From d9552d2826326a3a79a87c3ba9ec80c889c8c7a1 Mon Sep 17 00:00:00 2001 From: Timothy Cera Date: Wed, 24 Jul 2024 10:19:45 -0400 Subject: [PATCH 1/4] WIP --- demos/plotting_examples.ipynb | 431 ++++++++++++++++++++++++++++++++++ src/grib2io/_grib2io.py | 101 +++++++- tests/test_subset.py | 65 +++++ 3 files changed, 592 insertions(+), 5 deletions(-) create mode 100644 demos/plotting_examples.ipynb create mode 100755 tests/test_subset.py diff --git a/demos/plotting_examples.ipynb b/demos/plotting_examples.ipynb new file mode 100644 index 0000000..6e3d573 --- /dev/null +++ b/demos/plotting_examples.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "fb56f795-20b8-496f-adea-50b9e6aaa66e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:53.550756Z", + "iopub.status.busy": "2024-07-24T03:52:53.550622Z", + "iopub.status.idle": "2024-07-24T03:52:54.085554Z", + "shell.execute_reply": "2024-07-24T03:52:54.084990Z", + "shell.execute_reply.started": "2024-07-24T03:52:53.550746Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR 1: PROJ: proj_create_from_database: Open of /home/tim/anaconda3/envs/default311/share/proj failed\n" + ] + } + ], + "source": [ + "import grib2io\n", + "import numpy as np\n", + "import pyproj\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "53b5d3d7-bfd9-4687-bfc1-f92cfa804338", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:54.086439Z", + "iopub.status.busy": "2024-07-24T03:52:54.086070Z", + "iopub.status.idle": "2024-07-24T03:52:54.090243Z", + "shell.execute_reply": "2024-07-24T03:52:54.089795Z", + "shell.execute_reply.started": "2024-07-24T03:52:54.086425Z" + } + }, + "outputs": [], + "source": [ + "msgs = grib2io.open(\"../tests/data/gfs.jpeg.grib2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8cfef760-d3cc-4d8c-a23a-c3c76d4c0863", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:54.091691Z", + "iopub.status.busy": "2024-07-24T03:52:54.091466Z", + "iopub.status.idle": "2024-07-24T03:52:54.102368Z", + "shell.execute_reply": "2024-07-24T03:52:54.102056Z", + "shell.execute_reply.started": "2024-07-24T03:52:54.091675Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "90.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "msgs[0].latitudeFirstGridpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5dd9519b-b2a3-4aaa-b7ca-d167be32e9bf", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:54.102977Z", + "iopub.status.busy": "2024-07-24T03:52:54.102823Z", + "iopub.status.idle": "2024-07-24T03:52:54.105035Z", + "shell.execute_reply": "2024-07-24T03:52:54.104702Z", + "shell.execute_reply.started": "2024-07-24T03:52:54.102965Z" + } + }, + "outputs": [], + "source": [ + "proj_pars = msgs[0].projParameters" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "50aa1e62-6dbb-4ff6-8073-8928109a0eab", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:54.105616Z", + "iopub.status.busy": "2024-07-24T03:52:54.105492Z", + "iopub.status.idle": "2024-07-24T03:52:54.108101Z", + "shell.execute_reply": "2024-07-24T03:52:54.107730Z", + "shell.execute_reply.started": "2024-07-24T03:52:54.105604Z" + } + }, + "outputs": [], + "source": [ + "gfs_proj = ccrs.PlateCarree(globe=ccrs.Globe(semimajor_axis=proj_pars[\"a\"], semiminor_axis=proj_pars[\"b\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aa58c61d-f0d5-4117-98e3-421ed1d5752a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:54.108752Z", + "iopub.status.busy": "2024-07-24T03:52:54.108576Z", + "iopub.status.idle": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes(projection=gfs_proj)\n", + "ax.coastlines()\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle=':')\n", + "plt.contourf(lons, lats, msgs[0].data, cmap='turbo')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0c2242a5-8f4c-4ab3-aa96-2d23ef3bdb37", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:56.253534Z", + "iopub.status.busy": "2024-07-24T03:52:56.253415Z", + "iopub.status.idle": "2024-07-24T03:52:56.289089Z", + "shell.execute_reply": "2024-07-24T03:52:56.288695Z", + "shell.execute_reply.started": "2024-07-24T03:52:56.253522Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first_i, first_j [283] [316]\n", + "last_i, last_j [188] [935]\n", + "latitudeFirstGridpoint 19.25\n", + "longitudeFirstGridpoint 79.0\n", + "newmsg.nx, newmsg.ny 95 619\n", + "(721, 1440)\n", + "latitudeLastGridpoint 43.0\n", + "longitudeLastGridpoint 233.75\n", + "95 619\n", + "(array([[19.25 , 19.25 , 19.25 , ..., 19.25 ,\n", + " 19.25 , 19.25 ],\n", + " [19.28843042, 19.28843042, 19.28843042, ..., 19.28843042,\n", + " 19.28843042, 19.28843042],\n", + " [19.32686084, 19.32686084, 19.32686084, ..., 19.32686084,\n", + " 19.32686084, 19.32686084],\n", + " ...,\n", + " [42.92313916, 42.92313916, 42.92313916, ..., 42.92313916,\n", + " 42.92313916, 42.92313916],\n", + " [42.96156958, 42.96156958, 42.96156958, ..., 42.96156958,\n", + " 42.96156958, 42.96156958],\n", + " [43. , 43. , 43. , ..., 43. ,\n", + " 43. , 43. ]]), array([[ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", + " 232.1037234 , 233.75 ],\n", + " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", + " 232.1037234 , 233.75 ],\n", + " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", + " 232.1037234 , 233.75 ],\n", + " ...,\n", + " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", + " 232.1037234 , 233.75 ],\n", + " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", + " 232.1037234 , 233.75 ],\n", + " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", + " 232.1037234 , 233.75 ]]))\n" + ] + } + ], + "source": [ + "subset = msgs[0].subset(lats=(19.2, 43), lons=(233.7, 79))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "92801898-2a31-4fdb-ae0b-1a8690bee554", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:56.289745Z", + "iopub.status.busy": "2024-07-24T03:52:56.289576Z", + "iopub.status.idle": "2024-07-24T03:52:56.292025Z", + "shell.execute_reply": "2024-07-24T03:52:56.291620Z", + "shell.execute_reply.started": "2024-07-24T03:52:56.289731Z" + } + }, + "outputs": [], + "source": [ + "lats, lons = subset.latlons()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "53cba63e-c274-4fdb-93c8-236c6866cafd", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:56.292758Z", + "iopub.status.busy": "2024-07-24T03:52:56.292589Z", + "iopub.status.idle": "2024-07-24T03:52:56.296038Z", + "shell.execute_reply": "2024-07-24T03:52:56.295540Z", + "shell.execute_reply.started": "2024-07-24T03:52:56.292745Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((619, 95), (95, 619))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lats.shape, subset.data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "57dd0dcc-33c7-4142-86c2-cd6c81cfa75d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-24T03:52:56.296973Z", + "iopub.status.busy": "2024-07-24T03:52:56.296650Z", + "iopub.status.idle": "2024-07-24T03:52:57.029810Z", + "shell.execute_reply": "2024-07-24T03:52:57.029106Z", + "shell.execute_reply.started": "2024-07-24T03:52:56.296957Z" + } + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Shapes of x (619, 95) and z (95, 619) do not match", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_feature(cfeature\u001b[38;5;241m.\u001b[39mBORDERS, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 4\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_feature(cfeature\u001b[38;5;241m.\u001b[39mSTATES, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlats\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mturbo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/pyplot.py:2950\u001b[0m, in \u001b[0;36mcontourf\u001b[0;34m(data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2948\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mcontourf)\n\u001b[1;32m 2949\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcontourf\u001b[39m(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m QuadContourSet:\n\u001b[0;32m-> 2950\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2951\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 2952\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2953\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m __ret\u001b[38;5;241m.\u001b[39m_A \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m 2954\u001b[0m sci(__ret)\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:315\u001b[0m, in \u001b[0;36m_add_transform..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvalid transform: Spherical \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not supported - consider using \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlateCarree/RotatedPole.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 314\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m transform\n\u001b[0;32m--> 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:359\u001b[0m, in \u001b[0;36m_add_transform_first..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;66;03m# Use the new points as the input arguments\u001b[39;00m\n\u001b[1;32m 358\u001b[0m args \u001b[38;5;241m=\u001b[39m (x, y, z) \u001b[38;5;241m+\u001b[39m args[\u001b[38;5;241m3\u001b[39m:]\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:1655\u001b[0m, in \u001b[0;36mGeoAxes.contourf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(sub_trans, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mforce_path_ccw\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 1653\u001b[0m sub_trans\u001b[38;5;241m.\u001b[39mforce_path_ccw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 1655\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1657\u001b[0m \u001b[38;5;66;03m# We need to compute the dataLim correctly for contours.\u001b[39;00m\n\u001b[1;32m 1658\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _MPL_VERSION\u001b[38;5;241m.\u001b[39mrelease[:\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m<\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m8\u001b[39m):\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/axes/_axes.py:6536\u001b[0m, in \u001b[0;36mAxes.contourf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6527\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6528\u001b[0m \u001b[38;5;124;03mPlot filled contours.\u001b[39;00m\n\u001b[1;32m 6529\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[38;5;124;03m%(contour_doc)s\u001b[39;00m\n\u001b[1;32m 6534\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6535\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfilled\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 6536\u001b[0m contours \u001b[38;5;241m=\u001b[39m \u001b[43mmcontour\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQuadContourSet\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_autoscale_view()\n\u001b[1;32m 6538\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m contours\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:858\u001b[0m, in \u001b[0;36mContourSet.__init__\u001b[0;34m(self, ax, levels, filled, linewidths, linestyles, hatches, alpha, origin, extent, cmap, colors, norm, vmin, vmax, extend, antialiased, nchunk, locator, transform, negative_linestyles, clip_path, *args, **kwargs)\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegative_linestyles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 855\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegative_linestyles \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 856\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour.negative_linestyle\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m--> 858\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_args\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_levels()\n\u001b[1;32m 861\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_extend_min \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextend \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1523\u001b[0m, in \u001b[0;36mQuadContourSet._process_args\u001b[0;34m(self, corner_mask, algorithm, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1520\u001b[0m corner_mask \u001b[38;5;241m=\u001b[39m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour.corner_mask\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 1521\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_corner_mask \u001b[38;5;241m=\u001b[39m corner_mask\n\u001b[0;32m-> 1523\u001b[0m x, y, z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_contour_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1525\u001b[0m contour_generator \u001b[38;5;241m=\u001b[39m contourpy\u001b[38;5;241m.\u001b[39mcontour_generator(\n\u001b[1;32m 1526\u001b[0m x, y, z, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_algorithm, corner_mask\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_corner_mask,\n\u001b[1;32m 1527\u001b[0m line_type\u001b[38;5;241m=\u001b[39mcontourpy\u001b[38;5;241m.\u001b[39mLineType\u001b[38;5;241m.\u001b[39mSeparateCode,\n\u001b[1;32m 1528\u001b[0m fill_type\u001b[38;5;241m=\u001b[39mcontourpy\u001b[38;5;241m.\u001b[39mFillType\u001b[38;5;241m.\u001b[39mOuterCode,\n\u001b[1;32m 1529\u001b[0m chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnchunk)\n\u001b[1;32m 1531\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1563\u001b[0m, in \u001b[0;36mQuadContourSet._contour_args\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m<\u001b[39m nargs \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m:\n\u001b[1;32m 1562\u001b[0m x, y, z_orig, \u001b[38;5;241m*\u001b[39margs \u001b[38;5;241m=\u001b[39m args\n\u001b[0;32m-> 1563\u001b[0m x, y, z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_xyz\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_orig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1565\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1566\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _api\u001b[38;5;241m.\u001b[39mnargs_error(fn, takes\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom 1 to 4\u001b[39m\u001b[38;5;124m\"\u001b[39m, given\u001b[38;5;241m=\u001b[39mnargs)\n", + "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1610\u001b[0m, in \u001b[0;36mQuadContourSet._check_xyz\u001b[0;34m(self, x, y, z, kwargs)\u001b[0m\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 1609\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m z\u001b[38;5;241m.\u001b[39mshape:\n\u001b[0;32m-> 1610\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 1611\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShapes of x \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and z \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m z\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m 1613\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 1614\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShapes of y \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and z \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: Shapes of x (619, 95) and z (95, 619) do not match" + ] + }, + { + "data": { + "image/png": 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U1MjOzo4aN25M8vLytHnzZsrOzqZ+/fqRrq4uBQcH08ePH+nDhw+FRmB36NCBrly5QjwejwCQnp4eE/UhykvDEynb1ahRgy5dukS5ubnk4eFBu3btovv379OKFSvI39+f0tPTyczMjN6+fUvNmzen69evk7m5OYWGhhJRXnyAnJwck9LNyspiYkTfIiMjQyYmJjRs2DBq06YN1axZs0QBbzk5OXTlyhW6ePEiPXjwgIRCIZMjzn9sKSkp4vP5rA0iASENDQ3q1KkTHTx4sNS/ze+GUCikmzdv0qpVq+jevXu0aNEimj9//q9uloRCiIyMpDt37tCjR4/o0aNH5O/vTwBITk6OzMzMKDo6mlJTUwvdVkpKikxMTKhq1ark5OREkydPLjIItzzIyMiggIAAysjIoGfPntHdu3fp1atXlJKSQrm5uUREJC0tTQYGBqSoqEiKiooUHh5OSUlJVKtWLWrSpAnVrl2bpKWlCQB17tyZtLW1izze27dvSVlZmSwsLMQMgvDwcHr27BkdOXKEzp8/T9WqVaOPHz/+tPMujhkzZtDq1atJR0eHNDU1SUFBgfh8PvH5fIqOjqb09HSx9Q8cOEBnz54loVBIpqampK+vT5UrV2aBlra2tr/kPEqCxBj4hQiFQkpMTKT09HRKT0+ntLQ0SkxMJBMTE7K2tmYvyRYtWvxwwE2zZs3ozp07zBjYuXMnffz4kdauXUtERFpaWpSdnU0ZGRnUo0cPOnPmTIF9zJw5k1auXEnt2rUjU1NTUldXp4CAAPry5QvJyclRdnY2JSYmkpycHOnq6pKamhpxHEcyMjKkq6tLaWlpdO7cOeI4jsaMGUObN2/+oXMqCUlJSfTixQuaPXs2vXr1iuzs7Gjs2LE0YsSIn37sn0liYiJpaWkREVH37t0L/b0k/FrS0tJYP2hjY0NNmjQhY2NjioyMpODgYHrw4AHl5OSQrq4uWVtbU/Xq1al69epkaWlJFhYWZGpqWmEpobt376bZs2czLQElJSVq0qQJNWrUiLS0tJhgV4MGDcjIyIhtJxQKKTs7+4eNFI7j6PHjx/TgwQPy9vam58+fU2xsLBHlZRtNmzbth/ZfVkQp1lZWVtSmTRuKi4ujT58+UU5ODvF4PIqLixPzjm7YsIEmTpxY5P7u3r1Lzs7OP7/hZUBiDPxC+vTpQydPniz0Ox6Px0a9srKypK+vT/b29tSiRQsyNjamHTt20PHjx8W2qVWrFtna2pKhoSGpqKiQiooKKSsrk5SUFDVp0oSMjIzE5uWJ8lIC16xZQ6mpqWRoaEjy8vJUq1Ytio2NpZMnT5Kfnx+lp6eTUCgkc3Nz9n+BQECZmZlF6uZ/ey6i20dVVZXMzc1p+PDhVK9ePdLU1BQbGfws+Hw+nT9/nk6cOEEnT56kiRMn0tq1a/9I1bgbN25Q27ZtxT4rweMpoYJ49OgRDR48mNXimDRpEq1YsYKWLFlCq1atIo7jqGnTptSlSxfq0qVLhWSBpKWl0du3b+nLly8UHx8vVlgrIiKCFi9eTERE165dI2NjY6patWqFalNUrVpVTAZcXV2dDAwMaNeuXaSjo0PR0dFMXEtVVZXU1NRIU1OTGcT5CQ4OppiYGNLR0SEdHR2SlZUlImJ9YWnIycmhW7du0alTp+jhw4dkaGhIWlpalJaWRtnZ2aSurk56enokLS1NAwcOpMaNG9P79+/p2bNn5O3tTdHR0fT161fi8/nk4uJC06ZN+237HEk2wS/k2LFjLNr22bNneP/+PT58+ICbN29i586d2LhxI9asWYPBgweLReY2aNAAM2bMwMOHD3Hp0iU8fvwYfD4fHMfh48eP4PP5uHTpEiZPniwWuU1EWLduHYC8iP5z585BVVW10Ojf1q1bo27dulBWVkbr1q0xfvx4jBo1Cm3atIG0tDSICDVr1sS+fftQs2bNAts3adIEtra2YhkPpqamhR5LVVUVI0aMQGxsbIVc982bN0NKSgo9evRARkZGhRzzR8nOzkbTpk0hLy+P+vXrs2tXr149PH369Fc3T8L/ExQUJHZvh4aG4sGDB7C2toasrCwWLFiA+Pj4Mu378+fPuHDhAp4+fYrQ0FBkZ2ez7wQCQbH3cqdOnViblJSUoKCgwJ5jZWVl9t3Hjx/L1LYf5cCBA7Czs0P79u1hamoKKSmpIvsm0cLj8TB06FDs2rULhw8fxpkzZ3D16lUYGxsXur6+vj5GjRqFly9flrmdM2bMKHTfN2/eLMer8Wso6ftbYgyUM58+fYKhoSHMzc3x+vXrAt9//foV06ZNg56enthNp6CgUOhD0bx5c9SqVQtEBF1dXfadioqK2LrXrl0DADg6Oop9HhUVhSdPnsDKyop9JicnBzc3NwDAkSNHoKOjAyKCrKwsZs2ahR07doCI0LZtWxw4cAD3799n6xAR+vXrh40bN+LmzZt48uQJmjZtWuzD3blzZyQmJlbI9T9//jy7PtHR0RVyzLLy+PFjsRRMIsKYMWOwZcsWCASCX908Cf/PgQMHQERQVFREt27d8PnzZwwaNAhEhIYNG+Ldu3dl3ve2bdsKfWYmTZoEAOjatSuICDt37gSfz8ebN29w9epVnDt3DidPnoSdnR3q1KmD1NRUsf1yHPdD5/yzEAqFCAsLw/jx43H9+nX4+/sjMjISHz9+xKtXr3Dv3j1069atyPTqJUuW4N69ezh16hSOHTuGI0eOYNKkSTA2NoaioiL8/f1L3SYvLy9Uq1aNHUNkTImM8v79+2P8+PFYuHAhNm7ciMOHD+Pq1avw9vZGWlraT7hK5ctPMQYuXbpUZuv3R/ldb+78ZGVlsZto//79SE5OFvv+4sWLUFNTg6ysLLvRDh48iPDwcADArVu30KdPH+zYsQM3b97Evn370Lp1a3Tq1AknTpxA9+7d2f6fP38OX19f2NvbQ11dnY2+XVxcxB6etWvXIjMzE6mpqVi6dCkUFBTYiF900zs7O+PSpUvs5VmlShV07dpV7JonJydj+PDh2LdvX4Hz5jhOzJhRVlZGs2bN0LRpU2hrazNDo3Pnznj//j18fX3x+PFjCIXCcv8N3r9/z9px+vTp39ZDkJ2djTZt2hTo7CT8XowcOZL9NpmZmQCAa9eugYiwbNmyMt/DmZmZ8PT0hLy8PIYPH47w8HD4+vri+PHj7HjLli2DhoZGsYa2aGnQoEF5nvZvgVAoREZGBuLi4vDlyxeEhIQUeb0XLlwIIsL69evZZw8ePMDYsWNx/PhxhIWF4dGjR9i7dy/OnDkDf39/RERE4MiRI+wabty4ETt37sTKlSsxbdo0DB06FN26dYOTkxNq1KiBypUrQ05OTuy6GxgY4NSpU8jKyqqgq1J6wsLCyt8YEC2NGzcul0YWRUJCAi5duoSNGzdi+PDhqF+/PuTk5KCoqIiaNWvi0KFDWL16NebOnYv379//1LaUhvT0dHTp0oVdJykpKbi7u2PEiBFo2LAhu3kKe5gXLlz43f1nZ2ejXbt26Nq1K3vJff36FWpqanBzc0NoaCgAIDY2Fvv27UOPHj0gIyMDGRkZ5prj8XiQlZWFlpYWbG1tUblyZWYQfP36FQDEzmHBggUlOvd79+7h/Pnz8Pf3F3NzAkB4eDg8PT3FLG4iQqNGjXDq1Cn4+/sjNze3FFe6aDZt2lTg2jZr1gy3bt36rQxKkUE2fvx4bN68mRlNIvGpf4309HSsW7cOU6ZMwezZszFjxgy4urqiefPmcHNzw44dO+Dn5/dTDMjiyD+VV7duXZw/fx4vX75kHrbx48fj8+fPpdrn4cOHoaenB2lpaQwaNEjsRTJu3Dixe/dbr9uDBw8QHh6O2NhYfP36FWfOnIGqqiq6detWzmf+Z3Hnzh0Q5U1NDhs2DJ6enlBSUoKWllaJjCkiYsJoxcFxHNLT0/Hlyxd4e3ujdevWzGvUqVMnbN26FcePH8eePXvg6emJZcuWYdasWZg6dSpWrFiB3bt3482bNz//ggDw8/Nj/XtJjIFSBRCKGDduHG3cuPF7m5WY5ORk2rlzJ92/f59u375NOTk5Yt936dKFmjZtSkePHqW4uDgmaiKiatWq9PXrV9LV1SV9fX1SUFCgSpUqUdu2bUlVVZUiIyPJ0dGR0tLSWDpd3bp1y9RWPp9PQUFBFBgYSCoqKmRqakrDhw8nExMTmjJlCtWsWZOys7Pp+PHjFBISQp6enmRiYkK1atWiWrVqUdeuXUlVVZUMDAy+Gxy2e/duGjp06HfbtGbNGpo1axYJBAKytbWl1q1bU8uWLalNmzYUHh5O165do/fv31ODBg3o1atXdP78eQoICCBlZWUCQNeuXaNBgwaRkpISXbt2jbKyssQK5UyaNImMjIzIwsKCqlatSoaGhqSmplZowExERAQFBQWRk5NTATnZly9fUlBQEJmYmFBqaipNnTqViZfIyspS3bp1afLkydS7d+8fCjwsrJ4CEZGVlRUFBASUeb/lxfz582nJkiWFfjdo0CDat29fBbeo4sD/p5i9evWKAgICSEZGhnJycmjr1q0UHx9PZmZmLN1NFNQVHh5Ob968YftYvnw5zZo1q0Lam5aWRo8ePaKPHz/SmjVrKCIiosA6lSpVoqSkpBLv09nZme7fv09BQUEF7tOwsDDq1q0bvX37lkxNTSk6Opp27dpFt27dIjs7u2KFuv51kpOTacWKFXTp0iX68OEDNWnShK5fv06JiYn05s0bMjExIUtLS0pJSSF/f3+KjY2l5s2bk1AopKSkJLKysiq1BDb+v/zz5cuX6cqVK/To0SNWf0OkiaKiokLS0tKUkJDA7pNhw4bRrl27Sn2OYWFhFB0dTbq6uqSnp1dstsfdu3epRYsW7O9yzSaIiYkhPT29Uja/eE6fPk3//fcf8Xg8atSoEcXGxpKbmxu5uLiQtLQ0ycnJiengZ2dnk7+/PxkbG9PHjx/p7t279P79e3r+/Dm1bNmSMjIy6M6dOxQfH19spTRRqc7KlSuTvb09Va9endq1a8fOLyMjg27evEn3798ngUBAWlpa5OvrSzdv3iyQg0pEZGZmRqGhodS9e3d69OgRff36lcaMGUMBAQHk4+NDampq1KBBA2rbti3Vq1ePdHV1adiwYWKiO9+yYsUKmjlzZpHfi346Ho9HKSkpdOnSJXJ1dWXfL168mHR1dWnHjh2sMxVFldaoUYNmzZpF/fr1Ix6PR9bW1hQQEEAKCgp0/PhxqlKlCm3ZsoVCQkIoOjqawsPDC2QYSEtLk6KiIunp6ZGCggIlJSWx8rRaWlpkYmJCkydPZr9vYe2Pi4sjPz8/+vDhA124cIFu3LhBM2fOpOXLl5fZIIiNjSV9fX3q168fde7cmW7evMlesL9DOdrKlStTTExMod+FhYWRsbFxBbfo5xMVFUWenp60f/9++vr1KxHlZaBIS0tTbm4udevWjZYuXUpmZma0YMEC6tWrF9nZ2dHEiRPp8uXLtG3bNmrdujXbn6amJvXs2ZP+++8/srKyYiW1fyZpaWnk5+dHGRkZLOMmKSmJnJ2dqXr16kREtGnTJho/fjzZ2tqSgYEBGRgYUJUqVahevXrUqFEj0tHRoW3bttHo0aNp8uTJtHDhwgKZQAsXLqSlS5eShoYG1apVi65fvy6p01BKEhMTqVKlShUe4Z+RkUFCoZCUlZUL/c1yc3OpT58+dOfOHVqwYAFlZGRQSEgIZWdnk0AgIEVFRTIwMGAFotLT00lVVZWGDh1KYWFhBQaxSkpKpK6uTjk5OZSTk0NKSkpkbW1Ntra2ZGNjQ6amplSlShWqXbt2+RoDc+fOpRkzZpCKikoZLlMeJ0+epJUrV5K8vDypqKjQrVu3SEpKikJDQ8XyXMsDjuMoMzOTXrx4QXFxcdSgQQNKTk6m48ePk4KCAu3YsYMUFRVJIBAwb4O9vT0ZGxvTvXv3KDExkczMzEhFRYXi4uLI3NycOnbsSI0bNyYrKyt68+YN+fv7U+/evUlfX5/27dtH69evJzs7Ozp58iTJyspS9erVqWvXrpSVlUUPHjygly9fsgJDpqamlJ6ezjpHojwjpUePHjR58mRKT09n6mUpKSmUnJxML1++pNevX1NkZCRlZmaSgoIC2drakqWlJXEcR0ePHiWiPPW6TZs2Uffu3Qu9Njo6OhQXF0f9+/cnLy8vmjRpEm3YsIF0dHQoLS2NDAwMKCkpiWbPnk0TJkwgGRkZGj58OJ09e5batGlD8fHxFB8fT58+faLU1FSqXbs2devWjSpXrkzW1tZ0+/Zt8vT0pOTkZDIzM6PDhw9T7dq1SVFRsdD2JCYm0vjx4+nEiRPE5/Pp6tWr1K5duzL/7h07dqSbN28WqJK3detWGjVqVJn2+6NkZ2fTgwcP6MqVK3TlyhUKCgoiZWVlateuHXXo0IF69uwp5oH7G/Dz86M1a9bQ4cOHSVFRkdzc3IjP59Pz588pLi6OIiIiKD4+nubNm0ejR48mW1tbat68OY0ZM4ZcXFzo+fPnNHfuXJKVlaUlS5bQ/PnzSV5enqpVq0YnTpxgAllEeQaCiYkJtW3bljp37kwNGzYstENOS0uj1NRU0tfXL9FLNjc3l16+fMnKMk+ePJmaNm1KHMeRQCAgX19funHjBmloaNCuXbuY4d2zZ0+Kioqiz58/M+PPwcGB5OTk6MGDB0REVK1aNfLy8qIGDRqw4/Xt25elF8vKytLEiRNp9erVZf4NJPxeXLt2jVasWEHe3t6kqqpKFhYWzHuQkZFBkZGRFBMTQ3JycqSqqkrx8fEkKytL9vb2TJfm5s2b9PXrV/r69SulpqaSvLw8ycvLU0pKCn348IE+fPhAgYGBxOfzSU9Pj2JjY8s3tVC0ODo64urVq6WOeL5165bYflq3bo0tW7b8suIkHh4eePz4MQAgLi4OBw4cgJubG1q1aoUJEyb8UNGN3NzcQq9PYmIiHj16hN27d2PKlCno3r07qlevLja3IyMjU+ic1rcRtqJARCUlJZiYmLD5sREjRgAAMjIy8OTJE/To0UMsjoGI0KFDB+jp6WHAgAFi7RPNubdp0waurq4gIhw7dgwhISHQ1tZG7969xda/d+8eiAg9evQocK6rV68Wa6+SkhJ69uyJe/fuFVg3fzomEWH69Ok4duwYXr9+Xea5/uTkZLHsCgcHhwJR1+VNVlYWTpw4gcmTJ2PChAno27cvGjVqBCMjI3btq1SpAnd3d5w/f54Fpf1NcByHmzdvol27diDKS/1q3bo1IiIiEBgYCHV1dYwdOxb9+/cHAERFRcHMzAxv374FkJfGJ4oNWL9+PeTl5bF3794CxxEKhXj9+jUuXryIPXv2YPny5XBzc2PxF9ra2hgwYABOnz4t9iy2atWK3RMmJiZwcnISSyFLS0vDjRs3MG/ePDRr1qzQTB/RsydaVFVVISUlBTk5OfB4PCxevFjseoSFhWHfvn3o1q1bgaJhJiYmYvf4yZMnxbJMmjVrVq6/j4Tfg5LGv8TExGDy5Mnsvpk/f36J+sTc3Fzcv38f8+bNK/8AwoMHD6JFixaoWrUqiAiGhoaYNWsWAgICkJGR8d0GxsTEwMXFhaXFGRsb4/z58xUeFCRCRUWF5ef/atavX1/iQBeivOpuHMfB19cXPXr0gLKysljqYWG5vJcvX8bs2bPZ38bGxgUCMNPS0uDt7Q2O43Dp0iUQEXr37g1NTU1YWFgUyCbZtWsXiP6XCiUiKytLrD1EhLFjx7I0yUePHhW4Bp8/f8aOHTvQo0cPscAfZ2fnMuVJL1myBER56Uh+fn4/PYDw4cOHMDQ0BBHB3NwcNjY2cHZ2xoABAzB37lzs3LkT7969+60CGcubjIwM9OvXD0SE2rVr49ChQ7h+/To0NTURGhqK9PR0HD58WGybEydOgIhYVk1+goKCcP/+fZw5c6bE100gEODx48eYOXMmatSoAaK8zB1Rds/BgwdBRKwfEy337t1Dw4YNWaCrtrY2unXrhnXr1uHFixcYOHAgiAg2NjZYtmwZ9uzZg/379+PBgwfIzc2Fv78/Zs+eDW9v72Lbl56ejjNnzmDAgAFwcHDAkydPxL5/9eoVzM3NWTqvvLx8uQXYSvhzKet78qfqDHAchxcvXmD06NFiD5OMjAw0NTVRr149HD58GHw+v9D9paenY9q0aWLbLVy4sMj1fxbLly/H/fv3K/SYRSEQCPDixQucP38eW7ZswY4dO+Dl5YWzZ8/i/v37ePPmDVxcXNCzZ088fPiwyP18/foVJ0+exKpVq3Dw4EFcuXIFly9fZh1UcnIytLS0cPTo0e+2SSgUYvXq1ZCVlUXPnj1x4cIFPHz4EJmZmfj8+TO8vLzY6G/cuHFi2/L5fLHO1sXFBcnJyZg+fTqICKtWrSr0mDk5Ofjy5QtSU1ORmJiIixcvwtzcHLKysjAxMYGenl6hhkRhbReNrn623oC/vz9mzZoFaWlpODk54cOHD6Xans/nIzIyEgEBAUW+8DIyMnDp0iWcOnWqTF65iuDz58+oXbs2lJSUsG3bNrRt2xYhISHf3S4tLQ1+fn5FlmtesGABTExMCpTO3rt3LywtLTFp0iTs3r0bz549KzTffsWKFSDKS7O9c+cO1q5dCyJCp06dxFL3qlSpAkNDQ2zfvh0fPnyocKNNKBRi5cqVrD3q6urQ0NCAl5dXhbZDwt/FTzEGzM3NcePGDbHvRApYzZs3x9atW7Fy5Ur2ghBZ2wBw9uxZ1K9fHzY2NgVSzERLo0aNyni6ZUNFRUUsL1VC4WRkZOD169eF/mZNmjTBtm3bCs3nj4qKwtOnTxEYGAiO45hQy4oVKwq1cs+dO8f2W6lSJWzdupUpsHl6erIc31u3bpWo3TNnzgQRlVuNdY7j8OXLF1y+fBnLly/HqFGjYGtrC6I8kaPZs2eX2qDdsmWLmMu5Ro0a2LBhA4YOHQoNDQ1UrVoVbdu2hZKSUgEvz+/ErVu3oKWlBTMzM/j4+ODz589o1qxZsWl3HMfhyZMn2LJlC8aOHYslS5YgMDCwwHrh4eEFNDsAiAlpfet279ChA1q0aFHAO5V/uqxu3bq4fv06WrdujaZNm2Lw4MEFRukViUjcSLQ4Ojr+s6mmEsqPn2IMiBYNDQ2Ymppi5MiR4PP58PHxQUZGBh4/foyqVauKSeV6eXnB29ub/a2jo4OdO3fi2bNnCAkJgZubG2rXrg0TExNs2rSpXE6+pCxYsAAPHjyo0GP+idy5c0dM2lRFRQWDBw8u0h2amZmJcePGoWvXrujcuTPatm2LevXqgYhgbW1d5HFEc1u2trZo2bIliAh2dnZYvXo1y7eeMGFCiUdsV65cgZqaGgwNDVlsSFnIzMzE+PHjxaZe1NTUYGdnB1dXV5w7d65Mc//h4eEFXlLt27cHj8eDmZkZZs2ahfHjx6Nt27ZYsWIFAgMDkZiYCB6PB01NTRw7dqzM51ReCIVCLFmyBFJSUmjVqhVevXpVoriM1NRUNp0gKysLa2trZuxt2bKlwPp79uzBjBkzxH57oVCI8+fPQ1NTE82bN8erV68we/Zs6Ovrw8rKCj179mTXtV69eti/fz+CgoJw+vRpuLu748uXL+V6LX6U1NRU7Nq1C8uXL4efn9+vbo6Ev4QvX76UvzGwbt06TJw4EStWrMCAAQNAlBcU1r59exw/fhwDBgyAtLQ0Vq9ejb179yIuLg5A3ghg165dqF69OogILVq0wNSpU9G6dWvUqFEDU6ZMYaI8RISEhIQfvwIlwNraGrt3766QY/2JcByHQ4cOsXgBBQUF9O/fHw0bNmTGQZ06dTB79mwxoSE3NzcQETQ1NQsodo0cObJIta709HQsXLhQzPAwNjaGnJwczM3NyzRK+vLlC+zs7NC2bduyXgYMGTIECgoKmDt3Li5evMjiNX4UjuPYMyFaHj9+jOTk5EL3LxQK8e7dOwwfPpx5ZX4lISEhaNeuHXg8HubNmwc+nw8zMzN07tz5u9uKFOP279+PxMRErF27lrnsp02bVmD9Hj16YMOGDYXua/jw4bCyssKpU6fErmWVKlXY/3+X6UAJEn42cXFxsLa2hoWFBZycnJhX8afWJnj69CmWL18u9iJv2bIlNm7ciGnTpuHIkSNiAWdZWVlo1aoV2rZtC3Nzc2hpaaFy5coFouRFSng/GxcXF1y4cKFCjvWnEBoaih49ekBdXZ1N54hG6UR5kqzh4eEs0yD/4uDggOTkZEycOLHIqSCivODGrl27igUFhoeHY9SoUfDx8UFaWhqOHj2KWrVqQV9fH+Hh4T8UT7JgwQIoKyuXSbc8KysLVatWxcCBA8t8/OKIjo5G//79xa7PggULsHv3bly/fh137tzB6tWr0blzZxYDIS8vj7lz5yI9Pf2ntOl7REREYNSoUZCRkYG+vj6uXbuGkydP4siRI7h27VqxEtDR0dGYMmUKmxOfM2cONDU1ISMjg8GDB8PHx6dQQygrKwspKSmFfrd9+3bweDx2/fT09FhBr2bNmmHUqFGFTjNIkPA3kpCQINafbNmy5ecbAyJEc6mBgYFi2tr5l2fPnolt8+LFC7Rv3x6GhoZiVe9kZGQqLNWwZ8+eOHfuXIUc63cnOzsbV69eRZMmTaCnp4elS5di27ZtePLkiVg1vSpVqoDH40FVVRV16tQp8DuLshNiY2Px/v17fPnyBcnJySzgLSAgAGPGjGHrnz17FikpKbh9+7bYfubMmYODBw9CWloay5cv/6FzS01NhY2NDdTV1UsUOAnkpeXs3bsXVatWhby8fKFFp8qT6dOnQ0tLC/Xr14e+vr7Yy01JSQktW7bEggULcOvWrV9iBISFhcHT0xPNmjWDlJQUNDQ0sGrVKvbi79ixI+bPn1/k9r6+vhg0aBDk5OSgqqqKSZMmwdbWFlJSUhg7dizCwsKKPf7Lly+hpqaGpUuXFvp9cnIyLl++jFGjRuHgwYNlP1EJEv4C+Hw+3NzcoKysjKNHj1acMZCfzMxMDB06FH369IG9vT2I8grX5I/oXrVqFbS0tFC9enXUqlULhoaGuHz5Mp4+fVpkRPHPwN7eXtJxAPD29maBcFJSUli2bJnY92lpaYiIiMCBAwfQtm1blk4oWiwsLHD16lUsXLgQ48aNQ1BQEAYMGABra2t07NgRU6ZMgaenJ2bNmoV+/frh5MmT2LRpE+zs7JgB2KRJE7H87/xLeZQRTUpKQt++fUFEGD58+Hfd/OPHjwdRnn6CKP+9IsnJycHnz5/h5+dX4Vk2+YmPj0efPn1AlKfF3759e+zcubPASFsgEBRppPj4+EBGRgZGRkbw8PBAUlISJk2aBHl5+RJlhoi4evVqqdaXIOFPYfHixahTpw5q1qzJasyUlujoaBw6dAgDBgxAnTp1WJyVSMPmtyth/ODBAxARjIyMEBUV9cP7E1GWCzht2rR/Plr34sWLkJGRQd26dfHy5csSvXji4+PZi7pTp07w9fXFxYsX2WezZs1iL9KOHTuy0bWWlhbTGTA1NcW+ffsQHByMbdu2oXv37mJzvER5ugS3b98ut3PlOA47d+4EEWHbtm3Frrtnzx4QEYKDg8vt+H8inTt3RqVKlbB79+5in/+kpKQif6uuXbuiatWqYnElosj5b9MFi2PAgAGoWbPmX63TIOHvw9fXF5s2bcKkSZPQrVs3tG/fnnnCUlJSmB6KvLw81NTUSlwZmM/n48iRI+jZsycbzBERatWqhaFDh6Jr166oW7cu05b57YwBUf3uvn37/vC+AODMmTPsIsydOxdAXk37YcOGYe7cuXj16lWRnYeSkhI8PT3LpR1/Ivfu3YOcnBy6d+9ealETgUAgFgiYkZEBd3d3uLq6IiEhAVJSUmLBmRzHsd/h7du3LNJbR0cHFhYWmDFjBtuPr68vzp8//9MCSd3d3UFE6N69O6ZOnYpp06Zh3bp1YrXJXVxcYGRk9FcqBJaGjh07olOnTt9db8KECZCVlS0QL5CRkQEpKSl0795dTBshICAARFRszI5AIMDHjx9Zaqifnx98fX3LeCYSJFQs2dnZmDlzJlOmtLS0RLt27aCnp4fmzZtjwoQJYhlK0tLSWLNmTaH7iouLY+nYiYmJWLduHZteb9SoEdzd3XH48GFWyj4/P1V0qCzcunWLzYOK/nV2dsabN2+wefNmHDlypFT7S0tLY65c0XLs2DF8+vSJ/S0KuJKRkYGTkxM8PT0xY8YMdOjQAadOncKMGTPg7OyMq1ev4tChQwVGxb9KGbEi4DgONWvWRKNGjX7K1Ez9+vXRpk2bYo/v5eWFhQsXombNmqhXr16BdVJTUzFq1ChMnTq1XEeDAoEAe/fuRe3atWFpaQlLS0vIy8tDX18fS5Yswfz580FE2LNnT7kd809l/PjxxaaDihAKhYV2RACwe/du8Hg81K1blwVxent7g8fjQUFBAWvWrMHly5exadMmDBkyBHZ2dmKSvcrKypLpAQl/FN7e3rCxsYGsrCyWLVsmNti6evVqgalQDQ0N9O3bF7GxsXj+/DmWL1+OW7duYdasWUxFs3r16ujYsSOUlJQgKyuL/v37l6gc8m9nDOQfwRMRzMzMCtSa9vDwgEAg+K6r+vTp02LR6rKyshg3bhzTfici9OvXD3w+H6dOnUK9evVQs2ZNyMrKQlpaWiw4S7QfW1tbzJ8/H2/evMGjR4/QoUMHtGrVqtRqcn8KMTExICKcOHGi3PctFArZPHNiYuJ3158zZw60tbXFjK/MzEw4OjqytJgFCxaUezvzExoaCldXVygpKUFTUxMdOnT4pXP1vwuenp5FyuFeunQJ8+fPx+bNm8WmAApDVKcif3pgaGgoRo0aVWTWSf6lV69e5X5uEiQUhkAgQFRUFN68eYNr167hwIED2LBhA3bt2oXjx4/j6tWruHHjBkaMGAELCws0aNAA/fr1Y/oaor6ViAr1ZAkEAgwePBgyMjLQ1tbG5s2bsWzZskLvey0tLQwYMAB79+5F586dQUQYMGBAqVRVfztjAMib47h//z5u3bqF3NxcFkWeX91OlGPeokULuLm5oVGjRuDxeLh48SLbT4sWLUBEmDlzJpsr0dfXR4sWLeDp6Yng4OBC5VoFAgFzV4eFheHUqVNwdnaGkZERi9o2NTWFmpoa9u7di5UrV4LH4+Hs2bNl0sb/nfn8+TOICNWqVUOfPn3KVbJXJFNd0ikYUSZBw4YNcfz4cTx9+hRt27aFoqIis5KJCIsWLSq3Nv5rvH//Hp6enti4cSPmzZtXYlXGN2/egIhw6NAh5p0JDAxEhw4d2DMrJSVVpLv/9evX6Nq1K5SVldG6desCBhbHcfjvv/9AlKc62alTJwwbNgwdO3ZkGgwTJkyQGGYSKoQLFy7AzMyswEu5sGJVRkZGGDt2LNq3bw+ivFoWNjY2Yq7/b4XZQkND4eTkBB6PhzFjxiAxMREHDhyAvLw8XFxccOLECVy5cgUxMTF4/vx5gfdYREREqb2kv6UxUBzDhg2DlpaWWJph/iU4OBhfvnzBihUroKCgwNyOJ0+eRGRk5A+5kQUCAQ4fPsxET+Tk5GBoaAh5eXkoKSmhSpUqqFOnToXpH1QEQqEQnp6eGDVqFCpVqoSxY8eW275FbvYGDRoU6TrOj0AgwPr169G2bVv2exsbG+PSpUtsHZHlLDEISs/r16/FdCOI8mpJlOR+5jgOjRo1YoajqKM0NTXFqVOn2Ig/v7Gen0GDBkFXVxfz589HUlJSge+/fPnCUp+ICAMHDmTPsqj6oQQJP5uQkBAmrd+2bVucP38ez58/R2hoKIsbEggESE5ORlhYGPz9/dmLWlSAr3///hg/fjxWr16NY8eOsYJvISEhWLNmDdzd3aGmpgZjY2MmgiWSYHd0dCxSjO1H+eOMgW95/vw5Jk2aBFlZWUyfPh3q6upibv38noTyKtqSk5ODs2fPYv369ViwYAFatmzJbpBmzZpBR0fnrzIIRCxbtgyysrJYsmQJRo8ejRUrVuDhw4c/dHM+fvwYenp6cHJyKnHsBcdxOH36NG7cuFHoNiKDoKJlq/90GjVqhNq1a+PRo0diz07lypVLtL1AIMD169cxfPhwTJkyhQXnikptq6qqwtrautD7xdLSEmPGjCnweWZmJjp27MhcoS4uLiAiNG7cmK0zcODAIgOqJEgoD7KysrBo0SIoKCjA2Ni4VNUxS0J2djYaNGgAorwo/zFjxogZxWfPnmXP48+qk/PHGgPp6emYPXu22Pw/j8eDjIwMJk6ciMGDB4sFCH5bDvVnICpdqqGhge7du+Pt27d/VUnRrKwsWFtbQ1lZGba2tqzEtJKSEgYOHFho8ZiSILrRvy2TXFY4jkO3bt1ARP98lH9JSUpKAhFh+fLlyMrKwoEDB3Ds2DFoamqiXbt2pd6fKPgpf3Gl+fPnQ15enr30OY5DTEwM7t+/X2Rcypw5cyAvL48tW7bAwcGBPV/5jcD+/fvD2dn5r3rWJPw+XL9+HRYWFpCVlcWsWbN+iphX8+bNIScnh+PHjxe5ztevX6GkpITVq1eX+/GBP8wYSEpKgqurKxv95180NTWxePFiNseZkZEBNzc3zJ8/v8IEioRCISZMmAAVFRXo6upi8eLF0NPTw5UrVyrk+BWBUChkFjGfz8fr16+xdOlSGBsbQ0FBAV27dsXq1asRERFRov0lJyezueLyjEcQzVV3795dkmZWAjiOY9MvJ0+eBAA2Fefi4lLqUdD06dNhYGAAgUAAR0dHFgwsmlP18/MTK21et27dQj13Xbp0AY/Hw4EDB8Dn87Fq1SoQEfMuPH/+HGvWrEHDhg1RqVIlpkcQEhIi0RmQ8ENERkYyT5Szs/NPCxJ/9+7dd4O0r127xuT4S5tRV1J+e2OA4zisWrUKTk5OhcYIyMnJoX79+nj48GG5HfNHmTp1Kng8HhQVFdGpU6efEon/u5GRkYFly5ahZcuWUFRUhJSUFExNTWFtbY2GDRuiR48erPobx3F4/fo1lixZAn19faioqOD8+fPl2p709HRs374dlpaWkJGRQffu3TFv3jxcv35d8pLIR2BgIIYOHYouXbqI1ZEIDQ3FunXrmDrZzp07S7Xf5cuXQ0lJiQlLVa1atdDnd968eTh06BBiYmIQExODJ0+eICEhAUKhEGfOnEHdunWhqKiIJUuWAMgbEEREREAoFOL27duwsrLC5s2bMXv2bCxatAipqalIT0+HmZnZH11plOM4Zhy9f/8effv2ZX8vXLjwnxe5+pkIBAIsXboUysrK0NHRwcGDB39qnyF6VorLtLG0tISsrCzOnj373YycsvLbGwNt2rQp0IEMHz4ca9asQURERIXGJ3wPjuNw79493LlzB3379oWioiIUFBT+udznlJQU7Nq1C7NmzcLEiRPRpUsXEBFTnhNVoiMiuLm5ITw8/Ke1JTs7GytXrkTLli2hr68PIsLQoUN/2vH+FFJTUzFz5kzIycnB2NiY1ZUQTf3weDx0794d6enpGDlyJGRlZdGtWzcxwaXiSEhIgJKSEgskVFdXh5WVFTtGixYtcPbsWTx+/BizZs1iipPfZgvp6emhfv36cHd3x7x58+Dj44Pc3FwIhUKkpqaiefPmBUoM8/l8zJs375fIQ5eVd+/eseDKlJQU6OnpYePGjQDySoM3bdoU0dHR4DgOlStXxrZt25CRkYG9e/eKpeUKBAKcPHmy3OKj/kW2b98uFi8THh6O0NBQbNq0Cfv372fKf2/fvsW0adN++B108OBBEBGr3pufy5cvo0mTJiyL5mcaJb+9MbBhwwYYGxvDxcUFixcv/q3nBV++fCnWoYkkbevUqVPm+fS/gVevXoGIcO/ePQB5Ix2iii+ty3EcPDw8wOPx4ObmVuK0ub8NjuNgamoKRUVFLFiwAJmZmTAwMAARYciQIZg3bx5z7c+ZMwfZ2dlYu3Yt5OTksHjx4iL3+fr1a2zfvh0jR46Es7MziAhOTk7YuXMnc3HKysoWePFra2vDzc0NXl5eeP36NQYPHoxOnTrBxsaG1QR58eIFGjZsiIcPH0JaWhqPHz/+7nlGRUVhwIABFVbQrLScO3cOz58/BwDMnTsXVapUYd8dPny4SKEYkXrj48ePwePx4Ofnh0OHDqFbt25MTO1frKUSFhYmNiVbVPXK75GWloYjR45gx44dMDIygpKSElRUVFhQuqGhIR48eMB0aIoqilWadhMRTp8+LfY5x3EwMjKCvb09duzYgcjIyB86zvf47Y2BP4n8WvxEBKFQiGfPnsHQ0BA9e/b8rQ2Zn4lQKETlypUxYMAA9tnu3bshJSWFmJiYCm1LdnY2pk2bBmNjY9SpU6dCj/074eTkhNq1a7POct++fWIGrGjZvn0722by5MlQUFDAnDlzxOoLBAUFsWkAHo8HKSkpmJmZwd7eno3OExMT4e3tzYKvLl26BCLC1KlTSzyKFQqFCA4OxpYtW/D58+fvrn/hwgU4ODh8t9Lhr0Ik1JSYmIj09PQyuX/j4+PBcRwuX76M0aNHIzc3FwEBAez7nyXV/TuRlZWFuLg4rFu3DqqqquxzBwcHuLq6/tC+w8LCsGbNGsycOROJiYkIDw9H48aNxQLXraysfvQUYG5uzgJro6Oj0bNnT1SrVk3Mo/qzkRgD5UxgYCC7SXr27AkArMCEkpJSsRrrfzOil4woOM3f3x9EhFOnTv2S9syaNQuVKlX6Jcf+1eTm5qJJkyYwMDBAYmIivLy8YGtriypVqmDv3r0gIiZ5mn9klZqaiq5du7JYHSBv9NKuXTuYmJjg5s2bSElJwYgRI75b2CsiIgKvX78ucbEVABg3bhyUlJTEMgkK0yQAgB07dkBOTg7NmjVj7tffSTb88uXLmDNnTon0NcqKl5cXtLW1f/qIUkRiYiLOnj1b4Rk8GzduhKamJuLj48UGF1evXmXGaHm613NyciArKwttbW0cOnQIRFSgOmdpGTlyJMzNzcFxHPPCTZw4EcePH6+wGCeJMfAT+Pz5M+Tk5DBw4EAAeTeiSKFNFAj1r8FxHLp37w4igqurKzp37gx1dfUKG7kIhUK8e/cOu3btYvPjCgoKCAkJqZDj/06IJL/l5OQgJycHIoKDgwN8fX2Zml9R96rIDa2pqYkXL15gyJAhICKcPXsWKSkpiImJKXHnlZubi8DAwBKrBj569Aju7u7o3LkzgLx7Sk5OjlWW3LZtG2bOnAkAmDdvHszMzGBkZAQ+n4+tW7fCwcHhpygUPn/+HJqamswAev/+PR4+fCh2HfL/n8/no2fPnmjRogWysrJKZRCVhoSEBHh6erJYpvwGGp/PL/eXjFAoROvWrTF+/HgAQGxsLI4cOfLTX2Y+Pj44c+ZMkb+tv78/atWqVUDlr6ysXLmSzeGLPGo/atSJqrlOnz4dKioq6NatW7m0tTRIjIEK4uHDhzAzM4OamhrGjBmDixcv/nM58BzHYe/evdDQ0IC2tnaFaD8AeZ21qIhH/noTRPTbupB/Jrm5uThz5gw2bNiATZs2iV2DmTNnil2fHTt2YMOGDXB3d0ffvn2ZpKpISIjH42Hy5MnIzs6GiYkJeDxesWmlHMexEXrTpk2hra2N1q1bA8iruPa9Z8LLy4u94IRCIY4cOcJiP65fvw4HB4dCt5s7dy4ToQoKCvphL0FISAhWrFjBzmnTpk0sNmH8+PGwtLQEx3GIjIxE/fr1ce3aNQDAsWPH0KdPH+Tk5CA9PR2tW7eGh4fHD7Xle2RlZUFPTw9bt24FAKxbtw46OjolSrkWCARinpUrV64U2r8LhUK8evUKQUFBbArnwoULkJeXLzS+Iz09/YeNhJSUFJw9e/a762VmZmLcuHHlkhooFAohLy+PwYMHQ15eHhMmTICMjMwPV7XNycmBubk5iAgjR478YU9DWZAYAxXE5MmTC2RFqKmp4fDhw/9c5G92dna5aD/cuHED5ubmbHQrGu2bmprC2dkZTk5OsLS0hJSUFOrWrYt9+/axQDZR3vvPStP5E9m1a1eh6X+ysrKws7ND06ZNYWNjA11dXdSpUwcdOnTA+vXrwXEc7t69i8aNG4PH47GKg4Xh6uqKVatWAcir375o0SIMHjwYADBq1KhiS5Z7e3tDTk6OvdRKA8dxyM3NRVpaGmRlZZk34dKlS6WqOnn8+HGcPXsWa9asgZWVVaECNEKhkGU4CAQCDBw4kKUCnj59Gu7u7gDypl0GDhxYaBR5efP582cWs+Tt7Y0TJ06A4zgEBwfD1dW1QElpEe3bt2ejVJEy5YsXLwDkTf3t3bsXADBgwACoq6sXeMGLjK5z586hS5cu7HMrKyuMGDHiu+0+cOAAGjZsCCDPm7F06VI8fPgQAoEAjx49gpycHKKiokp8HUJCQn6ov01MTGTTnba2tpgwYQJcXV2hr6/PChCVFX9/f3ZtfwUSY6CCiIyMZNrt3y5mZmbYs2cPnjx5gsDAQPj6+iI0NBTv3r3D27dvER4ejrdv3yIiIgJv3rzBx48f4e3tja9fv8Lb2xsfP36Ej48Pvnz5grdv38LX1xdfvnyBj48PAgMD4e3tjZiYGDx79gzXrl1jEbcZGRk4evQoU/4LCQmBl5cXkpOT8fLlS/j4+MDPzw/z58//rYIfr1y5gr59+4ql/4is6vxLhw4dMHnyZOzevRtZWVlwdnaGmZkZK1lNRFBUVISNjQ17Qf3LPHjwADo6Ohg3bhw2bNiACxcu4Pjx48xwFQkGNWzYkEXBi9i1axczyoqrX7Fx40ZMnDix0O8uXLiAFStWoG7dunB3d2cv/dTUVAwbNgyRkZG4devWD3W6fD4fd+7cYS+QyZMno0OHDiUepQ4YMABubm5lPv7vxsuXL2FlZQWO4/D582d069YN9vb2eP36NQDg/v37ePbsGYC8F3toaCgzoN3d3dlvnZ2dzXT0C2P37t2YMmUKu85nz55lHh1vb2/MmTMHQN4ovnv37iy26urVq5g3bx6AvPtTSUkJGhoabNvTp0+X+LdLT08Hj8djxl9ZPBOiYmmvXr2CoaEhunbtip49e4KIMHv27FLv73dCYgxUMF++fMHQoUPFolHzL4MHD4a1tTXGjx+PGjVqwNTUFHPmzIGKigpq1qwJojydBSKCl5cXy5s3NzfHzJkzUbVqVVhZWWHKlCmwtLRkssyiQBdZWVkoKCgAyMuTzR/oOH78eJYqIysri65du6Ju3bpQV1dHt27dcOTIEdy4cQOBgYEIDAxkLt2EhIQKSZ3kOA5TpkwpcM20tLQgKysLFRUVJpJDRGIvE1G+++3bt5n7dtKkSejVqxcrkV3ewkd/IhzHoXfv3uyaiHQH5OXlsWbNGoSGhha6HZ/Px9mzZ9G3b184OzsX6+738fFhc/+i6qANGjTAtm3bEBcXBysrK+zbtw+9e/cGADx58gTS0tKFTiv5+flh8+bNP1QLhOM4ODk5sdTX4uDz+X+dN+nr16/IyclBlSpVoKenB1NTU7x8+bJCjs1xHAYNGiTmJfjvv/9w9erVAus+f/4c/fr1K2CIlpScnBzcunWLGYKzZs3C5s2bC6z35s0bPHnypNB9TJkyBXp6eggODgZRXiVOkbdRX1+/TO36XZAYA7+Iu3fvir24RMunT5/g7++PiIgIBAQE4MOHD4iKioKUlBQUFBRQrVo1hISE4O3bt4iPj4ePjw9CQkLYev7+/vD390dkZCQCAgLw6dMntq6vry/evn3LOr3MzEycP3+elV0OCwvDmTNnMHHiRPTs2RMfPnzA27dvceHCBfTv3x/Hjx+Ho6Mjk609e/YspKSk0KBBA/To0QMCgQBCofCnzHfl5uaiV69e7Do1atQI+/fvR0ZGBm7dugUej4dmzZohMzMTHMeJiePs3r2bbXf37l3cuHEDly5dQnR0NIKDg5kYTtOmTcu93X8aHMcxA6BVq1aYPHkyhg4dWuK6EUKhEF5eXrCzs0NiYiJ27NjBYhI+f/6MGzduYMGCBahbty46deqEoKAgxMfHo3r16rCwsICnpyeEQiEePHgAaWlp3LlzBwBgbGwMZWVljB49GkCe7kDLli0xfPhwyMrKIi4uDhzHlfpFIQpgXbVqFZYvX17suikpKahVq9YvdeX+TC5evCimyTB//nwcOnToF7ZInAcPHmD06NHMM7R58+YfGoRMnjwZa9euBZAXK3P06FEIBAIoKyuz4O/8nD59GrKyshg/fjwiIyPFBh1JSUkFxK/+NCTGwC8kPDycublFN1ZRkcW3bt1iozIfH58yBR8mJydj5MiR39XqT0pKwoEDB5iRkJ+4uDjMmjULq1atwrt37+Do6IgFCxZATU0NO3bswI0bN6CsrFyucqn5HzyiPGnb9u3bs/nax48fgyhPtCO/C1koFOLw4cMs4re4pV27diCifyIv+3usW7eOeaF0dXVx48aNEm975coVyMjIoG3btnj79i2kpaXRoEEDBAUFoWPHjmjdujWEQqHYtFNMTAz69euHypUro3///gDyRnFPnjxhEeIJCQkYMmQIrl+/DiAvCHDIkCE4efIkC2DcunUriIgpWhY1N8xxHKKjo7F69WrIy8tDUVGxRHrv2dnZGD16dIG0SR8fH6xevRrXrl3DkydPSjWH/StITExEw4YNWZ2RiRMnFpgmEwqFcHZ2/mWpv4Vx79491KpVi5UIJiIWxPmjdO/eHbt37wYAfPz4sYAq6rFjxyAtLY0+ffogNzcXCQkJrO9o2rRpkTEXfxISY+AXo6OjAwUFBaxYsQLjxo0Tm8cSCoVMajQpKQnp6enIysqCiooK5OXlS93pfPnyBUpKSli7di1yc3ORmZnJOoTMzExmYKSnp0NWVhY7duwosA9paWmoqamBKE9ZcfPmzbh//z6WLVuG5ORk1KhRA/Pnz0dYWBhatmxZLiky+acGtmzZAldXV3h4eLARp0AgQKVKldg6lpaWcHZ2Zmlve/bsQWxsLD5//syinW1tbQsYBBLPgDgRERFo3bo15OTkWDT89zh79iymTZuGT58+4fnz59i2bRuMjIxgamqKFi1aICkpCQcPHoSBgQHz3hARjIyM4OPjU2B/27Ztg4eHR4nmdwcPHoxx48bh3r17MDQ0ZFNoV69exblz5/Dy5Uu4uLiw2If8S2nLXQ8bNgyGhoYsFTN/dcbatWuXal8VzfPnz2FiYsI0Gjp27MjiAn5nRJ5HAExJtDy1I7Kzswvd39evX6GmpgYXFxdmYH78+FHs/tmyZUu5teNXITEGfjHdunWDtLQ0fH19sW3bNnTt2hVPnz7FokWLUKdOHfB4PACArKws1NXVERkZiePHj6NVq1ZYuHBhqY6VnZ2NIUOGwNvbm9V8uHbtGoRCIevQAODo0aMgIrRo0UJs++3bt6Np06YYO3YsXr16BTU1NaxevRr16tVDw4YN0blzZzRv3hxEefmyRHlCSz9KZmYmXF1dUb16daSnpyMtLQ1Lly7Fo0ePwOfzkZSUhEmTJhU78v/WU8Hn85GYmAiO43Djxg2sX7++wsRZ/iRycnLQvn17qKiolChHf+XKlWjVqhUGDhzIqh2uXLkSo0ePRkBAAL5+/Yphw4ZBRkaGiaz07t0bDRo0gFAoRFJSEkxMTPDu3Tvw+XzY2dnBzc2tWGOA4zg4OjqiTZs2cHJygp6eHhwcHKCtrc1+fx6Ph//++49NMQ0ePJjFkRQ2P10UfD4fixYtYlkqI0aMwNmzZ5GamoqjR4/iv//+g5ycXIH2CgQC3Lhx45frWoiqPZY0eK5v375YuXLlz2xSiRkyZAgmTZoEIG9Ks6yxA98iel9NnDgRVatWFbvPg4KCUK1aNWhra4tVVc3JyRHrX76VEv4TkRgDv5g1a9ZAUVERa9euhbm5OTp06ICDBw9i3759WLp0Kat4OGjQIOjq6rISrevWrWO65ElJSRg/fjzc3d3RqFEjBAQEoFGjRhg9ejTc3d1x8uRJtGjRAq1bt0b9+vXx4sULXLp0CdOnT0dERATrsEXzZyKt7G+jvlesWIGWLVsWCKB6/fo16tevDwsLCyQkJKBbt25ITEzE8OHDce/ePURGRsLT07NEGQmDBw+GiYkJPD09cfLkSQwePBj+/v7o378/s775fD5sbGwKDcIsrLBV//79f6tsiD8NT09PKCgolOgFEhwcjCpVqmDcuHGFBqGJSsKKFlNTU0RGRoLP56NZs2YYNGgQi87es2cPDAwM8OnTJ0RFRRXrCVuwYIHYNJIooNDY2BhEBFVVVaxevbrAvTFr1qwSXweBQID//vsPMjIy6NatG86ePQuO4xAeHg4jIyO2z7p16xa6rZqamlha5YMHDyq0gua2bdugpaVVqinGSZMm4ebNmwCA9evXY+TIkT+reQxR+/h8PgYPHsw8GGvXri3XIF9R5kLz5s0B5I3281eYffjwIbS0tGBpaVmgjsm9e/fYNNqnT5/+ikqoEmPgF+Pq6opq1aqVervk5GQsWLAAYWFh0NDQYG7yQYMG4dOnTxg0aBAWLFiARYsW4ebNmxg2bBjc3NxQpUqVElmxgwcPRv369cU+S09Ph4mJCbp161ZAwKNy5cowNTXF4cOHoaCggK9fv7LiNQ8fPoScnFyRoh+iiPKcnBxs3rwZpqamaNOmDUaOHIlKlSpBSUkJlpaWEAgEuHv3Luzt7aGkpFSkF0BeXh6tWrXCqlWrSqRhL6F4Zs6cCVNT0xKvn56ejrCwMAwdOhStWrXC7t272XRXu3btYGZmhsWLF7PpJktLS1aU6NKlS3j48CHzWon6kjp16kBdXV1Mfji/gScqfkVEePr0KftcJHstWvT19eHs7AwTExP2mYmJSYmCAjMyMkCUl80j4ty5c2w/Hh4euHv3rtiLY8mSJSzoMTw8nL00xo0bh+rVq1eYRHJgYCCuXbuGw4cPlzk1U1SJFMjr66dPn17u7f/w4QM0NTVx+fJlBAYGwtTUtERFqcpCRkYG/Pz8cO7cuQLfHTlyhMlZFxZHNHHiRBARjI2NoaGhgUmTJsHFxQVDhgz5KW2tCCTGwC9GS0sLurq6312vWrVqRc5LTZkyBb1798a6deuK3Ud8fDykpaVLpNq1b98+dO/eHXPmzIG7uzuCgoLQt29f1KtXD7Vq1YK6urqYO23YsGFQUFCAUChkI7gnT57A3t4ec+fOxYcPH8SsZy8vL3h4eCAmJgbTp0+HhYUF/vvvP/b9ypUr4eLiAi0tLTg6OsLR0ZGlu8nKyqJFixb4+PEj7OzsoKWlBSkpKdSsWRNHjx7955Qdy4KouE3Dhg2hp6eHxo0bo1WrVmjbti1GjhyJGTNmYMKECZg6dSpMTEzQvn37Qvfz5csXHD9+HB4eHjhx4gRevHiB/v37Q0ZGBpqamixjRhSPcf78ecjIyKBKlSrw9fWFm5sbiAjW1tbMk3D79m1s27YNPj4+ePHiBbKzs1GzZk0oKiqy2IWNGzfCzs4OAoEAX79+ZRoHhRl/ISEhWLRoEa5cuSL28nr69Cnat2/PPEy1a9dm5cZzc3MLBCDm5OSgQ4cOUFRUZJHj+YNbC0tv3LlzJ5YtW1bgc6FQyJQajxw5wtJ7AaBDhw6lUufMzc3F9evXiywvLRQKUa1aNQwbNqzE+/wer169goqKilhRpPIgIyMDy5Yt+6FU0dISFxfHphzS09OxePFiEBEGDBhQQBwtLS0Nu3btgqWlJfr164eYmBhMnDhRTLukqHoZvzsSY+AXEhkZCSkpKaanXhxaWlrFPswvX75kUwglOaZI9W327Nng8Xgsw6BXr15wdnaGs7MzeDweRowYgY4dO8Lf3x8tWrSAm5sb6tWrx9y4IiEYLy+vAi689PR0XLlyBePGjYOVlZXYiKRz586QkpJCQEAADh8+jAEDBsDLy4t9P27cONSuXRuzZ89mL5Q6dergyZMnYp30kSNH4OLiAg8PD8lUQCkQ6avb29tj/vz5cHV1Re/evdGtWzfUqFEDZmZmsLGxQbVq1eDo6IiwsDB4eXmxeyw7Oxtr1qxhHWB+T42ioiJmz56NFy9ewMHBAd26dQMR4cGDBwDy3LHW1taoV68ekpOTsWrVKja/37hxYygpKcHQ0BC6urpQUlICn8/HxYsXcfbsWRa1ff/+fWzfvh05OTm4e/dugRF7afj06ROTq9bS0sLly5exb98+VKlShR3vwYMHEAgEeP78OYhIrFRueHg4FBQUoK2tjTFjxhSZxZCZmVloKty5c+cwdOhQ9vfUqVNx/Pjx77Z75cqVuHTpEjiOg62tLRO9uXHjBgwMDNgct7+/P+7fv8/iBcoL0fN85MgRsdLWly5dYqmoIq9fUdvn98iMHTu2Qksvr169Gurq6lBUVAQRoXXr1tDQ0ICUlBTmzJlTaLv79+/PJM27dOmCzMxMPHjwgBkD0tLSvzwupKxIjIFfiEi4wtTUFPv27QOQF7xXt25dlqvfvXt3jB49GsHBwejXrx9GjBiB7OxseHl5sbQ+AGjQoAGT7SyO1NRU9OrVi1UPvHHjBrp27YrY2FgIhULY29ujRYsW6NChA+rVq1eowMrnz59hb28PorwSt6KXwPDhwzFs2DBYWVnh4cOHePLkCfz8/CAnJwcZGRnMnDmTjRAnTJiAWrVqsX02btxYLE1o8eLFLEJbFBlORBVe8vhv5f79+yAimJubIyoqCsHBwd8tmNOnTx9WR+DKlSvst4mOjgbHcYiPj8fz58+ZZ0hkxFlZWcHJyQlycnJYu3YtK+2tpqYGU1NTzJ07F/fv38euXbugo6MDImLGgcjIsLGxKbRNCQkJLNC2sGIxb968wdq1azFy5EgMGzaMpQZ+29GLJJVbtWoFTU1NvH79Ghs2bACQF3QnLS2NCxcuIDMzE/Xq1YOOjo7Yi93X1xfOzs6szSIvXlpaGnMzjx07Fra2thAKhRAKhd8VL1q1alWBEfKGDRvQqFEjTJkyBW3atMHq1asBiKfDBQQEYMGCBcjKyoJAIICenh4cHR1/2pTEihUrxFQZK1WqxLwhN2/ehLW1NXtuIyMjWd+2fv16yMvLs+0mTJiA9evX/5Q2vn//HgkJCTh48CBatWqFx48fM4/QsGHDsH79erRt2xZTp04tcmqRz+eDKC+dsVatWsx4VFdXh5OTEy5fvvzbp5UWh8QY+MVYWlpCWlqazcUtWrQIcnJyrBMwMjJCgwYN8OLFC5iYmKBatWrIyMhglminTp0A/G+6ITc3F3v27GHuu0+fPuHkyZPIyMhAUlISjIyMsGbNGgAo0q0I5HUodevWLVQHPiUlBY0aNcLmzZuRkpKCoKAghISEIDc3F69fvwaPx4OZmRm0tLTQvn17PH36FC9fvoScnBw6deqES5cuwdnZGUZGRmyfampqcHV1xeHDh8Wkhr9N2xIZTRJ+DJHGhUhOdurUqawaYHGkp6fj6tWrmD17NhQVFQtknORHlFnSsWNH5OTksPocDRo0wIoVK3D//n20bt2a/bbHjx9HTk4Ok0Bu1KgRXF1d4eDggD59+kBHR4e5+vft24fly5ezAFHRPjp27Iht27bBy8uLiWMpKCigTp06aNCgAcsgsLKywoIFC/Dw4UM8f/4c58+fx+XLl5lkuEjv4PXr11i/fj22bduGw4cPQygUYseOHZCRkUHlypXFFBlF7SbKU6Y7duwYatSoASMjI7x//x5v376Fn58fOI6DmpoaC9gtDI7jCr3f9+zZAxMTEzg6OmLChAmF9rUcx+HkyZOsRsTMmTPRo0ePClNOFGXpAHnG2MSJE1n6spGRERvAxMTEMNnj8qCo/iwhIQEyMjJYv349bt68ifHjx0NDQwNWVlY4evRoqY6hp6fHPAPz58/HxIkT0bFjx19SWKi8kRgDvxhTU9MCUfuZmZmQkZEpNne1Xbt2mDFjBvv7+vXruHbtGpvDFI26x4wZAyLCy5cvmVCHg4MDwsPDxSJ1C2PHjh1wdHRkKYwXL16Eubk5Vq1ahWrVqolFFt+7dw/Vq1dHTEwM3r9/jydPnuDEiROsnrhAIMDGjRtRu3ZtVK9evdDj3blzR2zuLf9Sp04drFq16q+I2v0d4DgOZmZm7DeMjIzE0qVLi91GKBSySH9FRUUMGzasyGC0U6dOsd/O2tqazb0+ePAATZo0gbKyMrS0tDBr1izcu3cPGhoabP2TJ0+CiDB58mTIysrC19cXI0aMgJaWFjp06CB2X+QvuSxapKWlWWDiyZMnxeZ9RR6A//77T0ybQrRUrVoV48aNw9WrV3Hnzh3UqlVLbL0dO3YgOjoa8+fPh4mJCbS1tVkEelZWVoH92dvbw9raukAa3Jo1a5CUlIT4+HiMGzeugMgN8L+Ut8uXL2PQoEHYunUrtLW1IRQKcfXqVdjb27MpiejoaKxYsQJv3rwBkDc6/93qbQiFQty4ceOHy/1+S/5U6fxlt/39/ZkBdOPGDeTk5EAoFGLp0qVlHlg0adIEcnJyUFVVRZs2bcrrFH4LJMbALyQrKws8Hg/W1tZin4uilsvyMOfk5GDIkCFszjE4OBiHDx9man2nT59GYGAgy8uvV68egLwa90pKSkyJrWfPnlBTU4OWlhabz/Ty8oKamhp27tyJlStXinWy27ZtAxGx0cigQYMKRKBzHAd7e3toaGhg0aJFGDNmDG7evIlnz55hzJgxWLNmDTp27Mg60lq1amHgwIFiEqkSygfRaMnT0xPv3r0rUSW3xYsXg8fjMVd/cYjqXgwdOhREVEDOOCYmBu7u7iy4UORFEC0i75Kuri527dqFTZs2iX0/d+7cQg3HM2fOgOM4pKSkfLeNAoGAxdqI5LvzX4fFixdj2rRp2Lx5M7Zs2YIHDx6IVSkUaWkQETIyMsRyz7t27VpAqbAwUlJSYGFhwZQVv4XjOKxfvx5ubm64c+cOOnbsCGtra8jKyooV3ImLi0PlypXh7e3N9vu3G85CoRBTpkwBj8djxqSmpiYOHDiA4cOHQ15eHiNGjGDXYcuWLdDV1QVRXtp0WbIq9PT0MGvWLOzduxc8Hq9QI+5PRWIM/GJEnWX+m0ooFMLf37/YUfuCBQt+KNhm//79rLMG8iR9a9asidu3bwPIq//erFkzzJ07Fy1atGCdjIju3btj0KBB7O8vX75g7969LOCqT58+ICImaXzhwgUYGxtDUVGRSd0SEfu/tLS0mG7A9OnTy3xuEr7PiBEjoKCggJCQEFSuXBkaGhrFrs9xHNTV1Vma3Pf4VkK6qHiEY8eOia3XpUsXuLq6supwIsGh169fs3XmzJmD3NxcNu3wvZoCZSU9PR1169ZF5cqVYWhoiJ49e8LW1hZRUVEQCoXo0qULLC0tWfAtx3EsZbE0Mr4ioyU3NxenTp0SM2IuX74MImLPn52dHYgIBw8eRGBgICwsLNCnTx98/vz5r3/5i4iLi4ObmxuLFfHw8ECrVq3E7iMrKytWY+PUqVPIzMxk3xVVhKgkWFtbY8iQIbh48SLzuP4tSIyBX8yXL18gIyMDV1dXsVS9+Ph42NnZsSCmbzE2Ni7STdWqVSvs2rWr2ON+/foV165d++484smTJ6GoqCgm18pxHExNTcXmmA8dOsQ6rIEDB8Ld3R3NmzeHiooKzp8/j1q1asHGxoYJmHTv3p3N8YrSwkSLyBUq4efh4OCA6tWrIyIiAn5+ft+93qIpJmdn5xLtXyAQoGnTptDU1MSoUaPYi4rP5+P169cIDg7G+/fvcf36dbHfXuRZAvICzIgIO3fuZHPo+TvgZ8+eMePgZxEdHY2goCDo6+vD3NwcmpqaaNGiBZo1a8amDfLj4+OD6tWro0qVKuyzuLg4fPjw4bvZLqdOnYKUlBTzhIkMaz8/P/b/b7OGdu7cCX19/RJVXPxb6NevH6SkpODi4sICoUXFxnr37s0CAGfMmAEiws2bN5GZmYn69euX2eMK/K988e7du2FtbY3GjRv/Vf2UxBj4DRCVIs4/lx4bGwtra2sYGhoW+sJev349zpw5U+j+ZGRkvltzPTIyEl5eXmUusJGTk4PPnz9j4sSJePXqFeuoMzMzUbVqVdja2uLLly9YsmQJVq1ahfbt2+PEiRPw8vICn8/HzZs30apVKwBA3bp1xV4IohQ0CT8PUbDbpEmTcPfu3WLTziIjIzFo0CAoKCiU6rfhOA7Jycks2wDIE64pLCakqFGWm5sbDA0NwXEcDAwMQESoUaMGgLypLSLCqFGjSnn2JYfjOMTGxiIyMhKPHj1CQkICEhMT4ejoCCLCxo0bC0T8Dxo0CPb29mxKYcuWLVBUVGTrvX79ushpmY8fPyI3NxfLli2DoaEhvLy8sGHDBubBa926tVisTkhICMaOHYuoqKgSyUX/DVy7dg08Hg+ysrLYvXs3du/eDXt7eyxYsABXr15l11YUDKqqqorz58/j1KlT0NTUhJSUVImFjHJzc8FxHLZt2wZlZWU4ODjAw8MD8vLy5a6x8KuRGAO/CSI3V371MqFQiC9fviAjIwO9evViLnwRz58/R4MGDdCvXz8AebKxDg4OaNOmDebPn4+RI0fiyJEj6NWrFw4ePIhevXrhzJkz6NWrF6pVqwYiwvXr19G2bVt06NABkZGROHr0KJo3b86CGseNG4d69eqxdCyBQMAKeqSlpYGIMHDgQJiYmEBPTw9fv37F0aNHmYTsx48fWdBX7969wePxYGBggNzcXGRkZCAjI4PdNyI3sYSKQRQYJysri+rVq+PSpUsA8gzRp0+fwtvbG7dv34ahoSHk5eVZFHhGRgbevHmDy5cvY+/evdiwYQNWrlyJVatWwdHRETY2NmjXrh369evHVAaJCGPGjMGhQ4fY3yKPkJmZWYFpKBGiFMiZM2ey7UTt4DgOa9euZSP0Fy9eQCAQQCAQwNvbGz4+PgVEhh4+fIigoCBcunSJRZ/7+vqyIknJycnQ0tJiKXujRo2CtrZ2gWkOoVDInll1dXUsW7YMJ0+ehJeXF0xNTSEjI4OOHTuC4zhkZGSwQFoAMDQ0xPz584v8XQQCAcaOHQtTU1Noa2ujWrVqTA307du38PDwgKmpKS5duoTIyEioqanBwcEBFhYW2LlzZ0l//j+aN2/eiBWcsrGxYf8XxUv5+/uLZat8+PABfD4fDg4OsLe3/+4xvLy8IC8vjwYNGoCIMGLECKSmpmLhwoXQ0tL62adY4UiMgd+EjIwMmJiYoHXr1gUs/KCgIGhpabGXKACYm5ujbdu2qF27Nnr37g0gT7tbNMpu2LAhBgwYgAMHDqBt27bYtWsX2rRpg2PHjjFDgIhw+/ZtKCkpMVW1/fv3o2HDhkzARVTchYgQFRWFmTNnQlVVFfXq1YOXlxdUVFTYw6Kuro4VK1agT58+TJhItBgaGsLAwIDNqfr4+IAoL7OhV69ebNQn8QpUHD169EDlypVhZ2eH2rVrQ11dHZMmTWIiLKJFpEUQGBgIFxeXAt8rKSlBTU0N0tLS6Nq1K8aPHw8LCwvIyMhg0qRJrKPW0dEBkOc237x5MysbPWHChCLbyHGcWL2JqVOnFvg+f4ZBzZo1mQYGUZ6I0YIFC5jSYf5F5GIeOHAgqzTIcRyqVauGvn37AgAOHjyIWbNm4cqVK4V60aKiouDq6gplZWW2XyMjI7i5uRVahRHIG9kmJCQgKytLLKc9MzMTy5Ytw+vXr5Gbm4vw8HDcuHEDs2fPxurVq5l3JS0tDfb29lBTU4NAIEBUVBTGjRuHDh06wMHBAZcvXy7ZDfCHk5KSggMHDmDDhg1MnVROTg63bt0SW69KlSogIhbQeeDAge++p5KTk6GrqwsLCwtYWVmJFbOytbVFx44df8o5/UokxsBvxPnz50GUl0b3bTBQUlISnj17Bj6fDw8PD2hpacHFxaXQ/ezevZulGBXGhw8fMHPmTGzbtg1paWlwcXEpMoCM4zgcPXoU27dvR2ZmJl6+fImlS5eKabK/fPkSa9aswa1btxAZGQk/Pz/cvXsXu3btwuXLl6GiogJzc3PUqFED+/btw/Hjx5GYmIjDhw/j7NmzePjwIS5cuAAiYqNTCRVLdHS02ItyyJAhmDhxIvbt24exY8cyL4KRkRFWr16Np0+fIjw8nBmuHMeJRdp/+PABW7duRXZ2NusX8stNiwgNDf2uMp5AIMCRI0cwdepUGBsbo1u3bli0aBHmzp2LtLQ05ObmwsfHB7du3UKrVq1Qu3ZtbNq0CRcvXoSNjQ0qVarEDJh58+Yx74IoxS0hIQFxcXHseKLCSSICAgIgIyNTbE0PkehSafLNPTw8YGZmhrVr14LP5yM3NxeVK1fGxYsXv7ttUlISTp48WWDg0KhRI8yYMQPW1tZ4+/btXzWn/S0cx7HUaTs7uyIj+6dNmwZVVVU2fXD48GEQERODEgqF2LJlC/777z/Uq1cPBgYGqFatGpSUlArsMzs7G9LS0hg/fvzPPblfgMQY+M0YPHgwiAhbt24tVETj2bNnUFJSgouLS5EP+sOHDxEWFlbkMTZt2oRp06axv4ODg3H06NEif7egoKBClf8cHBxAlKc8uH79ehgZGUFHRwexsbGIj4+HsbExpKSk0KJFC1bli4jYw5eSkgI+n48lS5agTp06UFRUZNkHEiqW3bt3gyhPbEdLSwuKiopMXEVGRgbTpk3DuXPnypSOlZOTA3Nzc/Tq1euH2ijyJuVfHBwcilz/8+fPBdZ/8OABiAg3btwosL5QKMSqVasKfXY+fPhQovTL0jB8+HBUqVIFMjIyrIjXt1r4hXH+/HmxYkwJCQmYM2cO7ty5g/j4eIwfPx76+vpQUFAoMgD5b0AU9zJ8+PBigzNFFS3Pnz+PFi1aQFFREfb29iwepF+/fuDxeKhbty4aNmyI2rVrw9raGvv378fHjx+xc+dOeHp6YsaMGTAxMYGcnFyJpN//NCTGwG9Ifhe7i4sLgoODxb53dXUFEYlF+OdHRUVFTJDoW0aOHIkOHTqwvzmOg52dHZycnLBjxw4EBARg+/bt8PPzw6dPn6CoqIhGjRqx9bOzs5GZmYmsrCw29XDq1CnMmjULy5Ytw6BBg6CmpoZ169bByckJzs7O6N69O7Zv3467d++ycq5GRkZsTk9OTk6sNoGEiuPu3buoVKkSBg4cCOB/te6Tk5OxefNm1KxZs1BN/aKIjIxEq1atWKXCUaNGic3l/giRkZFYv349Jk6cCFdX12IDwXJzc2FqagqivNLW/v7+8PT0BBEVWqUwNTUVmpqaBaaq+Hw+3N3di3T7l5WvX7/i2bNnSE9PL5WhMW3aNDg6OrK/OY7D4MGDmeDOf//9hxEjRuDUqVPFqoz+6bx+/ZplERTnUfTw8GD9qaOjI9asWYOYmBgWgCqKmxGJVYmmm06cOMHksBUUFKCvr48RI0b8lYYAIDEGfkvS0tJw+vRppn9NRFi0aBH7PiYmBh4eHkWOolVVVcXW/5axY8eiZcuW7G+O43DkyBH069cPW7duRa9evaClpYUaNWqw0dWkSZNw8eJFZGdnY9asWZCTk0NkZCSAvPQmImLRtWvXrkXlypXZfTBhwgQQiZeWFaUWamlp4cKFC2W/WBLKzNOnT1n9AEdHx0JLtZaFZ8+eoWvXrsyIXb16NYjy1PjevHlT4fnwIld6Wloa69xFMrQ3b97Ef//9xyL9CzN6srOzYW5uXq7pexzHlXp/YWFhWLlyJa5du1bAfZ2Wlsaua/7r6+/vX2Rw5t+AKCCwefPmRd5XGRkZuH79Onx8fMBxHCIiItCpU6cCXqPClr59+4pNf/3NSIyB35zo6GgWtdyrVy9kZGSgWbNmGD16dJGdd2xsbLE3sKhQSlFkZmYiODiYCZncvn2bBWn5+flh3759kJaWZnOoO3fuhIKCApv7LeyhfPv2LTIzM3H06FGmDz9+/Ph/Rijld+Ply5dQVlZG3bp1cfbs2WJ/h6ioKJw5c6bUVe84jsPXr18hFApx+PBhlj2goKAAXV1ddO3aFYcOHSryXg0JCUHPnj2hr68PGxsbuLi4FFlEpiSsWLECRHlSyk+ePAGfz4e5uTmkpKS+604XGRSxsbHFxg6UFNGUR0niA0SEhoaiXr16BUbBL1++hIWFBTw9PcWe6ydPnsDExERsSvBvRBS/JFJP/R4PHz4EUZ5a4aVLl8Re/gYGBhg1ahSOHDnC9C3+FSTGwB9AbGwsZGRkoKuri6ysLBY5K3K7nj59Wizndfr06ViwYAHWrl2L/v37Q19fH/b29rhz5w4WL14MU1NTNGjQoMTHf/PmDWxtbTF27FhmZY8YMULsd/72oVm1ahU2btwo9pmzszOkpKSgpqYmMQR+Mf3792dFr4rD29sbRITOnTuXWpPiyJEjqFSpEiulO2jQIEyfPh0rVqzAvHnzWK5+s2bNCtwLUVFR0NDQgImJCebOnYuxY8dCT08PRIQmTZpgzJgxePr0Kd68eVNgvjglJQWBgYFiLnKO42BpaQkigpqaGiIiIgDkzb/XrVuXTVGlpqYWayjv2rULampq363w+D0yMzPx7t27Eq0bExOD06dPQygUFvrM8Pl8tG3bFiYmJmLehh07dkBRURGTJk36obb+CfTt2xfKysolVgQUSWF36dIFRISVK1f+NCXLPwWJMfCHMH36dKiqqiIqKopZsfLy8ti9ezeUlZWhp6cHAEwAqGbNmqhZsyYqVaoEExMTDBgwAC9fvsTevXvh7u5eqvnb2NhYyMnJMYngFi1awMTEBN7e3jh37hzu3buHTZs24datW9i9ezeioqIgJSXFyt2KqFevHqSkpODn51d+F0ZCqUlISICCgkKxhYlOnToFb29vpKam4tixY2WaQoiIiMCpU6cQFRWFqKgoZGVlFXiZiWRdGzVqhHnz5uH58+cQCoUsu0Qk9QvkqXWuWbMGLVq0EBvNqaiooFu3btiyZQtGjhwpJmtNRFi2bBlGjx7NMnVEabOPHj3C3bt30bVrV1ZIaPjw4WjevHmR5yQQCIoNzi0tr1+/LlJ6WxS0K3qmi5tWOH78OObOnSuWXfD48WPY29tj6tSpLH7jbyUjIwOmpqYYMGBAidYX6VcQESur/q8jMQb+EBISEqCpqYkhQ4bAwsICjo6OqFWrFszNzeHr68sKnYSFhaFJkyZiebFA3sho4MCBbJRWWjIzM9n/w8LCoKqqihEjRkBOTg4WFhaQlpZmudaiIkh2dnZISUnB5MmToa+vDzU1NSYYI+HXERwcDCLClStXilzH0dGx3EaUFhYWRYrscByHvXv3olevXqzYjLW1NZsaKyxob//+/WjVqhW8vb3x+PFjLF26FI0aNQKPx4O2tjaWLFmCO3fuoGvXriDKU6CTlpbG8OHDoaWlxVIejY2NC5zjkydPmLJndnZ2oeqfz58/x+HDh7Fr1y4EBASIpSWWlq1bt6JZs2YFPvf394eysjJ8fHwgEAiQmJhYqMKgUCgsMp2R4zjs2rULDRs2hLOzM+bNm1ekd+FPRxQkOGXKlBKtn5mZiT59+sDJyUmSwfT/SIyBP4hNmzaBx+NBX18fVapUYcWGjh079t1tAwMDWf44n88XEyYpLpKZ4zgsWrQIlStXRvXq1bFgwQJMnz4dXl5eeP78udjoTFpaGkZGRpCWloaCggIqVaoEGRkZODk5sXzvbysZSqh4YmNjISUlhbVr14p97urqysrxhoeHlyjN7XucPn0aFhYWJZJu5fP5uH37Nrp06YIqVargwIEDpTpWQkKC2Ms7NTUVnTt3xqNHjyAQCGBjY4PBgwfj8+fPCAsLQ2BgYLFxEFOmTEGDBg3Eng9RCmb+xcrK6rt1BwojOzsbJ06cwI4dO5Ceng6O43D+/Hn4+voiKioKO3bs+K5uwdKlS2FnZ1fsbxUVFYXmzZtDQ0MDOTk5WLVqFQYPHvxDRsHvZlCcOHGCZV9JKBsSY+APgs/nM7U/IkKVKlVgb2+PSpUqlWg+9/Xr1wD+Vynu5cuXOHfuHAwNDQvtTBITE9GrVy8Q5ZWUrVatGipVqsQkZo2NjUFUsHjNyZMnoaqqiiFDhrB54c2bN+O///6DlJQUvnz5Uj4XREKpefLkCapXrw49PT1cvnxZLHZjwIABzBgoL3x8fDBp0qQfGj1/S3p6Ovz8/L5rrKSmprJnJX+ho3nz5qFq1arffYG/ffsWhw4dYv+vWbMmPn78iP79+4OIcO7cOSxZsoTd36Vl7969kJWVBVFegabc3Fy0b98e3bt3L/E+3r9/j927d393vWfPnmHbtm0QCoU4ePAgFi5cCCAvvbG08Q8zZsxg9SGCgoKK9TBVJN26dUOtWrV+dTP+WCTGwB8Gn8/H4sWLC4xO2rVrV+IpgPPnz6NHjx4A8goeqampic3NCgQCNGvWDDIyMlBWVi4QPZ2VlYW+fftiwoQJmDdvXrH513w+HzVq1ECrVq2QkpICfX19ls8uoWLgOA7Xr1/HwoULISsri3r16sHf3x9bt25F8+bNCxTa+d25e/cuiMTreBSFjo4Oy1wRPSt3794t9bx/cHAwKwjUvn17EP2vYuGQIUOgrq5e6n1yHMe8FPk/K4k2wKNHj0od0Llnzx7Mnz9fbFS/dOlSFpgM5BkXly9fxrp165ix5Ofnh69fv7LjPX78GEeOHGHVS//77z/07t27RB7Kn8Xt27chLS2NMWPG/LI2/OlIjIE/jOjoaCatKqrn3qpVKygpKUFGRqZMtbq/dfnNnj0bPB4PjRs3RlRU1A+3uX79+ujUqROAvCh2TU3Nv1om9XdjxIgRLKVu1KhRrJMfPXr0Ty99m5GRAW9v7zJXxyyM5ORkPHnyRCyOpShCQ0MLGM7fK+/9PW7evInGjRvD0NAQT58+RVJSEqpUqQJVVVW4uroWMK4yMzOLvd99fX3RtGnTIuV0ReTfh4WFRamrNW7YsAGDBw8GkNdXHz9+HP7+/kyNkeM4yMjIMPGd/fv3AwCrL9G6dWuxKROO4/Ds2TOYmZlBWVmZlSevaN6/fw85OTlYWlpKgpN/AIkx8IeRk5MDW1tbEOUVkHFycoK0tDSWLFkCLS0t1K5du0wPhIeHB1M1s7S0xIgRI8qtzQMGDGAjso0bN4KIcPDgQZYOpaWlxVK9JJQvZ8+eBRFh27ZtePfuHZo1a8Yi59XV1cHj8cpFGbAoRHPsJXlx/yxmz57NcvqHDBkCBQWFH1YTDAkJgY2NDTQ0NHD16lWEhYVhyZIl0NHRgaGhIdauXYvDhw+jcePG7FktynP38eNHODg4fHfao0uXLhg9ejSAvHTfsojhiAx/UbyPqIbJ9evX4evri5UrV4KIULVqVVZA6sqVKxg4cCDOnz/Ptg8PD8fgwYMRHR2N9+/fMznlikYgEKB+/fqwtrYutQ6GBHEkxsAfSEZGBlatWiU22pGTk2PuU1lZ2VJryE+dOhVaWlrIyclBpUqVitV8Ly0xMTFQVVVFixYtkJmZCSLCyJEjxdrv6upabseTkMeaNWtAROjQoQMTAJo+fTr27duHc+fO4dixY5g5cyZ7UYtEpsqTmJgYBAYGlut+Q0NDsWTJkhKnO8bHx0NFRQXTpk1DZmYm7Ozs0KBBgx/2TsXExMDW1haVKlXCiBEj8ObNG0RERKBbt26svK5I8ZCIsHjx4gIZAbdu3UK1atVKFKy5fPnychE8AvKMgqSkJOYlqlWrFkaPHg2hUIiwsDAsXboUCxYsQEREBK5fv442bdrg0aNHbPsLFy5g0KBBuH//PvtswYIF8PT0LJf2lZTNmzeDiMrkEZUgjsQY+IPJyclhUr9EeZLFW7ZsARFh1qxZpRo5CIVCFmAoSgMsT0R54yKt+CtXruDBgwfYsmULlJSUfpsgpL+FtLQ0yMvLY/To0eDz+YiLi8PRo0cRHx8Pd3d31KtXT2z9hIQEqKmpYfHixeWSRZCfr1+/lmvQ6OPHj6Gnp4dPnz6VeJtFixZBRkYGvr6+uHPnDogIs2fP/uG25H/+8usTZGZmsmyf/Iu0tDRq1qyJwYMHY/Xq1Vi7di06dOiANWvWwMPDAytXrsSMGTPg6uqKfv36FTp1EB0dXe4SuVlZWYiPj0dcXBz69++PoUOHQkZGhgUanjt37ruBhsuXL8esWbPKtV3FERAQwFKcIyMj4eHh8dfrKfxMJMbAH46oCptI6tXPzw/Tpk2DoqIievToUeqUp7i4OBBRgbSz8kDUcWpoaIDP52P+/PkgIsydO7fcj/Wvc+rUKRARHj9+jJycHGaEPX36FBzHFToq3rBhA3up5R/x/QhCoRBmZma/XN0tJycHVlZWsLe3R2ZmJkaPHg1FRcUypQTmJzw8nL3oa9euzTwgZ86cgZycHHR1dZlxfuXKFWzduhXDhg1DvXr1oKKiIubZU1dXh6amJszNzdGkSRNUrlwZ5ubmSEhIQFhYGPz9/SEUClGzZk3s27evHK5K4Rw/fhxr1qxBQEAAOx9dXV1WdfLp06fYsGFDAW8Px3HlXtmxKGJjY2Fra4vq1avjw4cPTOLczc2tQo7/NyIxBv5wEhMTWeU/0RIYGIizZ89CRkYGjRs3LrT8cFFcvnwZRFSqUVdp+PTpEwsmmzVrFmuzhPLj9u3b7LcXvfRXrVqFFy9eFNtZp6am4uzZsxg3bhx7hgsT3Sktx44dQ1JSEgQCwS8LMgPyUmuJiLn0paSkYGFhwTxiZWXXrl0Fpux4PB5cXFxw584dNG3aFHJycpgwYQJOnz4tVsMjMjISCQkJhU6jfPr0CfLy8nBxcYGVlRWGDh0KAPD09BTL/ilvAgICxKqaCgQCREREsHtp8eLFYlUTw8LCYG9vX2wFyfKC4zicOHEC2tra0NbWxvv379G+fXtUqlQJRIQ1a9b89Db8rUiMgb8AjuNw+PBh1hnZ29sDyMsp19fXR926dUscQ3Dw4EEQUbkF43z+/Bk7d+7EypUrMWbMGEybNg0HDhxgGvA2NjZo165duRxLQl7ZXjMzM7Ro0QI5OTl49uwZ7t+/DwcHh2Ln7a9cuQJLS0uxl39CQgJMTExw9uzZH24Xx3Ho1KkT9PT02KiW4zjw+XysX78e7du3L3G53RcvXqBOnTplmnowNzdn5bi9vb1Rp04dmJmZ/fDUyPv379G1a1dMmjQJmzdvxp49e+Dv7w8+n4+srCxMnz6d6XI4OTmV2CPh6ekJTU1NKCkpYd26dT/UxpIyYMAA5q2Li4tDlSpVsGTJEiQlJWH//v2IjY0V6x8SExPRuXNnvHr1Cn5+fkUGEwqFQowcORITJ04sdZs4jmNGChGhZ8+eiI2NRUBAAMuo4vF45SoV/a/xRxsDL168YKIgEvLyiImIufOAPLEUVVVV9O7du0T7WLNmDRQVFX844IvjOMyYMaPAnCmPx2NTBb169cLs2bPLNe3sX+fJkycgyqvv/unTJxARPDw80KVLl2JH+W/fvsXkyZPFjMaMjAzMnj2bpZeWxUuQnZ3NRpRTp07F4MGDERsbCwBwc3NDy5Yt8fDhQwwaNIjdcy9fviz2/gsMDMSoUaPYfkqDSCp7zpw5EAqFePfuHXg83g+73dPS0mBsbMzkcENDQ2Fubo727duLTcncvXsXMjIyaNq0aYkNgtTUVLi5uYGISq3KWBaEQiGys7MhEAhYUbT69evD0tISurq6CAkJKXJbOzs7MXnlYcOGQVFRkf1WY8eOxZ07d0o0pZCamoohQ4agatWqkJeXZ0Jn+atsxsfHs76lVatWP37y/zB/tDFQrVo1yMrKYsGCBXBxcfnnXypCoRAvXrwoELG8d+9eEBHevn373X1s2LABsrKyJe5os7Oz8ezZM1y6dAnnzp1jldhEGghExIRfvjUIRP+3s7OT5AeXA9nZ2bCwsIC2tjYuXboEPp+PR48e4dOnTz9s3GVmZsLa2rpEanciEhMTYWZmhqNHjxb4Lj09HX379oWmpqZYfIK/vz+IqFhvRFxcHM6ePQs/Pz8xmeO3b9+yKbHU1FS8evWKjWC/fPkCPz8/CIVCLF++nEX3A3nz4aL//wi5ubnMmPr48SNat26Nxo0bF+gP69atC3l5+VIFAV65coWNiPMfrzyDPd+/f4/169dj6NChYs9r/fr1QZRX/Cw4OLjYfeTk5ODTp09YtWoV+vTpgzlz5kBTU7OA9kL79u3Rtm1b3L59GxMnTsT27dvx6dMnfPnyBefOncPYsWOhra0NoryCbKJMisKuWe/evUFEv3QK6m/gjzYGHj9+jK5du7LgKCJC165df9p8959KRkYGVFRUMGjQoO+uGxcXh0qVKmHmzJnFrpebm8us/vwdh6KiIjIzM3HlyhXUr18f27ZtY9XqAgMDcfnyZfTr14/N8XXs2JEVqKlevTrWr19fLvPU/yIilykRwdDQEABw9OhREFGJXPAfP34sMr4kNzcXS5cuZS/f4n6j/Eb58uXL4e/vX+Jz4DgOt2/fZqNmDw8PVjhIhCiFtnfv3mIpsBoaGli9erXYOiKVwnHjxsHOzo6tKycnBwMDAwB5L+dOnTpViN7+9u3bQUSsnSUlKysLnTp1goyMDMs40NbWLtcpNjs7O8jJyaFGjRqwtLREz549MWXKFPD5fFSqVIndU9+Dz+ejSpUqMDAwwPDhw/Hs2TMAeWmUIlnqV69eoV69eiAiGBgYsPtWlIppaGiIqVOnIigo6LselIyMDIkhUA780caAiKSkJMyZMwcyMjIgIjRs2LBEUqX/Cr6+viy3vyS4ubnB0NCwWHnj8+fPgyhP5/3Fixf48uULzMzMoKOjU2L3Z/60LCsrK7EArJcvXyIyMhK5ubmIjY1FUlJSifb5L5OamgojIyNoaWnh4cOHAPKi3c+dO1doxbtvUVdXx4oVK767Hp/PR8OGDQtVL/z06RP09PRYFc2ScP/+fXh5eRX4nOM49OzZE0uWLAGQ90IMCgpCYGAg4uLiEBISUmrPgAhHR0eYm5sD+F+tjgkTJpSbISoqOiQaEYuqhhLlFQsri8YBn8/HsGHDIC0tjUqVKqFBgwZQUlIqFzXPBw8eQEVFBTNmzCj0+z59+kBGRgbe3t7F7uf58+eYMGECWrZsCXd3d7HfVV1dHRMmTEB8fDysrKygqamJgwcPguM4uLu7M0+hv7//b1cI6V/grzAGROTk5DDXElFeIR4J/ytZm39etji+fPkCAwMDsYjib1m+fDkUFBTY/kRZCN+WTv4eVatWLRBXQJRXa+HbqYWGDRtix44dP5wO9jdz8+ZNEBFTGSwNz549K1HmiUAgQIsWLZhIUXx8PHv5C4VCLFy4sMR1MpKTk6GmpoadO3cWuY7oHvPw8ICcnJxYTExZcXd3Z8V2gDzBHDk5OXTs2LHM6XEcxzGvpEAggKamJjw8PAAAHz58YN6MH82Fz87ORk5ODgv2/REDxsfHB82aNWPTAUVVSczMzGRFx4oqRw3k6YmYm5tDXl6+wD3o4+MDjuPQsWNHaGtrswGbSPVQtIi0DSRULH+VMQAATk5O7Kbau3fvL2vH74ZIja5+/folih04dOgQiAjTpk3D8+fPIRAIEBQUhNOnT2PUqFGQl5dH//79AeR1giJFweKCi76F4zhcuHABW7duxb1793D16lXcvHkT27ZtQ8+ePdnv+PTpU3h6erK/9fT0EBkZicDAQLx9+1biNcjH+/fvUbduXTG1uJ+B6AU9e/Zs1KxZE0T0XW394vZVktGtv78/KleuXC7FaETZN/nvV5G369atWyXax7Vr13Dx4kUAeS/Ltm3bQlFRkc1rBwQEQCgUIjQ0FA4ODiAi3Llz54fbLuLIkSMgojLf/+/fv4eWlhZsbW1x5syZ7/4GycnJkJKSgpGRkZgBIhQKsXbtWuZ5KcyY4vP5sLa2xurVq8Hj8ZgHis/nQ0pKCsOGDWOewtIKkAkEAvj6+iI6OrrUyqsS/sdfZwyEhIRg0qRJePr06S9rw+/KnTt3YGdnB3V1dbi7u2P//v1Fjio4jsOkSZOgqanJXPeil7G5uTlGjBgBf39/hIaGihlgpUFkoIiWO3fuMM307y1jx44V+7s089J/M7m5ueDxeMWOtIvi5s2bpc7TfvPmDYyNjSusjvyRI0fKJZ89KSkJ6urqMDQ0ZCNYjuNgbW0NJyenEhkngwYNEotg9/T0xI0bN9iUDMdxuHv3Luzs7FCpUiVMnz69XF9WomBLUaGh0vDixQvo6+ujVq1apfJUvH37FrKysmIBl0KhEObm5tizZw/77MCBAxg3bhyAPPXHZ8+eYevWrXj9+jVat24NfX19xMbGguM4KCsrs+dYVEipNIjiMESLZHBQNv46Y0BC8SQkJGDy5MmwsLAAEaFevXos1acwhEIh1qxZg2nTpuH69euIiIhAWloaiAju7u54+PAhqlevDiLChg0bStUWkSysaJk6dSqysrIwevRomJmZQV9fn6U2ycvLY/Lkydi5cyfmzp2LN2/esBiRatWqlSnN7G8kJycHUlJSZTIGPDw80Lhx45/QqqKZMGFCiWNZyptbt25BSkoKpqambDR7584d8Hg8VuJb1EaR5n5ycjKbfszJySnw3Lx79w4BAQGIjo5mCptSUlI/pRgUx3HQ1NQslVtdIBBgzZo1UFBQQIMGDcr03Li5uaFOnTpin31bJ+Lo0aNwcXFBYmIi9PT0cPbsWQgEAmzfvh3+/v5QU1NDx44dmX7A4cOHS+SdKAwnJyfUqlULbdu2BRH9sIjUv4rEGPiHefr0KerUqQMigq2tLSZMmFBonEVhhkJaWhoTFxFVpouMjCx1G9LT0/Hw4UM2SviWnJwcHDlypMSCNP86otiNp0+fYv369Th//vxPPV5OTs4PjXYPHTqETZs2lXj9Bw8e/HDFQRGZmZn477//QEQ4efIk+1w0JdW1a1dER0dj6tSp2L59OwBg//79UFZWLjQmIi0tDbq6ujAyMmLps82bNy/3OgL5adeuHdq3b1/i9UUGyoQJE8pcSXLcuHEs+PLVq1ewsbHBrFmzMHLkyGILKe3btw9EhFWrVkFNTQ1EeVLkPyJhnJmZCSkpKcyePZu9fzZu3Fjm/f3LSIyBfxyhUIibN2+if//+qFKlCogIDg4OWLJkCTw9PWFjYwNZWVk4Ozvj9u3bbDuRdsGYMWPYyH7MmDFYt24devbsCRcXF4wcOZKVSJVQMaxatQpycnLgOA49evQotu4Dx3HIyMhgbu309PRSu1iXLVuGOnXqlLpDT01NLVV8iYi6devC3d291NvlJy4uDp6ensw9bm9vL5ZaKBQKUbVq1ULz4729vREaGlrofgcPHgwiQosWLbB582ZcvXq13Is+fcuCBQugqalZ7HE4jsPy5cuZ4d+2bdsfOmaNGjWYQSHKbJCVlUWvXr3Epgq+JTU1FdHR0cjNzUVERASWLFkCHo+HNm3alDkoeOHChSAiLFu2DC4uLiAiSZ9TRiTGgAQGn8/H0aNH0bt3bygrK0NKSgo9e/bE2rVrWSTx0KFD8fHjRyQmJsLDwwMLFy5kc346OjqQl5eHlZUV6tevjypVqkBKSgpDhgz51af2T5CSkoLq1auLVc8TvaQvXLiAhIQEXL9+nRkI6enpICIcOXIEQN6ImMfjFZkJsH//fgQHB2PevHlo0aIFgDytj8JEhb7HnDlzYG9vX+IUsrCwMKSnpyMuLu6H+5cbN25AXl6eneeBAwdAlCfoI6rMR0Ro2bLld/eVmJiIY8eOoVevXiCi7+pzlDe+vr5i1QULQ1TMzNnZGRcvXvzhVEQPDw8YGhpCUVERy5YtY5kSJcn1z83NRVpaGjiOQ1hYGKZPnw4ZGZliMxSKY968eSzoUOIV+DEkxoCEQsnMzBRzzQuFQowfPx5EBAsLCwiFQggEAnTs2BHW1tZiI0M+n4+ZM2ciNjaWjSIkUb4/H5GIi0gFUkRiYiK0tbUxdepU7Ny5E82bN4dAIACfz8eRI0fw+fNnAHl1JO7evSu2bUREBIv2r1OnDurVq4fHjx9j2rRpP9TWwMDAUk1hdOrUCW3atPmhY+Zn165duHfvHtNMGDduHDQ1NaGnp4fHjx+zUXRRCAQCzJ49G9LS0iAi1KpVC5s2bSqz6/1HmDdvHmRkZBAYGFjo96JpvPJum8io4PP5kJGRgZOTU7GZGLdv32ayyjNmzED9+vVhY2MDd3d3VK1atUxtuH//vljc0aRJk3Dx4sUKq574NyExBiSUGI7jsGjRIhARtLS0oKurCwUFBcjJyWHnzp3MeIiLi4OCggL27t2LUaNGQUlJSaINUAGIalO4urqyeI5Jkybhw4cPWLx4MWRkZErlto6OjoacnByWLVsGIE/Vr3r16gWCxYC8l2vLli3ZlINQKCxy1J+ZmQmO45CcnFziTjs4OJgp2f0oEyZMgKamJqZPnw4/Pz8MGzaMua719PSgoKCAkSNHQkpKiqVLCoVCRERE4MGDB2jVqhXk5OQgJSWFhQsXljmlsrzIysqCkpJSkaqGDx8+BBH91EqHTk5O0NXVxeHDh5GamgpfX1+kpKQgJCSEZWucPn0aenp62LZtG4KDg2FsbIyxY8fi5MmTICJERESU6dh6enoFso3MzMzw4sWL8jzFvx6JMSCh1Dx//hzz589HixYtQERsdEREqFu3LsLCwph4ScOGDSU1xisQkbTr0qVLkZubCxsbGyxYsABhYWHw9vYukVt+586dTDznxo0bzIDgOI65eF++fClm4J05c4YpBUZEREBJSQmPHj1Cbm4uHBwc2NTEq1evYGRkhGrVqkFDQ6NE8//Jycn4+vVruanS3bhxAxs2bChgiPD5fPTo0UNMCOvIkSM4efIkbGxs2GempqZYuHDhb5W+7OTkVKQYU1hYGIioVKqQpWXz5s0gIjx+/Jj1C1ZWVhg8eDAMDAzYtc7/Gx44cADNmzfHly9fQERlDnYVZUaJihmJFkm/UzokxoCEHyIyMhJbtmyBrq4uixvIr11gYWGBPn36lGnfHMdh3bp1cHJywrNnz3DixAlJVsF3qFmzJrS1tZmAExFh//79ePPmDXR0dGBvb493794hNDQUI0aMKDTHfPDgwcV2pJ8+fYKsrCwcHR0xderUAiP2qKgoTJs2jXX827dvZyJIly5dwsqVK/F/7Z11XFX3/8df3Et3Sbd0GICF7cRZM8F25qzpZs3ajDmM6azp5pzOqVizO6ao2IEYhIBSAoJ014337w9+93y9I7xIKp/n43EfyjmfeJ8b5/M+7887Ro4cSfr6+jKF3EnM3NVdfEUiEe3Zs4fbBpEoM1UhFovp1atXXL0MycvCwoIOHz5Mjx8/LqdExMfHN3g424IFC0hXV7fCDIJHjhypcPuoNikuLiYAtGbNGjI2NubeNw0NDVq9enU5Re7dbUNJhtR3HZSrgyRKQeI74Obmxv0tJyf3Qdk4myJMGWDUGjt37iQej0fTpk0joVDI3SAkZmZZiYiIoMDAQK52+buvfv36saIkVSDxrl6yZAnFxsbSoEGDqEWLFpSamkrGxsakqqpKBQUFJBaLydnZWapioITi4uIqq9OdPHmSOnToQObm5jRw4EAaMGDAB8nq6OhIgwcPfm+7nJwcun//frUtA/v27SMdHR06fPgwEZU5UXbs2FGmOg1t27YlADRixAi6e/eulIKbn59Pp06doi1bttCIESO476ak1HNDkJCQQCoqKjR79uxy50aNGkWenp51On9+fj7x+XzOguLn50cXLlyg6dOnl1voHz16RGpqaty2xfHjxwmQrarqf7lz5w4VFRXR8uXLuXwn48aNIz8/P+5zGTNmTK1d56cMUwYYtYokG9jAgQO5/8saRx4eHk4uLi7cj9jMzKycMiBJQjRjxgwKCAigo0ePUlpaGhUXF9Phw4fpt99+o0WLFtGoUaPIx8enWiV3PwWEQiGNGTOG5OXlqUWLFpSQkCCVnfFdn4H/hs0lJydTQEAA5xj2888/c0/u9+/fJw0NDbK0tCR/f3/6+uuvKTw8nD7//HOZvO4rIikpiZo1a/beDHqFhYXUvn17GjlypJSS8j6veJFIRPHx8ZSUlESvX7+mc+fO0Q8//PBepeLEiRNcNU5vb2+KjIyk3bt305IlS8jb25t4PB731Nm6dWuys7NrFPe99evXE4ByRdp8fHxq1fmyMiTOwsrKytyTv1gsJnd3d3J3d6crV67QnDlzKCQkhH7++WeKjY3lCsx17dq12n5F9+7d46wRREROTk7l7heSF+P9MGWAUasIBALuBzhjxgxSU1MjBwcHLmSrKry9vcnOzo62bdtGt2/fpvz8fJoyZQoBZWlKJfvVX3zxhdQP3djYmPP+/u/Ly8urHq668REcHEyampo0bNgwKikpoYSEhHIWmrdv35KLiwsNHjyYBAIBzZw5kwwMDOjBgweUmppK3bt3p2+//ZaIypSBZcuWkaenJ+np6XELakZGRpWJZioiNDSUDhw4QGvXrqUpU6bI9N3YsmUL6erq0vbt27ljbm5u5fbBjx07RkRlis6YMWPozZs35O7uTvb29lKllatCkt2yQ4cOnD+MnJwcWVhYUM+ePem3336jEydOkK+vL6WmptLs2bNJU1OzwT3YCwoKSFNTUyq3xKVLlwiAVLhpXSAUCit9Eg8KCiJ1dXVuX//69ev08uVLMjc3J2VlZVq6dGm1FYHCwkJuPklYoqTy4X9fVeXaYPwPpgwwap0hQ4ZwP8TBgweTlpYWDR06lCIjIyk1NZV+/PFHLo/Bzz//TKWlpXT//n0CwDmhHT16lLp37845xL37ZFNcXEwvXrygly9fUlRUFE2YMIE6depEf/31F4nFYvL19eXmr6ry4qfO1q1bic/nk4+PD+3fv5/U1NSknorT0tK4m3dpaSmNHj2aFi9eTETE1Yj4b7a/+Ph4LoQtMTHxg2LWDx06RJqamiQnJ8eVGK6I+Ph4KcvOu5YMsVhMI0aMoNu3b5NYLKZ169Zx1e/u379Pubm51KxZM0pMTOTuS5Jrk4UbN25QSUkJPXv2jM6dO1dl/n5bW1uaPn26zGPXJRMmTCB7e3sSi8XcAi1xrJs5c2adlQYuKCjgfnMDBw4stxWTn59Pe/bsIaCslLqZmRk5ODhw/hzVITs7m7Pc6Ovrc1aI5ORkKSfCFStWkIaGRp1HUnwqMGWAUeuIxWLavn07l4jlXfOdgoICV1NASUmJeDwedenShZSUlMjZ2ZlbfGbPnk2///47zZ8/X0pJkJWCggKukuLWrVubbGjjyZMnCQDt37+f8vPzKSwsjBYuXMiFw1V2kxQIBOVCsxITE0lfX5+rKvfZZ5/R0qVLP0iuK1euVJjtUCwWc/P+9ddfZG5uLvVEP3ToUDIwMKAzZ85wx7Kzs2nAgAG0adMmun79upTVQkKzZs1o/vz51ZYzKCiIjIyMpGL4CwoK6PfffyehUEhPnz6tkSd8bSNJvnP06FGuUqK9vT33+6urbTNJGmzJq6KCV8HBwdz5Nm3afFD6cpFIJDXPf/OXfPfddwSUZYF811r4oU7MTQmmDDDqDLFYTB4eHtwPUlNTk0t53LFjR87bWE1NjUaNGiX1lCgUCik/P59KSkooKirqg55owsPDuaqL9vb2XOx9U8PFxYXGjx9PRGVVPfl8Pm3bto1evXpFZ86cqfTpPCgoiEaPHs1l6hMIBDR37lxas2YNqaur086dOyusZVEZYrGYTp48KXUDv3LlCq1du5bOnTtH+fn5dOzYMZKTk6Pw8HBKSkoqlyinf//+JCcnRzdu3JB53tTUVDpz5swHOahFR0fTwoULuSfdhQsXUv/+/UldXZ1evHhBEyZMIHNzc5mcEusDoVAoZZk7ceIEOTo60rRp02j06NFkbGxcaaXSmiCxytja2pKvry85ODhU2G7GjBm0YMGCD07T/PLlSwJAjo6O5b4baWlp1KpVKyllYeHChTRixAhycXH5oPmaEkwZYNQpJSUl9Mcff9C8efOkfqQHDhygiRMnkpKSEm3btq3Cp4SlS5eSlpYWpaWlkUAgqHYteIFAQGfPnqXFixeTk5MT6ejoNMnQRMmNesWKFeTt7U13796l/Px8atGiBTVr1qzCiAKisqe9vn37ltsKeP78Oe3cuVPm+YODg+no0aN048YNAiC1kH/22WfUtm1bcnFxoaKiIhKJRO/9nCWm5YKCApn26U+fPk3y8vJc7oSa8NlnnxGPx6PHjx9TfHw8ycvLV7vsc11TWlpK8+fP5+oEuLu705QpUygkJIQA1EmoXUREBPfbljhY1laSqHdJTk6udNxFixZJ3WO6d+9Ojx494nw+6mqL5FOBKQOMeiM5OZn69etH3333HRUUFFBGRgb17duXnJycKmz/5s0bunjxIhH9z8R469YtEovFdPnyZUpLS6tyvlGjRnFPQZKFqHnz5k2u3LFYLKaFCxcSj8cjeXl5zsnuhx9+IGtr63JRBVVx48YN+vvvvykqKoqWLVtG27dvp6NHj9Lr168rbC8QCMjZ2ZnLjvfuXHl5eaSurk7z58//oAVq5MiRNGvWrPe2EwqFdPfu3WqPXxEikYiT9auvviI9Pb1Gr2AOHTqUPDw8OFN+VSmDa8rWrVulFuRevXpVmPvgQ0hKSiJtbW0CUKEyunDhQgLAbRV8/vnnpKCgwMlS05oMnzpMGWA0KJKEMikpKVW2E4vF3BNsdnY2NW/evMqFICwsjFq3bk3+/v5c/1OnTpG+vj5paGiQjY0NaWpqkra2NvXu3Zt27twpk1f7x0xkZCS1atWK1NTU6NdffyWxWFzl01JycjI9fPiQ+/vZs2fUpk0bUlRULOexraurW+mWQVJSUqWm6eTk5A9eTA8cOMApNrJQVFREmzZt+qC96nXr1tGdO3e4vx8/fkxycnIfRWEcSUSBZLusNu7PAoGAHj58SC9evJAy15eWllLHjh3JwsKCq2poZWX1wamGJbx9+5Y8PT1JX1+f2rVrx/klvHvfEAqF3G942rRpUoqALEpjU4cpA4wGZePGjQSgSq/y/1JSUlJpURYJ+fn5lJGRQXv37pUa+8cffyR9fX0aMmQIzZo1i/r27Uvy8vIkJydHOjo67415/9jJzc2lGTNmEAAaPXo0BQcHk5eXF126dIk2b95Mvr6+5OHhQa1btyYzMzOysrKiEydOcOGc+vr6NGvWLLp48SK9fv2aMjIyKDExkVq3bk2tW7eWKgM8c+ZMztmwIqoy8R88eJC2b99ebm85JSWFRo8ezX3+fn5+MlcKfPXqFQ0bNoy0tbVlcih98OABRUdHU2lpKXXp0oV+/vlnIiqLZvH09CQXF5dG4yvwPg4dOkQXLlyo1u+sKiRZDSUvFxcXio+PJyKiKVOmkLW1NV25coXGjBlDpqam1K1btw82058+fZo0NTVJU1OTgoODKSEhgdq0aUMAyNXVtcI+BQUFtG7dOjIxMaHNmzd/8HU2JZgywGhQTp06RQCqtZ8rFovpyZMnFRaIEQgEnBf50aNHSU1NjV69esXdqAQCgVQK3IcPH9KDBw8oOTmZevfuTQCkYtk/VQ4fPkwqKiqkpqZGJiYmpKCgQAoKCtStWzeaPHkyTZw4kdzc3LgQLnt7ezpw4ECli+j169cJAA0ZMoS6d+9ORkZGBJRljKyMMWPGkLu7e4Vm5KtXr5KNjQ2nMEjCRoVCIbVr145T2rZs2ULLli2j1atX0/nz54moTOF5d+ERiUQUEhJCmZmZpKGhQZ9//jm5urpWGAGQlZVFmZmZlJqaSqqqqlzUQklJCTfm119/TYqKik26EE5kZCQBIG1tbdq5cydZWVmRk5MTpaSkcGmwJRUwJYWIoqKiqj3P6dOnicfjkYWFBQ0fPpyIygpNSRKSzZ07tzYvq0nDlAFGg7JhwwYCQOfOnZO5T2lpKWlpaUlp/CUlJfT999+Tj48PTZw4kYjKng5u3bpFSkpKNG3aNBKJRLRlyxaKiIiocNwnT55wccr/ja//FHnz5g19//33NHDgQNqwYUOFWzWFhYUUHR0t01Pdtm3byMXFhTp16kTz5s3jnt5Wr15dYfuoqCgKCAiQelp9+vQpl25acjw7O7vK8NL8/HyaPHkyLVmyhMRiMXXv3p1cXV25zHRHjhzhHFVPnTpFQUFBNGnSJM7nZPXq1RQfH08ikYg8PDzI0dGRBAIBBQYGlrNM/Pbbb01GYXwf586dI3l5eerduzfdvXuXjI2NydramoKDg0lVVZUmTJhAYrGYAgICCACFhoZWa/ygoCBSVVWlAQMGUHJyMrcFYGhoSHw+n+bPn9/giZ4+JZgywGgwYmNjqXPnzqSkpMSFr8nKzZs3pW4Ef/75J2lqatLJkyelHKTEYjHFxsZSQkICxcTEkLe3N7m7uxNR2VPgqVOnaM6cOVRaWkrx8fE0depU0tfXpwULFtTORTZhSkpKaMqUKcTn86v8fHNycujnn3+mwsJCWrp0KRkZGZVz8szKypI5V8SGDRuoWbNmdOTIESIievHiBfeU+l/EYjENHz6cBg0aREKhkFasWEGOjo4VZis8duwY8Xg8Lisjg+jy5cukr69PPXr0oLi4OGrRogWpqqrS4sWLCQCdP3+egoKCCJCtaqJQKKSVK1eSl5cXqaioUNu2bSk/P5/mzJlDxsbGVFpaSkpKSjRixIh6uLqmBVMGGA2CQCDgcgC8m0CmKoRCIeXm5pJQKKSSkhL64YcfOBNzXl4eXb58udIkOpIn2+3bt9OaNWsoNTWV/Pz8SEVFhQwMDOiLL77g2nz11VdkaGhI+fn5tXClTZv9+/cTUJbjf+nSpXTr1i2yt7cnNzc3butGsu8sqaMwefLkGnl+v3z5kubMmSOVdIiIKD09Xabv2n+VDoFAQMuXLycANHz4cPY0+h8uXrxIAGjixImUmppKXl5epKenR7q6uqSoqEjr1q0jS0tLcnNzq9TCFBISQidOnCB/f38CQEOHDqV169ZxmR/T09O5kFNJtEB184Y8efKkwkRXjDKYMsBoEL7++msCQH/++afMfZ4/f05AWSnb4OBg4vF4nINacXExmZmZ0TfffFOu36JFi2j06NEkFovp1q1bBIAOHz5MSUlJ9O+//1JISIhUhrKbN28S0HTrGtQmY8aMIQcHB5o1axYBoE2bNpG1tTWZmJhwZnpTU1Pq2LFjrcwnFosrDVOcN28eWVhYVNq3qKiIoqOjpbYGQkNDydPTk3g8Hq1cuZKFp1XCX3/9RaqqqjR+/HhKT0+n/v37c1UEAZCWlhYBoJs3b5br++rVK1JTU+PatmzZkjv36NEj2r9/v1R7SYGikSNHkkgkosLCQtq1axcFBQVxDp2lpaU0bdo0cnZ2poyMDBKLxdz4kyZNqtP34mOFKQOMekUsFlO/fv0IAKmrq5OysjIXHlbVE5dQKKTly5dTmzZtKD09neLj48nAwIAWLVrE+Q6IRKIKTcmPHz+mr7/+mps/JSWlyqd+gUBAbm5u5OzsXJNLZRCRmZkZmZqaUn5+Pl2+fJkyMzMpKCiIYmNjuYX16dOnlJycTGlpaXTy5Mkaxe3/+++/BKDCMEexWFxlaeY7d+4QAAoLCyMiIn9/f1JSUiInJ6c6SaDzqbFhwwaSl5eX2uKR+FioqqqSgYEBhYSElOu3evVqUldXp8DAQDp06BDx+XwuJHjs2LHliiy9m9Z4yZIllRYpk7ySk5MpPj5e6hijPEwZYNQrr1+/JqCsgFF6ejrt37+fxGIxJSQkkJ2dHc2bN4+io6PL9Xv79i2NHz9eKtlIRkYGTZo0qdaduSSJUzw9PWnr1q3vDWNkVIxIJCIjI6MKn8T8/PzIyspKymwcFhZG8vLyNUoQVFkGw9TU1HLpa/9LdnY2BQQEUH5+Pm3bto0A0Pjx42stHO9TJy0tjeTl5Wn27NlSn+vkyZO51MAVMWfOHLKzs+P+Pn78ON27d6/Sefr06UO2trb0zTffSBUlq+glSVpWWloqlSHxXcRiMedA2pRhygCjXpkwYQLp6OiUCydLTU0ld3d3UlZWpoCAgAaSroyYmBjq1KkTF74ElBVWWbRoET169IilNZURSdGcitIdnzlzpsL6AvHx8R+ct15Cbm4uXbx4kct2GBYWRpqamrRjxw6Z+kvqI8ydO5d91tVEEh30448/csdycnJITk6O2rZtW2Gfbdu2kYKCgkyZQUUiETk4OND06dOlTP8//PADzZkzh6tNMGDAgHKJjuTk5KSiUu7du0dz5szhol709PRo+PDh1crI+SnBlAFGvREVFcU5YVXEixcvaNiwYZyG/l+P7tDQUNq0aVO9JnqR5B4YPHgw6evrE1BWhXH37t31JsPHyqJFi0hRUbFCz/z/snPnzirLBMtCdnY29e/fnwwMDAgAXbp0iYiINm/eTJaWluTh4cFFGFTGli1bSFVVlYYOHdrknxQ/lCVLlpCioiJXQyI/P586dOhAAOjQoUPl2qenp5O2tjZ17969yrVDJBJRnz59CADt27ePqztQ0WvlypXl+j99+pQUFBQ4J1Jvb2+u/ZYtW+iHH34o1zc7O5uOHDlCR44cKeeQ+qnBlAFGvZGcnEwAyMzMTKqCnFgsLvedmT17Nqc0BAcH05IlS+jXX38lbW3ter1JR0dHc/vPAoGALl26RIMHD+b2K48cOdLkah3ISlRUFCkqKtKSJUuIqCzU8ObNm9znPXnyZHr27BklJyeTtrZ2tZxJK+LmzZukpKRE7u7u5ObmRnFxcURUZiLOzMykWbNmcd87iVPZu5w6dYqUlZVJR0eHW8gY1ScvL48MDQ2lwv9EIhF16NCBbGxs6NixY+X8gy5dukRycnJccaWKkGwxSr4nGzduJEVFRcrMzKRBgwYRADI2NqZLly7JFIYaHR1NAwcOJFVVVTI3N+fqHgwePJiIyiwWOjo6nMKgrKxMU6dOpatXr35QvYXk5ORGfa9gygCj3khOTuYy2r3rFPT999+TjY2N1Pfm+vXrtGXLFu7/Dg4OlJub2yie1goLC8s9ibRq1Yr69etH3t7etGzZsnJ11psqfn5+JCcnRydPnqT09HQyMjKiQYMGUW5uLhkYGHCm3Ly8vBqH7AkEAho1ahQBZamWK0MsFlPnzp1pwoQJ3DFJLLyHh8cnX6OiPti7dy8BoIMHD3LHwsPDydLSkgDQ0qVLy/UxNjamLl260MKFC+nWrVt09epVKUdQieOgxKfE1NSUhgwZQnPmzOF+h9bW1uUW6lu3btG+ffsoODiYevbsSU5OTuTt7U2LFi2itLQ0cnZ2lvot6+vrc1aCyZMnU3x8PCUlJdGqVauoWbNmBIDk5eVpyJAhdPHiRZkslSUlJcTn8wkATZ8+vVH6oTBlgFFvCIVCGj9+fLmbxPPnzzmTbmU8e/as2iWM65ILFy7Qs2fPKCkpifbt20fjxo2jAQMG0KBBg0hZWZkMDQ3fa5JuCohEIvLx8SF5eXnau3cvZWdnk0gk4kLC3ufhXx22bNlCioqKdPjwYZo+fXqlxZGIiK5du0a3bt3i/v7qq68IAFPiagmxWExjxowhBQWFcr/bkSNHkpubW7k+K1euJCcnJ9LT05NanM3MzDi/AuB/dUycnJyk2knuLZs2beLGLCkp4c7/d1wA5ODgwEU8ACBHR0fOGjBu3Lhy1iOhUEjLli0jZWVlcnNzI6CsSNeXX35ZZV0TsVhMK1as4OaRk5NrdNUumTLAqDd+/vlnkpOTIyMjI4qLi6PBgwdz+eTfx+jRo4nP59O+ffvqWMqaEx8fT0OHDiWgLBVvU3dCKy0tpSFDhhCfz6cDBw7Q1q1byd/fnyIiIsjf35/4fL7UttGHkpaWRvv376+WhSEpKYl69uxJfD6fVq1aVWMZGP+jtLSUevToQQYGBlLZBzt06FCl5SY7O5u2bt1KwcHBdPjwYerWrRu3iBoaGnLtqvIZeDdyZO3atdStWzfy8/Oj3bt3019//UU7d+4kZWVlatGiBRERDRs2jOuroKBALVq0oO3bt1e5lonFYnr06BEtWbKEsy7MnDmzSktBjx49uHnmz58v0/tYXzBlgFFvtG/fntzd3emPP/4gkUhE/fr1q9CjnKjsh+bn58cpCydPnqQVK1Z8NAurWCzmsta1atWKxo0bRydOnGhosRoMSaU/eXl5srGxoebNm9O5c+dIKBRytQhqyqtXr8jS0rJaisXjx49JU1OTTExM3ltGm1F9kpOTuQVwypQpJBQKycbGRuZKk0Rl68rVq1cpIyNDKoNgYWEhV0ukbdu2UsqAJE+BrGRkZNC5c+coOzubZs+eTZaWlsTj8UhDQ4O+/fZbev36dZX9xWIx/f7778Tn8zkfGaIyv5mMjAzy9fXltrAkr++++4527NhRac2N+oYpA4x6Y/bs2QSA+0FXtf+fmZlJY8eO5crGfqxcvnyZhg8fTg4ODgSg0iJJTQFJNEn79u2pZ8+e3KJ97do1mjNnjkxRB1WRn59PCxYsoDt37sjcJzo6mgCUy3LHqD3EYjHt2LGD+Hw+de3alZSVlal9+/a0bNmyGuWUkFROBEDTpk2j33//nfv7QxXMpUuXEgB68eIFJSYm0pIlS0hPT4/s7Oy4kNczZ86Qt7d3hf1nzZpFfD6f+vbtS2vXrq3UcnH06FESiURkbW1NAKp0nKwvmDLAqDckOcWXLVv2XkfAT+07VFBQQIqKipxTZFNFEuLn6urKLdr79u2jQYMG0Zs3b+pdHkle/aZstakvTpw4wcX0A+Cc8Xx8fGj37t104sQJ8vb2phkzZtDx48dl8tgPCAigbdu2cYWwAgMDK8xrISsS2SRpzonK6ibIycnRnj17iIjoypUrNGrUqAr7FxQUcEnL3r1GycvX11cqodKePXu4c2PHjq00lXZ9wJQBRr2Rn5/PVTObOnVqpe0kmndjc7CpKd27d6d+/fo1tBgNyq5duwiAlPPefxGJRDR8+HD69ddfqaioiObPny9TxTuisqfQefPm0e3bt2Vqn5+fT/b29qSoqPjeDIWMmiP5/AHQqlWraMmSJeWemiXJvmxsbCgqKqrGSaiqw8iRI8nQ0FAqNLGwsJAsLCzoyy+/rLSfUCik+Ph4Lpuq5Fru379PoaGh9Oeff9Lt27fLbXOKxWLOv0jyWrlyJf3www/1nteAKQOMemfjxo3E4/EqrTC4Z88eWrRoUZXe4B8jvr6+1KlTp4YWo0ERiURkYmJCs2fPLnfu7NmzNHXqVBKLxXTlyhWaO3cuZWdnk4eHB23btk2m8YuKisjR0ZHWrVsns0zjxo0jW1vbj8Yf5WNGLBbT06dPycvLi3g8ntQiqKamRpMnT6bTp09LKQ08Ho8GDBjAPVG/efOG1qxZQ2fOnKHc3Fx68eIFBQQEkEAgoKtXr9LevXul5szNzaWnT59WmOZcFkaMGEEAyNTUlP7880+KiorirBYCgYBWr15NRkZGBICaN29OmzZtoh49epCioiL9/fff7x1fJBJRbGwsXbt2jQBQly5duGs/f/48PXz4UOby3TWBKQOMeqegoIArBFTVdsGnVCpWLBaTsbFxpfnZmxL9+vUjFxeXcsd///13GjNmTI3GLikpIR6PRwcPHiSBQEAHDhx4743U19eX3N3dazQvo/rk5uZSQEAAHTt2jA4dOkRjxozhyppX9no3DBAAde7cucJ2IpGILly4UC6HQHXj+99NeSx5KSsrEwB6+/Yt3b59mzPxHzlyhHMSPH/+PI0bN44UFRXp/Pnz1cqPUlpaytVzkLwWL15c3be32jBlgNEgSMqQHj9+vMLzEi25sLCQBAIBhYaGVtjuY7EeSPYGa8tz/mPGxMSEfHx8ZG6fnZ1dqRVJwrlz5+jXX3+l4uJievPmDRUVFdHTp09JTk6OunTpQt7e3pVGC+zfv5+AiisdMuoXsVhMiYmJ9PjxY7p79y6dPHmSRo0aRTNmzKA7d+5Qr169pBZJDQ0NKQuDgoIC/fbbb9S9e/cKFYkP4enTp3T16lWKiIigrKws8vLyKqcYPH78mJO/a9eupKurSwEBAWRhYcE5zVZVKbUiZs2aRfLy8txcdR3twpQBRoPRrVs38vDwqNA8+++//3J+BXv37iV9ff1yP6aZM2dSt27d6kXWmnDs2DGSl5eniRMnMlM0EQ0fPpzs7Oxktvz88ccfBIAePnxYaZt79+6Rs7MzRUVFSR2/desWmZiYUP/+/SvdexYKhWRubk4dOnRolJnhGP8jPz+fXr16RaWlpVx0kmSLQWIB2rJlC3e8bdu2dOnSpVq1MpaWltL169fp2LFj5OfnR8+ePZM6n5mZSV5eXqSkpMTVUpC8JCmyZWHq1KlSfWsSeSELTBlgNBhXrlwhAFzhkMp48OAB3bx5k1atWkXr1q3jPH1v375N/v7+9M8//0iVNm5MHD9+nPh8Po0YMaJeCyw1Zh48eEAAaOvWrTK1F4lElJmZSYcPH6bBgwdTfHw8hYWFyfx+isViiouLo0WLFkllGCwpKeHCGTt27EgAOI9xxsfBlStXaOTIkTRw4EDOFH/o0CEyMjKikSNHylT86uXLl3T27Nlqh7Zu376dbG1tuUiGdykuLqbFixdTp06dpLY+Dh8+LPP4paWlFBgYyCnDACrNy1IbMGWA0WCIxWLq3r07ubi4vFdzf/bsGSkpKVHHjh3L7buPHz+epk2bVpeifhCSamxDhw5lisB/aNmyJX311VfV6nPhwgWytbWlVq1akZqaGl2+fJkSEhJo2rRpVFBQQFlZWRWmj01PT6cHDx6QpqYmeXp6ElGZImBmZsY5pZ0/f56UlZWlUtm+j/r0cmfUDSKRSOrpW1LDIC8vj65fv07h4eGV9lNVVSUA9P333793nsDAQAoICPhgy+CWLVuof//+FBkZ+UH9ZYEpA4wGRfKUuGvXLpnanzp1ikaNGlXhjyomJob27NnTKDLJicViGjduHGlqajbqSmUNQVJSEmloaFRYZvZ97Nmzh27fvs3dFCX3nLVr15KOjk65EESBQEDq6ur05ZdfEgD6/fffiajsZj5hwgSp6oSampoyJ7lKTk4mY2PjCqMiGB8Ht2/fpnbt2lXogPjuXn1FCsHly5e5SAczM7NPwtlZ1vWbBwajDmjbti3GjBmD7777Dm/fvn1ve11dXbx586bCc3fv3sWKFSsgEAhARHjz5g2EQmFti/xeiAijRo3C3r17sXXrVhgYGNS7DI2VoqIifPHFF9DU1MS0adNk7pecnIxmzZrB3NwcHTt2hL29PQBAU1MTISEhWLBgAf744w9ERkZK9ZOXl8ejR4/QqVMnREVFYfr06QAAHo+H3bt3w8rKimtrYWGBhIQEmeQ5c+YMkpOTsXnzZty8eVPm62A0DuLj49G1a1cUFhZi8uTJ2LJlC/Lz8xEXF4cTJ05gyZIlcHZ2BgDk5eWV63/+/Hnu36SkJBw6dKhe5W9QalOzYDDeJS0tjQBI5fT+UCRORG/fviVDQ0P6448/ajxmdbl//369hQN9bGzcuJEUFBSqXZgoJyeHVq9eXWWs+FdffUUODg5Sx2JjY+ngwYPcfvI///xDaWlpFfZv27YtTZ48+b2yrF+/ngCQl5cXOTg40MSJE6txJYz6RCwWU3BwMC1ZsoTWrl3LJZZq2bIllxSoIg4fPkwAKk00NHz4cGrfvj0REZmbm9PcuXOl6iZ8jLBtAkajoGXLluTl5fVec9uLFy9kMrunpKTQ6dOnGySr3MGDBwlg5XArolWrVjRs2LBq9Tl58mS1KrxdvHiRizyZOHEiWVlZEVGZX4C2tjb98ssvFfbr1q0bDRo0qMqxCwsLycbGhgBQQkICjRs3jlsUGI2LwsJC+uyzz6SSF7Vq1Yo2btz4XmfRPn36kIGBQaXRJWPHjuX8Tz7//HMCQPr6+vVee0RS9yEmJqbGYzFlgNEoOHPmDPF4PPL29qb4+PgK2yQkJJC8vDytWbNGpjFLS0upffv29e5DcPLkSQJAqamp9TpvYycjI4N4PF61Iz927NhBAwcOlKltWFgYAeBqQCQmJlJ2dja9ePGCNDU16ciRI5U6cXl7e1OHDh0qHfvNmzfUsmVLUlJS4mrXS8rrMr+QxoVAIKCxY8eSnJwceXh4UFJSEj19+pT09PQ45aAy61RhYSHxeDxq3bp1pSmBp06dSjY2NkREFBwcTP379ycAZGdnV2fXVBFJSUnc9dQ0bJkpA4xGw5UrV8jExIRUVVVp7dq15by1BQIBxcbGyvyll8SPnzp1ijIyMujWrVv14tV/9uxZAkCJiYl1PtfHhKRyXV0unN27dydTU9NyVeWEQiEdP36cDh48WGG/hw8fkpycXDlFJSsri3bv3k2DBw8mHo9HJiYm3CIiUTwAlMtvwGg4iouLuaf1/1ajzMzMpKCgILpx40aVVkh/f39SV1cnZWVlWrhwYbl7kaenJw0ePFjq2IoVK0hDQ6Na2QZrSmFhIfcdrEnSrMLCQjp16hRTBhiNh5ycHJozZw7x+XyysrKiv/76Syqd7Lve39VBUp2ussWgNtmwYQOpqKh8Eh7GtcnUqVPJxcWFbt68We2+aWlplRYrEolEnJk0JSWF1q9fT7t37yYiosePH9OcOXOIqCyevHnz5lLe4cXFxeTv70/NmjUjd3f3csri8uXLCQC5ubnRqlWrpKxWkqey//opMBqOf/75h1scDQwMajTW27dvadmyZaSgoEAeHh70zz//cIWI3q1iKOH69esEgC5evFijeavL1atXCQBZWFh8UH9JBk7JiykDjEZFaGgo+fj4cKa35ORkCggIIB6PR48ePar2eAUFBfVWInfcuHHcfiLjf0yfPp20tbWpc+fORFS2OLu7u1NISAgREf3222/UpUsXrn1iYiKXbnry5MmkpaVFPXv2pAMHDlBSUhLX78cff6RmzZpVeN9Zv3499ezZk4jKvgPTp08noVBIxcXFtHz5cq7EbJ8+fSg9PV2qb3FxMZmYmNCIESMqvJ5NmzYRAE4ORsPy9u1bUlFR4Ra19yUzk5U7d+5wDoeDBw8mHx8fUldXL+cTVFpaSvb29uTh4VEr81aH3377rdqpzvPz8+nrr7/mUipLfgtMGWA0Su7fv0/y8vJka2tL1tbW1KJFCy4PeHURi8W0c+fOD3oylZX8/HwyMzOjSZMm1dkcHyt+fn6koaHBmdSTkpJo6tSp3FP95cuXafny5URUdmMFQLt376aIiAiyt7cnNTU16t27N8nLy1OrVq3IxMSEiMpM+dV5GisoKKDevXuToqIizZw5s1Knr9TUVAJAx44dq/D8tGnTKiy4xGgYLly4QADI39+fjh49Wuupv3/99VcyMzMjDQ0N+vPPPytss337dgKqXxCpIZA4OgMgKysr5jPAaPwEBQWRs7MzmZmZkbOzM5c4prqUlJSQqakp/fXXX7Us4f+Q3JA+VGH5lAkNDSVVVVUaMWLEe2/UAoGALly4QK9fv+asRK9evSKiMt+SefPm0ciRI98753/nCQ8PJycnJ1JVVaWrV69W2VcoFJK6ujr99NNPFY5rYWFB06dPf68MjLonPT2dDAwMyMHBoU4XYpFIVGUVTMk2xdmzZxt9HZLY2Fjq0qULubm50cGDB5kywPg48PPzIz09vWr1WbJkCfn6+hJR2c175cqVlJmZWad7+ZI4euYvUDH+/v7Vcnby8/OjgICAcscFAgG3v1/Ve92jRw8aOnQoEZUVLTI0NCRnZ2d68uSJTPOPGDGiQg/xO3fuEAC6fv26TOMwap/i4mJ68uQJHTt2jDp16kRqamoV1gmoT0pKSriyyp07d673UMOawDIQMj4KLC0tkZGRgcePH+PatWsgovf2adWqFbp06QIAiI6Oxi+//IL58+eDz+fXiYxxcXH46aef0L9//zqb42NnxIgRMDExwY4dO2Rqf+7cObx48aLccXl5ecjLy+P27dtwc3NDbm5uhf3XrVuH4cOHAwBmz54NNTU1XL9+Ha1atZJpfkVFxQq/a/v374eZmRn3/WLUD8nJybh8+TJWrFgBW1tbtG7dGj4+PsjMzMSlS5dgZGTUoPIpKioiMDAQ58+fR3JyMpydnfH333/X6hxLly6FnJwcFixYgJKSklodWyZqU7NgMKqLJHb/yJEjJCcnV2UoV1BQEJ06darc8WnTpnGxwXVBt27dyNraWqZKaU2ZVatWkbKyMiUkJNR4rPj4eBo4cGCFJtn/RgZ4eXlRmzZtZLbanD9/ngDQxo0by42rr69P33333YcLzqgWhYWFNGXKFOLz+VzJYl9fXzp//ny1ygLXJwUFBaSiokI8Hq/SfAUfwt9//83t9bdp06bWHKPZNgHjo0ASu5+QkFAufl8oFNLVq1dJLBbT/fv3qVevXtStW7dyYxQVFVFKSgqlpqbWSsaud8nMzCR5eXn67bffanXcT5Hs7GwyMjKiPn36VJr3ITMzk7755ptqvZ++vr60efNmKi4upmfPnlGLFi3oyJEj3PkbN27IXEZWLBaTqakp6ejolIsyCAgIIAD08OFDmWVjfBiPHj0iY2NjMjMzIxUVFdq4cSM9fPiwVhfXuiQyMpLU1dVp4sSJtVrlUhLyCoDk5OS46ps1gSkDjI+Cbdu2Se3FJyQk0MaNG0kkElFERAQBoEuXLtG4cePI2dmZnj9/XulYJiYmtH379lqRq7i4mMLCwkhfX59UVVXpxYsXtTLup44k78OBAwcqPJ+ZmUk7d+6kBQsWyDymlZUV9erVi44ePUp8Pp8GDBggtWcrFosJQJVZBt+dH5Xkpfj666/JwsKi0TuIfQpIFry+fftSWFhYQ4vzQYwbN467jqruS9Xl3fwAtRHVwpQBxkfBnDlzyN7enojKQr5MTExIX1+f1q1bR2KxmG7fvk1EZSFpN2/epOLiYgoICCjn+SsWi+nQoUOVFquRheTkZFqwYAF5enoSj8cjAKSrq0tJSUkffoFNEAsLixoXpxIKhZyCeOjQIbpx4wY9ffqUJkyYQDo6OuU+f2dn5/fWHyAq+55oa2tXuBVgY2NDM2bMqJHcDNmQLHYNUWOktkhMTKQZM2YQAPLz86vVsdPS0mjz5s01yj4ogSkDjI+C/v37U58+fbi/Hzx4QEOHDq2w1jhRWQy7srIyXbp0qcLzCQkJ1KZNm2pVzzt16hQNHTqUNDQ0SFdXl0aPHk3bt2+nw4cPV7sKH4PIzs6u0gJEr169Ij8/v/cWe9q6dSvNmzev3PHMzMwKq8gBoLZt28ok36xZs8jMzEzqmMQKdfr0aZnGYHw4QUFBFaYU/ljp168f6ejo0I0bNxpalAph0QSMj4LIyEg4OjoCANLT0+Hi4oJjx47BycmpwvYmJia4ePEiPD09AQArVqyQqjluYmKCli1bIikp6b1z5+TkYMSIERg0aBASEhIwd+5cREVFYf/+/Zg2bRqGDx+Oli1b1sJVNi2srKwQERFR4bn4+Hj8+uuvyM/Pr3IMHR2dCtvo6OhAW1sbAEBEuHjxIvr16wcAGDlypEzyOTk5ISUlBVlZWQAAgUCAbdu2QUtLC97e3jKNwfhwgoKCAADdunVrWEFqif3798Pd3R09e/bEyZMnG1qcD6c2NQsGozqUlJQQn8+nHTt2EBGRurp6OQ/vqhCJRPTll1/SunXryp2bPHky+fr6VppIpLS0lHr27Emampp08OBBtk9ciyxdupT09fUrfE9v3LhBlpaWXNx4Tk6OVPKff//9l9seqKj/q1evqEOHDvTjjz+Sq6srASAPDw/av3+/zJ9hYmIiqaurk66uLk2aNIlL17pw4cIPuVxGNYmJiSEANHfu3IYWpdYoLS2lYcOGkYKCAgUHBze0OFKwbQJGoycyMpIA0LVr14ioLORLko2uOty7d4/y8vK4vwUCAbm7uxOASqMLfvnlF5KXl2fJZeoAiXd/RZ7QUVFRtHjxYi5yJCgoiFRUVDjPfm1tbRozZkyFC7tYLKY+ffoQAFJXVycAFBgY+EGKXGxsLM2ePZvMzc1p2rRp9OjRo3qtSteUmTt3LuczUFBQ0NDi1BqlpaVkampKs2bNamhRpGDbBIxGT0pKCoAy0z4RwcvLC82bN6/WGKWlpfDx8cGyZcu4Y8XFxZwJ2drausJ+Dx48QPv27T8ZU2VjolOnTtDS0kJAQEC5c3Z2dvjyyy9hZmaGyMhIeHh4ICgoCHp6egCAiIgI+Pv7Q05OrlxfOTk5nDp1Ch06dEBpaSk3V0Vt34eVlRU2bdqE169fY/v27fD09ASPx26H9YGCggL3/08piZeCggI8PDwQExPT0KJ8EOzbz2gwkpOTAQBGRkaYN28eunbtCoFAUK0xJJnBvv/+e3z++ec4ceIElJSUoK+vj7i4uEr7mZiY4M2bNzURn1EJfD4f3bp1w8GDByEUCqXO5eTkoKCgAD/88AOnADg7O3PnDQ0NqxxbUVEReXl5UFFRQbt27dgC/hHy448/Ql1dHW3btoWSklJDi1OrFBcXv9cfprHCfkmMBuP48eOwsbGBpqYmpk6diqVLl0o9NchK8+bNoaenBzc3N5SUlEBBQQFt2rSBubl5pX2ioqJga2tbE/EZVfD9998jMjISEydOhFgs5lL/3r17F56enpg2bRr09fXfO05JSUk5Z9Djx49DQUEBvXv3rhPZGXWLkpIS/Pz88PTpU4SFhTW0OLXK+PHjERgYiKdPnza0KNWnNvccGAxZSUxMJDk5uVpLEkREdPr0aTp79ux7261Zs6bW52aUR1JKdceOHaSnp0eHDx+m7Oxsevz4scxZ2yRjSJwMCwoKaMKECQSA7ty5U5fiM+qQ9PR0MjExoX79+jW0KLVKSEgIAeDyozQGmAMho1ETGxvL1Sh3d3evcRWwnJwc6tOnD3355ZdVtjt37hwBoCVLljCHsXpgxIgRZGRkRMuXL6/2Z5yXl0clJSXk7OxMysrKVFBQQL169SIVFRX6+++/60ZgRr2xc+dOAkDR0dENLUqtsWXLFlJQUGhUdUxkXb/lG8ggwWjivLvX6+LiUqMqXRkZGfjmm2/w7Nkz3Lt3r9J2+fn5mDFjBnr16gU/P78PcjxjVI/Vq1fDyckJAoEADg4OMvcrKipC+/btsX79eoSFhSEhIQH+/v74999/ceXKFfTs2bMOpWbUBy1atAAApKWlwcbGpoGlqR3Onz+PHj16QEdHp6FFqTbMZ4DRIFy9ehUA0K9fP+zbt4+7MXwIJ0+exKFDh3DgwAFYWFggPT0dX3/9NTIyMrg2JSUlmDBhAtLS0rB9+3amCNQT1tbWWLhwIdavX4/g4GCZ+6moqGDixImcQ2lsbCxmz56Nr776iikCnwitW7eGkZERtm/fXivjiUQivHjxAnfv3sWlS5fw+vXrWhm3Orx9+xaWlpb1Pm+tUJtmBgZDViSliy9fvlzjFLC5ubl08+ZN7u8bN26QnZ0dVwI0Li6OOnXqREpKSnTixIkazcWoPiUlJdSiRQvq0aPHB/U/efIkKSkpUY8ePaioqKiWpWM0JFu2bCEej0dv376tdt+4uDi6evUq+fv708iRI0lPT4/LXwCA5OXlycfHh5YvX15r5YCrIjExkfh8Pv355591Pld1YD4DjEZNcHAwASBfX19q0aJFrY8vFAqptLSUUlNTyc3NjczNzRuVU09Tw87Ojuzs7KrVRyAQcM6evr6+VFxcXEfSMRqKhw8fVqtsdF5eHm3fvp06deoktfC7u7vT999/TwEBARQWFkaxsbG0bt066tixI2loaBAAWr9+vdRYT5484TJh1pTMzEzq378/6ejoNLp1kikDjEbNrl27iMfjUWxsLAkEgg8ep7CwkKZPn04XL14sd87Pz4+7EQQEBNREXEYN8fT0JAsLi3LHxWIxCQQCevPmDQmFQsrKyqKgoCBavXo12dvbk5ycHC1atIg5e36CiEQimjVrFgHgMlL+l+LiYqkshWPHjiUA1KVLFzp69ChFR0dTSkpKlfNkZ2dzSsPjx4+JiOjChQsEgIYPH14r1+Lt7U1aWlqN0vLIlAFGo2bAgAHUvn17IiIuF/2HEBwcTCoqKjRv3rxyNcVXrFhBAOi7775jtQcaGMlN/Pbt2yQSiejff/+l5cuXk62tLXejVlFRkXraGzVqFD169KihRWfUEc+ePeM+63eVvfT0dJo/fz4ZGhoSj8cjOTk5MjIyIgUFBQJAW7ZsqfZcWVlZ1Lp1azI1NaWlS5dy83bq1KnG13HlyhUCQEeOHKnxWNUhLy+P4uLi3ttO1vVbjuj/s4FUQW5uLrS0tJCTkwNNTc0aeikwmjrx8fGwsbHBtm3b0LJlSwwYMAC7d+/GgAEDqj3W8+fPpSoLtm7dGhoaGlBQUEBAQADGjRuHPXv21KL0jA/h3c9Jkv1RS0sLAwYMgJeXFwwMDBAXFwcNDQ2IxWJ8+eWXUFFRaWCpGXVJamoqunTpgsjISAwdOhSdOnVCRkYGNm7ciMLCQnz99ddo2bIleDwe4uPjYWRkBHNzc/Tv3/+DHID37t2L8ePHlzv+9OnTGlUn7dq1K4RCIW7dulWvGTH79OmDS5cuYd26dfjqq6+4ap7/Reb1WxYNhFkGGLXJggULSFtbm/Lz8yk/P5+2bt1aIzPwoUOHOE3f19eXhgwZQp06daJDhw7VyOrAqF0KCwtp69atNHbsWLp+/Tqz1jBILBbTgQMHyMrKilRUVEhRUZG++eYbCg8Pr/W5hEIh7dixg/r27StlgXJ1daW0tLQPGvPq1asEgPbt21fL0r6f33//nbsGJyenSn9PzDLAaJQIBAKYmZlh5MiR+P7776Grq1srxUp8fHwQGBiIO3fuwN7evhYkZTAY9Q0R1UvYb3BwMPLz8zFu3DgkJSXB0tISISEhUFZWrtY4PXv2REFBAe7cuVPvdTLEYjFGjx6Nw4cPAwBWrVqFmTNnllujZV2/WZ4BRr1BRPj666+RmpqKiRMnYv78+Zg7d26tjP3jjz8iPT0dx44dq5XxGAxG/VNf+T/c3d3RpUsXxMbG4smTJ3j16hXOnDlTrTFKS0tx8+ZNjBw5skEKZvF4POzZsweenp4AyuqBVLZVINN4tSQXg/Fe8vLysHPnTpibm6NFixYYOHAgOnfuXCtjb968GSoqKhg8eHCtjMdgMJoGLi4ucHZ2xrp168qVH87Ozsa+ffuQmJgIoOxp/OzZs5gyZQoGDBgAgUDQoEmGlJSUcODAAe5vIsK0adMwY8YMWFlZ4eDBgzKPxdIRM+oNdXV18Pl8LFiwAADQu3dvqKqqytxfIBDg1atX0NXVRVZWFpKSkvD8+XMcPHgQQUFB2L17N5ycnOpKfAaD8Yni7++Pzz//HM2bN4elpSU8PT3RrVs3rFmzBm/evIGRkREuXryIb7/9Fjdv3oSzszOaNWuGNWvWoF+/fg0qu7W1NeTl5bFhwwakp6dj//79yMjIQG5uLsLCwtC/f3/ZBpLFUYE5EDJqi3bt2tHQoUMpJSWFLC0tKTU1Vea+fn5+Uo4/AEhBQYEGDRpEp0+fZg5pDAbjg8nNzaVjx47RggULqH379iQnJ0etWrWiL774grS0tAgAGRoa0tWrVxtaVI78/HxydXUlABQcHExEZQ7Vbdu25ZI5sUJFjEZHdnY2YmJi4OjoiMLCQnTt2pXLPS8Lenp6AIADBw7AzMwMpqamMDExYSFoDAajxmhoaGDo0KEYOnQoACArKwuamprg8/nIzs7GuXPn0KtXLxgYGDSwpP8jOTkZoaGhAMpCJFu3bo358+cjLy8PGzduRJs2bZCbmyvTWEwZYNQbgYGBSEtLw7Jly2BtbY29e/dW2V4sFuPUqVPQ0NBAhw4dcPz4cWhpaWHEiBEN4rDDYDCaDjo6OsjOzsbKlSthZmaGL774AuHh4bh8+TIMDQ3Rq1evBpMtIyMD3377LVRUVHDw4EHs3r0bM2bMgFAoRFJSEv755x8MGzasWmOy0EJGvbFy5Ups2bIF6enpSElJQWZmJlxcXCptf/bs2XKJiLp27YqAgIBaCUdkMBiMqrh06RL69OlT4bnS0lIoKCjUs0RlToLDhg2TipxSUVFBUVERNDQ08MUXX0g5FbLQQkaj4+nTp8jMzMTatWuxatUqTJw4scr2kqf/Vq1awdDQEHfv3sW1a9eYIsBgMOqFjh07wtLSEp07d8bp06fx9OlT/PbbbwCAnJycBpHpr7/+wrFjx7BhwwYsWbIEAKCvrw8A+Pzzzz+4JDSzDDDqBSKCnp4esrKyAAC//PIL9PT0KkwPKqGgoABWVlZIT08HAISHh7NoAQaDUa/8/vvv+Oabb5CUlISioiJYW1vDwcEB4eHh9b5defHiRQwfPhyDBw/Grl27MG7cOBw/fhzXr1+Hq6trheszswwwGhWFhYWcIgCU1RAwMzODSCSqtM+dO3eQk5OD1q1bY9euXXBwcKgPURkMBoPD19cXenp66NGjB7etOXHixHpVBF6+fAlfX1/07dsXnTt3xsaNG+Hh4YFDhw5h//798PLyqvGDOnMgZNQLxcXF3P+VlJRw48YNbN68Gc+fP4eVlVWFfU6cOAEbGxvcv38fioqK9SQpg8Fg/I9mzZrhxIkTWLNmDezs7LB8+fIqfZ1qkzdv3mDs2LG4du0aTExMsHfvXlhZWcHV1RUpKSkAgL59+9bKXEwZYNQLd+/eBVC2/+/u7o5evXph6tSpMDU1Ldf23r172LFjB/bu3Ysvv/ySKQIMBqNB6dixI86dO1evcz569AhffPEF5OXlcfDgQQwcOBAqKiro3LkzjIyMcP78eVhbW0NNTa1W5mPKAKNeCAwMRLNmzRAcHAw5OTlER0dj27ZtWLx4sZR568yZMxg0aBDs7e3Rs2dPzJs3rwGlZjAYjPonNzcXgwcPhoWFBc6ePQtDQ0MAwOHDh3Hnzh2cP38e7u7utTonUwYYdc6OHTuwYcMGTJgwgTv26NEjHD16FH5+fuXaGhkZ4dGjR9DQ0KhvURkMBqPBefv2LZKSkpCUlIT+/fvDx8cHwcHBOHLkCAYNGlRpuGNNYA6EjDpHYl77+++/ubCcESNG4OXLl+XCBMePH4+cnBw4OTlhwoQJDRa+w2AwGA2FnZ0dAgICsHnzZmhra2PNmjV4+PAhdu/ejePHj9dJdUcWWsioc+7fv4+OHTvC09MTx44dg6mpKV69egV7e/sK20dERGDSpEm4e/cuYmNjK3UwZDAYDEbVsNBCRqNh/fr1cHBwwO3bt2Fubo6ffvoJLVq0QEV6aFZWFhYuXIi7d++iT58+TBFgfNKkpqZi69atePLkCYqKihpaHEYThvkMMOqcFy9ewNvbm0vd+d1338HLy6tCU9ePP/6Ia9euYe/evRg5cmR9i8pg1BtExDmGAYCHhweCgoIaUCJGU4ZZBhh1zruZB5OSkpCamgpvb2+pNmlpaZg+fTp+++03LFmyBF9++WWD5P1mMOoTJSUl7v9ubm4NKAmjqcMsA4w6x8rKCtevX0dmZiZmz54NFRUV7Nu3T6rNqFGjEBwcjGXLlskcTlhaWgqRSITY2Fg4OTnViVMNg1FXyMnJITg4GOnp6VBXV0fLli0bWiRGE4ZZBhh1zsSJE5GYmIigoCBs3boVgwYNAhGhoKCAa5OVlYXPPvsMS5cufW+SoUePHqFz585QUlKCubk5XFxc0LdvX5SWltb1pTAYtYqzszO6dOkCd3d3VoCL0aAwZYBR58jLy4PP5+PmzZvQ0tLCkCFDMGHCBLRv3x5CoRD+/v54/Pgxjh49is2bN793vJ9//hm3b98GUFbXGwDCwsLqvWgIg8FgfCqwbQJGndOpUyds2bIFM2fOxC+//IKffvoJCxcuxP3792FkZMQt6ABw+/ZtzJ49