{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Load Modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from scipy.io import loadmat\n", "import pandas as pd\n", "import glob\n", "import numpy as np\n", "import xarray as xr\n", "import plotly.express as px\n", "import seaborn as sns\n", "import seaborn.objects as so\n", "import sparse\n", "\n", "import matplotlib.pyplot as plt\n", "from scipy.signal import lombscargle\n", "from astropy.timeseries import LombScargle\n", "from joblib import Parallel, delayed\n", "import astropy.units as u\n", "from astropy.visualization import quantity_support\n", "\n", "import re\n", "import os\n", "import continuousanalysis as ca\n", "import importlib as imp\n", "import scipy as sci\n", "import re\n", "import loadcontinuousmatlabfiles as lcm\n", "from seaborn import axes_style\n", "# import holoviews as hv\n", "# from holoviews import opts\n", "# hv.extension('bokeh')\n", "import h5py\n", "import dask\n", "\n", "import pync\n", "import IPython\n", "def ping(message=\"Done with script\"):\n", " # a=str(IPython.extract_module_locals()[1]['__vsc_ipynb_file__'])\n", " pync.notify(message,title=\"Script Done\", sound=\"default\")\n", "h=str(IPython.extract_module_locals()[1]['__vsc_ipynb_file__'])\n", "\n", "path='/Users/ryanmaloney/Documents/Matlab/'\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dask.config.set({\"array.slicing.split_large_chunks\": False})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import .nc file from matlab" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# ls Continuous Data (2hrs)_Batch1_Fly1_Trial1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "path='/Users/ryanmaloney/Documents/Matlab/'\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imp.reload(lcm)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F35_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F5_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F2_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F13_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F31_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F10_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F5_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F13_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F3_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F21_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F22_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F23_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F23_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F20_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F25_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F5_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F28_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F31_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F32_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T37.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F3_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F10_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F32_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F3_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F13_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F12_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F4_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F3_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F38_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F26_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F42_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F25_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F18_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F26_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F40_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F11_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F3_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F19_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F34_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F13_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F37_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F29_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F28_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F31_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F16_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F2_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F29_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F36_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F28_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F12_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F16_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F35_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F34_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F25_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F24_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F38_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F26_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F18_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F19_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F35_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T38.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F27_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F3_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F15_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F14_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F23_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F36_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F17_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F16_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F14_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F2_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F5_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F40_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F27_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F42_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F37_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F19_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F18_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F40_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F24_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F2_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F5_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F25_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F40_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F26_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F38_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F18_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F36_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F37_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F32_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F2_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F34_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F15_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F21_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F5_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F20_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F22_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F23_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F5_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F13_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F33_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F31_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/test_filename.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F30_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F3_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F4_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F10_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F33_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F32_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F12_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F27_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F23_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F22_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F20_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F12_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F13_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F16_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F2_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F10_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F31_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F30_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F9_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F6_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F21_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F20_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F23_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F22_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F1_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F23_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F7_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F22_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F19_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F21_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F7_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F11_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F9_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F1_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F12_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F33_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F9_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F32_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F30_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F13_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F18_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F1_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F42_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F26_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F38_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F22_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F41_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F14_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F34_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F24_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F35_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F17_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F36_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F6_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F1_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F31_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F1_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F17_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F15_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F14_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/my_file.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F8_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F40_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F41_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F25_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F39_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F42_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F15_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F16_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F37_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F16_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F37_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F36_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F28_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F18_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F8_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F42_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F39_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F27_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F26_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F17_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F38_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F25_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F42_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F24_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F41_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F34_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F1_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F10_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F38_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F1_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F25_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F42_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F11_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F37_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F8_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F34_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F7_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F19_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F6_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F36_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F9_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F32_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F9_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F10_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F10_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F13_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F37_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F13_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F32_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F8_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F9_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F6_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F23_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F39_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F20_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F6_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F21_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F13_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F35_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F22_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F28_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F36_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F35_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F19_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F33_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F24_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F27_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F39_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F38_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F3_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F4_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F18_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F3_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F11_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F27_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F40_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F40_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F39_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F25_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F24_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F18_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F19_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F26_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F14_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F29_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F36_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F28_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F14_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F35_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F34_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F40_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F4_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F21_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F22_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F4_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F12_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F2_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F5_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F29_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F18_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F35_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F31_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F12_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F2_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F5_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F11_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F30_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F4_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F10_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F34_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F30_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F28_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F13_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F12_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F5_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F26_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F13_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F30_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F31_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F4_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F3_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F10_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F31_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F30_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F14_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F29_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F23_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F21_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F5_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F23_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F21_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F20_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F23_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F22_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F20_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F14_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F12_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F33_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F32_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F37_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F19_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F18_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F42_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F2_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F38_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F41_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F27_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F31_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F2_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F24_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F40_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F15_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F14_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F3_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F29_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F33_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F3_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F17_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F16_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F29_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F16_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F4_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F34_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F36_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F37_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F42_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F15_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F34_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F3_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F40_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F41_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F24_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F26_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F38_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F20_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F35_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F30_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F19_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F30_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F14_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F36_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F29_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F17_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F17_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F16_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F38_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F27_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F42_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F19_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F22_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F26_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F41_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F22_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F7_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F42_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F25_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F40_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F41_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F42_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F7_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F31_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F37_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F29_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F1_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F6_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F15_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F2_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F34_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F9_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F15_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F14_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F1_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F6_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F21_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F20_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F41_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F23_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F22_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F10_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F12_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F13_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F31_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F30_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F10_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F17_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F33_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F2_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F32_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F6_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F7_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F22_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F21_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F7_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F20_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F30_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F1_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F30_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F1_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F11_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F9_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F10_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F22_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F30_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F17_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F1_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F23_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F9_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F39_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F21_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F37_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F11_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F8_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F12_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F7_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F39_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F27_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F26_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F38_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F18_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F25_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F24_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F37_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F29_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F37_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F36_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F15_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F36_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F11_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F36_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F8_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F14_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F6_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F32_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F24_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F40_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F27_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F9_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F42_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F27_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F39_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F38_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F1_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F41_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F35_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F34_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F13_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F37_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F36_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F28_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F2_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F39_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F27_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F26_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F4_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F3_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F38_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F24_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F18_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F33_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F40_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F11_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F3_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F18_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F25_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F24_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F41_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F39_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F26_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F18_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F19_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F28_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F36_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F14_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F6_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F16_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F40_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F28_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F35_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F14_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F4_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F3_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F21_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F4_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F22_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F3_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F35_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F31_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F29_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F5_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F32_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F2_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F13_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F12_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F30_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F11_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F10_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F5_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F2_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F12_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F16_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F30_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F31_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F28_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F4_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F32_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F13_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F12_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F23_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F26_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F31_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F13_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F31_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F10_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F4_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F2_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F5_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F23_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F20_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F21_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F23_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F5_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F23_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F22_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F20_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F14_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F10_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F33_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F32_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F41_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F5_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F31_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B3_F39_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F27_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F42_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F19_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F18_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F24_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F33_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F29_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F3_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F15_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F29_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F16_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F3_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F37_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F34_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F16_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F34_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F15_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F42_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F25_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F40_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F41_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F3_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F38_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F26_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F18_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F14_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F19_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F30_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F30_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F17_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F16_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F17_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F19_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F1_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B4_F18_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F42_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F38_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F22_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F41_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F26_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F8_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F41_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F25_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F42_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F8_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F39_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F42_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F6_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F1_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F15_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B8_F31_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F28_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F29_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F37_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F6_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F1_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F15_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F9_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F34_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F32_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B7_F21_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B6_F20_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F15_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F23_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F22_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F13_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F10_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T2.nc',\n", " ...]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glob.glob(path+'*.nc')\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "test=xr.load_dataset(path+'CirclingData_2h_B1_F1_T2.nc')\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 40559)\n",
       "Coordinates:\n",
       "  * Fly               (Fly) int8 1\n",
       "  * Batch             (Batch) int8 1\n",
       "  * recording_length  (recording_length) int8 2\n",
       "  * Trial             (Trial) int8 2\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, Batch, Fly, t) float64 5.5 7.4 ... 83.33 83.33\n",
       "    iny               (Trial, Batch, Fly, t) float64 55.61 57.6 ... 22.33 22.33\n",
       "    theta             (Trial, Batch, Fly, t) float64 2.908 2.849 ... -0.5641\n",
       "    r                 (Trial, Batch, Fly, t) float64 0.4525 0.4382 ... 0.4808\n",
       "    direction         (Trial, Batch, Fly, t) float64 nan 0.8083 ... nan nan\n",
       "    speed             (Trial, Batch, Fly, t) float64 nan 27.5 34.21 ... 0.0 0.0\n",
       "    turning           (Trial, Batch, Fly, t) float64 nan nan 0.06746 ... nan nan\n",
       "    angle             (Trial, Batch, Fly, t) float64 nan 0.8917 ... nan nan\n",
       "    timestamps        (Trial, Batch, Fly, t) float64 1.548e+09 ... 1.548e+09
" ], "text/plain": [ "\n", "Dimensions: (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 40559)\n", "Coordinates:\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * recording_length (recording_length) int8 2\n", " * Trial (Trial) int8 2\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, Batch, Fly, t) float64 5.5 7.4 ... 83.33 83.33\n", " iny (Trial, Batch, Fly, t) float64 55.61 57.6 ... 22.33 22.33\n", " theta (Trial, Batch, Fly, t) float64 2.908 2.849 ... -0.5641\n", " r (Trial, Batch, Fly, t) float64 0.4525 0.4382 ... 0.4808\n", " direction (Trial, Batch, Fly, t) float64 nan 0.8083 ... nan nan\n", " speed (Trial, Batch, Fly, t) float64 nan 27.5 34.21 ... 0.0 0.0\n", " turning (Trial, Batch, Fly, t) float64 nan nan 0.06746 ... nan nan\n", " angle (Trial, Batch, Fly, t) float64 nan 0.8917 ... nan nan\n", " timestamps (Trial, Batch, Fly, t) float64 1.548e+09 ... 1.548e+09" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.54780939e+09, 1.54780939e+09, 1.54780940e+09, ...,\n", " 1.54781658e+09, 1.54781658e+09, 1.54781658e+09])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test['timestamps'].values.squeeze()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "test['timestamps'].values=pd.to_datetime(test['timestamps'].values.squeeze(),unit='s', origin='unix').values.reshape(1,1,1,max(test['timestamps'].values.shape))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 40559)\n",
       "Coordinates:\n",
       "  * Fly               (Fly) int8 1\n",
       "  * Batch             (Batch) int8 1\n",
       "  * recording_length  (recording_length) int8 2\n",
       "  * Trial             (Trial) int8 2\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, Batch, Fly, t) float64 5.5 7.4 ... 83.33 83.33\n",
       "    iny               (Trial, Batch, Fly, t) float64 55.61 57.6 ... 22.33 22.33\n",
       "    theta             (Trial, Batch, Fly, t) float64 2.908 2.849 ... -0.5641\n",
       "    r                 (Trial, Batch, Fly, t) float64 0.4525 0.4382 ... 0.4808\n",
       "    direction         (Trial, Batch, Fly, t) float64 nan 0.8083 ... nan nan\n",
       "    speed             (Trial, Batch, Fly, t) float64 nan 27.5 34.21 ... 0.0 0.0\n",
       "    turning           (Trial, Batch, Fly, t) float64 nan nan 0.06746 ... nan nan\n",
       "    angle             (Trial, Batch, Fly, t) float64 nan 0.8917 ... nan nan\n",
       "    timestamps        (Trial, Batch, Fly, t) datetime64[ns] 2019-01-18T11:03:...
" ], "text/plain": [ "\n", "Dimensions: (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 40559)\n", "Coordinates:\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * recording_length (recording_length) int8 2\n", " * Trial (Trial) int8 2\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, Batch, Fly, t) float64 5.5 7.4 ... 83.33 83.33\n", " iny (Trial, Batch, Fly, t) float64 55.61 57.6 ... 22.33 22.33\n", " theta (Trial, Batch, Fly, t) float64 2.908 2.849 ... -0.5641\n", " r (Trial, Batch, Fly, t) float64 0.4525 0.4382 ... 0.4808\n", " direction (Trial, Batch, Fly, t) float64 nan 0.8083 ... nan nan\n", " speed (Trial, Batch, Fly, t) float64 nan 27.5 34.21 ... 0.0 0.0\n", " turning (Trial, Batch, Fly, t) float64 nan nan 0.06746 ... nan nan\n", " angle (Trial, Batch, Fly, t) float64 nan 0.8917 ... nan nan\n", " timestamps (Trial, Batch, Fly, t) datetime64[ns] 2019-01-18T11:03:..." ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# test=test.set_coords(['timestamp', 'Fly', 'Batch'])\n", "# test.unstack(sparse=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "test_sparse=test.unstack(sparse=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 40559)\n",
       "Coordinates:\n",
       "  * Fly               (Fly) int8 1\n",
       "  * Batch             (Batch) int8 1\n",
       "  * recording_length  (recording_length) int8 2\n",
       "  * Trial             (Trial) int8 2\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, Batch, Fly, t) float64 5.5 7.4 ... 83.33 83.33\n",
       "    iny               (Trial, Batch, Fly, t) float64 55.61 57.6 ... 22.33 22.33\n",
       "    theta             (Trial, Batch, Fly, t) float64 2.908 2.849 ... -0.5641\n",
       "    r                 (Trial, Batch, Fly, t) float64 0.4525 0.4382 ... 0.4808\n",
       "    direction         (Trial, Batch, Fly, t) float64 nan 0.8083 ... nan nan\n",
       "    speed             (Trial, Batch, Fly, t) float64 nan 27.5 34.21 ... 0.0 0.0\n",
       "    turning           (Trial, Batch, Fly, t) float64 nan nan 0.06746 ... nan nan\n",
       "    angle             (Trial, Batch, Fly, t) float64 nan 0.8917 ... nan nan\n",
       "    timestamps        (Trial, Batch, Fly, t) datetime64[ns] 2019-01-18T11:03:...
