICXLpEomm4Vb56NiXDEhpthdmViseuNeqG+y9Yaex2LyzM6/mo20twMA8IXB94ojKjiyMGfly4fFFu1AzBSwtmulwqoVKpJOKeBLOzM4kxlVvB1FRyrPQkqNfrKBZbh9VkDOYhrk1oiwmsVCor/rVyaPzTAtROSk9wq7MG1q3D/+k3/gEWFxbAAPw///W/get5ePi+O/HAPbfji5/7JK5euWyo2JbzC2St1cT0xqKJ5Y8bfeuFQPIapySrPCYfqg2CJuJhFAqOyX908SfhbvgfW2G22RKRSH0mGDPl0sRhjPipEol9a+EHVl+o9GosqHQ0sssTzIdgfDhS0k0QjdnrEmmdY/Rz1DxbrRs9l/0SCFs2zzHfp6kkkKpheQDmOMiuIAlkEBcalJNaJelQfWYv+ECKFZSPrYiIRmmoTSBjDH4QQDhnNiW0tO9p25Oe07rs+jgXaW23ITaEnMuoKMKuSjHsBiMTw4fHJAAM1FYz6nPRj0wz+xQoPeDyCwdfUa3TDKLNnTFoex3bMTBHXBLouK6wHYLZd60gJcNimepss50U1M1jBuuCRMKUc10Xrpug4mfJP7Mk8o4CzumhLpKchZwjk8stq72gzIPDHKTSpmqag8MBYaII0+ml0oDqfzl/6aHQ7plsLgfOOVw5/2HNadUGZcOZy+fBGNMXQ1Q612Hm4ZNzcTuYmAsp8EOBk2vR13y+YMyLWJ+SzuKcm2HgWkwZjZ/n6RB2dG8Q/aNoafQul82J0GtRQckViE5CZ5cZko4y9zZfzcBah4Ejn4q2ZTIZ7W6Jtimqnhv5lVs0+0BkA5P7TF9fX+K7JEilUokuX1ql7+joWJU6uLe3t+20juOgu7tH/6ZCilXyfQa0ZRPY0dGRyFkLBMTzkZERXL++fLD4nyT0Jgw8ABw7fQF+wPVp9Ld/55/h67/yP2BoWNhT/Jt/+8eJDJGCbDaL+x9sHYaG9hjnHDt23oB0ymuZxhZhP/b4J/TmQqVWQLTw6Lg8Ke1Gksq29eEj6zdg3eBQLHGrsb73/gcNtwOJdZDfT3zqc1F1nGubQGXATSdxoaML9z7wiM7fDEJNqCgxV6rjm/bcBs91tK2OKkcBlR5wDvTe/AhSiSqDyP8bZQ6e/MwX8CeHJgDAuMVLL3io9L0jG9Gx0ZObRfQ+RXxTUkmgM3ATWDqDjLv8GYwDaIYhstsfsA4C5ARN2hyGHE62Axtv26X7ygYtCZQb5vCuW+FePK8lJ0b9ZK4pfH7mZ7+aiGurk3fPnscxf34y8RIMtTMKOeB1D8Pp3NCWVBIAPvbxyD5GMZc0L8Wj5nOkt94PxphxAYaBw+hQCV3dPXjsE59sXTnI7WCHYe9td6DSCFBtBLIfosMPjRihLvR84We/rCVvraSeQLSWvvClLxtMrWKquPotO4tD+CrbtGlzTOojmGQW26UfkiG91NxVdVAkOPn5+Z/9UuKmo3ChkMvlcfdDj7ekn/Q5B3DTLXuwUGliqerrCxfK3s0oWjbjocc/if1vXoIDpi9eKHUwZe5VXY984jO4cHQsknbKvjDmuszQ3T+IW4d3y0d0rnJ4LDowqXruuOse5DOerk8x/5GPQWbQsk9/9nMo1UPyrjUjk06niS2mQJpZ/U0ZzB07dq5waDN/f+yxx1Gsh8ZzynOHnET2ZgyPfvzJFSVOqm96+/rQ198Xq9feV9WecMuttyGbjtvBq7YYknAAH3v8CZPZbYEY5xye5+Fj0r42Sbhiw8ZNm1bl8uXe++7XB7B24NHHHm87bXd3N267/Xb9W88DeQhZrQRQQVutu+222xAEAcIwjP2p54ODrR3U/jSAsh2iJw8FVCXF+fLuZGyo1Wq4TiKM2GXT34wxTE1OYGlx0Xy/zKQ9depE8iJW5TIzvbqKr97ZpzIK09NTGB9LVh0m1Xn6ZHIIO3shKzh65JDuWyDZJpBBMF+l4hLOnz2t+0JJArXbB8LUMcZw8dwZzM3OyHemPRtNr5id0pVjBjNHb3YqVZ2qKwgCHHr3HSmBAzzX3NBtCeHU2DVUpq/BkXhLHsBgKo3oErNnwcImsm2og5sBR330qMGkaXUH7XcuVNxhdQmzF49LXOP3a4NAtNmTDOj1c8cRlOfkpkj7z8ynCPbLL/zIeEbrt8H3fcyfFiHAHJvDhDleAeeozVxDWJ4Cc5hhtN4K3nz1JVTKZd3WVrZXQuIZonn1IJi8RUr705RaCJibncE7b7+xbP1+ENV35L2DGLs2qiXcnmOqrOj8BYDvP/UdwryZ5caY6TDE95/6LmZLDdT9KKoK3URpjvPnzuH9owk2W7a4STJBL73wHBYtmmSgxUxJz/e+/a1kVXO8KViYn8N777ya6Lsxadt979ABXLl00Sgr5JFEU+fnooznvv8dESGF0cOlsjVGjPg9+30RCtCwCYRF7xhDEHJMX7+KE++/F+0PBCelbZDJAQBvvPoS5qV7MQpq7VPpNucc3/32t2KSS4OWEuZ1aWkJL77wnH6n5rotFVO/3j96BOdlKEBjfCTdsBnOp5/6rqE5SBobRnB65unvmI0nddsH1NHRq3jv4LuJ0mjqz1E9fvvN1zE3E9mF2mpbzk3p5g+f/k7bkrByuYwXX3helyXwTGYAGWM4cfw4zp0922bpIqyb7yfHD0+Cp7/33ZUTSbh+/ToOHjDtGWlfJAkG2oG2mMA/+qM/+m+S5icJjYZgAvXpmbwziYGw11Gw0uSq1+uYmpowni03BrOzM6hUTRsDvaETIqAGdvz69UQJC+Vm1dcwDDE5MRHhwEl6qzEMwmBX2Y0kEWVO0gLAzPS0fhGV2Tpg9Qyx1eCgxFBJR7iW3DQbDZRLS3pBNoJQS3YYxMUQKuUpLi3C95vypG3aYHHOow1aqmiDSlQ2tQl0nIiYK0lhGAQoFZd0Go9FxC8MhV0dlQTWqlVwvylVRNGGTyWPodRBM8aAegWMOUi7TksCpKDhc4T1im6c6hM6LroOzsEDHyCXJVTfG3hAqTCZiF0q3XhY+6DuS1pOtVJOLDupHb4f6LBxLmGKkiDkHH6jAWgpa5wRtaFaregyFXNuM7MKGn4I7jcFA8yIOxR745WfzWZT20glAufSJlC47Gk0GnodKzxIUj1HFW7NBgntt0I7/SAAD0NUG4HQWmgpVJwRAIDA92Phy3QSe1FzoNFswvVcAw3qB48CgzgkmQcAM6IE3dB9PwBj0TxvNXd1W6lrE71e1cEnwj9qnqBcjEU2gYm3cbkMAyjHwnXMQ0DIeXRgVv0ahtpViaIzgLixn+QnULk2oX0FRGHqqC2qomumw/fkicBlvyS5TYmlVYdo2Y+UMaR42VWFgRgn6PLN+o06EtaFHcwApI5QuU2xymrFwAaBD1d6chC42Ic68kxmoocM22yCPrdd56wEtluWlWC15bfLvAJiXdP5Zc+l6PkqCkWb6uCHH26t7mw0Gkin08um+WkA13Vi9I8xgCV0mNLpK8ZInZwUKGInOptr+wX7VJ60TF3XRTqVTrSpUaNHF4uySVGQZAek3gZhiM7OTnOhIVKh2uClPGRZTpz0KV/JeWxiAUChUIi7qyF42ae2rq5uAz9lQ+VJOz0tURPHeHR0duu8Tc3ECeJvS3ny+QIymSyUjYvaJCizptJzzpHq6NY2dfS9Ixk312GG656+/gGEdZFGXeDgnKMRhnr81UbrpDJwMgW4jlDfci7sN6i6l95WZuk84DrILkMsFJ7NkMPJ9Viqi2QXKn4gXMQUeoT9jb3hhjxqtyd1Z162E/AqwvdgqxMx+T40vCExTZL9IeccbscAEMjoKdY4USYm4BxIZcG8DBzHgSvtFGNqQAKDQ8Pw5FoNQ8HYO4wZEl9VftMPwQr9ifaG0RyO8qQzGQwODrWUWAGROthzGfoHBpAvFCKJHyImQzFIynaQATqkl6qfKYQsJl+twy3btmFjXy5GCxSDpJ8BRuSSqJyEBsiKN23aJGzZQCTd9EAKkxZu377DeGceHiLGDQBS6TSGRjYYG3cSDVMwODgElukEtWFWTBTtI/Vy45YdODLHJVNHbwfLecNMHDdt24kxn8QOlrRP9bVCPORArqMTgwMdgsYQcw6AejSI1uKmzZuRkeYy9BClPBu4jjlWu27YbUiIk278K4Y6k8lgy9ZtLRn/aG0JvIaGhpEvdJhqcfJJ6TZH5FosaZrQPVDJH3bfeIvGe7nDHQfQ2d2NfD4ekcaet0riumXrNhGKNWHfSpo7N950S8LBK5mBTKfTOgzcSgdwABgeGRGRXdqEPXtuXZX6+NZb97adtrunR88vBfoyFJYXPi0Hq7od/DM/8zOYl1e3AeDChQt44IEHlsmRDL/3e78HxhiOHxdqq6mpKXz605/Grl27sGfPHrz1VuQdv1Kp4Od//uexc+dO3HDDDfjud9sXn1Lo7u5pq5M4B774pa8YRC8SQZsMIGNAf38/Hv7YYyIvKSeJ8HLOcedd92DDxo2xBckRZVCEnTGGz/7MF3R5qgyqYqV1eJ6HTzz5qZh6Rts8EGqgFttOEhcxYo4jPGibHnjokZg/MbseIDrdPvDI46Ttkfos5TKtjlXEvaunD7tv3qPTN3xFHAUu6mKI+r3zxpvR09svy4kmsuoPeiOPc6Br217i1JW8J5ctlHrXS6Vwy213asYtRaRkzTAkkkDxrGNgCNmedXAdJiWY6n20vHxpEMkYwDpGwBwPKceJETkbGn4It38bGGNRWxM4QM4Fo+lk8ugd2ZRYFuccgcRDqYNzA+vhpPNaEmgzXVq6JX/vtjYLe84Yc95xke7bCKV+pmXbrfZDDqQ6AC8vVLaKwSaMvd3qzVu360NJpN5PDn/XDEKwjiGjbPuwptSOYAzZbA5DIybDq4pVvEJDBoT2HAf96waRzRVkOVxs+KRcO7Thpi1bYzjadSnaA8awcaMZCnC5Dayjo9MwIDcLjn9fNzhkhNJSjE2rw+z6DRs1Dmo9Gr4RGTQT53op9PQOGBs6pRVRe0U5nZ1dyGZz5LDAtZROt5kg1DOwTktjlU9QKpEEqYtzju6+AXAu5ogtLaXtDEMOL51GZ2enfq8wDjmHK9cjEF286e7uMey9FcMUSMmZzeT19PXJS2pqTK2O5pFNtOM46LHGNEnapZ5ns1lkczk9j8xy5QGNRUxmZ2d0cGjFUKgh42GI7p4eQ8Js9zXd/1zHRT6Xx0qg6s1ms0hZdvN2W5U6ngMitJ9muluUTV5QF230va12BqTQZhU2fkk2860gCALkE3BZDpJwSTqArwZWxQQ++uijuPvuu7Fv3z5861vfwmOPPYbf+q3fWlWFhw8fxv79+7F582b97Ld/+7dx//3349y5c/gP/+E/4Bd+4Re0Xv0P/uAPkMlkcP78eTz//PP4jd/4DYMRbRdq0ncP7Sc6genz733nr4y09kLTi4ED169dN0I6qfJoOgpvvv6qVtkmLTZ6ggSAb//lX1hMX3QSsjfgSrmM5599Ji6+h7kIVIOPH3sf58+e1Q3VjCkpmz7/wfe/J1QBZCPnVl5axfPPPGU0SjFeERMYbSRj167ixLHIjqkRiNO34neC0JROHnjnTSwuzBtG91HfQDNqaiNZOPW2sREFRJWnmFFFpEvFJRx4502NL73l2yAXVpTd2rVzp1GdG4fHGOpBKF36JF8oAYBw6gQcxzH8CKo+o8A5hx9y1K++JyWW0DjTtqr+DzmHX5zB4tVzZjkah6hfFG7XTh5GUC/DlUxaK6ZU4fbGqy8aZSaBelcsLqF4VYTdshlXu60BD1GfuQpeX4LjOKY7DSux+v3W66/oZ9pwnTDzFBp+iGDiBKjakOKrNkPxgGNyYgznzp5e9uCo3Bi5DsP7hw+hXCpqtaFh7yhvh1LG5M3XXonRHRtU2mq5jMOHD8m2xw3jbRzPnz+LifGx5PLskx2At99Ktn00+HnSnqT0nHPttF0/AzA7M4XRS+fMQyySaKpA6vjxY9oNRsREJ2gmJGN95MA+cMBg7AOuJIHMoMW1ahWnj78PQNrzMsTpHIvqnBkbxdTkuIGfAu3HEtHB5uCB/fqQRfcIbQYjD4WKJh3Yvy8mXbT7T8Hc7CzOnj2zvNSNMLunT51EcSnqR2PfS/h+4N19ZjnM7L/I7IKjVqvH3H8tN4+vXxvFuJyP9nylzKJ6fvS993QYOFp3UlsZIOwNOQx6n8Q0AtLN2/lzMaYviaHmnOPc2TPa9VM7cHAVPgibzSbef/9o2+nHrl/HxPi48Yxq7eghdTWwqoghv/mbv4l7770Xjz/+OPr7+/HGG29gx44dbeev1+v4+3//7+M//+f/jMcff1w//9a3voVLly4BAO655x4MDQ3hrbfewmOPPYa//Mu/xJ/92Z8BALZt24aPfexjePrpp/H1r3+9ZR3UV5MaQNdrT0/PGGKGnVqSlgC+77ct/mWMIWgVzmcZNt44RSdMbEVYfd+HI20vVNkqbwx/Zto70Nq1jSIBDnECpOGlzE0iaobD4nY5HNAEMuU4cmOMbroGxIYFHNIAHjE7H0V8A9+H67laTSMYhwhXn4QnUZIBJVXi4IY6WEUk0HXJfgz96JavGp56EBjMKwA0mj6Yk4LrMNSCAEql55JxU1IjhRBzmGEzqMeAm4u40fABuaE1CeOcyDAGIRAG8FLJy1rZMwKRJLAhbbaUJEWdsOlhZCW1CUNCmD8AzWYAdc60VbQ2+CFH4DfE5uMwuA6gukzhw5Hse1Pnl4cGOyIPANQbTXAmxjvppjK3PkOyNuziOBdp1HxOewxBPdBRC5SkSeVVpg8Oi9oRs42TldvrijERusojoahoXk6yQ/4OgyCidzYHkDAMnHO4ck2qYYxCIEaMhW48wYH2CX2uqvSbwr5LHdLU5ks3XfWbc67XXvRMlOWxiGmjzFqASAIs4vpyPU9UWoVLEATg2vxDSSStrpG/Qy7CnaWluYGSNKuDr5IEqpvInCua5BljGHIemQ2QeadUz9SW1WAAuYmbsgmk0jqQvqR9zxhD0/fhJdF2OmhkrOxxU+MCpuIASwafA83AFy6FJB1T5cb2DHlo9f0A+VxWl88RzSemaXLUXj+I29Ul0SG1lqJyIilwK4m5sNnzlj3w0ny+ZYdn9E1C+e2omCkuK0VfstPbkka7PiWlXg2sShJ4+fJl/OZv/iZ++Zd/GVu3bsXv//7vr8o/4O/+7u/iF3/xF7Ft2zb9bHZ2FmEYGqFZtm7diqvSIenVq1exZcuWxHdJ8I1vfAPd3d36b9MmoRpzXS82SQGTkRKfInpIUpqkvs3n8xgcHl5R1azKH9mwITFiSCvYuesGY8IlTVzFFKRSKWzbHjHldLEl1bduaAh9/f0RcSXEOFYHhCqQTjq6oGm5SjW0+6Y9xuah1MFpjwnfWWFky9Pd24vhkfWyPUISCEQ3cBVPpxivbTt2IZvNaSkLZUI551JyGP3u2LBLM7chjzZwz3HkBh3ZcGVzeWzfeYNW92RcV499LVDq4IjZKKwbQbrQjbTHUJYuQpSNneovvYlwgHVtELZpCTF+bfDDEF7/DjgOi9lJqr5S5Ychh1voRX8Luz2VRqhnRcbO9TvA3IzhL5GCTWRuv/Ne47eaA1QyqQh6KptDqn+LYKQ9x1hj9gzzOYfTMQSWyovINMZhh9RHNse77rlfPxe2mJE/RRuafgi3fxdc10l8b9czNDSCLRYdsNvd0HOI4dbb7kBW+n1T4cXo2Ed2q2KtPvDQI6Ri65PUAYioQXtvu6PlulRpFfOxc+cNGBoaMQtJqEr9Pfzwx2J1AvGNTuH/sHQpo8tk5q1cIFp3A4PD2LJ9V6IDb5uuMQC33XEXCh2dWqojbn1HuGr85DzYc/dD4jY24eqU2YgdWzxfKOCGW+8Sc9ONaDplQBQEnGNk6w6MrE9eS5FdacS9PfrYJxKZY+FGVDqjJ3U99vFPiAMCzH4mTdQwPDKCW/bcarxMEgiofr3zrnvQ3dsbOzDSPUExygzAx5/4pMG8tpiS4BBuf+594OFlpX8CJ4HXzhtuwJYtWw3mTOGp05J8j338CX2gshlclU8dEELO8cSTn46laQUjIyPazUo7UtV77r2vpV/BpPyf+vRnVsRBQS6XW5WLmBtvugnbtkc0yZ6zSjCxGkYUWCUT+Mgjj+Af/aN/hD/90z/FG2+8gd7eXtx7770rZwSwb98+HDx4EL/xG78Re9dKNZD0fiUbqn/yT/4JFhcX9d/oqPD6v6y0jmyonHNs3rwlMVlS1ZlsVoeNA+Kbm41zb2+fjk3cKr3ARSA1MDgYm3Cx079a1I6D3p7euJSBpqW4pyOnnCstaAA6TmsirpQgQdxozMv0DJLxknimHIZ6GGi1mcqayWQ0kawTeysAkZTHiYiC53maCaS3T6kNlrajsxzzRrZxkTpY5w+FA2VxExNIO5EkUKmDHSdSqTYbPuA4SDkmE6gkgZxHkknOORD6cFwnUTpGT8aAvNXOAzgOgx9EG5wChZe+9BE04jGJZaIGsWdU/VorlwD5ux3iUSoVV0yj6itXqwiaDaM+LfWw8vhhiKBaAngIx3VkqD7TbsvOs7AQueNQF3IYkl3dNJpN8EZFSzwNfBFJhdSrpeISmlKjoBhPKrHhPHK3k/EcM5oDYPgiVHNRODwW5SdFXLBBzddatRqLuEAZIsrQAcD0zBSazYZYVxYRSKrzuoyQpA90LN5HilHzfR+Tk3FvCK3o8sLCPCrlaM4kSXTos9HRK1FUHMBgqmOVArg2ehmcc83UKWY47iYIqJRLmJ4W45QiF6GScPd5iIWZSTQbQsgRhBwhSSukjoSRYsCF82flO9I2mdeBaRLRbDZx+dIFMoYmwxrhLd7MzsxgdsYM2chgCgZoi8+fPwff97X00n6vfqt3p0+e1PNb9VeitJeLfrw+ejVizJfZkznnuDZ6FaViUfeHZjBbML0nj3+QyGQljpMf4PSpk1GbWPI+rSTQk5OTkTmWTKjmGwXVrydPnECD3uRfBhdAuOZpF4rFIs6fO7dyQgkXL1zAwsKCrp8y9K2etQOrYgJfeuklfO1rXwMgDCb/4A/+AN/4xjfayvv666/j9OnT2LZtG7Zu3Ypr167hU5/6FA5IvzcqlAsAXLlyRdsMbt68GZdJyCT6LgkymQy6urqMPyB54wGskzQHms0G3t3/tnl6ogvJKmT06pXEuJ6KGNmn6QP796HZbBonLW0nk4DfPtvecJmNem52FmfOno58SFnp7ZynT50QmwvZ/JIkh+rd4UMHdOPUhplEgAARcunUiSjOYci5lqClPSLVkpKb8evXMDM9FamRrFusNAwcA/DB+4f1jVBA3LyLpBBc2xAqf4OVsbPG2Pm+Kl9KAglzsDA/h7FrV3XbqD8/paYWJ3uRd/raJQSNKlIuQ6UZ2QxSiVMQkMVaHJPRURADm0mq12rwl8bhOMLeUPWJPQ+UlM8vz6FZXogXDKHKDqVYRcUtnr0q7GMUM5wEtKZzZ04mpknKO7cwj2ZxRksCk2175LoLOfylSSAM4FhOtFV/2zVcIDZSvpS2uQnqXgBo1uvglRm4bvR+OZZ3dnoKS4sL+ndMGomIsU85DBfPCQZAHToMH5HqmRP5d7xIw0+ShrEExJaWFjEzPWX4VGsJDBi7fg31ej1m4qHy2vkvWX75jOKYOS9qtZoOe0lVkyqJY0meFuZmUS2VCHNhMWfWsyvSLIjWzbmp4qIljF27IiR7TuRyRZuZyDQKz0q5hIX5WXBY5amDKIvodsg5FmemdFhCJdnkEK6totvukZnJ6LWrOi1I3dEFElWf8KoxOTFp9G3scMKjIAbz83Mol0tRX5GB1P1E8l6/NhrrW3rYUWtKvb1+fdQYx+S5ID6rlTIWF+d13apsG1RZM9PTaDYbmolsBaovxsevG89s+z3aHt9vYm5m2nhOmW3b7m9pcRFl6Vt0OQ2fOpiMJ9jW0rw2jFs2e8tBpVJpGU4vCebmZtFsRu6/7PFNMhNqB1bFBO7evTv27HOf+1xbeX/7t38bY2NjuHz5Mi5fvoyNGzfi+eefx2c+8xl89atfxR//8R8DAA4ePIiJiQntcoa+u3TpEl5//XV84QtfWA3aMeBJ3+WXZtOPhVAyJZFmWc1mMxYmptXJCxD2Dl4q1UI1nUwkjbIpAtaJs+k3tQ1LYl7rh9/0Be5EQmC3lVt57U1KEw3yjDFp10H8WoU82jQzrqPVdzp8mWUfoZlEyRD43HSjEIYBPM+Nbg0TpOgt2obPZbi7CA+O6IKEsPfjBuPgS9sepcLLkflQkxJK12Va+tBsNuG4HrKeg3KDtIt0t+8HEm+BgGJ0VpK++X4TgAPXdXSsWu1Am4yBYgIRBsikozlgbOB+qE+9adlu3/cB5iDlOoYrIWMjQ7Qx2swYxd4kSEC10QCHsO1RIfLsk7r6GoQhQomLJ23IVHq7HSZDJp6rW9uOk3Dxgwu/fJy5cN3o0onaFBPXqU9t2ZLr9cmhJgiFTWAUHztqr5AiqctHAiFNsFsgQNdjGIaaxjAsT1/AYaS3IWm+Kf9zMWZOtyH6DIIA6QQPAcIWj8doGOdcu5/ROCCqgz7jENoM1/M086MkcBQfxUxwzsEkzdCXrDiJwsLMPgrCEJ4MSecmRQ8i34NQrLNMJiPGgDJrEL4mw2iQAMTD6XEI/H25ZpUdLpdjVCjkDfpJL4bY+wNzHGSy2Wgd8OS1p96nUimk0ylDSuww06k6I3Q/l8trptYG+kzlzecLyXtGPDs8z4u8SkTD1PLQUSjEI5Qlqb4ZRD92dne1pKP2HHZdd1U3cnO5XHx/X0bS1mW5Z1oOGGNmKMAVIJ3OtLx9zGEKq1YDbVklfuITn8DLL7+MdevWxRY4Y2xVQZCT4F/9q3+FX/qlX8KuXbuQTqfxn/7Tf9IMwW/+5m/iV3/1V7Fz5044joM//uM/TtTRrwQteCf9W73OZNK45977YUMsj3ywddv2mHGnTQjUADHG8OBDjyQ6k+Q8+ST42MefsOplhrpELRaHMQwPj2jVNK3XQIrsIHfde5+Ii2j1DV1kdFI9vkwYLaPNXKiO77n/If0sCCPpnOcywQQi8uy/a/fNyGaixeYTn4IqP/39+JOfAWNMO5WmUhY/EJckGFOXKTiG9z6i+xQgl1Q8Ic2jESrWb9yMvnXrEYxdhOsIZo/JzlCSPodc7Bi66R5UZ+vIeA6KdXExRNy2jbpcSQLDkMMZ3COcp7YgXAaTlCrAG74ZrstQk9JL13E0UQcEY9uQtor59TdgaEPkToSxaBRrMrqP4zhaErjutscxdWYWKSt6SeJmAOAzn/85E1ed3ry8xDlHz8gmeAOTaNR9ZFOmOphOS8akP8ShW4DSceMSFzUXoP3DGPAzX4pC2DXDaPNPkgSydAfcoT3GuKiesdcrA3DbnXdHKmIrPSDNGxRD7Tn44s/9LZTqgd4kPYK3dp8iGSTHcfClr3zN6NdWjDUgaAy1U2YJCWkZH//Ek0a7kg+c0YWxL335q4ZUT41JEnR3d+OJJz9F5pasgzCP4rd4sOvmvTqtwpEeLBWDor5//os/h+liHYCQglENAG2synPHY5/BqYNjURxvyRxSh+Cqvk2bt2CbX8CZs7NIOYy0Nd5YDo6b734Q/QMDulxqZqIiETFENzM//8UvmX0Mc54otzsOAzo7O/HYJ57E1RkROECFE7RBTeWbb74lxmzFGEXZlpBzfOazPyM8H5B3hH+UZUdj9dnPfxGzpUZMVctpBgmDw+uxedNGw9F1hG9cS3H3vfcjl07Y8yjumjnk+NRnPifmj8H4J19Qy2ZzeOTRx2NqUdUGmo9zjht2744LWhLKVXk//oknVnV545Of+vTKiSQMDQ2tKtLanXfdlXip1Lat/GuxCfzmN78JADh06BAOHjyo/9TvHwcuX76MPXuEX7ihoSG88MILOHfuHE6cOIFHH31UpysUCvjLv/xLnD9/HmfPnsVXvvKVH6u+pEWTlKZSqWJhYX7l8rj4m56eQr1eN6UE8jNpLK5evbwsbkoFAIjbbFTVnHQyioxkOWZmpjE/PxfbRByLgKoE586cRkhu0VLcjUUlP0+ePG484CC3Qi0J0dLSIq5fvaJeGcbxKSeSBCpD/cuXzqNciuxGfPlexAeOXMQo4v7B0cPgXNju2H7ZmkEoVeEyUkTQRPn6OUkgRduUPZeK76tUrIwJdzUTE9dFnVAEX5QtJIsRE8gBjJ1+H+AB0q6DWjPQ7aKLsdkU/RyGHOH8ebieq/2aJfW/gvLiHPz5UbiuoyWpSkVOfbM1wgBBwFGbvICguij7Xc0Rka7qB+CyH5WKe+LkuwCAjMF4IREa9ToO7HvLMGXQjEYCEb5y8TyaCxNwHAf51PKkxg85mmPHAHCkZNoVLhTjFRLCrhnKeeCa9mxMSj+q87MI5y7C88yLIYyk078Zw+GD72J2ZipxXQNSwiMPGhmX4fkf/UDMRyn18+ScYRI30R6BW61Ww0vPPxtJRQgeEd6RzdqZ06dw5vSpeCJCOChuzz37DOq1WoyxFIyAzbxxPP3Ud43fSdI8BZMTE9j39lux95Tm0fTH3tuP2amJlmoqmyl86nvfjtzpMEZufTNrvgG1egP7X3kWgDDZUAwQh2QaeZQWAM6eOYULZ04g5EIbQRkPwXNENMwPOY688SKqKixhGDHJgGDyQy7NU+Sh5nvf/la8fTy6ta6ilHAOTExM4I3XXjEOCI7Vp5TJ2r/vHYxaFyKXmQ749rf+i5FGvRN7RZSfQewzT33326TuZergwMVzZ3D82PsAzEhNNi4q/UvP/wjlcjmmBdB1yOfq1VPf+StNi21zKruOmZlpvPn6qy0ZO1uC+O7+/bh65UpMxZwkvWOM4dt/FR/TVkxWEAT43ne/0zYTJsI7Hm0rLQC88PxzKBYj+1rKsLdiktuBtpjAkRFx02zLli0YHBzE+Pg4JiYmMDg4aNzc/WkGTUDIogMIUZS/y6UipqYmYyqXVgzk+Nh1bTdC6xJl0/rE0yuXLsUlbYSCUkJUr9cxMRHZGCTZ99GFMjc7q+0dFHDZxlgDOHDt2qhuO7VLpFIBWtfk+JjuSMqsRhtMVGe5XEKxWNTv/CCEHyjmSUjoIiLKMD87g1D6huJcRHgAhJpHEFIxWZUkcEYaeCsVqZbWyWeBZNSagVQzNsqmukdu4MoWziV2aKViEbVaQzN7lGmoNMnFD/m8tDAD5jhIOw6qddEGyowIpjPU9aJZ0dKuVup/1ffNWhUIm0ilHMHQci7t68w+V6resF4yIpVQqDSFJBAMmimrl4pgjCGdSlZPU1VZo9lArVaN5gni4aJo9sVySYajYssygZzLMauVAcaQTntaApdE1lQdlUoUfrGpJMItJIHNeg3goWYwaQq7HgagUikDiBg5u34/CPUN9LTHdChI7TuQSKIEg6rsVhn8ZlP4QZP0waADhAHTB5laTTyz1zDlilQG2S9KpWq9Et+tjVWHsOPRfKJ5qS0itUmK+iPSTlCJPOfCptX1XEEzEO9nW4oRWO65qM2vUQYD/EYD8myl1cFKYucS7kQxP81GA1xK79NOXDJF6bYKY5iWphVqnit7Q+WORs0dxfza+7DQgCgGMGLA/GYTnueJNcsBBtOMIdoSxDPf9w215ErbvU3D7TyCyZbzs9k0TKDUu1bMnY0L3TfUH82rTKboWtKHLtknNHyn3Ybl2ug3m/Bcb8W0Cny/CTdBsteS7rV4nly239IMIwmSTMmWg+Vcytjq89XAqvwEvvrqq/g7f+fvYGRkBJyLmzZ/8Rd/YUjuflpB01C9MavnDIwwSY1mE+m0afOiiTQtSz4IgwCpdFpPcI44rRb1CcahpfdxqVpQaZWdnG2/oBcZkQRGeDEd4oZBEi4ObQ9nNyqdSgufcvIUrNhPzaTSk1LIkc8XYpScCAJp0QKXQkH3e8OPNs2U46DiN6WkTzBL6XQamWxW96EvJVYZVxCHJgkSDwCdnd1iM+aRcb5SOVXkrTjHYYKZ5BzZjh6NL0ekDtZRQpyoP1PpFEIvh4AXkbIcFzd8aZcnJZicc3i5Triuh4zroO4LJtGz1Ku+L5jOMAiBVCH2XvWbDX4AsLRI7xO3NkA0FIxFN3+9TAEdZM7Q+VFphFodXMiIMty86JcMiR2rGDDAZkgY1g0OGdI/egpVEhVdt5cD87JgDkNHxiWbJUkjP+sBB093wmk6SKdXlkpyzrVLIUC4mFGS4zjhBkK4YJlOeNYFFcp80Wd9ff0ydJX5WjE36qDBGEPWdTC4UbiiUlI/z4lCxGl1oNzoHeZgw8aNsoGtN2pVYUdXl1h7ywADwGXm9Rs2aL+CXBKmpPCYaty2yOglinapg6FN9wAREWFICgUorvoGtCV97ulfh3y+0JKhsMdKhUZjUmqq7Hu1o2WKk+OgY3AjUBc2rqqvhXmM0VAwiIgemUUARZFeDaxithXeHMKTweCGzUin01qaJ9qpqmbwffPW8vYdOwx6yHmkAXEdpW0Q73O5HNZv2KhtSAVjafUPWShDw8ModHQYfUvTGep2Duy8YXesv9RnFOdZSlcdB9t37jTKtMeL0oTevl7kMunEdSzaotz7iOdbt21HKpWKMX5JbWWM4YYbdq/I5CrI5fLYlHBR1GbiVB+tX78B3d3dKzJNKt+NN96U+LxVnt27b2wTc6Cvv39VIeZ27NipbSvpeFCm+8dhA1fFBP7Df/gP8dRTT+G+++4DABw4cAC/9mu/hg8++ODHqPqnC8Ti4diyZSs2k5BO+kRG05KefvTxTySW1yr9F7/05WjTRCSBA+hpUuTu7u7G4594UpdnMJjWXGSMYc+te+OT1BYHkN+f+dznoVi/5RaEevPJz8QvASkCaodgWr9+I/oHo0264Qt1nVKjKlu+lFTD7r3zXhRyGSldgpYEZj0HoZIESikPY8BDj35cMofCIN0lFzGqvrgFm3JFPU46i6GdwvQg5CJObRCIzVGdztOEKdu6bSemyj7CcMnwyQdIZoWbase+Xbeh7AvmtkEujlB1bCQJ9OH0bEUq1WaQ8UwXWGEQqZSLJrFjBKKhDHl08zc3tA29lrGxGr9KMwQPORzPQSEl3Ovk1t+ApVkfuZSzIuFNZ7LYeUNEFKnKJWn+ZHrWgWVmBdOZMhk7qurikHGPO0aAhWnkcp5sHyN57CDpHDeSMIPq1rZnqYOVVId7ObB8H9Jpt2U79Thzjg2btyBLQl3RpaMlgUEoDnaugx27xKYb8BAOTEfgPpFmOUwY7W/Zsi2qF62JNwfQ3z9gxgylxIBF6RR+27fvBCzmPGqjuSmGYYht23dEtIhHjI3NPAAi5Jbh51Rt9vJPmTgo9Hr71+kwcDYYDIrEcYNkplXqgEgYjXwcAHOQ71kHTIbIpsxDsU5PkO/o6ISXC+CUmjpud6sDOwcwsG6Q+Cnl+p1S6wdc3DLmEBcUbJ+CipYpCTWVBHrpNPr6+jEhhdkOkiXY6lF3VzdyuVx02IK5/lR96t3gusG4xkm3L+pH9TnQvy7S5EgGke59lOFIpzPClhzWbXAWHXy0UIRzdPf0IuWZ/W1LGqO6OXr7+3W7OI/Mb5JUno7jRDHql5HaqXfZXM4IfdpKjaqe9/T0xN61AtHW9tN7nreqMHP5fN60CdQnWOugypNvU7eCVd0OLhQKmgEEgHvvvVdPhp92EEQqOh0Y407oxZnTJ3HuzGn6OBFU/mef+YERoWSlfE9999vGpDPsHqy0169dw/533o6IqpLsWfkh3735xmtGSDpVZrRISeEc+M63/1Kf+GkeJPyuVCp4/kc/1BTFJt5mRo6TJz7AhbNRP1aDQEtOMq4jbOs41xK4V57/oY7UEtlbAdmUozdyZVcDAM/98HsAhO1gyGH4CatKhogxIQlsLs1g8bJwbaKMuwVhiaa/spFjjOG9A+9gbHpKbmqmir4hb/l6jpAEBiHH1UOvigskjoN6U773ImYj5JENYlCvIpw7n8gE2psu50Bx7BJ4eQqZ/z95/x1j2ZHdh+OfuvHFzj3TkxNnmIbkklxmLrnkJi43cXe5KwmGZFi2ZUO2BQOGBVlygA3Ysi3ZsA0JNgwDTjIUdpe7q83LnIY5c3LumZ7O3S+/G6t+f1S4dcPree3w3RV+B5h5/e6rW7eqbtWpUyd8jm0K3EKiYSVCja8vAkM6Z1+HM0Ck6Ev4GoOgbJlgDFj58AjX2jrJWBQddgmApcV5HM2kiyrqg5yTZz94B1G/BWIQVG2ZmzapT93DGPyIgs6/DxCCStkWWVwGn7z7vR5eO/Ki+u5HQhNYgHfIGNBfvgTWXUapYNxJwRp46flnEUVR4RlKagIV5JFp4vlnn1IabADKJ5AyDcNQHGKWlxZx9OgHA7VtqbYBeP/dt9FYW8sPXHYjFZ/Pi5R0ahwyhz9lKmQM/X4fr716JG8q1G7RTYsXzp/D7MULKdMpkGD5KbxOsRG9++YriONQ1Sebkn2rsq0vvfCc0nIQkuTnzWWBIcD66jIun+XQPI5EEYiFT6DCY0kOHMeOfaigTWyROJfoA6f1iTGGo2+9qq5Fgv/o5mAg8YkLgwBvvPZqqg6A+xJGUnDUDpSXZi/i0qyGiUgyvtu6tATg9ddfTeHV6WOovyd5WHrlyIupOZ1db0RUQAiHnzlx4ljqeYPeEWXAmVMn0FhPz8eNFAlvvHYkNSaD9kfGOFrBm2+8nqrPUPwuf+eVK3O4InAugfTcLqJ33n5LjaP0Oywief2VV44MaG2eGo0G990dkk6fOoU1ua6HoNe0+QVgQ/6op927Gm0aLFoGiQDA//pf/wuf/ezwCNk/ayoyoQLahGdAt9tLpUYrrif5u9PpbGjXT/lMMZbD+ZHPSZ3sxD8/8FM+ADJReepejUEHvq/MzVfbXACA0bT5Lkv69AqDgENDZDmKXp6ky9uiLQyAJ8ykpkHgmiZ8ES0rhbdY+DsQCMYpTn9VJ9EESgw4vc9FKZm4SZZrhcKIAjSC4/K2xIwhjCli4asm34/UJADcVyMQwrllpjVkodA4SW0cZQncjEGI0gTq5t6Ycp9AYhDEQQAYlvJN22jcGYAoCAHDRNlJNIF6pG3SZyoOCRT1zOlSjpfSBBoGqpaFKKYqWEfHQhw0J6IgVO9UtrOI2cprXhACxBSawHTGnuxdQcwAAdJdK/H1ZIAM3DCiKN0Wmb3DsWS0ZnJnTBkHrTYslB0LWb6pb04EAAj323OcNJSTvqY8oW02TQJXCHyMMWXe0+ejvCa1QUEQwBZzPTUOGaYuD39BEOTdSAauwQKNiXhQVnMkXU5sDbJK1wLJa3oUaRAECoaKkGTzz8KlyK9RFMF13I1PxhrpvJAgrQnMVhEEEWLC3TVsg0eGJZG46dKEcJ4Uiy3P1vxmU/OSMWUtsDRXEHmAjbXDqBR8GaDmizYEkGZk6QajQztxn0A7ZUbXFYHSZCuvZf3H9EN4MvSZCP3MeKWEQiLB7IEwCBUsE4EULpAjqWEKw7zvW15gTJ5V5J9YdB8Ra8PNQApt6IcXhrBsu9AqUWShGNYP73/HJ3CzPn5hGGyqfNaNRbc0ZcexyHd3EG3KHPzf/tt/w+rqKv76X//rAHjgwuTkJP7gD/4AhPyfQ8X8vyalSStwQpU0MjqC0ZGx1LXsBFebBgH27N2rNEqFk5wkrJ4xhgPXHMzVrbdFMhXGGKqVKuyZ/MLX66aMqc1r246dKVNNocCmXTt46FBeC0gyE10wBsu2sXfvvkQTyJKxkJpVeYIHeKYT262qPsp0a6bNBY5AM/cSANdce0OCAcgSTWDV4TlNQ8pQsokyC1x3w038BC58sByNqfcj7vtmGjy60CjVMLZlWphnGLxYCEOmoaBopBDEGMPuvfsxC0cJezofkMKGLXzPojhGfechWAZnqpEM3tDaE1CqAlEoDBi1rdwsqS9ighzjY4yBVCZgdLuolCw0OkGqrfp780I+vvVdh+AOMDX7oYzIJqjZFoIoRmnmEAJCULI16JSckMQbOD45iZoAX9c3Hp3kbGcMqG7bB5xb5MK8FqggmZYuePgRBalvBwjBWNWBDFMacNBFqVTG9TcmabS8kG+ajpl+X/Kdk+o0SNBFrWQV9lEvTwB89M67oL8RXesiDzVS+C9ZJj561z0AoLADDW1NRFp0sGkQTE1vwdj4+EbnKVUeAG48fDNq9XpO7adrguVYUsZw7333p9ewWqsst1mWy2XcettHAZb4cOl4nHrdhBDs238AriN0zanzrRR05Xf+efj2u3ikfKZvWV4m77/nvgd4O5EIX/LZWRqfnMLI9gNYa3GfYDBNCDTSkDQEwOFbPoJTpxogAVUR9ikicr7wz1vuvE9poSJGYQAIGZRWjyIJDHFLJdwp5oDeR3mA5fxBPQZ79x+AbVk4u5oEwmRdavT5cf/HHhTBFSytWCggBuDBjz+sdyupkyVp+OS1qS1b4FbqqYwoKYWEdohgAA7ffAvGRkdSdReRvO9jH0+7TKm5Q5I+yraVyyXcc+/9OS1X1v+YX+PjWCnZA4W27PcHHvz4YL/8gvs++alPF14vMiNPT09jdHT0qnVLuvW225UP/zD0qU9/JvVdaX5JXss6TB8lbUoT+Oabb+L8+fM4ceIETpw4gfPnz+PNN9/8P4KK+f+S0mZU/pl9mZOT0xifGL/qwVXesk/gd+mbuL5E9QVLKeU5FLXf9bISR0q21S2VMDE5mdNgptqu3T82OqaAXKlYXWRQwwj3eRn2hA4AY+MTqWp0RiUFGdld27JTvkCBMNFy3D1DgS6XbA6zUtHcCmLKlNaralsK1sUUp+UoilCt1cCQpJeTQiBjDF6YCGrct5ChUuWLjYKh7ScBEsrsrJmDAQKPmsK8mB6gQAhSrtDGhTEDI4mpJ4q4htHVhKpeGIMKTSANQ8B0UuZg/dScXOMHlSgIQEwLNddWbS1rWkR5APAimYtZA87NkB/x6GDTNDimoeeJcUhH72b3GKmV8fv9fPquDOm3dtodUHCtqG0Z4vSav4cyxjWBkQcYFsYrtjLFJ21KN8r3PURhsoHKbCoV20iVJxDuBf0OCDFQL9tJH5VAlG97s9nMCZPJ2pSYiyLYizH0Oh0AHJBYCnuMJT6oABcEDAJ02i0EQVDIB7L9ZOAwVIZhZBqR+6rqW8kexpVwn9eMdLtdtDv5tG7Z1yTrX11ZzpnJeb+F8KtpuygDVpfmc75ugw7LYRjyrEGagJv1CdRN3I3GOro9ngrQFRhRVBtrrVMAIbg8ewmhSAWZ0gRKPioPJ+IAtrqQZK6QZn4p5EueJw/tvX4Pq6sr6nGyjXpgiB4Bu7gwD8/zVGCbob/AzLgQQnD+3Nn0de13/TvANVKzAp4rO9YSEF4/QKytrGBtfS217nUBOosHePH8OWFJKRbOk/2J33/+7Bk19zbSCjJwZIYr83NKs66Pg/4paW7ukoLx4c/cWEA+fvxYYZtzbWEMcRzj5IkThb8X1bG8vIyV5eWC0sV06uTJTWnsjh39MHl+pg3y4C2p2+0MXe+mhMA9e/Zs+O/nmbiAwpRmK9HmJQNJGcPrr72CTqe7oSlVn+TPPfuM+vtqc6vTauG9d9/OXR80cU+fPIElkZMUGLxwJL1yJEkxRzKf8otsI40pXnvlSKoCyYd0R2NJCwvzuHjhfMIotbYX9fvYh+/D8z313Yul4z4XCLyQ11CxeeaK995ODhFRzH3oDIOgbtt8E6dcuDEIQbfTxplTJ7lgIgCUHSMR4jwRwWuZBiJKETbmQXst3m/G0PIipRGTUcglTQj64N230A3yQRiMJT6BjvD564YhGhdPqE1f4gE6Gu5eOwwTobPbAPObcAvAUyUlJ3aG/tJFgEaol7g5mBCCqpsHDPWEcNpfOJuYnTJMNNA0gZZJsNZqwVuZg2EQlKzB2nH5nCtXLqPVbKavazdl7188dwJgBKZp8AAgrYxelDHAC2Kw1hXAMDFdS5vVZPt1Wl9bw/JSksNWCv6lAjN7TBnC9SsAizBRTmsCVZtIGofuzMk088/u0X5MEcccWigO+7h8aRaMQfkyyoAirgkUgowQDhcW5tFut9KNzPAjucYIgJMnTySp9DJrTd+s5fczZ06rulJal8yBlxCCZrOB1czGpfOO7JS4NHtR86nSzpdMBDdIQVO86MsXz+f6pf/j9/Kyfc/D3NxloY3klWeFQH2+rSwvod9t82AbISRLbE8FlKx14Py5M6CMiOwdidsAk+0TgnVMuR564fKsmieST1AmNKUGgcyQAnDevrq6IgSj5JkyulnPVQwIaLEgUOZlfdyhtVsG2hSlJtWfo1KFsgRaLDtn9Xel0+raKjrdTpqPk+w7ksIxMHd5VgW5qN+1Z6SU0Ixh7nIa35BrmrXySOrudjsKp7fo0CLrlM9cWlxEFIZKWJb/skKkpMuXLuWuFZWThxJ9/70aNdbXcxBtG9Hc3OWhBFJJly8n6Rqzt2XfQ6/XH7reoYTAxx577P9KmZ8lFW1wqUtiFAPfT0UPDUO6ULjRQ/zAU9FAOoNVsAOZN+sHPvenydRVtLD58/ORfalbWfKp/Iyys2cA+b6HUlnzNSs4pekbvO/7sG1HLfZ+yLVvlsEDG6QmsKzw6dKnf2k+HXVtEUmcJIkPAv6OGLh51iBQ0X5AIhBxGAfuE1gv85RLMQUafQ6aLAGYuSYwvfn2pe+fMPvK3vpC2JAI+O1+HzAsWIYBCqagYGQAAmPAuhcqX7w49AHDhmMZA5mypJgy0DCAYTuou6YILgHqbn7Z8kwmSOEXZs0pXsix6SzLhGkQrHd7YIYlhEAzEYwG8KXA95VvZZHiQhcKACAQmrosQHOWuCZFHBgME1NVrv3lWRQShq4PUxD4KZ9AmU2l6uTHJozFOJoOJqtpDxhVZ+YlSJ8s2Ve9PGOJOdgwCEA5TBRl3G1BD2BijClzsMTEDIMAju0o4S0tEBdoSpgIYhrA+PW/5WFDvvtBmlt5NQqFbyXRTa/p5+u8KgwTX0yp6ZHjRbRJITd2IB/1mhLukczTOAzhuKVUmSQNn3bwFJ9BGAEm9wdzxfqXvntp/7rk7whG4lsM5AQAKb/GlMGxExeGmFGlVZYWCcaS90xpjHI5Decl5wNFGh4G4rmu4wpBigifQJK6l/ebz5lif0B9TJOo4SiKUKvWVNnsPz1tXNKWUqru7N/6MwzDSNYeSbcn2zYGwM2k08u2PXWdUlTLlQ2DQXStokEMOK6brBsmIdSK7y1KGVdUTgapjIwMn9aNELIp8y5P7Te82bZWq2n91NuaPtTya1fZ0DUayifwlVdewW/+5m9uWObo0aNDP/RnTfKETQr+vu32O1AqlQplo/Qi5p/33vexVOaG7HP0+8fHJ1CrVnPP1El/7g03HFbYUPm25CEgpB9ITriVJzaxSTPCJ+B9H3sgc4TDQNq7d7/a5eVilwxTaQ5IMi5333s/bBmgQIgSUizTEDl9udmsavMo1fse/CSvy0hSoBkGQdk2sdr31WmaEGBichqjI2NgjKkcw2VN8+ZpAlwcM1S2HcKWGQ6BQBlDR2gCOfZeksVE0kOffATfOM01NbpmiYFrAgnhuHcAEBADU9ffDcvkkYxSCCxr2rqVbqS00KhuATFqqLiDl558FV5EYe+4GWEEVF1TCcYjjpUrK9PZbb/14+o0nKqTEJXNxLa5kGBUx1DafgMXAsUmKk/y+gYhzYO3fvQuGCJLik4pbaD2OXHzx8FeuwjbzqfI05k2pQz9CDDGDwKmhTHXTmlZ8gwOOHDw2tRzwyjxIc1SP45gbr8ZccwwWck7YqfWtXjOl77ytZyZ2BATXL4bqVWd2boFu3fMoOvHqQAmQKYN48+Q5uA77k58x1SftE1L8SXxx1e//guqYTrPyvdDpKT76tdSY6x4gCYZyT5dc/CQEh7kms7mVpZVEAJ85pFHVVaO9AE0gQvRBYGHPvflFIwIE/9JXiEFMUIIxicm8MBDn8Ryy08EQy2oQm8LA3DDzbeh0jiHoBvwXNhASgjX22IQ4POPPY7/8PJ5rgnUx0LfUJHkyP7Uo19W12LGx1dm/wAS4ZIxjm9Yc61cXRET7iOmoVLfMXBoMcYA2lxLDsAZwVXyDMYYHvvK49oaSgtxkgghYJSix1zc/+DDG7J2Pbf1Lbd8BK1+iF4Qpw4+2Xcv58kjn38M0tc9u48VzcvPf/Gx3N4V0yRTlX7/jp27sH/fXjV3r6Ypu+ve+zFasQu1hkV+e1/44pc2rE/eB3A0lIce/kTut0FtuuHGG69at06PfPbRoXECGWN49HOfH6gdzdLWrVuHbsdQLfj1X/91VKvVDf/9zb/5N4d+6M+S9MldNJRLS4tK85Pf7LR/4BGKa2srqdQ52dN5IiARrK+vwe/31bMTZpk5mYnrs7MXU6ceWSbrICvp/IVzqTrVr1qH5e/tdguLi4k5LdvZbN/nLs2iLzI0ZPhVzgTCGHDqRNr3oh8kARMEUD5/NdtGGEU4d+akqqMfxYhj4btmGxz3jzElkC0vLWBlmfs9BRF3Ii9JHCrG0BNBErZlII4pvOXzkAogBoaenwiBMU2w3iQz+uC9txOfxULQZ2CiwqNd1xoNtBcvwrYMRIxyzSMB6pqQt9qLlIYmbl4BYg9V1yqcf/rYN4MA/txRmKaJkkUULl1Ni4aT76Mf8Gd0Ln4gBLnkxCh5Rj/gmjmJlXfx0mX4a1dgmkZKiE5parQN/J03X0O/38+8+wGbKWO48qFISeemsfmy/aaMYX1tHax1CTBtVEREvKExPKJtWgBw8tiHWNSy6UjhXO+HfFY7iOBfegemaWLcTZ+8UytICjGU4umf/kj9Lv/pGhSZocU2DawszOHksQ+VBkk3NzKhDQK4yZIQgiMvvajM6kV8QI2P+ONH3/+eNq5IrWN9LBljaLVaePGF5xLhXar1dKFeu+eD99/DhfPn+HvQNYFaW3RLx/f+/DtApg4pOBlSkBVzjjKGF376/VwO2yzJdzt7aRbvvMndQqSgJX3msiDgDMCrL7+AbnNNIA4YqjxBWpBl4hnf/c63EMYMthDSc20RjYwpQ6/bwWsvPAVCiEAr4BaHiCVZUQAojeLRD97HqVMnc/Wp/NJCEyi15d//7neUOXgQGYQo7fET3/yz1D5QNJBSSFu4ModXX3l5gCmYpdLyMQa88PxzWFxcUPueMqlq46c383vf/sZATR6Q3g96vT5+9IM/T+2DaTeVdPtPHD+a8n3biBiAH//gz+F5npq7yTOKBcg/+9M/GapuAJifn8eLLzw/dPmXX3oRc3NzVy/4v9GWMAzxnW8n6R2zXcvy3tOnTg1d91CawH/yT/7J0BX+vNOguWuIU96Z06dw6223A9AYPyE5oQeMT/C5ucu4+ZaPpOrPMkg56ZeXFjFSryf1AKlNQC08wcDPnjmNm26+hTMiuQHpdWuMnjGG2QsXVITahicoBrTaLTSbjaSNBWOjm5fn569gZHwcEGOi35MsumR8rsxdxuHb5abARLo1wHV4Zoow4gJN3bHge300Je4UgG4YCX8rzqz70g9PmIMba2vKZO/HPApY19hJk61jGghDiri9gvGKK070TAlDXAhkahOR7251eQn+lr28DktH4k80fZNVbipqttqIfY8HocTJ7+OVZGm1+iHAhB9RvwUyUuGn18FvCACw7gWI+i24wqcuirh2dERAUejvqx/EYFEIg3o5BiHfpS9S2rkuh4NoNFtgcQzTJErwYuI/PYWVas/aKixNYwikA5/kupB/eO0WYMyISOjBGizKgE67CxaHgO3AkSDfJJnHTE4w8dFutzA2MaHuD4RZv1KQVqkZhKB+D4ZpYMx1CrSSssm8L2EUKb83vc1EKy81245lwPd6oJSq92GRRDiIGTdHG0RiBzK0Wk0FhzTwIKAd8nq9nnq4HEfZNr1dADfZy1R9atw0yva91+2iPjKSEiylkKP3V1K/xyG0pCZPNoSxfERuHMeIozAFjaLXmW2/1++nD7iZsZPtl/6ynU4XlNRBCFFg78ocrJnjpeDrez5ixuAYJLeLZg+xURDAMnlwmPQRJKJ+2zGEwMVU3wLfg2WNpupkSFIG2qaR+NERnj/aMC2B7pAXSpOc7LwPsfZOiyQw+Z4NQjBRJljvO7n1ps83fR74vg/TslXdXIhO36M/mqqgkCQwpvhgyOB7HiwzgxEq+IOujZTzL/B9VMdGc22UfcweOoOAw6Lp73sjSszFg/Ptyt/CIIBtO7nfBlEhlNP/JcrWnT0QEe1vg5AkFeQQtKnAkL/olJ0eZMCXosWTqkdU5HseyuVKSgCS92eFKuloWiqVcyp0ofVPnZYAwDJNmObgDAf8XqaYRKlUTl3X25/qO+G4fFXhN5Lf5BKGoiYbIahkfV5okqJJnnBlf8oViSjPny0DLVwheIVik6/YFve90ELrm0EIpjFOCcsitX2GYaBSrXIg3oJoPy+UwRtce2a6JUxUKpDOx17AfeNs21DJ6V0xzowx1OojCSafpgmkjAd+EAKMuZxpehGFVa7BsQyEVGaRMDCl+Z5JzSMX2A3ALmGqamf3odx7WuqGIHYFhmnAgOiLyU3kcr7IKvp+BDCKkcktqTr1+nyfC7/SFB0wE7DLXBOYiVZOaR0E1UdGuf8N8mupkFx+4Ck5MgOI1le1AfDDV9+LQKwyYNmpDT+7JiRVazU1fxlj6lCR1QQCwFInBNxRrlEtCBwhBKkFrGd/GLT2dM12pVLG6NgEpHtCyhwscOJkFhGDEExOTMItldL8Idem5Mr2nSITBUPunmST5uVN08TMtu2Fm6G+Ucjvo6OjGBEZZhJNYMGhV9Cu3Xt4HaISqX2SmkB5DeBZVWZ27MkFPpDMdzmfKpUqxian1G8MQrNHkhzf+tiMTU6DWC5MgwuBjDGVxaPIBXVm5x4VYJaN+s8KYIZlYtuOnSCEB4/JAzFjUGZdINE4jk9MYHR0LNH2EV6PjFp3hOAshee9+/Zx32ddg6rGh6RcjCilidk+K9Rr98jvtVoNMzPbCrXv3DLAUtarbdu3o1SuKF49yH1XCu779icp5pRwiLSwKMm0LOwVCBoD62TJfBqfmMTExGRBuWKBbd/+A+pgytszeLdkjPGUdClBcjAnq9Zq2L5jx8Dfs7Rjx07UNPetq9FmUsyZpolrrjl4VSFX9kxmXRmGNoUT+BeZpGBXNIS60PeVr/1C+jrJmzLkQp7esgUPTD+UmviKKSOtBWSM4c47704tGvWcrBQp6EtfeVyVlUUGTXLTNPH5Lz0mBJnicgzgOUQJz+eochVrZXQNgt7+jz34UPJbpt6i6w9/5nPKXw8AegEXzOquiSimiGIG0+AarnJ9BHfcfb8qu97nQpNrcw2SNM1K5++D190AyzQRxBxU2hbRrvJpQRSLYAcDlDKMHbwD4yXuhB0zprJ62LbJT/amkfjEEeDO+x/CS0d4ZKAOORLF3Nwro5YBwBmZRHWG5zhWeIAGwXTFVWMihUDDICDVGZhuDTN1EQGbHUttHBfbIayt18M0TVBw3znL4mOWPah4YQxiOdh93c2pdyLbQAAE4h1IIdAam4FRDWDbhtKkFLVH0s233p7CxMzOA7lJ8EMJhb31WuByq9D/UV8XIaUImAvijsKyLaFdYAM3IwDYvXe/EgJ5hhm+SZcyQiABsNwJYYztgmmZsMy8EMgPYUwtbNMwcN313L9HN18lWhSgI/ynHNvE1NQWhMyAF0PgWRpKsyODAwjh+W8ZgIPXXqc0gbLerD+gPk7XX3ejeok6f9H7KNtYKpexa/ee3PrX69X52NZt29XGJTfxLMKQfK8EwMGDh3LtpkLbpWMjyrbv2LM/leFGbfpau2Vb6yMjIC5DgAQPlDIGA2nIF5kTfWLLNmCtC9PkQiBlPIo3G2Sh+rp9N9gFX5jl0+9WWVuE5s8wLcxM8bSXXFjjbYgoz3eugvlEhMjo2LgKImDJVELMuPlVxzFlAHbu3A3JHgnyAOcyuAHgQuDOXbs1Piu1dek5I8fdLZVQ1yxO+nN1i5J8BxMTk3CcJGWnjmeYr4Rh68y29NjmiiQWKsMwsGXL1sQSQDJzlyTzAQBq1Rrq9TqkGV6+p8L9jDFs2ZL3fRskKMVxjJltSds3EhgJIbAsC+PC+lXUvyxVa7VNBZVOTk0NXZYxloKLS7UVSK074P8hTuBfZCpinrqQA3C7+09+/MPcfXzBJSc8gH+ePn0Sx4/xgJgiOS7t+0Dw5E9/pPzqUuVUmfT1b3/rG4WnP71OSSvLy3jhuWfS5mWtfrX4xEC88dqrOdiBQYuCAXjiG3+S0y4mEWxaWcZAKcUPvvtNVSdlgB9wE0LNNeGHFLFgppZp4NKFc/jg3bcgzcrrvUiZ2hjjQqBBiIKBeem5p9FqNrhQRpNMDFIA9oW2jmsCGVonXlR4hJQxhCq1m6HaIYWHZqOBIy88IwINCKoalIsfUeWrWBGas4unjqG3PIeSJXwXhSZQagoZY1zzKN4BXXgPpmVgqpxfpNm33OhH8C+8DtMyVZoy0zRSWVPkM4KIIu6soHs5nbZImsIYA8KQm5QkVt6FD99E3G/BsowNHeUlPfWTH6Tev3y/6po2D3q9Lpqn+TutupqpOdU2/unHFO0rF8C8BhcCIYWGfNYD+f2l555WaQaDiCIW4OBOxtGaAVjtBIjmP+BCoBRIMvXqC3t5aREfvPeOaGN6zgN8o+wFSZT4++++hfW1VWU61OFAZMYIPRjh6Sd/kn50ti1I1qrv+3jxxedUISb+G6QPOH/uLM6dPVP4m+Rlsh7GgJdffF6lvVR+VZkDhLqXAM8+81SuXkoZKMQhhyRQQ+trazhz/P0kSENrtD5/5N/Hjn6gcNYkvw6FUCd9AtV8BvDakedVtK5tGkpoNEk6OwcAMErxyssviEOfzOObf7cAf79ri1dwZfY8AIEFqq0j2zCEFURmDgHeefN1braHLuwmmkA7My9fevE5xDFV+4Y0pQOCh2oH+WaziffefXvgPiD3Mbk/nT51EosLC6k5YhC9LNQ7ZgBefPF51T/5TuRvvC9SUOZ/v/bqS6p9hCRRz0XC4OLCPM6dPa3GQzaqqCcEwPvvvYNOp6P1p1j4g+jHa6+8VKQ/SbVbUrfbxXvvvlPw5GI6d/YsFubnc9cH7ZOvHHl56LoB4MjLL129kKCVlZWr+vnpvT1x/NjQdW9KCLx06ZLylXn55ZfxB3/wB2i321e56+eDBjFNyYQIISkBjWU/WfJPUrfTge3YAzdNlvnebrfVSUFnyDqCu05xFKU2Qj19U9bBtu/1ufTP0oy+sGEE8Lw+XDeBq9F+ypXPMYfMJqTGUNzme57ypZCnfV847k9ULA6ezHhKNoMAnucn0DkEaArfNQmz4guTrzTN+r4H1y0hEM75jkXUSZMypoIETKGlMIxkk2AAgoAHbziWKQCWjRT8jOOWEp9FTYu15gVKEHNElGyv58F0XJRsA50gRhzHsCwDNTu5zxdRuXzwKCzbxqhM76cdLLLU6vTBiAHLMtANRCCCbaTM9PL0HoYxEIcYrVXEO0rPeC78ci3lSNkCIQR+3wMxHTiOmYLx0NujNMJIz5NB60lSt+chhgViEFRcK9XebNv8OEbY73KoHduC9K7LHi500lM0BRHXLFumIaA40gPa6AZglHIBsyAggGQklCAMFOh6tp/yQCgjrcuOhTDgcEgyEtgyE3MwFwJFxgjNLip5zqDNUD7cD9KQVSRVIE+B7xcmpU+Nv1aFr5UvOtSloFmkBKq+M9VH/T553fc8uI6bm1s53irb0vdgi8AdCawcMy5Aq6HTeCBlvM22xTXZiXmVKAFCHTR8H4bJeXXJysO16O2IKUMUhiiX+bjL9IryN8sgqbEiJIGt0vtJWZLRqGSaqj3y0RI/kkPEIEVSKJP+Xa7jpvquB25kyfd9OK6bM7/LTjLZbgj+TPmBF1LIzgh1st2UAUEYwrYSK4Z0sdHfK7Trnu/DLbnJu9PaQrLlwddeqeTm1rBOumZwkE9f0W+b8dljjKdI3ExaNw7EPxj/NVv/sJHBQB66brBiSJT/f+UT+KUvfQmUUszNzeEXf/EX8fLLL+NXf/VXN1PFzx0R/ZNwNX3R7+q7dqFWr2NcZNEA0nhUktHqAtOuXbtgWpZavFJoATjTy2ZI2Lf/QHGbCya565awfcfOgYssS9NbtqI+mqQAyzLC7P3XHDyUboPWX+nTIk+ZhBAcOHitaCMHfw5EVO1YyUIzCJQZ1iAEo2NjSq1PGdAVvmtlxxJCHWdSjsEZ6Z69++GWSvCERtEVWgICAQwsBLiQcu3ZyM4DIEQwd0qVJtC1DSFEJjh2pVIZ23btFcDMQN1Ocs2ueb4yycry9tgWWJU6qo6BtV4Elvkd4GZYtQFUt8CyLRXNKNdyETPr+hHM8T2wbQNtnwsdboG5kwpoGqs6wueAIMmIAaigFcMwMFLiDu/u1C7AdJTZXb4vVbc86Yvvh2+5LdfGQWRYDsyxXSCEw+ls5MvSj2NQdxzErsCW+VeRaHdVW5DMyZs/crsaMzkPpKCV1ZJ2+gGMif1CgM4eYDI8ABzKSfoxyYONbAOvU5jfCUHdNXDw2utRqdU1TWDCVqNYagITAOk777xbtU2vN0cEKLkl3HzLrUl/Bo4ixNrYp3wCB2m69L/vuPNulZ9cxyuVm7outADA3fckbhtqzYnDcSoilzHUx8ax58Ch1FqQ9aXmmfh33Q2HMSJSdkoBIxKaviKcycO33aX4iG0SxEIotAiHY6HaGLiOjQM38XF0rOQgVSRIhZRietsOniYTiZAL8IxDjmGkoGsY4+OY3qRF+0VgS8lKfLsZeBq4KGbK7UHOjdQ4ie9j4+M4fPMtG64hnW648TCmpqdTfVNnUDnm2sMeePAhJRjK3/QDihQMueBi4u577xfldMVEXjBh4Pvp/gMHCw9/ej/lXPvoHXcprL2soiN9H9f63v/gQ4W/F907OjqK2z96R2H5ovuvv+EGbNu+fajyAPDwJz45dNnNlt+xcyeuu/76q5aTXb79ox8duu5Nm4NLpRJ+8IMf4G/8jb+BP/7jP8apTYQi/zxQynyV+c22bewWC1+dktR9+brGxycxNjauftc3qYxyAYDA2tPbAqjZL58l6wiCQKWkA0OGQbAck3dsG5NT08Ud068JzjAyMopyqVxYpIimpreo9ur+XIphaG2hlGJsYkptEmFMhWBmoG7b6IbS54/75liWhWqtrur0lO+aKYJIGCxD4AQCqNW4z0g3jMDANYGya6HwEzQMgo4fg8YxKrURSLN0pGH5ORYXTqQQSAgQxxHcco0zb4Ng1HHUe1zqhkobJ4WNIKIwbRc118RKl2cGsawEF4+yxBcvjilADNiOndsYi4TwXq8PYtqwbRMtTwquZkpwAbgmLAwpCIsxWk20QDoj7wYR4igGMRITtxcEIIaJsoY7mNJqp7+ACa1G9sRfRCvtDijlQTx110wJk1kfn34UI+p31djIjWEjLamvZaNpBaHSLOvwHbJpnU4PAINTEKDCtAUu11+300k9mCDNNxh4cBEhwGjJQmO9AcuyEAmzqMSz5PORImKaIMN4ZP5Q2znjZnWZWkrnLzrp47m+vqaZ6dJtLnrm2tpq8rum3eI+fumyvu+j02nl3kkcUy7MGOlxb7VaoHGkUirqBw0lDGrtW1yYBwwuLEkza0ypcvcAEp5DGXBpYV4d4iS6AwPXuso1IoWLXreLRqvN+YUO3Kzef3JgjylDa20VhMUg4IK8RLWjDHBNQ1hvEuF0fn4uNe/k+/dFAJytQcREUYSFhXlNg5rXXlPFW7lZvdvpFAJ/p8ZQHMAvX5pVe0qy5uTBPQnmYwDmGx4+PHkmpcAwSKKJzFK/38Pa6qqqXw5hdn7J/iwtLsDrJ9krBu6pYr6dP3dmU9q0i+fPp77rlB3TtbU13vYh6eKFC8rlZBjaDCxLv9/HpdnZqxcUtDA/j2azWXhwTB/c+eepk8O3ZVNCoO/78H0fTz75JB56qFgC/4tAcqDkxJXfz587w1OjIfldllcLW56oGHDkpRcG5v7LLwrgheeeSb6LTwW8mmnT+toaPvjw/eSlA6lNVH7KfydOHFO5K7PcXglv2oUjL7/IwWNJoopXbde+E0IQxTFef+2V3LhIM3Y2+m9h/gouXbwg8mES9IOYRwMbBBXbVE71MqL3+Ifvwfc9pV2U0b1VR0QSZ7QBb7/5KgjhUDKUaXl/wTfnKOJ+e20vAg36CFYuAhCaQMZ9Ag3DgGNzn0NHAzOeu3QRS0uLiCiFZRgqMwgArArQZ9s2E+ick+8J7D4Lax2ugnccMwGUpUwJgVEUg7XmYLt2iqHzzSHNsBgDOuvriDvLcBxTAFwDJdEefW52gghRRBGtz8GI/NR7lGVWPB9xHMM0TdQFkPXyOe4/WHWtwo1INyH6noezp0+qcdbnYpEceP7yJYSdNRimgWomw0n2ANMNYgQrFwAQ2E4Cm5Eao0z9J48lWGLdKFJmQT3wQ67XXqMJ1l0V7yUr3KlGqetzly+pXMC6NkQPlpIuByMlEycFJqaEA9Gz13B/Mi4EmAZfS6dPnkxthtkx1Wl1dQWrK8uptsiyunlQjufZM6cRx7G6n2mDl30GA09NKesAEoGkqC29Xg+XCtJuxZpApM+L5cV5eN1u6kCcOswiuUYAnDp5Qgk+hECZd2Ugh06MMcyePw3KuFBnGESVN4VVRWnfCdBsNrCyuqyCNLL9I9pnSCnWF+fA4kgJhYwlOaBdk7uRGCTRhF04m+T2lW2llCGIOO+yNM2/7/uYv3JF5BWGGPf8eMtrq6srKr+znDc5AUxTFJw7dzZ3epICLlPP4+9qZqyE1fnknSZuoWmBXj6r22ljdXVZ7FlEtYkgrxVkjGFhYR5B4Kv7B5G0iF26eDEj4BTNRH49CEMsaML3RppDgKd1a7VaA3/P0qVLs0NrX2X5YanX66kD2DC0tLSEwPdTa15SURMvXx6+LZsSAn/pl34JMzMzmJ2dxb333ov5+fnCNCw/75QdNLmAer0eylfpjz4nPc9DuVxKC4dF9yDNtNVzkSzM7Cmv7/VRKVdSwlj2pKVr5HrdLqrlSppDZBuhPzjTWKKVUe0UH/1eHyUJaaEzcWEmyAKz9r1+Kn1OL4gRxRSWZaBsmmj73CewLDJXeP2+0kpGMeXZRAyCmmMiirlJxTalc7h8NjcjAkDFSYCe1/1AAUX3/Ags8lGpcPgDGUUaRdw3zhWawIqTmCs9rw/iuIjjxNdIDlPLE8KrzbOeUAaEUQzLslC3bYUH6GrZQmIhdBJCEAUBQAyUy3l4GDm0OnP3ul1urnUt9IMIhCSRvbrs0gx4nwkNUBcwPllmsdwLeNCKaaAmBMlIRF2PFGTR0O8lYlwct5Sb34mpKL2ullodnpLO5ObnQcQY0PEpT6dn2nBdHgmdhXKThyW9XZJ6YaSAm9N+hPw+r9cX47iBhkF7mO97HMIlsyEmbYGao3Vt7kgoIwk3ZBDC08sxpoRA3/NSfsFF/UnaxHmMW8qn3Rq04XmeV+wTmPkuhVn9eyo7B8nzycD3UBbjov9ElUYr3R/P81Eul5R5Oft89beoj2u/DFEXEb546aAK+S6CMAI1eLahmgA/l5BVtkixJ02GAPfZg2XDIFCYoHo79PGMKEMcRagIHiY1drJsyTQFJA5R89Q0zVQfGeP1hCIoyDYTgdT3eApOPU2fbhmgTNOUEoIoDHmKOfXeEsGZIHNoF99dx1HaSL28fjDk5Vkq/WJKmEu/MjAGBEFYuOfL+ZRbK5TyvYMlPo5Eqzx7AFa+uEMIX0EQol5LR0GntN+ZOuI4Hkpe0bVtm4n23UzZKIpQETBqw1JJ21NzWv7McJVKw6ev2xREzD/8h/8Qf/tv/22MjHDzWr1exze/+c3NVPEzI31DA5JJqwtfB/ZfA7fM8eTkKklOtunTLCHAvffdr06bRVM2pfFgDPd97MHkN1m3XmfyFdPTW7i/4QChLuXvQwhuvuVWjI6Pp8uSYnkQAD4uUszJB6aZMkttUKVSCXffc3+uLt1XRrsZe/buR0STiboqfOlcm2tqegLMuSY20DvuvV8t/iCi8ENuaqs6BmekMUPZSYI3Hnz40wAAX4BIlzUsPy4EUjiWgX4QwyyPYuf113CmzChCyhBFscivyu+pOkk2gusPfwRnGx4o7SqhQr4XCTJdsk0lVG45fC+owUGvOx7XCpdKybIKKVUBGVEUw5i8FpWKDAoZ9HbEO6htgTlGUS5ZykRestOCDBd8Q8QxxeiB2zAxNqbu12mpzU3VtmmjIvwKawfvhrcWYbpalEotESIJIRgZHcM99z+4ocuA/lx320GQqgfTNFNp7mTdevuaXgRjy80ga1dQLtvqUJTVCOjP/oxIXcUYQ1toll3byAUhRDEDKpMwJ/cr0Gr1Y7bP4vI99z2gAJGz4wHGXQCk32nVtvCxL30F/QgqErSsZa/xxEFF+itWqhV8+rOfSz0TyCxzOeEYcOPhm1K8S42atlHp4/6FLz6W6hsRRRnJsQaAEHz167+oGsDZXqLNyQo1M9u2YevMTG68IpoAJzNIH0GGw7fdxdcsSerTBRL5GuRU+Mrjv4BFkTKOECCIhSZQw6yRAo9pWbj+/kdw7v0llJ0EKDpmgJXSDPH7rjl4LU7FIzh/ocWDNNTY5Mc2pBS33f8wxgVosQzgkFiItkgRqeMFfuXxr6fGjBB5WOD+oLZ2gB2fmMDDn/gUzi52AfAc2VklAEOyTm69PfHvGhgQIMsD+IqEFmN5/i9ZtqmN0Re//FWsdQLIFUZI/j7Z/52796B8YB8iynJLKNHIybYC933sQbiW5v9M0oK33idCCL742FeuymPkPdVqFZ/49GdydRQJowBw4+HDQ9Sc1PHo5z4/VHlJj335K0OX3bp1K2a0tXQ1uvOuuzbcL5K1xct89tHPDSybpaE0geeF3f3YsWO4cuUKTpw4gWPHjmF2dhZNkf7oLxLpkzzRLgALi/OF0UC6lkOf4IuLi0mdKD5ty5fS7XTQabdS5YEk8TrJXF9cXEAs/BH0egc5k58/f3agL4V+SpR1nL9wLlWx3v6Ump8QrK2tYl1k9ODaleQ+eRrW6eKFc4iiUNXX9LkvnWObsE2CfijhNfj0O3f6pPIn8kKKUGgNRwSmYMx48IdlGvA9D+fPneFCWZRkBpHDsdrjQNOOZXLTcGcFVsBNKTFj8EKqNGIch4pgrGypfp88+j7aQrPmikhc2W8ZsCIFsZgyLJ89JqKLDZWRQ8fFC2KqhM6w2wLrr6JWGxyhJg8nMWXozF8Ewj6qrqUCEWqZ3LgEHFKHUgbvyjHU3UTA1PeL5W4oglZMkaovRvPcByAGwRYNs3AQY1+Yn8Oli+dT7UyXS5c/8cG7oIEH0zIx4gzOjsLANax04QOAENRqDmTe4EH39LpdvPf2G2otyPnkWGYK+oMJjV134SJYfw1lqUXNCDey77JPLz73NM8AwvRySaYB6XJgGDzTyvPPPAkACERqMRn0Q0gCdC5TlS0tLeLD999NbYQ586uUCAnw1huvY3VlJWcKLmoXAPz4h9/PbxaSZ2nVM3C/YwlXIzVxWQ2sPkZnzpzGieNZCCKugdP94yS9fuRZBF6/cF5liQD44ff/XP0tNYGMAY6RBgMmANZWV3D0rVdBCMFoKVmPgMhPThKcQUII3nvnbSzMcbNnWfjsFmm6uBDL8OazPwYDU2tR1m+AIw1EjCExOTN877vfzmsCY5pKIyjH8cKF83jn7be0CGOS8mXV/fYA4Llnn8Gq5ssm+TKQ1wIZBHjiicGKGV538j5arTae/MmPtPFNW6WyfTr+4fs4c5q7M5ipNiQwOro/6g+/9x2EUdplKrvXyboBDosGrX8b0ZW5y3jtyEuqfPZwma3jpZdexJUrV65ar7z3G3/2p0OVBThawbef+NbQ5U+eOIH33n136PI//MH3C5FYBh0Kvr3BHMjSUELg3/k7fwcA8LnPfS737/Of35y0/LMindEmAp128iEcW8e0rMITlC78ib9w9szpnHCYYtLa+1lvrGFtbU2VkaR8Qow0U7o0exGBdAjPnJiyfwPAhfPnU33MCrn6RT/wsTi/kNMCsoL7GGNYW11Br9dNNVwyH4kNpjOluUuzYCQRzBoeF1JcK60JlL5p81fmRD+5aU8BMjs2fIEFKM1p3W4H7VaLa1nCNIg0ADT6PIrWsQz4YQzaa6JkMGXuavscy8+yTJVqbKKcCG3zc5ew6gsNqJ2kOwOgQKbLDoc8CSKKXnNF5Yn1vAggSAVaeHGsAjKiXhsARb1iD2RykolSBnhN7uQ/VnVUhLSOW6j6LMaXek2FXyjHE6I+7lPI/RlHHAu9fh+h14dpGhgvWcg2Jzu/Ws1mytdsIxbNGLC2vAwGAssyeHANirXllPJczvA7gGFivMZBvU2SNiEmBxSgL4Il5LzriewdFTsBAZbj2wtihN0miEFQK9k5gUuta+1as9FQ5j2CtJmLMYauHyGOKSyLwDUIxzUDEEitn4CpoZom0DS4lrLX6YBSikLSF68YsGazoaJ39XbqflCyXQDHQkveQ2aBa8ShmTwh7DIlHMjHF03PdqsNwzRyZaQwo4RAxn3d2q1mLmJW1/zp1wAeBCO/GwQKbD4FtCxu7vX68CL+d9U2AcI1dpQlqdzk2BgE6HQ7CMV2V7Gs5KAAqRVN2hMzhjjwYIpczxLmJWKJuZzjEfKbwiAAYzSnLIhibg62SOLKwhhPj2eapshWJHBO9aj2zP7U7XRS0CY6n5aUzFXeHl0jmRXkCEnSQnqeB0MTsrN7n/6uKGPwvT5s2875c+prTm+n73uwLQv6XqvvN3pZxhioxmM2Isp43VZGaVO0V0ry+v1NgSjHQ7YFELA8m6h7s+WDIBhobi7ixZvxZRzKHPz9738fQKIR/ItIWV+I7BgxxiXijZzRdQbmeT7KlbK6rv+e15Jwv7dqrab4eyKQMvUs/XmB7/OMCCTZhIocZuVvul8HWEqZkPYXAV8Mo2OjOYlPb1Oy6fJ0d7X6iPpNjlc2VZQaU8ZSPgkS3sSxOR5dP4hhCp8/xpDy0+gKIdA0DYy7DvyY4+OVLK5xjKMIY2NjnJkKp2s9VRh/Fo+ilb5/E2MjkI7d6/2Im0VtA0HIBasR11JjVa2P4LIvNZWW6hNletQyXzaNXh9WZRSuxRm6hILRg0k6IY/KtWwLcRgDTg31sp1739m5xk1bBIZTwnTdxbmFtsAtzJ/buj7fyEv1cTiWkWPGBEDXE+Zg24Rrm1jvBIA7AtM0ULO1XMRMbhzpOizLQqW6sQ+Lvj5i0wUjFmzbRFn4bBWtD8oY+kEMmC5g2tg6VlbZJ5J6M6NDCKZEJDxjTGkCK7aR2wRaQQhGbBh2Sfk+Mgw+/TIGbNm6Vctrmt/Y1r0QUUThOCZsUMwIWJ4gpgq4WJLUBFpCG2TbNiYm05kCckxcW7Cjo2OoVCoD+YZso9TE6GmupCCa1apKYYGX35nalKVGSuePcgiqtSrGRsdS1+ThSgpHgPRpYxif3ALHsdMCgCb0Acl8YYxh+87dPMpb+AAGUTJ22T4bpgWrPgnEifk9klrXlPmY93d0dAxoWCAhX59E74A2ppJPbN+5R10LBY6ezEpjGkRYJ5J27ZGoElrfQoEvqENQEQKUq1XUR0bRoBIUOp0bWTZKBp7MbNuGcrmc5uU630Xaj2/fvv2F+5t+EJPlbdvGjp27VDmSYUyGQUDjhOePjU9gZJTzX2nm18dO3iy/79m7H6bBMVqhtRlI5iHA+QClFAeuSVLSDTLryt+q1RoqJTdXbpC/7MzMNlSvwsN0uuaag0OXNQwD+wdAuhXR2Ph4ym/+arRnz96rYhbqI7Wptg9dEsCRI0dy177xjW9spoqfKUntlb4RJcIO8Ljwj5FO6Az5E7H8Xi6X8PkvfCldvyZMJadr/u+662/ATTfdnGtTEmFLUs7vn/rMZ1ETQuPGvgD8ty899tWkrDydZfoo6x4dG8MD0idQ/2EAHb7plgSuRnRULrwi2IJPPPI5GEZiSm37MRfkRM5bX5g2Rx0bMaV4+NOPqnZ2wkjlyB0t2fBiHvkpc75OTm/BTR+5nQtlIjNI1bbUptYVgpptGQjDGLWd12Jm67QSWtc6ARjlcCFBFMM0uUlP0r0PfEIEgDDU3MSHLKZM+SqOCs1hO6YYO3g7HNtAN4pU1LFuDl7qJAEZsVMHGdlemEZNJ8YY/DiGObEPhlvG9hFbmJR5NGp2PnSE4LvlutuSTAkZJtgX+ZIdh5vkm5TAnj7A+y+0h1JQ00l+3blrD2a2bc9pFtLtTv6u7LweAIHjmColnb4+JMWUoecFMKozgGlj70SJ+5cVaBYk1esj2C9wKAGgI7J36AEo8t5WGIKVp2CUathS1wDM9b5KIUV8PXzzrYpXSC2XvnGt+76ACjLhOhauv4H77QWxbvrjGJUh5dluZN7g8ckp7NCwHFM91JmS+OGag4dUUJYuBKRu0wb+uhsOp+vNCgPa3yXXxYED16SEQum4L9+zLthv2bIVEwU5SaWZVM/SQRkURmBKACXpeSK/x5Ti2utuAJAEmEihzjITAUm+11K5Amt0C/eztCyApc3BQGJWBSHYum07YJdBCFL5o7PaKKlN3L1nv5ofYcxT10n/RENqAoX2jhgG9u0/kFoXlCVA4dlcxaMjo5iYnELEpLBHUoKujEKWJuudO3fB1nhUZtqmrg0SSCVIunSBkT85jo2ZmTQWns4H9HzSAN876kIhkOUX0pdTn4/bd+xUh8qsXCahg2RdjFGR8jCpbyNyHBdTU1OF5YquTUxOFgZNDaLNYAQCSPnLXo0qlYpKMzgMTU9PX3U8JMVxjOmCdHqDaFNC4K/8yq/g937v9wBwG/iv//qv41/+y3+5mSp+ZqQ0Y5nTCP+bf5c+Kan7MidXSbMXL+Ldd95WZYqeJ4kQgiMvPY/VFQ7hIqtL4GG41KYYMIDvPJGkXdPvSdqVPMHzPNV23cyhky4InjxxHB9+8H6u7ZLp6JoBxhh++uMfpvwRJJNnjOUWLGPAnz/xZwAEM2cMbaVZ4xozP+IpvsZLDlqNdbz03FOJ6SOMEEUMjmWi5JjoieAPKagc/eBdnD93BjFl8EQ98jfGhLADbkKKIorWiRdRszjTjhlDsxeAMQbXNRFEFJZpKIgZAPjx97+NXpDAf8gDrNT0GQbBZJUz5IuXL2P93Pso2Saafqg0j2OaeflSIxBZSwyw5dNA1MOIiA4etKYJ4RiI3TNHuE9dyUQYCsHYzZsQ+kEEGoVoHD+SdzAX87cn/RlLXBv7welT8JbOw7JMlIfA5XrlpefR6bRzc30QY5p96zmunSxZqdyxQHpuRjFDu9UCXTsLWA52j3GTx0Ya+dOnjuPy7AX1XfpLjpXNXPm2H8GbfQuGQbBjND12uuZap2ef+nEiCCGviW/6kYpA76yv4IP33gVjPDBEBoAQwsGLQymYCA3Se2+/hZWV5Zxf1yB66qc/Vn2Sm7fyI9Q0HoQQRFGE5599Oi0skrQGSOeBsxcv4MzpU2IIhEBSgFUiBbVXjryEbreXGjLpM2eQtDmYMYbXX362EOSZIct3eGq0t958TctBTOALd41EO5kE2p08cRyNpXkVnAMiNJLQNIHisG8Q4Plnn0YYccGtpB16FB6faIuEbXn/tRfVNakJDGMe5GGI8ZBdW1lcwAmRPlT1kYlUjlSmEVRDg/fefRvr62tq3AyScathybolAF54/ln1DnV/bB1eRr5Tz/Pw2qtHUu8ISIQ5Dr+UlD9//jwuamuJ11e8piljePv1VxGININGVmMM2e4EbPrIS88X1iUtXGq8ADQa6zj6wXvF5TPEGHD61AmsCPik4jLplfXiCwPaMuDel158YejyC/Pzm8IJfO9dnh5vWHp5iBRzsrf9fh/vv/fu0HVvSgh8/fXX8eKLL+LRRx/FPffcA6BYO/jzSMlJSXxPqY+FL408+WrlB23UrQH+Luqfup+fdhrrDbjltPmY35c31zDw9DypcnqbMu3vtNsCqia9y+qLUV8O3U5HmWBVfws2XfnZ7SbllTZV/KafYGVfsotbCiAjJZNH+4pcwCMlG71+j5u9BTX6Qmvo8DyvPeGHVxHO3P1uD5VKhQuTAjrGFUydawe5ts4wCPctJAxT1bLS6rT7SQSvdNrmmUj4wcY0TQHHkgRhMHBtgBQCt9cdEABL621YThll20AzCJUmUAqJALDQ8riGxSBgQQ/ErWCyop3qB8yvNY9rLE3R7zjmmUgmNCFQ3tvzI9DIR7VeSwnl+gHG9yMhlHGTwvxKG8x0YdtGynyp3hvTAMwJ4Hk9lMsyJd1gwUWapwLx3kqav2HORAWueem0OgAxALuECVcKgXkNtqyn3+spuATGkrzUYwUCctuPQaMIpm1je62UwrRU604TAihNIseLeAUANL1YQdLEoYdSpQzKmEhdl2Qt4XBHfE1wTSDQ6yfjKImoBhUMqLZ+i0xcujDY6/VSJiZdeynHURck+v0eSuWSCIZICzaJkJfwtn6vr8ZdHx8qNn4ZjKE/I5sJQ2+PXr/X78MpldVaAbjwZSDB2NP5med5YIbDhTpxiAtj4Roj57NoIwEXgnzK2+PaRo7HybZEMUMYBrCsxHUgpFQdBPWIYBkN7fn9nIaJMR4oRCEylGgHIa/fR8ktI2IUBOn8u3w8+eDpgReyrdk5kJ0SHIIo3xZ9DUrNHCHcr85x0r5m8t1m3xtlIo1hicOiycHTt51UPzQwbD1oRG+z5CWMAaHvw3VLufVWREz0tewO1uzlXDnYYPNyttxmUswB6fSLw5bfDKTMIJLvSc7zpO7h274piJiJiQn8yq/8Cv7aX/trqNVq+I3f+I3/Kx35/4L0jaRIcxfHMfbu3ccnKU2iygZpAsuVCmrVBKNIXzhyYifaMoKtM1sTQUoyWFHG1BcR+D26+TV1skeGKQAwDBN79u5LMQhZjzzl6kLk6Ng4JoVfUu4kl3kWIQT79h/gjvJ62wdoDuKYYv/BaxXDpgxKqJqoWPBDDhxdEr5ipVIJu3bvUfcvd0MwxoTfDtANuMAo/da2zMxgbGwcAeUn7bJtwLX4ph1Thr7Pn2UZQBwz1Lbtw2TJ5Zs0ZTx4A0DJ5UJgxU1SuAEM+w/diFcvCpxCYQ5mDPCiGGHINXrbqmX+7twq3PEtqDkmmv1ImX1nRPQvY0Czm+RwJO4orHIVM/Vi3w41dxjDhXUfxuguWLYlIowpymULdeG/qFPXj0AMEzv2X5u6Lt8jY0C/z8dltOJwLUtpDEa5D9c1VcYF/Z3L++U7P3TdjWmmmJ4kKY0UGIM1dQBYB+qVtNAqmZWs26cxvMiE4Y4BjoOKbaIbRimfPF1zAQC79uzF2BhP10hZkpd6vIDxNbwIZGwPLMvCTOXqTJoxho/c9tFCHiFJ4kW6tomtW7fADyYUqLljGgIOhGuOQpE2zBERq9ddfwOqteTQk904k4t8fD56x52pAyUVUpO+mcmxd10Xt3zktlTwSFYQ1Gnnrt1KAMhm24CqI/n7to/eAcdOMC7l+6SaRksdEhnD4VvvzGkCE8E63ZaR0VEcuvZ6xEgyVsjALcsgOR64a981MNaXYSER+nwawyQQmHxMaQIJIbj9zrtx7kIMxyTKPQGQAmwy7BFlMIiB2++4R41fLMY7YjzvsLxPpozbNrM95QfMf+eWCsa4P7MuPN7ykdtQrlYRN9r8wEqQ+p1qY8QA3H/fx3L+gFktsBzXSqWC2zRImexYS3xDSfv2HUDATM39KS+UAmKOMYY7774PhgpqkQoA8Rxk7gFw7/0P8rq0g6O+FxGSCIsTk1MKludqxBjD9YdvwuTU+FDlAeDjDyUuUBsJhIQQWJaF+z/2wNB179q9++qFNLrzrrs3hbH80MOfGLpsvV7HrbfdPnT5TWkCf+M3fgO/+7u/i7feegv/6T/9JzzyyCP4oz/6o81U8TOjq8n/lmVh337ulJoVhHIaDMYdjaemp3JlmD7TtXr27NmXn3SCY+qmJgauBSxKMacLf7IsZQzEINg6sw25G4ruJ0C1WuV+HRmBjmVukIxG+hcwyMWT+N8kTt/8Mwh8bNnC22KIBd4XmpqtNQetIERME2dpxhhGxsbUBrLe40JaxeV+fn3h91d3uPDj2DZK5TKCiCKKecSx3GhiyjWBhsGZbhxTuLU6Kq7JfXQYFwIJ4RuB1OZIn6Mw8FGqjSAQ/ndVOx3gEUUxLMvAWMkGYwydfgjLraDsGGh6MaIogmWZmBGp2xiATj9MhHPGYLslbKlIQFSkxk5/B7NrPTBiwrYtdH2qfND0DUyW94IYiCPMTKaBU3XyfR65PFHnieWb7R6I6cJxLO7DhvzGrDPqQdFmRVd9z0MY8X6NV51CDDT5GcQUnWYbgAHHdWCJDU36C+mBSJK6nY4GKssDCCyDFArIa70YNAph2ibq5as4VgstjQ4CLOc8xCcDz3DCGNdW9zptmJYlfMCE/5eYT1FMVbYbGeHabDZg25koZab9A9TAU8bQaadNRkV8TGrhup0OmNBakUzZove0vr4GSwRVKK2NUZwBhjGg1WwU/ADlH6dMmQD6fQ/9XrfQHFy097aaTfgB19JL4SqkVPlYZtsyP78AyriW0BZrgguNiTle51fLy8sIJUKBQXLDLf+OKYPX78H3eqp/EaUwBc+ToM86LuL62ioojXNtDKh0TTFTL2P+yhwMATjNxyMt0MtgHkPYnRcWF3Lt1MdSH+Jms4l+r5vS/GWVDtI/kBCCxYV5xHGsDuwb7ZMMwIJAcsi2QdcwSvL6HtZWl1NC4qD5C3DYHz3F3NXo0mySXWSYaNiLFy7knjmIOp2Oct8ahuavXIHneVcvKOjsmc2lxzt/7lzhb0XdWF9fR2N9fei2bEoI9DwPR44cwYEDB/D5z38eL7zwAv7jf/yPm6niZ0bylKOT1DAwxn1M5i7nUyIBxQP94vPPKqiH7MlZbh7qGUhSxqWESyQ5OvUFsrayiqNH309vFNrv2bpPnTxe6BshmSDLXHz5xRdUyrjUWBT0nTGGV4+8pH7UtUtys5SmYMqA1ZUlXDh/VpnZJPizaRqYqZSw6gXK3AsAZ06eQEf4GzIGtAXg8ljZVsEftkmUtu/tNzg2WCeIEDOZdYS3KYqZyuQQUo767y+cgi0wAYOYwvcjEIPnDZbBKtLctLS4gNnZizwfsJloHwEOQh3HPG+wFMTmzpwAjQLUXZ7bl1H++4iIiOQmulD9zZqXYDs2Jlx3wGk7eQdXltcRN+fgiroppXBFdLUYWvV+giBC3FlGLW5n6uNvizLG+00Idgmfuyunj4IxipJjFm/U8lOsj2MfvJtqpz4XstRqNdFd5umcZkZLqfaS9LTjQuDCJbDYg1tOrArZJiX3Exz78D0YhjQBcuBm2zJQss3c+lpte4jXZ2E7Ns/+kpV2M5rGxtoqFhbmc+Mgnw1w2BnG+EFl9sJZeF4fUUwRUZHCjEghkCmXA1dAhBw/djS3joseRsDNOufPnVXPZhmhISvkLS8tplJRSY2Lzkf0rp86eYLzMJb4ihVl95D3nTh+LHc9ZizBzENiyux0O1hevKK5i+QP03p75uYuoy0EXpkHOKQ8ICNJuZbM51MnjiFmBKbU7Amh0SJEWSd0HL5Tp04hipP3k3uv4kAexRTddhPtxioIeJrGxOTONbqyG3LdXJq9qPzk9HGRkeGlTJq6M2fPiBSWVKXDTGvrRLsA+EGA2dmLvP/qv8HjuLK6gvVGI3VNJx78k3w/d+6s8k2UL0jNT5Z+HGXAhfPJfJS+jPqz9ANTt9vF8tJSoWY5pfgQ9ywtLnAosiGIMeDC2TMqYGYYM+8FLSXs1ajZbKawGa9GV67MDUwh+3/aljAMMT9fjG+YsuSJIVhfW0O7lccUHESbEgL/83/+zynz7+7du/HCC8M5T3qeh8ceewyHDh3CRz7yETzyyCO4ICTzpaUlPPLIIzh48CAOHz6Ml15KnCB7vR5+6Zd+Cddccw0OHTqEJ554YjNNTlGxkMM/O+026vWR1CZFCsoBfLDDMIRl2WqT1MtJcwjAF1EcxzAtK8X8pRaP15c+CXY6bYyMjBaq17OMn4m2j9RHUtpEsLTZTR8E7p+R3WW1D01DyNNQ6X5G/FOHntDb3+10Ua5W1YTs+pECfx4rOWgIIa/qcqyuXreNaq0GxmSkKBdWJqsWopilnO11avpcw6b89hjgR1TBy/SCGLHfQ6VaU5rLnmbSlRGcZddUmoNetwvmlHh7DQOjbgKifKUlAKRFnt2YMvR7HbjlKmqOiUY/BI0pXNeCbXJIBB02Jo4pAArbtRWCfmboU8xyfb0FmA5KJRtr/UgJHVmKKeMg1bGPbSJbiF4nwH2lopDnDd477gqfqj5g2CjLvMG5mvX3zXI7Sm5eae1vtTugxIFhGtg+srH2rRdFCLodEMOCW3aVeS6L9ZZuU2LK8UKKKKawhUaIaGUAYGW9DUZMuK6VbLYs74cr+9rr9VCpVDfcVCRoeL1kodfrwSlVVDSoYyaaaZnthsPDJMKDLsQR+QeQG1Sv10OlIn0f8zBRVGc8SKe93IiPyYMqTwdZBkge5kUvL6loTHhe3XSqOb6ueyiXKjCVP1+xUCJ/87w+XOFTZRCRbpFSAcROxPXEzOvFMSi4htWxDMRCg2YbBo8sl+MJzqsZeB7ykjg06hpeXXiNKUPo+ypl3Gon4O4G4O/TNoyUrxsYQxD4qZRectwCkUu6LEDMpSDJgbAFaDPSYPu6sC/TDFbKBWnatL1Df0eB76sUnHJsZXv4YV1XfjCEYQjHdRPXIu09Fb0vy7JSz6Pi/RdZNYLAz7U9tTdmtPx6qr6rEQNgmKY6DF6N4jjeXFq3sDg93iCS6fGGpc20xfd91DQXkiLS31Ucx1dNf6vTpnwCAeCJJ57Au+++m1J9/ut//a+HuvfXfu3X8NnPfhaEEPzBH/wBfu3Xfg0//elP8Vu/9Vu4++678eMf/xhvvPEGHn/8cZw9exaWZeH3f//34bouzpw5g/Pnz+Oee+7BQw89hPHx8U21O7dhIZn0jAHXXnc9xsYn1O+saJfT6L77H0ipwZNFkJTRmczHPvZg6rkEMlw/YbxSqNuaSc8kf8uSrP/mW25FfXQ0V45pn3pXHvx43jdCbUYZpm/ZNu69/wFVUVYTqLRc4tZdu/coEGcAaHkRwpDCdbgP4FovQkwZqg7Xytz60btRE4JaEFEV2DFZteBHFGHG5PuASBnXDrlgVBOA04RAROjGqFUcdPohDKeM3YfuAMAZeNvnQqIhso8wBlQ0X5791xyCv9pBfH4eZcdATYA+MwCXm0lUsRQqJ6+/Ex4cVG0L6x3+ux4I0QtjpYGLwgjG5HUoV9xCASTLcGOjAnPqIMplC40ur7tWsnIaRC/kaemqO67F/gK/FEII2n7An28amBC4WqPX3YfG5R7qpeI8xrJNcl5/6pEvpNuaOvikZ+fI5DYY43vAKMFUdUBErrivG8ago/uAdQ/lisv9r5AW+rPte+Rzj6m/W16IiDK4IpWf3m8whq7PYMzcjHLZTn4f0GFCgAOHrgVYEoWaO3gxoO/zOTpdtfDwvZ9BJ2DotAMwBqXxIwDCiGe7sUyeO5YAeOzLjyd1yefKh2gaIIBjiX384U+mxkv1DUIo0m6VfkA5DXNBHwgBvvzVx2EYpoqILbpX//7lr34993sU85Rx0mQrhanpme0ojU+noFHkc4vo3vsewHLbRy9IMPkiKgRojT9ysyXFnZ/6Io6+fhmOZcIxk/JlSxPyRN2OY+P+T30Bp96ZU0EkcsyYYPSybEwZtu09gJu28f3FD2NQJNpJ2yAaFA2/75OffiQFOyPrkWkES5b0Leb3feXxr2O9xy0ZToHgLWFcAGB0dBSfeeSzqfecDeLT1+mtt96aaOoziguh9NW0dwSPffkrWG75AOG+8DJKOUuyri98+fFCk3TRHNsysw3bBcxKVgvMhU3ChUhx7c577s0dkItIWp6+qK2lq5FpmptK67b/wPCYfwA25bMHAF/+yleHLluv1/EpkR5vGLrx8GG0Wq2rFxS0KU3g3/27fxf/9b/+V/yX//JfEMcx/uRP/mRolWmpVMKjjz6qJtjdd9+Nc8LO/Wd/9mf4W3/rbwEA7rjjDmzdulVpA//0T/9U/bZv3z488MAD+O53vzvwOb7vo9Vqpf4BeSFK/04IMDc3B8dxUpoE/fSqn6jimGJR+mmQdD2JJlBcA9BYX0On18359Okh/nrD5i7NXtWHUadz57haXBc6lZYuU5bGMS5q6b9kX2Xf9DYSAMvLS8oXSNc8KL8VojElxnDmzGlEcazwwRZ6fcQxz+VrmwQtj/vJjJUtMABnTh1X2oJeEMMPud/diMNTpUkTm2kQ+L6Pc2dPgTGgE0QgBEpQA4ClvqdMze1+iLizCjfi5oWQUnS8WAlD0hF7pJTkET15/EN0/ACxCFxxNcYuAzxqIro2ihmWzhyFaXC8w2aP/17RsoGs+wGPKDYN+J02WHcJtRo3BetaoZwASBma85cBv41K2UZHaEfHqk7ufS73PMQxRbBwAmXTVGYa+T4BYLbdQxxzwOoRx0ZMGVZOvQsQYLwghV2yBoRw3VjHmZPHkjnCsvOeaL8xvPbBewg7DZEyzsqtEZ0aXgx/7kPAMFEWLgByTqmgpozg+OpLz6s2rqi81GYKMFduFI2FRbD2PCqVRCO5kQD+wbtvoynmeyIzpu+QgU4zdRvPP/MkCOFZJSgYT0kmygUx5X6CQhMYRRFefP5Z1Q+1PtWD0m27eOE8Tp86qdqQNQNnD3kvv/QCz17C0mu5qJ8A8JMf/1Bt4FQ4+g+CB2m32zjyUt7qE2maQCDhaadPHsPC5YspIXAQMQY8+ZMfwQ94qklTCFpSs2eZ6W2KMuCFp37AhTKLa4BleklbwyeV/Gp1ZRmvvnYElKXzhCdrEOqAG1KGC8few9oydwkIRHCHbGdJ+PLpB5UffT+/H/GAJZ5GUIdgYozh+3/+XYQCCFvWk9bwJnz1woULePvtt1SdOixXET3/3HMplwB9D0vS0cn1yvDtJ76VKEOQd8NQ7QbXTD/z0x+l1sWgtlDGcOL4MZw4fizpW0Y4l+2Ta/XJn/wIvV5vcOe05zIGfO8739xwLHRaXV3Fc88+M1xhAG+9+eZAP7wi+tY3vzE4E1ABffMbfzZ02UuXLuGVASgshOS1wS++8DwWFhaGrn9TQuDTTz+N7373u5iensa/+Tf/Bm+88QaWlpY2U4Wi//Af/gO+8IUvYHV1FZRSTE9Pq9/27t2L2dlZAMDs7Cz27NlT+FsR/e7v/i5GR0fVv127OBq6FIx00jcYndnKyQqkBTqIOnq9bspvCNhYyFxeWkS/282Z/eSJMst4z587C2LkTYaaskCRQQhmhVk9y9zkNW2PQbvdxvramqpMniz1cvrmsry0CF/4u6hITaQ1gbJuALh08YIwk/MrC0JDJlOptYUWZdQ1wRjDoubrsN4PEEXcdFyzbfSiCBEFKo5IudXtoCdSdHWExqBiJRiB80IbU3Ys9IMIca+ByTIfx4hRtP2YC0OWKSL+gEkhHBACXLk8i17MzT1lzVeOMaZ8FetCCPRjiu76Cg9IcCz0hBA4qkXDtoMQUajlDaYRxsdLKfgT2fbkffGNp9dYBQg3m/f8SGie8lq7yx0uZLP+OkpuIujoc+DUSh80prAdCyWLm8V6zXUQQrB9xFHPle9YbwsAtFstRHGUWkMbaXVOzF4BpYBlWxzDbQNa7YZAdw3EtFCt2qCQwkhewwhw015PHKgIBJQOS1L8AXKtc0f3XrMJAighUF8LWSIAVlaWYZkWsoKfJMo4aLhhEEyXS2i2Wtz0FyemPynk96NYmCC5JrDb7SKORT5w/ZSW7yYIIWg2mylzl9Tap9a39vfa6mrOzKSXle9MHlY77bYS3PVDXRF1O93C9xHFXPjNarM67TYcy9bW0OD5Qgj3wTJNS7WPR/PTFMaevD2mFN1ej2vybc4bwpj3Qc8jLpvb7XTgC17rmvktTx/TgMbwum1Uyty0J1PXUcZAweCKyFiOF8gfFPh+br5QBvgRf1+uFmDW9zwADGFMwYBCVxcdu7DX68ISrkTJYbt4DAGg02mrKH69Sfo+pj/O931Nc6inR8wfvvpeH4ZpJmbjgnGUZQGg1+3BdRxlKSpae3LsCSHotNtDm0kpY4iiaOA6zVK/30+lX7x6+V46C9dVKAzDTZmmhwlkkeR73lWzhejU7/c3BRGzKSGwVCoJDC2eSmzr1q2Ym8tHC12N/sW/+Bc4ffo0/vk//+cA8ift7ABlfeY2on/wD/4Bms2m+nfpUhLskZ3Yep3yBarT+YCyAGdwoyJ9kr6w1GLKtLvf76nyqh1ItAHqhMQYQPiEqhWkt8mf7LkpluM2FcNBZMer3+thfGIipb/X26HGQLQ9DAKMjI6qhzMI6BsglQ1APsU0zZSPCYd84f5sMnpXpoyjlPGsKILhrXg+ooiK/LY2eiHHYysLRh8EPsYnJkXEMXeqrijHYGC5E4IK2BdfCE47toyBMYFHJ/IGO46JQGzkW2uOYpaVag0NkTdY97+LKUNHCIHyei8IYJRHVCq8Xk/A4IjoW0IIGiJi2DANhL4P4taxc7Kq2pu8x/R7bfohB7Yt1TBSceAFEQzDUGDIOs01RaDNyBhsK/27ZNZz6xyrkGcLMdDveyClMRiGgX3jxbhcJMPkJyamUpoTKUjI9aJrBVY8ApgOHMdCdQPGyxh/Z7BKgGGiVnVE2rB0Pmq9eVEQYNv2Haptq10ZTW6qNqgNPaI8UKg8IszeGh/JtEPO13p9ZMP0eDFlCrh7quxieusM15KIPKN6f/tRDMqg/AQppdi2jad1kzxGkuI7SNZeuVzG+PiENg7p95TdVCenpgqxzaTwnr6dYfv2HTIeIsH6GyAFmqaBrdu25a6HMY9+ltq6WJgyS5Ua92tWh4t031ItYcC2HTtVISnUcS1qfosKwwi1LTtAGXcr4eVFyjiNj0veV3JdlASkUEnT1Eo+rI9jGDPUR8cxNlJXBzI+WpzKIkUkQRK1vHNXknZNEqUCzJ4gZeJkjGH3nr2IhHBJiOb/qrVLvoZatapSJKp2bDCW01u2DExHls3+AQC79+xNPbtIuJNtIsTArl27AU2bmNVCybKMAaNjoxgbH0/tTUAa8Fr3D9y1e/fQghplaRi1q5Hruti+fcfVCwqanJzCqHCxGoY2kzIujmMcPHho6PKVanVT2Ui2bduewyLdiDblE1iv19Hr9XD//ffjL//lv4yZmZlNSagA8Pu///t44okn8NRTT6FSqSjny+XlZaUNvHjxInYL/6bdu3fjwoULqd8effTRgfW7rlt4mtA3qeykJYTg8a//Av8NiXAlGYTa8MT9M9u2Y0YwxKI65clanqzuvOvuDA5bwoCyTNcgBF/68ldzwlVWaaCf2r742FdUPxjNdC5DM9u2YWbbdt43oZvX2yYFUUm3ffROEGGnknVLgTXbdgbgk595FH6cZAtp9EUKtpKlfP4sk0fQMsbw8U99Vj2u4YUqE0PNtTDf43ABMifsli0z2L5tO/ohRU8Ik1ITSBn3PwR4ZpIgoKjvvBY7tm5VGIGtPgdgdl3ub2gYBFvLiTPvvQ88jP/8Nlej61k/dKBomTKuFYQYO3g7SrYpfufC3pgGQ9Lo87zBTtVBbFVBxnZh9+TGjs+McTOyObYLZsAwXXdwZo6ntxtx80LgSof7Rm69/nbld5altY7PtWUlG5ZJEDECa8shENPArloeuDgloAKY3srX+QClVcrJHgCMqX2AdQmOkzapZ4kyhtWWB6O6DTAsTNTcRMMsn58SlnmO7BsO36KutX0ufNVdI2c6bvdDMHsERsVRZvysxK2vMwLgxsM3a5qUJEuFigz2Y4RhDNs2ULYJbhYp5vw45qY/J8lG4YlrrhAC7XIZB6+9Lj942vOhjeW27TtQqVQSDaCmIWJIvwvZ9uyBWX+fOg+L4xg33fIRECRmwmyKN50q1SrqBWmupDXD1HgaAExsmcHo6KjKKsGF0MFam0PXXo+QJSZpad4tmfk0iZRSjGw/AHbJR12siTDWcQ55eSnYVmt1lManYHgBSkKTpQuJIEn2ppBSbJnZgZE6d8SXqesi0c+KbYrAMd7OGAzXZDZ0LlQzBBE/0OhrwDAM7D9wDZZ6ctzSmkC578g0orWR0XS+W+3Fpw/7fIz37NkL07QKNa8yHZ1u4di374AQLOV7Spt45TUGBsuyUykPZR26UlsSIRyPlud5T4TP5ECSFiIJIRxGDVcnxhiiKMaevZsTAstbh0+lNj4xsanAkKKDwCBijG2qfLlc3lBDmvUVnpic3FTgyaY0gX/8x38My7Lwe7/3e7jxxhthGMamcgf/23/7b/HHf/zHePLJJzGmRTJ+7Wtfwx/+4R8CAN544w0sLCzg/vvvz/12/vx5PP/88/jiF7+4mWZfleavXMFrr74CID2Zdf8LfWG88fqrmLucaECz5rzsRvndb38r5S8gTTBq0UFjSIzh299M/AV04U8XACUtzs8X+upkSU6Q1197BbOXLqY0kEnDkk/Z3W9/889yvg4648/2SaWMM3jarK4XwjB4KrUgEnAepoFx18HiwjzeevVlPraEoNGPEcdcC1dxTPQjbjquO3wDf+fNV3Fl7pLQ8DA4Fje1SAfjrh/BFE74cUzROfUiaratTp4ywKJSsRFGFI5loi40e4wxPPmD74j2GthSSzRH3SBCEHBfxckqNxWen51F4/yHqLgWVvo+1w5ZJsZFNhACYLUXCdgYE3TpJEB9zNTTGqks0yMEaAcRuqdfgWmZmBlxEIZcO1pzpOk6uavjhQCL0TpxpDA7AwCVJaVS4RGyJ86fQ3/hLBzH5MEmmfJZjfkbr76MxtpqzvlbrY1Mm8689jQAoFy2c7iGOsWUodnugq6cAJwydk5wAdk2kuCK1NgAmL1wDmdOnQAgMsD4XJiXwoBBEi3KXLcP7/IHMBFgvJb2p8z2Q/bhqZ/+MPfMZFwY1rscKshxLMRBH68c4b7LQUxhGwafj+BLyRPz1zW5tvj0qRMK8oUVPSBDzz3zNMIwLNQEEiC38T75kx/l3n+ReR8AGo0G3nrj9USzz7hwMMgn8NjRD3Flbi4nkEnQYFOaYYVA+fpLzxREGrMU79Lpmad+kmDYkUQDJzV3+mMXlxZx4eQxEAJMVvj8TfIMpyPEDQK8/947WFlZFaDz1sB5wBgXlI6+/gLHEhQWBFMIZCbhcDQRY7CIyE8cx8rPUx9nDnFDFY+SNHf5Ek6fOimyjwAWSYLe5LsAkjRyb7/5OjfbIy20ZoU1+fezzzyd0vrq2vSskNfv9/Gqtu8BCUaj1MTrsDyXZs/j8qWLiTlYO1xk9ybGgDdePYIgCFIaVyq+yD1PnxMvvfBcIf8qolariQ/ff2e4wgBOnTyJhfn5qxcU9OILzw9dFgBeeP65ocuurKzgeCbN4EZ09OiHA2MvipbTSy++sOGBK0ub0gRu1STp3/md39nMrbh8+TL+3t/7e9i/fz8eeughAFw6f+211/Cv/tW/wi//8i/j4MGDcBwH//N//k+lFv77f//v41d/9VdxzTXXwDAM/OEf/iEmJiY2elQhJYwh/Z0QnrNQT4umJveAutbX13BIO9FnT07Jdb7gwiDI+QtIoU4PrJCQLLp2tagN+gtutZqo1jIgwQSpBUwEEyPgvjeHrr1+YOW6uQGAynsrT72ynUQwfp28fh9uqaw24VBF+xKMVyz0oxhhRFF2LdRKFlbmWqiPJG1vepEyxVomQVfkAa7ZFgzCzfC1kVEEMccQK1mGisoLIgpfaOsog0izRjBWcpSTeUdg9tWqDiJKURWwIQCHqHBKZRWdPF1N3kHTD4UJ0MBMjV9fWGvBKlUxWjKx0OXBGaZlYqKSRBSvtH0xfgQIerAqIxgr2Yn5VHuf+ubYELiAlm2hYhuIY4ZSycSoowU3iHt6fgQaeqjV6zktEP+EypJSLnMfrdmVVTCzBMdJg09nBQbJuHvdLmpijiktSn7qKArCGDCT5w0sF1G0Gy2AmIBp45qpknKK10lvV7fbQV2YGSnV8gaLqGx9M7rS9hF5XdjT6SwtRFv82c1S9Z0l1/V1NNftC+BuA6HfQ0mYXQKRwlDOR8Z4dChPSyh9WrvYsWM8PxAZgVc+M/C9FOzEoHGXG2qiVZGDIMci029wP7lypZLShG70rvq9Hiq7Kql5yliSSs0S605ep5TCsYcDww3DEKZI08aFeKJ88SQ+qGo/A/rdHmLLhUEIxlyeAUceTG3tZCo1gd1eD8ydgBEnecZln5NDOP8eMwaT8DRvUjNICOHXhVaP9pkC/+73+zlYE8YgfBSBkmWkzMHdbhelUknDMEReE8i4gEhE/eVyOacI0PcvnfIHOqYOyXIPkEFxvV4vPb9I8i8nrDPOI0emJpWQqI+jvF+/zfPSY5PaJ8V/RUqTIsr6K3piXDYqp5Pn9VEq590ZBlEcx0ODOcs9cljy+v1car+rlR8EP7MJWW8gbRoi5n+Xdu7cOfAUuHXrVvz0pz8t/K1areJP//RP/6+2RW5kcgAdt6TSqHGMqqRgkVp9cnIK9Xpa8NIZLaPJd4PwtGvZ5zLKbzKIkdpkojjGwUPXJmUL2i4jvBiASqWqoG2KxlcuCImxNTOzTZl1UtqIATvMNYeuVXXL6mWidl0TKE1KBw5dp57rR1RFtm6p2lyYiinGLANlx0StNoKqlsprtcvNwbUSn5YdnzNgmRN2+85dqFVrmG8GCCKKsbKpTtleGKPrc9+5KGaIY4rRXQdRcyxQygFtO50kwne94yvwZW5+JNh78Ho8dSKAaRqoa1HH7TBUmsDtNc54AqsKZ9TAeMXCpaaPKIxQKjuYrjiKGa6IvMGUMhB3DFalihEnv+R0cx9jwFI7BBnZCdux0Q1i4SfppHALGaSvYgTAxM5rri9UKkWUA1YDSVDLCqvBKI8JOJtiLqJfPXjoOjiCCRHtd3lg0j0QYspgTR4AW2eolKyU75Dsq6R+FMMLCQx3HHDL2FErQ2ZiGETbtu1ApZaY6voiSnyspB2cxCPmWwEwsguW42D3WCmluYS2oeib3q233VF4upZtv9L2EccUZcfCSH0E1xy6DgxAL6QwjSSXdiR8wgzCNZumQbB33wGMjGbwP7PqVW2MbrvjTrXOZdHsJqeJZLjj7ns0jU9SV9E7Hp+Y4PickFh/bCBQNAAcPHQt9yXONF1qAmW/Y8rxMa/7yB0ZmJ9smjOW+u32O+7iEcoC61FqAm0J8K3RxPQW2BM9kD7PF80YU0KXPNQoPmkQHLruRpy6FIAEHHBdDblWrxS+Qkpxy+13g0BkGBLt8KMErzSiDIbNx8p1Xdz+0Ttz/fTDGEHEMFpKm3t37doNw7JwsZGMheTN3ASc1AMAt9720Vw+aIMUCxyMcegy/f7EnSGBJJNuPLVaDYdvuTWljcumjyRCGI0Z4/O3VhXloPEslhJKCeE+mnfdc7+mIdb7lQBWJz6FDPfe/7HB/ChzfXRsHOOjgzMkZen6G268KtaeTg88+PGhywLAgx9/aOiyM9u2YXrLlqHLf+TW267qn6gPz2baAmzSHPwXnbJaQEn1Wh1T09MplTkI0A9jXFjpKX8QSTt27FSnhKINQ3d2DcMQe/buyz1X4iPpi4GAmxd27Ny1oW+EviAsy8LE5GRheZ3Zys/xiQlYqu0saZeuwRRfgyDAli1blWZRdpixxFyh1xOEASan+OQ2CNAXkC+mSTBddtHwA8Q0iRSmjGJ0dExpMlpCUzdZ5SbcXhjDNonC8quUKzAti6dwowwVDRuuG8Qq04cfxYjDENX6CFzbUKYZmS2kJIJSyk4iBAW+B7tURyCyT1Q0B+WGx1PG2baBiTLH2Wv3A9ilCkZdEysdnjfYtk1MlVw1Ju0Oj6qOhanKLZVENhHkmKaaAwSYW22DEguua6HpxQoCRfctIkhyJYP62DZRfCgJIqpS5Y3XeNsWVyUQtZUGh1XtSc8jyqgKCCsi/Wqv58EPeV9Gq47qX9GdvTBGt8mR7S3XwYgjtUEkNSb6JtXutFEqlUDAtS1+EMMkxXmD13ohaBjAtEzsqJZzbeXPSq55nqc9t0DLQgiWOjzQqWSb6HVaPP0X5dkhdKBo6bJggLsnmIRgfX0t56ujllXR+HS7OX/LLMnzW7fbBRXBKbp2U23kmb401tdhGIZmksv7J+u0srKcCjqR1gWZbYKnm2PJ4aTV5KZZTRiV92Wp0+nAF9kW5M+9iAv3umZP0urqKvyIa1lrwp0jjLnA7WiaQ/m8xcVFRODvpmRr5mKtTgYBqh5T9FsN0WauCZSaSUtkG4koE4E+DJ1OC0GQZAuR/uBBRHlkuMA4lf1eXl4CFfUCfP5ZpubHqII3eH1LiwvqHSaKhYRv6BrrKAqxurpSON6MJcoN2f/19XX42pyXz5XzRq+HMaSQHIYxNy4uzBeX0/oi6/c9D+vra1etE+Dvan1ttTBN26B2zV68uClt3WUtoPRq1O12sbaJ7CJLi4ubSjF3/ty5DbWS+jwA0unxhqH/vxICJUkNhqTnnn2amwMygpBjGdg9Wc4AnjK8+MJzSTmSFwT1r2trKzh+/GiqPJA46SpGLX4/eSKdAi5h6PmNggA48vKLWlmirqfaozXw1SMv536TQi/Us3h71tfXcerkiZT/BpBoAvXNhjHg/NkzWFlZVu3oBhECgfs3UythSWj6Rkp8Qn/w9hvJeIgIXEKAHaMOopjCCxlck5tguH/MawB4UAalDDU3EUxaQYhQPMsLY4T9NmjjisIc6wi/PokRKINVJEOdvXge5xfmEYrsEzUN2mS1GyGOeUCJKyBWLh9/l+cXdkw0+4FApLeUj2FMGbrdUDDnGKx5CW7ZvSpkCgDMzS8hbi2gXLZVyjhuIk+0HADHKvT9CFFzEROWnze3AOiFEXyhId09zrV5syfeB4iBailtXlZ/640hBEc/eA+5BZItB/7z6toaeitzAAG2j28cBNOPY3QWZsHCPtyyy536QVSAgtTQSW0JITx9nWHw7BthRLmGxjQwXnZUm2RXWr0Q8fpFOK6DiUoeNkOXeQghaKyv4YrIjSq7mxpTxtDoJy4Lly9dRLfb46nrYg4FYwip0gt53mDLJCK4ATj24QfpjYho1gGk2wIAJ0SKuawWLS+ccniYpcU0NpiuocnShfPnVKozBX6c0dzpdPzY0bQGUgiOEthY5tEFAD8IMXfhbKFGNxtoA3ABc3VlReUgBrjmzSQEtmnk5vWFc2fR932YhoGKSOsWxFTxbMYEpAvj43ry5EmeftIgSeAOkVq1pK9hRBH4PlYX5rjmjDEElAuXfsyBonkASZI3eHV5CY3GerqPEJHhABwrrQk8e+Y04ihGxCgIiJrbkvRUdwBw+vQp9T5oZtzSpnmGXq+n5q9ORQImIQRLi4totppKeAf4eFGa9tWT7Tp/7kzqkJStXylQxBicO3tamdrzLkpQ9zDG0O12sLw4HLYdYwyLC/Pw+lfHFJR0+vSpoc27AHD+/PAYga1WSwnfw9DclTkFuzYMnT175qoCrH7Y2kxKOmAT5uB2u40/+qM/wtGjXKA5fPgw/tJf+ks5s+jPM+kCW9Zkog+yznizjtK9Xm8ghISsX05sQghajUYOHgYQzBN5/6dGYx3XHDyUY/RZAVV+5bh3VqIxIPkysi+e7yeneZIsWgKtsEatVhMjI5l0dEgSxqeiigG0W01MzexU2pWVPod8qZVt1EoWVrtJSjjGgCiKuP8jIegHEfrC5LqrXkY/iBHEFGPCpBhHESzhK9nwudZgVAvqaIchoohipOqg50Wgfg+jW0b5BkEpljohwjCCZVmgoid1Ldq2224hGtmGKOpjpOygpPkOzYuUcZWSBcMg8PwYXhCg5rqoWhY6/RBgQLnMU8YBwkdRCF+hzxH5S2UnlwlDPwAwcNNqc60BYpdRrzs8wpVxE7aRKR/GlKeMizwc2prASMgxIeBBJmEQwjANXL+FpzfzvABGyUK9nA5S0d+lvBpFfMxSXFv/O0ONVgsRHJimiUPTGwuBfhzD6zRBTAfVejXRdBQII7JNjFLFzMOYIghjlF0LVTcN/QEA680OGEw4rsUhZLSBl5uTzgO63Q6qInuN/ptcP5RBmN85fmO/10W5UlFwJjI9GMAzTYQxFzLknNCFH3mo00df/97v91UqMiJu0P3+tK6AMY4np5u7NlAeAgC6va7iY1JDpKfq21D7KH6XvraOYcAg3EeTMuH3Vq7k8lzr96faIsrLNnDhi0f/22bazzGmDO1OBzCn4FgG99FjTGnsbM1PWVpaKBXZRGwjhSfJf0/cMMKYwu/3UK3ytvD7qNK61xxDBdFIDaXX9zA6PZ7qF2MM/YijIpSttNXF8zy45QriRgeEJD6BckRUYIi4IPclmZ6N35PnIYwx7p9YyaZpS+acwh8U3z3PQ3VsSs0pXXPM702eQQW/dl0XMdNS+Gl9Sz2XMmVxkoOt70n6O6KMawKr1eHMtYxxK1VROr1BZBjGpnD8NoN6EgbBpiBZ4igaCONTRIPaoo+/nBtBEKSjyYegoYTAubk53Hvvvdi5cyfuuOMOMMbwP/7H/8Dv/u7v4siRI9ixY3j8nZ8VpU4ume9F9n9DCnRIM2rbtnH3PfdtWD//zi/s3LULpi5ginqpUEcaGViG22+/Q0VOZzURsu3ykwF4+BOfShdgANFO5TpDty0LDz70iWImLwVkrcPbd+zEtu07UidF+akYF4GCjrnltjtg2GVeP4ArHU+ZXQGg5fGgiy1VnhnigYc/peBA2v1IafLGSw7aPgeKrrpcE2AaBh58+NMAY6oe6StICMGy0DKWHQvrXR/O6DSuP7ybmw2FEBhHMdySo7QXOgzMzbfdgecutUBpD6UMtMmaMOuOVHiEaTeIseUw93epOVaSTaSaYA4u9DyleQyDEMbU9ajVEvNo5pVpAgxDaE3AnNiD0ZqLpvDnk5k99Ds7IR+z0X03Yd+OvNMzIUAzCBGFEWzHxt6RKqKYonboXnSWfY6dl7x+fSqoCizTxCc/87mEgRcIgET8SAgwunUHjNFdgGHi4Hg1JbRmqRvEoPX9wPoJjIyWBVB0QWAIknn3mc8/pq5Jt4CSzQNcshtMx2cg225BpeIUaqV0IQ8A9u2/JqWpyG5alDEB3M0DnT5258PoBgzL7QCUMQUFA0CZA12HZ8ohhOCxrzyu1ky6HQT6ZcYYyuUyPvvo51Mamiwv4mX557XXXZ9b0xuZ7D7zyKNCu6RF+2vQIVke8dWv/UKuDsq4r60U2qUWq1yt4a4HPjkwW0i2nYdvuhmrnQCtfqQCTELKYBEt6ljr7z0PfRbPPXuW54sWQp8v8n1L7L5EgCH4zBcfx398/RLGy0bKvCz5q2R7QURRGxvHTQc/Ifoj2iGsCa6V5A02RTs/ctttShupW0r6ETfNu1q0MgB86ctfRTegiIUmMAvLo2sCCQEe18ZdCq259ov3NTMzw913MuMs+bcMupI13HX33VjvBggiMeflP5KeB/JhX/rK1/j3jIZZFxb153358V9I9YkQgNGkD4QQMGEW37lrN/bv24thiAG46977lSvAMPT4174+dFnTNPGVrw6fkm7vvn3Yu2/f0OU367P31ce/Vni9aB93XRef/8IX/++njftn/+yf4a/+1b+Kl19+Gf/u3/07/Pt//+9x5MgR/Oqv/ir+6T/9p0M/7OeFuNaM/93pdJQvQlbFrbRrGs1fuYJI+K8Mrju55+ypk4WSvNTUZNnk6VMnha+OLKe1KVPW9zxcvHgh9WyQfJvlCW9+/goajfXk96toC06dPA4qouPAEigJyrSINiUdMhz94D2YpqkCRuZEvt162UYYCwgXg2DUtRFFEc6cPK6edbnTQxRxIOeyZaIZ8HurwnyzvDiP5aVFxIxjw5kG9wOTYyK1dfWyDc+LEK7OYrdQUocxQ7MXKqDoKOYO0lNVCTRN8P7bb6AVMBWYIk2vlHFoGUKAiRrP+9vyfSyf+xCuzc3GMuBkaiQJcrnU8hCFEUzLhN9YBgtamJgoawwweae6IOaFFM1Lp0BogJGKrSB2do3lfd7mux6iiCKYP4qym4ePAYC5lo84imE7Niou11qunuIm1alacg8XTmVDki+LC/O4cO5MfqpkBRfx+cZ7byPqd2DZFqarpQ19W5t+jODyuyCmhfHxkkgZl8+goGsBX305gUNa8wIRpGGqw4Q84DEGrF25Ataax8iIOxD6RKf33nkLzWajWCCGyOSisrdYeO6ZJ8HA8QAZg9L6gAkhkPEoUscyQCnFs08/qSrMaUQ08xshBJcvXVIwEslrSd6T/r4IAV5/7VU0Go1cnwZ1+8c/+oHicbEIGLAKNIGEEMRxjKee/In6LklqAlVuX8b7MXvxAi6dPaUEMvlb0qZ0o15+6QXVdsMgHEqKcreMrCBJGcOPf/x9LnQ7JhfqGD/oWUSmYEsEjzDw8dzTP0VMmco8lGqPPMCI/ixeuoBL57gJNoopYqEJpIwLdHKspPD28gvPod1uK4GJECLyBsdaBpmk/T/43nd58AxjiRZQ+10KmYbBM8w8/dSTvL3aGOjvXdfsHT92DKdPnUxdy5qP9eCfn/7kx+h1e0qQTMO+MLWXydzSP/red3JBlUB+jhFC0Gg08MJzTyvfcVmGavNc36eOfvAezp45jWGIMoYnf/T9lC/m1ejbT3xr6LIrKyt4/rlnhy7/9ltvbSrF3He+/QRi4b87DH3rm8UwfEVBXwsLC3jpxatDxuk0lBD44osv4rd/+7dz13/7t38bL774YsEdP9+kT+KV5WW029w5vcgCkr10afbihi8wHfUGnDt7Bq7mUM2FKKZhYpHUwpudvZhuq3ZSZZlr6+vr6HTaub5lNzy5wSxcuZLGK4Q0NUH5/cm/AeDCuXOq7ZKpyk+dOUsTxoLwRzGE0/RqV2ixKjZ6foQgimGbBuqOjXazCa/fVYxnrs1Nx2XHgmsb6IR8Y62L9G3LS0ugMYeY6Yd8gyjZXEtAKcOKeNZI2YLvx4g7y9g7Psad1xlFux+CUgrXtRBEMUzTwJaKqzRVqytLaIi8xiPCVxDgG0HP4759W0dEdG2jAb/fR8k2YZkG+v0QxCDYPZGo+BfaAeKI5+uNOg2AEOzfWlcapiJi4LmAvfUlmLaD0bKDvh/BMAh2j6ahHADg7CqHKyFhJxVMkZQjOLvSB6MMpZIFyyBYaDQReB5M08Du8fS8BJATgNQBqajBBRLGsbOXEFHAcW0Fl6LXpzu5r3ZDoNcALAfjdVfAcyQbuXw3cr32ul1EQaDastjlKa+qwlSut4cB6K6vgRgEk3LsMoJrllZWllASgRtCNkj5D0uwc8Mg2DFSQrvVBMDN2gCSvMEE6EUxYgq4Aieu2+kgprFaxGlNSiJwye/NZgOmaebaylieTzHG/epk0EnWqT9LhPAUXXKgZIRvNjBE1tPpdNR3va1hzM1QUtiTfs6ddguWZSotXdaPMduu9bV12DZfizL7R0Rl3uC0+ZtShrVWS6Tj4+tPClWOqeUNFkyp1+3yHM7iQKnnlwagoq8ZuDbR67bhiAAlP6KgsgzjeI8RTfIGM8bQWF/jEC5aByPK4IlAFXkwkL/3+32eai9z4NF5vEyb2Ol0lQkzFcSnkS5QtTvtlMJBFwSZtudIajYasBxXPV8Kj0qg1Z8DwPM9/i4y2sgii1Wv14NpJu46enuKDmTdTgeOPWSqM8bdH4YFRGaMbcoHj6eYG94c3Ot14WwCnNnzvKH9Exnj6fGKqEjT722y7cCQ5mDLsgrTudi2val8fD9rKuKJnU4bE5OTSRloanF5QaNev5eDSkgxOO0WAp5qL+tMy6hc7OlF7Xkexicm1CYp21y0ERDwdHRTU9Mps5X+/KR9vIYgDDA+Np4fBL2cNkhuqQRH5H6U/VGmI9MAtOJxHGNkdExt3mHEBS9CCKZrNtp+BD/kEA0120K/62NqOgmTl1GXFdeCYxnohlzjIgMpGGMYGx/nEccxg2MZKSfwTp9rzGquyTM6uA6mR0fUBtHuh2CUA0X7QoicqZYhWXB9ZAxNEZ28VcOU6wcxPC+CZRmYEObj9W4PTn0SFdcSvn/c7HtgMknft9rlmkfLMhGHAUhlHPfsTWdcKFrEl9p9UJiwSjVM1SwFTTNRcnIQHnNNngmkPj6RY8KSMV9Z64Ex3m9CgEavC1Ieh2UZ2DdSVUKpEk4zgpJhGMqnNbspZBcUY8Cyb4BYJRF5nJn3mYm50PQB0wUsB2NVF5QxEQiQFUbE3IsjzMj0YoyJOcOSdHraA6KYIggojNIIJutuAfag/Ex+qNXqqFSraa0Lkg0yiCiCgB8gdtYqwJYZMAZ4Ivq7alnqBp4yjpsQHZOAhjF27tyVGkT5Z5Hw7joO6iOjik/INV+k2SMEGBsbT7BOM5tulhgTKeNYYrkwjPRhVH7K+SwzReiCYBjxvMFS2JOCl+WUUKuPqOwdg8zU8vrE5CRs11Up6KQW1TZISlMmrRCVya0AA2oyp3gQI6JUYfdBvjPxrMrkVjCfoe6ks4VIa0wsJJkgpqhUqpie4vuBNOnzQztDSQicBkkOwTMzM7lUfTFlKrClalmp/u7evQeRgLNJDjwCGYIxBeNCCDeFZzN0JL7cyZjIv0fqI7m9ST5XwgDpkcq7du+GIYQRShkMQ9Psa+9Manj37Nknm5Db07Lv1bQsbN++PdeW7D3yMWPj4xgdGy0snyXKGHbt3jtwfufKU7qptG6O4wxsexFNTExipCCbziDaTLq7OI5xzTUHC3/TfYTl2yqVy9hWkN5xIxpaCBxEm00b9/NGNx6+CTpnTmlCCMkJVJ/69CMbTr6U2QPA57/4WK5MzCQuV1pT4rouPvmpz4hnF9eZtE2mCEqfKPRFnG3n3ffcB9M080wZyWag//LJTz+StEH8k5HBynSEhGHc/9CnFJPt+jzQwzAIJisW1v0AYUxRL9uouhZKU9PYsWO76t96L1LmXIMQdAVGoMTru/aGw3AdB3PrnsAItBQmmB9SlS0kiBjimGH6hjvh2qZguAxt4bdXrzoIohgjFQdlxxSaRIq77v84nnz2PAgBdmlat4WuhzCkXBCr8LneMyqo7TyIsbKFuU6PB5zYFsa1aNtGNxAOxgaIMwqjPoEDo7XUu9U3dflKLjUCmFMHYTkWJspcCHRdE7XMOmMA1jsBGI2x5+Y7M+8xqb/R5idg7o9I4JsOjLE9sG0TM/UMAClJpyQjAHbt2pMEKJDMXNQuqHk3dQC4vIhyWUDhkHxxJrRPcyttGNUZwC5hquZwrZKmgciuMrdcSYDOAaz1OFD0RNnKDWS7H4HaozCrMaY1f0p9zNNrHbjp5o/kAsT0cZXBPrZtYKRk4KabbwUF1wQSwlPGEQCMcIgTxrgmyDINkFIJB6+9bkPzuP7MrTPbUK3VxPoqLqsLADcevinn+J7lZfwelqSMI1AwLwovM6PtY4zBdd1cajSuCaQKAgdI5t7o+BRGR0cGQs6ogDtR/8Frr0fAoLSRPNCGwTFMBWwsKQhD1HdeAzYboCrWrx9yGPuSmUBGyTFzSy6qW7YDl/qoaNlC1JASAOJQHlKKsckpTAmlgBem8waXRN5gXYOnJw3QfQJ7AQUh4LiEor9xHOPa665HJ5bwMGlNIAAVdEHAFQi79+xWhxBAx+PLC2FT09M5PLlEoOffeXo/vjdIH1jTIIiR7HdSyJPlYkoRhiH2HzykDiX6q5XaS50cp4QdO5Kg0UGHAbnjTE3l2z6IGIA9+/ZtiCeqUxzHOHDNNUOVBfi4bybgdXJqalPBGLv37Bm6LKV0KH9DORSlUgkjIyMDtYeF9w5T6IMPPsCWLVty/6anp/Hhhx8O/bCfF9Ln63ee+GbOvJsWwFiKCX/rGxsDV+vlm411PP0UB8HWN2e1II00/tOHH7yPkyeOF5p8itr20x//EK1WK7nG0iYAuelJekK0Xfr48S9pzaFczH3Pw4++/+eqPGMi0k5sPNn1d/H8OXz43tvK9NPxIvR9qUFz0A5k4AYPunjzlZewvrrMNwJAaA256ZinA+MYgRxXj+BH3/s2AKAThohiHuknMcHaXiTwCA20vAhxHKF18mXYJsf0ihkUXMt4zUUc83ZIJrI4fwXvvvUG+kLLs62SRGUu9DyRKzbBAPzwnbfgNZYxXbVwYqXH8wM7Fm+rGA+Zog4A6Py7sF0LFdtMvZ8iWm4H6J97jZuRGVSKsiz+GRPaTdpvoTd7LKlXmyeUJTA1dRHU8u4778FvLMB1TRUBrbSAyZRQ35958kfCLzSpV2kP5QXZJgAX33gWQCJ06qSEU8IjMRfnFkEb5wGnjL0TLiiDwpYr2uROHvsQV+Yu8zFlQE+YypUmULvhTKON/sW3YJnC91HMS13TmX3GUz/5YdoKkKGmHyq/1dhr4803XwNjTOWxrmqO6jJlXEkEi5w4fhSXZi9ezQ1X/f7s008iFlh5ujCdOmRqjXzyJz8aWFdWsGs0Gnjj9VcBJFGn0scte3gkhODkiePKTUX/XfrWKugiwRteP/I8QOMUBqpO2cP1M0/9VN1LCNcwxozl/OkA7gJzUqQLk/mig4iCMqQig7m5FTh54hguX7nENYJ6fdpBSbrnhJTi2BsvwyT8RwlYrQJeTFNlSJEC7rPPPJXTvMaUIYhF3mDLUP1dX1vHu++8pbKhmISosdPfk6z7+PFjmL+SYPMlfoeJJlMX8p5/9plCQUv6KQJpczBve3KQ1y1T2WDA5voajr7/Lq+DpPEJi+jUyeNYXJxXbSMFZZnQsALAi88/m2v7QGLAS5sov7q6ig8/eH+4usF9K5cWF4cu/9yzzwxdFth8irmjH35Q+JucL8khATh29CiWl5Y21Z6hNIFnzpzZVKV/kSgMQ1iWmRO6JEOiLNGKeJ4H29nYb0GvZ319DePj4yBSlSBILUhtY5Xlr7vu+pTGJLsByGuMcV+d1Iklsx71LhUtmOxmJ/sJwsFkx8bGE58ZluCCFcHDNFtN1EfHVZ2rfZ7Vw7FNzFRKeK3RR0wZxsrcPNJuNTA6Ng6D8M2kLfzuto3yNG99geulsPzAGVg7iBAzhrqbCHFLPQ+BENS6XoTY62G0PgLLNBB5ESJK0e9zmJRa2YYMVpGMqdVqwCxV4YcxHMtQwSgAsNSOEMcUrmtiS9XlWstGE87UNmytOfjwTEP43NkqxzEAtIQGLop4+phytZKLBMxqycCAtWYblJgolWylHS0LfEKd/JCi70dgYQ/TE+OpeuSb7gex8lccE5lMLi+tA1YZ1aozMFesfimKI9hCgyIPMmo+avOIAeh7PoIQIA5BrZy3EOjzuuvHaK83QYgNUipjlwiqKTrcS2Gz3W5h+46dauPvB1z7W9eEb9mmo0s9xGGAcqmEmYLIapb5HsdxWguoz2+xduY6fX6AcC14va6AkyHwIgrbTDDouDmYgxmXTJHysNPGzLa0ialoA5VXgiBAqZQHli7a91TwVqZ/gxQl3U5HpQGU5s5sgIJeX7vdxtatM6nrjHHYFYskEblKmAp9lMslZIXJrJlZtp2Je6VPXxhTMMZTrmWp3e4gtvhcGRe8RGUXyaSMA4Bet4fIrMJgSGF06ph7kr+FlAFxhIoY90BkfJHJAkq2iU4QqXUTMQqzAESdY5xSWALjVI17r4tqparyHMtMK/Ju5SsueH6/10Npe9ocXCRMAZw3KjinApL+mqY27llh3MjMAUmMibSBlQqgCaK8nvReJsv3ul1UynvU+PLribCs5gOT9dA0pMwGFEZR4bgPIp52bXhIFp5ibvjyXHAfHn5msynmBrW9aE/3fW9TbQeGFALHx8extraGvXv3pq6fP38ek5o/3V9Eyps5tL/FJxM7XxRFuP6GG1MbYZb011KpVDAxNppi3IwlDChrLtm6davyT9Q3TL1t+rVrr7teaelSASkFbYnjGIdvviXXzhTmlPbpui4OHDyo+s+QmAlTpmBxz+TkJEr1cRXssuYHCiNwvOpgWQRuTNW4ELb/4CG1yQURRccLYZoE+8bK3AE/4hiBrm0CjOH6wzcDANoB9wMbLyeCiXxWtWSj44UAMbD9wHU8BSBlaAcRgoBH6kphdqycpGCbmJxCqxUijNYwWnVS+XRXe6HI3Wuh4locKmJiB6xqFWOug9WWB0opymVLCQEx5SnqCCEI/Aikvg3laln59GWdrvV3strxYY7tQbXqYLXL+8qjlaXWijPPjs9BoO3qCA5esz/DhPlbWer68P0IpmliaoSPdVjdAsPleIpKu8crTvkGyvd6y0duT7XR0CcJ0xg8gE4/hDl1DYhPMF51Bgt0AJpBgIC5IM4I3LKLiZKLXhhnNCPpOXngmmtUisSuH8ELYliGgTHXTtXPAMyu9mGM74Pt2NimRSnLz5zwyxg+eufd+QZrbbm47ovocRvj4+OwSlUwxuBFaVDzMKboBTxatWyZMAyC/QcOYnJQZh+k1yoA3HlXcVuKN2mGu+6+d2Db+X3JjWPj46gKTMEsRqCsT85RBuDgwUOFvmaRCH4wFQ/i9173kTtSa0jWmW2HvH7HXfemfpNCnVuQe7g+No7y1r0gvSRlXCIEaj6Bog8HDh7Ce6dbMLrI5Q2Wz+Q8mWsCb7rtLphirfXjiPsoihzQrm2g6TGUrMQH9b777uf1aPVGWm5z106Ce6Tv2JVuIpDp407F+CWm5usxquDCpMYnLUTrfpv3feyBwjEHZAR4wn8opbj73o/JQQCQhm6R/ePtYpiYmsbU1ESqr7K/2SlJGcO1N9yIyalENpBWpGx5qQm85/6PDdxTs0QZcG+mrxvR9JYthfN3EN1w42EF0zYMfeyBB4cuu9nyW7ZuxeTU1NDlD990M0ZHRzeVkWQokfQ3f/M38dZbb+Wuv/TSS/it3/qtoR/2syRlitWu9Xo97Nq9Z6Dp1dAYIcA1gdu37yicrIQkeR8lURpjcmoqVZ6fepNE6TqVyxURiCHrTD6lACivxXGEsbHxq5qXAL7oOp0OprVAjKJThGIyAPpeH+MTaQFfmh9Sztrg7YqiGNVqXcHDLHU4fEetxLVY6z1+gt5RdzjcgltSWsOOF6kAiH2jVXT9WGEEGoRHa40Jf5H1vsBpcx0luKz2uLau4lro+REQedi2RTh3U4rZBs/t67g2gpCbNqdrEh6Gn3LXmc0BoV1Li2oE1jrcrDsisOb6QQw/iuHYFiqWhVafC2rVqiMc4bmWzvO48OX3uoBhoybgZVLvhRD1T9L60jpguRgdLaHthUprmfV/WfMC+H4MEnk4tDXP4AghWOj1EQYhTMvE/gmuxWy0ujBMC1N1NznRFwiABBzUlGkag414NAFwaWUNQcxBWWeEX2W2f3LOdMIIXnMdICbK1TIsQhRkhqxP3SXa2Wq14DhceO2JNIG2wJXUNeYAsNr2QEMfTsnBVMVN+pl7B/yz026r1HhZIV22f6Et/ErLNpqNdRimxcG3Q4qSbSgtixfGCGIGxyJKu7W2tpqK3k1pGjNjyxhTiAXDkIQo0dtKIPlFIixIWl9bUxojeT2b51dvz/Iyj5rWr0U08SWU2TNkAEK72UgJ83q7dG0gYwydTgdeECh3E4PwoBpCkIsuZwCWl1dUyrjpsiuEbgaT8DzDAJRQZxBgbu4KQsIhn1KCqa7RZjwggzKGfrsJk3DzKQf7Jsq061iG0P7xG7udDjw/vdlShlQGGVfgVzIAq6srCrhazvWUEChM83JfuDJ3mc/3zDspEqQJIVhcXEiZiPV5RlliTQH4ftDpdIQ2Lq3NS2lsxXiura4UaPJQSIwBVy5fhpPxY5ZrWs4VvY6lhfniynJ1M/S6XXQ3sT6Wl5Y25SN3aXZ2UwGvV+byWVoGUbfbRbMAymkQLS8tIQiCwt+KNKEXL1zYdLDuUELgCy+8gK9+9au567/8y7+MF17YHCbNz4pSWg9Bly9fwqXZiwNPIPLUJCWtox+8n0ArZCjro0EI8PorR0BI/kQswYpTJyLK8Pprr6TautHf62trOHPm1EBfrtQzwdNE6altWOYGxtL3v/v2W4jFwpFrXeIE6hFm8oT67ttvAkiYjMQIHK04XDMi/Le2Vcvw+n2cPv6hkm7nO32EoUi7Vraw6vGo17ESN/csLcxjYWGeZ9PweK7YLRUXEM9e6XBtXa1koedFCNYuY0uJawdCSnF53QeNOTyMJ3wHuSmJ9+uDd97CSo8j/NdLtsrlyVgCFD1R45vgWt/H6vkTHFDaNNDr8QU6pmnW5rp9BH4I0zbhN9fA/CZGRpyUgCEFruwcaMxdBIv6mBkvoyOiqydrCSi23BCaQYggiBAun8UuFXCS1GYQYKkTIRJBK9dN1hBRhrXzJ0AMgolqPlhCbTK8MnQ6bczOXlAmIJ3kPTo0y+mLFxC0GzAtE9dvLafKZg883TBCb/40QID6aEU5l0tTm+5PJu88cexD1c5WECKMKVzLQK2UZ3rNRgtxZwllka1GH+tkM036v7g4j/X1tdRGlX0/jR7HixwpWzh98gTiOFYRpBUtE0XX59lCHEsCRQMnjx/LvSNS8AwA8H0f588N54JDCMHS0mIubZU80AFpbRHAU5cFQvDSU8bpQoPSlAE4cTztc0oAITRxXiDzBgNA3wtw5eI5ZZrV+5g1DxuEYGVlGWsrq9zCIATHfsTXeKlAE3jx/Dn0+jxlXK1kgTEmAnOIEvJ0IePosePwI8DVzfWAmm+SIsoQhBGWLl0AIcKvT2QtCSIKR+QNjmliCWk2VrG6spLT5PoiZWBJvn/R/4sXzsPzfISUA0XrUcZAYrKVl06dOqmCZYo0qYmPINfsnTt7tvDQBSRYkLLP62trWF1d4WXFNRkYJOdKIkAyXJ69CN/ztGh1llFSpA81Z8+cTPJJS6FS/G5o5Rm4KfjcubO5Ng+iRmMda5tI03b58qVNQcScOXN6aJMtY2yTbW9gZWUTKebmLg9su5QjdP3A2bNnCt//RjRUT6+WvPgvAhWdWlZXVjA5OTXwRAPoJxdgbX0NE5OTAxeaPPEBSVqdrPO51BZKrZGkbrejTDRXI0KAtbU1TEwUm+J1gVf+a6wn5aUvhr4BZRdqtyv8DYmmRWUJqGwaQiD5WyZYXxPRuDN1B10/hhfGcC0DdddGs8n9AeWzzzV6iCKZy5cLOIwBI8LRvtlcx+jYGKKYohtw7U+tlESELncSQczzIjC/jY/s2S5O5QxrHS5Ulsu2wAgkGC8lghUDFEbgaNlSGmAJPUMIwZ6JEggB5tbWQJwSyo4F0yDodkMYhoFt40naoOPLXYUR6HcaIG4NU2PlHCSQ5L+SKfoRRb/VhFWqY/tYGR2PC857xhM8Qz7G3EwdRRS2xTBeT+OUydexKLKkOK6FmXoZ6+0+opiv58mqlcPW0xtGALSaPOWhera2Ycj5pfdlYXUd1HBg2RauHavnBUfte9uniPpdwHSxdWuNHy6IBgmSXWMsDa7eCkJEMceVlFG5OjVXG4BVQrXqcA2RttFl2899VFsYqY+oH0hGOotihq4ntNBlC51OG5VqDZ7As6zalhqnnh8hpgwWSUyUqbHKj3giUINrJev14SAnGGPotNsYGRkdWCbhQfyz02mjXq+ryGAAec3dRs8EF5qS9JHCFAyg3WmjUqultOlFbZGHx067g3K1mjKD+nEM20iEOv2eTqcDZpXgCB5AGbi5VhcCWeJbF1NumnUEXqM+bjpFMYXX66Je5VluIqWxI+p+3neeD5oyhn6vh2qtlnKJoJRnC4kpD16Th2IGbnkqVapcCCTp6GBC5KaeHKRTY1WwR+nro9frpVKRZfsn9yI5x/r9PtxyJfVb7l2JT0oZfN9DpcIjYKXLzyDi8yLts6enmUv3iyEIfFTKlaFkCco4NNpmonE9z8ul09uINuOzFwTBplLABb6/qbZvVH/RcJmmOdQ46jSU3jCKIrRarRwWTrPZRLhB9oyfR9LH58bDNwmgT/69UBjUjnkPfvzhFG5elqQ2TZ7CP/WZz+ZelEzOnXWkLZXKeODBh4buxx7hn0k0BqyAUAuad/sdd6FaqaROrerUp5x7iVqsn37kc2pTk2YBPb2UPC1KjcknPvM5tZH1ghjNHsft2z3uYKnLYV3qJRuuZcCdnMLUxLgam4vrXEgbKXPBrOnxqMgRh4MA33DjTTAtCx0/hhdRVGRkMLhPU6vH/QklpMrkDXfjwNZxbnphFK2eyD5S5RiBrmVia7mkNE6f+Mzn8O9e4+aILXVbjakfUvRE3fsnuHnTMxyMX3uH2IAYPC+CYRo4vC1hYhfWPMRxjJLlgrqTIPa4AIpOGG1WM0YAXG71YE5fB5sC0zUbnhfCNA1cO1HL+dfNCv+03Xd8gmsuxXX93S61PDDKhV/XMjDfD1DadxcYMbBtxMlrATVBEwB27d6DHTt3pdooJ516jjQZASjvOQz61vtwXO4HmpUxpTsDYzz1nzFxLUi3gWu2j3Kg6Ix5LD1EBJ/70lfVxYbPtb9VAYKdtJ0vhsCow5g8iImxkhK+E82F/gx+3+0fvRNEk9LVeBKAMO6D2BMwRNM1G49+8SvoBAzdkJ/SK1YiiPbCGBRM5bYlAB776tdS2o+sAC3HhhBganoKDz70MIqoyLx72+0fzfGk7HzR58fnv/gYDEOALAttgp7dA1pZgwBf/4VfzNUVxRx/0xK4jpTxYI5qfQx3fewTqfRtWa2qBEoGgBtvuhkrbR8dn6qUcX7MAy50oU7Wdc9Dj+DZZ86gooRAxssTAkd7JgEXVh5+9Mv4r+/OY6xkpoKrZN+o4GFBRFGu1XHnpz4LgB8eQ8pT0YUxw3jZUPxPCrg33HgYFTfxTSaib52QW1AqtpnS9D3y2c+h40eI15qQgNOGpkmVCgI5No9//Rc1LRtLvUd9XACgWq3i81/4Yko7KMtQlkB7yevX33ADGt0A/ZAm46UpLvg7ZcqX7+Of/AzHu5R73AaaE8aAx776dWVK3ogYgHK5jM994YsbF9T6fPDQdahsImXcpz79maHLAsDXvp5PkTiIXNfFF7/02NDlrzlYjPk3iB7+xCc3VX4z6fEkDSXy/tIv/RJ++Zd/Gevr6+ra+vo6/spf+Sv4xV/8xQ3u/Pmm9999B7Ztb+jbIBdGTCmOHb06HI6sanV5GfNXLqt6JKnckJnN7uzZ0+gKZP6rLRwAeOuNNyBNzbrpRq9Tp7fffF0Bg8rC+mJWp1nG4AcBPnz/PcXcOCwM0TaM9Al9/soc5uYuK3NCqx+hH0SwbRPT5RJW+j5iyjdsxzJw8tiH8Po9JUiudZP8uJQxdAMKxzJQt3nU5+uvvgxb+N9FMY8MllAifhiLzZknko9jinDhOEq2qbQVvV4CkxKKNGOjAjy53WrixPGjAmfQwM6RxFdupecjCDj8y1SZpx47c/oM/OYqxsom1j1ukrVsC7dMj6rxX2v7YJTBcUywxeMwLQP37R5RY6Y7aBva9+MrXfQvvQ+n5GK8YsL3OUagjucnx3yp5YPFIeIrx7WAk+TdGwRYbPS5SX60BMMgOHb2HPrLl+E4FnbXK7l5QrR7CQHef+cttFtNdc3IlM3Smy88CzCCatWBaxuJ8IW0EMAYsNINQBc+AJwKrt1a4enHhFCevYcAWF1ewvEP31fflzt8o1XwL1o7KANasydghB3s2VJT45vSApJEAASAZ57+aaKpyHaMCL9V4UowU3XxzJM/BpD4r9U1/6deyLXKJYubNBuNRuLqka26YCBPHD+GixcuFIxwngghePbpp+D7fk4bnHU5kYLKT374AzUPJfhxkS8mAdDudPHsM2kIDM4TuHbKzgh758+cwpULZ1NRr9l7pfadAXjh+WfR7nRAwNsQRtyUKvMw65lwYsbw0x9/D5TxHOESLD6kFLbB8whLNAMACIMALzz7JKKYouqaAmYpaa8+H8KYYWnuIi6eOcGfJbKWEAAx5QKdjBKWWUxePfISGmJPlGsnpgw94UZTsdIWtO//+XcQU6EpI0TTovJ6JX81CA/k+9H3vyfaylQwYWq9Kt5BMHvxIt59551C7bvUJOp7zssvvYTl5SW1puWaz/tu8r9/+oPvqt/S+YvzmxVjDD/6/p9z4RZpU3mRdnN+7jLefvONXD1FxBjw5uuvDu1DCPCUcRsJrdm2bybF3JUrV/DKkSNDl3/9tddw6dKloct/+4lvpbJ8SZJR2Vl64lvfHLpuSUMJgb/zO7+DsbEx7Nq1C7feeituvfVW7Nq1C/V6Hf/oH/2jTT/054EYY7hyZWOHTqkhIYSgsb6Ofr931TrlfUtLC8rRV1+XVCxGM8OwL81eVPAzw2hzL1+eVemNJOPWp0VK8KQUy0tLhZsbEZ00lDoIWF9dBY0j9V0yCMa4KTgLdbKyvAiiMbXlrgdKGaolG9NlF02RM3hLnfvbLc5fRkVoJWU+VscxsWPU4XlDY4a6a6BesmAYBGurKzAMgobPtYvTNQu2yTewtU7AmXzZxnI3AmiIssHNqFHMfW88jwtqW0SwwljNhWvxTWZ9dZWbjCKKkmPimrGa6tvFVhcAUK87mBDm46XFRdiOg12jLi60umCMoVZzMSrBkSHw60wDtm2CeU2UR0ewf6yWQN0oATA9xy6ueaBBB5VqSWU/qNUkqLXuT0jQ9kIYsYepmpOqQ9cC9HohLNvCtskKDALML66AmBZqNRsz9VJSHxJ4CFUPgKXFBZTLlZQGIdksEn9AOV/X17k/oMqRnPknNxnKGK4sNwBGgeo4do3ywA1HvBNlttL602yswdTAgDs+B+LeM+aqNsnxjWKKsNOAU6ngpm1p00vSdmni5t+bzQYsLU2b7gMHcO02Y0ClYmOm7KDX6/FnMZ6TtuKYqrAUSqq2hZJtoNVqwjS0urW2oODv1dXVgSmxpAlN3+xXVldQKpUG1q2eIe7vdPlhU6WAFMKTFGT0jbvdahW6A8U0CZYgSHD5Ou0myq6bjvLOtEF+EgBrq6solcqwTA46HYiI46plJZo6ycwY0GjzFInjFUtEJfPnVmwzFaBiGAS9bgdMaNzGSmZOy0wE0yTgm2rU66JS5uMeyQOvwdtVtSxQylPZSczOxvqaGnf+bvi48RR2BDXbTgRexuB5faUFdUxDBbLo9xpibrbbHRhmEqiUtLl4c2i321dN3KDz7WazgZJbUkKzbuLN+pCCEHXI0A+wWTcDSb1eL23+hXZAVeOfrK9epzN00gkGoN1ubSpNW/aAtBGFYTi0wAhwKJzNBGJ0Ou1chpmNKAiCQvO0fF96WymlmwqAkTRU603TxH//7/8d//gf/2O8/fbbAIDbbrsNBw4Mn4rlZ03ZOdDv9zXsq3yZxDmWX+90Oti+fcfA+nWTLAEQhSG279pVyIwHmb3Gx8eH7g9PL5dEV2W1HLJNsq87du5UgpzeTvlPr8MPfMxs3w5DbNh6vbapgRZL3swYpqamYQvn6TUv4JlC6i7Gqjb6Ide+7R3n2rRqpYJalQsmXsgd62tVBwfGy8qBebKSOPRPTU7xKD0ao2LznL9SWFj1AlimgZGKzbWPRoy9+/epKGXZ50rVxdYRF0tNDztHk/vBKEbHp0GWfEzUXUzUHCWkLXc52PXkaInjChIggoHxyQnsHa3gybNrsITQIzeGmDIEUQyn5PDUetVJjE+NwbUTAUcyRcmACeGbUKfnwRndgrGxEjp+DMcxsWW0rIRrIjoURBRRTFFyCa47sEtpAgGAivkVU4YooihVStg7VeW+mjDg1scwMVpO8BdJsZYMIKiPjHBhPcPs1euX5iKIjaEyDoc62D5ZEQcd0SZxEKIgIGI+rTQ6ILUtKI2NYEu5BEdtriTVLimA2raNkZFRMX48crNesrG9VlJtp+LE1fVjmG4Z7viEMqUbmX4SrSOEEOzYvqNQiyIXUhBTmCZBtWSjZjPs3rtP9bHumqi4ploSBgGqzv+Pvf8Os+zI7gPBX9z7/EtbaSozq7K8h2t477sBtGH7bpKiJHKlkaivRelbSUsuuaMVl9/ukDIc7eyKHA1n5xuRI0pkN9oAaHhfQKEKKAugvM9K730+f2/sH3Ej7om4cV+9bDbVDSLP92W+9+4Nc8L/4sSJcxy0poS5IZc5yg1V3FpEF+Cmpia0tes2Om2LmHzWF/DOoG8EVTiE48DzPGzevCUE/0wcBTthUZWOn1zs6bzHSCZJ11F6dlKVJJvNoaurQ/OEYXJOf/f09CKdSoqbuA5DzeNIOQ5aUkntApqsm/b1vcg4LjqbxHj0OZB0HDQlE1rdSilix/o+jPrS7SJ5H/DLEbrMa23KY31Xp+Iv4zpIuy7yKScwWi+ke8lAStm9vlvT15J17DCG5rSL5lRC1b0Pho39mwAAKddB2nHVfCnJYbqZmy1btpK0WV2JTS6XQy5G10xu0BNOaJaso6NT6KBLIE0AoinxcxmwZcsWUX9Gm5jSZgAA59iydWvAM7fMLSE5jCGXzyGbbqtTOp06OzrR1KD+PCA8azVKq3Uxl8lmsX4VgLSjo3NV3kga4V1Jyj0P27c37hlF0qruEm/fvv0TBfxMoh0wmUziwYcfiQ1r7ga6169HdvOm+ukH/xzGcOONN2q7RErmpRAAeODBh1dlcPKhh4XOkJx45HeNn+B3KpXE3ffcG3mnLSwslCZ2d68XPo8RLhRS/0fqOHHybs/efUiksmr3X/F85NMJ9K/LIp8WytutuRS2tjTBcRjuuuc+IX0Ijn8SrgCM29ubhG/QpIOephQyKRfcq+G+B4Q9K58D63IJ9AY+f10m3HPl0gn0tWUxX6yipa0J996xW6uMRMJBe3sWTWkXLbkkdnVllT5Ob18fJlaA7MAQelszCuTKSyX5fAq97TnkAr+jfdv3oOxksb5JALV8PoW+jrw6IvKDem1uzsBhHLmNe9Db26wWDlH3AUCiaN0RN/xattyI3q48yp7w97uxPUtuborg1ZqPVMLFuvXrcNve3doxj+OIQV2uCklZS2sWOzvFcXBTVx9a5paxoSOn0jRNz8ifjAG33HKrkq4YuF8dNyIAguVqFe1b96E4VcXWTt0wtjpyCsQuPgc8JOCs34XWjla0Be72BGBCZPFnDOjq6kZTc7Pot1yYD1nXnEZXLqPqxYEAiPPFCvK9W5FtaUVXPq10P+mYCLq8kEz6Pm666ZZwMaNVEqB0xoB8JonuYEOwd98NgMOQdl20Z5LISZdxEIt8R064EXQdhta2NuRy2cj4NEnOOZs2b0ETuXAQbkh1EC5p374btLaJCyc2Gx5uvOkm9SzhMqXXJ8OEuptC12wd0d8N02LIJB1lAsVxBJjs29CHzo51SjpLTbGIMur87NmzV+hiQsyLDgOakkF9EoDBIQyXb925D9dGq1ifDyVH+aS4HKSkWkwAnmw2g02btmBoooCOTDrUmQbUxsoHU0ezG7p70N3VqcqXTyaQTybQmkmgOS0ugmWSAXhzGHbv1MeepGxw8YrqrXmeJ2zMMoZcIhHoi7pkHuZkfhBzNnUXFoJ0+4agrb29rv9aKWmVtHXbNiQTbrDJ0NVJaPvI2t+xY6eoM8ILjO/qmetg67bQP64so9zUmXm0tbaipaUxYOQwoL+/H5lVAK9tq8QslPfrUVNTU+w6b6P1PT2rukhyPZdxtC/4vr8q3iU1fg3mbxgdO3oEg9euXTecWLAZXnnpBXVVO04ULo95XYfhuWd+aEkLwfGCXu2VSgUvv/h8w7wPDw3h+DFdh6LeAnPi+DEMDQ6GfLJQCijLQCUkr73ykrJNJCd4h4lJJCWPKILAjAGvvvhjJF2mwK3rMHQ0Z7C3O4eEy5BLOtjYlkZfWxbVcgkH97+hjqA4gJZsCps7cuhuEUd7Xfkk+psEUBkbGcKZkx+BMeEbtK8lhfZ8UonDE46DjuY0dnVn0JxJIDl3BR1eAS4ToKLqc7S0pLGxuwnNaRd9bVlsa2lSO+M3X30JJa+GruY09nRnA6kICyZ8Bx0tGexdn1MT6PiH72B7Rwb5dALppIN167LY0xOkF0hUcukEOjtzSLsVpIvD2NPfhoQb7MZdRx2pS8DjBL8rMyPIetPY2dOMTIJhfVsWe9dnleRYSltqPkdbPoXE5AW0sprQ6aJ/jpAWNufT6O7OY0trDi4DBo+9g472LG7qzas0hXQMSi9MXQ7yfbz1xqvqt/yTEhQVN4gzOTEGb/IKOjvz2N2ZVXzIxdYNNkdO8LsycRn5VA19fS1oyiSQSQo3dvTIifK0/83XAsAp2qEjl8Cm9gxayG1uKUEs1jzw8Y/R09OMpkwiLFcATGUZZD5Liwv44IODaiFU44J8pl0X65rS2NqRxci1KxgauIJkQkiI1ucyCoQwCP3ArmxG+cE+dPBdrKzEq5LQuYQxhjdee0Ubn3LMSl7kcS1jYu54Z/9bmlTfjCuJczF3nA3M1biOsGOXCTZ2ZljGgBPHj2JmZtbgF8pDijKBwoQU68iBt8RlCSlNMlCCDgg53nzjVaQSjlJ5cB2GtnR4CqDiAZidnsL45bPobUmhrymrwjelEsplHwvyTLoMZ0+fxOLcFPpakmgP/H5LkCjnD9nHsikXZ44egDyhTboMbekU8qkEOjIpYe80KSS+iWBD99abr2ntBoj5vTWdxPp8Gvl0CPIW5udx/OgRpIJ6y6Vc5VJOkjpeZwwXL1zQ1idT1cOkA+++E2tKREoY6RH9m2+8DjdYiwRAjPfAUSkVcfzoB2TOoBu0KE+jQ9cwOHBFvZd9V20MVFxR98eOfoBCoKJwPXIYw3vvvBXrl9qkarWKA+82bsZudGQE58+dazj8hyeOY34Vdv/efuvN2Ho2iXOO/W+/1XDaU1NTDd1bMGl1VgX/BtHU1CRuIh40KOnKq+J4slwqKcRv240xBrhg4EwY2Y0zq+O6YiGlu/vpqSl0dnY1zPvMzDQ6u8Lw1+tT09NT2HfDjWFYqTROt9kknUq5jGxGuvFi8CGkPemEo6zpS+Keh0QioUm6mpIJ7O7OYs+6ZjiMobcpjb5mhvZ8EmMjE+jsCg1oOwzY2pHBlvYM0klx1NLfnENnsziynZ+bQXtHBxzGsC6TQlMqgbZcEm7AV2sqiT3dWexub8ZS2cdksoy9m/vUrj3lONjU1YTd63Poa06jJeNiXRP1CuKjJS0A4N51LULyFdRHT1MKu3ubcEu3eF4ulNDTlsfeziYkEw42tKaw2NuCW3ub1S1A33WwYV0OvD2Hoasz6N3Si/u3tARK62ElOwRscMbAONCEArZt6cO9m1rBOcfenibsaW+JTLwJh2FHVxa1TAWbejvDozcOuA6C3TrD5q4mpJMO+lqElHZdLoENfS24tbstOpmrf4KWlhaFQ3cWDypcSMVvjsWFBfT3dSPd04Zd7c0KcHGErqoYBxwuwHlLoorerRtx49Z1yKWE0r3UCXSYrqMKLqRAqWRCXSzY3CZ2082ZRChNCYBLChxdHU24aVObOvYGyNGVIZFYXFxAa2urGpOyKqSEBgDyaRd7e/K4oasJSxPD2Ni/GemEg7YAXIRSHaAll0Q66ajLR8KES7PiIaJzhRDcmWQCIfrJOaxWG+rNB4tLwhSOqF6GTOBDW8YxdZgXFxfR2mqmL8AjAoANQF0EySQdNKVDVQNa5vCIWaRTKBSRz+UVGHKYMPjclk+iJZvA4EwBXc3pACACxZVldK9rx/qurNBphZBwtUrVETKnJF0HK8vLWL95I/JuXtnxZKDG+gEOhoQLsRFxGXKB4fF0QrRf0mXgXAB6NwC/SVdIj+kcL9sy4TK0ZVNIOQ7yBAwXVpbR3NyEdFJcSks4uks5xgQQk2Z3lleWNU8RtD5tVCwWY82gyPYyb4DLOdDnuq05Gg8AauUiWprykVODWF4KBbS2toW8kxMQM/2k66BULDZ8vCtvUzdKKysrqzIPU5Du8VYRfjWSvdVQpVJZlf7gat3jSWJ8NVqQn0ASk1grRibn1GTJGMPZM6exZ+8+axwKAiWdO3tGHAEFRIGgOamXy2WMjY4qUS59R3VtZNzZ2VnUqlV0r1/fUJmGh4bQ1t7e8MC5cOE8du7cFTuJmD3g4sXz2LlztzoSAkJTCnJBl1EqlQpGRoaxefNWNQkPTBVQqnrY3JlD0nUwPFtEOim8SMzPz6NaqaCrW3gvmStUMTJbREdTCj1tGRTKNZSqPlqywsr/2OgIWlrbkMvlMF+oCn2bQPrjc46JhTKWilV0taQxPFfExOBVPHTXzXCY0M8bmy/h3Mwitrc1B4sNsL41oybVy5cuYl3vZiwWa+hsTimJAgBcmy5gqVzFlo48MkkHhWIJl66NYOuWzWjOJHBhYhlzpQr2rW9BS+CGrub5eH9wFrmEi7PDo1gqe/jm7bvR2Rz1AwsQyQ4H3jh6Bn6+Gfds7UWhXEOx4qG3LaOkJKKtOMo1H9emC5gZu4Z7b71BgRvajMulGj4em0drKoXdPUIv7uCJs2hevwG7upuRS0dt6wm+gkWrUMDCwjx6DX+30b4j+sX4xCTOTZWQb2rGTX0tyBA/upR8LvT5fnTwBEbRis9u68ING8S4DN1WURAm8hi8NoDNW7aCQSjeTy2WkXAdIRU2yj88vYS3T13A7Tt3Yk9fswYoZThZdocxzC8swKvVlMtGznU9JgBYKtUwOldEZ3MateUZtLa1I53JCOPjgURNUrHigUP4mnUdhmsDA9i8ZQu5OMYUL7KKZJ4+5xi6dg2bpAkoS53LcckYsLJSwPLyMrq7u7Xntk9AbCAzmYw4buahuRY5J5nLwdDgIDb290dOPjw/9B4U9gXg6tUBbNm6RY0v2/Iin1erVUxPT2N9T2+oO8ehfHhfGFtGX3sGrQHQnp+fx3yhBiRz6G5NI5dytfBS0iXzHB0dRa5lHUoeQ1dzSjNtRfkAgHLNx9DQIHZs3QLGxKWycuCOrlIT6i2y7yRdBs/zMDExgQ0bdD1xz+fKikF7Pql4Wl5eRqVSQWtbO6o1X0lOKaBRfngdhsnJSbS0tDR81Dg0NIT+/qNx7lwAAQAASURBVP7Y977PQ5uFnGN4eFiFtxmiplQsFrGysoLOBt2XzczMIJvNNgymhoeHsWFDVCe3XviNGzdePyDEWrywsIDu7u7rB4boY67rNqy3NzY2hu7u7rq2lCmthvdarYbp6Wn09PQ0FH5paQm+76O1tRWLi4tY39GKhYWFumoCwKdQEih1gKS5FFPfxgwLCHuI1HirObGZk93c7Cxa29oiE474jOYxNzeLjRv7I8/j8PnS0iI21hnwlDjn8Gq1ugOMvlpZWUE6FdW3oBIyIFxM52dn0drcoimCb+4Ug1/av9rcmYPUlZybncaGjeLCDAfQnkuiLZCYAEA+k0CezHvzc7Po27AB4FC25yhPPa1prG8V3jxaMgmwOemlQezwN3Xm0Bfo1ZkSoKXlZXGxoyklPGgY72U5GARYHR2bxJb17crbxt7eZs0+IwPgOi4e2CoknU3FCfRv2o6mnH5zU+luaY0A9GVr2LevHy5j6viKBmQQUsNsysXu3iacnfWRCKQHzEivJZvA/QEf8jhqS1ce/f1tGsAyslA/Zmem0ZQPb0rHiarUGJmZwgN7diORSGj14UNfcKW6xA1tLr5903Z1nAyEgFLd3A2oWquiWCwoKZ7LGHraiNkc4zPDi3hsVw82bGjRwJyygWYUaXpqEr29vZp6hDlcWrIJtGTFwvDR5XH09PYCgLgVbFCW6LNxAHNzs9i8ZUsE/FH+GWPBzccleJ444pdWIGxgTtLM9DRyeWn01y4FpBLN8bEx7N6zR6VLTyU0yWTwbHpmGv2bdF1oZefUyKxaraBYWK6rv0ZvN8/NzQmJGgEnrsOULt3eDaH0FAAmxsexYeNGNDWFl5VcJtQvaLo0/G0EpNnAjixnOuFgaW4WbJvYuLsOU21LN4Yyj6WlJZQt/lldR/jNNmlqchKtbUIC71r6DKALB0aGh9H5mc9Yw9locmKiLgikYLNQKChzZCLf+uBrenq64du7gDhS3bV7d8PhJ8bHGwZG1WoVM9PTDYefn59flbeQifFxsd40SCPDw+gN5oLrEecc42NjDfO+tLSktdP1SPax1dKnTieQc47p6WkMXhuIAC1zIpS/h4cGMTs7o00iNtG2pLNnTqur5nFgjuZ14vgxJBIJLd243RljDB9/9GHD5V1eXsbly425oAKAsbFRTE1NhpIqhvjvAC5duoBSqRjyh1AXTC5uLlnoP/rwBBKuq3TMGNN10aTemQQ1p06dVHo+UqdE/QXPpY5duVzE4MBVdRTJAOVJQNOrCz6nJ8YxPzsjdMScUE8s5CXU2etsSmFxchCValndJky4jjoGd1jIYyLQj7xw+iRy6aQ6eqK809/yiOPiubNKOkD/GKDx5jKGwvIyRoaH1EKu3jthWZSdNQAT42NYXJgPw4LwIBsu+GMALl+6gJpXU40q13zVB2h4Bpw+fRKpZEIdLYblDNtZ8uUw4OL5c6LumH4LmJpukWkvzM1hcnxMa1dVJwjHo4w/NjqC4spyRJpH+6gsj8OAC+fPqj5NP1V448GF8+es45I+k4C2XC5j4OoVLd/wAkeUr5mpKUxNT2tgL7z4xbTfQvJ2JTCBYQeA1OUYAJw5fcpq0oLWIS3HhfPnrXVghmeMYX5+HuPjY9Z0bHPnyPBwXTBipnH+/Dnl3zmOf0oXLpyPhDFJztGlUglDQ4Na2Hrz/NTkpGY393p09eqV6zpWsJW1EeKc49Kliw3zMjc3h6mpyYbDjwwPo1QsXj9gQOfPn2tYMgYAFy9eaDjs8vIyxldhI3ByYgJLi4sNh798+VKs8MVGq6n3SqWCwcFrDYefnZnB7Ozs9QMGNDQ0GOtnuB596kAgAExNTqCnp7duY9Nd7OTEBLq712vv6KcZfmZmWonO43bD9LvveZFBIycgk8eVlRWrKYC43dz01BS6uhoThcvw19NPpIvlzPQ0OozwJlADwgXQq9XUrlJbyEl8KQ2qVatIp1IwLdnToymVPmOYm53Buo51aoGnQCIEGiFDs3Mz4gjQBJdBPIcAGjAh4e3o6IjwbQISCbR87iMV2ArT/hACHhmvGuh/0LoI+WEacAGTbgDXaZVL69oEafPzs2hrb9eecRqWlAMAFubn0d7errVh2L4hMFXgExCuomjWqtJFPAnAV1ZWkM/nFTCU5aJtQ+tzcXEBbe3rVN6OUY80T0C4u2tvayNtAz2sAZgKAT+yzmk5AWh9z6wH+qnqhvC3uLCg6UeR6rZK7hYWF9DW2qYAquyDQOhWjP4tLy8J3U2DJ9OOWGTc1AFq8nulUkE6nVbp2MpLaWV5WXNfZ0oWI+FXltEcc1Rlznucc5TLZatrtLh5/HoSLhpmeXl5VaY7VlZW/lrDr8Z12WrdohULBeTzjZtYKRYLseZnbOT7fsO28zjnqwKMxWJxVW7XVhv+r9PFXKlUWpVpm3K5vCpezPHRKH0qdQJrtRo45yEYMSZ7SfJ3pVKB67qqs8YdH0sqkUskNC1JZtxSqRRrHFaGl3n6vo9qtYp0Om09vjHJ9314ntewOL8WHB03OjBXo7zKuRDnp1LJ2OMtFTaIUKvVkLgO76o9OEe1VotMQLb25ZzD9/3AXIRjDStBkvysBWlTnTLzOy1AzSO8XEfrX6bvJvRbkZxkQnmR/DuBVDWyaOrJK6vzzIn6sqXlllSz1KONdZmr3MjE1geJxyFMZph9TF4eUIrk5Ln0R1qPomBJL5O8ECDCMg1ExEmXbPx7vg/HcXSARcthhPeD8JJHzvX2kc/EZ9S7g6nfR/kT/YBr4W3lkLxSXsz3cePEBJP16Hpzoxm2kTRXG/Yn4eWvO/xq+V/j/WcT/q+bl/9WtBqdwE+lJPCN119FMpDQ2Mjs9G++/pqaOOMws3xeLBZx6L0D6rlcsOkumgc6XIC45HH+3NkwvGW3TuOf/PgjTE9NRXg0O6P8vv+tN69rRZzGe+3VlxsGgL7v4803XmsoLCAkpNK0jVn1VLoBiHq7cP6cEp/L4PRWrXwupUbvHXgHhZUVq9QGQOR48OUXXyCmQqKmD+gRLCBM55gLvkPiUYnUwvwcjnxwKJRQ0ZfB7/BYluHSxQu4euVy5HiTyboh5QWAQwcPYGlpiVym0D/Ncr384vPiKE2rc0OyirC/vvrSC7pUz/xjYXvMzszgg0MHNf5s3yVOOX/2DAauXrGkGRaUAqR397+Fwsqy3j9Y9E/SC88/GwvgaB0hKOuLgXsuU1pmo/HxcRw9cjiajvEpy/Thhyc080zqaNfkLXjwyssvo1Ipa5sjWubwmZgXnnv2GYsZlmifZkx4fXj9tVcjc4xZFpn21StX8PFHH2nPRRlC8EzVWg6+9x4mJxs/Zvzxc882HJYxhueefabh8DMzM6syDXLq5ElcvtS42sxrr76CQqG+BylKzz7zo4bBQqVSwUsvvtBw2tcGBvDhiRMNhz908CAmJiYaDv/sMz9qOOxqw09PT6+qnU5+/PGq2unVV15GcRVH2avhvVwur6qdrl65sqp2eu/AgVW102rc41H6VILA+bm5ugBQToKcc6ysrKBcKWtgzDYZSpoYH0eamJLhsEvppCeO8fExNAWieRPwSaK/R4aHA6O50eNi2/fJyQlNKmlLm5Z5NfoT8/PzqxKHT09NRsTVZjNIaQdjom7yubxmvkC9hw5GOITSe3NzswYqTOBHgcDKynIooaHvLOELKysKvFOgpeIagHFmZhqZdEZ7bstDpjU5OYFcLq8BMvoeJA/GGCbHx9XRghnWBkopOGYxYRDUZ7FQMKTMdrAlv8/PzyKTDY0228ICYdvNTE8hn8tF0zP4kO+mpia1I53rraXFQsG6EVB9hWyglpaWlD6u5ME86qd1MTc3K45IVZpGe5HvgFCXyOZ0P82hyQzKk/hcmJ8ThtqNMpplkfMTLas5N5lgbWFhQUl3rwdIGBMXN5Q7OqMfyk+a5+zszKqO3goB741QpVJZlUusxYUFJJONm9eYn59T83YjNDc3t6qjt5LlEkkcLS8vr8oV2eLi4qpMiczPr573RtupVqtdV/eR0sryMlx3lWVdhaHo+fn5VRlzXk07FQqFVR1lr7Zdl5YW654QmrQa93iUPnW3g6vVKjZt3mLV87F9LxaL2LFzV11wRp/7vo9NmzZHnkvDyPK5nKBd10V3cAX8ekcvnHM0t7Ro4l1bGPpuw4aN6ujKBjLp71KphK3EZY4NvNJ31UoFW7Y2bqGcOY7V9Z4NWABAJpNFp7LgD5jeBzQJCQd6+/rgusKmGOcEJBpFYBDHkZs2bTYkY2QB5+EHg7j5KL3lMJK5w6LSHcYYHMdBr7WswlWVDC9Zy2ay6OzsJDcrCS9Mlz4Cwt1WihyTy3g+jSeLwjn6+zdpx7QUWPu0rgDUalVs37EjFmyZz13HRU+P3S0as8TJ5fNol/qM5F2clKxvw0Y12ZqbFpN830f/ps1W9Qtzk+QwBt/zsNlilV/2IamfKvtBKplCu9RPRPTY3SxTa2sr2owbe9IUCD3eFYCQRW7i0jKb32u1GrZu3x4Z23FzFWNsVS60stmsZqvuetTR0bkqECjnyUaoWq2uyiVWIplEb19980aUWlpaV+W2c+PG/lVd3FhNvQOrq5tcLoemVegbrradVsN7rVZbVTslUynlUrERso2netTfv2lVgorVlJUxtqp2ampqUhvCRqizs2tVOoGr7WOSPjE6gRcvXsSv/uqvYnp6Gm1tbfjTP/1T7Ntnt/NHSeoEjk7No7WlRaFxuds2S28uYvPz82hqalII3tTLMX/PzMxgXeDX10amfs/09DQ6OkKgY07mlD/OOebmxOWEuPxp+EqljEqlgqamZuuCbsaVdZNOZ7SFOa6e5ubm0NzcLPTkjOMrW6+anhYXZsz3ceGnpqfRZVmEaHgZrVarYWlxER0dYoFW5jUQhqNVUCgW4XkempqaYhdzerQ3v7CAXDardtzSvlrc4JmenkbHunVKBw+IqhJQcDE5NYXuruiFHJ8s/tJsiO/7mJudRWdnZ4Rv2+9yqYRKpYKWlhbNF61ZN7JeFxcXkU6nkE5LKVC0r4jyiE/R59vBGNWtDMOY+m+Tk1Po6urS0pXhKG/iubjN39XVZeWB8sIYUCyWUK1WNJugoUQsqHeCgKUURV6AuB5NT09j3bp1DS8sk5OTDdso830fs0G7NkLFYhG1Wq3hCwcLCwvIZDINSxempqaC8dqYdGE1Za3ValhYWNDmsnq0EkjiG1Wsn5ubQz6fb1hCthreVxu+UqlgZWWlYZC5tLQE13UbBgAzMzNobW1tWMq0Gt4555iammo4fLFYRLVava4emqSFhQWk0+mGpXWTk5PB3NFYn5yYmMD6Bu3v/nX3ydnZWTQ3Nzesn78a3s3wfyN1An/9138d//Af/kNcuHABv/Vbv4W///f//qriS1MfH314HDMz06HbIGqKg+lHHIwxvB3o1KljPUd/b/5+7dVXlBkD2x/Nl3OON15/jeSvSxZN/hYW5nHs6JG6+dPw1wYGcOnixUiYuLgffXgC09PT2nNbPclnb7/1Jnzft9RjNLzjMLzx+qsx9R4N6/se3nn7TWs6NLw0/TI7M41TJz9SfCuTMCQcTePyxQsYGRrU3LaZ+dA0jh5+Pzg+Fr+l72M35u+tN16D6zras0Tgukm6nZLxGQPefvN1a1lpeFmuleUlHD92xMq37ffgtQFcvXJZ/ab50ziyXj88cSw46tef2/qP4zC89ebrAKDlS8O4xvO3grKa/cvkjTGG5eVlHDn8QSwPZj8dHhrE5UuX1HM5jii/lE4cP4bZ2dmGF5U333h9VfPOasIvLS3hyOEPGg4/cPXqqvSjjh09goWFhYbDr4Z3zvmqws/NzeHE8WMNh7908WJDbj4lvX/o4Kp09lbDu+d5ePutNxsOPzExgdOnTjYc/szp05gYH284/Dv734bneQ2HX01Zi8Ui3g/0fRuh4aEhXLzQuMmXE8ePrcrUzmrH31tvvtFw2Pn5+VX3Sarvez1abZ9cDe+e5+Gd/W83HJ7SJ+I4eHJyEsePH8err74KAPjGN76B3/iN38DAwAC2BJb1JZXLZc04pJz0FhcXwDnHwJUr2LvvBiwuLkYkM+ZRipS8VSqVhvQcPM9DpVzGokWvznY8NT09jWw2o8LXOwZmjOHypUvI5/LW9G109eoVbN6yteHwAwMD2L1nb8Ph5+fmUCqVND0K86hZSmh830elXML07DwYhC09IdUSNxuNtRmTk5NIplKYmZ1XxmSlVIx6KWCMwfM5rly+jFQ6o9qbc6Dmc+2YVDmN56Ju9uy7CdNB+pQPKp3ygpumIyPDuPOuu7GwsAA/eO6wUC9Q8ia9Piwtr2BufkGTuNX80KCt73Plp1j6zZyfXyDlCuvC8zk8nyPhimP9y1euIJlKY35+QW0mZJklv7SOBgevYUPfBiwsLGj1IsN4PofPoYxsDw8PY98NN2pjJO6YkXOOleUlLAf23sxjWjO/arWqdtw0DCAkgTVP3NgWPqqB4cFBpFIpLCwsaOPSNlY5B64NDqK7q8vah2ueD58LF2cSDI6MjGDP3n0N9/mVlWVVVkD0Xw69vmX55bwh077eLd7hoSEkEsmGeRkZGUFPz/qGw4+OjuKmm29pOHyhsIKlpaWGwpZKJfi+33DaY6MjcN1Ew+HHx8ewadPm2PBm3U5OTIBz3nD6xWKh4bBLi4twHKfh8BPj42BgDYefnBhHZ0wfttH8/Fxk3Ysjzvmqyjo1NQWHNc77xPg4MtnsqtLftWv3qtqp0T5Zq9Vi12MbTYyPq5OQRmhqagq9PT0Nh5+ZnkatVms4fKlUbHxeWl7W+vuSgSnqEv8E0NGjR/nevXu1Z3feeSffv39/JOzv/u7vcgBrf2t/a39rf2t/a39rf2t/n9q/oaGh6+KrT4QkEKh/AYLS7/zO7+Cf//N/rn7Pz89j8+bNGBwc1IyqrtEngxYXF9Hf34+hoaGG9UzW6OeH1trvk01r7ffJprX2+2TTT9p+nHMsLS01dOnmEwEC+/v7MTw8HBrr5RxDQ0PYZLlJl06nrYrPra2ta4PgE0wtxq3oNfpk0Vr7fbJprf0+2bTWfp9s+knar1Gh1yfiYkh3dzduvfVW/Pmf/zkA4Ac/+AG2bNkS0QdcozVaozVaozVaozVao8boEyEJBIA/+ZM/wa/92q/h93//99HS0oI/+7M/+1mztEZrtEZrtEZrtEZr9ImlTwwI3L17Nw4dOrTqeOl0Gr/7u7+7Ksvba/TzQ2vt98mmtfb7ZNNa+32yaa39Ptn036L9PjHGotdojdZojdZojdZojdbop0efCJ3ANVqjNVqjNVqjNVqjNfrp0hoIXKM1WqM1WqM1WqM1+hTSGghcozVaozVaozVaozX6FNIaCFyjNVqjNVqjNVqjNfoU0hoIXKM1WqM1WqM1WqM1+hTSGghcozVaozVaozVaozX6FNIaCFyjNVqjNVqjNVqjNfoU0ifGWPRPSr7vY3R0FM3NzWCM/azZWaM1WqM1WqM1WqM1+msjzjmWlpbQ19cHx6kv6/sbDwJHR0fR39//s2ZjjdZojdZojdZojdbovxkNDQ1h48aNdcP8jQeBzc3NAERltLS0/Iy5WaM1WqM1WqM1WqM1+uujxcVF9Pf3K/xTj/7Gg0B5BNzS0rIGAtdojdZojdZojdboU0GNqMCtXQxZozVaozVaozVaozX6FNLfeEmgpEKFI1HhoMCYMQbOufrNOcCY/lwiadtv8x0lE4HHpWeLd730aFqSj9Wkb+Mnjlcz/dWkxTmHz8Wn6zBrORhj8H0ODiAIAs4BDsB1mFbPNE4cX7a6MMvFOeA4Ie8+BxgAz+fwOEcieOc67Lp1K78yJuLXfI6k66iy2PiK41P+rtR8MMa0+nAcBpkkLTHNxg/iO4ypfu7zsF49n6NS85F0naBsYdxyzQfnQCrhRNrB7CFVz0fN40glHPhB29Y8jlLVg+dzNGeTSDgMHHrfoOlWPB8uY0i4LJIXY1BtIsvrsLCul0o1zBeq6GxKIZtyYenCquz0leSn5nGVR7nqoSkjpsGVsgcASCcdOIRfhzGUqh6KFQ+uw5BPJ+A40b4ny5Vyw701YyK+LBsP2oGBY7lUQ7HqY11TComgrwHApfFlTCyXsLenBc3ZhKhLzuEGCt4yLZ9zbc5CUD7f56jUPHCwoH6ivPocmFgowXUYOvJJwWeQvqy36eUKCuUaelozSCbCMtVqNfhgcB0Hvs8xu1JBJumiOahHDsD3RR3zIHwikQA4UPM5PF/0mUrNx9BMAamEg2//h/cwdnkQ9z52E/74mzdhoejjn/3gI4wML+DLj+3Ebz+yTfTZoC+9eWkCxZqHx7d2IptOKt5fvziBg9cW8e19Pdi7oVm1P2MibqVSQTKVEs9IHdLxVfF8eD5HOuGgUqkgnU6rOcrzRbs1ZRKqj5drPnyfw4EPhwFV7mBivoS2fBLphAM36A++z1Gu+fji/7gfg/vfRPvNd+DF//5J5DNJTC6WkUu56G7NgAVpLhaqaMokMLdSwYGhGfzZ6xexeVMH/v1XbkDV4+Ccoy2XBGMMNZ/j9OgCfvO7HyOddvH/+dbN2NieQiadFv2NMTWfcgBVj+OfPXMKhz8cxb23bcDvP7kDqWQCVZ8hmXCQSTpwGQMYtDrknKPqcczOL6GzvRkJY64bmy9huVTDps4c0gkHS6UaHMaAWgk1lkLNFzzL9aDmcywVa7gytYL2XBLbuvPwPQ81z0M6nVZtJPMX41GfLwrFIjhzUawBzZkEGAOWSzVkUy7SCQecAx7n8DyO5XIN/8PLJ3HsyjKeuL0P/+DOTWjOJJB0HVVWHoxlz+dYLlbwvx68iLNTVXztM+vx+I71SCccJFwGl8yzDmPwOUe5XEbCdZBOp7U1igd93+ccKddR68/y8jJyudx1L29IWlxcbOhEs1RrKDnBe+NBP9k0PT0VWSzMiVE2zLvv7AcQD/hCMMFRrVZx8L0DwWR8fdHrtYEBXLl8WaVP49AOI+ngewdQqVS0NGx8ybTeeP01a9q2ck9NTeHE8eOR93FA8vixY5ientbSljzYANcrL790XV4klUslvP3Wm0FYnU8b/xcvXMDFCxe08tM4YV2Kv9dfexXlcjl4p7ejHPiUXnj+uQgP0Q2D4HV8fByHP3hfvWMATNZp3MMffIDR0VHtHQWoZvgfP/ejkG+SpgRM8h1jDJVKBS8+/+OG+iJjDOfPncPpU6dUnnKRjaNXXn4JyysrKk+TzHr80Q+e1t9L0Gz+ZsD01BTefutNMcFbNx3672NHPsC1gYEgfJiWH3xnRtynv/eXKvO4jQ2lp7/7lyS+WPB9brRBwNTVK1dw9Mhh1Sac8EuBtGzrN15/FXNzs9EyWvjgHPj+098Vi5Mlby8AVrI8i0tLePWVl6zp2OjUyY9w4cJ5FYbySvmSz59/7hnUqtVI/cqy+ka8Z374NEDqrV7XrCzN4PjhQ1o5zU0J5evo4UMYH9PHEiXRDmGGz/7oB2FZCM9q/Kl0AM/z8KKcB0gYlZwESAGwunrlEk6fOqlVNJeJIewH8nVl5GOsLC/p7ULWFvrY58D82XeNfqQVFTXSB5YWF3Dgnbe1PuMrUCLyqXlh+DOnT2Lw2lVZrLBtjTxkXb7y0o/VfEH50Dfs4bsXfvyseuYaO2Qxd4W/h4aGcOL4schY8H2OcrDRlBtDxoD3Dx3E7MyMqhfJsxlfgrsrR94CoPPBySeNVywWcfH4e0F+An165iSAsM9fOH8OFy9etM9fMg1Cr736CqrVaiSs4stI56UXX4gN+5PSpwYEFgsrDYVjjGFkeBhAOGGb4MyUTI2NjWmSAfM9BWsrKytYXFyMlVhREMQYw/T0NGq1WiwYMZ9NT09rz2wLunxWrVaxtLSogRBbOJnOyspyBJDSMpqSrvn5efXe98PJLYwT/vY5x8LCglbP9YB1uVxGuVzW0qN507pljGF5eTkSJvweLffK8rI1X8mXVhbfR6lUEnGt4fRnlUoZvu9HeJFtb5a5VCxZwYFMzwTNlWoluhhqE3X43fd91DzPCBsPwGq1aiya4NbwNXs9xMSX9eKoOtEXTpN3Wznl4mDyYgLtsH/HFUQHMzYK+6+vpWnbCMj39UB2LCyVAD2Snp425Smad2y2WphIbKM+Oel0FDRp7JI/kowWlgIdybND2kqGM6XqMl2zvDS9emWlzSsXfQnyaR3zmDqPJkTCx2yMOEFEqqyOY+0L3PJdzhauY+8/nHNU6ZwCgIFFgZCxjsj+yDmHwxwNzNvyUJ+cW6VX0ZMZRNL0I/Umw4bcUrCk+gtJgyHc7HHf1+vE0l/Aw8d+sFYljD5M21oD2wgk8XQMaGXU28S1tKttPpLp1xPWXGef+lOhT81xsOu4DYXjnCPf1ARAlyaJ32EYJZp2HOTz+UgaJhCRz9LpdEQKQQcXBQScc7S2tsZO7rZJsL293cqL+QwAUqkU8k1NsVIRumgyBrS0tCKZTMYuMmaenZ1dhNeQZ9tu3XVddHZ2GvUW5UWWpSloo7i6MJ+tX98Dxhztufz0fR6ZLPs2bNTKbhJ9lslmtLLawtBnHR2dyGazEfAfV6/9/ZtCXqDvVmkeDmNwHAcbNugmAVQZKBhjol5aWlpRq9XCYxBE5lCNNm7sRyKZVLtqmV4ERwUAf/OWrYJPow6iixOQyWTQ29en0qJHwIx8l9TZ1Y2m5mYyLo38oQOI7dt3aLzQfhb2CSgJz/YdO1VcU7pFqhOAGBsJV44NAmBZdCEGgP5Nm5FJZ7RnZpqMNPau3Xu0sFKqI4779RzS6TS2BPUeR7SvdXV1I5lMBs/1eg/Dh7zt2LkLjuNE+mHcrLBn776gv9nzp78T6Rz6Nm5QR8oibTJOACWRBYANG/vR1Nyiwsnn5mKOIN6+G26s279p2owx7Nl3QzwwJ3HAOdrXdQDc10GcEdknG+LEug3IZLKR9G38eT5HZv22unzUgsxclyGXy6J1x04DZMtNpixnEJ4x9PZtQFMuF5bHVkbC294bbxG/WVBfPDpG6Rxx82dujQV/Jthpa29HJpOx8uIQwCV52bFzF3JNTao8cp7UvpNKbd+8F3NLQMK9/glVMpXCuv5dmASLbEwpseBFT08vUqkkKaNtXQ3n7ZtvvkWoSsSQyd5tt91el9+fhD41IHBDf3/sgm7SF774pbqLMhACHcdx8MSTT0UAExCVSHHOsXXbNisANNOVn/fedz9SgQ6LTMeMR/N58KGHI+9NACU/29vblX4BzTPKu0hj9549WoeNO4KVz++5995IWBsxAIlEAjff8hmVn9DBYSpvsz16+/oifJqAl/7ed8MNSCYTVn6B6GRz4003K16uV4Z8vgn9mzapdEzwYUbbsHEjstlshE+66aC/d+zcFdGLo5Ov/M0hNiVbt20P8yb52rpzW3tbWB/W0oVxGRPgOJFIhMedljQV/2DYvHmLNS3CuqJUKoXu7vWaXg5gB71iw7MOuVyO9LswLckbHY+9vX11yheBsejp6SXphQsAXUClLlA2m4Xr6tOpCGLf6be1tsFJJOHT8OS7ihJ8dnR0avUg85V1Rfub4zhobWuLLassjyxyJpvVxjUFfCwoMAUTLa2tSgqk8UzLQsZec3OL2hTIsknJGw0P5sBxE8hkM/B42P5U3hTtM2m4iXBcm/pjEqDJZ9lcLjyWJWkKSViwsWeAH8w7OQmM5Bzk+xGgIwFAwk2AI156Ix97ng9wDsdNRI6Iw3om9YJAj9RJGBtHfWzUAt7kiYIE9pK0Y3UOpULAmJAwOo5j5Z0CQCltTao6j0pqQcLLQA4RwlBJsuxb9PCbcw5GNhl0zNOyq7IYksBwrmDhPIlAAoiwbqXuNwNi5z7P81UYN2b+Z1p4D4C+XvsG+qXJVKvVumujiVlqtVUo+zVIn5rj4KtXrtQFgHSS+v73vhsBTvY4HL7v44ffD/WeaIe1HVVdOH8eJz/+WHseJxHinOPVV17GsnE0aZPuSdDw3LPPRPiMOz6YnJzE+4cOxoYLgYj4fuTwYYyPj0fSonEpMHzhx8/FpgvoE3q1UlH6jBTomPFkvV6+dAmXDN2LOBAFAG+9+QZKpXIkrPptfL768ovazlVPO3zOOTAzPY0Tx49Z6yQsS7jr/OjDE5iamlL1RD+1+lG8vx5Jm2u8hd+r1SoOvLs/DIdoPVKSOqoSlDHQtozm8cGhg+rom6ZPs6Dtvf/tN2PzNtman5/Hxx99KNK11LMpaTh//gymp6a0STZcPPW0OQfeO/BOhIe4bZ7nc7x34F0SX6ov2OOPj43h6pXLYdlYCHbob0kffXQCxWKgWwm9zuRlBZrB+4fes+YrAYxMBwAKKys4dTKcY+pJ+QFg4OoVTEyE45oCC07FKgGfhz9439pvKbCn5Tl6+AMjbwtPcqEuLGDgyhX4CMexqUMm4zsALl08H+jVca0uaJqc8HPsyOFIXWjglZShXKng5McfqfLECQVk9xsfG8HE2Ki1T9G5hAc/qtNX4XuekA7SWJxH0vA5UBg5r8Ya7YsKKBKQXVhexpVLF7U09PaRY0rU77WrVzE/Pxc7V1DQzACc/CjUJafV4ht9EQA838dHH55Q+TrG/KLmyCD8zNQUxkaGtfQZY+qyFi0/B3Du3FmUi4E6DiO6u5QXUlczV88EwDdaTnNjUiisYHr4ChhjCjTa4khhxfDwEOZmZyPjw6xX+fyjjz6sLyAxXn0UzI8/TfrUgEAboKj3XoahgII+13UQomJlGsYED3FSPH23HC+qjjsejqN6oNEW1vY7biGxpW0L5/tRvUHxLvg04tiAMc2rHmiKA51ORCE5CnbUO8eJLM4UFGtrDb++foytXq/X1vK5CbLUe+hAQ6Ybn140fXmsSBdM0VbRxVpMunp7m7nYQBhJPpYnFszseln0curhzU2HHaCZ8eTzOP5VAM61/iLbIS6OWe+0/nwubqD7PAzr+z4cFkrTOI/RP5NzhoV3+TpgVy3A6gIADWf0Y/rd9/2IZE+mabAhC6Bt5GzTEJcrtBGfSt5pXwl1ZMVYokDYtvbKpH3fV43LEOrOaZL4aHRN4mbWLSdf4uZaCTpp2r7vgzn0ZnjYF+inLKs5duOI80BqZ7Q+HQuchxdDxHgWvNjGhUxT9hPHCfuAufng5Lu85S5All2PXIbTxrHvq5vtFEzKBxwCwEpg6nmemn8pwFKSPPLHAPieB8eNqntpahKkrmW5k46uqiGBJikyajUPfgCTHGNMyTiyPgHBu7wNTtc22c+YEa+R9pfUyFr7k9Cn5ji4uaVV+805x/z8PO66/Rb8H3/+l+ro8n/5n/8Y//n/+FN8+5d+2YLmTR1BIULfbejr0DxMal+3Di1EEd8UcZt57N69R+kR0ji235xz3HjjTVp+NgAl4zQ1NWm6QybIMkHS1q1b0dbWZg1jo8/ceps6KqFl0/kTAySZSOCW4Di4Hsk8e/v6YkGhrTy33nobEokkONfNw4jP6EJwxx13qcnHTE/yLKmtvR07ErvCMhnlM+Pv2r3Hes0/rgx33X2fxme9aSCZTOLOO++GlFzZSLSFSHvTpk3ajpmRMJybgAG46+57kE6lYxd/Cro4Bx548OEgHX3nT8sg37e1t+Omm29RkhuznmmdAMCePfvQ2pzX8pPpyfLQ3w89/KiejvzjYX2wAHEz5uDBhx7RysSDyre1wcb+fnKBKxw3agwh7FuMMdx51z1w0nlUfcKnkabDmALAjz32WfU8XByZrqvIxNFTS3Mz7rjz7sjxLP3Oyffde/YpVYk4orw99vjntHJFNilGQR597HNqATeDyT4t+cu0dWHX3p2YrYZzoCkJlHwzxnDb7XehuSmnPbeCxiD8Z594Sj3TgKkxj3EOZLNZPBD0AbNoLGibEKQA23bsAmNMSeS08CRdCQKbtt6qzNVoY82YJxkTIKll591IuLqZIMU/hGkbWc7Orm5s718fqQe1aUCoc+k6DDffejtSyQToFTGVh+r/4W3gzz75xUjaatwxPZ9kIoHPPfl5VOVLS4U6CDe8W7dvV4k5DPBU3YjOouo9SOKRxx6H7yRRqul1ZuYj66nv1kdwcbSIVMLRNqg+5xH92tb2dejefRsmVzwkmBPZqJlr4K233Y5kwiV1weDEboGBX/jyV2LfyfiUvvyVr9YN/5PQp0YSmM1kIgtKW1sb/r9/9B/x6//d/wmFQgGXLl7Ev/mD/xf+8N//TwCiO2ZT0iTBVZtxGYPGMSmVSiGdTl+XXylVoADQBKPyGc2H2ieiceh3mk6C6I3UK6/4arebF8e/nOzieDUHk2fcUjV5pZKWarWq3Q4135v8FYtFAFE7kXFUILfJqUTYRtVqFWXjiLQerSwvW80CxKU/Pz8bvLenR3nzfR+zc2F4y1yoSQdWVlawslJQ4IbmE5mvuTCH43Pf0NUhZSBhGYAxYr6DhuXGb0C0ETX1YFmbNdAwNT2ljqYp0DTjyGdDg9f055HyhhF9z8Pg4GCExzBNfXzNz81hdiZq8kXLhzwbGryGajWq32OCd9leFy9eUAuQyY7ZxoViEcNDg9eVNkggOTY2iqXFRS0tJeliLDJOz50/G+HPBDuqfnyO8+fOWnmh84L87heXMDUxrvXRwISblqfM6+qVS8HYFs8DFS5tMad5nj5lqOLIMlrqqFQq4dLF82TzZwejks+x0WHMzkxHwlCeeVAnAFCdulJ3QyfSF58+B4pjlzTbdCbV1EUaYHFhFteuXrHmLxOW4R3GcPnieSwtL4n4Bt+27D48fkSBNkmRcRf8VSoVnPzow5iBROb44NHw0BBGR0a08muJkrAcwLGjRzRduejpRRgWACYvfgQpCQSiF6to+vNzs5gbvgwmwxmbYhoWAM6dPYPZ2Vltoy37MhU6SDr43oFI3pRoWWq1Gj54/1Dd8D8JfWpA4NRU1E4gADz1+S/g3vvvx3//27+Ff/D3fw3/6v/x/8TgtWu4dPEivvrlL+KBe+/C3bd/Bn/yH/9nFYc2jO/7OBLoyJgTnE2yMzY6iqFgcbEdW5oT44cfnkC1Wo0AOBneBGXHjh65bl3IfOfn53Hl8qUIz7ajV8aAy5cvYWFhwTqRm7855/jowxOxR1JaHAggdebM6SBcNI6Zz8T4OKYmJ2PfmzydPXsmAI1RXm105vQplT+VtIp4Om9Li4sYJjoslHcTnHLOMTQ0iEKhUJdfSufPnVPfbUaoaZxqtYqrly+Hz7Uw0MAbIGzzzUvQGOEjbHt5bDRw5TIB95IHHQzQCfrypYuhZICbbauDtsLKCsbHx8jCTECjpakmx8dRjKlHE6ByDlwhOnuqbixlBsSxzsjwIHlOxp3Kg5GxNIeFhfnIMafMx0xneHhIbXp0qRrTjrFl5KGhwagkTfZH43m5VMLMtB2MRDZKAGZnpq39MW58SBNasUQWdp9zjI2NqLzMBV+ECYARY/ArRSwtLobHjUwo5GuccK6OfacmJ+AFAIABSreO9jM6J42OjuggoU5ZK+WyqsdwXiR9ywi/MD+PUqlo7efgoSRUjh+/MBdpC1ovdKx6Pkd1eVYbE1qVAIEBadHG1XIJywGok2mZ4amkdW5uBjXLpsS26QCA6alJUQZu9u/wu5T+VqtVzSamnAs4DxOnOpHLy0solcNNNRBuSgA9PwZganpK00WkGwgblRZm4TgMKWLzRc4FMh/Je7lURrVUgOsYOoF0biJpLywsrOryxnTMOFXZkP5Rq9WwsLDQcNqN0qcGBJr6YJT+7R/+v/G97/4F8vk8/t5/9w/gc45f+7u/gn/zb/9HHDh0GG+9exD/2//6v+DECd2wsjx+iQMiJgiggMJ2NGoj/Vg2mk9cGo08t+3EKe/R3/WPX2k8xpjSj6kH/iQw4TzUq4urFhuP15NKqsUouEEmd2e2OLZs40CuSDOclszJW4aNA+31JCM0DRHG4MngR5NgEf0uM4xt1+6bNraMfKUU2PdD0xaOE1jXNxZaCfYoST0jCWx4WGXRduZcHUuGQIsu1PonYwyOG+8xxCyLvC2pwIiRvyyD/J4iEntO0pHvafsyx0EimSR6mtF2kzwDghem+rsO5uRvJ0AFjEGdHpjAVbtEQvhLpVLWjaOWX/CZSCQC00+0nHbpCGMM2UxWex4B05zE5xz5fJMW1iSqX+a6LtKZNDxSrqTLjPDi03XESUkikVDg2fPJOAsypGVqbm5WY5DyrY53jfHaRMyF2eqDlj+VSiFFVCU0UMeIx58A8CazeUPvNOCVlJ1+uukcEq5j7++co+yR275uIrg5H/KoAzSotct1GLLZHJKplDrCleUz61BSa2ubVgeKDVI5LBjwjDG0trVp9e0EHos4iHF31XfTyGV1czXhaZSeDwfQ2tIK13Ubkqr6HHCyzeBceHZS7W+EVfOz68LNCF6SDXj1yOfz1pM+Tv4EL+JbZ2dnXX5N6ujouC4Pq6VPjU5gZ2c3ALqAhO/efWc/srkcrl69guXlZWzatAlnz5zG3/2VX1JhlpaXcO7MGdx6623qGecciYSLhx551ArqTLDh+8JEjPkuPFKImkJ55NHHkEql6izUevinvvBFLT+bVE1Sd/d6dHR0RMEQt+sQ3nX3PcpOYNxREy3XF7/0C1aeqTSAMTEwM9ksPvfEk9bwtnLu3bcvtk5kHhR0fekXvlzXHpPiJ/j+la99I5Kv/l3mI0y+bNi40SKpsQOBhx5+JHTR5YeSNk3qiLCevvaNb6v8bTYNKeXzeXzBqHfrAhY8u+UznwHnHBWPbg6ifMvfX/36N1CuCrdaeng7V9/89i9pk5njMBGX0biiD/Rt3IiN/RtBsHUsMcZw3wMPIptyFX/xmwfx+bVvfEst/k6wqYnLIpvN4PNf+FJ0MxQTfu/effD98IiNzjORhZJzPPnUF7BQqKLqRceZjb761a9bJSFCWV2P09XdjbZ1nZq+YITIq9sD/VfKt0kSEHDO8YVf+DJ8H5o0hIah5DgOnnjqC9bsORcSIN/nATBy0LJ+I3bu3o0L08uazpqKx6GNjfsefBhJ1wk3ewRAU0marOPPPvF57be1agIg1dLaijvvvlelawM8NJ3de2+A4zhYLntaGA28cB5cZnGwbs89qkxxPMmoVc9H8/Y7dH1mph/VV2rhSUdPby9aspvU+FKbE4T1Jy/rOQy45dY7kEgkRH8EQNVDzIJzznH/w49p7R2C1aigIJ/L4c677sFyVU+DxqEXzrbv2BG5SKKtGSRfBuC+Bx5EDS64ZoA//NQ3oRztO2/F2ExVmXwR+dsMawMtbevQ3LsNi2Wu3Df6PL6dbrjxJmTSKS0NWoXhJlE8ue/+ByJpxVE6ncbtd9zZcPhG6VMjCSwEeiPmDn1mZgb/53/6j/EX3/sBPvfEk/id/+v/BYOD19DR2Yn3j57AB8c+xAfHPsTZC1fwy7/ytyPpVqs1XCZHqiaFIE9MLJMTE1YRcCh10YHj6VMnle6buUu1gTVqqsQxrMtTCQBjDPPzc7h65YpVwmYDtWfOnMbS0pKWt628kj54/5DGY5wEjgEoFYs4dvRoLJA2f1++dEnpjZhls+W3/+23YvO30dtvviEmD5KWWdeSRkdHles1+jYum/cPHcTc3JwKY06aJp+vvPyC/j6GZwbhi/Kd/W9pz+hRliT57OTHH2Pg6lVrombzMsbw4+eeFd4xtM2FPTwAPPfsj4w0aR8mkzWAwWvXcPTIEcWzDYgqaQmAg+/ux9TU9Y5Twjx+9MPvR/iwNhEHlpaW8crLL0XC2eqec+Djjz7ExcD1Gs1bxqESAMYYnn3mh0LNIyY9Bn3sPv30d6280mMyGXdkeBiHDh6w6jqRIgIQYOnAO29jfGxMlVPWv/rNwuNYxhh++PR39fanhTWoVC7hxcBlmJm3AjhEt3dp9DLOnzutpIOMhbpbtJByH/HKCz/WTBZ5vmFX0ACNz1r6gPxOQR3nwNTkJA4GZoXMejCJAzh6+H2MBmohtF+bVeMHiosLZ98m8cNLF4C8wRryWK3VMH/2gGarzrytKvUhXdfB4NXLOB2YCQrBVlgXns/hBRdJEg7D/jdfxdLSkq6KgPhNz0vP/TA2DB0rHMDc7Czeeedt3UyOObcg9NV94vgxDF67po0bFU4KH0geco5h9AX5Hs6r4vvI8bfgOEyT7JnR5O+RwQHMDJyDw0I7gRRgmvTO229hfn6B8GsPK3n68XP62KhH8/PzyqXtT5M+NSCwXJIgUG+Nf/ZPfwN/61f+Du688y78D3/wb/HmG6/j9KnTyGVz+K9//p9VuCuXL2NuLtThkJ+eV8P42FjDQGp+fh7LS0sqTAgyorsoxphKWztmtQAq+Zva8ZO7PfFd541zjmKhgMXFhUidxNHc7Cwxb8AieZsSM6qzR3mygZlqtYrFxYUgfhQImbSysrKqyxVzs7Mwp6s4qQsgdLxs6drASaVcDi8oQC+XrfwrKyvW97FlXV6OTMymhEPm7Xke8ZHMImFMqlQCF3MkiJQ2UAmC5L1YKIj0jKqjYISGL8nNFwkbx43neVr/0qUL0fDlctmoP/2TEmPQ+kucdFQ+rRFeBA/QyqePx8A9Xky5ohIGLnzvxhwv2dLxPS9eMMr1svueB8QAFluf9zxPMxFjSncAw51b8CxSLuM9mAA8zDA/YwLNqielY4ATzHU1CgJd0h9ZeBzsMMO8DQ/TjAPA5kba9l3mVfNqcF03EpdzvY/LMvm+D9d1ETetyKN7qQtqNbjN7PVfqXoAc6J+d8n3KjE94/teJH06Z8gjWMdxkE44gWmTqJkVsygcxNSRwbfJs+TP8z0kXFcLZx+jck31tONdJU0zgRT08pubIalyQtvW8zwwJlRI3KBDReZohBLlmidMxDAmdKIZwr5la+eaV0Mi4Ubex5ahzhpkho1ro78qNXQcXE+JXZLjOMrVy88jOW5oVV6ClB/94Ps4e+Y0/n//+5+Bc3GU9h//5H/D3/lbv4jnX3oN/+r//n/D//Tv/xCe76Gzswv/+5/9uXLLRhuPei2QFNe4iURCHe/qkjl9YZFgKZfP1214euwJAM3NzQaAkt+jE53rJjR9HVu6tBz5fJPwFmEANApmadnb162LAFhb/XCI/tPevi54Z4Ku6IyRy+WQCbxuUF7jJvbOri6rXqjWDsGf7/tY39MbmehD3vUBmk5n0Nraag1j43/duo66R/wc+u6sr2+DdcKh6QtgxuG6Ltav79H4DtsrLLOk1tZW5PJ5UD0u3+fKFzadaBlj2NjfLw5OWPzkJclhDP2bNlsnbxvlcjmgo1PxLAFW5KAmKE93T0/snBNUh9ZWmzdviV2gZZ48CJxMJJS7PtNlnBkHEG2aNnTlaHgz3uYtW8GYY61GG+jdtn2H9biVk8SltDeby6Gnp1drN5tEXT7q3bBB9AHKNx1HoIsYw44dO3W9V6N8DKEtNtd1sSVQgYkD/xXfU/zl29ehfV0HRrzwaDOVCBddCZI4F/1r2/btSicQnKPGQ7AoM2WkDnbu3h2pP1kX2uYaQD6Xx0bpslHyL+dqvTLBHGG2qrm5BfJagNaGdI4JdAKbereGdUiBvCW+x4F05yatLsxyVGryeJdhXUcncikLqAviyaNgxoBMwsH2HTuRSqXUhRvavnRzJGnPDTdq9SA3SRzRy2vZXA5btm0XaYHp4UnZZbz+jf1oIR5vaP+y+ZG+8cabtP4tTL3QvkKP8hma+7aj4jJlgokWWAeSHC1tHUi3z4MxxBqLltE5gJ27diNN3EFG2sn43YhZNEm5XA47du66fsBVUkMgsKmOf1n5vLe3FyPkeO7njdavD20myY73tW98E1/7xjdVJ2SM4cGHHsbA8DgYY/jhMz8GEA9eANEwn3viydgdpgmm9t1wgzUd2zPGGD73xJOBcn0UlNlAxJNPfT62rUxA0tvXi96+3ki4OLr9jjvCCZfwawNsjDE89PAjGrAwgShdIPP5PD5z662xvJvl2LptW6yOn61e7r7n3shurF5et91+h7Vs5gLNGNDR2YmOzo4wfegTOI3PGMOu3buRyWQi9SfCRRfLG4gLO5mHCUQlZTIZbA0mXJPoxCsjr+/p0dwSynzMeBJw7tq9RymL63ECxXqj7+/cJRZdDUySdMPyc7S0tKCpqSkiuYiUI/js69sQa25JlpXO21u2btMnedD2JUViQCqV1FwT0nLKslFqa2+Hmwh9B8s6kupDGkhiDBs2bFTAxXLaGZafi7w2bAh1TikYNNPlnCOTzaKtrnUykj5jaG9rV/Uo0xMGrvWNmqTO7m5RX8yQBgWJ0nnAcRx0dOjK77I6pUSp7PlqE5LOZpHN5eEvVtXFhZQbGDEmYNcPjk+bg0sB8rl1TImXAARYV88lOGAsGh6Am0igqalZ20zTMGb3zGSySKZSqNbk5isMJDc0nHNxHMw5UjEbcBv5nMPN5JFJxB/eydvBDmNIJlwNjEh+5BxS8zm8QA846TJk0hk4rgsp/BbpIHYQZrNZa/+WcwutMYeRy0SW9KSEVPUZ1xW65yq++HQtbcS5uMBl6+tqM81Ce5qe7wMJsQE33cBFNjNMjOOEm4QbXGSJnZQUr46SBMr1Tl+zw7QB1NVTN+d43/cjrgB/GtTQcfAtt9yijmrMP/m8u7v7p87cT5NmZ6X9MfGbAhJT4vP9p7+nxY2bXBhjmJ+fx8svvRgJb/sEgKNHj+DqlSuRtGia9PMHBi+2Y1ia7w9/8H2YFIeprly+jI8/+sjKe/R4hOH1115VLuyud1zLOcdzz/yIxI8uoBTUTM/M4OB7B6xA2PzNGMOJ48cxNjpqXaBt9MLzP9YlBAbRx7VaDa+/9oqVD3lMSf+uXLmsmXGxVTeV7u5/+y2USiWtLSkJcBL+fv3Vl0nZ6QRoxOMcc7OzOHH8aGxdmE8//vgjTE1Oqt9Uj9QGSN98/bWo7g1CiVBYXqBSreLAO29H8pUAiY47zoFr1waUfq1ts2DS4fcPaXYCzfpgxvP9b7+p82EZGE5QwXOzc8qFXRQCRPvamdOnMDUVqj/I9UItjka+777zdqw+oFi8mKpn3/dx8L13Y9KK8jY2OoJrA1f1hdngmYLIY8eOKFt7kfekmLL/HjpwQMWlZaV5SVpaWsSZUyetZZRhK56vbqmvTAxidnqKeL8A0uRGLEN4m9RhDMeOvK/x7PnQJTyET845Pjh0UOMxrl0ZhPmZq1cvq/5pC0/H4pnTJ1HQVD3I+yB/cStXeDlZHjyjuVBTfDNGvMuIz1KhgOL4ZSTITWlzLvCC9km4DCOD1zA1NaGVi/ZHLwCBnAMpl+HEsSP6Jo3Zga78fYKYIpPtT+tJ9TXGMDM9hatXL+sFonXIoXQiAeDsmdNCBYa+57onHMqXdAXIjOdm/weAUrGAxZHLYMyQ7HFZDn39HRsZRGFxRs3VDovWSRAdAPDhh8cjwhbJvy3esWNHLU/tNDs7K/S3f8rUEAj8D//hP/xUwvwsiTEngqzjpEC+YbSYghfz+FXpL5i7yRiJnU/0b8x3lOJ4o4DUDFer1TRdGO1IzQKmJO82MGkruxm+nnS4kXLp/jQ9q/5NHPl+6CrI5KNeeURci/s6yTtC3R4ZNy5tJdEwdDXM3ST9zhhDrVbTdoCxUruYOpBHHTaq1XQ9Jg18G2EZY8L5uuOQxSRaL5ELRpRHOxviva/rg8nymyBS9tGaoQsUR1LP1Tf6jA3km8/kAmJrI/rMJ3pMqox1+pjv+0i40V097VdmfuB2XceQ73COcWL6lzzulDxxFV6a8bHPSaG+FMi4jmnPGB4FsLHzFBQvot8lyxbyJCSBcq5KMA7HdQOdQMF/0glNeXCE7a+t4VzoP/ucw3WIXqvGK68795pjXY4luSmjAMFWJb7vB+oyelVwUlGCT3E72Ha8yAG9nxFemOMibXN4K8ME5nGSroOa59Xtj7Wgzh2HIZdyFMCybXhkPPnnc3H8DRb2Oco23URCrhuOq72jJCWBDiPrEpkfJdilc2K4SWGkXfQygoaR5a558CE28knH0YCx2qgjzKdW88Ahwil9TAMIamua52kqU/raUX9tpGSeOAGAZ6wbPy1qKMUHHoi/xlypVJBKpeqG+XmgTDptWSBCgBMe9TFs3b4d5sIfd8SbSqWU7lAc0fA9Pb1oJa7XTD7kbzkh7bK4pKOgh6bjOA727rvBClpsgKKjs1OzE2eLR9PfuXN35OjQLAMt743BMWYc0eZoamoSek+kHeh32gacc2zevAWtra0RUG7yIn9T0z5yQo8DWclEAjfddEt93kkWvX194sjeKJONOOe49dbblKkdk1/bpHTHnXdHdtqUB5pOa1ub1mfkQm8eacnnO3ftVi7s6E5Y5aGVmeGee+/TeTWqkKaRSCRw5513AxATvAlC9HoB+vs3xeu90Q1cMKnfccddWn/UxwXZgQfxHrj/wesucrIQrW3t2HtD6ILxepuSPXv3IZMl7ssQBZuUlEs68owhPIaVyxaDsJ133/0PRsKreHQh5BwbNvaLiwR1iILTu++5D5lMBrKL0EWddgeZx8OPPhYCPVjqM1isHcbQ1taOWz5zmwUohOmXaj54cPTbt20XOjvX48rEklIhocBHSoR8LiRnDz7yuOLN56Ek0CH80Tp7+NHH9X4iy0DmGcnXxv5NxuUg6f3BtvEF7rzrXuTyecwXPW3jJUCM2Oh4QhQIOC56b7pXlUmNUy1NMrenc8ht3ItsyiF1qIPRai0E03v33Yhsxj5Xcy5uB8s8swkHd3/2CcgOQDcVpkRelvyxzz2lxroEurQvULC2sX8T/A0bUPLDTZksgwzvMAY3MPVz7/0PCP1glRaiG0cS94mnvqCkw3HllbfNc03NaNl6M+CKSzYmmBMb4zChbbv2Ijt1DQ5YMIcF+TPiS5zU0VOfD93phetWaFQ77F8iTpwZNRv1b9qEvg0bGg7fKK3qdvCXvvQlZdoCAC5fvox7A5+7q6Hf+73fA2MMpwKzGpOTk3jqqaewc+dO3HjjjThwIHSlUigU8Mu//MvYsWMHdu3ahR/+8IdxydaldMZiwJFz68DeuLEfgH0XbX4mEgl0dHZGdpG2vBhjyOZyyrVbHFijHWXdunWKFzpRxeVh80kryVzgzcs8NvBE80qlUte9nUTLIQ3W0skkmke4u7elbQJASVRaV48PGl48D9/bJAGAkEjZAJr5W36XN2wJRoklxhiWl5fhOE4M+I7yQ13Y6fxG+SuXy9otWHNSVtK4INriwoLWF+knbRspfZHj34lpT0rVahWLS4sRXjmgTegyr+XFRVSrlVjwxLn+ZXJyPFg45MISV4/ib3xiXC9j8J6RT/m9UFhRt9UpxRV3ampS8xQggylPBka8kZHh2KNFGV7Wf6VSweTEuF1KZ3k4OzuDlZVlklaUaVVXjGHAcC/G6HcWXShpeEAH/tJsiRMgmqXFRUxOTpD0QrAjWS9WAxMxDCjPTaBcLmnSwYwxzqWNSgaGK5cuan3cN8ctD8tUq9UwMKCr4pjVR+epqckJ5U1HPou0maxGAJcuntdcWcrnEd6DfrI8NqDaWNZdkFFkPBaWl1BdmNLM5dB24gBqgUekhMswOHhVO5o2yyhuZIv6TSdcnD19ClSqJ8P5Et2RPCuVCi6ePaN+K6Ar53qE/YYBmBgfw/TUZOSImwXMB/dkFHA/feqkum1PN3UuC08SKJ8fnTiuDXguGSLlkLSwMI/C1Agcx9AJDL6atjWvXb2MysoyHIeFcx7hS/6WKR0nx7thG8JK1WoVHxoOKCjP5rgdGR7WzKL9tGhVIPDhhx/GHXfcgUOHDuF73/seHnnkEfzWb/3WqjI8fvw43n//fWzaFErPfvu3fxv33HMPLl68iP/0n/4TfuVXfkVNqH/4h3+IdDqNS5cu4ZVXXsF3vvMdDYg2SpVK1JyIJHpr1Pc53nn7LetibwMOM9PTOH/uLEyKi3/61EksERMx8jkNyxhTtzMPHXxPA2M2HiQVi0WcPGn4xqyzWF+7NoAJYlLGxq/4FM+PHj1ct2zmOzkg6gFXSTPT0xgaku70oNI0JYAyvQsXziu9Oko0Ds3zo0C/S8bXQDX0haxQKODSpQvWXb+tHMPDQ5ibna2rN0bjnT59Sss/UgYSn3OOM6dP6aDG4IfWz/zcnPLXy3k4scbxcvXqFWVSJo53uXADwPnz5+q2JS1KsViM+Ou1xxGRxsfHlA9byT+ACLiWc/zFCxcUoDVZ0ub3YLW5FAAGs2wyqnKBxcViMTM9rYOgmIUFAIYHB1GpVKxl49D545zj8qVLkcVKls3jPNQJhAD2Y6Oj6r0Z3hzZ05OTWNZ0qqLtJYEDAzBw9Qoxs8JVHQDRI1dA2HOEzJcuvggXUfl0eXlJuQyTYNxMr1Tl6kSisjCNaq2Kmrzk4DCkybG8bGufc7iMKdd+nPNAj1D4Gg77fVg/pVJJuTuLLOKW+XFublZZxqg3fclXI8NDkbmRGfVX9Tjg1wDGUFnQeVF9UQoBSB7lQgFeeRm5pBORvsp5quZJnUAHM1OTqNXIZlDmIdPzPSVpzbgOJsZHtTJKoKOpFwV/tWoVM7PT6pmUkIXzVggOOYDF+XkUVgoEIJKyKvAY6EQy4cvaNPEjx6Zt5RsfFzYuHQtAVr+D9ErFImqlFTjSDRydVGS8gDfOhY51tVpVF0PUhgNEoBBGx8REdD1FkJbJk3CnZ8cyvh/tcEtLSxHd3Z8GreqA+Td/8zdx11134dFHH0VHRwfeeecdbN++veH45XIZ//gf/2P81//6X/Hoo4+q59/73vdwNVB4vPPOO7F+/XocOHAAjzzyCL773e/iT//0TwEAW7duxUMPPYRnn30Wv/ZrvxabB13UFoNFJZGI2m+SE4qURMhndCdmggkTYJj6XSaZEjyqQ2idnE0AU2cXby7s1Wo1VjpmlocxoXshbxuZgMQ8WpNhbHp7JqiySe/iJHqSarUaEm5CTQgmD2aetVot1j0azVN/r5dFlTWGl+uR4qUq+oCa+Hi4M64nwa23uZA/pR00c9NqAz4AUK1VVX+UYcyFiLGw3rT+a2kaHiQQSlbsqyEnYWUwz9BLUuWs26aulW+9DOEzGY4mq43lIDyVHDMjHcqDXASkfpcqm+QD5tiQXgR81QeuR4wx5R7PJAmUSBPB8zzt2JvWnuwL5rtEMmkdy+HvME7SCKuDx2iJbCohMcMaHFydNjgM8NTzsLyFqgdwwHEdZJKA47io+rUAGEb14OhRJr0B63OgxjkSLJTa0A0e931ks7mgje0G4Gk9uY6LdDqt13GdOUyelHDuqbTMpX+lWgN8YfMvrUxcmXUmKOzH4qjbTWaQTYb6lRSUAEISCABJ10EylUIymQp5pnYgAzAqT0dSjquZCpMbFynV1UrAAc/z0dQcnjipsao2DiKG3BC4iYQy5xXOeWGSvjEOc9mcphMIhGarFI/kXXNLS4RPHazxYIwGbgWTOi8w4xEAnkql4CQTEfNiceNcutPTy6pvjGhZpck5k2xdLJlM/rWY4VsVCBwYGMBv/uZv4ld/9Vdx6tQp/P7v/z7++I//uGHG/tW/+lf423/7b2Pr1q3q2czMDHzfR1dXl3q2ZcsWDA6KHd7g4CA2b95sfWejP/iDP8Dv/d7vRZ4L0yb2OGIRCSfKhx5+RL2zTRJ0UV/f04P2deusEwl9Jnddt99xp/JHScPEHT8+/tnPWSbw6ELMmPCLefc9+vF8XFgA2LfvhtCfqsG//lvU0ZNPfb4uH2ZZpAs7VieOpC1bt0TAUhz4ZkyYn8lms1agZQNXpu6FVj4jj4516/DAQw9rkzDlR3/Occedd4Ixu0kODWwGn1/92tdj68Ekx3HwC1/5mvWd7LeUl507dxHDvqbEILqAPfnU5+E4DopVn6RD89B/f/2b30bFj58EAfHOcRg6OzuF/hjsk5oJUu6+594IgDPD0yOrb377l4z3MfwwASy+8c1vi0tBxju6qErauXOXksbo4e2XkJ586gtC2d6SP9UlknG+/s1vY2qpoo0NQDquFzqBcux0dHTg0ceE7pspAYL2W0CO2++8C6VqCERs0nJa/q9+/ZsWrkO+TID45a9+HV6QBl3AFbiSesYc2LJlW6z7OjmvlAL7dmDAZ+59EC2tbSiPjoNzsfAn3BCMcB5603AZwxd+4asK5PkSHDqILNo+52hta8NDjzwWpgW9f8u+Jbm94aabRXkgxxI1iB69HPLUF78s0okrLICFShXwPXAwbL3zMS28KXVjpH5Tbd3I9WWRTbiar2Wm/oW+gNNJB3fdc3/oTs8yMCqBJNBxGLIJF48/8Xl4BPwwkL5DxghnQL6pCffe/1CkeFT1gdbB7j17AcYwu1JVmzW1aSSf8uLFY5/9XHR+ZrpUWqXDOT77uSexYrikE2Gic3xzexcy3VvEnGCbY6Bv+nbc8BkkFgaRCI6D6cmKWa2cA489/tkIHzRtuQFjTJjZoTrW16PtO3ZEBB8/DVpVig8++CD+xb/4F/iTP/kTvPPOO2hvb8ddd93VUNxDhw7hyJEj+M53vhN5V2+SMt9fb/H8nd/5HSwsLKi/oaEhmYpViBFKC8RLz/MwErj+Mcmc/DkX17YXFxe1Dq0WKzpYg3yuXLmseUWIK5OcfC+cP69NxHESSkBIPUeGh2N3qmZZrl65oky+0OcmaJFg4+OPPqzbNvRZpVLBqZMfx7YXfcwADA8N1T2aNsHhsaNHlOX9enxIev/QwUhbm98lTZMjfhsoom3hc+CjDz+M6I+ZdUjp7bfe1J5HJivCUqlUwvsH34uGswEAxnDx4gVcGxgIwpPJmy4aJK0333gd1WpNe1ePXiHmkEweGPT8xsfGNJ2XyM7YAP0fvH8Q09PTShoWRzLe8z9+NgLUze+SisUCXn/1lYjEIBIyWAXOnjmNS5cuxJZV5iPzevH5H1uPg2Vck6Vnf/QD0BMIGpZz3czJ2OiocMFoS1xbGAVgOXjgXUyMj2vHnGadhD52GX70g6e15BgLb2yaVCgW8dKLwn4qXSy1OiV5nT1zChfOnQny1MNLHsrV0ETMmQOvoVQsoeaJwI7jIJVwVN9iLJQEug7DM9//blBvQh/Q88URJrGkotp8bGwU772731aLGsmoh947gLHRUAeLSlDpZkQ+e+YHIS/0OJp+WShXZANj9PhbZOOq9xFtjACYH76M0uQVZAypqASoAESdAcilXLz28vOhkwdLQ1b98Lg9k3Dx7A+f1tpTa1e6meTA5OQEDh3YH123ST3RN0cOH8LI8FCYHqD1TakTKOlHP3hapSE/TRuBsi/VajU8/9wz2mZEfqfzkSzDpcsXsDRyWdPxi5AE1Rw4uP81VIoroZ1AmZxlDWEsdE0px7EEvHJsUpqamop1A2cbe0cOH8bwsB2b/FVoVSDw9ddfxy/+4i8CEDfW/vAP/xB/8Ad/0FDc/fv349y5c9i6dSu2bNmC4eFhPPnkkzh8WOiZTU1NqbDXrl1TOoObNm3CQLCome9slE6n0dLSov0BiAwySWZHrlarGB4aiiz25nGpTHN2ZgaLCzoAMI9J6UQ8dO1aXfBHgSPnHIOD16zAywSbgHAvNj8/XxdUUjKV2Wm+tmNY6V80TmpJqVKpKDd79WG7oIXFBc0HqJm3KdGYGB9veFfEOcdM4K+5Xt1LKhQKKBaLVuANhDtuOUEtLMwb7vTq12XoNzgKisSLkO9KpYJi4PKQLjwmoJV9obCyot1o1OrB8mx+ft5qlkVucuhkDQBLy1KfVQeZtvRLpZLWvzR+ofdjAFheXrGCbhqXTvBFw5NRvbatVqvwJC8W4KQBTwbdDWC48kfqQ429woo4wpL8MZUUySvMs1y26A8iuPmKEBxyIHQDSMLRxc3kpVQuKZMydDMaqVtSNzQ5VVxEb3VXqtVwPpN1wmg9ks0GE/OA40TN29BFu6LMlThwfQ9OIoGqL/UEEXGVJtlxwEDlup4v+ofr2OemGlGXkQuzjWSK1WpVqTPQMU3Bic5XnQ1y8G626AFeFeA+UcXRg6lpQPZnAF6tCsZc3d+twUOV+AKuVmvaMb9JVd9X85gwVaMfXKvxKX8gBP1erYZkIrRuYIJh2jcZRD26hpFjxRYX9eYyFgAts615APrCSqIharUaEsmkGptmGG48WC6WwJxEcMmkzsY3iFitVQHHVX1Qm++4KRSgz3UzRZzwJ4tYq9Wsxp/j1qZarfqzMxYtabfhcgcAvvjFL1pCRum3f/u3MTo6ioGBAQwMDGDjxo145ZVX8PnPfx7f+ta38Md//McAgCNHjmB8fFyZnKHvrl69iv379+PLX/7yatgGUGfAGxVeq9VWde7OOVfW9q8npeRc6GZIO0Km9EL+cR5KO7KGazRbPiodxpCPcf9EFwMZP5VKxej32P0eNzU3q/c2cGOWlbpSiwJjEhZAKplCLperC2BDsAS0trUp3UqaR1w7r+voiPBhSsfk90QigZbA/IxZJtsOsLm5Wd34juOdkjSsbttYqAoh8Ts7Q1UJs3Rm3Hy+CXlN3SDCkpZGT0+PMm8T5R2KT/m5YcMGtRpoxSWAUcbLZDKq3k1+ufEbEMeemYzU1zHBVsi7lBxs7N8U2azR8JQ/13XRu2GDCGMU1rZ4tLS2orWlVa/vOqtGf/8mOEw3ZUI3QHpdcfRv2qzlxyCBRlQKkMvn0d29PsIrNx7Ieli/vgfZwFyNWT9U+iKLtHVbVK9bA3QkvnCntzm6yFrigwvXkW3t7UraRTcxkodKoBPIHIZNm7bCcRxxgQKA6zpIGtIvpWvmMGzdtkM9l7eGky6z6o/lcnn09oUmNrSTGoN/xoTpJzqWbGWkfW77jt0aiLbRXKEmArgp9PRvJjxG50XaN5xsK1LNbUgH9hwpAJTZ1TwhTW1Kudi6bTvR9dXHEYcAjF4QPuEy7Ny1RwMqsmzaZiPIKJfPY8PGjRE+5SaNbmAAMU6b8uHaIfmg5JA+tnffDVp8uvmlzxkA5jjKK5HZvyhvMtN0azvcXKu4HexEK5urf4LW92+D4yaQkm7mDCEQJcaAfTfcSLKrjweampqwecvWyHPGWESdAQA2bdpc1/rHT0oN6QQ+/vjjeOONN9DV1WVddCeJx4GfhP7Nv/k3+Dt/5+9g507hv/A//+f/rDrwb/7mb+Lv/b2/hx3Befgf//EfK7Mpq6HrNYikpqYmPPX5L1jD28DXzbfcYgVMtjicA1/+ytdipTihxFC8930fX/7KV1X8hUIV4/MlbF+fD3cmpD02btyI/v5+AzDZ+WKM4b77H4gA0Xr0xJNPER71fmCWo6mpCXfdfa/Y4VsWapOtXbt3X9f8TJgnx4MPPVznfUgyvwcfepjssMPwWvsEn93r16OnpydSVhvYYAy44cabkM3mUPOj/cNGd951t8aflr5RMdlsVk2KNE/Odb0nydPG/n5kDXNIZhlpDjcT35VmX5A31Gi5b7r5M7CRmS4gXKk1E0Vpkxf6jHNg67ZtyOdzCKtBguQQVPlEGrdn7z5Vf/ST1pHkP5VKYcvWbZF8TXmOLGtnZ5e2GaRgwWHClyulbdt3RJBE2PfpM/F8+46dKj8FFnmoEyhNrDjBxq4pn7ceZdvMZggQmAkXNtscEDxzGFN2TrXxEXynx9Ic4ni2t7fvutJ9Gae5qUVdCqDPZT0AApCAiwtQG/s2gDEWHFdyuG50QZTDzOFcufbjCLxgBNIt4fJM5ymdyWiSFE4+eVBWj7RzS2ur6gN0vJnrs2zfzkCv3dSBpD8FCPQBJ6E2SDadSflIHaE7CTippGYiRvLOgvDVmjgByKcdtLa21T0pqQUZJBIOHAa0B+70rOCJPuBCv7appSUcb5aNHQXI2cCdXqVqkdCBmvwR1NLcovVnABEAFlYAR2tLq3Vs0PaVVPY5WCKNhBua6JKBuZkAACedBpiDtOGuz9xYyQ1Fk+EK0DKla5TNRv2Nx520Oa4ba6f3r0INSQL//M//HABw9OhRHDlyRP3J3z8JDQwM4MYbBWpev349Xn31VVy8eBGnT5/Gww+HC3w+n8d3v/tdXLp0CRcuXMA3vxmvxFyPbA1h61QTExM48O47se/1NDneP3QQIyMj2kIeF48x4PtPfzcSzgRs8nmlUsGzxPVaPu2iKZOIeAWR8U6fOoVTJ6Mummxl5pzj2Wd+FHsEq4cVv3/w/ac1XukxkwmURkZG8P6hgxqf9L3J38H3DmBiYkJ7RweXKcV87pkfWaVyNqBeKBTwyssvAYh6v7DRubNncPHihcjzsLw6/6+8/JI6UqufsqAXnv9xA6EE7xPj4zhx/JjKi0oabIv7kcMfYGbGdJEYbnNNYZasFynJM4lO5sViEe/ufztIVKZNwnL9t9BPvGKVRJEkFNB76803UK1Wo3xTfnhoC+7N11+NvDN5kWlMTkzg5McfRdIzpRZS+vHRhycwNxvaiJOLjJTUg/wGgLfffF2rQxZTP0Cg53noPfWe8k0ldJIGrl7BtWsDESmgb0hIJB048A6q1VpET4/WJ92vvPvO21FeSTno4j4zPY0zZ06pRVarC1XOMPHTp09ibm5WqzdKDpgAL5zDcR2cPvY+fC70BLnPFUiRQI0hPPb1alUcO/KByl+WKcFC939SF4wDGLo2gKGha+E4luUCkTYRHo8dOSyO4skzWecUzHAuKurw++9F+q7Zg5dL4mII/CpWxq4oAMcYyIWuqI7g8vgAUBW6abRtJHEIf8CMAW2ZBI5+cChsC0vFl2u+uqzIvRpOHCNu4MgmWT0jZRkbHcZYoOOnbRJ4uEmiIOzD40dRrlTUxiYErjyoU9EPZEKHD4euAGn+pkQQABYWFnD+3NnIEb3cCJh7gWtXL8MvLSHpOhHPS1pbBf3m9IfCzFnSYapN4oQ+nufh+PGjKowp1DHzGBsdxcgqdPxOfvyR1ZzXX5UaAoG9vb0AgM2bN6O7uxtjY2MYHx9Hd3e3dnP3k0BSoiC+hw0pn1UrFev7ODBVKpUiZlPMdOnf9SR0FFhVq1VNryPhOtiwLqstbnSgSh0DU1Jl8iw/a7Va7M4iTmJngt04QOXVakilkpF4ehohKJFlBaBNsnF5SOmR+cwmoaS6FzZAGUZAGD6RjKRj5hVXVjNMo1JoW1haL7KubFWu2rRaNVzSGeFWyQdd2KvVqtC/0Z7Hpy/D28C6yQfn9fsjjQvoumr2Y3j9t+wD9TYAaoJHUI+G/g3XwkbnCFooOc9or4P+Xq1W4QbuxTQpOcJ+TY/HatWq3h+NT7NEZh+wLVocYX/S6iD49Ek70QW95gkdLMoD5V/lF3z3PLvek8hDfJZrQhLoui5cR5S54gmpViKQwMgFXpiBEe98wgtHAA4Bzb+u2qxAmk9KRucUZpRBSigDb1iyLNQQdbhpCNOnc6YOwMOwy6VAEggf+WxK5ekYLUE3epxzeNUqHFfopvG4tgkkgdmka2eAUDWwKZhKufD9aBvZZizZP2tkXMu6dxj1dqPnXSX9UdssqLLSizRMO1VQG18QA/WQoJ8rfUNliBrGum2UoViugLkJJOTxruSH2ctc4yKfhGtXq6BE50fpmlQbdnJfwoM+XqnE6ARaGIG+Fvw0aVUmYt566y38rb/1t9Db2wvOOSYmJvAXf/EXmuTu55Xozrzeop7OZNDT0xuEpY0O9YwCiO7u9cjn81YJlCn94pxj2/btkTRsxLlQlN6+fYeWnjmB0Q7Z0dGBbC4XWaDiwu/cuSv2CFYHbeJz7959ER7jFtXmlhZl6ymulFRytGXLVmU6h8XEoXnddPMtUdtNMbykUins3btPA8+Sf42/YJHu6enVdCvrHQdziCNVMXmFi4Cmk2Tkedttt0fSsy1KnHO0r1unmRSS6cfR7j170ax0N+liokt3JN1xx53W57b+m8lklCtAOVkzhJOaCZI2bdqMVDptAfLh4hH2aSgXczZeAMPWIue4+577jDBheibw7OjsRL65JRYwahsnCN0eqn8jgFkkqqJ773tA370gBOxmltlsFrfeekckDbGQhjyxoFybt25Dikz+cgGU380S3X3v/Ugk3Nj3ojwhenngwXD+pm0K6ICHQRyTNzW1aJuDKKAP+/ONN96CJoseU9j2wTGmLySB99wveKlUhdceVx7bBZMFY4ExbSbq8fY77wmBa7DwJoybnLIet27b0ZDvVcn7fQ88pHwBU8Bsfkp68OHHImNJLfrB90I50AnMd2LP3htV36PpUxCEIH6ubycSmZx+QUG2jQSKgY5fLuHilsCdHox0JJVqoe/gTCaLe+57UN98E17oMw5g89btyKaTapw5TOqxh/WnmgzCzWAymUKlTK05ACyoF3GZJ7yt+/hnnwh5RmAj0NUvGMrNR1d3N3JNzaiROpfkB/n7QQQOoGv7DUjMTQbHwWFatMzqWh3n2Hnng7h6qYR0Qp/POV28AkqlUnj4Ed0kljHthWVnDDt37bKuWTZ9QAB49LHH1f2Dnyat6mLIP/2n/xTPPPMMjh8/jhMnTuCZZ57Bb/zGb/zUmfrrpHoLKGPiIsb6np4gbLgIxgGMjs5O67k+TZOm09e3wWoN3JY+YwydXV2xYM5MP5PNRi6G2MJLag8UtuMkYxT81mo1Fb5e+pR3CursYcLvyWTSKgmMy8eUGMXVn+Q9RQbP9SR7vu9rkj2rJIvsSsXzsEx1hE1WI9cR3gl7lXIZjLiYux4VCwXi/cEAU4jWa6lUsra7jSqVSsQ1GjN20FSCtRx4xjHTpNITSktLiwTMweibQdzgned5KKwsa2PTtimRz5aXl1Ezb8EG/GoAOficnp7SFjPzvaqDIP/p6SlNKibrxedSBSEse6lUxNLykpLG0XTDG65hmWanp1Glt/i1/MP4so4nAg8KGoBjpvs38VmrVpVbtzCdMC16pMYBLMzPY3lFNytFC2DW08jIkCqH+V6ChErNA/wa3ISLibFh+ByoBCAlmXQi4FvqkFWKK5idCa1KeFzUXcqNjhXOOcbHRlEuFq16aSaPjDFcu3Y1lEyRzZQkahfP8zwMXhsQgAzmvB+WuVgO2tErwi8sqzR1FZ8QuHIujnmL0yNw4MNhDJblQyTpidvUuUQCVy5dtAcKqFARx+2u66BUWMbY6LB1XEaAKQdGR4axsrIcGW+SLbO9zp4+Td6R8Q1R5xLUA2LuPX/ubGRzYWIp2Senp6YwFXiB0cIb/VHS8JVL4F4NCXIxJDKmSXmvnj8LxoCU6xjpR/vYysoKBq5e0QCx/M4DpmmsawMDmJ+fj6QTRx+eON7wOrCa06dVgcB8Po+77w5363fddVcs6Ph5I32Rjq/Iy5cuYWhQui8LJ2KbhAEA3nv3nYhJDuvxS5DGu+/st0oHbDQ1NaVcY8k0aB5m+qdOfqzs/sUBHQpipEu6RjqWdElnkxDR9CVdGxjA+JjdhY4eRwy2I0cOW/mwATDOOY4HenK2cObvmelpMUHXCU/pwvlzWFkpqHC2o2xKyhYe2QnHpV8sFnHhwvkID+axosxldHRE6aZRgGGSjP/xyY9iw3EVNnx26pSuQ0rzMWludhajxG4aXRzNOuJcuKSL0zll0ONxLnQx6cJJ+6ZMXupkVcolDAxcrduO9NX4+BgWYiZcrbjB9wvnz4W80VckrFzEOOe4cP6cVi66oOplEkBqcmI8BFdGP1AXQ4L6GRwaFJsBAzDaWolBzGE6L9JjAskn+KxVqxgeHoo89412lc+npiaxtBiCddPGmwQvMvOrV3VfvTQfCZaqVXn062L4mmjTcmDsOh1INEH6ibyAVSquYC7QfwUCsyccSDiOdnQoaXxsDKVKOfZkQpvbAFwbuBpr6ForBxcbJAm+TeEBTaJSEcfBTq0IVEIzRIyUUT4DRH8vex4K02NgzH7rWYQDarXQ+POwdKenlS/8vlLxAy86DOVSAXNzcyKssanS6if4nJqcRK0amjiSbUkvktH1djQAmDbpmIgbzrHSTzatF7kZoyTznJ+fw8ryisqXFtgEjpwDM5Pj4FyoVsk4cfMd58DMhGjTpBualDFDy3oqFgpYmJ+3z8/GJwBMz0xr5pmuR2OBibZGaDXprtpYtLwkAgD/5b/8F3z+85+vE+Pnh7SOZ5EYAKITV6oVJWUypWP0OFbGr8bo34QTgN2DhbygUA/gSZ0UMy4FQzI8YwzlctmqU6XZtbOkYUtbrzs97TieaZ1WCO9mufRn4tMLJGRmmBAEhPxLw7KN7nYqlQqShBddomoPn07H60pSSYnObBgmbmKR9XI9cClTL5fLSs9Tr29r8vA9L3R3ZrBoSiZkPcbxEMc7QEEcQoDBufrNGJTOC7PwUq8MNp5VnQfp1GpVpFKpuvzTS0BKPzFgPrpp0Cd36d4xzNcyLgiPJliiZRQLYAhiQt20aF9UNyUJM16tZu2PmkN7mQZglTSb41PyXKtVtSMmc27R3KtyHkjJU6o/UtBkKz84j+gpm3mVgyPSZNJBInCnVwn026ShaClhA0KdQHBfcxtX9QPpIdGbM3PNpOKP07S5HkAqlY548ZB1AkjQIZ76Xg25XF4LE+ljAMplT4wTl6E1l1P5RoARDyVhK9UawFwkUsmoEWrCux9I9jIJB9mM3SUdIABjIQDe6aQLcB660yP1YN2UQ+hpZtKZcG6W5Q3CSAmbNDieMU7KyDBWv6XEuVaroaWlVaXtcR4ZJ5SY4yCTzUaPihHOTbSOanABx1U6fioMD4ElHUsslYPDGFJu1CSP4oGs9fmmpgjgJXuicJMEIJmwu4GLW9dWYx6GOlK4Hq1KJ/BP//RPMTMzg3/wD/4BALFAdXR04I/+6I/A2F/dVMx/K5ITezhBi+eMATt27LSeu8tGNiWCDz78iNYJZFg9v2Ci8DkeCsLHNTSN29vXh+7162OlYSbIvOvue9Dc3BzhMQ4AP/65J2LLZfLf2tqKu+6+py7AoeFvvOkmw4et7aguKDOAJz//Ba18Jq8m4Hzyqc9HAIBNGsgYw5atW+F5XpCfCb6iE+WDDz+CXJ0jfkqMAb8gzfhYQJdZ5vb2djzw4EPXrUNJn7n1dqXfJfMTadtB1Fe++nV7Xav4zAj/NVH+mN0rI+XYum0b+j1fuJci9abSNIDG4597QtkgdCx9wBx7X//mtyI8mO0qJTPt7evw6GOfDdKJH3cy/h133KkuDtD6gKX9AeAb3/rFKLBjTC0ufiACk2P/m9/+JdSIO724dmIM2L59B/r6t2KxWFMLnKwjP8Cb1IbZE08+qVyixREFgl//1i+iUvPV4qNdaFCbGPGuta0Nn/3ck4Q/qXcHEiesy8/cepuSxJl9ii508stXvv5NgFvMoHCudLakJDCVSuCpX/g6Fos1lGtiEcuk3HAlDfKUt2A3b9mKtlyotiFtCyblZpLp3fqhRx7V5wgy/9CyM4j0v/zVr5ssK2mZSfmmZjzy+OeCY2y7hEmVlftIdmxRNg59DiQooOehniwAFGoeWvfch1QyqV8MIcn7PPSv6zoMn/+Fr2j5Sv4lFSs1MMaQSbno69uArZs3oVj1NZBi8i7pjrvvQ1MmvGjnOHofoW4rGQO++KWvYLFU03gWwF7w7BPJdy6Xw6PE9ZpsIzp30L6+Z89elKoelkpeeIxLNl2MMXA/9B3cc9P9GDw7hYTj6JtTFqYr24Bzjs23P4T5oSVhmsfo55Q458q0mOPoc1YU9Ipfd9x556rcwD3x5FMNh12NreNVSQKPHj2Kq1ev4ty5czh37hyuXr2Ko0eP/pVMxfwsiMwpxnOG8bGxyKJSD2yMEvMwQNh55Hcar1wuYWpyMgJWaHwqUp8YH8fKSijqNiV/NK44kjpP9MHst2fppzrCskxYJl+zs7PKrVscgKVlv3D+PIrF4LiD6yDaRic//ijCh/ld/hUKBVw4f94KGM0/ABi4elWZTYnWW3RQH37/kJWH8Jke/1Dg1i0aLhp3fHwcly9diq0L89FHHx4nR/zhXxyGfGf/29YNSWSBhvCMcuTwB0rXR+eDa4sjAFy8cAHDw8PaMbAMSwG95PXN11/TVCVMUGWW4dVXXrYXisSRUpLJ8VGcPPlR3U0Jff7B+4dCTy00DCmDzIAxhpdefD42TVpWzjmWl5fx1huvR8pE60KOa58DZ06fiuhsMSMclfK98OMfK11MJa3gkSZT9PxzzyiQzhHtA4IPEXt8dBSHP3hf8anCGEBD9qlD7x3AZGDKidaDBLIq7YDRHz/zQxU/Wi9SYizKlmA17H/9ZXDOQ2CYcCN91+cCNFw+fwYXL5xV6VaDvpam1hpkXMbw0gs/tuvA0u88lDg+84Ono2HJuJBxGQMmxkfx/sEDUFLxmLDVqpAE8plLmJ0Kj/ccgkgkoJLfC9Ua5k6+iYTraDqBDCFwqRHjz7VKBS8+90ykbKQQKFbExZuk6+DihXM4feqkag9ZXzS++uQcr730vPLWQ8eDF4Ath8RlAH7w9F/qt3fJnzQPI4+5pyYn8c7bb4XzY7BWm/YqZfkPvncAI0OhOgMlmqf8vHDgRXDOkUrYj5jpZ61axal3XgFjiBgsN+cQxhguXDiPjz/+SJsPaffR4jCGl196sa7bVpO+//T3rM9tNB14yGqEVgUCN2/eXPfvk0Bao5Ddlhx4V69eUaJUG5ijcQHgyuVLVkmEbfJYXl7G9PSU9txcsB2iED4xMY4y0amiwMbkjTGGwcFrRtrR8ss0PM/D6MiI9b2tDHOzc5pCOJWymOCUMabpjtHwphRO0pjhki7uO+ccxWIRCwvzVsmPSZxzzMxMo1qpxtRH9Nnk5IR1h6YPbvHd87xQnwaIlNGkleVllMvl2HBmCWZnZ+AGkg1z82KLPz8/R/gw6lD+BY9KpRJKpZLSddMX52ja0ke2mPSiZVPpBC/n5+fU7fOIVMT47fu+2vDYKAQnkvciPM+rW9c0r4WFRSGZllICHr6jbSepQHmRYCom/XK5DM+PHr9QsEslIIVCQT2gixsQHgdTSeDyyrK4pYoQeMq0ZD50kS4Flx9k+6uxEymvcDFnk9B7QT1RqQsAFAorSCQS6p1Mh/JD06tUKprupCSfS31CRgBf2O9qNdG2maQbGRM1Li4S1GoVuMQlnTgOBtJEid/c9GSC2+pxxMmXmldTc5w+7mjdBnlXKprnHdsFTwFuRT9x4CGZTAngH3ZGrS0lP0sVD9znSCac0BUZ6VSci+Nzz/PhOAy+V1XH6nFUkiAw4aBWrSBpCW+CWMlPrVpBKlXHbZwxR9GNjQSu8k+6SZT1ValUkKCmnHhwecuoF5lvtVpFQqr6sHBjEQfwfB7oTSbDOZUGoD8rlSo8R6iEmOPUbCPOOarE5AthPzIPS6rEqG/9NOinrhP41a9+9acS5mdJtINGgVQYrlqtxp7T2yYPN5gQTTAkP+liX61Whe5FHaBA0/B9jmygNxLHC03H1JGSZHtWrVbR2tYWK5kz43HOkcvmI2WidUnTSqVSyGQyBLzo4UyWTDMoZtq03J7nob19XWxYsy2SqRRy+ZwG4OLqhTGGtvZ27ZktvCyX59WUGzjz+MlGiURCudOzbRTMibcp36S5pItrLvm8K/BaYPIs7Xc5DNpC0tHRGTmqM6XdkvL5PPL5plgJFCBv24r2XS9NLUFvwziwvmHDhshzs4yS10w6jY51Hcb7eM7WdayLqHnIhQjQjSczQHMvpp4bi5uMknBd9Pb2XbftJXvNLS3INxPzMySMH9Rd6BEI2LixX+g3Grxo/CBst01kQ26bJyQvjDHkMll0dXVrvNBFSwd7DJ1d3colHSfp0ASkBAcQrq5MXmiZHQYFjFLpBDZs7Be3gytCqpVNRZcoWUftbW1oaQvHaiWQBKbcqBtEzjk2b9kCZtncme0mgC8XXmDMdxIYG/0lm8uhp7ePrDNM6yOSajUP4D6SLZ3IN+WV3pwJMiitVHyk1vUh4TAdXBIQtlIR6i6JhINkMoHNW7ZZ0wIQGOMWdZ50HbS3t2Od9BgCfbyaxDnQv3mrWvfUc8h2oR5wRDvt3LVb8Urj+JwrNQf5PJfPo39jv1X/zsZbb18f8vkmmNXCjHgI4jX3bAVjDPkUVbFhWly10XUctPRsgeMwzWczmA7y5deOjk5tLMl3ZlXK/Lbv2Kl7sOHR0ztKpom2epTPR3FDHDWkE3jo0CH81m/9Vt0wp0+fbjjTnwUpZC56pxh4TnSX95Wvfo3EibqjMj+/8c1vxUoKzYV4w4YN2Bj4XDTTpc8k3R/4T6ZkAiIa56tf+7oGFmRS0jI8pXQ6jc898STqEeVt1+7dmpSSlssGph597HE4TnQXHwJm2iZc6TswxsB9P2Jnj5a3s7MTXYHpHPouTop58823BNKLML0wXLTMDz/yWGTBlyT7jCyD6yZwz733WXfNNtqwcWOsdEyvH8HYrbffgXQgvdD7CgCi3yh3o3fdfY+WTj1qbm7G3n1iYokshIzB3Htv2boVTiJFdv5cSS9MTwoA8JlbbzPSI+kb+XHOcdPNt0SeqUUlKIsEa+s6OtGaT9dte9pPpa6vJ+8UKL5gpX033KiAvgI7iE7+HMK26Jat2zSJSNjXwt/y/fr1PfCcNDxLPcjyUc8QO3ftFouTJbxZbt/3sX3n7kgYXQrHlQ5irrkZiUSbVibJByP1I8va27dBbZIj7tEQShtlpM3EVR8dI5xzJQGrVWsAY8hn0+jp24AS58LwMQOySQO0cY6az+EyJkBgS6t6VQ1MpKRdNwIiGIANG/tDPghAo/Uq69nzPGzcuClSb2Z9yrfpdAa5bE6FCS+G6CZmalVxCSbV1KZdatFwtLEmFCs+Ek2dytUZ59KtYBhpvlyB73MkEgwJx0H3+vXWtCWVqoGkNeUim8sjJzfhxkaXkuSqs6tLuzBDZ4qIOR/PQ/f6HrW5MKnmy9vw4n0ikUBLW1tE5UTmLzc7kpryTUil06j6CI7KQxM9DLpXHZ8DTrYJWAFygekhlRILPxmEzVff95HItcBlDJnAwwg1caPWryBqMplEU1OMiTboZoAAoLW11XKJyxodvu9HhBP1qJETEkkNSQK/853vBFKA+L9/9I/+UcOZ/ixJn5yjEpBnfvRD9ft6i2ipVMLLL70YeW4ej8pJ5/SpUzhz+vR105Xvn3v2GRSLRav0xCaJe/q7f0me28ochh0bG8OBd9+JgI64sh86eACjo6NauerRD77/tAIm8eUUn9VqBc8/96zKtx44AoDz587h1MmTVhBqy+/ll14UR3CR/I06DdJ67pkfqh1hvXphTJjxOfjeAfEutqQhHT1yBKMjIzFpR8v/wo+ftQBp+anXk+d5eIX0x7iql/leuXwZZ8+cCQGGsRCZ8fe//RYKhYK1nBIk0c3WKy+9IL4b+dIyyOdzc3M4/MH7iheTV7nmyfo5e/pjjI2OxvZDs2+89srLESmEtV4ZwLmPN19/NQIm42h4aEjp12rpkXQZC6U4H7x/ECuBagWdhziITiBZTd9683UtvXq0uLiI40ePWMeE7dn5c2cxNjYaPkfYD1U5CIDd//abUEr0BJjaeOOc4939b0WfqzzEb6/mAcwBCnO4evkiaj4PjjYd5FMBoOOhgfGa78N1GE5+fAIry0sqzYq8GGKxEwgAB955WxtfZN3XxiMDUFxZwYcnjlmlREDU08vVK5cwPj6qS5SZrsfGOVALTMTUJs+GaYJH8gHCDcFyxcPytY/gknIx2QZBhIVKVd0Onp+dxqXADJVtEuAByHYcB5mki7OnT2JpcTF8T8Jx8lDW05H3D+rpBZ+eOvYNeSuXSjhx/GgAmoyNIAv1CSVAGrh6BaOjI2ouoXMKzUvS4Q8OCemqESC6yRRja+zch+CcI592rOlJSTbnwMzMDGZHrgS3g50o6gNdU4WJq4X5BfX7enTo0MHIs7j5rFKpKPehjdDIcFTVK44akgT+7u/+bsMJfhLIPGakv00dPDo50AmEc6GbJpE8fW6mL78XigWsq3OMaX6WSqXoEZbBE43vByYS4spMeRP6CGntHeWVMaZJEIvFovXWtCnRVPwYthPNupELo+9zFIslpImjdhvR9EvlUsRRNy0n/QSEztb1bkuZ4EPyZ2t7SpVytI1svMu45bIoa/R4jihlg+at++aUnyaIYhCbknSMKgO3TFyqXhjAGpi0ykGfMaUsIk3ddIYGIki+vh8uLFoblcqqHulu35R+ylurlXIFKXJMbo4/s700Xo1POZ7U2CAmhWz1RokxhnIlHEsmaRKDoI9VK1Vt7FH+PJ+DgSER8YYT5UWOTxqyUi4LLy0Gv/q4COusUikjTXinfY0xwGWGcWLLOLBJRwFh7sO1eOhgkCBc9MtatQa4LtKODzeRRNX3FQjMSUkgC/UTPR/IJMQFiDQxWVSqCXCYSsQr8Uv9MhA+o+M87AMSCMm8aXv5st8AgT5YSuuPcvyC5CcBbyrpIJlwFTiUkjBZN7S9ihUhM06TSzImzRXF5Zpk0oXnVUPj+EzX3eRc2hQUksCUy1AtVZAkY89Wd3IjwIHI2JJ143MiKQvKVa1UkE6nhS4e6UcyHaGmEhpurlQqaGpqDiWB5jxH+gEQmn6qejxsI6MMsk0831c6gS2pROTI2JwCi6UyOBNmeVJS91RuFC35VKvVoM/IfMOy0ghxa0k9splcq0fVSuX6gQJalYmYTzKZ4CI6WMV3qk9jLkCM6c9d10V//6aY8ObujqO9rR3t69Zp+dY7ttuydasmTbQdfdH0d+/ZGwMuonlkczn0bdhgTVN+p1fd+/s3Kb29uIWWPtu77wYtrWh9AGIPDCSTCezatZu8E5UdBzC7u7qRW4WR8r179wXun/T2EWkSw7jB8xtjjiVN4hxoaW0NwEh8/jTutm3bNZ1AOtlTniTdcuutmrTIfC/j+lzYY7spcOsWkTIi2hY9vb2a7cfrTUw333wLksmkMrOiQnNdp07SbXfcaeVX4yvgs6W1Fbt274m8t/VnANi5axfaLbqbcePprrvuMcKKT3mERMvjui5uu+1OWTQD7OqLOuccfX0bQJUBKMvyOwW/t3zmViRTaT1O8OlzDtcRx8Hy2b333qcAJAWulA8WlKm5pQV79t6gldWcazgPgceu3Xs0fVyZqgZEyPN77n9AK7tZJ5Qc5uDue++3vpNSI59z+J4PMAcd6/vQv7kfo+UaPI8DDMgkQ/dejAG+JzxMJByGG2+7AxmiM131xLFiItAJZMZm6f4HH46RYkftPDa3tOCWW25V5Q8CRuKygLEdu3Yjk8miTOYSLUyQT60mjr7X33AnyZtIxchGUNZvteajaeutCtyK6ZFcagQwX6yBc3HRo7e3D2mXCBQQ9g8JvCoVX9XvZ264A/lcHj4jBpHpjhS0XwD3Pxyqy9By6mUR35ubm3H7HXfBC+rJ7ClecBws09u1e4/SgWZM3BxW65HRRgDwyGOf1eoiUumEfA607boTC2Ugn0xowSjQl+l3dPWgZcN2eIyFHkMs7RsUDbfdcSdampsV2NY2bsywLwlE1LHqzb+5XA73P/Bg7HuTduzc2XDYVd0O/ptAciAAUQDo+z527NylfusAJ5qW67rY2N+vLeTRCTeU2rW2taG1tTWy85Rxzc8+opxO05bx6EJQq9UUqLNJFWnagNBfWLduXeyiaaafy+VCSU2MRITWaWtrq5ZGXD4MQt+hta0tCKeXl6at0nKcVXmqyWazVld9QAheJBCoVqtoyjdF2lTLnzyTF4nkJGJuFEyq1WoRo9uU5A7YjB8FQ9G0y+WycmAehosCX0mlUin0jWp5b2ZRLpetA0Eaigbpo7VaNTRrQhKyAVgAKBYL2kIcV39SV21pceG6fmBpGvL4lczJEUAX8lJErVYNpQ5UgmHha3F+PuI1SJQtumAD4sY3Y/ZbyZ4v9L2k8V3P8zBHPJ3Y4tDn4vZ5SZVR4yf4DHUCGaYmJyJSHUkOiyYyNTEBeTTo83Dh14By8FksFrE4P6+kaTSMrMblak1YY3BdoLSAaqWCYs0Lj4MDSaBMs+YLnUCHMYwMDyPhuqouK57QFaQAWpLPOUZHhiM8mvOqLPLiwgKWlha1ujX1JOm7sZER+J5HDPND68+yzF5VSAKd4owCfhKQ03xoHRWLBVSXZiISTlqXiyVxMcR1GOZmprTxZFLVI36Gkw6uXbksbjabQgzKPKHBgSsqjNmm8mKYTGpxYR5T01OqjCZ4FHUa+g0eGhpEJZBiyf5Fwazot2Gely9dtI5PunGRz0vFEpamhEm3HLnYQoEgpfHxMRSXF+A6LFQxCBI2+zvnQrVC1gOM97Z6PHv2jPY7bs4DgIWFBYwMD8e+N+natWsNh/3UgEC6m5RkdvpSqaTZfDMXfRMYDFy9isGgsk3AE82f4eB7B8RCaiEbcDvwzv4I8KMgk+YzOzuLM6dPRSY1mj+lc2fPYGZ6Whuw9Ui6mKM8xpWjVqvh6JHDWr71ROAjwyMYGhJujqTZCLvUTsT/6MMTaqIw87b9Pnr0iBV8yEWeThiLi4uafldcWeXzgYGrmA/sz10vDgAcO3Y0th55MMvR3fTHH54AgFgQK9PhnGN6agojwUJn25jQ34CwE1mpVGL5NVvrZGADK36/GsYpFAq4cvmShVcoPmi+IyPDmJudjUoiLGVlDDhL+nqEhwhg5jhz+pTizwQtCsMG7xfm5zU9uTDdIDz0uhkYuIpKMK6j0m6oMSbLe/7cWZUGXdB8HvgNJuUql8u4dvWq4l2fC6L1Mzk5gfmFecAoo1Y3qg2AixcvRMJIPhQIJvlevnQpkraJFXnwb2lpUfPrSruZ3HTNlSrwah4cx4G3MIVSqYyVqpAEuq6DXCCxkZsjL7hNmnQcDFy9pAGsqsfhOsFROmGKMQavVtMWUduNeMU7gLm5WSwuLFjqj4AGWY8AhgYHIqDIJJ9zdQmGLU2o/OQFmdDIsmhbyePKygpqhXnkko4GhOjcVawGl2KSLqamJlAsFkOejXIKczJcHbcPXhuwemuylcX3PIwSN4PaxlfWT1BJnAMLC/OqHsWJhT7+ZL+Sx8Gjw8PKRJsYH2TdRbj5kDQ0eC3kk+k8UKAPCDBdXpxDMukgn0xofJvEOcfU9DQqpRIchyk3czIfRsIhyHNoaJCoh+n1GG7ewufDxL5hvfURAJYWF7FI9DavR9L1XiO0KhA4NDSkFt/33nsPf/RHf4Ql4iT+k06FQkGZNaEUt0gWi8WIrlkc+OJc6BtmA08U1zt+45wrcwYU+JlpyvxKxSKy2VxdAEL5K5VKyOaEuRrz1q9JcXzGLcBUN01bHNQCpk8cpXJJuTmy5WTWFdXxM8GzlT/LO9txGmMM5VJJuCHiPLYtKcnw9H29trW1o55J+FVz02ZsYsKihRuCcqVM3EVFQQKdGAHoOqf1kB3lP4ZvtTCxMO1McPsxIjnh0UmvUi5b3ZeFv6H0jgBhmsc84tcl92Eb+L4P5jgREGKrftkf0+lMZKIXEjqSB8L+LvXwdH709jIBomvo/XEuXKI5UpKEoA+QejE3pCbvVG+T8kMljz4pj+aakiRkgn0zPwpSIu+Cf9Wq0NmjengyqDwOnixUwH0Ox3WQcjwk0ykslYVfW8dhyBnecjxfANSk4yCbCiXBns9R9X3l45Wu7JzzyJxhSoHCsKI8tWo1oqdMJVJmXXieh2QqJbx2WBLlnIsyVysAc5DPZbT2cR0WmabkxqRSrsJJptGUdiN8yCjFwOB2KuGA+344liwb40K5pmwK5lOu0GXUChTznYuTj3xTk+r70sSN1I901JjjwaevzJypCyNcJRdeJiFmqyTvtHxxlM5kAM4JgI72e0mVmgeWzAqTL27MHEwAXs3zwRKpULrMVJAwOAtvf2fSmYY8gHDO4Xkempub9XSuI1xZzemXTRc3jlalE/iVr3wFBw8exMjICH7pl34JDzzwAPbv34+nn356Ncn8TCgcyLBO/oCwVfeZW2/TJ05DIkU7zdZt2xSo0/PSO6BM54GHHo5tbFNixrlwMUfTs8WR79b39KCjszOSvvxNwQLnHJ+59Talm2byYCvP45974rrhJGWzWTzw4ENWPsM0g3cAdu7chUTCtUryTCkWADz62OPEKGcdQBWk8dQXvmgNYwOQ3evXoz04Jq9HLFhpbr/jTqRSKU2HyuSf0pd+4ct139NZL5lM4nNPfl7rG2b/pe927twVa6QWCKUGkj73xJNIpVIoxbiLkuBY0le+9nX45JkpPZETPGNAZ2cX7g/6gA5+xCxtSrTvuPOu4FkUMAFikZAeCRhj+Po3vmUHQlZQyPDVr31DW1Q4D0GrTzsjhFs3Knm17eKpJOOzTzwpTM8EfMsLG/L4UMaVvH3jW7+I2YKn1Rsg+PA4F27PgmdtbW2a7pBcaM24sq1uu/0OZf6D8kjrih7jfuNbvxg5CgzBXbTtvvGtXxQ6oTwETIyR+pA8cWDT5i3o798cPQZmcrFmGF+qBAAqidvuexDt6zqxOD4OzjkSCQdNge9pyYVwyybq6Bd/6ZexXBK6cDXPR9XjSLrBJQOjazQ3N+OJp76g1R83BhOV5N1g0a2ldW72vM9/6SsiTDleId/zOVArA46L2x//kmoLhxyHMvmMtF+ipQPNm2/Cupzuo17WJ+cc5cDXcjrh4u577gslV5YxslSpBcfBQD7l4vGvfl35apZtx6T5NCVOFR/JVBqfe+qL2sY55EPf3DMG7A70fKeWytF9JokjN0Sfe/KpoA7sJw4yX/n5C1/+qnC/SAPJdQ6hoIQDaGlbh+YtN4MxKHM7tgzkhnPLvpuRnrsmJIFkYqVzCB3XX/ryV7Qqp8uZmZPjOPjCF7+kftvMuFHasnVr/EsL3Xd/1LxcHK36ODiTyeCFF17Ar//6r+Mv/uIvcOFC9Djh55lsDSRpbm4ucsxYT6ozNjqq2XxTa4m2aIcZURdzNsBAw66srGCWuDqLS1M+GxsdtbtEMgBUeCR1TvFej2S88+fOxeZv0vT0tHItRdMJF35dSnLp4gV1TM6N8CYfQOhiziynja9yuYyzZ86oMPTTbAsGYGhwENOBa7+4ugzzA04cP6b0wUwgZes30kWXfB9ZZEiU2dlZq+kRk+Qif/bMaeG9RJWThJF5knjvvrNfAwsm0fJ4nof3DryrJaIAiAEIGGMYvHYNV65cjh079CnnHO8fOogSuZkfF8cPwNubb7ymvaNtSvsaACwuLuDI4Q9UGnLhtIFYBuHWbSzweCN3+YoHCgSDF68GZnmYEYYBpK+H4+/lF18A5xxSzUgmL6RcQq9NPhseGsLHH38EIARnCsAg2q4H33sXc7NzVime7NNUEvj8j5/V6lECOs5113Wcc6ysrOCtN17TJKQMUftnMtLZ06eUSoAOhkNJ7MyKAHFuwsXF999CpVIVho99eRysz1FScpR0Gf7iu99T9VDxODxfSAgdikqD8o+OjuDIB4c0cCD5MdgGY8D7Bw9gbGxUm4/CPs40IAAAzz/7w7BOSL3R9Cs1H6hVwBnD+Im3FICg8yFH+EyCl7mhyyhOXEFLOpR8q+PgIP1SYCw6mXDwxqsvYWVlRYRVG62QD2lOJpFw0JxM4tkfPq2DuiCsQztyQDPTU3jvnbciUm8/6LtmXzh8+BCuXRtQdUjnfSDw4AEBshhj+P7T341suijopptcz/fxg6e/JywGGPmqPoqwPw9cuYyFa2fgBjb/EPRfG6jnHDi4/3WUlheQcJky1q6PK70fff97f6n6t0rH/Az60MLCAl55+SUtrXqCh+PHjuHSxYux70167pkfNRx2VSCwXC6jXC7jtddew6OPPrqaqD9zovVLBwVtsPGxMaws667RTFI7C85x4fw5Tfwbpqnr7Mnfl+WEGAMEaR4LCwuYmZlWeem8s8jvkZFhVCqVuh2J5jl4baBuWPquWq1ifHysLs+U97nZWSzHuACjYFnS6Kjuf7leDpxzZa8w7iiQ0srKilLwricJlPPIzMw0arVa3XJSKdHI8DASiSToPGSTMMrfk5OT2rOIlIyH35eXllAulxsC3pwLnUCbbg+Tq4tRpJmZGdEWPGYhJ2UoFosoFoqq7WwqirT8i4sLVjNBQLiTpzQ9NaUuesQVVZnkYAxLgQs7rYzQx6d8VigUUKvVEEohQ35V3cjyApifnwdjds8SOiAUY295ZTmyyTLHqHzu1Wool8tK506mCwSGc7mQesr2oK70FMA1eJJ8c4gLDVJKbvYZGV9eXnAdhmKhEPIs0wq6oVITCeKWSyVdmmrUj8ovSGhlZRlOsNHUgDYL+89ioQr4PhzXAcplOK6L5Yo4DnZdhnSS2GeD0PsDgARjauPIGEO56sHzhSTQMcThDEJtwzE2vbTttXrkot7TyowPmSMQJYbQTZfcpCgJKamkuVIV8GoAuDrKlu9N1QD6rlgqwnGTaEnpB3ccofu9SmArL5N0lTmvEHjoet/LAQh0HIbmVAKeV7PkzSObUkCoSqTIKYx87UlwqlSLxF+5VFJu3cw1l0O3Lch5aOYsDkiRKVK7CCcBmgCiYVzarqVSET5cOI6UFpO51/JZLJXAnQSSAWiMA/eqvAilgzR/uVmidVCOqL+gLpVX6WLO5/a510arAoG//Mu/jJ6eHgwODuK+++7D2NgYcha3Zj/vZJOUAEJxNBfoyUXDRoGYzzlS0pbUdVrR87xQ94YsVJIPCgwZY6iUy2hqbr4u6JLp1Wo15PP5CACNo4zlGNssp/xdKpXQRtwz1asfueC1ELdYtrTNRdLsR/XqU+pSWMGOQdVqNWJpnUqLVDw5Gbsu8jHpS3KcMG5be3ssH+bzWk24mIsDiSKSno48mg7TJJOoATiy2aymnmDT9aQsdXV1EQv7OhtqPQ9eeJ6H7p4exSKdCOWCQYFkKpVGa2tbtHwI8Sitn7b2dnL73Bot5ItzzSOCrV9RUOY4Drq710fbCaEEQ8ZhEP2rKegDdJzaNiqcc/T29hl5Sl6i/HmepzxXyIVFvq15wcUQoh+VzWbRsa7DKsm10bqOjsAOZXTDKHnxCH8bNvarcHIRC/W79PycwD2eJKnkL/uDyV9LSytaiUcPSr4vJEBLpSrge0gkEti6aQsAhlI18GubdCM3YquByxeHc+UajXOOSs2HD2E/UNlUVhsWjnQmg+7u9VpaclMgg8o6dhjQ1d0dmSOlhE6LH1Tkps1bNAmjFi/4HFhcAXwPSGawMTBFJu3kOSy8Kcw58X7BOZxUHqmmFnRkQlNU1A+v73NUa+J4tzXrom9jv/CQBMmeLglcUi7mXOQSLrZs2xFIDOkGKVoxPufIpDPo7etT5Zdl8wmYo3W1fn0PmvJiLLlEAKCvfeFctT1w1Sf7olmH1JwTYwzbd+w08tX7IE3DyTXBybQhGRwFxwF6Set6N8BJpJBKONrcRluYjq+dO3dp87Oxv9TCptNpbN68hbyzMEOop6dnVR5Ddmxv3ETMqnQC/+W//Jf4jd/4DbS0tIAxhubmZnz/+99fTRI/M5KDS0PppMNzznHPvfdpceTzOInDN7/1bQW6bOCJHku5rouvff0bkQVFLFJRm3hbtm6LDEiTD5rHY49/NpJ/PaLu8UyeaZ05DkNTUxMee/xxazp00ZXxd+/ZA8acWOV7k7/PPfGkpuSvJDRGWWW8J5/6fCzPJnV1dQUu5uIHGl0MbrzpZrXTtZXVBHCPPPpYJK148OgoXcnrtQ9jwo5fMpm09L9oHwKEmzbqM5LywiF2fLQe7r7n3jr5679zuRxuvOlmJRWSQI4Wg0bp37RJuywRB5BkOW4PbArScpnl8Hlov+72O+609j1bvbS1taGtrR1S0ip37dolDwJ4Nm/ZipaWZjgOg+9xDeyY5QQM/TGmnzLIxc1XUj4Hu/buQxnQ/AEzJiSBQAgCOefo6OgQbgMRLICRDUeYFwDs2LELyWQyslGTi54EKiyItGfvvnDcQd9kyAsqCOo9nU6jP1i4OE2XzFM+58LwOAO6utajuaUlKtnh4XHwcqkK+Bxu0sXOHTvgc45yxQM4kEo5GghkACoBCEywEDAwxlD1xDydcAnwJXzl8026n1ZYKEAejDlYv74X2Ww2IijQ6pHwtWnzFrXGKBAf1Kc0xH5+qgR4VSCdRV/fhgDoyflO/PmkbhQlc0gkUsilXM0tnWTZD0AwALRmEtjQuzEso2UuWi4LY9yuy5BwODZu7NePK80IpO8nUyk057vDMRc8l5JtaZ5HptfW1o5UJoNK2bdsNMUGLMEcuMHLnsDfuGRd1Gk4tpU+ZxCmp6dXtRlgaVcGwBcFKzMXTjqHdOAyjsv3iiESn3Nkmtvgz3CkXL2Py3lPLw+PbDIYHUwGOY6jbAarsAbRcmdzuVUJ3Gja16OGJIFXAxMFZ86cwejoKM6dO4czZ85gcHAQC5Zr9D+vRMGFPimJyn7pxRdQrVa1hT7uCBGA5mJOpG/feQPA2NgY3gvci5lky+/wB+9jZMTu+sXWYZ7+7l/GSpjMBcH3ffzg+9HLPNGFQ3y/cvkyjh07auXFFv/NN17HTKDPaL6n6Uo2v/+972rh4vARYwzLy8uGLkUUIFM6dfIkzp87ZwWAtjgvPP8cqlW72ZSo1BP40Q9/ELzU+bHRxMQE3j90UPWTaF/R0zh65DDGiWs0xuqbivnxc8/E9O9oWWu1Gl5+6UU16V9Pcnz1yhVhggjRRUIthiwcA+++s19ZDtD6lVFWSS+9+ALhN9p/5YIBAMtLC8pVHw1jxpW8nD59SplyQmR3Hm4QJb3x2itq86faJPgnd/d0UXjtlZc0UBnWKVQ6cpGfnBjHqUDHTwG7gGqeD+lHVc4Fx48dwezsLABd+hPWVcgfA/CGoSupykny8gIl9HK5jPcOvBMJK298OsGNVVm2a1ev4NrVK5ZyhnWv6p8Dhw6+i0rVrqIi45bLHsB9JJIJnPhA8CIlgalUQi26QZIoB/btlhfn8fHHH6q0SzUPPgcyicC4NAEunHOcO3sa01OTsUBepi/fvbP/Te0ZfWf2Yd/38d67+7V5gUq6ZPixuSLge+C1AuaGBxVvDohUivQH2S8Xrp2Bw8vIUP1IUqc1T7qBY+jMJ3CItKltrC5XRP0mEg6qpQI+OnEssjmjAE/G4wAGLgv3eIC+idKk6UyCJIaD772rbxQZySMoo7RHyRhT/dGx4CdZ97KvT01OKnNLWjhG9JxlBADnz56CV1pGKrhx7pqdgYV5cACnjr0PznlgsNw+P8p2LpXKOHb0sMrSVodAWKbhoSEMDQ5a06TlkHT0yGGrWbQ4OnLkg4bDNiQJ/Cf/5J/g+eefxxe/+MXIO8YYrly50nCGPyuinVv+NiU7S4uL2m5RPjcXGAmkpGuWeqBLUkHTMaFgK8oLIGxs1XN1ZgJHz/cjkhFbGRhjyhROXJom/ysrK8hlc5G04mh5eVmZnzHTt4K2GOmGjYrFotoR2aQ/5vNCoYDe3l7tncaTkb50/3S9MgKB6RESLk6vTvFeKCCbjd/N0QnO5xzFYgHZnG72h4IPFY8CFSMt+V3GkdGKxaL1ZjtNkxZHmEPKRvXAZNoIDcWK8IVI+qbEiT4zFz+zKkWfFt8rgaklW180pYKAMJ/U1bVem5wBAgBh2Ovzfe3SlAzvMHqcJZ7XarXIBSs77wJYFUtFpNIZsfiTjQDnoX4UtUlWLJaQyWSUBNTWw6z6mWb+5nvGUCmFfUDWA0O40TDs46JQLKI5ON6VR4GmKpvDQvRVJmaCKElA7zCGcln40k0mE3CZuAEuQWAm5WpGlBmAqu/DYUCtKky+yLYpewIEpl1HgWVVds4VLyaoo+XTJU6OkuTQ/iiLRzcC0iyPlOI5zH4zd3qpBHAfzAFa8zkl+XOCAcF56BZR8uhxjlq5jEQqjYRLLqeElQnP58oNXHsmhXGmp6GXEShWfHCfI5Vy4XsVzZwXrbdowwmdwEymW6Utx7K6cW7Uq+95gZ4y18aw5EV6f3EYU/OpqpMgbd0VHVf9WerVcR7thxSsC0k2ML9SAnPbkEo6gcmX6HiSeXAe+qLOJZ3IeKLfORcmiNT6roUFpHoenffK5bIy+dLImmrqEF6PrnfSRKkhEPj8888DCCWCf1PIrKhNmzdbFxUZloKrarWK7Tt3RoCkGVe+T6XT6Al0qijYMtte5tPdvV7pvtFj3zjetu/YEVsuW5l37IjqDMR1xLa2Niuoi6MtW7YqkGkbMOYiv5ccSXHzJfRBkk6nsY0cA9mIPu/p6cG6jo7IOx0kh+/23XhTRLHc5J8xoVzveT4+85lbtQXFBLqUl9a2NjQ1N9cd9HTB3rptO5oDvVC5MNuisWDGuzlwcwVEJQDmhJdIJHDjjTepI5GYIqt66untheMmI0eGUmokM5V1dMONN8cqMtO+LMPfdvsdqixx3VfWQWtLC9qb9liBv63vb922HS2tbQAnwI9Hx7asoNvvuCtMN2RafciLFZLuuPNuGiRClKfu7vVobu2whq95oi7pceKNN96EpqYmrf0igA6hF5A777pHSVuYEVb2I2m6JpvLhq7RgsASiDBmXAyBGNduMrQpyHloy9RGd95zr/W9BACMBZJAiKPfO++5Hz4Pbd7l07pXB84FCGQM6OzqROuGTpWmPCaW7r24iiPaau++G9Ha1hYZe3Ez2j33PxDpS77RZ+RRbiqVxF333BuRiNM8OAcWF8sA50g0d2HLpi0qHerhhK4NnHNUPB/Zvj1IZTJwHWHo2ewHVc9HtSokgW3pJLY/8JCWt+Rd8laSZnaSLlpb2tB+863hS0J0Iyl/79y9F+0thptBsoGhqgwA8PAjj2p5q8/gWc3nQkcvSO/hR3T1GkoMer329fVhXVc3PFVfAHj0hjIP/lq37ANbnEfKle73whKaY4UD2PKZezE64qM57aoxpXgxOk4+n8NdgXpN7DyAENDu2LkzYue0Hj38yKPX9ZCkh4+vR5NWdTHk4MGDkWefBBuBgL6w2OqdsVDJlD4T4e3HwtsNMGI/9hCZ5fN5dHV3hzs5smDZ0u7s6lI+FOnz8HhJXwA3bNio8W3+0Xecc/TUkY6ZlEyllBu4uDj0KGhdRwcBfPFxAGEMt7OrS6vDemDT87yIfUMb0bq16VLQxYVSNpONzV+XPAKVSlmBOqp/ZOYhqVKpRC4e0bTN+pGXieRxYnxZo32ULkT6kaR4Jg0cK7Mj2vjgJB3RHsVCQRgtZkYYTqVCYf41r6blL9OyHZFQryWWPYAiyWuxuGKRvtm9zHDOwxuzkndICUO4QNB0CoUVxbv0+csQHlFpUsZSSXk4kPzXo4X5eXCIOjUXK3lM6zrhRmBmdkYp+atFVOM3/M45x/z8XPgb0X4VSlOEi7lqtaoWUFkvVPIp8+MApqenQlAXJEk3D1pdcmBmetp+7MphSAI50gmOlWVx47tSEfXZkksZ8UK/wSuLC6jUqupdyRPAJu3al7Sx0RG4gYu5OKIgbiKwhiB/y3rQwEKwAVpeXhIWCNQG1w5CVlbEyZHrF+AEioLKMwuz9/tCrYbS3ASSqUTYHgbI93yuPKw0J11lyYHyTy9OFIPj4HTSxdLinHKpKMIaAJnpEsIR4i0E5HkoqAjLzBgwMDAQGfOcdBRhHF2ELRQKmJgY125X0/ozx+r4+DgKK4XIqYc27wWf4BxDVy8DzAlMxNQx6B/EGQlM2+RT4XFwXH+en5vHzPS09kz9kYhybA1eu6aOdxvBgRdXYYqvUqlgMOC9EVoVCPy7f/fv4t/9u38HQEjCvvOd7+Bf/+t/vZokfmZE29sm6alWq3jn7beCsHbgRBez4aEh5XLJtmiadOzoEaysrFgHui3+u+/sj6RnAj9JhUIBxwOdPVMqafscuHoVI8PDsSDUpCOHP4iYTbFJJeWzQ0Rfq14cDuET8RzxoSh373F06eJFzAU6UiaZ9QgAH7x/KNwlRsLrEwvnHMeOHo7N3wTu01NToa4Z4sGLpPPnzqJATHJQvqkEUNKHx4/F9Jfos3K5jHPnzkTC2PIChI3LqclJgJMLAHXo5MmPhS9gWz1CLDJ0WEl3dyYYlc8oP4uLSxgYuBp5Z5KUNowND8W49IpufBhjOPnxR8KUE6djmfJACsI5zp45reLK947Bs4wyNzuL8TEdMMT1NQAYuHpFmPygKyspH0NgMy14ds7wL0rYjIynUqmEawMDIS+IzhlK348Jv8ESNEpQIfkAotLhSxcvqDaU6VBgpAVnwMWL57W8dT7kRsoDGEMmWcPkxBg4F88YY2jNhYaiWQDYpSRwanwUxZUV1daVAIhn3FBaQgHBxQvnxbGkyaeFfM5x9fJlo+6g6glBuhLoLMzPYXZmRhyJknqTseVnqSSOvllpDswX/PpKBzSUGNJ+t1CpojQ7ikzSVX3LlOqL42Bx0cPxK5gYG1XxKZCSn8WKMJfUlElgbnYGy4HurksStYMdjsGBK5HNi+SBQWxglOQPwLWBK6qtbenJvggAy0tLmA6AlDaXG38SpI2Pj6FYKqpAalwgnHdktj4HpkaHwRhD2uKDmdNKglAJmRwTOvlJ6blL4z38zhiwsDgvzGLx+htZSSMjxD3e9TokoNyqNkKlUknVYyO0KhB4+PBhvPvuu/jCF76Ae+8Vok+bdPDnnWxHgcvLy5pZCPmpH92GrbW0vITmpubrStFknKXFRTQ1NUXStfPEFWO29E1J4vLysko7TF/Pn4ZfWVnRwtuIgohSkeoO1S+vLi3TwbPki3b6leVlohuhYwxbXoXCCnIx7nOsEjYg0EnR0zXTFlKJstKPiSsb/b6ysoJcLmcAivj6KRQKmj6jjW/bfEBBrBVgAIqXMI4e19xIUF6s+RnPSqUSMhm7DqFv9LV6ZOO/WCggl8+DdPu6+RSLxdg+oOcVGEf2fc2eJ12oqZQUEJvBerdIKbCQEtKsUY96X9PjF4oFpNO6i0QJcOTt4MT/n73/DrfkuO5D0V91904nhzlz5szMmTyDSCQiZzBTzAQpWZemJIt+8vdJsp4tfdQlpc+Sdd+1aNm0n2w/ypKvryWZvJZIiQEgiAwQGAADDAaTcz45p51Dh3p/dFd1VXX13r1p0SSE85vvzN67u8KqvGrVqrW4S6v4sune1apVZHM5SYeK0ck/aXj7tFarIiPo1TEmg3msEPsBAesD8vjgzBCrG/Gdpjez8G4wT9gNX5rXmQKyuQ7/dnDdZwIHcpZUTo/6dWQSAs9pSHNSLTgOFo1Ly0ypXCfQ9Fk2N9mBq77I3IXwcg6fy+C7PMzlctLlCHGjwOLXa4Ek0KDo6MgFZaLSJozbwgxIXK7YAAx+KURtU8C/LOPf9jUAz+H9MdzcSoSgUveP2zsyFlzHRjaXlTbDvN0VBtKjPnOUzWYix8RsbLJbvmzOYScZQHSzyfQfWX+3bRsdHZ1CHQoMsZAf29y4ruvrKbP6YuOYMClc2AYUgANfEpxLGQGTKIwNsHh+u9brDSDtnwplLVOWNCI6/lzX5fWuvtPxGwC0KlNxaHY/QIVt25JLulZoy0TMwMAAfuEXfgH/+B//Y3R1deE3fuM32lJW/ElC5ND5sRRhQ82vZGZ2Qsck+GmEz3bu3BW5RNIs/F333Cv4gSWR94xG1h8eCkyPNDuaZOjt7cUtt97Gf4vlUpkyALj2uuu0x5LqcSeri4ff875YKaSOvvcJbq7UMKqkZGTzCIY3aa7Waz4B4N2339HWVfkPffhnNDT43z1p8iDIpNN4z3vfr01HJzXdvWcPnzCbHS8wPPTwI7ze1bb3mBhBSOJnPhq6mBPrTZdNf38/Hnjwoab5i/TddPPN/iLsRfVN2aQqPvvoxz4OmCbkW4AENFAkUyUsH//kpyN5UpVLCN5v3rIZm0Y2xUrRGFjed997Hzqz+rGn6++fevQz/juBDnEjJZKVTqfwkY99ImgfEikvkdKh2HvNNXBdLwgXbRhuIibI5wMf/DDyVReOoFfIFiw38DubMg3ObH7msz8X5A+pDEAoOWLo7+/Hw4+8Tyo7P6YDW6zD8jA9TEDuW+xYmhD5luYnP/2Z4CgbktRLLTX7/emAdpVu16Og8PuM3bABYmDz6Chuu2MPFgoN2LYLYhAMdcnLk+f5OnKmQXDfAw9y8zEUQN3xLxVITKBQ7k9/5mcjDIWhtCdDNpvFxz7xKU65GCYy9gjBvmuuAyEExZoTpCtLoWhQZifQdezedw+6urqDyxThLWzGvIhjZqXioPf6h5BOhX6DRcPSHgVKtu8GLp02MDg4iK0Pv1dqB/6dMEmgz2R3ZVO45dZ3+0bDbU+iWfwU+/vPfPzTfEMlMYI0VGUQ59RPfOozWCk3IsawPUr5uGHvto6OYsvWUdkmnzCe1Xa6/4EHUW24KAWbBj5HK+FokM7Ibe9B/uoKOtLChlBIX0Q6k8Hmmx/E+GIJWdOUNkMiWP3s23etoA8ZbuyYygmbH9l88IEPfiiSZzN8/BOfTBx2YGAA99x7X+LwbUkCf+M3fgNf+cpXcPjwYfzpn/4pPvShD+Eb3/hGO0n8xMAmNUCW9DEsLy+HXgWkeFHdPUopJicmtDeJVQaSLR4zirkXHSPF7AWWy2UsLS42lRaKHZe5jBPDNWNILpz3XcbFMbtqfhcvhMc64gKry2NtbQ2zMzNaRkKUaLHHY1evcq8I4mSj0sGeHTt6JJG7Oxbv+LGjWqab0SdiYWEBU1OT2nKpaVAKnDlzWjrebSUlPfTmwXACVfJgO1gG27ZxVDHLI/ZhFVOTk5iemuJ0iKSwSVCm5c3Q44LmvVqSV/a/rK0zFpYQZhqColQq4eiRw5wWNbDKcF44fx6z0pGq3u6m5/lSk1dffpG76kuCF55/npeTQDY4y7Jht1oXFxZw4vgxiUHzaRI2Agib6tjRI1hcXGw63vhYAPDM008ChGjNvbhB+dhi4jgOnnvmaanNpYVXyWd8fAxnz55WdAblBZRJbQwCvLo/NOMT0urTK96wZfGe+sET/NgslKCGdttowBhSAIViEftfepGXX83Dpw0+Y2SaICvjGL96GbbnBUebJvoURl/0rfzic09xLx0AULN93bJMygTTYxMlQT9Q3OOpdSiWdWF+Hm+8/po0j4pHnKxe2eJ++NBBzM5M8zoRdfcYGo6HRuBXuDH2hs9Ie5QfB7OwqmvA1YqNwrlXtUeYrDLX6g1QCqTTFqbGruDk8WNSkHDd88tRD5jAnqyJV/e/iJXA1WTc2Ob0AHjysW9rGWemQiDGqZRKePaZp7jEVH2v2sU8eeI4Ll48r5e+I8qAff/x76HeaEjMtuSJR2gA16O4cuBp30tKxowwpeoR+8L8LMZPHoRlEuQsEybRCzwYrW+8/hqmJieDtPxn4gaKj5PgwbcCk26t1gwAsSbd4nDl8mU+/yZBW0xgrVbDgQMHsHv3bnz0ox/F/v378Z//839OHPeTn/wk9u3bh1tuuQUf+tCHMBborywsLOBDH/oQ9u7dixtvvBGvvhrqlFUqFfz8z/889uzZg3379uE73/lOTA7/c5iemkStWuW/o8yLzAieO3c2cluHSwuETsiOoy5fCv3+NWPSCCFYXV1FPr8WkUqK0guJ9ukpX1+Lpx9MMBrbEZRSXL1yhStJqxID9l2kf2Z6SpuODqsrKyhXykF8Xflkqda0krbKOIbx/Gczgt28VqjX69yXrlrnEiMS5LuyugK7obcRKMZju97ZmRnFbWBzuuYVf8oivGCWZUmUSiXO2DOmrtl8sbKyrGwSwnes7OK8Nzc3i1QqzfWaIkwp5DZeWVmRjkJZuqrxWkIICoUCHGGBboXV1RW+QSBCOiIoDW+trq6syEe2MZsjwJ9Ai8VCpO5EyZooxCuWihJT7ylps8WQ076yrL2kooLFKRWLvP+EZfU/3eCok0lTarUaXE++dBJ3c51SX+WEMeOxCAgxDYLVtdXIDe5wIQ3TZdXDXdgx6YZAKwnKwbKuVir8OFhdYN3AcLbrUTi2A5gp9Bh1GKaFmuv6Nu9MA90pwetFEM92KVIGQblUFNzjATXHlyhlU9Hx6B/FxvulZkwNY4QqlTKfH7uyFrpzKT2TFPSNcqkEK5UKJKhEqgeGuu35ZSUG0qmwLxmIbgjY2COEoFAqA4T4zK1QLrEbzJd9N3CplIFqrYpUKnBJx9OSaW40HBCDoL/DQqVUQiadifQbyv8LaQIAh+mxaarCFMTCHvXVDaxUKmLeiM3x6o3iarWKlJXSMpls3HGGj/pqSpaVkseSEoaV33Y82IFee3fGFPosY/Ip7+sAUKnUQIkF0zCQNU2AtSsRNg5CmerBRTtWF+KnSJdujW2Fer3e8tRRRK1WQzqd/IS2LSbwv/yX/yId/27btg3790eNjcbhV37lV3D+/HkcO3YMH/3oR/Erv/IrAIAvfelLuPvuu3Hx4kX8+Z//OT73uc9xpuarX/0qMpkMLl26hGeeeQa/+qu/itXV1XbIlhDHCJRKJXQHnlAAvbRQXWyaMQAis9ZoNNDJ9QGjaakMSrVSQXfgdk0ncVN3OkwHIOxYUamRmJ/vVkqfpopKpcJv+4q0xoW3bRu9PX3ad358Ic8gX5H2VmhH16FWq2EouHmsMi/8O//PZxj6+6Ju4HS0UwqkUinfVR/00jQV/Qnd/lDq25/bMDQkPReZZ7X/WJaFnphb07rFoLe3Vzux6I5TKaUYCmgRaQjjgIenwUw6tHFjpN69QCSg1m4mkw1M4WjJD+MHiywztZQEtm1LHhEY/YR/lwtjmRYGBzfI/kdJvA3I7u4edHZ1Rcaa2hPY600jmwNmV07P9Shszz/qZHl7nofNW7aG0kojXlUF8F1B9g8M8vzEHNh3V5BWbdjgWyBQ24hLAlk/CN5tGQ0tEEgbTE1Gpmli08iIJElSpV11x4PruDBMA1uGBtDV3YN8w4bjuLAsA50BMxPGA1zPtwU4um27NP/WHQ8mgVZi5routu/cGSGV/5bamiCTzWJ42O9jhaqNUs3hGxSRaWC09Q8MorOzSxoHcn8ASg0nYAIJBrZsk+rQYLfBSehBhPXRat1Fum8TujIml+SJZaAAlgMmMGOZ6O7uwuCGIWkzJTIujkfRaPg6st0ZE5tGNiMt2AlU25L1I7ZZ2r59Z2Scss2Sz5yGm+RUKsWtVqgMJlN/EMdW/8AA93TB1TTE9VrI0yAEu3bvURjDaED2qG47yGwYhWkSdKYYUxc9/mZIZbJI9w7CNPyLWmJiIpPJmMfhTZvQ3d0VXXdFIhDON7t270Y70Jl0i0NvX19k7WiGtnQCAeA73/kOjh07Jh0//pt/829axstms/iZn/kZ/vvuu+/GH//xHwMAvvWtb3EbhHfccQeGh4fx6quv4uGHH8Y3v/lN/MVf/AUAYOfOnXjwwQfx2GOP4Zd+6ZfaJZ1DlbABwCPv8fUodEe/uviPfuazkfTEeCIymYyvU4VwIhfjiqCUYt8110SeqWHFZ4+8570RJk2NI8b95Kc+HaFRB0opOjo6JJd0unzEtPfu2wcIulS6coh4+JH38KNp3TKrMjuiy7hW6O7uxl1338udpcfSEgzcvXv3xbqM0+Ghhx+BaZpwAr+dzXZ1nueBuZjTSYJV6Wtvby/6+2+OpKPmw+Lt3buPK5uLEjX/QVhGBq4zQlX3abLdS0J8I65333NfhIFneVFKpWPu/oEBDGwIbbjpyiBi3zXXoKurK0q3UgSmP3SroMvG8hTrQoRhGLj5lls1NLA48nHT0MaNnDlmOluMLna8w6UShODa667zN1VCfYiSIEYTY65ufNfNcCiFRYhEg0d9cxlZI8XbIJ1O49prrw3pVMrH8wg+hzYOw0xnI/1DPIryBOnLtdddL7U3y8evN0V657q4/vob+W/GA8bZl8zmcti6bbusTxd8Oq6/+E+WKnBdF1bKwujIZnR292JxeQnU871Z5CxT6hB2YN8ubZjYKSyKHqVouBQZy0BK0BM0gk/qedi39xqprtiFDPbQX9D9G7KdnV3I9IcSUlYGVaLJ+s3I5s3I5XKolBqSjqTIVBVtG9RuAIaJrTt3+gavKeX9igSVz8dcwATVHQ+Z/k1cj41AlgZTCqyUfYFJJmWiu7sHvT1Rv+2sDI5LYduu7w40bWLz4NaAcdPoBFKACuVxPQ+jgc9jVo9GkLDrUaRMEqhb+HSl02kMj2xGg8rjTewHEMrT19uHHtFvu7hOKuWhALZs2er3Q4ExF8eeiFrdhtUzBJsAnYKEWUxPYtiMFIxcr98+bCwIzDSni8/Xfchmc1zVIk4ewOphZGRzIikgg2jSrRWy2WxbksO2JIH/7J/9M/z5n/85/ut//a9wXRd//dd/rXUPlgT/8T/+R3zsYx/D8vIyPM/jkgYA2LFjByYClyoTExPYHnQ89Z0O9XodhUJB+gNkCYqqMwUAj33vu00bRTyKtW0bT3z/cf5OXcBVXLl8GceOHuVh4yVN/rsXnn9OuuKtk06Kz/72b74VS7eKRqPB3d3paFWfXTh/HsePHYsNozLUzz37DFY0ktrIAhZUwd9865thuWJoZmUtl8uSezEdxHzOnD6Nc2fPNJUwiW337NNPhUdeWjrk79/9zrel3X8zrK2t4eXABFF8BuHX06dO4uqVKxpmUd9uTz35BJeeR3bqiEoCv//4Y6FkLrKzD8gJXiwsLODgwTcAQNJ34rQIEgBKKY4eOcz1QnVFVB8/+cT3W9YfW6QJAr06DcQND/ubnprCycBNG6vf8Dg21OticQ++foCbIBKPg0W6xYXy6aeehKjnyNLVFader2P/y34fUHXuXI/C9SDpKo1dvYJLFy8K6cbXDyHAgVf3o1KucMlPRJJCiMS8vfDcM5E0mDFppqfGohcKBbx58I1QMkRpYBYoShQBcPH82YhNOUCQGoHg/GIFNGACL7x1AI7jYrFsw/M8pFKm5DcY8I0ie9SX9r304vNS3dmu7+NVtW0I+PYNT548LtFOGLdAwrph/MTpk8extKToZFP5goj46pWXX4R4vCmXN6i/hgM0aqCEoD5xEoDISId1yPo4i5dfnEVjZQrdGZMzhqy/sjYu1X3Vi1zGxPGjh1EqFbUbNgCoNNxA59Jnsl9/bT+/DQ6EcwWLKMatlss4dviQJCFmmyQguBQiRBi7cpm7RhP1HkVGnMUDgNdff83Xy0fYv/hYU+ISAK/sf4m3C6trcSyLa9PK2hoKU5d8ncC0JaUnMb5B3ufPnUV1bRmWafgmm8TGh8wPAIi4X+TpS2sl24h5eO2V/dr5UYeF+XlcOH++dcAAJ08cj+j6NkNbTOALL7yAxx57DENDQ/h3/+7f4dChQ1hYWGgnCQDAH/7hH+LixYv4V//qXwHQS8NExB3l6fCVr3wFvb29/G90dDRIQw7HfrO0RZ2R6O5YvvBRKMgu3XRHzGL8fD7PzaDojtrYJ8u3kM9HTLjo8mAdUTRW22oxLRQK/Ei1WVgaLGyFYgGdnVFaVHrY72KxyHdzcfQzyYe+bPHxisViy+NgMbxqOkcXRkSlWkFXl970iNrv2NFksmHs08LqUZe/2q1LxWIgHWNMqp65YPEc29ZalA89jYRxJVdnVE+P+Eg048PmQkkiBlkiVCqV0NHZGStl0mXWmgn026BRr6EjMAsRDUOlT5GW8F1oAkUc0yx3nfkZkdkRc9VJu0V6VVQqZeQ6OnyJk2AQmhDG4FBuI5AQgopg9sevy7AfqLRQ6qtu5Do6Qv0j4T37zm0AKicS7Bs7LrbYjVVWL4pJIT5+xcVZkPLUqjXffBLY/BkypewYcGK1Bng+E2hQD4ZpYrnsSExgyAT43jI8AGkApnBDtWZ7sD1fEqiTTPruGkOXhxLjKggHiBRetkCgs3VHA65RlHLFqQ2UGg5g+xexNg928bFBQMLjYEYH8fuW5/l6b2Y6i75sOLaZ5IwxtJWaA8Mg6MxYqAuuADlTI9CxXKkH5mQIOgOPFeJ7XRz2vKZJm9NKQ2bODTpOvR6aFFIvy1Aa2sVkkjPmGk1k6qjmOyDr8LWYOkABFMtlX8fPNJAzo/OkxPgSglKlCphppEyDm7CR0qR6PXORdvZM3Wg7joNUKpVYEqgzzdQMfj3qvTXp0BYTmM1mA5trBLZtY3h4GNPKrddW+OpXv4rvfOc7eOqpp9DR0YHBwKXX4uIiDzM+Po5t23y9iW3btvELJOo7Hb785S8jn8/zv8nJ6G5UBGvMHYHOSBxUxmvHjjC8KplTF6ju7m7JK4b6Xl28tm7bpm10PfNAsXfvvkhaKuPMnluWhV27WusjsF3L4MAgNg5v1NKrw549e2FaVoRRVNNmy9j119+gLZPuezab5S7jWtEB+C7jkupGUEpxw43vSiSR8j893HzzLREmIi5OV1cXdu3ere0fDGIao9u2c/0YPw192zLcdPOt+s2LJm1KKd797tsjkq04hq2vvx/bg/4ebp7kcEyHjBCCPXv3obe3V3vkomNkfVqaMVShRC6VsnDLrbdpmSyd6sPwpk3YOrotIn2QJAECx3Td9TdI5pPEhUa3Qbnjjrtk6Qn0fYFSoKOjAzfccBNf6BkdlPq3Rymo7x82SGN023ZsGhnhY5GFZXVlKDTddvsdSKUsiWEVmRvW9wxCYAC4+57QjAQLI+qpsfwAoLu3F9dcez0vi6gDxtIWsWvPXl83TakXSim/ALNUqAGui1Q6hdvvvAceBYpVG6BAJhNKAlm8emAL0DIN3MVoJ77JE9ejyFpRw/oE/jH57j37YhkcXldB+BtuvAk9ypEqMyclSnjYPHbXPfeHYXi7CvMvgHzNBZwGkMrhvrvu4semhETN7DCDy45HYXQPIdM/wm9Ki+3J2qIaeFjJpkzcctsd6GjiE3yhUoPrUliWic6UhXsDF3Nq/9b14VxnN951y62RemR+g7kUOyjXnr37sHF4JJZR84/fCZcgPvDAQxGBgLiZUTc2Dz70MGe8WR/RHdlTCmR6+pAZ2gHTNJANfAGzMlMhrE8Xxabd1yDV1YtMyuBu7XRlYP1edNPGNmvi+7BMBKZp4oGHHo4mFoOto6OBmlUy3H7HnejqSq473xYT2N3djUqlgvvvvx+/+Iu/iN/8zd9s6+z53//7f4+/+qu/wnPPPYe+vj7+/LOf/Sy+9rWvAQAOHTqEubk53H///ZF3V69excsvv4yPf/zjkbQZMpkMenp6pD8GfwL1v4udrdFoYO++UA9P9Q2q6kkZhoGtgYRRhG5nAACdXV3c7ZrK8IlHVwybNo0kFhXX63WMBkxxHPMkpu95HjYOy3b5RKgMRiqVilzcUBle8V1f4J8zPn1B+lqvY2hj4Iw8kCLomEeWnm3b0uWKOKZajNfKpqCYbybmRpWu7NVqNXIRI5a5IwS1Wi30AauZTTyl7zQaDXTkclwCJNKrA5cOcImRevM7DFupVEKn8cLkHyshrVSQyUQ3JVJ5hUmvVqvyeUFtIzUH1/W46zKxb4gQj9rq1TKslLyTj9tUAUAhn0eG2ecEIkyx54XmWwghqFQrMAwD7PiZLbpqOwB+f2Sun6T8oa/TYqEAZthGNKoL+Ewg4DM4DCvLy5H5VUxXnM8AoLCW52F4OZX6dAIpXL1eQ61Wi7xnBnxViVZ+bU06hvTDgOuOqfPe4sKcpFsJIS5buNdKft1ZaQul4hoopSgFtvbSaROWaUjlrTkuLAI49YpPe5B3teHCo0AubUj9i31fWV4CpaFJoWabMIMQzM3NIJVKRTYO4iASZlosLsxzaadUb8L35bIDuDYAF91BLxD1MxHUDasrSv2LQtXVJRgG0J1K8XEt+nR2PYqG43LTJ/NzMxFuRdwELASXSCzLQIpQLMwvSGHU/hAeewJra6uoVSqRdEXpMqsvAt/iBtuIqLb/mKTTCDYShBBMTckmVnTNROH3u1q1isXFRWGDFj2VESX1i0uLcOo1pCzDlzALu0BxXkAwpsbHr4ICSFtmsCEKNxUsv3DDAYyPXY1ukEWiBRSLRd9bk1AfzaCagGuFSxcvSpemWqEtJvCv/uqvYFkW/u2//be44YYbYBhGYt/BU1NT+K3f+i2sra3hkUcewS233IK77vIdr//RH/0RDhw4gL179+KXfumX8PWvf50fbX3xi19EtVrFnj178MEPfhBf+9rXMCBISNqBuOCJx0Nzs7M4f+5sEEYYZBpGihCCE8ePYW1tLSJti2Ne9r/8UnSHGrPYUUpx4NVXImnoGC7ANzvC9Ibi0hXTOnP6FPIal1tqfqysB157NZZxUfP0PA8HD74R2Y2Facu/Z2dmMRZcCBIXrrj0z545jVKpFPterZs3A7t8unaR+BdC4Lkujh3V21aKSlV9PY0pQcocx8AwnDt7BlXBBFFcHiyFo0cOa+tDl0W9XseZ06e06Yr9kn3Ozc5ifm4u1DFqQjfg13u90YjkzSVNRGZqTgR2yuKSFfOr1aq4fPlSy4mQLTSL87NYWV6WpRYKAyJu2s6ePQM3sClIgk5GiL9Yc/vcQXyDAGdOnQzLF2zpfYlXtDylYhETE+ORsqou5himp6e46Sf1opLj+gtiSvAWcvbMqaaTOVFoOnfubIQxVMEYlXKxgNnAvZj4TmJMhHJNTU6gXClziQtjSPxAah4U58+eDY9sBTo9yi5lhL50MxkLU1cvwqO+SzMA6MymJP1IJgk0DYJqKY/lJf/kiAKo2IEHDCu0EcjjAZgYH/M9QAS0hu1NuHhJjBPnp9Vk4YTAjm1jYnyMSxJN4aiW5Q8As2v+0TclFHalAEL8elAvCHG6ANRcF+XFaVgmRV8mFUojBZoajgfb9mAYBH05E1cvX5JoFhkSQgiWKzYo9c3JULvmM42i6EqIJzFuAJYWF1AulyQ1BiAcm6aS19iVy0E42aQNY9g8GppEAnwdWEJEQU10TSDw+1CxVMLC4gKgSEalfATa5+dn4dRrSFsGlzxqp6cgz8nxy6DERMoMaZdOB4TNIQDu9lIMx2lR1oZCPi9ZOGk1/87MTGs3m3EYHx9rmaaItm4HDwsSpN/93d9tJyq2bt0aO9EPDw/j2Wef1b7r7OzEN7/5zbbyioN6XOY/85X2+/r6Aei5cvXZ2toaegMpUDNmTgSbzHV6SyKKxSK6urubMoti/LxCCxeJa8tKJNpZOnFST/FZHCRJR6CzRzWTii79YrHAJbVsYKlDUxo8hULkmCYpnXE0MEbGp70nYVmpRAt/qtkIMLTSZ2THKeIEG04+OhpC6FweNkOpVEJPT0/kGFOEmE2pVOImjmSiwyM0dRccB3+SD+tJ1Nts0m34QlMtlzC0dUvzTBDWU7lU8l16hSSHu3khT9YPmNQilFrRiGFZhnK5HHEZpwNrknK5jI29g9qjJTs46hS9DtiB7pBYL6pUmNUnpR6IIbjDgn5TReFLbMrlMroU3UexbdQyVypldHR0cmaHMTCAnJc0fwiruDhGKPVpqNf9Y8xMyr9N7HnUl4gSoDMr2AgM0mQmdBr1KjoDfUMAKAdMoOjeSyxTtVZFR2eH1NnVcCLJkukvsR6DPiGWqV6rcT1P9ZifMSiUUsznqwD1YJrgrtEASHYWVXUM2/Pg2g2kcx3oyJghfUJfqNku1/Hb0JlCwzQ5zSEtYZsUai4opUinTcBrICfQwpk+nn4oEAAAu1FH52C/pPdGSFQSyIrheS5S6QwadTciCaQ0NFpuBvqplmVFNjBS3xKe2/U6OoP+GAohKJekS8lQinKtDmJlkEtbvM7VRNl8AAAe8eu7I2VIF17EEws2PziOgw5Bh1QVMghkgBBfJ7AzgdtLBtd1uS5mEqi2P1uhbRMxf18gTqzbd+yQXLqJnV/3/Y4774ocM8YdBQOAzjSI7uiWEIJcLod777s/MgB13yml2LlrF5eaNmNCGO69736pA7YK/74PfDAiCYtjUDs7O3Hf/Q/475SyqWUFfHMy7OjNX7iitIj5PfjQw225jPvwz3wkUidSvSM8quju7sb9TdyuyWkA111/PQzDiL3gouK973t/rHIvpVG7cqLLOJanykCxMvT39+NBQcdEJEN3A/FdN90EwzBge1GaGS3i0w//zEdArFSkLZlCOJOWMTCXcbpyqmGHhoYwwPqMpvpYeZl3gXffcSeyKTOYZFv3909++lG/nQKzJOJFGe6nlYQ975Of/oyQt3yBSZ3Yt2zZguHAZqFu8yQ+J8Qfe/mqA9uVvUp4NDCbAgLL9MeDYYRmqORNAGJE5gQf/9SjgkpFSC9rTzcwIG+ZBDt37cKuXbsiNKteHBgeec/7/KNkliBCSZqYAjO98qnP/Ky23pgkkFCgUvGlG109HXj/hx/EcslGNdAJ7OtMyxJVAHXXhWkA1153A/pyIZNYYkygzpMQBT7wwZ+RuInIhR4qu9PjtCvBRMkOS6Kntxfv+8CHYDNzJ0pjsfpdWq4Anovspj3YHehwuwHTwlyLiYykX14P3fvuQzqbQS5tan10lxsu6nUXhmFgIJPG3Y/+rPaiB+CnXag6oJ7PBI5s2oQ927eiZrswgrDhNleO63nU1zfMWJFxqhp9DoqOTz36We5KTyct5mZlAqaK9femkuwgqS2jo9i4eQvyFZvPiyxfNka8IAIFsOHad8OcH0M2ZQZMXbiBEUECAq574EMYP7UoeRdR65KlYVkWPha4deMMonYu8D/btRHIzNclxac+/Si3ipIEbR0Hv90hLgDiOf/ly5ciRlNFqMzgxPi49D5Oekip70JrcWEhVrqmMnsz09OoVCpcmtcsH0IIzp09yxkpMd84nD1zOpYpUstbLpcxLlzKEenUYWpyEmura0G4MHz4Jw/wkydO8MHUSoBFKcWxo0cSS/sqlQrOnT0beS7Wq7ieToyNYSnQ02glDSYEOHL4ML+VncRv8ME3Xo8No2ZXLpe5WZPw6I1JS6Pxr165gtmZmeiLmLIcfON1NALPKCpJOgb/lf0vx29yKFv8/J/FYhFHAnd3KuNMEe0/58+d5+7udGB9kekPvfbKy/C8eLuManlfeO65IKHgfZCmGNMIdvVra2s4cvgtvjkILwPo2/f06VOYnopejAulSnL5n3/2mYB2EllQHI/CNBCYo/D/nnnqqdi0VSwvLeLoW6H5DjEc6+diGxw7ekRy1cfA/BerxX3yB98PmPiQ+RcvNIiGwCmlePrJJ2S6WbjABI1HAbvhe9AwG3mcOn4Etuf50kECDHWl/Q1DEM9x/TpKGQTH3nrDPwoUmCUAyFlmtK2I7zKOMOIEmokQhtHnui6e/sH3JUaKjTu1zUAIxq9ewemTx7kXFPGmL5MEehQoFut+IssXUMyv+WWifl2zcoq2CymlqDoulk+9jEzK5JsDNo+yOi3ZdmDyhSBHXTz3zJM8LXF+Y9+rDd81ai5t4erlCzh75lQoEBE5QMj9ngJ46cXnUC6VIn2QuTs0gnmc5f297/ytcPlF3rh61L9FbBoEJiHI5/N4/rlnY/u3VPcIXMwFx/YsX4qoLiuF3+cOvvi0L1VNRVkezvAGUT0KvP7s9+FR/7KRmr9EFyFYXFzES4L5L1Wiq8Y/9OabXA0qCb79t3/TlpvMdkzGAe8wJpAtHA3HQ812eWNdunBBOq6VGAWlUzmOg3Hh/D9OuseeLS8vo1DIaxk6XR5TU5PaBpcGpJDPlSuXkU6nJWZLDSPmOaUsuHELKSG++7pKRW83T5f+zMwMp103mEXpK+DrLmQyGS590p26ieXRLVpxWFtbQ70eul3TloHnAcwvhC7dWjHflPrtlE6nw517Ey7W8zzp9nscHQyFfB4OvywhL+oiaYzhX1pabO69Rkl/ZmbGr3fNO6brIv5eZS7jlOc0oJ351aTUPyZ3XTcRs04pxcrKEizB/ZcOhBDuMm55cUFwF6bfwIjpF4vBjliQAhFhsveZIv93sVCQyh8uLFHGgVKKtdUVgfZQTy7kj2X68vk1EMMMbfAJ+bCF1DL9enRdF9VquBlUNwBq7eYLhUDKKr8T6WaST5MAa6ursAQzFWxu9FgZlPTLpRKvKC5dFhkeAfV6HY7jaOlg/n/rruv70jUIeiwbpmn6lxwavlRrS29aKrATeFSxDAOVUgHZNHPRRVGxXRjE1wnkm2oh72qtGpVERb74xatUKiDCcaGvOxoYdRZuv7LxUC6XYFpWeCQqbH5Z8pRSVMr+XGTQOjLZ0L0YP/5H2B8Z8jUHXqPhS6+IzIj60i6Kku3AcVyYpgHLsyUXhmI7sk1Apc50Li3UKuXAVIkwbmKGLaUUpUIeuVw2skEQXUeyzSohhG8049Jjxt8J8S96mGZUyqiSxHjVSrkcUZWII58CKJYqgGEhmzKldZe1I6sfL6Cr2ghsLwqXjXQlodQ34yM6GQiPjOU4/himqFYr3MVcEti2nfiih+u6sXUeh8THwcViEd/4xjdw+vRpAMCNN96Iz33uc2258fppAKUUC4U6bMfDjiH/aDGVSknSNBYOkCUjTLInulGLY+5Y/Eq5jIGBQa0UT5TesWf1ej3iXqzZUXOO3yClkXfqglitVrFB48lBd4xMqe9vc3BQ7/lBt8h7nou+gHaVWRHjMYlsZ2dn2LlbdFzHcSSD4s1oB/yBMzi4IZYZYZMVg0EI+vr7YvNnkw1j+vr6+vw+I146iIHrupI+ra4M4mzhui6/NS3Sq+bjf6dIWRZ6hdv28sYEoRQseL5hwwYQYgBwoztnZVJ1HAcjmzcH3JD/3hBmRcY08Pgxt88ZGWotZbJZQac1Es2vDy/U1Rse3iT192awbRtbtoa3+Fmbh4uVzDCYpinR7mnGlIiOzk509/SAKX632mxs2jQSSDSV8tFA342EuniObWPbjh3CuFaYQN6m/mc6lcKGoY1Sfuw7W+SYBM4wCHp7eyNzdyhxDaXbLP/NW7dKumsEgmkOIQ9CfLul2wQD/1JZPQoKX8rlNBzAMDE82IOBwSGUXc/3ZmEa2NyVkTqE4/q2AHMWQc/goG/LMShPzfYCv8GmdmPDLDnwxZ6FkeaosGyjo9ulcDppDnvW2dmJjs5OLvHSMdAeBerVOkAIsn1DyOU6gg0GlY4aWb2ytJfLDqzejchlLCBgWFjbsCmj4rhwXYp02kBn1sLW0aj5NLHdmDmZno40enrS6O0f4GUnYgSEkklWbyNbRznjBYTNI7p/CxlZiu07dnKbnOKGB9S/hc4kgQBgpVLYulW2uKGeHrHvFEB//wB6evr4pkdtUxr85zObQKp/BIZrIJcyIpJuddS6rovOjdvQIMywtP74mOWbzWaxZcuWsHyg0jyq9oehoY1NddtV7BbMorWC53mSybgkSMQETk9P495778XWrVtxxx13gFKK//7f/zu+8pWv4MCBA7wC3i7Y0u/rZrEG+/RnPqtl+nTo6enB+97/gdjw6nHsNddeGytZ0h3Lvue975PSasZceJ6Hj3z0Y9q01fiEEGQyGckFHIMuD0IItm3fHrp0SyDZufOuu2FasmkQ8bu6SH7ggx9qmSaDaZpgupUsrWY0jYyMwNy6VfuOSW3Edrj5lluRy8Ub5FSzeuDBh4TJqXndEEK4rmSz9Fk6G4aGsJlPKiGNcUzG9TfcIBnFlvoifHG/mP5dd98TsZfXDHfceTc8MMYgfM6oEXWBBgYHMTQc9e3LaFHz27fvGv+SirAh0o0Xpj91W+Ayjm0m1PTU+DffeptgdsP/VA0/s4W7r78/kKqzXXuwOMbU0e7deyXD0mo4tRw33nQzbEolMzCsHK5HkTYN4VYuwQ033KikB6ksTNoACvT29aM/lZEWbQDSd1F3a+eu3RFlc6avlzbDUxE2Tq699npeHpf3+zBtkfkyTRN79oYmt8Q2YfRXHBd2wwYxTezbMoT+DRuxulaFbbswTRMjnTlp8WQeVVKGgR07dyGV9ucZx6OoOb50M5c2IxsNz/Wwb++1nMawMuWvjMaUZWHHzl1SvbB47MhTdE/W29ePru5uVGxZGiZy7HXbRaPWAIiBruGtMA2DGztm7cXaVqzTpVIdqf4t6MiEOt+qRKhUd+F5FKmUic5cFkO9UUaK1yEFanUHhBAMdKTQP9CN3t4+OGK+QUMyJp+3LwVGt+3gtFKhH7qBSTXuXo1SeK6H3Xv28k1zSE9Ai0el4+90Oo1Nmzfzfg0hfQh5snrvHxhAtqsLdTecG8U+KDGElMLq3QiyCnRnzLDOxbAINwiO4yIzuAWkBmREySrkfs6QzmTQ3a13eyn2fe4er7+/Ld32LTHrmA6e52nN1zVDIhnj//F//B/4whe+gNdeew1//Md/jP/wH/4DDhw4gF/+5V/GH/zBH7SV4U8SkWMyAiwsLHJ3Xs0Wc9aYx44exaWLF7UMnJgG+3z8se9xe1y6xU1lGr/1zb9OzJCWSiU883SoNxQtn/z77JkzOH1KNiWi5i/aSHzu2WfaUjD9zt82NxckklOv1/CDJ77P82wGSikuX7okua9rVTcvv/RDrKysaBknRfAGAuCx732nCd1y29m2jSd/8IS80DaRTI1dvRpxvafNJ0jn9ddexYrgjlGUWsnl8J99//HHtM91TKrjOL6rszgalOqcGB/HiUA/kUBmQpjkQozz+oEDEu0hTYyhkjcHzA1gM4baCxgyz3XwvOLqTM5DZgAnxsdxlpnOIWE9qlI0FuPNN17HWmC6wZechYu9jvll7utUiv3FQN4U2nYDL77guzpTpa8e9f0GW0ZoM21yYhznz52L9HNWh2oZDh58HWv5tYj0gUmPxD5qEKKtR8b0qe6/Go06XhXcXInpiGA0jo9dxZXLF6EDY0TLtgPXcWFaJipjp1AulVBxXDiOi1TKQE8uJY1R2w0Z5VdfekFKr2Z7SJkEmZRgDipo72qtioNvHIi0EWGVg1CCSQgwNnYFY2NXwvpSxp4nji1CcOjgATRqNeE4mPmZDRtoudyA3bABw4QzexKEBEwQQuPg4qaUfa7lC6jNnEe34C1ErHLPo1ipOPA8imzGwtT4FUxPR12qhoycf9wOAgx1WXjzwGtoBMeejOkTdw0U4eaNUorXX3kpqI+QjpCZBb/gAgDFfB5HjxwOJYHK+GH1xXw9Xzx/DrOB4wl+AUYpr9jn9+9/KfA4IkvcdDqBrkcxdfLNwKuKqd3UiX1tdWUZc1fOwjQIOiwzVAch4bgS8zl39gwWFxb0tIp5BO26/+WXogQ0wSv7X04cdnFxEWfPnG4r/URM4CuvvILf+Z3fiTz/nd/5HbzyyiuaGD+dEBdHNlmsra6gp6dXG078zcKvrCyjW5FcsE+VoSKEoFatSjtu9b26QKsLpRhPlfKtrqygt7dPm6YujbX8mvbYUJY8hjQWAxdzSfW7QKIeK2T6w2erq2vcYDgfxDH5EBI1bdMKhUKhZXjGmDRnQaNtls/nJekVe/6j0sImPbbAFAr5iJHzZogscIKkK2TUCKelu7tbe2tYB2ayyE83fE6FP1N4USoV0d3To9XNUetIZP6bkcH1dCol7RFmXPrFYhFdXd28DiiFsCgRwQhwYDuvXPJpZ5IFKi8sItPPj/2onC9nvqjsVoqZ2QmZrHDM2a4XXAwJ0ykKpnOkDSfEOQf8s1wqoaurO5TUCPSwTQ9beC3Vll0Q2PFku5GMMfJp7wyleR7zvSwzUyxNZk5GeeXn4XogIFip2HAd32VcJ1xkch2YLzXgOi7Sactn6ARaHNfzvYIEkhkipFd3PWRMA5mUKRTa/1opl33zMEKc8OJDSD/rI5VyOeLik/UZxgTQMAlUKhVkOzq4NEzXjyeKZaDRAAwTuYCh8yhggEhHpSwfNizy+SLMTA69GTOWQV8s+0xgR8ZCveq7uwvbL6wOv64o6nVf0rqhM4VGo4ZMNiv1ZyJGkOqASrqSvCopBN2+cJzU6tXg2Dsqrfdp8S93pQJ/z7VaNfDUEw0rMn+MAfVcvxwgsuRbPrP3w9dtF05wIasrLfcrKN8pBSrVGqiRhkEIOiyLEyAynGIfqNVqyAprvJie2L7imG9Hx68dw8/1Wg3ZbHJzMkDC42DLsrR+SVOplPb5TzOYHhWbSHMdHVyPTZXAqUwS4J/nxxmrVnf/hBDsu+ZaKV3dsR6f6BwHN9z4LilvMYyYLiEE2VwOO3ftimUaWRz2fnjjsKQTqJMcikds111/Q6QDxh3Dep6Hd73rptj6kNIAkMtlsSfQXYhjcsR6Gx4exqBGnzEO1113fUv/jHzBoxQ33XxL87BBuT2PIpPJ4LrA3Z26W1XDA77rslY2AsW+ce31NzQ9mhZBCOFHpLp0AVEC5qsE3HDju5pKLkVsGhlBKq33FsKZJIF5uf76G7WmcMSjn5D20GUcpXI6Yv0xUnPZLG66+RYpviiVUPvmlq1bkc5kFZ01eVERKbrhxps47YxxYvkAYVt7Qbw77rybP48b0wy5XA433HizJJnzaQl84lLK3aQBwOjoqO/vVhjHurTZ05tuuRWpVDpkABAF9wZiENx9971SPQKh7mVoq9CP0NnZiRvfdTPPm+mBqnqDbCO4bdsOaVGUaAjmlysrdcBuIJXuxu133AbDtDCdb4B6FJmMGWF2bNfX7eqwLNx9z3283R2XouFQdGcI0mbojxcBI9zV1YUb33VzeIwnNBN/RsK5YOeu3dz2I+9PNNR5A8D1gA0C3HHXPbBMEx51BCaKdRi/103mG4BdAyUGdt58ZzCPeP7xsuCeT92Eu6kO5IZ3oidr8jEgMngUgZs9+Bc9du/dh56ebmlsinA86utcGgT9mRS233N/U3WHoGq4tO/uex/Q376l8I1ew9fzIwAGBzegt7sbLpSbwUE5RdUECuCaa69HT093JG1xEyPm/MADD8n6rsFZOhHiciat4aJn162oGwSdKZMXmG1g+SYxCN87MIiOTTtATAO5lBlhjFW86103obdX3LSHdgJ9hk+O/NDDj8QnpoAQIpn/aoVNIyMRffJWSMRiNmP02nEb99MAdbI2TRMbg0qLY6bEz/6BAanMOskb+23bNrcj1ooWSv2LGyObN8fSoj53HIf7XhYlhboFg1LfGGeHMMHpwF3/eJ5WChhHV7lcxuCGDaGERBNHXEyr1Sq/ABPHjoj1Wq/X2zKw2apfEuGzXKmgu1uvqBs9mvfL6hvFRqTtRdoZyoJUpyU9mk2Cml5Im09Ls7Kqx4mFQgEdHR1aBo6lKaKQzyOby0V2z/7kHx6XMtQada1ERH1GKZXUJHThGdiCUSkVInORGE+to9WVFcn+p6iHpUrqAF+CJUo+GKchKuIzVGs12LYNFYRE6xTw65EEGyrZxR8NmEAgZYa+cpcWF/mtf3We0I3AQj4fMGYyYyyCmX9p1Gta7zU6e2+Af5MYNGR2mccPHorzPP6X+bnZ2DXD9Xwj05NLZcBzkMqkUMqvwKPAYrEOSimyWUuRoAA1xw0SqKNcLnHGreH4F0Y6UkZo9ksgf2Vlxb+tLtJKQsmXCEKA2dkZ32hx8IxJ5sR+4fniWBD4t9UBSJtnzgwGDbFQsgG7BhCgN+XAIOHYkb3L+PkxczOltRUQeOjJ6g1FU+ozgYQA3dkU5mamYZmmVveWwpeI2bYLyzLQn8lgfm4ustERyy0mUS6VUCoUpE2UTwe7tBVuyiiA5eVF7qVFZ2zdP8ol3HDz5OREoH8eNp/YlxmzyvKenp6SzBRRVim8bsK1cDVfQLWYh2EQdKSscBMGQViCMM7s7Dwa9Tosk0geQxjUvjM+djV2XtKtbZMT0SP7OJRKJa16TRzm5+bacjEHJGQCT548iY0bN0b+hoaGcErRMXs7QFwYX31lf0RipTt6BXzG6MBrr0ppiO/V30tLS7h44bwk5WFxdc8uXbzYtMHVRe7Ng29EmJS4sABw8OAbsWkzutmgKJVKOB3jikyHyYkJzM3NxTKX6uMTx4+hXq/7tKLpRgsAcOTwW00ldSJs2+Z6bHFgg54QYGFuDrMzUXtvgL4ez587i2q1KunaNMORI4djaff4ERz77eFk4HYtpCE+j5XlZUwLtIvBdMZlL1++hFKpJB37hXGjfeno0SMwDN8dl3q0zCZsPuFRilOaelfHE8tjZWUFMzPTLevPCezATU+MoVIOTRYRovpVltM5fvyYPzlzBiZk0igNFfFZGc6cPhXOAcGfeJOQCPnk19YwPT2lXXB1mJgYR6FYBBBKP9hYazgeDAKkrdBw+qlTJ/nCojLy7DtXxIfvMk5enOMWa4JSIY+5OdncEiEkelwcJDA5OYFSuRTWC9/8sMTl8Xvu3FmYpqnt8+zIc7lQAyhFNpfBlUsX4XkUa4EbuWw2aiqk5vhmYBqVEubn5/mcUW24cD2KzrQZ6p4J3WBmehLVaiUsD5Xfs7piffl8QLsogWX9AJClWv6cfSEoF+U+gFXa18oNwHUAQtHjFHhds+NgVq+eIDWyXQ/FxVkQr4HewGUcH2+8LikqwUWPnqyFyxfPh32G0xjSMV+qwXX9SyRdGQtjVy7ytpTKFaQtVlO+kMfK0gJUsLoTpccEwNz0tG+aR6GBNYHjytLVSxfOB31Gbh4+9og8t12+cplLntk8EEqnQ0aNAphbWUW9sArLMtBpWaFkj+jnwKmpKTj1OlKmgZQZ43aUJUEILl0KffXGC2/CuFevXtGG0SGfz2N5JTkTODMzzdfVpEh0lnvp0qXWgX7KoWPaaNCDVWZMd+RJqe9aLHRzFn87lT1fWV7GwMBgJB1d2v6iuBxrTVy3QNfrdeH4Kir9E+M6jsN9eSbB6spKxFRNszKsra1hy5at0uLQrI4K+bykJ9dyIU3IADJa+gTdxziwGltdW0Vvb+vwYvrcrAniB76IpmGEyU11G8ialU2C0sIOf3Lu7dHXo465Kgb6ibpjCp0UMlwEw8WKHXcwRpLRWgtcaOnKq2PzfJ3T1qYSmBeLSqCzJ9McX1bP87i3EEA2ecFvRwdhG40GUum0YKzXfy569giFg8SnPUKLsBAK3yn1/QwPbd7mu8kiROo3duAtxPee4D/jOk9Q5itAyoMCcBzbt7Gm0CvVEQ2PYktl0VVfmCbTa1OPg8ulEka37Qw2AlGft+w5Y1Co50n26vx69NuRSczyeX+hymYMWJYFj1IUa75ktSubCvXUgiyqjgvTIHAaVXR2hjcrCw0blAJdaTNkAEnYrtVKBV2dXXJ/FN6HdRlKiK3AZiEQ9tvI+CUEjuMglU5z0zspVvcQ+wv1j2ydOmjKwrYN/YE0NbhMIdyoZfXker79P7teQ6Z7E3rTzOKCTIdHgWrd4X6DAd/lHaOdM70BJosVuK6HVMqACS/qXowzXET86UsR6zV0dEZPMxwvtPcnFAP1Rh3ZwBSOehwM3ocMWIHDYb+/JJvjGw07sNEKmNzCWHT8syeFagWwMj7zm7Ii5RPDUwD5ShWwepCywtv6KvPPuhqlNKJ2JM0DyjPHcdpyAWc3GujsaM/FXDs3j4GETGB/fz9WVlawY8cO6fnVq1f5ceRPO3SSCEop3vO+9ycKD/h6Pffce78Un33XxWf6eqrOV1y8O++6W+vXNy78Bz/0Yak8Yhy1rIZh4AOa8GocxmhsGhnBBo1dvrjy3nrbbTCZa7EmzDG7DPDhj3xUWiiaRAOlNGIKpxn6+/tx7333x75Xd5U33viutpRvP/DBDyGbzaLueFxq0gwfD1wK6UABaUfa1dWFR97zvph2VOJSiuuuvY6lEmXgEJWyvue970M6nUa1obMRGFXi/uSnPg1PyJu9ZselojQqm83iQx/+iLZ/qMwypRR79u4NyqU398LgBG7W3vPe9yGT1h996/L8lOACTm1zXt6g3VOpFD76sU9I71RGkKXjeT7tjusGddN8LgCAhx95D/JVBw03JILFsR0vMBQdShM+87M/F9IobUyj8S0rhY9+/FNK/TKmIfztHzkT3+6YsukFwuNitUs/9Mh7OSMeGgZWdiMCPvkoc/8lH7kzsyApw0C53AAIQUdnFh/+2KewUvZdxhFC0J1NSfORQYCa6zOBe/bsQW/O4nQXg9utHYHEV73U8ODD74kwgDo7H+znZ3/2H8jPAkbOJJAWfr/eLXz8k4/6F2rYpopSwDCkvsYkgeamXXjwrnt4f/dNziCo19DmJqVA0XbQteNWmCZBTzolGehmqNsuGg3GBFr45Gd+VpDMq15pgMm1BjzPQzptIptJ4aOf+DRqTni0L4YPmRcAHrB9x25kUrJpI0qpJD0WJdYPP/we1GwXhaoT2bhS+H2B2cUkBHj0s6GbwVZIp1P4xCc/jdWyHap2IHpphtVrz6atyI00YBmEu6mLA6XA7nffg9fenELGCnVTw01JNPKng/4upxNuIsQ4qVSq6Xqgol0Xc0zf8O/cbdxv//Zv4/Dhw5Hnr776Kr70pS8lzuwnCV3jLS4uYnlpSbvDFjs0+371yhU0GnUeTk1X3bEfO3o04t1AF57hxPFjWs8Pus5XrVZx/tw5KU/pCIPKR3DTU1NYXIiK81Wa2CRz6uRJrc5THA6+8TpSlhWpO0abvwiHtB5682BIN5oL+hYXF3Hl8uXEtJw7exYrKyvad1yKxURZAA689ipMM34oiOUAgNdefSXCUMShUCg0NQ8jSXABXLp0EXOzs8LCH0oAdIz76wdeg+M4eloVRgAAXvrhi1yipeNfxXLVajUceO016RlPS5IE+vmNj43h8uXoqQGXZgrPCCF48+BB/2haw3yKYMdkzz/7TCyTpcJxHPzwxecl2rkuG5PSBOWh8MeHaFqBvZe9IISSwENvvoGV5Wgfi0pS/c+nn3oSXqADxS4DsGNBx6OwjNBbCKUUzzz9lNw3hA2UtKhSitmZaZw8cUw6mheZXkp9hX0aSGzeOvQmFpS5gEmnAEguNQHgySce55IyCGFEMCaCUopnn/pB2A8V+hkTUK81AMNCys3jxNG3UHM81GouiEHQ0yEz+h4FGq6HtElw8uhb0lF2yXZBCNCRku25sTI9+cTjkfYQpTOM6WJ0PvH495S89cy9QYDF+VkcfvMN3i6i4Wcx+NpaDfBcGMVJFFbmeb2wG7V8rAvpl20HiydeRiplIpcOj6dNztRTFKsOGg3fZVxf1sKT3/8eLyPLQ6RjoeT7ZU6nTeSXF3DozTekwcloYfTzDQSAY4cPYW56KlIPqq9pN0jv8ce+wyXPphLH3wz4cZi+7Xe//bdSPWs3kgGNs7NzeO3VVyPli/D6wUR3+vhhVJZmkbKMcKMlhBM/KYCXnnkcrushbRGuOkLFNCWaCL73nb/V0Kr3pjM/P49XX9mveaPHoTffjLhubYbvfufbbbmYAxIygfv378ejjz4aef75z38e+/cnL9BPG+ZmZ7n/Vz4ABCZKla6NBQqgKtMohmFpAb4eEJN2qWmpf67rYn5+XkpThJrn8vIybLvBn6mMijqIZmdnJQZTl4f4fHzsqvaWZxzm5+d1G2xOC1s8KXwzIqJieqtlfamJyzUdZmdnEtHOJpWFhfnI8RWDykx7noe1tTVJgboZVmNsFYoQDfwuLoRu0Vi96ehgz+aEsuqOYFXGcXV1NTSN0gL5fF6S/IjRfImIbO5kaXkJFtO5UejQYW52puXRCPM1axCELuASoFgscqaD00EFTxgIGQICYG1t1aed+Hm6QrnF+AyLi4tIZ7NcSgDo+zGLUioVJRucDK7newtJGeHRU6Vc1kqCdXQQ4ptPMq1Qj05kWtlvzwtNjKwsL2vHh+P6njdE6TYhBOVyGUyy5LB0BGaEhQMBavUav4gRzod+OBZ3rdaAXbcB00SO2LBSKZRsm7uMG+lJC+7XmLcQD2nTQLmYR4a5jIPPLJkGQadl8QYQGc9q4IddBWt3Fp4AaNgNvh5wuoN0RJ6X3Q4vl0qwUqnw1rXK/QVjplCoAdRDynTQkcsEafgbG1ESyOiiAEoNF269jmxgLof3VaH/LlbrcBwPlmWik/gqBIxusV+yL4WKv6nPBuZkTDPUOWUSNaYLKI116rtf1Lk6E1UI2AbbIL6pEqmyBXiBBJFtsBzHkeo9Dqwdq4G7OwiMs1g/QChxpACW14qAkUI2ZfqSQE3aNPiPUopy4N3F1zOVb/Or8DwPdrAJV9doPjaE8NVKBZaV/DJtpVJuy8Vco9Fo61QLSMgExi2QgJ5j/2mEbgGt1qr8RqsIkaESmTzXddHX1xdh4FgcdefO9AeT1FG5XMbIyEgs3WIahBDUazXuQkulXbebcl1XMrGi32mFcTs6OxMzgZ7nxZrNCdMG3+3WajVs3hy62VGZFV36cUfTOliWFeuWR5SOAP4A3bAhPm213iuVCkZHtwnt0pwWz/OwqckNcY+GklLAPy4YGOgPaGSSwPjd8dDGjZLfa7GcqnTBdV1sZdbnSTQ9kXFg4UdG/Nvq6iKoSnAJIUin0hEd2LAc0dvBA4ODyLSY4ER/t5uDm/Mi4vqN67oYZZbzxSB80Q3dowFAKpWWXCSGi4p4wSZMpq+vD52dnZJ+YTN6RkY2ByYpmETBf+54/oKYMgSXcY6Dbdu2x24exD5BKUU6k8aGDUMRfUYRTGJjEIK+vr6ITmAoHSaRKhPdebE+ydQAGEPCwruui+2B2hCloQ4mIaH9u/lKLfAbbGJDfxcGBodQsh3/aNM0MNqbluqxHtwATpsGBgYGuC6m51GU6z7jymwEEoSMPgEwKrhRk7pBkD6/8GEQeK6Lnbt2K3M6eL2pSGcy2Dg8zCWBhpAmC+1Rimq5BhAD2d5B9HT79kWZyzQxnIhi3YXVO4xc2kQmZUo+r/26BVbrDbiuB8sy0GWZ2LFrN5h+JiswY25cL9S57MhYyOWyGNo4LKmMMGZQZXgoBfoHBiNmrsJj7XAzyMb6jp27An/fUaaLbbIs0/BvX3su9uzdJwtgIrUdIpPNYiiYU0WmT1yDw3oFaLYXRjqHjoyFtGVI65wIGoTvGPLnmc50uDHjeq9K/TiOI6m1qFDrMpPNcgsgSbBhw1Bbrnl3Kt5ukiCRTqDjOCgUCpGFNZ/Pt3Vk+JOEunsGgNtue3eseQ2xQ/vxKd7/gQ9KytrxO3WfYXz/Bz6YmL5sNot777s/spuIy2Pr6GigHKuXGqoSoNve/e6WTJ14nNyObSLXdfHQw4/E0iszx77rPWavkLCtbxPs3LWrLWXaO++6u6X9SjZpOK6Lu++9r2lbit9TqRRue/e7fdqF53HxNw4Pt6SdH0lRimuvu76lORmRnnfffof+HU877O+e5+Hdt98hGIrW08LQ29uL/oFBsMMFcbFn0hBTGB+j27ZHLkv47/T033LrbaH0IEa30gkkgdRzY+0h6pDL5bgdSoKQcTUJY5hZgf2PkZERbiiaSSpIwCSaRngUy6Jcd/2NACEtJcH+XODghptugU0pTMOQbps6LoVDPXSaFq/LTCaNvfv2xW7UVAwMbEAq1ylJ/qJ0+J+WSbBn7z6kUla4EQrKzPT1gLD/eJ6Ha6+/QWp3ILjcEoxdElQKAWAahl/vxJcdUuFoiumPzZdtOLYD0zKxc2QDNmwcxqWZNdi2L9Ua7shJ5Wg4HlwPyJoGduzYiUxwoaHhUtQcDymDIBsYl+b1FJRi3zXXRhgKkTGhNDDXEjxX/bRyhoSEElY2d/T09KCzsxNlO5SGccY4qFzXo6hVagAh6N64mdtPFKXobKPhf/pM0ErZRqpvBB1ZC5ZBUEfYFxntq1XHP7ZMG+hIG9i0azevN5Gx8+D350rdl1h1Z1Po6e5GrqNDq4qj/qYAhjeNoFO49MWZfsYAG4JuHgF27NwVbAJCppwV3KMARah/6lJff56lLS4I6gkXpRSdnZ3o7EnDhiyhlRhX9h+lcHN9ICkbXdmUpDdIhD4sNDiyA5uBPNCh9CltPpRix46dmlA+1Pkhl8slurTIMLhhQ1um0Ua3RX1Ht0IiSeDP//zP4/Of/zxWA5dKALC6uop/9I/+Ef7BP/gHTWL+9EBtOEopvv0332p5LMpg2zYef+x7sUyXn0eYydTkJA69+aY0IeiOkRneOvQmpiYnQ3G/wJDpwj/x/cdh27ZW8qeTDn7vu/Fu0cRwgG+b6Pnnnm0ZnuHK5cs4depkorQp9XXwlpaW+O9W+P7jjyWWOFNKuRu1uDj+ZO5P7kuLi3jzjdcTpU0IwcmTJzA+NiYdLTaj7blnn4m9ss/6oSFMSk8/+YQ2XPQZUKtWY10QiWoHDFcuX8a5s2e5hEaFyoi9fuA1rK2ucdrYpygFFW2APfP0kxGJHwBBty584bounn3maW14EYz5WlqYw4njx2PLquLY0aNYmJ+PjB9uB5PVD/x2fPaZpyLpNaPtuWefDsKEz+LCriwv4/BbbwKAtIhT6nsLcT3/wgbD2TNnMD01FT29UNJl9fnySy/CcRzhZnD4yb6L+lkvPv9sJA127K4q+OfzeV93jIRMCmN4VBBCcPnSRUyMj/m/heeEhAzDcjn0FlIfO45KpYJS3YUbuIzb2Jnh8Q0SGtPOmCb2v/Q82Evb8dBwKNIWQS4dqt0w6Uu5XMEbb7wWbIREOnWMD8HE2FWMXQ11jxl/wOpNhEGAtw4dRLVS4UwkPzoU+nrD8Xy/wYYJd+4ESNAWFDSo66BthY2kQQjWSjWUJ06hN5eO2n4kPl1rVQeuS5HJWFhbnMXlwFWfODcx1GzX9xtsEPR2pnHi2GEUi4XYCyFivVBK8dorP9SOa3bjPtwMAtTz8Mr+l/hGUSwjO3JmkkBCCOZmZ3H+3DkeRgexT544fgyrqytAkDeLoZvTKICrRw/AMAzkUobUb4kaEL5pnvOHXwchBF0Zk+vHiuUQ9oOYn5vDubNntPTqinLq5Am+9iUBc2n74woPJGQCf/d3fxd9fX0YHR3FrbfeiltvvRWjo6Po7u7Gv/gX/6LtTH9SUBchwzC0jJa4gLJnS0tL2CAcHavHhGo+LLx6RCxCfLe8vIwBzU3rOMau0WhIV/ybMSK2bSfy7MLoWVlZkY7GWmF5eRkD/YMBHc3TJsQ3sSIeHyfh75IygcVisamrO3UXuLq60vQoW1xIKfXtw/X192sXEh1qtVqsJFBl0jzPAzEMqc3FPxVrq6tNd5WEyBMdM53DFrZm/RHwJf09fX386M9Q0mMeDxit4qdEh+Z5qVRKpC7BGMhKqbUbQBGFQh49vb3gR1wBrSLvIuqd0UBKx2kmvkSDK/uLC3ujjmxwjM3aJk4iSAhBqVhEd3cPl+SI77zgUkg2ZfI+VSwWuFRSSgtyXbJ5qlGvI8f0E0m8iStfCiLqLodpusFmhPlyZbHLxSK6A3d0DKbQERjN7H2lVEJ3t+w2UGQCUiZBse678MrkMuiGh1xHJyq2BxCgszON3o5UOE7hM00pk6A7lfI1E4N3dmA/sjvjH5mqi3W5VESXYNZEpVWsV4MA5XIJHZ1dYR2TaHl5OxCCSqmEjs5OLskWGQYWbrVs+/pu6Q505FL8ooFFjEhdM11MwyAolYpIZXPY0OW7LSMEwfFpGL7meL4/3GwKXqPGTYkQlbND4HvZ9SWtQ51W6NqPCBeVpDYL+xgAyaahzNjJt3INwtwGdvhMmtDfWXyP+ukx/bxyuczNmoTjVd5wMloopSiVStw9nthPCNH4DqaBdQGDIJc2ghvZhPeFsLz+Z71WA6w0UqaB7rQltSfLx+Blp6hVq9xFYhzEsVitVtsy4dLslEmF4zg/kge3RDFM08Rf/uVf4vd+7/dw5MgRAMBtt92G3W1eX/5JQ9zhu66LW297t/ReXcjEDpjJZLDvmmv581YNMzQ0FNE3VBlC8ffu3XukY8BW6d9yy63a8uniua6Ldyc4SmNxu7q6sHffvpbpMmzZssXXTYthVtRF613vuinidSUOlFLcprRTM1iWhZs1daOCHVMMDm5AOsbsiAp2XMTsJyYZnLfHHNeqdAD+DvquwBVZa1qA7p5uXHvd9RJ9IkyBSQOAbdu3o6+vTzJN0Qy33nobUqlURNJNgviGYk3/zjvv1jJ8uoU3k8kkaidQf/HbumULujqTqwRcf8ON6Onp5sdsCBYrkQmzTIMfyd1zz73SomAavnRD5+rOMAzccdfdwftQShPHAA9tHEJP3wAsM7wdzOBRIGuayAqSrGuuvU5rozMuj7vvuU/qR9p+SXwTNCYB7r3/Af5YPJq2TANpMyyTQYC+gQFck+sQmGGClMmOg0OGk8XZs3cvunt85ptCNiHE8rAdD1bKQkd3Bx584EYYhoGa7SGdttDTk0GWGX5GuDjnLBPZlIF77rtPOHIF0hbBQM5Cxooe3XX39ODGm26WnqmeSNgzw/D12Jj/WpFBZrdYDQLQQAfOMgjuuOtuZNJpkHqd+8DlF6WCMTNdrsA0Tbi5buy65Vrf1iHxJb/iJQXfRFDwaRBYmRwGdl2Poc4U17kT9e5YfafTJno709i9ewRdHTlfMkaEchKAMFUI00Aul8Jwdwrv3nkHOjtysL2wLdVuI7bvvfc/EBnHNGDyRObLNAg6OnK4/fY7/UtGgbSPCGkSABnTCPTz/CNM6dIi9H2Zfb/1tnejq6sLDS98x9pILoDfjltuvAMzVQu9WRNW0L9lhhcgvvICUuk0dtxwG5apiaxp8o2gjjk1CMGWrVsQB90wvPmWW2P11XVoRy2LEIIHHnwocXiGtq6R7N69G5/97Gfx2c9+9m3HAKrMWD6f5z724iQiogSmWChwm4hJFn+dmzOdxJDlZ1lWonQBX4rSpVEWjYu/traG3jb0EFQJUyu6KpUKcrmcttOr9DF9SYY4XTCGYrHIDRAnwcrKSlOdOlnKBqytrrQ1KCuVCm8r3ZGYCNu2JfMtcfQwKUKhkEe2DZ/BqysrsVJG7j1BwPLSEjo6OiSGQU1TRLlc5gsQk6KJn5ZgTb9UKgUMkj5d9fny8nIyl5MESJsEK8uLTSWqKtZWV2Ga/nEOk1SIN5mt4LlBCBy7gYpyi9Q0CExTL+lcW1sLvvthVZMqMm3Bje+ML11Q+4xBgGzKQEfA+BgEWJifjxry1ZTTX1Ap1tZWtDbSRFgGQdoyUCoW4ToOT0vQe0faMkLXdcHzpYUFWFbIoJoEgRcFgQ6hrDMz08hmM7yPMQaL2UHMBNKvTC6D/v4cqsVV3g4dHWls7M1xV12MuTANgp60BdhV1Gs1zuAYBkFP1sRALg3LjEp3VpaXI2oRjLEUN6xGwPDPzkzzemf5cuaLt1fYn5YW5oGgX6UDw8KMbva97npIZVIwOzrRYzZ4eunAXAmrG9MgvH+kLQOkXkIuQ7CxM82ZKcY0snpNWwQdHSls6M5gcX4WpmFIEnvGtLExkM1Y6OpKYagjg4U5Pzw7kjaIMjfyuiJwHRurK0vaPp4ySSCFDaV4hbU11Gs1qe5ExpqVkXnjmJ2Zkft1fDcGAMxMTyNlWdIYFuueb1KDT6+0gs5cCoMdljCXyacsbE6rlApIeTV0ZS10pize1uq0xuY00cKICtZOIsbHxtqS1k1PTSUOWyqVfBePbaK9u8RvY6iM3ZnTp1CpVLRhdZ39zTcPNr0lreJwjJsz3bNSqYQLF84nTntmehrzc3OJw184f07rKzQOhw+/1VZZjx8/Jk20zVAoFNpymzM9NYXlNnQozp87i0aj0TRMeGxEcOzY0cRlpZTi5MkTfhoJwjO3aM0gTi6TExMotmHk87Tg5iySrhFlUpkbQN2uX0Wj0cDFixekI8yQcSCBS6UwkYX5eSzGmPLR8cpXr1xO5N7INPyj0tOnTrbVJ88GejpscSNENn3CFijD8PXeZmfDhYhLY2KY/KnJSZRLJYmpjAMhBOfOneX1pbaJZRrozPhmQBgzee7c2cTlrFarmJqcaEqLL70jyFgGFhfmuW63tAga/uUKy5QXx6tXL/uqM8GYSQUSHM6ICdIRALh44TyX6vANRLD6py0D2bSJ7qyJ7r4ubNqQw9TYFaQtA305E/39WWwb7BAkb37amZSJgWwGjUoeK8tLnKFImQRDHWkM57JclzGU2BBMToyjUa9LDI1IKwNb6M+dO8s3eKJ0VNTdI8Ezw/D1Hw3i12smZcIyQld+jPnKWSY6ezrR2Wmi1y1wZi8bhGfhUiZByjLCz8oyejMetnZ1wCQEaZNwOgjxmfqBnIW+nixG+9K4fOE8LMvkG0pDqQ/LNNDXmcZAbw4jHVlcuXwpYC7lPqlKSg0C1KoVLM3PScwc6z+ZlImM0B9Mk2BpcR6lUpHXnbhJMgIGMJsykQ42BFeYCzhGQ4vN9aVLF0NmOWgrSZdVaFfXbsAqL2KwJ4NNHTmkTYNfauL9QghfXFtGxq1goCOFnkyK32COmzMnJydiN/qGEY1z5crlxMIez/PaWivX1taw3IafYYb2D5DfplCZlKWlJdx3f3KzI67gxqkVqtVqW7dZlxYXm5opiYRfWsKWLfFiaF34u+6+J3F4T+P2KQ7VarUte4LLS0vt6RuuLGPbtu2Jw6+sNNfxY2ATTTs6F6VS6G6r1UQFMNd7rUznhOmsrq5gcxvtKtLTKm1xkm0lwfRpWUV/f7+2nIZBkFIW07W1tVg3g1pJXULXfinTgGUwjwzJ9qy1Wi1iO9Evs8wEsne+C8OQFsMgSMGIbeNCIY+RzZsT95tqtYrurk5uU01ENmUAHSkuIWunPwK+pLyvr69lm7IFt1gsaseHZRDkUmZEclEsFtHX28MXM3aEx377R+FEYLLYMWR4bMnC5VIGPArsGMhgy5YeXLsxjT70IJsysbk7i12benDbli7paI8AyKVNDHamsZavcXMZhPibg9GuDgx0pcN8AxD4kuyenu5wIQ6UYYkQxq9rZohZyJeQwKCzf1TILiAQApgAXMfmEk9mwsU0Cb9CTykADxjKZTC8qRd2toJ9mwaC43aDM7Gs3Zg0zfMoLBPYkHHRO7QJG7oz3ASOZYR0mAawtSeLbUOduGawE0sTLtdTVdU9WN1vG+xA1fYw2JVGinnDoCGjLjJ3otDErtd4PYp90wANGMBwPrQoQaNRR9/QxmCzILQJ8SXJactAV9bifb5Wq7WlJ2eaoScP/+Y+YJmy/q5v0oCC2nWMDvUhNdiBke4slxirG1tCg2Nz18b2wT6kh7Lo70zxY37d5gHwN8vNaI9sONrYyNZqtbZuBjc0p49JQGicKOHvCQqBr9S5pTVJsbxcLrdVYe2E9zwP9Xo9MSPoOE7gzid6BKRDo9HwB8KPiSkVF9FWoJSi0Wi0tPfG4LouPM9LdhQIv24IIYnLql6Y+bsMT6nvgzkp7Z7nwfO8xOJ/13UDqUwyZqddReB2wlNK29oM8JvOCWl3Xdd3Y2YlS7+dTVi74dulXWcNoBmYD+P497ICfavw7aYvIo72ZuadRLWVZnH9PiOEV8K4gfHuC7Ml7J9cxoOjg7hmxN/ETK1UcXmlhOs29mCkT557HI/6t2ADkx2srhyXYq1ioytr+cw0z8tnBNTTn/DiH4Ln0fDyxiksg1hiyt9H02NxmOmjuXwN/783JtCbNfH/umMU/Z1pvx6ofwmHNTu7ScuMNV+aK6HccHDjll5kAuZZ5fMvz5dxYaWIW0b6sKk3EzI0yuaPEP+G9f5Li6CgeGjPRs7cBDxrLJPD0ogbH05wOYd54hDhelS7OfE8iobj+e3ZxoZHR1ez+P5NZODMVAGz5SpuHx1Ab0eq6YbJ8yiuLpZRrrvYtbETnRnzf4rGnxQKhQKGB3v9y30t1J3eMcfBYkM2Gg28dejNxHFnZmbaclt2+tSpWLdlOrz26iuJLKYz/PDFFxIvcJ7n4ZX9LydOe3FxEadONjf3IuLsmTOYm51tHTDAa6++kugYkOG5Z59pq6wvvvB84rQXFhaaunRTcfrUKcxMNz/eFfHySz9seTQt4umnnmyLAWzHjM/09HSsiRUdjh45grk2VA6efurJttwVffd7j+HSfDlR2FKp1Jbpg0sXL3KTE0nw2quvcD2/JGjXZNEPnvh+0zAiAzg3N4c3Dx5MTMvRI0cwOTmZOPzTTz2pte0aV57HH/ueFEYnXRb1Ql/64QvhkSSB8Ocfg6ZNA9dv6cav3LUDqfIsLpz3j8q3b+jAI3s3YnN/TjgK9v/SpoFc2sQrL7+EfH6N52+ZBBu605wBDJkg//v3H39MsgARHnNDPi41/PfMtJRftvA4lOmaMRgEmJmaxJG3DnGdR355ICg3O2rd0p/Dlx/ZjQczC2gUlvhxML+gwI9sg2Ne09eVu/Dm87h1Wy+yaZ8JES8nsL/dw514/zXD2NyfwxOPfy+U6CGkm+nkZSwDD+0ZwiN7N6KUX8Vrr7wclq+FWsPJEydw9Yr+WNIyjQgD+PRTT6JarcYyW4ZBeLkA39VZUlQqFTz91JMAWm/CCCG4cuki6otXcNf2QfR3NmcAAeCV/S+hE1Xs29SViAFsh3bP8xKZamOYmprCwTfeSBz+4BtvYKoNHUKGdwwTKGJ+fr6tnfbszExiSRfg6wkkNfgL+C7d2pG8rbah/LmystLWTiZOKT0Os7MzbV3cmJmZSSz6p5S2tTi3Exbwj+HbKev8/FxbZZ2fn08sgaWUolgsJk57dXW1LSng8tJSW8f2CwvzbUnK8/l8WxJPz2ngui3JLuSsra211U7LMW7R4rCwsNBWWeN0iXUol8ttjb/82lpbc83KynJbUv58Pt+W5Lsmuv9SoJarWCxGbv2LjBbTkWL6XIX8GjqyWa6ozxgo8biZSakMg2B1ZVlqJzF/6VJD8F1yTakJy8rIxl4qlZLoVdNjdAC+pCWTyUgXjwwC6eKGr4tHMNCZQrVcDG4eh7dp+Q1oRH836jWkLFNipEOJXXj5JJsyQT0XDrcZG5XqsXrPBt5HyqUiUqnk46kQ4zIuDvl8vq0+3I5QoFwutzXvlUoldHfmArNDrcdhoVBAd1eHxKQ2Qzu012q1tviOSrmc+NQJ8E0ctTNPMrxjdAJF2I2G5E6oFSilGG7i+ktFR0dHWzdON22Kun+LQ7VabWqhXEWj0cD27TsShweATRr3dXHIZDL81nQSjIyMJB4Itm23Rbtt223VjWEYbZU1m80l0jdk2LJlS+J2bZd213Xb0pVMpVJtud7r7u5pqw9vHR1tHShAvV6PeGZohS1btiYO29HRIblIbIWNw8OJFy1KaVtzh+M4bbVrOp3Wel2JQ29vX1v2E9uh3bZt7NqV3AqEYRiSizkdxPHQ3d2NgcHBUKKovCdUdpu4aWRzpJ2aja8k40OMr7aTyjiKmlMdHR3IZLPR28f8iJlKxoIHNwz6enUIDVADgqcNwcwQpRTbNX1GLCoLx1RUdu+Jui6L+57OZPjclEQbrK+3ry0vF9u2bW9LPaGd8WGaZlvzXnd3d1uWMTZubO3hSUQ7tFNK23LrluvoQEcbm9MNG4baEj4xvGN0AueXw7Nxdk6edIFOqsQOhOZnkoZvNBqwbTuxJKJS8e1OJV208vk8urq6Eh+psgsBSZH0IgbgL4iVSiUxc8F28kkHZaFQQC6XS7x7Wl5exsDAQOJ+sLy8nJjhdV2XK+0nQbVaBaU0sZQ0n8+jo6MjcVmXlpYwODiYuKzM2HkSOI6DUqmUuKxMOpa0rKurq+jq6kpc1sXFRQy1wfC2E77R8M3JJC1rsViEZVmJ+/Dy8rJ/0SPheF1YWMDGwNRVK1BKsbS0lLis1WoVtm0nHq9ra2vI5XKJ56aFhQUMDQ2BEBLoEjbXVWynrLZtx16C0cE3cUQSz8PLy8vo7e2FZVktdUQppZifn8fw8DCA0IWhaGydhWOM2eLiYqSscTpw1WoVjUYj8WZgbW0N2Ww2sbR8fn4eGzduTDx3iGVtBdu2URDMr7VCsViEYRiJ22lpaQl9fX2JpYdzc3NNfb2L8DwPi4uLictaqVRg23bidlpdXfU3GwnH09zcHIaHh0EIWdcJbIUnfxB1zRUHSinXQUiCfD6P1w+8ljj82NWrOHc2uUmII4ffwsLCQuLwzz/3bEtbdSLaKatt223p4M3NzeH4saOJw58+dQpTbeg7vbL/5bZM4Tz37DNtKfe3o4O3uLiIo0cOJw5//tw5TIyPJw5/4LVXUS4n06kD/H7QTllfeP65xGn7upXJ2/Xc2bNtteurr+xv69ilnXaybbstfcPZ2VmcOX06cfiTJ05gYX4+cfgfvvhCW7qV7Yy/UqmENw8m1zEaHxvD5UuXEoc//NYh5PP5xOFffOH5iAQtDpTStsq6tLSEE8ePJQ5/7uzZiL26Znj1lf1ct1LVlVRBCMFLP3wxLCuPEw0H+EzdG68f0Kajw9TkJC5dvJiY9qNHDrfVTj988YW2dGB/+OILidNeXl7GyRPJdZXPnzvXdju1s/699MMXE4etVqttjaepycm2xlO77ST2sXbwjmIC2Y3HVoNWRLuSsfm5OQwPJz86XliYx8aEOwk/fPLdMIC2bu62e9u0XYlLu6ZwlpeX2jrWYy7jkqBdAXjbEtLl5TZd7y1p3QbGgUm4fxzI5/Ntpb22utrSFI4Ufm0V/W0cq7dzM79dEyvtSPkBX2evnfCFQr6t46h2bp8z15dJkUQqoIZvz1Xf332fZG1ZqVTaMiPCXPUlDl8qag3wx6EdCwoiROYvrp+2Mv30vzp8O2hnvQGAcqnU0u2aiEql3NYRab1eb4uedtBun2w3fLtWPX5UvGOOg5mJGMdxMDs7i9GEOkzlchnlcjkx48U8ISSdbKenpzE0NJRYoXN8bAzbd+xIFLbd8LZtY2FhIbENwmKxiEajkViUv7i4iI6OjsQL+sTEBLZu3Zp4oRsfH8f27cn0RTzPw/T0dOJ+UKlUUC6XEzO9KysrSKVSiZnS6elpDA8PJ2bCJyYmsG1bMv0uSimmpqYSl7VaraJUKiUuK7ukkrSsMzMz2Lhx44+lrJ7nYWZmBlu3JtMhrFQqqFQqiY++V1ZWkE6nEy+i09PTbenBTk5OJm4n13UxPz+PzZs3JwpfLpdRr9cTH5EuLS2hs7Mz8UI0NTWVuN7bDd9oNLC6upr46K1QKIBSmpgpnZ+fx8DAQGIGvB3aKaWYmZnh8yozXRJ3U7VaraJSqSSeV1dWVpDJZBLPq9PT09jchp3LdsrqOA6WlpYSH6kWi0V4npe4nRYXF9HT05OYsWu3T05PTyde/2q1GkqlUuK5Y21tDalUKnE7zc7OYnh4OPHcIdL+9/I4+OLFi7j33nuxb98+3HnnnThz5syPlM7MzExbO7iJ8fG2dk3jY2OJF0PAlxwmZQBt225LPLyystKWxGtmZqat20WTExNt7WwmJybauoW5tLiYeABUKhWUS6XEaS8sLLQlMZqZnm6rbsbHxtoq69zsbGKmyLZtrLZhgmh5eRluG0cic7Ozbdnkmxgfb2vHOjszk7isjuNgpQ0r+Kurq7DbMMszPzfXljRtYny8rfljbna2rYtQ7ZR1ZWWlrbLOzsz82MuaFNVqFfk2bvMvLy9rTdvEYWZ6uq0+PDkx0dZNzHbKWiwWpblJNWytYnFxsa0jzOmpqbZOcGZnZhLPfa7rYrEN9aO1tTXU2lDJWZifb0v9YWJ8vK15uJ12qlQqbbldW1lZaUtNpR3zYoDfru1csGnnmFzE24YJ/Cf/5J/gV37lV3DhwgX89m//Nr7whS+0FZ91+vPnzra1+J88eaItcfL58+cSp99oNNpyC7O4uIjV1eSL/+TERFvmLC624boOAE6dOtnWgDx/Prnttmq1iomJ5Dpyiwt5/tuGAADzqUlEQVQLbZmIGR8bQ72J+QsVZ8+eaWtROX/+XOIB7HkeLl1KrtOzsrKC5eXkrvRmpqfb0h+8ePFCW5uHduqGUoqLFy8kTnttbQ2Li8kXodmZmbbKeunSxbYWoXPnzr6ty9pOu164cL4tPdLLl5PrO62urmJpSe9mUIeZ6WlU25jL2qEdQFvjr9FoYHx8LHH4leXliN3YZh6HJicm2mIumLu7pGinrKVSSXKp2AqLCwttCSquXr3SFsN78eKFtvQT2ylroVBoa/zNzc62JXi4fPlSW+OvHdpt226rT4p4W5iIWVhYwJEjR/Dss77C96OPPopf//Vfx9jYGHYoR531el0aQKxDMp+sszMzuPOuu1FI6KO1GhwDJgHLN2naMzMz6OrqShx+7OpVdHYmDz85MY4b3nVT4vCzs7N49+13/FTUzfT0NDo6Otso6wQ626jLmZlpbNm6JXH4leVluK6bKDylFI16PXHaa2uryGYzicNPTU4im8m2UZdT2LN3X+LwzI5m0vCNej2xjcNKuQzTNJO30/QU0ul0W2NqdNu2n46yViptpT03O4NUKpU8/NxcW2Vlvp3bKWvSsOVSCZZlJa/3uTmYRtgPKKVwXMr9ObNnzOvEwsI8enp7k4+p1VU4jvNjKevq6gpSqTbKOj/Pb2wCYblCI9EyU7O8vIjRbaOR9ONuIYvzMDM3E8dkUkphNxo87bhb2SLt7fThpSX/uDbxvLqyknheBdprp3q9DkqpNryuLpcWF0CpfnzodI2Xl5eR6+hoY55f+7H1yWKhILVTUehrLUHfBnjrrbfoddddJz2744476MsvvxwJ+/u///sUgeee9b/1v/W/9b/1v/W/9b/1v3fi3+TkZEv+6m0hCQT09qN0+PKXv4zf/M3f5L/X1tawfft2TExM/NhuU67jx4dCoYDR0VFMTk62dbNxHT8dWG+/tzfW2+/tjfX2e3vjR20/GnjBSXJp7G3BBI6OjmJqaoqbMKGUYnJyUntjMJPJaHX4ent71wfB2xg9Pe15sFjHTxfW2+/tjfX2e3tjvf3e3vhR2i+p0OttcTFk48aNuPXWW/GNb3wDAPDtb38bO3bsiOgDrmMd61jHOtaxjnWsIxneFpJAAPizP/sz/NIv/RL+8A//ED09PfjLv/zLnzRJ61jHOtaxjnWsYx1vW7xtmMBrrrkGr7/+etvxMpkMfv/3f//HZjV8HT9erLff2xvr7ff2xnr7vb2x3n5vb/yvaL+/9x5D1rGOdaxjHetYxzrWEcXbQidwHetYxzrWsY51rGMdf7dYZwLXsY51rGMd61jHOt6BWGcC17GOdaxjHetYxzregVhnAtexjnWsYx3rWMc63oFYZwLXsY51rGMd61jHOt6BWGcC17GOdaxjHetYxzregXjb2An8UeF5HmZmZtDd3R3xP7yOdaxjHetYxzrW8fcJou9gw2gu6/t7zwTOzMxgdHT0J03GOtaxjnWsYx3rWMf/MkxOTmLr1q1Nw/y9ZwK7u7sB+JWx7kB7HetYxzrWsY51/H1GoVDA6Ogo53+a4e89E8iOgHt6etaZwHWsYx3rWMc61vGOQBIVuPWLIetYxzrWsY51rGMd70CsM4HrWMc61rGOdaxjHe9A/L0/DmaYXKmhx8nw3xSAKCilwnfHcWCZZiQNNQ4AeJT64a0UVMkrpYjAcRwAgGlZoJSi2nDhUf82jw71Wg3pTCbxzeZqtYJcrkP7jlJINDqOA8d2kM1ltbRS+HSxd7VaFZaVgmlZIMF7gwCZlAmCMG0WvlQsoKs72RG853molEstw7M2qFarAIBcLsfbTqxDQggIAMMgoJRibXUV3T292ptSatEppcivrqC3f4CnJb4TQQhBo15HrVZFd09vNG3KwoXfi4U8Mpks0pmMkI4ch/02CMHS4jw2DA1H0tZ1Cc/zsLqyhMENGyNtquth5XIJoBSdXd28fKzudPWyvLSAvv5BWEH/bYX52RkMj2xuGY5SoFGvo1QqoH9wiOfH3qll8Nt0Bel0BrmOTm3ZxDQAYG5mCps2N1eSFjE7PYlNm+VLZWI+Yp+vlEtoNBroHxjkc4QX5G0Qwr8zLC3MobunH6mgD4h0UurHJcTvuxTA7NQENm3ZBg9U064UBAQ0eOfYNpaXFzC0abN2XKsorK0iZZro7u0NyuWXgBDAEL775SWYm57A8MgWmMEcaRCirX8j6POT41exZduOIB2iLS8bH5VKGcVCHhs3bRbe09i4Swtz6OjsQkdnFwDAo4BLKa8Ll1J4lPpzLChmxq9idMcu3m5EmNEp5P7meR7mpsawaXQXr2O13hk8SlHKr8FxbPQMDPH3BHLbs3ISAixMT2Jo4yakMxmYhAR1HNa3ismrlzC6cw8sYgjhQ5gGgRE8r9drWF6cx5bRHVJ9sfdiOQFgaXEe6XQavX39vP9JZaWszD7GLl/A9l17eR9thauXzmPnnmsi+Yr1Yho+YaVCHtVqhfcB9hyQxxzrD3PTE+gbGERHR2dkrqY0XKdYnAtnz2D3NddF1nKxfCwv27YxNTmGXbv3RuqChRdpWlpcgGEQDARzWLScYS8iBLh66QJ27NoNy7Ik2g0Slk8Mf/7saVx73Q1SB+FlY4EAlIsNbf46vGMkgbPTk9rnNPgTW/aJ7/6N2NP4d5VppPAZqWeffDyyiMfh8qULuHDujLYD+tnJb157+XnUatX4BBW89OwPYt+pNC4tLuDksbcSLRQAcOrYW1hZXgzTaxH+5Ree1j6nmu+Neh1vvPpSMkIAjF+9jMnxK/KgbcIoHzywH3YjHBitmIbX9r/gD1glTV0eK8tLOHf6pCYt+Tv7ee70SayuLEfCigyjSOfB115uSrMIx3Fw+OCBKC2Q24sEf7PTk5gcHwtooFJ4mT6fKTlx5BDq9VoCSnwcPvhaonCEAKVSAefPnIoNozL741cu8f4ojU1KtQvT8bfeaEoDawP2d+ytg5G8xbzEZytLi5ieuNpyYWG4eO40KpVy4nng1LFDzWkXcqnWq7h8Nr4eVSzMTGJxcU5Y6ON7GqUUZ08chee6WjoZxIXr9PG3WtLAGKNSIY/p8atSfuJ3tV2nxq+gVMxzikVyPKUcBAQXTh1tSQf/7nlN65Et5yzO6vIiluZn/WeMoQSNXRsmLp/nY8lTGXtNE1w4fRwGSGR+0KFWrWL8yqVY2lXMTU8hv7rSNIyY1/nTJ/yNYsLF48KZE/y7sKTK6QdJFQpr0nrdKo+xK5dRrVRiwxIhbUopzp/x5+rIHKf5bdsNXL10QaIvkj4J54ylxXmsLi/HhCORfC6eP8vHktq/dWU5d+ZUhF+RiY5unFvhHcMEeh4blOGCGFtPothGWJ2pECec5MKdkE7qA8j5GOpLBTpJ048KrXRPYjbkXVyrrAh82nWLoJq2NgBPJ/o9LppY5+KnX436BUJuE8rzURdyHaMTxo+2hW5QMuZIO/ko9UlinrNnrev/fw7x9RqWuyX+DmxtikOLfUakWwlnMS7V0ObTxkyoS1vzXS29uLjEIdqPhO9N8k9Cf3OGLT58ICvjz0MpWzRO5IQDVCuVk8OENLQqB+v7PBgR3zXvb5TKErpI3xLK2LSuNO88z2va36NxfNmfkXSIUArDMBMt2D6THN9G0fA0tu706wKVytp6LvDD/yjrk268+3n6n54XP6dKcxib4z0PRuJK15ct7K/CmGBpNwkvzumE+OHbmagp9UAMgwscSJM24Ot2XFrJs5VA6P/sTPlTjkKhgN7eXrx25BxGt++Q3sUV/PTJ47jhxpti0xTjeZ6H82fP4Nrrb+TPmvF5y0uL8FwXQ8Ob4FGKSt2Vjn5UjF25iC1btyOVTsfSI+LyhXPYve9aPd1Upq1aKSO/tsqPyNRjAoqQeQaAhbkZdHb3oKOzS3scrGL86iVs37knEd2O42BuZgpbg2MjiW6ETBv7zK+tglKKvuDI1qdbrj/x6GN6ahJDwyO+2F2TvvSbUsxMTWDL6PYIY6lrp1q1inK5hMENQ0o6+rKuLC8i19GpPbaPMI6EYG56CiNboseYccfBSwvz2LhpJH6zKHwvl4qglCY6tqeUYmlxAf0Dg/wosBUW5+cwNLwpUVi70UCpVEJPX7+Sb5RuSimKhTysVBrZbE7bhoC8IVhZWsTABv0xjZgPQ1x41g/99P3PWq0K13HQLdSjp6GBoVhYQzbXBdOyOL3s2JhJjfkiB3D1BJeGx8FcAiEyOhSwXQfVcgldPb2JGIZatQLTNJDL5vgcQQSJkyEwNoQQlAp5dPf08EVKPQ5mZWH0ra2toqe3j7+PXeCpf/xm2w3kOjr5WG+2RFUrZaRSaZipFAjC42CPUnjwP10vSB8UlWIxoB38CJ19MhrEctSqZWRyXfCCMkn0KvXesOugngcrnY0wiGK6LJl6pYJsLgfTMGEQAtOQj6fV8OVSCd1d3f6xL+KPgwHA81y/HnMdUv2Jc6JHw/iNeh3EMJBOp0Bp6+PgaqWMXEenVFfNEA0fLSM79nUcB9TzkEqnQQiRjoOB8PiVHa026nWk0imYpikxv6IqE8vDp6WKdDYbq/YildvzYNs2sllBlYzq5wDA77+EEFhWVNNOPQoGAKfRQDqTlhhASikvs8QUAmg06sgyNSI+/hTGkBCUCgVcu30I+Xy+pVWUd4wkMBNUHCGhFESqOKG39Gh0uwA5nigJ7Orq4mHiGEAWN5XydeqoEJ6f/Wt2Vul0pi3piz8Qmu+0monjm41lz/MgSgK5FC8ivfQ/G8HxqzYvTfriMSNVwlDl07EbcB07lgYVlXJJWlSlfKgshieEoJDPa0XzOkmt49gol4rBe/GdnpZysSgdTbcKz488E0gLPc/D0uJCbHpqLZVLJZSKRW1auvIvLczx44skiFPD0KFer2FpcaGpdEFsq9XlJX4MpEInqZocv9KShnAjRDHVJLzaP8vFAlaXl6Q0RJpVzEyONz1WVyXkY5fOa6Sl+t9OvY6ZybHYtFUsL8yhsLbq5yswgAYI1wkUs7p8/rTEZMs0BBsl4dmlc6d5WK0URgjsHwVORPKMw+TYFVTKpabCl1BniuDS2fBYUpSK6uA4DVw5dyqgXU5LpZ+CYnl+FisL8wmo9nHl3Cm4th17MiB+AsC5E4ejeSNaT4T4mzu/z8gSQTE9Mdvxq5eQX9MfB+um1hNH3hTeN28pz/Nw8qiszhC3/gDA0vwspoP+S1qkTwGcOXkU1Uq5aRgxhcMHX9MezRLN93K5hLMnj0bSjAgSgrVz/MolLC7M6emgYU9jRXojUPVh7wGZH1DLfmD/S1o6koyVOLxjmMDFYGEENBUnisIpxZFDet0hkflj8DwPx46EHTyuv7J4CwvzmJme0obR6bxcOHMKdqOuT1SDs6eOaZkFlcGjFCgVi5gcu5KIx6TwF65SscB/h4ws67Rh2gBwIdCnkfKNSd91HFy5eF4Ss8eRReAzAMtLixINzZiHscsX4QaXctRQLK5Y95cvnJXei58iKKWolMuYm5kOwojvhO9CnLnZGVSYDksMvWLcq1cu8meiFEgHz3UxNXE1mp6Qlxi1kF/F6uoy73sqM8zKyDAzNQHbsaX3zTA5HqUlDrVaLXYCVWkCfOa4VtVP/vyCixB+rglDyiUd/JNibkYep7r6YyiXSigW1iJhVZoZlhbm4blO7OU0tV8szM9Iv5tVvWPbyAcMaauwgK+HV6tWtEeoKiilWAz03nRQldkBYGlhlsdtBkKAeq2KMptjNPOhisLaChzH1jJDhqalVhaTM2mu40b05ETdPVVqVy2XUatFNyU6Zp1SIL+6DI/6YkrGXMpH9HK8tZUlbXo6NBr1cK4WJGK6OYnCv8RnNxrC/NK80zBaWoFSf41km4wkqNVroY4fTye+0IXCmi+gkPKVw9dtl5dtLUb3kQkExHwdx0apVIqun1Je4fdyuQS73vxShhh3bW0Vzc6PxfFEKUU+L9dj7PrRlAIZ75jbwQxMBP7hh+4E4Eurrl6+iGuuuwEAsGv3XnzgIx+PcjSQFwHd7ouHoxpJY/C7WCjg8W9/EzfcdMvfSXmaQeRtVQllHLOiTUf53opxCY+pmteZmgdNEI6FjR5XyLsotusiAV3syF0tjwiRESSGzBg2uwXXTP9GR7v4XWIChPpLxJjTaDhKKUhwAzquXdXHpnJjulmZjUB/RQzXDKbmWCQOlAKWKd86Vo+hpKMtw4RhxB9Lq/SlUqlkdARxxdvbcX2TtYF/BJQKn8XQzGClMiDE0Oo4Rb8D6Uw2kq9PVzRtSoB0NrzxH2FCIOvRWakULE3deKASI0XhHydmczmpD2j7g3DUluTYkD02DZOXNcnN00w261tmgMp4+8fCKjq7wqMxfrEjZkYgBOhsoiahxkul00CT/hhJu7MLpimPD91xMENXdy+P2wqmYaKjM/QWoc734lEyAZDN5ZBOZ0AImlqrYOjp7W9NhJBvr6C2w2jg3yHPg2mN6lOz+bWrqyfSfwkhXJWJAMimTE5L/8BgNH1o1nT4c0xvX792PRdpZ2l3dHQi16G3ziGmyzC4YUhYo/VSdZYPAAxtHJaPoiHUJQk/kq1GQfh3ik7gkbPj2LDRN7MhFnhyfAwfe999OHYh2PUT4usCDW5omq7julwHoZBf44NCZLYiizyAKxcv4DMfeQRHLkz7UqRAJzAO5VIRuY7OWCfQihATlXKRm/tQw0XK4DhwHBvZbC4aHvIOEvB1LwzLgmmGOoCEAGnLkPQdWPxatYpcLsd/M+ikH5RS2I2GtPA2g+u6oJRy3Qv9xQzCzVQ0bDtyDb/ZpQ7f7E8yBoZJLAgxdHsH/zfCiYblJ+pS6QatpCoQk3fc3MiYcBZXzLeZyZu/S7C+qTKrapbqmFHpEsuiYxCB5vp3rehT02q1GREnZ3EBYPmr9a3m16ysLBwrD1XiqYxNxKwJROlZ/FIg67P5+m4mMYIyIKITKDINoBSmGc5HKhMY6gr6DIXjOE11SMUiuZ4HSimfW1vBcRwYpinQoNcJZOV1bQfpdNScl44WSik81wFMC5QiVifQ758UruPAA/Fpl+o3mg8hvsQ2nUrBIIZWJ1Dsm4QA9XoduUw2aCv5ljBBVCeQeh5njlgaOp1ACl83zbQsGIYR6bvi+GP1Uq/X+LrRqp0opWg06sgEzL1urIm0ObZ/0mClUvyZqhsnPqtXq8jmsj7typwu0s6krZVKBdkYM2oqPNf12ymbjZXai2thrVqDaVl87VDnBhaeBvRUKmV0C+pkLJyogxtmSlGtlH31M019cFrIuk6gFqVyqakUCwBsx8HnP/NRPPqR9+J9992G3/gnv4RKpQIK4Ft/9XV8/mc/jn/+q1/AR997H44dPoQnH/8u3nvPzfjEBx7Af/p3/xo7h3IoFUsAgONH38LnPvUhfOx99+Gj77kbT37/uwCA3/mtX0OhkMdHHr4Tn3j/fS3pvnjujFZ/LA4njx7momvxTwSrh2Ihj7FLFyL1omNeKYDLF8+iGIj1dXVJlc+3Dr6mDecFk7T4vFGv42hgwiNOF0/8G796CdMTY/x3K7x5YD/sYHIR48TFffWl52LTEuuUUmBxfh6njx+V3ol1IX2nFMcPH8TK8pJWwiqH9cM//9Tj0nspnNLO1UoV+198LnK0odYp+7t84RyuXDrP02qFl55/Co16VD1BrRP2/ZknvhelUy1z8GBhfg6HDx6QaPWUBUjM7+hbb2BhflYrTYv0eQo8+8R3+XdPoJXlIdZrvV7Hi08/Hul3kf4YpHH10nlcOHtSeBa158frAcArLzwl66nG1U/w47nvf5vbv/Mou9srl5WZRMmvLuPIgf1BGvp/Yp5nj7+F2akJn2lC9OiQPWN0PfeD70SYJX3/8o8CX3jyu9p6EOuD/c1MjuHcqWNy34VSJ8LfoQMvoZhfC9tSKJ+YD8P+Zx8LJF3RuZGXN/gsFws4fODl8D2rN/aPt5dvh/Dy+dOYC3TZtOkrdL3xwpPRY0wljFhPrz73A97GqvkbFfOz0zh9PEaHkNMW5nbk0AGsLMcf8ao888vPPpFo3gX8I/6D+1/k5WiWLuCPpbFABYbT3SSvV196Fo16PTKnq8wre/bi009o04yow8A3o3bi6FuJJWsnjr6FlaWFGJWssE+zVy8+E5p0i1M3Ymg0Gtj/w+fCgiiMerONazO8Y46D4xTIxda1TBP/8c/+Ai+/+Bw+/ujP4V988f+Nr/+3P8Ov/Po/BwC8dfAAfvDiG9i5ew+WFhfwhc89im99/3lcOHcG87Ohzk4hv4bf/a1/iv/7f3wHGzeNYGV5CR9/3724/c578Gu/+SX80y98Dk/88CAoBSoNt6n0YmV5keuNaMkn8nfRjl9sHPidpdGoo1gKLwVIi6kmfKlU5PpgkjQkZlut072IK6dHPRQLeSm9ZoxavV6HZSaU1AFcP4al32oCK8dclgjTCD9d14ko+cdL7gjqdf8WYTN6xd1dvVaT6rtZ+h710Ah0SJMc29qOzetRlFLFSUrsekORJKr5h7RTitgNDCujGN31XFBNX4+j3xH6ogod/dRzw3dCFN3kyey7tVIJYNm4rtv0KI8v5mL4mEoWmctImSgBSHicqz0OFkybNAvHJU2ex1UCdHp0cYWhFJE6UuF5HldPSALXddsKTz0aGz6sc5lZFhHXzz3qSyUJUVQlEN4m5lLuoAd5Ae2ibUAq9bPwNjIhvuUFvfH6sH3FMQmE7cMv7FBFyg6/T4r1Hltu4bnneZyWZvOFjnFqBkr9NgUh0Y2ZSI/w3fNcpDXqCeocyH67rgdDkDQ364+EMEZYluZLa1/wzqMUnudGzM80LTH1+0yreVSXWNxpFoPnujBMU2Ii1XKx5+0wg+8YJtA0zXDygnhEBv7M9Sj+r//8n/D4t7+JP/kPX0WxkMcd99wPj/rSq9vvvBfbdu6G61EceetN3HDTLdi+aw8mJsbw6M9/Hv+ff/Hb8CjFoTdfx8T4Vfyjf/AJnj+lFJcvXfDpCAaEJ+yYAd3OAch2dMIgpnSdn7/XlLOrp1fboXUdzDQtdHR2xg4YRiNDR0cXLCslS0Mgpy2m1Nc/qEgI1GNByp9Rzw/vqVZTY5DLdsBKpSJGVkUQSkGD/PoHNgAg2vR1SWwYHtEYcA0nDtGWVSqTQU/fQNNjfT++/9nXP4BUOt18skVwJAdwy/lcx4XED3JCDAwNj0i74WZ5dHb1wLSsWGO16sQ7NLIZlBiCJC2qE0b5f8Dm0W2x9UKVz2w2i4HBjdKY1B2VsueDGzYikw2PmNQFU0wbALZs26mVDojlCI/qDYxs2a70X33fIQC6e/oi4dj8Ik7MLJ+R0e2SPpiOKQZCic+WHbu41IlSAERuX1EylM7msHFki5bxY3FEJmVgaBi5QH+MpUMgH02KdG7ffY284DRpX0IIduzaGxn7Mj1hH+ru7Ys9qtPlsnl0O1Lp0PIDNPUhmoDZvjvefJaKTCaDzdt2RBi5OAwMbUIml4PoJSQ8eg2YPyH+9j3XRjZU7CiexRXz3rnvupY0M3T39Gl16+Libt2+U6vLpo4XNkb2XHNDJJxu/QKAVCqNnXuuiZQnTnI1uHET168VoW6a2O+9114vqRu0Ykyvu/EmXg52kz2OAevs6vbnDSinY+LGkJWF+vXY1dOtnYtYPDFOs7sBIo0E/vH4dde/K5om2Hj90fCOYQK7BzehWHP4b9ZApZoDSinyFRtPfOevceDV/fjm06+ho7Mb/+PP/zMOHzyAfMVGte4ilc0hX/GlD6WqDdejKNY83HL3e7AWSJrWKjZKVQd7rrkB/+WbT/L8WIedmhgDMQyslhvwKNBwPGnCUjvO3pvuQtklQLURG0bEvlvvxWql0fS4gMV3M10YGN2L5UpD2uGK4fhiD4re0V2omRYWK+FxIAFBxjS0nX7ztTdjruhLyNjONY4u16UY3Hkt5kuyRC22rN2DcImBuWKV08HoFH8zuvq3X4PFig1CHClcHPq37eVpi0y6jp4GTcHsHeJlZYhjUEnvMPKuhVIhaiJEZ4Kic2QHZgpVeQIl0YmXUsBzHaQGRjCdl73MiLQwXS2DAFWrA4ZhYjpfDVwV+WFcRSDH+ofZO4y5UgMgyVQUzP5NmC7ItMQxbPU60Mh0YbpQBSGASQjXgWMMkNgfa5lOrLgEhWIyjzpedz+mSzEnAgpcxwHt7sdcuSbVMV/chf5FQFCGBWIY8Eo1Xr/NbMXZmS4s1h0Q25cgirp+nDkWnrkd3Vis1kCBppsNkxDAA1Kd3Sg2HN6fXKpXwwCAqpkGoUCj6o9rI1h0VMkzS8vO5LBQCetFxxgZQb00HAeNTCcWq3VeLypEioq2B2KYWKrFW0QQ86uZKRQdD5VaI2SQgzRZmQGhzrI5FG2bl086/iMC8wagbjtwrDSKgRqJzmgwS5sCaBACQgxUHCd6cS1SBsBLpVCyHZgk1OUTx79YTs/z4AW0mIRIOpssnGWEpn0qtguPmCg1gvkuGHOWoPfpUsr1PR2PwPYIENyiZSoA6jzDNx2GhZoTThLN5tOG48A1TNQcLxJOpN0M5h8vkO66HoULUWIXSlh9xsj/7VECxwMQnK6oOoE8L86QE9icdj3dJNhk2Y4LCgJHnRCFkouwHRceNWC7YT9S82dxDELgeB4cV/YqAzA7m1QKX2/YcD1fWKXmL0o1DULhNjlpUvGOYQLXluawYaBP6hQU7NYQQS5toloqYGBwEG+8/Cwefv+H8cS3/wdGt+1EZ8ZEJmXAMgx0Znwbf3fdfTf+4H//dcyMXcD41cuYDcxJdGRM3H3PPfg/v/xPceqtV3H3/Q8DAM6eOoHd+65FcXkO1UoZWQsghgnLiPrdFHfGz7/wBB5874cTG4t++unv4kOf+GyisDNTs1iYncHNt9+tfc92SGwSOPjWAey59gbu2xXwn6ctvZL4G8++gA994jOJaCkW8jh37CAeeM8HE4U/c+kUOrt6sGnXngiTJh+R+xPCW8//EB/42KOxR3A+zWE6h557GR/6+GelNOOYl6mlGawuL+HGW+/w40OeGtRd5sW3juH6m25DX2en9FyMx6gkBHjr+Vfx4Zg2VctTzK/h4rnjuP+RD8SWU8TCpdPo7OrB8IaoUW8dr3H0xTfwgY+G9aibQsX+e+T5g9j38c8kOpqeXplFeXEBu0dCP8miJEKVKo8dPo89170L/T098s48ZgE+dvIwrvnoo1IaKhPNUC4VcPniaezeEu2P4q1ZnwkElq/MIJPNYWjDYLAwKfQqaRw/fQQ73/8xGJYp5c0YGbZZYs+PnDqK3dtlqZS4FohHiMvL85ifmcL1t93N36l5iLgydglde65Bb08vT0fcEAAhA2SA4K1TR7Bv525eft0GjzEXVdfG6YunsUc11C+MJZH2heV5Xwob43tVBCHAkbMnseWhYaRTFmdcRF/B4ncAuHrqGLZt28mZVLUuxDotV8pYmryC4U0j2vypUv9XpsawcfNWpLq6tXO6TDvB2Jnj2L5jV3jRQ2AZ1GnK8VyMnzuJnUF4Vu+sLxICpIzwMH92eQF2o4HhDRslCbR44cASaLp64TT6+/uRymWDOT9qANyP6H9cOnMcO3eFcwaFTLBY3GKlgtnxy9iyZRughGMQL4DMTk1gcGgjunv6uGqJWncis3z25DHs2L1XTplL0eS4nufh/Onjgd9jPb0iVpaWUCkX+KVSMbxuKbl88Ry6unvCC47ikbM63wA4d+oEdu25RpIRs5Me8RQB8C+RXLl8AVuFscTqJ2wfEpxc6sujQyImsBKnTyfAMAxks9mW4X5SSJkGrOBGG6tYSilSpv8rYxn4uf/tH+KHz/4Af/QHv4vv/PXXcec992F+dgYZy49rGEAm5aexZfMI/tW//Y/4jX/88zAME4/+3D9EKpVCf083DMPA//3/fBtf+Ze/gz/6gy/DsW1s3jKKP/vv30J/Xy/e8/4P45PvvQu5XAf+xw/2axbGsOkNUGTSFiyruZ4M3+mZBGnLiO3UIiwCpFOmxMRRhMyIOhmYhCKTspASwus8hvDwJkEmJd8KVI8XGeqWgZRp8PDioi7+ZjANP+10k3oRbwcbBkE2He3ucUyJZRDe1q2QMvw6yArh1WTFn6bhbz7SVlQDSzfhWUG9tAoLADUTyKYtZFk9augVjz4tkyCbsZBJJbuNaRkE2XQyMxiAv0EQzTM0yyJtkGh/bMIEGoQim7GC/t7alZllEGRi+osapWEAaVOmRWXugVByY4AibZlIBW3Kxk9cPib8dlJtVLLyikr7lAIpQpBhhuAVqT1jGkOmALAstsGkIJr7fyLzYwHIWCYyZiglEiWdoscQwK+XrMXC+iF9yZfi9gpAg1CkTTFtDZMu/EwRAitlImsl7I+EImtZfG5nN4NZnbheyAQSEL8PmOEtfgIjwiQzKbkFirSVQspg60ZYNh1jTagHy/TDR6XA8mD1Jd0G0obJpd6ipFHcbBICUJsibVlIm/5NYtWHMAFgmQZnCgxCkU6ZsEwihIDkfcOjAuMBikw6hZRJ4FESGWsqk0dAYZlGojYy4SFlWXyt1UUxDBIyNJ4LyzRhEJk5DEsRzn3+rWrC0xbB1i4xjm27SKVSsMyoXVlVKuyn7yGdSiFlymukDgQAoR4y6RRfg1XBhJgn22ipa5h4g1t8ZghtFJZJ7jPsl0hvKyQyEcNsg8Wdm1NKMTIygunp6cQZ/68CMxHz+vGL2Lx1FIB+Mhdb7MTRw7jpttul3qqrpFKxCCuVxtiVSzhx7DC+9f/8Bf7miReb0jM7Ow3LSmHD0EY4roea7TUdSBfPncbufdfFmojRhd977Q0tw1HqGwquVqsYDnTOpPeAtDABwNTEVWzYuAmZbI7XoUHAGQ4Vly+ew5591woTZbTu2e9GrYalxXlsHt0uPY/D0uI8Mpksurp7tH2TLU5sEI5duYQdu/Zo8w7rJFAI9jxMjl3B9mCnq04Oal6lYgGNRh0DgvQirkkp/Jt7g4NDsdJdVQIzNTGGrdt28PprIsxEo17H2toqhjZu4vk1w+rKEtKZLDo7u7TlVMs7OzOFkc1RF3ZxmI1xeaeCUn+nazca6O7t0x7pqIzV8uIiunv7kEqlmkpeGRbnZzE03FqqA/hmKkrFAvoUm2Jq/2FtUSrkkUqnuSvAOCaQ5bW0OI/BIVHiKS+66rHtytICegeHNJsLhYEBRaNeQ6Nhc1NROombiFJhDbmOTqRTaWlxBaLmYQghyK8soT8woSWaxGHlEMee7bgoFQvoCdpULatIO+D3AcMwkElofqSwthr2lyAth3qgFPw2teg2rpRfQ29fv3SUGneb2G404DoNpHKdPKwKfksYlLuwM6yUhglk9Rc+qxby6O7t1zKBaljP81CtlNHT3cMlhyIYM8TqnV1Uyyj2JWUmMJwDy8UCOjq7YJpGVIotjD+GUjGPru7eREyg4zhw7AbX9YxjAhlptVoVKcv3rGUa0Y0FgjKyflYuFdHVLdpEDOcOJhETJWblchkdnbJZFp+uqIDCN6BNkc1mFMmvDJZ+pVJBJpOVTCgxmnThq9UKuoQTIXHsiPEI/As2vkm3bIQp5nUSfPk7NxFz8803w3VdXy9B+WPPN27cmCSpnxgygZ2fpgxggKHhYd5Tm3Xxv/i//gSf/MAD+NVf/t/wt//jv+Nf/3//RHqvLtYU/qDMKBLTZkeUzWwEqkXwO2vU5l/4Xn1KkErFMCJCPAbDMGAa8RImKT/4yvVxcwRrCwaX+jcaxTZSw/C0KYVjB94/YupOlSTadqjDpvYDX+IZMjy246HRiN6CFtMV0WjU4TpRV2o60tikJTNc2iJwlIrFRAwg4JdTdKFElL8oLSVulyukJ95P8moTMxIqXNfF2upyorCEAJVSCZVKOXbDyWhjWF6c9+2hCe+bLUoLczOx79R6bTRqWFn2PdKwPym88mx1ZRm1ajLdREKAuekJ6URCBPslvp+ZGk90c9cAQTG/hmJ+hecV0iz0Y4H6uakJeE6oO6ZCfTYxdkmbt67u67Uq99SShGFYWpiTbvK3wpWL5/j3SP8mMu0EBOOBOST2W4RKXjG/iqUmbuBURm967DJqtXhXgCwPls/VC2dahmWo16qYVMymMOjG9vzMNNZWwrHXqurPnT7RPIAa/uQxP+9WExKA/NoKZibH+e/oaYf8e+zSeZTLpcS0nDwmm8JZLTdQt/2NAJ/7gjzqtRrOnzkZ5i3REZ13ZqYnsLQw17T+xDROHX0LcRYOdOohRxXvZPrTA7+nra4s4cqlC+whb9RIfSI6XzVDIibwP/2n//R3EuYniUorO4GsUgnBwddfi0zEuq7+67/5v+OvH3sav/d//lt864nnsXuvfPNMOsoK0hi/ehnLi4uClIM2nRxVn4txYLuc0yeORoqk0sSwvLiAecU1Fg8npMtw6dwZ1BUXdmwHLsZhZT1z6hj/rb5T45RLRUyMXYmEjauZ6ckx7vO26S3bYAAxF3Ys78iiLkwAvgu7cxIj1GyyW1lawrJimkddgDg98F3YOQLzokoJ1B3npYtnef+Ls3HGUC6XJXdnVPlTaZmbmUKlXFZ2/lT6FDF2+WKiiR/w63FqfIyXSSqX8Jt9X1tdwdrqSlMphPhuenKc+zFWmX4dppq4sFOLWqlUsNzExZhaM8uL8xLz3UzaBfgXxKgmnA6u68aOU5WR8UBRLhVRCswt6fugLBFcnJ+FI5rP0cQTf85OxbvfU+u/XqthdWVJ+06HtdVl1ANGKkndLM7PhBJG5Z0a3aMelgSXd5FLCoSF8z9rlTJ3YZcEa8uLgJYB0IdXXdjxS20kurDbjQYKisuwMF607MXCmtaepw4EPvOddFwDwPKS74Y1SRvVKhVUBKau2ZoE+Bsq1X4iD6ujRfE3PlWqoNRwpDpkedh2g5sik2nQl6NcLHKTW61AKbC0tCCY2mkdp5ltRgZWtlq1imq1Eo5cLlmXj4IB/QXDOCRiAu+///7Yd43ADlizMD8NYO6ltHXDRl2wcsdJoHhw4XsrO1gqE+m6Lvdc4Tce4X8+KW20ngLXdSWpoVCkKF0ksA0VY8lfR4XruVrL/2oZ1Q6ok0YR5Z3n+rbK1LBEl05wZMvKKtZfHMQqENMVGTyWjkEoTCu53pvrulxCqiKq2+HXu2lEdfxYeFl6o6kDEg3HwGxJhcdd+o0MZyo9j7epWg/6AiWb+FnahhnqxkjlEn6z7z4tzdWUZbqoX9bIcz3i+jqjQQKlMFMpfRtB0y6ESK6rZF0dWceHUt9sBkGLug7geS4y2VxLA8Ei4iT8Pv1yfqZpwjLMSPuI38UY2Zz+tCEOmeD4SqeyoeZnmhZ375ekTdXjTjl9+bfnupILuzhzL/zyhEGkUxvdwioWKZXOaN0kxkm+sh3qxTCiDed/J9ojTD9eVOJlpVJIpdPCmNZG5ejs6g6Z6Rbjm1La1J2eCsM0kFMuwTVDNpuLeHdi0BWju7dX+v2u4T4MdqYlIYwUvic8xmZCCXX9Zfmk0hmu4sHCN0NPT6+wLsWHY6/6+vrDZ+qRsXIMbloWujTewPjGTaAx4RTtx0+iE8jw0Y9+FF//+tfR3+8TfvnyZfzsz/4sDh+OWiZvhj/4gz/Av/yX/xInT57EjTfeiIWFBfzCL/wCLl++jEwmgz/90z/lTGWlUsEXvvAFHDp0CIZh4F//63+NT3/604nzYjqBRy9OY2BgMCIqlX4HVbG4MI8h5TaQCDG+bdsol0pcz4R1PKKEZ/mUS74eYSaTge14qDvNjePm11bQ2zfQ9IiMwfM8lEtFdPeEg0I86Wbf+a6o4VtZz4h+RgVaVZ2sSrmEbK5DYnpNQdleZTjL5RI6dboXwndWT74LOwfZbFaqv4jELnjWqNd9nREz/uIJmxQp9SUSzPWPKo1U4/t147uw00mY1HZwA2lUlBY9E27bNlIpi5eShdFNGgYhkgu7VpM5kyz7LpSE5zFl1t2qVRcD9Xg4SV8U00+6sVEnbfWyhPpM7Ne6eD8KWkVV61Ey6UFCvSbRjEOzyxA6aaCok0WF8KI3D5kmKoVhF0RalyWUCJoCs8DMj6g3oNUyst+6fsXGntYkjKYu2BjwaFT9ohlzIr0P0hJ1AqXbwUF004gq3ou0SDVEATZDm4TwSyMsTRbepRS+oWACiuitY1XaT0CCCxAWCNFfDBHBJGMp04zoBBIIOoGs/wUCAXXsqTqBLL4/J6V4m+mk8X6N+O88x4GZSiUaa+L8qJsTCZEvQ9iNhu8yzjCCyxNyWcPNo9/3GvU6ssGtZvbcnwflPFg92o4bbjSgXwdYjnajAcM0o3O7Eo6hXqsFtOjnakYzS8VpNJDNyZsk9TIM6+OObYMQ+Ia0lTVOpIUQglKxgGu2/Rjcxj300EO4/fbb8frrr+Nb3/oWHn74Yfz2b/92O0ngyJEjeOONN7Bt2zb+7Etf+hLuvvtuXLx4EX/+53+Oz33uc3ACHZWvfvWryGQyuHTpEp555hn86q/+KlZXV9vKEwCX8HHOnz3WBJ2IOTaimvDlUhGLC/NSxxYbhv1m+U5PTqBWrUh5s4VbdzR86fxZHqYVGo06rl6+ID2T6FI65dzsFPcwEulIShoAcObkUYkOguggEHH8sH+UHTfIxAG4uryEybHLUt4iM6g+O3f6BMqitxOh7nT1eOiNV6W8pTSF8IQQVMolnDz2Fv8tDkZdO1y9dAHzs/pLUbpjtdf3vyBPrDETBsOrP3xOCsv+dJidnsL5M6fiE1Nw+OBrKARut2S6KS+7uND+8NkfRMLGoVQs4M3AfVkSnDtzEpNjV3j+ItTqIQR44envt8XwvfjUY7Hv1GRmpsZx5sTRyJgXx7KIQwdeljzktGJ8n3/yezGbkGj8UrGAg6883zQ9BgME588cx9TVyy3DsuXolWcfh+0KNlRj5B2Mpme//20hDZH2aLzZ6QmcOHww9r2Kw6/vx8ryYoyUPPr0+R98R/odJy0lIKhVyzj40jPSs2ge4fcr505hQphPGdMkmpxh4Q0CHHj+B9yLTRzEsfvqM49r6dB1neWFOZw6/Eb0hZi28P3YoQNN1Rl4XsHn809+T6BRYWCVz3q9hldefDqgtfUG7+qlc7h66VwQvmVwvPrDZ9Bo1BOvNc89+RinJXLjl30GX5YW5nHsreb1yOJRAMePvMn1QiMSXU285596XBtWB7vRwEvPPx3QJzDdnOZwTSIArly6gEsXzkkFiggZSHhBKinaYgK/+MUv4r/9t/+GRx55BP/8n/9zvPTSS/i5n/u5xPHr9Tp+7dd+DX/yJ38iNda3vvUt/Nqv/RoA4I477sDw8DBefdVftL/5zW/ydzt37sSDDz6Ixx6Ln8zjwHTDAD1TItba1MS4Ng1dRyyXS4EeUzQ5cQFhi8fCwjxs245lKFTEMRc62I0GVwYWJ5u4DlnM51Gv15oyFeK7xYV56bhZkr4gmsbqymIkrLqAsu+VShnVajWy6OrajALIr61KTF+ryWhtbSWSHtvVqpKGRqOOqqDk30rfrFwq8t1uGCeelmKxGGkUXf8BfMlIuRx1YRdX3Hq9Btd1Irt3Oe3wSaVcjjzz0ydQGWvAd7+YlPFyHJtv5sK8VVrCZ/UaM84dlUJQRGn0dZ7iLf7L+VDuOzr6Lkqbf/FINYwhxIHcjxuNBgyDCEy6WAa5PJQiAbMQzleu48ZMWiEzwvsLKFzHAUmgFMTi2rYDy1DsFdKorUKdrmhkvCpldWzHd6UW0z7qY8exYRpmjEQ42kfbged6XC3Ipz1Kk5iF67ra4904+C7vdGaomsfjbajUt5y2E6GlmSDDcVxeVl166jPxdnWrenWdkJZEkkDHbanmIYXXqNeI/UxfnuhcBUTXa8dxtCpNani21jtuqAIlzkXipxS/SdWpuquOpk0lGoQ5mMKvd6uJugEViGyDB2yPCRwbG8MXv/hF/OIv/iJ27NiBP/zDP2x5G0rE7/3e7+Ef/sN/iJ07d/Jny8u+EujQUGheY8eOHZiYmAAATExMYPv27dp3OtTrdRQKBekPgK9jIISTKomJYoI/brpDPGYAIhOeH5UGOi9RWnRMo2VZgdhdFvVK8YTfuY7kuhSUUm4WQiA/FlYqhUwm5jaxJo3uwByLrlzqb8/z0NPbz9+1mq6tVAodXV2RtOLidXZ3JzagDQADAxu0aYuSLlbvhmGir39Ay/zpJsiOzi5Jb8QPF1//G4b0N+l1xxfU87QmfOKQzebQ09vnx2VpKWHEMvQNDCKTyWrLpSv38MjmxIuvaVrYMCSrVUR200I99fT2oSPQTVJ1mVhbidgkmKppqRNKKTc/pEIY+hwdnZ0R8zBAlPlj2LBxGOlMVkhLTw97v2V0R9MxIR4bpVIWNo5sEWgQFzk5FQMEff2D6BJUQsJFQmGigribR3dw/6iEQHsczPOmFKM7divpROlmcTq7ujEY9Pck/WZ4ZGusvVldfJWWZjAtCyOjOwKaw39x6BvYgO5gLEVo0cTbumM3t1cohSXyd/Z76849UlrNaMl1dGHDcHQeiIsxsmUUHYEennh8qgMFfLduCq1iHmLPsVIpbNsZNS4fh/7BDYHbziC/mA0vw87d+2AKOoGtGM29197Qco5m6Ozqxpat27RrEqWUe3pha/2WrdvQ0S3r4UXn0/Bv77U3xpaLxeXj2kphz77wMqnKJLJ1iXnwGdo4HLt2qOVpZ3vUFhP4wAMP4Ld+67fwZ3/2Z9i/fz/6+/tx5513Jor7+uuv49ChQ/jVX/3VyLuICLfJjq9Vh/jKV76C3t5e/jc66tsGNJtdDAkTByjFz3z80+FvRkNMlK2j23HTLbcl2O35adx17wPoDZRB5UU/WkZCCB5634ebJyygu6cXt95xTxBXQ4Mi8du973ps3LTZFyk3SZelddd9D/Oy6JhEOQ7BXfc+GJummt/G4RFs37kn8j6OrutvvEVW2G3RALfcfpc0makLl4iOzi7sUfx0Nstn6/YdeoYhZhK4/l23+O+16SvtRAiuveGm2HAq+gcGOdMYmeQ0+W3fuVvSffTT1klI/B3p7n3XJpYEZjJZbN0WZbwkKaXwfcPQMLq7e5RdfTPaky9ElFKMbt/V5L1MS0dnF1+41EVQR8vQxhGk0xmtJJClL36ObBltKkkQ45upFDYEdh99eoS5QkjFA4UHilxXt7R55CcCMbPYhuERrTRUoh/BhsnzsFHwoKHWDZTf6XQaPYJf5TiwfLt6emEFvoDl99GTE0op+gJ7ha16JIW/oPo2BVtLAQkB0tkst1eYBN1889V6fHieh27hUkArmJaFjs4uEBDOnDfLJZ3JSP53mw1ZAn9T3SycJEBRhA2tYFpW6EEDyqYC0TUkk83xzbmWFmGTRamHjk55A647WhXjpgO9c/6Mv4uOD8M0kdJIMeX6CP/SmVAwoV2DBZo8z0NOuGTF131NHAZuE5WXPxqmXbTFBD7//PP8+Nc0TXz1q1/FV77ylURxX375ZZw7dw47d+7Ejh07MDU1hQ9+8IN48803AQCLi+HR4fj4ONcZ3LZtG8bGxrTvdPjyl7+MfD7P/yYnQ3MGOkkeAKm1bNvG0098T3rOO4km6oVzZ3HuzKlEOgAUwHNPfV86Umul9Pz043/bOuEAC/OzOPLmgdj36o70yJsHsLQ47wtC1bCchrCjvfD047GSEBX1WhWvvPSc9Ew38BguXziH8SsXtXTo8nv5haf5UaNOF0T9zXTZpMGrpMkWmsX5WZw+cSSSZ9ykdPzwm5I+WEiDfiJ4+YVnog+FOOJnrVrBoddfjYSJS/vKpQuYmhyLTV/Fgf0v+jfcueRNMc5KiPT32ssvJpYELi3O4/zZUD+RMyNKP2Q4deIoCoW8XiqpSf+NV38Y+05FvVbF8SMHE4T0MTl2WTK104wOADj65mvwXLfJpkj+PHLwVW24yDEdfH3ZqxfPa8Ornj0A4Mr50ygV8pE843DirXDOEMOK+nXscaNew5nj4djQzQfi75mpca7SotvcqzSeOnoIrqJC0OwG9clA37BZEZmLuMLaKqYDG4et9AEBYPzS+ZYmYsQinTl6KDDAHE8NC+/YDVw6E9rmixsTDAuzU1ien22irxkw5MHrsyePwhH0POMkfAwnj4SmyLRrkfC9VMhjPMZmoQ4TVy5yk0VxNIvpnwr0scNwUeIZjY7t4OypE/IRsDh/CXkBwPzsLBbmZnmZVJ5Azev86RNwXFuQpsufUlkIcPp4dN3QgRCgWMzjqlKP8piQaRm7esmfH5W0JEaQNOt9erTFBF5zzTWRZx/5yEcSxf3Sl76EmZkZjI2NYWxsDFu3bsUzzzyDD3/4w/jsZz+Lr33tawCAQ4cOYW5ujt8OFt9dvXoVL7/8Mj7+8Y/H5pPJZNDT0yP9AWGHa1VBjuOElZ9A4uE4dlMdA0CeKB3b5qYkRLHv3wV8nYFU0zBiv3Kd8EaYSm8rsLqMMl/+p+PYsIIdVKFq+7f+mqQvhmfps7C68K6r14/Q0aKDRynyFTsyeRBCgpty0aPmuIXIdRykNLSo0iXpaBP6vsg0E1h+jm1H9EaadRe/Xpr3ARFeYPYnXmdLLyFstjAzOI4jt2mLwefYtrYeWyFJf3XaHRuurMfUjNFh6SfVH2P9IslkTRHoYMV45VH1yAwQeK4L00rxvpR0elHbR3VNBjA9ppAW8bWuP7iuBytQf2l2RM7Td2zJ1I4cjmi/x4HVDfcj7DrcTFAraR2lCHQr2QmSfCua3ayOZfpjmMxwfnTa05NzHBDTjKQbtyn3TZFF61GSwiN+flXDi3k4TjgnJWmHON1KxvypScT1FQJ5bgQA27FhWWbTo26ZlrDeCZrPxYDfTimLmSzSh0u4J45I+uyGDcuMbyPpRAS+niefTzVHQe0yfwyJeuF73/tevPDCCxgaGoocWxJCsLCw8CNm7+OP/uiP8PnPfx579+5FOp3G17/+db7Af/GLX8Qv//IvY8+ePTAMA1/72tcwMDDQdh7SYgyhc2h2p6PbdrRMi02uvX19EsPQatId3bEzwrw0E33v3L2vKS0iOjo7MTyi1x9TFwRK/SOpbK5DK33QUbM7OCLV7YJYmgypVBo7du8FAHRlLW7+ILKLCZ5tHB5BOk4XSAjLsO+6G7XSP0k5WJCyXnfjzVJYgxD05PS2qPoHBtHZJZu2Uc0PiNixe29Ed1PXnOzZDbfc1kRfT2YEM9kc9l17Q2IJ7OatoxGvMWJe6vd33XJ7U2m0ejv4lnffGRtWxeCGIfQoNrziQKnfprmOzkRpUwrccvvdmuf6dsrlOnDtDTdHwqtpMmzZthPpNnROb7njniBP+bl+bFPceue9CY4wfQxsGEKPcnSozjPstweK3dfdGNh9i457Le133hdPA5UXuVy2AzfcfHsLykOMbt8J07KkPhSmTSPpv/vuBxJ6SPILdPs9DyrP9eEJCPo3bIzUY1xcQoB9N96CVKZDG1bH5N0a0BLHYIqS8EwmixtuvSuWFhVbd+zml3eaMR3s3R33PhQRTohMlPgMAO596L3afiI+Y3PG4IZhru+dBNe961atPUciTEihxI7gnofeF5Uas6BKf8lksrjzXrUPRCXYDNt37gnXBSFdIsRleq0UwD0PPCJtqFg8Ob/w3YPv/aD2OSub+G5oeBMGB6NqRLrwBMCt775DnpP46Y1MDE3KlQZIJAn8xje+AQB46623cOjQIf7Hfv8oGBsbw403+kqUw8PDePbZZ3Hx4kWcPn0aDz30EA/X2dmJb37zm7h06RIuXLiAz3zmMz9SfgyRnY+4PYNf8SObt0TCUyUK++zs7EJ3d29LnRqGwQ0hI91KsuJ5HgY2DCEpDMOM1dWI7rZ8I66MgU3Sb7IdHZHdE4G8KWGfrudxnT3VJ6ZEM2cgKb/ooWOQ1Gq1TFU6RiP1ycCkOmpbxumiMumurp10DEqjUY8YIlaPuUQ4jfibodJkQykcuwHXc2OPAFRUK5X4tDXfmWqCerNOV5e2baNWTX47uFIuc/d+sTQJC2N+dQVEY9tMB0IguaSL1o9MY71WlbwWxKXJsLa6or3BG8eMx7kX09WV6zhYjTGDEtISvi0V8qhVyrIUJ+a7AYLFuRmlzzbJCMDC7LS2n3qgkeeVaoV7AEmCpcV5NBqNhJI73xOQTjKjrUfX5S7pGKRFV0klv7qCUmEtGeEAZiauAlS+9a9KA31plk/b9PiVpp4aRNrq1TKW52e087IOi3Mz3CONyJTFbdivXDjbtM7FOK7r4kqMuoEuzsryIlaWFgNaWs8FY5cv6MeSdqNMcencab4eqpclVFTKJUyOX42d+9W8ZqbGtW4JKUIGUMzvzKnj/IiWzc1xkkNKKc6eOtG0bCxtQvw5Y05j/UM8MRJx7uwp3yJCi11AuxLBREzgyIivCLx9+3Zs3LgRs7OzmJubw8aNG6Wbu28XRBpRWHnXVpZx7szpSNi4wXbuzGnk81F9MJ608vutN16LDytIMQCfuTiVUMcA8F2AMX2HOIhSu1PHjoB6niwh1NDMcCrw0Rj3/v/P3p8HW5Jc9eH4J+uu79637+/1Pj3To9kXaaTRAlj4S3yFDDa/wDiQzWYUxgQGgggCwgYbQg4M/gJ22BhwmCACYXCwCAmNGG2zz/QyPd3TPb3v/fb93X2te29V5e+PrMzKzMq69zZffaUZeKej+t3KyuXkye3kyZPnyH2zXCxidWWxZ3xe7uLdW2jUJNdCUrrwzovi6uV3gnzQ/Vii1bJx9/aN/o7fKMX25gbyuUBHtdfR563rV0NhOlMs1+GG77vSlKO+Gy+XS8pE0WvKXV1eRLMZzQiqOFLcuXktsn46Q9jptLHqu4HrB3K72yhHuLpi+ap9htu4jNJJ0sNXFu4o34HoflCrVlDIRZ9YKJM8AbY31oQ3JCWeUlYQzm1cBvlF95dOp93T9JNc11Ihj2q1En30qIVvdfHTakq7ubasbOR4GpOv4ma9irLPfPe8kQ3mjqzTw+2WzNRsrpktP5jK6rRb2N1W5zv1uFPtL7VyEU1/I8CXym5Su53NtZCxa/2dMQUsr12/TU23jvVNYbPRQNW3z9mPi69ibifSxJEJ5P5lmoMYngwcp4Oiby+21/xCwDYlzSZjSPth7nM7W70RlmB3e9O4GTVBy26iWjbrEcvAPxcKecWPfK/68s2djoZJGuh0Ooq/5jCDH/QKSoFGrQrbbirfu11q2d3eDhJ3gf626AHckwLOq6++in/+z/855ubm2GK5vY0/+7M/UyR371aIZCq01brjdBBPGPQXIvJ1HLP+GAei/xbSJRkFc+6ddltYNu8HOu22uOUllyEzfnyRozTQv1GOQXjaLuUodTKMPULYQidbZTeBXG5H0pXsJgFk+WsSU6q6ftPp6XQ696Qn12m3kckO9i3xcn3bU/1ENx2Lqd/lBcPXT5Rw5ymjsnAcp2t/lHEg/rbWdOtS/82kkp2eepgKLp2O0WMMyy9cB+4ZpddkyPGRw1XmLJDg8rz61dnjeHmeqkPIJYBRTSy7imIMVXRn6DVn6HVyPRfpRLav/sU8hVBxhNULKIXkNjAIMy1gADudSGpHezKDTggRtOJZJBLJnv0eclyBm7lf8nxc10VKuV0ZxI0yGp3Qbh4ThL17sDLYyUqsy7yhp02kwreaddxkKX/K4H7PRHuA3VJNJpP6wZWPRxgU93gRkiUOrusiOzjEykb3uZ8CIJaFVDy6rjokEom+9XEJYe7x5PlcxDHhQyFMiwVh6twhp0skEkimUj3XGA66WpDJAw4H13UxNDLaddzJG0gSi2FAOt419Xe5/gOZTKAve49Hvt3gntzGPfbYY/jDP/xDfOhDTJfhzJkz+PSnP43Lly9/wxD6RgN3G3d1cQdD/iURIxPoQ7PRQLvdEmZcQvE1KORzGBoeiezk+uLBXdJ5HkWz7RolHBwcx0GzXou0VaVDo15DLBZX/F0quFBV0hS4pDPvdLjODvzflUoJI5ouSCphGY972+02PNft289orV5DOj3AjmElHKKgXqsqR9+6Lhizr8Tq5bouOu020gMDIZ044/Fuix3v9svw2M2mqKeJjjJ1PErRabWE/qNOOVUiReC6LqjnCYZazs80D3Q6/CjbEvEFLhJTxHelbQNjZ2KkCWH+mmVfw72Au7oy6XgZmUDXk5yvUxFProeMl+vHD+/Kza7tujEi+iLN8DfHl/uQ/JmPgxBTZJJsSvXSj27F2NPim1zBmQwMy0aeTWNbB0LUSyAmO4GyTq+8YOvjiIcJZljgb3b/JvcDOT4M6fS0Om09yty3UVDhMo5SdeG2iCq9A8w05EW6HvvOL4Z4VE3DyqWCIZTL6nb5xAJgEQuEMJdxMk6mLkopRYxYiFlEtJUcLWYRxfWaqa/L3z0apOeX9rixc09rHwUP+D7BfW60H/ZB9vNuypP3L0L4mkPFHCO7uZPnveDypjrHqHrhahkAM4wuq/rwOukgNmARBpp1/CmFmB8TiXgkMy+PD9d1EY9ZCu4AjK7yGO5tpJLJYA0gJCRF5vn/f+Y2LpvNCgYQAD74wQ8iew+Ood8NoC/KAvxOXS4XQ8dpUTsQgLmBiyrHJD3g+gsU5s4nQ61axm6ErpEJ1tdWhNeFfiBwSad2WBNe7XYLKwt3IiRz4bDtzXXk87vhDxrw/G5cvsCOpuV8u6S79M7bipSKS7bCzAtQKOSwrLnRokocddd559Z11KuVQFrWA869FRzx8526vPuW27pt27j0ztuR9dMnyNXFu9g0mCqJgjOnjsNxXOM3/ZIHBXDy9Zf6qiOlFPndHdy4Kpu16J7u0jtvK3p74TzV9zde+qpYBPj3qDFi20285ZuIicJXxm/h9g2sRLhSU6Xy7DnxygtwOh3jOJWlXBxe/frfKN+7QW5nC5fOn+n72ObSudM9XYDJTXHiheeVA45uzdRu2Tj96tdDcbj/Yc5s8DZZunMDC7euR+AQ9CsOx1/+Kmy72VP6zdO9/JW/jkZWK2t3exPvnIlWr9HhyvnT2N3aUCR43Ri1V7/y1xqzJ5WvtXKn08bJF59X8jRpZ/G6ri7cwsLNsBqJqR0A4M1Xv45mvdaXdBdQXfv1gkJuF2dPvc7w64IP/3b5/Blsbqz633vPHbprPxMIptt1Q+4dKaUKAyjD4t3bypyk46TX5+RrL6JWq4pxHUVOvna/8Hx0f1TXEKBcyuPsm28Y8TTBlQvnQvyD6Ric4/nVL31BYQB1kENc1wt9j4J7NhbNL4kAwP/5P/8H3/3d/Rsz/laCkSkLbUMpdnd2UKvVQmmVdynZ0uJdIXXRwdQPVleWjAPHJP6uVauo3oMi8+72Jlwv3PgmKQdg1hvheMtSQIBJx8qloiKlk0mnQ7lURKdl1gWSBx9vj53tTXakqsWJGkuF/G5IQhAlcWk2GsJfcy8ghKCYz4HC7MvZBLLbQM5EmJJRAK12C81mPcQcmoBSilq9Bs81M3VmXPLKzjVoy0BSI+dfr1Yjj910sO0mHEkvKZxOjV+vVbve9FQWGAo0tEsturRDLq/daodc0qlp1Q2B3YzeHJkm7Ea9xrwWaOFK35U+tmxbjIlePabV6t/LEsu7Gbp4pOAk9TcPVOjgdRufHBzHMeqveaCwQEKMeKsVXILSpZUhvMDoohotDqeQ6eg6Tl+XQsQRvzD5In8z49NptxGPx4VkDzAzanJGXL9Pl7jo0kOn01ZMvlD/n44Pr6tjkDCZ+iGX6jqdNuLaJb6u84cyziOq5/91nUBdJkz7cDpZneEeDhJNqBnzFrfJNVzVNuZ0CZt+ki9g6MDUlCRrHoheY3r1bbEW+uOv0+5uhkpf9zvttuL1yiS9FfM2yyBgAHVhh4Zbp4dbShnuSSfws5/9LPL5PP7Vv/pXANiEMDExgd/93d/FN8JUzDcDdOaDvQREdV0HSV9vRI6rvEsUtyxLOR4Tx3kIdyLP84T5jqjBo+wAPA/pAbOJAhPELKY3ErX71yeje3FJ53kuBoeGRec3LY5ymBWLCdz1bzptCIDs4DC7GdoXLh5GRpmZoH4mIcuykB0ejt71abuvgWw2pPfUDUbHJ6QjMp5nRF8DMDY+GepbJiCEIJ1OM/3ESNzVth6fmDRKZEyTi+d5iieKbnhQSpFIJDE6Fm2eSe9zwyOjXemo4z41Mxv63g0nHl8fnybIDg5hcMh8LGI6npqanQuOafSyeVmUj3eKuX0HeuLMv6fSAxif6P/W/+j4lGL2p9sRrwWCmX0H+8KF5UUwNb8vtB/W+zOH4ZFRocsmHwdH6W7N7jvAjuy7SOtFmQD2+15dCNRjSRZHfU+l05iYkvqAYdKVj2/Hp2buaT6dP3hEyUc+Dtb1AS0rhlnJLaF88USsCRJhssOjSBs8o3DQ48/vP+zbWwzCAulY+MTi8H1hbzp6W/LX9MAAZnyLGKYuo/e3qZm5SF1fExy6r38zZxaxcOi+B8S7wgQpOLGQ0bFxsw5/xBp84PD9ITuUehx5Pjnqu3XrZ0OVHhgwekiS8eX5EgCz+/YLFTUT6Ezhsfc9zNJKnUPgQ5Q/fUloBW73ohO4vLzc9fu78aYw1wm8IukEKtCl+iYm0MRgmfQmTB1QBsf1YHc8ZWLrZq+tH+jn0oEMruchZtLXQlgSKOt3CUaQAMm4xXZBrodUItDncxxmmNUydH6dPgRMh1C2gWSiAK+f53lwHEfEN0sXAp3ATqcDEKLsvHW9Mfm33Wz6/qD7G0iNRh0DA2GG2tR/Op0OPNdl+XfJk+u9NJsNpJIpIX2R6WdCr1atIDsY6L7yxdRUV9d1YTcbfbuAsptNWJaluIDqBpVyCYNDI0LXSK4bEIwl/q1YzAvmnn8Xv6G2c8tuwXE6GDAsRqb+UKuUkR7IRBoi1pMUC3nG3MtxoLYllw65rotqpYxxfzPg0W6bPCZlJJYVbJKkuIJp8aVslAKlYg7DI+NG+1/cTIlID4pibgdjk9Mhxs4E7ZaNdsvGyMiYpMKg6gTyehJCUC0XMTCQYcr1EhMozz28b3qUnU7wjYZZTzP47bguSoUcJqdmQu1tgnqtCgCi/3KdQI8y93kepZBPxQq5bYyNTzLjwhKTZsKHECC3vYmxqTkhOZSZQJ6Wx2+1mmg26hganeh6xMzoQ1Ap5pHJZJEeGFB0AqOmnJ3NdczN74dFCGIaw00IQcwKdM08z8Pu9iZmJH/TgKpf53pUzM21agWUehgeGQ31XX0MAsDO1gbGJ6fFfNqrnTbXVzC3T/Xypc8HXCew3W6hUi5hcmoGFiHCp3VQV56G1bdY2EV6IIOsf5FP7mMBwx6kW1lewr4Dh4ybYn0j41GKzbUV7DP4HNcFGwBQrZTheQ5Gx8y2/+T8LQJsbaxjanparGOyvqAenwBYWVrAoSP3hRi9YNwGL/ncDp544MA3Xifw0KFDXZ93M/RaznmfPPHGqyiXiqroWc5HlvRQ4MvPhfUddKkX/1spl/DGK8yVmjzYZOZPHlBXLp7D6tJCD8wDfL72N5+H54UnNfm4KAinfeuNUDD7XVcvnleOaznulkUUBhAATh1/FZVSUcmHaH/l/F/4yhdDeho6PTidKuUS3jrxWt+4X796CesrSyou2oIk/37l68/3lTdP98rXvqwebfIyEDBtPGxrfRU3r4cvUkUxvefeOoVquaQewxvjsr+vv/S1UH7KZEICvcB6rYpzb0W7GdThzq3r2Fg368Ca4MRrLwlMGFOrMn36gnfq9Vf6znt3Z5OZt9HCoxakqxfPo2pwXWUCQoAzJ15V2k7u93obNBt1XPZ1/GgXBpDDyuId4ZIu6qgzGF/A+dPH4XjRLuk48KPDd946IWjcaz9ZzO1i1TfNw6FbOTevXkK12h8dgWj3eKby2i0bVy+eC0tmJEmX3Jc3Vpexu7WhxJUZMEolO6SguHr+jFCt6Me/76Wzb4p4JgZQ/l0pFsJ0lHqJ3g4rd28YXalFtdcVzeUhn1uUME7HdgvXjG4vzXlvrq9ia4ObtzGDnPTaxXPC7l8/AovL58+EcFAkmiRgfiulIpY1Oip4aEzp3Vs3USmVIjcXen0unjsjmC0ZlE2yn7fT6eDqpXeM40EX8hACbG9uYGtzIxxXk9TyX9cuXxDqNabTGx0unDvTk4/h/aJa6e7uUIa+mMDv+77v+4bEeVeBthXhxK3XakbzDTScBACMRjB5fP1vs9nwTbLQSAZEhrbdUnQGegH1PGXnxPE0dWKT2ZRuHbDVaiGR6h+XTrslTCbIi6iCW8RvHXT6cF2Kbjp7cn7tdqtnfL28/i5L+CZZktFmdvTS2u1AJyWKLiIfQtButxBPpYzHxzJzH3VEqEtU5KPvtqaT0gva7ZZQlTBBaKNhuBWoTOLSu+u6RppH0cjpdAJn6iK/aGryPtAX3to7QXjRlaM4nQ4SyaSxD5vqFOWWkB9h6eB5HtNl6911Wd/VcumWrtNpw+I3zyXGUVfM4PVwHFWnKor+f5vDDG4Sy8TYmxTmO+0Wm08R7iM8msy4uQaXdLJNP7VvqnMFPw6W08nA3IEmlW8mnUAe3mm3FVy4FDBq2jFuFkJxfFza7b5MEPE8ZH3DqGaT0Yrqvz3LouH66e/tdsuoV2ccWxyXZDIynmlDwTdZ+pgO6ez5Zs7C7Wemk+uq5rxEfKkvqXQM5l8epx8mvJ8wk43TKOhLJ/DNN9/EL/7iL3aNc/Vq+KbTuwkEwUxnJFIrT8/MRJpYMR2t7De4mIsaSMlkCnPz+0N5mMsiGJ+citRjMsFhRZfCHEcwpZTiyP1hXY0otEZGx8AvwPBdkL67kRmVA4eOIG2wJxYFR489rMaVRPu63bf0wAAOHDrSJbdAWkMpxdz8fmHyJ0onSYb3PfJ4dL7aREYp8ODDj4WYsKgSxiYmw+oE2l/589EH3qcwXnIcE+P38GNPRUoVAVXqnMlmcfSB90VgGoYDB48odihNIDOljz35ga79XMafAnj86We65KsyGOOTUxgZnxDfuumbAcADDz16T/pgj7//g6F2UXCXfg9ks3jgfY9G4s37DEftwKH7jDbi5LLk8fTY02b3YvpRMC/vsQ88a+wb8oUIzqCMT80ofZJ/5xdDBE4+jR98+AlkIvTBTEdsT/VwSSf/TQ9k8NBjT5vpbWjbA0fuF0wjhdk2oCzBe+KDHxWb5H6MRT/5oY8Z85HpyGF0cgrDvr5sP8fBDzzyBLLZYG7neZoYJQB4WnePp+SnQnogg8ee/mC43AgO4+CR+yNtaPJ+JJf3gQ9/e99mogDggx/9eChMbnsZr4mpGUUlRMSHeqxL/YSPPvE0BjIZJZ6Cv/b+ke/4TkXq1m2rn0oP4APPfkwJk9PoZR26735YEZdETek//G0fVy/xRZzY8Hp8xz/8v4P0kuBKzC9S3pNT013xkKEvSeBP/dRPIZvNdn1+8id/su9Cv+UQsVBQAPsOHArdxJOPVOWknucpfoajdggsD4p4IoGJqSkRt7v+HkV2cEjSGwqjr0uCxienQjjqwHc/HvWUCwp62RxHDpYVQ8Y3BxS1A5EHx0AmC0u67Svnq5fFjJUGxplNf+XfrusqAz8KhDTA8+7pNlssHj3BiUmR77qdDpLJlFkKFzqiYBKAWCxulGyIeOBl0OBGI1VvK4s4CPDxKEXHdUS+JsmnHNZu2Uof7EWaRr2GmBXQJijXwHBQoGm4kcvHiJyGgt2YVSz5SwuPUcevpprL6CXlLZcKikFvZUxDfQCEVBlCdESAf7NeR6fdUo+NDfTg6YrFPDxP1QcO2lJrXwqU8oGbNir/k+civ+ROu4VapWxkAOX0HGrlEpx2W6JF2FWcDNub62IzGDlepfg70nFtL2lWs1FHzT9qNvVbHXI7m3BcJ5LmgCoJ3FpfldpBvb2rp+20W4qHGd1IsM7oVYr5nm4JZRpvri4ZmObotBu+96VQnoawWq0qPICY8pf7IsA8TbX9G+v9nKysLN4xxjFBp93Gmo+7egRsxq2Y2xWeVEyb5WC9Y0zQ4t1bRsJR7S+Hu7du9FwD+Nd6tYKNddU8F99wyOsdh/WVZTQa3fuADLeuXdHmX2r8TQB0HAe3ZfUXsak30ynKjaUJ+mICf/VXf7Xn8/M///N9F/otg6gFF0HDn3rj1Z6MFIdmo4EL58+GmD+9Mfn7yuKC0L3o52jywtunQf0LGfoA4ty/zJScPxOt36XjWCzkseQPZv0bwy9IBwALt68LRWyBh1ZfeRG7cO6MkRGRw3h4q2Xjuu+jsR8mbWtjHTnDTXSVWQ/yuXb5grjYYkqjw7VLFyLj6vHLpRLWVpaMjARLo9Z1ZemuWCz6qatuA0tOJ09EFOwYaOH2TfG915F2bmdH2HLk/akb3L19A67nivgcopLdvnE1ki6Q3gmARq2GrXWzPURTPbbWV327aX0MVACLt29KUlAtfy2u4zhY8XVx+bdutCwV8yF7iFHRCYC15YWQ2Z9utF9ZuqPROxyZS+3sZiPsSk2ivJyWgCC3uxVpPskkWVtZvBNI00zH95Qq88K6xLwYTWNJvyvlIio+A9ALKFgf8FyvqzSHxWX12Fpb7pkvZ/Zato38bhc3g1qppUIu0gyRaaHeWl0GkVQluhmKBpgbQw5hBj8oBwDqtUpfdOQCgd3tTbgRtkX1dQAAtnwbgf2MvXbLNjKkIVx83EvFfKTbS1kSyGFjbdVItKg+sbWxrkjXutWgXq+hLum/ynHVY2T2N7e7I+gYmmMMOG5vhfUHo/DptNsoFMI2V0PzsB9Q+UbrBP7dAGr4FdYLCKWSIsu7CwrAbtlI6UeeBukVB9sOx+8Guls301/+mzE50vGNhqv+3mrZSPk6e/KkHaU/Zts2EsmUMY7Qs5AlAn3s5Dm0Wy2kUum+F/R2y/Z1/FTGTwcepNtjCnAK/gqplkfheTT0LSwJYI+i+9il7QXu7VbY7ZbGGHOdFaA3I+dJcVuazl4UDrxOrVbLd+kVpgN/5389CrRabSQS6tG0zuBFMXxRIPfHhC9RVcac0q/8B76OXx9uuvTfUTjI0PZ1zfRvUe3K9A31sWTAAxLumm4lpdHHTFFHl3LXEJJAX0cq8phTmQcp3E7HiEsUsGNAs/QiyJePjTZS6YHIuHq/cTqdrjqnPB58DKhH70mn1aRrJqOk6w8OdPF2pBubph5FIpUK0V0eUzIzGE8kutrQlHFzXbfruiH3OUoBz/UUD0bd2pMCACFIpsKnGVHp7mUNcz0PmayqQtINJ67uE8QNn37IkEwmQ1YflPyk355HkclklPy6AVOZCVQf5LzUeYH9jcdjRjqa8AIQqVYhypP0Fz3PxfDwaGgtl8v3EzGepkffUsqh/a6871HgJmIuL25jqId+HaXA5saa0NsDIKQtJiK1bBv1eg1j4+Yr4TqUikWkUmmkBgaYy7gepM/t7GByelrg1g08z0OpWMD4xGRPvAF2SYV6HgYyWaMETr+9XCkXkckOKbogiZiFeMzMpJSLBYx0sSkng+M4aNnNwH+llCV/l9FrNhuIx+KBUq1WV33HWK1UMDQ83B8zQCnq9RoGB4dCmwU9OaXMYK3nun2bTbHtJhKJFGKx7oOUH3fYdgMDfeqyeZ7HFtKUzAjKearv3NhypD4QVJq2W9GMl6y/wqHdavmTopnwijkHz4PremJC123EyXXxKI10SRfegbMw13WFMXKZ2SJaWRwn3dWVCbhZC+ZeDorLO7nf6v3TE9J9VTJJeVq5rqAgFPAQMBzcRp0cR64/9TnKKL+4oq6S9El2EaeHs7CgT/JvkflKeqfdQDbhwesuQ5gZVvujHsfxwuZhdCkodxunlKMxgvwCiEdVmgd4SZsSKX+TSzkTCQjxXcX5tI1reor6OOX4xggJmYgR4Za5bXg+hITdGurjVR8L+gYGUNeEflgH06mEnsyyAmPcMl6KyzhD3yM+ofTLkIDu3QVinIr+Y9BfhfZNdo/H+56pD3KcmBtLppusXySS8+X4UN8Fp4yK7NovBJQiFpOubMk6hIAYpyAElVIRDx+Z/cabiHmvg0nCwaUclDI7Vfnd3ZA0gw8MXVpTKhXQbJhF13L+/PfG+qpx8JgkMZQCayuLYclIxNNo1LG7sxWWyugSJv/Z3lhHo14X9QrjTpXft29cjVwUdRw9j+LWjWuRUiX9ye/uYntr09wu/F2q691bN2C37CCMUmWSkmvjUeDKpXeCvCTacCma+E0p6o06bt+4Gmpzz0BDAFhbXkQ+px4bmerInwtn3xJMgvxE0f/8W28KWnhafP1vbmcHC3duiXi8zryeLI+gztcuX0ClUgrVyURHCuDNE68q8fR08nuz2cD5M6e6LhTyt+WFu1hbXWKLuGF86O135uRrzK0bVePJ+PI6UwCnXnshJK0EzAaJ87kdXLt4LhJvpQwKXL14DvncjlT/CHz878df+qo/D2hx9L4LCqfTwcnXvq4wfQrjITMkFFhbuoulOzdC8WQayfmcPfEymo06Y5ykMuT8xfijVLjH67X+U0pRKuRx/q2TxjYy0ebaxXPCdI4xT+33K197LpRvFG6e5+HEi38TCtfj8oV7fXkBt69dDH030dSjwLmTrzFdzJ7yJQbHNdy7eS6plEo4e+IVJb4JePC1S+9EmhYLM8AULzz/eaajGlG+Hv7i81/oiwEEgM21FVx552zkdx2fMydeQ1HSgWVxzLQhAL763F+FwoM+puLYqNfwuj/2+oGb1y5j8c7NSAmgXuaLX3lObKz7KeGrX/p85CVBGQgh2Npcxzlf3YvHIfJD1DLPnnmzDwwY3BMTuLq6Kq4enzx5Er/7u7+LarXaI9W7A4IFUX8PFvlmo4HNzXXjYm9i3LiLOYVpkRdsBGV6lGLx7h1Qf5egMAIIL9AUzC8xx5E/UUxdrVpBpVQ04m6C3e0tOI5jZFRCR58U2NncBEDUuDDVkx0zFvI5gbPr78BcT6oHgvqWSkXYtq3UU66rXu+drU0Q3wsBp6N8jMvSsPIopcjvbivpmSQpSCe3RbPeEO7LgvxU2sjtXCgU4LqeCJf7lenJ53ZBLMvwjeHLaMTyd1wXxVIhRAtOU/1vrV5Fu93W6qjSx5PqXCzkYVkxtX6eShv5qZTLSjyZHp5GI9u20Wq3Rb08LS5/eJ2r1bKY3aj2PdzvgXKxCCueELTj48c0TjzPQ93XHzSPbXVhtRtNPw/z2KACT1ZWtVoBITEpjrkfc2g06gFjJfUV6uPi0oAha9k2KKe1H+ZSqsRhebF/drPue+gwb0LkMU0p0KjXEY8n1W/8H6+PxAi22y0FV31MyHMld6Un5lteZ/FXpUvTbgpprReBv4yn0wnmL5cynD0Zd4lB7rTbEOoyhrwCQ9Psd6fdClzSSfRV0oOKubJlN5EwmNqRaSqD67pKmJ6/DJ0IsymiHC283bKFaRP5W0BXXg7763nUKFHn/ZPHpZRGmnKKAmYGhavLqDjzvEkofv/mbfS6d4N2p923iTZKKTqS6RzTdz2Va3AFGIm3IX1X3FsqLhR6+6i0aLe+wSZiOPyTf/JPcOrUKayvr+MHf/AH8bGPfQyvv/46Pve5z91LNt8SoP6iqIRpcRrNJlLpDFydCwrlxf7adgvZoRHB5MhlmcD1PFixOBzX8xkUFRcujufp48lkCJeovO1WC+lMtifuHDzqIZEakCReKi78iIuHJ9MDcKS8CQDLCzOalDJcBodHNVyCctgcEkxErkeRGsjCcc24h0JjcVjxFDpuNKNrESLSpbPD6Lj90bHlOBgcHkXb6c8BtxWLI5HKoNMjPi8tOzyq4EJCMQBC2O92q4XRiekQLlFHGR4sDAyO9I17Ip3x6eiJdg5MMISPpUbGp9B2POViEo+rQ8ehGB2fkuhCxf+mnXUskUYyM6jgrvdJub4jE1PSghbRb/xg13UxPj0f6gNAIEmzQAKpWjyG7Og42hGXiTjwY7ns8CgSqTQcznQjTB+5yaZm98GRxlfAZAVzAg9zKMXYzCw6Pi6uLAUUC2oQFk9nkcpklbHKv+u6hQQEIxPToFZMxI9ZFITyY0mfNpTAIyx8am4/HH81jxESant+7OhRChJPYHxqVuSt9yche/IZ7sGRcSQHsoyh8+lhPNKmDPe5/QfhUE/QmDOCrsc3mAGT5ngeZvYfhBsx7qlGy1R2EAOxOFxKhechGQJhAvsxPj0LK5EMzafyFGgRXgjBzIHD8CgbCy4CmvM4ct+JJZKYnJkP+gqCMWRRRh/P89MQgtGJKWR8F5+8P1rSQaY81ikF9h88om6IBE0MzJHn4ZDkks5EThm/THYI6YFsiGEV5fnjjh9yTs/tEzqHwRykT05B+OGjD4TnrjBKoBRIJJIhk27dTsHGxicxMjIaSQ/qI8jrevj+YwBI0OahrHnbstPA+32XdPr8EH6nyA4OIpnSXMJKFVfmVUoxvy9QaesF96QT+PTTT+P8+fP4gz/4A+zs7ODf//t/jyeeeAIXL17su8BvNnCdwONX1oTNPUqjHTV7HqOyibmRJzEKU6dQy9YXLg4Opeh4npQXDeVv4uWidoosfbSBYzkvngdj8jQaSBjL6yWXpgidJ7AOF7dIaGFheTPJmOn4WNW38ctyHOY32HTb0FCftmZo1RRH1jNiRmJNF0MCxoQDd2BvaTu6KN66ZTeRSKYCPShzNFFey24iPZAJ9R2THpXnuswETSrswk5P7wFwWjZILG7U8TMN82a9ZnS7FoW7Xa8h448hC2G9IE+K22m3WB/poejPoVGrIZkeCJlnMuMCNGtlZIZGQno3pnZyHQftFtM5NUm5ZSAEsBsNWLFYcNkjgpkDWH+ulUvIDo8oOoEsXWDri9MrbhE0yiWMjU+IBVpIdyn8v1zqC3RaNlzHQSoTbid58eTQqJaRGBhALJ4QjG2UR25CgGoxj5HxCdG/LOW7rMNE4LkumtUyhsbGjTlyvS2ue2Y36gAhQqeVk1qsYVr6Yn4XI2MTPXUxRV2LOWTHAh3oQELJ6RrE7bRb6NgNDI2Mafpp5hFbr5QQT6XZ2JNooafhv4q7Wxibmo2cJ2Qg1EOlkMPY1IyvG6jqvAGqPpldr4NSD4NDI8LFnAxxi+kKcv3NQm4HI6MTiqkrrkvIGUkg0APd2VoXLuY4kyxviuT+02630KhVMOb7vnZp0L9MB8q1UgmJZBKpCJNelq+nyfvM9uYaZuYYAyPrOQKqLiA/At1cXxV+u3vpojbqNbTb7a7+z3k+lFLkdrYxMjomLlBGrTMcr/XVFew7cNAQK8iXp3GcDkr5HGbn55U4Jp1AQgjKxQLiibhyryEqLgFw8/pVfNfH3t+XTuA9SQJbrRZarRZefPFF/NzP/dy9JP2Ww/86s4JkZlBM+pbFJmS+g3RdD4U1ZrphaN9RiGM0GkgQuY9Gj1I4LsXmpRMYf+ApxFMDcH2mihsVldMDrMHW334Js0//Q2VRkcXC/AgKAFrVImrrdzD+4AdEPA6KhMRPU1m+itTQGNITaqeS01Ip/9yFlzDxxD/squDrugHDWLj0Ciae+IfKd860yrtIAGgVNtCpFTF48JFQ/HicTfAdyW9y+fbbyMw9gHh2RKmXjht/L15+GaOP/kMj3vLCSymF126itnQBw8c+HIojmHApH3v7LkgsgdTkwb4U3MvX3sDQsWdBYolQu/CyZChdeQUjj3w8Mj8Zt041h05xA5mDzHg1V4COwr2xcgmJsXnEBycj85ehev01DD30D0LhOhMDAPAc1O6cxtCD36a0I5X6utwPWrllwHWQmjnadYPCy6vfOY2Bg0/ASkbfyOQ4AUDl2qsYffQ7mS6M1E7yWOXgNEpobS8ge+RpYz11SUJj7Trig6NIjs4ZcA1LtBgdv6PnMRkhBPG4BWfpJI589LtRb3YEvu22C8fx4DjsggynZ7u4AbdZxcD8gwoTquPMobbwNgbmjiGeGYEJ5PiEEJSuvIKxx8JjKXRpgACuXUd99QqGH/iQEkfPk7dJY+MWYsk00lPhscT7snwTv3jlNYw/+u0gki3KbtLmwqWXMP74/2WkgwyWRdAp78AubWH48OOiHQLVh3Cfqdw9j/T0IaSGGYPML3Lpp0kcdt95CdNP/1+hfDgt5DLcdgvFG6cx9cR3hOJyZoiAX5ggqG7cASEEYwceEN8AvnmAYKJi/qWEjfOvYObRjyCWTIs85XEiA6UUK6e/hsMf+W5GK/mCECGIx1i+cctCKmGhVc6hvHYXR576sHLSYzvU90fMJOQxiyBmAatXzmF4eg7DU8G6xMcPX0s5fhYBLr/6Ah75B9+DuEYXHl8G13Vw7fhLePzj/4iVK10wkdeBuB+2s7KAtt3AgQcfExeEOL3lC0Mxfx2/dPIEHnzyGWSHRpSNQFxiTi2JeT/5xuv42Hf///yNkI8HiFqGz6jXyyXcvvoOPpgdExd++PjWL2MBwLUrNzAyOoYDh1MK8yczlpxWAHD8ZP86gffEBH7qU5/C7Owsjh07ho985CPY3NxEpg+jve8GsDsu3HZgCyluEXgxS+y4Hc9Ds15DMjMExw2OGPh3AJAvwloEcOw6kxYQ1uGJ35gWZbIyXVoYJx7SSTbByQNZBjGuag5oNoNsmuuZqAupziR1Yi6GRoeQHkwJ/JT8pHQA0BxIYHJMdWAfJW1y2zYwPoSZyYwq3SBmyUvFiQFD4xieGVRdLfmSEADiCJ1SivgWwfShScRT3KyBmWngIfH1DPYfiFjktPd2laLUHsfMobEQ3ibmruAmkR6dQnaS7RblOuq/AWB5J439988YaSeHWYTdHlvPD2P//ZNKuJ4nwMJq2020R6cxft8km+gFEyhJrqUqbLcHMHZ4HslBM230MlZKQzj4vimRj+GSnYBOo4acM435h6YZExhTb8KyPKQFfSmPWCqN4blppa9GMUqrtQHMP7oPVqz3lEQIwUp5BIfeN6VMiPKmS4ZG3kV9cBpTx6aUcBMjDQA7dBlDs/PIjE1JcaM3AoyOvS30E0IQox2UO+N44uAoam3P19Ok6LgeOo4Hx6NwXE8wG6W1Eqg3gJEDkwr9ovDZrKYx9cCsGEvdcAGA1Z1BHDoyZhzHen+wyx4q3jimD4+F8lHzZovjbiuJzNgkBqdGxTfGKARtJqvFrKwlcejAmF+/IC/+Lvcj6nmwRjM4MMfMjyinHQaGtu7l0Y6PYmySzUnxmCX0UY1HgusEY5MjSGUzYvPP8TWV0RyIY2I4MJ3CVoCgfDl+GzacwQxGs2EpuczEcDp2LA+JzCCyaTY2uJROZqDZrVSfgaEuMgMDwkyIzDCEhAmE+JJqM4PINSIIKNqOB7tpw7MSsDseXGl9bHU8cXTO6sHWwlK9ibgbg9f09Tf9I34eV5/7Sk0HW5V22FczpT7TGMyZ7WYdhRawmGf2GWP+yZSuRsCZsNxWBZZlobrVEPE5cEaTkCB8YbsKN+8iXq37dGffEz5jrG/KFos2MhtVxKxAp5UQKAwtl8w3ijnsljpw10pIJyxYCHRSeX6cp6CUYnF9F+NeCmuJAuIxRlveFznunDYxi2CxVA+1ZxTcs4mYUqmE4eFhWJaFWq2GcrmMffv23UsW31Tgx8FvXF5VXLCZJGALd25hbHxSiIvlr/pCRinFpfNn8LjBPU+QJvjtui5uXr2Ihx9/Go5HhY5PVLpCbgftVgtz+w8Yd8FKfFAs3b6Juf0He7rG4gPj5pULePDRJ41x+JGAkOzZTWytr+LQ0WPKYEkbju4IgN2tDcSSSYyNTyrhUXD7xlUcPfZQyLYRgdoGRIp///tUKaMsoeGTJ6UUtVoF1XIZc/sOiIm5Gzm31lcxPDKKzKDZPVpo4N+5gSP3R7tek49IHMfB5uoSDhwJdGqijuwIYWZ2qOeKoxf1e1hKura8iOnZecT7VKxeWbiNg5KrwW7QspsoFfOYndsfqdcnQyG3g1Q6jewgV8EI6mXqzyvLd7H/0H1KXJZ30Bd5WkopNlaXsO/gESWPqPuNjXoNLbuJ0fEppXwdF7545HY2MTQyqthz1HXT+I4eADbXlrFv/+GeeFggcJ0OivldTM3Oi3rpOoGe9LtaLoFYBAOD4SMdk3rI7tY600/r80g1t7mOackkFtdz1I0XExB0Wk3YzTpGxiZ9SUUEA+gv8uViHumBDFLpAWXMiHaU6EQpmzc4XYDwxkRmCjzPQzG3g4npWf9boAPI/lIlfrNRBzwXg0PmDZKud1jK7yIzPIZYPC4kO6paDceJGccu7GxhbHrWuAmRwSIETqeNZq2GkfEJxIjal4CArrzu9WoF8XgCmUw2ZOKGgIjjYIDRPr+7jYmpGYafP7ewdEQwbYGQgKKU38X45LSk+2buwxYIWnYTnU4Hg0PDxtvkOh0rpSIy2UHEDBdbAtyC+hfzu2K+k5knUV9J8ua6Duq1qnAJKnCgquoBz6ZWq8GyLLFG9loLSoU8RsfGGd0M+elQzOcxNjFhnN/0NE6ng3bLxuDQcOibacNcq1aQSqWRTCa7Sr05bK4t4x997PFv3HHw4uIijhw5gmvXrgEANjZUS9fvZiaQw2A6jsF09+run5vGyOiY4l6Kg4khfOD+oz3z5Pl02i6O3X8U2XQcbcdD0tNvY6mSPSeTRmpsBOlEfz4ap8ZHMT48qEkLoxfduZkZZBOSkU0JDxlvALDaBPtmZpFNxBWxczJuXmgG0gkMDY8g1QfuFMDYyBAyKcPtN0P8TqeDibFRQZeoI2E+wJugGB0ZFrj2kqTELYqhwaw47ozE26dtJpVEqktcuRS708JwNot0PMBdntRU6QFAvA4ymWwknSEWar/feB1kM/0ZcqWUIkaowF0mh84oAUDL7WAgmUCih31DDo7dxMjICJJ9xKcAaKeFlCGuPKFzaLVsxKjXNW+5PtVGA6lEwrhpAcILXrNSxsz0LGKxmDia0fPkxzUUgFOvizbtxgRQylRqqOMgTiyh5G+BZSaYQFBQ/6JAq1bG8Ni4kTamiyHNchHp+QNBmVwZXWPYKCg814NdqyDpH79yBkNmSuT6N4p1EM9BOh7QRa8u34C5HsVGIYfRQ/ch5dtCk+NyBkKmba2wi/2+fle3yywUFM16A51GFek4YxqZFCVgojlTyOlULBeQyQyG+kzUHFnc2sD4xLQ4OdDxkC+GEAKUd7YwMzsfYoJ0CatFALtRg9OsI2lNiaNA/k2nJQBs7G5hZv4AUjErpAdm+UygHJbfWsPs7CzPBYAs9VJt2LVaNsr5bczOzgaScUGbcF/OlXKIxeJIjI6AUpU54iBvtnfXlvHgw4+HXHEKtQBCJH/OwO21ZczPzKpH4zLTSwJ9wEqpgnoph/mZKaVMeQ2T027mtzA+OYlMckjJ19SHAeDm6l3sm+vPB6/rutjdWMS8H1855tfyBYDd0i5cp42h6fHQiZEp7dqdZdz/wPswkEkoNOB1Jn4BPP6VzcW+8Ab6NBHzMz/zMwCAf/SP/lHo+Z7v+Z6+C/tWQtvxej7HX3sVdttFq6OGt7R4HZeJxk++8WrPPDsue7a2d3D9+jV25OOGH9c/BuJHFLeuX0WxWPTDqQh33PDTcSnePn0Sjgd0/HfHpf7Rkv9XeuxWG5ffeVu8B3n7eEhP2/OwvrGGtbVldDwPbf9xvaAc+W/Hpbhy6QIazWaoXLk8+ffF82cj4yl4uR6q1Spu37ym0K3bs7q8iHxut2c8/ly+cK6veNwsy9VL58Vv2QSOeHy9I9ejKBTyWF9dZvG8wFyLSKvlvbRwB7VaTTEdo+Igm1FhLuZMuAYLY/DYdgt3b9804i2/8+87W5vI7e6G6xfx3L19HU6n01dcSinu3rzuS/nYo+Ml06VSrWJzfVWYSuGP0nek8M3NNVSrFTjUg0PZsRX/7VAvjPuta4DPyERJ9ThNHc/D0t1bAX5ULV/HqVQqYHd3m+HhpxG4eDSEz/rqEuxW25iviZbLC3eUuos+4pfD/3qUmWTZ2ViTLqZIetA0XJ9CbgfVSsVYT+Xxw5cWbsPV6ibawH8XY5x6WF66K+gh6im1mVyHcrWKfG5Hqie7FSyYQVDl287WBpqtsBmqqHqsLy8CJLioo+PBpY0UFJ1OB1sbvD9qZpMgMVVgYeVSAdVKCdwsjCzFlB/XY8/m2io8zxVGsHX8udSTjx3uq5d2EXPxMhqNJnK7u2KzpZpXCvLk8fO7u745r4DhoobxzP+ury4BijkvyRwSNKYNwMb6ih8WfBe0pJDyZqd81Uo5wBkqA8jz4bC9tQGnE6iEyd9kUvE6c/d4/UCn00Y+p7rHIzAzgIQQlEsFNBr1EMNnYgAB5h6P+Ef3PD9lzyAzhgTY2gi7pIuCviSBzz//PAAmEXyvwkAqjkwqrohZ9Z1O3KIYNuhpyED93UW73UF2II1sKh4pUpa5dOrYGB8dQiYVg90hioKxKb3TbmJ0eMgoTTPpvCTiFtKJME9vOvauNpoYHho0xgcM+nKdFkaHBpFJxBS9iUTMMu6mvLaN8ZFhJCIkgfKxLaVAMmYhk9R2itDox3F32xgdHkYmpXZdGV/hyYEC1GlhfHQemVQ8tFvV03Il4uFsWJpm2hU7joOBVBIDSVMbGerttDEyPKjUNUq0TwhhuI8MIavVNSpNKm6F4kb1zU6zg5GhbGhMRAHxWHxe124XigDA67QwOjyIeLy/276JGBH6snq+/JVP8pbbxlA2g0xCly5EFOC0MTqYRUaSfOtSAxlSMUvEjaon3423Wx1k0kkxlqJwEMG+NHggHlPqBQRSRB7ugQJOByNafKEDhDCdkjELaYPkmI83+XfLbSMzMIBU3Ap5DOFxg+NhAE4HQ0PDSGteDky0oZTCoi6GMxlAkhrKOMjYe56HdCwmJJ7yN9OxXdxzMZTNIu2f2riUwiOB7USPUl8328/D85BJDyBhWQruyrxFAwlZMpFAguvUSTOHfoTtUmZ9IJvNCn1nGeQ2E+B6yGYyDBeot2NNQOBhID2AOLFCt4MJ+IWIoP1SyZTQWeNhav7SGug6GBwcZPElfKk/8QoGVswRFOl0OpBa9pg3EokEEvHA3iIrPWhTXndCmDRtYGBASMNk/Ui5vuKVeshms4puHwWXTlLBVPGv7Cg47TNK8mUdjaHyQXZ5KMpX0gWpnA4bG9w0GVHSaOnA6DmYGVTWOBkPnazxeBzpdFqtP68XCUtk7+Wuxj3pBJ46dQof+chHlLDPfe5z+IEf+IG+C/xmQy+3cfJAWV9bxbw4jggIamTSHAfFfA6T0zN94VGrMaPamexQXy7j8rldjI6Nw7JiysQZGX93BxNT0yHcZfx5h+t0Omg2ahgeGQt1aLED5GkpRbNRhxWLKT4jucs4fVBQSlEq5jE61p8rPQAol4ohvY4o6LTb6DgdDAxkI+PIg6TRqCOZTIWMeEbRslatCD2NXgu653mwmw1ksoM925PjTint6mJOFvXbzSaSqVRIv8ukM0Ip89Khu5iTF17+zv56aLfaSKXTfp7mYxEe1ul0GJOs01GOL71zl3EmMOHesoP4ct/T60IB38Wc29MwK6+747i+4rx2FBiRrtPpCMO/Mo4yyC7jKOXun4Kb3aYxy5lOxdyS4bu8GHueB3YyHLaXpzAw/JjS80CIFXmcykFWQoiR4AKB6ShYLEy87l1uEKnzSPTmgrelzozL80m3eUx+5wwxlxDJ0jEZ5ONuk65pUIeAweO0EpfJNCZQMY3lv+v58zzlvDidOVPH3/XxykF3GRcwUf6tYJjT8fz0yzgyU2hqBxl3mdbyGL/H6wShfDlzpDN7lFJxTCyrzOjSNFOYvn7Jcbj5t3tBNHThT8qX14FLKPllHlMbysyoGF86Qy/RQg7juMjH5vLRL89TqEFRimqlgocOT3/j3cb9yI/8CH7rt34LAJsof+qnfgr/+T//53vJ4lsGJOohvCEp8rs7Snz9N5F+V8slNJtNIaJVRbUk1Hm2NtZBPa/vgbO2vBDyKyjwIdKkQhhDurG+YsadqHgDQDG3g1qlEuyUuu1CCcHywm0xIIIHSn2BYCDcvXWjrzoCjElbW1nqO/7G2goqpZJSf/m3jA/AXCiZ6xVOSwhw7fI7oTA5HhDQs1IqYMV3z2TqAzouC3duolqtSDiY03A4f/ZNWJr9xGDyUycBSikuvP2WEk+miV7v7c11rK8uKcyKsa7++9VL59CyG5G00MfImVOvh5gHvQ4cGvUaLl84G5Sv1UHBBexCy/bmWiTd5PgAcPbUa/Bctyvuch3eOv6yIS8Cvf8DTJH9+uV3tDbScJbqdf3yOyjmd0PfAfOievKVr0GmIEFgm5MDZwBd18Xp116MjKeHbawsYunW9RDOFsK3RQkhOHf6OOq1CqJAb4fXX/xyZFxA7Re1agVnT74W+i7mFqhtdOvaZawvLwQMoSw9NcCpl78C1w2OAk2XWhTcv666mOMMpSldbnsT1y+8reEejD+9e145dxqF3E4QhzPqETzK676LuVCbQGVGALbRfO2F54M42tyow+LdW7h9/UrfvNHJV19Avda/l7AXv/yFvuOWigWcPs7c45nmcg58/rj8zttYXV6MzE9P+fW/CdzdmeYtGSilRpd0elze7zbXV3Dx/FvG+CY4feK1kLvRUDl8fgPwN3/9lwG+BgZQ/m3bTbz8wlf6xuWemMAzZ87g+PHj+OQnP4kPf5jZXTt16tS9ZPGtBcOI5IRttVpYX1sJLfz6wsTT5HK7qPsLutog2oLtv69E+HLU4/G/66vLXdGXJ4xmo4ZyMW/EO5QHgEJuF61WS6m/KR6H9ZUlxHuY7pDrsL25YVg0w8wmIQSNWk1MKr0YUgC+j1bPjy/npf8NcInF5ONX06TC/nqeF9LrMMXjUKtUhFFkIMy86EeJ+dxO5K1NecAHDEZOyUPZTUp/CQC72YDdbETWUYdKuQzPC+hoYhZlyO/uKK6rouLzv9VyyYinksb/22w04Po+N3Uw1aVSLiHm6xn1OpYG2A3FWDzu79ijxwcFAErRqNUU/EI4Sb+bjbpgraLi83I57iYXYIA+d7C/drMR0DbEmLG/nGlr200gggmS0/LfzMVczDiO+G+ZEatVy0JaaxrLLO+ADi27aaybCdotWxgK16XEPF8Zmo26oKPcllGGsTst26iaYOrHnueBukF/DMye+HE0WrZtG7F4TEgBe0GrGbiY0+ltYsA9iXk1gRy71bIRi+nqIzI91Xe72UCyT2sCANBsNpBO8dMDcx+QQce921Bl7u4CXKIYNBG/zeIb1xYtLqXMY5e+ARXf4Qsx/HfXcYxG9wnCI4wQRvekZlw++B4us91qCSPUJjDRUpRLQz50IH1Cux19CmOCe2ICx8fH8SM/8iM4deoUtra28LM/+7NdK/LuAqLOxj7wt2a9ppiQCaXWFrmWzTwQ8E4hT35EfycE7XbbPzYMJla988rMQyLCZ6GODwDYTRtDw6NqvWjwV+9PjtPB4JDZBIpIL9UhnkgoZkcIVKOmcr09z0M2Oxi9gGr17jgdjIyOR9IhJO0hBNkI8y0yfpzGQ8MjkUyoDu1WCxOTYXMsKv4yI04x6h9jR01YcrnJZAqZTFatF1QayulGxydCeUTh4rkOpqZnte/Rk3Q8HsfwyGjXusowODQsjo7l8qP+ctUEORyI3nSMT6m38Lq1VHpggI09rW9Ete/E1DQC6XV0vgRs8p+RTKb0Woji8ThGJ6JVH/TxNzQ8gnQmY57ktbmJEGB6bj4UT48j0gOYmp3vWkcZ0gMZpQ/wpUqWpslHhOOTU8FC1wUfnnpuX2+D65zRjMXimJ6Z64OFYjA0MoJsl/lah2npxrQOOmqu42D+4BGjNNUEqfQARiemlLjRyzQwNjGFAU1nS4xjUMVThwWC+f2HNcYzGgghmD9wqMt39X14ZAQjo+ORzFmwAWEwu++AYI7C6hrhTLjZp1B+Wr4AkEylMTMfWBqh2l8dxiamMDwyIlQP9DL08g4fOdo1PxkoZS7pQuER8QcHhzE5Fa0apqebnd9n1DnUgVfl6P3HlHxC64UUFo8lcPDQ4Z55i7T3ohP4sz/7szh58iT+8i//EtevX8dP//RP49d+7dfwQz/0Q30X+M0GrhN4hesEEhK5HRGShYi8elGqFyEppeJ28d9WnyIq336kPywuENa28b+B6/Ko4a6kwwQ/ZSphhZSZxYJBqeJiTgYixQXYTpFYlqLc2g06nY7Qv4oCjhcFczGn73SjaO+6LjzPC+mDmYBSdvQST8RhWVaIZnoJTO/NRnpAHfimWnBdGNtuKjp+YUYx+N3pMO8TyWSyq84Ol5w1mw2kUum+7cnVa9WezDcH13VhN5vI+C7pugw5AIBt2wAQ6ARquki6nlKtWsFAdtDg9N5cSLVSCm2S5PxkcDodtNotgTsHud8SBLRv1GtIJlOC7iY9Rl5/QoBSsYjh0TFjPDH+pPBSoYChsTFFj0rXRePf2i2b6ctmB3vOVQBQr5aRGcginkj4zJhZH5BLMsrFvLIxCcoP5h++KXYcB7VKGSO+zVVdYqvrTdbrNRBCMJDJagu6ykTyfEqFPIZGRpk5L4D5bUa0TmA5v4PxqWkjo6brA3babTTqdQyOsA0e1weUbQXysgDWvxLJJBKSge4oJpCAoJzfZRsT+E4GJLqboLizjamZOeVSCI/OL4XIkuNOp630d0ICXTNZFxMASsU8stkhJFPJ8IVABGk47GxvYnpmri+9QMdxUC7mhc1CE8jeTGqVMohlITs4ZDSVQqQwAmB3ewsTU1PKeqCvXzJdNzbWMTenmrPjtNCZq5Zto16vYlyy0aqvmnLepWIeqfRASCebxQtw5um2N9Yxv1/17yvTQh9TG+sr2Lf/IMtDkizyMUoRrHv1Wg3FQg4feuLBb7xOoG3bOHXqFI4ePYrv+Z7vwRtvvIH/+T//571k8S0ETQygjbob165g8e6d/nIiwMsvfAUtf/GSO5DSSaTfX37u8yyumMyjJVOlYh6nj7/aFy4AcPXSeaxGHDcrC6r/++vP/7WiH9MLXvgbhruQWmkDlAMBsLu1iXNvnWQLmvbNtHu5cO4tbG2s9Y3L15//664MoKyv5XkeXvrqc33nvb66HKlDGC4HePutEygV89FxIA98gle+9jeh73qegaTZxsnXXuoiYVTfF+/ewsLtm8alxyQpO338VXF83A+89uJX+45brZRw4e3TSn3kv7pE7vaNq9ja7K8PEAKcev3FsES/y2J08tUX+8Y9v7uNm9cuhcYx7896O1y//A5KvipGFL4yvPnGS364YROm1cHpdHD+9BtanGjct9ZXsbZ0N1K3TIcr59+CbTeVuFz6pKenAN468UpPnHm6ZqOGqxff9sOij+x58MriHexsbYj4UWOcL47nTr9hzDNKJ/DCWyeM4TLOHEqFHJbv3FAYuW7edBZvXhXqDyLPCHmdRYBLZ8K4RLWr0+ngsqRrZppHZdjaWMN6Dx1rOf3ld95Gy+5/HjjfhY46NBt13Lx60fjNVN/V5QXsbm+F+xTkDUPw7fzZU30LUygFzr9lVl0zSdeKxTwWbt9U5zCofUUu+vaN66iUSl3Hp/zp7bdOit/d9B4JGDP9zttnBAMYxFHjc9jaWsfaWv/mbe6JCfyDP/gD5fj34MGDeOONN7qkCMC2bXzf930fjh07hieffBKf+MQnsLS0BADY2dnBJz7xCTzwwAN49NFHceJE0NEajQY+9alP4f7778exY8fwhS/0r2iqgi6qUd8r5RLSA76uA8wDTe4Q9Wol0I/R42klep4Hp4fOk3xkWatWQ8cF3aBeq2EgY74ta+J7PdfR9ORUvHUJUzwe7zrxyNCo15Dxj4MJuL/E7rhnucQI3eMCEO3Wj+TTNtyW1UHOp9lgN337lao26jVfchFmbKL6BEF0PeUu2WgEdAnjHA5rNhrIDma1nar50gkhBM1mM9RnoiYjk0mEbtBsNEISTxPenGbsVnMWbSes62kC1/WU/ivjpjMQTqeDeBfJrs6kNpsNDKQDjwLdUCGEwG42kR4YCE3+/dLKlIantO0mUj36LwcPlOl3+d45ZMmh3jc5tFs20tIRPyHRTFRUfzbhDbCb7al0+Ng7/M7jN4QJjCiQ2zbqdriF8C1qUE/r/6a8g99t20Y8mQYBwfhAUhiY1m8bi/gt+550sEwQ1V1k/a7w+CGhcBZfU9uQfguGxw/stO2+jiUDRinITZFWGRj3lt0M4dINWr6enC4BNAEh7Ng+kQiMJ0fhzHD1jCdXOm14mrZth1TdmKRexYEDo7vZm0e3OZ/hFrFB8v+2bDuwzOGftOlrtgwtu3VPep735DsYAL7whS/gwoUL4ggHAH7zN3+zr7Q/8RM/ge/+7u8GIQS/+7u/i5/4iZ/ACy+8gH/7b/8tnn32WXzta1/D2bNn8U//6T/F3bt3EY/H8du//dtIpVK4c+cOFhcX8eEPfxgf//jHMTY2do+Yi62EFBTMlkPDIxgZCfLstr+gFDhw+Egggg5yV35zCYLjOLj/gQcBBMY4Fcy0nXIqlcbMXP9eWMYnpzA0PNIzHmdWDvdwFSaj53kujh57SBGbWxGDDmC6Y0MjAS66VEIsTv773L794uitnz3dA767uH52gMSycPTYQ0p5FMHkqeuSjI6NI51Oh46eouDI0WNISy6xZNCPGSiABx95PCQdFb+JSpt0Ko3DR4+F6xRB+JnZeQwNDxtNOYRwoxQPPfp4aNI2HwWxsEefeDoitzAMDg3jkNbHoqRTlAIHDh3ByOiY8IzSq2l74aLX45Eu8WXdWQDMfZbpGJ3HhyoVOnrsIQxkwsx6lITssaeeEd+VI1EDbolkEsceflzJQ5fs8/QWCKbm9yGRTIWOWqPgwUefCjHI8qUEJvkIxvrj7/+Qj4e6oTDB0PCIGHs8nj7eZDh03zFkBwfV8dGlfz7xgWeN+ZiYWNejePyZD0v5GJMKmo1NTWNobBIUFIVmu6sUEACOvu8x4R6xH3j0/R8GpRBHwTrIbZBMpvDwEx/oImlWP+zbf0hcsAniROPy+Ps/hEQy2XPM8Sze/+zHNOaKKn9lGBoZVfqvMV/RTynue+BBpNOaiSu/7GCjHVTmQx/9jqBsw1ytMHgUeOaj3xGpUyenoQCmZucwOT2tbvD5XGFI/8jjT3ZxSaiWSQjBR7/942q5XRopPZDGB5/9CI8o6kOImfc4fOQ+1Ou1yPx0uCdJ4M/93M/hj/7oj/CHf/iHcF0Xf/7nf458Pvo4TIZ0Oo1PfvKTorLPPvssFhbYEeZf/uVf4t/8m38DAHjmmWcwMzMjpIF/8Rd/Ib4dOXIE3/7t347nnnsuspxWq4VKpaI8CkTIcycmp5AdDCbzbuOeUor9+w/2FRdgO5b9Bw8pRco7J73zxuJxxe9uLxgyKO0HuKrvnkcx6uetLxSmecDpdDA+OR1iKroxGMOGSyqUBhb05TxS6QHEE4nII3WeJ8B2/4NDQ10HjFyfdquFYZ85lnd5UYuR6zrISBcOejGalqKLEqajnLrTbneVFiiLO5g3h4GBgZ7K0Rxsu4mkopOk4s/rzMM8N9p3tSnveCLR99FLo16P1KvkdBIPgGqlgoRh56qXxtPwDai8AEUtRt2OvE3VqVZKoUWUQu8//C9FpVJCIkJRXi/LdT0xOduOh61GU4sjeVKgTErueW6IUeUgM4UeKMrFgmJ/UI8vdOD8v5VyEfJoo9R8KYSC3X5s1usKnnqdOd6UUpRLRbiu05VJkPtAbmcTlhUYC9cfHQr+LX5eZjewmw3Ua9GLotwfAaBcyMGVbgdzjxVBPdUCd7bWDH3GjJTrURR2NyXm3xyPXxCp1ypodFnQdR24ne3NkKqPunGgStjm2ppxPtVpArC1YGd7S8mnG5SLBTTvgRnZXFsR9IiSGMv4ra0uRealY2c3G8jvbofWl6ha5La30Gw0xBgzrZEyCZYW7hh1w5ULgJJEfrmLtRCR1k9TrZSRk6xWdGNgCWH2jjutds/8OdwTE/jyyy/jueeew9TUFP7Lf/kvOHv2LHZ2utu6iYLf+Z3fwfd+7/cin8/D8zxMTQUKmIcPH8bKCrN7t7KygkOHDhm/meA3fuM3MDIyIp4DB6RbYboUUGqVU8dfC+WlE5u/t2wb586+JbLR4+gda31tBasrS4GtKW0nrYvSr148h1bLRr9w/ky0mR4dP7tZx42rl0JShSjYWFvB7taGUid+xKvw0/77lUvn0emEOyDfpeg0vXj+rBIuL7ZBHdjXeq3a0wahnP/G6jIKvk02U9n6UenVi+/cky3HSBuE2m8CoFIpY31lqevRgEzP9eUlVCrlnkcJHK5eeofZFJTimo7iuETmxrXLSpxu0p1ysajobfZiwleWFmA3VQZHPnZVHgC3rl/p6/iUELYRWLxzI4R7FP6FfA6FXC4yP/2odGXxbshcjUJTpS4Ei7dv9sSb79rtZgNba2zuGkjEMJfVLglpR5aF3W2mLB9BGrmbWiBYW7gTWS+GOxF/CQHWFm5Lx0phPUD5tVGrYXdnS+CpSlmoEp8Qgt3tzVAf0EHGcenuLXHEL8+JukoD77+rS3eVMrvdxq1VSigXcpF6ejou2xtrcDodEZ95tYD4Lertl7m5shQea4ayCAgcp4OdjXUlTKe7zIhXi0XUq2WpTKl8cWIRhG2sLoOb0JLrFvwmShh3MRfC1dB/Gs068rltY3wTFHK7aBo2Ybo0m8Pq8iIs0psl4WhtrK7cw8a0hnKxwMrsI/7uzpZxHYvqQRurK75NVzU86nRlY321dzww+lTKZVQq5j5ggs3NdTiuWf3MBPfEBKbTaWG8ttPpYGZmBuvr670TavDrv/7ruH37Nv7Tf/pPAMKLium4NOqbDv/u3/07lMtl8ayuBsQW+egcUD8LEALiV6sV3/RIMMHrcZX4lTKGh0dDuOuSGg71Wq3vm5j3AoQAtWpVMQ/Tq+qNeg3ZoSGFGZGlXyIf/73ZqBv1E/ViqJ6BIQ7RHkYX7dZmlwo06jVks0FdeWk6w8Bp3263FIlUt7w9zwORb0xH9AFebrNWRSabjY6vvdfrdWSzus5eJDqgHu1601fXk0tq9Yw24UFQrwc3g3sdkQO8D/Sv06rjx97DzCylbBOT9r3FmBlpNbTZjMbFJO2wpfjdmCKWvr8FKMi7ifRAJpJJ18uw7QbSXXQCQxu8VjOk32ViCAmB4rYSCBgak5FoAGi1zHqeej34z5bdRCqd7ou5ZwhIFgUMdJXz8Vw3JGnuxuA57ZbQrewHHSa1Zycr+lGwR1VmGkDfN+wBwG23lDbVmVdOf35Bp6PF5/OvSXgAMPNf+pGqDvLyl0hEa4Tpa5vT7iDrz+3ddAFlXExjL+iTRPrL8ognEsqGK5RW4EYjbQTyeHIwM9GWFfOK+BtRd8/zkEylDdJ0M0SZdFN5F/a3024r6xKg3gzWwXFdRnfDOIvCLXUPupj3ZCLmO7/zO/H888/jF3/xF1EoFDA7O4uTJ0/irbf6t5T927/92/jzP/9zvPTSSxgdHQUAZLNZLC0tCWngBz/4Qfzmb/4m/sE/+Ad45JFH8NnPfhbPPMN0af7ZP/tn+OQnP4kf+7Ef66u8wETMDoaGh4OerTFg21ubmJ3fF2JMTMRpNppotWyMSqYbusUvl4pIpwdgxZNoOcEuLUr3THYB1wsopSgW8hifiD4+lqvcabfQbrcUm4iyqFs+XiCEHdWlUmmFOUrGmcs4U88pFQsY9c1C9IN7tVLG8MhoSIfOBO12G67r+Hp4ZmZENjUju4zT89YvO1BKUfOZe/l7N9ybjbpiSkSmI9H+djodUM8LKRvLXVGWMLWaTSSSSXbkLOUVtYixyxgZpcwo00Ge5zFjpenA6Gs3PVXHcfxJt7fpHIAdwyeSSWPZcl8khC2qLdtGStLF5PGAoM3kY0XHcRCPJ0LtaaoHPxrTL5KYgFLGIMficbFAmY5g2V9Wluu6SMTjTErpmcczB89jLuNisRg8CV9+JMrrIf/2fNskch9h8Tk9gvwdzzWYzQlwDu17PYpYLIhvEaL5EIaQ+rFyA69BJuAMEzc1Qg2MnYk2ni/y4Pl6NOxW0CQo4Hl5FHB86Rc3D0NpcIRr+bjHdNpA7W8ynVxPLVM2D8PTelR1EedS1Vi0SToZI8z2oEUCn8G8mChD18SPFyO6pwjim4hRw2T6EqJ+56ZudAaP98GwoCKYe4J68W/mdonCJVwvs39g2TxMsGEK4hHtncfTfW/L6Uzh+hzT53bFmHd0nHA99Dbh8XSzODyd2BBL7S/qr+XBx2mjVsMDBya/8SZi/uzP/gzxeBy/9Vu/hUceeQSWZeFzn/tc3+n/63/9r/izP/szvPjii4IBBIAf+IEfwO/93u8BAM6ePYutrS187GMfC31bXFzE66+/jn/8j//xvaDtg7Tl553Ff8qVMmq1mphQ5IdLCuTfW5sbcFxX6LgpDw3rs9y9fQvxREJcCtH1XPSB0s27SJCGi4pLgX4MVR9RBwSDeW11BS27pdZRwT149yg7qrPicd/uFlW+8TD5uXn9qjHc9BSLeayvrQq66PTRn+WFO6hWqqxuHg3Vl1IoeVw6f9bX01DzltOyhZm9X75wXnk30tL/m8vlsLy0CNej4vGkcvS/d25eQ6VSDtFATuf65XoexdnTJ0Esi+Ej5aWWB/H77Fun/PR+flJeHo/vP1ubm1hauCvqxPOS+7nnBWPh6qULqFYrSp/Ry3el+G+eeK1LHPVvu93B2dMnBL5GWkp5r6+tYPHubeNYkvWd+HPx3Fuo12vK+JXbUx8Hp954CSAk1DZ6G1HKbuO+/eZxMR6C/KlKS/9ZXryDlaW7YrES/czHndu24/m8fep1NO2mP+6kukGyh4fgefPVF8JjAhItJZ3AarmMi2+fEvF43jwvGS8K5qZtY3VF0Nn0BGOE4vjLX4Xrusa5Tm8HgLmYY3SkoTL099zOFi6ff0vg5sm00fIFgGsXz6Gwu6XOn1oa+feJF58HiMrE6QwgT+NRD6de+rIiIdTjyLCzvorbVy+ouPC85PL83xfOnEA5wgyVSZL10pe/qOVNQ++ux+zV2raN133TT9QYNygHABbv3MTtm1dD+UXpbp58zexizkQXhvtfi/7RCyrlEk6+HnbvKNoSKpN27fJFLC7clhjYIB4QFkC89NXn4DidEKMn4mvhX/3S53sj7cPuzhbOnDpu/GZims+deRPrayuiv8sxAny4MAN47gufE96g+oF7uh08MxMYffzlX/7le0mKtbU1/PzP/zzuu+8+fPzjHwcApFIpvPXWW/h//p//Bz/8wz+MBx54AMlkEn/yJ38irv//wi/8An78x38c999/PyzLwu/93u9hfLw/SZMMLmUGRU2wu7ODSrksJnhAJbQOS0sLeOjRJ8TOP2q3w4OXFhfw6FPPoON6Ik0UdDodbG2uC1xMu3gZ8rk8arU6HJcqHdpUB0op1tdWkRkchmO4GMAmT1WisrG+isfe/6xAgBDAcT14HgmV0el0sLu7g44bnnhMu8JcroBGs9m38ez19TUMjU6g48rSVDUON7QMAFtbWz4u5slNhlarhUKxKMyUyFHkCYLnnc/n0Wp3RPxesLm5iYmZfT3j853ubi4Hu+Mp5UdtPJuNOmq1BlqOZ5QC6lLPfKEAz3XR6vRnK3Jrewv7Dt/fNb7cV/OFIlqdwNwL/ybH4f2hWq3CbrXRdryuCxH/ls8XkEymFDqG+3lQTm53F0cfTnSluzzGKpUq2o4HDzTsvks6MrUIUK5U0ZHoGLWg8DrkiwWMjE2g5XqCQeOSJM6AyfF38zm8L55A2/XgUSiGhXl6Ht+jQK1eRcfzeiyi7GOlWoVLCVr+WGJSCOmok0CRChbLZQyMjKLjUkWKItOJEMmAcrUGSmJiLpDjudJcQinguA4ato22y8ee/x1EeedhlVoNLrHQ9hc63VC0TBuGSxkz+w6i43lKnlF0atq2YMgtokr7OHh+GS27KTbEpjiylIWAoN6og8TicKkHCiIkc/D7rM4IVmtVWImkkG7KUkNPoiP7S9DutAUjzsNkDpbnEY8RVGpNkFiM4S/HkXCWxyMzK5Xpud5xnBr1ujiWlL9R6T+KQHrFzagp84DE3Mi9wW7aSMSDy2pUzttP7/njlFJm+mloZJTF16TMSp39b61WG1YsrjD/hMcnRMLHZ4T9vqhlH6oHATv5MF2E09Pw+brZtJFIplh5lI8nacshS4Khzv39wD2biPnbwv79+yM7z8zMDF544QXjt2w2i7/4i7/4f12+LCVQwgHU6nUMj471ZNA4NJtNDA4NG+ujd3YC+GYb+mN0GvWacjO4V5JWy8aIL1WNanbBRBACz3OFK7WgDN5pqLQIsL+63Tz9CEMus203MDk1HTapEMxSSrDnOhgbG2fxIzqtPKhiloXh4WHEtALkcUZIcHQ2MjISihsFTaeF2dk5ccwddYRGKfEXSGBsfAKJmNkzCksbDMZ0KoXh4WHEY6rwPYpUE5MTSMTUtohK0/QczM3PCxMrvGyeVu93qUQc2bFRJX43GBocxMjwkFH3ydQ/Z6ZneuYtaAkP8/P7EI/xyV6Wpqh1AQjSqRRG+6Q7pcytX3agP900SilmZueQiBHRzuocHuTBjuIszM3tQzJuiQ0U/2Ya60PZLMZHxpC0LKVuFEGf9STmZGZmDqm47gfWZ/oIlRgaANTD3Px+xK2g/3abO5LJOKamZxD3x0eML1IGRpAQYHhoGCNDbOxxUlAAMahHVLzMuX37xdjjMwsHiwZzIyWA2/Fw4OARgYNhVChvA+k0ElMzTPWDAjECeAj6ggvKMvZhZHQc2cEhKX/AowQgYQIREMzuY5Yf+FzHFnwdJ5/+HsWs76ZNZ1qVY1r/W2ogg2QyBe6SLjgOVePyPMYnpo12/DxQpT4c9h04HOp73VRn5vcfDDNSER0nOziEoeERn8nmuKpApR+zc/sRiydU6RzU35whpJRi38HDflyVOZQqIgqIJeKYmdsnmDR58ytQ8BlBSilGRseFCRfPo8bxQUjwbb/vdk3JT6MPX1M7joODR+4XzHegMyqhLdIQpNJpTM3Ma+VThbHjaSiY28tMdiioK+P0NJpQQYPD992PPlkZlgXthzN5D4NRJ1ACvWP2AsobWteFkH7LkgCZ+bQ7rnqs0Odg7Qae5tItCmc5fixmdnOmi+LZUZwnXDMRMElbKq7r1rBvrusy3YaQ7o1ZmtXmhqh96Zdec53Osgs4E524XgUfjB3tAoScL4/Pf/NdaEIyhdKtPfhuTq6rKSqvc7OH4WpFQuZ5aLXsUPwoPqbdagGEIJlM+sdS3ftjo1HHwIDZf21QVuC6qF6rKrqP3ejiui5adjPkds0ElDJzL4QQZWes9FetnFq1goEMcxkXNVJk3Lq5jNOh03GET/BuwDcbdrMBy4ohlUoZpZgBPixNpVxCdnAYxFdRkL+L8Selq5SKyI6MqvM9DZgMnhZguozNVhOZ7FBID8q04DUbNaSSSeELmBvSlb2FyP7By8U8RsbG/TFGlP7E+5GsN1fM5zA2IW9mtXlXwqvVasFpt5CJ8AUspyWEoFopI5lKIZlkdJf1AE0u4yqFXYxNqr59gTAd+cJbyucwPD4l6sSB5ylLEVt2k13GyA4Jya6et8AdBI1KCQPZLJKJlCLZNUmdLRAUdrcxOT0rXMbJsSzCdAJ5e3meh1Ihj3HN/zlnxnk/43k06jV4nscEGjD3X5mBK+VzGBoeQczXDY464eGQ29nCpObPnOfJxxDXg3McB5VSEeOTU33pBNaqFcRiljJWhaqBId3O9jbGJiaZizke34Az/7a9uYEZg99umfnleXfabVSrZcXFnMiP6HqcQLVcQjKZ1C4KmvUjAWBnax0zs/PBeFQ2bFJ9/bI21tcwPDyMBw9OfeN1Av9OAOt94iGE4KvPfxGO47CG7fLw5F9+7q/UnRsxxPOfYiGPk2+8CkBi3kW6oFH57+uXL2Bp4XYoPApe+frzwn1dP1X++t/8lRJuisuhWinh9PFXVJy1/ORvN69fweLdW6F8RBqtrDde+ipadjPo/Np3bpSaPy98+YvR0k5psBGwgXb6+KuhdhFxNdrfuXkNy4t3jN9NcPzVF4xmfILJSq3TK197vmdcHr9SLuHsmyf0rhoJt29ex9ryolKeXG91UiQ49frL6LTbSt263Th89cWv9iVJA5jtuXfOnu4rLgDcuXkNG+srGi6QfqvlvvnGy0zXDOH+YlqM7sVlXLlUwLVL55Uwvdp88QKYu7vczkbXtpGlym++/nJfG00Op4+/bMYBYVoV87tYuH7FiK8JFq5fQSmfU/LTDUXL9JRdxikbDH2xBmNGzp563bjJNeGV39nCwp0b6hjV8JX75c0rF1CvVvruk+ffVL1aMeGJjhv763Q6uPx2//13Z2MNG8sLPs4y/VTc+PvNS+dhNxoKc2mBCF1MOQwA3nnreGguYfiGTcu07CauXHhbCdO9ZMjMy/rqEjbXV7v2XzndpfNvCbMp/Qgrzp02672Z6lOvVXAjwsWczAByWF68g93tLWNcoqUDmBk1z/NCfUx/eK3O+e7xTPOvpb2XigXcuXHdiLsJ7t6+iUI+ymxVMJ74c+bUiQBvvhnQ5shg/aN4680TofHTDfo+Dq5Wq/jTP/1TXL3KFEMfffRR/It/8S8wNPSNN2XyzYaWbRtdEAHh3UK73RbSn34GT7lUwtDQkCLRME2iHCqVcshbSLfdVrtlRxqK1sF1XWHWRGZAqEF6BDDTNvwWsYhH9COyIKxeLWNyknmK0KUX+m+A6d6k06rLrSiSup4X6vjdJqJ6vcokvz50m7IoZTeDDxy6TwnrJQlMS8c0Om3kZAx3c81MwfV6TRlXMiNhgnq9iknDbXLOKOlt22oxV1RRuiNynT2f7voxSyQutRoyg72lgBwa9RqmZ8M77ijodBxxS1mWMAPqpgpg/T0W730rmINtMG/Uba1r1GvYf+BwKFzuN03HxUA8BkLYJYIoqb3MrFMw6UI8nhAMQhRw/KJM4QRSF7UfNRo1YXpElmDp9TBtnniYLBWRwWQephvD1mjWQ1LvbnNks1nv6uqMECbduJcjMQ4tu4nUwEAkTUzxuckiGWR9T/4eI8SPz3CXbQRG3QwG/HaTuk0ULbkLw36AEBZ/bGKir/gAv8Uf5K+3ry6x7RcopQx3Oe+euDQxORXcUeDjRl+bOHiug6TBuoF+YsI3P+Fbz9F4tVo2kumwI4AoGrRaTeGilscjhvh8zFJKFXNkYkyHylPnx36hLyZwfX0dH/nIR7B//34888wzoJTif//v/43f+I3fwKlTp7BvX/8uzr7VIBYNqcMevu+oMa5pDnE6HRx738PGhVlhAvyyBjIDGBwcDDE6UfPTxOR0yAVct0X3yP0PRn7TwXUcHHvfo6FwtkCFwzKZLOZ9fZcg3Gw6AwCmZuYwGuHOL6TaQYEH3/ewmQmBtriDLegPPfpECBd14gm+ZQayClPH86Gm34Rgdn5/pGkbEzP4Ps3tWhRNAIB6Hh554qkQjsa4AAYHh5A6fJ+ygHeDfQcOYXRsXD3Kh8pYyPV99PGnFNx1RWT5m+u6ePypZ0RYL+Z4eGRUOeboBQcO34eR0XCfiaLno0++P5K5ld+5KsNjTz7TNy7Do2PIDo10bUueNyEEh48e6+muMZMImFDuMg4I9ym9OEIIHnnyA4qOYDecxiem4U2Yj1xNaY8ee6QvW6Sc1txlnA661IUCiMcTeOSJ94vvvXix6Zn5kPmWbnDs4ccVZl3t9+xFns8e+8CzCsMVLOjqhQ8CgmQ6jfsffiLEAKoXBIJ0M/sOIh5ha09nBAHgwceeRlyzKSdfONLhiQ9+tOv4l6Vk2aGhnm7aZDh039GefoP52kAAPPXBjxjVMEzzged5ePpDH+uJA59XRsbGlU2MGOP6GuuH3X/sIdU8l4SvaZw88+FvU+ok0hnWJUopPvjR74jMS1/7J6am+zKLxtM89MjjGBwaNq596hzG0nz02z8uXkw0kfGyLAsf+di398RFhr5G3n/8j/8Rn/70p3Hy5En8t//23/Df//t/x6lTp/DjP/7j+MxnPnNPBX6rQN+9skCCdruNfZILOD0Nfzh0Oh3Mze9XJhR9ktZ3saPjE4HSuI6DBplsVuhH9bpM4rouxsb738nZto2JKVVvgeNvKsdxOhgZGRXxgLCNKblDxmKxvqSSvDxu106EQxoAUNvMbjaEPUGWR3fa2LaNwaEhJU8FBy0fp9MRtuoCPU71L6Q03fQw9TKbjToGBrJipyo/OhAwSSBf5LrFlfOXHbVTw9/A3IrHbBbSwKyDzODxh1+SatRriMXjRrqYoFatKIZKoxgYrrtTKZd936U0CKfBd1UqSRUvFHobUqj9oru7rfBTKZUQj8eDsg20DNIzN20Jg45qWLeW4V7zzWXofVeuJw9t1GtwI276cp00/s0DRbGYgxWLhfqL3nf4e6mQQyzq9EOjved5qJbLSj35d242KjBtw6TqnU471CZRbZzP7TA9SQNtdNoCzBMFZzzkT/rtaoBtfCvcU4TeB7VWpaColkrotFVPEeIGrx9HTpff3ey6S9NvOhdz2yCEiNvFUeCBot1uoVwIm4cx6+6x/tju4mlKZi4oBbbW1xmTGjH/631ne2sjVGaU9K9lN1GKMG2j9kf2UsrnhFqTukE112VtdVmRjunxBaPv57W+thIdB1wCyN6bjTqKEce1LE/1PbezLVxZBnHMfYIQgpWlRWG6jF0kUTdROqwsL4oI+nqp/65Va8jncvckie2LCTx+/Dh+6Zd+KRT+S7/0Szh+3Hzu/24DhbhSL8jndnD39i0lDjU8HO7cuo5iIW9cmHW6UwBvnz6lFhmFn98hLtyDPkqlXMLd293dqMmwtryIQi4XuTDzMF6PyxfOwZXsDYXqpy04F8+/bVzsTcBdwHXrqnLSrfVV5Hd3Fd02Yxo/0Y2rl3zXT6rUyHSsSQjB1QgXcCaw7SYWJHdhyqILlXn1KDNEnsuF3SsGO08JfwB3bl4PLUTd4PqVy0p6E/CjvE67LfQ2A/zVVB4F7Daz8ba7s9V1QtTbd+H2TXTanVCcqDnp9o2rSjy5HvrliXa7JdyFmRgGvR753W3jWJWZPLm8pYXbjPGS8uaMTlBmkObureuhML3e/FvLbmJzddnA4Kg48LDc7jYq5aKRUZGZP874rC7eUfxBm9LJsLRwK8QEc7uDKn4UzWYDWxuB5yXPQHf5Uktudwu1akV8k+vJac/rSynFyuIdn+ENt6X8l7fFiq+7S30ayEwxparUzm42kNvZEvXX5z6dBvncDhqNakii6FKq5MGZwfXlBZVpAQ2VJdN/fXkBlHLXm0SUCwTtydugWa+jkNsJzS8mugLAru/vVg/XacrzWV68A2L5l/40xivMMANrywvmDbXWVgDbyJY0BtaUr8B9ZyvESMnjQU+3vHiXXVg0lA3IEnDWN9ZWlsO4SOXIqWvVKoqFQtd5S8F9exOddkvLP2omBlaW7grPa4QQSTgQTkMpxdrqiiCCjI7+mxBmDqlULHQtX4e+mMB4PG7UmUskEpG6dO9qkM7YSqUSRn27g4LB0H5D+l0qFjEyNqYc0/Hf8nV1/rguO6PnHU2XLAKsofkCnUimjMdyAepBWLVSwrAvqesHeHwdbx14/2m3VL03Xr4uBSQk0B2T8zPlT/3waqWMoZERNZ+IBwAq1QqG/fgqLqqeEs+rIbk6E/Gh0k8MQkrVvCIeDrVKBUNDI1I+0gPpL5hidqNWwdDQsLLjM7UBr0e9VlF0MXsdCSv5SjQz9bVqtYyhoZGu9YxZBNk0u7Vdr9XE0UVYZyWsnF733QzqtAnhTCQ8Rdk6HYnyrVGrIjs4qNDW8r9bhno06nUf93C/5P5gebhF2I3ZbCYLAnajknljIOKCkrClJ5WhK4kb60IgvMvoY1upq4Sf3agjmxmUbgTyNuV1DVyLWSBo+zbceHmWhoP+HiNSPoR5o+CPjJNFCFp2Qxy9RdGam44hhMBuNpHJDIq4QT/g5cltQNBu2cj6tNHHmx6mlA3/NjP0ugZt2m75dJHiKPTU2qnTYjp+3HwLf7inD5GP/8/ptBUpvOnSDg+nlDIPQIY5UncXZ4HRRW5TWXdM7mccOp12oG+o1TOgZUAjSimSybD+mHHMUop43BTXPDF12i2jdQNTG7D47ZBOK9HSyRCLxZSy5fHkoyvAcV2kUuoRvL4Wy9k7ThuZbMaIp543i+/2rZcPAHHf3Z16GqPOAUHeDgbSA70XARG/I8ZSv9CXiZinn34a58+fv+dv7wbQTcQQSFw/pajVaojHYl0Vanl8AiCfz2FsfCJE5CgqFvI5jI5PoNl2Q9IiPYnruqhVKxgZHROMCctb1cPiv1u2DY96XU2PyMDMa2SNLrRkG4kcT928BjcPY+pflFLUa1XffiJ8PFUpkBze6XTgOo6maGzegRIwJea4v+mQd8Ni8EMtp16vIWswU2JsJkrRaDaQMfg81usIsHbix8dSFkYgBGi32iCW5ZvDic6fL5hcwbtbXLlv2E3mM5bHlzcjgCqddD0PjuMYTefI9eT5O47DFl9xoSgo14RTy7aRDLnGi3CjRuFvfJKh70Jap0mHXNdFLBZsPOW6mcYT0L9vV6ZULeWtZRgsCgFtkn58k+knud4M98DckhzPVFfu9UU3uqlLl/i7yWVcV/CYyzi+7Kh2AhEaV9xUFA+T50SZNrqhYoLu9hO5BCQ0n3ZBXZ4PPRpI6VxKlXksIB+FRSxlfjDp6/F3x6XC9IreB/gxbiBJpAAJypLNyOhLukX45sVCzFLN8kQBZ9C52RROP908jE5rHmZyGRemZ7j/8XAT8LjhNVCXxnX3Sc7rpM9TlmRfksVV6yi/U0phEaLUW15n9Hd9DdZ/y+nUunX/bqqf+C2lU2zsSnMpa8swE8jHI0+k2p5k4SE6gUkzv6Fu4y5fvozp6enQMzU1hStXrvSTxbsGlC5JCG7euIoEX7QiWpfvGCiAO7duGBlAE6PeqNextrYi9GUi8fBhc31VuNnRGUC1PBZ2++bVexL7Xrt8QWEA9aMRDoQwCeZt3+SE3LmiBsju9pbQGYnatMiD8taNa2g2Gko8U1U43c+fPR1aQKOPhCkunjsTDgeM022hkMPywh1jXjw/mc53bl5HpVxS8YzYNQLA2dMnfPF/ZBECPx6flRuOI0svKaUoFQu4KZkG8dekyGXl7q3ryO1uR+IgMwAAcObUG76UVy3XiD+lOH3iNWO4CarlEq5ceLtrH5ZxWbh9A9uba5qEJTjOIVpZZ0+9DqejHk132yG/+YZqTkZuUz1ZrVLGJUl1QzA+UMeujPvG6nIofhScOfGKr1fXNZrA661Xv278bkrfqFZx6ezJ7hkj6EN3b13D2sqiEtatrNNvvIy2djzWDd546StBHujOAFYr5UjTI0C4nRZuXsWGbz4pPAeHSzr92ovotFuqYV7oTH7w+9TLAe69oFYu4+KZE8Z8THDn+mWsLd+V4nf33fz6S19V1Ei69TFKgZe+8kX2OyKOXEypWMC506qpnW7537hyEat9uD/l5b/6wvNirHabvzh8/fm/9nEkyl+dAQSAQn4XZ988rjJyXfK+cvE81laWAvyoeV7n8OKXnxMbTh34mimn/8pzgYu5busYIcDuzjbePPmGqJion4Sc3ArvvH0GK8tLuBfo6yz3zp3oBfK9AlFtuLq0iPc/8yx76TEqHcfB9tZm5Hc9eaGQQ6vZVKUZEj56abtbG5iZ369KuCIkL5RSrC0v4aFHn+yKswy5HdWuUreOXatWQnbwouITAuzu7iAtXZPXGWP9fXN9FYeOmG9lh/IHG8iEkBDNTIPIthtoNOoCN3ngUIRpXyrklUUc0nfTRJfb2cLM3FwYT4lpkBnsYj7Xl5SGgElIuT6VCXR8KuWSIlGKypdDIbeL0bH+LxMV8rnAJEsXiQ7ApEW1Whj3qD5c8229Re7kAaGvxBne/UOHNWmY+lvuC6VCXjFC3Y0BpJSiWa8bcFcXFp5HvVYVbapKhcxllEtFzO07oODSbRGtlEqIJ5N9SR2oL8lWw6I3Y816DbFYXEiq9A2MPEYIGOM1M7ev64LF01PK+mSSu7kS36I3bC27EQo3zY8B7jGBp6zDKJOTS70atarvIUnPP+wKDmDxE74B7d77a4pOxEUMk3kZu1lXjlQJCWwEmqSBjXpNGau9jvgajTriCTn/riw7nI7TNT+5/nazjpjhODiqT9jNOsYlY+HdgCA46dH7oFnoQOE6TigMWnw+xuxms6vaml5Es9HAlGTk2iQRlqHdbhlP10xAKYXnuaH1PQps22bzr0FSLiSAUnij0eh6ymOCvpjAsbExFAoFHD58WAlfXFzExD3YGfpWgtKG0mRtsq0VlbZRq2J6Jrz4c9Db0242MTE1bdyBmPpUx2Fu1Po9zx/IZPrufJ1OB6MRN4lNuNjNwA5TsAh2WUQ9V7kmry+eOiSTKWR9UyK9BhmllB2RS2HdcGnZNmZ9W4shKSfC9fUoFdbew0cDYQYmnkgIF0QBjtHSz/HJya5xZOi0bczvO9CTJjJMTc90/S5nk0ylI82amGgj2x+UJYEmJqbdamHecNPerPAMgBBMTM10lfzI6dNpFXdOoyiGYXJm1ihFN4HT6WBWYtIUPLX0/Hh8UqN7t6PybDaLoeFRwybGiA6mZueiGSeN8fFcF3P7VLp37WcWwYShzwjGMYT7IIaHR4P6dckaAGbm9yleRVje5j7gui7m9x9W8u2Geiwex5Q/B8vxOO6e5iJ6cHgYg5rHmChvHgAwPb8/RHfidzBZOkgp4DouZg8cNh6zmo6DY/EEJqZnxaUQAJEMIAAMDY8KnVZWx+5M+Pz+g12YbfXddV3sP3TEr3vvNo0nkoLuMkRtZoZGRpGN8ABjgn0HDwe49ojrui4O3Xe/EW95juV4JZMpxRYpbz99rucwOjah2JjtBQd8OvYDruvi8H0PGL/JGy+A4ZhOpzFr8FwSSusnmpqewfDwCDx9IHRL249O4E/+5E/iu77ru/D93//9Svif/Mmf4M0338Tv//7v913gNxtCbuM4UBocc1mWyrGYJiywBuS3epRvMpMnSb4cx4EVi6HteHBcTZJnwLXT6ShSl27SAkqZw+1+DUNyt2jxeFw4dee+aWU9HoFLu41YPO5fZWf1SsatSF+892Ko1BTffPQZ0NFz3eDYXqRRXVbxfNqtFmLxGJN2aM1plC74btR44+mTi06bRqPeh/4g++t5Hmy7KeJ3W5wtQtBpt0ApkDIYH+Ug94t6vYZUagBx3yiyiY7yxFKtlDHYw7ZdUIdAz1MOM+EDMOabUtq3maB6rYpUekCYoJG/cbzl8Eq5iKHhMaVe3YDFH+2JC8D6TKfT8S9vqHgCsrQskASmkikkU0nGiPGjOph14ErFAkZGxyNvd+p1LRULGB4dN9zW5fGD8FarhVa7haHBYWN8vT52vYZkKolUMi3qJi4mSPXkkphSgbmMsywrtIkV0ghpYdVdxgVlh3Frt1pot23hkzaURnuvVyuIJ5NIpdLwKNMD5PTnuoGUBpLAUn4XYxNTkuRFah9JGsiZskJuR7iMi0nLgnwczNN32i3YzSYGtQtrUUaq7VoViWQCmQF26aCXTmAht4OJyWkkYlZITUP24c7Dd3e2FAPKrA58YwJRf4DRvdmssz5Jw/2PAw+qVcuIJ5LMuD/CDKl+SpDf3cb45HQkUyrjTjjuPoMseznRxx0Bo3uzUcfo+ISgnO5eUsalUi4hnkgK3fleG2zm7m5Gq5+Kjww7W5uYng0Y5Ch9QABwO23UalVMSK79+Dgz1btSLiGZCFzMmTZp8gWhna1NTM/Mol6rfWN1At94440QAwgAP/zDP4w33njDkOI9AIRgbXUF586e1rcP5ugA3jl7GutrK6EdqJ+deOdhX3v+i3BdzV8wohevF57/goQeMf7m79VKGW++8XL3OkqwePcW7t5irm34zcducObUG6hWSn3n/9JX/6ZnHE4bx3Hwygtf7is+wOxTXbl0PqQrIk8MMly+eE4c20cxgHKa1178imIKpxdz8erXvxy5EdD7Q6mYx4U+3KjxgXz75jWsrS4p/UgHebI+9cYrgnEUk3lEGRTA6y9/PVLSEuxAWQ6Neg1n3zyhxiHhm5tcOri8eBeLvsvDfuDcmVOoG2z5EWKuw8nXXlLq0g1aLRtnT77eNy6b66tYuns7NA1EHalefucs6tLRdy8pmTxWu+qzgo2Ps6dei2QA9bDc9iaW79w0ehYRzJlUn5tX3kG1xMzPKBcjNLpTP/+3TrwiFutudaQIXMapOEa3Vm53Gwu3b4TGaVQ/vnntEiqSCYyorPn09o6kxyYYCv+fIlEFhUc9vHP6uLgRzCGKqSvmdrBy96bGIJoljRYBFm9eRbVUFDgA3S+FvHP6jRATJIPK3FOce/O49j1aKpjP7eLuLWZerNdYApgpJ5PNQrkseV44d/p4zxMt/t2jFG+/2Z+pOUKYisqdW/27abtz85owV6PQLCL+mVMqLr3EZG+fPtH1u9yGhXwOd26acVcFD+zvrRvXkM/viu8hXAhRwk+deKPvk0QOfR0HdztyvNcC301QzOfYMaChDqZ2zxfyePixJ/t2R+S4DiwrBur7Og2VIS0G7VYLiYT5LN+kD1guFvqyUs6hXCwIsbWlLQymHXqlUkJmcCQkNo9iqjyYJ0uTVK1cLmFweNR4o9IEpWIBQyOjxt0ex4ObYACY/tWxhx41xuf8vvzFdT1hvFOPq5fTsm3EE0lxC5GaCCIFlcslDI6MBLhEVJMQRsNisYT7p+ZE/uLIU/orQ7PRRCKVDvAxZw+ALdAeBRxPNRKtA/sGFItFZAaHlBuXap3V8HK5jOnZuVB8NW2QslqtIjWQheN6oTh6n2H40zAu/l+9n1XKFaSzYdyjoFqtIDs4JCT2hABUQiuYyIP4qYFBuB4N3YiV9ZPk28Fyf/Q8bmSZhbnSt2qtilQmG8I9iimsVitI+/GjpmPqc1WEMNd+qUxWKZMi+G6BdTYudeBzhW6MWR5HfPw16jWkBwZCkl3RhlK/45Lm9EBWGJ0W9Ia5L9frDHcKKJI/cUPY4/Xxb//6kxcFBajG+EkFEOLrpaVScClLa3I9JzPOzWYdaV+dSL4VLP/ltAGYzcKU5qrP5C8YCGhtUjGQL0HwPtZq2ZLh8vA8QbUfzWYDA5lM5Bqg08duMNypFtd0I/hegVsI6Ab8FjDAdL7TAxmYdHCj5kjTKZW8qVEFOzREY/5bz7/T6UQaXZfXTJ7GtpuRKmgmynH3pMoarMXnkkQd936hLybQcRxUKpWQWLFcLqOj3b57twKbBLgFexY2PDqOiakpuF537xMc9u0/jERqIHIhEu9+Xvc/+DA6rufnH8RVJjj/Q9txcOyRx8WC2EtxPJ0ZxFxmsK9FjlJgYnoWgyPjYpGT8TDtqo8eewSEWAFzQZgZGRM763keHnz48dBirksPxOJoxXHkgfeF4sv4svTs98j4FDKZrHKkri8YhAQ03n/ofiTTLL5+FCYPJv7twUeeVNoohI9IT+G4Hh589MmeuHPIDo0hOzQmMY1hoTN7Zy9z+w8hMzSCjhv4G46arADgocefhuMF6btNAp2Og0ef/IDUxxCdhlKkM4M4dPRBxlxodDDB3P6DGBweVerKQZlI/TwefPQpQOpjUtEhJpBSikeefCbE2BulYwBS6QEcffCRYHE2LXLS78nZfUgPZOBwRkr6KLcTlxAde/hJxBJJOC7VcFLlSwBrs8ee/hBjpGRGkKqMjHAflkzivoceg0M9ZQPA0/BSuDRrbGYOsXgSTmheMvu/PfS+RxBLZdBxKTNrQoJ48tEwYwIJHn76Q2h7XohJkePBrwNiCRx79CmBC68XvwBBObfp4zc6PYtYLIa2L4lXxwRC9T/84KMgqTRs1xX05NI3+Tfxe+z7nv4gOmIeCBhRHQgIPMvCA49/wMfdbwui3Qz2yyEgGJmcBQjQ6al/xeh88MGHkchk0fE8JoskevtQRQL58PufRdv1JNuNwUwQt4gwOcPqZuHRpz8Ex2X9j9ti5JjJKgoAMDY5w45WDRsw0/Rx/8OPITM45I9FHpeG1jPif3ziAx9R5lt9vvZABXLEsvDE+5+VTCIpBBfriAeW4fjkDCYmZ5TNhcKc8jQ+fg88/Bgyg8OMBj3meEIIPvDhb1PMbKkuA7X5wLLw9Ac/qtBOqHJBF55QTE7PYnJqOoijzb16mocffRxD3GuXVH4Un/fRb/sO84cu0BcT+KlPfQo//MM/jM9+9rMY833DFotFfPrTn8YP/uAP3nOh3wogAOB3DE5AiwBD3LBtF9E5IWznns1mEY+ZT9DVvsUM1U5OTgqDs5SEBwPLn3UCu17D+PiEekwrHbfp0GrWMTO/v+exrsiKUmQzZp09nZ9xXRepZAKJeFDXmEWEDqEOlXIFY6OjSMa7axdQyiazVqOG8dGRUHwT0wAA7WYV++bnhf0onRx608UsD6mEapRVT8MHlN1sIp1KIOG3q5gMoLYVaycLlWINw0PZnnXlYNdKmNt3IDK+zCgTArjtBoYHDbtWQz9wXRfwOkgnzO6T9IW0Wa0jO5BC2vdnK44KEWYuKYBWvYKx8QmBe69dZrVcUPyId5M0AoDbaiCdCNvNk+vAJ/datYKYRZGMWz2ZdQKgUC0hnQm3UxQDWy3sYurYQ4hFjG9ZRw4AWvUyUgn1drCOBy/PbjbgdlpMDQNEqW/Cl1bJi1itUkY6FsdALG5goDQ2kwKb+Rz2Hb4P8bglGDM5ncqsAe1KGVlfEd9CYBzaVE+n04HTrCk+kHm58jEXZw6qxQrihCIZs0L9y8TAb+Z3MX/gIFLSjV8gWhJYK+ziwL79vmTVLA3k4DoO7HIJqf0HFQbWNfQBQoB6qQbitJGKWca5wxN/2Y9qfhfjM7NIaX3GdFGEgKC8s43ZmXnEJft+nP4hoEA1n8PBfQeFnhynIyFhfcBauYZOq4lE3BLj1JK+e5qUuJzfwcQU0zdUGCgdDT94Z2MNU/4lLlnCbXFGRklDkdvZxMzcvCKtErQggf1CixBUy1XYjRpi1gwsK2z3jqfhdS/mdjA6PiHWA16mjIVIS4HNtWVMTk4pjJleWxm/7c01zM7NhZk/A161agW1aglT09PKeAjh4Yfnd7cVBw86A8j/8tClpQU8+dQHVFxVYgOEiPG4urrS10USGfpayX75l38Zo6OjOHDgAJ566ik89dRTOHDgAIaGhvAf/sN/uKcCv1VA+D6KjRwQQnDh/Fmj9Xv54QOp2ajj7q3rsCzVknxgUT54LAJsb65jd2tTNI7wNgA9LcNr4fYNtG077AUBMOJ1+cI5xONxJb5JX4u/X7v8jojLH86gch1B/jRqZWyuLbF6+fUVxiwNZWyur6FcLgS0VgaBHJ+F3b551WhXyW8aNhFIZd24cokZtuV08SdRyyJ+ewSeCVzXxe0b10ITK8+bSO1ACFAs5LC7sxmUrbVV0F6sLdaWF9BqNo10V/uNj/vVS6KdTPS2tDrfunFNSS97rJC9WMQsgma9iq31VSW+SiPVq8DO1gYq5aKoK6eLpdPGD7t7+wYo9YTEQeAb6sMsbOHWDc3TRjgep2Wn3cbK4t2gfEOfJVJ4sZBD2fcBG2pLQ9utLi/A6bSVRYiXrdPWIgQLt28gFosZxwOvtyXVa2Xxtvgm90Wi0SRmMf1d5u9W7VemelqEYHtzDS27yfLyayCYNdEX2T+LECzfvYl4PC68gAQGhgnixFL6nkUI1pbuIE4I4ryuRK2nOhdUUMjtKHTR8xPzhEWQ39lEs9GQ+p/Z0wr/trx4G0SyoRnQJmgzeRyuLd2R2hHGOvOnY9so7m6HwuMWe2QvKTFCUM7vwm7UFfoShe5qH9pcXQjNqToeMWIhRizELYKt1QXBAMakthF4EUs8TquJ0u4Ww9Onralfxvy6VIo52PWa0n7yGAzGO6P9+uoSACrVS53X9XlzbXlBtAmPE3jSUec9t9NBbmvd2GfEWCLB2KmWC6jVKsHaaqn9Sx5/bL1ZBagbXke1uZSHrS0vKmNTbUt1/vM8Fxu+n2E5rvxXpmOtUkalVNL6QJAvj8vrsLWxDqfTlugQzCs6D2FZBKvLS4jHY35fD+LK87w833D7hlIT9oS+dQL/+I//GL/yK78ivIM8/fTTOHq0Pztv7wbgjQyfKbNtGyn/timfdIybIZ+ahfwuxqemBCNp3Df5HB8BUMzvYm7f/tDuV28c/l4uFTA2Pq5MhkG+QdxA5OwhJtmes6RveiGNZgPZbDZkiR0Ak1BqlamWixiTpZIk2LnJkwCHcqmAI0ePQd2tqjjIzEi9ym5syzu5KOD6aaGohl4eIwTlYgnDwyOQb1rxna6sX8MXpXKpiJHRMQVPPQ7/DbDbWsfe97C5nXQUwcx3yF4oeJ56WkKYfkkykQjRWsZL5AGgWmHeZUzxVUTYTrFWKePAoSNK+RFCNcg3g3nbBTRUj5YAdaGnlBr7GgcPFI06cwMYs4i/4w728ZSLGxCEN6rMVZ9seqSbugSzDzccWriCYz5VsmpZREj5o7oar6/t3yaPWb7OmbSVppKkj9OsUa9icGgI3B6iVDW/bVT9u0a9hoNHHlAkcgwv3jcDJAkBCKVIxOPaJQMS6qOEEDiOg1QiIca2LvnT39t2A8NDQ4hLpwC69JgQpipiEUab6ZlZ0Sc9CScLqloMAOZ2zbfLFzU3yuGWFRPtGCMEhAKWf7JICGDRIHK72cDQ0DDiRJfs+ZJVray23cTI5JTwnsKBH5RQMCksJ4XTaiEzkAnFBwm3GyFAIhZnTCACRtCPHhpLtZaNwcGhYJMhGDQWR/YwQQgzLZX14wd1IkICa2k4dlotZAayUlyI/mgCrvcmr6FalYO8OzYy2UFFqidH4PMER9XptDGYzQrGiOMu00MGx+lgIJMNzdX6JRs2pggSiYSyNnEwzR6ddguDg0Oh9UuvBpFxHxoMjR85jhzuOh1kMplQPL0P8J+JeFwNl+NLeQPMbWAmkxFMcb/Ql4mY9zJwEzHXlyQTMYSwo4JmE5nBsGsxeY7m0G634Xke0pr5iyi9J7vZRCyRQNtFV70kDo16Tfjn7AWEEGGmJGrBkuN6nod2q4UBg3KsST/GcRx4nodUKqUcLUQBM8kSD9xKKYNT7cAUjDY6LiqDK9OSot1q9TQ7wpXzvT7corF8WRmy6Zx+oN1uI5FIRE4Spvg6LiEGWeAUmP3plT1vV8cJmMxux6ScNrKkrRc4jnNPvsGZS7fg2NBUzwAXCtfzEI/F2KUACXf5eIozHJ7n+cxlf8fw/dRV/uZ5nmICxTSO5G88vm5eieupycB1joNx4ZeppeNvgY2v8FGhzkSxsPAiry9ALLeAMeV1jWL+1I1KQHddf5jHD3RqqT+2iNKOrL5hWjFpFDH2XVMbyLpUMq1NZq7EhkRioER/kuLyYtilKd5vgnz0Y3jxTik4z2m65cuZkIBBoYEUB9G0B/gizufe4OhXMAHabwBG3XZOA3ZpKJiXOehrkzwHszrL/UY9rud1DNebdF2XoupPgRAT2K0cDro6hZz+G83eRDF5ehzAoBJAwsIUSHH1MCMzKeXP28QicjsRISA4sm/iG2ci5u8CKMcQAO7euY1qtaKIVsWiQdQjPQLgwrmzQT4IOHEhzrWCo1uLMNdffAEN4mpHfP5fUIpL599WwqKOauMxC6XCLlYWbmvh7JHjxWPsGGLpzk1UywURR32sUBkXzr4JC9TXA7R8JWRVPC/j99ap15FMxDVxP5R3fqRBPRfnz5yKPNqwiFqP/M4WFu/eCsXR8eBl3Lp+GZVS3nhsRbQ6WITg9InXjHSOek4ffyVEMx0f/rSaDbxz9s1Q+8nHOHERTrC+soS15bs9cWBHWcCFt0/DblRVWkiP35UFbd54+WvG9jb1NYsAJ159oWsc+Snmd3Ht0nml7+k4x2NBfW9dv4zdrQ3lmCs43ocoj+P/5usvAZ4bonkUTsdf/qpKaz4e5MfHq9Ws49ybbwQ4xoI0iZiFZNxCQhozK4t3sLxw20hLpT4+fuffOoFGraL2yS5958TLX0HMMreT3ncseHjz1a8Zx3VCPITVIW4hv72BO9cuijD+JGIEibha32TcwvVLb6Nc2GHfY2w+MM03/Hnt68+Hxr0+RmU6vP7C86H6R81llUIOV86d9tvCEvWMxywRJqe5fe0idjZWRJ7KX//hNIrHLLz56tdgeS6SMQsJiz1Jy0LCIkhYBEmLHdUmLAvJmIXTL38FKSuGlBVDMmaJJxWzkI7HkI7FkPLfW40KLr51XLSHTuuE1BbJuIWVuzexvnwHqUQMqUQ4ntx+iRjBm6+/hLZd7zq2LRIcnb781S+JtYqHWURWIwmOZTttGyde+Vq4D8b0d0bH9eW7uHPzitIfE1J/DMYZw//tN19HtVxAQs9P6gtyH3nhy18MjTm5b8l1BaV46StfCs0X+lrLn631VVy5cC5y3Ol9+J0zbyK/uxWKE7f0erB6f/VLXxA8Q8xi87gYH1JdePovP/d5ZZ6Q24wgSBsjBDs7Wzh96oQqJuwD+t/mv8dBdAowRm1jbRkzH/igIlqVdznKjocQbG2s4dmPfFTZeYX2GFJ4sVgAtOMzkZ/Aib2Vi0VQyhY4KRst62AHUtzdQSqRCF3U0Hf8vOTc9gbmZmcQj4X3q9zEhSwVye1uIZMZiBQpy8GUUtTKJcQtEh0JAe6lclFMILo0IYjt5w2gVMghnUwK2pjAoxBH2rtbWzh0+D6Gu9hKBfnpuVRLBaRTkquliDIo2NFus14L1xXh/gJKUasUkUokFLqbcOBJysUcJqamIi/giLR+/rmdbTzx1AcQs9ikHb4ozivOtvcy7tHHwCz7eq0OCzDWNYQ4mB/gVCol6qrXU25j12MGiOfm5hGzCDwPoEqfCn57/odatYxUKtmfZI8yd178sk83KQIhBHajilQygWQ87N/ZJJGoV0uYmJhCMkbgkABHSiks6h8JS5KVSjEvjrtkSUuUP9pOy1aOX+U669L1armCZCLODArLEgNJ6sDfCYBWo4JsdgCpRCz03RS/Wipi+KGHkIpbvgQtMG/D6eLL4kApc6HFL5R5VKWDrnbSbrVBQJW6ymVzevOwVrOGdDqFZJxJYLnki9PSohwjlq5Zr2Bubh6JuCXqxqWHlIbn2lazjnQqGVJToVLnpCKMwnU6SMSIQjvevrq0y23bSKeS4jKR/F2X9AFA265henoWKb9P8m+WRBilT9YqyGYyIJqpK9FX5Mt/hB0dWhKC+hG/fG+51mr4Yzvov1HtZBGCdrOOzMCAcinLdKTJw5v1GgazWeVyHmdyTOB22sq85FJZOhvkTSnQaDWR8AUUChBV8sihZTeQTqnrjSyBlvEnAJqNGjLptHJhVPAZPmFkGrmuI+oZoouGi9PpBEe7Mj0i1ku70UAymRDMYr/w94YJ1I+G4rE4xiUXbXLH4ZO0DBMTk4hpR1FRZG40mzhwgLlxkgcax0MHp9PCwYMHxWRo0hkTeQGwCMXs/D7EY5ba4YJClPd0KompqUlVRwPBIhTTSpqZnlE6qpZ18BtAs2nj4MFDbNAQIi0KYfwJAajn4MDBAwHDaywjWFTjsRimZ2e6MoFEmgQGB7MY1fQNuzGbs3NzXRlYuT4Nu4n7jt7PLqnwvHkcafZhadjubP+B/cptwG6QSqUwPTklbQZkm3O6OQGCqakpZDNpUUe9GsrWg1Ls239AtFOog2sIUuri0JH7EPdvD+r0sLQs0qk4xsdm2aIInzHX+opMr6GhQYyPjyFuEXhSPOozUew3Y6oIAfbvP4BkPGyv1ETXVquN+44eFcxIVNchPh1SMQsHDwY3uDl55H2EXNBgdhBTU1P+TWJPYgwCH8iUBsehs3NzyGbSjCYIjq5k5oLTBpRi/8GDEi7qmBLo+CjZMeDIfUeMzIXpfTCbxdDQENIJy7hI6WFTUxMYGxlGPCYxXnJ7gfcDxnwcOHhQ9F9La0tuHYD3mw7xcN/R+0PMOmeKZLAIQTYzgMHsPsEEeh4VqgSySgFPOzE+jonxUeX2PI/L20Iu5+ChwyGrAjyN4KX8MpxOB0fvP8ria/kox37+h0w6gQMH9ivMt2DsOL0k+k+MjWF6eoK1k7RGyajJa8nBgweRSiZ8Bj08t8pjgFKKw4cP97wZzMtIxuM4ePBQqJ2i+tr42CiGh4eRSsRU4vE8Rd7s99zsLIYHM0F9pHxN6g+H7zsa3OKnFETbUMlzTZwAR+67D/EYUcJFPA23oaFBJBNJjVGD6LsB7mx8z87OYnRkWNm4y3EUegE4cuQ+ZX4PTU18sBPAAcX9DzwQ6kt6dA7ZTBbJVNLfNISiRsLfG53AhfW80AmklKJWqwXmYXpAp9OB4zhGnTqen7xINxoNxONxOIgp9uSi9AnqtRpT6IzFwhOy/yKH1yoVUZeonQRBwDhUymUMa66NAH9XT6kYp5SynUrLtpGVdCWlfqnUBQCazSYsyxKXbHRc9L5Yq1bvyedxuVTCyOho5NV+Wb+KACiWihjzL3roOMt6E5RSdDodtFstDHbpB/LmoNlswLIsoRfaa+RUK2VksoPCpZuSL9S2o5SiWGCXg9Tyo0dzPpfD+MSEph9m5vHa7baoaz8GReu1GhLJBJLJVCg/U9JioYDR0VFmdBvaZCnrgfk45nK7mJiYFNIlgTxkaYsf3/NQLpcwOjpmLNzS+n3TtuG5LrLZLHT9Lhl4cKlUQjab7arrKQfncruYnJxi40WSioFyO3XBOwXzzjA+MRUylCxFE2W02200Gg0Mj4yGmAt9PSWEoFarImbFMJDJKBIKeSGV55FiPoexsbGQ/m7UeM3ldjHl+4/mkjfZAyaBz1R5FI7rolwqYtx3GWfSN+NleZRJLjzqYnAwGH8yAyiPEYsApXIJ2ewgEolEiAHUbU0SQpDb2cbU9Iy/KAZ01+PySwPb21uY8F2dmZgQXleA6UG3WszdXZROn/xeq1aQTqWQHhhQ2sXE4BFCsLu7jempaUUH1sgw+LC5uYmxyRkjQ8dPe3iSTqeDWrWC0fGJyH4o07FeqyJmWchms8pphMy0sjqz91xuF6OjY4j7lxp6GZTe2d7C9MysFidUDQBMX5b1ycA9nrwB0Of8eq0G13UxNDISWsfkfsYxLBTyGB4a7qpTLqfb3trEjMFlnIK+9LK9tYlZP354Y6+ms20bdrOJ0bExFUkNb/63XCwilU4jk8mgUqngwMzYnk6gDJw7JoRZnT95/DUljBD1qrj8rK4s4ca1y8ZvhEDoX3FJ26UL57G7vYUYQUg3h+t1yOEnX38JhLpI6no6/Lei/2Hh5Re+IumymPVzOE6dThuvv/qSoism6x7IOhHxGMHO1gauX70kdGaEzpqk8yHr1Ny8egk7m+uK/kZcw0PWkzr++ivsKEL7ZsIvZhG89OJXlTiKWQcNP891cPK1lxV9LoGv9M7rkt/ZUutq0JmUaXPj6iXsbm+K8vU6K0+M4MTrr4B6Tlf9Ev7ELILXXlF18LikV39iFkHLbuL0qeOSbkmAkwm3rfVV3L19XehBmfDlfxMxgiuX3kGpkBM0kHXiQmktguOvvhS0kVZHXhc5/MRrL4uwhChbHRe87zdqZVx+5yyScQspwyPi+s/a0h1sri0hlYiJMSPrbCW0552330Kr2VD0BGV66HU+8drL4XgCf4KkNM4tULx18g2BO8cxlYgJ/NMJC6kECyvnt7F85zrSCR4eE086Kf1OxJBOWFi4cRW1Uh4DCQsDSRYu8k6Eyzt94rVAFy5uifbmfULvfydee0X0O/49EVP7DR9XdqOGqxfPK20nly3q5P/eXFvE7ua6wJnXKZ0I6pGMB3S4+PZboG1b0JHXQS5DpvPZU68HNOT9Rco7nYgxmiVjIJ6Di2dPYSAZw0DCErTmOA8kLIHbQDKGUm4La0u3WXz/4Xhn/Dx53IFkDHeuX0azVlZoIOOl62eeeuNV85whv/tj3gJw8vhr4fla6pdJ3i/jFmqVIm7fuBIeT1qfS/v4rSzcQjG3FcSXaJ2IS3n74+nt0ydhgYo+pY83fd455fdJGe+oOdNu1HHpnXOh+cekRxizCDbWV7CxtqzMbaExLeF09cJ5tBr1cJ5yWdKaeerE68b5ztRO1HVx+uQJSb8v4B2E9E56trc3cfv2DfCjYBGPRyGyniDBtWtXUCoUoligSPh7cxwMBDp4u9tbmJmZUXTeZK5aZroBIL+7g337D0TqyCl5ELbzf+r9TFeLy4BMEjvekG6njcHMQLTEQvrAzYjELKJsDKKEUsVCHtNTUwJ3npN/gsP0aKRyS4UcpqeCI0kzLsERVbGYx/seelg1CdKFTHaziaFsNhRuOnKknoe4FROmFKiUN5He+VFsrlTwj+357pqIb/pui+2cCpiaVI/Jo6SHAHNf99BDj4j81WOFcJpOu42sLD027OZ4AtdzhZ06OS6R36XMy6UiJsbHA/MU3bomAaqVEiZ8A6t6/UQ06UO1zOKbzF+YFBv50b2xjn58dvwK2O0WUqkUmyy1HTxvswAvZqtucmIcibhZt1LffderFRw8fITpSkLdLcvV4NCoVTE6OiLiC7DMiQhhkzwohJ5bIP0jSmH1cgnjoyNI6UarYQa7UcP42Ghg0FsunvAj4+BDs17FxLhqqD1KsgCwMZVKJkIfiV+A3HQdx0EsHlOMtBMaJgml/sbENwLP60pj2nwD9dSk1ahh//4D4ihbRskkUWnWaxgeDsymMB1LquhkypK1RIyEjmu55FAmIyEEtVYDY6MjiiF1E3CJk9tuYNyPb0n9QsGZBL9bzbp/bGi4R6y1A/Xz0h0TUARSG7lPd9ptpJMpxC1iPgqmLDYP69gNjA4NMj1PTd3EVI+23cTICFNRMI07vRqddhsD6VToe2jq8+duCyQ0LynEkH7bjQYGsxllvSE0LM0KcG9gdGxcPa4lYR0/gPV9u9nA4NCgsv4F0k9JGi/NaTIuus6kLAW17SYy0jqvnwro9GJWNDJGvkOfB6kfn7vH67YchPL6+3IcvLxZEGLRfC6HWCyGkbGxsNhWogYn8OraCqanZtQjTxPn5Yct3L2Dw/cdDY7QtHVaH2SLC3dxJMrmon6s12ljZ2cb+/cfiK60NFLL5TKcTgcTk5Pqt4hW397awtDIMDKZTPSqKcHy0iIOHjpsPEYzFbO0uIAjR+6LXARlcB0HG5sbOHjgoJHh1fOvVqto2TYmp6b6wiW3s4OBbBaD0jGHPMnp1V9eXsKBg4fE8Y+JcdXjHzp02BhPp5bjONja2sK+/fsj40AKr1SrcNptcRxsYo5k2N3ZQWZwkNmo6pIvwPrn8vISDhw42PPYmMPKyjIOHjzUV9x2u41CPo/ZubkQ3U38Y6VSAfU8jI6OGvPT67y9tYWRsTGkuR1Qw5GUDKurK9h/4GCILlHp1tZWsX//AYVhjZoOmraNerWKyampKN5YYexKxSLi8QQGhwZF3G79Z2N9HTOzsyH1Chl3mdlZXV0RuMv5mRYix3GQ293FzGxwVKePPbku9VoNnU5HeJXqNcZzu7sYGhpCKp3uvofx67K+toZ9+/ezvHtlToD1tTXs378/tNjq8zLAjt7q1RqbJ2VkIsopl0uIJxLIZrLK/B41/jY3NjA7Nycxqd3H1cb6Oubm5/saf47joJDPY3J6WkFA6VscCFDzj0hHR0akzYuKuFxqbncXwyMjPc1ucVw31tcxv29f6IJKlAmXrc1NQZsoVRWettlswrZt0cei+rlQDyoWkUylkM0wv8e6fVA5D0IINjc2MDM7G2ZwDUIEANja2MDsvNlDhz6fdTodlEslTEjrk6mr8bBKpQIQgmFJXSmqfwHAzs4OxsfHEY/HUalUsH/vOFgDiXKbmxsYGR0NdntE/c7fOfdfKhaRSqeCeHo6KcxxHdi+54+YJhLWTXeAAKVSsbt+nFbGzs42MgPhhVxBX3rZ2txQ9Psicfdx2tnZYrfMEITphYkgSlEulQRTpGdLtfdGvQ7Xt8tnIF2oTrndXSTj6s1dfaDIf7c3NzDg69xExZGf9fU1xuxG4KDjVywUJP0fEvoup2k2m7CbTWM+lmEhyOdysDQ66nHk8O3NDeF0XZdom2B9bRUZiTZKntKxAp8QC7mcmbHXpKoAUK/X0Ww0xDc5jm6CiddVhEl4czx0DwRbG+tIJsM3g2VGRi5jY30N6VRYEgEtHq9rbndXaRNVaq++VyoVtGwbQCB5kk1J6V4Ucjs7Co0Veih1Z2nW11aRSiVFPnI6xayU/21raxOxWCxEY5k+PB1nFuQ4VIqr06tULKLVbimMi46H3E4721vM/qAU1u1ZW11BOp0O9RFTPyKEYHt7K8jbin4IYXNNvVYN9RHZFIc4TiUEhVwOruuIdrC0uPqzsb6GRCwmeXswtK/0bG5uKLRR+qT2eK6LHV5XIDTH6GnKpRIajXqwxoTwgVLnna1NQOq7Yq0ytBEhBCvLS0j5409/lLg+Ppsb60q/08eW3Pfr9Toq5ZIaR6sfz8siBPlcDu1WKzTm5DrLYRtrq4j7Jyz6Nx03ANhYX0PM0Ieh4UPAmO/NrU3lG1ed0NccPp5q9ZqxPeV2Bdi43NrahOu6ynoemY4wgYxJ/7wX/P1hAiXu4drVKyBChq/F01Z0x3Fw+9bNyGz15PndXezubvfkcPjr6uoKavVan5UAbt+4Dtd1QxtUqv3m+V++dJEtoF3wl9G8fv0akwwY8tLzqNfrWFtdjfwuv1MwBeBSsRiaBE0CRwJgYfEu2p22eJeZShlHnuba1StIJAKmsZew4Natm33ttAG2i1taXAjKliQn8kLDYXd3B4VC3phXIJEI8lhdWUarZYt3/jdKgnX92jVhh7LX7T4AuH37ViSTIONDKUW73cbS0qISx/SbP7ncLjOJxL9JcULSMb+udssWC7NcV/7IcON6UFeiTdqmRfXOnduROOtg2zbW11Yjv+v13d3ZFn1Ybh/1KDKg6/LyojBIHpWv/Pfm9WuiD/fCBwAW7tyOZI7139VKBbs725F56rC1tYlatdoVF7mt7t65rV008Pu3jrv/3PX7pBxuig8wQ+SLC3eN8fRFlRCCYqGAQj4Yf7oEhY9jDhtrq6xPGvDRgQC4eeM6u/gg5d9NSrMg90kNZ6r9rdfr2PKZC31u1OtKwQQD1Uolck2QmS6AMQvUN0geNWfLc+vC3TuhPKIYGc/zRDvJ+elp+d9KqYR8PheKI8/vMmxurMP2XSp2m7n5t7t3bjE1FR0HA6NGACwu3AEQNlFjKqvZaGB7a1PcJQC4uoEZp1xuF5VyWZQfoXElyltZXoLnupH56bC0eFdg2ueyBuDvExPoE6XdbiOZ6i7WlmF3ZwdThuNFDvog3dndCd106pYut7uDmemZ0PeoDlgslTA+MaF8M00Oohwa9rJgmkgAJr3KDGQicZXfKYB8PofJqUklLAoImARocmqqK9MiT6TFPNPxM+EAhOvQ6XRUV4CInihc12UuebrgLEMhnxd0V8o1MC0A2/WNj0+EwgXu0iillKJQyGPMj68ziaZ07bbZA4wOPA/d44MJD/67XCqJW8q6zpBcX/63WqkodVU2JIY6VCpljI2NGxlEHSeAbcT02+dRdfU8T9xK1PHn7zJOlXKZ3b7jOPBHax8O1WpVucFtYvxkqFWrys1805GYovNDCJNEoPsiB/j6wX3QhZdbq1YxPDwSuT/VcW/UahgeHoEOOsPFwxqNhnL0JGiil+M/lnYCojNDcnn1eh0j0g3PUP20MhqNOnMbaIgr58vTNZoNDA8NKzQxbYDl/OKJROQGVmk/SgXDqNNAxwNgUsxhH3edKTPVwW42kfWPDPV25Y9cF7vZxODgoHENCJ20EOZNSQ7XaSqntW0bg0NDRrqb0jbtJrLZQYVpoTDUQ6zfLQxmB/tmjDyPIpk0e2AiJHg4WLHwNYkohorTUZ/idGaTQ7vdRsbXh6c0rNKgM4Wddlvc+o/Cnf+mFIjFJNd+9wDvGZ3A27dv40d/9EeRy+UwOjqKz372s3j44Yd7pgvpBJKwe6teum88vhisXbZ8fJHhi64+wPVBrbu3kuObflPPA4kwG2BqSM8LfAzrA9wErqf6JO42+fD8Ta68OIkU/CUbBDrepndT3nK+ehoagUskblH6JwgYgX6HRwifiLryPE1MA+83UWXqjEyUVM+Et9wn9bQmfPT4Mu56mUC4rXSzSRoyzJ9pl7bScTL9jvrWLX6vPLrhodOiV1pT/F5gyi+K8elWvjxGdKa8H1zEfKP3s4h8+8VdSc/bq0tcaPH1FU6fC0LzjSal7zaaKQ273+sXTLTSIQqHbnOhHG6KG5UHD7uX/EReJGBQ+G85zDS39wI5vRwGhMPlMrrF6VWGqcxe6aLyMNGhW1463qZ0XdgIJX2v9tDLBBjfs2/675hO4L/+1/8aP/ETP4Fbt27hF3/xF/HpT3/63jMhwDvn3sbG+rocBHCumoQb5ZWXXoDdbKrhhnj8/fnnvqjoR/BvpgmAUoovf+k5IWpXmAUtfwq2G37lpRd6Srl4+pXlJVy+8I4S1i3NO+feFkdjehr9AYCXXvgamr7eWzc8ePy/ee6L4WMF6Z3vjvkE/vyXnhPhcn4mKBUKOHH8dZGnnk7H6dbNG7hx/Vo0zhrDc/LEG8jt7qrxuhwdcdxD7agdvXJ47otfEAxg1PEej99oNPDSC18L4R21CC0tLuDSxQshhtMkxSSE4Oxbp7G1uRkp5ZSPQnk7ua4bySDqded1lcNMZQBMsvDC174Sma/OjC0uLuDCO+e74iGnOfvWaWxubBhxMKX/8t+wupq+m+rypS9+oa8jRoC164tf/2qYgY+If/fuHVy+dNH4TZbO8PzePHkCu9vbkbjq6b/0xS9IfozD+erwpS9+wZiPiUEpFIt49dWXQ2oselqe/saN68bxSrW4HF599WUUSyVjXjoQAF/8wl8JhrTbAzDaffGvPx+WnmkMJ5/7tre28ObJEyJuN6YMAC68cx4LC3dD5ZqAAPjaV55Ho9mMPA7WafTFL/xVZNkAhK1LCsC2W/jy819SGSMpjb6uLS0t4vz5c0p5VMuTx/UocOrkSWz444+a8qX+479/8a8/Hzk+hF1K/tCgrlE0pFK6QrGIl19+SfnGyxb4Sxldv3YVN3z1KZGGBrgodabAKy+/iFKpFGpTY1+jwF9//q8U/JQ6SmGMRhRf/MLnA3p1H94KvCeYwJ2dHZw/fx4/9EM/BAD4/u//fiwuLmJpaan/TPxetbG+rhynSp9CYQRALpdDVjJpojN3MlBK0dL0SsQ3Q5p6rYZYjyNJuayd7S2j0WcdN55mZ3sLIxE3KgFp0vL/bm5uMoO8fUKhUFBoY8Kbg0wbWfKpMA5SunKphHQ6+qhLzz+X28WQb3Q2UhdJKndnZ1scdZn0yng4h92dnRAtTQxUwLw0+8qXEIJ2uy10dEz5ynEppSjk8+plHymNSUpYyOeFYfSovGWmcHd3R/QzXWfPJA1sNOqKLqaprjyN4zjodDpGWphwKxYKwuxBt3g8vGTok93omcvthmjZDZq+Ifio/GTwPE+pKxAwZSacyqUSO/7pQRMOpWJRXGzSwcSQFwr54NjQIB3VoWXbfRt1dxyHKbGbcDGEVSsVpFPpyPyUDQv8+UCrq0nazaFULIp+0G1+5fl32u0esQLmp9FsKidDpg07/0sB1GpVJFOpaAmq9rdcKWPAoJYj5ymnqVWrgQH7CNzl8LZUVxknE8NZq9WQTCYjmW0dl2q1ipRf127Av1erFaQHBpTNiqmO/G+71Qox63ocDq7noeM4IdyVdFKZjXpd1NWUn55HrVZDwlfHMOWtp5PbSaeDKX5La6eoelAwKwTEN9QfhUMUvCfsBK6urmJ+fl5RDj948CBWVlZw+PBhJW6r1UKr1RLvZV8Rs1qpABTIZLNwHZddv5ZHsD5C/fCp6WlUKtWggC4zSrvVwvy+fShXKuYI2oxRKhTC8U2zig+NRh1jYxPR+Wvguh7SmUxkfH0SG8gMwKOUxTfgIcjjf5ucnu6JC8+i5dOm5LdHKE8trFgqYXp2rmv+ctM1bRvDI6Oh+HqT8moRQpBMJlk/gLkrQHofGR1Fs9kUks+I7sK+UYopiTaR8fz3aq2K+XlGm6hFVNk81GsYHR9HuVLpegzEf3ueh2Q6LepqylvtBxk4jmOkDRCWkk5OTaFcLnfrugLsZpP1eb8f9EpTr9UwPjEh4utl6/h7lCKVHghwNzA3cpnpgQFQSkX+pjgyTPh1NeUJLU3LtjE3P89M3PSxNa/XahgdGzPiwkFv72QqbcTHpBKQyWaZmYo+6koIiayrKf9ms4G5+XmU/bqaypfD7GYTw6MjKJfLxg2hqbx4PK7EB1h762Y/CCEYHhkR47UftQ5e137i1mpVTE/PhOayKOh0OhjMDnbNX6ZBMpEECEJjJCrtxOSU6O+9aEkpxeT0dN99slarYWx8IkR3kwoJY84oUoY+GYV7emDA9wjUX/zJqWmlj5nqysOdTgezs7NKXbupdtg28wBT0fpw1KlGLB5HPJ4wz6uG9EPDI7BbLbTa7Z6nJgAwPT2j5N1NNaXZaGBqalrEr1aj5z8d3hNMINDfzhUAfuM3fgOf+cxnQuGPHjv8/wVae7AHe7AHe7AHe7AH7zqoVqsY6XJ6COC9cTFkZ2cHDzzwAPL5PLupRCnm5uZw+vTpnpLAUqmEQ4cOYWVlpScx9uDdB5VKBQcOHMDq6mpPBdc9ePfBXvu9t2Gv/d7bsNd+723427YfpRTVahXz8/M9L0u+JySB09PTeOqpp/Cnf/qn+LEf+zF8/vOfx+HDh0MMIACkUimjOYmRkZG9QfAehuHh4b32ew/DXvu9t2Gv/d7bsNd+723427Rfv0Kv9wQTCAD/63/9L/zYj/0Yfv3Xfx3Dw8P44z/+4281SnuwB3uwB3uwB3uwB+9ZeM8wgQ8++CDefPPNbzUae7AHe7AHe7AHe7AHfyfgPWEi5v8NpFIp/Oqv/mpfHgf24N0He+333oa99ntvw177vbdhr/3e2/DNaL/3xMWQPdiDPdiDPdiDPdiDPfjGwt95SeAe7MEe7MEe7MEe7MEehGGPCdyDPdiDPdiDPdiDPfh7CHtM4B7swR7swR7swR7swd9D+DvNBN6+fRsf+chHcOzYMXzwgx/EtWthB+R78K0D27bxfd/3fTh27BiefPJJfOITnxD+oHd2dvCJT3wCDzzwAB599FGcOHFCpGs0GvjUpz6F+++/H8eOHcMXvhB2XL8H31z4zGc+A0IIrly5AmCv/d4r0Gq18NM//dN44IEH8Mgjjwj/7Hvt996Ar3/963j/+9+Pp556Co8++qgwnbbXfu9O+Nmf/VkcPnxYmSuBv317eZ6Hn/mZn8HRo0dx//334/d///fvHSn6dxg+/vGP0z/6oz+ilFL6uc99jj777LPfWoT2QIFms0m//OUvU8/zKKWU/o//8T/od33Xd1FKKf2X//Jf0l/91V+llFJ65swZevDgQdrpdCillH7mM5+hP/qjP0oppXRhYYHOzMzQQqHwTcd/DxicO3eOfuITn6AHDx6kly9fppTutd97BX7u536O/szP/IwYgxsbG5TSvfZ7L4DneXR8fJxevHiRUkrp4uIiTaVStFKp7LXfuxRef/11urq6Sg8dOiTmSkr/9uPtj//4j+l3fud3UsdxaD6fp4cOHaLXr1+/J5z+zjKB29vbdGRkRBDS8zw6MzNDFxcXv7WI7UEknD17lh49epRSSmk2m6U7Ozvi2zPPPENfffVVSimlDz/8MD1z5oz49gM/8AOC2d+Dby7Ytk2fffZZurCwoExse+337odarUZHRkZotVoNfdtrv3c/cCbw9ddfp5RSevHiRTo/P09brdZe+73LQWcC/7bt9clPfpL+5V/+pfj2C7/wC4KZ7Bf+zh4Hr66uYn5+HvE4s4dNCMHBgwexsrLyLcZsD6Lgd37nd/C93/u9yOfz8DwPU1NT4tvhw4dF262srODQoUPGb3vwzYVf+ZVfwQ/90A/hyJEjImyv/d4bcPfuXUxMTODXfu3X8IEPfADf9m3fhpdffvn/3979x1Rd/XEcf8IVakIrf8yyBiJ6N4cX7k25AtPJJURzq3TNiIl1rWTF7ccsZDZaq7nKEuzHIK3+iEwzB8uJo7TSCbPMpNlVkDAiMci1TJqhq8al8/3DdccV/ALphMt9Pf5inHM/n/c5730u730+53xQ/oJEWFgYFRUV3H333UyaNIk5c+awadMmOjs7lb8gcjnX25XI5YgtAuHCRdKT0SsRh62XXnqJ5uZmXnzxRaD/3PVsV16HxldffUVdXR0ej6dXm/I3/HV1dfHjjz+SkJDAN998Q1lZGTk5Ofh8PuUvCPh8PtauXUtVVRUnT55k7969uN1uQNdfsLmcfF1uLkdsERgTE0N7ezs+nw+4MDltbW3ExsYOcWRysZKSErZv386uXbsYPXo048aNA+D06dP+PidPnvTnLjY21r+B5OI2uXpqa2tpampi8uTJxMXF0d7ezoIFCzh06BCg/A13kyZNIjw8nNzcXADsdjuTJ0/mu+++A5S/4c7r9XLq1Clmz54NgNPp5Oabb+bo0aOA8hcsLufv3RXJ5aAeHgeZ9PT0gI0hKSkpQxuQ9LJ+/XozY8aMXguT3W53wELZmJgY//rO5557LmCh7IQJE8yZM2euZtjSh57rXJS/4JCVlWU+/vhjY4wxra2tZvz48ebUqVPKXxD45ZdfzHXXXWeampqMMcY0NzebMWPGmPb2duVvmLt4TeB/zVd5ebnJzMz0bwyJjY01jY2Ng4plRBeBTU1NJjU11VitVjNz5kzT0NAw1CFJD21tbQYw8fHxxm63G7vdbmbNmmWMufAFl5WVZaZOnWoSEhJMTU2N/3Pnzp0z2dnZZsqUKcZqtZrKysqhGoL00POLTfkLDi0tLSY9Pd3YbDZjt9vN9u3bjTHKX7DYunWrsdlsJikpySQmJpoPP/zQGKP8DVcej8fccsstxmKxmBtvvNG/EfK/5svn8xmPx2Pi4+NNfHy8KS0tHXRM+t/BIiIiIiFoxK4JFBEREZFLUxEoIiIiEoJUBIqIiIiEIBWBIiIiIiFIRaCIiIhICFIRKCIiIhKCVASKiIiIhCAVgSIyZOLi4pg2bRp2ux2r1cqiRYs4cOCAv/2tt97itddeG8IIYefOnRQWFg5pDDD4uWhtbWXUqFE4HA68Xm+//VesWMH+/fv77ZeRkcHYsWMpKysbcCwiMjzpZdEiMmTi4uKorq7GZrMBUFVVhdvt5tNPPyUlJeWqxeHz+Rg1atRVO9/V0NraSnJyMr/99tsVP/by5ctJTk7mscceu+LHFpGrR3cCRWTYWLRoER6Ph5KSEgCef/55Vq1aBcDBgweZOXMmDocDm83Gxo0bgQsFSV5eHpmZmUybNo3ly5fz999/A9DZ2UleXh6zZs0iKSmJRx55hK6uLgBcLhfPPPMMmZmZLFiwgNOnTzN//nwSExNJSkrigQceAOC9995jyZIl/hjXrVvH9OnTSUxMJDc3l7Nnz/pjXbp0KXfeeScJCQncdtttdHR09DnOwsJCnE4nDoeD9PR0mpubAaitrWXKlCn+zz366KPk5+cPeC7643K5WLlyJS6XC6vVSmFhIf/eB3C5XFRXV/PPP/9w++23s379egBaWlqIiYnxxygiI4eKQBEZVpxOJ8eOHev1+7Vr11JQUIDX66WhoYGcnBx/29dff01VVRXHjh2jo6ODN954A4CCggLmzp3LoUOHOHLkCD6fL+AxptfrZffu3ezdu5ctW7YQFxdHfX09R48e9RdBPe3atYvy8nK+/PJL6uvriYqKoqioKCCOTZs20djYyIQJE3j77bf7HOPq1aupq6vD6/WSn5/Pk08+CUB6ejorVqzA7XZTWVnJgQMH+nwE/P/moj+NjY18/vnnHDlyhH379lFZWRnQHh4ezpYtW3jzzTepqakhOzub4uJirFbrgM8hIsFBRaCIDCuXWqGSkZHBCy+8wJo1a/jiiy8YM2aMv+3ee+8lOjoai8XCgw8+yJ49ewDYsWMHxcXFOBwObr31Vvbv3x9wR+u+++4jIiICgNTUVHbv3k1BQQE7d+4kKiqqVwx79uwhNzeXG264AYD8/Hz/uQAWLlzI2LFjAUhLS6OlpaXPsXz22WekpaVhs9lYs2ZNwJq9p59+mq6uLvLy8qioqODaa68d1Fz0x+12ExERwejRo1m2bFlA/P8aP348mzdvZv78+SQnJw+qyBSR4KEiUESGlbq6Ov8awZ5WrlxJdXU1EydOpKioCI/Hc8ljhIWFARcKyh07duD1evF6vRw/fpwNGzb4+0VHR/t/TktLw+v1kpKSwkcffYTT6aS7uzvguMYY/7EvPhcQULBZLBZ8Pl+v2H766SeeeOIJPvjgAxoaGti2bRt//fWXv72zs5MTJ04QFRXFr7/+2uf4BjMX/bl4PP/69ttvGTduHO3t7ZcszEUkuKkIFJFho6qqio0bN/LUU0/1ajt+/Djx8fHk5eVRVFTEwYMH/W2VlZWcP3+e7u5uysvLmTdvHgB33XUXL7/8sr8Y+/333/nhhx/6PPeJEyeIjo4mOzub0tJSvv/+e86dOxfQJysri23bttHZ2QnAO++84z/XQJ09e5bIyEhuuukmjDG9dtk+9NBDLF26lIqKCpYtW8aZM2cGNRf92bx5Mz6fjz///JOtW7f2Gf/hw4cpKSnh8OHDGGNYt27doMYoIsFhZG2HE5Ggs2TJEq655hrOnz9PQkICn3zyCampqb36lZaWsm/fPiIjI7FYLAFr9ubOncvixYtpa2sjNTWVxx9/HIDXX3+d1atX43A4CA8PJyIigldeeYWpU6f2On5NTQ2vvvoqFouF7u5uiouLuf766wP6LFy4kPr6etLS0ggLCyMpKSngzuJAJCYmcs899zB9+nRiY2PJysryt5WVldHR0cGzzz5LeHg4Dz/8MPfffz/V1dUDnov+zJgxg3nz5vHzzz+zePHigE0vAH/88Qc5OTm8++67TJw4kffffx+n08mcOXOYPXv2oMYqIsObXhEjIkFNryvpW1+viHG5XKxatYo77rjjso6tORcZGfQ4WERkBLJYLERGRg74ZdEDlZGRQW1tbZ8bZ0QkuOhOoIiIiEgI0p1AERERkRCkIlBEREQkBKkIFBEREQlBKgJFREREQpCKQBEREZEQpCJQREREJASpCBQREREJQSoCRURERELQ/wDl2uBbRrsU4gAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 30 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 5. Rectify the frames\n", + "\n", + "Now that we have our fitted transformation model, we can rectify the spectral frames with the `TiltCorrection.rectify()` method. The process is straightforward - the method takes a CCData object containing the input frame data and applies the fitted geometric transformation to rebin the pixels. This rebinning uses an exact flux-conserving approach to ensure data quality. The output is a new CCDData object where all spectral features line up with the detector rows and columns." + ], + "id": "18b01edd419fa004" + }, + { + "cell_type": "code", + "id": "8ff2cb79-fe5c-4be1-8f0a-6c320ef7a646", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:47:19.243442Z", + "start_time": "2025-05-13T09:47:18.765257Z" + } + }, + "source": "rectified_data = [s.rectify(d.data) for d in frames]", + "outputs": [], + "execution_count": 31 + }, + { + "cell_type": "code", + "id": "576a031f-a4bc-4e41-b8de-c5d185bf9279", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:48:10.416622Z", + "start_time": "2025-05-13T09:48:09.784557Z" + } + }, + "source": [ + "fig, axs = plt.subplots(4, figsize=(6.3, 5), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(rectified_data):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=150_000))\n", + " axs[i].text(0.01, 0.9, labels[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + " axs[i].grid(which='both', axis='x')\n", + " axs[i].set_xticks(np.linspace(10, 1000, 19))\n", + "plt.setp(axs, ylabel='CD axis [pix]')\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 35 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's check how well our rectification worked by comparing emission line positions. Here we overlay the reference row spectrum from the original arc frame with spectra sampled from different rows in the rectified data. You can see the spectral features line up beautifully across various rows after rectification - the emission lines match with sub-pixel precision, showing our tilt correction worked perfectly.\n", + "id": "7380d9d0da0475ca" + }, + { + "cell_type": "code", + "id": "079c3cca-9e47-46f4-8557-233c50e6fa00", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:47:45.893443Z", + "start_time": "2025-05-13T09:47:45.824633Z" + } + }, + "source": [ + "fig, ax = plt.subplots(1, figsize=(6.3, 2), sharex='all', constrained_layout=True)\n", + "\n", + "ref_row = s.ref_pixel[1]\n", + "rec_rows = [s.ref_pixel[1], 150, 450]\n", + "\n", + "ax.plot(arcs[0].data[ref_row])\n", + "for i, r in enumerate(rec_rows):\n", + " ax.plot(50_000*(i+1) + rectified_data[0][r])\n", + "plt.setp(ax, xlabel='Dispersion axis [Pix]', yticks=[])\n", + "plt.setp(ax, xlim=(50, 200));" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 34 + } + ], + "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/docs/tilt_correction/tilt_correction.rst b/docs/tilt_correction/tilt_correction.rst new file mode 100644 index 00000000..9a63c12a --- /dev/null +++ b/docs/tilt_correction/tilt_correction.rst @@ -0,0 +1,40 @@ +Tilt Correction +=============== + +In astronomical spectroscopy, tilt correction is a calibration step that addresses optical +distortions and misalignments in spectroscopic instruments. These distortions cause wavelength +to vary along the cross-dispersion (spatial) axis, resulting in spectral features appearing +tilted or curved across the detector rather than being perfectly aligned with detector columns. + +Tilt correction is performed by modeling a two-dimensional tilt function that describes how +wavelength positions shift across the spatial axis. This function can be determined empirically +from arc lamp calibration spectra by measuring how the centroids of emission lines vary along +the cross-dispersion axis. + +Once characterized, the tilt function enables transformation of two-dimensional spectroscopic +images so that wavelengths become aligned along straight lines parallel to the detector axes (a +process known as 2D rectification). This alignment is essential for achieving accurate +wavelength calibration and performing robust sky subtraction. + +In the `specreduce` package, the tilt function is represented as a 2D polynomial using an +``~astropy.modeling.models.Polynomial2D`` instance of a specified degree. The +``~specreduce.tilt_correction.TiltCorrection`` class implements this correction through several +steps: + +1. Identifying emission lines in one or more arc lamp calibration spectra for a given number of + cross-dispersion sample positions +2. Fitting a 2D polynomial model to characterize the geometric distortion +3. Computing a transformation that maps the tilted features to straight lines +4. Applying this transformation to rectify the observed frames + + +Tutorials +--------- + +The following tutorial provides hands-on examples demonstrating the usage of the +``~specreduce.tilt_correction.TiltCorrection`` class. + +.. toctree:: + :maxdepth: 1 + + osiris_example.ipynb diff --git a/docs/wavelength_calibration.rst b/docs/wavelength_calibration.rst deleted file mode 100644 index f34d7dec..00000000 --- a/docs/wavelength_calibration.rst +++ /dev/null @@ -1,53 +0,0 @@ -.. _wavelength_calibration: - -Wavelength Calibration -====================== - -Wavelength calibration is currently supported for 1D spectra. Given a list of spectral -lines with known wavelengths and estimated pixel positions on an input calibration -spectrum, you can currently use ``specreduce`` to: - -#. Fit an ``astropy`` model to the wavelength/pixel pairs to generate a spectral WCS - solution for the dispersion. -#. Apply the generated spectral WCS to other `~specutils.Spectrum1D` objects. - -1D Wavelength Calibration -------------------------- - -The `~specreduce.wavelength_calibration.WavelengthCalibration1D` class can be used -to fit a dispersion model to a list of line positions and wavelengths. Future development -will implement catalogs of known lamp spectra for use in matching observed lines. In the -example below, the line positions (``pixel_centers``) have already been extracted from -``lamp_spectrum``:: - - import astropy.units as u - from specreduce import WavelengthCalibration1D - pixel_centers = [10, 22, 31, 43] - wavelengths = [5340, 5410, 5476, 5543]*u.AA - test_cal = WavelengthCalibration1D(lamp_spectrum, line_pixels=pixel_centers, - line_wavelengths=wavelengths) - calibrated_spectrum = test_cal.apply_to_spectrum(science_spectrum) - -The example above uses the default model (`~astropy.modeling.functional_models.Linear1D`) -to fit the input spectral lines, and then applies the calculated WCS solution to a second -spectrum (``science_spectrum``). Any other 1D ``astropy`` model can be provided as the -input ``model`` parameter to the `~specreduce.wavelength_calibration.WavelengthCalibration1D`. -In the above example, the model fit and WCS construction is all done as part of the -``apply_to_spectrum()`` call, but you could also access the `~gwcs.wcs.WCS` object itself -by calling:: - - test_cal.wcs - -The calculated WCS is a cached property that will be cleared if the ``line_list``, ``model``, -or ``input_spectrum`` properties are updated, since these will alter the calculated dispersion -fit. - -You can also provide the input pixel locations and wavelengths of the lines as an -`~astropy.table.QTable` with (at minimum) columns ``pixel_center`` and ``wavelength``, -using the ``matched_line_list`` input argument:: - - from astropy.table import QTable - pixels = [10, 20, 30, 40]*u.pix - wavelength = [5340, 5410, 5476, 5543]*u.AA - line_list = QTable([pixels, wavelength], names=["pixel_center", "wavelength"]) - test_cal = WavelengthCalibration1D(lamp_spectrum, matched_line_list=line_list) \ No newline at end of file diff --git a/docs/wavelength_calibration/osiris_arcs.fits b/docs/wavelength_calibration/osiris_arcs.fits new file mode 100644 index 00000000..830b8fb8 Binary files /dev/null and b/docs/wavelength_calibration/osiris_arcs.fits differ diff --git a/docs/wavelength_calibration/shane_kast_blue_600_4310_d55.fits b/docs/wavelength_calibration/shane_kast_blue_600_4310_d55.fits new file mode 100644 index 00000000..8c9a6a0c Binary files /dev/null and b/docs/wavelength_calibration/shane_kast_blue_600_4310_d55.fits differ diff --git a/docs/wavelength_calibration/wavecal1d_example_01.ipynb b/docs/wavelength_calibration/wavecal1d_example_01.ipynb new file mode 100644 index 00000000..c3eabc49 --- /dev/null +++ b/docs/wavelength_calibration/wavecal1d_example_01.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Tutorial 1: Basic Interactive Workflow\n", + "\n", + "This notebook demonstrates a basic interactive workflow for wavelength calibration of\n", + "astronomical spectra using the `specreduce.wavecal1d.WavelengthCalibration1D` class with a single\n", + "calibration lamp (arc) spectrum. It serves as a general introduction to the class.\n", + "\n", + "The interactive workflow consists of these key steps:\n", + "1. Loading the arc spectrum data\n", + "2. Initializing the wavelength calibration parameters\n", + "3. Finding emission line positions in the spectrum \n", + "4. Inspecting catalog line wavelengths\n", + "5. Manually identifying initial line matches\n", + "6. Evaluating and refining the wavelength solution\n", + "7. Applying the solution to calibrate spectra\n", + "\n", + "For this demonstration, we use a [Helium-Mercury-Cadmium (He-Hg-Cd) calibration lamp](https://mthamilton.ucolick.org/techdocs/instruments/kast/images/Kastblue600HeHgCd.jpg) spectrum\n", + "obtained with the [Kast Double Spectrograph](https://mthamilton.ucolick.org/techdocs/instruments/kast/index.html) on the [Shane 3-meter telescope](https://www.lickobservatory.org/explore/research-telescopes/shane-telescope/) at Lick Observatory. The Kast blue channel configuration uses a 600 lines/mm grating covering approximately 3200-5700 Å at moderate resolution.\n", + "\n", + "This interactive approach provides hands-on experience with the calibration process while\n", + "allowing careful validation of the results. For automated reduction pipelines, alternative\n", + "methods are available and covered in Tutorial 3." + ], + "id": "4fd312a6be58ac88" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:56.462836Z", + "start_time": "2025-04-24T09:34:55.274752Z" + } + }, + "cell_type": "code", + "source": [ + "import astropy.units as u\n", + "import numpy as np\n", + "\n", + "from astropy.io.fits import getdata\n", + "from astropy.nddata import StdDevUncertainty\n", + "from matplotlib.pyplot import subplots, rc\n", + "\n", + "from specreduce.compat import Spectrum\n", + "from specreduce.wavecal1d import WavelengthCalibration1D\n", + "\n", + "rc('figure', figsize=(6.3, 2))" + ], + "id": "d0349a731069a333", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 1. Read in the Arc Spectrum\n", + "\n", + "First, we load the arc lamp flux data and create a `specutils.Spectrum` object, assuming that the flux is measured in counts (digital number units, `u.DN`). We also include measurement uncertainties. While these uncertainties are not directly used in fitting the wavelength solution, they are required by the default line-finding routine (`specutils.fitting.find_lines_threshold`).\n" + ], + "id": "bcd9f3e0dda780e9" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:56.502353Z", + "start_time": "2025-04-24T09:34:56.497014Z" + } + }, + "cell_type": "code", + "source": [ + "flux = getdata('shane_kast_blue_600_4310_d55.fits', 1).astype('d')\n", + "arc_spectrum = Spectrum((flux - np.median(flux)) * u.DN,\n", + " uncertainty=StdDevUncertainty(np.sqrt(flux)))" + ], + "id": "63bc459f2c36d759", + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", + "metadata": {}, + "source": [ + "## 2. Initialize the Wavelength Calibration Class\n", + "\n", + "Now we instantiate the `WavelengthCalibration1D` class. This class manages the input data, fitted model, and provides methods for solution fitting and spectrum calibration.\n", + "\n", + "Key initialization parameters:\n", + "\n", + "- `ref_pixel`: Reference pixel coordinate near detector center. The polynomial fit centers around this pixel to improve numerical stability.\n", + "- `degree`: Degree of the `astropy.modeling.polynomial.Polynomial1D` model for pixel-to-wavelength mapping. Higher degrees enable more complex curves but require more lines for stability and risk overfitting.\n", + "- `line_lists`: Either lamp names (e.g., 'HeI', 'NeI', 'ArI') recognized by `specreduce.calibration_data.load_pypeit_calibration_lines`, or NumPy arrays of theoretical line wavelengths. Here we provide lamp names for our single spectrum.\n", + "- `arc_spectra`: Observed arc spectra as `specutils.Spectrum` objects, used for automatic line finding if `obs_lines` is not provided.\n", + "- `obs_lines`: Pre-identified line pixel centroids. If provided, `arc_spectra` is not needed for line finding.\n", + "- `pix_bounds`: Detector pixel range `(min_pix, max_pix)`. Inferred from `arc_spectra` if provided; required with `obs_lines`.\n", + "- `line_list_bounds`: Wavelength range `(min_wav, max_wav)` for filtering `line_lists`. Defaults to `(0, +inf)`.\n", + "- `wave_air`: If `True`, converts PyPEIT's vacuum wavelengths to air wavelengths.\n", + "\n", + "In this example, we provide:\n", + "- One arc spectrum\n", + "- Reference pixel at 1000 \n", + "- Polynomial degree 4\n", + "- Lamp list `['CdI', 'HgI', 'HeI']`\n", + "- Wavelength filter range 3200-5700 Angstroms\n", + "\n", + "The `plot_observed_lines` method displays the arc spectra and input lines in pixel space. At this stage, it shows only the raw input spectrum since no lines have been identified or fitted.\n" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:56.628938Z", + "start_time": "2025-04-24T09:34:56.543497Z" + } + }, + "cell_type": "code", + "source": [ + "wc = WavelengthCalibration1D(ref_pixel=1000, degree=4, arc_spectra=arc_spectrum,\n", + " line_lists=[['CdI', 'HgI', 'HeI']],\n", + " line_list_bounds=(3200, 5700), unit=u.angstrom)\n", + "wc.plot_observed_lines();" + ], + "id": "52091185a7a5e148", + "outputs": [ + { + "data": { + "text/plain": [ + "