u8rxvvnmG8jJycHFxQVFRUXw9PSEt7c35OXZ15nBYDA+BJZngFEvvHnzRqoOgYaGBng8HnJycuDh4YEZM2Zg0qRJ0NTUxPXr12s91SaDwWA0RVieAUajwsTEBElJSZzHdF5eHnJycqCiooIDBw5g6NChuHjxIvT19eHh4YEePXpg2bJluHnzZgNLzmAwGJ8+TBlg1BsmJiYIDg7G33//DRsbGwCAkZERli1bhosXL8LBwQHm5uYAgOvXr+Onn35C165dK0xOxGAwGIzag20TMBoEkUiEM2fO4NKlS3j27BkePHjAnePxeHBxcYGOjg4WLVpUJ0U5GAwGoykg6/rNlAFGoyAuLg5RUVHQ1dWFh4cHyxnAYDAYtYCs6zdzv2Y0CqysrFgdAgaDwWggmM8Ag8FgMBhNHKYMMBgMBoPRxGHKAIPBYDAYTRymDDAYDAaD0cRhygCDwWAwGE0cpgwwGAwGg9HEYcoAg8FgMBhNHJZngPFR8/btW5w6dQp5eXkYPXo0jI2NG1okBoPB+OhgygDjo4KIEBQUhBMnTuDMmTMIDw/nzgUEBODixYsNKB2DwWB8nDBlgNHoKSwsxL59+3Dp0iWcPn26wjYdOnSAn59fPUvGYDAYnwZMGWA0ah49eoSBAwfi7du3EIvFAAAVFRX88ccfcHR0RGJiIjp16gQDA4MGlpTRFJCUcmnqtTPEYjECAgKgo6MDd3d38HiN3/3s0qVLuHDhAubPnw8LC4uGFqfRwZQBRqODiHDhwgWsWrUK9+7dQ8uWLXHs2DF07NgRnp6eePToEde2bdu2DSgp41OGiBAaGopz587h3r17ePHiBZKSklBSUgIdHR2YmZnB29sbP/zwA7S0tBpa3CpJSEjAvXv34OvrWyNFhohw//59zJs3D/fu3QMAWFhYYP78+fjqq6+grKxcYb/MzEzs27cP4eHhSEtLw+PHj+Hm5oaFCxeiS5cu751XLBYjOzsbmpqakJevfNkiIqSlpSEpKQlFRUUgIhQUFODNmzdYtmwZEhIS8ODBA9y7d++jUGDqE1a1kNGoiI2NxVdffYWAgAB07twZHTp0QEBAAB4/fgwAMDMzQ0xMDBQUFBpYUsaniFAoxMOHD1FSUoI9e/Zg3759UFdXh5eXF1xdXWFhYQElJSVkZGQgJiYGu3fvRqtWrbBp0yZ07dq1QS0GpaWlKC4uhoaGBtLS0vD69WsUFxdDIBBg+vTpiIyMxOjRozFr1iyYmZkhPz8fubm50NXVhZKSEtLS0sDj8WBrawtVVVUUFRVBJBLh9evXAICioiLMnz8fgYGBcHV1xebNmyEvL4/du3dj//79MDQ0xIQJE6CjowM5OTlkZWUhISEBMTExXInyli1bQktLCy1atEBgYCCCg4Nhb28PHo+HNm3aoHfv3mjdujWMjIwgLy+P6Oho7Nq1C3///TcKCwuhoKAAc3NzaGhoIDo6GkVFRdDQ0ICZmRm0tLQQHh6OrKysCt+fZs2aYfLkyVi7di169OiB7t27o6SkBMXFxRAKhcjNzUVJSQmICEKhEHJyclBWVoaGhgaMjY0xatQoWFpa1tvnWVuwEsaMjxIHBwdkZGRg9+7duHfvHtauXQs3Nzd8//33sLOzQ6tWrZhGz6gViAgvX77E5cuXkZaWBpFIhH379iExMREAoKqqigULFmDx4sVQVFSscIyjR49i6dKliIyMhJaWFszMzDBkyBCMHDkSzZs3R1xcHP788088ePAA7u7usLOzg5WVFczMzKCnpwd5eXkYGRl9sBIhFApx8eJFbNq0CXfu3EFpaSl4PB63pSbB0tISo0aNwpYtW1BYWFjlmIqKilBWVkZubm65c3Z2dvjll1/Qr18/8Pl87nhUVBR+/vlnnDt3jltQJe+HtbU1OnToAF9fXxgaGnJ9xGIxTp8+jWvXrkFOTg4BAQFSDsESmjVrhmnTpsHNzY1TcnJycmBjYwMNDQ3k5OQgMTERWVlZcHR0hLOzM8zNzaGqqgoejwdlZWWYmJhASUkJAHD69GmsW7cOkZGRUFFRgbKyMuTl5aGhoQEVFRUAgLy8PIgIxcXFyMvLQ2hoKAAgPj7+o9tiYMoA46PE3t4erq6u+Pvvv6GtrQ13d3c8fPhQ6sbDYABli/m9e/egpKQEFxeXSk3UEhITExESEoLIyEiEhobi2rVriI2NhaKiIvT19VFQUIBhw4ZhwoQJ0NXVhaWl5XvHlMhx7do1PH78GJGRkTh69Cjy8vK485qamvjss88QGhqKuLg4CAQCqf79+/fHqVOnZP6Oi0Qi+Pv748iRI7h16xby8/PRuXNn+Pj4wMDAANnZ2TA0NISFhQVUVVUhEAjg4uICPp+P0tJShISEICUlBWpqatDS0kJWVhZKSkqgq6sLIsLDhw9RXFwMU1NT8Pl8mJmZAQDy8/PRs2fPKs30NSUjIwMhISFIT0+HQCCAoaEhOnXqVKkyVl8cOHAAY8aMAVCmEHl5ecHDwwPNmzeHlZUVZ60Ayr4PkZGRsLKykun7U9cwZYDRqCEi7N27F6dOnYKysjI2bNiAmJgY9OnTB2PHjsXvv/+OKVOmYNeuXTA3N4evry/mzJnD3ZgYTZuioiIMGzYM586dAwBYWVlh4cKF0NbWhpqaGvr27Qs+n48XL15gx44duHXrFoKDgwEAysrKcHR0RMeOHfH555+je/fuUFdXr1XZAgMDkZKSAgMDA3Tp0oUbXywWIyUlBYmJicjIyEB8fDxmzJiBVatWYfHixe8du6SkBD4+Pjh37hw+++wz9OzZE127dkX79u2bvFNjXfP27VsEBATg7t27uHfvHkJDQ1FaWsqdNzQ0hKmpKQoKChAZGQl1dXX4+Phg5MiR8PT0hLa2doNYNZkywGjU7N69G5MmTUL37t0RERGBZs2aITo6Gq1atcLFixc5LTswMBDHjx/HgQMHoKSkhKCgIJiYmDSw9E0ToVCIy5cvw8rKCjweD1paWlKfRXZ2NoKCgqCgoABLS0uoqanh6dOnSE5ORmFhIUQiEZSUlGBoaAgDAwPo6+ujefPmMs2dmJiIK1eu4Pnz50hISMDt27eRl5cHf39/GBkZYdmyZbhx4wZEIhEAwMbGBh4eHrh69SqUlZXRrVs3fPHFF/Dy8oKZmVmjsDQVFRXh+PHjGDduHIYOHYojR45InT979ixu3bqFvLw8ZGVlIS4uDpGRkSgqKsLRo0fxxRdfNJDkDKDMQpOUlIT4+HjEx8fj5cuXSElJARGhZ8+eiIiIwN9//424uDgAZVsPhoaGcHV1xapVq9CqVat6+R4yZYDRqFm8eDEOHTqEuLg4PH36FJ07d0Z+fj5++ukn/PDDD+Xap6SkoGXLlnB1dcWxY8ego6PTAFI3LbKysvDw4UM8e/YMERERuH79OndjAwAlJSUsXboUn332Gefk9d+9agl8Pp8zU7+Lj48P+vXrh+joaOTm5kIgEICIIBKJkJeXh9jYWLx+/RrJycmQk5ODra0trKys4OrqismTJ8PZ2ZkbKz8/H2KxGJGRkfjjjz8QERGBdu3aYdGiRY0u9DQhIQEODg4oKiqCmpoarly5gg4dOgAos5qtXbsWS5YsgZWVFXR1daGlpQVzc3PY29tjwIABcHNza+ArYMgCESE8PBwvXrxAWloaUlJSsH//fsTExEBdXR3jxo1D165dYWBgAJFIBGVlZaioqHD/19XVhaGhYY2UBqYMMBoVJSUluHz5Mm7duoXExETuCT8wMBBAmVPPoEGDwOfzcfHiRXh7e5cb49q1axg6dCjc3d0REBBQ35fQqElPT8eRI0e4/eLRo0fj/PnzmDlzJhQUFNC7d28MGzYM+fn5iImJQVJSEtLT06GiosLlabh58ybCwsIQGxuL+Ph4ZGdnAwA0NDRgZ2eH9u3bY/To0cjLy4OysjIuXryIX375hbtx+fn5oX///uDxeNizZw+0tLTg4+MDExMTbu9UJBIhNTUVaWlpCAoKwooVK5CQkABTU1Noa2tDUVERcnJy4PP5UFVVhZWVFSwtLeHk5ITevXtDW1u74d7kWiQlJYVLne3l5YU7d+4AAEJCQjB9+nTcuXMHixcvxurVqxtSTEYdUFRUhIcPH+Lq1avYtWsXUlJSqmwvLy8Pe3t7dOjQAR4eHujVq5fMFjWAKQNNluzsbOjo6MDFxQXm5ubQ0dGBpqYmzMzMoKqqiuzsbEyaNKleQ2REIhGsra2RkJAARUVFKCkpIS8vD8ePH8eQIUOQk5OD8PBwdOzYEXZ2dkhLS0NCQgLU1NTKjfXnn39i6tSpyMvLq9V93o+Za9euYdiwYcjNzYW2tjYyMjKgoKCAkpISeHl5wdbWFvv37+ee2lVVVWFmZgZ9fX3k5+fj+fPnAAAjIyO0aNECNjY2sLS0hJWVFecBX9l+dExMDLKzs+Hk5MR5YlcHkUiE4uLiCj/rTxkigqOjI6KiorBo0SKMGDECT548wbx582BkZIR169ahb9++zA+gCZCZmYn09HTIy8ujqKgIRUVF3P8lfiUhISG4d+8ewsPDIRQK8eeff+Krr76SaXyZ12+SgZycHAJAOTk5sjRnNADBwcE0adIkmjt3LgF476ugoKDSscRiMf366680e/ZsevPmTY1lE4vF5OzsXKEcpqam3P+1tLSoT58+JCcnR0lJSRWOFRERQXw+n9avX19juT5W0tLS6OLFi7R06VJq1aoVAaDOnTvT27dviYjoxYsXtHnzZlq3bh0lJCQQEVFWVhbFxMRQcnIyicVibiyxWEwBAQH07NkzqeOMukUoFNLIkSPL/R769u1LmZmZDS0eoxFSWlpKgwYNIgB0+PBhmfvJun4zy0AtsWPHDkybNg3e3t4YMWIE+vTpA2NjY0RFRWHJkiVwcnJC8+bNYWFhgaioKPB4PEyZMqXcOEVFReDxeFxMrKzs3bsX48ePB/C/VKny8vJcGJOSkhI8PDygpKQENTU19OnTB69fv8arV6+QkJCA9PR0ODg4wNzcHPHx8bh8+TLU1NRQUFAAfX19LuZ21KhRcHFxgaqqKgwNDWFpaYnmzZtj165d2LlzJ3r16gVjY2Pk5ubCzs4On332GczMzFBcXIyjR4/ixYsXiIiIwO3bt8Hj8VBaWoqioiIUFxcDALS0tLB69WrMmDGj0msdNGgQ7t+/j6SkpEbhCFZf7Nu3D35+fnj58iUAQEdHB59//jlGjRqF/v37s6fIjxCJFaykpAR2dnbQ19dvaJEYjYzS0lIkJibiu+++w9mzZ+Hv74/hw4fL3J9tE9QAIsLr168RHh4OBQUFGBoaoqioCKmpqdDW1oaVlRXk5eVRUlKC5ORk5OTkYP369eX2sYcMGYL58+fDy8urwnnGjBkDkUgEZ2dnCAQCPHv2DP/++y9UVFSwYcMGjB8/HikpKdDT03tvxr3i4mLY29tDWVkZ/fv3h7GxMaZMmQIFBQWEhobi4MGD2Lp1K2cq5vP5MDU1hZ2dHSwtLaGjo4PQ0FCkpaVBS0sLX375Jb744gtcuXIFL1++hLy8PAoLC7F3715kZGRwqT7fxdPTk7uxqaurIzExEfLy8lwiIcnemKmpKRwdHWFqagoDAwMYGhrCzMwMdnZ2aNGiRaXXWlJSgufPn2Py5MlIS0tDYmJik0lAdP/+fc7BbPXq1fDx8YGtrS1TABiMT5CkpCTcuXMHN27cwK5duyAQCKCsrIwjR45UO4qEKQPvIBaLUVpayjkxCYVChIeHIyMjg0tDmZ6ejoiICISEhCA4OLjSlJa1hba2NhwdHSEvL4+wsDAoKSnB0dERffr0QUhICPbv3w9jY2MkJydDW1sb3t7eMDc3h6WlJby8vKCtrY38/Hy8fv2aWxTj4uLw888/c3PweDycPXsWffv2BVBmdcjNzYVYLIaBgUGNnqpFIhHS0tLw6tUrREREoH379nB1dZVqk5WVhb179yI6Ohp6enpwdHRE27ZtYW1tXa1F7PHjx5g4cSK3t+3g4IADBw7Aw8Pjg+X/WCAixMTE4M8//8S6desAAFevXsVnn33WwJIxGIzaRigUYu/evfj2229RUFAAIyMjTJs2jUuHLXE6rQ5MGQAQFxeHoUOHIiwsDADQs2dPvHnzBlFRUSgoKJBqKycnh+bNm8PNzQ3u7u5o3bo1XF1dIRKJkJKSAlVVVRgYGCArKwvx8fEgIigoKMDIyIh7bzIzM5GVlYXCwkIoKSmBz+dDJBJxLyKCsrIyzM3N4eHhUeliTEQ4ceIEbt++jXbt2iE8PBw3b95ESkoK4uLiUFJSItVekjpTEmP9LvPnz8f69etr6R2tW8RiMV6/fo2IiAjEx8cjLCwMgYGBeP78OVq2bIlvv/0W5ubm6Nq16ydbmyAtLQ3Pnj3D48ePuYIqku/foEGDMGrUKPTr16+hxWQwGLXM1atXMXnyZMTHx2Ps2LHYsGEDmjVrVuNxm7QyQP+fDvKHH37A8ePHMX78eJiZmeHRo0ewsLCAnZ0d2rVrx6Xb1NDQgLa29kex/1xSUoInT55w8cmmpqYwNjYGj8eDQCBAamoqEhMTuXhwX1/fBjel0zs5voVCIRQVFTl/hoyMDGzfvh137txBREQEp6Tx+XxYW1ujc+fO8Pb2hq+vb52mQa1LiAjJycmIjY1FQkICkpKSkJSUhJycHJSUlCAnJwdv375FTEwMMjIyAADq6upo06YN2rVrh44dO6Jr165cIiYGg/FpERMTA1dXV3h5eWH9+vVo3bp1rY39SSgDK1euREhICEaOHAl9fX3k5OQgIyMDqamp0NHRwdixY6GoqIjMzExERkbi+fPnuH//PgICApCUlMSNc+HCBfTp06fe5GaAqyZWkbXiv+jp6WHAgAFwcnKCq6srnJycYGZm9tEu/kCZlSMkJASBgYHYvXs3nj17xp2TKHG6urpQVFSEpqYmjIyMYGFhAScnJ7Ro0QK2trYNrsQxGIz64ddff8W3336L7OzsWi+H/UkoA25ubly1qHfR1tZGXl4e+Hw+l7EMKDOXt2rVCp06dUKfPn1gY2MDXV1d6Orq1pvMjDKICN7e3uWcKh0dHbFmzRoIhUIoKChASUkJHTt2rPenXpFIhPDwcMTHxyM2Nha6urqwtbVFq1atqh3JkZKSgrCwMERERHA+FPfv30d2djYUFRXh7e2NCRMmwMHBARYWFh+FdY3BYNQfAQEB8Pb2Rv/+/XHixIlafRD6aJSB8ePH4/jx47CysoKBgQHatGmDYcOGwd3dHevXr8eCBQvg4OCAbt26YfTo0Wjbti2UlJQQFRWFCxcuQE1NDbq6urCzs+O86RkNi0gkwvjx43Hr1i3Ex8eXO5+QkNBgBYfEYjH27NmDtWvXciF6CgoKXAimtrY2hg8fjl9++eW9SY2ICMOGDcOxY8e4cWxsbGBra4t27dqhc+fOaNeu3Qcl42EwGE2LkydPwsfHByoqKlBUVISioiKuXLlS49TTH40y8K5XeatWrRAXF4fs7Gy0a9cON27cwKFDh7Bv3z7cv38fAoEAnTp1gpaWFjQ1NaGrqws9PT1oa2tDSUmJq1vt4eHBrAENSHR0NGxtbQEAmzdv5mqOGxsbo2fPng0WS52Wlobx48fjwoULGDlyJMaMGQM3NzeYmpoiPz8fEREROHPmDDZt2oQJEyZg27ZtVY6XnJwMExMTeHp6YubMmbCyskJeXh4yMjK4srACgQBeXl7o0aNHPV0lg8H4GCgpKcGmTZu46oeFhYU4f/68VJsxY8bA39+/RvN8NMpAUlISvv/+e+zfv7/c/vKdO3e4GP3i4mLs2LED9+/fR15eHnJzc5GZmYmMjAzk5OSguLhYKu7dxsYGbdu2xeLFi9GiRYtalZlRNZmZmejVqxceP34MMzMzrF+/Hl26dIG+vr7MdckLCwu5tLoKCgpQVVWFsrIySkpKsHHjRvz+++/o2rUrVq5cWWme7sTERDx//hyxsbEICQnBP//8Az6fD39//0p9SN6+fYt+/fohJiYGmZmZVcpYVFQEZ2dnqeI9ElRVVaGkpISsrCxoaGjg9u3b7HvIYDAAlFlPvby88OTJE3To0AFKSkpQUlKClZUVbG1tYWJiAgMDA7Ru3brGa+5Howy8O0dgYCDS09Ohp6cHIyMjtGnTpsp49OLiYpSUlEBLSwtEBIFAgJcvX2LPnj349ddfUVpaCmdnZy60kFG/BAcHY/LkyXjy5Al3TENDAyYmJrC3t4ejoyPnMW9ubi71WY8bNw779u2rdGxbW1u8evUKy5cvx4oVK8qdj4mJkVISVFVVMXz4cMydOxd8Ph8pKSlISkpCXFwc4uLi8PLlS0RERCA1NRVAmb+KJK9BVeTm5uLBgwdISEjgclPweDzk5OQgPT0dN2/eREhICADg3r17aN++/XvHZDAYny5FRUWYNGkSDh06hMuXL6NXr151Ot9HpwxUxcyZM/Hbb79xf+vp6aGwsBBFRUUAAGtra7i6uiI+Ph6RkZEoKSmBgoIC2rdvjyVLlqB37971LjPjfyQkJODp06fIyMhARkYGEhISEBUVxTnwAWV79a6urrC2toaOjg7i4+Nx+vTpKsfV1dVFREREhbG4sbGx6Ny5s1RUSUXo6Ohw4aaurq5wdnZGmzZtYGlpidTUVNy4cQNBQUHIysqCWCyGWCxGUVERkpKSkJiYiKSkJAiFQqkxFRUVoa+vD319fTRr1gw2NjYwMzPD/PnzoaqqWs13j8FgfEps3rwZc+bMAQCsWbMGCxYsqNPIoU9KGQgMDES3bt3KHdfU1MSWLVvw9OlTvHjxAtbW1nBxcUHr1q3h4eHBHLc+AlJSUvDw4UOEhoYiNDQUCQkJyM7ORnZ2Npf2WIKKigrs7OxgZWUFMzMzdO3aFQMHDqzQ+z8tLQ3Pnz/HgwcP8OzZM6SkpCAxMRFv3rzh6iD8F0VFRejq6sLc3Bympqa4evUq8vPzYWVlBSMjI/B4PK5uhKmpKUxNTWFmZgZzc3OYm5tDX18fOjo6UFVVZWmCGQxGhRQXF+PMmTP4999/sXv3btja2mLs2LHw9vaGh4dHrSdU+6SUgXeJiIjAlStXsH79eiQkJNSLmYXRMBARsrOzkZCQgISEBERGRiI0NBTPnz9HWFgYiouLYWJigqVLl8La2hoBAQF49uwZnj9/ztVBUFBQ4NI429jYwMnJCTY2NlBSUgKPx4OcnByKi4uRm5vLpaVOSEhAfHw8nJycsGTJEhgZGTXwO8FgMD5FHj58iM2bN+P8+fPIzc2FiooK2rRpg7Zt28LT0xMODg5o3rx5jUKvG1QZICIUFhYiNzcXenp6MjuNyUpmZib09PQAlNVylzydsbDCpoNIJEJISAg2btzIeduamprC09MTbm5uaNmyJVq0aIHmzZt/FJklGQxG00UoFCIoKAh3797F3bt38ejRI7x+/Zo7b2hoiOXLl6NPnz7Q09ODurq6zNbHOlEGevbsCUdHRzg5OSE0NBQPHz5Ey5Yt0bNnT4SFheHcuXNITExETk4Ot4+qoqICLy8vdOzYER06dICjoyMMDQ1rZMIPDw+Hi4uL1DEdHR2MHj0apqam0NPT45INSWoE8Hg88Pl8Ls1tQ1s4GLVHUFAQlJWV4eLiwszzDAbjkyA9PR2vXr1CdHQ0zp8/j0OHDnHnFBUVYWZmhuHDh0NDQwMpKSl4+/YtsrOzYWZmhubNm3M5TwwMDGBhYVG7ykDHjh2RkZGBqKgoWFpaolOnTnj8+DHCw8Oho6ODAQMGwMHBAdra2tDS0oKGhgaioqJw48YN3L17VypUS0NDA4aGhrC0tETz5s1ha2sLY2NjaGlpQVtbmxuDz+fj7du3SElJQXJyMvLz86GmpsaFmRUVFaGwsBD37t3D8+fPkZ2djaysrHLldd+lT58+uHDhgqyfCYPBYDAYDQYRITQ0FMnJyZwj9vPnz3H06FHw+XwYGhrCyMgImpqaSExMRHR0dLnKu3WyTSAQCCAvL889haWkpHB51qu6mFevXiEuLg5v377lFvi4uDhER0fj1atXyMvLe++boqysXKkDmLy8PCwsLKCrq4sXL16Uq0wo4eDBgxg5cuR752IwGAwG42MkKysL0dHRiIyMxJgxYz4uB8Li4mLOkzwnJwfZ2dkQCoUwMjKCkZERDAwMoKCgAJFIhMLCQuTn56OgoAB5eXlITU1FTEwMYmNjkZ6eDmNjY86XQFLZr1mzZmz/mMFgMBhNBlnX70ZVFk5ZWZlb+KtCUnaYlXRlMBgMBqPmsBqpDAaDwWA0cZgywGAwGAxGE4cpAwwGg8FgNHGq5TOQm5tbV3IwGAwGg8GoZWRdt2VSBhQVFWFkZARzc/MaCcVgMBgMBqN+MTIyem8mYJlCC4GysL/S0tJaEYzBYDAYDEb9oKio+N50/TIrAwwGg8FgMD5NmAMhg8FgMBhNHKYMMBgMBoPRxGHKAIPBYDAYTRymDDAYDAaD0cRhygCDwWAwGE0cpgwwGAwGg9HEYcoAg8FgMBhNnP8DuZpHbjTKK6wAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes(projection=gfs_proj)\n", + "ax.coastlines()\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle=':')\n", + "plt.contourf(lons, lats, subset.data, cmap='turbo')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83924ebe-833e-4804-b6ce-e41742d59705", + "metadata": { + "execution": { + "iopub.status.busy": "2024-07-24T03:52:57.030227Z", + "iopub.status.idle": "2024-07-24T03:52:57.030567Z", + "shell.execute_reply": "2024-07-24T03:52:57.030482Z", + "shell.execute_reply.started": "2024-07-24T03:52:57.030474Z" + } + }, + "outputs": [], + "source": [ + "subset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c40ed50-670e-4857-850e-0c6c3209c929", + "metadata": { + "execution": { + "iopub.status.busy": "2024-07-24T03:52:57.030933Z", + "iopub.status.idle": "2024-07-24T03:52:57.031104Z", + "shell.execute_reply": "2024-07-24T03:52:57.031025Z", + "shell.execute_reply.started": "2024-07-24T03:52:57.031018Z" + } + }, + "outputs": [], + "source": [ + "msgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7ef44bc-9c2a-4d88-91bc-3a7037466bf3", + "metadata": { + "execution": { + "iopub.status.busy": "2024-07-24T03:52:57.031650Z", + "iopub.status.idle": "2024-07-24T03:52:57.031881Z", + "shell.execute_reply": "2024-07-24T03:52:57.031796Z", + "shell.execute_reply.started": "2024-07-24T03:52:57.031787Z" + } + }, + "outputs": [], + "source": [ + "subset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f527023d-eb46-4f12-a54f-3404d40352a3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/grib2io/_grib2io.py b/src/grib2io/_grib2io.py index c15bade..5972a4b 100644 --- a/src/grib2io/_grib2io.py +++ b/src/grib2io/_grib2io.py @@ -469,20 +469,20 @@ def write(self, msg): msg GRIB2 message objects to write to file. """ - if isinstance(msg,list): + if isinstance(msg, list): for m in msg: self.write(m) return - if issubclass(msg.__class__,_Grib2Message): - if hasattr(msg,'_msg'): + if issubclass(msg.__class__, _Grib2Message): + if hasattr(msg, "_msg"): self._filehandle.write(msg._msg) else: if msg._signature != msg._generate_signature(): msg.pack() self._filehandle.write(msg._msg) else: - if hasattr(msg._data,'filehandle'): + if hasattr(msg._data, "filehandle"): msg._data.filehandle.seek(msg._data.offset) self._filehandle.write(msg._data.filehandle.read(msg.section0[-1])) else: @@ -1334,6 +1334,97 @@ def interpolate(self, method, grid_def_out, method_options=None, drtn=None, return msg + def subset(self, lats, lons): + """ + Return a spatial subset. + + Uses the minimum and maximum values in `lats` and `lons`. + + Parameters + ---------- + lats + List or tuple of latitudes. The minimum and maximum latitudes will + be used to define the southern and northern boundaries. + + The order of the latitudes is not important. The function will + determine which is the minimum and maximum. + + The latitudes should be in decimal degrees with 0.0 at the equator, + positive values in the northern hemisphere increasing to 90, and + negative values in the southern hemisphere decreasing to -90. + lons + List or tuple of longitudes. The minimum and maximum longitudes + will be used to define the western and eastern boundaries. + + The order of the longitudes is not important. The function will + determine which is the minimum and maximum. + + The longitudes should be in decimal degrees with 0.0 at the prime + meridian, positive values increasing eastward to 360. There are no + negative longitudes. West longitudes are converted to east + longitudes by adding 180 to the absolute value of the west + longitude. + + Returns + ------- + subset + A spatial subset of a GRIB2 message. + """ + if self.gdtn not in [0, 1, 40, 10, 20, 30, 31, 110, 32769]: + raise ValueError('Subset only works for regular lat/lon, Gaussian, mercator, stereographic, lambert conformal, albers equal-area, and azimuthal equidistant grids.') + + newmsg = Grib2Message( + np.copy(self.section0), + np.copy(self.section1), + np.copy(self.section2), + np.copy(self.section3), + np.copy(self.section4), + np.copy(self.section5), + ) + + msglats, msglons = self.grid() + + la1 = np.min(lats) + la2 = np.max(lats) + lo1 = np.min(lons) + lo2 = np.max(lons) + + first_lat = np.abs(msglats - la1) + first_lon = np.abs(msglons - lo1) + max_idx = np.maximum(first_lon, first_lat) + first_i, first_j = np.where(max_idx == np.min(max_idx)) + + print("first_i, first_j", first_i, first_j) + last_lat = np.abs(msglats - la2) + last_lon = np.abs(msglons - lo2) + max_idx = np.maximum(last_lon, last_lat) + last_i, last_j = np.where(max_idx == np.min(max_idx)) + print("last_i, last_j", last_i, last_j) + + setattr(newmsg, "latitudeFirstGridpoint" , msglats[first_i[0], first_j[0]]) + print("latitudeFirstGridpoint", newmsg.latitudeFirstGridpoint) + setattr(newmsg, "longitudeFirstGridpoint" , msglons[first_i[0], first_j[0]]) + print("longitudeFirstGridpoint", newmsg.longitudeFirstGridpoint) + setattr(newmsg, "nx" , np.abs(first_i[0] - last_i[0])) + setattr(newmsg, "ny" , np.abs(first_j[0] - last_j[0])) + print("newmsg.nx, newmsg.ny", newmsg.nx, newmsg.ny) + print(self._data.shape) + setattr(newmsg, "data" , np.copy(self._data[ + min(first_i[0] , last_i[0]) : max(first_i[0] , last_i[0]), + min(first_j[0] , last_j[0]) : max(first_j[0] , last_j[0])])) + if self.gdtn in [0, 1, 40]: + setattr(newmsg, "latitudeLastGridpoint" , msglats[last_i[0], last_j[0]]) + print("latitudeLastGridpoint", newmsg.latitudeLastGridpoint) + setattr(newmsg, "longitudeLastGridpoint" , msglons[last_i[0], last_j[0]]) + print("longitudeLastGridpoint", newmsg.longitudeLastGridpoint) + if self._sha1_section3 in _latlon_datastore.keys(): + del _latlon_datastore[self._sha1_section3] + newmsg.grid() + print(newmsg.nx, newmsg.ny) + print(newmsg.grid()) + + return newmsg + def validate(self): """ Validate a complete GRIB2 message. @@ -1589,7 +1680,7 @@ def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out, a,newshp = _adjust_array_shape_for_interp(a,grid_def_in,grid_def_out) # Set lats and lons if stations, else create array for grids. - if grid_def_out.gdtn == -1: + if grid_def_out.dtn == -1: rlat = np.array(grid_def_out.lats,dtype=np.float32) rlon = np.array(grid_def_out.lons,dtype=np.float32) else: diff --git a/tests/test_subset.py b/tests/test_subset.py new file mode 100755 index 0000000..1cda305 --- /dev/null +++ b/tests/test_subset.py @@ -0,0 +1,65 @@ +import itertools +from pathlib import Path + +import grib2io +import pytest +import xarray as xr +from numpy.testing import assert_allclose, assert_array_equal + + +def _del_list_inplace(input_list, indices): + for index in sorted(indices, reverse=True): + del input_list[index] + return input_list + + +def _test_any_differences(da1, da2, atol=0.005, rtol=0): + """Test if two DataArrays are equal, including most attributes.""" + assert_array_equal( + da1.attrs["GRIB2IO_section0"][:-1], da2.attrs["GRIB2IO_section0"][:-1] + ) + assert_array_equal(da1.attrs["GRIB2IO_section1"], da2.attrs["GRIB2IO_section1"]) + assert_array_equal(da1.attrs["GRIB2IO_section2"], da2.attrs["GRIB2IO_section2"]) + assert_array_equal(da1.attrs["GRIB2IO_section3"], da2.attrs["GRIB2IO_section3"]) + assert_array_equal(da1.attrs["GRIB2IO_section4"], da2.attrs["GRIB2IO_section4"]) + skip = [2, 9, 10, 11, 16, 17] + assert_array_equal( + _del_list_inplace(list(da1.attrs["GRIB2IO_section5"]), skip), + _del_list_inplace(list(da2.attrs["GRIB2IO_section5"]), skip), + ) + assert_allclose(da1.data, da2.data, atol=atol, rtol=rtol) + + +def test_da_write(tmp_path, request): + """Test writing a single DataArray to a single grib2 message.""" + target_dir = tmp_path / "test_to_grib2" + target_dir.mkdir() + target_file = target_dir / "test_to_grib2_da.grib2" + + datadir = request.config.rootdir / "tests" / "data" / "gfs_20221107" + + with grib2io.open(datadir / "gfs.t00z.pgrb2.1p00.f012_subset") as inp: + print(inp[0].section3) + newmsg = inp[0].subset(lats=(43, 32.7), lons=(117, 79)) + + print(inp[0]) + print(newmsg) + print(newmsg.section0) + print(inp[0].section0) + print(newmsg.section1) + print(inp[0].section1) + print(newmsg.section2) + print(inp[0].section2) + print(newmsg.section3) + print(inp[0].section3) + print(newmsg.section4) + print(inp[0].section4) + print(newmsg.section5) + print(inp[0].section5) + + print(inp[0].data.shape) + print(newmsg.data.shape) + + with grib2io.open(target_file, mode="w") as out: + out.write(newmsg) + assert False From 03c861a039b320e8ecb7ea38b4dff09d1eeca1d7 Mon Sep 17 00:00:00 2001 From: Timothy Cera Date: Sun, 4 Aug 2024 01:43:33 -0400 Subject: [PATCH 2/4] Added spatial subset for both messages and grib2io xarray. --- demos/plotting_examples.ipynb | 424 +++++++++++++++------------------- src/grib2io/_grib2io.py | 60 ++--- src/grib2io/xarray_backend.py | 64 +++++ tests/test_subset.py | 121 ++++++---- 4 files changed, 348 insertions(+), 321 deletions(-) diff --git a/demos/plotting_examples.ipynb b/demos/plotting_examples.ipynb index 6e3d573..141f95f 100644 --- a/demos/plotting_examples.ipynb +++ b/demos/plotting_examples.ipynb @@ -1,16 +1,33 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "2f1a893c-0867-4845-9bd6-d4db8e2b9007", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-30T23:06:48.619292Z", + "iopub.status.busy": "2024-07-30T23:06:48.619050Z", + "iopub.status.idle": "2024-07-30T23:06:48.622102Z", + "shell.execute_reply": "2024-07-30T23:06:48.621682Z", + "shell.execute_reply.started": "2024-07-30T23:06:48.619276Z" + } + }, + "source": [ + "Geographic Subsetting of GRIB2 Messages\n", + "=======================================" + ] + }, { "cell_type": "code", "execution_count": 1, "id": "fb56f795-20b8-496f-adea-50b9e6aaa66e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-24T03:52:53.550756Z", - "iopub.status.busy": "2024-07-24T03:52:53.550622Z", - "iopub.status.idle": "2024-07-24T03:52:54.085554Z", - "shell.execute_reply": "2024-07-24T03:52:54.084990Z", - "shell.execute_reply.started": "2024-07-24T03:52:53.550746Z" + "iopub.execute_input": "2024-08-04T05:14:32.359155Z", + "iopub.status.busy": "2024-08-04T05:14:32.359086Z", + "iopub.status.idle": "2024-08-04T05:14:32.672753Z", + "shell.execute_reply": "2024-08-04T05:14:32.672427Z", + "shell.execute_reply.started": "2024-08-04T05:14:32.359147Z" } }, "outputs": [ @@ -24,6 +41,7 @@ ], "source": [ "import grib2io\n", + "import wat\n", "import numpy as np\n", "import pyproj\n", "import matplotlib.pyplot as plt\n", @@ -31,148 +49,99 @@ "import cartopy.feature as cfeature " ] }, + { + "cell_type": "markdown", + "id": "53317570-7b44-4bc5-94a0-e1e83c066c36", + "metadata": {}, + "source": [ + "Global Forecast System Weather Model\n", + "------------------------------------" + ] + }, { "cell_type": "code", - "execution_count": 2, - "id": "53b5d3d7-bfd9-4687-bfc1-f92cfa804338", + "execution_count": 10, + "id": "66b0afed-223e-43fa-a2e4-2427c3d3aa57", "metadata": { "execution": { - "iopub.execute_input": "2024-07-24T03:52:54.086439Z", - "iopub.status.busy": "2024-07-24T03:52:54.086070Z", - "iopub.status.idle": "2024-07-24T03:52:54.090243Z", - "shell.execute_reply": "2024-07-24T03:52:54.089795Z", - "shell.execute_reply.started": "2024-07-24T03:52:54.086425Z" + "iopub.execute_input": "2024-08-04T05:20:30.107950Z", + "iopub.status.busy": "2024-08-04T05:20:30.107826Z", + "iopub.status.idle": "2024-08-04T05:20:30.111975Z", + "shell.execute_reply": "2024-08-04T05:20:30.111625Z", + "shell.execute_reply.started": "2024-08-04T05:20:30.107941Z" } }, "outputs": [], "source": [ - "msgs = grib2io.open(\"../tests/data/gfs.jpeg.grib2\")" + "msgs = grib2io.open(\"../tests/data/gfs.jpeg.grib2\")\n", + "proj_pars = msgs[0].projParameters\n", + "gfs_proj = ccrs.PlateCarree(globe=ccrs.Globe(semimajor_axis=proj_pars[\"a\"], semiminor_axis=proj_pars[\"b\"]))\n", + "levels = [4800, 4950, 5100, 5250, 5400, 5550, 5700, 5850, 6000]" ] }, { "cell_type": "code", "execution_count": 3, - "id": "8cfef760-d3cc-4d8c-a23a-c3c76d4c0863", + "id": "c7d0ef26-64e5-4dbe-a68b-3ac66a2cf1d9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-24T03:52:54.091691Z", - "iopub.status.busy": "2024-07-24T03:52:54.091466Z", - "iopub.status.idle": "2024-07-24T03:52:54.102368Z", - "shell.execute_reply": "2024-07-24T03:52:54.102056Z", - "shell.execute_reply.started": "2024-07-24T03:52:54.091675Z" + "iopub.execute_input": "2024-08-04T05:14:32.677177Z", + "iopub.status.busy": "2024-08-04T05:14:32.677099Z", + "iopub.status.idle": "2024-08-04T05:14:32.680302Z", + "shell.execute_reply": "2024-08-04T05:14:32.679834Z", + "shell.execute_reply.started": "2024-08-04T05:14:32.677169Z" } }, "outputs": [ { - "data": { - "text/plain": [ - "90.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "0:d=2022-02-10 00:00:00:HGT:Geopotential Height (gpm):500 mb:1 day, 0:00:00\n" + ] } ], "source": [ - "msgs[0].latitudeFirstGridpoint" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5dd9519b-b2a3-4aaa-b7ca-d167be32e9bf", - "metadata": { - "execution": { - "iopub.execute_input": "2024-07-24T03:52:54.102977Z", - "iopub.status.busy": "2024-07-24T03:52:54.102823Z", - "iopub.status.idle": "2024-07-24T03:52:54.105035Z", - "shell.execute_reply": "2024-07-24T03:52:54.104702Z", - "shell.execute_reply.started": "2024-07-24T03:52:54.102965Z" - } - }, - "outputs": [], - "source": [ - "proj_pars = msgs[0].projParameters" + "print(msgs[0])" ] }, { - "cell_type": "code", - "execution_count": 5, - "id": "50aa1e62-6dbb-4ff6-8073-8928109a0eab", - "metadata": { - "execution": { - "iopub.execute_input": "2024-07-24T03:52:54.105616Z", - "iopub.status.busy": "2024-07-24T03:52:54.105492Z", - "iopub.status.idle": "2024-07-24T03:52:54.108101Z", - "shell.execute_reply": "2024-07-24T03:52:54.107730Z", - "shell.execute_reply.started": "2024-07-24T03:52:54.105604Z" - } - }, - "outputs": [], - "source": [ - "gfs_proj = ccrs.PlateCarree(globe=ccrs.Globe(semimajor_axis=proj_pars[\"a\"], semiminor_axis=proj_pars[\"b\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "aa58c61d-f0d5-4117-98e3-421ed1d5752a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-07-24T03:52:54.108752Z", - "iopub.status.busy": "2024-07-24T03:52:54.108576Z", - "iopub.status.idle": "2024-07-24T03:52:54.115671Z", - "shell.execute_reply": "2024-07-24T03:52:54.115252Z", - "shell.execute_reply.started": "2024-07-24T03:52:54.108740Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(721, 1440)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "3871f973-7e87-4416-b0fe-587810e9e6bc", + "metadata": {}, "source": [ - "lats, lons = msgs[0].latlons()\n", - "lats.shape" + "Included with the installation of Cartopy, whenever the ```projection``` keyword is used with Matplotlib, for example, ```fig.add_subplot(projection=...)``` a Cartopy GeoAxes is created rather than a Matplotlib Axes. A Cartopy GeoAxes includes lots of features to plot maps." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "321416c8-19c6-4da2-9c77-95a3d654d7a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-24T03:52:54.116579Z", - "iopub.status.busy": "2024-07-24T03:52:54.116345Z", - "iopub.status.idle": "2024-07-24T03:52:56.253010Z", - "shell.execute_reply": "2024-07-24T03:52:56.252716Z", - "shell.execute_reply.started": "2024-07-24T03:52:54.116566Z" + "iopub.execute_input": "2024-08-04T05:27:30.587985Z", + "iopub.status.busy": "2024-08-04T05:27:30.587854Z", + "iopub.status.idle": "2024-08-04T05:27:31.482399Z", + "shell.execute_reply": "2024-08-04T05:27:31.482099Z", + "shell.execute_reply.started": "2024-08-04T05:27:30.587976Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -180,159 +149,80 @@ } ], "source": [ - "ax = plt.axes(projection=gfs_proj)\n", + "lats, lons = msgs[0].latlons()\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(projection=gfs_proj)\n", "ax.coastlines()\n", "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", "ax.add_feature(cfeature.STATES, linestyle=':')\n", - "plt.contourf(lons, lats, msgs[0].data, cmap='turbo')" + "cntr = plt.contourf(lons, lats, msgs[0].data, cmap='turbo', levels=levels)\n", + "fig.colorbar(cntr, location=\"bottom\")" ] }, { - "cell_type": "code", - "execution_count": 8, - "id": "0c2242a5-8f4c-4ab3-aa96-2d23ef3bdb37", - "metadata": { - "execution": { - "iopub.execute_input": "2024-07-24T03:52:56.253534Z", - "iopub.status.busy": "2024-07-24T03:52:56.253415Z", - "iopub.status.idle": "2024-07-24T03:52:56.289089Z", - "shell.execute_reply": "2024-07-24T03:52:56.288695Z", - "shell.execute_reply.started": "2024-07-24T03:52:56.253522Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "first_i, first_j [283] [316]\n", - "last_i, last_j [188] [935]\n", - "latitudeFirstGridpoint 19.25\n", - "longitudeFirstGridpoint 79.0\n", - "newmsg.nx, newmsg.ny 95 619\n", - "(721, 1440)\n", - "latitudeLastGridpoint 43.0\n", - "longitudeLastGridpoint 233.75\n", - "95 619\n", - "(array([[19.25 , 19.25 , 19.25 , ..., 19.25 ,\n", - " 19.25 , 19.25 ],\n", - " [19.28843042, 19.28843042, 19.28843042, ..., 19.28843042,\n", - " 19.28843042, 19.28843042],\n", - " [19.32686084, 19.32686084, 19.32686084, ..., 19.32686084,\n", - " 19.32686084, 19.32686084],\n", - " ...,\n", - " [42.92313916, 42.92313916, 42.92313916, ..., 42.92313916,\n", - " 42.92313916, 42.92313916],\n", - " [42.96156958, 42.96156958, 42.96156958, ..., 42.96156958,\n", - " 42.96156958, 42.96156958],\n", - " [43. , 43. , 43. , ..., 43. ,\n", - " 43. , 43. ]]), array([[ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", - " 232.1037234 , 233.75 ],\n", - " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", - " 232.1037234 , 233.75 ],\n", - " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", - " 232.1037234 , 233.75 ],\n", - " ...,\n", - " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", - " 232.1037234 , 233.75 ],\n", - " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", - " 232.1037234 , 233.75 ],\n", - " [ 79. , 80.6462766 , 82.29255319, ..., 230.45744681,\n", - " 232.1037234 , 233.75 ]]))\n" - ] - } - ], + "cell_type": "markdown", + "id": "a7120e7e-b45a-42e4-97da-d05d7c45fadd", + "metadata": {}, + "source": [ + "Subset to the extent of the Continental United States\n", + "-----------------------------------------------------" + ] + }, + { + "cell_type": "markdown", + "id": "a25798ab-90c8-404e-a0ba-0a3bb1b8f923", + "metadata": {}, "source": [ - "subset = msgs[0].subset(lats=(19.2, 43), lons=(233.7, 79))" + "The ```lats``` and ```lons``` can be given in any order and can have any number of values. The minimum and maximum of each are used to define the bounds. The ```lons``` start at 0 and increase positive eastward, so something like 125 W longitude would be entered as (360-125)." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "92801898-2a31-4fdb-ae0b-1a8690bee554", + "execution_count": 12, + "id": "0c2242a5-8f4c-4ab3-aa96-2d23ef3bdb37", "metadata": { "execution": { - "iopub.execute_input": "2024-07-24T03:52:56.289745Z", - "iopub.status.busy": "2024-07-24T03:52:56.289576Z", - "iopub.status.idle": "2024-07-24T03:52:56.292025Z", - "shell.execute_reply": "2024-07-24T03:52:56.291620Z", - "shell.execute_reply.started": "2024-07-24T03:52:56.289731Z" + "iopub.execute_input": "2024-08-04T05:28:47.013510Z", + "iopub.status.busy": "2024-08-04T05:28:47.013361Z", + "iopub.status.idle": "2024-08-04T05:28:47.030966Z", + "shell.execute_reply": "2024-08-04T05:28:47.030642Z", + "shell.execute_reply.started": "2024-08-04T05:28:47.013500Z" } }, "outputs": [], "source": [ - "lats, lons = subset.latlons()" + "subset = msgs[0].subset(lats=(24.5, 49.4), lons=(360-125, 360-66))" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "53cba63e-c274-4fdb-93c8-236c6866cafd", + "execution_count": 13, + "id": "401f060f-fb16-4ca2-acc7-cf4dd85ea955", "metadata": { "execution": { - "iopub.execute_input": "2024-07-24T03:52:56.292758Z", - "iopub.status.busy": "2024-07-24T03:52:56.292589Z", - "iopub.status.idle": "2024-07-24T03:52:56.296038Z", - "shell.execute_reply": "2024-07-24T03:52:56.295540Z", - "shell.execute_reply.started": "2024-07-24T03:52:56.292745Z" + "iopub.execute_input": "2024-08-04T05:28:55.666372Z", + "iopub.status.busy": "2024-08-04T05:28:55.665938Z", + "iopub.status.idle": "2024-08-04T05:28:55.777395Z", + "shell.execute_reply": "2024-08-04T05:28:55.777201Z", + "shell.execute_reply.started": "2024-08-04T05:28:55.666339Z" } }, "outputs": [ { "data": { "text/plain": [ - "((619, 95), (95, 619))" + "" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "lats.shape, subset.data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "57dd0dcc-33c7-4142-86c2-cd6c81cfa75d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-07-24T03:52:56.296973Z", - "iopub.status.busy": "2024-07-24T03:52:56.296650Z", - "iopub.status.idle": "2024-07-24T03:52:57.029810Z", - "shell.execute_reply": "2024-07-24T03:52:57.029106Z", - "shell.execute_reply.started": "2024-07-24T03:52:56.296957Z" - } - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "Shapes of x (619, 95) and z (95, 619) do not match", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_feature(cfeature\u001b[38;5;241m.\u001b[39mBORDERS, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 4\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_feature(cfeature\u001b[38;5;241m.\u001b[39mSTATES, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlats\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mturbo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/pyplot.py:2950\u001b[0m, in \u001b[0;36mcontourf\u001b[0;34m(data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2948\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mcontourf)\n\u001b[1;32m 2949\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcontourf\u001b[39m(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m QuadContourSet:\n\u001b[0;32m-> 2950\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2951\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 2952\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2953\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m __ret\u001b[38;5;241m.\u001b[39m_A \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m 2954\u001b[0m sci(__ret)\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:315\u001b[0m, in \u001b[0;36m_add_transform..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvalid transform: Spherical \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not supported - consider using \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlateCarree/RotatedPole.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 314\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m transform\n\u001b[0;32m--> 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:359\u001b[0m, in \u001b[0;36m_add_transform_first..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;66;03m# Use the new points as the input arguments\u001b[39;00m\n\u001b[1;32m 358\u001b[0m args \u001b[38;5;241m=\u001b[39m (x, y, z) \u001b[38;5;241m+\u001b[39m args[\u001b[38;5;241m3\u001b[39m:]\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:1655\u001b[0m, in \u001b[0;36mGeoAxes.contourf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(sub_trans, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mforce_path_ccw\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 1653\u001b[0m sub_trans\u001b[38;5;241m.\u001b[39mforce_path_ccw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 1655\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1657\u001b[0m \u001b[38;5;66;03m# We need to compute the dataLim correctly for contours.\u001b[39;00m\n\u001b[1;32m 1658\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _MPL_VERSION\u001b[38;5;241m.\u001b[39mrelease[:\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m<\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m8\u001b[39m):\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/axes/_axes.py:6536\u001b[0m, in \u001b[0;36mAxes.contourf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6527\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6528\u001b[0m \u001b[38;5;124;03mPlot filled contours.\u001b[39;00m\n\u001b[1;32m 6529\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[38;5;124;03m%(contour_doc)s\u001b[39;00m\n\u001b[1;32m 6534\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6535\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfilled\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 6536\u001b[0m contours \u001b[38;5;241m=\u001b[39m \u001b[43mmcontour\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQuadContourSet\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_autoscale_view()\n\u001b[1;32m 6538\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m contours\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:858\u001b[0m, in \u001b[0;36mContourSet.__init__\u001b[0;34m(self, ax, levels, filled, linewidths, linestyles, hatches, alpha, origin, extent, cmap, colors, norm, vmin, vmax, extend, antialiased, nchunk, locator, transform, negative_linestyles, clip_path, *args, **kwargs)\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegative_linestyles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 855\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegative_linestyles \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 856\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour.negative_linestyle\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m--> 858\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_args\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_levels()\n\u001b[1;32m 861\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_extend_min \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextend \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1523\u001b[0m, in \u001b[0;36mQuadContourSet._process_args\u001b[0;34m(self, corner_mask, algorithm, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1520\u001b[0m corner_mask \u001b[38;5;241m=\u001b[39m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour.corner_mask\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 1521\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_corner_mask \u001b[38;5;241m=\u001b[39m corner_mask\n\u001b[0;32m-> 1523\u001b[0m x, y, z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_contour_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1525\u001b[0m contour_generator \u001b[38;5;241m=\u001b[39m contourpy\u001b[38;5;241m.\u001b[39mcontour_generator(\n\u001b[1;32m 1526\u001b[0m x, y, z, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_algorithm, corner_mask\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_corner_mask,\n\u001b[1;32m 1527\u001b[0m line_type\u001b[38;5;241m=\u001b[39mcontourpy\u001b[38;5;241m.\u001b[39mLineType\u001b[38;5;241m.\u001b[39mSeparateCode,\n\u001b[1;32m 1528\u001b[0m fill_type\u001b[38;5;241m=\u001b[39mcontourpy\u001b[38;5;241m.\u001b[39mFillType\u001b[38;5;241m.\u001b[39mOuterCode,\n\u001b[1;32m 1529\u001b[0m chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnchunk)\n\u001b[1;32m 1531\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1563\u001b[0m, in \u001b[0;36mQuadContourSet._contour_args\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m<\u001b[39m nargs \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m:\n\u001b[1;32m 1562\u001b[0m x, y, z_orig, \u001b[38;5;241m*\u001b[39margs \u001b[38;5;241m=\u001b[39m args\n\u001b[0;32m-> 1563\u001b[0m x, y, z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_xyz\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_orig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1565\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1566\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _api\u001b[38;5;241m.\u001b[39mnargs_error(fn, takes\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom 1 to 4\u001b[39m\u001b[38;5;124m\"\u001b[39m, given\u001b[38;5;241m=\u001b[39mnargs)\n", - "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1610\u001b[0m, in \u001b[0;36mQuadContourSet._check_xyz\u001b[0;34m(self, x, y, z, kwargs)\u001b[0m\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 1609\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m z\u001b[38;5;241m.\u001b[39mshape:\n\u001b[0;32m-> 1610\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 1611\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShapes of x \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and z \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m z\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m 1613\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 1614\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShapes of y \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and z \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mTypeError\u001b[0m: Shapes of x (619, 95) and z (95, 619) do not match" - ] }, { "data": { - "image/png": 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U1MjOzo4aN25M8vLytHnzZsrOzqZ+/fqRrq4uBQcH08ePH+nDhw+FRmB36NCBrly5QjwejwCQnp4eE/UhykvDEynb1ahRgy5dukS5ubnk4eFBu3btovv379OKFSvI39+f0tPTyczMjN6+fUvNmzen69evk7m5OYWGhhJRXnyAnJwck9LNyspiYkTfIiMjQyYmJjRs2DBq06YN1axZs0QBbzk5OXTlyhW6ePEiPXjwgIRCIZMjzn9sKSkp4vP5rA0iASENDQ3q1KkTHTx4sNS/ze+GUCikmzdv0qpVq+jevXu0aNEimj9//q9uloRCiIyMpDt37tCjR4/o0aNH5O/vTwBITk6OzMzMKDo6mlJTUwvdVkpKikxMTKhq1ark5OREkydPLjIItzzIyMiggIAAysjIoGfPntHdu3fp1atXlJKSQrm5uUREJC0tTQYGBqSoqEiKiooUHh5OSUlJVKtWLWrSpAnVrl2bpKWlCQB17tyZtLW1izze27dvSVlZmSwsLMQMgvDwcHr27BkdOXKEzp8/T9WqVaOPHz/+tPMujhkzZtDq1atJR0eHNDU1SUFBgfh8PvH5fIqOjqb09HSx9Q8cOEBnz54loVBIpqampK+vT5UrV2aBlra2tr/kPEqCxBj4hQiFQkpMTKT09HRKT0+ntLQ0SkxMJBMTE7K2tmYvyRYtWvxwwE2zZs3ozp07zBjYuXMnffz4kdauXUtERFpaWpSdnU0ZGRnUo0cPOnPmTIF9zJw5k1auXEnt2rUjU1NTUldXp4CAAPry5QvJyclRdnY2JSYmkpycHOnq6pKamhpxHEcyMjKkq6tLaWlpdO7cOeI4jsaMGUObN2/+oXMqCUlJSfTixQuaPXs2vXr1iuzs7Gjs2LE0YsSIn37sn0liYiJpaWkREVH37t0L/b0k/FrS0tJYP2hjY0NNmjQhY2NjioyMpODgYHrw4AHl5OSQrq4uWVtbU/Xq1al69epkaWlJFhYWZGpqWmEpobt376bZs2czLQElJSVq0qQJNWrUiLS0tJhgV4MGDcjIyIhtJxQKKTs7+4eNFI7j6PHjx/TgwQPy9vam58+fU2xsLBHlZRtNmzbth/ZfVkQp1lZWVtSmTRuKi4ujT58+UU5ODvF4PIqLixPzjm7YsIEmTpxY5P7u3r1Lzs7OP7/hZUBiDPxC+vTpQydPniz0Ox6Px0a9srKypK+vT/b29tSiRQsyNjamHTt20PHjx8W2qVWrFtna2pKhoSGpqKiQiooKKSsrk5SUFDVp0oSMjIzE5uWJ8lIC16xZQ6mpqWRoaEjy8vJUq1Ytio2NpZMnT5Kfnx+lp6eTUCgkc3Nz9n+BQECZmZlF6uZ/ey6i20dVVZXMzc1p+PDhVK9ePdLU1BQbGfws+Hw+nT9/nk6cOEEnT56kiRMn0tq1a/9I1bgbN25Q27ZtxT4rweMpoYJ49OgRDR48mNXimDRpEq1YsYKWLFlCq1atIo7jqGnTptSlSxfq0qVLhWSBpKWl0du3b+nLly8UHx8vVlgrIiKCFi9eTERE165dI2NjY6patWqFalNUrVpVTAZcXV2dDAwMaNeuXaSjo0PR0dFMXEtVVZXU1NRIU1OTGcT5CQ4OppiYGNLR0SEdHR2SlZUlImJ9YWnIycmhW7du0alTp+jhw4dkaGhIWlpalJaWRtnZ2aSurk56enokLS1NAwcOpMaNG9P79+/p2bNn5O3tTdHR0fT161fi8/nk4uJC06ZN+237HEk2wS/k2LFjLNr22bNneP/+PT58+ICbN29i586d2LhxI9asWYPBgweLReY2aNAAM2bMwMOHD3Hp0iU8fvwYfD4fHMfh48eP4PP5uHTpEiZPniwWuU1EWLduHYC8iP5z585BVVW10Ojf1q1bo27dulBWVkbr1q0xfvx4jBo1Cm3atIG0tDSICDVr1sS+fftQs2bNAts3adIEtra2YhkPpqamhR5LVVUVI0aMQGxsbIVc982bN0NKSgo9evRARkZGhRzzR8nOzkbTpk0hLy+P+vXrs2tXr149PH369Fc3T8L/ExQUJHZvh4aG4sGDB7C2toasrCwWLFiA+Pj4Mu378+fPuHDhAp4+fYrQ0FBkZ2ez7wQCQbH3cqdOnViblJSUoKCgwJ5jZWVl9t3Hjx/L1LYf5cCBA7Czs0P79u1hamoKKSmpIvsm0cLj8TB06FDs2rULhw8fxpkzZ3D16lUYGxsXur6+vj5GjRqFly9flrmdM2bMKHTfN2/eLMer8Wso6ftbYgyUM58+fYKhoSHMzc3x+vXrAt9//foV06ZNg56enthNp6CgUOhD0bx5c9SqVQtEBF1dXfadioqK2LrXrl0DADg6Oop9HhUVhSdPnsDKyop9JicnBzc3NwDAkSNHoKOjAyKCrKwsZs2ahR07doCI0LZtWxw4cAD3799n6xAR+vXrh40bN+LmzZt48uQJmjZtWuzD3blzZyQmJlbI9T9//jy7PtHR0RVyzLLy+PFjsRRMIsKYMWOwZcsWCASCX908Cf/PgQMHQERQVFREt27d8PnzZwwaNAhEhIYNG+Ldu3dl3ve2bdsKfWYmTZoEAOjatSuICDt37gSfz8ebN29w9epVnDt3DidPnoSdnR3q1KmD1NRUsf1yHPdD5/yzEAqFCAsLw/jx43H9+nX4+/sjMjISHz9+xKtXr3Dv3j1069atyPTqJUuW4N69ezh16hSOHTuGI0eOYNKkSTA2NoaioiL8/f1L3SYvLy9Uq1aNHUNkTImM8v79+2P8+PFYuHAhNm7ciMOHD+Pq1avw9vZGWlraT7hK5ctPMQYuXbpUZuv3R/ldb+78ZGVlsZto//79SE5OFvv+4sWLUFNTg6ysLLvRDh48iPDwcADArVu30KdPH+zYsQM3b97Evn370Lp1a3Tq1AknTpxA9+7d2f6fP38OX19f2NvbQ11dnY2+XVxcxB6etWvXIjMzE6mpqVi6dCkUFBTYiF900zs7O+PSpUvs5VmlShV07dpV7JonJydj+PDh2LdvX4Hz5jhOzJhRVlZGs2bN0LRpU2hrazNDo3Pnznj//j18fX3x+PFjCIXCcv8N3r9/z9px+vTp39ZDkJ2djTZt2hTo7CT8XowcOZL9NpmZmQCAa9eugYiwbNmyMt/DmZmZ8PT0hLy8PIYPH47w8HD4+vri+PHj7HjLli2DhoZGsYa2aGnQoEF5nvZvgVAoREZGBuLi4vDlyxeEhIQUeb0XLlwIIsL69evZZw8ePMDYsWNx/PhxhIWF4dGjR9i7dy/OnDkDf39/RERE4MiRI+wabty4ETt37sTKlSsxbdo0DB06FN26dYOTkxNq1KiBypUrQ05OTuy6GxgY4NSpU8jKyqqgq1J6wsLCyt8YEC2NGzcul0YWRUJCAi5duoSNGzdi+PDhqF+/PuTk5KCoqIiaNWvi0KFDWL16NebOnYv379//1LaUhvT0dHTp0oVdJykpKbi7u2PEiBFo2LAhu3kKe5gXLlz43f1nZ2ejXbt26Nq1K3vJff36FWpqanBzc0NoaCgAIDY2Fvv27UOPHj0gIyMDGRkZ5prj8XiQlZWFlpYWbG1tUblyZWYQfP36FQDEzmHBggUlOvd79+7h/Pnz8Pf3F3NzAkB4eDg8PT3FLG4iQqNGjXDq1Cn4+/sjNze3FFe6aDZt2lTg2jZr1gy3bt36rQxKkUE2fvx4bN68mRlNIvGpf4309HSsW7cOU6ZMwezZszFjxgy4urqiefPmcHNzw44dO+Dn5/dTDMjiyD+VV7duXZw/fx4vX75kHrbx48fj8+fPpdrn4cOHoaenB2lpaQwaNEjsRTJu3Dixe/dbr9uDBw8QHh6O2NhYfP36FWfOnIGqqiq6detWzmf+Z3Hnzh0Q5U1NDhs2DJ6enlBSUoKWllaJjCkiYsJoxcFxHNLT0/Hlyxd4e3ujdevWzGvUqVMnbN26FcePH8eePXvg6emJZcuWYdasWZg6dSpWrFiB3bt3482bNz//ggDw8/Nj/XtJjIFSBRCKGDduHG3cuPF7m5WY5ORk2rlzJ92/f59u375NOTk5Yt936dKFmjZtSkePHqW4uDgmaiKiatWq9PXrV9LV1SV9fX1SUFCgSpUqUdu2bUlVVZUiIyPJ0dGR0tLSWDpd3bp1y9RWPp9PQUFBFBgYSCoqKmRqakrDhw8nExMTmjJlCtWsWZOys7Pp+PHjFBISQp6enmRiYkK1atWiWrVqUdeuXUlVVZUMDAy+Gxy2e/duGjp06HfbtGbNGpo1axYJBAKytbWl1q1bU8uWLalNmzYUHh5O165do/fv31ODBg3o1atXdP78eQoICCBlZWUCQNeuXaNBgwaRkpISXbt2jbKyssQK5UyaNImMjIzIwsKCqlatSoaGhqSmplZowExERAQFBQWRk5NTATnZly9fUlBQEJmYmFBqaipNnTqViZfIyspS3bp1afLkydS7d+8fCjwsrJ4CEZGVlRUFBASUeb/lxfz582nJkiWFfjdo0CDat29fBbeo4sD/p5i9evWKAgICSEZGhnJycmjr1q0UHx9PZmZmLN1NFNQVHh5Ob968YftYvnw5zZo1q0Lam5aWRo8ePaKPHz/SmjVrKCIiosA6lSpVoqSkpBLv09nZme7fv09BQUEF7tOwsDDq1q0bvX37lkxNTSk6Opp27dpFt27dIjs7u2KFuv51kpOTacWKFXTp0iX68OEDNWnShK5fv06JiYn05s0bMjExIUtLS0pJSSF/f3+KjY2l5s2bk1AopKSkJLKysiq1BDb+v/zz5cuX6cqVK/To0SNWf0OkiaKiokLS0tKUkJDA7pNhw4bRrl27Sn2OYWFhFB0dTbq6uqSnp1dstsfdu3epRYsW7O9yzSaIiYkhPT29Uja/eE6fPk3//fcf8Xg8atSoEcXGxpKbmxu5uLiQtLQ0ycnJiengZ2dnk7+/PxkbG9PHjx/p7t279P79e3r+/Dm1bNmSMjIy6M6dOxQfH19spTRRqc7KlSuTvb09Va9endq1a8fOLyMjg27evEn3798ngUBAWlpa5OvrSzdv3iyQg0pEZGZmRqGhodS9e3d69OgRff36lcaMGUMBAQHk4+NDampq1KBBA2rbti3Vq1ePdHV1adiwYWKiO9+yYsUKmjlzZpHfi346Ho9HKSkpdOnSJXJ1dWXfL168mHR1dWnHjh2sMxVFldaoUYNmzZpF/fr1Ix6PR9bW1hQQEEAKCgp0/PhxqlKlCm3ZsoVCQkIoOjqawsPDC2QYSEtLk6KiIunp6ZGCggIlJSWx8rRaWlpkYmJCkydPZr9vYe2Pi4sjPz8/+vDhA124cIFu3LhBM2fOpOXLl5fZIIiNjSV9fX3q168fde7cmW7evMlesL9DOdrKlStTTExMod+FhYWRsbFxBbfo5xMVFUWenp60f/9++vr1KxHlZaBIS0tTbm4udevWjZYuXUpmZma0YMEC6tWrF9nZ2dHEiRPp8uXLtG3bNmrdujXbn6amJvXs2ZP+++8/srKyYiW1fyZpaWnk5+dHGRkZLOMmKSmJnJ2dqXr16kREtGnTJho/fjzZ2tqSgYEBGRgYUJUqVahevXrUqFEj0tHRoW3bttHo0aNp8uTJtHDhwgKZQAsXLqSlS5eShoYG1apVi65fvy6p01BKEhMTqVKlShUe4Z+RkUFCoZCUlZUL/c1yc3OpT58+dOfOHVqwYAFlZGRQSEgIZWdnk0AgIEVFRTIwMGAFotLT00lVVZWGDh1KYWFhBQaxSkpKpK6uTjk5OZSTk0NKSkpkbW1Ntra2ZGNjQ6amplSlShWqXbt2+RoDc+fOpRkzZpCKikoZLlMeJ0+epJUrV5K8vDypqKjQrVu3SEpKikJDQ8XyXMsDjuMoMzOTXrx4QXFxcdSgQQNKTk6m48ePk4KCAu3YsYMUFRVJIBAwb4O9vT0ZGxvTvXv3KDExkczMzEhFRYXi4uLI3NycOnbsSI0bNyYrKyt68+YN+fv7U+/evUlfX5/27dtH69evJzs7Ozp58iTJyspS9erVqWvXrpSVlUUPHjygly9fsgJDpqamlJ6ezjpHojwjpUePHjR58mRKT09n6mUpKSmUnJxML1++pNevX1NkZCRlZmaSgoIC2drakqWlJXEcR0ePHiWiPPW6TZs2Uffu3Qu9Njo6OhQXF0f9+/cnLy8vmjRpEm3YsIF0dHQoLS2NDAwMKCkpiWbPnk0TJkwgGRkZGj58OJ09e5batGlD8fHxFB8fT58+faLU1FSqXbs2devWjSpXrkzW1tZ0+/Zt8vT0pOTkZDIzM6PDhw9T7dq1SVFRsdD2JCYm0vjx4+nEiRPE5/Pp6tWr1K5duzL/7h07dqSbN28WqJK3detWGjVqVJn2+6NkZ2fTgwcP6MqVK3TlyhUKCgoiZWVlateuHXXo0IF69uwp5oH7G/Dz86M1a9bQ4cOHSVFRkdzc3IjP59Pz588pLi6OIiIiKD4+nubNm0ejR48mW1tbat68OY0ZM4ZcXFzo+fPnNHfuXJKVlaUlS5bQ/PnzSV5enqpVq0YnTpxgAllEeQaCiYkJtW3bljp37kwNGzYstENOS0uj1NRU0tfXL9FLNjc3l16+fMnKMk+ePJmaNm1KHMeRQCAgX19funHjBmloaNCuXbuY4d2zZ0+Kioqiz58/M+PPwcGB5OTk6MGDB0REVK1aNfLy8qIGDRqw4/Xt25elF8vKytLEiRNp9erVZf4NJPxeXLt2jVasWEHe3t6kqqpKFhYWzHuQkZFBkZGRFBMTQ3JycqSqqkrx8fEkKytL9vb2TJfm5s2b9PXrV/r69SulpqaSvLw8ycvLU0pKCn348IE+fPhAgYGBxOfzSU9Pj2JjY8s3tVC0ODo64urVq6WOeL5165bYflq3bo0tW7b8suIkHh4eePz4MQAgLi4OBw4cgJubG1q1aoUJEyb8UNGN3NzcQq9PYmIiHj16hN27d2PKlCno3r07qlevLja3IyMjU+ic1rcRtqJARCUlJZiYmLD5sREjRgAAMjIy8OTJE/To0UMsjoGI0KFDB+jp6WHAgAFi7RPNubdp0waurq4gIhw7dgwhISHQ1tZG7969xda/d+8eiAg9evQocK6rV68Wa6+SkhJ69uyJe/fuFVg3fzomEWH69Ok4duwYXr9+Xea5/uTkZLHsCgcHhwJR1+VNVlYWTpw4gcmTJ2PChAno27cvGjVqBCMjI3btq1SpAnd3d5w/f54Fpf1NcByHmzdvol27diDKS/1q3bo1IiIiEBgYCHV1dYwdOxb9+/cHAERFRcHMzAxv374FkJfGJ4oNWL9+PeTl5bF3794CxxEKhXj9+jUuXryIPXv2YPny5XBzc2PxF9ra2hgwYABOnz4t9iy2atWK3RMmJiZwcnISSyFLS0vDjRs3MG/ePDRr1qzQTB/RsydaVFVVISUlBTk5OfB4PCxevFjseoSFhWHfvn3o1q1bgaJhJiYmYvf4yZMnxbJMmjVrVq6/j4Tfg5LGv8TExGDy5Mnsvpk/f36J+sTc3Fzcv38f8+bNK/8AwoMHD6JFixaoWrUqiAiGhoaYNWsWAgICkJGR8d0GxsTEwMXFhaXFGRsb4/z58xUeFCRCRUWF5ef/atavX1/iQBeivOpuHMfB19cXPXr0gLKysljqYWG5vJcvX8bs2bPZ38bGxgUCMNPS0uDt7Q2O43Dp0iUQEXr37g1NTU1YWFgUyCbZtWsXiP6XCiUiKytLrD1EhLFjx7I0yUePHhW4Bp8/f8aOHTvQo0cPscAfZ2fnMuVJL1myBER56Uh+fn4/PYDw4cOHMDQ0BBHB3NwcNjY2cHZ2xoABAzB37lzs3LkT7969+60CGcubjIwM9OvXD0SE2rVr49ChQ7h+/To0NTURGhqK9PR0HD58WGybEydOgIhYVk1+goKCcP/+fZw5c6bE100gEODx48eYOXMmatSoAaK8zB1Rds/BgwdBRKwfEy337t1Dw4YNWaCrtrY2unXrhnXr1uHFixcYOHAgiAg2NjZYtmwZ9uzZg/379+PBgwfIzc2Fv78/Zs+eDW9v72Lbl56ejjNnzmDAgAFwcHDAkydPxL5/9eoVzM3NWTqvvLx8uQXYSvhzKet78qfqDHAchxcvXmD06NFiD5OMjAw0NTVRr149HD58GHw+v9D9paenY9q0aWLbLVy4sMj1fxbLly/H/fv3K/SYRSEQCPDixQucP38eW7ZswY4dO+Dl5YWzZ8/i/v37ePPmDVxcXNCzZ088fPiwyP18/foVJ0+exKpVq3Dw4EFcuXIFly9fZh1UcnIytLS0cPTo0e+2SSgUYvXq1ZCVlUXPnj1x4cIFPHz4EJmZmfj8+TO8vLzY6G/cuHFi2/L5fLHO1sXFBcnJyZg+fTqICKtWrSr0mDk5Ofjy5QtSU1ORmJiIixcvwtzcHLKysjAxMYGenl6hhkRhbReNrn623oC/vz9mzZoFaWlpODk54cOHD6Xans/nIzIyEgEBAUW+8DIyMnDp0iWcOnWqTF65iuDz58+oXbs2lJSUsG3bNrRt2xYhISHf3S4tLQ1+fn5FlmtesGABTExMCpTO3rt3LywtLTFp0iTs3r0bz549KzTffsWKFSDKS7O9c+cO1q5dCyJCp06dxFL3qlSpAkNDQ2zfvh0fPnyocKNNKBRi5cqVrD3q6urQ0NCAl5dXhbZDwt/FTzEGzM3NcePGDbHvRApYzZs3x9atW7Fy5Ur2ghBZ2wBw9uxZ1K9fHzY2NgVSzERLo0aNyni6ZUNFRUUsL1VC4WRkZOD169eF/mZNmjTBtm3bCs3nj4qKwtOnTxEYGAiO45hQy4oVKwq1cs+dO8f2W6lSJWzdupUpsHl6erIc31u3bpWo3TNnzgQRlVuNdY7j8OXLF1y+fBnLly/HqFGjYGtrC6I8kaPZs2eX2qDdsmWLmMu5Ro0a2LBhA4YOHQoNDQ1UrVoVbdu2hZKSUgEvz+/ErVu3oKWlBTMzM/j4+ODz589o1qxZsWl3HMfhyZMn2LJlC8aOHYslS5YgMDCwwHrh4eEFNDsAiAlpfet279ChA1q0aFHAO5V/uqxu3bq4fv06WrdujaZNm2Lw4MEFRukViUjcSLQ4Ojr+s6mmEsqPn2IMiBYNDQ2Ymppi5MiR4PP58PHxQUZGBh4/foyqVauKSeV6eXnB29ub/a2jo4OdO3fi2bNnCAkJgZubG2rXrg0TExNs2rSpXE6+pCxYsAAPHjyo0GP+idy5c0dM2lRFRQWDBw8u0h2amZmJcePGoWvXrujcuTPatm2LevXqgYhgbW1d5HFEc1u2trZo2bIliAh2dnZYvXo1y7eeMGFCiUdsV65cgZqaGgwNDVlsSFnIzMzE+PHjxaZe1NTUYGdnB1dXV5w7d65Mc//h4eEFXlLt27cHj8eDmZkZZs2ahfHjx6Nt27ZYsWIFAgMDkZiYCB6PB01NTRw7dqzM51ReCIVCLFmyBFJSUmjVqhVevXpVoriM1NRUNp0gKysLa2trZuxt2bKlwPp79uzBjBkzxH57oVCI8+fPQ1NTE82bN8erV68we/Zs6Ovrw8rKCj179mTXtV69eti/fz+CgoJw+vRpuLu748uXL+V6LX6U1NRU7Nq1C8uXL4efn9+vbo6Ev4QvX76UvzGwbt06TJw4EStWrMCAAQNAlBcU1r59exw/fhwDBgyAtLQ0Vq9ejb179yIuLg5A3ghg165dqF69OogILVq0wNSpU9G6dWvUqFEDU6ZMYaI8RISEhIQfvwIlwNraGrt3766QY/2JcByHQ4cOsXgBBQUF9O/fHw0bNmTGQZ06dTB79mwxoSE3NzcQETQ1NQsodo0cObJIta709HQsXLhQzPAwNjaGnJwczM3NyzRK+vLlC+zs7NC2bduyXgYMGTIECgoKmDt3Li5evMjiNX4UjuPYMyFaHj9+jOTk5EL3LxQK8e7dOwwfPpx5ZX4lISEhaNeuHXg8HubNmwc+nw8zMzN07tz5u9uKFOP279+PxMRErF27lrnsp02bVmD9Hj16YMOGDYXua/jw4bCyssKpU6fErmWVKlXY/3+X6UAJEn42cXFxsLa2hoWFBZycnJhX8afWJnj69CmWL18u9iJv2bIlNm7ciGnTpuHIkSNiAWdZWVlo1aoV2rZtC3Nzc2hpaaFy5coFouRFSng/GxcXF1y4cKFCjvWnEBoaih49ekBdXZ1N54hG6UR5kqzh4eEs0yD/4uDggOTkZEycOLHIqSCivODGrl27igUFhoeHY9SoUfDx8UFaWhqOHj2KWrVqQV9fH+Hh4T8UT7JgwQIoKyuXSbc8KysLVatWxcCBA8t8/OKIjo5G//79xa7PggULsHv3bly/fh137tzB6tWr0blzZxYDIS8vj7lz5yI9Pf2ntOl7REREYNSoUZCRkYG+vj6uXbuGkydP4siRI7h27VqxEtDR0dGYMmUKmxOfM2cONDU1ISMjg8GDB8PHx6dQQygrKwspKSmFfrd9+3bweDx2/fT09FhBr2bNmmHUqFGFTjNIkPA3kpCQINafbNmy5ecbAyJEc6mBgYFi2tr5l2fPnolt8+LFC7Rv3x6GhoZiVe9kZGQqLNWwZ8+eOHfuXIUc63cnOzsbV69eRZMmTaCnp4elS5di27ZtePLkiVg1vSpVqoDH40FVVRV16tQp8DuLshNiY2Px/v17fPnyBcnJySzgLSAgAGPGjGHrnz17FikpKbh9+7bYfubMmYODBw9CWloay5cv/6FzS01NhY2NDdTV1UsUOAnkpeXs3bsXVatWhby8fKFFp8qT6dOnQ0tLC/Xr14e+vr7Yy01JSQktW7bEggULcOvWrV9iBISFhcHT0xPNmjWDlJQUNDQ0sGrVKvbi79ixI+bPn1/k9r6+vhg0aBDk5OSgqqqKSZMmwdbWFlJSUhg7dizCwsKKPf7Lly+hpqaGpUuXFvp9cnIyLl++jFGjRuHgwYNlP1EJEv4C+Hw+3NzcoKysjKNHj1acMZCfzMxMDB06FH369IG9vT2I8grX5I/oXrVqFbS0tFC9enXUqlULhoaGuHz5Mp4+fVpkRPHPwN7eXtJxAPD29maBcFJSUli2bJnY92lpaYiIiMCBAwfQtm1blk4oWiwsLHD16lUsXLgQ48aNQ1BQEAYMGABra2t07NgRU6ZMgaenJ2bNmoV+/frh5MmT2LRpE+zs7JgB2KRJE7H87/xLeZQRTUpKQt++fUFEGD58+Hfd/OPHjwdRnn6CKP+9IsnJycHnz5/h5+dX4Vk2+YmPj0efPn1AlKfF3759e+zcubPASFsgEBRppPj4+EBGRgZGRkbw8PBAUlISJk2aBHl5+RJlhoi4evVqqdaXIOFPYfHixahTpw5q1qzJasyUlujoaBw6dAgDBgxAnTp1WJyVSMPmtyth/ODBAxARjIyMEBUV9cP7E1GWCzht2rR/Plr34sWLkJGRQd26dfHy5csSvXji4+PZi7pTp07w9fXFxYsX2WezZs1iL9KOHTuy0bWWlhbTGTA1NcW+ffsQHByMbdu2oXv37mJzvER5ugS3b98ut3PlOA47d+4EEWHbtm3Frrtnzx4QEYKDg8vt+H8inTt3RqVKlbB79+5in/+kpKQif6uuXbuiatWqYnElosj5b9MFi2PAgAGoWbPmX63TIOHvw9fXF5s2bcKkSZPQrVs3tG/fnnnCUlJSmB6KvLw81NTUSlwZmM/n48iRI+jZsycbzBERatWqhaFDh6Jr166oW7cu05b57YwBUf3uvn37/vC+AODMmTPsIsydOxdAXk37YcOGYe7cuXj16lWRnYeSkhI8PT3LpR1/Ivfu3YOcnBy6d+9ealETgUAgFgiYkZEBd3d3uLq6IiEhAVJSUmLBmRzHsd/h7du3LNJbR0cHFhYWmDFjBtuPr68vzp8//9MCSd3d3UFE6N69O6ZOnYpp06Zh3bp1YrXJXVxcYGRk9FcqBJaGjh07olOnTt9db8KECZCVlS0QL5CRkQEpKSl0795dTBshICAARFRszI5AIMDHjx9Zaqifnx98fX3LeCYSJFQs2dnZmDlzJlOmtLS0RLt27aCnp4fmzZtjwoQJYhlK0tLSWLNmTaH7iouLY+nYiYmJWLduHZteb9SoEdzd3XH48GFWyj4/P1V0qCzcunWLzYOK/nV2dsabN2+wefNmHDlypFT7S0tLY65c0XLs2DF8+vSJ/S0KuJKRkYGTkxM8PT0xY8YMdOjQAadOncKMGTPg7OyMq1ev4tChQwVGxb9KGbEi4DgONWvWRKNGjX7K1Ez9+vXRpk2bYo/v5eWFhQsXombNmqhXr16BdVJTUzFq1ChMnTq1XEeDAoEAe/fuRe3atWFpaQlLS0vIy8tDX18fS5Yswfz580FE2LNnT7kd809l/PjxxaaDihAKhYV2RACwe/du8Hg81K1blwVxent7g8fjQUFBAWvWrMHly5exadMmDBkyBHZ2dmKSvcrKypLpAQl/FN7e3rCxsYGsrCyWLVsmNti6evVqgalQDQ0N9O3bF7GxsXj+/DmWL1+OW7duYdasWUxFs3r16ujYsSOUlJQgKyuL/v37l6gc8m9nDOQfwRMRzMzMCtSa9vDwgEAg+K6r+vTp02LR6rKyshg3bhzTfici9OvXD3w+H6dOnUK9evVQs2ZNyMrKQlpaWiw4S7QfW1tbzJ8/H2/evMGjR4/QoUMHtGrVqtRqcn8KMTExICKcOHGi3PctFArZPHNiYuJ3158zZw60tbXFjK/MzEw4OjqytJgFCxaUezvzExoaCldXVygpKUFTUxMdOnT4pXP1vwuenp5FyuFeunQJ8+fPx+bNm8WmAApDVKcif3pgaGgoRo0aVWTWSf6lV69e5X5uEiQUhkAgQFRUFN68eYNr167hwIED2LBhA3bt2oXjx4/j6tWruHHjBkaMGAELCws0aNAA/fr1Y/oaor6ViAr1ZAkEAgwePBgyMjLQ1tbG5s2bsWzZskLvey0tLQwYMAB79+5F586dQUQYMGBAqVRVfztjAMib47h//z5u3bqF3NxcFkWeX91OlGPeokULuLm5oVGjRuDxeLh48SLbT4sWLUBEmDlzJpsr0dfXR4sWLeDp6Yng4OBC5VoFAgFzV4eFheHUqVNwdnaGkZERi9o2NTWFmpoa9u7di5UrV4LH4+Hs2bNl0sb/nfn8+TOICNWqVUOfPn3KVbJXJFNd0ikYUSZBw4YNcfz4cTx9+hRt27aFoqIis5KJCIsWLSq3Nv5rvH//Hp6enti4cSPmzZtXYlXGN2/egIhw6NAh5p0JDAxEhw4d2DMrJSVVpLv/9evX6Nq1K5SVldG6desCBhbHcfjvv/9AlKc62alTJwwbNgwdO3ZkGgwTJkyQGGYSKoQLFy7AzMyswEu5sGJVRkZGGDt2LNq3bw+ivFoWNjY2Yq7/b4XZQkND4eTkBB6PhzFjxiAxMREHDhyAvLw8XFxccOLECVy5cgUxMTF4/vx5gfdYREREqb2kv6UxUBzDhg2DlpaWWJph/iU4OBhfvnzBihUroKCgwNyOJ0+eRGRk5A+5kQUCAQ4fPsxET+Tk5GBoaAh5eXkoKSmhSpUqqFOnToXpH1QEQqEQnp6eGDVqFCpVqoSxY8eW275FbvYGDRoU6TrOj0AgwPr169G2bVv2exsbG+PSpUtsHZHlLDEISs/r16/FdCOI8mpJlOR+5jgOjRo1YoajqKM0NTXFqVOn2Ig/v7Gen0GDBkFXVxfz589HUlJSge+/fPnCUp+ICAMHDmTPsqj6oQQJP5uQkBAmrd+2bVucP38ez58/R2hoKIsbEggESE5ORlhYGPz9/dmLWlSAr3///hg/fjxWr16NY8eOsYJvISEhWLNmDdzd3aGmpgZjY2MmgiWSYHd0dCxSjO1H+eOMgW95/vw5Jk2aBFlZWUyfPh3q6upibv38noTyKtqSk5ODs2fPYv369ViwYAFatmzJbpBmzZpBR0fnrzIIRCxbtgyysrJYsmQJRo8ejRUrVuDhw4c/dHM+fvwYenp6cHJyKnHsBcdxOH36NG7cuFHoNiKDoKJlq/90GjVqhNq1a+PRo0diz07lypVLtL1AIMD169cxfPhwTJkyhQXnikptq6qqwtrautD7xdLSEmPGjCnweWZmJjp27MhcoS4uLiAiNG7cmK0zcODAIgOqJEgoD7KysrBo0SIoKCjA2Ni4VNUxS0J2djYaNGgAorwo/zFjxogZxWfPnmXP48+qk/PHGgPp6emYPXu22Pw/j8eDjIwMJk6ciMGDB4sFCH5bDvVnICpdqqGhge7du+Pt27d/VUnRrKwsWFtbQ1lZGba2tqzEtJKSEgYOHFho8ZiSILrRvy2TXFY4jkO3bt1ARP98lH9JSUpKAhFh+fLlyMrKwoEDB3Ds2DFoamqiXbt2pd6fKPgpf3Gl+fPnQ15enr30OY5DTEwM7t+/X2Rcypw5cyAvL48tW7bAwcGBPV/5jcD+/fvD2dn5r3rWJPw+XL9+HRYWFpCVlcWsWbN+iphX8+bNIScnh+PHjxe5ztevX6GkpITVq1eX+/GBP8wYSEpKgqurKxv95180NTWxePFiNseZkZEBNzc3zJ8/v8IEioRCISZMmAAVFRXo6upi8eLF0NPTw5UrVyrk+BWBUChkFjGfz8fr16+xdOlSGBsbQ0FBAV27dsXq1asRERFRov0lJyezueLyjEcQzVV3795dkmZWAjiOY9MvJ0+eBAA2Fefi4lLqUdD06dNhYGAAgUAAR0dHFgwsmlP18/MTK21et27dQj13Xbp0AY/Hw4EDB8Dn87Fq1SoQEfMuPH/+HGvWrEHDhg1RqVIlpkcQEhIi0RmQ8ENERkYyT5Szs/NPCxJ/9+7dd4O0r127xuT4S5tRV1J+e2OA4zisWrUKTk5OhcYIyMnJoX79+nj48GG5HfNHmTp1Kng8HhQVFdGpU6efEon/u5GRkYFly5ahZcuWUFRUhJSUFExNTWFtbY2GDRuiR48erPobx3F4/fo1lixZAn19faioqOD8+fPl2p709HRs374dlpaWkJGRQffu3TFv3jxcv35d8pLIR2BgIIYOHYouXbqI1ZEIDQ3FunXrmDrZzp07S7Xf5cuXQ0lJiQlLVa1atdDnd968eTh06BBiYmIQExODJ0+eICEhAUKhEGfOnEHdunWhqKiIJUuWAMgbEEREREAoFOL27duwsrLC5s2bMXv2bCxatAipqalIT0+HmZnZH11plOM4Zhy9f/8effv2ZX8vXLjwnxe5+pkIBAIsXboUysrK0NHRwcGDB39qnyF6VorLtLG0tISsrCzOnj373YycsvLbGwNt2rQp0IEMHz4ca9asQURERIXGJ3wPjuNw79493LlzB3379oWioiIUFBT+udznlJQU7Nq1C7NmzcLEiRPRpUsXEBFTnhNVoiMiuLm5ITw8/Ke1JTs7GytXrkTLli2hr68PIsLQoUN/2vH+FFJTUzFz5kzIycnB2NiY1ZUQTf3weDx0794d6enpGDlyJGRlZdGtWzcxwaXiSEhIgJKSEgskVFdXh5WVFTtGixYtcPbsWTx+/BizZs1iipPfZgvp6emhfv36cHd3x7x58+Dj44Pc3FwIhUKkpqaiefPmBUoM8/l8zJs375fIQ5eVd+/eseDKlJQU6OnpYePGjQDySoM3bdoU0dHR4DgOlStXxrZt25CRkYG9e/eKpeUKBAKcPHmy3OKj/kW2b98uFi8THh6O0NBQbNq0Cfv372fKf2/fvsW0adN++B108OBBEBGr3pufy5cvo0mTJiyL5mcaJb+9MbBhwwYYGxvDxcUFixcv/q3nBV++fCnWoYkkbevUqVPm+fS/gVevXoGIcO/ePQB5Ix2iii+ty3EcPDw8wOPx4ObmVuK0ub8NjuNgamoKRUVFLFiwAJmZmTAwMAARYciQIZg3bx5z7c+ZMwfZ2dlYu3Yt5OTksHjx4iL3+fr1a2zfvh0jR46Es7MziAhOTk7YuXMnc3HKysoWePFra2vDzc0NXl5eeP36NQYPHoxOnTrBxsaG1QR58eIFGjZsiIcPH0JaWhqPHz/+7nlGRUVhwIABFVbQrLScO3cOz58/BwDMnTsXVapUYd8dPny4SKEYkXrj48ePwePx4Ofnh0OHDqFbt25MTO1frKUSFhYmNiVbVPXK75GWloYjR45gx44dMDIygpKSElRUVFhQuqGhIR48eMB0aIoqilWadhMRTp8+LfY5x3EwMjKCvb09duzYgcjIyB86zvf47Y2BP4n8WvxEBKFQiGfPnsHQ0BA9e/b8rQ2Zn4lQKETlypUxYMAA9tnu3bshJSWFmJiYCm1LdnY2pk2bBmNjY9SpU6dCj/074eTkhNq1a7POct++fWIGrGjZvn0722by5MlQUFDAnDlzxOoLBAUFsWkAHo8HKSkpmJmZwd7eno3OExMT4e3tzYKvLl26BCLC1KlTSzyKFQqFCA4OxpYtW/D58+fvrn/hwgU4ODh8t9Lhr0Ik1JSYmIj09PQyuX/j4+PBcRwuX76M0aNHIzc3FwEBAez7nyXV/TuRlZWFuLg4rFu3DqqqquxzBwcHuLq6/tC+w8LCsGbNGsycOROJiYkIDw9H48aNxQLXraysfvQUYG5uzgJro6Oj0bNnT1SrVk3Mo/qzkRgD5UxgYCC7SXr27AkArMCEkpJSsRrrfzOil4woOM3f3x9EhFOnTv2S9syaNQuVKlX6Jcf+1eTm5qJJkyYwMDBAYmIivLy8YGtriypVqmDv3r0gIiZ5mn9klZqaiq5du7JYHSBv9NKuXTuYmJjg5s2bSElJwYgRI75b2CsiIgKvX78ucbEVABg3bhyUlJTEMgkK0yQAgB07dkBOTg7NmjVj7tffSTb88uXLmDNnTon0NcqKl5cXtLW1f/qIUkRiYiLOnj1b4Rk8GzduhKamJuLj48UGF1evXmXGaHm613NyciArKwttbW0cOnQIRFSgOmdpGTlyJMzNzcFxHPPCTZw4EcePH6+wGCeJMfAT+Pz5M+Tk5DBw4EAAeTeiSKFNFAj1r8FxHLp37w4igqurKzp37gx1dfUKG7kIhUK8e/cOu3btYvPjCgoKCAkJqZDj/06IJL/l5OQgJycHIoKDgwN8fX2Zml9R96rIDa2pqYkXL15gyJAhICKcPXsWKSkpiImJKXHnlZubi8DAwBKrBj569Aju7u7o3LkzgLx7Sk5OjlWW3LZtG2bOnAkAmDdvHszMzGBkZAQ+n4+tW7fCwcHhpygUPn/+HJqamswAev/+PR4+fCh2HfL/n8/no2fPnmjRogWysrJKZRCVhoSEBHh6erJYpvwGGp/PL/eXjFAoROvWrTF+/HgAQGxsLI4cOfLTX2Y+Pj44c+ZMkb+tv78/atWqVUDlr6ysXLmSzeGLPGo/atSJqrlOnz4dKioq6NatW7m0tTRIjIEK4uHDhzAzM4OamhrGjBmDixcv/nM58BzHYe/evdDQ0IC2tnaFaD8AeZ21qIhH/noTRPTbupB/Jrm5uThz5gw2bNiATZs2iV2DmTNnil2fHTt2YMOGDXB3d0ffvn2ZpKpISIjH42Hy5MnIzs6GiYkJeDxesWmlHMexEXrTpk2hra2N1q1bA8iruPa9Z8LLy4u94IRCIY4cOcJiP65fvw4HB4dCt5s7dy4ToQoKCvphL0FISAhWrFjBzmnTpk0sNmH8+PGwtLQEx3GIjIxE/fr1ce3aNQDAsWPH0KdPH+Tk5CA9PR2tW7eGh4fHD7Xle2RlZUFPTw9bt24FAKxbtw46OjolSrkWCARinpUrV64U2r8LhUK8evUKQUFBbArnwoULkJeXLzS+Iz09/YeNhJSUFJw9e/a762VmZmLcuHHlkhooFAohLy+PwYMHQ15eHhMmTICMjMwPV7XNycmBubk5iAgjR478YU9DWZAYAxXE5MmTC2RFqKmp4fDhw/9c5G92dna5aD/cuHED5ubmbHQrGu2bmprC2dkZTk5OsLS0hJSUFOrWrYt9+/axQDZR3vvPStP5E9m1a1eh6X+ysrKws7ND06ZNYWNjA11dXdSpUwcdOnTA+vXrwXEc7t69i8aNG4PH47GKg4Xh6uqKVatWAcir375o0SIMHjwYADBq1KhiS5Z7e3tDTk6OvdRKA8dxyM3NRVpaGmRlZZk34dKlS6WqOnn8+HGcPXsWa9asgZWVVaECNEKhkGU4CAQCDBw4kKUCnj59Gu7u7gDypl0GDhxYaBR5efP582cWs+Tt7Y0TJ06A4zgEBwfD1dW1QElpEe3bt2ejVJEy5YsXLwDkTf3t3bsXADBgwACoq6sXeMGLjK5z586hS5cu7HMrKyuMGDHiu+0+cOAAGjZsCCDPm7F06VI8fPgQAoEAjx49gpycHKKiokp8HUJCQn6ov01MTGTTnba2tpgwYQJcXV2hr6/PChCVFX9/f3ZtfwUSY6CCiIyMZNrt3y5mZmbYs2cPnjx5gsDAQPj6+iI0NBTv3r3D27dvER4ejrdv3yIiIgJv3rzBx48f4e3tja9fv8Lb2xsfP36Ej48Pvnz5grdv38LX1xdfvnyBj48PAgMD4e3tjZiYGDx79gzXrl1jEbcZGRk4evQoU/4LCQmBl5cXkpOT8fLlS/j4+MDPzw/z58//rYIfr1y5gr59+4ql/4is6vxLhw4dMHnyZOzevRtZWVlwdnaGmZkZK1lNRFBUVISNjQ17Qf3LPHjwADo6Ohg3bhw2bNiACxcu4Pjx48xwFQkGNWzYkEXBi9i1axczyoqrX7Fx40ZMnDix0O8uXLiAFStWoG7dunB3d2cv/dTUVAwbNgyRkZG4devWD3W6fD4fd+7cYS+QyZMno0OHDiUepQ4YMABubm5lPv7vxsuXL2FlZQWO4/D582d069YN9vb2eP36NQDg/v37ePbsGYC8F3toaCgzoN3d3dlvnZ2dzXT0C2P37t2YMmUKu85nz55lHh1vb2/MmTMHQN4ovnv37iy26urVq5g3bx6AvPtTSUkJGhoabNvTp0+X+LdLT08Hj8djxl9ZPBOiYmmvXr2CoaEhunbtip49e4KIMHv27FLv73dCYgxUMF++fMHQoUPFolHzL4MHD4a1tTXGjx+PGjVqwNTUFHPmzIGKigpq1qwJojydBSKCl5cXy5s3NzfHzJkzUbVqVVhZWWHKlCmwtLRkssyiQBdZWVkoKCgAyMuTzR/oOH78eJYqIysri65du6Ju3bpQV1dHt27dcOTIEdy4cQOBgYEIDAxkLt2EhIQKSZ3kOA5TpkwpcM20tLQgKysLFRUVJpJDRGIvE1G+++3bt5n7dtKkSejVqxcrkV3ewkd/IhzHoXfv3uyaiHQH5OXlsWbNGoSGhha6HZ/Px9mzZ9G3b184OzsX6+738fFhc/+i6qANGjTAtm3bEBcXBysrK+zbtw+9e/cGADx58gTS0tKFTiv5+flh8+bNP1QLhOM4ODk5sdTX4uDz+X+dN+nr16/IyclBlSpVoKenB1NTU7x8+bJCjs1xHAYNGiTmJfjvv/9w9erVAus+f/4c/fr1K2CIlpScnBzcunWLGYKzZs3C5s2bC6z35s0bPHnypNB9TJkyBXp6eggODgZRXiVOkbdRX1+/TO36XZAYA7+Iu3fvir24RMunT5/g7++PiIgIBAQE4MOHD4iKioKUlBQUFBRQrVo1hISE4O3bt4iPj4ePjw9CQkLYev7+/vD390dkZCQCAgLw6dMntq6vry/evn3LOr3MzEycP3+elV0OCwvDmTNnMHHiRPTs2RMfPnzA27dvceHCBfTv3x/Hjx+Ho6Mjk609e/YspKSk0KBBA/To0QMCgQBCofCnzHfl5uaiV69e7Do1atQI+/fvR0ZGBm7dugUej4dmzZohMzMTHMeJiePs3r2bbXf37l3cuHEDly5dQnR0NIKDg5kYTtOmTcu93X8aHMcxA6BVq1aYPHkyhg4dWuK6EUKhEF5eXrCzs0NiYiJ27NjBYhI+f/6MGzduYMGCBahbty46deqEoKAgxMfHo3r16rCwsICnpyeEQiEePHgAaWlp3LlzBwBgbGwMZWVljB49GkCe7kDLli0xfPhwyMrKIi4uDhzHlfpFIQpgXbVqFZYvX17suikpKahVq9YvdeX+TC5evCimyTB//nwcOnToF7ZInAcPHmD06NHMM7R58+YfGoRMnjwZa9euBZAXK3P06FEIBAIoKyuz4O/8nD59GrKyshg/fjwiIyPFBh1JSUkFxK/+NCTGwC8kPDycublFN1ZRkcW3bt1iozIfH58yBR8mJydj5MiR39XqT0pKwoEDB5iRkJ+4uDjMmjULq1atwrt37+Do6IgFCxZATU0NO3bswI0bN6CsrFyucqn5HzyiPGnb9u3bs/nax48fgyhPtCO/C1koFOLw4cMs4re4pV27diCifyIv+3usW7eOeaF0dXVx48aNEm975coVyMjIoG3btnj79i2kpaXRoEEDBAUFoWPHjmjdujWEQqHYtFNMTAz69euHypUro3///gDyRnFPnjxhEeIJCQkYMmQIrl+/DiAvCHDIkCE4efIkC2DcunUriIgpWhY1N8xxHKKjo7F69WrIy8tDUVGxRHrv2dnZGD16dIG0SR8fH6xevRrXrl3DkydPSjWH/StITExEw4YNWZ2RiRMnFpgmEwqFcHZ2/mWpv4Vx79491KpVi5UIJiIWxPmjdO/eHbt37wYAfPz4sYAq6rFjxyAtLY0+ffogNzcXCQkJrO9o2rRpkTEXfxISY+AXo6OjAwUFBaxYsQLjxo0Tm8cSCoVMajQpKQnp6enIysqCiooK5OXlS93pfPnyBUpKSli7di1yc3ORmZnJOoTMzExmYKSnp0NWVhY7duwosA9paWmoqamBKE9ZcfPmzbh//z6WLVuG5ORk1KhRA/Pnz0dYWBhatmxZLiky+acGtmzZAldXV3h4eLARp0AgQKVKldg6lpaWcHZ2Zmlve/bsQWxsLD5//syinW1tbQsYBBLPgDgRERFo3bo15OTkWDT89zh79iymTZuGT58+4fnz59i2bRuMjIxgamqKFi1aICkpCQcPHoSBgQHz3hARjIyM4OPjU2B/27Ztg4eHR4nmdwcPHoxx48bh3r17MDQ0ZFNoV69exblz5/Dy5Uu4uLiw2If8S2nLXQ8bNgyGhoYsFTN/dcbatWuXal8VzfPnz2FiYsI0Gjp27MjiAn5nRJ5HAExJtDy1I7Kzswvd39evX6GmpgYXFxdmYH78+FHs/tmyZUu5teNXITEGfjHdunWDtLQ0fH19sW3bNnTt2hVPnz7FokWLUKdOHfB4PACArKws1NXVERkZiePHj6NVq1ZYuHBhqY6VnZ2NIUOGwNvbm9V8uHbtGoRCIevQAODo0aMgIrRo0UJs++3bt6Np06YYO3YsXr16BTU1NaxevRr16tVDw4YN0blzZzRv3hxEefmyRHlCSz9KZmYmXF1dUb16daSnpyMtLQ1Lly7Fo0ePwOfzkZSUhEmTJhU78v/WU8Hn85GYmAiO43Djxg2sX7++wsRZ/iRycnLQvn17qKiolChHf+XKlWjVqhUGDhzIqh2uXLkSo0ePRkBAAL5+/Yphw4ZBRkaGiaz07t0bDRo0gFAoRFJSEkxMTPDu3Tvw+XzY2dnBzc2tWGOA4zg4OjqiTZs2cHJygp6eHhwcHKCtrc1+fx6Ph//++49NMQ0ePJjFkRQ2P10UfD4fixYtYlkqI0aMwNmzZ5GamoqjR4/iv//+g5ycXIH2CgQC3Lhx45frWoiqPZY0eK5v375YuXLlz2xSiRkyZAgmTZoEIG9Ks6yxA98iel9NnDgRVatWFbvPg4KCUK1aNWhra4tVVc3JyRHrX76VEv4TkRgDv5g1a9ZAUVERa9euhbm5OTp06ICDBw9i3759WLp0Kat4OGjQIOjq6rISrevWrWO65ElJSRg/fjzc3d3RqFEjBAQEoFGjRhg9ejTc3d1x8uRJtGjRAq1bt0b9+vXx4sULXLp0CdOnT0dERATrsEXzZyKt7G+jvlesWIGWLVsWCKB6/fo16tevDwsLCyQkJKBbt25ITEzE8OHDce/ePURGRsLT07NEGQmDBw+GiYkJPD09cfLkSQwePBj+/v7o378/s775fD5sbGwKDcIsrLBV//79f6tsiD8NT09PKCgolOgFEhwcjCpVqmDcuHGFBqGJSsKKFlNTU0RGRoLP56NZs2YYNGgQi87es2cPDAwM8OnTJ0RFRRXrCVuwYIHYNJIooNDY2BhEBFVVVaxevbrAvTFr1qwSXweBQID//vsPMjIy6NatG86ePQuO4xAeHg4jIyO2z7p16xa6rZqamlha5YMHDyq0gua2bdugpaVVqinGSZMm4ebNmwCA9evXY+TIkT+reQxR+/h8PgYPHsw8GGvXri3XIF9R5kLz5s0B5I3281eYffjwIbS0tGBpaVmgjsm9e/fYNNqnT5/+ikqoEmPgF+Pq6opq1aqVervk5GQsWLAAYWFh0NDQYG7yQYMG4dOnTxg0aBAWLFiARYsW4ebNmxg2bBjc3NxQpUqVElmxgwcPRv369cU+S09Ph4mJCbp161ZAwKNy5cowNTXF4cOHoaCggK9fv7LiNQ8fPoScnFyRoh+iiPKcnBxs3rwZpqamaNOmDUaOHIlKlSpBSUkJlpaWEAgEuHv3Luzt7aGkpFSkF0BeXh6tWrXCqlWrSqRhL6F4Zs6cCVNT0xKvn56ejrCwMAwdOhStWrXC7t272XRXu3btYGZmhsWLF7PpJktLS1aU6NKlS3j48CHzWon6kjp16kBdXV1Mfji/gScqfkVEePr0KftcJHstWvT19eHs7AwTExP2mYmJSYmCAjMyMkCUl80j4ty5c2w/Hh4euHv3rtiLY8mSJSzoMTw8nL00xo0bh+rVq1eYRHJgYCCuXbuGw4cPlzk1U1SJFMjr66dPn17u7f/w4QM0NTVx+fJlBAYGwtTUtERFqcpCRkYG/Pz8cO7cuQLfHTlyhMlZFxZHNHHiRBARjI2NoaGhgUmTJsHFxQVDhgz5KW2tCCTGwC9GS0sLurq6312vWrVqRc5LTZkyBb1798a6deuK3Ud8fDykpaVLpNq1b98+dO/eHXPmzIG7uzuCgoLQt29f1KtXD7Vq1YK6urqYO23YsGFQUFCAUChkI7gnT57A3t4ec+fOxYcPH8SsZy8vL3h4eCAmJgbTp0+HhYUF/vvvP/b9ypUr4eLiAi0tLTg6OsLR0ZGlu8nKyqJFixb4+PEj7OzsoKWlBSkpKdSsWRNHjx7955Qdy4KouE3Dhg2hp6eHxo0bo1WrVmjbti1GjhyJGTNmYMKECZg6dSpMTEzQvn37Qvfz5csXHD9+HB4eHjhx4gRevHiB/v37Q0ZGBpqamixjRhSPcf78ecjIyKBKlSrw9fWFm5sbiAjW1tbMk3D79m1s27YNPj4+ePHiBbKzs1GzZk0oKiqy2IWNGzfCzs4OAoEAX79+ZRoHhRl/ISEhWLRoEa5cuSL28nr69Cnat2/PPEy1a9dm5cZzc3MLBCDm5OSgQ4cOUFRUZJHj+YNbC0tv3LlzJ5YtW1bgc6FQyJQajxw5wtJ7AaBDhw6lUufMzc3F9evXiywvLRQKUa1aNQwbNqzE+/wer169goqKilhRpPIgIyMDy5Yt+6FU0dISFxfHphzS09OxePFiEBEGDBhQQBwtLS0Nu3btgqWlJfr164eYmBhMnDhRTLukqHoZvzsSY+AXEhkZCSkpKaanXhxaWlrFPswvX75kUwglOaZI9W327Nng8Xgsw6BXr15wdnaGs7MzeDweRowYgY4dO8Lf3x8tWrSAm5sb6tWrx9y4IiEYLy+vAi689PR0XLlyBePGjYOVlZXYiKRz586QkpJCQEAADh8+jAEDBsDLy4t9P27cONSuXRuzZ89mL5Q6dergyZMnYp30kSNH4OLiAg8PD8lUQCkQ6avb29tj/vz5cHV1Re/evdGtWzfUqFEDZmZmsLGxQbVq1eDo6IiwsDB4eXmxeyw7Oxtr1qxhHWB+T42ioiJmz56NFy9ewMHBAd26dQMR4cGDBwDy3LHW1taoV68ekpOTsWrVKja/37hxYygpKcHQ0BC6urpQUlICn8/HxYsXcfbsWRa1ff/+fWzfvh05OTm4e/dugRF7afj06ROTq9bS0sLly5exb98+VKlShR3vwYMHEAgEeP78OYhIrFRueHg4FBQUoK2tjTFjxhSZxZCZmVloKty5c+cwdOhQ9vfUqVNx/Pjx77Z75cqVuHTpEjiOg62tLRO9uXHjBgwMDNgct7+/P+7fv8/iBcoL0fN85MgRsdLWly5dYqmoIq9fUdvn98iMHTu2Qksvr169Gurq6lBUVAQRoXXr1tDQ0ICUlBTmzJlTaLv79+/PJM27dOmCzMxMPHjwgBkD0tLSvzwupKxIjIFfiEi4wtTUFPv27QOQF7xXt25dlqvfvXt3jB49GsHBwejXrx9GjBiB7OxseHl5sbQ+AGjQoAGT7SyO1NRU9OrVi1UPvHHjBrp27YrY2FgIhULY29ujRYsW6NChA+rVq1eowMrnz59hb28PorwSt6KXwPDhwzFs2DBYWVnh4cOHePLkCfz8/CAnJwcZGRnMnDmTjRAnTJiAWrVqsX02btxYLE1o8eLFLEJbFBlORBVe8vhv5f79+yAimJubIyoqCsHBwd8tmNOnTx9WR+DKlSvst4mOjgbHcYiPj8fz58+ZZ0hkxFlZWcHJyQlycnJYu3YtK+2tpqYGU1NTzJ07F/fv38euXbugo6MDImLGgcjIsLGxKbRNCQkJLNC2sGIxb968wdq1azFy5EgMGzaMpQZ+29GLJJVbtWoFTU1NvH79Ghs2bACQF3QnLS2NCxcuIDMzE/Xq1YOOjo7Yi93X1xfOzs6szSIvXlpaGnMzjx07Fra2thAKhRAKhd8VL1q1alWBEfKGDRvQqFEjTJkyBW3atMHq1asBiKfDBQQEYMGCBcjKyoJAIICenh4cHR1/2pTEihUrxFQZK1WqxLwhN2/ehLW1NXtuIyMjWd+2fv16yMvLs+0mTJiA9evX/5Q2vn//HgkJCTh48CBatWqFx48fM4/QsGHDsH79erRt2xZTp04tcmqRz+eDKC+dsVatWsx4VFdXh5OTEy5fvvzbp5UWh8QY+MVYWlpCWlqazcUtWrQIcnJyrBMwMjJCgwYN8OLFC5iYmKBatWrIyMhglminTp0A/G+6ITc3F3v27GHuu0+fPuHkyZPIyMhAUlISjIyMsGbNGgAo0q0I5HUodevWLVQHPiUlBY0aNcLmzZuRkpKCoKAghISEIDc3F69fvwaPx4OZmRm0tLTQvn17PH36FC9fvoScnBw6deqES5cuwdnZGUZGRmyfampqcHV1xeHDh8Wkhr9N2xIZTRJ+DJHGhUhOdurUqawaYHGkp6fj6tWrmD17NhQVFQtknORHlFnSsWNH5OTksPocDRo0wIoVK3D//n20bt2a/bbHjx9HTk4Ok0Bu1KgRXF1d4eDggD59+kBHR4e5+vft24fly5ezAFHRPjp27Iht27bBy8uLiWMpKCigTp06aNCgAcsgsLKywoIFC/Dw4UM8f/4c58+fx+XLl5lkuEjv4PXr11i/fj22bduGw4cPQygUYseOHZCRkUHlypXFFBlF7SbKU6Y7duwYatSoASMjI7x//x5v376Fn58fOI6DmpoaC9gtDI7jCr3f9+zZAxMTEzg6OmLChAmF9rUcx+HkyZOsRsTMmTPRo0ePClNOFGXpAHnG2MSJE1n6spGRERvAxMTEMNnj8qCo/iwhIQEyMjJYv349bt68ifHjx0NDQwNWVlY4evRoqY6hp6fHPAPz58/HxIkT0bFjx19SWKi8kRgDvxhTU9MCUfuZmZmQkZEpNne1Xbt2mDFjBvv7+vXruHbtGpvDFI26x4wZAyLCy5cvmVCHg4MDwsPDxSJ1C2PHjh1wdHRkKYwXL16Eubk5Vq1ahWrVqolFFt+7dw/Vq1dHTEwM3r9/jydPnuDEiROsnrhAIMDGjRtRu3ZtVK9evdDj3blzR2zuLf9Sp04drFq16q+I2v0d4DgOZmZm7DeMjIzE0qVLi91GKBSySH9FRUUMGzasyGC0U6dOsd/O2tqazb0+ePAATZo0gbKyMrS0tDBr1izcu3cPGhoabP2TJ0+CiDB58mTIysrC19cXI0aMgJaWFjp06CB2X+QvuSxapKWlWWDiyZMnxeZ9RR6A//77T0ybQrRUrVoV48aNw9WrV3Hnzh3UqlVLbL0dO3YgOjoa8+fPh4mJCbS1tVkEelZWVoH92dvbw9raukAa3Jo1a5CUlIT4+HiMGzeugMgN8L+Ut8uXL2PQoEHYunUrtLW1IRQKcfXqVdjb27MpiejoaKxYsQJv3rwBkDc6/93qbQiFQty4ceOHy/1+S/5U6fxlt/39/ZkBdOPGDeTk5EAoFGLp0qVlHlg0adIEcnJyUFVVRZs2bcrrFH4LJMbALyQrKws8Hg/W1tZin4uilsvyMOfk5GDIkCFszjE4OBiHDx9man2nT59GYGAgy8uvV68egLwa90pKSkyJrWfPnlBTU4OWlhabz/Ty8oKamhp27tyJlStXinWy27ZtAxGx0cigQYMKRKBzHAd7e3toaGhg0aJFGDNmDG7evIlnz55hzJgxWLNmDTp27Mg60lq1amHgwIFiEqkSygfRaMnT0xPv3r0rUSW3xYsXg8fjMVd/cYjqXgwdOhREVEDOOCYmBu7u7iy4UORFEC0i75Kuri527dqFTZs2iX0/d+7cQg3HM2fOgOM4pKSkfLeNAoGAxdqI5LvzX4fFixdj2rRp2Lx5M7Zs2YIHDx6IVSkUaWkQETIyMsRyz7t27VpAqbAwUlJSYGFhwZQVv4XjOKxfvx5ubm64c+cOOnbsCGtra8jKyooV3ImLi0PlypXh7e3N9vu3G85CoRBTpkwBj8djxqSmpiYOHDiA4cOHQ15eHiNGjGDXYcuWLdDV1QVRXtp0WbIq9PT0MGvWLOzduxc8Hq9QI+5PRWIM/GJEnWX+m0ooFMLf37/YUfuCBQt+KNhm//79rLMG8iR9a9asidu3bwPIq//erFkzzJ07Fy1atGCdjIju3btj0KBB7O8vX75g7969LOCqT58+ICImaXzhwgUYGxtDUVGRSd0SEfu/tLS0mG7A9OnTy3xuEr7PiBEjoKCggJCQEFSuXBkaGhrFrs9xHNTV1Vma3Pf4VkK6qHiEY8eOia3XpUsXuLq6supwIsGh169fs3XmzJmD3NxcNu3wvZoCZSU9PR1169ZF5cqVYWhoiJ49e8LW1hZRUVEQCoXo0qULLC0tWfAtx3EsZbE0Mr4ioyU3NxenTp0SM2IuX74MImLPn52dHYgIBw8eRGBgICwsLNCnTx98/vz5r3/5i4iLi4ObmxuLFfHw8ECrVq3E7iMrKytWY+PUqVPIzMxk3xVVhKgkWFtbY8iQIbh48SLzuP4tSIyBX8yXL18gIyMDV1dXsVS9+Ph42NnZsSCmbzE2Ni7STdWqVSvs2rWr2ON+/foV165d++484smTJ6GoqCgm18pxHExNTcXmmA8dOsQ6rIEDB8Ld3R3NmzeHiooKzp8/j1q1asHGxoYJmHTv3p3N8YrSwkSLyBUq4efh4OCA6tWrIyIiAn5+ft+93qIpJmdn5xLtXyAQoGnTptDU1MSoUaPYi4rP5+P169cIDg7G+/fvcf36dbHfXuRZAvICzIgIO3fuZHPo+TvgZ8+eMePgZxEdHY2goCDo6+vD3NwcmpqaaNGiBZo1a8amDfLj4+OD6tWro0qVKuyzuLg4fPjw4bvZLqdOnYKUlBTzhIkMaz8/P/b/b7OGdu7cCX19/RJVXPxb6NevH6SkpODi4sICoUXFxnr37s0CAGfMmAEiws2bN5GZmYn69euX2eMK/K988e7du2FtbY3GjRv/Vf2UxBj4DRCVIs4/lx4bGwtra2sYGhoW+sJev349zpw5U+j+ZGRkvltzPTIyEl5eXmUusJGTk4PPnz9j4sSJePXqFeuoMzMzUbVqVdja2uLLly9YsmQJVq1ahfbt2+PEiRPw8vICn8/HzZs30apVKwBA3bp1xV4IohQ0CT8PUbDbpEmTcPfu3WLTziIjIzFo0CAoKCiU6rfhOA7Jycks2wDIE64pLCakqFGWm5sbDA0NwXEcDAwMQESoUaMGgLypLSLCqFGjSnn2JYfjOMTGxiIyMhKPHj1CQkICEhMT4ejoCCLCxo0bC0T8Dxo0CPb29mxKYcuWLVBUVGTrvX79ushpmY8fPyI3NxfLli2DoaEhvLy8sGHDBubBa926tVisTkhICMaOHYuoqKgSyUX/DVy7dg08Hg+ysrLYvXs3du/eDXt7eyxYsABXr15l11YUDKqqqorz58/j1KlT0NTUhJSUVImFjHJzc8FxHLZt2wZlZWU4ODjAw8MD8vLy5a6x8KuRGAO/CSI3V371MqFQiC9fviAjIwO9evViLnwRz58/R4MGDdCvXz8AebKxDg4OaNOmDebPn4+RI0fiyJEj6NWrFw4ePIhevXrhzJkz6NWrF6pVqwYiwvXr19G2bVt06NABkZGROHr0KJo3b86CGseNG4d69eqxdCyBQMAKeqSlpYGIMHDgQJiYmEBPTw9fv37F0aNHmYTsx48fWdBX7969wePxYGBggNzcXGRkZCAjI4PdNyI3sYSKQRQYJysri+rVq+PSpUsA8gzRp0+fwtvbG7dv34ahoSHk5eVZFHhGRgbevHmDy5cvY+/evdiwYQNWrlyJVatWwdHRETY2NmjXrh369evHVAaJCGPGjMGhQ4fY3yKPkJmZWYFpKBGiFMiZM2ey7UTt4DgOa9euZSP0Fy9eQCAQQCAQwNvbGz4+PgVEhh4+fIigoCBcunSJRZ/7+vqyIknJycnQ0tJiKXujRo2CtrZ2gWkOoVDInll1dXUsW7YMJ0+ehJeXF0xNTSEjI4OOHTuC4zhkZGSwQFoAMDQ0xPz584v8XQQCAcaOHQtTU1Noa2ujWrVqTA307du38PDwgKmpKS5duoTIyEioqanBwcEBFhYW2LlzZ0l//j+aN2/eiBWcsrGxYf8XxUv5+/uLZat8+PABfD4fDg4OsLe3/+4xvLy8IC8vjwYNGoCIMGLECKSmpmLhwoXQ0tL62adY4UiMgd+EjIwMmJiYoHXr1gUs/KCgIGhpabGXKACYm5ujbdu2qF27Nnr37g0gT7tbNMpu2LAhBgwYgAMHDqBt27bYtWsX2rRpg2PHjjFDgIhw+/ZtKCkpMVW1/fv3o2HDhkzARVTchYgQFRWFmTNnQlVVFfXq1YOXlxdUVFTYw6Kuro4VK1agT58+TJhItBgaGsLAwIDNqfr4+IAoL7OhV69ebNQn8QpUHD169EDlypVhZ2eH2rVrQ11dHZMmTWIiLKJFpEUQGBgIFxeXAt8rKSlBTU0N0tLS6Nq1K8aPHw8LCwvIyMhg0qRJrKPW0dEBkOc237x5MysbPWHChCLbyHGcWL2JqVOnFvg+f4ZBzZo1mQYGUZ6I0YIFC5jSYf5F5GIeOHAgqzTIcRyqVauGvn37AgAOHjyIWbNm4cqVK4V60aKiouDq6gplZWW2XyMjI7i5uRVahRHIG9kmJCQgKytLLKc9MzMTy5Ytw+vXr5Gbm4vw8HDcuHEDs2fPxurVq5l3JS0tDfb29lBTU4NAIEBUVBTGjRuHDh06wMHBAZcvXy7ZDfCHk5KSggMHDmDDhg1MnVROTg63bt0SW69KlSogIhbQeeDAge++p5KTk6GrqwsLCwtYWVmJFbOytbVFx44df8o5/UokxsBvxPnz50GUl0b3bTBQUlISnj17Bj6fDw8PD2hpacHFxaXQ/ezevZulGBXGhw8fMHPmTGzbtg1paWlwcXEpMoCM4zgcPXoU27dvR2ZmJl6+fImlS5eKabK/fPkSa9aswa1btxAZGQk/Pz/cvXsXu3btwuXLl6GiogJzc3PUqFED+/btw/Hjx5GYmIjDhw/j7NmzePjwIS5cuAAiYqNTCRVLdHS02ItyyJAhmDhxIvbt24exY8cyL4KRkRFWr16Np0+fIjw8nBmuHMeJRdp/+PABW7duRXZ2NusX8stNiwgNDf2uMp5AIMCRI0cwdepUGBsbo1u3bli0aBHmzp2LtLQ05ObmwsfHB7du3UKrVq1Qu3ZtbNq0CRcvXoSNjQ0qVarEDJh58+Yx74IoxS0hIQFxcXHseKLCSSICAgIgIyNTbE0PkehSafLNPTw8YGZmhrVr14LP5yM3NxeVK1fGxYsXv7ttUlISTp48WWDg0KhRI8yYMQPW1tZ4+/btXzWn/S0cx7HUaTs7uyIj+6dNmwZVVVU2fXD48GEQERODEgqF2LJlC/777z/Uq1cPBgYGqFatGpSUlArsMzs7G9LS0hg/fvzPPblfgMQY+M0YPHgwiAhbt24tVETj2bNnUFJSgouLS5EP+sOHDxEWFlbkMTZt2oRp06axv4ODg3H06NEif7egoKBClf8cHBxAlKc8uH79ehgZGUFHRwexsbGIj4+HsbExpKSk0KJFC1bli4jYw5eSkgI+n48lS5agTp06UFRUZNkHEiqW3bt3gyhPbEdLSwuKiopMXEVGRgbTpk3DuXPnypSOlZOTA3Nzc/Tq1euH2ijyJuVfHBwcilz/8+fPBdZ/8OABiAg3btwosL5QKMSqVasKfXY+fPhQovTL0jB8+HBUqVIFMjIyrIjXt1r4hXH+/HmxYkwJCQmYM2cO7ty5g/j4eIwfPx76+vpQUFAoMgD5b0AU9zJ8+PBigzNFFS3Pnz+PFi1aQFFREfb29iwepF+/fuDxeKhbty4aNmyI2rVrw9raGvv378fHjx+xc+dOeHp6YsaMGTAxMYGcnFyJpN//NCTGwG9Ifhe7i4sLgoODxb53dXUFEYlF+OdHRUVFTJDoW0aOHIkOHTqwvzmOg52dHZycnLBjxw4EBARg+/bt8PPzw6dPn6CoqIhGjRqx9bOzs5GZmYmsrCw29XDq1CnMmjULy5Ytw6BBg6CmpoZ169bByckJzs7O6N69O7Zv3467d++ycq5GRkZsTk9OTk6sNoGEiuPu3buoVKkSBg4cCOB/te6Tk5OxefNm1KxZs1BN/aKIjIxEq1atWKXCUaNGic3l/giRkZFYv349Jk6cCFdX12IDwXJzc2FqagqivNLW/v7+8PT0BBEVWqUwNTUVmpqaBaaq+Hw+3N3di3T7l5WvX7/i2bNnSE9PL5WhMW3aNDg6OrK/OY7D4MGDmeDOf//9hxEjRuDUqVPFqoz+6bx+/ZplERTnUfTw8GD9qaOjI9asWYOYmBgWgCqKmxGJVYmmm06cOMHksBUUFKCvr48RI0b8lYYAIDEGfkvS0tJw+vRppn9NRFi0aBH7PiYmBh4eHkWOolVVVcXW/5axY8eiZcuW7G+O43DkyBH069cPW7duRa9evaClpYUaNWqw0dWkSZNw8eJFZGdnY9asWZCTk0NkZCSAvPQmImLRtWvXrkXlypXZfTBhwgQQiZeWFaUWamlp4cKFC2W/WBLKzNOnT1n9AEdHx0JLtZaFZ8+eoWvXrsyIXb16NYjy1PjevHlT4fnwIld6Wloa69xFMrQ3b97Ef//9xyL9CzN6srOzYW5uXq7pexzHlXp/YWFhWLlyJa5du1bAfZ2Wlsaua/7r6+/vX2Rw5t+AKCCwefPmRd5XGRkZuH79Onx8fMBxHCIiItCpU6cCXqPClr59+4pNf/3NSIyB35zo6GgWtdyrVy9kZGSgWbNmGD16dJGdd2xsbLE3sKhQSlFkZmYiODiYCZncvn2bBWn5+flh3759kJaWZnOoO3fuhIKCApv7LeyhfPv2LTIzM3H06FGmDz9+/Ph/Rijld+Ply5dQVlZG3bp1cfbs2WJ/h6ioKJw5c6bUVe84jsPXr18hFApx+PBhlj2goKAAXV1ddO3aFYcOHSryXg0JCUHPnj2hr68PGxsbuLi4FFlEpiSsWLECRHlSyk+ePAGfz4e5uTmkpKS+604XGRSxsbHFxg6UFNGUR0niA0SEhoaiXr16BUbBL1++hIWFBTw9PcWe6ydPnsDExERsSvBvRBS/JFJP/R4PHz4EUZ5a4aVLl8Re/gYGBhg1ahSOHDnC9C3+FSTGwB9AbGwsZGRkoKuri6ysLBY5K3K7nj59Wizndfr06ViwYAHWrl2L/v37Q19fH/b29rhz5w4WL14MU1NTNGjQoMTHf/PmDWxtbTF27FhmZY8YMULsd/72oVm1ahU2btwo9pmzszOkpKSgpqYmMQR+Mf3792dFr4rD29sbRITOnTuXWpPiyJEjqFSpEiulO2jQIEyfPh0rVqzAvHnzWK5+s2bNCtwLUVFR0NDQgImJCebOnYuxY8dCT08PRIQmTZpgzJgxePr0Kd68eVNgvjglJQWBgYFiLnKO42BpaQkigpqaGiIiIgDkzb/XrVuXTVGlpqYWayjv2rULampq363w+D0yMzPx7t27Eq0bExOD06dPQygUFvrM8Pl8tG3bFiYmJmLehh07dkBRURGTJk36obb+CfTt2xfKysolVgQUSWF36dIFRISVK1f+NCXLPwWJMfCHMH36dKiqqiIqKopZsfLy8ti9ezeUlZWhp6cHAEwAqGbNmqhZsyYqVaoEExMTDBgwAC9fvsTevXvh7u5eqvnb2NhYyMnJMYngFi1awMTEBN7e3jh37hzu3buHTZs24datW9i9ezeioqIgJSXFyt2KqFevHqSkpODn51d+F0ZCqUlISICCgkKxhYlOnToFb29vpKam4tixY2WaQoiIiMCpU6cQFRWFqKgoZGVlFXiZiWRdGzVqhHnz5uH58+cQCoUsu0Qk9QvkqXWuWbMGLVq0EBvNqaiooFu3btiyZQtGjhwpJmtNRFi2bBlGjx7NMnVEabOPHj3C3bt30bVrV1ZIaPjw4WjevHmR5yQQCIoNzi0tr1+/LlJ6WxS0K3qmi5tWOH78OObOnSuWXfD48WPY29tj6tSpLH7jbyUjIwOmpqYYMGBAidYX6VcQESur/q8jMQb+EBISEqCpqYkhQ4bAwsICjo6OqFWrFszNzeHr68sKnYSFhaFJkyZiebFA3sho4MCBbJRWWjIzM9n/w8LCoKqqihEjRkBOTg4WFhaQlpZmudaiIkh2dnZISUnB5MmToa+vDzU1NSYYI+HXERwcDCLClStXilzH0dGx3EaUFhYWRYrscByHvXv3olevXqzYjLW1NZsaKyxob//+/WjVqhW8vb3x+PFjLF26FI0aNQKPx4O2tjaWLFmCO3fuoGvXriDKU6CTlpbG8OHDoaWlxVIejY2NC5zjkydPmLJndnZ2oeqfz58/x+HDh7Fr1y4EBASIpSWWlq1bt6JZs2YFPvf394eysjJ8fHwgEAiQmJhYqMKgUCgsMp2R4zjs2rULDRs2hLOzM+bNm1ekd+FPRxQkOGXKlBKtn5mZiT59+sDJyUmSwfT/SIyBP4hNmzaBx+NBX18fVapUYcWGjh079t1tAwMDWf44n88XEyYpLpKZ4zgsWrQIlStXRvXq1bFgwQJMnz4dXl5eeP78udjoTFpaGkZGRpCWloaCggIqVaoEGRkZODk5sXzvbysZSqh4YmNjISUlhbVr14p97urqysrxhoeHlyjN7XucPn0aFhYWJZJu5fP5uH37Nrp06YIqVargwIEDpTpWQkKC2Ms7NTUVnTt3xqNHjyAQCGBjY4PBgwfj8+fPCAsLQ2BgYLFxEFOmTEGDBg3Eng9RCmb+xcrK6rt1BwojOzsbJ06cwI4dO5Ceng6O43D+/Hn4+voiKioKO3bs+K5uwdKlS2FnZ1fsbxUVFYXmzZtDQ0MDOTk5WLVqFQYPHvxDRsHvZlCcOHGCZV9JKBsSY+APgs/nM7U/IkKVKlVgb2+PSpUqlWg+9/Xr1wD+Vynu5cuXOHfuHAwNDQvtTBITE9GrVy8Q5ZWUrVatGipVqsQkZo2NjUFUsHjNyZMnoaqqiiFDhrB54c2bN+O///6DlJQUvnz5Uj4XREKpefLkCapXrw49PT1cvnxZLHZjwIABzBgoL3x8fDBp0qQfGj1/S3p6Ovz8/L5rrKSmprJnJX+ho3nz5qFq1arffYG/ffsWhw4dYv+vWbMmPn78iP79+4OIcO7cOSxZsoTd36Vl7969kJWVBVFegabc3Fy0b98e3bt3L/E+3r9/j927d393vWfPnmHbtm0QCoU4ePAgFi5cCCAvvbG08Q8zZsxg9SGCgoKK9TBVJN26dUOtWrV+dTP+WCTGwB8Gn8/H4sWLC4xO2rVrV+IpgPPnz6NHjx4A8goeqampic3NCgQCNGvWDDIyMlBWVi4QPZ2VlYW+fftiwoQJmDdvXrH513w+HzVq1ECrVq2QkpICfX19ls8uoWLgOA7Xr1/HwoULISsri3r16sHf3x9bt25F8+bNCxTa+d25e/cuiMTreBSFjo4Oy1wRPSt3794t9bx/cHAwKwjUvn17EP2vYuGQIUOgrq5e6n1yHMe8FPk/K4k2wKNHj0od0Llnzx7Mnz9fbFS/dOlSFpgM5BkXly9fxrp165ix5Ofnh69fv7LjPX78GEeOHGHVS//77z/07t27RB7Kn8Xt27chLS2NMWPG/LI2/OlIjIE/jOjoaCatKqrn3qpVKygpKUFGRqZMtbq/dfnNnj0bPB4PjRs3RlRU1A+3uX79+ujUqROAvCh2TU3Nv1om9XdjxIgRLKVu1KhRrJMfPXr0Ty99m5GRAW9v7zJXxyyM5ORkPHnyRCyOpShCQ0MLGM7fK+/9PW7evInGjRvD0NAQT58+RVJSEqpUqQJVVVW4uroWMK4yMzOLvd99fX3RtGnTIuV0ReTfh4WFRamrNW7YsAGDBw8GkNdXHz9+HP7+/kyNkeM4yMjIMPGd/fv3AwCrL9G6dWuxKROO4/Ds2TOYmZlBWVmZlSevaN6/fw85OTlYWlpKgpN/AIkx8IeRk5MDW1tbEOUVkHFycoK0tDSWLFkCLS0t1K5du0wPhIeHB1M1s7S0xIgRI8qtzQMGDGAjso0bN4KIcPDgQZYOpaWlxVK9JJQvZ8+eBRFh27ZtePfuHZo1a8Yi59XV1cHj8cpFGbAoRHPsJXlx/yxmz57NcvqHDBkCBQWFH1YTDAkJgY2NDTQ0NHD16lWEhYVhyZIl0NHRgaGhIdauXYvDhw+jcePG7FktynP38eNHODg4fHfao0uXLhg9ejSAvHTfsojhiAx/UbyPqIbJ9evX4evri5UrV4KIULVqVVZA6sqVKxg4cCDOnz/Ptg8PD8fgwYMRHR2N9+/fMznlikYgEKB+/fqwtrYutQ6GBHEkxsAfSEZGBlatWiU22pGTk2PuU1lZ2VJryE+dOhVaWlrIyclBpUqVitV8Ly0xMTFQVVVFixYtkJmZCSLCyJEjxdrv6upabseTkMeaNWtAROjQoQMTAJo+fTr27duHc+fO4dixY5g5cyZ7UYtEpsqTmJgYBAYGlut+Q0NDsWTJkhKnO8bHx0NFRQXTpk1DZmYm7Ozs0KBBgx/2TsXExMDW1haVKlXCiBEj8ObNG0RERKBbt26svK5I8ZCIsHjx4gIZAbdu3UK1atVKFKy5fPnychE8AvKMgqSkJOYlqlWrFkaPHg2hUIiwsDAsXboUCxYsQEREBK5fv442bdrg0aNHbPsLFy5g0KBBuH//PvtswYIF8PT0LJf2lZTNmzeDiMrkEZUgjsQY+IPJyclhUr9EeZLFW7ZsARFh1qxZpRo5CIVCFmAoSgMsT0R54yKt+CtXruDBgwfYsmULlJSUfpsgpL+FtLQ0yMvLY/To0eDz+YiLi8PRo0cRHx8Pd3d31KtXT2z9hIQEqKmpYfHixeWSRZCfr1+/lmvQ6OPHj6Gnp4dPnz6VeJtFixZBRkYGvr6+uHPnDogIs2fP/uG25H/+8usTZGZmsmyf/Iu0tDRq1qyJwYMHY/Xq1Vi7di06dOiANWvWwMPDAytXrsSMGTPg6uqKfv36FTp1EB0dXe4SuVlZWYiPj0dcXBz69++PoUOHQkZGhgUanjt37ruBhsuXL8esWbPKtV3FERAQwFKcIyMj4eHh8dfrKfxMJMbAH46oCptI6tXPzw/Tpk2DoqIievToUeqUp7i4OBBRgbSz8kDUcWpoaIDP52P+/PkgIsydO7fcj/Wvc+rUKRARHj9+jJycHGaEPX36FBzHFToq3rBhA3up5R/x/QhCoRBmZma/XN0tJycHVlZWsLe3R2ZmJkaPHg1FRcUypQTmJzw8nL3oa9euzTwgZ86cgZycHHR1dZlxfuXKFWzduhXDhg1DvXr1oKKiIubZU1dXh6amJszNzdGkSRNUrlwZ5ubmSEhIQFhYGPz9/SEUClGzZk3s27evHK5K4Rw/fhxr1qxBQEAAOx9dXV1WdfLp06fYsGFDAW8Px3HlXtmxKGJjY2Fra4vq1avjw4cPTOLczc2tQo7/NyIxBv5wEhMTWeU/0RIYGIizZ89CRkYGjRs3LrT8cFFcvnwZRFSqUVdp+PTpEwsmmzVrFmuzhPLj9u3b7LcXvfRXrVqFFy9eFNtZp6am4uzZsxg3bhx7hgsT3Sktx44dQ1JSEgQCwS8LMgPyUmuJiLn0paSkYGFhwTxiZWXXrl0Fpux4PB5cXFxw584dNG3aFHJycpgwYQJOnz4tVsMjMjISCQkJhU6jfPr0CfLy8nBxcYGVlRWGDh0KAPD09BTL/ilvAgICxKqaCgQCREREsHtp8eLFYlUTw8LCYG9vX2wFyfKC4zicOHEC2tra0NbWxvv379G+fXtUqlQJRIQ1a9b89Db8rUiMgb8AjuNw+PBh1hnZ29sDyMsp19fXR926dUscQ3Dw4EEQUbkF43z+/Bk7d+7EypUrMWbMGEybNg0HDhxgGvA2NjZo165duRxLQl7ZXjMzM7Ro0QI5OTl49uwZ7t+/DwcHh2Ln7a9cuQJLS0uxl39CQgJMTExw9uzZH24Xx3Ho1KkT9PT02KiW4zjw+XysX78e7du3L3G53RcvXqBOnTplmnowNzdn5bi9vb1Rp04dmJmZ/fDUyPv379G1a1dMmjQJmzdvxp49e+Dv7w8+n4+srCxMnz6d6XI4OTmV2CPh6ekJTU1NKCkpYd26dT/UxpIyYMAA5q2Li4tDlSpVsGTJEiQlJWH//v2IjY0V6x8SExPRuXNnvHr1Cn5+fkUGEwqFQowcORITJ04sdZs4jmNGChGhZ8+eiI2NRUBAAMuo4vF45SoV/a/xRxsDL168YKIgEvLyiImIufOAPLEUVVVV9O7du0T7WLNmDRQVFX844IvjOMyYMaPAnCmPx2NTBb169cLs2bPLNe3sX+fJkycgyqvv/unTJxARPDw80KVLl2JH+W/fvsXkyZPFjMaMjAzMnj2bpZeWxUuQnZ3NRpRTp07F4MGDERsbCwBwc3NDy5Yt8fDhQwwaNIjdcy9fviz2/gsMDMSoUaPYfkqDSCp7zpw5EAqFePfuHXg83g+73dPS0mBsbMzkcENDQ2Fubo727duLTcncvXsXMjIyaNq0aYkNgtTUVLi5uYGISq3KWBaEQiGys7MhEAhYUbT69evD0tISurq6CAkJKXJbOzs7MXnlYcOGQVFRkf1WY8eOxZ07d0o0pZCamoohQ4agatWqkJeXZ0Jn+atsxsfHs76lVatWP37y/zB/tDFQrVo1yMrKYsGCBXBxcfnnXypCoRAvXrwoELG8d+9eEBHevn373X1s2LABsrKyJe5os7Oz8ezZM1y6dAnnzp1jldhEGghExIRfvjUIRP+3s7OT5AeXA9nZ2bCwsIC2tjYuXboEPp+PR48e4dOnTz9s3GVmZsLa2rpEanciEhMTYWZmhqNHjxb4Lj09HX379oWmpqZYfIK/vz+IqFhvRFxcHM6ePQs/Pz8xmeO3b9+yKbHU1FS8evWKjWC/fPkCPz8/CIVCLF++nEX3A3nz4aL//wi5ubnMmPr48SNat26Nxo0bF+gP69atC3l5+VIFAV65coWNiPMfrzyDPd+/f4/169dj6NChYs9r/fr1QZRX/Cw4OLjYfeTk5ODTp09YtWoV+vTpgzlz5kBTU7OA9kL79u3Rtm1b3L59GxMnTsT27dvx6dMnfPnyBefOncPYsWOhra0NoryCbKJMisKuWe/evUFEv3QK6m/gjzYGHj9+jK5du7LgKCJC165df9p8959KRkYGVFRUMGjQoO+uGxcXh0qVKmHmzJnFrpebm8us/vwdh6KiIjIzM3HlyhXUr18f27ZtY9XqAgMDcfnyZfTr14/N8XXs2JEVqKlevTrWr19fLvPU/yIilykRwdDQEABw9OhREFGJXPAfP34sMr4kNzcXS5cuZS/f4n6j/Eb58uXL4e/vX+Jz4DgOt2/fZqNmDw8PVjhIhCiFtnfv3mIpsBoaGli9erXYOiKVwnHjxsHOzo6tKycnBwMDAwB5L+dOnTpViN7+9u3bQUSsnSUlKysLnTp1goyMDMs40NbWLtcpNjs7O8jJyaFGjRqwtLREz549MWXKFPD5fFSqVIndU9+Dz+ejSpUqMDAwwPDhw/Hs2TMAeWmUIlnqV69eoV69eiAiGBgYsPtWlIppaGiIqVOnIigo6LselIyMDIkhUA780caAiKSkJMyZMwcyMjIgIjRs2LBEUqX/Cr6+viy3vyS4ubnB0NCwWHnj8+fPgyhP5/3Fixf48uULzMzMoKOjU2L3Z/60LCsrK7EArJcvXyIyMhK5ubmIjY1FUlJSifb5L5OamgojIyNoaWnh4cOHAPKi3c+dO1doxbtvUVdXx4oVK767Hp/PR8OGDQtVL/z06RP09PRYFc2ScP/+fXh5eRX4nOM49OzZE0uWLAGQ90IMCgpCYGAg4uLiEBISUmrPgAhHR0eYm5sD+F+tjgkTJpSbISoqOiQaEYuqhhLlFQsri8YBn8/HsGHDIC0tjUqVKqFBgwZQUlIqFzXPBw8eQEVFBTNmzCj0+z59+kBGRgbe3t7F7uf58+eYMGECWrZsCXd3d7HfVV1dHRMmTEB8fDysrKygqamJgwcPguM4uLu7M0+hv7//b1cI6V/grzAGROTk5DDXElFeIR4J/ytZm39etji+fPkCAwMDsYjib1m+fDkUFBTY/kRZCN+WTv4eVatWLRBXQJRXa+HbqYWGDRtix44dP5wO9jdz8+ZNEBFTGSwNz549K1HmiUAgQIsWLZhIUXx8PHv5C4VCLFy4sMR1MpKTk6GmpoadO3cWuY7oHvPw8ICcnJxYTExZcXd3Z8V2gDzBHDk5OXTs2LHM6XEcxzGvpEAggKamJjw8PAAAHz58YN6MH82Fz87ORk5ODgv2/REDxsfHB82aNWPTAUVVSczMzGRFx4oqRw3k6YmYm5tDXl6+wD3o4+MDjuPQsWNHaGtrswGbSPVQtIi0DSRULH+VMQAATk5O7Kbau3fvL2vH74ZIja5+/folih04dOgQiAjTpk3D8+fPIRAIEBQUhNOnT2PUqFGQl5dH//79AeR1giJFweKCi76F4zhcuHABW7duxb1793D16lXcvHkT27ZtQ8+ePdnv+PTpU3h6erK/9fT0EBkZicDAQLx9+1biNcjH+/fvUbduXTG1uJ+B6AU9e/Zs1KxZE0T0XW394vZVktGtv78/KleuXC7FaETZN/nvV5G369atWyXax7Vr13Dx4kUAeS/Ltm3bQlFRkc1rBwQEQCgUIjQ0FA4ODiAi3Llz54fbLuLIkSMgojLf/+/fv4eWlhZsbW1x5syZ7/4GycnJkJKSgpGRkZgBIhQKsXbtWuZ5KcyY4vP5sLa2xurVq8Hj8ZgHis/nQ0pKCsOGDWOewtIKkAkEAvj6+iI6OrrUyqsS/sdfZwyEhIRg0qRJePr06S9rw+/KnTt3YGdnB3V1dbi7u2P//v1Fjio4jsOkSZOgqanJXPeil7G5uTlGjBgBf39/hIaGihlgpUFkoIiWO3fuMM307y1jx44V+7s089J/M7m5ueDxeMWOtIvi5s2bpc7TfvPmDYyNjSusjvyRI0fKJZ89KSkJ6urqMDQ0ZCNYjuNgbW0NJyenEhkngwYNEotg9/T0xI0bN9iUDMdxuHv3Luzs7FCpUiVMnz69XF9WomBLUaGh0vDixQvo6+ujVq1apfJUvH37FrKysmIBl0KhEObm5tizZw/77MCBAxg3bhyAPPXHZ8+eYevWrXj9+jVat24NfX19xMbGguM4KCsrs+dYVEipNIjiMESLZHBQNv46Y0BC8SQkJGDy5MmwsLAAEaFevXos1acwhEIh1qxZg2nTpuH69euIiIhAWloaiAju7u54+PAhqlevDiLChg0bStUWkSysaJk6dSqysrIwevRomJmZQV9fn6U2ycvLY/Lkydi5cyfmzp2LN2/esBiRatWqlSnN7G8kJycHUlJSZTIGPDw80Lhx45/QqqKZMGFCiWNZyptbt25BSkoKpqambDR7584d8Hg8VuJb1EaR5n5ycjKbfszJySnw3Lx79w4BAQGIjo5mCptSUlI/pRgUx3HQ1NQslVtdIBBgzZo1UFBQQIMGDcr03Li5uaFOnTpin31bJ+Lo0aNwcXFBYmIi9PT0cPbsWQgEAmzfvh3+/v5QU1NDx44dmX7A4cOHS+SdKAwnJyfUqlULbdu2BRH9sIjUv4rEGPiHefr0KerUqQMigq2tLSZMmFBonEVhhkJaWhoTFxFVpouMjCx1G9LT0/Hw4UM2SviWnJwcHDlypMSCNP86otiNp0+fYv369Th//vxPPV5OTs4PjXYPHTqETZs2lXj9Bw8e/HDFQRGZmZn477//QEQ4efIk+1w0JdW1a1dER0dj6tSp2L59OwBg//79UFZWLjQmIi0tDbq6ujAyMmLps82bNy/3OgL5adeuHdq3b1/i9UUGyoQJE8pcSXLcuHEs+PLVq1ewsbHBrFmzMHLkyGILKe3btw9EhFWrVkFNTQ1EeVLkPyJhnJmZCSkpKcyePZu9fzZu3Fjm/f3LSIyBfxyhUIibN2+if//+qFKlCogIDg4OWLJkCTw9PWFjYwNZWVk4Ozvj9u3bbDuRdsGYMWPYyH7MmDFYt24devbsCRcXF4wcOZKVSJVQMaxatQpycnLgOA49evQotu4Dx3HIyMhgbu309PRSu1iXLVuGOnXqlLpDT01NLVV8iYi6devC3d291NvlJy4uDp6ensw9bm9vL5ZaKBQKUbVq1ULz4729vREaGlrofgcPHgwiQosWLbB582ZcvXq13Is+fcuCBQugqalZ7HE4jsPy5cuZ4d+2bdsfOmaNGjWYQSHKbJCVlUWvXr3Epgq+JTU1FdHR0cjNzUVERASWLFkCHo+HNm3alDkoeOHChSAiLFu2DC