" ], "text/plain": [ "\n", "Dimensions: (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 40559)\n", "Coordinates:\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * recording_length (recording_length) int8 2\n", " * Trial (Trial) int8 2\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, Batch, Fly, t) float64 5.5 7.4 ... 83.33 83.33\n", " iny (Trial, Batch, Fly, t) float64 55.61 57.6 ... 22.33 22.33\n", " theta (Trial, Batch, Fly, t) float64 2.908 2.849 ... -0.5641\n", " r (Trial, Batch, Fly, t) float64 0.4525 0.4382 ... 0.4808\n", " direction (Trial, Batch, Fly, t) float64 nan 0.8083 ... nan nan\n", " speed (Trial, Batch, Fly, t) float64 nan 27.5 34.21 ... 0.0 0.0\n", " turning (Trial, Batch, Fly, t) float64 nan nan 0.06746 ... nan nan\n", " angle (Trial, Batch, Fly, t) float64 nan 0.8917 ... nan nan\n", " timestamps (Trial, Batch, Fly, t) datetime64[ns] 2019-01-18T11:03:..." ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_sparse" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGdCAYAAADzOWwgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwz0lEQVR4nO3df3hU5Z3//9fkx0wSEoYkhMRokKipK4JV0YsVbQWBoC0i5aqsi6XZb6mLotGIFJfSKnpdJi1WYBcMSj8usNKI7bZU/miR4I9YLnQl0VRBSy8rLSAkGTBOfhgnIXO+f+AcZ5JJSEKSc5LzfFzXXDBn7pm85yTnzGvuc59zuwzDMAQAAOBgMVYXAAAAYDUCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcLw4qwsYKoLBoI4fP66UlBS5XC6rywEAAD1gGIYaGxuVnZ2tmJiu+4EIRD10/Phx5eTkWF0GAADog6NHj+qCCy7o8nECUQ+lpKRIOrNCR44caXE1AACgJxoaGpSTk2N+jneFQNRDocNkI0eOJBABADDEnG24C4OqAQCA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4xGIAACA4zHbPQBHCgaD8vl8kqSMjAzFxPD9EHAyAhEAR/L5fCooLZdhBPXU/KuVkZFBMAIcjC0fgGMlpKTK5YpRUVmlCkrLzR4jAM5DDxEAx3Mnj5LbHW91GQAsRA8RAABwPAIRAABwPAIRAABwPAIRAABwPAZVA4Ak48vrEgWDQUlSZmYmp+ADDkIgAgBJgWa/isoq1R5o1um2Nv3vyn9VZmam1WUBGCQEIgD4kjt5lILxcYppbbO6FACDjP5gAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeJxlBsBRgl9eb8jn80mG1dUAsAtLe4jeeOMN3XrrrcrOzpbL5dLvf//7iMcNw9CqVauUnZ2txMRETZ06VQcPHoxoEwgEVFhYqNGjR2vEiBGaM2eOjh07FtGmvr5eCxculNfrldfr1cKFC/XZZ58N8LsDYEc+n08FpeUq3Fyh1jZOrwdwhqWBqLm5WV//+te1YcOGqI+vXr1aa9as0YYNG7R//35lZWVp5syZamxsNNsUFRVpx44d2r59u/bu3aumpibNnj1b7e3tZpsFCxaourpau3bt0q5du1RdXa2FCxcO+PsDYE8JKanyJHutLgOAjVh6yOyWW27RLbfcEvUxwzC0bt06rVy5UvPmzZMkbd26VZmZmSorK9PixYvl9/v13HPP6fnnn9eMGTMkSdu2bVNOTo727NmjWbNm6cMPP9SuXbv01ltvafLkyZKkX/7yl7ruuut06NAhXXrppYPzZgEAgG3ZdlD14cOHVVNTo/z8fHOZx+PRjTfeqH379kmSqqqq1NbWFtEmOztbEyZMMNu8+eab8nq9ZhiSpH/+53+W1+s120QTCATU0NAQcQMAAMOTbQNRTU2NJHWaSygzM9N8rKamRm63W6mpqd22GTNmTKfXHzNmjNkmmpKSEnPMkdfrVU5Ozjm9HwAAYF+2DUQhLpcr4r5hGJ2WddSxTbT2Z3udFStWyO/3m7ejR4/2snIAADBU2DYQZWVlSVKnXpy6ujqz1ygrK0utra2qr6/vtk1tbW2n1/f5fN3OZO3xeDRy5MiIG4ChKxgMqra2ltPtAURl20CUm5urrKwslZeXm8taW1tVUVGhKVOmSJImTZqk+Pj4iDYnTpzQgQMHzDbXXXed/H6/3n77bbPN//3f/8nv95ttAAx/nG4PoDuWnmXW1NSkjz76yLx/+PBhVVdXKy0tTWPHjlVRUZGKi4uVl5envLw8FRcXKykpSQsWLJAkeb1eLVq0SA899JDS09OVlpamZcuWaeLEieZZZ5dddpluvvlm3XXXXXr22WclSf/+7/+u2bNnc4YZ4DAJKalnbwTAkSwNRJWVlZo2bZp5f+nSpZKkgoICbdmyRcuXL1dLS4uWLFmi+vp6TZ48Wbt371ZKSor5nLVr1youLk7z589XS0uLpk+fri1btig2NtZs86tf/Ur333+/eTbanDlzurz2EQAAcB6XYRgcTe+BhoYGeb1e+f1+xhMBQ1Btba0WP1+pLxrrFeNJVjDQ1OW/ba1tKiv6VrfjDAEMDT39/LbtGCIAAIDBQiACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACMOwFg0H5fD6JiYoAdIFABGDY8/l8Wrxhp1rb2qwuBYBNWTrbPQAMFndSzydlNowve5QkZWRkKCaG747AcMdWDgAdtH3eqKKyShWUlpvBCMDwRg8RAEThTh4ltzve6jIADBJ6iAAAgOMRiAAAgONxyAwAumAEGVwNOAVbNwB0IdDsZ3A14BD0EAFANxhcDTgDPUQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDx4qwuAAAGSjAYlM/nk8/nkwyrqwFgZwQiAMOWz+dTQWm5Ak1+nW4/bXU5AGyMQ2YAhrWElFR5kr1WlwHA5ghEAADA8QhEAADA8QhEAADA8QhEAADA8QhEAADA8TjtHgDOwvjyekaSlJGRoZgYvksCww1bNQCcRaDZr6KyShWUlpvBCMDwYutAdPr0af3kJz9Rbm6uEhMTddFFF+nxxx9XMBg02xiGoVWrVik7O1uJiYmaOnWqDh48GPE6gUBAhYWFGj16tEaMGKE5c+bo2LFjg/12AAxh7uRRSkhJtboMAAPE1oHo5z//uZ555hlt2LBBH374oVavXq0nn3xS69evN9usXr1aa9as0YYNG7R//35lZWVp5syZamxsNNsUFRVpx44d2r59u/bu3aumpibNnj1b7e3tVrwtAABgM7YeQ/Tmm2/qtttu07e//W1J0rhx4/TCCy+osrJS0pneoXXr1mnlypWaN2+eJGnr1q3KzMxUWVmZFi9eLL/fr+eee07PP/+8ZsyYIUnatm2bcnJytGfPHs2aNcuaNwcAAGzD1j1EN9xwg1555RX99a9/lST9+c9/1t69e/Wtb31LknT48GHV1NQoPz/ffI7H49GNN96offv2SZKqqqrU1tYW0SY7O1sTJkww2wAAAGezdQ/Rww8/LL/fr3/6p39SbGys2tvb9cQTT+hf//VfJUk1NTWSpMzMzIjnZWZm6h//+IfZxu12KzU1tVOb0POjCQQCCgQC5v2GhoZ+eU8AAMB+bN1D9OKLL2rbtm0qKyvTO++8o61bt+oXv/iFtm7dGtHO5XJF3DcMo9Oyjs7WpqSkRF6v17zl5OT0/Y0AAABbs3Ug+tGPfqT/+I//0B133KGJEydq4cKFevDBB1VSUiJJysrKkqROPT11dXVmr1FWVpZaW1tVX1/fZZtoVqxYIb/fb96OHj3an28NAADYiK0D0eeff97pAmixsbHmafe5ubnKyspSeXm5+Xhra6sqKio0ZcoUSdKkSZMUHx8f0ebEiRM6cOCA2SYaj8ejkSNHRtwAAMDwZOsxRLfeequeeOIJjR07VpdffrneffddrVmzRj/4wQ8knTlUVlRUpOLiYuXl5SkvL0/FxcVKSkrSggULJEler1eLFi3SQw89pPT0dKWlpWnZsmWaOHGiedYZAABwNlsHovXr1+unP/2plixZorq6OmVnZ2vx4sV65JFHzDbLly9XS0uLlixZovr6ek2ePFm7d+9WSkqK2Wbt2rWKi4vT/Pnz1dLSounTp2vLli2KjY214m0BAACbcRmGYVhdxFDQ0NAgr9crv9/P4TNgiKitrdXi5yv1RWO9mutPKiVrnIKBJsV4krv9t6u28XGxeuJbucrIyGBOM2CI6OnnN1szgGEnGAyqtrb2zLxj/fiVjznNgOHL1ofMAKAvfD6fCkrLFWjyKzH9/H59bXfyKLnd8f36mgCsRyACMCwxESuA3uCQGQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcDwCEQAAcLw4qwsAgP4SDAbl8/nk8/kkw+pqAAwlBCIAw4bP51NBabkCTX4lpp9vdTkAhhACEYBhJSEl1eoSAAxBjCECAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACgF4yvrwAZDAYtLoUAP2EQAQAvRRo9uueTXvOXBEbwLBAIAKAPnCP8FpdAoB+RCACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACAACORyACAACOF2d1AQBwroJfTrbq8/kkw+pqAAxFBCIAQ57P51NBabkCTX4lpp9vdTkAhiACEYBhISEldVB/nmEEzdnuMzIyFBPDCARgKGMLBoA+aPu8UUVllSooLTeDEYChq0+B6KKLLtKpU6c6Lf/ss8900UUXnXNR4T755BN973vfU3p6upKSknTllVeqqqrKfNwwDK1atUrZ2dlKTEzU1KlTdfDgwYjXCAQCKiws1OjRozVixAjNmTNHx44d69c6ATiPO3nUoPdMARgYfQpEf//739Xe3t5peSAQ0CeffHLORYXU19fr+uuvV3x8vP74xz/qgw8+0FNPPaVRo0aZbVavXq01a9Zow4YN2r9/v7KysjRz5kw1NjaabYqKirRjxw5t375de/fuVVNTk2bPnh31PQAAAOfp1RiinTt3mv9/+eWX5fV6zfvt7e165ZVXNG7cuH4r7uc//7lycnK0efNmc1n46xuGoXXr1mnlypWaN2+eJGnr1q3KzMxUWVmZFi9eLL/fr+eee07PP/+8ZsyYIUnatm2bcnJytGfPHs2aNavf6gUAAENTrwLR3LlzJUkul0sFBQURj8XHx2vcuHF66qmn+q24nTt3atasWbr99ttVUVGh888/X0uWLNFdd90lSTp8+LBqamqUn59vPsfj8ejGG2/Uvn37tHjxYlVVVamtrS2iTXZ2tiZMmKB9+/Z1GYgCgYACgYB5v6Ghod/eFwAAsJdeHTILBoMKBoMaO3as6urqzPvBYFCBQECHDh3S7Nmz+624jz/+WBs3blReXp5efvll3X333br//vv1P//zP5KkmpoaSVJmZmbE8zIzM83Hampq5Ha7lZqa2mWbaEpKSuT1es1bTk5Ov70vAABgL3067f7w4cP9XUdUwWBQ11xzjYqLiyVJV111lQ4ePKiNGzfq+9//vtnO5XJFPM8wjE7LOjpbmxUrVmjp0qXm/YaGBkIRAADDVJ+vQ/TKK6/olVdeMXuKwv33f//3ORcmSeedd57Gjx8fseyyyy7Tb3/7W0lSVlaWpDO9QOedd57Zpq6uzuw1ysrKUmtrq+rr6yN6ierq6jRlypQuf7bH45HH4+mX9wEAAOytT2eZPfbYY8rPz9crr7yikydPqr6+PuLWX66//nodOnQoYtlf//pXXXjhhZKk3NxcZWVlqby83Hy8tbVVFRUVZtiZNGmS4uPjI9qcOHFCBw4c6DYQAQAA5+hTD9EzzzyjLVu2aOHChf1dT4QHH3xQU6ZMUXFxsebPn6+3335bmzZt0qZNmySdOVRWVFSk4uJi5eXlKS8vT8XFxUpKStKCBQskSV6vV4sWLdJDDz2k9PR0paWladmyZZo4caJ51hkAAHC2PgWi1tbWQeldufbaa7Vjxw6tWLFCjz/+uHJzc7Vu3TrdeeedZpvly5erpaVFS5YsUX19vSZPnqzdu3crJSXFbLN27VrFxcVp/vz5amlp0fTp07VlyxbFxsYO+HsAAAD25zIMo9dzQz/88MNKTk7WT3/604GoyZYaGhrk9Xrl9/s1cuRIq8sBEKa2tlaLn6/UF431ivEkKxho6vRvc/1JpWSNi/pYX9qG2rjd8Xp24TWdznYFYA89/fzuUw/RF198oU2bNmnPnj264oorFB8fH/H4mjVr+vKyANArweCZCVZ9Pp/U6692APCVPgWi9957T1deeaUk6cCBAxGPne10dwDoLz6fTwWl5Qo0+ZWYfr7V5QAYwvoUiF577bX+rgMA+oTJVQH0hz6ddg8AADCc9KmHaNq0ad0eGnv11Vf7XBAAAMBg61MgCo0fCmlra1N1dbUOHDjQadJXAAAAu+tTIFq7dm3U5atWrVJTU9M5FQQAADDY+nUM0fe+971+m8cMAABgsPRrIHrzzTeVkJDQny8JAAAw4Pp0yGzevHkR9w3D0IkTJ1RZWemoq1cDAIDhoU+ByOv1RtyPiYnRpZdeqscff1z5+fn9UhgAAMBg6VMg2rx5c3/XAQBDkvHl9CGSlJGRoZgYLu8GDEV9CkQhVVVV+vDDD+VyuTR+/HhdddVV/VUXAAwJgWa/isoqFRcfp61LZjLJKzBE9SkQ1dXV6Y477tDrr7+uUaNGyTAM+f1+TZs2Tdu3b1dGRkZ/1wkAtuVOHqX4uFh6ioAhrE9bbGFhoRoaGnTw4EF9+umnqq+v14EDB9TQ0KD777+/v2sEANsL9RQVlJabwQjA0NGnHqJdu3Zpz549uuyyy8xl48eP19NPP82gagCO5U4eJbc73uoyAPRBn3qIgsGg4uM7b/Tx8fEKBoPnXBQAAMBg6lMguummm/TAAw/o+PHj5rJPPvlEDz74oKZPn95vxQEAAAyGPgWiDRs2qLGxUePGjdPFF1+sSy65RLm5uWpsbNT69ev7u0YAAIAB1acxRDk5OXrnnXdUXl6uv/zlLzIMQ+PHj9eMGTP6uz4AAIAB16seoldffVXjx49XQ0ODJGnmzJkqLCzU/fffr2uvvVaXX365/vSnPw1IoQAAAAOlV4Fo3bp1uuuuuzRy5MhOj3m9Xi1evFhr1qzpt+IAoCvB0BWiDasrATAc9CoQ/fnPf9bNN9/c5eP5+fmqqqo656IA4Gx8Pp8Wb9ip1rY2q0sBMAz0KhDV1tZGPd0+JC4ujguSARg07qTOvdUA0Be9CkTnn3++3n///S4ff++993Teeeedc1EAAACDqVeB6Fvf+pYeeeQRffHFF50ea2lp0aOPPqrZs2f3W3EAAACDoVen3f/kJz/R7373O33ta1/Tfffdp0svvVQul0sffvihnn76abW3t2vlypUDVSsAAMCA6FUgyszM1L59+3TPPfdoxYoVMowzp3e4XC7NmjVLpaWlyszMHJBCAQAABkqvL8x44YUX6g9/+IPq6+v10UcfyTAM5eXlKTU1dSDqAwAAGHB9ulK1JKWmpuraa6/tz1oAAAAs0ae5zAAAAIYTAhEAAHA8AhEAAHA8AhEAAHC8Pg+qBgArhCZ1ZWJXAP2JQARgSPH5fCooLVegya/T7aetLgfAMMEhMwBDTkJKqjzJXqvLADCMEIgAAIDjEYgAAIDjEYgAAIDjEYgAAIDjEYgAAIDjEYgAAIDjEYgAAIDjEYgAAIDjcaVqAOhHxpdTi0hSRkaGYmL43gkMBWypANCPAs1+FZVVqqC03AxGAOyPHiIA6Gfu5FFyu+OtLgNAL9BDBAAAHI9ABAAAHI9ABAAAHI9ABAAAHI9ABAAAHI9ABAAAHI9ABAAAHG9IBaKSkhK5XC4VFRWZywzD0KpVq5Sdna3ExERNnTpVBw8ejHheIBBQYWGhRo8erREjRmjOnDk6duzYIFcPAADsasgEov3792vTpk264oorIpavXr1aa9as0YYNG7R//35lZWVp5syZamxsNNsUFRVpx44d2r59u/bu3aumpibNnj1b7e3tg/02AACADQ2JQNTU1KQ777xTv/zlL5WammouNwxD69at08qVKzVv3jxNmDBBW7du1eeff66ysjJJkt/v13PPPaennnpKM2bM0FVXXaVt27bp/fff1549e6x6SwAAwEaGRCC699579e1vf1szZsyIWH748GHV1NQoPz/fXObxeHTjjTdq3759kqSqqiq1tbVFtMnOztaECRPMNtEEAgE1NDRE3AAAwPBk+7nMtm/frnfeeUf79+/v9FhNTY0kKTMzM2J5Zmam/vGPf5ht3G53RM9SqE3o+dGUlJToscceO9fyAQDAEGDrHqKjR4/qgQce0LZt25SQkNBlO5fLFXHfMIxOyzo6W5sVK1bI7/ebt6NHj/aueAAAMGTYOhBVVVWprq5OkyZNUlxcnOLi4lRRUaH/+q//UlxcnNkz1LGnp66uznwsKytLra2tqq+v77JNNB6PRyNHjoy4AbBOMBhUbW2tfD6fZFhdDYDhxtaBaPr06Xr//fdVXV1t3q655hrdeeedqq6u1kUXXaSsrCyVl5ebz2ltbVVFRYWmTJkiSZo0aZLi4+Mj2pw4cUIHDhww2wCwP5/Pp4LSchVurlBrW5vV5QAYZmw9higlJUUTJkyIWDZixAilp6eby4uKilRcXKy8vDzl5eWpuLhYSUlJWrBggSTJ6/Vq0aJFeuihh5Senq60tDQtW7ZMEydO7DRIG4C9JaSknr0RAPSBrQNRTyxfvlwtLS1asmSJ6uvrNXnyZO3evVspKSlmm7Vr1youLk7z589XS0uLpk+fri1btig2NtbCygH0RDAYlM/n41AZgAE15ALR66+/HnHf5XJp1apVWrVqVZfPSUhI0Pr167V+/fqBLQ5AvwsdKgs0+ZWYfr7V5QAYpoZcIALgPEPxUJnxZc+WJGVkZCgmxtZDNgHHYwsFgAEQaParqKxSBaXlZjACYF/0EAHAAHEnj5LbHW91GQB6gB4iAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeAQiAADgeMx2D8CWgsGgfD6ffD6fZFhdDYDhjkAEwJZ8Pp8KSssVaPIrMf18q8sBMMxxyAyAbSWkpMqT7LW6jHNifNnTFQwGrS4FQDcIRAAwgALNft2zac+ZQ38AbItABAADzD1iaPdyAU5AIAIAAI5HIAIAAI5HIAIAAI5HIAIAAI5HIAIAAI5HIAIAAI5HIAIAAI5HIAIAAI7HXGYAbIVJXQFYgUAEwFaY1BWAFQhEACwX6hUK/T8hJdXiigA4DYEIgOVCvUKS9Ivbr7S2GACORCACYAvDuVfIML7qAcvIyFBMDOezAHbDVgkAA6zt80YVlVWqoLTcDEYA7IUeIgAYBO7kUXK7460uA0AX6CECAACORyACAACORyACYBtGMKiTJ09yQUYAg45ABMA2As1+PbztT2pta7O6FAAOQyACYCvupBSrSwDgQAQiAADgeAQiAADgeAQiAADgeFyYEQAGiRFkCg/ArtgaAWCQBJr9TOEB2BQ9RAAwiJjCA7AneogAAIDj0UMEAIOMsUSA/bAVAsAgYywRYD/0EAGABRhLBNgLPUQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxCEQAAMDxbB2ISkpKdO211yolJUVjxozR3LlzdejQoYg2hmFo1apVys7OVmJioqZOnaqDBw9GtAkEAiosLNTo0aM1YsQIzZkzR8eOHRvMtwIAAGzM1oGooqJC9957r9566y2Vl5fr9OnTys/PV3Nzs9lm9erVWrNmjTZs2KD9+/crKytLM2fOVGNjo9mmqKhIO3bs0Pbt27V37141NTVp9uzZam9vt+JtAQAAm7H1dYh27doVcX/z5s0aM2aMqqqq9M1vflOGYWjdunVauXKl5s2bJ0naunWrMjMzVVZWpsWLF8vv9+u5557T888/rxkzZkiStm3bppycHO3Zs0ezZs0a9PcFAADsxdY9RB35/X5JUlpamiTp8OHDqqmpUX5+vtnG4/Hoxhtv1L59+yRJVVVVamtri2iTnZ2tCRMmmG2iCQQCamhoiLgBAIDhacgEIsMwtHTpUt1www2aMGGCJKmmpkaSlJmZGdE2MzPTfKympkZut1upqaldtommpKREXq/XvOXk5PTn2wEAADYyZALRfffdp/fee08vvPBCp8dcLlfEfcMwOi3r6GxtVqxYIb/fb96OHj3at8IBAIDtDYlAVFhYqJ07d+q1117TBRdcYC7PysqSpE49PXV1dWavUVZWllpbW1VfX99lm2g8Ho9GjhwZcQMAAMOTrQORYRi677779Lvf/U6vvvqqcnNzIx7Pzc1VVlaWysvLzWWtra2qqKjQlClTJEmTJk1SfHx8RJsTJ07owIEDZhsA1ggGg6qtrT0z47thdTUAnMzWZ5nde++9Kisr00svvaSUlBSzJ8jr9SoxMVEul0tFRUUqLi5WXl6e8vLyVFxcrKSkJC1YsMBsu2jRIj300ENKT09XWlqali1bpokTJ5pnnQEYXMFgUD6fTz6fT8t+Xa1As1+J6edbXdagM75cD5KUkZGhmBhbf0cFhjVbB6KNGzdKkqZOnRqxfPPmzfq3f/s3SdLy5cvV0tKiJUuWqL6+XpMnT9bu3buVkpJitl+7dq3i4uI0f/58tbS0aPr06dqyZYtiY2MH660ACOPz+VRQWq5A05kg5Ol+yN+wFWj2q6isUnHxcdq6ZGa3h/EBDCxbByLDOHsfusvl0qpVq7Rq1aou2yQkJGj9+vVav359P1YH4FwkpKSevZEDuJNHye2Ot7oMwPHonwUAAI5n6x4iAHACxhIB1mOrAwCLhcYSFZSWm8EIwOCihwgAbICxRIC16CECAACORyACAACORyACAACORyACAJsInW0WDAatLgVwHAIRgEERmreMD/uuBZr9umfTHs40AyxAIAIwKHw+n+5Y/Rs+7M/CPcJrdQmAI3HaPYBB405K+SoQMbt9VIbBRRoBKxCIAAyo8JntW5saVVRWqfZAsyNnt++Jts8bmfAVsACBCMCACp/Z/nT7aSUmj1Iwnl1Pd7hIIzD46IsFMCBCg6h9Pp8SklPlSWZsDAD74msagAER3jPE4TEAdkcgAjBgElJSrS5h2AiNxZIYbA0MBAIRANhY+KD0Zb+ullxisDUwAAhEAGBjHQ89MtgaGBgEIgCwISPsEFlCMocegYFGIAIAGwo0+7lmEzCICEQAYFNurtkEDBpOUwAAAI5HIAIAAI5HXywADCEG1yMCBgRbEoB+FT5lBzPa97/QYOuC0nIzGAE4d/QQATgnHa+gzJQdA4/JX4H+RyACcE5CAcgwgnpq/tWSuG4OgKGHQATgnCWkpOqLxnqumwNgyCIQAei18MNkwWDQXM51cwZPaHA1A6uB/sFWBKDXQofJCkrLderUKavLcaRAs1/3bNrDwGqgn/BVDkCfJKQwTshq7hFeq0sAhg0CEQAMUYbBNYmA/sLWAwBDVNvnjVyTCOgnBCIAGMLcyaO6PHwZukhm+MB3ANERiABgmAgFoFAI8vl8umP1b+g9AnqAMUQAeiz0IWtOy+GyuiKEC539J0lbl8yUJHkYeA30CIEIQI91nJaD6SPsh7P/gL4hEAHolfAPXCMY1MmTJ5nE1WJG2IUy6bkD+oZABKBLoUNkoUG5p06digg/gWa/Ht5WrfSLJlpUIaQzv4fwaVPi42IjA5I6T8LLKfpAJAIRgE7Cxwot+3W1As1+xXpGRJ2nzJ2UYlGVCBc+bUp4QIpxJ0nqPL4oIyMjIiCF2hCW4FQEIgCddBwr5HFJMZ5k5ikbQkIBqa21zVwWfrgz2gDsO1b/RtuX367MzMzBLRawAfZugMN1dSiFwbnDQ/jVrDuOL+r4O+aMNDgZgQhwuC4PpTBQelgIXc06dLiTMwOB6DhQDEAJKalmb4HP59PiDTvV2tZ2lmdhqHAnj5Inmd4foDv0EAGQFHnqtjtxpMXVAMDgIhABkBR5ZtLp9tNWl4MB0t01i8LHG3G2GZyGQATAFDoz6XT9SatLwQDpeM2i8DFFrc1nxhvFxsXoqflXKyMjIyIYcS0jDGcEIsChOs1LBscIv2ZRxx4jd/IoBQNNKiqrVFx8nLYumWmeht9xAD6n52M4IRABw0hvvsF3vNYQnKmrQ6Xu5FFRz0jjcgwYrghEwDDQ8crScnX+Bt8xLEl8uOGMsx0q7dSbyFxpGIYIRMAw0LG3JzSXVWgOslCb5f/7ngwF9dT8q88s5FAZumFEmcIl/O8rfMoPiXFFGNoIRMAQ03HC1ZiYGAWDwYjenvDDIKE5yD73f6r0iyaa40OizUsGhOs4ANvj+mr5PZv26H9XnglEBaXlMowzQTs9PV3Smb/LrgISg7NhRwQii3W1Y2CHMXRFOzQVup+enn5mxnid/fcdLfiEriId6g2K9YxQbFyMVtw0tlNvT+gwSGgOstNhF1oMH1QLdKervxV32DQfCSmp+qKxPiKEh85UGz9+vKTIXqTeDM5mXzh09eR3Z6ffL3tEi3U1bUKoi9pQUE9+90rzD6WvfzBW/NGdy8/szXODwaBqa2sldf+ttC/68h5Cv9PQN2ZJ5u9yxU1j9bNXj5ljfDr+vsOXf/DBBxEzzYc+YCQpIflMb1CMJ1nBQJMe3vYnpV80sV/eM9ATneZIU4cQHmjq1IskfTWRbHiPZmg7i7aN9SQ82elDFT0b0xhqF9rPddVmMBGIbCDaDNShsSDBQJPu+q+XlDImp9MpsCE92Rmc6+my5xIM+vIzu3tux56TU6dOafH6nUpMz+6X66eEtw0Gg/r/nnmlUx1ne72O35hDv8tQcAmNwehqbIbP59Pi9TvlvXD8VzPNd3Ooy52U0uN1C/SHjnOkRROflGJuJwnJqZ1CVOi+z+fTfc+9qg2Lbuq07UpdD/7v6oPXnI9PfQtI3fXcd/Xlq6se3fCf3ZfX7S/d9Vz3pvemJ0c1QvvN0OdY6GzFjs8NTRPkHTveFnPsOSoQlZaW6sknn9SJEyd0+eWXa926dfrGN75hdVmddNz43UkpEafARvujCu+RiNZFHXpdo5sP8u7++MN7rKIFjmjP78nP7Olzw5/X8ZBRe6BZMZ6kiOundAxG4QFr893TzfcmfbUDCr12+M71F7dfGXVn3HGdZ2RkmIfDwq/r0/FwQyi4dDc2I7Q8xpPU6edyqAt2cra/x46hqWOoD7/f3h6Meu2jkGj7ka5OJgjfhkPbe/jzwkXrnerqC5nP59N3H9usxLTsLq/RFNovRXsfPXndrr7Uhdcaei+h53Z1P/y5XfVcd9czE63esx3ViLbf7Dg4P/zEDjtNE+SYPeuLL76ooqIilZaW6vrrr9ezzz6rW265RR988IHGjh1rdXmdL6ffTZtof1QJyan6oqledz+7W88sPtM+/I89JPSBG+34frTQ0zHpdwwcoQGUodeIdhZTx5+Znp6uYDAYsbGeOnWqy3pD7yn0s06dOhV5yKjD6cLRglFoHRlGUIcOHdLPXj1mHooK7bgkddq5njx5MuKbbHivVGidh35O6HBYqLfnbLocm8HVojGMdPpS0MX90/Un5U4e1ekMyVOnTnXaj4SGEYTvC6TOXzTi42LN7b3jlznpq/1e4X+/ru0P3x7x4R7eoxVeiztxZNQvqKHnSGf2S253fETPT7RLXYQ/N/S6vd3HdtxvhkJPaB2FdNxnha+j2traiDNSw78kRqs3fN1E6+UO7Te7+r2EB2E7TRPkmEC0Zs0aLVq0SD/84Q8lSevWrdPLL7+sjRs3qqSkxOLqOv/B9KRNtEMoLldMpz/2qMf5A02dwlPoDzp8g1xx09iIHU7483tzFlPH53zu/1QpY3Iint9VveHvKbzt2XT8mV8dtqpW+kUTzUNR4T+z48411Dba++343kKHwzxcowXos2hnSHbc1kLDCKIePg4LXNG24fAvSaH9nsudGPXDPdp2H/oA7+qSBCFGMNgpjJ15QFHDRMeLYvZmH9txH/ZFU33EOoq6HsPWUce24euoY72hQ/kdawjv5Q6t865+L+H37fTFz2UYxrC/Eklra6uSkpL0m9/8Rt/5znfM5Q888ICqq6tVUVHR6TmBQECBQMC87/f7NXbsWB09elQjR/ZfF19dXZ3u/n+vRRz+6fhvS0O9kjMuiPpYT9qePn1awdbPlZh6Xqe28UkpUR8L/dtQe1SpY/+pzz+7L2071tuT1x3IddQf72mgXm+o/GzqdGaddl1H0fYxZ9sXRnu9rvYb0fafHdv2936uL6/Xm3UUut9+un1Afu9x8XF65ofTNGbMmH77fA1paGhQTk6OPvvsM3m93i7bOaKH6OTJk2pvb+90jDQzM1M1NTVRn1NSUqLHHnus0/KcnJwBqREAACfL+/nAvn5jYyOBKMTlijyWYRhGp2UhK1as0NKlS837wWBQn376qdLT07t8zkAKJdz+7qEaLlg/3WP9dI/10z3WT9dYN92zw/oxDEONjY3Kzs7utp0jAtHo0aMVGxvbqTeorq6uy1PBPR6PPB5PxLJRo0YNVIk9NnLkSDa6brB+usf66R7rp3usn66xbrpn9frprmcoxBFXr3K73Zo0aZLKy8sjlpeXl2vKlCkWVQUAAOzCET1EkrR06VItXLhQ11xzja677jpt2rRJR44c0d133211aQAAwGKOCUT/8i//olOnTunxxx/XiRMnNGHCBP3hD3/QhRdeaHVpPeLxePToo492OoyHM1g/3WP9dI/10z3WT9dYN90bSuvHEafdAwAAdMcRY4gAAAC6QyACAACORyACAACORyACAACORyAaoubMmaOxY8cqISFB5513nhYuXKjjx49bXZbl/v73v2vRokXKzc1VYmKiLr74Yj366KNqbW21ujTbeOKJJzRlyhQlJSXZ4mKjVistLVVubq4SEhI0adIk/elPf7K6JNt44403dOuttyo7O1sul0u///3vrS7JNkpKSnTttdcqJSVFY8aM0dy5c3Xo0CGry7KNjRs36oorrjAvyHjdddfpj3/8o9VldYtANERNmzZNv/71r3Xo0CH99re/1d/+9jd997vftbosy/3lL39RMBjUs88+q4MHD2rt2rV65pln9OMf/9jq0myjtbVVt99+u+655x6rS7Hciy++qKKiIq1cuVLvvvuuvvGNb+iWW27RkSNHrC7NFpqbm/X1r39dGzZssLoU26moqNC9996rt956S+Xl5Tp9+rTy8/PV3NxsdWm2cMEFF+hnP/uZKisrVVlZqZtuukm33XabDh48aHVpXeK0+2Fi586dmjt3rgKBgOLj460ux1aefPJJbdy4UR9//LHVpdjKli1bVFRUpM8++8zqUiwzefJkXX311dq4caO57LLLLtPcuXNVUlJiYWX243K5tGPHDs2dO9fqUmzJ5/NpzJgxqqio0De/+U2ry7GltLQ0Pfnkk1q0aJHVpURFD9Ew8Omnn+pXv/qVpkyZQhiKwu/3Ky0tzeoyYDOtra2qqqpSfn5+xPL8/Hzt27fPoqowVPn9fkliXxNFe3u7tm/frubmZl133XVWl9MlAtEQ9vDDD2vEiBFKT0/XkSNH9NJLL1ldku387W9/0/r165miBZ2cPHlS7e3tnSZ4zszM7DQRNNAdwzC0dOlS3XDDDZowYYLV5djG+++/r+TkZHk8Ht19993asWOHxo8fb3VZXSIQ2ciqVavkcrm6vVVWVprtf/SjH+ndd9/V7t27FRsbq+9///sarkdAe7tuJOn48eO6+eabdfvtt+uHP/yhRZUPjr6sH5zhcrki7huG0WkZ0J377rtP7733nl544QWrS7GVSy+9VNXV1Xrrrbd0zz33qKCgQB988IHVZXXJMXOZDQX33Xef7rjjjm7bjBs3zvz/6NGjNXr0aH3ta1/TZZddppycHL311lu27pLsq96um+PHj2vatGnmRL7DXW/XD85sP7GxsZ16g+rq6jr1GgFdKSws1M6dO/XGG2/oggsusLocW3G73brkkkskSddcc43279+v//zP/9Szzz5rcWXREYhsJBRw+iLUMxQIBPqzJNvozbr55JNPNG3aNE2aNEmbN29WTMzw7wg9l78dp3K73Zo0aZLKy8v1ne98x1xeXl6u2267zcLKMBQYhqHCwkLt2LFDr7/+unJzc60uyfYMw7D1ZxSBaAh6++239fbbb+uGG25QamqqPv74Yz3yyCO6+OKLh2XvUG8cP35cU6dO1dixY/WLX/xCPp/PfCwrK8vCyuzjyJEj+vTTT3XkyBG1t7erurpaknTJJZcoOTnZ2uIG2dKlS7Vw4UJdc801Zm/ikSNHGHP2paamJn300Ufm/cOHD6u6ulppaWkaO3ashZVZ795771VZWZleeuklpaSkmD2NXq9XiYmJFldnvR//+Me65ZZblJOTo8bGRm3fvl2vv/66du3aZXVpXTMw5Lz33nvGtGnTjLS0NMPj8Rjjxo0z7r77buPYsWNWl2a5zZs3G5Ki3nBGQUFB1PXz2muvWV2aJZ5++mnjwgsvNNxut3H11VcbFRUVVpdkG6+99lrUv5WCggKrS7NcV/uZzZs3W12aLfzgBz8wt6uMjAxj+vTpxu7du60uq1tchwgAADje8B9cAQAAcBYEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4HgEIgAA4Hj/P2nK+XSWNrZ4AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(test['turning'].squeeze())" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(np.log(test['speed']).squeeze())" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(test['inx'],test['iny'])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F5_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F2_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F13_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F5_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F22_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F23_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F31_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F32_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T37.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F3_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F10_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F3_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F26_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F40_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F11_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F19_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F34_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F31_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F2_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F29_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F36_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F28_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F16_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F26_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F18_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F19_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T38.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F27_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F3_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F15_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F14_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F37_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F40_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F24_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F26_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F38_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F18_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F36_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F37_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F2_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F23_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F13_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F3_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F4_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F10_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F20_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F12_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F13_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F9_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F23_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F22_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F1_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F21_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F11_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F1_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F13_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F41_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F14_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F34_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F24_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F35_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F6_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F1_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F1_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F8_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F39_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F42_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F15_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F16_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F37_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F8_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F17_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F25_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F42_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F24_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F41_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F34_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F10_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F38_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F25_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F42_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F37_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F6_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F32_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F10_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F13_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F9_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F20_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F21_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F35_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F18_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F11_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F27_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F40_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F18_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F19_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F26_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F14_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F29_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F36_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F28_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F22_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F12_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F2_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F5_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F31_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F2_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F5_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F4_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F10_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F30_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F13_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F31_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F4_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F3_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F29_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F23_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F20_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F20_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F37_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F24_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F40_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F15_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F14_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F3_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F29_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F3_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F16_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F34_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F36_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F37_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F26_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F38_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F35_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F30_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F14_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F26_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F41_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F42_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F37_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F29_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F1_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F6_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F15_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F1_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F6_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F23_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F22_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F10_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F12_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F13_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F2_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F6_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F21_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F30_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F1_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F1_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F17_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F9_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F21_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F11_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F7_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F25_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F24_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F37_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F36_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F9_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F42_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F27_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F39_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F38_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F41_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F35_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F34_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F2_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F40_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F11_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F18_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F26_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F18_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F19_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F28_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F36_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F14_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F16_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F22_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F31_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F5_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F2_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F13_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F12_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F5_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F2_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F30_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F31_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F4_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F31_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F13_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F4_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F20_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F23_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F20_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F10_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F24_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F29_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F3_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F15_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F3_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F37_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F34_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F16_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F38_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F26_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F18_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F14_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F30_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F41_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F26_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F8_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F8_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F19_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F39_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F42_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F6_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F1_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F15_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F28_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F29_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F37_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F6_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F1_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F15_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F23_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F22_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F13_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F10_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F24_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F1_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F2_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F23_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F1_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F30_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F32_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F1_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F17_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F21_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F10_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F25_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F24_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F41_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F15_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F28_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F32_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F37_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F37_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F36_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F9_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F41_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F39_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F27_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F38_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F42_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F13_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F12_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F2_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F5_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F5_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F31_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F22_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F23_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F10_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F32_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F30_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F31_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F3_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F21_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F19_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F11_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F40_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F35_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F34_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F35_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F2_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F20_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F31_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F16_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F22_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F29_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F36_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T37.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F39_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F37_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F27_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F18_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F19_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F33_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F41_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F26_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F14_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F3_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F17_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F37_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F24_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F25_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F40_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F18_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F38_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F42_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F25_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T38.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F2_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F36_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F17_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F9_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F21_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F13_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F33_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F3_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F35_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F20_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F12_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F23_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F22_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T37.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F21_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F32_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F11_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F13_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F6_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F11_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F41_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F21_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F24_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F35_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F35_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F14_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F7_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F29_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F1_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F6_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F1_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F8_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F26_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F39_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F40_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F27_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F18_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F37_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F34_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F23_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F15_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F17_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F16_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F8_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F41_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F42_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F25_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F24_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F19_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F34_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F42_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F25_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F27_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F38_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F19_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F29_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F37_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F36_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F17_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F7_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F5_T26.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F22_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F32_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F13_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F33_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F38_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F17_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F8_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F27_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F23_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F31_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F7_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F13_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F11_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F10_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F15_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F40_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F14_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F9_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F30_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F21_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F9_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F24_T37.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F20_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F24_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F30_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F41_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F34_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F34_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F8_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F15_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F14_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F5_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F2_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F15_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F4_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F2_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F35_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F28_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F36_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F29_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F37_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F17_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F4_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F37_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F4_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F4_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F38_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F39_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F27_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F30_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F33_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F15_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F32_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F12_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F8_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F5_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F30_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F34_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F40_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F21_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F12_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B4_F21_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F20_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F32_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B6_F31_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F19_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F10_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F36_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B3_F11_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B5_F36_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F32_T14.nc',\n", " ...]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glob.glob(path+\"CirclingData_2h*.nc\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# all2hrdata=xr.open_mfdataset(glob.glob(path+\"CirclingData_2h_B2*T[1 2].nc\"))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T2.nc']" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glob.glob(path+\"CirclingData_2h_B2*T[1 2].nc\")[0:2]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Trying to load raw matlab files together" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This path isn't particularly fruitful, as the files can't be combined along time, being different lengths" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# xr.open_mfdataset(glob.glob(path+\"CirclingData_2h_B2*T[1 2].nc\")[0:2])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "testfiles=glob.glob(path+\"CirclingData_2h_B2*T[1 2].nc\")[0:2]\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F41_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B2_F39_T2.nc']" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testfiles\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 13118)\n",
       "Coordinates:\n",
       "  * Fly               (Fly) int8 41\n",
       "  * Batch             (Batch) int8 2\n",
       "  * recording_length  (recording_length) int8 2\n",
       "  * Trial             (Trial) int8 2\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, Batch, Fly, t) float64 ...\n",
       "    iny               (Trial, Batch, Fly, t) float64 ...\n",
       "    theta             (Trial, Batch, Fly, t) float64 ...\n",
       "    r                 (Trial, Batch, Fly, t) float64 ...\n",
       "    direction         (Trial, Batch, Fly, t) float64 ...\n",
       "    speed             (Trial, Batch, Fly, t) float64 ...\n",
       "    turning           (Trial, Batch, Fly, t) float64 ...\n",
       "    angle             (Trial, Batch, Fly, t) float64 ...\n",
       "    timestamps        (Trial, Batch, Fly, t) float64 ...
" ], "text/plain": [ "\n", "Dimensions: (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 13118)\n", "Coordinates:\n", " * Fly (Fly) int8 41\n", " * Batch (Batch) int8 2\n", " * recording_length (recording_length) int8 2\n", " * Trial (Trial) int8 2\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, Batch, Fly, t) float64 ...\n", " iny (Trial, Batch, Fly, t) float64 ...\n", " theta (Trial, Batch, Fly, t) float64 ...\n", " r (Trial, Batch, Fly, t) float64 ...\n", " direction (Trial, Batch, Fly, t) float64 ...\n", " speed (Trial, Batch, Fly, t) float64 ...\n", " turning (Trial, Batch, Fly, t) float64 ...\n", " angle (Trial, Batch, Fly, t) float64 ...\n", " timestamps (Trial, Batch, Fly, t) float64 ..." ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file1=xr.open_dataset(testfiles[0])\n", "file1" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 8103)\n",
       "Coordinates:\n",
       "  * Fly               (Fly) int8 39\n",
       "  * Batch             (Batch) int8 2\n",
       "  * recording_length  (recording_length) int8 2\n",
       "  * Trial             (Trial) int8 2\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, Batch, Fly, t) float64 ...\n",
       "    iny               (Trial, Batch, Fly, t) float64 ...\n",
       "    theta             (Trial, Batch, Fly, t) float64 ...\n",
       "    r                 (Trial, Batch, Fly, t) float64 ...\n",
       "    direction         (Trial, Batch, Fly, t) float64 ...\n",
       "    speed             (Trial, Batch, Fly, t) float64 ...\n",
       "    turning           (Trial, Batch, Fly, t) float64 ...\n",
       "    angle             (Trial, Batch, Fly, t) float64 ...\n",
       "    timestamps        (Trial, Batch, Fly, t) float64 ...
" ], "text/plain": [ "\n", "Dimensions: (Fly: 1, Batch: 1, recording_length: 1, Trial: 1, t: 8103)\n", "Coordinates:\n", " * Fly (Fly) int8 39\n", " * Batch (Batch) int8 2\n", " * recording_length (recording_length) int8 2\n", " * Trial (Trial) int8 2\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, Batch, Fly, t) float64 ...\n", " iny (Trial, Batch, Fly, t) float64 ...\n", " theta (Trial, Batch, Fly, t) float64 ...\n", " r (Trial, Batch, Fly, t) float64 ...\n", " direction (Trial, Batch, Fly, t) float64 ...\n", " speed (Trial, Batch, Fly, t) float64 ...\n", " turning (Trial, Batch, Fly, t) float64 ...\n", " angle (Trial, Batch, Fly, t) float64 ...\n", " timestamps (Trial, Batch, Fly, t) float64 ..." ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file2=xr.open_dataset(testfiles[1])\n", "file2" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "xr.concat([file1, file2], dim=\"t\").to_netcdf('concatbyt.nc')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "file1s=file1.stack(metadata=[\"Fly\", \"Batch\", \"recording_length\", \"Trial\", \"t\"])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "file2s=file2.stack(metadata=[\"Fly\", \"Batch\", \"recording_length\", \"Trial\", \"t\"])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "fileboths=xr.merge([file1s, file2s])" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# fileboths.to_netcdf('testboth.nc')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Fly: 2, Batch: 1, recording_length: 1, Trial: 1, t: 13118)\n",
       "Coordinates:\n",
       "  * Fly               (Fly) int8 39 41\n",
       "  * Batch             (Batch) int8 2\n",
       "  * recording_length  (recording_length) int8 2\n",
       "  * Trial             (Trial) int8 2\n",
       "  * t                 (t) int64 0 1 2 3 4 5 ... 13113 13114 13115 13116 13117\n",
       "Data variables:\n",
       "    inx               (Fly, Batch, recording_length, Trial, t) float64 59.62 ...\n",
       "    iny               (Fly, Batch, recording_length, Trial, t) float64 4.5 .....\n",
       "    theta             (Fly, Batch, recording_length, Trial, t) float64 -1.372...\n",
       "    r                 (Fly, Batch, recording_length, Trial, t) float64 0.4645...\n",
       "    direction         (Fly, Batch, recording_length, Trial, t) float64 nan .....\n",
       "    speed             (Fly, Batch, recording_length, Trial, t) float64 nan .....\n",
       "    turning           (Fly, Batch, recording_length, Trial, t) float64 nan .....\n",
       "    angle             (Fly, Batch, recording_length, Trial, t) float64 nan .....\n",
       "    timestamps        (Fly, Batch, recording_length, Trial, t) float64 1.551e...
" ], "text/plain": [ "\n", "Dimensions: (Fly: 2, Batch: 1, recording_length: 1, Trial: 1, t: 13118)\n", "Coordinates:\n", " * Fly (Fly) int8 39 41\n", " * Batch (Batch) int8 2\n", " * recording_length (recording_length) int8 2\n", " * Trial (Trial) int8 2\n", " * t (t) int64 0 1 2 3 4 5 ... 13113 13114 13115 13116 13117\n", "Data variables:\n", " inx (Fly, Batch, recording_length, Trial, t) float64 59.62 ...\n", " iny (Fly, Batch, recording_length, Trial, t) float64 4.5 .....\n", " theta (Fly, Batch, recording_length, Trial, t) float64 -1.372...\n", " r (Fly, Batch, recording_length, Trial, t) float64 0.4645...\n", " direction (Fly, Batch, recording_length, Trial, t) float64 nan .....\n", " speed (Fly, Batch, recording_length, Trial, t) float64 nan .....\n", " turning (Fly, Batch, recording_length, Trial, t) float64 nan .....\n", " angle (Fly, Batch, recording_length, Trial, t) float64 nan .....\n", " timestamps (Fly, Batch, recording_length, Trial, t) float64 1.551e..." ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fileboths.unstack()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Fly Batch recording_length Trial t \n", "39 2 2 2 0 59.625000\n", " 1 59.666687\n", " 2 59.166687\n", " 3 59.543457\n", " 4 59.543457\n", " ... \n", "41 2 2 2 13113 88.562500\n", " 13114 88.562500\n", " 13115 88.562500\n", " 13116 88.562500\n", " 13117 88.562500\n", "Length: 21221, dtype: float64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fileboths[\"inx\"].to_pandas()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "fdask=fileboths.to_dask_dataframe()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Dask DataFrame Structure:
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
metadataFlyBatchrecording_lengthTrialtinxinythetardirectionspeedturningangletimestamps
npartitions=1
0objectint64int64int64int64int64float64float64float64float64float64float64float64float64float64
21220.............................................
\n", "
\n", "
Dask Name: concat-indexed, 59 tasks
" ], "text/plain": [ "Dask DataFrame Structure:\n", " metadata Fly Batch recording_length Trial t inx iny theta r direction speed turning angle timestamps\n", "npartitions=1 \n", "0 object int64 int64 int64 int64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64\n", "21220 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: concat-indexed, 59 tasks" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fdask" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/dask/utils.py:1070: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", " return getattr(__obj, self.method)(*args, **kwargs)\n" ] }, { "data": { "text/html": [ "
Dask DataFrame Structure:
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Batchrecording_lengthTrialtinxinythetardirectionspeedturningangletimestamps
npartitions=1
float64float64float64float64float64float64float64float64float64float64float64float64float64
.......................................