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1tbXZ1wfzQ6NSGWP0L//yL7rrrrv02muvSZIuvPBCSYe/ZJX6R0XZ84LX5rWByD6M52gfTvF4XBdccIFmz56tJUuWFL0d1ntcXV2tIUOGSHIvBDqroc8+++ygPU92X2/evDnvdTkrgS0tLZLc+QKFvqzPkVAopAkTJkg6/IVqsPccFnSy6O3bt6uzs1PpdFqBQMC+JBIJJZNJpVIppVIpvfXWWxmPe/DBB3X11VcrHA4rHA4rFAopEAhIUsa3x1yu93dfOp22L1VVVaqqqpIxJuNitTH7duu+3t5erVy5MuP5fvzjH+uGG25QdXW1pMMblHM9u3fvtjfUq666qs9kuGjRIo0ePVo1NTUKh8OKRCJKpVL2gbpHYvXPYN0eCh0eCr29vf2e7DiZTKqjo0PNzc2qra1VVVWV0un0Ed8TY4zd/9bJSZ2XZDKpQ4cO9enfn/zkJ5o5c6aGDBmiRCKh3t5e+/071iUYDNrXQ6GQ0um03n77bT3yyCOaPn26vvSlL9ntMsZkPC4QCKi7u1uBQEDBYFDxeFyxWEzxeNy+3H333XrnnXf0zDPP6K677tKePXu0bNkySdLw4cPV2dmpDRs2aN68eRo3bpwaGxtVU1OjZDKphx56yH6NTz75pCZPnqxQKKREIqF0Oq1IJNLn9aTTabW3tysQCKipqUk1NTV9+th5Pfv/udwXDAbtsWxtw/2tJ3udqVRKyWTSHlvW+11VVWVv39b2YV2c88Oxbt++fbueffZZbdmyRX/5y1/s119bW6t7771Xy5YtU3t7uyZOnKjzzjtPJ598spqbm9XT06OnnnpKJ510kqZPn67q6moFg0GFw2Gl02n7PQ8EAkqlUhnzxdEuzuVeffXVjHH705/+VLNmzVIgEFA8Hlc4HJZ0uOqSTCbtY9icc2UgENChQ4dUW1trr9vaFq3+tfrV+lNSzvfAORYswWDQPvVHXV2dYrGYPdadr8Wab470/n788cfauHGj6urqVFtbqwcffDDjtf7yl7/U5z73OUUiEfs9D4VCWrVqlR3Kvvvd79rjPxQKqbe3137OZDKpUChk3x6Px3Xo0CENHz7c7gdLKpXSwYMH1dnZqbVr19rvvbU7eMGCBdq5c6dGjBihuro69cfa7uvq6uzniMVi9rgIhUIKBoNKJpOKx+P26+vt7bU/r2KxmNauXat/+7d/s9f74osv6uc//7n+5m/+RsFgsM97lL3t5HrfoUOH7GOIL7roIq1cuVKPPfaYTjvtNEUiEQWDQYVCIR06dEjPPvuswuGwxo8fn/FarPHT3t6uJ554QpI0cuRI+xfCCxcuVCgU0siRIxWNRhWNRlVVVWUHbudYraurUzAYtI/J3b59u/7whz/o5JNP1gknnKBIJGLPr0OGDMmYE2KxmN2H/bHmPsuRxru1XPbnmTVnVFVV2dcDgYCGDh1qhynn+q3t0vo3kUjYfXqk98f6rMjOFoFAQMlkUr29vQOeR+LxuHbu3Gn/3qCpqUmnnnqq/Xkyd+5cnXvuufbnQzQatecYawwEg8H8vwyZPHR3dxtJvrvcddddA1q+trbWxONxM2XKFNfb7oXLhRde6HobCrk8+eSTZvLkya63ww+XSCRiFi9ebIwx5ne/+52prq52rS2nnHKK6/1RqsuNN97oehskma997Wtm0aJFrrdDkjnhhBNK8jy/+tWvirKeSCRi1q9fb1avXm0CgYDr/cfl/y/Nzc3mo48+MsYYc+WVV+a1ju7u7gHluYIqgaFQSOFwuE+Vx7rdSqjBYFDNzc16+OGH9dhjj+mZZ55RLBZTMpm0q4ZOznSfy/Wj3Wd9e3FWq7Lvs9pnLef81hMMBhWJRFRfX6/LL79cd955p8aMGaOFCxeqp6fH/maRSCQyqknhcFgTJkzQrbfeqnA4rCVLlmju3LmqqqrSxIkTtWDBAu3du1e9vb1KJBLq7u5WXV3dUY+hKcXt1nsQiUTsb7/9McbYFSxn31ljwPq/VSGw+lRSn/5tbW3VpZdeqptuukk//vGPtW7dOrW1tSkWi6mmpkZDhgwZ8LerVCplf6sLh8Pq7OzUmDFjtHv3bvuboNU+52OHDh1qV4YikYiqq6sVjUbtfolGo/af9KmqqlJjY6MaGxt18cUX6/LLL9ell16qJ554Qi+88II6OzvV0dGhWCymaDSquro6jRs3TmeffbYef/xxtbe3yxhj93c8Hs+onlqXhoYGDRs2THv27LEPjM/uS+f1gd7X09Njf3t2bif9rce6bo17ZxXb+pZtvQfJZDJjLrAqFNmXo90eDAbV2NiosWPH6rjjjtP111+vuro6uzJ2ySWXaNOmTVq1apXa2trU1tamjz76SPF4XF1dXWpsbFRHR4fi8bj9nluVAmu8Ws+Ta8XZ+kY+ffp0zZo1S3PnztWGDRu0Y8cO+/Vb67Zev9WvzrnS2lasZazrVj9aY1HK/DGM8z1w3mb+rzJrvU5jjF09dlY/nXNBdmVSOjynjR492q4KNTU1acKECfrWt76lc889V7/61a+0Y8cOe+4LBALq7e1VKBTSsGHD9I//+I9avHixtm7dqlQqpXg8rtra2owT4lqV30gkot7eXtXW1qq3t9eublj9VVVVpdraWg0fPlwjRozQZz/7Wd18882KRCKqra3Vc889p46ODnV2dqqnp+eoezusvjb/9+fSrOeorq5WOp3O+DwKh8P2GAkEAopGo4rH4/Yem6FDh+qMM87QpEmTNG3aNH3zm9/Utm3b1NPTY1cYs9+jfP6NRCI6/vjjdf7552vmzJlasmSJnnjiCe3du1fJZNK+pFIptba2avTo0dqzZ09GVT2RSCiVSmn06NGaMGGCLrvsMp166qmSpNWrV+vxxx/Xli1bdODAAcXjcfX29trzn3McG2Psk3Nb23sgENCJJ56oeDyuvXv32ttYMpnUxx9/rHA4bI81qwLo/GV/9meS9TzW58rRxru1TPY6rDnfGrfBYFCHDh1SLBZTJBLJ2C6MMQqHw4rH44pGo0qlUvZeAueeDee/1meFMzM493YNGTJkQHOItceqqalJxx9/vG6++Wb7WM0HH3xQjY2N2rRpkzo7O+3PW+v9iUaj9pi1Pr/yOZl4wBwtGfRj//79amhoUHd3t31wLgAAAEov31zGyaIBAAB8iBAIAADgQ4RAAAAAHyIEAgAA+FBevw62fkvS399IBQAAwOCz8thAf+ubVwi0/r5ra2trPg8HAABAkR04cEANDQ05L59XCLT+rl17e/uAnsyP9u/fr9bWVm3bto3T6RwDfZU7+ip39FXu6Kvc0Ve5o69yl29fGWN04MAB+88B5iqvEGidDLWhoYE3NEdDhw6lr3JEX+WOvsodfZU7+ip39FXu6Kvc5dNX+RTl+GEIAACADxECAQAAfCivEBiNRjV37lz7b3fi6Oir3NFXuaOvckdf5Y6+yh19lTv6Knel7qu8/nYwAAAAvI3dwQAAAD5ECAQAAPAhQiAAAIAPEQIBAAB8KK8QOH/+fI0dO1bV1dU666yztGrVqmK3q6z98Ic/1Gc/+1nV19erqalJf//3f69NmzZlLDN79mwFAoGMy+c///mMZXp7e/X1r39dI0eOVF1dnWbMmKG//vWvpXwpg27evHl9+qG5udm+3xijefPmqaWlRTU1Nbrwwgu1cePGjHX4oZ8k6YQTTujTV4FAQLfccoskf4+p3//+97r00kvV0tKiQCCgpUuXZtxfrHHU2dmpa6+9Vg0NDWpoaNC1116rrq6uQX51xdVfXyUSCd15552aMGGC6urq1NLSoq985SvasWNHxjouvPDCPmPtqquuylim0vtKKt4254e+OtLcFQgE9K//+q/2Mn4YV7nkg3KarwYcAv/7v/9bt912m7773e9qw4YN+sIXvqBp06apvb19oKvyrFdffVW33HKL3njjDa1YsULJZFJTpkzRwYMHM5abOnWqOjo67Muzzz6bcf9tt92mX//611qyZIlee+01ffzxx5o+fbpSqVQpX86gO/300zP64d1337Xvu//++/XAAw/ooYce0ltvvaXm5mZdcsklOnDggL2MX/rprbfeyuinFStWSJIuv/xyexm/jqmDBw9q4sSJeuihh454f7HG0TXXXKO3335bzz//vJ5//nm9/fbbuvbaawf99RVTf33V09Oj9evX65577tH69ev19NNP63/+5380Y8aMPsvOmTMnY6wtXLgw4/5K7ytLMbY5P/SVs486Ojr0i1/8QoFAQP/wD/+QsVylj6tc8kFZzVdmgD73uc+Zm266KeO2U045xXznO98Z6Koqxu7du40k8+qrr9q3XXfddeayyy476mO6urpMOBw2S5YssW/bvn27qaqqMs8///xgNrek5s6dayZOnHjE+9LptGlubjY/+tGP7NtisZhpaGgwP//5z40x/umnI/nGN75hTjzxRJNOp40xjCmLJPPrX//a/n+xxtF7771nJJk33njDXmbNmjVGknn//fcH+VUNjuy+OpK1a9caSWbr1q32bRdccIH5xje+cdTH+KWvirHN+aWvsl122WVm8uTJGbf5cVxl54Nym68GVAmMx+Nat26dpkyZknH7lClTtHr16oGlzwrS3d0tSRoxYkTG7a+88oqampr0qU99SnPmzNHu3bvt+9atW6dEIpHRly0tLRo/fnzF9WVbW5taWlo0duxYXXXVVfrwww8lSZs3b9bOnTsz+iAajeqCCy6w+8BP/eQUj8f1n//5n7r++usVCATs2xlTfRVrHK1Zs0YNDQ0655xz7GU+//nPq6GhoaL7r7u7W4FAQMOGDcu4/fHHH9fIkSN1+umn64477sioUviprwrd5vzUV5Zdu3Zp+fLluuGGG/rc57dxlZ0Pym2+Cg3kxezdu1epVEqf+MQnMm7/xCc+oZ07dw5kVRXDGKNvfetbOu+88zR+/Hj79mnTpunyyy/XmDFjtHnzZt1zzz2aPHmy1q1bp2g0qp07dyoSiWj48OEZ66u0vjznnHP0H//xH/rUpz6lXbt26fvf/77OPfdcbdy40X6dRxpPW7dulSTf9FO2pUuXqqurS7Nnz7ZvY0wdWbHG0c6dO9XU1NRn/U1NTRXbf7FYTN/5znd0zTXXZPyx+lmzZmns2LFqbm7Wn/70J/3zP/+z/vjHP9qHKPilr4qxzfmlr5weffRR1dfXa+bMmRm3+21cHSkflNt8NaAQaHFWJqTDLzT7Nr+49dZb9c477+i1117LuP3KK6+0r48fP15nn322xowZo+XLl/fZMJwqrS+nTZtmX58wYYImTZqkE088UY8++qh9gHU+46nS+inbokWLNG3aNLW0tNi3Mab6V4xxdKTlK7X/EomErrrqKqXTac2fPz/jvjlz5tjXx48fr5NPPllnn3221q9frzPPPFOSP/qqWNucH/rK6Re/+IVmzZql6urqjNv9Nq6Olg+k8pmvBrQ7eOTIkQoGg31S5u7du/ukWj/4+te/rmeeeUYrV67U6NGj+1121KhRGjNmjNra2iRJzc3Nisfj6uzszFiu0vuyrq5OEyZMUFtbm/0r4f7Gkx/7aevWrXrxxRd144039rscY+qwYo2j5uZm7dq1q8/69+zZU3H9l0gkdMUVV2jz5s1asWJFRhXwSM4880yFw+GMseaXvnLKZ5vzW1+tWrVKmzZtOub8JVX2uDpaPii3+WpAITASieiss86yS7eWFStW6Nxzzx3IqjzNGKNbb71VTz/9tF5++WWNHTv2mI/Zt2+ftm3bplGjRkmSzjrrLIXD4Yy+7Ojo0J/+9KeK7sve3l79+c9/1qhRo+zdAs4+iMfjevXVV+0+8GM/LV68WE1NTfrSl77U73KMqcOKNY4mTZqk7u5urV271l7mzTffVHd3d0X1nxUA29ra9OKLL+q444475mM2btyoRCJhjzW/9FW2fLY5v/XVokWLdNZZZ2nixInHXLYSx9Wx8kHZzVe5/8blsCVLlphwOGwWLVpk3nvvPXPbbbeZuro6s2XLloGuyrNuvvlm09DQYF555RXT0dFhX3p6eowxxhw4cMDcfvvtZvXq1Wbz5s1m5cqVZtKkSeaTn/yk2b9/v72em266yYwePdq8+OKLZv369Wby5Mlm4sSJJplMuvXSiu722283r7zyivnwww/NG2+8YaZPn27q6+vt8fKjH/3INDQ0mKefftq8++675uqrrzajRo3yXT9ZUqmUOf74482dd96Zcbvfx9SBAwfMhg0bzIYNG4wk88ADD5gNGzbYv2gt1jiaOnWqOeOMM8yaNWvMmjVrzIQJE8z06dNL/noL0V9fJRIJM2PGDDN69Gjz9ttvZ8xfvb29xhhj/vKXv5jvfe975q233jKbN282y5cvN6eccor5zGc+46u+KuY2V+l9Zenu7ja1tbVmwYIFfR7vl3F1rHxgTHnNVwMOgcYY8/DDD5sxY8aYSCRizjzzzIxTo/iBpCNeFi9ebIwxpqenx0yZMsU0NjaacDhsjj/+eHPdddeZ9vb2jPUcOnTI3HrrrWbEiBGmpqbGTJ8+vc8yXnfllVeaUaNGmXA4bFpaWszMmTPNxo0b7fvT6bSZO3euaW5uNtFo1Jx//vnm3XffzViHH/rJ8sILLxhJZtOmTRm3+31MrVy58ojb3HXXXWeMKd442rdvn5k1a5apr6839fX1ZtasWaazs7NEr7I4+uurzZs3H3X+WrlypTHGmPb2dnP++eebESNGmEgkYk488UTzT//0T2bfvn0Zz1PpfVXMba7S+8qycOFCU1NTY7q6uvo83i/j6lj5wJjymq8C/9doAAAA+Ah/OxgAAMCHCIEAAAA+RAgEAADwIUIgAACADxECAQAAfIgQCAAA4EOEQAAAAB8iBAIAAPgQIRBARZs3b54+/elPF219r7zyigKBgLq6uoq2TgBwAyEQgOfNnj1bgUBAgUBA4XBY48aN0x133KGDBw/qjjvu0EsvveR2EwGg7ITcbgAAFMPUqVO1ePFiJRIJrVq1SjfeeKMOHjyoBQsWaMiQIW43DwDKDpVAABUhGo2qublZra2tuuaaazRr1iwtXbo0Y3dwLBbT6aefrq9+9av24zZv3qyGhgY98sgjkiRjjO6//36NGzdONTU1mjhxon75y1+68ZIAYFBRCQRQkWpqapRIJDJuq66u1uOPP65zzjlHf/d3f6dLL71U1157rS666CLNmTNHknT33Xfr6aef1oIFC3TyySfr97//vb785S+rsbFRF1xwgRsvBQAGBSEQQMVZu3at/uu//ksXX3xxn/s+/elP6/vf/77mzJmjq6++Wh988IGWLl0qSTp48KAeeOABvfzyy5o0aZIkady4cXrttde0cOFCQiCAikIIBFARfvvb32rIkCFKJpNKJBK67LLL9LOf/Uzz58/vs+ztt9+u3/zmN/rZz36m5557TiNHjpQkvffee4rFYrrkkksylo/H4/rMZz5TktcBAKVCCARQES666CItWLBA4XBYLS0tCofDR1129+7d2rRpk4LBoNra2jR16lRJUjqdliQtX75cn/zkJzMeE41GB6/xAOACQiCAilBXV6eTTjopp2Wvv/56jR8/XnPmzNENN9ygiy++WKeddppOO+00RaNRtbe3s+sXQMUjBALwlYcfflhr1qzRO++8o9bWVj333HOaNWuW3nzzTdXX1+uOO+7QN7/5TaXTaZ133nnav3+/Vq9erSFDhui6665zu/kAUDScIgaAb7z//vv69re/rfnz56u1tVXS4VDY1dWle+65R5J033336d5779UPf/hDnXrqqfriF7+oZcuWaezYsW42HQCKLmCMMW43AgAAAKVFJRAAAMCHCIEAAAA+RAgEAADwIUIgAACADxECAQAAfIgQCAAA4EOEQAAAAB8iBAIAAPgQIRAAAMCHCIEAAAA+RAgEAADwof8FgpyEDXMJC1EAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "f9756d12-0f58-4f75-bcdd-76bfe4d5f9ee", + "metadata": {}, + "source": [ + "## 3. Find Line Pixel Positions\n", + "\n", + "Next, we detect emission lines in the arc spectrum using the `find_lines` method. This method uses `specreduce.line_matching.find_arc_lines`, which performs peak detection to identify lines and calculate their centroids (in pixel coordinates). The detected line centroids are stored in the `ws.observed_lines` attribute as a list of NumPy masked arrays (one array per input arc spectrum). Initially, none of the lines are masked.\n", + "\n", + "The plot now overlays vertical lines at the pixel coordinates of the found lines.\n", + "\n", + "**Note:** If we had already identified the line centroids by other means (e.g., manually, or from another program), we could have supplied them directly during initialization using the `obs_lines` argument, skipping this step." + ] + }, + { + "cell_type": "code", + "id": "e5bb4f88-355f-4278-9ec4-15876f4712e2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:56.786768Z", + "start_time": "2025-04-24T09:34:56.639220Z" + } + }, + "source": [ + "wc.find_lines(fwhm=4, noise_factor=15)\n", + "wc.plot_observed_lines(value_fontsize=6);" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "dbfb2d5b-a7dd-46a5-ba82-1ee06cb130e6", + "metadata": {}, + "source": [ + "## 4. Inspect the Catalog Line List\n", + "\n", + "When we initialized `WavelengthCalibration1D`, we provided lamp names (`line_lists=[['CdI', 'HgI', 'HeI']]`). Internally, the `_read_linelists` method loaded the corresponding known wavelengths (filtered by `line_list_bounds`), converted them to the specified `unit` (Angstroms by default), and stored them in the `ws.catalog_lines` attribute.\n", + "\n", + "Similar to `observed_lines`, `catalog_lines` is a list of masked arrays (one per arc spectrum) sorted by wavelength. We can visualize these theoretical line positions using the `plot_catalog_lines` method.\n" + ] + }, + { + "cell_type": "code", + "id": "da59f18b-1c86-410a-a5a6-dc9c94302cff", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.106982Z", + "start_time": "2025-04-24T09:34:56.795915Z" + } + }, + "source": "wc.plot_catalog_lines(value_fontsize=6);", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "The `plot_fit` method allows us to simultaneously visualize the observed lines (bottom panel, pixel space) and catalog lines (top panel, wavelength space). \n", + "\n", + "Since we have not yet calculated the pixel-to-wavelength transformation, the bottom panel displays pixel coordinates. At this stage, we should not expect to see any matches between the observed and catalog lines.\n" + ], + "id": "915e48bb113ab04e" + }, + { + "cell_type": "code", + "id": "e95afe59-471d-4669-aafe-72b1f7d79d53", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.359476Z", + "start_time": "2025-04-24T09:34:57.115932Z" + } + }, + "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, value_fontsize=6);", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "806d1730-5325-4537-825e-da686ffcda21", + "metadata": {}, + "source": [ + "## 5. Fit an Initial Solution Manually\n", + "\n", + "To establish an initial relationship between pixels and wavelengths, we need to match some observed lines to their corresponding catalog wavelengths. \n", + "\n", + "The `WavelengthCalibration1D` class offers several ways to fit:\n", + "1. `fit_lines(pixels, wavelengths)`: Fits the model using explicitly provided pairs of corresponding pixel and wavelength values. This is useful for manual identification of a few bright, unambiguous lines to get a starting solution.\n", + "2. `fit_global()`: Performs a global optimization to find an initial model by minimizing distances between transformed observed lines and the KDTrees of theoretical lines. This requires good initial guesses for wavelength and dispersion bounds but is more suitable for an automatic pipeline.\n", + "3. `refine_fit()`: Improves an existing fit by automatically matching lines based on the current solution and performing a least-squares fit on the matches. This method is called by the `fit_lines` and `fit_global` methods by default, but can also be called manually.\n", + "\n", + "In this example, we'll use `fit_lines` with a few manually identified pairs. We visually inspect the plots above (or use an interactive tool in a real scenario) to find approximate correspondences. We also set `match_pix` and `match_wav` to `True` to match the approximate pixel and wavelength line values with the more accurate observed line centroids and catalog wavelength values." + ] + }, + { + "cell_type": "code", + "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.375755Z", + "start_time": "2025-04-24T09:34:57.369398Z" + } + }, + "source": [ + "wc.fit_lines(pixels=[496, 967, 1077, 1655, 1999],\n", + " wavelengths=[3890, 4360, 4473, 5087, 5462],\n", + " match_obs=True, match_cat=True)" + ], + "outputs": [], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "id": "616e53c5-20e5-411e-a983-1feba2db30dc", + "metadata": {}, + "source": "After applying the initial solution, we can plot the mapping of observed pixel values to wavelengths (`obs_to_wav=True`). In this plot, matched lines between observed and catalog data appear as solid blue vertical lines, while unmatched lines are shown as dotted lines.\n" + }, + { + "cell_type": "code", + "id": "40883c16-11b3-402b-bd0b-c688c44deef2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.644862Z", + "start_time": "2025-04-24T09:34:57.417724Z" + } + }, + "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, obs_to_wav=True, value_fontsize=6);", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "id": "524b217f-b2cd-49b9-a011-eb3a11fa14ee", + "metadata": {}, + "source": [ + "## 6. Evaluate the Initial Fit: Plot Residuals \n", + "\n", + "Let's examine the quality of our initial fit. The `plot_residuals` method calculates the differences between the theoretical wavelengths of matched lines and the wavelengths predicted by the current model at their observed pixel positions. We can also calculate the RMS of these residuals using the `rms` method.\n" + ] + }, + { + "cell_type": "code", + "id": "343801bc-65fa-41c9-929b-72565cdee31d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.756868Z", + "start_time": "2025-04-24T09:34:57.654630Z" + } + }, + "source": "wc.plot_residuals(space='wavelength');", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 9 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.772258Z", + "start_time": "2025-04-24T09:34:57.769326Z" + } + }, + "cell_type": "code", + "source": "wc.rms()", + "id": "b2cfafee7b41f185", + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.10779117337629601)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "id": "983fe041-41e9-47a7-a179-fead64e171ba", + "metadata": {}, + "source": [ + "The fit looks good! Now, we can move on to use the solution.\n", + "\n", + "### 7. Use the Wavelength Solution\n", + "#### 7.1 Rebin a Spectrum to Wavelength Space\n", + "\n", + "A primary goal of wavelength calibration is to transform spectra from the detector's pixel grid to a physical wavelength grid. The `resample` method does this, converting a `Spectrum` object from pixel space to wavelength space.\n", + "\n", + "Key parameters for `resample`:\n", + "- `spectrum`: The input `Spectrum` object (assumed to be on a pixel grid corresponding to the calibration).\n", + "- `nbins`: The number of bins desired in the output wavelength grid. If `None`, defaults to the number of pixels in the input spectrum.\n", + "- `wlbounds`: The desired `(start_wavelength, end_wavelength)` for the output grid. If `None`, defaults to the wavelengths corresponding to the first and last pixels.\n", + "- `bin_edges`: Explicitly define the wavelength edges of the output bins. If provided, `nbins` and `wlbounds` are ignored.\n", + "\n", + "The method uses the fitted pixel-to-wavelength and wavelength-to-pixel transformations along with their\n", + "derivatives to map the input pixel bins to the output wavelength bins. It performs an exact\n", + "flux-conserving rebinning, meaning the total flux in the output spectrum matches the total flux\n", + "in the input spectrum (adjusted for the units transformation from counts/pixel to counts/wavelength_bin).\n", + "\n", + "Here, we demonstrate by resampling the original arc spectrum itself. In a typical workflow, you would apply this `resample` method (using the `ws` object derived from the arc lamp) to your *science* spectrum observed with the same instrument setup." + ] + }, + { + "cell_type": "code", + "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.960397Z", + "start_time": "2025-04-24T09:34:57.818056Z" + } + }, + "source": [ + "spectrum_wl = wc.resample(arc_spectrum)\n", + "\n", + "fig, ax = subplots(constrained_layout=True)\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", + "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", + "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", + "ax.set_title(\"Arc Spectrum Resampled to Linear Wavelength Grid\")\n", + "ax.autoscale(enable=True, axis='x', tight=True)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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rr79iw4YN6NKli5hm27ZtCAkJQadOnSTbq3///pLmmYceeghAybM+//3vf3H9+nWz6/zhhx/Qs2dPuLi4QKFQQKlUYuXKlWabOSMiIuDs7Cx+NjQrDRw4ULJPDNPLltvV1RVDhgyRTIuKioJer8fvv/9e7nbZtm0bIiIi4O/vLynzwIEDAUA87vbu3VvuOu5HQkIC8vPzMXbsWMn0pk2bok+fPlVuYqypsuXp0KEDgHvbd+fOndBqtRgzZoxk+zg4OCA8PFzSZJebm4s333wTrVq1gkKhgEKhgIuLC/Ly8szu86effrrK+YyIiMA///yDGzduID09HadOnULv3r0BAOHh4fjzzz+RlZWFq1evIikpSWwKBoBLly4hKioKvr6+kMvlUCqVCA8PBwBJvsaNG4dr165h9+7d4rTVq1fD19dXPB7i4+Nx584dREdHS7aHXq/HgAEDkJiYaPLohEFNvmtu/xQUFCAtLQ3AveNz+PDhknQjR46s8rY16NSpE5o1ayZ+dnBwQHBwcJWvMWUtWbIESqVS/OvYsaNJmqocA+fOncONGzcQFRUluRYEBgaiR48eNcqbPeIgkFXowoUL+P333zFo0CAQETIzM5GZmYlhw4YBgNlnQ/z8/CSfb926BQBo0qRJjfMRGBiIiRMnYuXKlTh//jw2bdqEgoIC8XkWA4VCgcaNG0umGR4aT09PBwDcvHkTRAQfHx/JxUipVOLgwYPiMz61kW/AdHvUhJOTk0kArVKp4OHhYZJWpVKhoKCgSssdPnw4EhMTER8fjxUrVsDV1RUjRoyQBPc3b97EX3/9ZbKtXF1dQUTi9urVqxd+/vlnMTho0qQJQkJCJEPObN68GcOHD0dAQAC+++47JCQkIDExEc8//7zZPJctn0qlqnB62WX4+PiYLLPs8WDOzZs3sXXrVpMyt2/fHgDEMqenp1e4jpoy5M3csePv719h3mtD2XPI0LErPz8fAMRnRh966CGTbbRp0ybJc3JRUVH4/PPP8cILL2Dnzp04fPgwEhMT4eXlJS7PWHXOF+PnAuPi4iCXy9GzZ08AwCOPPAKg5LnAss8D5ubm4tFHH8WhQ4cwd+5cxMXFITExEZs3b5aUEyi54fDz88Pq1asBABkZGfjll18wZswYyOVyyfYYNmyYyfb48MMPQUS4c+eO2TLU5LuV7Z/09HQoFAqT88TcsVqZsusyrM/cvjNmCBzLBotRUVFITExEYmJiuT19q3IMGM4Bc+fa/Z5/9oR7B7MKrVq1CkSEH3/80eyYY2vXrsXcuXPFiyEAk1ozLy8vACW982rL8OHDsWDBApw6dUoyXavVIj09XXLhMjykb5jm6ekJQRCwf/9+s72WDdNqK9/mahENAV3Zh96NfzwtwcvLC127dgVQ0ju4bdu2CA8Px7Rp07Bt2zYAJdvL0dGx3IfBPT09xf8PHToUQ4cORWFhIQ4ePIgFCxYgKioKzZs3R1hYGL777jsEBQVh06ZNku1S1bHCqqtsBxfA9Hgwx9PTEx06dMC8efPMzvf39xeXcfjw4XLXUVOGvKWkpJjMu3HjhmSb1wfD+n/88UcEBgaWmy4rKwvbtm3DrFmz8NZbb4nTCwsLyw2KqlPr3qtXL8jlcsTFxUGtVqNz585wcXEBALi5uaFTp07Yu3cv7ty5A4VCIQaIe/bswY0bNxAXFyfW/gEwO0yKXC7H6NGj8dlnnyEzM1Mc227cuHEm22Pp0qXl9mAtLwC7n++Wp3HjxtBqtbhz544kELzf47I6Hn/8cXz11Vf45ZdfxI46AODt7S129HN1dTV77lflGDCcI+bKZMlyNnRcE8jKpdPpsHbtWrRs2RJ79+41+Zs+fTpSUlLw66+/Vric4OBgtGzZEqtWrar2j725H0Gg5E4+OTlZ/DE2tn79eslnw5h3hmaiyMhIEBGuX7+Orl27mvyFhoYCAHr06AGNRoPly5eDiMrNY9m78KowDGT6119/Sab/8ssvVV5GXXj00UcxZswYbN++HQkJCQBKttfFixfRuHFjs9vL3KCsarUa4eHh+PDDDwFA7DEqCAJUKpXkIp+ammq2d3BtyMnJMdmm33//PWQyGXr16lXu9yIjI3Hq1Cm0bNnSbJkNx11ERES566iK8o6dsLAwODo64rvvvpNMv3btGvbs2YO+fftWafl1pX///lAoFLh48aLZ7WO4sRAEAURkcrP1zTffVKmHdmU0Gg0efPBBsSbQcI4bhIeHY+/evYiLi8PDDz8sBoiG469svlasWGF2PePGjUNBQQE2bNiANWvWICwsDA888IA4v2fPnmjUqBHOnDlT7vYw1FaXdT/fLY8hsN20aZNk+saNG03S1uT6VRVPPvkk2rVrh/nz5+Ps2bO1umwAaNOmDfz8/LBhwwbJ9fnKlSuIj4+v9fXZKq4JZOX69ddfcePGDXz44YcmF1cACAkJweeff46VK1dWOqr8F198gcGDB6N79+6YNm0amjVrhqtXr2Lnzp0mQZuxefPm4Y8//sCzzz4rDueSlJSEzz//HOnp6Vi8eLEkvUqlwkcffYTc3Fw89NBDiI+Px9y5czFw4ECxeahnz5548cUXMW7cOBw5cgS9evWCs7MzUlJScODAAYSGhmLixIlwcXHBRx99hBdeeAGPPfYYJkyYAB8fH1y4cAEnTpzA559/DgBi0Pjhhx9i4MCBkMvl6NChQ4UXbl9fXzz22GNYsGAB3N3dERgYiN9++01sjqpPH3zwATZt2oR3330Xu3fvxtSpU/HTTz+hV69emDZtGjp06AC9Xo+rV69i165dmD59Orp164b33nsP165dQ9++fdGkSRNkZmaKz/8YfpQiIyOxefNmTJo0CcOGDUNycjI++OAD+Pn5mX2+9H41btwYEydOxNWrVxEcHIwdO3bg66+/xsSJEyXPOZX1/vvvIzY2Fj169MCUKVPQpk0bFBQU4PLly9ixYweWL1+OJk2aYMyYMfjkk08wZswYzJs3D61bt8aOHTuwc+fOKuXP8Padr776Cq6urnBwcEBQUBAaN26Md999F2+//TbGjBmDkSNHIj09HXPmzIGDgwNmzZpVpeUfPHjQ7PTw8HCxprsmmjdvjvfffx/vvPMOLl26hAEDBsDd3R03b97E4cOH4ezsjDlz5sDNzQ29evXC4sWL4enpiebNm2Pfvn1YuXIlGjVqVOP1G4uIiMDixYshCIJ402EQHh6OTz75BEQkeVa4R48ecHd3x8svv4xZs2ZBqVRi/fr1OHHihNl1PPDAAwgLC8OCBQuQnJyMr776SjLfxcUFS5cuRXR0NO7cuYNhw4bB29sbt27dwokTJ3Dr1i0sW7bM7LLv57vlGTBgAHr27Inp06cjOzsbXbp0QUJCgjh8ivHQWobr15IlSxAdHQ2lUok2bdqI4yvWlFwux88//4z+/fvj4YcfxoQJE9C7d2+4u7sjMzMThw4dwokTJ8odPqYyMpkMH3zwAV544QU8+eSTmDBhAjIzMzF79mxuDq6OeuqQwhqAJ554glQqlThkhTkjRowghUJBqampYs/WxYsXm02bkJBAAwcOJI1GQ2q1mlq2bEnTpk2rMA8HDx6kV155hTp27EgeHh4kl8vJy8uLBgwYIPZKNYiOjiZnZ2f666+/qHfv3uTo6EgeHh40ceJESQ9Tg1WrVlG3bt3I2dmZHB0dqWXLljRmzBiT4VV27NhB4eHh5OzsTE5OTtSuXTv68MMPxfmFhYX0wgsvkJeXFwmCIOlpBzPDkRikpKTQsGHDyMPDgzQaDT333HN05MgRs72DnZ2dTb4fHh5O7du3N5keGBhIgwYNKnebGlSUt9dff10yDERubi793//9H7Vp04ZUKhVpNBoKDQ2ladOmiUPqbNu2jQYOHEgBAQGkUqnI29ub/vWvf9H+/fsly164cCE1b96c1Go1tW3blr7++mux12Nl+SvvGDPXG9awfeLi4qhr166kVqvJz8+P3n77bSouLjZZl3GPSaKS4UGmTJlCQUFBpFQqycPDg7p06ULvvPOO5Hi6du0aPf300+Ti4kKurq709NNPU3x8fJV6BxOV9GwOCgoiuVxu8p1vvvmGOnToIG7zoUOHSoZfKY9he5T3Z+hhWbbchp6rZXuCl9eb/eeff6aIiAhyc3MjtVpNgYGBNGzYMMmwJobt4+7uTq6urjRgwAA6deoUBQYGUnR0dKXrroyhp67x8C8Gd+7cEUcWiI2NlcyLj4+nsLAwcnJyIi8vL3rhhRfo2LFj5e63r776igCQo6NjuUNV7du3jwYNGkQeHh6kVCopICCABg0aJDkuzQ0RU9XvGs4T46F1ylvmnTt3aNy4cdSoUSNycnKixx9/nA4ePGh2BIWZM2eSv7+/uK0M+7m8a0l4eDiFh4eb3QZlZWVl0fz58+mhhx4iNzc3UigU5O3tTY8//jh98cUXlJeXJ6atqFd7ecfgN998Q61btyaVSkXBwcG0atUqio6O5t7BVSQQVdDOxVgDMnbsWPz444/Izc2t76wwK9C7d2/cvn3b5LlRxuzV999/j1GjRuGPP/7gHrQMADcHM8YYYzZnw4YNuH79OkJDQyGTyXDw4EEsXrwYvXr14gCQiTgIZIwxxmyMq6srNm7ciLlz5yIvLw9+fn4YO3Ys5s6dW99ZY1aEm4MZY4wxxuwQDxHDGGOMMWaHOAhkjDHGGLNDHAQyxhhjjNkh7hhSB/R6PW7cuAFXV9dqvQKJMcYYY8yAiJCTkwN/f3/JIN+1hYPAOnDjxg00bdq0vrPBGGOMMRuQnJyMJk2a1PpyOQisA4bX7SQnJ8PNza2ec8MYY4yxhig7OxtNmza979f4lYeDwDpgaAJ2c3PjIJAxxhhj96WuHi3jjiGMMcYYY3aIg0DGmN377e+bGL4iAVfS8+o7K4wxZjEcBDLG7N74tUdwOOkOZv1yur6zwhhjFsNBIGOMlbpbpKvvLDDGmMVwEMgYY6XcnZT1nQXGGLMYDgIZY6yUmwMHgYwx+2EVQeDvv/+OwYMHw9/fH4Ig4Oeff5bMHzt2LARBkPx1795dkqawsBCTJ0+Gp6cnnJ2dMWTIEFy7dk2SJiMjA6NHj4ZGo4FGo8Ho0aORmZkpSXP16lUMHjwYzs7O8PT0xJQpU1BUVFQXxWaMWRkZv+GHMWZHrCIIzMvLQ8eOHfH555+Xm2bAgAFISUkR/3bs2CGZP3XqVGzZsgUbN27EgQMHkJubi8jISOh0957xiYqKwvHjxxETE4OYmBgcP34co0ePFufrdDoMGjQIeXl5OHDgADZu3IiffvoJ06dPr/1CM8asDoHqOwuMMWYxVjFY9MCBAzFw4MAK06jVavj6+pqdl5WVhZUrV+Lbb7/FY489BgD47rvv0LRpU+zevRv9+/fH33//jZiYGBw8eBDdunUDAHz99dcICwvDuXPn0KZNG+zatQtnzpxBcnIy/P39AQAfffQRxo4di3nz5pU78HNhYSEKCwvFz9nZ2dXeBoyx+kccAzLG7IhV1ARWRVxcHLy9vREcHIwJEyYgLS1NnHf06FEUFxejX79+4jR/f3+EhIQgPj4eAJCQkACNRiMGgADQvXt3aDQaSZqQkBAxAASA/v37o7CwEEePHi03bwsWLBCbmDUaDb83mLEGimNAxpg9aRBB4MCBA7F+/Xrs2bMHH330ERITE9GnTx+x9i01NRUqlQru7u6S7/n4+CA1NVVM4+3tbbJsb29vSRofHx/JfHd3d6hUKjGNOTNnzkRWVpb4l5ycfF/lZYzVD64JZIzZE6toDq7Ms88+K/4/JCQEXbt2RWBgILZv346nnnqq3O8RkeR9e+bevVeTNGWp1Wqo1epKy8EYY4wxZi0aRE1gWX5+fggMDMT58+cBAL6+vigqKkJGRoYkXVpamliz5+vri5s3b5os69atW5I0ZWv8MjIyUFxcbFJDyBizPdwxhDFmTxpkEJieno7k5GT4+fkBALp06QKlUonY2FgxTUpKCk6dOoUePXoAAMLCwpCVlYXDhw+LaQ4dOoSsrCxJmlOnTiElJUVMs2vXLqjVanTp0sUSRWOM1SeOARljdsQqmoNzc3Nx4cIF8XNSUhKOHz8ODw8PeHh4YPbs2Xj66afh5+eHy5cv4+2334anpyeefPJJAIBGo8H48eMxffp0NG7cGB4eHpgxYwZCQ0PF3sJt27bFgAEDMGHCBKxYsQIA8OKLLyIyMhJt2rQBAPTr1w/t2rXD6NGjsXjxYty5cwczZszAhAkTyu0ZzBizHRwDMsbsiVUEgUeOHEFERIT4+bXXXgMAREdHY9myZTh58iTWrVuHzMxM+Pn5ISIiAps2bYKrq6v4nU8++QQKhQLDhw9Hfn4++vbtizVr1kAul4tp1q9fjylTpoi9iIcMGSIZm1Aul2P79u2YNGkSevbsCUdHR0RFReE///lPXW8CxpgVIO4ZwhizIwLxVa/WZWdnQ6PRICsri2sQGWsAmr+1HQAwtJM/lox4sJ5zwxhjJeo6nmiQzwQyxlhd4Ftixpg94SCQMcZKcQzIGLMnHAQyxlgpfjqGMWZPOAhkjLFSHAIyxuwJB4GMMWbAUSBjzI5wEMgYY4wxZoc4CGSMsVL82jjGmD3hIJAxxkpxvxDGmD3hIJAxxkpxEMgYsyccBDLGWCluDmaM2ZMqvzv4l19+qfbCH3/8cTg6Olb7e4wxZik8NiBjzF5VOQh84oknqrVgQRBw/vx5tGjRorp5YowxizGOATkeZIzZk2o1B6empkKv11fpz8nJqa7yzBhjtYbK+T9jjNm6KgeB0dHR1Wrafe655+Dm5lajTDHGmKUYNwdzTSBjzJ5UuTl49erV1VrwsmXLqp0ZxhizNKrgE2OM2TLuHcwYs2v8TCBjzF5VuSYQAGQyGQRBqDCNIAjQarX3lSnGGLMUHhaGMWavqhUEbtmypdx58fHxWLp0KQ+3wBhrUCQ1gfWXDcYYs7hqBYFDhw41mXb27FnMnDkTW7duxahRo/DBBx/UWuYYY8yS+CaWMWZPavxM4I0bNzBhwgR06NABWq0Wx48fx9q1a9GsWbPazB9jjFmMnmNAxpgdqXYQmJWVhTfffBOtWrXC6dOn8dtvv2Hr1q0ICQmpi/wxxlid4uZgxpi9qlZz8KJFi/Dhhx/C19cXGzZsMNs8zBhjDQl3DGGM2atqBYFvvfUWHB0d0apVK6xduxZr1641m27z5s21kjnGGKtr0iFiOCBkjNmPajUHjxkzBsOHD4eHhwc0Gk25f9X1+++/Y/DgwfD394cgCPj5558l84kIs2fPhr+/PxwdHdG7d2+cPn1akqawsBCTJ0+Gp6cnnJ2dMWTIEFy7dk2SJiMjA6NHjxbzOXr0aGRmZkrSXL16FYMHD4azszM8PT0xZcoUFBUVVbtMjLGGgcM+xpi9qlZN4Jo1a+okE3l5eejYsSPGjRuHp59+2mT+okWL8PHHH2PNmjUIDg7G3Llz8fjjj+PcuXNwdXUFAEydOhVbt27Fxo0b0bhxY0yfPh2RkZE4evQo5HI5ACAqKgrXrl1DTEwMAODFF1/E6NGjsXXrVgCATqfDoEGD4OXlhQMHDiA9PR3R0dEgIixdurROys4Yq1/82jjGmN0iKwOAtmzZIn7W6/Xk6+tLCxcuFKcVFBSQRqOh5cuXExFRZmYmKZVK2rhxo5jm+vXrJJPJKCYmhoiIzpw5QwDo4MGDYpqEhAQCQGfPniUioh07dpBMJqPr16+LaTZs2EBqtZqysrKqXIasrCwCUK3vMMbqR1Z+EQW+uY0C39xGUV8n1Hd2GGNMVNfxRLV7B+v1eqxatQqRkZEICQlBaGgohgwZgnXr1tXJ8zRJSUlITU1Fv379xGlqtRrh4eGIj48HABw9ehTFxcWSNP7+/ggJCRHTJCQkQKPRoFu3bmKa7t27Q6PRSNKEhITA399fTNO/f38UFhbi6NGj5eaxsLAQ2dnZkj/GWMPAr41jjNmragWBRIQhQ4bghRdewPXr1xEaGor27dvjypUrGDt2LJ588slaz2BqaioAwMfHRzLdx8dHnJeamgqVSgV3d/cK03h7e5ss39vbW5Km7Hrc3d2hUqnENOYsWLBA8kxk06ZNq1lKxli94cCPMWanqv1M4O+//47ffvsNERERknl79uzBE088gXXr1mHMmDG1mkkAJu8sJqJK32NcNo259DVJU9bMmTPx2muviZ+zs7M5EGSsgTAeIoZrAhlj9qRaNYEbNmzA22+/bRIAAkCfPn3w1ltvYf369bWWOQDw9fUFAJOauLS0NLHWztfXF0VFRcjIyKgwzc2bN02Wf+vWLUmasuvJyMhAcXGxSQ2hMbVaDTc3N8kfY6xhkA4WzVEgY8x+VCsI/OuvvzBgwIBy5w8cOBAnTpy470wZCwoKgq+vL2JjY8VpRUVF2LdvH3r06AEA6NKlC5RKpSRNSkoKTp06JaYJCwtDVlYWDh8+LKY5dOgQsrKyJGlOnTqFlJQUMc2uXbugVqvRpUuXWi0XY8w6GId9XBPIGLMn1WoOvnPnToU1Yj4+Pia1cVWRm5uLCxcuiJ+TkpJw/PhxeHh4oFmzZpg6dSrmz5+P1q1bo3Xr1pg/fz6cnJwQFRUFANBoNBg/fjymT5+Oxo0bw8PDAzNmzEBoaCgee+wxAEDbtm0xYMAATJgwAStWrABQMkRMZGQk2rRpAwDo168f2rVrh9GjR2Px4sW4c+cOZsyYgQkTJnDtHmM2yrhDG8eAjDF7Uq0gUKfTQaEo/ytyuRxarbbamThy5IikidnwfF10dDTWrFmDN954A/n5+Zg0aRIyMjLQrVs37Nq1SxwjEAA++eQTKBQKDB8+HPn5+ejbty/WrFkjjhEIAOvXr8eUKVPEXsRDhgzB559/Lsn/9u3bMWnSJPTs2ROOjo6IiorCf/7zn2qXiTHWMFC5HxhjzLYJVI1xXWQyGQYOHAi1Wm12fmFhIWJiYqDT6Wotgw1RdnY2NBoNsrKyuAaRMSt3K6cQD83bDQB4qLk7fni5Rz3niDHGStR1PFGtmsAxY8ZU2iO3LnoGM8ZYXTHuDKLnmkDGmB2xitfGMcZYvTEK/PTcM4QxZkeq3Dv4r7/+gl6vr/KCT58+XaPnAxljzJKMwz6uCWS26syNbDz15R+Iv3C7vrPCrEiVg8AHH3wQ6enpVV5wWFgYrl69WqNMMcaYpUhfG8dRILNNr278E8euZiLqm0P1nRVmRarcHExEePfdd+Hk5FSl9EVFRTXOFGOM1QduDma2KreQW+aYqSoHgb169cK5c+eqvOCwsDA4OjrWKFOMMWYp/No4Zg/cnVRIySqo72zYrb9TsvHHhdt4vmcQZLKKO9haUpWDwLi4uDrMBmOM1Q+SdAypv3wwVpdcHKrVD5TVIiLCwCX7AQAtvJzR54HyX7phadV6bRxjjNka6WvjOApktkleyfBurO7cLbo3dnJqVmE95sQUB4GMMbtmHPjxM4HMVsn4177e6IyuK3Ir2w9Wlh3GGLMsbg5m9kDGNYH1Rqe7d2HRWtlFhoNAxhgrxc3BzFZV9rYvVneMawJ1HAQyxpj1kI4TWH/5YKwucQhYf/RGgV+Rtuov3bCEGgWBu3fvLnfeihUrapwZxhizNOm7gzkKZLbJikYlsTvGNYGFthAEDho0CNOnT5cMCH3r1i0MHjwYM2fOrLXMMcZYXeNnApk94GcC64/O1moCf//9d2zduhUPPfQQTp8+je3btyMkJAS5ubk4ceJEbeeRMcbqjPTdwRwFMttk/Eygnu92LEpvFPcV6WwgCOzWrRv+/PNPdOjQAV26dMGTTz6J6dOnY8+ePWjatGlt55ExVgV3i7TcsaEGjLcZbz5mq4ybg62th6qtM24OtomaQAA4d+4cEhMT0aRJEygUCpw9exZ3796tzbwxxqroRmY+2r23E2NXJ9Z3VhocHiya2QPj5mCt3roCEVtnc83BCxcuRFhYGB5//HGcOnUKiYmJYs1gQkJCbeeRMVaJzceuAQD2/XOrnnPS8PAzgQ2fVqfHygNJ+Dslu76zYrWMB4vmmkDLMg4CC7W6ClJaXo1eJrhkyRL8/PPPGDhwIACgffv2OHz4MN5++2307t0bhYXW9VoUxmydwtqGoW9QuHdwQ7cxMRkfbDsDALi8cFA958Y6CUaDxGh1fJxbkjXXBNYoCDx58iQ8PT0l05RKJRYvXozIyMhayRhjrOoUPP5DjXFNYMPHNYDVo7Wyzgm2zvjm0iY6hpQNAI2Fh4fXODOMsZrhILDm+JnAhk+l4JrwyhjXRnFzsGXZXE3g+++/X+H89957r0aZYYzVDDcH15zkjSH1lw12HzgIrJxxD1VuDrYsax4sukZB4JYtWySfi4uLkZSUBIVCgZYtW3IQyJiFGdcEanV6Dgqrgd8Y0vCpFfL6zoLV00tqAq0rELF1NvfauD///FPyd+rUKaSkpKBv376YNm1abecRADB79mwIgiD58/X1FecTEWbPng1/f384Ojqid+/eOH36tGQZhYWFmDx5Mjw9PeHs7IwhQ4bg2rVrkjQZGRkYPXo0NBoNNBoNRo8ejczMzDopE2O1xTjos7ZnTqyd5JlAbiZrkNRGNYG8D80zvsHh5mDLkjQHW9n1udaqC9zc3PD+++/j3Xffra1Fmmjfvj1SUlLEv5MnT4rzFi1ahI8//hiff/45EhMT4evri8cffxw5OTlimqlTp2LLli3YuHEjDhw4gNzcXERGRkKnu9dlOyoqCsePH0dMTAxiYmJw/PhxjB49us7KxFhtMK74Kyy2rotMQ8K/jQ2TyugEsLbmNmth3AJcbGWBiK2zuWcCy5OZmYmsrKzaXKSEQqGQ1P4ZEBE+/fRTvPPOO3jqqacAAGvXroWPjw++//57vPTSS8jKysLKlSvx7bff4rHHHgMAfPfdd2jatCl2796N/v374++//0ZMTAwOHjyIbt26AQC+/vprhIWF4dy5c2jTpk2dlY2x2sI/gtVjXEPCzcENk8zocYginR6O4ObhsoxrSHV8t2NR1vzGkBoFgZ999pnkMxEhJSUF3377LQYMGFArGTPn/Pnz8Pf3h1qtRrdu3TB//ny0aNECSUlJSE1NRb9+/cS0arUa4eHhiI+Px0svvYSjR4+iuLhYksbf3x8hISGIj49H//79kZCQAI1GIwaAANC9e3doNBrEx8eXGwQWFhZKxkbMzubhCphlGd/Y8/M+1SMdIoZ/HBsi487xXMtlnnHgV8wdQyzKmpuDaxQEfvLJJ5LPMpkMXl5eiI6OxsyZM2slY2V169YN69atQ3BwMG7evIm5c+eiR48eOH36NFJTUwEAPj4+ku/4+PjgypUrAIDU1FSoVCq4u7ubpDF8PzU1Fd7e3ibr9vb2FtOYs2DBAsyZM+e+ysfY/dAZBX58l189kppA67o+syqSBji8E82RPBPI28ii9LZWE5iUlFTb+aiU4e0kABAaGoqwsDC0bNkSa9euRffu3QEAgiAdK42ITKaVVTaNufSVLWfmzJl47bXXxM/Z2dlo2rRpxQVirBYZX9P5Lr96jGNmHdcENkjW/MyVtTAORPhG0bKMr8/W9rhOgx1HwtnZGaGhoTh//rz4nGDZ2rq0tDSxdtDX1xdFRUXIyMioMM3NmzdN1nXr1i2TWkZjarUabm5ukj/GLEnHF/ga4x/Hhs/4+OeaQPMktaV8nFuUNd+kVLkm0LimqzIff/xxjTJTHYWFhfj777/x6KOPIigoCL6+voiNjcWDDz4IACgqKsK+ffvw4YcfAgC6dOkCpVKJ2NhYDB8+HACQkpKCU6dOYdGiRQCAsLAwZGVl4fDhw3j44YcBAIcOHUJWVhZ69OhR52VirKZ0Rj98/CNYPWWHFNHrSdLRgFk/nc74R5YDHHMkNd783INF2URz8J9//lmldJU1v9bUjBkzMHjwYDRr1gxpaWmYO3cusrOzER0dDUEQMHXqVMyfPx+tW7dG69atMX/+fDg5OSEqKgoAoNFoMH78eEyfPh2NGzeGh4cHZsyYgdDQULG3cNu2bTFgwABMmDABK1asAAC8+OKLiIyM5J7BzKrpJBd4/hGsjrKbS0cEGTgIbEi0/ExgpfSS2lK+RlhS2Y4hVXlUzVKqHATu3bsXly5dQvPmzSGTWb4V+dq1axg5ciRu374NLy8vdO/eHQcPHkRgYCAA4I033kB+fj4mTZqEjIwMdOvWDbt27YKrq6u4jE8++QQKhQLDhw9Hfn4++vbtizVr1kAuvzecwPr16zFlyhSxF/GQIUPw+eefW7awjFWT8Z099w6unrI9grmHcMOj5+bgSkneHcxBoEWVvTEv0umt5i031eoY0rp1a6SkpIg9aJ999ll89tlnFT4vV1s2btxY4XxBEDB79mzMnj273DQODg5YunQpli5dWm4aDw8PfPfddzXNJmP1QjJEDF/gq8UkCOQYosHRWvEzV9ZCEgTyQW5RJkGg1nqCwGpV6VGZi+WOHTuQl5dXqxlijFUfd26oubK/h9xDuOGx5nHYrIXxYc03ipZV9ppiTTcqDbZ3MGPsHuOLOvf8q56yNYEcRDc8PBBy5SRvreBA2aLKdj6zpu1frSBQEASThxmt5eFGxuyZdIgY67nANASmzcEcRDQ0PFh05YyP6/wiXT3mxP5Yc01gtZ4JJCKMHTsWarUaAFBQUICXX34Zzs7OknSbN2+uvRwyxiplHPhxTUj1lG395Y4hDY/xM27W9ANrTYwDkfxiDgItyaQm0IqO0WoFgdHR0ZLPzz33XK1mhjFWM8aVH9ycWT0mzcEcBDY4/Exg5YyP80IOAi2q7DXZmt4aUq0gcPXq1XWVD8bYfeAhMmqu7AWaW9MbHm4Orpzxcc01gZZVtnHGmoJA7hjCmA0w7hjCNYHVY26waNawSAaLtqIfWGtifF3gINCyyj6nbU3NwRwEMmYDjGsCtRwEVkvZoa+4Y0jNZN0txoBPf8fS385bfN3cHFw542tEQTFvI0sqe0ha0zHKQSBjNoDfBlBzJjWBHATWyNqEyzibmoOPYv+x+Lp5iJjK6bljSL0p+9wx1wQy1sAcupSOQZ/tx5HLd+o7K2YZ1/7xEDHVU1+vjdt4+Cp+OJJskXVZQm6htt7WreM3hlTKeBsV8BAxFmXujSHWgoNAxqpgztYzOH0jG8OWJ9R3VszSc01IjdVHEJhTUIy3Np/E6z/+heyC4jpfnyXUZ4cMrY13DDl65Q6upN/f27n4mcD6UzYIzKvHG6ayOAhkrAqy8q37h1paE8hBYHWYvjGk7tdpXBNwO6ew7ldoAcbBV9nnLOua3oZrAk/fyMLTyxIwcMn++1qO8S4p4CDQospeYzLuFtVTTkxxEMhYFVj7i3EkQ8Rwc3C1mLw72AJBtHFtbZqNBIEC7p0klh4CQ2vDtVwxp1IBAHfvswlXOlg0XyMsqew1JdOKKhU4CGSsCqw9CDS+yOi4Obha6qM52LjWzNprmatKIb93klj6tWSSpk4be95N2qu35mWTPBNoY4GytTMJArkmkDVkOQXFGLv6MBbvPFvfWbEY41oOayTpHcnNwdVSNuizxBA7xkNE2MoPsvFmzCuy7DNPxsf//daYWRuZ0R3o/dww1FYwyarPcHw6qeQAgMy71nPjx0Egq7bVf1xG3Llb+GLvxfrOisU0qJpAbg6ulrIxnyU6Fhg/t2YrQYvxdrN0mSRBoI0FOMZN69n3FQTe+7+tNZlbO0NTvIezCgA/E8gauBwb6c1oS4xrr3iw6OqpjzG8jAMmW2m+NC6TpXs/avXG29N6el7WBuPj435qAm25ydzaGc4Nb1c1AK4JZFasKg/FC0bVYrY4HIM5Vl4RKH1jCD8TWC1lD3mLB4E2UitjfNxZvCbQaB/aSs2qgfHxUdMgsOxbcAq1en4zjgUVa0u2tberAwDreg6Yg0Amupp+F53e34UPtp2pMJ1xQGQvz5YYB77WePHU8RAxNVZ2OBNL9Gwt0tperUxRPdYE6vS2V7NqUBs1geZGDLB0D257Zjg3vN1KagK5OZhZpYUxfyOnQIuVB5Kq/B17eQelceBrTe99NNDZ+GC5dclkNH9LPBNotI76fNNGbTKuCbR0TUd91kLWNeOawJo2I5oL+GylBrohKCrTHFxQrLeaChQOApnoTl7V7k6MLyjWciDXNeOOIdZY08A1gTVXL83BRuuo6nln7Yyfy7P02IdaSe9g2wiqDYyvNylZ+TVaRqHRzbqydCgfe7l2WwPD+e7urIJcVrL9reW5QA4CmUitkIv/ryiQMA4CbWWg28pYe82N8UCw3MxTPWVrTi0RBBbaYBBYZFQbl5pVYNF1GwdK9VHD9d8jydh5OrVOlm1cnhuZNduuhdqSZagVMjgqS67zthYsWzPD74dKLkMjRyUAIDPfOs57DgLrCRHhq98vIv7C7frOikituHc4/J2SXW464x/Jp5fFW/wVUfXB+Ecmp8D6Lp7GQYU1PXTcEJTt7V6krfsgwngcvXQbCQK1RsF0araFg0CjQKlYRxZ9JOJEcibe+PEvvPTt0TqpXTNe5rXMGtYEll4f1AoZNE4lQQhfJyzHcDyqFDI0Kt3+GXnWsf05CKwn8RfTMX/HWUR9c8hq3nVpHEj8dS2rgnTSC92ineew+o+qP0fYEBk/Z2SNNYHGgYy50egz8ooQvngvpmz405LZahDKBvWWqEm9a3QMpefaRm26ceB108JBYNlaLUs+F3jqxr1r5T83c2p9+cYB7vWM+2sOVivl8HAqHavOSoIQe2DoHaySy9CodPtncU2gdfvyyy8RFBQEBwcHdOnSBfv339/Lu8u6nJ4n/v/k9fIDLksyvpBeSMstN13ZH81lcRcxZ+sZ/Hk1o87yVp+IqMzD2dZx8hrLzr+3T8w9a/LTsWu4kn4Xv5y4YfGem9bOMABv6aM6SLFAU2aeUZCScbfIJmrTjQOv8pqDcwu1WBRzFmdTy29pqC69nkw6qFkysL6dc+96cPpG7ZXLwLgV4nZuYY2eSTbcuDso7wUhd6zwOmarCktvkJRyGTxdSrb/tRoG9LWNg0AzNm3ahKlTp+Kdd97Bn3/+iUcffRQDBw7E1atXa20dV+/cFf9vLcGTcSBxPq38O9rb5Vxgj16xjnLUtoJiveSVWLVxob9bpK1x01Hynbt45ftjiDmVIk7LNqoJvHLnrkltrXHNzCkruemoLzo9IfnOXcScSkHYgt/w8/EbAIAHm7kDAPacTcPl23mSwOzSrdxaDZ6Nl1WsI5t4LvBm9r3rwu3cQknzsMGaP5LwZdxFDPh0f609k2Z8gxbQyBEAcL2GzaY1Ydz0vf2vlApS1kzZWk3j3w6DYl3FvU0Nx5daIYefpmSsuitGFRGs7hARckpvNJUKGTo0aQTAen4vBbKFW9Ba1q1bN3Tu3BnLli0Tp7Vt2xZPPPEEFixYYJK+sLAQhYX3LoDZ2dlo2rQpJq85ALWTCwglPzxaPUGr00OrJ8SeuSmm93RRI6xlYyhkAmSCALms5H2RRACBSv8toZCVTDcMDiwIJe+1FYR7PVj1+pLv6ankfZ5EJH5fTyXTZULJOvRU8vzM3SId4s7dEvMkCEB4sBcUMgGCUPLmXD0R8gp1SLiUDgAY1a0Z1h+6Fxi7OSjwcFBjyGWAvLQshnUAKP1c+q9M+jZe43KULbfhMwDIjbrpygRDuQUQEfRE0OnvpRX3T7EeWr0ebg7K0m1wb5voiSTb05BfwzQ9Ea5n5uPU9XuBXyMnJboGekCtkEEQSsp6L6+lay/NswABEEpqK7R6gk5PKNLqcfRKBhQyAT1becJRJRf3nWF/6amks4e+9DslZSPoCIi/cFvsDRkS4Iab2YW4lVMIQSi50yzS6qFxVMLXzQFujgo4KOXYf/7es6cBjRzRoYkGSrlMsqUMW1YQgIy7xSAiuDupoJTLoKeS/Ov1BKW8ZP8ZxkssewERULJPJK/aI7P/FQMtHZU8U2boOacnkhwPhn1hOHZVCjnkQsn3DNtIZ9hGpdtLEAQUa/Uo1pX8FZb+/3ZukcnzUCq5DFte6YHoVYniTY6zSg5fjQOKdHok38mHs0qO0CYaKGQycX1anR46PUHjpIKAkvPccEwKKMmrQi4DUckPtSFvCZfSJTcWzTycEOzjKtZGGmYZpxGE0uXJZCAQtLqS7aGQCZDJDMeONL3hX8M5otWReE7dW4sAB6UMWh1JOhiVx3CMFun0uJCWCzcHJZQKQXKOAEAbH1d4OKugUpQcP4VaPQ4n3RHnt/ByFoM2yTlvdL6L1z4z0wzn2s2sAtzIKoBaIcOjrT2x++80OCrlaO7pDJVCVno8onR/COK1BmWnGV2D7qUr2XYyoeS6YdiHhr+s/GKcL9Nq8mCzRnBSySGXyUq37r31S7Yj7nXAM2x2QxmLdYT8Ih1SsvJxO7cIMgEICdDgr2tZ0Dgq0cLLWfy9AEpu7PKLdQhs7AyNoxIKWcl1LL9Yj9zCYiTfKQmKewV7oU8bL8zeWjIWbCtvF7g6KEBUch0zPBduONf0+nvnneEczCnQIi27EC28nOHlqoZWV3LMG34rjH9vjH+7jEMN4/KK/ze338vZ98bLI8ny7n2QTpeu5978SvJUZj0wXg9gpqxG+SydkVuoFa83e6aHIz2vCM8sTwAAPODrCgelHGqFDIrSXttEJddxwzFTXJCH9ZP6ICsrC25ubqhtHASWUVRUBCcnJ/zwww948sknxemvvvoqjh8/jn379pl8Z/bs2ZgzZ47J9KZT/wuZ2qlO81vbmrg74gFfN+z++2aF6QIaOeKniT0w4qsEXE43vTO1Rd1beOCva1lWOw7ZxN4t0SFAg+k/nLDaPFqjvg9447nugYh4wBunrmdh5uaTOJOSXedD7biqFejT1hv/K62JtAW923ghoJGj5ObQEmQC8Hr/B/BIK09Erz5s8ZrVgEaOCA3QIKaOegj3CvbCs12bYvKGYyZDGlVVCy9nfD6yM/wbOWDMqsMVPvfNapdKLsOEXkF4vf8D0OkJo745iIOX7lT+RQD6wrtI/nQ4B4GWcuPGDQQEBOCPP/5Ajx49xOnz58/H2rVrce7cOZPvlFcTuHjrMTi7lOw0hUyAQi5AIRMgl5VE/SH+Gng4qxB3Lg13i3TQ6vXQ6UvvuIxrFAx3rrg3Mr7xGyyIpLWFMsFw9yqU/v/eHa7hu4bvGGqPHJQyuDooEdaiMdwcldh7Ng3peYWltTD3lqtWytDMwxnt/d3goJTjbpEWKVkF8HBS4cCF28gt1EprrvQk3lUbauCM7ywFCEY1APfyZFxu47toXZk7N0OtmbkyQ9weJdPzi3XiXb6hRtK4xsZw5ygTUFJTWZpOJZehja8rQvw1yCnU4uS1LCSl50Gn04t384blGNceiLWaRJDLZZALhv0vwFElh1wmICUzv6SGrbTsxrWb8tJaRplMgFy4969cJqC9vxuSM+6CCPByVcNP4wiv0oFIcwu1uJiWi5wCLXIKipFfrINcJqBT00bQ6QnHrmbibpFWvIOnMne3QMmzQ2qFHJl3i1Cso9LappI8FelKjlPDeGNlGdfqGO8H47pfwcxXjbehoXZVp6fSWuV7+1dPJbWpOj2JeZKXnleGWnRDDYlSIYNKLkClkEEpv/fX0ssZmXeL0czDCTKZaWaKtHpcSc/D7dwiFOv08HBWoVCrw7WMfOip5JhWlJ7HMkFAVn4xBEBSk2k43ot1JNYIltSWAG4OSoQ20cDTRY0LaTk4l5qL7IJi8zV5uHdsGs4rwzaSCUJJrbDhi6Xb0FCDYfi/YTsavmNYruG8vFukg1JeUqaKEEhy/ni6qiErrXFt6uGE1t4uEISSRyaS79xFkU6PIq0eMqFkH+j0hE5NG0EuE3DsaoakxtfccWA4bu6dV0ZHU+k0FwcFWnq5wMetpJmzoFiHc6k5yCg9dg21NYbaT+MWBr04j8T9ZjjugHu1P3oqOdaUckNrjSDWxDVxd0JLb2coZDIcuXwHaTmFpS0sJB7xhvWUrRwvOa+l5UTpceSsVsBZpYCzWo5gH1co5TLcyMzHhbRcFBTrJLXfXi5qtPJ2wYVbubhbqIO29BhxVMrhqJIjyNMZni7qe+smQtLtPKRkFSC3UCu29Bg6RRnOIcO10Pj/ckGAq4MCGXeLcCunUKy1KtYaH7zGLQuC0f9N96vxfi/9qtk05va/YXpl64FkGZWvB+WuX7i3fKN1GT5Jp5fkR6WQoYWXs2QINq1Oj1M3spGdX4xCrR6FWp044LkglNQE60sP1LzcbDzfJ4SDQEsxBIHx8fEICwsTp8+bNw/ffvstzp49W+kysrOzodFo6mynMcYYY8z21XU8wR1DyvD09IRcLkdqqrRaPy0tDT4+PvWUK8YYY4yx2sVBYBkqlQpdunRBbGysZHpsbKykeZgxxhhjrCFT1HcGrNFrr72G0aNHo2vXrggLC8NXX32Fq1ev4uWXX67S9w0t7NnZtT9mFGOMMcbsgyGOqKsn9zgINOPZZ59Feno63n//faSkpCAkJAQ7duxAYGBglb6fk1Myxl7Tpk3rMpuMMcYYswM5OTnQaDS1vlzuGFIH9Ho9bty4AVdXV0kv3uoy9DJOTk62yw4mXH77Lb89lx3g8nP57bf89lx2wLT8RIScnBz4+/tDVknv/ZrgmsA6IJPJ0KRJk1pbnpubm12eDAZcfvstvz2XHeDyc/ntt/z2XHZAWv66qAE04I4hjDHGGGN2iINAxhhjjDE7xEGgFVOr1Zg1axbUanXliW0Ql99+y2/PZQe4/Fx++y2/PZcdsHz5uWMIY4wxxpgd4ppAxhhjjDE7xEEgY4wxxpgd4iCQMcYYY8wOcRDIGGOMMWaHOAisY8uWLUOHDh3EgR/DwsLw66+/ivPHjh0LQRAkf927d5cso7CwEJMnT4anpyecnZ0xZMgQXLt2TZImIyMDo0ePhkajgUajwejRo5GZmWmJIparsrKXLbfhb/HixWKa3r17m8wfMWKEZD3WWHZzFixYAEEQMHXqVHEaEWH27Nnw9/eHo6MjevfujdOnT0u+11D3v7GyZS8uLsabb76J0NBQODs7w9/fH2PGjMGNGzck37OV/W9u39vyuV+WufLb8vk/e/Zsk3z7+vqK8239vK+o/LZ+7le2763uvCdWp3755Rfavn07nTt3js6dO0dvv/02KZVKOnXqFBERRUdH04ABAyglJUX8S09Plyzj5ZdfpoCAAIqNjaVjx45RREQEdezYkbRarZhmwIABFBISQvHx8RQfH08hISEUGRlp0bKWVVnZjcuckpJCq1atIkEQ6OLFi+IywsPDacKECZJ0mZmZkvVYY9nLOnz4MDVv3pw6dOhAr776qjh94cKF5OrqSj/99BOdPHmSnn32WfLz86Ps7GwxTUPd/wbmyp6ZmUmPPfYYbdq0ic6ePUsJCQnUrVs36tKli+S7trD/y9v3tnzuGyuv/LZ8/s+aNYvat28vyXdaWpo439bP+4rKb+vnfmX73trOew4C64G7uzt98803RFRyQAwdOrTctJmZmaRUKmnjxo3itOvXr5NMJqOYmBgiIjpz5gwBoIMHD4ppEhISCACdPXu2bgpRQ8ZlL2vo0KHUp08fybTw8HDJD0dZDaHsOTk51Lp1a4qNjZWUR6/Xk6+vLy1cuFBMW1BQQBqNhpYvX05EDX//l1d2cw4fPkwA6MqVK+K0hr7/Kyq/PZz71dn/tnT+z5o1izp27Gh2nj2c9xWV3xxbOvcrK7u1nffcHGxBOp0OGzduRF5eHsLCwsTpcXFx8Pb2RnBwMCZMmIC0tDRx3tGjR1FcXIx+/fqJ0/z9/RESEoL4+HgAQEJCAjQaDbp16yam6d69OzQajZimvpVXdoObN29i+/btGD9+vMm89evXw9PTE+3bt8eMGTOQk5MjzmsIZX/llVcwaNAgPPbYY5LpSUlJSE1NlexbtVqN8PBwMe8Nff+XV3ZzsrKyIAgCGjVqJJnekPd/ZeW39XO/qvvfFs//8+fPw9/fH0FBQRgxYgQuXboEwD7Oe6D88ptja+d+ZWW3pvNeUZMCsuo5efIkwsLCUFBQABcXF2zZsgXt2rUDAAwcOBDPPPMMAgMDkZSUhHfffRd9+vTB0aNHoVarkZqaCpVKBXd3d8kyfXx8kJqaCgBITU2Ft7e3yXq9vb3FNPWlorIbW7t2LVxdXfHUU09Jpo8aNQpBQUHw9fXFqVOnMHPmTJw4cQKxsbEArLvsALBx40YcO3YMiYmJJvMM+fPx8ZFM9/HxwZUrV8Q0DXX/V1T2sgoKCvDWW28hKipK8tL4hrz/Kyu/rZ/71dn/tnb+d+vWDevWrUNwcDBu3ryJuXPnokePHjh9+rTNn/dAxeVv3LixJK2tnfuVld3aznsOAi2gTZs2OH78ODIzM/HTTz8hOjoa+/btQ7t27fDss8+K6UJCQtC1a1cEBgZi+/btJhdEY0QEQRDEz8b/Ly9Nfaio7MZWrVqFUaNGwcHBQTJ9woQJ4v9DQkLQunVrdO3aFceOHUPnzp0BWG/Zk5OT8eqrr2LXrl0m5TJWNp9Vybu17/+qlh0oeVB8xIgR0Ov1+PLLLyXzGur+r0r5bfncr87+B2zv/B84cKD4/9DQUISFhaFly5ZYu3at2AnAFs97g4rK/9prr4nzbPHcr6zs1nbec3OwBahUKrRq1Qpdu3bFggUL0LFjRyxZssRsWj8/PwQGBuL8+fMAAF9fXxQVFSEjI0OSLi0tTbyT9PX1xc2bN02WdevWLZO7TUurStn379+Pc+fO4YUXXqh0eZ07d4ZSqZRsH2st+9GjR5GWloYuXbpAoVBAoVBg3759+Oyzz6BQKMT8lb1zK7tvG+L+r6zsOp0OQMmPwPDhw5GUlITY2FhJTYA5DWX/V7X8xmzp3K9O+W31/Dfm7OyM0NBQnD9/XuwpaovnfXmMy29gq+d+WebKbqy+z3sOAusBEaGwsNDsvPT0dCQnJ8PPzw8A0KVLFyiVSrEKHABSUlJw6tQp9OjRAwAQFhaGrKwsHD58WExz6NAhZGVliWmshbmyr1y5El26dEHHjh0r/f7p06dRXFwsbh9rLnvfvn1x8uRJHD9+XPzr2rUrRo0ahePHj6NFixbw9fWV7NuioiLs27dPzHtD3f+VlV0ul4s/AufPn8fu3btNmonMaSj7vyrlL8uWzv3qlN9Wz39jhYWF+Pvvv+Hn5yc2cdrieV8e4/IDsOlzv6yyZS+r3s/7anUjYdU2c+ZM+v333ykpKYn++usvevvtt0kmk9GuXbsoJyeHpk+fTvHx8ZSUlER79+6lsLAwCggIMBkqoEmTJrR79246duwY9enTx2x38Q4dOlBCQgIlJCRQaGhovXeVr6jsBllZWeTk5ETLli0z+f6FCxdozpw5lJiYSElJSbR9+3Z64IEH6MEHH7T6spenbI+3hQsXkkajoc2bN9PJkydp5MiRZoeKaIj7vyzjshcXF9OQIUOoSZMmdPz4cclwCYWFhURke/vfuPy2fu6bY663p62e/9OnT6e4uDi6dOkSHTx4kCIjI8nV1ZUuX75MRLZ/3ldUfls/9ysquzWe9xwE1rHnn3+eAgMDSaVSkZeXF/Xt21cMgu7evUv9+vUjLy8vUiqV1KxZM4qOjqarV69KlpGfn0///ve/ycPDgxwdHSkyMtIkTXp6Oo0aNYpcXV3J1dWVRo0aRRkZGZYqplkVld1gxYoV5OjoaDL+ExHR1atXqVevXuTh4UEqlYpatmxJU6ZMMRlTyRrLXp6yP4R6vZ5mzZpFvr6+pFarqVevXnTy5EnJdxrq/i/LuOxJSUkEwOzf3r17icj29r9x+W393DfHXBBoq+e/Ydw/pVJJ/v7+9NRTT9Hp06fF+bZ+3ldUfls/9ysquzWe9wIRUfXqDhljjDHGWEPHzwQyxhhjjNkhDgIZY4wxxuwQB4GMMcYYY3aIg0DGGGOMMTvEQSBjjDHGmB3iIJAxxhhjzA5xEMgYY4wxZoc4CGSMMcYYs0McBDLGGGOM2SEOAhljjDHG7BAHgYwxxqps2rRpGDFiBLKzs+s7K4yx+8RBIGOMsSrT6/XgV84zZhs4CGSMsVrQu3dvTJ06tb6zAaBmeenduzcEQYAgCDh+/Hi56ZYsWYJNmzbBzc1NnDZ27Fjxuz///HPNMs0YszgOAhljDcLy5cvh6uoKrVYrTsvNzYVSqcSjjz4qSbt//34IgoB//vnH0tm0uNoMPidMmICUlBSEhISYzIuPj4dcLseAAQNM5i1ZsgQpKSm1kgfGmOVwEMgYaxAiIiKQm5uLI0eOiNP2798PX19fJCYm4u7du+L0uLg4+Pv7Izg4uD6y2mA5OTnB19cXCoXCZN6qVaswefJkHDhwAFevXpXM02g08PX1tVQ2GWO1hINAxliD0KZNG/j7+yMuLk6cFhcXh6FDh6Jly5aIj4+XTI+IiAAAxMTE4JFHHkGjRo3QuHFjREZG4uLFi2LaFStWICAgAHq9XrK+IUOGIDo6GgBARFi0aBFatGgBR0dHdOzYET/++GO5ea1K+t69e2PKlCl444034OHhAV9fX8yePVuSJicnB6NGjYKzszP8/PzwySefSGr+xo4di3379mHJkiVic+zly5cBlDy7V9GyqyMvLw///e9/MXHiRERGRmLNmjU1XhZjzHpwEMgYazB69+6NvXv3ip/37t2L3r17Izw8XJxeVFSEhIQEMQjMy8vDa6+9hsTERPz222+QyWR48sknxaDvmWeewe3btyXLzcjIwM6dOzFq1CgAwP/93/9h9erVWLZsGU6fPo1p06bhueeew759+8zms6rp165dC2dnZxw6dAiLFi3C+++/j9jYWHH+a6+9hj/++AO//PILYmNjsX//fhw7dkycv2TJEoSFhYnNuCkpKWjatGmVll0dmzZtQps2bdCmTRs899xzWL16NXcOYcwWEGOMNRBfffUVOTs7U3FxMWVnZ5NCoaCbN2/Sxo0bqUePHkREtG/fPgJAFy9eNLuMtLQ0AkAnT54Upw0ZMoSef/558fOKFSvI19eXtFot5ebmkoODA8XHx0uWM378eBo5cqT4OTw8nF599dVqpX/kkUckaR566CF68803iYgoOzublEol/fDDD+L8zMxMcnJyoldffdVkvcYqW7Y55pZj0KNHD/r000+JiKi4uJg8PT0pNjbWJB0A2rJlS7nrYIxZF64JZIw1GBEREcjLy0NiYiL279+P4OBgeHt7Izw8HImJicjLy0NcXByaNWuGFi1aAAAuXryIqKgotGjRAm5ubggKCgIAyXNto0aNwk8//YTCwkIAwPr16zFixAjI5XKcOXMGBQUFePzxx+Hi4iL+rVu3TtKsbFCd9B06dJB89vPzQ1paGgDg0qVLKC4uxsMPPyzO12g0aNOmTZW2VUXLro5z587h8OHDGDFiBABAoVDg2WefxapVq6q9LMaYdTF9+pcxxqxUq1at0KRJE+zduxcZGRkIDw8HAPj6+iIoKAh//PEH9u7diz59+ojfGTx4MJo2bYqvv/4a/v7+0Ov1CAkJQVFRkSSNXq/H9u3b8dBDD2H//v34+OOPAUBsNt6+fTsCAgIk+VGr1SZ5rE56pVIp+SwIgvh9Km1uFQRBkoaq2Axb0bKrY+XKldBqtZKyEBGUSiUyMjLg7u5e7WUyxqwDB4GMsQYlIiICcXFxyMjIwOuvvy5ODw8Px86dO3Hw4EGMGzcOAJCeno6///4bK1asEIeROXDggMkyHR0d8dRTT2H9+vW4cOECgoOD0aVLFwBAu3btoFarcfXqVTHorEh105enZcuWUCqVOHz4sPicX3Z2Ns6fPy9Zrkqlgk6nq/F6KqLVarFu3Tp89NFH6Nevn2Te008/jfXr1+Pf//53naybMVb3OAhkjDUoEREReOWVV1BcXCwJhsLDwzFx4kQUFBSInULc3d3RuHFjfPXVV/Dz88PVq1fx1ltvmV3uqFGjMHjwYJw+fRrPPfecON3V1RUzZszAtGnToNfr8cgjjyA7Oxvx8fFwcXERexDXNH15XF1dER0djddffx0eHh7w9vbGrFmzIJPJJLWDzZs3x6FDh3D58mW4uLjAw8OjytuyMtu2bUNGRgbGjx8PjUYjmTds2DCsXLmSg0DGGjB+JpAx1qBEREQgPz8frVq1go+Pjzg9PDwcOTk5aNmypVhzJpPJsHHjRhw9ehQhISGYNm0aFi9ebHa5ffr0gYeHB86dO4eoqCjJvA8++ADvvfceFixYgLZt26J///7YunWr+HxhWdVNX56PP/4YYWFhiIyMxGOPPYaePXuibdu2cHBwENPMmDEDcrkc7dq1g5eXl8kYfvdj5cqVeOyxx0wCQKCkJvD48eOS3sqMsYZFoKo+YMIYY6xe5eXlISAgAB999BHGjx9fq8vu3bs3OnXqhE8//bTGyxAEAVu2bMETTzxRa/lijNUdrglkjDEr9eeff2LDhg24ePEijh07Jo5bOHTo0DpZ35dffgkXFxecPHmyWt97+eWX4eLiUid5YozVHa4JZIwxK/Xnn3/ihRdewLlz56BSqdClSxd8/PHHCA0NrfV1Xb9+Hfn5+QCAZs2aQaVSVfm7aWlpyM7OBlAyFI2zs3Ot548xVvs4CGSMMcYYs0PcHMwYY4wxZoc4CGSMMcYYs0McBDLGGGOM2SEOAhljjDHG7BAHgYwxxhhjdoiDQMYYY4wxO8RBIGOMMcaYHeIgkDHGGGPMDnEQyBhjjDFmhzgIZIwxxhizQxwEMsYYY4zZof8HF+XTCu84trwAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 11 + }, + { + "cell_type": "markdown", + "id": "618a1d04-c7ff-4f05-8679-bf94ea2f045a", + "metadata": {}, + "source": "Let's verify that the total flux is conserved, as expected:" + }, + { + "cell_type": "code", + "id": "26ba254f-69a6-43c0-bcfc-86e2707a2c55", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.984800Z", + "start_time": "2025-04-24T09:34:57.981797Z" + } + }, + "source": [ + "spectrum_wl.flux.sum()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$244192.09 \\; \\mathrm{DN}$" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 12 + }, + { + "cell_type": "code", + "id": "68ea6434-cf95-4503-8c68-bfc27209e3df", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:58.034538Z", + "start_time": "2025-04-24T09:34:58.031958Z" + } + }, + "source": [ + "arc_spectrum.flux.sum()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$244192.09 \\; \\mathrm{DN}$" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 13 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "#### 7.2 Access the WCS\n", + "\n", + "For cases where rebinning is not desired or when you want to use alternative rebinning methods,\n", + "you can directly access the pixel-to-wavelength transformation through the `gwcs` property. This\n", + "property returns a [GWCS (Generalized World Coordinate System)](https://gwcs.readthedocs.io/)\n", + "object that provides methods for converting between pixel and wavelength coordinates." + ], + "id": "d712829f7ae5d751" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:58.087021Z", + "start_time": "2025-04-24T09:34:58.083231Z" + } + }, + "cell_type": "code", + "source": "wc.gwcs", + "id": "1d74837351f08c69", + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\n", + " [1]: \n", + "Parameters:\n", + " offset_0 c0_1 ... c3_1 c4_1 \n", + " -------- ----------------- ... ----------------------- ---------------------\n", + " -1000.0 4393.545398623673 ... -1.1679853938796083e-08 9.589069917248972e-14)>" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 + }, + { + "cell_type": "code", + "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:58.142518Z", + "start_time": "2025-04-24T09:34:58.139303Z" + } + }, + "source": "wc.gwcs.pixel_to_world(0)", + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$3428.3395 \\; \\mathrm{\\mathring{A}}$" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 15 + }, + { + "cell_type": "markdown", + "id": "4d21e847-568b-4c90-a945-205b16332f5e", + "metadata": {}, + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/wavelength_calibration/wavecal1d_example_02.ipynb b/docs/wavelength_calibration/wavecal1d_example_02.ipynb new file mode 100644 index 00000000..9889a6cd --- /dev/null +++ b/docs/wavelength_calibration/wavecal1d_example_02.