4uLiAiSZ9TRiTGgAQGn8/H0aNH0bt3bygrK0NKSgo9e/bE2rVrWSTx0KFD8fHjRyQmJsLDwwMLFy5kc346OjqQl5eHlZUV6tevjypVqkBKSgpDhgz51af2T5CSkoLq1auLVc8TvaQvXLiAhIQEXL9+nRkI6enpICIcOXIEQN6ImMfjFZkJsH//fgQHB2PevHlo0aIFgDytj8JEhb7HnDlzYG9vX+IUsrCwMKSnpyMuLu6H+5cbN25AXl6eneeBAwdAlCfoI6rMR0Ro2bLld/eVmJiIY8eOoVevXiCi7+pzlDe+vr5i1QULQ1TMzNnZGRcvXvzhVEQPDw8YGhpCUVERy5YtY5kSJcn1z83NRVpaGjiOQ1hYGKZPnw4ZGZliMxSKY968eSzoUOIV+DEkxoCEQsnMzBRzzQuFQowfPx5EBAsLCwiFQggEAnTs2BHW1tZiI0M+n4+ZM2ciNjaWjSIkUb4/H5GIi0gFUkRiYiK0tbUxdepU7Ny5E82bN4dAIACfz8eRI0fw+fNnAHl1JO7evSu2bUREBIv2r1OnDurVq4fHjx9j2rRpP9TWwMDAUk1hdOrUCW3atPmhY+Zn165duHfvHtNMGDduHDQ1NaGnp4fHjx+zUXRRCAQCzJ49G9LS0iAi1KpVC5s2bSqz6/1HmDdvHmRkZBAYGFjo96JpvPJum8io4PP5kJGRgZOTU7GZGLdv32ayyjNmzED9+vVhY2MDd3d3VK1atUxtuH//vljc0aRJk3Dx4sUKq574NyExBiSUGI7jsGjRIhARtLS0oKurCwUFBcjJyWHnzp3MeIiLi4OCggL27t2LUaNGQUlJSaINUAGIalO4urqyeI5Jkybhw4cPWLx4MWRkZErlto6OjoacnByWLVsGIE/Vr3r16gWCxYC8l2vLli3ZlINQKCxy1J+ZmQmO45CcnFziTjs4OJgp2f0oEyZMgKamJqZPnw4/Pz8MGzaMua719PSgoKCAkSNHQkpKiqVLCoVCRERE4MGDB2jVqhXk5OQgJSWFhQsXljmlsrzIysqCkpJSkaqGDx8+BBH91EqHTk5O0NXVxeHDh5GamgpfX1+kpKQgJCSEZWucPn0aenp62LZtG4KDg2FsbIyxY8fi5MmTICJERESU6dh6enoFso3MzMzw4sWL8jzFvx6JMSCh1Dx//hzz589HixYtQERsdEREqFu3LsLCwph4ScOGDSU1xisQkbTr0qVLkZubCxsbGyxYsABhYWHw9vYukVt+586dTDznxo0bzIDgOI65eF++fClm4J05c4YpBUZEREBJSQmPHj1Cbm4uHBwc2NTEq1evYGRkhGrVqkFDQ6NE8//Jycn4+vVruanS3bhxAxs2bChgiPD5fPTo0UNMCOvIkSM4efIkbGxs2GempqZYuHDhb5W+7OTkVKQYU1hYGIioVKqQpWXz5s0gIjx+/Jj1C1ZWVhg8eDAMDAzYtc7/Gx44cADNmzfHly9fQERlDnYVZUaJihmJFkm/UzokxoCEHyIyMhJbtmyBrq4uixvIr11gYWGBPn36lGnfHMdh3bp1cHJywrNnz3DixAlJVsF3qFmzJrS1tZmAExFh//79ePPmDXR0dGBvb493794hNDQUI0aMKDTHfPDgwcV2pJ8+fYKsrCwcHR0xderUAiP2qKgoTJs2jXX827dvZyJIly5dwsqVK/F/7Z11XFX3/8df3Et3Sbd0GICF7cRZM8F25qzpZs3ajDmM6azp5pzOqVizO6ao2IEYhIBSAoJ014337w9+93y9I7xIKp/n43EfyjmfeJ8b5/M+7887Ro4cSfr6+jKF3EnM3NVdfEUiEe3Zs4fbBpEoM1UhFovp1atXXL0MycvCwoIOHz5Mjx8/LqdExMfHN3g424IFC0hXV7fCDIJHjhypcPuoNikuLiYAtGbNGjI2NubeNw0NDVq9enU5Re7dbUNJhtR3HZSrgyRKQeI74Obmxv0tJyf3Qdk4myJMGWDUGjt37iQej0fTpk0joVDI3SAkZmZZiYiIoMDAQK52+buvfv36saIkVSDxrl6yZAnFxsbSoEGDqEWLFpSamkrGxsakqqpKBQUFJBaLydnZWapioITi4uIqq9OdPHmSOnToQObm5jRw4EAaMGDAB8nq6OhIgwcPfm+7nJwcun//frUtA/v27SMdHR06fPgwEZU5UXbs2FGmOg1t27YlADRixAi6e/eulIKbn59Pp06doi1bttCIESO476ak1HNDkJCQQCoqKjR79uxy50aNGkWenp51On9+fj7x+XzOguLn50cXLlyg6dOnl1voHz16RGpqaty2xfHjxwmQrarqf7lz5w4VFRXR8uXLuXwn48aNIz8/P+5zGTNmTK1d56cMUwYYtYokG9jAgQO5/8saRx4eHk4uLi7cj9jMzKycMiBJQjRjxgwKCAigo0ePUlpaGhUXF9Phw4fpt99+o0WLFtGoUaPIx8enWiV3PwWEQiGNGTOG5OXlqUWLFpSQkCCVnfFdn4H/hs0lJydTQEAA5xj2888/c0/u9+/fJw0NDbK0tCR/f3/6+uuvKTw8nD7//HOZvO4rIikpiZo1a/beDHqFhYXUvn17GjlypJSS8j6veJFIRPHx8ZSUlESvX7+mc+fO0Q8//PBepeLEiRNcNU5vb2+KjIyk3bt305IlS8jb25t4PB731Nm6dWuys7NrFPe99evXE4ByRdp8fHxq1fmyMiTOwsrKytyTv1gsJnd3d3J3d6crV67QnDlzKCQkhH7++WeKjY3lCsx17dq12n5F9+7d46wRREROTk7l7heSF+P9MGWAUasIBALuBzhjxgxSU1MjBwcHLmSrKry9vcnOzo62bdtGt2/fpvz8fJoyZQoBZWlKJfvVX3zxhdQP3djYmPP+/u/Ly8urHq668REcHEyampo0bNgwKikpoYSEhHIWmrdv35KLiwsNHjyYBAIBzZw5kwwMDOjBgweUmppK3bt3p2+//ZaIypSBZcuWkaenJ+np6XELakZGRpWJZioiNDSUDhw4QGvXrqUpU6bI9N3YsmUL6erq0vbt27ljbm5u5fbBjx07RkRlis6YMWPozZs35O7uTvb29lKllatCkt2yQ4cOnD+MnJwcWVhYUM+ePem3336jEydOkK+vL6WmptLs2bNJU1OzwT3YCwoKSFNTUyq3xKVLlwiAVLhpXSAUCit9Eg8KCiJ1dXVuX//69ev08uVLMjc3J2VlZVq6dGm1FYHCwkJuPklYoqTy4X9fVeXaYPwPpgwwap0hQ4ZwP8TBgweTlpYWDR06lCIjIyk1NZV+/PFHLo/Bzz//TKWlpXT//n0CwDmhHT16lLp37845xL37ZFNcXEwvXrygly9fUlRUFE2YMIE6depEf/31F4nFYvL19eXmr6ry4qfO1q1bic/nk4+PD+3fv5/U1NSknorT0tK4m3dpaSmNHj2aFi9eTETE1Yj4b7a/+Ph4LoQtMTHxg2LWDx06RJqamiQnJ8eVGK6I+Ph4KcvOu5YMsVhMI0aMoNu3b5NYLKZ169Zx1e/u379Pubm51KxZM0pMTOTuS5Jrk4UbN25QSUkJPXv2jM6dO1dl/n5bW1uaPn26zGPXJRMmTCB7e3sSi8XcAi1xrJs5c2adlQYuKCjgfnMDBw4stxWTn59Pe/bsIaCslLqZmRk5ODhw/hzVITs7m7Pc6Ovrc1aI5ORkKSfCFStWkIaGRp1HUnwqMGWAUeuIxWLavn07l4jlXfOdgoICV1NASUmJeDwedenShZSUlMjZ2ZlbfGbPnk2///47zZ8/X0pJkJWCggKukuLWrVubbGjjyZMnCQDt37+f8vPzKSwsjBYuXMiFw1V2kxQIBOVCsxITE0lfX5+rKvfZZ5/R0qVLP0iuK1euVJjtUCwWc/P+9ddfZG5uLvVEP3ToUDIwMKAzZ85wx7Kzs2nAgAG0adMmun79upTVQkKzZs1o/vz51ZYzKCiIjIyMpGL4CwoK6PfffyehUEhPnz6tkSd8bSNJvnP06FGuUqK9vT33+6urbTNJGmzJq6KCV8HBwdz5Nm3afFD6cpFIJDXPf/OXfPfddwSUZYF811r4oU7MTQmmDDDqDLFYTB4eHtwPUlNTk0t53LFjR87bWE1NjUaNGiX1lCgUCik/P59KSkooKirqg55owsPDuaqL9vb2XOx9U8PFxYXGjx9PRGVVPfl8Pm3bto1evXpFZ86cqfTpPCgoiEaPHs1l6hMIBDR37lxas2YNqaur086dOyusZVEZYrGYTp48KXUDv3LlCq1du5bOnTtH+fn5dOzYMZKTk6Pw8HBKSkoqlyinf//+JCcnRzdu3JB53tTUVDpz5swHOahFR0fTwoULuSfdhQsXUv/+/UldXZ1evHhBEyZMIHNzc5mcEusDoVAoZZk7ceIEOTo60rRp02j06NFkbGxcaaXSmiCxytja2pKvry85ODhU2G7GjBm0YMGCD07T/PLlSwJAjo6O5b4baWlp1KpVKyllYeHChTRixAhycXH5oPmaEkwZYNQpJSUl9Mcff9C8efOkfqQHDhygiRMnkpKSEm3btq3Cp4SlS5eSlpYWpaWlkUAgqHYteIFAQGfPnqXFixeTk5MT6ejoNMnQRMmNesWKFeTt7U13796l/Px8atGiBTVr1qzCiAKisqe9vn37ltsKeP78Oe3cuVPm+YODg+no0aN048YNAiC1kH/22WfUtm1bcnFxoaKiIhKJRO/9nCWm5YKCApn26U+fPk3y8vJc7oSa8NlnnxGPx6PHjx9TfHw8ycvLV7vsc11TWlpK8+fP5+oEuLu705QpUygkJIQA1EmoXUREBPfbljhY1laSqHdJTk6udNxFixZJ3WO6d+9Ojx494nw+6mqL5FOBKQOMeiM5OZn69etH3333HRUUFFBGRgb17duXnJycKmz/5s0bunjxIhH9z8R469YtEovFdPnyZUpLS6tyvlGjRnFPQZKFqHnz5k2u3LFYLKaFCxcSj8cjeXl5zsnuhx9+IGtr63JRBVVx48YN+vvvvykqKoqWLVtG27dvp6NHj9Lr168rbC8QCMjZ2ZnLjvfuXHl5eaSurk7z58//oAVq5MiRNGvWrPe2EwqFdPfu3WqPXxEikYiT9auvviI9Pb1Gr2AOHTqUPDw8OFN+VSmDa8rWrVulFuRevXpVmPvgQ0hKSiJtbW0CUKEyunDhQgLAbRV8/vnnpKCgwMlS05oMnzpMGWA0KJKEMikpKVW2E4vF3BNsdnY2NW/evMqFICwsjFq3bk3+/v5c/1OnTpG+vj5paGiQjY0NaWpqkra2NvXu3Zt27twpk1f7x0xkZCS1atWK1NTU6NdffyWxWFzl01JycjI9fPiQ+/vZs2fUpk0bUlRULOexraurW+mWQVJSUqWm6eTk5A9eTA8cOMApNrJQVFREmzZt+qC96nXr1tGdO3e4vx8/fkxycnIfRWEcSUSBZLusNu7PAoGAHj58SC9evJAy15eWllLHjh3JwsKCq2poZWX1wamGJbx9+5Y8PT1JX1+f2rVrx/klvHvfEAqF3G942rRpUoqALEpjU4cpA4wGZePGjQSgSq/y/1JSUlJpURYJ+fn5lJGRQXv37pUa+8cffyR9fX0aMmQIzZo1i/r27Uvy8vIkJydHOjo67415/9jJzc2lGTNmEAAaPXo0BQcHk5eXF126dIk2b95Mvr6+5OHhQa1btyYzMzOysrKiEydOcOGc+vr6NGvWLLp48SK9fv2aMjIyKDExkVq3bk2tW7eWKgM8c+ZMztmwIqoy8R88eJC2b99ebm85JSWFRo8ezX3+fn5+MlcKfPXqFQ0bNoy0tbVlcih98OABRUdHU2lpKXXp0oV+/vlnIiqLZvH09CQXF5dG4yvwPg4dOkQXLlyo1u+sKiRZDSUvFxcXio+PJyKiKVOmkLW1NV25coXGjBlDpqam1K1btw82058+fZo0NTVJU1OTgoODKSEhgdq0aUMAyNXVtcI+BQUFtG7dOjIxMaHNmzd/8HU2JZgywGhQTp06RQCqtZ8rFovpyZMnFRaIEQgEnBf50aNHSU1NjV69esXdqAQCgVQK3IcPH9KDBw8oOTmZevfuTQCkYtk/VQ4fPkwqKiqkpqZGJiYmpKCgQAoKCtStWzeaPHkyTZw4kdzc3LgQLnt7ezpw4ECli+j169cJAA0ZMoS6d+9ORkZGBJRljKyMMWPGkLu7e4Vm5KtXr5KNjQ2nMEjCRoVCIbVr145T2rZs2ULLli2j1atX0/nz54moTOF5d+ERiUQUEhJCmZmZpKGhQZ9//jm5urpWGAGQlZVFmZmZlJqaSqqqqlzUQklJCTfm119/TYqKik26EE5kZCQBIG1tbdq5cydZWVmRk5MTpaSkcGmwJRUwJYWIoqKiqj3P6dOnicfjkYWFBQ0fPpyIygpNSRKSzZ07tzYvq0nDlAFGg7JhwwYCQOfOnZO5T2lpKWlpaUlp/CUlJfT999+Tj48PTZw4kYjKng5u3bpFSkpKNG3aNBKJRLRlyxaKiIiocNwnT55wccr/ja//FHnz5g19//33NHDgQNqwYUOFWzWFhYUUHR0t01Pdtm3byMXFhTp16kTz5s3jnt5Wr15dYfuoqCgKCAiQelp9+vQpl25acjw7O7vK8NL8/HyaPHkyLVmyhMRiMXXv3p1cXV25zHRHjhzhHFVPnTpFQUFBNGnSJM7nZPXq1RQfH08ikYg8PDzI0dGRBAIBBQYGlrNM/Pbbb01GYXwf586dI3l5eerduzfdvXuXjI2NydramoKDg0lVVZUmTJhAYrGYAgICCACFhoZWa/ygoCBSVVWlAQMGUHJyMrcFYGhoSHw+n+bPn9/giZ4+JZgywGgwYmNjqXPnzqSkpMSFr8nKzZs3pW4Ef/75J2lqatLJkyelHKTEYjHFxsZSQkICxcTEkLe3N7m7uxNR2VPgqVOnaM6cOVRaWkrx8fE0depU0tfXpwULFtTORTZhSkpKaMqUKcTn86v8fHNycujnn3+mwsJCWrp0KRkZGZVz8szKypI5V8SGDRuoWbNmdOTIESIievHiBfeU+l/EYjENHz6cBg0aREKhkFasWEGOjo4VZis8duwY8Xg8Lisjg+jy5cukr69PPXr0oLi4OGrRogWpqqrS4sWLCQCdP3+egoKCCJCtaqJQKKSVK1eSl5cXqaioUNu2bSk/P5/mzJlDxsbGVFpaSkpKSjRixIh6uLqmBVMGGA2CQCDgcgC8m0CmKoRCIeXm5pJQKKSSkhL64YcfOBNzXl4eXb58udIkOpIn2+3bt9OaNWsoNTWV/Pz8SEVFhQwMDOiLL77g2nz11VdkaGhI+fn5tXClTZv9+/cTUJbjf+nSpXTr1i2yt7cnNzc3butGsu8sqaMwefLkGnl+v3z5kubMmSOVdIiIKD09Xabv2n+VDoFAQMuXLycANHz4cPY0+h8uXrxIAGjixImUmppKXl5epKenR7q6uqSoqEjr1q0jS0tLcnNzq9TCFBISQidOnCB/f38CQEOHDqV169ZxmR/T09O5kFNJtEB184Y8efKkwkRXjDKYMsBoEL7++msCQH/++afMfZ4/f05AWSnb4OBg4vF4nINacXExmZmZ0TfffFOu36JFi2j06NEkFovp1q1bBIAOHz5MSUlJ9O+//1JISIhUhrKbN28S0HTrGtQmY8aMIQcHB5o1axYBoE2bNpG1tTWZmJhwZnpTU1Pq2LFjrcwnFosrDVOcN28eWVhYVNq3qKiIoqOjpbYGQkNDydPTk3g8Hq1cuZKFp1XCX3/9RaqqqjR+/HhKT0+n/v37c1UEAZCWlhYBoJs3b5br++rVK1JTU+PatmzZkjv36NEj2r9/v1R7SYGikSNHkkgkosLCQtq1axcFBQVxDp2lpaU0bdo0cnZ2poyMDBKLxdz4kyZNqtP34mOFKQOMekUsFlO/fv0IAKmrq5OysjIXHlbVE5dQKKTly5dTmzZtKD09neLj48nAwIAWLVrE+Q6IRKIKTcmPHz+mr7/+mps/JSWlyqd+gUBAbm5u5OzsXJNLZRCRmZkZmZqaUn5+Pl2+fJkyMzMpKCiIYmNjuYX16dOnlJycTGlpaXTy5Mkaxe3/+++/BKDCMEexWFxlaeY7d+4QAAoLCyMiIn9/f1JSUiInJ6c6SaDzqbFhwwaSl5eX2uKR+FioqqqSgYEBhYSElOu3evVqUldXp8DAQDp06BDx+XwuJHjs2LHliiy9m9Z4yZIllRYpk7ySk5MpPj5e6hijPEwZYNQrr1+/JqCsgFF6ejrt37+fxGIxJSQkkJ2dHc2bN4+io6PL9Xv79i2NHz9eKtlIRkYGTZo0qdaduSSJUzw9PWnr1q3vDWNkVIxIJCIjI6MKn8T8/PzIyspKymwcFhZG8vLyNUoQVFkGw9TU1HLpa/9LdnY2BQQEUH5+Pm3bto0A0Pjx42stHO9TJy0tjeTl5Wn27NlSn+vkyZO51MAVMWfOHLKzs+P+Pn78ON27d6/Sefr06UO2trb0zTffSBUlq+glSVpWWloqlSHxXcRiMedA2pRhygCjXpkwYQLp6OiUCydLTU0ld3d3UlZWpoCAgAaSroyYmBjq1KkTF74ElBVWWbRoET169IilNZURSdGcitIdnzlzpsL6AvHx8R+ct15Cbm4uXbx4kct2GBYWRpqamrRjxw6Z+kvqI8ydO5d91tVEEh30448/csdycnJITk6O2rZtW2Gfbdu2kYKCgkyZQUUiETk4OND06dOlTP8//PADzZkzh6tNMGDAgHKJjuTk5KSiUu7du0dz5szhol709PRo+PDh1crI+SnBlAFGvREVFcU5YVXEixcvaNiwYZyG/l+P7tDQUNq0aVO9JnqR5B4YPHgw6evrE1BWhXH37t31JsPHyqJFi0hRUbFCz/z/snPnzirLBMtCdnY29e/fnwwMDAgAXbp0iYiINm/eTJaWluTh4cFFGFTGli1bSFVVlYYOHdrknxQ/lCVLlpCioiJXQyI/P586dOhAAOjQoUPl2qenp5O2tjZ17969yrVDJBJRnz59CADt27ePqztQ0WvlypXl+j99+pQUFBQ4J1Jvb2+u/ZYtW+iHH34o1zc7O5uOHDlCR44cKeeQ+qnBlAFGvZGcnEwAyMzMTKqCnFgsLvedmT17Nqc0BAcH05IlS+jXX38lbW3ter1JR0dHc/vPAoGALl26RIMHD+b2K48cOdLkah3ISlRUFCkqKtKSJUuIqCzU8ObNm9znPXnyZHr27BklJyeTtrZ2tZxJK+LmzZukpKRE7u7u5ObmRnFxcURUZiLOzMykWbNmcd87iVPZu5w6dYqUlZVJR0eHW8gY1ScvL48MDQ2lwv9EIhF16NCBbGxs6NixY+X8gy5dukRycnJccaWKkGwxSr4nGzduJEVFRcrMzKRBgwYRADI2NqZLly7JFIYaHR1NAwcOJFVVVTI3N+fqHgwePJiIyiwWOjo6nMKgrKxMU6dOpatXr35QvYXk5ORGfa9gygCj3khOTuYy2r3rFPT999+TjY2N1Pfm+vXrtGXLFu7/Dg4OlJub2yie1goLC8s9ibRq1Yr69etH3t7etGzZsnJ11psqfn5+JCcnRydPnqT09HQyMjKiQYMGUW5uLhkYGHCm3Ly8vBqH7AkEAho1ahQBZamWK0MsFlPnzp1pwoQJ3DFJLLyHh8cnX6OiPti7dy8BoIMHD3LHwsPDydLSkgDQ0qVLy/UxNjamLl260MKFC+nWrVt09epVKUdQieOgxKfE1NSUhgwZQnPmzOF+h9bW1uUW6lu3btG+ffsoODiYevbsSU5OTuTt7U2LFi2itLQ0cnZ2lvot6+vrc1aCyZMnU3x8PCUlJdGqVauoWbNmBIDk5eVpyJAhdPHiRZkslSUlJcTn8wkATZ8+vVH6oTBlgFFvCIVCGj9+fLmbxPPnzzmTbmU8e/as2iWM65ILFy7Qs2fPKCkpifbt20fjxo2jAQMG0KBBg0hZWZkMDQ3fa5JuCohEIvLx8SF5eXnau3cvZWdnk0gk4kLC3ufhXx22bNlCioqKdPjwYZo+fXqlxZGIiK5du0a3bt3i/v7qq68IAFPiagmxWExjxowhBQWFcr/bkSNHkpubW7k+K1euJCcnJ9LT05NanM3MzDi/AuB/dUycnJyk2knuLZs2beLGLCkp4c7/d1wA5ODgwEU8ACBHR0fOGjBu3Lhy1iOhUEjLli0jZWVlcnNzI6CsSNeXX35ZZV0TsVhMK1as4OaRk5NrdNUumTLAqDd+/vlnkpOTIyMjI4qLi6PBgwdz+eTfx+jRo4nP59O+ffvqWMqaEx8fT0OHDiWgLBVvU3dCKy0tpSFDhhCfz6cDBw7Q1q1byd/fnyIiIsjf35/4fL7UttGHkpaWRvv376+WhSEpKYl69uxJfD6fVq1aVWMZGP+jtLSUevToQQYGBlLZBzt06FCl5SY7O5u2bt1KwcHBdPjwYerWrRu3iBoaGnLtqvIZeDdyZO3atdStWzfy8/Oj3bt3019//UU7d+4kZWVlatGiBRERDRs2jOuroKBALVq0oO3bt1e5lonFYnr06BEtWbKEsy7MnDmzSktBjx49uHnmz58v0/tYXzBlgFFvtG/fntzd3emPP/4gkUhE/fr1q9CjnKjsh+bn58cpCydPnqQVK1Z8NAurWCzmsta1atWKxo0bRydOnGhosRoMSaU/eXl5srGxoebNm9O5c+dIKBRytQhqyqtXr8jS0rJaisXjx49JU1OTTExM3ltGm1F9kpOTuQVwypQpJBQKycbGRuZKk0Rl68rVq1cpIyNDKoNgYWEhV0ukbdu2UsqAJE+BrGRkZNC5c+coOzubZs+eTZaWlsTj8UhDQ4O+/fZbev36dZX9xWIx/f7778Tn8zkfGaIyv5mMjAzy9fXltrAkr++++4527NhRac2N+oYpA4x6Y/bs2QSA+0FXtf+fmZlJY8eO5crGfqxcvnyZhg8fTg4ODgSg0iJJTQFJNEn79u2pZ8+e3KJ97do1mjNnjkxRB1WRn59PCxYsoDt37sjcJzo6mgCUy3LHqD3EYjHt2LGD+Hw+de3alZSVlal9+/a0bNmyGuWUkFROBEDTpk2j33//nfv7QxXMpUuXEgB68eIFJSYm0pIlS0hPT4/s7Oy4kNczZ86Qt7d3hf1nzZpFfD6f+vbtS2vXrq3UcnH06FESiURkbW1NAKp0nKwvmDLAqDckOcWXLVv2XkfAT+07VFBQQIqKipxTZFNFEuLn6urKLdr79u2jQYMG0Zs3b+pdHkle/aZstakvTpw4wcX0A+Cc8Xx8fGj37t104sQJ8vb2phkzZtDx48dl8tgPCAigbdu2cYWwAgMDK8xrISsS2SRpzonK6ibIycnRnj17iIjoypUrNGrUqAr7FxQUcEnL3r1GycvX11cqodKePXu4c2PHjq00lXZ9wJQBRr2Rn5/PVTObOnVqpe0kmndjc7CpKd27d6d+/fo1tBgNyq5duwiAlPPefxGJRDR8+HD69ddfqaioiObPny9TxTuisqfQefPm0e3bt2Vqn5+fT/b29qSoqPjeDIWMmiP5/AHQqlWraMmSJeWemiXJvmxsbCgqKqrGSaiqw8iRI8nQ0FAqNLGwsJAsLCzoyy+/rLSfUCik+Ph4Lpuq5Fru379PoaGh9Oeff9Lt27fLbXOKxWLOv0jyWrlyJf3www/1nteAKQOMemfjxo3E4/EqrTC4Z88eWrRoUZXe4B8jvr6+1KlTp4YWo0ERiURkYmJCs2fPLnfu7NmzNHXqVBKLxXTlyhWaO3cuZWdnk4eHB23btk2m8YuKisjR0ZHWrVsns0zjxo0jW1vbj8Yf5WNGLBbT06dPycvLi3g8ntQiqKamRpMnT6bTp09LKQ08Ho8GDBjAPVG/efOG1qxZQ2fOnKHc3Fx68eIFBQQEkEAgoKtXr9LevXul5szNzaWnT59WmOZcFkaMGEEAyNTUlP7880+KiorirBYCgYBWr15NRkZGBICaN29OmzZtoh49epCioiL9/fff7x1fJBJRbGwsXbt2jQBQly5duGs/f/48PXz4UOby3TWBKQOMeqegoIArBFTVdsGnVCpWLBaTsbFxpfnZmxL9+vUjFxeXcsd///13GjNmTI3GLikpIR6PRwcPHiSBQEAHDhx4743U19eX3N3dazQvo/rk5uZSQEAAHTt2jA4dOkRjxozhyppX9no3DBAAde7cucJ2IpGILly4UC6HQHXj+99NeSx5KSsrEwB6+/Yt3b59mzPxHzlyhHMSPH/+PI0bN44UFRXp/Pnz1cqPUlpaytVzkLwWL15c3be32jBlgNEgSMqQHj9+vMLzEi25sLCQBAIBhYaGVtjuY7EeSPYGa8tz/mPGxMSEfHx8ZG6fnZ1dqRVJwrlz5+jXX3+l4uJievPmDRUVFdHTp09JTk6OunTpQt7e3pVGC+zfv5+AiisdMuoXsVhMiYmJ9PjxY7p79y6dPHmSRo0aRTNmzKA7d+5Qr169pBZJDQ0NKQuDgoIC/fbbb9S9e/cKFYkP4enTp3T16lWKiIigrKws8vLyKqcYPH78mJO/a9eupKurSwEBAWRhYcE5zVZVKbUiZs2aRfLy8txcdR3twpQBRoPRrVs38vDwqNA8+++//3J+BXv37iV9ff1yP6aZM2dSt27d6kXWmnDs2DGSl5eniRMnMlM0EQ0fPpzs7Oxktvz88ccfBIAePnxYaZt79+6Rs7MzRUVFSR2/desWmZiYUP/+/SvdexYKhWRubk4dOnRolJnhGP8jPz+fXr16RaWlpVx0kmSLQWIB2rJlC3e8bdu2dOnSpVq1MpaWltL169fp2LFj5OfnR8+ePZM6n5mZSV5eXqSkpMTVUpC8JCmyZWHq1KlSfWsSeSELTBlgNBhXrlwhAFzhkMp48OAB3bx5k1atWkXr1q3jPH1v375N/v7+9M8//0iVNm5MHD9+nPh8Po0YMaJeCyw1Zh48eEAAaOvWrTK1F4lElJmZSYcPH6bBgwdTfHw8hYWFyfx+isViiouLo0WLFkllGCwpKeHCGTt27EgAOI9xxsfBlStXaOTIkTRw4EDOFH/o0CEyMjKikSNHylT86uXLl3T27Nlqh7Zu376dbG1tuUiGdykuLqbFixdTp06dpLY+Dh8+LPP4paWlFBgYyCnDACrNy1IbMGWA0WCIxWLq3r07ubi4vFdzf/bsGSkpKVHHjh3L7buPHz+epk2bVpeifhCSamxDhw5lisB/aNmyJX311VfV6nPhwgWytbWlVq1akZqaGl2+fJkSEhJo2rRpVFBQQFlZWRWmj01PT6cHDx6QpqYmeXp6ElGZImBmZsY5pZ0/f56UlZWlUtm+j/r0cmfUDSKRSOrpW1LDIC8vj65fv07h4eGV9lNVVSUA9P333793nsDAQAoICPhgy+CWLVuof//+FBkZ+UH9ZYEpA4wGRfKUuGvXLpnanzp1ikaNGlXhjyomJob27NnTKDLJicViGjduHGlqajbqSmUNQVJSEmloaFRYZvZ97Nmzh27fvs3dFCX3nLVr15KOjk65EESBQEDq6ur05ZdfEgD6/fffiajsZj5hwgSp6oSampoyJ7lKTk4mY2PjCqMiGB8Ht2/fpnbt2lXogPjuXn1FCsHly5e5SAczM7NPwtlZ1vWbBwajDmjbti3GjBmD7777Dm/fvn1ve11dXbx586bCc3fv3sWKFSsgEAhARHjz5g2EQmFti/xeiAijRo3C3r17sXXrVhgYGNS7DI2VoqIifPHFF9DU1MS0adNk7pecnIxmzZrB3NwcHTt2hL29PQBAU1MTISEhWLBgAf744w9ERkZK9ZOXl8ejR4/QqVMnREVFYfr06QAAHo+H3bt3w8rKimtrYWGBhIQEmeQ5c+YMkpOTsXnzZty8eVPm62A0DuLj49G1a1cUFhZi8uTJ2LJlC/Lz8xEXF4cTJ05gyZIlcHZ2BgDk5eWV63/+/Hnu36SkJBw6dKhe5W9QalOzYDDeJS0tjQBI5fT+UCRORG/fviVDQ0P6448/ajxmdbl//369hQN9bGzcuJEUFBSqXZgoJyeHVq9eXWWs+FdffUUODg5Sx2JjY+ngwYPcfvI///xDaWlpFfZv27YtTZ48+b2yrF+/ngCQl5cXOTg40MSJE6txJYz6RCwWU3BwMC1ZsoTWrl3LJZZq2bIllxSoIg4fPkwAKk00NHz4cGrfvj0REZmbm9PcuXOl6iZ8jLBtAkajoGXLluTl5fVec9uLFy9kMrunpKTQ6dOnGySr3MGDBwlg5XArolWrVjRs2LBq9Tl58mS1KrxdvHiRizyZOHEiWVlZEVGZX4C2tjb98ssvFfbr1q0bDRo0qMqxCwsLycbGhgBQQkICjRs3jlsUGI2LwsJC+uyzz6SSF7Vq1Yo2btz4XmfRPn36kIGBQaXRJWPHjuX8Tz7//HMCQPr6+vVee0RS9yEmJqbGYzFlgNEoOHPmDPF4PPL29qb4+PgK2yQkJJC8vDytWbNGpjFLS0upffv29e5DcPLkSQJAqamp9TpvYycjI4N4PF61Iz927NhBAwcOlKltWFgYAeBqQCQmJlJ2dja9ePGCNDU16ciRI5U6cXl7e1OHDh0qHfvNmzfUsmVLUlJS4mrXS8rrMr+QxoVAIKCxY8eSnJwceXh4UFJSEj19+pT09PQ45aAy61RhYSHxeDxq3bp1pSmBp06dSjY2NkREFBwcTP379ycAZGdnV2fXVBFJSUnc9dQ0bJkpA4xGw5UrV8jExIRUVVVp7dq15by1BQIBxcbGyvyll8SPnzp1ijIyMujWrVv14tV/9uxZAkCJiYl1PtfHhKRyXV0unN27dydTU9NyVeWEQiEdP36cDh48WGG/hw8fkpycXDlFJSsri3bv3k2DBw8mHo9HJiYm3CIiUTwAlMtvwGg4iouLuaf1/1ajzMzMpKCgILpx40aVVkh/f39SV1cnZWVlWrhwYbl7kaenJw0ePFjq2IoVK0hDQ6Na2QZrSmFhIfcdrEnSrMLCQjp16hRTBhiNh5ycHJozZw7x+XyysrKiv/76Syqd7Lve39VBUp2ussWgNtmwYQOpqKh8Eh7GtcnUqVPJxcWFbt68We2+aWlplRYrEolEnJk0JSWF1q9fT7t37yYiosePH9OcOXOIqCyevHnz5lLe4cXFxeTv70/NmjUjd3f3csri8uXLCQC5ubnRqlWrpKxWkqey//opMBqOf/75h1scDQwMajTW27dvadmyZaSgoEAeHh70zz//cIWI3q1iKOH69esEgC5evFijeavL1atXCQBZWFh8UH9JBk7JiykDjEZFaGgo+fj4cKa35ORkCggIIB6PR48ePar2eAUFBfVWInfcuHHcfiLjf0yfPp20tbWpc+fORFS2OLu7u1NISAgREf3222/UpUsXrn1iYiKXbnry5MmkpaVFPXv2pAMHDlBSUhLX78cff6RmzZpVeN9Zv3499ezZk4jKvgPTp08noVBIxcXFtHz5cq7EbJ8+fSg9PV2qb3FxMZmYmNCIESMqvJ5NmzYRAE4ORsPy9u1bUlFR4Ra19yUzk5U7d+5wDoeDBw8mHx8fUldXL+cTVFpaSvb29uTh4VEr81aH3377rdqpzvPz8+nrr7/mUipLfgtMGWA0Su7fv0/y8vJka2tL1tbW1KJFCy4PeHURi8W0c+fOD3oylZX8/HwyMzOjSZMm1dkcHyt+fn6koaHBmdSTkpJo6tSp3FP95cuXafny5URUdmMFQLt376aIiAiyt7cnNTU16t27N8nLy1OrVq3IxMSEiMpM+dV5GisoKKDevXuToqIizZw5s1Knr9TUVAJAx44dq/D8tGnTKiy4xGgYLly4QADI39+fjh49Wuupv3/99VcyMzMjDQ0N+vPPPytss337dgKqXxCpIZA4OgMgKysr5jPAaPwEBQWRs7MzmZmZkbOzM5c4prqUlJSQqakp/fXXX7Us4f+Q3JA+VGH5lAkNDSVVVVUaMWLEe2/UAoGALly4QK9fv+asRK9evSKiMt+SefPm0ciRI98753/nCQ8PJycnJ1JVVaWrV69W2VcoFJK6ujr99NNPFY5rYWFB06dPf68MjLonPT2dDAwMyMHBoU4XYpFIVGUVTMk2xdmzZxt9HZLY2Fjq0qULubm50cGDB5kywPg48PPzIz09vWr1WbJkCfn6+hJR2c175cqVlJmZWad7+ZI4euYvUDH+/v7Vcnby8/OjgICAcscFAgG3v1/Ve92jRw8aOnQoEZUVLTI0NCRnZ2d68uSJTPOPGDGiQg/xO3fuEAC6fv26TOMwap/i4mJ68uQJHTt2jDp16kRqamoV1gmoT0pKSriyyp07d673UMOawDIQMj4KLC0tkZGRgcePH+PatWsgovf2adWqFbp06QIAiI6Oxi+//IL58+eDz+fXiYxxcXH46aef0L9//zqb42NnxIgRMDExwY4dO2Rqf+7cObx48aLccXl5ecjLy+P27dtwc3NDbm5uhf3XrVuH4cOHAwBmz54NNTU1XL9+Ha1atZJpfkVFxQq/a/v374eZmRn3/WLUD8nJybh8+TJWrFgBW1tbtG7dGj4+PsjMzMSlS5dgZGTUoPIpKioiMDAQ58+fR3JyMpydnfH333/X6hxLly6FnJwcFixYgJKSklodWyZqU7NgMKqLJHb/yJEjJCcnV2UoV1BQEJ06darc8WnTpnGxwXVBt27dyNraWqZKaU2ZVatWkbKyMiUkJNR4rPj4eBo4cGCFJtn/RgZ4eXlRmzZtZLbanD9/ngDQxo0by42rr69P33333YcLzqgWhYWFNGXKFOLz+VzJYl9fXzp//ny1ygLXJwUFBaSiokI8Hq/SfAUfwt9//83t9bdp06bWHKPZNgHjo0ASu5+QkFAufl8oFNLVq1dJLBbT/fv3qVevXtStW7dyYxQVFVFKSgqlpqbWSsaud8nMzCR5eXn67bffanXcT5Hs7GwyMjKiPn36VJr3ITMzk7755ptqvZ++vr60efNmKi4upmfPnlGLFi3oyJEj3PkbN27IXEZWLBaTqakp6ejolIsyCAgIIAD08OFDmWVjfBiPHj0iY2NjMjMzIxUVFdq4cSM9fPiwVhfXuiQyMpLU1dVp4sSJtVrlUhLyCoDk5OS46ps1gSkDjI+Cbdu2Se3FJyQk0MaNG0kkElFERAQBoEuXLtG4cePI2dmZnj9/XulYJiYmtH379lqRq7i4mMLCwkhfX59UVVXpxYsXtTLup44k78OBAwcqPJ+ZmUk7d+6kBQsWyDymlZUV9erVi44ePUp8Pp8GDBggtWcrFosJQJVZBt+dH5Xkpfj666/JwsKi0TuIfQpIFry+fftSWFhYQ4vzQYwbN467jqruS9Xl3fwAtRHVwpQBxkfBnDlzyN7enojKQr5MTExIX1+f1q1bR2KxmG7fvk1EZSFpN2/epOLiYgoICCjn+SsWi+nQoUOVFquRheTkZFqwYAF5enoSj8cjAKSrq0tJSUkffoFNEAsLixoXpxIKhZyCeOjQIbpx4wY9ffqUJkyYQDo6OuU+f2dn5/fWHyAq+55oa2tXuBVgY2NDM2bMqJHcDNmQLHYNUWOktkhMTKQZM2YQAPLz86vVsdPS0mjz5s01yj4ogSkDjI+C/v37U58+fbi/Hzx4QEOHDq2w1jhRWQy7srIyXbp0qcLzCQkJ1KZNm2pVzzt16hQNHTqUNDQ0SFdXl0aPHk3bt2+nw4cPV7sKH4PIzs6u0gJEr169Ij8/v/cWe9q6dSvNmzev3PHMzMwKq8gBoLZt28ok36xZs8jMzEzqmMQKdfr0aZnGYHw4QUFBFaYU/ljp168f6ejo0I0bNxpalAph0QSMj4LIyEg4OjoCANLT0+Hi4oJjx47BycmpwvYmJia4ePEiPD09AQArVqyQqjluYmKCli1bIikp6b1z5+TkYMSIERg0aBASEhIwd+5cREVFYf/+/Zg2bRqGDx+Oli1b1sJVNi2srKwQERFR4bn4+Hj8+uuvyM/Pr3IMHR2dCtvo6OhAW1sbAEBEuHjxIvr16wcAGDlypEzyOTk5ISUlBVlZWQAAgUCAbdu2QUtLC97e3jKNwfhwgoKCAADdunVrWEFqif3798Pd3R09e/bEyZMnG1qcD6c2NQsGozqUlJQQn8+nHTt2EBGRurp6OQ/vqhCJRPTll1/SunXryp2bPHky+fr6VppIpLS0lHr27Emampp08OBBtk9ciyxdupT09fUrfE9v3LhBlpaWXNx4Tk6OVPKff//9l9seqKj/q1evqEOHDvTjjz+Sq6srASAPDw/av3+/zJ9hYmIiqaurk66uLk2aNIlL17pw4cIPuVxGNYmJiSEANHfu3IYWpdYoLS2lYcOGkYKCAgUHBze0OFKwbQJGoycyMpIA0LVr14ioLORLko2uOty7d4/y8vK4vwUCAbm7uxOASqMLfvnlF5KXl2fJZeoAiXd/RZ7QUVFRtHjxYi5yJCgoiFRUVDjPfm1tbRozZkyFC7tYLKY+ffoQAFJXVycAFBgY+EGKXGxsLM2ePZvMzc1p2rRp9OjRo3qtSteUmTt3LuczUFBQ0NDi1BqlpaVkampKs2bNamhRpGDbBIxGT0pKCoAy0z4RwcvLC82bN6/WGKWlpfDx8cGyZcu4Y8XFxZwJ2drausJ+Dx48QPv27T8ZU2VjolOnTtDS0kJAQEC5c3Z2dvjyyy9hZmaGyMhIeHh4ICgoCHp6egCAiIgI+Pv7Q05OrlxfOTk5nDp1Ch06dEBpaSk3V0Vt34eVlRU2bdqE169fY/v27fD09ASPx26H9YGCggL3/08piZeCggI8PDwQExPT0KJ8EOzbz2gwkpOTAQBGRkaYN28eunbtCoFAUK0xJJnBvv/+e3z++ec4ceIElJSUoK+vj7i4uEr7mZiY4M2bNzURn1EJfD4f3bp1w8GDByEUCqXO5eTkoKCgAD/88AOnADg7O3PnDQ0NqxxbUVEReXl5UFFRQbt27dgC/hHy448/Ql1dHW3btoWSklJDi1OrFBcXv9cfprHCfkmMBuP48eOwsbGBpqYmpk6diqVLl0o9NchK8+bNoaenBzc3N5SUlEBBQQFt2rSBubl5pX2ioqJga2tbE/EZVfD9998jMjISEydOhFgs5lL/3r17F56enpg2bRr09fXfO05JSUk5Z9Djx49DQUEBvXv3rhPZGXWLkpIS/Pz88PTpU4SFhTW0OLXK+PHjERgYiKdPnza0KNWnNvccGAxZSUxMJDk5uVpLEkREdPr0aTp79ux7261Zs6bW52aUR1JKdceOHaSnp0eHDx+m7Oxsevz4scxZ2yRjSJwMCwoKaMKECQSA7ty5U5fiM+qQ9PR0MjExoX79+jW0KLVKSEgIAeDyozQGmAMho1ETGxvL1Sh3d3evcRWwnJwc6tOnD3355ZdVtjt37hwBoCVLljCHsXpgxIgRZGRkRMuXL6/2Z5yXl0clJSXk7OxMysrKVFBQQL169SIVFRX6+++/60ZgRr2xc+dOAkDR0dENLUqtsWXLFlJQUGhUdUxkXb/lG8ggwWjivLvX6+LiUqMqXRkZGfjmm2/w7Nkz3Lt3r9J2+fn5mDFjBnr16gU/P78PcjxjVI/Vq1fDyckJAoEADg4OMvcrKipC+/btsX79eoSFhSEhIQH+/v74999/ceXKFfTs2bMOpWbUBy1atAAApKWlwcbGpoGlqR3Onz+PHj16QEdHp6FFqTbMZ4DRIFy9ehUA0K9fP+zbt4+7MXwIJ0+exKFDh3DgwAFYWFggPT0dX3/9NTIyMrg2JSUlmDBhAtLS0rB9+3amCNQT1tbWWLhwIdavX4/g4GCZ+6moqGDixImcQ2lsbCxmz56Nr776iikCnwitW7eGkZERtm/fXivjiUQivHjxAnfv3sWlS5fw+vXrWhm3Orx9+xaWlpb1Pm+tUJtmBgZDViSliy9fvlzjFLC5ubl08+ZN7u8bN26QnZ0dVwI0Li6OOnXqREpKSnTixIkazcWoPiUlJdSiRQvq0aPHB/U/efIkKSkpUY8ePaioqKiWpWM0JFu2bCEej0dv376tdt+4uDi6evUq+fv708iRI0lPT4/LXwCA5OXlycfHh5YvX15r5YCrIjExkfh8Pv355591Pld1YD4DjEZNcHAwASBfX19q0aJFrY8vFAqptLSUUlNTyc3NjczNzRuVU09Tw87Ojuzs7KrVRyAQcM6evr6+VFxcXEfSMRqKhw8fVqtsdF5eHm3fvp06deoktfC7u7vT999/TwEBARQWFkaxsbG0bt066tixI2loaBAAWr9+vdRYT5484TJh1pTMzEzq378/6ejoNLp1kikDjEbNrl27iMfjUWxsLAkEgg8ep7CwkKZPn04XL14sd87Pz4+7EQQEBNREXEYN8fT0JAsLi3LHxWIxCQQCevPmDQmFQsrKyqKgoCBavXo12dvbk5ycHC1atIg5e36CiEQimjVrFgHgMlL+l+LiYqkshWPHjiUA1KVLFzp69ChFR0dTSkpKlfNkZ2dzSsPjx4+JiOjChQsEgIYPH14r1+Lt7U1aWlqN0vLIlAFGo2bAgAHUvn17IiIuF/2HEBwcTCoqKjRv3rxyNcVXrFhBAOi7775jtQcaGMlN/Pbt2yQSiejff/+l5cuXk62tLXejVlFRkXraGzVqFD169KihRWfUEc+ePeM+63eVvfT0dJo/fz4ZGhoSj8cjOTk5MjIyIgUFBQJAW7ZsqfZcWVlZ1Lp1azI1NaWlS5dy83bq1KnG13HlyhUCQEeOHKnxWNUhLy+P4uLi3ttO1vVbjuj/s4FUQW5uLrS0tJCTkwNNTc0aeikwmjrx8fGwsbHBtm3b0LJlSwwYMAC7d+/GgAEDqj3W8+fPpSoLtm7dGhoaGlBQUEBAQADGjRuHPXv21KL0jA/h3c9Jkv1RS0sLAwYMgJeXFwwMDBAXFwcNDQ2IxWJ8+eWXUFFRaWCpGXVJamoqunTpgsjISAwdOhSdOnVCRkYGNm7ciMLCQnz99ddo2bIleDwe4uPjYWRkBHNzc/Tv3/+DHID37t2L8ePHlzv+9OnTGlUn7dq1K4RCIW7dulWvGTH79OmDS5cuYd26dfjqq6+4ap7/Reb1WxYNhFkGGLXJggULSFtbm/Lz8yk/P5+2bt1aIzPwoUOHOE3f19eXhgwZQp06daJDhw7VyOrAqF0KCwtp69atNHbsWLp+/Tqz1jBILBbTgQMHyMrKilRUVEhRUZG++eYbCg8Pr/W5hEIh7dixg/r27StlgXJ1daW0tLQPGvPq1asEgPbt21fL0r6f33//nbsGJyenSn9PzDLAaJQIBAKYmZlh5MiR+P7776Grq1srxUp8fHwQGBiIO3fuwN7evhYkZTAY9Q0R1UvYb3BwMPLz8zFu3DgkJSXB0tISISEhUFZWrtY4PXv2REFBAe7cuVPvdTLEYjFGjx6Nw4cPAwBWrVqFmTNnllujZV2/WZ4BRr1BRPj666+RmpqKiRMnYv78+Zg7d26tjP3jjz8iPT0dx44dq5XxGAxG/VNf+T/c3d3RpUsXxMbG4smTJ3j16hXOnDlTrTFKS0tx8+ZNjBw5skEKZvF4POzZsweenp4AyuqBVLZVINN4tSQXg/Fe8vLysHPnTpibm6NFixYYOHAgOnfuXCtjb968GSoqKhg8eHCtjMdgMJoGLi4ucHZ2xrp168qVH87Ozsa+ffuQmJgIoOxp/OzZs5gyZQoGDBgAgUDQoEmGlJSUcODAAe5vIsK0adMwY8YMWFlZ4eDBgzKPxdIRM+oNdXV18Pl8LFiwAADQu3dvqKqqytxfIBDg1atX0NXVRVZWFpKSkvD8+XMcPHgQQUFB2L17N5ycnOpKfAaD8Yni7++Pzz//HM2bN4elpSU8PT3RrVs3rFmzBm/evIGRkREuXryIb7/9Fjdv3oSzszOaNWuGNWvWoF+/fg0qu7W1NeTl5bFhwwakp6dj//79yMjIQG5uLsLCwtC/f3/ZBpLFUYE5EDJqi3bt2tHQoUMpJSWFLC0tKTU1Vea+fn5+Uo4/AEhBQYEGDRpEp0+fZg5pDAbjg8nNzaVjx47RggULqH379iQnJ0etWrWiL774grS0tAgAGRoa0tWrVxtaVI78/HxydXUlABQcHExEZQ7Vbdu25ZI5sUJFjEZHdnY2YmJi4OjoiMLCQnTt2pXLPS8Lenp6AIADBw7AzMwMpqamMDExYSFoDAajxmhoaGDo0KEYOnQoACArKwuamprg8/nIzs7GuXPn0KtXLxgYGDSwpP8jOTkZoaGhAMpCJFu3bo358+cjLy8PGzduRJs2bZCbmyvTWEwZYNQbgYGBSEtLw7Jly2BtbY29e/dW2V4sFuPUqVPQ0NBAhw4dcPz4cWhpaWHEiBEN4rDDYDCaDjo6OsjOzsbKlSthZmaGL774AuHh4bh8+TIMDQ3Rq1evBpMtIyMD3377LVRUVHDw4EHs3r0bM2bMgFAoRFJSEv755x8MGzasWmOy0EJGvbFy5Ups2bIF6enpSElJQWZmJlxcXCptf/bs2XKJiLp27YqAgIBaCUdkMBiMqrh06RL69OlT4bnS0lIoKCjUs0RlToLDhg2TipxSUVFBUVERNDQ08MUXX0g5FbLQQkaj4+nTp8jMzMTatWuxatUqTJw4scr2kqf/Vq1awdDQEHfv3sW1a9eYIsBgMOqFjh07wtLSEp07d8bp06fx9OlT/PbbbwCAnJycBpHpr7/+wrFjx7BhwwYsWbIEAKCvrw8A+Pzzzz+4JDSzDDDqBSKCnp4esrKyAAC//PIL9PT0KkwPKqGgoABWVlZIT08HAISHh7NoAQaDUa/8/vvv+Oabb5CUlISioiJYW1vDwcEB4eHh9b5defHiRQwfPhyDBw/Grl27MG7cOBw/fhzXr1+Hq6trheszswwwGhWFhYWcIgCU1RAwMzODSCSqtM+dO3eQk5OD1q1bY9euXXBwcKgPURkMBoPD19cXenp66NGjB7etOXHixHpVBF6+fAlfX1/07dsXnTt3xsaNG+Hh4YFDhw5h//798PLyqvGDOnMgZNQLxcXF3P+VlJRw48YNbN68Gc+fP4eVlVWFfU6cOAEbGxvcv38fioqK9SQpg8Fg/I9mzZrhxIkTWLNmDezs7LB8+fIqfZ1qkzdv3mDs2LG4du0aTExMsHfvXlhZWcHV1RUpKSkAgL59+9bKXEwZYNQLd+/eBVC2/+/u7o5evXph6tSpMDU1Ldf23r172LFjB/bu3Ysvv/ySKQIMBqNB6dixI86dO1evcz569AhffPEF5OXlcfDgQQwcOBAqKiro3LkzjIyMcP78eVhbW0NNTa1W5mPKAKNeCAwMRLNmzRAcHAw5OTlER0dj27ZtWLx4sZR568yZMxg0aBDs7e3Rs2dPzJs3rwGlZjAYjPonNzcXgwcPhoWFBc6ePQtDQ0MAwOHDh3Hnzh2cP38e7u7utTonUwYYdc6OHTuwYcMGTJgwgTv26NEjHD16FH5+fuXaGhkZ4dGjR9DQ0KhvURkMBqPBefv2LZKSkpCUlIT+/fvDx8cHwcHBOHLkCAYNGlRpuGNNYA6EjDpHYl77+++/ubCcESNG4OXLl+XCBMePH4+cnBw4OTlhwoQJDRa+w2AwGA2FnZ0dAgICsHnzZmhra2PNmjV4+PAhdu/ejePHj9dJdUcWWsioc+7fv4+OHTvC09MTx44dg6mpKV69egV7e/sK20dERGDSpEm4e/cuYmNjK3UwZDAYDEbVsNBCRqNh/fr1cHBwwO3bt2Fubo6ffvoJLVq0QEV6aFZWFhYuXIi7d++iT58+TBFgfNKkpqZi69atePLkCYqKihpaHEYThvkMMOqcFy9ewNvbm0vd+d1338HLy6tCU9ePP/6Ia9euYe/evRg5cmR9i8pg1BtExDmGAYCHhweCgoIaUCJGU4ZZBhh1zruZB5OSkpCamgpvb2+pNmlpaZg+fTp+++03LFmyBF9++WWD5P1mMOoTJSUl7v9ubm4NKAmjqcMsA4w6x8rKCtevX0dmZiZmz54NFRUV7Nu3T6rNqFGjEBwcjGXLlskcTlhaWgqRSITY2Fg4OTnViVMNg1FXyMnJITg4GOnp6VBXV0fLli0bWiRGE4ZZBhh1zsSJE5GYmIigoCBs3boVgwYNAhGhoKCAa5OVlYXPPvsMS5cufW+SoUePHqFz585QUlKCubk5XFxc0LdvX5SWltb1pTAYtYqzszO6dOkCd3d3VoCL0aAwZYBR58jLy4PP5+PmzZvQ0tLCkCFDMGHCBLRv3x5CoRD+/v54/Pgxjh49is2bN793vJ9//hm3b98GUFbXGwDCwsLqvWgIg8FgfCqwbQJGndOpUyds2bIFM2fOxC+//IKffvoJCxcuxP3792FkZMQt6ABw+/ZtzJ49u8rxvvnmG8jJycHFxQVFRUXw9PSEt7c35OXZ15nBYDA+BJZngFEvvHnzRqoOgYaGBng8HnJycuDh4YEZM2Zg0qRJ0NTUxPXr12s91SaDwWA0RVieAUajwsTEBElJSZzHdF5eHnJycqCiooIDBw5g6NChuHjxIvT19eHh4YEePXpg2bJluHnzZgNLzmAwGJ8+TBlg1BsmJiYIDg7G33//DRsbGwCAkZERli1bhosXL8LBwQHm5uYAgOvXr+Onn35C165dK0xOxGAwGIzag20TMBoEkUiEM2fO4NKlS3j27BkePHjAnePxeHBxcYGOjg4WLVpUJ0U5GAwGoykg6/rNlAFGoyAuLg5RUVHQ1dWFh4cHyxnAYDAYtYCs6zdzv2Y0CqysrFgdAgaDwWggmM8Ag8FgMBhNHKYMMBgMBoPRxGHKAIPBYDAYTRymDDAYDAaD0cRhygCDwWAwGE0cpgwwGAwGg9HEYcoAg8FgMBhNHJZngPFR8/btW5w6dQp5eXkYPXo0jI2NG1okBoPB+OhgygDjo4KIEBQUhBMnTuDMmTMIDw/nzgUEBODixYsNKB2DwWB8nDBlgNHoKSwsxL59+3Dp0iWcPn26wjYdOnSAn59fPUvGYDAYnwZMGWA0ah49eoSBAwfi7du3EIvFAAAVFRX88ccfcHR0RGJiIjp16gQDA4MGlpTRFJCUcmnqtTPEYjECAgKgo6MDd3d38HiN3/3s0qVLuHDhAubPnw8LC4uGFqfRwZQBRqODiHDhwgWsWrUK9+7dQ8uWLXHs2DF07NgRnp6eePToEde2bdu2DSgp41OGiBAaGopz587h3r17ePHiBZKSklBSUgIdHR2YmZnB29sbP/zwA7S0tBpa3CpJSEjAvXv34OvrWyNFhohw//59zJs3D/fu3QMAWFhYYP78+fjqq6+grKxcYb/MzEzs27cP4eHhSEtLw+PHj+Hm5oaFCxeiS5cu751XLBYjOzsbmpqakJevfNkiIqSlpSEpKQlFRUUgIhQUFODNmzdYtmwZEhIS8ODBA9y7d++jUGDqE1a1kNGoiI2NxVdffYWAgAB07twZHTp0QEBAAB4/fgwAMDMzQ0xMDBQUFBpYUsaniFAoxMOHD1FSUoI9e/Zg3759UFdXh5eXF1xdXWFhYQElJSVkZGQgJiYGu3fvRqtWrbBp0yZ07dq1QS0GpaWlKC4uhoaGBtLS0vD69WsUFxdDIBBg+vTpiIyMxOjRozFr1iyYmZkhPz8fubm50NXVhZKSEtLS0sDj8WBrawtVVVUUFRVBJBLh9evXAICioiLMnz8fgYGBcHV1xebNmyEvL4/du3dj//79MDQ0xIQJE6CjowM5OTlkZWUhISEBMTExXInyli1bQktLCy1atEBgYCCCg4Nhb28PHo+HNm3aoHfv3mjdujWMjIwgLy+P6Oho7Nq1C3///TcKCwuhoKAAc3NzaGhoIDo6GkVFRdDQ0ICZmRm0tLQQHh6OrKysCt+fZs2aYfLkyVi7di169OiB7t27o6SkBMXFxRAKhcjNzUVJSQmICEKhEHJyclBWVoaGhgaMjY0xatQoWFpa1tvnWVuwEsaMjxIHBwdkZGRg9+7duHfvHtauXQs3Nzd8//33sLOzQ6tWrZhGz6gViAgvX77E5cuXkZaWBpFIhH379iExMREAoKqqigULFmDx4sVQVFSscIyjR49i6dKliIyMhJaWFszMzDBkyBCMHDkSzZs3R1xcHP788088ePAA7u7usLOzg5WVFczMzKCnpwd5eXkYGRl9sBIhFApx8eJFbNq0CXfu3EFpaSl4PB63pSbB0tISo0aNwpYtW1BYWFjlmIqKilBWVkZubm65c3Z2dvjll1/Qr18/8Pl87nhUVBR+/vlnnDt3jltQJe+HtbU1OnToAF9fXxgaGnJ9xGIxTp8+jWvXrkFOTg4BAQFSDsESmjVrhmnTpsHNzY1TcnJycmBjYwMNDQ3k5OQgMTERWVlZcHR0hLOzM8zNzaGqqgoejwdlZWWYmJhASUkJAHD69GmsW7cOkZGRUFFRgbKyMuTl5aGhoQEVFRUAgLy8PIgIxcXFyMvLQ2hoKAAgPj7+o9tiYMoA46PE3t4erq6u+Pvvv6GtrQ13d3c8fPhQ6sbDYABli/m9e/egpKQEFxeXSk3UEhITExESEoLIyEiEhobi2rVriI2NhaKiIvT19VFQUIBhw4ZhwoQJ0NXVhaWl5XvHlMhx7do1PH78GJGRkTh69Cjy8vK485qamvjss88QGhqKuLg4CAQCqf79+/fHqVOnZP6Oi0Qi+Pv748iRI7h16xby8/PRuXNn+Pj4wMDAANnZ2TA0NISFhQVUVVUhEAjg4uICPp+P0tJShISEICUlBWpqatDS0kJWVhZKSkqgq6sLIsLDhw9RXFwMU1NT8Pl8mJmZAQDy8/PRs2fPKs30NSUjIwMhISFIT0+HQCCAoaEhOnXqVKkyVl8cOHAAY8aMAVCmEHl5ecHDwwPNmzeHlZUVZ60Ayr4PkZGRsLKykun7U9cwZYDRqCEi7N27F6dOnYKysjI2bNiAmJgY9OnTB2PHjsXvv/+OKVOmYNeuXTA3N4evry/mzJnD3ZgYTZuioiIMGzYM586dAwBYWVlh4cKF0NbWhpqaGvr27Qs+n48XL15gx44duHXrFoKDgwEAysrKcHR0RMeOHfH555+je/fuUFdXr1XZAgMDkZKSAgMDA3Tp0oUbXywWIyUlBYmJicjIyEB8fDxmzJiBVatWYfHixe8du6SkBD4+Pjh37hw+++wz9OzZE127dkX79u2bvFNjXfP27VsEBATg7t27uHfvHkJDQ1FaWsqdNzQ0hKmpKQoKChAZGQl1dXX4+Phg5MiR8PT0hLa2doNYNZkywGjU7N69G5MmTUL37t0RERGBZs2aITo6Gq1atcLFixc5LTswMBDHjx/HgQMHoKSkhKCgIJiYmDSw9E0ToVCIy5cvw8rKCjweD1paWlKfRXZ2NoKCgqCgoABLS0uoqanh6dOnSE5ORmFhIUQiEZSUlGBoaAgDAwPo6+ujefPmMs2dmJiIK1eu4Pnz50hISMDt27eRl5cHf39/GBkZYdmyZbhx4wZEIhEAwMbGBh4eHrh69SqUlZXRrVs3fPHFF/Dy8oKZmVmjsDQVFRXh+PHjGDduHIYOHYojR45InT979ixu3bqFvLw8ZGVlIS4uDpGRkSgqKsLRo0fxxRdfNJDkDKDMQpOUlIT4+HjEx8fj5cuXSElJARGhZ8+eiIiIwN9//424uDgAZVsPhoaGcHV1xapVq9CqVat6+R4yZYDRqFm8eDEOHTqEuLg4PH36FJ07d0Z+fj5++ukn/PDDD+Xap6SkoGXLlnB1dcWxY8ego6PTAFI3LbKysvDw4UM8e/YMERERuH79OndjAwAlJSUsXboUn332Gefk9d+9agl8Pp8zU7+Lj48P+vXrh+joaOTm5kIgEICIIBKJkJeXh9jYWLx+/RrJycmQk5ODra0trKys4OrqismTJ8PZ2ZkbKz8/H2KxGJGRkfjjjz8QERGBdu3aYdGiRY0u9DQhIQEODg4oKiqCmpoarly5gg4dOgAos5qtXbsWS5YsgZWVFXR1daGlpQVzc3PY29tjwIABcHNza+ArYMgCESE8PBwvXrxAWloaUlJSsH//fsTExEBdXR3jxo1D165dYWBgAJFIBGVlZaioqHD/19XVhaGhYY2UBqYMMBoVJSUluHz5Mm7duoXExETuCT8wMBBAmVPPoEGDwOfzcfHiRXh7e5cb49q1axg6dCjc3d0REBBQ35fQqElPT8eRI0e4/eLRo0fj/PnzmDlzJhQUFNC7d28MGzYM+fn5iImJQVJSEtLT06GiosLlabh58ybCwsIQGxuL+Ph4ZGdnAwA0NDRgZ2eH9u3bY/To0cjLy4OysjIuXryIX375hbtx+fn5oX///uDxeNizZw+0tLTg4+MDExMTbu9UJBIhNTUVaWlpCAoKwooVK5CQkABTU1Noa2tDUVERcnJy4PP5UFVVhZWVFSwtLeHk5ITevXtDW1u74d7kWiQlJYVLne3l5YU7d+4AAEJCQjB9+nTcuXMHixcvxurVqxtSTEYdUFRUhIcPH+Lq1avYtWsXUlJSqmwvLy8Pe3t7dOjQAR4eHujVq5fMFjWAKQNNluzsbOjo6MDFxQXm5ubQ0dGBpqYmzMzMoKqqiuzsbEyaNKleQ2REIhGsra2RkJAARUVFKCkpIS8vD8ePH8eQIUOQk5OD8PBwdOzYEXZ2dkhLS0NCQgLU1NTKjfXnn39i6tSpyMvLq9V93o+Za9euYdiwYcjNzYW2tjYyMjKgoKCAkpISeHl5wdbWFvv37+ee2lVVVWFmZgZ9fX3k5+fj+fPnAAAjIyO0aNECNjY2sLS0hJWVFecBX9l+dExMDLKzs+Hk5MR5YlcHkUiE4uLiCj/rTxkigqOjI6KiorBo0SKMGDECT548wbx582BkZIR169ahb9++zA+gCZCZmYn09HTIy8ujqKgIRUVF3P8lfiUhISG4d+8ewsPDIRQK8eeff+Krr76SaXyZ12+SgZycHAJAOTk5sjRnNADBwcE0adIkmjt3LgF476ugoKDSscRiMf366680e/ZsevPmTY1lE4vF5OzsXKEcpqam3P+1tLSoT58+JCcnR0lJSRWOFRERQXw+n9avX19juT5W0tLS6OLFi7R06VJq1aoVAaDOnTvT27dviYjoxYsXtHnzZlq3bh0lJCQQEVFWVhbFxMRQcnIyicVibiyxWEwBAQH07NkzqeOMukUoFNLIkSPL/R769u1LmZmZDS0eoxFSWlpKgwYNIgB0+PBhmfvJun4zy0AtsWPHDkybNg3e3t4YMWIE+vTpA2NjY0RFRWHJkiVwcnJC8+bNYWFhgaioKPB4PEyZMqXcOEVFReDxeFxMrKzs3bsX48ePB/C/VKny8vJcGJOSkhI8PDygpKQENTU19OnTB69fv8arV6+QkJCA9PR0ODg4wNzcHPHx8bh8+TLU1NRQUFAAfX19LuZ21KhRcHFxgaqqKgwNDWFpaYnmzZtj165d2LlzJ3r16gVjY2Pk5ubCzs4On332GczMzFBcXIyjR4/ixYsXiIiIwO3bt8Hj8VBaWoqioiIUFxcDALS0tLB69WrMmDGj0msdNGgQ7t+/j6SkpEbhCFZf7Nu3D35+fnj58iUAQEdHB59//jlGjRqF/v37s6fIjxCJFaykpAR2dnbQ19dvaJEYjYzS0lIkJibiu+++w9mzZ+Hv74/hw4fL3J9tE9QAIsLr168RHh4OBQUFGBoaoqioCKmpqdDW1oaVlRXk5eVRUlKC5ORk5OTkYP369eX2sYcMGYL58+fDy8urwnnGjBkDkUgEZ2dnCAQCPHv2DP/++y9UVFSwYcMGjB8/HikpKdDT03tvxr3i4mLY29tDWVkZ/fv3h7GxMaZMmQIFBQWEhobi4MGD2Lp1K2cq5vP5MDU1hZ2dHSwtLaGjo4PQ0FCkpaVBS0sLX375Jb744gtcuXIFL1++hLy8PAoLC7F3715kZGRwqT7fxdPTk7uxqaurIzExEfLy8lwiIcnemKmpKRwdHWFqagoDAwMYGhrCzMwMdnZ2aNGiRaXXWlJSgufPn2Py5MlIS0tDYmJik0lAdP/+fc7BbPXq1fDx8YGtrS1TABiMT5CkpCTcuXMHN27cwK5duyAQCKCsrIwjR45UO4qEKQPvIBaLUVpayjkxCYVChIeHIyMjg0tDmZ6ejoiICISEhCA4OLjSlJa1hba2NhwdHSEvL4+wsDAoKSnB0dERffr0QUhICPbv3w9jY2MkJydDW1sb3t7eMDc3h6WlJby8vKCtrY38/Hy8fv2aWxTj4uLw888/c3PweDycPXsWffv2BVBmdcjNzYVYLIaBgUGNnqpFIhHS0tLw6tUrREREoH379nB1dZVqk5WVhb179yI6Ohp6enpwdHRE27ZtYW1tXa1F7PHjx5g4cSK3t+3g4IADBw7Aw8Pjg+X/WCAixMTE4M8//8S6desAAFevXsVnn33WwJIxGIzaRigUYu/evfj2229RUFAAIyMjTJs2jUuHLXE6rQ5MGQAQFxeHoUOHIiwsDADQs2dPvHnzBlFRUSgoKJBqKycnh+bNm8PNzQ3u7u5o3bo1XF1dIRKJkJKSAlVVVRgYGCArKwvx8fEgIigoKMDIyIh7bzIzM5GVlYXCwkIoKSmBz+dDJBJxLyKCsrIyzM3N4eHhUeliTEQ4ceIEbt++jXbt2iE8PBw3b95ESkoK4uLiUFJSItVekjpTEmP9LvPnz8f69etr6R2tW8RiMV6/fo2IiAjEx8cjLCwMgYGBeP78OVq2bIlvv/0W5ubm6Nq16ydbmyAtLQ3Pnj3D48ePuYIqku/foEGDMGrUKPTr16+hxWQwGLXM1atXMXnyZMTHx2Ps2LHYsGEDmjVrVuNxm7QyQP+fDvKHH37A8ePHMX78eJiZmeHRo0ewsLCAnZ0d2rVrx6Xb1NDQgLa29kex/1xSUoInT55w8cmmpqYwNjYGj8eDQCBAamoqEhMTuXhwX1/fBjel0zs5voVCIRQVFTl/hoyMDGzfvh137txBREQEp6Tx+XxYW1ujc+fO8Pb2hq+vb52mQa1LiAjJycmIjY1FQkICkpKSkJSUhJycHJSUlCAnJwdv375FTEwMMjIyAADq6upo06YN2rVrh44dO6Jr165cIiYGg/FpERMTA1dXV3h5eWH9+vVo3bp1rY39SSgDK1euREhICEaOHAl9fX3k5OQgIyMDqamp0NHRwdixY6GoqIjMzExERkbi+fPnuH//PgICApCUlMSNc+HCBfTp06fe5GaAqyZWkbXiv+jp6WHAgAFwcnKCq6srnJycYGZm9tEu/kCZlSMkJASBgYHYvXs3nj17xp2TKHG6urpQVFSEpqYmjIyMYGFhAScnJ7Ro0QK2trYNrsQxGIz64ddff8W3336L7OzsWi+H/UkoA25ubly1qHfR1tZGXl4e+Hw+l7EMKDOXt2rVCp06dUKfPn1gY2MDXV1d6Orq1pvMjDKICN7e3uWcKh0dHbFmzRoIhUIoKChASUkJHTt2rPenXpFIhPDwcMTHxyM2Nha6urqwtbVFq1atqh3JkZKSgrCwMERERHA+FPfv30d2djYUFRXh7e2NCRMmwMHBARYWFh+FdY3BYNQfAQEB8Pb2Rv/+/XHixIlafRD6aJSB8ePH4/jx47CysoKBgQHatGmDYcOGwd3dHevXr8eCBQvg4OCAbt26YfTo0Wjbti2UlJQQFRWFCxcuQE1NDbq6urCzs+O86RkNi0gkwvjx43Hr1i3Ex8eXO5+QkNBgBYfEYjH27NmDtWvXciF6CgoKXAimtrY2hg8fjl9++eW9SY2ICMOGDcOxY8e4cWxsbGBra4t27dqhc+fOaNeu3Qcl42EwGE2LkydPwsfHByoqKlBUVISioiKuXLlS49TTH40y8K5XeatWrRAXF4fs7Gy0a9cON27cwKFDh7Bv3z7cv38fAoEAnTp1gpaWFjQ1NaGrqws9PT1oa2tDSUmJq1vt4eHBrAENSHR0NGxtbQEAmzdv5mqOGxsbo2fPng0WS52Wlobx48fjwoULGDlyJMaMGQM3NzeYmpoiPz8fEREROHPmDDZt2oQJEyZg27ZtVY6XnJwMExMTeHp6YubMmbCyskJeXh4yMjK4srACgQBeXl7o0aNHPV0lg8H4GCgpKcGmTZu46oeFhYU4f/68VJsxY8bA39+/RvN8NMpAUlISvv/+e+zfv7/c/vKdO3e4GP3i4mLs2LED9+/fR15eHnJzc5GZmYmMjAzk5OSguLhYKu7dxsYGbdu2xeLFi9GiRYtalZlRNZmZmejVqxceP34MMzMzrF+/Hl26dIG+vr7MdckLCwu5tLoKCgpQVVWFsrIySkpKsHHjRvz+++/o2rUrVq5cWWme7sTERDx//hyxsbEICQnBP//8Az6fD39//0p9SN6+fYt+/fohJiYGmZmZVcpYVFQEZ2dnqeI9ElRVVaGkpISsrCxoaGjg9u3b7HvIYDAAlFlPvby88OTJE3To0AFKSkpQUlKClZUVbG1tYWJiAgMDA7Ru3brGa+5Howy8O0dgYCDS09Ohp6cHIyMjtGnTpsp49OLiYpSUlEBLSwtEBIFAgJcvX2LPnj349ddfUVpaCmdnZy60kFG/BAcHY/LkyXjy5Al3TENDAyYmJrC3t4ejoyPnMW9ubi71WY8bNw779u2rdGxbW1u8evUKy5cvx4oVK8qdj4mJkVISVFVVMXz4cMydOxd8Ph8pKSlISkpCXFwc4uLi8PLlS0RERCA1NRVAmb+KJK9BVeTm5uLBgwdISEjgclPweDzk5OQgPT0dN2/eREhICADg3r17aN++/XvHZDAYny5FRUWYNGkSDh06hMuXL6NXr151Ot9HpwxUxcyZM/Hbb79xf+vp6aGwsBBFRUUAAGtra7i6uiI+Ph6RkZEoKSmBgoIC2rdvjyVLlqB37971LjPjfyQkJODp06fIyMhARkYGEhISEBUVxTnwAWV79a6urrC2toaOjg7i4+Nx+vTpKsfV1dVFREREhbG4sbGx6Ny5s1RUSUXo6Ohw4aaurq5wdnZGmzZtYGlpidTUVNy4cQNBQUHIysqCWCyGWCxGUVERkpKSkJiYiKSkJAiFQqkxFRUVoa+vD319fTRr1gw2NjYwMzPD/PnzoaqqWs13j8FgfEps3rwZc+bMAQCsWbMGCxYsqNPIoU9KGQgMDES3bt3KHdfU1MSWLVvw9OlTvHjxAtbW1nBxcUHr1q3h4eHBHLc+AlJSUvDw4UOEhoYiNDQUCQkJyM7ORnZ2Npf2WIKKigrs7OxgZWUFMzMzdO3aFQMHDqzQ+z8tLQ3Pnz/HgwcP8OzZM6SkpCAxMRFv3rzh6iD8F0VFRejq6sLc3Bympqa4evUq8vPzYWVlBSMjI/B4PK5uhKmpKUxNTWFmZgZzc3OYm5tDX18fOjo6UFVVZWmCGQxGhRQXF+PMmTP4999/sXv3btja2mLs2LHw9vaGh4dHrSdU+6SUgXeJiIjAlStXsH79eiQkJNSLmYXRMBARsrOzkZCQgISEBERGRiI0NBTPnz9HWFgYiouLYWJigqVLl8La2hoBAQF49uwZnj9/ztVBUFBQ4NI429jYwMnJCTY2NlBSUgKPx4OcnByKi4uRm5vLpaVOSEhAfHw8nJycsGTJEhgZGTXwO8FgMD5FHj58iM2bN+P8+fPIzc2FiooK2rRpg7Zt28LT0xMODg5o3rx5jUKvG1QZICIUFhYiNzcXenp6MjuNyUpmZib09PQAlNVylzydsbDCpoNIJEJISAg2btzIeduamprC09MTbm5uaNmyJVq0aIHmzZt/FJklGQxG00UoFCIoKAh3797F3bt38ejRI7x+/Zo7b2hoiOXLl6NPnz7Q09ODurq6zNbHOlEGevbsCUdHRzg5OSE0NBQPHz5Ey5Yt0bNnT4SFheHcuXNITExETk4Ot4+qoqICLy8vdOzYER06dICjoyMMDQ1rZMIPDw+Hi4uL1DEdHR2MHj0apqam0NPT45INSWoE8Hg88Pl8Ls1tQ1s4GLVHUFAQlJWV4eLiwszzDAbjkyA9PR2vXr1CdHQ0zp8/j0OHDnHnFBUVYWZmhuHDh0NDQwMpKSl4+/YtsrOzYWZmhubNm3M5TwwMDGBhYVG7ykDHjh2RkZGBqKgoWFpaolOnTnj8+DHCw8Oho6ODAQMGwMHBAdra2tDS0oKGhgaioqJw48YN3L17VypUS0NDA4aGhrC0tETz5s1ha2sLY2NjaGlpQVtbmxuDz+fj7du3SElJQXJyMvLz86GmpsaFmRUVFaGwsBD37t3D8+fPkZ2djaysrHLldd+lT58+uHDhgqyfCYPBYDAYDQYRITQ0FMnJyZwj9vPnz3H06FHw+XwYGhrCyMgImpqaSExMRHR0dLnKu3WyTSAQCCAvL889haWkpHB51qu6mFevXiEuLg5v377lFvi4uDhER0fj1atXyMvLe++boqysXKkDmLy8PCwsLKCrq4sXL16Uq0wo4eDBgxg5cuR752IwGAwG42MkKysL0dHRiIyMxJgxYz4uB8Li4mLOkzwnJwfZ2dkQCoUwMjKCkZERDAwMoKCgAJFIhMLCQuTn56OgoAB5eXlITU1FTEwMYmNjkZ6eDmNjY86XQFLZr1mzZmz/mMFgMBhNBlnX70ZVFk5ZWZlb+KtCUnaYlXRlMBgMBqPmsBqpDAaDwWA0cZgywGAwGAxGE4cpAwwGg8FgNHGq5TOQm5tbV3IwGAwGg8GoZWRdt2VSBhQVFWFkZARzc/MaCcVgMBgMBqN+MTIyem8mYJlCC4GysL/S0tJaEYzBYDAYDEb9oKio+N50/TIrAwwGg8FgMD5NmAMhg8FgMBhNHKYMMBgMBoPRxGHKAIPBYDAYTRymDDAYDAaD0cRhygCDwWAwGE0cpgwwGAwGg9HEYcoAg8FgMBhNnP8DuZpHbjTKK6wAAAAASUVORK5CYII=", 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", 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" ] }, "metadata": {}, @@ -340,68 +230,120 @@ } ], "source": [ - "ax = plt.axes(projection=gfs_proj)\n", + "lats, lons = subset.latlons()\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(projection=gfs_proj)\n", "ax.coastlines()\n", "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", "ax.add_feature(cfeature.STATES, linestyle=':')\n", - "plt.contourf(lons, lats, subset.data, cmap='turbo')" + "cntr = plt.contourf(lons, lats, subset.data, cmap='turbo', levels=levels)\n", + "fig.colorbar(cntr, location=\"bottom\")" + ] + }, + { + "cell_type": "markdown", + "id": "214a3be9-f81b-4978-9320-6a21fdf99ba1", + "metadata": {}, + "source": [ + "Geographic Subsetting of Xarray DataArray and Datasets\n", + "======================================================" ] }, { "cell_type": "code", - "execution_count": null, - "id": "83924ebe-833e-4804-b6ce-e41742d59705", + "execution_count": 14, + "id": "930f74d2-07b8-4c2f-8c33-ea005c37540e", "metadata": { "execution": { - "iopub.status.busy": "2024-07-24T03:52:57.030227Z", - "iopub.status.idle": "2024-07-24T03:52:57.030567Z", - "shell.execute_reply": "2024-07-24T03:52:57.030482Z", - "shell.execute_reply.started": "2024-07-24T03:52:57.030474Z" + "iopub.execute_input": "2024-08-04T05:29:02.635061Z", + "iopub.status.busy": "2024-08-04T05:29:02.634600Z", + "iopub.status.idle": "2024-08-04T05:29:02.647903Z", + "shell.execute_reply": "2024-08-04T05:29:02.647663Z", + "shell.execute_reply.started": "2024-08-04T05:29:02.635030Z" } }, "outputs": [], "source": [ - "subset" + "import xarray as xr\n", + "ds = xr.open_mfdataset([\"../tests/data/gfs.jpeg.grib2\"], engine=\"grib2io\")" + ] + }, + { + "cell_type": "markdown", + "id": "f3db282c-f4fd-4ce9-a1fa-d78c51a1ad79", + "metadata": {}, + "source": [ + "Subset to the extent of the State of Maine, United States\n", + "---------------------------------------------------------" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7c40ed50-670e-4857-850e-0c6c3209c929", + "execution_count": 15, + "id": "44e86285-07d0-4985-bd85-a5c3362ec1f3", "metadata": { "execution": { - "iopub.status.busy": "2024-07-24T03:52:57.030933Z", - "iopub.status.idle": "2024-07-24T03:52:57.031104Z", - "shell.execute_reply": "2024-07-24T03:52:57.031025Z", - "shell.execute_reply.started": "2024-07-24T03:52:57.031018Z" + "iopub.execute_input": "2024-08-04T05:30:21.074200Z", + "iopub.status.busy": "2024-08-04T05:30:21.074069Z", + "iopub.status.idle": "2024-08-04T05:30:21.188094Z", + "shell.execute_reply": "2024-08-04T05:30:21.187728Z", + "shell.execute_reply.started": "2024-08-04T05:30:21.074192Z" } }, "outputs": [], "source": [ - "msgs[0]" + "xrsubset = ds[\"HGT\"].grib2io.subset(lats=(43.083, 47.433), lons=(360-71.25, 360-66.95))" ] }, { "cell_type": "code", - "execution_count": null, - "id": "e7ef44bc-9c2a-4d88-91bc-3a7037466bf3", + "execution_count": 16, + "id": "54b050a0-2128-4d43-9390-49695f9ea455", "metadata": { "execution": { - "iopub.status.busy": "2024-07-24T03:52:57.031650Z", - "iopub.status.idle": "2024-07-24T03:52:57.031881Z", - "shell.execute_reply": "2024-07-24T03:52:57.031796Z", - "shell.execute_reply.started": "2024-07-24T03:52:57.031787Z" + "iopub.execute_input": "2024-08-04T05:30:22.062014Z", + "iopub.status.busy": "2024-08-04T05:30:22.061629Z", + "iopub.status.idle": "2024-08-04T05:30:22.316174Z", + "shell.execute_reply": "2024-08-04T05:30:22.315890Z", + "shell.execute_reply.started": "2024-08-04T05:30:22.062006Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "subset" + "fig = plt.figure()\n", + "ax = fig.add_subplot(projection=gfs_proj)\n", + "ax.coastlines()\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle=':')\n", + "cntr = plt.contourf(xrsubset.longitude, xrsubset.latitude, xrsubset.values, cmap='turbo', levels=levels)\n", + "fig.colorbar(cntr, location=\"bottom\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "f527023d-eb46-4f12-a54f-3404d40352a3", + "id": "67c4ec72-bc85-40e5-ab8f-4d5982b4a8d0", "metadata": {}, "outputs": [], "source": [] diff --git a/src/grib2io/_grib2io.py b/src/grib2io/_grib2io.py index 5972a4b..c70ac45 100644 --- a/src/grib2io/_grib2io.py +++ b/src/grib2io/_grib2io.py @@ -1371,7 +1371,9 @@ def subset(self, lats, lons): A spatial subset of a GRIB2 message. """ if self.gdtn not in [0, 1, 40, 10, 20, 30, 31, 110, 32769]: - raise ValueError('Subset only works for regular lat/lon, Gaussian, mercator, stereographic, lambert conformal, albers equal-area, and azimuthal equidistant grids.') + raise ValueError( + "Subset only works for regular lat/lon, Gaussian, mercator, stereographic, lambert conformal, albers equal-area, and azimuthal equidistant grids. Grid Definition Template Numbers of 0, 1, 40, 10, 20, 30, 31, 110, and 32769 are supported." + ) newmsg = Grib2Message( np.copy(self.section0), @@ -1384,44 +1386,42 @@ def subset(self, lats, lons): msglats, msglons = self.grid() - la1 = np.min(lats) - la2 = np.max(lats) + la1 = np.max(lats) lo1 = np.min(lons) + la2 = np.min(lats) lo2 = np.max(lons) first_lat = np.abs(msglats - la1) first_lon = np.abs(msglons - lo1) - max_idx = np.maximum(first_lon, first_lat) - first_i, first_j = np.where(max_idx == np.min(max_idx)) + max_idx = np.maximum(first_lat, first_lon) + first_j, first_i = np.where(max_idx == np.min(max_idx)) - print("first_i, first_j", first_i, first_j) last_lat = np.abs(msglats - la2) last_lon = np.abs(msglons - lo2) - max_idx = np.maximum(last_lon, last_lat) - last_i, last_j = np.where(max_idx == np.min(max_idx)) - print("last_i, last_j", last_i, last_j) - - setattr(newmsg, "latitudeFirstGridpoint" , msglats[first_i[0], first_j[0]]) - print("latitudeFirstGridpoint", newmsg.latitudeFirstGridpoint) - setattr(newmsg, "longitudeFirstGridpoint" , msglons[first_i[0], first_j[0]]) - print("longitudeFirstGridpoint", newmsg.longitudeFirstGridpoint) - setattr(newmsg, "nx" , np.abs(first_i[0] - last_i[0])) - setattr(newmsg, "ny" , np.abs(first_j[0] - last_j[0])) - print("newmsg.nx, newmsg.ny", newmsg.nx, newmsg.ny) - print(self._data.shape) - setattr(newmsg, "data" , np.copy(self._data[ - min(first_i[0] , last_i[0]) : max(first_i[0] , last_i[0]), - min(first_j[0] , last_j[0]) : max(first_j[0] , last_j[0])])) - if self.gdtn in [0, 1, 40]: - setattr(newmsg, "latitudeLastGridpoint" , msglats[last_i[0], last_j[0]]) - print("latitudeLastGridpoint", newmsg.latitudeLastGridpoint) - setattr(newmsg, "longitudeLastGridpoint" , msglons[last_i[0], last_j[0]]) - print("longitudeLastGridpoint", newmsg.longitudeLastGridpoint) - if self._sha1_section3 in _latlon_datastore.keys(): - del _latlon_datastore[self._sha1_section3] + max_idx = np.maximum(last_lat, last_lon) + last_j, last_i = np.where(max_idx == np.min(max_idx)) + + setattr(newmsg, "latitudeFirstGridpoint", msglats[first_j[0], first_i[0]]) + setattr(newmsg, "longitudeFirstGridpoint", msglons[first_j[0], first_i[0]]) + setattr(newmsg, "nx", np.abs(first_i[0] - last_i[0])) + setattr(newmsg, "ny", np.abs(first_j[0] - last_j[0])) + + # Set *LastGridpoint attributes even if only used for gdtn=[0,1,40]. + # This information is used to subset xarray datasets. + setattr(newmsg, "latitudeLastGridpoint", msglats[last_j[0], last_i[0]]) + setattr(newmsg, "longitudeLastGridpoint", msglons[last_j[0], last_i[0]]) + + setattr( + newmsg, + "data", + self.data[ + min(first_j[0], last_j[0]) : max(first_j[0], last_j[0]), + min(first_i[0], last_i[0]) : max(first_i[0], last_i[0]), + ].copy(), + ) + + newmsg._sha1_section3 = "" newmsg.grid() - print(newmsg.nx, newmsg.ny) - print(newmsg.grid()) return newmsg diff --git a/src/grib2io/xarray_backend.py b/src/grib2io/xarray_backend.py index 3335065..9490fb8 100755 --- a/src/grib2io/xarray_backend.py +++ b/src/grib2io/xarray_backend.py @@ -749,6 +749,29 @@ def to_grib2(self, filename, mode: typing.Literal["x", "w", "a"] = "x"): da.grib2io.to_grib2(filename, mode=mode) mode = "a" + def subset(self, lats, lons) -> xr.Dataset: + """ + Subset the DataSet to a region defined by latitudes and longitudes. + + Parameters + ---------- + lats + Latitude bounds of the region. + lons + Longitude bounds of the region. + + Returns + ------- + subset + DataSet subset to the region. + """ + ds = self._obj + + newds = xr.Dataset() + for shortName in ds: + newds[shortName] = ds[shortName].grib2io.subset(lats, lons).copy() + + return newds @xr.register_dataarray_accessor("grib2io") class Grib2ioDataArray: @@ -1014,3 +1037,44 @@ def to_grib2(self, filename, mode: typing.Literal["x", "w", "a"] = "x"): with grib2io.open(filename, mode=mode) as f: f.write(newmsg) mode = "a" + + def subset(self, lats, lons) -> xr.DataArray: + """ + Subset the DataArray to a region defined by latitudes and longitudes. + + Parameters + ---------- + lats + Latitude bounds of the region. + lons + Longitude bounds of the region. + + Returns + ------- + subset + DataArray subset to the region. + """ + da = self._obj.copy(deep=True) + + newmsg = Grib2Message( + da.attrs["GRIB2IO_section0"], + da.attrs["GRIB2IO_section1"], + da.attrs["GRIB2IO_section2"], + da.attrs["GRIB2IO_section3"], + da.attrs["GRIB2IO_section4"], + da.attrs["GRIB2IO_section5"], + ) + newmsg.data = np.copy(da.values) + + newmsg = newmsg.subset(lats, lons) + + da.attrs["GRIB2IO_section3"] = newmsg.section3 + + mask_lat = (da.latitude >= newmsg.latitudeLastGridpoint) & ( + da.latitude <= newmsg.latitudeFirstGridpoint + ) + mask_lon = (da.longitude >= newmsg.longitudeFirstGridpoint) & ( + da.longitude <= newmsg.longitudeLastGridpoint + ) + + return da.where((mask_lon & mask_lat).compute(), drop=True) diff --git a/tests/test_subset.py b/tests/test_subset.py index 1cda305..f17f8c6 100755 --- a/tests/test_subset.py +++ b/tests/test_subset.py @@ -1,65 +1,86 @@ -import itertools -from pathlib import Path - -import grib2io import pytest import xarray as xr -from numpy.testing import assert_allclose, assert_array_equal +from numpy.testing import assert_array_equal +import grib2io -def _del_list_inplace(input_list, indices): - for index in sorted(indices, reverse=True): - del input_list[index] - return input_list +@pytest.fixture() +def inp_ds(request): + datadir = request.config.rootdir / "tests" / "data" / "gfs_20221107" -def _test_any_differences(da1, da2, atol=0.005, rtol=0): - """Test if two DataArrays are equal, including most attributes.""" - assert_array_equal( - da1.attrs["GRIB2IO_section0"][:-1], da2.attrs["GRIB2IO_section0"][:-1] - ) - assert_array_equal(da1.attrs["GRIB2IO_section1"], da2.attrs["GRIB2IO_section1"]) - assert_array_equal(da1.attrs["GRIB2IO_section2"], da2.attrs["GRIB2IO_section2"]) - assert_array_equal(da1.attrs["GRIB2IO_section3"], da2.attrs["GRIB2IO_section3"]) - assert_array_equal(da1.attrs["GRIB2IO_section4"], da2.attrs["GRIB2IO_section4"]) - skip = [2, 9, 10, 11, 16, 17] - assert_array_equal( - _del_list_inplace(list(da1.attrs["GRIB2IO_section5"]), skip), - _del_list_inplace(list(da2.attrs["GRIB2IO_section5"]), skip), + filters = { + "typeOfFirstFixedSurface": 103, + "valueOfFirstFixedSurface": 2, + "productDefinitionTemplateNumber": 0, + "shortName": "TMP", + } + + ids = xr.open_mfdataset( + [ + datadir / "gfs.t00z.pgrb2.1p00.f009_subset", + datadir / "gfs.t00z.pgrb2.1p00.f012_subset", + ], + combine="nested", + concat_dim="leadTime", + engine="grib2io", + filters=filters, ) - assert_allclose(da1.data, da2.data, atol=atol, rtol=rtol) + yield ids -def test_da_write(tmp_path, request): - """Test writing a single DataArray to a single grib2 message.""" - target_dir = tmp_path / "test_to_grib2" - target_dir.mkdir() - target_file = target_dir / "test_to_grib2_da.grib2" +@pytest.fixture() +def inp_msgs(request): datadir = request.config.rootdir / "tests" / "data" / "gfs_20221107" - with grib2io.open(datadir / "gfs.t00z.pgrb2.1p00.f012_subset") as inp: - print(inp[0].section3) - newmsg = inp[0].subset(lats=(43, 32.7), lons=(117, 79)) + with grib2io.open(datadir / "gfs.t00z.pgrb2.1p00.f012_subset") as imsgs: + yield imsgs + - print(inp[0]) - print(newmsg) - print(newmsg.section0) - print(inp[0].section0) - print(newmsg.section1) - print(inp[0].section1) - print(newmsg.section2) - print(inp[0].section2) - print(newmsg.section3) - print(inp[0].section3) - print(newmsg.section4) - print(inp[0].section4) - print(newmsg.section5) - print(inp[0].section5) +@pytest.mark.parametrize( + "lats, lons, expected_section3", + [ + pytest.param( + (43, 32.7), + (117, 79), + [ + 0, + 380, + 0, + 0, + 0, + 6, + 0, + 0, + 0, + 0, + 0, + 0, + 38, + 10, + 0, + -1, + 43000000, + 79000000, + 48, + 33000000, + 117000000, + 1000000, + 1000000, + 0, + ], + id="subset_1", + ), + ], +) +def test_message_subset(inp_msgs, inp_ds, lats, lons, expected_section3): + """Test subsetting a single DataArray to a single grib2 message.""" + newmsg = inp_msgs[0].subset(lats=lats, lons=lons) + assert_array_equal(newmsg.section3, expected_section3) - print(inp[0].data.shape) - print(newmsg.data.shape) + newds = inp_ds["TMP"].grib2io.subset(lats=lats, lons=lons) + assert_array_equal(newds.attrs["GRIB2IO_section3"], expected_section3) - with grib2io.open(target_file, mode="w") as out: - out.write(newmsg) - assert False + newds = inp_ds.grib2io.subset(lats=lats, lons=lons) + assert_array_equal(newds["TMP"].attrs["GRIB2IO_section3"], expected_section3) From 46d69a7114d52e1b46f3fa10a176812509270db1 Mon Sep 17 00:00:00 2001 From: Timothy Cera Date: Tue, 6 Aug 2024 08:39:02 -0400 Subject: [PATCH 3/4] fixed an introduced bug in unrelated code and reversed some format changes. --- src/grib2io/_grib2io.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/grib2io/_grib2io.py b/src/grib2io/_grib2io.py index c70ac45..4d7e7a1 100644 --- a/src/grib2io/_grib2io.py +++ b/src/grib2io/_grib2io.py @@ -469,20 +469,20 @@ def write(self, msg): msg GRIB2 message objects to write to file. """ - if isinstance(msg, list): + if isinstance(msg,list): for m in msg: self.write(m) return - if issubclass(msg.__class__, _Grib2Message): - if hasattr(msg, "_msg"): + if issubclass(msg.__class__,_Grib2Message): + if hasattr(msg,'_msg'): self._filehandle.write(msg._msg) else: if msg._signature != msg._generate_signature(): msg.pack() self._filehandle.write(msg._msg) else: - if hasattr(msg._data, "filehandle"): + if hasattr(msg._data,'filehandle'): msg._data.filehandle.seek(msg._data.offset) self._filehandle.write(msg._data.filehandle.read(msg.section0[-1])) else: @@ -1680,7 +1680,7 @@ def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out, a,newshp = _adjust_array_shape_for_interp(a,grid_def_in,grid_def_out) # Set lats and lons if stations, else create array for grids. - if grid_def_out.dtn == -1: + if grid_def_out.gdtn == -1: rlat = np.array(grid_def_out.lats,dtype=np.float32) rlon = np.array(grid_def_out.lons,dtype=np.float32) else: From aa20ccfbc956ebcaa89bce8b17f59ed6510c12a6 Mon Sep 17 00:00:00 2001 From: Timothy Cera Date: Thu, 8 Aug 2024 15:29:38 -0400 Subject: [PATCH 4/4] Will only work on regular grids and clarified documentation. --- src/grib2io/_grib2io.py | 60 ++++++++++++++++++++++++++++++++++------- 1 file changed, 51 insertions(+), 9 deletions(-) diff --git a/src/grib2io/_grib2io.py b/src/grib2io/_grib2io.py index 4d7e7a1..da09a48 100644 --- a/src/grib2io/_grib2io.py +++ b/src/grib2io/_grib2io.py @@ -1338,7 +1338,18 @@ def subset(self, lats, lons): """ Return a spatial subset. - Uses the minimum and maximum values in `lats` and `lons`. + Currently only supports regular grids of the following types: + + | Grid Type | gdtn | + | :---: | :---: | + | Latitude/Longitude, Equidistant Cylindrical, or Plate Carree | 0 | + | Rotated Latitude/Longitude | 1 | + | Mercator | 10 | + | Polar Stereographic | 20 | + | Lambert Conformal | 30 | + | Albers Equal-Area | 31 | + | Gaussian Latitude/Longitude | 40 | + | Equatorial Azimuthal Equidistant Projection | 110 | Parameters ---------- @@ -1359,20 +1370,44 @@ def subset(self, lats, lons): The order of the longitudes is not important. The function will determine which is the minimum and maximum. - The longitudes should be in decimal degrees with 0.0 at the prime + GRIB2 longitudes should be in decimal degrees with 0.0 at the prime meridian, positive values increasing eastward to 360. There are no - negative longitudes. West longitudes are converted to east - longitudes by adding 180 to the absolute value of the west - longitude. + negative GRIB2 longitudes. + + The typical west longitudes that start at 0.0 at the prime meridian + and decrease to -180 westward, are converted to GRIB2 longitudes by + '360 - (absolute value of the west longitude)' where typical + eastern longitudes are unchanged as GRIB2 longitudes. Returns ------- subset A spatial subset of a GRIB2 message. """ - if self.gdtn not in [0, 1, 40, 10, 20, 30, 31, 110, 32769]: + if self.gdtn not in [0, 1, 10, 20, 30, 31, 40, 110]: raise ValueError( - "Subset only works for regular lat/lon, Gaussian, mercator, stereographic, lambert conformal, albers equal-area, and azimuthal equidistant grids. Grid Definition Template Numbers of 0, 1, 40, 10, 20, 30, 31, 110, and 32769 are supported." + """ + +Subset only works for + Latitude/Longitude, Equidistant Cylindrical, or Plate Carree (gdtn=0) + Rotated Latitude/Longitude (gdtn=1) + Mercator (gdtn=10) + Polar Stereographic (gdtn=20) + Lambert Conformal (gdtn=30) + Albers Equal-Area (gdtn=31) + Gaussian Latitude/Longitude (gdtn=40) + Equatorial Azimuthal Equidistant Projection (gdtn=110) + +""" + ) + + if self.nx == 0 or self.ny == 0: + raise ValueError( + """ + +Subset only works for regular grids. + +""" ) newmsg = Grib2Message( @@ -1391,6 +1426,8 @@ def subset(self, lats, lons): la2 = np.min(lats) lo2 = np.max(lons) + # Find the indices of the first and last grid points to the nearest + # lat/lon values in the grid. first_lat = np.abs(msglats - la1) first_lon = np.abs(msglons - lo1) max_idx = np.maximum(first_lat, first_lon) @@ -1406,8 +1443,10 @@ def subset(self, lats, lons): setattr(newmsg, "nx", np.abs(first_i[0] - last_i[0])) setattr(newmsg, "ny", np.abs(first_j[0] - last_j[0])) - # Set *LastGridpoint attributes even if only used for gdtn=[0,1,40]. - # This information is used to subset xarray datasets. + # Set *LastGridpoint attributes even if only used for gdtn=[0, 1, 40]. + # This information is used to subset xarray datasets and even though + # unnecessary for some supported grid types, it won't affect a grib2io + # message to set them. setattr(newmsg, "latitudeLastGridpoint", msglats[last_j[0], last_i[0]]) setattr(newmsg, "longitudeLastGridpoint", msglons[last_j[0], last_i[0]]) @@ -1420,6 +1459,9 @@ def subset(self, lats, lons): ].copy(), ) + # Need to set the newmsg._sha1_section3 to a blank string so the grid + # method ignores the cached lat/lon values. This will force the grid + # method to recompute the lat/lon values for the subsetted grid. newmsg._sha1_section3 = "" newmsg.grid()