\n", "
\n", "
Dask Name: truediv, 65 tasks
" ], "text/plain": [ "Dask DataFrame Structure:\n", " Batch recording_length Trial t inx iny theta r direction speed turning angle timestamps\n", "npartitions=1 \n", " float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64\n", " ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: truediv, 65 tasks" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fdask.groupby(\"Fly\").mean()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "fulllist=glob.glob(path+\"CirclingData_2h_B1_F2_*.nc\")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T31.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T21.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T15.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T35.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T11.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T25.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T34.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T10.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T24.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T30.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T20.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T14.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T1.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T5.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T4.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T7.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T19.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T3.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T29.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T2.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T38.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T28.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T6.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T18.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T37.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T13.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T27.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T33.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T9.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T23.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T17.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T32.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T8.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T22.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T16.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T36.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T12.nc',\n", " '/Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B1_F2_T26.nc']" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fulllist" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "xarraysubset=xr.open_mfdataset(fulllist, combine=\"nested\", concat_dim=\"t\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Trial: 38, t: 2634600)\n",
       "Coordinates:\n",
       "    Fly               int8 2\n",
       "    Batch             int8 1\n",
       "    recording_length  int8 2\n",
       "  * Trial             (Trial) int8 1 2 3 4 5 6 7 8 9 ... 31 32 33 34 35 36 37 38\n",
       "    timestamps        (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    iny               (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    theta             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    r                 (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    direction         (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    speed             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    turning           (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    angle             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Trial: 38, t: 2634600)\n", "Coordinates:\n", " Fly int8 2\n", " Batch int8 1\n", " recording_length int8 2\n", " * Trial (Trial) int8 1 2 3 4 5 6 7 8 9 ... 31 32 33 34 35 36 37 38\n", " timestamps (Trial, t) float64 dask.array\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, t) float64 dask.array\n", " iny (Trial, t) float64 dask.array\n", " theta (Trial, t) float64 dask.array\n", " r (Trial, t) float64 dask.array\n", " direction (Trial, t) float64 dask.array\n", " speed (Trial, t) float64 dask.array\n", " turning (Trial, t) float64 dask.array\n", " angle (Trial, t) float64 dask.array" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xarray_1fly=xarraysubset.set_coords(\"timestamps\").squeeze()\n", "xarray_1fly" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# xarray_1fly=xarray_1fly.swap_dims({\"t\":\"timestamps\"})" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Trial: 38, t: 2634600)\n",
       "Coordinates:\n",
       "    Fly               int8 2\n",
       "    Batch             int8 1\n",
       "    recording_length  int8 2\n",
       "  * Trial             (Trial) int8 1 2 3 4 5 6 7 8 9 ... 31 32 33 34 35 36 37 38\n",
       "    timestamps        (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    iny               (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    theta             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    r                 (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    direction         (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    speed             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    turning           (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    angle             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Trial: 38, t: 2634600)\n", "Coordinates:\n", " Fly int8 2\n", " Batch int8 1\n", " recording_length int8 2\n", " * Trial (Trial) int8 1 2 3 4 5 6 7 8 9 ... 31 32 33 34 35 36 37 38\n", " timestamps (Trial, t) float64 dask.array\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, t) float64 dask.array\n", " iny (Trial, t) float64 dask.array\n", " theta (Trial, t) float64 dask.array\n", " r (Trial, t) float64 dask.array\n", " direction (Trial, t) float64 dask.array\n", " speed (Trial, t) float64 dask.array\n", " turning (Trial, t) float64 dask.array\n", " angle (Trial, t) float64 dask.array" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xarray_1fly" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# xarray_1fly['timestamps']=pd.to_datetime(xarray_1fly['timestamps'], unit='s', origin='unix')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:           (Trial: 38, t: 2634600)\n",
       "Coordinates:\n",
       "    Fly               int8 2\n",
       "    Batch             int8 1\n",
       "    recording_length  int8 2\n",
       "  * Trial             (Trial) int8 1 2 3 4 5 6 7 8 9 ... 31 32 33 34 35 36 37 38\n",
       "    timestamps        (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "Dimensions without coordinates: t\n",
       "Data variables:\n",
       "    inx               (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    iny               (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    theta             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    r                 (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    direction         (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    speed             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    turning           (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>\n",
       "    angle             (Trial, t) float64 dask.array<chunksize=(38, 71424), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Trial: 38, t: 2634600)\n", "Coordinates:\n", " Fly int8 2\n", " Batch int8 1\n", " recording_length int8 2\n", " * Trial (Trial) int8 1 2 3 4 5 6 7 8 9 ... 31 32 33 34 35 36 37 38\n", " timestamps (Trial, t) float64 dask.array\n", "Dimensions without coordinates: t\n", "Data variables:\n", " inx (Trial, t) float64 dask.array\n", " iny (Trial, t) float64 dask.array\n", " theta (Trial, t) float64 dask.array\n", " r (Trial, t) float64 dask.array\n", " direction (Trial, t) float64 dask.array\n", " speed (Trial, t) float64 dask.array\n", " turning (Trial, t) float64 dask.array\n", " angle (Trial, t) float64 dask.array" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xarray_1fly" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAHFCAYAAADR1KI+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAABMg0lEQVR4nO3dfXyN9f8H8Nd1ZjvbbDtsa3fMNrdhIzfFEDO5WVpuSrc/bXQnVEwJFaNsJUSJ+OY7fCO+5SZRWLGpUAjJtNDGsJnbnRk2O+fz+0M7X8c2zv11bef1fDyuRzvXuW7e1+XS3t6fm0sSQggQERERKZhK7gCIiIiI7oQJCxERESkeExYiIiJSPCYsREREpHhMWIiIiEjxmLAQERGR4jFhISIiIsVjwkJERESKx4SFiIiIFI8Jix0tWbIEkiRVubz22muG7cLDw5GYmChfoNXQarWYPn06YmJiEBQUBC8vL0RFReH999/HtWvXLD5uYmKi0b1wcXFBw4YN8dhjj+GPP/6w6JhXrlxBcnIyMjIyLNq/4s9qz549Fu1vS8XFxRg/fjz69OmDu+66C5IkITk5We6wZJeYmIjw8HCjdUq5N0r7O5ySkoJ169ZVWq+k53zr1q0YPnw47r77btStWxcNGjTAgAEDsHfvXrlDI4WqI3cAziAtLQ1333230bqQkBCZojHdiRMnMGfOHAwdOhRJSUnw8vLCjz/+iOTkZKSnpyM9PR2SJFl0bA8PD2zduhUAUF5ejqNHj+Ldd99Fly5dcPjwYTRo0MCs4125cgVTp04FAMTExFgUk1KcP38eixYtQtu2bTFw4EB89tlncoekWDt37kTDhg3lDkNxUlJS8Oijj2LgwIFyh1KtBQsW4Pz583j11VfRqlUrnD17FrNmzULnzp2xefNmxMbGyh0iKQwTFgeIjIxEx44d5Q7DbBEREcjNzUXdunUN62JjY1G3bl28/vrr+Pnnn9GtWzeLjq1SqdC5c2fD527duqFRo0bo1asXNm7ciBdeeMHq+GuqsLAwXLx4EZIk4dy5c3ZLWK5fvw5JklCnjjL+N2BJPDc/Q1SzfPLJJwgICDBa169fPzRt2hQpKSlMWKgSNgkpzOXLl1GvXj28+OKLlb7Lzc2Fi4sLPvjgA4fEUrduXaNkpcJ9990HAMjLy7Pp+TQaDQDA1dXVsO7s2bMYOXIkWrVqBS8vLwQEBCA2NhY//vijYZvc3FzcddddAICpU6camppuLtH/+eefePLJJxEYGAi1Wo1GjRrhmWeeQWlpqVEMxcXFeOmll+Dv7w8/Pz8MHjwYp0+ftul13klF/LaUkZEBSZLwn//8B+PGjUODBg2gVqtx9OhRAMD333+PXr16wcfHB56enujatSt++OGHSscx5T7+8ccfGDBgAOrXrw93d3fcc889WLp0qVnxLFmyBC1atIBarUbLli2xbNmyKq/r1iahiiaPbdu23fHPsbS0FOPGjUNQUBA8PT3RvXt37N2712bNO1qtFq+99hoiIiLg5uaGBg0aYMyYMSgpKal0DaNHj8Z//vMftGzZEp6enmjbti02bNhQ6Zhff/012rRpA7VajcaNG2Pu3LlITk42el4kSUJJSQmWLl1qeJZurToq4Tm/NVkBAC8vL7Rq1crm/2+h2kEZ/7Sq5XQ6HcrLy43WVfevSC8vLwwfPhyLFi3CjBkzDL/EAWD+/Plwc3PD8OHD73g+U17CrVKpoFKZn7NWNOW0bt3a7H1vVnFPKpqEXn/9ddSvXx/9+/c3bHPhwgUAwJQpUxAUFITLly9j7dq1iImJwQ8//ICYmBgEBwdj06ZN6NevH5599lk899xzAGBIYg4cOIBu3brB398f06ZNQ7NmzZCfn4/169ejrKwMarXacL7nnnsO/fv3x4oVK5CXl4fXX38d//d//2e45uoIIaDT6Uy6bjkrGhMnTkR0dDQ+/fRTqFQqBAQE4PPPP8czzzyDAQMGYOnSpXB1dcXChQvRt29fbN68Gb169QJg2n3Mzs5Gly5dEBAQgI8++gh+fn74/PPPkZiYiDNnzmD8+PF3jGfJkiUYNmwYBgwYgFmzZqGoqAjJyckoLS01+Xk15c9x2LBhWLVqFcaPH4/Y2FhkZWVh0KBB0Gq1Vt/nK1euoEePHjh58iQmTZqENm3a4NChQ5g8eTIOHjyI77//3ijJ2LhxI3bv3o1p06bBy8sLM2bMwKBBg5CdnY3GjRsDADZt2oTBgweje/fuWLVqFcrLyzFz5kycOXPG6Nw7d+5EbGwsevbsibfffhsA4OPjY/b9qYq9n/OioiL89ttvrK5Q1QTZTVpamgBQ5XL9+nXDdmFhYSIhIcHw+dixY0KlUokPP/zQsO7q1avCz89PDBs27I7nDQsLq/a8Ny9Tpkwx+5oOHDggPDw8xKBBg8zet0JCQkKV8QQHB4uffvrptvuWl5eL69evi169ehnFcPbs2WqvKTY2VtSrV08UFhZWe9yKP6uRI0carZ8xY4YAIPLz828b1+3+rG9dzHG76zLHtm3bBADRvXt3o/UlJSXC19dXxMfHG63X6XSibdu24r777jOsM+U+PvHEE0KtVosTJ04YrY+LixOenp7i0qVLt41Hp9OJkJAQ0b59e6HX6w3rc3NzhaurqwgLCzPa/tZ7Y+qf46FDhwQA8cYbbxht98UXXwgARn8fTXHr3+HU1FShUqnE7t27jbb76quvBADx7bffGl1DYGCg0Gq1hnUFBQVCpVKJ1NRUw7p7771XhIaGitLSUsO64uJi4efnV+m5qlu3bpXXoNTnvMLTTz8t6tSpI/bs2WPR/lS7scLiAMuWLUPLli2N1t3uXx+NGzfGQw89hPnz5+PVV1+FJElYsWIFzp8/j9GjR9/xfN98802lpo6qmNvxNzc3Fw899BBCQ0Ot7lfh4eGB7du3AwD0ej1OnTqFuXPn4sEHH8SmTZsQHR1t2PbTTz/FokWLkJWVZXRdt3ZkrsqVK1eQmZmJZ5991lBxuZ2HH37Y6HObNm0AAMePH0dQUFC1+8XHx2P37t13PL7cHnnkEaPPO3bswIULF5CQkFCpCtivXz/MmDEDJSUlkCTJpPu4detW9OrVC6GhoUbrExMT8d1332Hnzp3o169ftfFkZ2fj9OnTSEpKMqpAhIWFoUuXLsjNzTXpOu/055iZmQkAeOyxx4y2e/TRRzF06FCTznE7GzZsQGRkJO655x6j+9q3b19IkoSMjAzExcUZ1vfs2RPe3t6Gz4GBgQgICMDx48cBACUlJdizZw9Gjx4NNzc3w3ZeXl6Ij4/HkiVLzIpPic/522+/jeXLl+Pjjz9Ghw4d7HIOqtmYsDhAy5Ytze50++qrr6JXr15IT09Hnz598MknnyA6Ohrt27e/476tWrUyuUnIVMePH0fPnj1Rp04d/PDDD/D19TV53+rOfes96du3L0JDQ5GUlISdO3cCAGbPno1x48ZhxIgReOedd+Dv7w8XFxe8/fbbOHz48B3Pc/HiReh0OpNHkvj5+Rl9rmguunr16m338/X1NWq+U6rg4GCjzxXNCY8++mi1+1y4cAEqlcqk+3j+/PlK5wD+lxyfP3/+tvFUfF/VL82goCCTE5Y7/TlWnCcwMNBouzp16lTa1xJnzpzB0aNHjfpj3ezcuXO3jbci5op4L168CCFEpXiBytdgCqU951OnTsW7776L6dOnm/SPMnJOTFgUKjY2FpGRkZg3bx68vLzw22+/4fPPPzdp3yZNmhj+ZXY7U6ZMMWkOi+PHjyMmJgZCCGRkZNhtGKmnpyeaNGmCAwcOGNZ9/vnniImJwYIFC4y2LS4uNumYvr6+cHFxwcmTJ20a662WLl2KYcOGmbStKcmkvdzamdff3x8A8PHHH1c74iYwMBA6nc6k++jn54f8/PxK6ys6dFacr7p4Kn6RFhQUVDpGVessVXGeM2fOGA2hLy8vr5RUWcLf3x8eHh7497//Xe335qhfvz4kSarUXwWw7X25E3s851OnTkVycjKSk5MxadIka8KjWo4Ji4K98sorGDFiBIqKihAYGIghQ4aYtJ8tm4ROnDiBmJgY6HQ6ZGRkICwszKQYLHH58mUcPXrUaPSAJElGnWIB4Pfff8fOnTuNmh2q+xeih4cHevTogS+//BLTp083+xeFqWpKk9Ctunbtinr16iErK+uO/7I15T726tULa9euxenTp42er2XLlsHT0/OOw5BbtGiB4OBgfPHFF0bNQsePH8eOHTtsNn9R9+7dAQCrVq0yqlp+9dVXlZrGLPHQQw8hJSUFfn5+iIiIsPp4devWRceOHbFu3TrMnDnT0Cx0+fLlKkcT3VydsSVbP+fvvPMOkpOT8dZbb2HKlCk2Oy7VTkxYFOz//u//MHHiRGzfvh1vvfWWUdv17URFRdnk/IWFhejZsyfy8/OxePFiFBYWorCw0PB9w4YNjaotFbOQmlK21+v12LVrl+HnU6dO4aOPPsLFixeNqj4PPfQQ3nnnHUyZMgU9evRAdnY2pk2bhoiICKNfLN7e3ggLC8PXX3+NXr16wdfXF/7+/ggPD8fs2bPRrVs3dOrUCRMmTEDTpk1x5swZrF+/HgsXLjTqO2ApPz8/mzQlVPjuu+9QUlJiqCRlZWXhq6++AgA8+OCD8PT0BADDiJq0tDSLhuJ6eXnh448/RkJCAi5cuIBHH30UAQEBOHv2LA4cOICzZ88aqlum3McpU6Zgw4YN6NmzJyZPngxfX18sX74cGzdurDTqrSoqlQrvvPMOnnvuOQwaNAjPP/88Ll26hOTk5Nv2rTBX69at8eSTT2LWrFlwcXFBbGwsDh06hFmzZkGj0Vg0eu5mY8aMwerVq9G9e3eMHTsWbdq0gV6vx4kTJ7BlyxaMGzcOnTp1MuuY06ZNQ//+/dG3b1+8+uqr0Ol0+OCDD+Dl5WUYTVchKioKGRkZ+OabbxAcHAxvb2+0aNHCqmsCbPucz5o1C5MnT0a/fv3Qv39/w/8PKnCOHapE1i6/tVxFj/pbRwrc6tYRBjdLTEwUderUESdPnrRDhLdXMZKjuuXWkSv+/v6ic+fOdzxuVaOEAgICRI8ePcTatWuNti0tLRWvvfaaaNCggXB3dxft27cX69atEwkJCZVGjHz//feiXbt2Qq1WVxrpkZWVJYYMGSL8/PyEm5ubaNSokUhMTBTXrl0TQlT/Z1VxD7Zt22bqbbOJ2430ysnJMWz38ccfCwBi06ZNtz1exXV8+eWXVX6fmZkp+vfvL3x9fYWrq6to0KCB6N+/f6Xt73QfhRDi4MGDIj4+Xmg0GuHm5ibatm0r0tLSzIrns88+E82aNRNubm6iefPm4t///neVf+a3Pofm/Dleu3ZNJCUliYCAAOHu7i46d+4sdu7cKTQajRg7dmw1d7JqVf0dvnz5snjrrbdEixYthJubm9BoNCIqKkqMHTtWFBQUGF3DqFGjTDrm2rVrRVRUlOHev/fee+KVV14R9evXN9pu//79omvXrsLT01MAED169DD7/thbjx49bD7KiGo3SQgZG9TptsrKyhAeHo5u3brhv//9r9zh3FZWVhZat26NDRs2GM2jQvb12GOPIScnp0Y2RynRjh070LVrVyxfvhxPPfWU3OHc0fXr13HPPfegQYMG2LJli9zhENkVm4QU6OzZs8jOzkZaWhrOnDmDCRMmyB3SHW3btg3R0dFMVhxI/NMJ2tTO2GQsPT0dO3fuRIcOHeDh4YEDBw7gvffeQ7NmzTB48GC5w6vSs88+i969eyM4OBgFBQX49NNPcfjwYcydO1fu0IjsjhUWBarolxAcHIwpU6ZUOU0/EVnnl19+wbhx45CVlYXi4mL4+/ujb9++SE1NNQy3vlMHXEtni7bUY489hh07duDs2bNwdXVF+/btMWnSJKO5bYhqKyYsRETVuNM7nRISEsyetI2ILMMmISKiatypb5C9hskTUWWssBAREZHiOa7xlYiIiMhCtb5JSK/X4/Tp0/D29r5jezQRETk3IQSKi4sREhJi1w7V165dQ1lZmdXHcXNzg7u7uw0iUr5an7CcPn260ptjiYiIbicvL89u7027du0a/Dy8cAU6q48VFBSEnJwcp0haan3CUjHtel5eHnx8fGSOhsgxJm7Mwt2BXvjovwexwf97fDE7A/95ehpeGBIJL7c6mDp7A9a7/wChF2j979Vyh0ukGFqtFqGhoTZ5ZUd1ysrKcAU6PI0GcLOiZ0YZ9FhecAplZWVMWGqDimYgHx8fJizkNNSeXvDw8oaL2hPe7m5wl1RQuXnCva43PNR1oHJ1h5erK4Re8O8FURUc0YXAAyq4SZYnLC5ONmSm1icsRERESuQiSXCxIjFygXTjzUtOgqOEiIiIZKCSABcrFpWZuc6CBQvQpk0bQ4tDdHQ0vvvuO/tcnB0wYSEiInICDRs2xHvvvYc9e/Zgz549iI2NxYABA3Do0CG5QzMJm4SIiIhkYJMmITPEx8cbfZ4+fToWLFiAXbt2oXXr1hbH4ShMWIiIiGRQ0bRj8f7//Fer1RqtV6vVUKvVt91Xp9Phyy+/RElJCaKjoy0PwoHYJERERFSDhYaGQqPRGJbU1NRqtz148CC8vLygVqsxYsQIrF27Fq1atXJgtJZjhYWIiEgGtmoSunWesdtVV1q0aIH9+/fj0qVLWL16NRISEpCZmVkjkpZa//JDrVYLjUaDoqIizjdBpDDXrpTApeQ8Er47g0tXrmNZ2Wr4/N84ZI96Dk2f7o/f536FUm0pcuavQkIHzlhN9ueI3xkV53jdLRxqK+ZhKRV6fFCWa1WsDzzwAJo0aYKFCxdaHIejsEmIiIjISQkhUFpaKncYJmGTEBERkQwcPUpo0qRJiIuLQ2hoKIqLi7Fy5UpkZGRg06ZNFsfgSExYiIiIZCDBumYOc1OdM2fOYOjQocjPz4dGo0GbNm2wadMm9O7d24ooHIcJCxERkRNYvHix3CFYhQkLERGRDBzdJFTTMWEhIiKSga0mjnMWTFiIiIhkcCNhsabC4lw4rJmIiIgUjxUWIiIiGbBJyDxMWIhINu6edQHPuljxTKN/1nQBAER98S0AoNNDowAA3eUIjmRz6fIVnLuqwyltKT7bmYvf/yjEhVOFSPvtE7zadzImf/4GBv73TahjnpY7VKuw06152CREREREiscKCxERkQxUVjYJOVvFgQkLERGRDNgkZB5nS9CIiIioBmKFhYiISAYcJWQeJixEREQyYMJiHjYJERERkeKxwkJERCQDdro1DxMWB1h98DQePLYKczUP4ZVza6HtMwrtnkvDwp8+xeVyPR6e8wT2zduM6NX/Rp2QFnKHS0Qkq3penqjnBTS9yxs9mvjf9E0iDgHA7HiZIrMtF1jZJCRsFkqNIGuT0IIFC9CmTRv4+PjAx8cH0dHR+O677wzfJyYmQpIko6Vz584yRkxERGQbqn8qLJYuKiuqMzWRrBWWhg0b4r333kPTpk0BAEuXLsWAAQOwb98+tG7dGgDQr18/pKWlGfZxc3OTJVYiIiKSj6wJS3y8cVlv+vTpWLBgAXbt2mVIWNRqNYKCguQIj4iIyG6sHiXkXAUW5YwS0ul0WLlyJUpKShAdHW1Yn5GRgYCAADRv3hzPP/88CgsLb3uc0tJSaLVao4WIiEhprGkOsrbDbk0ke8Jy8OBBeHl5Qa1WY8SIEVi7di1atWoFAIiLi8Py5cuxdetWzJo1C7t370ZsbCxKS0urPV5qaio0Go1hCQ0NddSlEBERkZ3IPkqoRYsW2L9/Py5duoTVq1cjISEBmZmZaNWqFR5//HHDdpGRkejYsSPCwsKwceNGDB48uMrjTZw4EUlJSYbPWq2WSQsRESkOm4TMI3vC4ubmZuh027FjR+zevRtz587FwoULK20bHByMsLAwHDlypNrjqdVqqNVqu8VLRERkC1bPw8ImIXkJIapt8jl//jzy8vIQHBzs4KiIiIhITrJWWCZNmoS4uDiEhoaiuLgYK1euREZGBjZt2oTLly8jOTkZjzzyCIKDg5Gbm4tJkybB398fgwYNkjNsIiIiq6msnEuF87A40JkzZzB06FDk5+dDo9GgTZs22LRpE3r37o2rV6/i4MGDWLZsGS5duoTg4GD07NkTq1atgre3t5xhm+2RqBAgaiwmAADGwxNA/pejAYw2bHP/05Ptdv4RUjjmFv2GvfED8fPeArww9zEEzD+DtBM/o/PhXzD8P79hc7uT6Jzui33T4+wWBxER/Y/kIkFSWZ50SExYHGfx4sXVfufh4YHNmzc7MBoiIiJSKtk73RIRETkjlYsElRUVFjYJERERkf25qCCprBj7IjnX2w+ZsBAREclAUkmQrJhMRYJzVVgUN6yZiIiI6FassBAREclA5SJBZUWFReVkFRYmLERERDKQVNb1YZGEc/VhYZMQERERKR4rLERERDJgk5B5mLA4gU9FLgCgS+Z2dPlnXemz//s+Y2wPAMC+GIeGRSS7U1NfxF1jpkN1+SxGN4zDx4XbAd11RKX+gQ265bh6/jIWPvoenvnXGNy3aavc4VItI7lwlJA52CREREREiscKCxERkQxuVFis6HQLvQ2jUT4mLERERDJgHxbzsEmIiIiIFI8VFiIiIhlIkgTJipcfSnrnqrAwYSEiIpKBykUFlRV9WFTCuRpJmLAQERHJwOphzcK5KizOlZ4RERFRjSQJUbtfRqDVaqHRaFBUVAQfHx+5wyEissi5j8bB56mxWJBdhmdzP4dH90HQnz6GQzMWoEHaWpTOehXHf/gTGyYuRGr/VnKHW2M54ndGxTnS7++CunUsb+goKS9H7x93OM3vNzYJERERyYB9WMzjXFdLRERENRIrLERERHKwstMtnKzTLRMWIiIiGagkCSor5mFRSc6VsLBJiIiIyAmkpqbi3nvvhbe3NwICAjBw4EBkZ2fLHZbJmLAQERHJQHJRWb2YIzMzE6NGjcKuXbuQnp6O8vJy9OnTByUlJXa6QttikxAREZEMrH75oZlT82/atMnoc1paGgICArB37150797d4jgchQkLERFRDabVao0+q9VqqNXqO+5XVFQEAPD19bVLXLbGJiEiIiIZVEzNb80CAKGhodBoNIYlNTX1jucWQiApKQndunVDZGSkvS/VJpxmptsn/5WJ5z95HZsOnYW/Wx2M/XkeGn94Br+134/68U8jf8kCHFz6K+JyD8gdMhERycSRM93+NPABeLla3tBx+Xo5uq37Hnl5eUaxmlJhGTVqFDZu3IiffvoJDRs2tDgGR2KTEBERkQxULrCyD8uN//r4+JiVXL388stYv349tm/fXmOSFYAJCxERkVMQQuDll1/G2rVrkZGRgYiICLlDMgsTFiIiIhlIKgmSFRPHmbvvqFGjsGLFCnz99dfw9vZGQUEBAECj0cDDw8PiOByFCQsREZEMVCorX36oM2/fBQsWAABiYmKM1qelpSExMdHiOByFCQsREZETqOljbJiwEBERyUCy8uWHVr04sQZiwkJERCQDS6bXv3V/Z+JcV0tEREQ1EissREREMpBUKkgqKyosVuxbEznNTLf2nLWQbu/a1au4umQqfo4Zi77+pdjctj/eGTEb27sXQtw3EKorF+HmX3MmLyKi2suRM93+NvxheLm5Wnycy2XX0f7f653m95us6dmCBQvQpk0bwyx90dHR+O677wzfCyGQnJyMkJAQeHh4ICYmBocOHZIxYiIiIpKDrAlLw4YN8d5772HPnj3Ys2cPYmNjMWDAAENSMmPGDMyePRvz5s3D7t27ERQUhN69e6O4uFjOsImIiKz3T6dbSxew063jxMfH48EHH0Tz5s3RvHlzTJ8+HV5eXti1axeEEJgzZw7efPNNDB48GJGRkVi6dCmuXLmCFStWyBk2ERGR1SSVdQmLs/VhUczV6nQ6rFy5EiUlJYiOjkZOTg4KCgrQp08fwzZqtRo9evTAjh07qj1OaWkptFqt0UJERKQ0FZ1urVmciexXe/DgQXh5eUGtVmPEiBFYu3YtWrVqZXjHQWBgoNH2gYGBhu+qkpqaCo1GY1hCQ0PtGj8RERHZn+wJS4sWLbB//37s2rULL730EhISEpCVlWX4XpKMZ/ITQlRad7OJEyeiqKjIsOTl5dktdiIiIkvdaNpxsWKR/Ve4Q8k+D4ubmxuaNm0KAOjYsSN2796NuXPn4o033gAAFBQUIDg42LB9YWFhparLzdRqNdRqtX2DJiIishJnujWP4q5WCIHS0lJEREQgKCgI6enphu/KysqQmZmJLl26yBghEREROZqsFZZJkyYhLi4OoaGhKC4uxsqVK5GRkYFNmzZBkiSMGTMGKSkpaNasGZo1a4aUlBR4enriqaeekjNsIiIiq6lUKqis6Dhrzb41kawJy5kzZzB06FDk5+dDo9GgTZs22LRpE3r37g0AGD9+PK5evYqRI0fi4sWL6NSpE7Zs2QJvb285wyYzuXt4wP2l9/DQP58fOn3Q8DMAwLOuDFEp25Wr1/DX/w1A9MmmmPPReHj274fxPUdBm38MRatewD5dEDxdXfBH2854vDDrzgckIsVhk5B5ZE1YFi9efNvvJUlCcnIykpOTHRMQERERKZLsnW6JiIicESss5mHCQkREJANJsvJtzZJzJSzOdbVERERUI7HCQkREJAM2CZmHCQsREZEMmLCYhwkLERGRDFQuKqisSDqs2bcmcq6rJSIiohqJFRYiBfL0cMc9qzfjasWKc4cx9Kbv7/3nv61tMGncV4Gt0feDIXj4QizSHwvEmX/NRsrUzdC4uuCBFn749vA5JJzYh6hgjdXnIqL/kVSSdaOEVNW/CLg2YsJCREQkA/ZhMY9zXS0RERHVSKywEBERyYAVFvMwYSEiIpIBZ7o1j3NdLREREdVIrLAQERHJQHJxgcrFxar9nQkTFiIiIhmwD4t5nOtqiYiIqEZihYWIiEgGrLCYhwkLkZN79MwhAMC2fz43mLIQn0z53/exjg/JaicvXIbvtk/h1rQNmn2ixb7ZD8L77J8IGPUdXt6Whry0L9FxxJN46dKfcodKTkxSWTlKyIp9ayImLERERDJghcU8znW1REREVCOxwkJERCQDSSVZV2Hhyw+JiIjI3tiHxTzOdbVERERUI7HCQkREJANJ5QJJZcVMt1bsWxMxYSEiIpKDyuXGYs3+ToRNQkRERKR4rLAQERHJQaW6sVizvxNhwkJEtU5DXy/gkdcAADmL/lnp1R7nv20P4M0bn5/gLLfOpOxSIVxOHoSoF4SDo8fiQdeHsSPsF6jreWH5/eMw1uco9OH34NgbLzksJsnFxao3Ljvb25qdKz0jIiKiGokVFiIiIjmw061ZmLAQERHJQaWyMmFxrkYSJixEREQy4Ey35nGuqyUiInJS27dvR3x8PEJCQiBJEtatWyd3SGZhwkJERCQHyeV//VgsWSTzmpNKSkrQtm1bzJs3z04XZF9sEiIiIpKDgzvdxsXFIS4uzvLzyYwJCxERUQ2m1WqNPqvVaqjVapmisR82CREREcmgotOtNQsAhIaGQqPRGJbU1FSZr8w+ZK2wpKamYs2aNfjzzz/h4eGBLl264P3330eLFi0M2yQmJmLp0qVG+3Xq1Am7du1ydLiKssSvJbKWfIU3C79A8YkzCHigFzY99i7+NfVfmLRyAr7IPIE5f62CNjAS9bw85Q6XiEhWbvUCgHq9AADt1m1BPgBgNADgdQBAUwBAk5lLgX9pHBOUjZqE8vLy4OPjY1hdG6srgMwVlszMTIwaNQq7du1Ceno6ysvL0adPH5SUlBht169fP+Tn5xuWb7/9VqaIiYiIlMXHx8doqa0Ji6wVlk2bNhl9TktLQ0BAAPbu3Yvu3bsb1qvVagQFBTk6PCIiIvvhxHFmUVSn26KiIgCAr6+v0fqMjAwEBASgXr166NGjB6ZPn46AgIAqj1FaWorS0lLD51s7IxERESmBo19+ePnyZRw9etTwOScnB/v374evry8aNWpkcRyOopj0TAiBpKQkdOvWDZGRkYb1cXFxWL58ObZu3YpZs2Zh9+7diI2NNUpKbpaammrU+Sg0NNRRl0BERKRYe/bsQbt27dCuXTsAQFJSEtq1a4fJkyfLHJlpFFNhGT16NH7//Xf89NNPRusff/xxw8+RkZHo2LEjwsLCsHHjRgwePLjScSZOnIikpCTDZ61Wy6SFiIiUR6WyrlnHzH1jYmIghLD8fDJTRMLy8ssvY/369di+fTsaNmx4222Dg4MRFhaGI0eOVPl9bR1/TkREtQzf1mwWWRMWIQRefvllrF27FhkZGYiIiLjjPufPn0deXh6Cg4MdECEREZF9SCoXSFYkHdbsWxPJ2odl1KhR+Pzzz7FixQp4e3ujoKAABQUFuHr1KoAbHYRee+017Ny5E7m5ucjIyEB8fDz8/f0xaNAgOUMnIiIiB5KEjA1akiRVuT4tLQ2JiYm4evUqBg4ciH379uHSpUsIDg5Gz5498c4775jcL0Wr1UKj0aCoqMhoYh2yzqYm7dD5jTi86DYQyx+oi16rzmJTk32Q3NzRYN4FqL19UV52FR///BkeL8ySO1wiIpM44ndGxTnObf0CPlZM7Km9fAX+sU86ze832ZuEbsfDwwObN292UDRERESOwyYh8yhmWDMRERFRdRQxSoiIiMjpcKZbszBhISIikoOD52Gp6ZzraomIiKhGYoWFiIhIBo5+l1BNx4SFiIhIDpzp1ixMWIiIiOTAhMUs7MNCREREiscKC1mk37F9AIBV/3zOHAcAMQCA8w/evGUSiJxNxAtf4e0X7sO4SYsxePhADGgTjG9bd8Yjd/uhSb8WqBvshylvfINPRa7coZKMJJUKkhUjfazZtyZiwkJERCQHycomIYlNQkRERESKwgoLERGRHCQJkKyoG1TzAuHaigkLERGRHCSVlQmLczWSONfVEhERUY3ECgsREZEMhKSCsKJKYs2+NRETFiIiIjmwScgsznW1REREVCOxwkJERCQHSbJupI+TjRKShBBC7iDsSavVQqPRoKioCD4+PnKHQ0RUa1xaNAlfjF+DgW/0gt+It/FV81g8fHwvuk7bht3j7wEASOVl0P73E/iN/kDeYE3kiN8ZFee4cGgHfLy9LD9O8WX4tu7iNL/fWGEhIiKSATvdmse5rpaIiIhqJFZYiIiI5MBRQmZhwkJERCQHJixmca6rJSIiohqJFRYiIiI5sMJiFiYsREREMhCSZOUoIeeah8W50jMiIiKqkThx3C1iPszEnz/tx/qSr/HrzlP4/V+rsOBePS743Y3/NLgHXTs3QIeln+JC3YYI0NR1wBUQEZGjOHLiuHNHDsDH29vy4xQXw79ZW04cR0RERHbEqfnNwiYhIiIiUjxWWIiIiOTAUUJmYcJCREQkA75LyDxMWIiIiOQgqQBV7a2wnDx5EuvXr8eJEydQVlZm9N3s2bPNPh4TFiIiIrKpH374AQ8//DAiIiKQnZ2NyMhI5ObmQgiB9u3bW3RMZadnREREtVVFHxZrFoWaOHEixo0bhz/++APu7u5YvXo18vLy0KNHDwwZMsSiY5pUYfnoo49MPuArr7xiUSBEREROpRZ3uj18+DC++OILAECdOnVw9epVeHl5Ydq0aRgwYABeeukls49pUsLy4YcfmnQwSZKYsBARETm5unXrorS0FAAQEhKCY8eOoXXr1gCAc+fOWXRMkxKWnJwciw5eE2WM7QGM7QHgVdx30/q7ACQV/2X4HODowGq5rAItfm3dCW3vD8Vn6/9C/Y3fITlKj78mTUDzcS+j9WdXsC/mKHK7voBWQbV/RkcicgK1uMLSuXNn/Pzzz2jVqhX69++PcePG4eDBg1izZg06d+5s0TFlvdrU1FTce++98Pb2RkBAAAYOHIjs7GyjbYQQSE5ORkhICDw8PBATE4NDhw7JFDEREZFtVLz80PJFuTPdzp49G506dQIAJCcno3fv3li1ahXCwsKwePFii45p0SghWw1VyszMxKhRo3DvvfeivLwcb775Jvr06YOsrCzUrXvjPT0zZszA7NmzsWTJEjRv3hzvvvsuevfujezsbHhb8Q4GIiIiso/GjRsbfvb09MT8+fOtPqbZCYsthypt2rTJ6HNaWhoCAgKwd+9edO/eHUIIzJkzB2+++SYGDx4MAFi6dCkCAwOxYsUKvPjii+aGT0REpAy1uEmocePG2L17N/z8/IzWX7p0Ce3bt8fff/9t9jHNvlp7DFWqUFRUBADw9fUFcKPvTEFBAfr06WPYRq1Wo0ePHtixY0eVxygtLYVWqzVaiIiIFKfi5YfWLAqVm5sLnU5XaX1paSlOnTpl0THNrrDYY6gScKOvSlJSErp164bIyEgAQEFBAQAgMDDQaNvAwEAcP368yuOkpqZi6tSpFsVAREREllu/fr3h582bN0Oj0Rg+63Q6/PDDDwgPD7fo2GYnLPYYqgQAo0ePxu+//46ffvqp0nfSLVmkEKLSugoTJ05EUlKS4bNWq0VoaKjFcREREdmFTE1C8+fPxwcffID8/Hy0bt0ac+bMwf333295HDcZOHDgjdAkCQkJCUbfubq6Ijw8HLNmzbLo2GYnLPYYqvTyyy9j/fr12L59Oxo2bGhYHxQUBOBGpSU4ONiwvrCwsFLVpYJarYZarbYoDiIiIkeR4+WHq1atwpgxYzB//nx07doVCxcuRFxcHLKystCoUSOLY6mg1+sBABEREdi9ezf8/f2tPmYFs6/WlkOVhBAYPXo01qxZg61btyIiIsLo+4iICAQFBSE9Pd2wrqysDJmZmejSpYu5oRMRESmHDFPzz549G88++yyee+45tGzZEnPmzEFoaCgWLFhg00vLyckxJCvXrl2zyTHNrrDYcqjSqFGjsGLFCnz99dfw9vY29FnRaDTw8PCAJEkYM2YMUlJS0KxZMzRr1gwpKSnw9PTEU089ZfF5iYiIaotbB5dU19JQVlaGvXv3YsKECUbr+/TpU+1AFkvp9XpMnz4dn376Kc6cOYO//voLjRs3xttvv43w8HA8++yzZh9T1rc1V2R0MTExRuvT0tKQmJgIABg/fjyuXr2KkSNH4uLFi+jUqRO2bNnCOVhqmVZBPmh1/jAA4JOb1y/7BgCQ/fE/nx0clzWW+LVE18O/Ylmje/Dmhd+xu1cfXLt0DZ+M+wR5xy9ha/hPqOPpjtPb96PZgq/kDpeIHOzGxHGWj/Sp2PfWfppTpkxBcnJype3PnTsHnU5X5UCWioKBrbz77rtYunQpZsyYgeeff96wPioqCh9++KH9EhZfX1/89ddf8Pf3R/369avt8AoAFy5cMPnkQog7biNJEpKTk6u8+URERDWVEDcWa/YHgLy8PPj4/O+VJXfqx2nOQBZLLVu2DIsWLUKvXr0wYsQIw/o2bdrgzz//tOiYJr/8sKKiMWfOHItORERERLbn4+NjlLBUx9/fHy4uLpWqKbcbyGKpU6dOoWnTppXW6/V6XL9+3aJjmpSwVAxNKi8vBwD07dvXMIKHiIiIzKcXAnorSizm7uvm5oYOHTogPT0dgwYNMqxPT0/HgAEDLI6jKq1bt8aPP/6IsLAwo/Vffvkl2rVrZ9ExzerDUqdOHbz00ks4fPiwRScjIiKiG8Q/izX7myspKQlDhw5Fx44dER0djUWLFuHEiRNGzTa2MGXKFAwdOhSnTp2CXq/HmjVrkJ2djWXLlmHDhg0WHdPsTredOnXCvn37KmVNREREpGyPP/44zp8/j2nTpiE/Px+RkZH49ttvbf47PT4+HqtWrUJKSgokScLkyZPRvn17fPPNN+jdu7dFxzQ7YRk5ciTGjRuHkydPokOHDoa3Kldo06aNRYEQERE5E724sVizvyVGjhyJkSNHWn5iE/Xt2xd9+/a12fFMTliGDx+OOXPm4PHHHwcAvPLKK4bvJEky9DKu6mVHREREZEwIYdJo2dvtr3RlZWUoLCw0zIBbwZJZdU1OWJYuXYr33nsPOTk5Zp+EiIiInMeRI0cwfPjwShPSWVPcMDlhqcjk2HeFiIjIenI1CTlCYmIi6tSpgw0bNiA4ONgm87yY1YfF1hPLENVmif/M3PvOtWMAgPt3/gwA+F93s34AgGZPOjgwO5v78994sWQbvv6/WZg0+B18/tdHKMg6h0b3hiAtcRYydp7AG0teR4MQb8T+/qvc4RLJSsE5h1X279+PvXv34u6777bZMc1KWJo3b37HpMWcmW6JiIicVW2usLRq1Qrnzp2z6THNSlimTp0KjUZj0wCIiIio5rv5JYzvv/8+xo8fj5SUFERFRcHV1dVoW1Nm5r2VWQnLE088gYCAALNPQkRERMZq2yihevXqGbXCCCHQq1cvo20c0umW/VeIiIhsR//PYs3+SrJt2zbDz7m5uQgNDYWLi4vRNnq9HidOnLDo+GaPEiIiIiK6VY8ePQw/x8bGIj8/v1KrzPnz5/HAAw8Y3lFoDpMTllsnfSEiIiLLCXFjsWZ/papo+rnV5cuX4e7ubtExzZ6an4iIiKxXG0cJJSUlAbjRjeTtt9+Gp6en4TudTodffvkF99xzj0XHZsJCRERENrFv3z4ANyosBw8ehJubm+E7Nzc3tG3bFq+99ppFx5ZELe+cotVqodFoUFRUZNEwKiIiezn26hN4SPUU6vqoUXi8EOeP/oaiVS/glHsork18BmPbv4a1d+dA234Q7vLxvPMByWqO+J1RcY6snFPwtuIcxVotWkU0UOTvt2HDhmHu3Lk2jYsVFiIiIhnUtlFCN0tLS7P5MVU2PyIRERGRjbHCQkREJAMBK0cJ2SySmoEJCxERkQz0QkBvRcZizb41ERMWIiIiGQhYVyVxrnSFfViIiIioBmCFhYiISAa1ceI4e2LCQkREJAcrp+Z3tjYhNgkRERGR4rHCQkQkkyZzV+Kw0ZqhAIAwAFi0Bt8CALrgLgfHZQ+bmrTDvft/Rr28X5F2OQJPtwlEnYNbMLHLGExPfweB7x5Gk+hu+HFSDDw9LHs5Xk2jh4DeijKJNfvWRExYiIiIZFCb39ZsD2wSIiIiIsVjhYWIiEgGHCVkHiYsREREMmCTkHnYJERERESKxwoLERGRDDhKyDxMWIiIiGTAJiHzMGEhIiKSAd/WbB72YSEiIiLFkzVh2b59O+Lj4xESEgJJkrBu3Tqj7xMTEyFJktHSuXNneYKtwvUzOWg6cg0uX7kK3fEDiBq/EeO/OQT9Xz9Dl3cQ5acO44+n+8sdJhGR7Pod2wc/b0+4tIrBc/eFwcPdHa73PoyZ1/+GOuZpXPr+Xex9p5/TzHILADq99YszkTVhKSkpQdu2bTFv3rxqt+nXrx/y8/MNy7fffuvACImIiOyjoknImsWZyNqHJS4uDnFxcbfdRq1WIygoyEERERERkRIpvg9LRkYGAgIC0Lx5czz//PMoLCyUOyQiIiKr6YWAzoqFFRYFiYuLw5AhQxAWFoacnBy8/fbbiI2Nxd69e6FWq6vcp7S0FKWlpYbPWq3WUeESERGZ7MbU/NaMErJhMDWAohOWxx9/3PBzZGQkOnbsiLCwMGzcuBGDBw+ucp/U1FRMnTrVUSESERGRAyi+SehmwcHBCAsLw5EjR6rdZuLEiSgqKjIseXl5DoyQiIjINBwlZB5FV1hudf78eeTl5SE4OLjabdRqdbXNRURERErBiePMI2vCcvnyZRw9etTwOScnB/v374evry98fX2RnJyMRx55BMHBwcjNzcWkSZPg7++PQYMGyRg1EREROZqsCcuePXvQs2dPw+ekpCQAQEJCAhYsWICDBw9i2bJluHTpEoKDg9GzZ0+sWrUK3t7ecoVMRERkExWjfazZ35lIQtTuK9ZqtdBoNCgqKoKPj4/c4ZANnP1wLKJ+CseFvw9gzZndeO/Vj3D0198R2qYlACChTzMktg2Ce9FJ1AluJnO0RFSTOOJ3RsU51v12DHW9LP8HeMnlYgxs38Rpfr/VqD4sREREtYVOL6CzYmyyNfvWRDVqlBARERE5J1ZYiIiIZCCsHCVUy3t0VMKEhYiISAY6cWOxZn9nwiYhIiIiMjJ9+nR06dIFnp6eqFevntzhAGDCQkREJIuKieOsWeylrKwMQ4YMwUsvvWS3c5iLTUJEREQyUPIooYp38i1ZssRu5zAXKyxERESkeKywUI1z19gPUTD2f58fAgD0rLyhJyeNq02ynolHQMfm8Lq7FQ7PX4VhrcZg+cvdIEnAsEW/IP/vAvzWfj92z/4ecbkH5A6X6I5s9S4hrVZrtL62vlOPFRYiIiIZVIwSsmYBgNDQUGg0GsOSmppa5fmSk5MhSdJtlz179jjwDpiHFRYiIqIaLC8vz2hq/uqqK6NHj8YTTzxx22OFh4fbMjSbYsJCREQkA1s1Cfn4+Jj0LiF/f3/4+/tbfD65MWEhIiKSgV4voLdipI81+97JiRMncOHCBZw4cQI6nQ779+8HADRt2hReXl52O+/tMGEhIiKSgd7KmW7t+e7DyZMnY+nSpYbP7dq1AwBs27YNMTEx9jvxbbDTLRERERlZsmQJhBCVFrmSFYAVFiIiIlnYqg+Ls2DCQkREJAOdENBZkXRYs29NxCYhIiIiUjxWWIioRmi17BvDz+36PIv9N333y+Te//w0FHGvODIqkpPu+AH4Pfsl5h9Yg/BW/mj/3Xe4uvQd9DvbC4PuD8djUUHwclXh2KAHEb01U+5wK1HyKCElYsJCREQkAx2sGyWks1kkNQObhIiIiEjxWGEhIiKSAUcJmYcJCxERkQw4Ssg8bBIiIiIixWOFhYiISAZ6vYCOo4RMxoSFiIhIBjorExZr9q2JmLAQERHJgAmLediHhYiIiBSPFRaFWfjLcfQIrw/1B6Nw7Ls/cf/OzZjg2wHT/p0Az/t6QarjCn3RedTp0F/uUImIZOUS1haXvm8L4F3DOveX3sMvt2wXoMBZbgFAp7euSqLT2zCYGoAJCxERkQzYJGQeNgkRERGR4rHCQkREJANWWMzDhIWIiEgGnIfFPGwSIiIiIsVjhYWIiEgGOmFlk5CTvUuICQsREZEM2IfFPGwSIiIiIsVjhUVhXuwUduOHmf9BxMwbP35YnmO0jT2zzFLtBZSunoNk38fw29/n8fIDzTBInYOJkUOxYfRcnPr9F5yZ1Bzq2GfsGAURUe3HCot5ZK2wbN++HfHx8QgJCYEkSVi3bp3R90IIJCcnIyQkBB4eHoiJicGhQ4fkCZaIiMiGyvXC6sWZyJqwlJSUoG3btpg3b16V38+YMQOzZ8/GvHnzsHv3bgQFBaF3794oLi52cKRERES2VVFhsWZxJrI2CcXFxSEuLq7K74QQmDNnDt58800MHjwYALB06VIEBgZixYoVePHFFx0ZKhEREclIsZ1uc3JyUFBQgD59+hjWqdVq9OjRAzt27Kh2v9LSUmi1WqOFiIhIafRWVlc4cZxCFBQUAAACAwON1gcGBhq+q0pqaio0Go1hCQ0NtWucREREltAJYfXiTBSbsFSQJMnosxCi0rqbTZw4EUVFRYYlLy/P3iESERGRnSl2WHNQUBCAG5WW4OBgw/rCwsJKVZebqdVqqNVqu8dHRERkDQ5rNo9iKywREREICgpCenq6YV1ZWRkyMzPRpUsXGSMjIiKyHkcJmUfWCsvly5dx9OhRw+ecnBzs378fvr6+aNSoEcaMGYOUlBQ0a9YMzZo1Q0pKCjw9PfHUU0/JGDURERE5mqwJy549e9CzZ0/D56SkJABAQkIClixZgvHjx+Pq1asYOXIkLl68iE6dOmHLli3w9vaWK+RaT+3jC/WwaZhttDYE75f9jfcBAPFyhEUkO+/ur6Fwy7to98ZmFJ3OQ8nZPER07oHfEr2hC2yG6b9ewr2N6sPXow66hPvJHS7VAGwSMo+sCUtMTAzEbXo5S5KE5ORkJCcnOy4oIiIiB9AJPXR6vVX7OxPF9mEhIiIiqqDYUUJERES1md7KJiFnmziOCQsREZEMdHoBFfuwmIwJCxERkQzK9YBkRdJR7lxdWNiHhYiIiJSPFRYiIiIZsEnIPExYiIiIZMCExTxsEiIiIiLFY4WFiMgExdtnAgD+nDug0ncuAKb2DXJwRNXb/0hfPHLXC5i7Phl1Az0R8G069BA4ev4KDuZrkV90DUVXypByaB7eaDkKS/x3w61xawS89jMOfP4qci6VInlDFt78bBxe6DYBGXd9h80f/4iwYG/cvysdwsUVb2miMPP633Jfao3GCot5mLAQERHJgPOwmIdNQkRERKR4rLAQERHJQKcXVs3DwiYhIiIisjshBIQVScftXh5cG7FJiIiIiBSPCQsREZEM9Hph9WIPubm5ePbZZxEREQEPDw80adIEU6ZMQVlZmV3OZyo2CREREclACGFVs469moT+/PNP6PV6LFy4EE2bNsUff/yB559/HiUlJZg5c6ZdzmkKJixEREQyEHor+7DYqcLSr18/9OvXz/C5cePGyM7OxoIFC2RNWNgkRERERLdVVFQEX19fWWOQRC3vZqzVaqHRaFBUVAQfHx+5w6Fa4Py811F3eDJcDv2AKd3G4pGje9HazxUZUd1w79gHUK//k1hfEoxBkcFyh0pEZnLE74yKc3Se9g3quNe1+Djl10qwa3I88vLyjGJVq9VQq9W2CBUAcOzYMbRv3x6zZs3Cc889Z7PjmosVFiIiIhkIvfULAISGhkKj0RiW1NTUKs+XnJwMSZJuu+zZs8don9OnT6Nfv34YMmSIrMkKwD4sRERENVpVFZaqjB49Gk888cRtjxUeHm74+fTp0+jZsyeio6OxaNEim8RqDSYsREREMrDVKCEfHx+Tmq/8/f3h7+9v0rFPnTqFnj17okOHDkhLS4NKJX+DDBMWIiIiGVg7l4q95mE5ffo0YmJi0KhRI8ycORNnz541fBcUJN9byZmwEBERkcGWLVtw9OhRHD16FA0bNjT6Ts5xOvLXeIiIiJxQxTws1iz2kJiYaGiuunWREyssREREcrA26XCytzWzwkJERESKxwoLkZn8Rn9w44d7H0ZK6cOG9f2O7TP8PMjRQTlA6NAl2PXREFy4Vo7/7DmJJxePRWhsFOa/8TXevbs7WsU9il+T2sDNv+GdD0ZE0AsByYpmFn3tnve1EiYsREREMhDCyncJMWEhIiIie1Pqyw+Vin1YiIiISPFYYSEiIpKBXg9IVk0cZ8NgagAmLERERDKw1dT8zoJNQkRERKR4rLAQERHJQOhvLNbs70yYsBAREclArxdW9mFhkxARERGRokiilvfa0Wq10Gg0KCoqgo+Pj9zhkEx0xw/gnbsfQcf6Hui3dw2u7/4OmxoPwUffH8EDUUEYU/hfnNjyK1qmrZc7VCKSkSN+Z1Sc4+7Rq+Ci9rT4OLrSK/hz3uNO8/tN0RWW5ORkSJJktAQFBckdFhERkdWU+rZmpVJ8H5bWrVvj+++/N3x2cXGRMRoiIiKSg+ITljp16rCqQkREtQ5ffmgeRTcJAcCRI0cQEhKCiIgIPPHEE/j7779vu31paSm0Wq3RQkREpDRsEjKPohOWTp06YdmyZdi8eTP+9a9/oaCgAF26dMH58+er3Sc1NRUajcawhIaGOjBiIiIi01S8rdnihRUW5YiLi8MjjzyCqKgoPPDAA9i4cSMAYOnSpdXuM3HiRBQVFRmWvLw8R4VLREREdqL4Piw3q1u3LqKionDkyJFqt1Gr1VCr1Q6MioiIyHxCL6ya/I1NQgpWWlqKw4cPIzg4WO5QiIiIrFLx8kNrFmei6ITltddeQ2ZmJnJycvDLL7/g0UcfhVarRUJCgtyhERERkQMpukno5MmTePLJJ3Hu3Dncdddd6Ny5M3bt2oWwsDC5Q6MaxiWsLZKvHjV8rvNwMwwCMCiyolr3Flo+CZRdKsTAlUexKD8NgSMn4cvI/lBv/R7DX52H2fvXQOPqgkfPHJLlGoiodrF2pI+zNQkpOmFZuXKl3CEQERHZhV4vAL780GSKbhIiIiIiAhReYSEiIqqthF4HoddZtb8zYcJCREQkAyYs5mGTEBERESkeKyxEREQyEHq9lRUWvQ2jUT4mLERERDIQOh2EzoqExYp9ayImLERERDIQwso+LMK5Ehb2YSEiIiLFY4WF6CZu9QLw7YgAAF0AAE+dzQIAPLI1BUCK3c6bXajFi8v3oUmwD15YnIRO6dvsdi4iUgaOEjIPExYiIiIZMGExD5uEiIiISPFYYSEiIpIBKyzmYcJCREQkA87DYh42CREREZHiscJCREQkA71eB1hRYdGzSYiIiIjsjX1YzMMmISIiIlI8VliIFKBFgA8yxva48eEJ8yaNW/jLcTyVnYYma30w5OuP0NXP0zDhHREpFyss5mHCQkREJAedDkJlRdLBlx8SERGRvQlhXadbvvyQiIiISGFYYSEiIpKB0Outq7A42cRxTFiIiIhkIKych8XZOt2ySYiIiIgUjwkLERGRDG68S8i6xV4efvhhNGrUCO7u7ggODsbQoUNx+vRpu53PFExYiIiIZFAxD4s1i7307NkT//3vf5GdnY3Vq1fj2LFjePTRR+12PlOwDwsREREZGTt2rOHnsLAwTJgwAQMHDsT169fh6uoqS0xMWKhWK81Yjk9do/Fq18Zyh2I3L3YKAzolo/AZAEiSOxwiMpGtOt1qtVqj9Wq1Gmq12qrYbnbhwgUsX74cXbp0kS1ZAdgkREREJAu9Xmf1AgChoaHQaDSGJTU11SbxvfHGG6hbty78/Pxw4sQJfP311zY5rqWYsBAREdVgeXl5KCoqMiwTJ06scrvk5GRIknTbZc+ePYbtX3/9dezbtw9btmyBi4sLnnnmGQghHHVZlbBJiIiISAZCpwckK5qEdDdGCfn4+MDHx+eO248ePRpPPPHEbbcJDw83/Ozv7w9/f380b94cLVu2RGhoKHbt2oXo6GiLY7YGExYiIiIZOPpdQhUJiGXnulFZKS0ttWh/W2DCQkREJAOh11lXYbHTsOZff/0Vv/76K7p164b69evj77//xuTJk9GkSRPZqisA+7AQERHRTTw8PLBmzRr06tULLVq0wPDhwxEZGYnMzEybjj4yFyssREREMlBqhSUqKgpbt261y7GtwYSFiIhIBkpNWJSq1icsFR2Fbp1Yh5xDackVXHMt5p8/EZmk4v8VDhm+q7sOq86iu26rSGqEWp+wnD9/HsCNiXXIeU2QOwAiqlGKi4uh0Wjscmw3NzcEBQWhIOu/Vh8rKCgIbm5uNohK+SQh5ywwDnDp0iXUr18fJ06csNvDV1totVqEhoYiLy/PpDH9zo73y3S8V6bjvTKPre+XEALFxcUICQmBSmW/cSnXrl1DWVmZ1cdxc3ODu7u7DSJSvlpfYal44DQaDf/ym8jUSYjoBt4v0/FemY73yjy2vF+O+Metu7u70yQatsJhzURERKR4TFiIiIhI8Wp9wqJWqzFlyhRZJ7upKXivzMP7ZTreK9PxXpmH98t51PpOt0RERFTz1foKCxEREdV8TFiIiIhI8ZiwEBERkeIxYSEiIiLFqxUJy/z58xEREQF3d3d06NABP/744223z8zMRIcOHeDu7o7GjRvj008/dVCk8jPnXmVkZECSpErLn3/+6cCI5bF9+3bEx8cjJCQEkiRh3bp1d9zHWZ8rc++VMz9XqampuPfee+Ht7Y2AgAAMHDgQ2dnZd9zPGZ8tS+6VMz9bzqDGJyyrVq3CmDFj8Oabb2Lfvn24//77ERcXhxMnTlS5fU5ODh588EHcf//92LdvHyZNmoRXXnkFq1evdnDkjmfuvaqQnZ2N/Px8w9KsWTMHRSyfkpIStG3bFvPmzTNpe2d+rsy9VxWc8bnKzMzEqFGjsGvXLqSnp6O8vBx9+vRBSUlJtfs467Nlyb2q4IzPllMQNdx9990nRowYYbTu7rvvFhMmTKhy+/Hjx4u7777baN2LL74oOnfubLcYlcLce7Vt2zYBQFy8eNEB0SkXALF27drbbuPMz9XNTLlXfK7+p7CwUAAQmZmZ1W7DZ+sGU+4Vn63arUZXWMrKyrB371706dPHaH2fPn2wY8eOKvfZuXNnpe379u2LPXv24Pr12vuqbkvuVYV27dohODgYvXr1wrZt2+wZZo3lrM+VNfhcAUVFRQAAX1/farfhs3WDKfeqAp+t2qlGJyznzp2DTqdDYGCg0frAwEAUFBRUuU9BQUGV25eXl+PcuXN2i1Vultyr4OBgLFq0CKtXr8aaNWvQokUL9OrVC9u3b3dEyDWKsz5XluBzdYMQAklJSejWrRsiIyOr3Y7Plun3is9W7VYr3tYsSZLRZyFEpXV32r6q9bWROfeqRYsWaNGiheFzdHQ08vLyMHPmTHTv3t2ucdZEzvxcmYPP1Q2jR4/G77//jp9++umO2zr7s2XqveKzVbvV6AqLv78/XFxcKlUICgsLK/2LpEJQUFCV29epUwd+fn52i1VultyrqnTu3BlHjhyxdXg1nrM+V7bibM/Vyy+/jPXr12Pbtm1o2LDhbbd19mfLnHtVFWd7tmqzGp2wuLm5oUOHDkhPTzdan56eji5dulS5T3R0dKXtt2zZgo4dO8LV1dVuscrNkntVlX379iE4ONjW4dV4zvpc2YqzPFdCCIwePRpr1qzB1q1bERERccd9nPXZsuReVcVZni2nIFt3XxtZuXKlcHV1FYsXLxZZWVlizJgxom7duiI3N1cIIcSECRPE0KFDDdv//fffwtPTU4wdO1ZkZWWJxYsXC1dXV/HVV1/JdQkOY+69+vDDD8XatWvFX3/9Jf744w8xYcIEAUCsXr1arktwmOLiYrFv3z6xb98+AUDMnj1b7Nu3Txw/flwIwefqZubeK2d+rl566SWh0WhERkaGyM/PNyxXrlwxbMNn6wZL7pUzP1vOoMYnLEII8cknn4iwsDDh5uYm2rdvbzTsLSEhQfTo0cNo+4yMDNGuXTvh5uYmwsPDxYIFCxwcsXzMuVfvv/++aNKkiXB3dxf169cX3bp1Exs3bpQhaserGB5565KQkCCE4HN1M3PvlTM/V1XdJwAiLS3NsA2frRssuVfO/Gw5A0mIf3pvERERESlUje7DQkRERM6BCQsREREpHhMWIiIiUjwmLERERKR4TFiIiIhI8ZiwEBERkeIxYSEiIiLFY8JCREQ1xvbt2xEfH4+QkBBIkoR169aZfQwhBGbOnInmzZtDrVYjNDQUKSkptg+WbKpWvK2ZiMwTExODe+65B3PmzJE7FCKzlJSUoG3bthg2bBgeeeQRi47x6quvYsuWLZg5cyaioqJQVFSEc+fO2ThSsjUmLEREVGPExcUhLi6u2u/Lysrw1ltvYfny5bh06RIiIyPx/vvvIyYmBgBw+PBhLFiwAH/88QdatGjhoKjJFtgkRORkEhMTkZmZiblz50KSJEiShNzcXLnDIrKJYcOG4eeff8bKlSvx+++/Y8iQIejXrx+OHDkCAPjmm2/QuHFjbNiwAREREQgPD8dzzz2HCxcuyBw53QnfJUTkZIqKihAXF4fIyEhMmzYNAHDXXXfBxcVF5siIzCNJEtauXYuBAwcCAI4dO4ZmzZrh5MmTCAkJMWz3wAMP4L777kNKSgpGjBiBJUuW4J577sEHH3wAnU6HsWPHon79+ti6datMV0KmYJMQkZPRaDRwc3ODp6cngoKC5A6HyGZ+++03CCHQvHlzo/WlpaXw8/MDAOj1epSWlmLZsmWG7RYvXowOHTogOzubzUQKxoSFiIhqBb1eDxcXF+zdu7dSxdDLywsAEBwcjDp16hglNS1btgQAnDhxggmLgjFhISKiWqFdu3bQ6XQoLCzE/fffX+U2Xbt2RXl5OY4dO4YmTZoAAP766y8AQFhYmMNiJfMxYSFyQm5ubtDpdHKHQWS2y5cv4+jRo4bPOTk52L9/P3x9fdG8eXM8/fTTeOaZZzBr1iy0a9cO586dw9atWxEVFYUHH3wQDzzwANq3b4/hw4djzpw50Ov1GDVqFHr37l2pKYmUhaOEiJxQeHg4fvnlF+Tm5uLcuXPQ6/Vyh0Rkkj179qBdu3Zo164dACApKQnt2rXD5MmTAQBpaWl45plnMG7cOLRo0QIPP/wwfvnlF4SGhgIAVCoVvvnmG/j7+6N79+7o378/WrZsiZUrV8p2TWQajhIickJ//fUXEhIScODAAVy9ehU5OTkIDw+XOywiomoxYSEiIiLFY5MQERERKR4TFiIiIlI8JixERESkeExYiIiISPGYsBAREZHiMWEhIiIixWPCQkRERIrHhIWIiIgUjwkLERERKR4TFiIiIlI8JixERESkeExYiIiISPH+H+HP19oP8cLEAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xarray_1fly['theta'].plot()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Input DataArray is not 1-D.