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", + "metadata": {}, + "source": [ + "# Tutorial 2: Interactive Workflow With Multiple Arc Spectra\n", + "\n", + "This tutorial demonstrates the interactive wavelength calibration workflow using multiple arc lamp\n", + "spectra observed with the [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)\n", + "[Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/), and the mixing of custom line\n", + "lists with the [PypeIt](https://github.com/pypeit/PypeIt) line lists.\n", + "\n", + "We'll follow the Tutorial 1 steps but use three arc lamp spectra (HgAr, Ne, and Xe) observed with\n", + "the OSIRIS R1000R grism configuration, which covers approximately 5100-10000 Å at moderate\n", + "resolution. In addition, we use a custom line list for the HgAr arc spectrum with line wavelength\n", + "values taken from the [official GTC Osiris line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt)." + ] + }, + { + "cell_type": "code", + "id": "7853ed1c-5b05-42a2-9413-4054769c6032", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:20.483405Z", + "start_time": "2025-04-24T09:52:19.294588Z" + } + }, + "source": [ + "import astropy.units as u\n", + "import numpy as np\n", + "\n", + "from astropy.table import Table\n", + "from astropy.nddata import StdDevUncertainty\n", + "from matplotlib.pyplot import subplots, rc\n", + "\n", + "from specreduce.compat import Spectrum\n", + "from specreduce.wavecal1d import WavelengthCalibration1D\n", + "\n", + "rc('figure', figsize=(6.3, 2))" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", + "metadata": {}, + "source": [ + "## 1. Initialize the Wavelength Calibration Class\n", + "\n", + "First, we load the data for the three arc lamps (HgAr, Ne, Xe) from the example FITS table\n", + "`osiris_arcs.fits`. We create a list of `specutils.Spectrum` objects, one for each lamp.\n", + "\n", + "Next, we prepare the corresponding line lists. For the HgAr lamp, we define a custom list as a\n", + "NumPy array containing known air wavelengths specific to this GTC/OSIRIS setup, derived from the\n", + "[official GTC line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt).\n", + "For the Neon (Ne) and Xenon (Xe) lamps, we simply provide their standard identifiers (`'NeI'`,\n", + "`'XeI'`) within lists. `WavelengthCalibration1D` will use\n", + "`specreduce.calibration_data.load_pypeit_calibration_lines` internally to fetch these standard\n", + "lists.\n", + "\n", + "Finally, we instantiate the `WavelengthCalibration1D` class:\n", + "- `ref_pixel=1000`: Sets the reference pixel for the polynomial fit.\n", + "- `degree=4`: Specifies a 4th-degree polynomial for the pixel-to-wavelength model.\n", + "- `arc_spectra=arc_spectra`: Provides the list of `Spectrum` objects.\n", + "- `line_lists=[hgar_lines, ['NeI'], ['XeI']]`: Provides the list of corresponding line data\n", + "(matching the size of `arc_spectra`). Note how we mix the custom array and lists of standard names.\n", + "- `line_list_bounds=(5100, 10000)`: Filters the line lists to include only lines within this approximate wavelength range (in Angstroms).\n", + "- `unit=u.angstrom`: Explicitly defines the wavelength unit.\n", + "- `wave_air=True`: Inform the class that the provided line lists (both custom and standard PypeIt lists for these lamps) contain **air** wavelengths. The class will handle conversions appropriately if needed for internal consistency or specific outputs, but the primary fitting coordinate system will be based on these air wavelengths." + ] + }, + { + "cell_type": "code", + "id": "a78d101a-e1af-4c5e-8264-d3eb99d31e15", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:20.527027Z", + "start_time": "2025-04-24T09:52:20.516251Z" + } + }, + "source": [ + "lamps = 'HgAr', 'Ne', 'Xe'\n", + "\n", + "tb = Table.read('osiris_arcs.fits')\n", + "arc_spectra = [Spectrum(tb[f'{l}_flux'].value.astype('d')*u.DN, \n", + " uncertainty=StdDevUncertainty(tb[f'{l}_err'].value.astype('d'))) \n", + " for l in lamps]" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "427b44e7-67f4-4121-bc6f-7275f708ee6a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:21.050386Z", + "start_time": "2025-04-24T09:52:20.564790Z" + } + }, + "source": [ + "hgar = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106,\n", + " 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", + "\n", + "wc = WavelengthCalibration1D(ref_pixel=1000,\n", + " degree=4,\n", + " arc_spectra=arc_spectra,\n", + " line_lists=[hgar, ['NeI'], ['XeI']],\n", + " line_list_bounds=(5100, 9900),\n", + " unit=u.angstrom,\n", + " wave_air=True)\n", + "\n", + "wc.plot_fit(figsize=(6.3, 6), plot_values=False);" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:21.807706Z", + "start_time": "2025-04-24T09:52:21.060075Z" + } + }, + "cell_type": "code", + "source": [ + "wc.find_lines(fwhm=4, noise_factor=15)\n", + "wc.plot_fit(figsize=(6.3, 6), value_fontsize=5);" + ], + "id": "2c02abdf62aee58c", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "806d1730-5325-4537-825e-da686ffcda21", + "metadata": {}, + "source": "## 2. Fit a Solution Manually" + }, + { + "cell_type": "code", + "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:21.828538Z", + "start_time": "2025-04-24T09:52:21.819638Z" + } + }, + "source": [ + "wc.fit_lines(pixels=[175, 797, 1499, 1579, 1620],\n", + " wavelengths=[5461, 6931, 8822, 9048, 9165],\n", + " match_obs=True, match_cat=True)" + ], + "outputs": [], + "execution_count": 5 + }, + { + "cell_type": "code", + "id": "0fbe56d7-beb8-40e4-98e9-a17a00ba4342", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:22.166514Z", + "start_time": "2025-04-24T09:52:21.867629Z" + } + }, + "source": "wc.plot_fit(figsize=(6.3, 6), plot_values=False, obs_to_wav=True);", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "524b217f-b2cd-49b9-a011-eb3a11fa14ee", + "metadata": {}, + "source": "## 3. Evaluate the Fit" + }, + { + "cell_type": "code", + "id": "343801bc-65fa-41c9-929b-72565cdee31d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:22.379476Z", + "start_time": "2025-04-24T09:52:22.176146Z" + } + }, + "source": "wc.plot_residuals(space='wavelength');", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "The previous fit looks good overall, with two clear outliers while the remaining lines are well-fitted. Let's refine the fit by calling `refine_fit` with a small `match_distance` parameter to deliberately exclude these outliers, then remove them completely.\n", + "id": "a2173e9fbdf680cd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:22.496738Z", + "start_time": "2025-04-24T09:52:22.390880Z" + } + }, + "cell_type": "code", + "source": [ + "wc.refine_fit(max_match_distance=0.5)\n", + "wc.remove_ummatched_lines()\n", + "wc.plot_residuals(space='wavelength');" + ], + "id": "6c85a0d25186986e", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "id": "983fe041-41e9-47a7-a179-fead64e171ba", + "metadata": {}, + "source": "## 4. Rebin a Spectrum to Wavelength Space" + }, + { + "cell_type": "code", + "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:52:22.630036Z", + "start_time": "2025-04-24T09:52:22.506456Z" + } + }, + "source": [ + "spectrum_wl = wc.resample(arc_spectra[0])\n", + "\n", + "fig, ax = subplots(constrained_layout=True)\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", + "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", + "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", + "ax.set_title(\"Arc Spectrum Resampled to Linear Wavelength Grid\")\n", + "ax.autoscale(enable=True, axis='x', tight=True)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 9 + }, + { + "cell_type": "markdown", + "id": "4d21e847-568b-4c90-a945-205b16332f5e", + "metadata": {}, + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/wavelength_calibration/wavecal1d_example_03.ipynb b/docs/wavelength_calibration/wavecal1d_example_03.ipynb new file mode 100644 index 00000000..a7efe304 --- /dev/null +++ b/docs/wavelength_calibration/wavecal1d_example_03.ipynb @@ -0,0 +1,398 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", + "metadata": {}, + "source": [ + "# Tutorial 3: Non-Interactive Workflow\n", + "\n", + "This notebook focuses on a **non-interactive workflow** for 1D wavelength calibration that is\n", + "particularly useful for **automated data reduction pipelines** where the instrument configuration\n", + "(spectrograph, grating/grism, approximate wavelength range) is well-characterized beforehand.\n", + "\n", + "Instead of requiring manual identification of initial line pairs (as shown in Tutorials 1 and 2\n", + "using `WavelengthCalibration1D.fit_lines`), this workflow uses the\n", + "`WavelengthCalibration1D.fit_global` method. This method uses a global optimization algorithm to\n", + "determine the wavelength solution. It achieves this by automatically finding the best polynomial\n", + "coefficients that minimize the overall distance between detected arc lamp lines and a provided\n", + "catalog of theoretical line wavelengths.\n", + "\n", + "During the optimization, the algorithm uses a KD-tree data structure to efficiently find\n", + "the nearest catalog lines to each detected line. This approach speeds up the distance\n", + "calculations needed in each iteration of the optimization, making it practical to\n", + "search through large parameter spaces with many detected and catalog lines.\n", + "\n", + "A key requirement for `fit_global` is providing reasonable initial **bounds** for the wavelength\n", + "and dispersion (Å/pixel) at a chosen reference pixel. These bounds are derived from **prior\n", + "knowledge** of the instrument setup and guide the optimization search.\n", + "\n", + "Like Tutorial 2, this example uses three arc lamp spectra (HgAr, Ne, Xe) obtained with the R1000R\n", + "grism of the [Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/) at the\n", + "[Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/).\n" + ] + }, + { + "cell_type": "code", + "id": "7853ed1c-5b05-42a2-9413-4054769c6032", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:53.842644Z", + "start_time": "2025-04-24T10:19:52.651102Z" + } + }, + "source": [ + "import astropy.units as u\n", + "import numpy as np\n", + "\n", + "from astropy.table import Table\n", + "from astropy.nddata import StdDevUncertainty\n", + "from matplotlib.pyplot import setp, subplots, close, rc\n", + "\n", + "from specreduce.compat import Spectrum\n", + "from specreduce.wavecal1d import WavelengthCalibration1D\n", + "\n", + "rc('figure', figsize=(6.3, 2))" + ], + "outputs": [], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 1. Initialize the Wavelength Calibration Class", + "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:54.380911Z", + "start_time": "2025-04-24T10:19:53.876680Z" + } + }, + "cell_type": "code", + "source": [ + "lamps = 'HgAr', 'Ne', 'Xe'\n", + "hgar_lines = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106,\n", + " 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", + "\n", + "tb = Table.read('osiris_arcs.fits')\n", + "arc_spectra = [Spectrum(flux=tb[f'{l}_flux'].value.astype('d')*u.DN,\n", + " uncertainty=StdDevUncertainty(tb[f'{l}_err'].value.astype('d')))\n", + " for l in lamps]\n", + "\n", + "wc = WavelengthCalibration1D(ref_pixel=1000,\n", + " degree=4,\n", + " arc_spectra=arc_spectra,\n", + " line_lists=[hgar_lines, ['NeI'], ['XeI']],\n", + " line_list_bounds=(5100, 9900),\n", + " unit=u.angstrom,\n", + " wave_air=True)\n", + "\n", + "wc.plot_fit(figsize=(6.3, 6), plot_values=False);" + ], + "id": "49a5d5d6-af0f-4bb1-8a08-ce14cdad023f", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 2. Perform Automated Fit using `fit_global`\n", + "\n", + "With the `WavelengthCalibration1D` object initialized, we proceed to calculate the wavelength solution automatically.\n", + "\n", + "**Step 1: Find Lines**\n", + "We first execute `ws.find_lines()`. This step automatically detects emission lines in each of the input arc spectra (`arc_spectra`) and calculates their pixel centroids. These detected centroids are stored internally in `ws.observed_lines`. The `fwhm` (estimated line width in pixels) and `noise_factor` parameters help the algorithm distinguish real lines from noise; these may need tuning depending on the data quality.\n", + "\n", + "**Step 2: Global Fit**\n", + "Next, the core of the non-interactive workflow: `ws.fit_global()`. This method initiates the global optimization search.\n", + "- **No Manual Input:** Notice we do *not* provide any specific pixel-wavelength pairs.\n", + "- **Bounds are Key:** Instead, we *must* provide `wavelength_bounds` and `dispersion_bounds`. These constrain the optimizer's search space for the polynomial coefficients. They represent our *a priori* knowledge about the instrument setup.\n", + " - For this GTC/OSIRIS R1000R example, we know the approximate central wavelength is ~7430 Å and the dispersion near the center is ~2.62 Å/pixel. We provide bounds around these values: `wavelength_bounds=[7420, 7470]` Å and `dispersion_bounds=[2.5, 2.7]` Å/pixel.\n", + "- **Optimization Process:** The differential evolution algorithm searches for polynomial\n", + "coefficients within these constraints (and narrower limits for higher-order terms) that best map\n", + "the detected `observed_lines` to the `catalog_lines` by minimizing the sum of distances to the nearest catalog neighbors (using internal KDTrees for efficiency).\n", + "- **Refinement:** We set `refine_fit=True`. After the global optimization finds an initial solution, this triggers an automatic call to `ws.refine_fit()`. This internal step uses the initial solution to explicitly match observed and catalog lines within a tolerance (`match_distance_bound`) and then performs a standard least-squares fit using *only* these matched pairs. This typically results in a higher-precision final solution.\n", + "\n", + "**Step 3: Visualize Result**\n", + "We then call `ws.plot_fit()` to visualize the outcome. Because we provided multiple arc spectra, it generates pairs of plots for each frame (HgAr, Ne, Xe). The top plot shows the catalog lines, and the bottom plot shows the observed lines (and the spectrum itself) mapped onto the final derived wavelength scale (`obs_to_wav=True`). This allows a visual check of the alignment and fit quality across all input lamps." + ], + "id": "49076f6f-4695-4c58-9359-d1473cc54bd6" + }, + { + "cell_type": "code", + "id": "46937bc3-b120-46f6-870e-85f1da5907db", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:55.786544Z", + "start_time": "2025-04-24T10:19:54.391017Z" + } + }, + "source": [ + "wc.find_lines(fwhm=4, noise_factor=15)\n", + "\n", + "wc.fit_global(wavelength_bounds=[7420, 7470],\n", + " dispersion_bounds=[2.5, 2.7],\n", + " refine_fit=True)\n", + "\n", + "wc.plot_fit(figsize=(6.3, 6), plot_values=False, obs_to_wav=True);" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 3. Evaluate the Fit\n", + "\n", + "After obtaining the wavelength solution through the global optimization and refinement steps, it's important to assess the quality of the fit. In an automated data reduction pipeline, this evaluation should be done systematically by implementing quantitative quality checks based on:\n", + "\n", + "- RMS of residuals between fitted and catalog wavelengths\n", + "- Number and distribution of successfully matched lines\n", + "- Behavior of residuals across the wavelength range\n", + "- Expected accuracy for the specific instrument configuration\n", + "\n", + "These metrics help ensure the wavelength calibration meets the required precision for scientific analysis.\n", + "\n", + "Now, let's take a quick look at the fit results. As expected, the solution matches what we got in Tutorial 2 - we just arrived at it without having to manually identify any matching lines!\n" + ], + "id": "54bc4a147af94dd1" + }, + { + "cell_type": "code", + "id": "343801bc-65fa-41c9-929b-72565cdee31d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:55.889469Z", + "start_time": "2025-04-24T10:19:55.804835Z" + } + }, + "source": "wc.plot_residuals(space='wavelength');", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.033388Z", + "start_time": "2025-04-24T10:19:55.945594Z" + } + }, + "cell_type": "code", + "source": [ + "wc.refine_fit(max_match_distance=0.5)\n", + "wc.remove_ummatched_lines()\n", + "wc.plot_residuals(space='wavelength');" + ], + "id": "aa4420dc480aae9f", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 4. Apply the Solution", + "id": "b608003fb6c44d8d" + }, + { + "cell_type": "code", + "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:20:08.247555Z", + "start_time": "2025-04-24T10:20:08.116156Z" + } + }, + "source": [ + "spectrum_wl = wc.resample(arc_spectra[0])\n", + "\n", + "fig, ax = subplots(constrained_layout=True)\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", + "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", + "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", + "ax.set_title(\"HgAr Arc Spectrum Resampled to Linear Wavelength Grid\")\n", + "ax.grid(True, alpha=0.5)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 10 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "As a final check, let's make sure that the wavelength grid is indeed linear, and that the flux\n", + "was conserved as it should." + ], + "id": "21e11b25c0b14760" + }, + { + "cell_type": "code", + "id": "f15d4c4e-b721-4348-9f2b-078b3c348515", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.344842Z", + "start_time": "2025-04-24T10:19:56.342286Z" + } + }, + "source": [ + "np.diff(spectrum_wl.spectral_axis)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$[2.5969455,~2.5969455,~2.5969455,~\\dots,~2.5969455,~2.5969455,~2.5969455] \\; \\mathrm{\\mathring{A}}$" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 7 + }, + { + "cell_type": "code", + "id": "ae8c6cdb-2fa3-4916-9ba0-5dab3d19acdc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.411956Z", + "start_time": "2025-04-24T10:19:56.409172Z" + } + }, + "source": [ + "spectrum_wl.flux.sum()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$839275 \\; \\mathrm{DN}$" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "cell_type": "code", + "id": "960d0392-0a8c-4d37-ae94-940e84a1c600", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.497673Z", + "start_time": "2025-04-24T10:19:56.495039Z" + } + }, + "source": [ + "arc_spectra[0].flux.sum()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$839275 \\; \\mathrm{DN}$" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 9 + }, + { + "cell_type": "markdown", + "id": "4d21e847-568b-4c90-a945-205b16332f5e", + "metadata": {}, + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/wavelength_calibration/wavelength_calibration.rst b/docs/wavelength_calibration/wavelength_calibration.rst new file mode 100644 index 00000000..c242cec8 --- /dev/null +++ b/docs/wavelength_calibration/wavelength_calibration.rst @@ -0,0 +1,253 @@ +.. _wavelength_calibration: + +Wavelength Calibration +====================== + +In spectroscopy, the raw data from a detector typically records the intensity of light received +at different detector positions (pixels). However, for scientific analysis, we need to know the +physical wavelength (or frequency, or energy) corresponding to each pixel. **Wavelength +calibration** is the process of determining this relationship, creating a mapping function or +model that converts pixel coordinates to wavelength values. + +This is often achieved by observing a calibration source with well-known emission or absorption +lines at specific wavelengths (e.g., an arc lamp spectrum). By identifying the pixel positions of +these known spectral features, we can fit a mathematical model that describes the dispersion of +the spectrograph. + +The ``specreduce`` library provides the `~specreduce.wavecal1d.WavelengthCalibration1D` class +to facilitate this process for one-dimensional spectra. Tools for calibrating 2D spectra will +be introduced soon. + +1D Wavelength Calibration +------------------------- + +The `~specreduce.wavecal1d.WavelengthCalibration1D` class encapsulates the data and methods +needed to perform 1D wavelength calibration. The class supports multiple workflows with varying +levels of user interaction. It can be used: + +* manually, +* as part of an interactive pipeline, or +* as part of a fully automated pipeline. + +The typical workflow involves these steps: + +1. **Initialization**: Create an instance of the class, providing either an observed arc lamp + spectrum or pre-identified observed line positions, along with a catalog of known line + wavelengths. `~specreduce.wavecal1d.WavelengthCalibration1D` supports the use of multiple + arc spectra, and the initialization can also be done using a list of arc spectra (or a + list of line arrays identified from multiple arc spectra) and a list of catalogs for each arc + spectra. +2. **Line Identification (Optional)**: If arc spectra were provided, identify the pixel + locations of emission lines within them. +3. **Matching and Fitting**: Determine the correspondence between observed line pixels and + catalog wavelengths, and fit a model (a polynomial) to represent the + pixel-to-wavelength transformation. This can be done manually by providing matched pairs or + automatically using global optimization techniques. +4. **Inspection**: Evaluate the quality of the fit using residuals and diagnostic plots. +5. **Applying the Solution**: Use the fitted model (often accessed as a `~gwcs.wcs.WCS` object) to + calibrate science spectra or resample spectra onto a linear wavelength grid. + +Tutorials +--------- + +The following tutorials provide hands-on examples demonstrating the usage of the +`~specreduce.wavecal1d.WavelengthCalibration1D` class. These step-by-step guides cover +both basic and advanced functionality to help you get started with wavelength calibration. + +.. toctree:: + :maxdepth: 1 + + wavecal1d_example_01.ipynb + wavecal1d_example_02.ipynb + wavecal1d_example_03.ipynb + +Quickstart +---------- + +1. Initialization +***************** + +You instantiate the :class:`~specreduce.wavecal1d.WavelengthCalibration1D` by providing basic +information about your setup and data. A reference pixel (``ref_pixel``) is required, which serves +as the anchor point for the polynomial fit, and ``degree`` can be used to set the degree of the +polynomial for the fit. + +You must provide *either* a list of arc spectra (or a single arc spectrum) *or* a list of known +observed line positions: + +* **Using an Arc Spectrum**: Provide the arc spectrum as a `specutils.Spectrum1D` + object via the ``arc_spectra`` argument. You also need to provide a ``line_lists`` argument, + which can be a list of known catalog wavelengths or the name(s) of standard line lists + recognized by `specreduce` (e.g., ``"HeI"``). + + .. code-block:: python + + wc = WavelengthCalibration1D(ref_pix=512, + arc_spectra=arc_hei, + line_lists=["HeI"]) + +* **Using multiple Arc Spectra**: + + .. code-block:: python + + wc = WavelengthCalibration1D(ref_pix=512, + arc_spectra=[arc_he, arc_hg_ar], + line_lists=[["HeI"], ["HgI", "ArI"]]) + +* **Using Observed Line Positions**: If you have already identified the pixel centroids of + lines in your calibration spectrum, you can provide them directly via the ``obs_lines`` + argument (as a list of NumPy arrays). In this case, you *must* also provide the detector's pixel + boundaries using ``pix_bounds`` (a tuple like ``(min_pixel, max_pixel)``). You still need to + provide the ``line_lists`` containing the potential matching catalog wavelengths. + + .. code-block:: python + + # Assume observed line pixel centers were found previously + obs_he = np.array([105.3, 210.8, 455.1, 512.5, 680.2]) + + # Pixel range of the detector + pixel_bounds = (0, 1024) + + wc = WavelengthCalibration1D(ref_pix=512, + obs_lines=obs_he, + line_lists=["HeI"], + pix_bounds=pixel_bounds) + +* **Using Observed Line Positions From Multiple Arcs**: + + .. code-block:: python + + obs_he = np.array([105.3, 210.8, 455.1, 512.5, 680.2]) + obs_hg_ar = np.array([234.2, 534.1, 768.2, 879.6]) + pixel_bounds = (0, 1024) + + wc = WavelengthCalibration1D(ref_pix=512, + obs_lines=[obs_he, obs_hg_ar], + line_lists=[["HeI"], ["HgI", "ArI"]], + pix_bounds=pixel_bounds) + + +2. Finding Observed Lines +************************* + +If you initialized the class with ``arc_spectra``, you need to detect the lines in it. Use the +:meth:`~specreduce.wavecal1d.WavelengthCalibration1D.find_lines` method: + +.. code-block:: python + + # Find lines with an estimated FWHM and noise factor + wc.find_lines(fwhm=3.5, noise_factor=5) + + # Access the found lines (pixel positions) + print(wc.observed_lines) + +This populates the `~specreduce.wavecal1d.WavelengthCalibration1D.observed_lines` attribute. + +3. Matching and Fitting the Solution +************************************ + +The core of the process is fitting the model that maps pixels to wavelengths. + +* **Global Fitting for Automated Pipelines**: If you have + `~specreduce.wavecal1d.WavelengthCalibration1D.observed_lines` (either found automatically or + provided initially) and + `~specreduce.wavecal1d.WavelengthCalibration1D.catalog_lines` (from ``line_lists``), but don't + know the exact pixel-wavelength pairs, you can use + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_global`. This method uses a global + optimization algorithm to find the best-fit polynomial parameters by + minimizing the distance between predicted line wavelengths and the nearest catalog lines. You + need to provide estimated bounds for the wavelength and dispersion at the ``ref_pixel``. + + .. code-block:: python + + # Estimate wavelength and dispersion around the reference pixel + # (e.g., Wavelength around 7500 AA, Dispersion ~2 AA/pix) + wavelength_bounds = (7450, 7550) + dispersion_bounds = (1.8, 2.2) + + wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=30, refine_fit=True) + + Setting ``refine_fit=True`` automatically runs a least-squares refinement after the global + fit finds an initial solution and matches lines. + +* **Fitting Known Pairs for an Interactive Workflow**: If you have already established explicit + pairs of observed pixel centers and their corresponding known wavelengths, you can use + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_lines` to perform a direct + least-squares fit. + + .. code-block:: python + + # Assume these are matched pairs + pixels = np.array([105.3, 512.5, 780.1]) + wavelengths = np.array([6965.43, 7503.87, 7723.76]) + + wc.fit_lines(pixels=pixels, wavelengths=wavelengths, refine_fit=True) + + When ``refine_fit=True`` is set, the method automatically identifies matching pairs between + observed and catalog lines, then performs a least-squares refinement using **all matching lines**. + This goes beyond the subset of lines provided to :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_lines`, + resulting in a more complete wavelength calibration. + +After fitting (either way), the pixel-to-wavelength +(`~specreduce.wavecal1d.WavelengthCalibration1D.pix_to_wav`) and wavelength-to-pixel +(`~specreduce.wavecal1d.WavelengthCalibration1D.wav_to_pix`) model transforms are calculated. + +4. Inspecting the Fit +********************* + +Several tools help assess the quality of the wavelength solution: + +* **RMS Error**: Calculate the root-mean-square error of the fit in wavelength or pixel units + using :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.rms`. + + .. code-block:: python + + rms_wave = wc.rms(space='wavelength') + rms_pix = wc.rms(space='pixel') + print(f"Fit RMS (wavelength): {rms_wave}") + print(f"Fit RMS (pixel): {rms_pix}") + +* **Plotting**: Visualize the fit and residuals: + + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_fit`: Shows the observed line + positions mapped to the wavelength axis, overlaid with the catalog lines and the fitted + solution. Also shows the fit residuals (observed - fitted wavelength) vs. pixel. + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_residuals`: Plots residuals vs. + pixel or vs. wavelength. + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_observed_lines`: Plots the + identified observed line positions (in pixels or mapped to wavelengths). Can optionally + overlay the arc spectrum. + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_catalog_lines`: Plots the catalog + line positions (in wavelengths or mapped to pixels). + +5. Using the Solution +********************* + +Once satisfied with the fit, you can use the wavelength solution: + +* **Convert Coordinates**: Use :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.pix_to_wav` and + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.wav_to_pix` to convert between pixel and + wavelength coordinates. + + .. code-block:: python + + pixels = np.array([100, 500, 900]) + wavelengths = wc.pix_to_wav(pixels) + print(wavelengths) + +* **Get WCS Object**: Access the `~gwcs.wcs.WCS` object representing the solution via the + :attr:`~specreduce.wavecal1d.WavelengthCalibration1D.gwcs` attribute. This is particularly + useful for attaching the calibration to a :class:`~specutils.Spectrum1D` object. + +* **Rebin Spectrum**: Resample a spectrum onto a new wavelength grid using + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.resample`. The rebinning is + flux-conserving, meaning the total flux in the output spectrum matches the total flux + in the input spectrum. + + .. code-block:: python + + # Resample the original arc spectrum onto a linear grid of 1000 points + resampled_arc = ws.resample(arc_spectrum, nbins=1000) + + # The resampled spectrum now has a linear wavelength axis + print(resampled_arc.spectral_axis) diff --git a/pyproject.toml b/pyproject.toml index 52565e49..315c06cc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ test = [ "pytest-astropy", "photutils>=1.0", + "matplotlib>=3.7", "tox", ] docs = [ @@ -27,6 +28,8 @@ docs = [ "matplotlib>=3.7", "photutils>=1.0", "synphot", + "nbsphinx", + "ipykernel" ] all = [ "matplotlib>=3.7", diff --git a/specreduce/__init__.py b/specreduce/__init__.py index ad1f3f06..d703b86b 100644 --- a/specreduce/__init__.py +++ b/specreduce/__init__.py @@ -3,7 +3,6 @@ from specreduce.core import * # noqa from specreduce.wavelength_calibration import * # noqa - try: from .version import version as __version__ except ImportError: diff --git a/specreduce/compat.py b/specreduce/compat.py index 8d9b42fd..274098d6 100644 --- a/specreduce/compat.py +++ b/specreduce/compat.py @@ -1,7 +1,7 @@ import specutils from astropy.utils import minversion -__all__ = [] +__all__ = ["Spectrum"] SPECUTILS_LT_2 = not minversion(specutils, "2.0.dev") diff --git a/specreduce/tests/test_tilt_correction.py b/specreduce/tests/test_tilt_correction.py new file mode 100644 index 00000000..66e20066 --- /dev/null +++ b/specreduce/tests/test_tilt_correction.py @@ -0,0 +1,117 @@ +import numpy as np +import pytest +from astropy.modeling import models +from astropy.nddata import StdDevUncertainty +from astropy.wcs import WCS + +from specreduce.tilt_correction import TiltCorrection, diff_poly2d_x +from specreduce.utils.synth_data import make_2d_arc_image + + +# Arc frame creation code taken from Tim Pickering's example notebook +@pytest.fixture +def mk_arc_frames(): + + blue_channel_header = { + "CTYPE1": "AWAV-GRA", # Grating dispersion function with air wavelengths + "CUNIT1": "Angstrom", # Dispersion units + "CRPIX1": 1344 // 2, # Reference pixel [pix] + "CRVAL1": 5410, # Reference value [Angstrom] + "CDELT1": 1.19 * 2, # Linear dispersion [Angstrom/pix] + "PV1_0": 5.0e5, # Grating density [1/m] + "PV1_1": 1, # Diffraction order + "PV1_2": 8.05, # Incident angle [deg] + "CTYPE2": "PIXEL", # Spatial detector coordinates + "CUNIT2": "pix", # Spatial units + "CRPIX2": 1, # Reference pixel + "CRVAL2": 0, # Reference value + "CDELT2": 1, # Spatial units per pixel + } + blue_channel_wcs = WCS(header=blue_channel_header) + + tilt_mod = models.Legendre1D(degree=2, c0=25, c1=0, c2=50) + + arcs = [] + for ll in (["HeI", "NeI", "XeI"], ["ArI"]): + arc = make_2d_arc_image( + nx=512, + ny=128, + linelists=ll, + wcs=blue_channel_wcs, + line_fwhm=3, + tilt_func=tilt_mod, + amplitude_scale=1e-2, + ) + arc.wcs = None + arc.uncertainty = StdDevUncertainty(np.full_like(arc.data, 5)) + arcs.append(arc) + return arcs + + +def test_diff_poly2d_x_valid_derivative(): + model = models.Polynomial2D(degree=2, c0_0=1, c1_0=2, c2_0=3, c0_1=4, c1_1=5, c0_2=6) + derivative = diff_poly2d_x(model) + assert derivative.degree == 1 + assert derivative.c0_0 == 2 + assert derivative.c1_0 == 6 + assert derivative.c0_1 == 5 + + +def test_diff_poly2d_x_zero_x_coefficients(): + model = models.Polynomial2D(degree=2, c0_0=1, c0_1=2, c0_2=3) + derivative = diff_poly2d_x(model) + assert derivative.degree == 1 + assert derivative.c0_0 == 0 # All x coefficients are zero, so derivative is zero + assert derivative.c0_1 == 0 + + +def test_init_default_params(mk_arc_frames): + tilt_correction = TiltCorrection(ref_pixel=(128, 64), arc_frames=mk_arc_frames) + assert tilt_correction.ref_pixel == (128, 64) + assert tilt_correction.disp_axis == 1 + assert tilt_correction.mask_treatment == "apply" + assert len(tilt_correction.arc_frames) == 2 + + +def test_find_lines(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + np.testing.assert_array_equal(tc.cd_samples, np.array([14, 28, 43, 57, 71, 85, 100, 114])) + + +def test_fit(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + + +def test_plot_fit_quality(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + tc.plot_fit_quality() + + +def test_plot_wavelength_contours(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + tc.plot_wavelength_contours() + + +def test_rectify(mk_arc_frames): + arcs = mk_arc_frames + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=arcs, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + tc.rectify(arcs[0]) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py new file mode 100644 index 00000000..5a5e9014 --- /dev/null +++ b/specreduce/tests/test_wavecal1d.py @@ -0,0 +1,388 @@ +# specreduce/tests/test_wavecal1d.py +import astropy.units as u +import numpy as np +import pytest +from astropy.modeling import models +from astropy.modeling.polynomial import Polynomial1D +from astropy.nddata import StdDevUncertainty +from gwcs import wcs +from numpy import array +from matplotlib import pyplot as plt +from matplotlib.figure import Figure +from specreduce.wavecal1d import WavelengthCalibration1D, _diff_poly1d +from specreduce.compat import Spectrum + + +ref_pixel = 5 +pix_bounds = (0, 10) +p2w = models.Shift(ref_pixel) | models.Polynomial1D(degree=3, c0=1, c1=2, c2=3) + + +@pytest.fixture +def mk_lines(): + obs_lines = array([1, 2, 5, 8, 10]) + cat_lines = p2w(array([1, 3, 5, 7, 8, 10])) + return obs_lines, cat_lines + + +@pytest.fixture +def mk_matched_lines(): + obs_lines = array([1, 2, 5, 8, 10]) + cat_lines = p2w(obs_lines) + return obs_lines, cat_lines + + +@pytest.fixture +def mk_wc(mk_lines): + obs_lines, cat_lines = mk_lines + return WavelengthCalibration1D( + ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10) + ) + + +@pytest.fixture +def mk_good_wc_with_transform(mk_lines): + obs_lines, cat_lines = mk_lines + wc = WavelengthCalibration1D( + ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10) + ) + wc._p2w = p2w + wc._calculate_p2w_inverse() + wc._calculate_p2w_derivative() + return wc + + +@pytest.fixture +def mk_arc(): + return Spectrum( + flux=np.ones(10) * u.DN, + spectral_axis=np.arange(10) * u.angstrom, + uncertainty=StdDevUncertainty(np.ones(10)), + ) + + +def test_diff_poly1d(): + p = _diff_poly1d(Polynomial1D(3, c0=1.0, c1=2.0, c2=3.0, c3=4.0)) + np.testing.assert_array_equal(p.parameters, [2.0, 6.0, 12.0]) + + +def test_init(mk_arc, mk_lines): + arc = mk_arc + obs_lines, cat_lines = mk_lines + WavelengthCalibration1D(ref_pixel, line_lists=cat_lines, arc_spectra=arc) + WavelengthCalibration1D( + ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10) + ) + + with pytest.raises(ValueError, match="Only one of arc_spectra or obs_lines can be provided."): + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, obs_lines=obs_lines) + + arc = Spectrum(flux=np.array([[1, 2, 3, 4, 5]]) * u.DN, + spectral_axis=np.array([1, 2, 3, 4, 5]) * u.angstrom) + with pytest.raises(ValueError, match="The arc spectrum must be one dimensional."): + WavelengthCalibration1D(ref_pixel, arc_spectra=arc) + + with pytest.raises(ValueError, match="Must give pixel bounds when"): + WavelengthCalibration1D(ref_pixel, obs_lines=obs_lines) + + +def test_init_line_list(mk_arc): + arc = mk_arc + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists=["ArI"]) + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists="ArI") + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists=[array([0.1])]) + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists=array([0.1])) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"], ["ArI"]]) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", ["ArI"]]) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", "ArI"]) + WavelengthCalibration1D( + ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1]), array([0.1])] + ) + WavelengthCalibration1D( + ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ["ArI"]] + ) + with pytest.raises(ValueError, match="The number of line lists"): + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"]]) + + +def test_find_lines(mocker, mk_arc): + wc = WavelengthCalibration1D(ref_pixel, arc_spectra=mk_arc) + mock_find_arc_lines = mocker.patch("specreduce.wavecal1d.find_arc_lines") + mock_find_arc_lines.return_value = {"centroid": np.array([5.0]) * u.angstrom} + wc.find_lines(fwhm=2.0, noise_factor=1.5) + assert wc._obs_lines is not None + assert len(wc._obs_lines) == 1 + + wc = WavelengthCalibration1D(ref_pixel) + with pytest.raises(ValueError, match="Must provide arc spectra to find lines."): + wc.find_lines(fwhm=2.0, noise_factor=1.5) + + +def test_fit_lines(mk_matched_lines): + lo, lc = mk_matched_lines + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) + wc.fit_lines(pixels=lo, wavelengths=lc) + assert wc._p2w is not None + assert wc._p2w[1].degree == wc.degree + + wc = WavelengthCalibration1D(ref_pixel, obs_lines=lo, line_lists=lc, pix_bounds=pix_bounds) + wc.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) + assert wc._p2w is not None + assert wc._p2w[1].degree == wc.degree + + wc = WavelengthCalibration1D(ref_pixel, degree=5, pix_bounds=pix_bounds) + wc.fit_lines(pixels=lo[:3], wavelengths=lc[:3]) + + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) + with pytest.raises(ValueError, match="Cannot fit without catalog"): + wc.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) + with pytest.raises(ValueError, match="Cannot fit without observed"): + wc.fit_lines(pixels=lo, wavelengths=lc, match_cat=False, match_obs=True) + + +def test_observed_lines(mk_lines): + wc = WavelengthCalibration1D(ref_pixel) + assert wc.observed_lines is None + obs_lines, cat_lines = mk_lines + wc = WavelengthCalibration1D(ref_pixel, obs_lines=obs_lines, pix_bounds=pix_bounds) + assert len(wc.observed_lines) == 1 + np.testing.assert_allclose(wc.observed_lines[0].data, obs_lines) + + wc.observed_lines = obs_lines + assert len(wc.observed_lines) == 1 + np.testing.assert_allclose(wc.observed_lines[0].data, obs_lines) + + wc.observed_lines = wc.observed_lines + assert len(wc.observed_lines) == 1 + np.testing.assert_allclose(wc.observed_lines[0].data, obs_lines) + + +def test_catalog_lines(mk_lines): + wc = WavelengthCalibration1D(ref_pixel) + assert wc.catalog_lines is None + obs_lines, cat_lines = mk_lines + wc = WavelengthCalibration1D( + ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=pix_bounds + ) + assert len(wc.catalog_lines) == 1 + np.testing.assert_allclose(wc.catalog_lines[0].data, cat_lines) + + wc.catalog_lines = cat_lines + assert len(wc.catalog_lines) == 1 + np.testing.assert_allclose(wc.catalog_lines[0].data, cat_lines) + + wc.catalog_lines = wc.catalog_lines + assert len(wc.catalog_lines) == 1 + np.testing.assert_allclose(wc.catalog_lines[0].data, cat_lines) + + +def test_fit_lines_raises_error_for_mismatched_sizes(): + pixels = array([2, 4, 6]) + wavelengths = p2w(array([2, 4, 5, 6])) + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) + with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): + wc.fit_lines(pixels=pixels, wavelengths=wavelengths) + + +def test_fit_lines_raises_error_for_insufficient_lines(): + pixels = [5] + wavelengths = p2w(pixels) + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) + with pytest.raises(ValueError, match="Need at least two lines for a fit"): + wc.fit_lines(pixels=pixels, wavelengths=wavelengths) + + +def test_fit_lines_raises_error_for_missing_pixel_bounds(): + pixels = [2, 4, 6, 8] + wavelengths = p2w(pixels) + wc = WavelengthCalibration1D(ref_pixel) + with pytest.raises(ValueError, match="Cannot fit without pixel bounds set."): + wc.fit_lines(pixels=pixels, wavelengths=wavelengths) + + +def test_fit_global(): + lines_obs = array([2, 4, 5, 6, 8]) + lines_cat = array([500, 550, 600, 650, 700, 750, 800]) + wavelength_bounds = (649, 651) + dispersion_bounds = (49, 51) + wc = WavelengthCalibration1D( + 5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat + ) + wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=10) + np.testing.assert_allclose(wc._fit.x, [650.0, 50.0, 0.0, 0.0], atol=1e-4) + assert wc._fit is not None + assert wc._fit.success + assert wc._p2w is not None + + wc = WavelengthCalibration1D( + 5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat + ) + wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=10, refine_fit=False) + + +def test_resample(mk_arc, mk_wc, mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + spectrum = mk_arc + resampled = wc.resample(spectrum, nbins=10) + assert resampled is not None + assert len(resampled.flux) == 10 + assert resampled.flux.unit == u.DN + np.testing.assert_almost_equal( + spectrum.flux.value.sum(), resampled.flux.value.sum(), decimal=10 + ) + + wc = mk_wc + with pytest.raises(ValueError, match="Wavelength solution not yet"): + wc.resample(mk_arc) + + wc = mk_good_wc_with_transform + with pytest.raises(ValueError, match="Number of bins must be positive"): + wc.resample(mk_arc, nbins=-5) + + +def test_pix_to_wav(mk_good_wc_with_transform): + pix_values = np.array([1, 2, 3, 4, 5]) + wc = mk_good_wc_with_transform + wavelengths = wc.pix_to_wav(pix_values) + np.testing.assert_array_equal(wavelengths, p2w(pix_values)) + + pix_values = np.ma.masked_array([1, 2, 3], mask=[0, 1, 0]) + wavelengths = wc.pix_to_wav(pix_values) + np.testing.assert_array_equal(wavelengths.data, p2w(pix_values)) + np.testing.assert_array_equal(wavelengths.mask, np.array([0, 1, 0])) + + +def test_wav_to_pix(mk_wc): + wav_values = np.array([500, 1000, 1500]) + wc = mk_wc + wc._w2p = lambda x: x / 10 # Mock wavelength-to-pixel conversion + pixel_values = wc.wav_to_pix(wav_values) + np.testing.assert_array_equal(pixel_values, np.array([50, 100, 150])) + + wav_values = np.ma.masked_array([500, 1000, 1500], mask=[0, 1, 0]) + pixel_values = wc.wav_to_pix(wav_values) + np.testing.assert_array_equal(pixel_values.data, np.array([50, 100, 150])) + np.testing.assert_array_equal(pixel_values.mask, np.array([0, 1, 0])) + + +def test_wcs_creates_valid_gwcs_object(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wcs_obj = wc.gwcs + assert wcs_obj is not None + assert isinstance(wcs_obj, wcs.WCS) + assert wcs_obj.output_frame.unit[0] == u.angstrom + + +def test_rms(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + assert np.isclose(wc.rms(space="wavelength"), 0) # Perfect match, so RMS should be zero + assert np.isclose(wc.rms(space="pixel"), 0) # Perfect match, so RMS should be zero + with pytest.raises(ValueError, match="Space must be either 'pixel' or 'wavelength'"): + wc.rms(space="wavelenght") + + +def test_remove_unmatched_lines(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wc.match_lines() + wc.remove_ummatched_lines() + assert wc.catalog_lines[0].size == wc.observed_lines[0].size + + +def test_plot_lines_with_valid_input(): + wc = WavelengthCalibration1D(ref_pixel) + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc._cat_lines = wc._obs_lines + fig = wc._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig = wc._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, ax = plt.subplots(1, 1) + fig = wc._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, axs = plt.subplots(1, 2) + fig = wc._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig = wc._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + +def test_plot_lines_raises_for_missing_transform(mk_wc): + wc = mk_wc + with pytest.raises(ValueError, match="Cannot map between pixels and"): + wc._plot_lines(kind="observed", map_x=True) + + +def test_plot_lines_calls_transform_correctly(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wc._plot_lines(kind="observed", map_x=True) + wc._plot_lines(kind="catalog", map_x=True) + + +def test_plot_catalog_lines(mk_wc): + wc = mk_wc + wc._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] + fig = wc.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, ax = plt.subplots(1, 1) + fig = wc.plot_catalog_lines(frames=0, axes=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, axs = plt.subplots(1, 2) + fig = wc.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + +def test_plot_observed_lines(mk_good_wc_with_transform, mk_arc): + wc = mk_good_wc_with_transform + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = wc.plot_observed_lines( + frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True + ) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + assert len(fig.axes) == 1 + + +def test_plot_fit(mk_arc, mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wc.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) + assert isinstance(fig, Figure) + assert len(fig.axes) == 2 + assert fig.axes[0].has_data() + assert fig.axes[1].has_data() + wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) + + +def test_plot_residuals(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + + fig = wc.plot_residuals(space="pixel", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig = wc.plot_residuals(space="wavelength", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, ax = plt.subplots(1, 1) + wc.