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mxarray_1fly\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msortby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimestamps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtheta\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mplot()\n", "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/dataset.py:6965\u001b[0m, in \u001b[0;36mDataset.sortby\u001b[0;34m(self, variables, ascending)\u001b[0m\n\u001b[1;32m 6963\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_array \u001b[38;5;129;01min\u001b[39;00m aligned_other_vars:\n\u001b[1;32m 6964\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 6965\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput DataArray is not 1-D.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6966\u001b[0m (key,) \u001b[38;5;241m=\u001b[39m data_array\u001b[38;5;241m.\u001b[39mdims\n\u001b[1;32m 6967\u001b[0m vars_by_dim[key]\u001b[38;5;241m.\u001b[39mappend(data_array)\n", "\u001b[0;31mValueError\u001b[0m: Input DataArray is not 1-D." ] } ], "source": [ "xarray_1fly.sortby(\"timestamps\")['theta'].plot()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Input DataArray is not 1-D.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[52], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mxarray_1fly\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msortby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimestamps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/dataset.py:6965\u001b[0m, in \u001b[0;36mDataset.sortby\u001b[0;34m(self, variables, ascending)\u001b[0m\n\u001b[1;32m 6963\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_array \u001b[38;5;129;01min\u001b[39;00m aligned_other_vars:\n\u001b[1;32m 6964\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 6965\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput DataArray is not 1-D.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6966\u001b[0m (key,) \u001b[38;5;241m=\u001b[39m data_array\u001b[38;5;241m.\u001b[39mdims\n\u001b[1;32m 6967\u001b[0m vars_by_dim[key]\u001b[38;5;241m.\u001b[39mappend(data_array)\n", "\u001b[0;31mValueError\u001b[0m: Input DataArray is not 1-D." ] } ], "source": [ "xarray_1fly.sortby(\"timestamps\")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xarray_1fly['theta'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xarray_1fly=xarray_1fly.sortby(\"timestamps\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xarray_1fly" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xarray_1fly.to_netcdf('testfly.nc')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.sum(np.isnan(xarray_1fly[\"inx\"]).compute())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.arange(1,5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "lcm.import_dask_matlab_files(path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oneflylist=glob.glob(\"/Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B5_F40_T*.nc\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(oneflylist)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "glob.glob(\"CirclingData_2h_B1_F[0-9].nc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test single fly load" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24 1 1\n", "['inx', 'iny', 'theta', 'r', 'direction', 'speed', 'turning', 'angle']\n", "Speed Logged\n", "theta\n", "r\n", "direction\n", "speed\n", "turning\n", "angle\n", "speed_logged\n", "Circling_Summary_24h_B1_F1.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 1 1\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (Bins_direction: 200, Fly: 1, Batch: 1, Freq: 1000,\n",
       "                           Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n",
       "                           Measure: 4, Bins_turning: 200,\n",
       "                           Bins_speed_logged: 200, Bins_r: 200)\n",
       "Coordinates:\n",
       "  * Bins_direction        (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Fly                   (Fly) int8 1\n",
       "  * Batch                 (Batch) int8 1\n",
       "  * Freq                  (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n",
       "  * Shuffled              (Shuffled) <U17 'Non-shuffled Data' 'Shuffled'\n",
       "  * Bins_angle            (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n",
       "  * Bins_theta            (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "    recording_length      int8 24\n",
       "  * Measure               (Measure) object 'Mean' 'Std' 'Min' 'Max'\n",
       "  * Bins_turning          (Bins_turning) float64 -6.252 -6.189 ... 6.189 6.252\n",
       "  * Bins_speed_logged     (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n",
       "  * Bins_r                (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n",
       "Data variables: (12/19)\n",
       "    direction_bins        (Bins_direction, Fly, Batch) float64 202.0 ... 196.0\n",
       "    direction_psd         (Freq, Fly, Batch, Shuffled) float64 0.0008327 ... ...\n",
       "    angle_bins            (Bins_angle, Fly, Batch) float64 6.663e+03 ... 7.35...\n",
       "    angle_psd             (Freq, Fly, Batch, Shuffled) float64 0.0009525 ... ...\n",
       "    theta_bins            (Bins_theta, Fly, Batch) float64 1.166e+03 ... 436.0\n",
       "    theta_summary         (Measure, Fly, Batch) float64 -0.001312 ... 3.141\n",
       "    ...                    ...\n",
       "    r_bins                (Bins_r, Fly, Batch) float64 4.0 5.0 14.0 ... 0.0 0.0\n",
       "    r_psd                 (Freq, Fly, Batch, Shuffled) float64 0.09462 ... 6....\n",
       "    r_summary             (Measure, Fly, Batch) float64 0.3572 0.0961 ... 0.5467\n",
       "    direction_summary     (Measure, Fly, Batch) float64 0.009362 1.828 ... 3.142\n",
       "    speed_summary         (Measure, Fly, Batch) float64 3.356 6.626 0.0 489.7\n",
       "    angle_summary         (Measure, Fly, Batch) float64 0.0287 0.8736 -1.0 1.0
" ], "text/plain": [ "\n", "Dimensions: (Bins_direction: 200, Fly: 1, Batch: 1, Freq: 1000,\n", " Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n", " Measure: 4, Bins_turning: 200,\n", " Bins_speed_logged: 200, Bins_r: 200)\n", "Coordinates:\n", " * Bins_direction (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imp.reload(lcm)\n", "path='/Users/ryanmaloney/Documents/Matlab/'\n", "\n", "x1=lcm.importonefile(path, d=24, b=1, f=1, save_timeseries=False, override=True)\n", "ping(h)\n", "\n", "x1\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (Freq: 1000, Fly: 1, Batch: 1, Shuffled: 1)>\n",
       "array([[[[1.19346348e-02]]],\n",
       "\n",
       "\n",
       "       [[[1.19343076e-02]]],\n",
       "\n",
       "\n",
       "       [[[1.19339695e-02]]],\n",
       "\n",
       "\n",
       "       [[[1.19336199e-02]]],\n",
       "\n",
       "\n",
       "       [[[1.19332586e-02]]],\n",
       "\n",
       "\n",
       "       [[[1.19328851e-02]]],\n",
       "\n",
       "\n",
       "       [[[1.19324989e-02]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[2.09828208e-07]]],\n",
       "\n",
       "\n",
       "       [[[7.91301036e-06]]],\n",
       "\n",
       "\n",
       "       [[[1.19662968e-05]]],\n",
       "\n",
       "\n",
       "       [[[8.23062709e-06]]],\n",
       "\n",
       "\n",
       "       [[[5.28428461e-07]]],\n",
       "\n",
       "\n",
       "       [[[2.68287287e-07]]],\n",
       "\n",
       "\n",
       "       [[[2.19152874e-05]]]])\n",
       "Coordinates:\n",
       "  * Freq      (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.839 4.919 5.0\n",
       "  * Fly       (Fly) int8 1\n",
       "  * Batch     (Batch) int8 1\n",
       "  * Shuffled  (Shuffled) object 'Non-shuffled Data'
" ], "text/plain": [ "\n", "array([[[[1.19346348e-02]]],\n", "\n", "\n", " [[[1.19343076e-02]]],\n", "\n", "\n", " [[[1.19339695e-02]]],\n", "\n", "\n", " [[[1.19336199e-02]]],\n", "\n", "\n", " [[[1.19332586e-02]]],\n", "\n", "\n", " [[[1.19328851e-02]]],\n", "\n", "\n", " [[[1.19324989e-02]]],\n", "\n", "...\n", "\n", " [[[2.09828208e-07]]],\n", "\n", "\n", " [[[7.91301036e-06]]],\n", "\n", "\n", " [[[1.19662968e-05]]],\n", "\n", "\n", " [[[8.23062709e-06]]],\n", "\n", "\n", " [[[5.28428461e-07]]],\n", "\n", "\n", " [[[2.68287287e-07]]],\n", "\n", "\n", " [[[2.19152874e-05]]]])\n", "Coordinates:\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.839 4.919 5.0\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * Shuffled (Shuffled) object 'Non-shuffled Data'" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p=xr.load_dataarray(\"/Users/ryanmaloney/Continuous Turns/speed_psd_not_shuffled.nc\")\n", "p" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (Freq: 1000, Fly: 1, Batch: 1, Shuffled: 1)>\n",
       "array([[[[6.57800811e-07]]],\n",
       "\n",
       "\n",
       "       [[[6.57856461e-07]]],\n",
       "\n",
       "\n",
       "       [[[6.57913975e-07]]],\n",
       "\n",
       "\n",
       "       [[[6.57973414e-07]]],\n",
       "\n",
       "\n",
       "       [[[6.58034842e-07]]],\n",
       "\n",
       "\n",
       "       [[[6.58098328e-07]]],\n",
       "\n",
       "\n",
       "       [[[6.58163940e-07]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[4.90398873e-06]]],\n",
       "\n",
       "\n",
       "       [[[2.31082712e-06]]],\n",
       "\n",
       "\n",
       "       [[[1.53909382e-05]]],\n",
       "\n",
       "\n",
       "       [[[2.95598859e-06]]],\n",
       "\n",
       "\n",
       "       [[[4.06901022e-06]]],\n",
       "\n",
       "\n",
       "       [[[1.17984613e-05]]],\n",
       "\n",
       "\n",
       "       [[[1.65525675e-05]]]])\n",
       "Coordinates:\n",
       "  * Freq      (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.839 4.919 5.0\n",
       "  * Fly       (Fly) int8 1\n",
       "  * Batch     (Batch) int8 1\n",
       "  * Shuffled  (Shuffled) object 'Shuffled Data'
" ], "text/plain": [ "\n", "array([[[[6.57800811e-07]]],\n", "\n", "\n", " [[[6.57856461e-07]]],\n", "\n", "\n", " [[[6.57913975e-07]]],\n", "\n", "\n", " [[[6.57973414e-07]]],\n", "\n", "\n", " [[[6.58034842e-07]]],\n", "\n", "\n", " [[[6.58098328e-07]]],\n", "\n", "\n", " [[[6.58163940e-07]]],\n", "\n", "...\n", "\n", " [[[4.90398873e-06]]],\n", "\n", "\n", " [[[2.31082712e-06]]],\n", "\n", "\n", " [[[1.53909382e-05]]],\n", "\n", "\n", " [[[2.95598859e-06]]],\n", "\n", "\n", " [[[4.06901022e-06]]],\n", "\n", "\n", " [[[1.17984613e-05]]],\n", "\n", "\n", " [[[1.65525675e-05]]]])\n", "Coordinates:\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.839 4.919 5.0\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * Shuffled (Shuffled) object 'Shuffled Data'" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ps=xr.load_dataarray(\"/Users/ryanmaloney/Continuous Turns/speed_psd_shuffled.nc\")\n", "ps" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (Freq: 1000, Fly: 1, Batch: 1, Shuffled: 2)>\n",
       "array([[[[1.19346348e-02, 6.57800811e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.19343076e-02, 6.57856461e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.19339695e-02, 6.57913975e-07]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[5.28428461e-07, 4.06901022e-06]]],\n",
       "\n",
       "\n",
       "       [[[2.68287287e-07, 1.17984613e-05]]],\n",
       "\n",
       "\n",
       "       [[[2.19152874e-05, 1.65525675e-05]]]])\n",
       "Coordinates:\n",
       "  * Freq      (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.839 4.919 5.0\n",
       "  * Fly       (Fly) int8 1\n",
       "  * Batch     (Batch) int8 1\n",
       "  * Shuffled  (Shuffled) object 'Non-shuffled Data' 'Shuffled Data'
" ], "text/plain": [ "\n", "array([[[[1.19346348e-02, 6.57800811e-07]]],\n", "\n", "\n", " [[[1.19343076e-02, 6.57856461e-07]]],\n", "\n", "\n", " [[[1.19339695e-02, 6.57913975e-07]]],\n", "\n", "\n", " ...,\n", "\n", "\n", " [[[5.28428461e-07, 4.06901022e-06]]],\n", "\n", "\n", " [[[2.68287287e-07, 1.17984613e-05]]],\n", "\n", "\n", " [[[2.19152874e-05, 1.65525675e-05]]]])\n", "Coordinates:\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.839 4.919 5.0\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * Shuffled (Shuffled) object 'Non-shuffled Data' 'Shuffled Data'" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xr.concat([p, ps], dim=\"Shuffled\")" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (Bins_direction: 200, Fly: 1, Batch: 1, Freq: 1000,\n",
       "                           Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n",
       "                           Measure: 4, Bins_turning: 200,\n",
       "                           Bins_speed_logged: 200, Bins_r: 200)\n",
       "Coordinates:\n",
       "  * Bins_direction        (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Fly                   (Fly) int8 1\n",
       "  * Batch                 (Batch) int8 1\n",
       "  * Freq                  (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n",
       "  * Shuffled              (Shuffled) <U16 'Non-shuffle Data' 'Shuffled'\n",
       "  * Bins_angle            (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n",
       "  * Bins_theta            (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Measure               (Measure) object 'Mean' 'Std' 'Min' 'Max'\n",
       "  * Bins_turning          (Bins_turning) float64 -6.252 -6.189 ... 6.189 6.252\n",
       "  * Bins_speed_logged     (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n",
       "  * Bins_r                (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n",
       "    recording_length      int8 24\n",
       "Data variables: (12/19)\n",
       "    direction_bins        (Bins_direction, Fly, Batch) float64 202.0 ... 196.0\n",
       "    direction_psd         (Freq, Fly, Batch, Shuffled) float64 0.0008327 ... ...\n",
       "    angle_bins            (Bins_angle, Fly, Batch) float64 6.663e+03 ... 7.35...\n",
       "    angle_psd             (Freq, Fly, Batch, Shuffled) float64 nan ... 2.58e-05\n",
       "    theta_bins            (Bins_theta, Fly, Batch) float64 1.166e+03 ... 436.0\n",
       "    theta_summary         (Measure, Fly, Batch) float64 -0.001312 ... 3.141\n",
       "    ...                    ...\n",
       "    r_bins                (Bins_r, Fly, Batch) float64 4.0 5.0 14.0 ... 0.0 0.0\n",
       "    r_psd                 (Freq, Fly, Batch, Shuffled) float64 nan ... 6.115e-06\n",
       "    r_summary             (Measure, Fly, Batch) float64 0.3572 0.0961 ... 0.5467\n",
       "    direction_summary     (Measure, Fly, Batch) float64 0.009362 1.828 ... 3.142\n",
       "    speed_summary         (Measure, Fly, Batch) float64 3.356 6.626 0.0 489.7\n",
       "    angle_summary         (Measure, Fly, Batch) float64 0.0287 0.8736 -1.0 1.0
" ], "text/plain": [ "\n", "Dimensions: (Bins_direction: 200, Fly: 1, Batch: 1, Freq: 1000,\n", " Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n", " Measure: 4, Bins_turning: 200,\n", " Bins_speed_logged: 200, Bins_r: 200)\n", "Coordinates:\n", " * Bins_direction (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Fly (Fly) int8 1\n", " * Batch (Batch) int8 1\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'speed_psd' (Freq: 1000, Shuffled: 2)>\n",
       "array([[           nan, 6.57416783e-06],\n",
       "       [           nan, 6.57439323e-06],\n",
       "       [           nan, 6.57462613e-06],\n",
       "       ...,\n",
       "       [           nan, 1.27195389e-05],\n",
       "       [           nan, 4.84185271e-06],\n",
       "       [           nan, 1.95181723e-05]])\n",
       "Coordinates:\n",
       "    Fly               int8 1\n",
       "    Batch             int8 1\n",
       "  * Freq              (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.919 5.0\n",
       "  * Shuffled          (Shuffled) <U16 'Non-shuffle Data' 'Shuffled'\n",
       "    recording_length  int8 24
" ], "text/plain": [ "\n", "array([[ nan, 6.57416783e-06],\n", " [ nan, 6.57439323e-06],\n", " [ nan, 6.57462613e-06],\n", " ...,\n", " [ nan, 1.27195389e-05],\n", " [ nan, 4.84185271e-06],\n", " [ nan, 1.95181723e-05]])\n", "Coordinates:\n", " Fly int8 1\n", " Batch int8 1\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) Get Bins 633\n", "#bleh, it's the first call on xarray that made \"non shuffle\" the variable name." ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'p' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m xr\u001b[38;5;241m.\u001b[39mconcat([xr\u001b[38;5;241m.\u001b[39mDataArray(np\u001b[38;5;241m.\u001b[39mexpand_dims(\u001b[43mp\u001b[49m, axis\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m]), coords\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFreq\u001b[39m\u001b[38;5;124m\"\u001b[39m:f,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFly\u001b[39m\u001b[38;5;124m\"\u001b[39m:x_array\u001b[38;5;241m.\u001b[39mFly, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch\u001b[39m\u001b[38;5;124m\"\u001b[39m:x_array\u001b[38;5;241m.\u001b[39mBatch, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShuffled\u001b[39m\u001b[38;5;124m\"\u001b[39m:[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNon-shuffled Data\u001b[39m\u001b[38;5;124m\"\u001b[39m]}), \n\u001b[1;32m 2\u001b[0m xr\u001b[38;5;241m.\u001b[39mDataArray(np\u001b[38;5;241m.\u001b[39mexpand_dims(ps, axis\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m]), coords\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFreq\u001b[39m\u001b[38;5;124m\"\u001b[39m:f,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFly\u001b[39m\u001b[38;5;124m\"\u001b[39m:x_array\u001b[38;5;241m.\u001b[39mFly, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch\u001b[39m\u001b[38;5;124m\"\u001b[39m:x_array\u001b[38;5;241m.\u001b[39mBatch, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShuffled\u001b[39m\u001b[38;5;124m\"\u001b[39m:[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mShuffled\u001b[39m\u001b[38;5;124m'\u001b[39m]})], dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShuffled\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mNameError\u001b[0m: name 'p' is not defined" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'x' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[73], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mx\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspeed\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined" ] } ], "source": [ "x[\"speed\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xr.DataArray(np.expand_dims(a[0], axis=[1,2]), {\"Bins_turning\":np.mean([a[1][0:-1], a[1][1:]], axis=0),\"Fly\":x1.Fly, \"Batch\":x1.Batch})\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for varlist in list(x1.variables):\n", " if \"summary\" in varlist:\n", " print(varlist)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testlist=glob.glob(\"Summary Data/*\")\n", "testlist[0:4]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_summary=xr.open_mfdataset(testlist[0:10], data_vars=[\"speed_psd\"])\n", "test_summary\n", "# print('hi')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tsm_pds=test_summary[\"speed_psd\"].squeeze().compute().to_dataframe().reset_index()\n", "tsm_pds.dropna()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=tsm_pds.dropna(), x=\"Freq\", y=\"speed_psd\", color=\"Shuffled\").add(so.Line(), so.Agg()).add(so.Band(), so.Est(), group=\"Shuffled\").scale(x=\"log\", y=\"log\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import re\n", "re.search(x1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if \"summary\" in \"speed_logged_summary\":\n", " print('yes')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for summary in list(x1):\n", " re.search(r'*summary',summary)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "x2=lcm.importonefile(path, d=2, b=1, f=2, save_timeseries=False, override=True)\n", "x2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x2=xr.open_dataset(\"Circling_Summary_2h_B1_F2.nc\")\n", "x2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x2[\"speed_psd\"].to_dataframe().reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=x2[\"speed_psd\"].to_dataframe().reset_index(), x=\"Freq\", y=\"speed_psd\", color=\"Shuffled\").add(so.Line(), so.Agg()).add(so.Band(), so.Est(), group=\"Shuffled\").scale(x=\"log\", y=\"log\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x3=lcm.importonefile(path, d=2, b=2, f=1, save_timeseries=False)\n", "x3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# x3=lcm.importonefile(path, d=2, b=2, f=1, save_timeseries=False)\n", "x4=lcm.importonefile(path, d=2, b=2, f=2, save_timeseries=False)\n", "x4" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(x1.dims)\n", "print(x2.dims)\n", "print(x3.dims)\n", "print(x4.dims)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(x1['inx'].name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# for foobar in x1:\n", "# x1[foobar+\"_summary\"]=xr.concat([x1[foobar].mean(dim=\"t\").compute().expand_dims(dim={\"Measure\":[\"Mean\"]}),\n", "# x1[foobar].std(dim=\"t\").compute().expand_dims(dim={\"Measure\":[\"Std\"]}),\n", "# x1[foobar].min(dim=\"t\").compute().expand_dims(dim={\"Measure\":[\"Min\"]}),\n", "# x1[foobar].max(dim=\"t\").compute().expand_dims(dim={\"Measure\":[\"Max\"]}),\n", "# ], dim=\"Measure\")\n", "# x1[foobar+\"_bins\"]=x1[foobar].histplot\n", "# # print(x1[foobar].max().values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.expand_dims(x1[\"speed\"],axis=[1,2]).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "x1_bins=lcm.get_bins(x1)\n", "x1_bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1_bins.to_netcdf('x1_bins.nc')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1_t=xr.open_dataset('x1_bins.nc')\n", "x1_t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=x1_bins[\"speed_psd\"].plot()\n", "plt.xscale(\"log\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)a\n", "x2_bins=lcm.get_bins(x2)\n", "x2_bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1[\"angle\"]\n", "y=x1[\"timestamps\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f=ca.calcfreq(fres=1000, log=True, fmax=5, fmin=1/60/60/24/30)/u.second" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=lcm.calcpowerforfly(x1[\"angle\"], x1[\"timestamps\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xr.DataArray(np.expand_dims(a.squeeze(), axis=[1,2]), coords={\"Frequency\":f,\"Fly\":[1], \"Batch\":[1]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(a[1],a[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f=ca.calcfreq(fres=100, log=True, fmax=5, fmin=1/60/60/24/30)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f/u.second" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[2].power(f/u.second)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1[\"angle\"].compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1[\"angle\"].compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.array(x1[\"timestamps\"], dtype=float)*u.second*10**-9" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ls.autopower()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ls.Autopower()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1[\"angle\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load and calculate power/bins for each fly" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pwd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "path=\"/Users/ryanmaloney/Documents/Matlab/ContinuousData/\"\n", "b=1\n", "d=2\n", "f=1\n", "oneflylist=glob.glob(path+\"CirclingData_\"+str(d)+\"h_B\"+str(b)+\"_F\"+str(f)+\"_T*.nc\")\n", "oneflylist" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xarraysubset=xr.open_mfdataset(oneflylist, combine=\"nested\", concat_dim=\"t\").compute().stack({\"thack\":[\"Trial\", \"t\"]}).squeeze().swap_dims({\"thack\":\"timestamps\"})\n", "xarraysubset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "\n", "duration=[2,24]\n", "batch=np.arange(1,8)\n", "fly=np.arange(1,43)\n", "\n", "duration=[2]\n", "batch=np.arange(1,2)\n", "fly=np.arange(1,2)\n", "\n", "\n", "iterables=[duration, batch, fly]\n", "index=pd.MultiIndex.from_product(iterables, names=[\"Duration\", \"Batch\", \"Fly\"])\n", "index\n", "\n", "Parallel(n_jobs=8, verbose=20)(delayed(lcm.importonefile(path, i[0],i[1],i[2])) for i in index)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2 1 8\n", "Circling_Summary_2h_B1_F8.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 4\n", "Circling_Summary_2h_B1_F4.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 2\n", "Circling_Summary_2h_B1_F2.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 1\n", "Circling_Summary_2h_B1_F1.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 6\n", "Circling_Summary_2h_B1_F6.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 3\n", "Circling_Summary_2h_B1_F3.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 7\n", "Circling_Summary_2h_B1_F7.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 5\n", "Circling_Summary_2h_B1_F5.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 9\n", "Circling_Summary_2h_B1_F9.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 10\n", "Circling_Summary_2h_B1_F10.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 11\n", "Circling_Summary_2h_B1_F11.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 12\n", "Circling_Summary_2h_B1_F12.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 13\n", "Circling_Summary_2h_B1_F13.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 14\n", "Circling_Summary_2h_B1_F14.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 15\n", "Circling_Summary_2h_B1_F15.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 16\n", "Circling_Summary_2h_B1_F16.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 17\n", "Circling_Summary_2h_B1_F17.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 19\n", "Circling_Summary_2h_B1_F19.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 18\n", "Circling_Summary_2h_B1_F18.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 20\n", "Circling_Summary_2h_B1_F20.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 21\n", "Circling_Summary_2h_B1_F21.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 22\n", "Circling_Summary_2h_B1_F22.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 23\n", "Circling_Summary_2h_B1_F23.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 24\n", "Circling_Summary_2h_B1_F24.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 25\n", "Circling_Summary_2h_B1_F25.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 27\n", "Circling_Summary_2h_B1_F27.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 26\n", "Circling_Summary_2h_B1_F26.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 29\n", "Circling_Summary_2h_B1_F29.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 28\n", "Circling_Summary_2h_B1_F28.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 30\n", "Circling_Summary_2h_B1_F30.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 31\n", "Circling_Summary_2h_B1_F31.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 32\n", "Circling_Summary_2h_B1_F32.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 33\n", "Circling_Summary_2h_B1_F33.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 34\n", "Circling_Summary_2h_B1_F34.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 36\n", "Circling_Summary_2h_B1_F36.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 35\n", "Circling_Summary_2h_B1_F35.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 37\n", "Circling_Summary_2h_B1_F37.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 38\n", "Circling_Summary_2h_B1_F38.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 39\n", "Circling_Summary_2h_B1_F39.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 40\n", "Circling_Summary_2h_B1_F40.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 41\n", "Circling_Summary_2h_B1_F41.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 1 42\n", "Circling_Summary_2h_B1_F42.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 1\n", "Circling_Summary_2h_B2_F1.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 2\n", "Circling_Summary_2h_B2_F2.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 4\n", "Circling_Summary_2h_B2_F4.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 3\n", "Circling_Summary_2h_B2_F3.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 5\n", "Circling_Summary_2h_B2_F5.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 6\n", "Circling_Summary_2h_B2_F6.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 7\n", "Circling_Summary_2h_B2_F7.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 9\n", "Circling_Summary_2h_B2_F9.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 11\n", "Circling_Summary_2h_B2_F11.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 13\n", "Circling_Summary_2h_B2_F13.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 15\n", "Circling_Summary_2h_B2_F15.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 17\n", "Circling_Summary_2h_B2_F17.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 19\n", "Circling_Summary_2h_B2_F19.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 21\n", "Circling_Summary_2h_B2_F21.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=8)]: Done 2 tasks | elapsed: 2.7s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2 2 8\n", "Circling_Summary_2h_B2_F8.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 10\n", "Circling_Summary_2h_B2_F10.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 12\n", "Circling_Summary_2h_B2_F12.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 16\n", "Circling_Summary_2h_B2_F16.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 18\n", "Circling_Summary_2h_B2_F18.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 14\n", "Circling_Summary_2h_B2_F14.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 20\n", "Circling_Summary_2h_B2_F20.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 22\n", "Circling_Summary_2h_B2_F22.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 23\n", "Circling_Summary_2h_B2_F23.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 25\n", "Circling_Summary_2h_B2_F25.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 27\n", "Circling_Summary_2h_B2_F27.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 29\n", "Circling_Summary_2h_B2_F29.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 31\n", "Circling_Summary_2h_B2_F31.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 33\n", "Circling_Summary_2h_B2_F33.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 35\n", "Circling_Summary_2h_B2_F35.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 37\n", "Circling_Summary_2h_B2_F37.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 24\n", "Circling_Summary_2h_B2_F24.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 26\n", "Circling_Summary_2h_B2_F26.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 28\n", "Circling_Summary_2h_B2_F28.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 32\n", "Circling_Summary_2h_B2_F32.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 30\n", "Circling_Summary_2h_B2_F30.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 34\n", "2 2 38\n", "Circling_Summary_2h_B2_F38.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "Circling_Summary_2h_B2_F34.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 36\n", "Circling_Summary_2h_B2_F36.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 39\n", "Circling_Summary_2h_B2_F39.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 5\n", "Circling_Summary_2h_B3_F5.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 1\n", "Circling_Summary_2h_B3_F1.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 9\n", "Circling_Summary_2h_B3_F9.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 13\n", "Circling_Summary_2h_B3_F13.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 17\n", "Circling_Summary_2h_B3_F17.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 25\n", "Circling_Summary_2h_B3_F25.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 21\n", "Circling_Summary_2h_B3_F21.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 40\n", "Circling_Summary_2h_B2_F40.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 6\n", "Circling_Summary_2h_B3_F6.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 2\n", "Circling_Summary_2h_B3_F2.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 14\n", "Circling_Summary_2h_B3_F14.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 10\n", "Circling_Summary_2h_B3_F10.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 22\n", "Circling_Summary_2h_B3_F22.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 18\n", "Circling_Summary_2h_B3_F18.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 26\n", "Circling_Summary_2h_B3_F26.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 41\n", "Circling_Summary_2h_B2_F41.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 3\n", "Circling_Summary_2h_B3_F3.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 7\n", "Circling_Summary_2h_B3_F7.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 11\n", "Circling_Summary_2h_B3_F11.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 15\n", "Circling_Summary_2h_B3_F15.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 23\n", "Circling_Summary_2h_B3_F23.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 19\n", "Circling_Summary_2h_B3_F19.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 27\n", "Circling_Summary_2h_B3_F27.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 2 42\n", "Circling_Summary_2h_B2_F42.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 4\n", "Circling_Summary_2h_B3_F4.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 8\n", "Circling_Summary_2h_B3_F8.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 16\n", "Circling_Summary_2h_B3_F16.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 12\n", "Circling_Summary_2h_B3_F12.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 24\n", "Circling_Summary_2h_B3_F24.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 28\n", "Circling_Summary_2h_B3_F28.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 20\n", "Circling_Summary_2h_B3_F20.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 33\n", "Circling_Summary_2h_B3_F33.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 29\n", "Circling_Summary_2h_B3_F29.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 3\n", "Circling_Summary_2h_B4_F3.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 37\n", "Circling_Summary_2h_B3_F37.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 41\n", "Circling_Summary_2h_B3_F41.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 7\n", "Circling_Summary_2h_B4_F7.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 11\n", "Circling_Summary_2h_B4_F11.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 15\n", "Circling_Summary_2h_B4_F15.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 34\n", "Circling_Summary_2h_B3_F34.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 38\n", "Circling_Summary_2h_B3_F38.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 4\n", "Circling_Summary_2h_B4_F4.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 30\n", "Circling_Summary_2h_B3_F30.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 42\n", "Circling_Summary_2h_B3_F42.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 8\n", "Circling_Summary_2h_B4_F8.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 16\n", "Circling_Summary_2h_B4_F16.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 12\n", "Circling_Summary_2h_B4_F12.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 35\n", "Circling_Summary_2h_B3_F35.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=8)]: Done 64 tasks | elapsed: 2.9s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2 4 5\n", "Circling_Summary_2h_B4_F5.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 1\n", "Circling_Summary_2h_B4_F1.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 39\n", "Circling_Summary_2h_B3_F39.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 31\n", "Circling_Summary_2h_B3_F31.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 9\n", "Circling_Summary_2h_B4_F9.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 17\n", "Circling_Summary_2h_B4_F17.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 13\n", "Circling_Summary_2h_B4_F13.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 36\n", "Circling_Summary_2h_B3_F36.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 2\n", "Circling_Summary_2h_B4_F2.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 6\n", "Circling_Summary_2h_B4_F6.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 40\n", "Circling_Summary_2h_B3_F40.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 10\n", "Circling_Summary_2h_B4_F10.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 3 32\n", "Circling_Summary_2h_B3_F32.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 18\n", "Circling_Summary_2h_B4_F18.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 14\n", "Circling_Summary_2h_B4_F14.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 19\n", "Circling_Summary_2h_B4_F19.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 27\n", "Circling_Summary_2h_B4_F27.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 35\n", "Circling_Summary_2h_B4_F35.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 9\n", "Circling_Summary_2h_B5_F9.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 1\n", "Circling_Summary_2h_B5_F1.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 17\n", "Circling_Summary_2h_B5_F17.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 25\n", "Circling_Summary_2h_B5_F25.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 33\n", "Circling_Summary_2h_B5_F33.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 20\n", "Circling_Summary_2h_B4_F20.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 28\n", "Circling_Summary_2h_B4_F28.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 10\n", "Circling_Summary_2h_B5_F10.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 36\n", "Circling_Summary_2h_B4_F36.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 2\n", "Circling_Summary_2h_B5_F2.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 26\n", "Circling_Summary_2h_B5_F26.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 18\n", "Circling_Summary_2h_B5_F18.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 34\n", "Circling_Summary_2h_B5_F34.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 21\n", "Circling_Summary_2h_B4_F21.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 29\n", "Circling_Summary_2h_B4_F29.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 37\n", "Circling_Summary_2h_B4_F37.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 11\n", "Circling_Summary_2h_B5_F11.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 3\n", "Circling_Summary_2h_B5_F3.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 19\n", "Circling_Summary_2h_B5_F19.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 27\n", "Circling_Summary_2h_B5_F27.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 35\n", "Circling_Summary_2h_B5_F35.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 22\n", "Circling_Summary_2h_B4_F22.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 30\n", "Circling_Summary_2h_B4_F30.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 38\n", "Circling_Summary_2h_B4_F38.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 12\n", "Circling_Summary_2h_B5_F12.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 4\n", "Circling_Summary_2h_B5_F4.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 20\n", "Circling_Summary_2h_B5_F20.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 28\n", "Circling_Summary_2h_B5_F28.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 36\n", "Circling_Summary_2h_B5_F36.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 23\n", "Circling_Summary_2h_B4_F23.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 31\n", "Circling_Summary_2h_B4_F31.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 39\n", "Circling_Summary_2h_B4_F39.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 13\n", "Circling_Summary_2h_B5_F13.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 5\n", "Circling_Summary_2h_B5_F5.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 29\n", "Circling_Summary_2h_B5_F29.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 21\n", "Circling_Summary_2h_B5_F21.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 37\n", "Circling_Summary_2h_B5_F37.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 24\n", "Circling_Summary_2h_B4_F24.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 40\n", "Circling_Summary_2h_B4_F40.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 32\n", "Circling_Summary_2h_B4_F32.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 14\n", "Circling_Summary_2h_B5_F14.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 30\n", "Circling_Summary_2h_B5_F30.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 6\n", "Circling_Summary_2h_B5_F6.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 22\n", "Circling_Summary_2h_B5_F22.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 38\n", "Circling_Summary_2h_B5_F38.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 25\n", "Circling_Summary_2h_B4_F25.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 41\n", "Circling_Summary_2h_B4_F41.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 33\n", "Circling_Summary_2h_B4_F33.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 15\n", "Circling_Summary_2h_B5_F15.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 7\n", "Circling_Summary_2h_B5_F7.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 31\n", "Circling_Summary_2h_B5_F31.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 23\n", "Circling_Summary_2h_B5_F23.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 39\n", "Circling_Summary_2h_B5_F39.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 26\n", "Circling_Summary_2h_B4_F26.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 42\n", "Circling_Summary_2h_B4_F42.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 4 34\n", "Circling_Summary_2h_B4_F34.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 16\n", "Circling_Summary_2h_B5_F16.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 8\n", "Circling_Summary_2h_B5_F8.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 32\n", "Circling_Summary_2h_B5_F32.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 24\n", "Circling_Summary_2h_B5_F24.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 40\n", "Circling_Summary_2h_B5_F40.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 5 41\n", "Circling_Summary_2h_B5_F41.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 7\n", "Circling_Summary_2h_B6_F7.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 31\n", "Circling_Summary_2h_B6_F31.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 15\n", "Circling_Summary_2h_B6_F15.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 23\n", "Circling_Summary_2h_B6_F23.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 5\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F5_T*.nc\n", "End 2 7 5\n", "2 6 39\n", "Circling_Summary_2h_B6_F39.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 13\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F13_T*.nc\n", "End 2 7 13\n", "2 5 42\n", "Circling_Summary_2h_B5_F42.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 8\n", "Circling_Summary_2h_B6_F8.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 16\n", "Circling_Summary_2h_B6_F16.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 32\n", "Circling_Summary_2h_B6_F32.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 24\n", "Circling_Summary_2h_B6_F24.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 40\n", "Circling_Summary_2h_B6_F40.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 6\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F6_T*.nc\n", "End 2 7 6\n", "2 7 14\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F14_T*.nc\n", "End 2 7 14\n", "2 6 1\n", "Circling_Summary_2h_B6_F1.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 9\n", "Circling_Summary_2h_B6_F9.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 17\n", "Circling_Summary_2h_B6_F17.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 33\n", "Circling_Summary_2h_B6_F33.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 25\n", "Circling_Summary_2h_B6_F25.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 41\n", "Circling_Summary_2h_B6_F41.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 7\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F7_T*.nc\n", "End 2 7 7\n", "2 7 15\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F15_T*.nc\n", "End 2 7 15\n", "2 6 2\n", "Circling_Summary_2h_B6_F2.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 10\n", "Circling_Summary_2h_B6_F10.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 18\n", "Circling_Summary_2h_B6_F18.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 34\n", "Circling_Summary_2h_B6_F34.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 26\n", "Circling_Summary_2h_B6_F26.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 16\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F16_T*.nc\n", "End 2 7 16\n", "2 6 42\n", "Circling_Summary_2h_B6_F42.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 8\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F8_T*.nc\n", "End 2 7 8\n", "2 6 3\n", "Circling_Summary_2h_B6_F3.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 19\n", "Circling_Summary_2h_B6_F19.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 11\n", "Circling_Summary_2h_B6_F11.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 27\n", "Circling_Summary_2h_B6_F27.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 17\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F17_T*.nc\n", "End 2 7 17\n", "2 6 35\n", "Circling_Summary_2h_B6_F35.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 9\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F9_T*.nc\n", "End 2 7 9\n", "2 7 1\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F1_T*.nc\n", "End 2 7 1\n", "2 6 4\n", "Circling_Summary_2h_B6_F4.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 20\n", "Circling_Summary_2h_B6_F20.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 12\n", "Circling_Summary_2h_B6_F12.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 28\n", "Circling_Summary_2h_B6_F28.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 36\n", "Circling_Summary_2h_B6_F36.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 18\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F18_T*.nc\n", "End 2 7 18\n", "2 7 10\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F10_T*.nc\n", "End 2 7 10\n", "2 7 2\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F2_T*.nc\n", "End 2 7 2\n", "2 6 5\n", "Circling_Summary_2h_B6_F5.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 21\n", "Circling_Summary_2h_B6_F21.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 13\n", "Circling_Summary_2h_B6_F13.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 29\n", "Circling_Summary_2h_B6_F29.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 19\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F19_T*.nc\n", "End 2 7 19\n", "2 6 37\n", "Circling_Summary_2h_B6_F37.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 11\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F11_T*.nc\n", "End 2 7 11\n", "2 7 3\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F3_T*.nc\n", "End 2 7 3\n", "2 6 6\n", "Circling_Summary_2h_B6_F6.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 22\n", "Circling_Summary_2h_B6_F22.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 14\n", "Circling_Summary_2h_B6_F14.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 30\n", "Circling_Summary_2h_B6_F30.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 6 38\n", "Circling_Summary_2h_B6_F38.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 20\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F20_T*.nc\n", "End 2 7 20\n", "2 7 4\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F4_T*.nc\n", "End 2 7 4\n", "2 7 12\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F12_T*.nc\n", "End 2 7 12\n", "2 7 21\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F21_T*.nc\n", "End 2 7 21\n", "2 7 29\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F29_T*.nc\n", "End 2 7 29\n", "2 7 37\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F37_T*.nc\n", "End 2 7 37\n", "24 1 3\n", "Circling_Summary_24h_B1_F3.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "24 1 19\n", "24 1 11\n", "24 1 27\n", "24 1 35\n", "2 7 22\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F22_T*.nc\n", "End 2 7 22\n", "2 7 30\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F30_T*.nc\n", "End 2 7 30\n", "2 7 38\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F38_T*.nc\n", "End 2 7 38\n", "24 1 4\n", "2 7 23\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F23_T*.nc\n", "End 2 7 23\n", "2 7 31\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F31_T*.nc\n", "End 2 7 31\n", "2 7 39\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F39_T*.nc\n", "End 2 7 39\n", "2 7 24\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F24_T*.nc\n", "End 2 7 24\n", "2 7 32\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F32_T*.nc\n", "End 2 7 32\n", "2 7 40\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F40_T*.nc\n", "End 2 7 40\n", "2 7 25\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F25_T*.nc\n", "End 2 7 25\n", "2 7 33\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F33_T*.nc\n", "End 2 7 33\n", "2 7 41\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F41_T*.nc\n", "End 2 7 41\n", "2 7 26\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F26_T*.nc\n", "End 2 7 26\n", "2 7 34\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F34_T*.nc\n", "End 2 7 34\n", "2 7 42\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F42_T*.nc\n", "End 2 7 42\n", "2 7 27\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F27_T*.nc\n", "End 2 7 27\n", "2 7 35\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F35_T*.nc\n", "End 2 7 35\n", "24 1 1\n", "Circling_Summary_24h_B1_F1.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "2 7 28\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F28_T*.nc\n", "End 2 7 28\n", "2 7 36\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_2h_B7_F36_T*.nc\n", "End 2 7 36\n", "24 1 2\n", "Circling_Summary_24h_B1_F2.nc already exists in /Users/ryanmaloney/Documents/Matlab/ , skipping this\n", "24 2 1\n", "24 2 9\n", "24 2 17\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F1.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F35.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F9.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F17.nc\n", "Circling_Summary_24h_B1_F11.nc\n", "Circling_Summary_24h_B1_F27.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F19.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F4.nc\n", "End 24 1 35\n", "24 1 36\n", "End 24 2 1\n", "24 2 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F2.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F36.nc\n", "End 24 2 2\n", "24 2 3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F3.nc\n", "End 24 1 11\n", "24 1 12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F12.nc\n", "End 24 1 27\n", "24 1 28\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F28.nc\n", "End 24 2 3\n", "24 2 4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F4.nc\n", "End 24 1 36\n", "24 1 37\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F37.nc\n", "End 24 2 9\n", "24 2 10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F10.nc\n", "End 24 2 17\n", "24 2 18\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F18.nc\n", "End 24 1 4\n", "24 1 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F5.nc\n", "End 24 1 37\n", "24 1 38\n", "End 24 1 12\n", "24 1 13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F13.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F38.nc\n", "End 24 1 19\n", "24 1 20\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F20.nc\n", "End 24 1 5\n", "24 1 6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F6.nc\n", "End 24 2 18\n", "24 2 19\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F19.nc\n", "End 24 2 4\n", "24 2 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F5.nc\n", "End 24 1 6\n", "24 1 7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F7.nc\n", "End 24 1 13\n", "24 1 14\n", "End 24 2 10\n", "24 2 11\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F14.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F11.nc\n", "End 24 1 14\n", "24 1 15\n", "End 24 1 28\n", "24 1 29\n", "End 24 2 19\n", "24 2 20\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F20.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F15.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F29.nc\n", "End 24 2 11\n", "24 2 12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F12.nc\n", "End 24 1 7\n", "24 1 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F8.nc\n", "End 24 2 5\n", "24 2 6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F6.nc\n", "End 24 1 38\n", "24 1 39\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F39.nc\n", "End 24 2 6\n", "24 2 7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F7.nc\n", "End 24 1 8\n", "24 1 9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F9.nc\n", "End 24 1 20\n", "24 1 21\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F21.nc\n", "End 24 1 15\n", "24 1 16\n", "End 24 2 20\n", "24 2 21\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F21.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F16.nc\n", "End 24 2 21\n", "24 2 22\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F22.nc\n", "End 24 2 12\n", "24 2 13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F13.nc\n", "End 24 2 7\n", "24 2 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F8.nc\n", "End 24 1 9\n", "24 1 10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F10.nc\n", "End 24 1 29\n", "24 1 30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F30.nc\n", "End 24 1 39\n", "24 1 40\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F40.nc\n", "End 24 2 22\n", "24 2 23\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F23.nc\n", "End 24 2 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "24 2 25\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F25.nc\n", "End 24 1 40\n", "24 1 41\n", "End 24 2 13\n", "24 2 14\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F14.nc\n", "End 24 1 16\n", "24 1 17\n", "Circling_Summary_24h_B1_F41.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F17.nc\n", "End 24 2 25\n", "24 2 26\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F26.nc\n", "End 24 2 23\n", "24 2 24\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B2_F24_T*.nc\n", "End 24 2 24\n", "24 2 33\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F33.nc\n", "End 24 1 17\n", "24 1 18\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F18.nc\n", "End 24 1 21\n", "24 1 22\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F22.nc\n", "End 24 1 22\n", "24 1 23\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F23.nc\n", "End 24 2 26\n", "24 2 27\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F27.nc\n", "End 24 2 33\n", "24 2 34\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F34.nc\n", "End 24 1 10\n", "24 2 41\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F41.nc\n", "End 24 1 41\n", "24 1 42\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F42.nc\n", "End 24 1 42\n", "24 3 7\n", "Only One trial detected for Circling_Summary_24h_B3_F7.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B3_F7.nc\n", "End 24 2 34\n", "24 2 35\n", "End 24 2 14\n", "24 2 15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F35.nc\n", "End 24 3 7\n", "24 3 8\n", "Only One trial detected for Circling_Summary_24h_B3_F8.nc\n", "Circling_Summary_24h_B3_F8.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F15.nc\n", "End 24 3 8\n", "24 3 9\n", "Only One trial detected for Circling_Summary_24h_B3_F9.nc\n", "Circling_Summary_24h_B3_F9.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 9\n", "24 3 10\n", "Only One trial detected for Circling_Summary_24h_B3_F10.nc\n", "Circling_Summary_24h_B3_F10.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 10\n", "24 3 11\n", "Only One trial detected for Circling_Summary_24h_B3_F11.nc\n", "Circling_Summary_24h_B3_F11.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 1 23\n", "24 1 24\n", "Couldn't Find any files here: /Users/ryanmaloney/Documents/Matlab/CirclingData_24h_B1_F24_T*.nc\n", "End 24 1 24\n", "24 1 25\n", "End 24 3 11\n", "24 3 12\n", "Only One trial detected for Circling_Summary_24h_B3_F12.nc\n", "Circling_Summary_24h_B3_F12.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F25.nc\n", "End 24 2 41\n", "24 2 42\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F42.nc\n", "End 24 3 12\n", "24 3 13\n", "Only One trial detected for Circling_Summary_24h_B3_F13.nc\n", "Circling_Summary_24h_B3_F13.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 13\n", "24 3 14\n", "Only One trial detected for Circling_Summary_24h_B3_F14.nc\n", "Circling_Summary_24h_B3_F14.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 14\n", "24 3 15\n", "Only One trial detected for Circling_Summary_24h_B3_F15.nc\n", "Circling_Summary_24h_B3_F15.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 15\n", "24 3 16\n", "Only One trial detected for Circling_Summary_24h_B3_F16.nc\n", "Circling_Summary_24h_B3_F16.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 2 42\n", "24 3 1\n", "Only One trial detected for Circling_Summary_24h_B3_F1.nc\n", "Circling_Summary_24h_B3_F1.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 16\n", "24 3 17\n", "Only One trial detected for Circling_Summary_24h_B3_F17.nc\n", "Circling_Summary_24h_B3_F17.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 1\n", "24 3 2\n", "Only One trial detected for Circling_Summary_24h_B3_F2.nc\n", "Circling_Summary_24h_B3_F2.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 17\n", "24 3 18\n", "Only One trial detected for Circling_Summary_24h_B3_F18.nc\n", "Circling_Summary_24h_B3_F18.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 2 27\n", "24 2 28\n", "End 24 3 2\n", "24 3 3\n", "Only One trial detected for Circling_Summary_24h_B3_F3.nc\n", "Circling_Summary_24h_B3_F3.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F28.nc\n", "End 24 3 3\n", "24 3 4\n", "Only One trial detected for Circling_Summary_24h_B3_F4.nc\n", "Circling_Summary_24h_B3_F4.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 4\n", "24 3 5\n", "Only One trial detected for Circling_Summary_24h_B3_F5.nc\n", "Circling_Summary_24h_B3_F5.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 18\n", "24 3 19\n", "Only One trial detected for Circling_Summary_24h_B3_F19.nc\n", "Circling_Summary_24h_B3_F19.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 5\n", "24 3 6\n", "Only One trial detected for Circling_Summary_24h_B3_F6.nc\n", "Circling_Summary_24h_B3_F6.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 19\n", "24 3 20\n", "Only One trial detected for Circling_Summary_24h_B3_F20.nc\n", "Circling_Summary_24h_B3_F20.