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) + + with pytest.raises(ValueError, match="Invalid space specified"): + wc.plot_residuals(space="wavelenght", figsize=(8, 4)) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py new file mode 100644 index 00000000..7f7d6320 --- /dev/null +++ b/specreduce/tilt_correction.py @@ -0,0 +1,543 @@ +import warnings +from typing import Iterable, Sequence, Literal + +import matplotlib.pyplot as plt +import numpy as np +from astropy.modeling import models, Model, fitting +from astropy.nddata import StdDevUncertainty, NDData +from numpy import ndarray, repeat, tile +from scipy.optimize import minimize +from scipy.spatial import KDTree +from specutils import Spectrum1D + +from specreduce.compat import Spectrum +from specreduce.core import _ImageParser +from specreduce.line_matching import find_arc_lines + +__all__ = ["TiltCorrection"] + + +def diff_poly2d_x(model: models.Polynomial2D) -> models.Polynomial2D: + """Compute the partial derivative of a 2D polynomial model with respect to x. + + Generates a new 2D polynomial model representing the derivative of the input + model in the x-direction. The coefficients of the resulting model are calculated + by multiplying the coefficients from the input model by their respective x + index and reducing the order in the x-dimension. + + Parameters + ---------- + model + An `astropy.modeling.models.Polynomial2D` model. + + Returns + ------- + models.Polynomial2D + A new 2D polynomial model representing the derivative of the input model + with respect to x. The degree of the resulting model will be decreased + by 1 in the x-dimension. + """ + coeffs = {} + for n in model.param_names: + ix, iy = int(n[1]), int(n[3]) + if ix > 0: + coeffs[f"c{ix-1}_{iy}"] = ix * getattr(model, n).value + return models.Polynomial2D(model.degree - 1, **coeffs) + + +class TiltCorrection: + def __init__( + self, + ref_pixel: tuple[float, float], + arc_frames: Sequence[NDData], + n_cd_samples: int = 10, + cd_sample_lims: tuple[float, float] | None = None, + cd_samples: Sequence[float] | None = None, + disp_axis: int = 1, + mask_treatment: Literal[ + "apply", + "ignore", + "propagate", + "zero_fill", + "nan_fill", + "apply_mask_only", + "apply_nan_only", + ] = "apply", + ): + """A class for 2D spectrum rectification. + + Parameters + ---------- + ref_pixel + A reference pixel position specified as a tuple of floating-point coordinates + (dispersion axis, cross-dispersion axis). + + arc_frames + A sequence of arc frames as `~astropy.nddata.NDData` instances. + + n_cd_samples + Number of cross-dispersion (CD) samples to generate. + + cd_sample_lims + Tuple specifying the limits for calculating cross-dispersion sampling. + + cd_samples + A list of cross-dispersion locations to use. Overrides ``n_cd_samples`` if provided. + + disp_axis + The index of the image's dispersion axis. + + mask_treatment + Specifies how to handle masked or non-finite values in the input image. + The accepted values are: + + - ``apply``: The image remains unchanged, and any existing mask is combined with a mask + derived from non-finite values. + - ``ignore``: The image remains unchanged, and any existing mask is dropped. + - ``propagate``: The image remains unchanged, and any masked or non-finite pixel + causes the mask to extend across the entire cross-dispersion axis. + - ``zero_fill``: Pixels that are either masked or non-finite are replaced with 0.0, + and the mask is dropped. + - ``nan_fill``: Pixels that are either masked or non-finite are replaced with nan, + and the mask is dropped. + - ``apply_mask_only``: The image and mask are left unmodified. + - ``apply_nan_only``: The image is left unmodified, the old mask is dropped, + and a new mask is created based on non-finite values. + """ + self.ref_pixel = ref_pixel + self.disp_axis = disp_axis + self.mask_treatment = mask_treatment + + # IMPLEMENTATION NOTES: the code assumes that the image-parsing routines ensure that the + # cross-dispersion axis is aligned with the y-axis (1st array dimension) and the + # dispersion axis with the x-axis (2nd array dimension). However, this should not be + # visible to the end-user. The rectified spectra are returned with the original axis + # alignment given by the ``disp_axis`` argument. Also, I've decided to use `x` and `y` + # naming instead of `col` and `row` because this leads to (slightly) more readable code. + # The methods that are visible to the end-user use `disp` and `cdisp` naming. -HP + + # An ugly hack that should be changed after the refactoring of image parsing. + ip = _ImageParser() + self.arc_frames = [] + for f in arc_frames: + im = ip._parse_image(f, disp_axis=disp_axis, mask_treatment=mask_treatment) + self.arc_frames.append(NDData(im.flux, uncertainty=im.uncertainty, mask=im.mask)) + + self.arc_frames = arc_frames + self.nframes = len(arc_frames) + self._ny, self._nx = self.arc_frames[0].data.shape + + self._lines_ref: Sequence[ndarray] | None = None + self._samples_rec_x: Sequence[ndarray] | None = None + self._samples_rec_y: Sequence[ndarray] | None = None + self._samples_det_x: Sequence[ndarray] | None = None + self._samples_det_y: Sequence[ndarray] | None = None + self._arc_spectra: Sequence[Spectrum1D] | None = None + self._trees: Sequence[KDTree] | None = None + + self._shift = models.Shift(-self.ref_pixel[0]) & models.Shift(-self.ref_pixel[1]) + + # The rectified space -> detector space transform + self._r2d: Model | None = None + + # Calculate the cross-dispersion axis sample positions + slims = cd_sample_lims if cd_sample_lims is not None else (0, self._ny) + if cd_samples is not None: + self.cd_samples = np.array(cd_samples) + else: + self.cd_samples = slims[0] + np.round( + np.arange(1, n_cd_samples + 1) * (slims[1] - slims[0]) / (n_cd_samples + 1) + ).astype(int) + self.ncd = self.cd_samples.size + + def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0) -> None: + """Find arc lines from the provided arc frames for all cross-dispersion samples. + + This method locates spectral arc lines from the provided arc frames, calculates + their centroids, and organizes them into reference lists and sample arrays + for further analysis. + + Parameters + ---------- + fwhm + Full width at half maximum of the spectral line to be detected, used + by the line-finding algorithm. + noise_factor + A multiplier for noise thresholding in the line-finding process. + """ + self._arc_spectra = [] + self._samples_rec_x = [] + self._lines_ref = [] + samples_x = [] + samples_y = [] + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + + for i, d in enumerate(self.arc_frames): + self._arc_spectra.append([]) + samples_x.append([]) + samples_y.append([]) + + # Find the line centroids for the reference row + spectrum = Spectrum( + d.data[self.ref_pixel[1]] * d.unit, + uncertainty=d[self.ref_pixel[1]].uncertainty.represent_as(StdDevUncertainty), + ) + lines = find_arc_lines(spectrum, fwhm, noise_factor=noise_factor) + self._lines_ref.append(lines["centroid"].value) + + # Find the line centroids for the sample rows + for s in self.cd_samples: + spectrum = Spectrum( + d.data[s] * d.unit, + uncertainty=d[s].uncertainty.represent_as(StdDevUncertainty), + ) + lines = find_arc_lines(spectrum, fwhm, noise_factor=noise_factor) + samples_x[i].append(lines["centroid"].value) + samples_y[i].append(np.full(len(lines), s)) + self._arc_spectra[i].append(spectrum) + + self._samples_det_x = [np.concatenate(lpx) for lpx in samples_x] + self._samples_det_y = [np.concatenate(lpy) for lpy in samples_y] + self._samples_rec_y = [repeat(self.cd_samples, lref.size) for lref in self._lines_ref] + self._samples_rec_x = [tile(lref, self.cd_samples.size) for lref in self._lines_ref] + + self._trees = [ + KDTree(np.vstack([lx, ly]).T) + for lx, ly in zip(self._samples_det_x, self._samples_det_y) + ] + + def fit(self, degree: int = 3, method: str = "Powell", max_distance: float = 10) -> None: + """Fit a 2D polynomial transformation from rectified space to detector space. + + The transformation is calculated by minimizing the sum of distances between transformed + samples and their corresponding detector-space targets. The minimization is performed in + two stages: an initial minimization of a kd-tree based sum of line-line distances using + `scipy.optimize.minimize` and a refinement using least-squares optimization of matched + lines. + + Parameters + ---------- + degree + The degree of the final 2D polynomial model. + method + The optimization method used during the initial fitting stage. + max_distance + The maximum allowable distance to constrain the minimization.. + """ + model = self._shift | models.Polynomial2D(3) + + coeffs = np.zeros(10) + coeffs[0] = self.ref_pixel[0] + coeffs[1] = 1 + transformed_points = [tile(a, (2, 1)).T.astype("d") for a in self._samples_rec_y] + + def minfun(x): + coeffs[4:] = x + total_distance = 0.0 + for i, t in enumerate(self._trees): + transformed_points[i][:, 0] = model.evaluate( + self._samples_rec_x[i], + self._samples_rec_y[i], + -self.ref_pixel[0], + -self.ref_pixel[1], + *coeffs, + ) + total_distance += np.clip(t.query(transformed_points[i])[0], 0, max_distance).sum() + return total_distance + + self._initial_optimization_result = res = minimize(minfun, np.zeros(6), method=method) + coeffs[4:] = res.x + self._r2d = self._shift | models.Polynomial2D( + model[-1].degree, **{model[-1].param_names[i]: coeffs[i] for i in range(coeffs.size)} + ) + + # Calculate the final fit using least-squares optimization between matched lines + self.refine_fit(degree) + self._calculate_derivative() + + def refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0) -> None: + """Refine the rectified space -> detector space transformation model parameters. + + Refines the polynomial fit model parameters for matching features with a specified + degree and match distance bound. The refinement includes matching lines, + updating a polynomial model, and optimizing the parameters using a least squares + fitter. The derivative is recalculated after the optimization. + + Parameters + ---------- + degree + Degree of the polynomial used in the Polynomial2D model. + match_distance_bound + Maximum acceptable distance between features to be considered a match. + """ + rx, ry, ox = self.match_lines(match_distance_bound) + model = self._shift | models.Polynomial2D( + degree, **{n: getattr(self._r2d[-1], n).value for n in self._r2d[-1].param_names} + ) + model.offset_0.fixed = True + model.offset_1.fixed = True + for i in range(degree + 1): + model.fixed[f"c{i}_0_2"] = True + + fitter = fitting.LMLSQFitter() + self._r2d = fitter(model, rx, ry, ox) + self._refined_optimization_result = fitter.fit_info + self._calculate_derivative() + + def match_lines( + self, max_distance: float = 5, concatenate: bool = True + ) -> tuple[ndarray, ndarray, ndarray] | tuple[list[ndarray], list[ndarray], list[ndarray]]: + """Match the reference arc line locations with the detector-space targets. + + Parameters + ---------- + max_distance + Specifies the maximum allowed distance for matching lines. Matches beyond this distance + will be ignored. + + Returns + ------- + tuple of numpy.ndarray + A tuple containing three concatenated numpy arrays representing: + - x-coordinates of matched rectified-space lines. + - y-coordinates of matched rectified-space lines. + - x-coordinates of matched detector-space lines. + """ + matched_det_x = [] + matched_rec_x = [] + matched_rec_y = [] + for iframe, tree in enumerate(self._trees): + x_mapped = self._r2d(self._samples_rec_x[iframe], self._samples_rec_y[iframe]) + l, ix = tree.query( + np.array([x_mapped, self._samples_rec_y[iframe]]).T, + distance_upper_bound=max_distance, + ) + m = np.isfinite(l) + matched_det_x.append(tree.data[ix[m], 0]) + matched_rec_x.append(self._samples_rec_x[iframe][m]) + matched_rec_y.append(self._samples_rec_y[iframe][m]) + + if concatenate: + return ( + np.concatenate(matched_rec_x), + np.concatenate(matched_rec_y), + np.concatenate(matched_det_x), + ) + else: + return matched_rec_x, matched_rec_y, matched_det_x + + def _calculate_derivative(self): + """Calculate the derivative for the rectified space -> detector space transformation.""" + if self._r2d is not None: + self._r2d_dxdx = self._shift | diff_poly2d_x(self._r2d[-1]) + + def rec_to_det(self, disp: ndarray, cdisp: ndarray) -> tuple[ndarray, ndarray]: + """Transform coordinates from the rectified space to detector space. + + Parameters + ---------- + disp : ndarray + The dispersion-axis coordinates to be transformed. + cdisp : ndarray + The cross-dispersion coordinates, returned as is. + + Returns + ------- + tuple of (ndarray, ndarray) + A tuple containing the transformed dispersion-axis coordinates as the first element + and the original cross-dispersion-axis coordinates as the second element.. + """ + return self._r2d(disp, cdisp), cdisp + + def rectify( + self, + flux: NDData, + nbins: int | None = None, + bounds: tuple[float, float] | None = None, + bin_edges: Iterable[float] | None = None, + ): + """Resample a 2D spectrum from the detector space to a rectified space. + + Resample a 2D spectrum from the detector space to a rectified space where the wavelength + is constant along the cross-dispersion axis. The grid edges are based on the specified + number of bins, bounds, or bin edges. The resampling is exact and conserves flux (as + long as the rectified space covers the whole detector space.) + + Parameters + ---------- + flux + 2D spectrum as an NDData instance. The dispersion and cross-dispersion axis alignment + should be the same as in the arc frames. + nbins + Number of bins in the rectified space. If None, the number of bins will be set + to the number of columns in the `flux` input image. + bound + Tuple specifying the start and end coordinates for the rectified space along the + x-axis. If None, the bounds default to (0, number of columns in ``flux``). + bin_edges + Explicitly provided edges of the bins in the rectified space. If None, bin + edges are automatically calculated as a uniform grid based on ``nbins`` and + ``bounds``. + + Returns + ------- + rectified_flux : ndarray + NDData instance containing the flux values rectified into the uniform grid + defined by ``nbins``, ``bounds``, or ``bin_edges``. + """ + + # TODO: In the future, we want to make sure that we don't copy the data unless absolutely + # necessary. + ip = _ImageParser() + im = ip._parse_image(flux, disp_axis=self.disp_axis, mask_treatment=self.mask_treatment) + flux = im.flux.value + + ny, nx = flux.data.shape + ypix = np.arange(ny) + nbins = nx if nbins is None else nbins + l1, l2 = bounds if bounds is not None else (0, nx) + + bin_edges_rec = bin_edges if bin_edges is not None else np.linspace(l1, l2, num=nbins + 1) + bin_edges_det = np.clip(self._r2d(*np.meshgrid(bin_edges_rec, ypix)), 0, nx - 1e-12) + bin_edge_ix = np.floor(bin_edges_det).astype(int) + bin_edge_w = bin_edges_det - bin_edge_ix + + rectified_flux = np.zeros((ny, nbins)) + weights = np.zeros((ny, nx)) + + # Calculate the derivative of the rectified space -> detector space transformation with + # respect to the detector coordinate (dx_rec / dx_det). This is needed for flux + # conservation, as it represents how the pixel width changes. + dtdx = self._r2d_dxdx(*np.meshgrid(np.arange(nx), np.arange(ny))) + + # Calculate a normalization factor 'n' for flux conservation. This factor accounts for the + # change in pixel size due to the distortion, and ensures that the total flux in each row + # is conserved after rectification + n = flux.sum(1) / (dtdx * flux).sum(1) + + ixs = np.tile(np.arange(flux.shape[1]), (flux.shape[0], 1)) + ys = np.arange(flux.shape[0]) + + for i in range(nbins): + # Get the detector pixel indices (left and right edges) for the current rectified bin. + i1, i2 = bin_edge_ix[:, i : i + 2].T + + # Create a mask 'm' where the left and right detector pixel edges are the same. + # This means the entire rectified bin falls within a single detector pixel. + m = i1 == i2 + + # For rows where the rectified bin falls within a single detector pixel, + # the rectified flux is the detector flux in that pixel, scaled by the width of the + # rectified bin in detector coordinates and the derivative dtdx. + if m.any(): + rectified_flux[:, i] = ( + (bin_edges_det[:, i + 1] - bin_edges_det[:, i]) * flux[ys, i1] * dtdx[ys, i1] + ) + + # For rows where the rectified bin spans multiple detector pixels, calculate the + # rectified flux as a weighted sum of the detector flux, multiplied by dtdx, + # within the span [imin, imax]. + if not m.all(): + imin, imax = i1.min(), i2.max() + 1 + ixc = ixs[:, imin:imax] + w = weights[:, imin:imax] + w[:] = 0.0 + w[(ixc > i1[:, None]) & (ixc < i2[:, None])] = 1 + w[ys, i1 - imin] = 1.0 - bin_edge_w[:, i] + w[ys, i2 - imin] = bin_edge_w[:, i + 1] + rectified_flux[~m, i] = (flux[~m, imin:imax] * dtdx[~m, imin:imax] * w[~m]).sum(1) + + # Apply the normalization factor to conserve flux + rectified_flux *= n[:, None] + if self.disp_axis == 0: + rectified_flux = rectified_flux.T + + return NDData(rectified_flux * im.unit) + + def plot_wavelength_contours( + self, + ndisp: int = 50, + ncdisp: int = 100, + disp_values: Sequence[float] | None = None, + ax: plt.Axes | None = None, + figsize: tuple[float, float] | None = None, + line_args: dict | None = None, + ): + """Plot wavelength contour lines in detector space. + + Parameters + ---------- + ndisp + The number of dispersion-axis lines. + ncdisp + The number of cross-dispersion axis points for each disp-axis line. + disp_values + A sequence specifying the dispersion-axis coordinates explicitly. If not + provided, it will be automatically calculated based on the arc frame + dimensions. + ax + The Matplotlib Axes on which to plot. If None, a new figure and Axes + are created. + figsize + Tuple specifying the size of the figure to create, applicable only if + `ax` is None.. + line_args + A dictionary of line properties (e.g., color, linewidth, linestyle). + These properties modify the default styling provided for grid lines. + If None, default styles are used. Default is None. + + Returns + ------- + figure : matplotlib.figure.Figure + The Matplotlib figure containing the plot. If an Axes instance is passed + to `ax`, the associated figure is returned. + """ + largs = {"c": "k", "lw": 0.5, "alpha": 0.5, "ls": "--"} + if line_args is not None: + largs.update(line_args) + + if ax is None: + fig, ax = plt.subplots(figsize=figsize) + else: + fig = ax.figure + + if disp_values is None: + disp_values = tile(np.linspace(0, self.arc_frames[0].data.shape[1], ndisp), (ncdisp, 1)) + else: + ndisp = len(disp_values) + rows = tile(np.linspace(0, self.arc_frames[0].data.shape[0], ncdisp)[:, None], (1, ndisp)) + + ax.plot(self._r2d(disp_values, rows), rows, **largs) + return fig + + def plot_fit_quality( + self, figsize=None, max_match_distance: float = 5, rlim: tuple[float, float] | None = None + ): + fig = plt.Figure(figsize=figsize, layout="constrained") + gs = plt.GridSpec(2, 2, width_ratios=(4, 1), height_ratios=(1, 3), figure=fig) + + ax1 = fig.add_subplot(gs[1, 0]) + ax2 = fig.add_subplot(gs[0, 0]) + ax3 = fig.add_subplot(gs[1, 1]) + + rxs, rys, dxs = self.match_lines(max_match_distance, concatenate=False) + for i, (rx, ry, dx) in enumerate(zip(rxs, rys, dxs)): + residuals = dx - self.rec_to_det(rx, ry)[0] + ax1.scatter(rx, ry, s=50 * abs(residuals), label=f"Arc {i+1}") + ax2.plot(rx, residuals, ".") + ax3.plot(residuals, ry, ".") + ax1.legend(loc="upper right") + ax2.set_xlim(ax1.get_xlim()) + ax3.set_ylim(ax1.get_ylim()) + plt.setp(ax2.get_xticklabels(), visible=False) + plt.setp(ax3.get_yticklabels(), visible=False) + plt.setp(ax1, xlabel="Dispersion axis [pix]", ylabel="Cross-dispersion axis [pix]") + ax2.set_ylabel("Residuals [pix]") + ax3.set_xlabel("Residuals [pix]") + + if rlim is not None: + ax2.set_ylim(rlim) + ax3.set_xlim(rlim) + + return fig diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py new file mode 100644 index 00000000..254b0a86 --- /dev/null +++ b/specreduce/wavecal1d.py @@ -0,0 +1,1080 @@ +import warnings +from typing import Sequence, Callable, Literal + +import astropy.units as u +import numpy as np +import gwcs +from astropy.modeling import models, Model, fitting +from astropy.nddata import VarianceUncertainty +from gwcs import coordinate_frames as cf +from numpy import ndarray +from numpy.ma.core import MaskedArray +from scipy import optimize +from scipy.interpolate import interp1d +from scipy.spatial import KDTree +from matplotlib.pyplot import Axes, Figure, setp, subplots + +from specreduce.calibration_data import load_pypeit_calibration_lines +from specreduce.line_matching import find_arc_lines +from specreduce.compat import Spectrum + +__all__ = ["WavelengthCalibration1D"] + + +def unclutter_text_boxes(labels): + to_remove = set() + for i in range(len(labels)): + for j in range(i + 1, len(labels)): + l1 = labels[i] + l2 = labels[j] + bbox1 = l1.get_window_extent() + bbox2 = l2.get_window_extent() + if bbox1.overlaps(bbox2): + if l1.zorder < l2.zorder: + to_remove.add(l1) + else: + to_remove.add(l2) + + for label in to_remove: + label.remove() + + +def _diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: + """Compute the derivative of a Polynomial1D model. + + Computes the derivative of a Polynomial1D model and returns a new Polynomial1D + model representing the derivative. The coefficients of the input model are + used to calculate the coefficients of the derivative model. For a Polynomial1D + of degree n, the derivative is a Polynomial1D of degree n-1. + + Parameters + ---------- + m + A Polynomial1D model for which the derivative is to be computed. + + Returns + ------- + A new Polynomial1D model representing the derivative of the input Polynomial1D model. + """ + coeffs = {f"c{i-1}": i * getattr(m, f"c{i}").value for i in range(1, m.degree + 1)} + return models.Polynomial1D(m.degree - 1, **coeffs) + + +class WavelengthCalibration1D: + def __init__( + self, + ref_pixel: float, + unit: u.Unit = u.angstrom, + degree: int = 3, + line_lists: Sequence | None = None, + arc_spectra: Spectrum | Sequence[Spectrum] | None = None, + obs_lines: ndarray | Sequence[ndarray] | None = None, + pix_bounds: tuple[int, int] | None = None, + line_list_bounds: tuple[float, float] = (0, np.inf), + wave_air: bool = False, + ) -> None: + """A class for wavelength calibration of one-dimensional spectral data. + + This class is designed to facilitate wavelength calibration of one-dimensional spectra, + with support for both direct input of line lists and observed spectra. It uses a polynomial + model for fitting the wavelength solution and offers features to incorporate catalog lines + and observed line positions. + + Parameters + ---------- + ref_pixel + The reference pixel in which the wavelength solution will be centered. + + unit + The unit of the wavelength calibration, by default ``astropy.units.Angstrom``. + + degree + The polynomial degree for the wavelength solution, by default 3. + + line_lists + Catalogs of spectral line wavelengths for wavelength calibration. Provide either an + array of line wavelengths or a list of catalog names. If `None`, no line lists are used. + + arc_spectra + Arc spectra provided as ``Spectrum`` objects for wavelength fitting, by default + None. This parameter and ``obs_lines`` cannot be provided simultaneously. + + obs_lines + Pixel positions of observed spectral lines for wavelength fitting, by default None. This + parameter and ``arc_spectra`` cannot be provided simultaneously. + + pix_bounds + Lower and upper pixel bounds for fitting, defined as a 2-tuple (min, max). If + ``obs_lines`` is provided, this parameter is mandatory. + + line_list_bounds + Wavelength bounds (inclusive) as a range (min, max) for filtering usable spectral + lines from the provided line lists, by default (0, np.inf). + + wave_air + Boolean indicating whether the input wavelengths correspond to air rather than vacuum; + by default `False`, meaning vacuum wavelengths. + """ + self.unit = unit + self._unit_str = unit.to_string("latex") + self.degree = degree + self.ref_pixel = ref_pixel + self.nframes = 0 + + self.arc_spectra: list[Spectrum] | None = None + self.bounds_pix: tuple[int, int] | None = pix_bounds + self.bounds_wav: tuple[float, float] | None = None + self._cat_lines: list[MaskedArray] | None = None + self._obs_lines: list[MaskedArray] | None = None + self._trees: Sequence[KDTree] | None = None + + self._fit: optimize.OptimizeResult | None = None + self._wcs: gwcs.wcs.WCS | None = None + + self._p2w: Model | None = None # pixel -> wavelength model + self._w2p: Callable | None = None # wavelength -> pixel model + self._p2w_dldx: Model | None = None # delta lambda / delta pixel + + # Read and store the observational data if given. The user can provide either a list of arc + # spectra as Spectrum1D objects, or a list of line pixel position arrays. Attempts to give + # both raises an error. + if arc_spectra is not None and obs_lines is not None: + raise ValueError("Only one of arc_spectra or obs_lines can be provided.") + + if arc_spectra is not None: + self.arc_spectra = [arc_spectra] if isinstance(arc_spectra, Spectrum) else arc_spectra + self.nframes = len(self.arc_spectra) + for s in self.arc_spectra: + if s.data.ndim > 1: + raise ValueError("The arc spectrum must be one dimensional.") + self.bounds_pix = (0, self.arc_spectra[0].shape[0]) + + elif obs_lines is not None: + self.observed_lines = obs_lines + self.nframes = len(self._obs_lines) + if self.bounds_pix is None: + raise ValueError("Must give pixel bounds when providing observed line positions.") + + # Read the line lists if given. The user can provide an array of line wavelength positions + # or a list of line list names (used by `load_pypeit_calibration_lines`) for each arc + # spectrum. + if line_lists is not None: + if not isinstance(line_lists, (tuple, list)): + line_lists = [line_lists] + + if len(line_lists) != self.nframes: + raise ValueError("The number of line lists must match the number of arc spectra.") + self._read_linelists(line_lists, line_list_bounds=line_list_bounds, wave_air=wave_air) + + def _init_model(self): + self._p2w = models.Shift(-self.ref_pixel) | models.Polynomial1D(self.degree) + + def _read_linelists( + self, + line_lists: Sequence, + line_list_bounds: tuple[float, float] = (0.0, np.inf), + wave_air: bool = False, + ): + """Read and processes line lists and organize them for further use. + + This function handles line lists provided in various forms, applies wavelength bounds + filtering, and processes the data for efficient spatial querying using KDTree structures. + It also accounts for whether the input wavelengths are in air or vacuum. + + Parameters + ---------- + line_lists + A collection of line lists that can either be arrays of wavelengths or ``pypeit`` + lamp names . + line_list_bounds + A tuple specifying the minimum and maximum wavelength bounds. Only wavelengths + within this range are retained. + wave_air + If True, convert the vacuum wavelengths used by ``pypeit`` to air wavelengths. + """ + + lines_wav = [] + for lst in line_lists: + if isinstance(lst, ndarray): + lines_wav.append(lst) + else: + lines = [] + if isinstance(lst, str): + lst = [lst] + for ll in lst: + lines.append( + load_pypeit_calibration_lines(ll, wave_air=wave_air)["wavelength"] + .to(self.unit) + .value + ) + lines_wav.append(np.ma.masked_array(np.sort(np.concatenate(lines)))) + for i, lst in enumerate(lines_wav): + lines_wav[i] = lst[(lst >= line_list_bounds[0]) & (lst <= line_list_bounds[1])] + + self.catalog_lines = lines_wav + self._trees = [KDTree(lst.data[:, None]) for lst in self._cat_lines] + + def find_lines(self, fwhm: float, noise_factor: float = 1.0) -> None: + """Find lines in the provided arc spectra. + + This method determines the spectral lines within each spectrum of the arc spectra + based on the provided initial guess for the line Full Width at Half Maximum (FWHM). + + Parameters + ---------- + fwhm + Initial guess for the FWHM for the spectral lines, used as a parameter in + the ``find_arc_lines`` function to locate and identify spectral arc lines. + noise_factor + The factor to multiply the uncertainty by to determine the noise threshold + in the `~specutils.fitting.find_lines_threshold` routine. + """ + if self.arc_spectra is None: + raise ValueError("Must provide arc spectra to find lines.") + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + line_lists = [ + find_arc_lines(sp, fwhm, noise_factor=noise_factor) for sp in self.arc_spectra + ] + self.observed_lines = [ + np.ma.masked_array(np.transpose([ll["centroid"].value, ll["amplitude"].value])) + for ll in line_lists + ] + + def fit_lines( + self, + pixels: Sequence, + wavelengths: Sequence, + match_obs: bool = False, + match_cat: bool = False, + refine_fit: bool = True, + refine_max_distance: float = 5.0, + ) -> None: + """Fit the wavelength solution model using provided line pairs. + + Fits the pixel-to-wavelength transformation model using explicitly + provided pairs of pixel coordinates and their corresponding wavelengths. + This method uses linear least squares fitting. + + Optionally, the provided pixel and wavelength values can be "snapped" + to the nearest values present in the internally stored observed line + list and catalog line list, respectively. This can correct for small + inaccuracies in the input pairs if the internal lists are populated. + + Parameters + ---------- + pixels + A sequence of pixel positions corresponding to known spectral lines. + wavelengths + A sequence of the same size as ``pixels``, containing the known + wavelengths corresponding to the given pixel positions. + match_obs + If True, snap the input ``pixels`` values to the nearest + pixel values found in `self._obs_lines` (if available). This helps + ensure the fit uses the precise centroids detected by `find_lines` + or provided initially. + match_cat + If True, snap the input ``wavelengths`` values to the + nearest wavelength values found in `self._cat_lines` (if available). + This ensures the fit uses the precise catalog wavelengths. + refine_fit + If True (default), automatically call the ``refine_fit`` method + immediately after the global optimization to improve the solution + using a least-squares fit on matched lines. + """ + pixels = np.asarray(pixels) + wavelengths = np.asarray(wavelengths) + if pixels.size != wavelengths.size: + raise ValueError("The sizes of pixel and wavelength arrays must match.") + nlines = pixels.size + if nlines < 2: + raise ValueError("Need at least two lines for a fit") + + if self.bounds_pix is None: + raise ValueError("Cannot fit without pixel bounds set.") + + if self._p2w is None: + self._init_model() + + # Match the input wavelengths to catalog lines. + if match_cat: + if self._cat_lines is None: + raise ValueError("Cannot fit without catalog lines set.") + tree = KDTree(np.concatenate([c.data for c in self._cat_lines])[:, None]) + ix = tree.query(wavelengths[:, None])[1] + wavelengths = tree.data[ix][:, 0] + + # Match the input pixel values to observed pixel values. + if match_obs: + if self._obs_lines is None: + raise ValueError("Cannot fit without observed lines set.") + tree = KDTree(np.concatenate([c.data[:, 0] for c in self._obs_lines])[:, None]) + ix = tree.query(pixels[:, None])[1] + pixels = tree.data[ix][:, 0] + + fitter = fitting.LinearLSQFitter() + m = self._p2w[1] + if m.degree > nlines: + for i in range(nlines, m.degree + 1): + m.fixed[f"c{i}"] = True + m = fitter(m, pixels - self.ref_pixel, wavelengths) + for i in range(m.degree + 1): + m.fixed[f"c{i}"] = False + self._p2w = self._p2w[0] | m + + can_match = self._cat_lines is not None and self._obs_lines is not None + if refine_fit and can_match: + self.refine_fit(refine_max_distance) + else: + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() + if can_match: + self.match_lines() + + def fit_global( + self, + wavelength_bounds: tuple[float, float], + dispersion_bounds: tuple[float, float], + popsize: int = 30, + max_distance: float = 100, + refine_fit: bool = True, + ) -> None: + """Calculate a wavelength solution using global optimization. + + Determines an initial functional relationship between pixel positions + and wavelengths using a global optimization algorithm (differential + evolution). This method does not require pre-matched pixel-wavelength + pairs. It works by finding model parameters that minimize the + distance between the predicted wavelengths of observed lines + (``self._obs_lines``) and their nearest theoretical counterparts + (``self._cat_lines`` accessed via KDTree). + + Optionally, this initial solution can be immediately refined using a + least-squares fit on automatically matched lines. + + Parameters + ---------- + wavelength_bounds + Bounds (min, max) for the wavelength value at the ``ref_pixel``. + Used as a constraint in the optimization. + dispersion_bounds + Bounds (min, max) for the dispersion (d_wavelength / d_pixel) + at the ``ref_pixel``. Used as a constraint in the optimization. + popsize + Population size for the ``scipy.optimize.differential_evolution`` + optimizer. Larger values increase the likelihood of finding the + global minimum but also increase computation time. + max_distance + Maximum distance (in wavelength units) allowed when associating + an observed line with a theoretical line during the optimization's + cost function evaluation. Points beyond this distance contribute + this maximum value to the cost, preventing outliers from having + excessive influence. + refine_fit + If True (default), automatically call the ``refine_fit`` method + immediately after the global optimization to improve the solution + using a least-squares fit on matched lines. + """ + + if self._p2w is None: + self._init_model() + model = self._p2w + + def minfun(x): + total_distance = 0.0 + for t, l in zip(self._trees, self._obs_lines): + transformed_lines = model.evaluate(l, -self.ref_pixel, *x)[:, None] + total_distance += np.clip(t.query(transformed_lines)[0], 0, max_distance).sum() + return total_distance + + # Define bounds for differential_evolution. + # Assumes first 3 coefficients correspond to wavelength, dispersion, and curvature. + bounds = np.concatenate( + [ + [wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3]], + np.zeros((model[1].degree - 2, 2)), + ] + ) + self._fit = fit = optimize.differential_evolution( + minfun, + bounds=bounds, + popsize=popsize, + ) + self._p2w = models.Shift(-self.ref_pixel) | models.Polynomial1D( + self.degree, **{f"c{i}": fit.x[i] for i in range(fit.x.size)} + ) + + # Update the model with the best-fit parameters found + if refine_fit: + self.refine_fit() + else: + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() + self.match_lines() + + def refine_fit(self, max_match_distance: float = 5.0, max_iter: int = 5) -> None: + """Refine the fit of the pixel-to-wavelength transformation. + + Refines the fit of a polynomial model to data by performing a fitting operation + using matched pixel and wavelength data points. The method uses a linear least + squares fitter to optimize the model parameters based on the match. + + Parameters + ---------- + + degree : int, optional + The degree of the polynomial model used for fitting. Higher values + allow for more complex polynomial models. Default is 4. + + max_match_distance : float, optional + Maximum allowable distance used to identify matched pixel and wavelength + data points. Points exceeding the bound will not be considered in the fit. + Default is 5.0. + """ + + model = self._p2w[1] + fitter = fitting.LinearLSQFitter() + rms = np.nan + for i in range(max_iter): + self.match_lines(max_match_distance) + matched_pix = np.ma.concatenate(self.observed_lines).compressed() + matched_wav = np.ma.concatenate(self._cat_lines).compressed() + rms_new = np.sqrt(((matched_wav - self.pix_to_wav(matched_pix)) ** 2).mean()) + if rms_new == rms: + break + else: + self._p2w = self._p2w[0].copy() | fitter( + model, matched_pix - self.ref_pixel, matched_wav + ) + rms = rms_new + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() + self.match_lines(max_match_distance) + + def _calculate_p2w_derivative(self): + """Calculate (d wavelength) / (d pixel) for the pixel-to-wavelength transformation.""" + if self._p2w is not None: + self._p2w_dldx = models.Shift(self._p2w.offset_0) | _diff_poly1d(self._p2w[1]) + + def _calculate_p2w_inverse(self) -> None: + """Compute the wavelength-to-pixel mapping from the pixel-to-wavelength transformation.""" + p = np.arange(self.bounds_pix[0] - 2, self.bounds_pix[1] + 2) + self._w2p = interp1d(self._p2w(p), p, bounds_error=False, fill_value=np.nan) + self.bounds_wav = self._p2w(self.bounds_pix) + + def resample( + self, + spectrum: Spectrum, + nbins: int | None = None, + wlbounds: tuple[float, float] | None = None, + bin_edges: Sequence[float] | None = None, + ) -> Spectrum: + """Bin the given pixel-space 1D spectrum to a wavelength space conserving the flux. + + This method bins a pixel-space spectrum to a wavelength space using the computed + pixel-to-wavelength and wavelength-to-pixel transformations and their derivatives with + respect to the spectral axis. The binning is exact and conserves the total flux. + + Parameters + ---------- + spectrum + A Spectrum1D instance containing the flux to be resampled over the wavelength space. + nbins + The number of bins for resampling. If not provided, it defaults to the size of the + input spectrum. + wlbounds + A tuple specifying the starting and ending wavelengths for resampling. If not + provided, the wavelength bounds are inferred from the object's methods and the + entire flux array is used. + bin_edges + Explicit bin edges in the wavelength space. If provided, ``nbins`` and ``wlbounds`` + are ignored. + + Returns + ------- + 1D spectrum binned to the specified wavelength bins. + """ + if self._p2w is None: + raise ValueError("Wavelength solution not yet computed.") + + if nbins is not None and nbins < 0: + raise ValueError("Number of bins must be positive.") + + flux = spectrum.flux.value + if spectrum.uncertainty is not None: + ucty = spectrum.uncertainty.represent_as(VarianceUncertainty).array + ucty_type = type(spectrum.uncertainty) + else: + ucty = np.zeros_like(flux) + ucty_type = VarianceUncertainty + npix = flux.size + nbins = npix if nbins is None else nbins + if wlbounds is None: + l1 = self._p2w(0) - self._p2w_dldx(0) + l2 = self._p2w(npix) + self._p2w_dldx(npix) + else: + l1, l2 = wlbounds + + bin_edges_wav = bin_edges if bin_edges is not None else np.linspace(l1, l2, num=nbins + 1) + bin_edges_pix = np.clip(self._w2p(bin_edges_wav) + 0.5, 0, npix - 1e-12) + bin_edge_ix = np.floor(bin_edges_pix).astype(int) + bin_edge_w = bin_edges_pix - bin_edge_ix + bin_centers_wav = 0.5 * (bin_edges_wav[:-1] + bin_edges_wav[1:]) + flux_wl = np.zeros(nbins) + ucty_wl = np.zeros(nbins) + weights = np.zeros(npix) + + dldx = self._p2w_dldx(np.arange(npix)) + n = flux.sum() / (dldx * flux).sum() + for i in range(nbins): + i1, i2 = bin_edge_ix[i : i + 2] + weights[:] = 0 + if i1 != i2: + weights[i1 + 1 : i2] = 1.0 + weights[i1] = 1 - bin_edge_w[i] + weights[i2] = bin_edge_w[i + 1] + flux_wl[i] = (weights[i1 : i2 + 1] * flux[i1 : i2 + 1] * dldx[i1 : i2 + 1]).sum() + ucty_wl[i] = (weights[i1 : i2 + 1] * ucty[i1 : i2 + 1] * dldx[i1 : i2 + 1]).sum() + else: + flux_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * flux[i1] * dldx[i1] + ucty_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * ucty[i1] * dldx[i1] + flux_wl = (flux_wl * n) * spectrum.flux.unit + ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(ucty_type) + return Spectrum(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) + + def pix_to_wav(self, pix: MaskedArray | ndarray | float) -> ndarray | float: + """Map pixel values into wavelength values. + + Parameters + ---------- + pix + Input pixel value(s) to be transformed into wavelength. + + Returns + ------- + Transformed wavelength value(s) corresponding to the input pixel value(s). + """ + if isinstance(pix, MaskedArray): + wav = self._p2w(pix.data) + return np.ma.masked_array(wav, mask=pix.mask) + else: + return self._p2w(pix) + + def wav_to_pix(self, wav: MaskedArray | ndarray | float) -> ndarray | float: + """Map wavelength values into pixel values. + + Parameters + ---------- + wav + The wavelength value(s) to be converted into pixel value(s). + + Returns + ------- + The corresponding pixel value(s) for the input wavelength(s). + """ + if isinstance(wav, MaskedArray): + pix = self._w2p(wav.data) + return np.ma.masked_array(pix, mask=wav.mask) + else: + return self._w2p(wav) + + @property + def observed_lines(self) -> list[MaskedArray]: + """Pixel positions of the observed lines as a list of masked arrays.""" + return [lines[:, 0] for lines in self._obs_lines] + + @observed_lines.setter + def observed_lines(self, line_lists: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): + if not isinstance(line_lists, Sequence): + line_lists = [line_lists] + + self._obs_lines = [] + for lst in line_lists: + lst = MaskedArray(lst, copy=True) + + if (lst.ndim > 2) or (lst.ndim == 2 and lst.shape[1] > 2): + raise ValueError( + "Observed line lists must be 1D with a shape [n] (centroids) or " + "2D with a shape [n, 2] (centroids and amplitudes)." + ) + + if lst.mask is np.False_: + lst.mask = np.zeros(lst.shape[0], dtype=bool) + + if lst.ndim == 1: + lst = np.tile(lst[:, None], [1, 2]) + lst[:, 1] = np.arange(lst.shape[0]) + + self._obs_lines.append(lst) + + @property + def catalog_lines(self) -> list[MaskedArray]: + """Catalog line wavelengths as a list of masked arrays.""" + return self._cat_lines + + @catalog_lines.setter + def catalog_lines(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): + if not isinstance(lines_wav, Sequence): + lines_wav = [lines_wav] + self._cat_lines = [] + for lst in lines_wav: + if isinstance(lst, MaskedArray) and lst.mask is not np.False_: + self._cat_lines.append(lst) + else: + self._cat_lines.append(np.ma.masked_array(lst, mask=np.zeros(lst.size, bool))) + + @property + def gwcs(self) -> gwcs.wcs.WCS: + """GWCS object defining the mapping between pixel and spectral coordinate frames.""" + pixel_frame = cf.CoordinateFrame( + 1, + "SPECTRAL", + (0,), + axes_names=[ + "x", + ], + unit=[u.pix], + ) + spectral_frame = cf.SpectralFrame( + axes_names=("wavelength",), + unit=[self.unit], + ) + pipeline = [(pixel_frame, self._p2w), (spectral_frame, None)] + self._wcs = gwcs.wcs.WCS(pipeline) + return self._wcs + + def match_lines(self, max_distance: float = 5) -> None: + """Match the observed lines to theoretical lines. + + Parameters + ---------- + max_distance + The maximum allowed distance between the query points and the KD-tree + data points for them to be considered a match. + """ + + for iframe, tree in enumerate(self._trees): + l, ix = tree.query( + self._p2w(self._obs_lines[iframe].data[:, 0])[:, None], + distance_upper_bound=max_distance, + ) + m = np.isfinite(l) + + # Check for observed lines that match to a same catalog line. + # Remove all but the nearest match. This isn't an optimal solution, + # we could also iterate the match by removing the currently matched + # lines, but this works for now. + uix, cnt = np.unique(ix[m], return_counts=True) + if any(n := cnt > 1): + for i, c in zip(uix[n], cnt[n]): + s = ix == i + r = np.zeros(c, dtype=bool) + r[np.argmin(l[s])] = True + m[s] = r + + self._cat_lines[iframe].mask[:] = True + self._cat_lines[iframe].mask[ix[m]] = False + self._obs_lines[iframe].mask[:, :] = ~m[:, None] + + def remove_ummatched_lines(self): + """Remove unmatched lines from observation and catalog line data.""" + self._obs_lines = [np.ma.masked_array(lst.compressed()) for lst in self._obs_lines] + self._cat_lines = [np.ma.masked_array(lst.compressed()) for lst in self._cat_lines] + + def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: + """Compute the RMS of the residuals between matched lines in the pixel or wavelength space. + + Parameters + ---------- + space + The space in which to calculate the RMS residual. If 'wavelength', + the calculation is performed in the wavelength space. If 'pixel', + it is performed in the pixel space. Default is 'wavelength'. + + Returns + ------- + float + """ + self.match_lines() + mpix = np.ma.concatenate(self.observed_lines).compressed() + mwav = np.ma.concatenate(self.catalog_lines).compressed() + if space == "wavelength": + return np.sqrt(((mwav - self.pix_to_wav(mpix)) ** 2).mean()) + elif space == "pixel": + return np.sqrt(((mpix - self.wav_to_pix(mwav)) ** 2).mean()) + else: + raise ValueError("Space must be either 'pixel' or 'wavelength'") + + def _plot_lines( + self, + kind: Literal["observed", "catalog"], + frames: int | Sequence[int] | None = None, + axs: Axes | Sequence[Axes] | None = None, + figsize: tuple[float, float] | None = None, + plot_values: bool | Sequence[bool] = True, + map_x: bool = False, + value_fontsize: int | str | None = "small", + ) -> Figure: + if frames is None: + frames = np.arange(self.nframes) + else: + frames = np.atleast_1d(frames) + + if axs is None: + fig, axs = subplots(frames.size, 1, figsize=figsize, constrained_layout=True) + elif isinstance(axs, Axes): + fig = axs.figure + axs = [axs] + else: + fig = axs[0].figure + axs = np.atleast_1d(axs) + + if isinstance(plot_values, bool): + plot_values = np.full(frames.size, plot_values, dtype=bool) + + if map_x and self._p2w is None: + raise ValueError("Cannot map between pixels and wavelengths without a fitted model.") + + if kind == "observed": + transform = self.pix_to_wav if map_x else lambda x: x + linelists = self._obs_lines + lc = "C0" + else: + transform = self.wav_to_pix if map_x else lambda x: x + linelists = self._cat_lines + lc = "C1" + + if linelists is not None: + for iax, (ax, frame) in enumerate(zip(axs, frames)): + lines = linelists[frame] + ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=":", color=lc) + ax.vlines(transform(lines[~lines.mask].data), 0, 1, color=lc) + if plot_values[iax]: + for i, l in enumerate(transform(lines.data)): + if np.isfinite(l): + ax.text( + l, + 0.25 + 0.25 * (i % 3), + f"{l:.0f}", + rotation=90, + ha="right", + va="center", + bbox=dict(alpha=0.8, fc="w", lw=0), + size=value_fontsize, + ) + + if (kind == "observed" and not map_x) or (kind == "catalog" and map_x): + xlabel = "Pixel" + else: + xlabel = f"Wavelength {self._unit_str}" + + if kind == "catalog": + axs[0].xaxis.set_label_position("top") + axs[0].xaxis.tick_top() + setp(axs[0], xlabel=xlabel) + for ax in axs[1:]: + ax.set_xticklabels([]) + else: + setp(axs[-1], xlabel=xlabel) + for ax in axs[:-1]: + ax.set_xticklabels([]) + xlims = np.array([ax.get_xlim() for ax in axs]) + setp(axs, xlim=(xlims[:, 0].min(), xlims[:, 1].max()), yticks=[]) + return fig + + def plot_catalog_lines( + self, + frames: int | Sequence[int] | None = None, + axes: Axes | Sequence[Axes] | None = None, + figsize: tuple[float, float] | None = None, + plot_values: bool = True, + map_to_pix: bool = False, + value_fontsize: int | str | None = "small", + ) -> Figure: + """Plot the catalog lines. + + Parameters + ---------- + frames + Specifies the frames to be plotted. If an integer, only one frame is plotted. + If a sequence, the specified frames are plotted. If None, default selection + or all frames are plotted. + + axes + The matplotlib axes where catalog data will be plotted. If provided, the function + will plot on these axes. If None, new axes will be created. + + figsize + Specifies the dimensions of the figure as (width, height). If None, the default + dimensions are used. + + plot_values + If True, the numerical values associated with the catalog data will be displayed + in the plot. If False, only the graphical representation of the lines will be shown. + + map_to_pix + Indicates whether the catalog data should be mapped to pixel coordinates + before plotting. If True, the data is converted to pixel coordinates. + + Returns + ------- + Figure + The matplotlib figure containing the plotted catalog lines. + + """ + return self._plot_lines( + "catalog", + frames=frames, + axs=axes, + figsize=figsize, + plot_values=plot_values, + map_x=map_to_pix, + value_fontsize=value_fontsize, + ) + + def plot_observed_lines( + self, + frames: int | Sequence[int] | None = None, + axes: Axes | Sequence[Axes] | None = None, + figsize: tuple[float, float] | None = None, + plot_labels: bool = True, + plot_spectra: bool = True, + map_to_wav: bool = False, + label_kwargs: dict | None = None, + ) -> Figure: + """Plot observed spectral lines for the given arc spectra. + + Parameters + ---------- + frames + Specifies the frame(s) for which the plot is to be generated. If None, all frames + are plotted. When an integer is provided, a single frame is used. For a sequence + of integers, multiple frames are plotted. + axes + Axes object(s) to plot the spectral lines on. If None, new axes are created. + figsize + Dimensions of the figure to be created, specified as a tuple (width, height). Ignored + if ``axes`` is provided. + plot_labels + If True, plots the numerical values of the observed lines at their respective + locations on the graph. Default is True. + plot_spectra + If True, includes the arc spectra on the plot for comparison. Default is True. + map_to_wav + Determines whether to map the x-axis values to wavelengths. Default is False. + label_kwargs + Specifies the keyword arguments for the line label text objects. + + Returns + ------- + Figure + The matplotlib figure containing the observed lines plot. + """ + + largs = dict(backgroundcolor="w", rotation=90, size="small") + if label_kwargs is not None: + largs.update(label_kwargs) + + if frames is None: + frames = np.arange(self.nframes) + else: + frames = np.atleast_1d(frames) + + if axes is None: + fig, axes = subplots(frames.size, 1, figsize=figsize, constrained_layout=True) + elif isinstance(axes, Axes): + fig = axes.figure + axes = [axes] + else: + fig = axes[0].figure + axes = np.atleast_1d(axes) + + transform = self.pix_to_wav if map_to_wav else lambda x: x + xlabel = f"Wavelength [{self._unit_str}]" if map_to_wav else "Pixel" + + ypad = 1.3 + + for iax, iframe in enumerate(frames): + ax = axes.flat[iax] + if plot_spectra and self.arc_spectra is not None: + spc = self.arc_spectra[iframe] + vmax = spc.flux.value.max() + ax.plot(transform(spc.spectral_axis.value), spc.flux.value / vmax) + else: + vmax = 1.0 + + labels = [] + for i in range(self._obs_lines[iframe].shape[0]): + c, a = self._obs_lines[iframe].data[i] + if self._obs_lines[iframe].mask[i, 0] is True: + ls = ":" + else: + ls = "-" + + ax.plot(transform([c, c]), [a / vmax + 0.02, 1.27], "0.75", ls=ls) + if plot_labels: + labels.append( + ax.text( + transform(c), + ypad, + f"{transform(c):.0f}", + ha="center", + va="bottom", + **largs, + ) + ) + labels[-1].zorder = a / vmax + + if plot_labels: + fig.canvas.draw() + unclutter_text_boxes(labels) + tr = ax.transData.inverted() + ymax = max( + [ + tr.transform_bbox(label.get_window_extent()).max[1] + for label in labels + if label.figure is not None + ] + ) + else: + ymax = ypad + + setp(ax, xlabel=xlabel, yticks=[], ylim=(-0.02, ymax + 0.02)) + ax.autoscale(True, "x", tight=True) + return fig + + def plot_fit( + self, + frames: Sequence[int] | int | None = None, + figsize: tuple[float, float] | None = None, + plot_values: bool = True, + obs_to_wav: bool = False, + cat_to_pix: bool = False, + value_fontsize: int | str | None = "small", + ) -> Figure: + """Plot the fitted catalog and observed lines for the specified arc spectra. + + Parameters + ---------- + frames + The indices of the frames to plot. If `None`, all frames from 0 to + ``self.nframes - 1`` are plotted. + + figsize + Defines the width and height of the figure in inches. If `None`, the + default size is used. + + plot_values + Whether or not to display value annotations over the plotted lines. + + obs_to_wav + If `True`, transform the x-axis of observed lines to the wavelength domain + using `self._p2w`, if available. + + cat_to_pix + If `True`, transforms catalog data points to pixel values before plotting. + + Returns + ------- + matplotlib.figure.Figure + The figure object containing the generated subplots. + """ + if frames is None: + frames = np.arange(self.nframes) + else: + frames = np.atleast_1d(frames) + + fig, axs = subplots(2 * frames.size, 1, constrained_layout=True, figsize=figsize) + self.plot_catalog_lines( + frames, + axs[::2], + plot_values=plot_values, + map_to_pix=cat_to_pix, + value_fontsize=value_fontsize, + ) + self.plot_observed_lines( + frames, + axs[1::2], + plot_labels=plot_values, + map_to_wav=obs_to_wav, + ) + + xlims = np.array([ax.get_xlim() for ax in axs[::2]]) + if obs_to_wav: + setp(axs, xlim=(xlims[:, 0].min(), xlims[:, 1].max())) + else: + setp(axs[::2], xlim=(xlims[:, 0].min(), xlims[:, 1].max())) + + setp(axs[0], yticks=[], xlabel=f"Wavelength [{self._unit_str}]") + for ax in axs[1:-1]: + ax.set_xlabel("") + ax.set_xticklabels("") + + axs[0].xaxis.set_label_position("top") + axs[0].xaxis.tick_top() + return fig + + def plot_residuals( + self, + ax: Axes | None = None, + space: Literal["pixel", "wavelength"] = "wavelength", + figsize: tuple[float, float] | None = None, + ) -> Figure: + """Plot the residuals of pixel-to-wavelength or wavelength-to-pixel transformation. + + Parameters + ---------- + ax + Matplotlib Axes object to plot on. If None, a new figure and axes are created. + space + The reference space used for plotting residuals. Options are 'pixel' for residuals + in pixel space or 'wavelength' for residuals in wavelength space. + figsize + The size of the figure in inches, if a new figure is created. Default is None. + + Returns + ------- + matplotlib.figure.Figure + """ + if ax is None: + fig, ax = subplots(figsize=figsize, constrained_layout=True) + else: + fig = ax.figure + + self.match_lines() + mpix = np.ma.concatenate(self.observed_lines).compressed() + mwav = np.ma.concatenate(self.catalog_lines).compressed() + + if space == "wavelength": + twav = self.pix_to_wav(mpix) + ax.plot(mwav, mwav - twav, ".") + ax.text( + 0.98, + 0.95, + f"RMS = {np.sqrt(((mwav - twav) ** 2).mean()):4.2f} {self._unit_str}", + transform=ax.transAxes, + ha="right", + va="top", + ) + setp( + ax, + xlabel=f"Wavelength [{self._unit_str}]", + ylabel=f"Residuals [{self._unit_str}]", + ) + elif space == "pixel": + tpix = self.wav_to_pix(mwav) + ax.plot(mpix, mpix - tpix, ".") + ax.text( + 0.98, + 0.95, + f"RMS = {np.sqrt(((mpix - tpix) ** 2).mean()):4.2f} pix", + transform=ax.transAxes, + ha="right", + va="top", + ) + setp(ax, xlabel="Pixel", ylabel="Residuals [pix]") + else: + raise ValueError("Invalid space specified for plotting residuals.") + ax.axhline(0, c="k", lw=1, ls="--") + return fig