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 6\n", "24 3 23\n", "Only One trial detected for Circling_Summary_24h_B3_F23.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B3_F23.nc\n", "End 24 3 20\n", "24 3 21\n", "Only One trial detected for Circling_Summary_24h_B3_F21.nc\n", "Circling_Summary_24h_B3_F21.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 23\n", "24 3 24\n", "Only One trial detected for Circling_Summary_24h_B3_F24.nc\n", "Circling_Summary_24h_B3_F24.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 2 35\n", "24 2 36\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F36.nc\n", "End 24 1 30\n", "24 1 31\n", "End 24 3 24\n", "24 3 25\n", "Only One trial detected for Circling_Summary_24h_B3_F25.nc\n", "Circling_Summary_24h_B3_F25.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F31.nc\n", "End 24 3 25\n", "24 3 26\n", "Only One trial detected for Circling_Summary_24h_B3_F26.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B3_F26.nc\n", "End 24 1 18\n", "End 24 3 21\n", "24 3 22\n", "Only One trial detected for Circling_Summary_24h_B3_F22.nc\n", "Circling_Summary_24h_B3_F22.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "24 3 31\n", "Only One trial detected for Circling_Summary_24h_B3_F31.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B3_F31.nc\n", "End 24 3 26\n", "24 3 27\n", "Only One trial detected for Circling_Summary_24h_B3_F27.nc\n", "Circling_Summary_24h_B3_F27.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 22\n", "24 3 39\n", "Only One trial detected for Circling_Summary_24h_B3_F39.nc\n", "Circling_Summary_24h_B3_F39.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 39\n", "24 3 40\n", "Only One trial detected for Circling_Summary_24h_B3_F40.nc\n", "Circling_Summary_24h_B3_F40.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 31\n", "24 3 32\n", "Only One trial detected for Circling_Summary_24h_B3_F32.nc\n", "Circling_Summary_24h_B3_F32.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 40\n", "24 3 41\n", "Only One trial detected for Circling_Summary_24h_B3_F41.nc\n", "Circling_Summary_24h_B3_F41.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 41\n", "24 3 42\n", "Only One trial detected for Circling_Summary_24h_B3_F42.nc\n", "Circling_Summary_24h_B3_F42.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 32\n", "24 3 33\n", "Only One trial detected for Circling_Summary_24h_B3_F33.nc\n", "Circling_Summary_24h_B3_F33.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 27\n", "24 3 28\n", "Only One trial detected for Circling_Summary_24h_B3_F28.nc\n", "Circling_Summary_24h_B3_F28.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 28\n", "24 3 29\n", "Only One trial detected for Circling_Summary_24h_B3_F29.nc\n", "Circling_Summary_24h_B3_F29.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 42\n", "24 4 1\n", "End 24 3 33\n", "24 3 34\n", "Only One trial detected for Circling_Summary_24h_B3_F34.nc\n", "Circling_Summary_24h_B3_F34.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 34\n", "24 3 35\n", "Only One trial detected for Circling_Summary_24h_B3_F35.nc\n", "Circling_Summary_24h_B3_F35.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 35\n", "24 3 36\n", "Only One trial detected for Circling_Summary_24h_B3_F36.nc\n", "Circling_Summary_24h_B3_F36.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 2 28\n", "24 2 29\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F29.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 29\n", "24 3 30\n", "Only One trial detected for Circling_Summary_24h_B3_F30.nc\n", "Circling_Summary_24h_B3_F30.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F1.nc\n", "End 24 3 36\n", "24 3 37\n", "Only One trial detected for Circling_Summary_24h_B3_F37.nc\n", "Circling_Summary_24h_B3_F37.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 30\n", "24 4 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F5.nc\n", "End 24 3 37\n", "24 3 38\n", "Only One trial detected for Circling_Summary_24h_B3_F38.nc\n", "Circling_Summary_24h_B3_F38.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 3 38\n", "24 4 13\n", "End 24 2 36\n", "24 2 37\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F37.nc\n", "End 24 4 5\n", "24 4 6\n", "End 24 2 15\n", "24 2 16\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F16.nc\n", "Circling_Summary_24h_B4_F13.nc\n", "End 24 2 37\n", "24 2 38\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F38.nc\n", "End 24 2 29\n", "24 2 30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F30.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F6.nc\n", "End 24 1 25\n", "24 1 26\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F26.nc\n", "End 24 2 30\n", "24 2 31\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F31.nc\n", "End 24 2 38\n", "24 2 39\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F39.nc\n", "End 24 1 26\n", "24 4 21\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 1 31\n", "24 1 32\n", "Circling_Summary_24h_B4_F21.nc\n", "End 24 2 16\n", "24 4 29\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F29.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 2 39\n", "24 2 40\n", "Circling_Summary_24h_B1_F32.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F40.nc\n", "End 24 2 40\n", "24 4 37\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F37.nc\n", "End 24 2 31\n", "24 2 32\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B2_F32.nc\n", "End 24 2 32\n", "24 5 3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F3.nc\n", "End 24 4 29\n", "24 4 30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F30.nc\n", "End 24 4 37\n", "24 4 38\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F38.nc\n", "End 24 5 3\n", "24 5 4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F4.nc\n", "End 24 4 1\n", "24 4 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F2.nc\n", "End 24 4 13\n", "24 4 14\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F14.nc\n", "End 24 1 32\n", "24 1 33\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F33.nc\n", "End 24 4 14\n", "24 4 15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F15.nc\n", "End 24 4 6\n", "24 4 7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F7.nc\n", "End 24 4 2\n", "24 4 3\n", "End 24 5 4\n", "24 5 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F3.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F5.nc\n", "End 24 4 15\n", "24 4 16\n", "End 24 4 38\n", "24 4 39\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F16.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F39.nc\n", "End 24 4 30\n", "24 4 31\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F31.nc\n", "End 24 1 33\n", "24 1 34\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B1_F34.nc\n", "End 24 1 34\n", "24 5 6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F6.nc\n", "End 24 4 21\n", "24 4 22\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F22.nc\n", "End 24 4 3\n", "24 4 4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F4.nc\n", "End 24 5 5\n", "24 5 7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F7.nc\n", "End 24 4 7\n", "24 4 8\n", "Only One trial detected for Circling_Summary_24h_B4_F8.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F8.nc\n", "End 24 4 31\n", "24 4 32\n", "End 24 4 39\n", "24 4 40\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F32.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F40.nc\n", "End 24 4 8\n", "24 4 9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F9.nc\n", "End 24 5 6\n", "24 5 8\n", "End 24 4 4\n", "24 5 9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F9.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F8.nc\n", "End 24 4 16\n", "24 4 17\n", "End 24 5 7\n", "24 5 10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F10.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F17.nc\n", "End 24 5 9\n", "24 5 11\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F11.nc\n", "End 24 5 11\n", "24 5 12\n", "End 24 4 9\n", "24 4 10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F12.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F10.nc\n", "End 24 5 10\n", "24 5 13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F13.nc\n", "End 24 4 40\n", "24 4 41\n", "Only One trial detected for Circling_Summary_24h_B4_F41.nc\n", "Circling_Summary_24h_B4_F41.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 4 41\n", "24 4 42\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 4 32\n", "24 4 33\n", "Circling_Summary_24h_B4_F42.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F33.nc\n", "End 24 4 22\n", "24 4 23\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F23.nc\n", "End 24 4 42\n", "24 5 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F1.nc\n", "End 24 4 23\n", "24 4 24\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F24.nc\n", "End 24 5 8\n", "24 5 14\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F14.nc\n", "End 24 5 13\n", "24 5 15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F15.nc\n", "End 24 4 17\n", "24 4 18\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F18.nc\n", "End 24 5 12\n", "24 5 16\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F16.nc\n", "End 24 4 10\n", "24 4 11\n", "Only One trial detected for Circling_Summary_24h_B4_F11.nc\n", "Circling_Summary_24h_B4_F11.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 4 11\n", "24 4 12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F12.nc\n", "End 24 4 33\n", "24 4 34\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F34.nc\n", "End 24 5 16\n", "24 5 17\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 5 1\n", "24 5 2\n", "Circling_Summary_24h_B5_F17.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F2.nc\n", "End 24 4 24\n", "24 4 25\n", "Only One trial detected for Circling_Summary_24h_B4_F25.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F25.nc\n", "End 24 4 25\n", "24 4 26\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F26.nc\n", "End 24 5 2\n", "24 5 18\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F18.nc\n", "End 24 4 34\n", "24 4 35\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F35.nc\n", "End 24 5 14\n", "24 5 19\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F19.nc\n", "End 24 5 18\n", "24 5 20\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F20.nc\n", "End 24 5 20\n", "24 5 21\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F21.nc\n", "End 24 4 18\n", "24 4 19\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F19.nc\n", "End 24 4 35\n", "24 4 36\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F36.nc\n", "End 24 5 19\n", "End 24 5 15\n", "24 5 22\n", "24 5 23\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F23.nc\n", "End 24 5 17\n", "24 5 24\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F24.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 4 12\n", "24 5 25\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F25.nc\n", "Circling_Summary_24h_B5_F22.nc\n", "End 24 5 25\n", "24 5 26\n", "End 24 5 23\n", "24 5 27\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F26.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F27.nc\n", "End 24 4 26\n", "24 4 27\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F27.nc\n", "End 24 5 21\n", "24 5 28\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F28.nc\n", "End 24 5 24\n", "24 5 29\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F29.nc\n", "End 24 4 19\n", "24 4 20\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F20.nc\n", "End 24 4 36\n", "24 5 30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F30.nc\n", "End 24 5 26\n", "24 5 31\n", "End 24 5 27\n", "24 5 32\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F32.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F31.nc\n", "End 24 5 32\n", "24 5 33\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F33.nc\n", "End 24 5 28\n", "24 5 34\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F34.nc\n", "End 24 5 34\n", "24 5 35\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F35.nc\n", "End 24 5 31\n", "24 5 36\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=8)]: Done 476 tasks | elapsed: 122.4min\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F36.nc\n", "End 24 5 29\n", "24 5 37\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F37.nc\n", "End 24 4 27\n", "24 4 28\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B4_F28.nc\n", "End 24 5 22\n", "24 5 38\n", "End 24 5 37\n", "24 5 39\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F38.nc\n", "End 24 5 30\n", "24 5 40\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F39.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F40.nc\n", "End 24 4 20\n", "24 5 41\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F41.nc\n", "End 24 5 35\n", "24 5 42\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B5_F42.nc\n", "End 24 5 33\n", "24 6 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F1.nc\n", "End 24 5 41\n", "24 6 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F2.nc\n", "End 24 6 1\n", "24 6 3\n", "End 24 5 36\n", "24 6 4\n", "Only One trial detected for Circling_Summary_24h_B6_F4.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F4.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F3.nc\n", "End 24 6 4\n", "24 6 5\n", "End 24 5 38\n", "24 6 6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F6.nc\n", "End 24 6 2\n", "24 6 7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F5.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F7.nc\n", "End 24 5 39\n", "24 6 8\n", "End 24 5 40\n", "24 6 9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F9.nc\n", "Circling_Summary_24h_B6_F8.nc\n", "End 24 5 42\n", "24 6 10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F10.nc\n", "End 24 4 28\n", "24 6 11\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F11.nc\n", "End 24 6 9\n", "24 6 12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F12.nc\n", "End 24 6 12\n", "24 6 13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F13.nc\n", "End 24 6 6\n", "24 6 14\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F14.nc\n", "End 24 6 11\n", "24 6 15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F15.nc\n", "End 24 6 3\n", "24 6 16\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F16.nc\n", "End 24 6 7\n", "24 6 17\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F17.nc\n", "End 24 6 10\n", "24 6 18\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F18.nc\n", "End 24 6 14\n", "24 6 19\n", "End 24 6 13\n", "24 6 20\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n", "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F20.nc\n", "Circling_Summary_24h_B6_F19.nc\n", "End 24 6 18\n", "24 6 21\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F21.nc\n", "End 24 6 8\n", "24 6 22\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F22.nc\n", "End 24 6 5\n", "24 6 23\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F23.nc\n", "End 24 6 15\n", "24 6 24\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F24.nc\n", "End 24 6 24\n", "24 6 25\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F25.nc\n", "End 24 6 17\n", "24 6 26\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F26.nc\n", "End 24 6 21\n", "24 6 27\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F27.nc\n", "End 24 6 16\n", "24 6 28\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F28.nc\n", "End 24 6 20\n", "24 6 29\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F29.nc\n", "End 24 6 19\n", "24 6 30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F30.nc\n", "End 24 6 23\n", "24 6 31\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 6 28\n", "24 6 32\n", "Circling_Summary_24h_B6_F31.nc\n", "End 24 6 22\n", "24 6 33\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F32.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F33.nc\n", "End 24 6 30\n", "24 6 34\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F34.nc\n", "End 24 6 25\n", "24 6 35\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F35.nc\n", "End 24 6 26\n", "24 6 36\n", "End 24 6 31\n", "24 6 37\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F36.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F37.nc\n", "End 24 6 34\n", "24 6 38\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F38.nc\n", "End 24 6 35\n", "24 6 39\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F39.nc\n", "End 24 6 39\n", "24 6 40\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F40.nc\n", "End 24 6 27\n", "End 24 6 29\n", "24 624 42 \n", "6 41\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F41.nc\n", "End 24 6 37\n", "24 7 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B6_F42.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F1.nc\n", "End 24 6 32\n", "24 7 2\n", "Only One trial detected for Circling_Summary_24h_B7_F2.nc\n", "Circling_Summary_24h_B7_F2.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "End 24 7 2\n", "24 7 3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F3.nc\n", "End 24 6 41\n", "24 7 4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F4.nc\n", "End 24 7 3\n", "24 7 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F5.nc\n", "End 24 7 4\n", "24 7 6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F6.nc\n", "End 24 6 38\n", "24 7 7\n", "End 24 7 6\n", "24 7 8\n", "End 24 7 5\n", "24 7 9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F7.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F8.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F9.nc\n", "End 24 6 42\n", "24 7 10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F10.nc\n", "End 24 6 36\n", "24 7 11\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F11.nc\n", "End 24 6 40\n", "24 7 12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F12.nc\n", "End 24 6 33\n", "24 7 13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F13.nc\n", "End 24 7 1\n", "24 7 14\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F14.nc\n", "End 24 7 12\n", "24 7 15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F15.nc\n", "End 24 7 15\n", "24 7 16\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F16.nc\n", "End 24 7 11\n", "24 7 17\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F17.nc\n", "End 24 7 7\n", "24 7 18\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F18.nc\n", "End 24 7 9\n", "24 7 19\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F19.nc\n", "End 24 7 8\n", "24 7 20\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F20.nc\n", "End 24 7 20\n", "24 7 21\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F21.nc\n", "End 24 7 19\n", "24 7 22\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F22.nc\n", "End 24 7 14\n", "24 7 23\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F23.nc\n", "End 24 7 13\n", "24 7 24\n", "End 24 7 17\n", "24 7 25\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F25.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F24.nc\n", "End 24 7 10\n", "24 7 26\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F26.nc\n", "End 24 7 26\n", "24 7 27\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F27.nc\n", "End 24 7 18\n", "24 7 28\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F28.nc\n", "End 24 7 16\n", "24 7 29\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F29.nc\n", "End 24 7 29\n", "24 7 30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F30.nc\n", "End 24 7 25\n", "24 7 31\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F31.nc\n", "End 24 7 22\n", "24 7 32\n", "End 24 7 23\n", "24 7 33\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F32.nc\n", "End 24 7 21\n", "24 7 34\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F33.nc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F34.nc\n", "End 24 7 31\n", "24 7 35\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F35.nc\n", "End 24 7 27\n", "24 7 36\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F36.nc\n", "End 24 7 28\n", "24 7 37\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F37.nc\n", "End 24 7 24\n", "24 7 38\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F38.nc\n", "End 24 7 37\n", "24 7 39\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F39.nc\n", "End 24 7 30\n", "24 7 40\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F40.nc\n", "End 24 7 32\n", "24 7 41\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F41.nc\n", "End 24 7 36\n", "24 7 42\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log\n", " result_data = func(*input_data)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Circling_Summary_24h_B7_F42.nc\n", "End 24 7 34\n", "End 24 7 39\n", "End 24 7 33\n", "End 24 7 38\n", "End 24 7 41\n", "End 24 7 42\n", "End 24 7 40\n", "End 24 7 35\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=8)]: Done 588 out of 588 | elapsed: 229.5min finished\n" ] } ], "source": [ "imp.reload(lcm)\n", "\n", "lcm.import_dask_matlab_files(path, override=False)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#interrupted: next step check the ones I ran now have speed PSD before solving the rest" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1_Angles24h_dt.nc Circling_Summary_24h_B1_F14.nc\n", "1_Angles24h_dt_dropna.nc Circling_Summary_24h_B1_F15.nc\n", "1_Angles_24h.nc Circling_Summary_24h_B1_F16.nc\n", "1_DownsampleMeans24h.csv Circling_Summary_24h_B1_F17.nc\n", "1_DownsampleMeans2h.csv Circling_Summary_24h_B1_F18.nc\n", "1_Power24h.nc Circling_Summary_24h_B1_F19.nc\n", "1_Power2h.nc Circling_Summary_24h_B1_F2.nc\n", "1_Timestamps24h.nc Circling_Summary_24h_B1_F20.nc\n", "1_Timestamps2h.nc Circling_Summary_24h_B1_F21.nc\n", "24hrContinuosdata.pdf Circling_Summary_24h_B1_F22.nc\n", "2_Angles24h_dt.nc Circling_Summary_24h_B1_F23.nc\n", "2_Angles24h_dt_dropna.nc Circling_Summary_24h_B1_F25.nc\n", "2_Angles_24h.nc Circling_Summary_24h_B1_F26.nc\n", "2_Power24h.nc Circling_Summary_24h_B1_F27.nc\n", "2_Power2h.nc Circling_Summary_24h_B1_F28.nc\n", "2_Timestamps2h.nc Circling_Summary_24h_B1_F29.nc\n", "\u001b[34m2hData\u001b[m\u001b[m/ Circling_Summary_24h_B1_F3.nc\n", "3_Angles24h_dt.nc Circling_Summary_24h_B1_F30.nc\n", "3_Angles24h_dt_dropna.nc Circling_Summary_24h_B1_F31.nc\n", "3_Angles_24h.nc Circling_Summary_24h_B1_F32.nc\n", "3_Power24h.nc Circling_Summary_24h_B1_F33.nc\n", "3_Power2h.nc Circling_Summary_24h_B1_F34.nc\n", "3_Timestamps2h.nc Circling_Summary_24h_B1_F35.nc\n", "4_Angles24h_dt.nc Circling_Summary_24h_B1_F36.nc\n", "4_Angles24h_dt_dropna.nc Circling_Summary_24h_B1_F37.nc\n", "4_Angles_24h.nc Circling_Summary_24h_B1_F38.nc\n", "4_Power24h.nc Circling_Summary_24h_B1_F39.nc\n", "4_Power2h.nc Circling_Summary_24h_B1_F4.nc\n", "4_Timestamps2h.nc Circling_Summary_24h_B1_F40.nc\n", "5_Angles24h_dt.nc Circling_Summary_24h_B1_F41.nc\n", "5_Angles24h_dt_dropna.nc Circling_Summary_24h_B1_F42.nc\n", "5_Angles_24h.nc Circling_Summary_24h_B1_F5.nc\n", "5_Power24h.nc Circling_Summary_24h_B1_F6.nc\n", "5_Power2h.nc Circling_Summary_24h_B1_F7.nc\n", "5_Timestamps2h.nc Circling_Summary_24h_B1_F8.nc\n", "6_Angles24h_dt.nc Circling_Summary_24h_B1_F9.nc\n", "6_Angles24h_dt_dropna.nc Circling_Summary_24h_B2_F1.nc\n", "6_Angles_24h.nc Circling_Summary_24h_B2_F10.nc\n", "6_Power24h.nc Circling_Summary_24h_B2_F11.nc\n", "6_Power2h.nc Circling_Summary_24h_B2_F12.nc\n", "6_Timestamps2h.nc Circling_Summary_24h_B2_F13.nc\n", "7_Angles24h_dt.nc Circling_Summary_24h_B2_F14.nc\n", "7_Angles24h_dt_dropna.nc Circling_Summary_24h_B2_F15.nc\n", "7_Angles24hdt.nc Circling_Summary_24h_B2_F16.nc\n", "7_Angles_24h.nc Circling_Summary_24h_B2_F17.nc\n", "7_Power24h.nc Circling_Summary_24h_B2_F18.nc\n", "8_Angles24h_dt.nc Circling_Summary_24h_B2_F19.nc\n", "8_Angles24h_dt_dropna.nc Circling_Summary_24h_B2_F2.nc\n", "8_Angles_24h.nc Circling_Summary_24h_B2_F20.nc\n", "8_Power24h.nc Circling_Summary_24h_B2_F21.nc\n", "AR0_BialekAnalysis.ipynb Circling_Summary_24h_B2_F22.nc\n", "\u001b[31mCentroidarray.mat\u001b[m\u001b[m* Circling_Summary_24h_B2_F23.nc\n", "\u001b[34mCirclingData\u001b[m\u001b[m/ Circling_Summary_24h_B2_F25.nc\n", "CirclingData_24h_B1_F1.nc Circling_Summary_24h_B2_F26.nc\n", "CirclingData_24h_B1_F10.nc Circling_Summary_24h_B2_F27.nc\n", "CirclingData_24h_B1_F11.nc Circling_Summary_24h_B2_F28.nc\n", "CirclingData_24h_B1_F12.nc Circling_Summary_24h_B2_F29.nc\n", "CirclingData_24h_B1_F13.nc Circling_Summary_24h_B2_F3.nc\n", "CirclingData_24h_B1_F14.nc Circling_Summary_24h_B2_F30.nc\n", "CirclingData_24h_B1_F15.nc Circling_Summary_24h_B2_F31.nc\n", "CirclingData_24h_B1_F16.nc Circling_Summary_24h_B2_F32.nc\n", "CirclingData_24h_B1_F17.nc Circling_Summary_24h_B2_F33.nc\n", "CirclingData_24h_B1_F18.nc Circling_Summary_24h_B2_F34.nc\n", "CirclingData_24h_B1_F19.nc Circling_Summary_24h_B2_F35.nc\n", "CirclingData_24h_B1_F2.nc Circling_Summary_24h_B2_F36.nc\n", "CirclingData_24h_B1_F20.nc Circling_Summary_24h_B2_F37.nc\n", "CirclingData_24h_B1_F21.nc Circling_Summary_24h_B2_F38.nc\n", "CirclingData_24h_B1_F22.nc Circling_Summary_24h_B2_F39.nc\n", "CirclingData_24h_B1_F23.nc Circling_Summary_24h_B2_F4.nc\n", "CirclingData_24h_B1_F25.nc Circling_Summary_24h_B2_F40.nc\n", "CirclingData_24h_B1_F26.nc Circling_Summary_24h_B2_F41.nc\n", "CirclingData_24h_B1_F27.nc Circling_Summary_24h_B2_F42.nc\n", "CirclingData_24h_B1_F28.nc Circling_Summary_24h_B2_F5.nc\n", "CirclingData_24h_B1_F29.nc Circling_Summary_24h_B2_F6.nc\n", "CirclingData_24h_B1_F3.nc Circling_Summary_24h_B2_F7.nc\n", "CirclingData_24h_B1_F30.nc Circling_Summary_24h_B2_F8.nc\n", "CirclingData_24h_B1_F31.nc Circling_Summary_24h_B2_F9.nc\n", "CirclingData_24h_B1_F32.nc Circling_Summary_24h_B3_F1.nc\n", "CirclingData_24h_B1_F33.nc Circling_Summary_24h_B3_F10.nc\n", "CirclingData_24h_B1_F34.nc Circling_Summary_24h_B3_F11.nc\n", "CirclingData_24h_B1_F35.nc Circling_Summary_24h_B3_F12.nc\n", "CirclingData_24h_B1_F36.nc Circling_Summary_24h_B3_F13.nc\n", "CirclingData_24h_B1_F37.nc Circling_Summary_24h_B3_F14.nc\n", "CirclingData_24h_B1_F38.nc Circling_Summary_24h_B3_F15.nc\n", "CirclingData_24h_B1_F39.nc Circling_Summary_24h_B3_F16.nc\n", "CirclingData_24h_B1_F4.nc Circling_Summary_24h_B3_F17.nc\n", "CirclingData_24h_B1_F40.nc Circling_Summary_24h_B3_F18.nc\n", "CirclingData_24h_B1_F41.nc Circling_Summary_24h_B3_F19.nc\n", "CirclingData_24h_B1_F42.nc Circling_Summary_24h_B3_F2.nc\n", "CirclingData_24h_B1_F5.nc Circling_Summary_24h_B3_F20.nc\n", "CirclingData_24h_B1_F6.nc Circling_Summary_24h_B3_F21.nc\n", "CirclingData_24h_B1_F7.nc Circling_Summary_24h_B3_F22.nc\n", "CirclingData_24h_B1_F8.nc Circling_Summary_24h_B3_F23.nc\n", "CirclingData_24h_B1_F9.nc Circling_Summary_24h_B3_F24.nc\n", "CirclingData_24h_B2_F1.nc Circling_Summary_24h_B3_F25.nc\n", "CirclingData_24h_B2_F10.nc Circling_Summary_24h_B3_F26.nc\n", "CirclingData_24h_B2_F11.nc Circling_Summary_24h_B3_F27.nc\n", "CirclingData_24h_B2_F12.nc Circling_Summary_24h_B3_F28.nc\n", "CirclingData_24h_B2_F13.nc Circling_Summary_24h_B3_F29.nc\n", "CirclingData_24h_B2_F14.nc Circling_Summary_24h_B3_F3.nc\n", "CirclingData_24h_B2_F15.nc Circling_Summary_24h_B3_F30.nc\n", "CirclingData_24h_B2_F16.nc Circling_Summary_24h_B3_F31.nc\n", "CirclingData_24h_B2_F17.nc Circling_Summary_24h_B3_F32.nc\n", "CirclingData_24h_B2_F18.nc Circling_Summary_24h_B3_F33.nc\n", "CirclingData_24h_B2_F19.nc Circling_Summary_24h_B3_F34.nc\n", "CirclingData_24h_B2_F2.nc Circling_Summary_24h_B3_F35.nc\n", "CirclingData_24h_B2_F20.nc Circling_Summary_24h_B3_F36.nc\n", "CirclingData_24h_B2_F21.nc Circling_Summary_24h_B3_F37.nc\n", "CirclingData_24h_B2_F22.nc Circling_Summary_24h_B3_F38.nc\n", "CirclingData_24h_B2_F23.nc Circling_Summary_24h_B3_F39.nc\n", "CirclingData_24h_B2_F25.nc Circling_Summary_24h_B3_F4.nc\n", "CirclingData_24h_B2_F26.nc Circling_Summary_24h_B3_F40.nc\n", "CirclingData_24h_B2_F27.nc Circling_Summary_24h_B3_F41.nc\n", "CirclingData_24h_B2_F28.nc Circling_Summary_24h_B3_F42.nc\n", "CirclingData_24h_B2_F29.nc Circling_Summary_24h_B3_F5.nc\n", "CirclingData_24h_B2_F3.nc Circling_Summary_24h_B3_F6.nc\n", "CirclingData_24h_B2_F30.nc Circling_Summary_24h_B3_F7.nc\n", "CirclingData_24h_B2_F31.nc Circling_Summary_24h_B3_F8.nc\n", "CirclingData_24h_B2_F32.nc Circling_Summary_24h_B3_F9.nc\n", "CirclingData_24h_B2_F33.nc Circling_Summary_24h_B4_F1.nc\n", "CirclingData_24h_B2_F34.nc Circling_Summary_24h_B4_F10.nc\n", "CirclingData_24h_B2_F35.nc Circling_Summary_24h_B4_F11.nc\n", "CirclingData_24h_B2_F36.nc Circling_Summary_24h_B4_F12.nc\n", "CirclingData_24h_B2_F37.nc Circling_Summary_24h_B4_F13.nc\n", "CirclingData_24h_B2_F38.nc Circling_Summary_24h_B4_F14.nc\n", "CirclingData_24h_B2_F39.nc Circling_Summary_24h_B4_F15.nc\n", "CirclingData_24h_B2_F4.nc Circling_Summary_24h_B4_F16.nc\n", "CirclingData_24h_B2_F40.nc Circling_Summary_24h_B4_F17.nc\n", "CirclingData_24h_B2_F41.nc Circling_Summary_24h_B4_F18.nc\n", "CirclingData_24h_B2_F42.nc Circling_Summary_24h_B4_F19.nc\n", "CirclingData_24h_B2_F5.nc Circling_Summary_24h_B4_F2.nc\n", "CirclingData_24h_B2_F6.nc Circling_Summary_24h_B4_F20.nc\n", "CirclingData_24h_B2_F7.nc Circling_Summary_24h_B4_F21.nc\n", "CirclingData_24h_B2_F8.nc Circling_Summary_24h_B4_F22.nc\n", "CirclingData_24h_B2_F9.nc Circling_Summary_24h_B4_F23.nc\n", "CirclingData_24h_B3_F1.nc Circling_Summary_24h_B4_F24.nc\n", "CirclingData_24h_B3_F10.nc Circling_Summary_24h_B4_F25.nc\n", "CirclingData_24h_B3_F11.nc Circling_Summary_24h_B4_F26.nc\n", "CirclingData_24h_B3_F12.nc Circling_Summary_24h_B4_F27.nc\n", "CirclingData_24h_B3_F13.nc Circling_Summary_24h_B4_F28.nc\n", "CirclingData_24h_B3_F14.nc Circling_Summary_24h_B4_F29.nc\n", "CirclingData_24h_B3_F15.nc Circling_Summary_24h_B4_F3.nc\n", "CirclingData_24h_B3_F16.nc Circling_Summary_24h_B4_F30.nc\n", "CirclingData_24h_B3_F17.nc Circling_Summary_24h_B4_F31.nc\n", "CirclingData_24h_B3_F18.nc Circling_Summary_24h_B4_F32.nc\n", "CirclingData_24h_B3_F19.nc Circling_Summary_24h_B4_F33.nc\n", "CirclingData_24h_B3_F2.nc Circling_Summary_24h_B4_F34.nc\n", "CirclingData_24h_B3_F20.nc Circling_Summary_24h_B4_F35.nc\n", "CirclingData_24h_B3_F21.nc Circling_Summary_24h_B4_F36.nc\n", "CirclingData_24h_B3_F22.nc Circling_Summary_24h_B4_F37.nc\n", "CirclingData_24h_B3_F23.nc Circling_Summary_24h_B4_F38.nc\n", "CirclingData_24h_B3_F24.nc Circling_Summary_24h_B4_F39.nc\n", "CirclingData_24h_B3_F25.nc Circling_Summary_24h_B4_F4.nc\n", "CirclingData_24h_B3_F26.nc Circling_Summary_24h_B4_F40.nc\n", "CirclingData_24h_B3_F27.nc Circling_Summary_24h_B4_F41.nc\n", "CirclingData_24h_B3_F28.nc Circling_Summary_24h_B4_F42.nc\n", "CirclingData_24h_B3_F29.nc Circling_Summary_24h_B4_F5.nc\n", "CirclingData_24h_B3_F3.nc Circling_Summary_24h_B4_F6.nc\n", "CirclingData_24h_B3_F30.nc Circling_Summary_24h_B4_F7.nc\n", "CirclingData_24h_B3_F31.nc Circling_Summary_24h_B4_F8.nc\n", "CirclingData_24h_B3_F32.nc Circling_Summary_24h_B4_F9.nc\n", "CirclingData_24h_B3_F33.nc Circling_Summary_24h_B5_F1.nc\n", "CirclingData_24h_B3_F34.nc Circling_Summary_24h_B5_F10.nc\n", "CirclingData_24h_B3_F35.nc Circling_Summary_24h_B5_F11.nc\n", "CirclingData_24h_B3_F36.nc Circling_Summary_24h_B5_F12.nc\n", "CirclingData_24h_B3_F37.nc Circling_Summary_24h_B5_F13.nc\n", "CirclingData_24h_B3_F38.nc Circling_Summary_24h_B5_F14.nc\n", "CirclingData_24h_B3_F39.nc Circling_Summary_24h_B5_F15.nc\n", "CirclingData_24h_B3_F4.nc Circling_Summary_24h_B5_F16.nc\n", "CirclingData_24h_B3_F40.nc Circling_Summary_24h_B5_F17.nc\n", "CirclingData_24h_B3_F41.nc Circling_Summary_24h_B5_F18.nc\n", "CirclingData_24h_B3_F42.nc Circling_Summary_24h_B5_F19.nc\n", "CirclingData_24h_B3_F5.nc Circling_Summary_24h_B5_F2.nc\n", "CirclingData_24h_B3_F6.nc Circling_Summary_24h_B5_F20.nc\n", "CirclingData_24h_B3_F7.nc Circling_Summary_24h_B5_F21.nc\n", "CirclingData_24h_B3_F8.nc Circling_Summary_24h_B5_F22.nc\n", "CirclingData_24h_B3_F9.nc Circling_Summary_24h_B5_F23.nc\n", "CirclingData_24h_B4_F1.nc Circling_Summary_24h_B5_F24.nc\n", "CirclingData_24h_B4_F10.nc Circling_Summary_24h_B5_F25.nc\n", "CirclingData_24h_B4_F11.nc Circling_Summary_24h_B5_F26.nc\n", "CirclingData_24h_B4_F12.nc Circling_Summary_24h_B5_F27.nc\n", "CirclingData_24h_B4_F13.nc Circling_Summary_24h_B5_F28.nc\n", "CirclingData_24h_B4_F14.nc Circling_Summary_24h_B5_F29.nc\n", "CirclingData_24h_B4_F15.nc Circling_Summary_24h_B5_F3.nc\n", "CirclingData_24h_B4_F16.nc Circling_Summary_24h_B5_F30.nc\n", "CirclingData_24h_B4_F17.nc Circling_Summary_24h_B5_F31.nc\n", "CirclingData_24h_B4_F18.nc Circling_Summary_24h_B5_F32.nc\n", "CirclingData_24h_B4_F19.nc Circling_Summary_24h_B5_F33.nc\n", "CirclingData_24h_B4_F2.nc Circling_Summary_24h_B5_F34.nc\n", "CirclingData_24h_B4_F20.nc Circling_Summary_24h_B5_F35.nc\n", "CirclingData_24h_B4_F21.nc Circling_Summary_24h_B5_F36.nc\n", "CirclingData_24h_B4_F22.nc Circling_Summary_24h_B5_F37.nc\n", "CirclingData_24h_B4_F23.nc Circling_Summary_24h_B5_F38.nc\n", "CirclingData_24h_B4_F24.nc Circling_Summary_24h_B5_F39.nc\n", "CirclingData_24h_B4_F25.nc Circling_Summary_24h_B5_F4.nc\n", "CirclingData_24h_B4_F26.nc Circling_Summary_24h_B5_F40.nc\n", "CirclingData_24h_B4_F27.nc Circling_Summary_24h_B5_F41.nc\n", "CirclingData_24h_B4_F28.nc Circling_Summary_24h_B5_F42.nc\n", "CirclingData_24h_B4_F29.nc Circling_Summary_24h_B5_F5.nc\n", "CirclingData_24h_B4_F3.nc Circling_Summary_24h_B5_F6.nc\n", "CirclingData_24h_B4_F30.nc Circling_Summary_24h_B5_F7.nc\n", "CirclingData_24h_B4_F31.nc Circling_Summary_24h_B5_F8.nc\n", "CirclingData_24h_B4_F32.nc Circling_Summary_24h_B5_F9.nc\n", "CirclingData_24h_B4_F33.nc Circling_Summary_24h_B6_F1.nc\n", "CirclingData_24h_B4_F34.nc Circling_Summary_24h_B6_F10.nc\n", "CirclingData_24h_B4_F35.nc Circling_Summary_24h_B6_F11.nc\n", "CirclingData_24h_B4_F36.nc Circling_Summary_24h_B6_F12.nc\n", "CirclingData_24h_B4_F37.nc Circling_Summary_24h_B6_F13.nc\n", "CirclingData_24h_B4_F38.nc Circling_Summary_24h_B6_F14.nc\n", "CirclingData_24h_B4_F39.nc Circling_Summary_24h_B6_F15.nc\n", "CirclingData_24h_B4_F4.nc Circling_Summary_24h_B6_F16.nc\n", "CirclingData_24h_B4_F40.nc Circling_Summary_24h_B6_F17.nc\n", "CirclingData_24h_B4_F41.nc Circling_Summary_24h_B6_F18.nc\n", "CirclingData_24h_B4_F42.nc Circling_Summary_24h_B6_F19.nc\n", "CirclingData_24h_B4_F5.nc Circling_Summary_24h_B6_F2.nc\n", "CirclingData_24h_B4_F6.nc Circling_Summary_24h_B6_F20.nc\n", "CirclingData_24h_B4_F7.nc Circling_Summary_24h_B6_F21.nc\n", "CirclingData_24h_B4_F8.nc Circling_Summary_24h_B6_F22.nc\n", "CirclingData_24h_B4_F9.nc Circling_Summary_24h_B6_F23.nc\n", "CirclingData_24h_B5_F1.nc Circling_Summary_24h_B6_F24.nc\n", "CirclingData_24h_B5_F10.nc Circling_Summary_24h_B6_F25.nc\n", "CirclingData_24h_B5_F11.nc Circling_Summary_24h_B6_F26.nc\n", "CirclingData_24h_B5_F12.nc Circling_Summary_24h_B6_F27.nc\n", "CirclingData_24h_B5_F13.nc Circling_Summary_24h_B6_F28.nc\n", "CirclingData_24h_B5_F14.nc Circling_Summary_24h_B6_F29.nc\n", "CirclingData_24h_B5_F15.nc Circling_Summary_24h_B6_F3.nc\n", "CirclingData_24h_B5_F16.nc Circling_Summary_24h_B6_F30.nc\n", "CirclingData_24h_B5_F17.nc Circling_Summary_24h_B6_F31.nc\n", "CirclingData_24h_B5_F18.nc Circling_Summary_24h_B6_F32.nc\n", "CirclingData_24h_B5_F19.nc Circling_Summary_24h_B6_F33.nc\n", "CirclingData_24h_B5_F2.nc Circling_Summary_24h_B6_F34.nc\n", "CirclingData_24h_B5_F20.nc Circling_Summary_24h_B6_F35.nc\n", "CirclingData_24h_B5_F21.nc Circling_Summary_24h_B6_F36.nc\n", "CirclingData_24h_B5_F22.nc Circling_Summary_24h_B6_F37.nc\n", "CirclingData_24h_B5_F23.nc Circling_Summary_24h_B6_F38.nc\n", "CirclingData_24h_B5_F24.nc Circling_Summary_24h_B6_F39.nc\n", "CirclingData_24h_B5_F25.nc Circling_Summary_24h_B6_F4.nc\n", "CirclingData_24h_B5_F26.nc Circling_Summary_24h_B6_F40.nc\n", "CirclingData_24h_B5_F27.nc Circling_Summary_24h_B6_F41.nc\n", "CirclingData_24h_B5_F28.nc Circling_Summary_24h_B6_F42.nc\n", "CirclingData_24h_B5_F29.nc Circling_Summary_24h_B6_F5.nc\n", "CirclingData_24h_B5_F3.nc Circling_Summary_24h_B6_F6.nc\n", "CirclingData_24h_B5_F30.nc Circling_Summary_24h_B6_F7.nc\n", "CirclingData_24h_B5_F31.nc Circling_Summary_24h_B6_F8.nc\n", "CirclingData_24h_B5_F32.nc Circling_Summary_24h_B6_F9.nc\n", "CirclingData_24h_B5_F33.nc Circling_Summary_24h_B7_F1.nc\n", "CirclingData_24h_B5_F34.nc Circling_Summary_24h_B7_F10.nc\n", "CirclingData_24h_B5_F35.nc Circling_Summary_24h_B7_F11.nc\n", "CirclingData_24h_B5_F36.nc Circling_Summary_24h_B7_F12.nc\n", "CirclingData_24h_B5_F37.nc Circling_Summary_24h_B7_F13.nc\n", "CirclingData_24h_B5_F38.nc Circling_Summary_24h_B7_F14.nc\n", "CirclingData_24h_B5_F39.nc Circling_Summary_24h_B7_F15.nc\n", "CirclingData_24h_B5_F4.nc Circling_Summary_24h_B7_F16.nc\n", "CirclingData_24h_B5_F40.nc Circling_Summary_24h_B7_F17.nc\n", "CirclingData_24h_B5_F41.nc Circling_Summary_24h_B7_F18.nc\n", "CirclingData_24h_B5_F42.nc Circling_Summary_24h_B7_F19.nc\n", "CirclingData_24h_B5_F5.nc Circling_Summary_24h_B7_F2.nc\n", "CirclingData_24h_B5_F6.nc Circling_Summary_24h_B7_F20.nc\n", "CirclingData_24h_B5_F7.nc Circling_Summary_24h_B7_F21.nc\n", "CirclingData_24h_B5_F8.nc Circling_Summary_24h_B7_F22.nc\n", "CirclingData_24h_B5_F9.nc Circling_Summary_24h_B7_F23.nc\n", "CirclingData_24h_B6_F1.nc Circling_Summary_24h_B7_F24.nc\n", "CirclingData_24h_B6_F10.nc Circling_Summary_24h_B7_F25.nc\n", "CirclingData_24h_B6_F11.nc Circling_Summary_24h_B7_F26.nc\n", "CirclingData_24h_B6_F12.nc Circling_Summary_24h_B7_F27.nc\n", "CirclingData_24h_B6_F13.nc Circling_Summary_24h_B7_F28.nc\n", "CirclingData_24h_B6_F14.nc Circling_Summary_24h_B7_F29.nc\n", "CirclingData_24h_B6_F15.nc Circling_Summary_24h_B7_F3.nc\n", "CirclingData_24h_B6_F16.nc Circling_Summary_24h_B7_F30.nc\n", "CirclingData_24h_B6_F17.nc Circling_Summary_24h_B7_F31.nc\n", "CirclingData_24h_B6_F18.nc Circling_Summary_24h_B7_F32.nc\n", "CirclingData_24h_B6_F19.nc Circling_Summary_24h_B7_F33.nc\n", "CirclingData_24h_B6_F2.nc Circling_Summary_24h_B7_F34.nc\n", "CirclingData_24h_B6_F20.nc Circling_Summary_24h_B7_F35.nc\n", "CirclingData_24h_B6_F21.nc Circling_Summary_24h_B7_F36.nc\n", "CirclingData_24h_B6_F22.nc Circling_Summary_24h_B7_F37.nc\n", "CirclingData_24h_B6_F23.nc Circling_Summary_24h_B7_F38.nc\n", "CirclingData_24h_B6_F24.nc Circling_Summary_24h_B7_F39.nc\n", "CirclingData_24h_B6_F25.nc Circling_Summary_24h_B7_F4.nc\n", "CirclingData_24h_B6_F26.nc Circling_Summary_24h_B7_F40.nc\n", "CirclingData_24h_B6_F27.nc Circling_Summary_24h_B7_F41.nc\n", "CirclingData_24h_B6_F28.nc Circling_Summary_24h_B7_F42.nc\n", "CirclingData_24h_B6_F29.nc Circling_Summary_24h_B7_F5.nc\n", "CirclingData_24h_B6_F3.nc Circling_Summary_24h_B7_F6.nc\n", "CirclingData_24h_B6_F30.nc Circling_Summary_24h_B7_F7.nc\n", "CirclingData_24h_B6_F31.nc Circling_Summary_24h_B7_F8.nc\n", "CirclingData_24h_B6_F32.nc Circling_Summary_24h_B7_F9.nc\n", "CirclingData_24h_B6_F33.nc Circling_Summary_2h_B1_F10.nc\n", "CirclingData_24h_B6_F34.nc Circling_Summary_2h_B1_F11.nc\n", "CirclingData_24h_B6_F35.nc Circling_Summary_2h_B1_F12.nc\n", "CirclingData_24h_B6_F36.nc Circling_Summary_2h_B1_F13.nc\n", "CirclingData_24h_B6_F37.nc Circling_Summary_2h_B1_F14.nc\n", "CirclingData_24h_B6_F38.nc Circling_Summary_2h_B1_F15.nc\n", "CirclingData_24h_B6_F39.nc Circling_Summary_2h_B1_F16.nc\n", "CirclingData_24h_B6_F4.nc Circling_Summary_2h_B1_F17.nc\n", "CirclingData_24h_B6_F40.nc Circling_Summary_2h_B1_F18.nc\n", "CirclingData_24h_B6_F41.nc Circling_Summary_2h_B1_F19.nc\n", "CirclingData_24h_B6_F42.nc Circling_Summary_2h_B1_F2.nc\n", "CirclingData_24h_B6_F5.nc Circling_Summary_2h_B1_F20.nc\n", "CirclingData_24h_B6_F6.nc Circling_Summary_2h_B1_F21.nc\n", "CirclingData_24h_B6_F7.nc Circling_Summary_2h_B1_F22.nc\n", "CirclingData_24h_B6_F8.nc Circling_Summary_2h_B1_F23.nc\n", "CirclingData_24h_B6_F9.nc Circling_Summary_2h_B1_F24.nc\n", "CirclingData_24h_B7_F1.nc Circling_Summary_2h_B1_F25.nc\n", "CirclingData_24h_B7_F10.nc Circling_Summary_2h_B1_F26.nc\n", "CirclingData_24h_B7_F11.nc Circling_Summary_2h_B1_F27.nc\n", "CirclingData_24h_B7_F12.nc Circling_Summary_2h_B1_F28.nc\n", "CirclingData_24h_B7_F13.nc Circling_Summary_2h_B1_F29.nc\n", "CirclingData_24h_B7_F14.nc Circling_Summary_2h_B1_F3.nc\n", "CirclingData_24h_B7_F15.nc Circling_Summary_2h_B1_F30.nc\n", "CirclingData_24h_B7_F16.nc Circling_Summary_2h_B1_F31.nc\n", "CirclingData_24h_B7_F17.nc Circling_Summary_2h_B1_F32.nc\n", "CirclingData_24h_B7_F18.nc Circling_Summary_2h_B1_F33.nc\n", "CirclingData_24h_B7_F19.nc Circling_Summary_2h_B1_F34.nc\n", "CirclingData_24h_B7_F2.nc Circling_Summary_2h_B1_F35.nc\n", "CirclingData_24h_B7_F20.nc Circling_Summary_2h_B1_F36.nc\n", "CirclingData_24h_B7_F21.nc Circling_Summary_2h_B1_F37.nc\n", "CirclingData_24h_B7_F22.nc Circling_Summary_2h_B1_F38.nc\n", "CirclingData_24h_B7_F23.nc Circling_Summary_2h_B1_F39.nc\n", "CirclingData_24h_B7_F24.nc Circling_Summary_2h_B1_F4.nc\n", "CirclingData_24h_B7_F25.nc Circling_Summary_2h_B1_F40.nc\n", "CirclingData_24h_B7_F26.nc Circling_Summary_2h_B1_F41.nc\n", "CirclingData_24h_B7_F27.nc Circling_Summary_2h_B1_F42.nc\n", "CirclingData_24h_B7_F28.nc Circling_Summary_2h_B1_F5.nc\n", "CirclingData_24h_B7_F29.nc Circling_Summary_2h_B1_F6.nc\n", "CirclingData_24h_B7_F3.nc Circling_Summary_2h_B1_F7.nc\n", "CirclingData_24h_B7_F30.nc Circling_Summary_2h_B1_F8.nc\n", "CirclingData_24h_B7_F31.nc Circling_Summary_2h_B1_F9.nc\n", "CirclingData_24h_B7_F32.nc Circling_Summary_2h_B2_F1.nc\n", "CirclingData_24h_B7_F33.nc Circling_Summary_2h_B2_F10.nc\n", "CirclingData_24h_B7_F34.nc Circling_Summary_2h_B2_F11.nc\n", "CirclingData_24h_B7_F35.nc Circling_Summary_2h_B2_F12.nc\n", "CirclingData_24h_B7_F36.nc Circling_Summary_2h_B2_F13.nc\n", "CirclingData_24h_B7_F37.nc Circling_Summary_2h_B2_F14.nc\n", "CirclingData_24h_B7_F38.nc Circling_Summary_2h_B2_F15.nc\n", "CirclingData_24h_B7_F39.nc Circling_Summary_2h_B2_F16.nc\n", "CirclingData_24h_B7_F4.nc Circling_Summary_2h_B2_F17.nc\n", "CirclingData_24h_B7_F40.nc Circling_Summary_2h_B2_F18.nc\n", "CirclingData_24h_B7_F41.nc Circling_Summary_2h_B2_F19.nc\n", "CirclingData_24h_B7_F42.nc Circling_Summary_2h_B2_F2.nc\n", "CirclingData_24h_B7_F5.nc Circling_Summary_2h_B2_F20.nc\n", "CirclingData_24h_B7_F6.nc Circling_Summary_2h_B2_F21.nc\n", "CirclingData_24h_B7_F7.nc Circling_Summary_2h_B2_F22.nc\n", "CirclingData_24h_B7_F8.nc Circling_Summary_2h_B2_F23.nc\n", "CirclingData_24h_B7_F9.nc Circling_Summary_2h_B2_F24.nc\n", "CirclingData_2h_B1_F1.nc Circling_Summary_2h_B2_F25.nc\n", "CirclingData_2h_B1_F10.nc Circling_Summary_2h_B2_F26.nc\n", "CirclingData_2h_B1_F11.nc Circling_Summary_2h_B2_F27.nc\n", "CirclingData_2h_B1_F12.nc Circling_Summary_2h_B2_F28.nc\n", "CirclingData_2h_B1_F13.nc Circling_Summary_2h_B2_F29.nc\n", "CirclingData_2h_B1_F14.nc Circling_Summary_2h_B2_F3.nc\n", "CirclingData_2h_B1_F15.nc Circling_Summary_2h_B2_F30.nc\n", "CirclingData_2h_B1_F16.nc Circling_Summary_2h_B2_F31.nc\n", "CirclingData_2h_B1_F17.nc Circling_Summary_2h_B2_F32.nc\n", "CirclingData_2h_B1_F18.nc Circling_Summary_2h_B2_F33.nc\n", "CirclingData_2h_B1_F19.nc Circling_Summary_2h_B2_F34.nc\n", "CirclingData_2h_B1_F2.nc Circling_Summary_2h_B2_F35.nc\n", "CirclingData_2h_B1_F20.nc Circling_Summary_2h_B2_F36.nc\n", "CirclingData_2h_B1_F21.nc Circling_Summary_2h_B2_F37.nc\n", "CirclingData_2h_B1_F22.nc Circling_Summary_2h_B2_F38.nc\n", "CirclingData_2h_B1_F23.nc Circling_Summary_2h_B2_F39.nc\n", "CirclingData_2h_B1_F24.nc Circling_Summary_2h_B2_F4.nc\n", "CirclingData_2h_B1_F25.nc Circling_Summary_2h_B2_F40.nc\n", "CirclingData_2h_B1_F26.nc Circling_Summary_2h_B2_F41.nc\n", "CirclingData_2h_B1_F27.nc Circling_Summary_2h_B2_F42.nc\n", "CirclingData_2h_B1_F28.nc Circling_Summary_2h_B2_F5.nc\n", "CirclingData_2h_B1_F29.nc Circling_Summary_2h_B2_F6.nc\n", "CirclingData_2h_B1_F3.nc Circling_Summary_2h_B2_F7.nc\n", "CirclingData_2h_B1_F30.nc Circling_Summary_2h_B2_F8.nc\n", "CirclingData_2h_B1_F31.nc Circling_Summary_2h_B2_F9.nc\n", "CirclingData_2h_B1_F32.nc Circling_Summary_2h_B3_F1.nc\n", "CirclingData_2h_B1_F33.nc Circling_Summary_2h_B3_F10.nc\n", "CirclingData_2h_B1_F34.nc Circling_Summary_2h_B3_F11.nc\n", "CirclingData_2h_B1_F35.nc Circling_Summary_2h_B3_F12.nc\n", "CirclingData_2h_B1_F36.nc Circling_Summary_2h_B3_F13.nc\n", "CirclingData_2h_B1_F37.nc Circling_Summary_2h_B3_F14.nc\n", "CirclingData_2h_B1_F38.nc Circling_Summary_2h_B3_F15.nc\n", "CirclingData_2h_B1_F39.nc Circling_Summary_2h_B3_F16.nc\n", "CirclingData_2h_B1_F4.nc Circling_Summary_2h_B3_F17.nc\n", "CirclingData_2h_B1_F40.nc Circling_Summary_2h_B3_F18.nc\n", "CirclingData_2h_B1_F41.nc Circling_Summary_2h_B3_F19.nc\n", "CirclingData_2h_B1_F42.nc Circling_Summary_2h_B3_F2.nc\n", "CirclingData_2h_B1_F5.nc Circling_Summary_2h_B3_F20.nc\n", "CirclingData_2h_B1_F6.nc Circling_Summary_2h_B3_F21.nc\n", "CirclingData_2h_B1_F7.nc Circling_Summary_2h_B3_F22.nc\n", "CirclingData_2h_B1_F8.nc Circling_Summary_2h_B3_F23.nc\n", "CirclingData_2h_B1_F9.nc Circling_Summary_2h_B3_F24.nc\n", "CirclingData_2h_B2_F1.nc Circling_Summary_2h_B3_F25.nc\n", "CirclingData_2h_B2_F10.nc Circling_Summary_2h_B3_F26.nc\n", "CirclingData_2h_B2_F11.nc Circling_Summary_2h_B3_F27.nc\n", "CirclingData_2h_B2_F12.nc Circling_Summary_2h_B3_F28.nc\n", "CirclingData_2h_B2_F13.nc Circling_Summary_2h_B3_F29.nc\n", "CirclingData_2h_B2_F14.nc Circling_Summary_2h_B3_F3.nc\n", "CirclingData_2h_B2_F15.nc Circling_Summary_2h_B3_F30.nc\n", "CirclingData_2h_B2_F16.nc Circling_Summary_2h_B3_F31.nc\n", "CirclingData_2h_B2_F17.nc Circling_Summary_2h_B3_F32.nc\n", "CirclingData_2h_B2_F18.nc Circling_Summary_2h_B3_F33.nc\n", "CirclingData_2h_B2_F19.nc Circling_Summary_2h_B3_F34.nc\n", "CirclingData_2h_B2_F2.nc Circling_Summary_2h_B3_F35.nc\n", "CirclingData_2h_B2_F20.nc Circling_Summary_2h_B3_F36.nc\n", "CirclingData_2h_B2_F21.nc Circling_Summary_2h_B3_F37.nc\n", "CirclingData_2h_B2_F22.nc Circling_Summary_2h_B3_F38.nc\n", "CirclingData_2h_B2_F23.nc Circling_Summary_2h_B3_F39.nc\n", "CirclingData_2h_B2_F24.nc Circling_Summary_2h_B3_F4.nc\n", "CirclingData_2h_B2_F25.nc Circling_Summary_2h_B3_F40.nc\n", "CirclingData_2h_B2_F26.nc Circling_Summary_2h_B3_F41.nc\n", "CirclingData_2h_B2_F27.nc Circling_Summary_2h_B3_F42.nc\n", "CirclingData_2h_B2_F28.nc Circling_Summary_2h_B3_F5.nc\n", "CirclingData_2h_B2_F29.nc Circling_Summary_2h_B3_F6.nc\n", "CirclingData_2h_B2_F3.nc Circling_Summary_2h_B3_F7.nc\n", "CirclingData_2h_B2_F30.nc Circling_Summary_2h_B3_F8.nc\n", "CirclingData_2h_B2_F31.nc Circling_Summary_2h_B3_F9.nc\n", "CirclingData_2h_B2_F32.nc Circling_Summary_2h_B4_F1.nc\n", "CirclingData_2h_B2_F33.nc Circling_Summary_2h_B4_F10.nc\n", "CirclingData_2h_B2_F34.nc Circling_Summary_2h_B4_F11.nc\n", "CirclingData_2h_B2_F35.nc Circling_Summary_2h_B4_F12.nc\n", "CirclingData_2h_B2_F36.nc Circling_Summary_2h_B4_F13.nc\n", "CirclingData_2h_B2_F37.nc Circling_Summary_2h_B4_F14.nc\n", "CirclingData_2h_B2_F38.nc Circling_Summary_2h_B4_F15.nc\n", "CirclingData_2h_B2_F39.nc Circling_Summary_2h_B4_F16.nc\n", "CirclingData_2h_B2_F4.nc Circling_Summary_2h_B4_F17.nc\n", "CirclingData_2h_B2_F40.nc Circling_Summary_2h_B4_F18.nc\n", "CirclingData_2h_B2_F41.nc Circling_Summary_2h_B4_F19.nc\n", "CirclingData_2h_B2_F42.nc Circling_Summary_2h_B4_F2.nc\n", "CirclingData_2h_B2_F5.nc Circling_Summary_2h_B4_F20.nc\n", "CirclingData_2h_B2_F6.nc Circling_Summary_2h_B4_F21.nc\n", "CirclingData_2h_B2_F7.nc Circling_Summary_2h_B4_F22.nc\n", "CirclingData_2h_B2_F8.nc Circling_Summary_2h_B4_F23.nc\n", "CirclingData_2h_B2_F9.nc Circling_Summary_2h_B4_F24.nc\n", "CirclingData_2h_B3_F1.nc Circling_Summary_2h_B4_F25.nc\n", "CirclingData_2h_B3_F10.nc Circling_Summary_2h_B4_F26.nc\n", "CirclingData_2h_B3_F11.nc Circling_Summary_2h_B4_F27.nc\n", "CirclingData_2h_B3_F12.nc Circling_Summary_2h_B4_F28.nc\n", "CirclingData_2h_B3_F13.nc Circling_Summary_2h_B4_F29.nc\n", "CirclingData_2h_B3_F14.nc Circling_Summary_2h_B4_F3.nc\n", "CirclingData_2h_B3_F15.nc Circling_Summary_2h_B4_F30.nc\n", "CirclingData_2h_B3_F16.nc Circling_Summary_2h_B4_F31.nc\n", "CirclingData_2h_B3_F17.nc Circling_Summary_2h_B4_F32.nc\n", "CirclingData_2h_B3_F18.nc Circling_Summary_2h_B4_F33.nc\n", "CirclingData_2h_B3_F19.nc Circling_Summary_2h_B4_F34.nc\n", "CirclingData_2h_B3_F2.nc Circling_Summary_2h_B4_F35.nc\n", "CirclingData_2h_B3_F20.nc Circling_Summary_2h_B4_F36.nc\n", "CirclingData_2h_B3_F21.nc Circling_Summary_2h_B4_F37.nc\n", "CirclingData_2h_B3_F22.nc Circling_Summary_2h_B4_F38.nc\n", "CirclingData_2h_B3_F23.nc Circling_Summary_2h_B4_F39.nc\n", "CirclingData_2h_B3_F24.nc Circling_Summary_2h_B4_F4.nc\n", "CirclingData_2h_B3_F25.nc Circling_Summary_2h_B4_F40.nc\n", "CirclingData_2h_B3_F26.nc Circling_Summary_2h_B4_F41.nc\n", "CirclingData_2h_B3_F27.nc Circling_Summary_2h_B4_F42.nc\n", "CirclingData_2h_B3_F28.nc Circling_Summary_2h_B4_F5.nc\n", "CirclingData_2h_B3_F29.nc Circling_Summary_2h_B4_F6.nc\n", "CirclingData_2h_B3_F3.nc Circling_Summary_2h_B4_F7.nc\n", "CirclingData_2h_B3_F30.nc Circling_Summary_2h_B4_F8.nc\n", "CirclingData_2h_B3_F31.nc Circling_Summary_2h_B4_F9.nc\n", "CirclingData_2h_B3_F32.nc Circling_Summary_2h_B5_F1.nc\n", "CirclingData_2h_B3_F33.nc Circling_Summary_2h_B5_F10.nc\n", "CirclingData_2h_B3_F34.nc Circling_Summary_2h_B5_F11.nc\n", "CirclingData_2h_B3_F35.nc Circling_Summary_2h_B5_F12.nc\n", "CirclingData_2h_B3_F36.nc Circling_Summary_2h_B5_F13.nc\n", "CirclingData_2h_B3_F37.nc Circling_Summary_2h_B5_F14.nc\n", "CirclingData_2h_B3_F38.nc Circling_Summary_2h_B5_F15.nc\n", "CirclingData_2h_B3_F39.nc Circling_Summary_2h_B5_F16.nc\n", "CirclingData_2h_B3_F4.nc Circling_Summary_2h_B5_F17.nc\n", "CirclingData_2h_B3_F40.nc Circling_Summary_2h_B5_F18.nc\n", "CirclingData_2h_B3_F41.nc Circling_Summary_2h_B5_F19.nc\n", "CirclingData_2h_B3_F42.nc Circling_Summary_2h_B5_F2.nc\n", "CirclingData_2h_B3_F5.nc Circling_Summary_2h_B5_F20.nc\n", "CirclingData_2h_B3_F6.nc Circling_Summary_2h_B5_F21.nc\n", "CirclingData_2h_B3_F7.nc Circling_Summary_2h_B5_F22.nc\n", "CirclingData_2h_B3_F8.nc Circling_Summary_2h_B5_F23.nc\n", "CirclingData_2h_B3_F9.nc Circling_Summary_2h_B5_F24.nc\n", "CirclingData_2h_B4_F1.nc Circling_Summary_2h_B5_F25.nc\n", "CirclingData_2h_B4_F10.nc Circling_Summary_2h_B5_F26.nc\n", "CirclingData_2h_B4_F11.nc Circling_Summary_2h_B5_F27.nc\n", "CirclingData_2h_B4_F12.nc Circling_Summary_2h_B5_F28.nc\n", "CirclingData_2h_B4_F13.nc Circling_Summary_2h_B5_F29.nc\n", "CirclingData_2h_B4_F14.nc Circling_Summary_2h_B5_F3.nc\n", "CirclingData_2h_B4_F15.nc Circling_Summary_2h_B5_F30.nc\n", "CirclingData_2h_B4_F16.nc Circling_Summary_2h_B5_F31.nc\n", "CirclingData_2h_B4_F17.nc Circling_Summary_2h_B5_F32.nc\n", "CirclingData_2h_B4_F18.nc Circling_Summary_2h_B5_F33.nc\n", "CirclingData_2h_B4_F19.nc Circling_Summary_2h_B5_F34.nc\n", "CirclingData_2h_B4_F2.nc Circling_Summary_2h_B5_F35.nc\n", "CirclingData_2h_B4_F20.nc Circling_Summary_2h_B5_F36.nc\n", "CirclingData_2h_B4_F21.nc Circling_Summary_2h_B5_F37.nc\n", "CirclingData_2h_B4_F22.nc Circling_Summary_2h_B5_F38.nc\n", "CirclingData_2h_B4_F23.nc Circling_Summary_2h_B5_F39.nc\n", "CirclingData_2h_B4_F24.nc Circling_Summary_2h_B5_F4.nc\n", "CirclingData_2h_B4_F25.nc Circling_Summary_2h_B5_F40.nc\n", "CirclingData_2h_B4_F26.nc Circling_Summary_2h_B5_F41.nc\n", "CirclingData_2h_B4_F27.nc Circling_Summary_2h_B5_F42.nc\n", "CirclingData_2h_B4_F28.nc Circling_Summary_2h_B5_F5.nc\n", "CirclingData_2h_B4_F29.nc Circling_Summary_2h_B5_F6.nc\n", "CirclingData_2h_B4_F3.nc Circling_Summary_2h_B5_F7.nc\n", "CirclingData_2h_B4_F30.nc Circling_Summary_2h_B5_F8.nc\n", "CirclingData_2h_B4_F31.nc Circling_Summary_2h_B5_F9.nc\n", "CirclingData_2h_B4_F32.nc Circling_Summary_2h_B6_F1.nc\n", "CirclingData_2h_B4_F33.nc Circling_Summary_2h_B6_F10.nc\n", "CirclingData_2h_B4_F34.nc Circling_Summary_2h_B6_F11.nc\n", "CirclingData_2h_B4_F35.nc Circling_Summary_2h_B6_F12.nc\n", "CirclingData_2h_B4_F36.nc Circling_Summary_2h_B6_F13.nc\n", "CirclingData_2h_B4_F37.nc Circling_Summary_2h_B6_F14.nc\n", "CirclingData_2h_B4_F38.nc Circling_Summary_2h_B6_F15.nc\n", "CirclingData_2h_B4_F39.nc Circling_Summary_2h_B6_F16.nc\n", "CirclingData_2h_B4_F4.nc Circling_Summary_2h_B6_F17.nc\n", "CirclingData_2h_B4_F40.nc Circling_Summary_2h_B6_F18.nc\n", "CirclingData_2h_B4_F41.nc Circling_Summary_2h_B6_F19.nc\n", "CirclingData_2h_B4_F42.nc Circling_Summary_2h_B6_F2.nc\n", "CirclingData_2h_B4_F5.nc Circling_Summary_2h_B6_F20.nc\n", "CirclingData_2h_B4_F6.nc Circling_Summary_2h_B6_F21.nc\n", "CirclingData_2h_B4_F7.nc Circling_Summary_2h_B6_F22.nc\n", "CirclingData_2h_B4_F8.nc Circling_Summary_2h_B6_F23.nc\n", "CirclingData_2h_B4_F9.nc Circling_Summary_2h_B6_F24.nc\n", "CirclingData_2h_B5_F1.nc Circling_Summary_2h_B6_F25.nc\n", "CirclingData_2h_B5_F10.nc Circling_Summary_2h_B6_F26.nc\n", "CirclingData_2h_B5_F11.nc Circling_Summary_2h_B6_F27.nc\n", "CirclingData_2h_B5_F12.nc Circling_Summary_2h_B6_F28.nc\n", "CirclingData_2h_B5_F13.nc Circling_Summary_2h_B6_F29.nc\n", "CirclingData_2h_B5_F14.nc Circling_Summary_2h_B6_F3.nc\n", "CirclingData_2h_B5_F15.nc Circling_Summary_2h_B6_F30.nc\n", "CirclingData_2h_B5_F16.nc Circling_Summary_2h_B6_F31.nc\n", "CirclingData_2h_B5_F17.nc Circling_Summary_2h_B6_F32.nc\n", "CirclingData_2h_B5_F18.nc Circling_Summary_2h_B6_F33.nc\n", "CirclingData_2h_B5_F19.nc Circling_Summary_2h_B6_F34.nc\n", "CirclingData_2h_B5_F2.nc Circling_Summary_2h_B6_F35.nc\n", "CirclingData_2h_B5_F20.nc Circling_Summary_2h_B6_F36.nc\n", "CirclingData_2h_B5_F21.nc Circling_Summary_2h_B6_F37.nc\n", "CirclingData_2h_B5_F22.nc Circling_Summary_2h_B6_F38.nc\n", "CirclingData_2h_B5_F23.nc Circling_Summary_2h_B6_F39.nc\n", "CirclingData_2h_B5_F24.nc Circling_Summary_2h_B6_F4.nc\n", "CirclingData_2h_B5_F25.nc Circling_Summary_2h_B6_F40.nc\n", "CirclingData_2h_B5_F26.nc Circling_Summary_2h_B6_F41.nc\n", "CirclingData_2h_B5_F27.nc Circling_Summary_2h_B6_F42.nc\n", "CirclingData_2h_B5_F28.nc Circling_Summary_2h_B6_F5.nc\n", "CirclingData_2h_B5_F29.nc Circling_Summary_2h_B6_F6.nc\n", "CirclingData_2h_B5_F3.nc Circling_Summary_2h_B6_F7.nc\n", "CirclingData_2h_B5_F30.nc Circling_Summary_2h_B6_F8.nc\n", "CirclingData_2h_B5_F31.nc Circling_Summary_2h_B6_F9.nc\n", "CirclingData_2h_B5_F32.nc Continuous2h_all.nc\n", "CirclingData_2h_B5_F33.nc \u001b[34mContinuousData\u001b[m\u001b[m/\n", "CirclingData_2h_B5_F34.nc \u001b[34mData\u001b[m\u001b[m/\n", "CirclingData_2h_B5_F35.nc F0B1.csv\n", "CirclingData_2h_B5_F36.nc F0B7.csv\n", "CirclingData_2h_B5_F37.nc F10B1.csv\n", "CirclingData_2h_B5_F38.nc F11B1.csv\n", "CirclingData_2h_B5_F39.nc F12B1.csv\n", "CirclingData_2h_B5_F4.nc F13B1.csv\n", "CirclingData_2h_B5_F40.nc F14B1.csv\n", "CirclingData_2h_B5_F41.nc F15B1.csv\n", "CirclingData_2h_B5_F42.nc F16B1.csv\n", "CirclingData_2h_B5_F5.nc F1B1.csv\n", "CirclingData_2h_B5_F6.nc F1B7.csv\n", "CirclingData_2h_B5_F7.nc F1C_PSD_24h.pdf\n", "CirclingData_2h_B5_F8.nc F2B1.csv\n", "CirclingData_2h_B5_F9.nc F2B7.csv\n", "CirclingData_2h_B6_F1.nc F3B1.csv\n", "CirclingData_2h_B6_F10.nc F3B7.csv\n", "CirclingData_2h_B6_F11.nc F4B1.csv\n", "CirclingData_2h_B6_F12.nc F4B7.csv\n", "CirclingData_2h_B6_F13.nc F5B1.csv\n", "CirclingData_2h_B6_F14.nc F5B7.csv\n", "CirclingData_2h_B6_F15.nc F6B1.csv\n", "CirclingData_2h_B6_F16.nc F6B7.csv\n", "CirclingData_2h_B6_F17.nc F7B1.csv\n", "CirclingData_2h_B6_F18.nc F7B7.csv\n", "CirclingData_2h_B6_F19.nc F8B1.csv\n", "CirclingData_2h_B6_F2.nc F8B7.csv\n", "CirclingData_2h_B6_F20.nc F9B1.csv\n", "CirclingData_2h_B6_F21.nc \u001b[34mHandedness Drift\u001b[m\u001b[m/\n", "CirclingData_2h_B6_F22.nc IFI continous data.png\n", "CirclingData_2h_B6_F23.nc LombScarglePSD.pdf\n", "CirclingData_2h_B6_F24.nc Mean Power Spectrum.png\n", "CirclingData_2h_B6_F25.nc \u001b[34mPowerSpectrum\u001b[m\u001b[m/\n", "CirclingData_2h_B6_F26.nc Power_b0f0.nc\n", "CirclingData_2h_B6_F27.nc \u001b[34mRollingAverage_Y\u001b[m\u001b[m/\n", "CirclingData_2h_B6_F28.nc \u001b[34mSummary Data\u001b[m\u001b[m/\n", "CirclingData_2h_B6_F29.nc \u001b[34m__pycache__\u001b[m\u001b[m/\n", "CirclingData_2h_B6_F3.nc analyze_centroids.ipynb\n", "CirclingData_2h_B6_F30.nc angle_analysis.ipynb\n", "CirclingData_2h_B6_F31.nc angle_processing.ipynb\n", "CirclingData_2h_B6_F32.nc ar0.py\n", "CirclingData_2h_B6_F33.nc blacknand3.csv\n", "CirclingData_2h_B6_F34.nc concatbyt.nc\n", "CirclingData_2h_B6_F35.nc continuosturns.ipynb\n", "CirclingData_2h_B6_F36.nc continuousanalysis.py\n", "CirclingData_2h_B6_F37.nc heading_30s.pdf\n", "CirclingData_2h_B6_F38.nc justrunit.py\n", "CirclingData_2h_B6_F39.nc loadcontinuousmatlabfiles.py\n", "CirclingData_2h_B6_F4.nc lowpassfilteredflytraces.pdf\n", "CirclingData_2h_B6_F40.nc meanstd_continuousdata.pdf\n", "CirclingData_2h_B6_F41.nc meboot.py\n", "CirclingData_2h_B6_F42.nc pall2.pdf\n", "CirclingData_2h_B6_F5.nc \u001b[34mpartemp\u001b[m\u001b[m/\n", "CirclingData_2h_B6_F6.nc test.csv\n", "CirclingData_2h_B6_F7.nc test.ipynb\n", "CirclingData_2h_B6_F8.nc test.nc\n", "CirclingData_2h_B6_F9.nc testboth.nc\n", "Circling_Summary_24h_B1_F1.nc testflox.ipynb\n", "Circling_Summary_24h_B1_F10.nc testfly.nc\n", "Circling_Summary_24h_B1_F11.nc testpar.ipynb\n", "Circling_Summary_24h_B1_F12.nc violinpolot1.png\n", "Circling_Summary_24h_B1_F13.nc\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## test new circling summary" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ryanmaloney/Continuous Turns/Circling_Summary_2h_B5_F14.nc',\n", " '/Users/ryanmaloney/Continuous Turns/Circling_Summary_24h_B3_F16.nc',\n", " '/Users/ryanmaloney/Continuous Turns/Circling_Summary_2h_B2_F26.nc',\n", " '/Users/ryanmaloney/Continuous Turns/Circling_Summary_24h_B4_F24.nc']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testlist=glob.glob(\"/Users/ryanmaloney/Continuous Turns/Circling_Summary*\")\n", "testlist[0:4]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (Bins_direction: 200, Fly: 4, Freq: 1000,\n",
       "                           Shuffled: 2, Bins_angle: 200, Bins_turning: 200,\n",
       "                           Measure: 4, Bins_speed_logged: 200, Bins_r: 200,\n",
       "                           Batch: 4)\n",
       "Coordinates:\n",
       "    Trial                 int8 ...\n",
       "  * Bins_direction        (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Fly                   (Fly) int8 14 16 24 26\n",
       "  * Freq                  (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n",
       "  * Shuffled              (Shuffled) object 'Non-shuffle Data' 'Shuffled'\n",
       "  * Bins_angle            (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n",
       "  * Bins_turning          (Bins_turning) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Measure               (Measure) object 'Mean' 'Std' 'Min' 'Max'\n",
       "  * Bins_speed_logged     (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n",
       "  * Bins_r                (Bins_r) float64 0.15 0.45 0.75 ... 59.25 59.55 59.85\n",
       "  * Batch                 (Batch) int8 2 3 4 5\n",
       "    recording_length      (Batch) int8 2 24 24 2\n",
       "Data variables: (12/19)\n",
       "    direction_bins        (Bins_direction, Fly, Batch) float64 dask.array<chunksize=(200, 4, 1), meta=np.ndarray>\n",
       "    direction_psd         (Freq, Fly, Batch, Shuffled) float64 dask.array<chunksize=(1000, 4, 1, 2), meta=np.ndarray>\n",
       "    angle_bins            (Bins_angle, Fly, Batch) float64 dask.array<chunksize=(200, 4, 1), meta=np.ndarray>\n",
       "    angle_psd             (Freq, Fly, Batch, Shuffled) float64 dask.array<chunksize=(1000, 4, 1, 2), meta=np.ndarray>\n",
       "    theta_bins            (Bins_turning, Fly, Batch) float64 dask.array<chunksize=(200, 4, 1), meta=np.ndarray>\n",
       "    theta_summary         (Measure, Fly, Batch) float64 dask.array<chunksize=(4, 4, 1), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    r_bins                (Bins_r, Fly, Batch) float64 dask.array<chunksize=(200, 4, 1), meta=np.ndarray>\n",
       "    r_psd                 (Freq, Fly, Batch, Shuffled) float64 dask.array<chunksize=(1000, 4, 1, 2), meta=np.ndarray>\n",
       "    r_summary             (Measure, Fly, Batch) float64 dask.array<chunksize=(4, 4, 1), meta=np.ndarray>\n",
       "    direction_summary     (Measure, Fly, Batch) float64 dask.array<chunksize=(4, 4, 1), meta=np.ndarray>\n",
       "    speed_summary         (Measure, Fly, Batch) float64 dask.array<chunksize=(4, 4, 1), meta=np.ndarray>\n",
       "    angle_summary         (Measure, Fly, Batch) float64 dask.array<chunksize=(4, 4, 1), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Bins_direction: 200, Fly: 4, Freq: 1000,\n", " Shuffled: 2, Bins_angle: 200, Bins_turning: 200,\n", " Measure: 4, Bins_speed_logged: 200, Bins_r: 200,\n", " Batch: 4)\n", "Coordinates:\n", " Trial int8 ...\n", " * Bins_direction (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Fly (Fly) int8 14 16 24 26\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) object 'Non-shuffle Data' 'Shuffled'\n", " * Bins_angle (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n", " * Bins_turning (Bins_turning) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Measure (Measure) object 'Mean' 'Std' 'Min' 'Max'\n", " * Bins_speed_logged (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n", " * Bins_r (Bins_r) float64 0.15 0.45 0.75 ... 59.25 59.55 59.85\n", " * Batch (Batch) int8 2 3 4 5\n", " recording_length (Batch) int8 2 24 24 2\n", "Data variables: (12/19)\n", " direction_bins (Bins_direction, Fly, Batch) float64 dask.array\n", " direction_psd (Freq, Fly, Batch, Shuffled) float64 dask.array\n", " angle_bins (Bins_angle, Fly, Batch) float64 dask.array\n", " angle_psd (Freq, Fly, Batch, Shuffled) float64 dask.array\n", " theta_bins (Bins_turning, Fly, Batch) float64 dask.array\n", " theta_summary (Measure, Fly, Batch) float64 dask.array\n", " ... ...\n", " r_bins (Bins_r, Fly, Batch) float64 dask.array\n", " r_psd (Freq, Fly, Batch, Shuffled) float64 dask.array\n", " r_summary (Measure, Fly, Batch) float64 dask.array\n", " direction_summary (Measure, Fly, Batch) float64 dask.array\n", " speed_summary (Measure, Fly, Batch) float64 dask.array\n", " angle_summary (Measure, Fly, Batch) float64 dask.array" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_newdata=xr.open_mfdataset(testlist[0:4])\n", "test_newdata" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Trialrecording_lengthspeed_summary
MeasureFlyBatch
Mean14212NaN
3124NaN
4124NaN
5125.916266
16212NaN
..................
Max24512NaN
262122207.718750
3124NaN
4124NaN
512NaN
\n", "

64 rows × 3 columns

\n", "
" ], "text/plain": [ " Trial recording_length speed_summary\n", "Measure Fly Batch \n", "Mean 14 2 1 2 NaN\n", " 3 1 24 NaN\n", " 4 1 24 NaN\n", " 5 1 2 5.916266\n", " 16 2 1 2 NaN\n", "... ... ... ...\n", "Max 24 5 1 2 NaN\n", " 26 2 1 2 2207.718750\n", " 3 1 24 NaN\n", " 4 1 24 NaN\n", " 5 1 2 NaN\n", "\n", "[64 rows x 3 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_newdata[\"speed_summary\"].to_dataframe()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Trialrecording_lengthspeed_psd
FreqFlyBatchShuffled
3.858025e-07142Non-shuffle Data12NaN
Shuffled12NaN
3Non-shuffle Data124NaN
Shuffled124NaN
4Non-shuffle Data124NaN
.....................
5.000000e+00263Shuffled124NaN
4Non-shuffle Data124NaN
Shuffled124NaN
5Non-shuffle Data12NaN
Shuffled12NaN
\n", "

32000 rows × 3 columns

\n", "
" ], "text/plain": [ " Trial recording_length speed_psd\n", "Freq Fly Batch Shuffled \n", "3.858025e-07 14 2 Non-shuffle Data 1 2 NaN\n", " Shuffled 1 2 NaN\n", " 3 Non-shuffle Data 1 24 NaN\n", " Shuffled 1 24 NaN\n", " 4 Non-shuffle Data 1 24 NaN\n", "... ... ... ...\n", "5.000000e+00 26 3 Shuffled 1 24 NaN\n", " 4 Non-shuffle Data 1 24 NaN\n", " Shuffled 1 24 NaN\n", " 5 Non-shuffle Data 1 2 NaN\n", " Shuffled 1 2 NaN\n", "\n", "[32000 rows x 3 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "speed_psd_truncated=test_newdata[\"speed_psd\"].compute().to_dataframe()\n", "speed_psd_truncated" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(speed_psd_truncated.reset_index(), x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "duration=[2]\n", "batch=np.arange(1,8)\n", "fly=np.arange(1,43)\n", "\n", "iterables=[duration, batch, fly]\n", "index=pd.MultiIndex.from_product(iterables, names=[\"Duration\", \"Batch\", \"Fly\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Parallel(n_jobs=-1, verbose=10)(delayed(lcm.importonefile(path, i[1],i[0],i[2])) for i in index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pd.MultiIndex.from_product(iterables, names=[\"Duration\", \"Batch\", \"Fly\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "lcm.import_dask_matlab_files(path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lcm.calcpowerforfly(x1[\"angle\"], x1[\"timestamps\"].squeeze())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.isnan(y).sum().values\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lcm.calculatepowerforonefly(x1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xm_bins=xr.merge([x1_bins, x2_bins])\n", "xm_bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xm_bins[\"Bins_direction\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sldf=xm_bins[\"speed_logged_bins\"].to_dataframe()\n", "sldf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=sldf, x=\"Bins_speed_logged\", y=\"speed_logged_bins\", color=\"Fly\").add(so.Bars())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1_bins[1][1:].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1_bins[1][0:-1].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.mean([x1_bins[1][0:-1], x1_bins[1][1:]], axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xr.DataArray(x1_bins[0], {\"Bins\":np.mean([x1_bins[1][0:-1], x1_bins[1][1:]], axis=0)})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1_bins[0].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1_bins[1].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "varnames=list(x1)\n", "varnames" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "while 'inx' in varnames: varnames.remove('inx')\n", "while 'iny' in varnames: varnames.remove('iny')\n", "if 'speed' in varnames: varnames.append('speed_logged')\n", "\n", "varnames" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for a in varnames:\n", " print(a)\n", " if \"r\"==a:\n", " print(a)\n", " # if \"r\" in a:\n", " # print(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=x1['direction'].plot.hist(bins=np.linspace(-np.pi, np.pi, 201))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.pi" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1[\"speed\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1[\"direction\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=x1[\"direction\"].plot.hist(bins=np.linspace(-1, 1, 201))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=x1[\"direction\"].plot.hist(bins=np.linspace(-1, 1, 201))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[0].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[1][0:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[1][0:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bin_medians=np.cumsum(np.diff(a[1]))+np.mean(a[1][0:2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for a,b in enumerate(x1):\n", " print(a, b)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1[\"speed\"].plot.hist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for foobar in x1:\n", " a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "turning=x1[\"turning\"].plot.hist(bins=np.linspace(-np.pi*2, np.pi*2, 300))\n", "turning[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "turning[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(x=turning[])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "turning[0].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(np.cumsum(np.diff(turning[1]))+np.mean(turning[1][0:2])).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "histstuff=pd.DataFrame({\"Bincounts\":turning[0],\"binboundaries\":(np.cumsum(np.diff(turning[1]))+np.mean(turning[1][0:2])),})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=histstuff, x=\"binboundaries\", y=\"Bincounts\").add(so.Bars())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xr.merge([x1,x2])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x12=xr.concat([x1,x2], dim=\"Fly\")\n", "x34=xr.concat([x3,x4], dim=\"Fly\")\n", "x1234=xr.concat([x12,x34],dim=\"Batch\")\n", "x1234" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Second try with new matlab data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "path='/Users/ryanmaloney/Documents/Matlab/'\n", "\n", "a1=lcm.importonefile(path, 2,6,11, override=True)\n", "a1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a1.drop(\"thack\").to_netcdf(\"test.nc\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a1=a.squeeze().compute().stack({\"thack\":[\"Trial\", \"t\"]}).swap_dims({\"thack\":\"timestamps\"})\n", "# a1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"End\", 1, 2, 3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "a2=lcm.xarrayone_processing(a1)\n", "a2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "a3=lcm.get_bins(a2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#loading a second fly\n", "b1=lcm.importonefile(path, 2,2,4, override=True)\n", "b2=lcm.xarrayone_processing(b1)\n", "b3=lcm.get_bins(b2)\n", "ping(h)\n", "b3\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#loading a second fly\n", "c1=lcm.importonefile(path, 2,2,1, override=True)\n", "c2=lcm.xarrayone_processing(c1)\n", "c3=lcm.get_bins(c2)\n", "ping(h)\n", "c3\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# #loading a second fly\n", "imp.reload(lcm)\n", "d1=lcm.importonefile(path, 2,3,5, override=True, save_timeseries=False)\n", "ping(h)\n", "d1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "glob.glob(\"Circling_Summary*\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "abc=xr.merge([b3,a3, c3, d1])\n", "abc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a.expand_dims(\"Fly\").chunk({\"Fly\":1, \"Trial\":8}).compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# testing parallel processing\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lcm.importonefile(file_path,2,1,1, override=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "override=True\n", "duration=[2,24]\n", "batch=np.arange(1,8)\n", "fly=np.arange(1,43)\n", "\n", "file_path='/Users/ryanmaloney/Documents/Matlab/'\n", "\n", "\n", "iterables=[duration, batch, fly]\n", "index=pd.MultiIndex.from_product(iterables, names=[\"Duration\", \"Batch\", \"Fly\"])\n", "# index\n", "\n", "Parallel(n_jobs=1, verbose=5)(delayed(lcm.importonefile)(file_path, i[0],i[1],i[2], override=override) for i in index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate a nested list for xarray to have all data in dask\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load all of the Circling SUmmary data produced by lcm.get_bins (usually generated by import_dask_matlab_files) into one xarray\n", "\n", "This is replicated in loadallsummary data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch=[]\n", "batch=1\n", "nestedlist_batch.append(glob.glob(\"Summary Data/Circling_Summary_2h_B\"+str(batch)+\"_F[0-5].nc\"))\n", "nestedlist_batch\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# test1=xr.open_mfdataset(nestedlist_batch[:3], combine=\"nested\", concat_dim=[\"Batch\", \"Fly\"])\n", "# tes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dask.config.set({\"array.slicing.split_large_chunks\": True})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Load summaries alltogether to try and get " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch=[] \n", "batch=1\n", "# batch1=xr.open_mfdataset(glob.glob(\"Summary Data/Circling_Summary_2h_B\"+str(batch)+\"_F*.nc\"), combine=\"nested\", concat_dim=[\"Fly\"]).stack(flyid=[\"Batch\", \"Fly\"])\n", "batch1=xr.open_mfdataset(glob.glob(\"Summary Data/Circling_Summary_2h_B*_F*.nc\"), combine=\"nested\", concat_dim=[\"Fly\"]).stack(flyid=[\"Batch\", \"Fly\"])\n", "\n", "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "batch1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "batch1[\"turning_bins\"].sum(\"flyid\").plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "lcm.makehistogram(batch1, \"turning\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ba_df=batch1[\"turning_psd\"].to_dataframe().drop(columns=[\"Batch\", \"Fly\"]).reset_index()\n", "ba_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ba_df.dropna(subset=\"turning_psd\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(so.Plot(data=ba_df, x=\"Freq\", y=\"turning_psd\", color=\"Shuffled\")\n", ".add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\")\n", ".add(so.Band(), so.Est())\n", ".theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n", ".label(x=\"Frequency (Hz)\", y)\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b1ts=batch1[\"angle_summary\"].to_dataframe().drop(columns=[\"Batch\", \"Fly\"]).dropna()\n", "b1ts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b1ts_p=b1ts.reset_index().pivot(columns=\"Measure\", index=[\"Batch\", \"Fly\", \"Trial\"], values=\"angle_summary\")\n", "b1ts_p" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b1ts_p" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def makePSD(summary_xarray, data_category, xlabel=\"\", ylabel=\"Power\", title=\"\"):\n", " if len(xlabel)<1:\n", " xlabel=data_category .capitalize()\n", " if len(title)<1:\n", " title=\"Distribution of \"+data_category.capitalize()+\" across all Fly Behavior\"\n", " datalabel=data_category+\"_psd\"\n", " ba_df=batch1[datalabel].to_dataframe().drop(columns=[\"Batch\", \"Fly\"]).reset_index().dropna()\n", " psd=(\n", " so.Plot(data=ba_df, x=\"Freq\", y=datalabel, color=\"Shuffled\")\n", " .add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\")\n", " .add(so.Band(), so.Est())\n", " .theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n", " .label(x=\"Frequency (Hz)\", y=ylabel, title=title)\n", " )\n", " return psd\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "batch1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "spsd=batch1[\"speed_psd\"][:,0,:].to_dataframe()\n", "spsd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "makePSD(batch1, \"speed\", title=\"Speed PSD\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "makePSD(batch1, \"r\", title=\"R PSD\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "makePSD(batch1, \"turning\", title=\"Turning PSD\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "makePSD(batch1, \"angle\", title=\"Angle PSD\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary_xarray=batch1\n", "data_category=\"turning\"\n", "xlabel=\"\"\n", "ylabel=\"Power\"\n", "title=\"Turning PSD\"\n", "if len(xlabel)<1:\n", " xlabel=data_category .capitalize()\n", "if len(title)<1:\n", " title=\"Distribution of \"+data_category.capitalize()+\" across all Fly Behavior\"\n", "datalabel=data_category+\"_psd\"\n", "ba_df=batch1[datalabel].to_dataframe().drop(columns=[\"Batch\", \"Fly\"]).reset_index().dropna()\n", "so.Plot(data=ba_df, x=\"Freq\", y=datalabel, color=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=ylabel)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary_xarray=batch1\n", "data_category=\"turning\"\n", "xlabel=\"\"\n", "ylabel=\"Power\"\n", "title=\"Turning PSD\"\n", "if len(xlabel)<1:\n", " xlabel=data_category .capitalize()\n", "if len(title)<1:\n", " title=\"Distribution of \"+data_category.capitalize()+\" across all Fly Behavior\"\n", "datalabel=data_category+\"_psd\"\n", "ba_df=batch1[datalabel].to_dataframe().drop(columns=[\"Batch\", \"Fly\"]).reset_index().dropna()\n", "so.Plot(data=ba_df, x=\"Freq\", y=datalabel, color=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=ylabel)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "psd=makePSD(batch1, \"turning\")\n", "psd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=b1ts_p.reset_index(), x=\"Mean\", y=\"Std\").add(so.Dots()).limit(x=(-1,1), y=(0,1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch=[] \n", "batch=1\n", "# nestedlist_batch.append(glob.glob(\"CirclingData_2h_B\"+str(batch)+\"_F*.nc\"))\n", "test1=xr.open_mfdataset(glob.glob(\"Circling_Summary_2h_B\"+str(batch)+\"_F*.nc\"), combine=\"nested\", concat_dim=[\"Fly\"]).stack(flyid=[\"Batch\", \"Fly\"])\n", "# sum1=test1.stack(flykey=[\"Fly\", \"Batch\"])\n", "for batch in np.arange(2,7):\n", " # nestedlist_batch=[]\n", " print(batch)\n", " # nestedlist_batch.append(glob.glob(\"CirclingData_2h_B\"+str(batch)+\"_F*.nc\"))\n", " test2=xr.open_mfdataset(glob.glob(\"Circling_Summary_2h_B\"+str(batch)+\"_F*.nc\"), combine=\"nested\", concat_dim=[\"Fly\"]).stack(flyid=[\"Batch\", \"Fly\"])\n", " test1=xr.concat([test1,test2], dim=\"flyid\")\n", "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=test1[\"direction_bins\"].sum(dim=\"flyid\").to_dataframe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lcm.makehistogram(test1, \"direction\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load all summary data for both 2h and 24h\n", "Because there are a different number of flies, this may require some amount of coercion" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(42,)\n", "2\n", "(84,)\n", "3\n", "(126,)\n", "4\n", "(168,)\n", "5\n", "(210,)\n", "6\n", "(252,)\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (Bins_direction: 200, flyid: 252, Freq: 1000,\n",
       "                           Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n",
       "                           Measure: 4, Bins_turning: 200,\n",
       "                           Bins_speed_logged: 200, Bins_r: 200)\n",
       "Coordinates: (12/14)\n",
       "    Trial                 int8 ...\n",
       "  * Bins_direction        (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Freq                  (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n",
       "  * Shuffled              (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n",
       "  * Bins_angle            (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n",
       "  * Bins_theta            (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "    ...                    ...\n",
       "  * Bins_turning          (Bins_turning) float64 -6.252 -6.189 ... 6.189 6.252\n",
       "  * Bins_speed_logged     (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n",
       "  * Bins_r                (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n",
       "  * flyid                 (flyid) object MultiIndex\n",
       "  * Batch                 (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n",
       "  * Fly                   (flyid) int64 8 9 35 6 25 11 40 ... 34 24 41 10 1 5 4\n",
       "Data variables: (12/19)\n",
       "    direction_bins        (Bins_direction, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    direction_psd         (Freq, Shuffled, flyid) float64 dask.array<chunksize=(1000, 2, 1), meta=np.ndarray>\n",
       "    angle_bins            (Bins_angle, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    angle_psd             (Freq, Shuffled, flyid) float64 dask.array<chunksize=(1000, 2, 1), meta=np.ndarray>\n",
       "    theta_bins            (Bins_theta, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    theta_summary         (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    r_bins                (Bins_r, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    r_psd                 (Freq, Shuffled, flyid) float64 dask.array<chunksize=(1000, 2, 1), meta=np.ndarray>\n",
       "    r_summary             (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    direction_summary     (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    speed_summary         (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    angle_summary         (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Bins_direction: 200, flyid: 252, Freq: 1000,\n", " Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n", " Measure: 4, Bins_turning: 200,\n", " Bins_speed_logged: 200, Bins_r: 200)\n", "Coordinates: (12/14)\n", " Trial int8 ...\n", " * Bins_direction (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n", " * Bins_angle (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n", " * Bins_theta (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n", " ... ...\n", " * Bins_turning (Bins_turning) float64 -6.252 -6.189 ... 6.189 6.252\n", " * Bins_speed_logged (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n", " * Bins_r (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n", " * flyid (flyid) object MultiIndex\n", " * Batch (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n", " * Fly (flyid) int64 8 9 35 6 25 11 40 ... 34 24 41 10 1 5 4\n", "Data variables: (12/19)\n", " direction_bins (Bins_direction, flyid) float64 dask.array\n", " direction_psd (Freq, Shuffled, flyid) float64 dask.array\n", " angle_bins (Bins_angle, flyid) float64 dask.array\n", " angle_psd (Freq, Shuffled, flyid) float64 dask.array\n", " theta_bins (Bins_theta, flyid) float64 dask.array\n", " theta_summary (Measure, flyid) float64 dask.array\n", " ... ...\n", " r_bins (Bins_r, flyid) float64 dask.array\n", " r_psd (Freq, Shuffled, flyid) float64 dask.array\n", " r_summary (Measure, flyid) float64 dask.array\n", " direction_summary (Measure, flyid) float64 dask.array\n", " speed_summary (Measure, flyid) float64 dask.array\n", " angle_summary (Measure, flyid) float64 dask.array" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imp.reload(lcm)\n", "summary_2h_xarray=lcm.loadallsummarydata(path=\"Summary Data/\",duration=2)\n", "summary_2h_xarray" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Trialrecording_lengthBatchFlyturning_psd
FreqShuffledBatchFly
3.858025e-07Non-shuffled Data1812180.000081
912190.000011
35121350.000024
612160.000049
25121250.000445
...........................
5.000000e+00Shuffled641126410.000016
10126100.000010
112610.000210
512650.000031
412640.000011
\n", "

502000 rows × 5 columns

\n", "
" ], "text/plain": [ " Trial recording_length Batch Fly \\\n", "Freq Shuffled Batch Fly \n", "3.858025e-07 Non-shuffled Data 1 8 1 2 1 8 \n", " 9 1 2 1 9 \n", " 35 1 2 1 35 \n", " 6 1 2 1 6 \n", " 25 1 2 1 25 \n", "... ... ... ... ... \n", "5.000000e+00 Shuffled 6 41 1 2 6 41 \n", " 10 1 2 6 10 \n", " 1 1 2 6 1 \n", " 5 1 2 6 5 \n", " 4 1 2 6 4 \n", "\n", " turning_psd \n", "Freq Shuffled Batch Fly \n", "3.858025e-07 Non-shuffled Data 1 8 0.000081 \n", " 9 0.000011 \n", " 35 0.000024 \n", " 6 0.000049 \n", " 25 0.000445 \n", "... ... \n", "5.000000e+00 Shuffled 6 41 0.000016 \n", " 10 0.000010 \n", " 1 0.000210 \n", " 5 0.000031 \n", " 4 0.000011 \n", "\n", "[502000 rows x 5 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary_2h_xarray[\"turning_psd\"].to_dataframe().dropna()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(41,)\n", "2\n", "(82,)\n", "3\n", "(124,)\n", "4\n", "(166,)\n", "5\n", "(208,)\n", "6\n", "(250,)\n" ] } ], "source": [ "imp.reload(lcm)\n", "summary_24h_xarray=lcm.loadallsummarydata(path=\"Summary Data/\",duration=24)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (Bins_direction: 200, flyid: 250, Freq: 1000,\n",
       "                           Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n",
       "                           Measure: 4, Bins_turning: 200,\n",
       "                           Bins_speed_logged: 200, Bins_r: 200)\n",
       "Coordinates: (12/14)\n",
       "    Trial                 int8 1\n",
       "  * Bins_direction        (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Freq                  (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n",
       "  * Shuffled              (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n",
       "  * Bins_angle            (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n",
       "  * Bins_theta            (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "    ...                    ...\n",
       "  * Bins_turning          (Bins_turning) float64 -6.252 -6.189 ... 6.189 6.252\n",
       "  * Bins_speed_logged     (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n",
       "  * Bins_r                (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n",
       "  * flyid                 (flyid) object MultiIndex\n",
       "  * Batch                 (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n",
       "  * Fly                   (flyid) int64 23 6 17 33 42 2 13 ... 12 26 36 22 16 32\n",
       "Data variables: (12/19)\n",
       "    direction_bins        (Bins_direction, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    direction_psd         (Freq, Shuffled, flyid) float64 dask.array<chunksize=(1000, 2, 1), meta=np.ndarray>\n",
       "    angle_bins            (Bins_angle, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    angle_psd             (Freq, Shuffled, flyid) float64 dask.array<chunksize=(1000, 2, 1), meta=np.ndarray>\n",
       "    theta_bins            (Bins_theta, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    theta_summary         (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    r_bins                (Bins_r, flyid) float64 dask.array<chunksize=(200, 1), meta=np.ndarray>\n",
       "    r_psd                 (Freq, Shuffled, flyid) float64 dask.array<chunksize=(1000, 2, 1), meta=np.ndarray>\n",
       "    r_summary             (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    direction_summary     (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    speed_summary         (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>\n",
       "    angle_summary         (Measure, flyid) float64 dask.array<chunksize=(4, 1), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Bins_direction: 200, flyid: 250, Freq: 1000,\n", " Shuffled: 2, Bins_angle: 200, Bins_theta: 200,\n", " Measure: 4, Bins_turning: 200,\n", " Bins_speed_logged: 200, Bins_r: 200)\n", "Coordinates: (12/14)\n", " Trial int8 1\n", " * Bins_direction (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n", " * Bins_angle (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n", " * Bins_theta (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n", " ... ...\n", " * Bins_turning (Bins_turning) float64 -6.252 -6.189 ... 6.189 6.252\n", " * Bins_speed_logged (Bins_speed_logged) float64 -6.965 -6.895 ... 6.965\n", " * Bins_r (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n", " * flyid (flyid) object MultiIndex\n", " * Batch (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n", " * Fly (flyid) int64 23 6 17 33 42 2 13 ... 12 26 36 22 16 32\n", "Data variables: (12/19)\n", " direction_bins (Bins_direction, flyid) float64 dask.array\n", " direction_psd (Freq, Shuffled, flyid) float64 dask.array\n", " angle_bins (Bins_angle, flyid) float64 dask.array\n", " angle_psd (Freq, Shuffled, flyid) float64 dask.array\n", " theta_bins (Bins_theta, flyid) float64 dask.array\n", " theta_summary (Measure, flyid) float64 dask.array\n", " ... ...\n", " r_bins (Bins_r, flyid) float64 dask.array\n", " r_psd (Freq, Shuffled, flyid) float64 dask.array\n", " r_summary (Measure, flyid) float64 dask.array\n", " direction_summary (Measure, flyid) float64 dask.array\n", " speed_summary (Measure, flyid) float64 dask.array\n", " angle_summary (Measure, flyid) float64 dask.array" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary_24h_xarray\n", "# .stack(globid=(\"flyid\", \"recording_length\"))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "summary_both=xr.concat([summary_2h_xarray,summary_24h_xarray], dim=\"recording_length\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (Bins_direction: 200, Freq: 1000, Shuffled: 2,\n",
       "                           Bins_angle: 200, Bins_theta: 200, Measure: 4,\n",
       "                           Bins_turning: 200, Bins_speed_logged: 200,\n",
       "                           Bins_r: 200, flyid: 252, recording_length: 2)\n",
       "Coordinates: (12/14)\n",
       "  * Bins_direction        (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Freq                  (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n",
       "  * Shuffled              (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n",
       "  * Bins_angle            (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n",
       "  * Bins_theta            (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Measure               (Measure) object 'Mean' 'Std' 'Min' 'Max'\n",
       "    ...                    ...\n",
       "  * Bins_r                (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n",
       "  * flyid                 (flyid) object MultiIndex\n",
       "  * Batch                 (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n",
       "  * Fly                   (flyid) int64 1 2 3 4 5 6 7 8 ... 36 37 38 39 40 41 42\n",
       "    Trial                 int8 1\n",
       "  * recording_length      (recording_length) int8 2 24\n",
       "Data variables: (12/19)\n",
       "    direction_bins        (recording_length, Bins_direction, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    direction_psd         (recording_length, Freq, Shuffled, flyid) float64 dask.array<chunksize=(1, 1000, 2, 1), meta=np.ndarray>\n",
       "    angle_bins            (recording_length, Bins_angle, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    angle_psd             (recording_length, Freq, Shuffled, flyid) float64 dask.array<chunksize=(1, 1000, 2, 1), meta=np.ndarray>\n",
       "    theta_bins            (recording_length, Bins_theta, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    theta_summary         (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    r_bins                (recording_length, Bins_r, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    r_psd                 (recording_length, Freq, Shuffled, flyid) float64 dask.array<chunksize=(1, 1000, 2, 1), meta=np.ndarray>\n",
       "    r_summary             (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    direction_summary     (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    speed_summary         (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    angle_summary         (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Bins_direction: 200, Freq: 1000, Shuffled: 2,\n", " Bins_angle: 200, Bins_theta: 200, Measure: 4,\n", " Bins_turning: 200, Bins_speed_logged: 200,\n", " Bins_r: 200, flyid: 252, recording_length: 2)\n", "Coordinates: (12/14)\n", " * Bins_direction (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n", " * Bins_angle (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n", " * Bins_theta (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Measure (Measure) object 'Mean' 'Std' 'Min' 'Max'\n", " ... ...\n", " * Bins_r (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n", " * flyid (flyid) object MultiIndex\n", " * Batch (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n", " * Fly (flyid) int64 1 2 3 4 5 6 7 8 ... 36 37 38 39 40 41 42\n", " Trial int8 1\n", " * recording_length (recording_length) int8 2 24\n", "Data variables: (12/19)\n", " direction_bins (recording_length, Bins_direction, flyid) float64 dask.array\n", " direction_psd (recording_length, Freq, Shuffled, flyid) float64 dask.array\n", " angle_bins (recording_length, Bins_angle, flyid) float64 dask.array\n", " angle_psd (recording_length, Freq, Shuffled, flyid) float64 dask.array\n", " theta_bins (recording_length, Bins_theta, flyid) float64 dask.array\n", " theta_summary (recording_length, Measure, flyid) float64 dask.array\n", " ... ...\n", " r_bins (recording_length, Bins_r, flyid) float64 dask.array\n", " r_psd (recording_length, Freq, Shuffled, flyid) float64 dask.array\n", " r_summary (recording_length, Measure, flyid) float64 dask.array\n", " direction_summary (recording_length, Measure, flyid) float64 dask.array\n", " speed_summary (recording_length, Measure, flyid) float64 dask.array\n", " angle_summary (recording_length, Measure, flyid) float64 dask.array" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary_both" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot histograms over all data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n" ] }, { "data": { "text/plain": [ "array(3.14159274)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary_both[\"theta_summary\"].max().values" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imp.reload(lcm)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 13, "metadata": { "image/png": { "height": 378.25, "width": 636.65 } }, "output_type": "execute_result" } ], "source": [ "lcm.makehistogram(summary_both, \"direction\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 49, "metadata": { "image/png": { "height": 378.25, "width": 636.65 } }, "output_type": "execute_result" } ], "source": [ "ahist=lcm.makehistogram(summary_both, \"angle\")\n", "ahist.save(\"angle_hist.pdf\")\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 50, "metadata": { "image/png": { "height": 378.25, "width": 636.65 } }, "output_type": "execute_result" } ], "source": [ "turnhist=lcm.makehistogram(summary_both, \"turning\")\n", "turnhist.save(\"turning_hist.pdf\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 16, "metadata": { "image/png": { "height": 378.25, "width": 636.65 } }, "output_type": "execute_result" } ], "source": [ "a.limit(y=(0,5*10**6))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABdoAAAN6CAYAAACZvvvqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAB2HAAAdhwGP5fFlAADaoUlEQVR4nOzdd3yN9///8edJQoxIrFCzalaLSu1RRbU0tgalpVqdRpUOtKiqlrZGrQ7aUj61UqOoEbu11SaIPWJFIpEl8/z+yC/X9xw5WU6SEzzut5vb7Zxzrdc5OTknntf7er1NZrPZLAAAAAAAAAAAcE+cHF0AAAAAAAAAAAD3M4J2AAAAAAAAAADsQNAOAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHYgaAcAAAAAAAAAwA4E7QAAAAAAAAAA2IGgHQAAAAAAAAAAOxC0AwAAAAAAAABgB4J2AAAAAAAAAADsQNAOAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHYgaAcAAAAAAAAAwA4E7QAAAAAAAAAA2IGgHQAAAAAAAAAAOxC0AwAAAAAAAABgB4J2AAAAAAAAAADs4OLoAgAgt5o2bZqmT5+e5jp58uRRgQIF5OnpqWrVqql169Zq3ry5XF1d09xu2LBhWrZsmSRp7ty5atCgQZbVnezGjRtas2aNXnvtNbv207JlSwUGBkqSTp48abWsV69e2rNnjyRp48aNKlu2rF3HygqJiYmaP3++WrduLU9PT6tlu3fvVu/evSVJnTt31vjx4x1RYq61cuVKLVy4UKdPn1ZERIQKFiyo2rVra+bMmZneV0hIiNasWaN///1XZ86cUXBwsOLj41W0aFGVLFlSDRs2VIsWLVS7du2sfyIPkGrVqkmS6tevr3nz5jm4GuQ2ly9f1nPPPSfJ9nsk+f1TpkwZbdq0KdP7X7p0qYYPH37P9e3du1fu7u6SrL9Tx40bpy5dutzzfu+F5WuVGpPJpHz58snT01OPP/64nn/+ebVr105OTtk7NulB+27KjX8bAAAA5ASCdgCwQ1xcnMLCwhQWFqbTp0/r77//VpkyZTR27Fg1btzYYXX98ccfmjx5sqpXr2530H4/OX78uEaMGKGjR4+qefPmji7nvvLjjz/q+++/t3osLCzsngKm2bNna/r06YqIiEix7OrVq7p69aoOHjyon376Sc8++6xGjBih8uXL32vpAJAlzGazoqOjdfHiRV28eFF+fn763//+p0mTJhEWAwAAIF0E7QCQAd7e3mrbtq3VY2azWbGxsbp165bOnDmjTZs26dq1awoMDNQbb7yhCRMmqF27dg6pd8yYMQ45rqP9/vvvOnr0qKPLuO9ERUVpxowZkpJGdPbo0UNeXl5KTEzMdLg0ceJEYwR83rx51aJFCz399NMqXry4nJ2dFRoaqmPHjsnPz09hYWHaunWr/P39NX/+fMJ2IBdr0KCBMeo6o/Lnz59N1dinaNGi+vLLL1M8Hhsbq/DwcJ07d06rVq1SUFCQDh06pHfffVcLFy6Um5ubA6oFAADA/YKgHQAyoGLFimrVqlWa6wwfPlxTp07VrFmzZDabNWzYMBUrVkyNGjVKse748ePvm0vD76XdQG7VoEGDFO1vIJ07d05xcXGSpBYtWujzzz+/p/38999/RsheoUIFzZw5U48++qjNdT/++GN98skn2rJli4KCgtS/f38tX75czs7O9/YkAGSr0qVLp/s9eL/Inz9/us9lwIABeu2113T06FGdOnVKc+fOVb9+/XKowvsbba4AAMDDislQASCL5M2bVx999JHefPNNSUltZUaPHq2EhAQHVwakLSoqyrhdtWrVe97Pb7/9ZtyePHlyqiG7JHl4eGjKlCnGiPmAgABt2bLlno8NAFnJzc1Nn332mXF/8eLFDqwGAAAA9wOCdgDIYh988IGqVKkiSTp//ryWL1/u2IKAdCQmJhq3XVzu/WK3I0eOSJIKFy6sJ554It318+XLpx49ehj3kyfPA4Dc4Omnnzba31y9etXmvBMAAABAMlrHAEAWy5Mnj958800NHTpUkrR8+XK99NJLVusMGzZMy5YtkyTNnTtXDRo0SLGfHTt2aOnSpTpw4ICCgoLk5OSkokWLqkaNGmrVqpXatm1r1Wbj8uXLeu6556z2sWfPHlWrVk2S1LlzZ6NdTfLx8+bNqyNHjmj16tX66aefdO7cORUsWFBVqlTRJ598opo1a6ply5YKDAyUpHTbrgQFBWnmzJnavHmzrl+/rkKFCql69epq166dOnTokGpbkOQay5Qpk2armqVLl2r48OGSki7rHzhwoCSpV69eKUJay9ciue7du3cbPYYtX4+73b59W4sXL9bWrVt16tQpRUREqFChQqpYsaKaN2+uHj16pNqr17LGRYsWqXbt2lq9erWWL18uf39/hYaGqnDhwvLy8pKPj4+effbZVJ9vZuzdu1dLlizR/v37dePGDUlSiRIlVKdOHXXu3Fn169dPsY2t12369OmaPn26JKl+/fqZagEQHh4uScYEwR4eHulu07RpUx0+fFhFihRR7dq1UyxPfv8l13Lq1Cn98ssv2rVrl4KDg+Xm5qaaNWvKx8dHrVu3Tvd4cXFxWr58ufz8/HT8+HGFhoaqYMGCqlChgpo1a6ZXXnlFhQsXTnc/t27d0vz58/XPP//owoULioiIUOHChVW9enW98MIL6tSpk/LkyZPmPhITE7Vq1SotX75cx44dU2RkpEqUKKGmTZvqzTffzNKe9aGhoVq6dKl27dqlgIAAhYWFKT4+Xu7u7nrsscfUuHFj9ejRQ0WKFElzP3fu3NHatWu1cuVKnTt3Tjdu3FD+/PlVqVIltWzZUj169FChQoWstrH8bPrwww/VtWtXjR8/Xps3b1ZMTIxKliypli1batiwYVbbXbhwQQsXLtTOnTt1+fJlxcTEqEiRIqpRo4ZeeOEFtW/fPs1WQ2azWX5+flq1apWOHDmimzdvKm/evCpWrJhq166tF198US1btsy27TMqKipKf/31l3bs2KFjx44pNDRUsbGxKlSokMqWLauGDRuqZ8+eKlWqlN3Hyo3ef/99rVu3TpI0YcIEtW/fPs31Bw8erNWrV0uS1qxZo4oVK2Zrffnz51d0dHS665nNZq1Zs0Zr1qzRkSNHFBwcrHz58qls2bJq0qSJXn31VT3yyCMZPu7OnTu1cOFC7d+/X7du3VKRIkVUtWpVvfTSS/L29k63lo0bN2rjxo06dOiQgoODFRERoYIFC8rT01N16tTRSy+9pKeeespqu61bt+rtt9+WJLVu3VpTp05N8zh+fn7G93Dv3r2NKwAsv1s2btyY6lwf9/K9lSz5u6Fp06aaOXOmfvzxR/3555+6efOm8bfSt99+S199AACQowjaASAbtGjRQs7OzkpISNCBAwcUHR2d4Unh4uPj9emnn+qvv/5KsSwwMFCBgYFat26dZs2apVmzZmXqP+62+Pr6asSIEcb92NhYHThwQGXKlMnUfg4dOqQvvvhCYWFhxmPBwcHatm2btm3bprlz5+qnn35SyZIl7ao3u/n5+WnkyJEKDQ21ejwkJEQhISH677//9Msvv+ibb75R8+bN09xXTEyM3n33XW3evNnq8aCgIPn5+cnPz09dunTRV199JSene7vILCwsTJ999pnWr1+fYtmFCxd04cIFLV26VK1bt9a4ceNUsGDBezpORpQvX14nT56U2WzWF198oW+++SbdsPnxxx9PN8xJtnHjRg0ZMkR37twxHrt165b++ecf/fPPP3r++ec1ceJEubq62tz+5MmTGjhwoC5cuGD1eGhoqA4ePKiDBw9q9uzZGjdunJ5//vlU61i9erVGjRplnFhIFhQUpKCgIP3zzz/69ddfNWPGDFWqVMnmPsLDw9W/f3/t3r3b6vHAwEAtWrRIK1eu1IQJE9J8PTJqyZIlGjt2rFWLoGQ3b97UzZs3tXfvXs2ePVs//PCD6tWrZ3M/x44d08CBA40Tb8ni4uJ04MABHThwQHPnztUPP/ygWrVq2dxHZGSkXnnlFZ05c8Z47O6fR2JioqZNm6aff/45Reut69ev6/r169q4caN+/fVXTZs2TRUqVLB5nPfff1/btm1LUWtkZKQuXryoFStWqH79+vrhhx9SnBywd/uM2rp1q4YOHapbt26lWJb8mXP48GHNmTNH3377rV588cV7Ok5u5uPjYwTty5cvTzNoDw8P18aNGyVJXl5e2R6yBwcHKyQkRJJUrly5VEPbq1evasCAASkm446NjZW/v7/8/f01d+5cDR06VK+88kqax4yPj9eIESPk6+tr9fiNGzd048YNbdu2TStXrtTUqVNtfr5eunRJ/fr1U0BAQIplySdBT58+rUWLFqlPnz7GiWEp6cSnp6engoKCtGXLFkVERKQZVK9cudK43blz5zSf1911ZOX31rBhw7RixQrj/vXr11W8eHFCdgAAkOMI2gEgG3h4eKhcuXI6f/684uLidOjQITVs2DBD2/70009GyF6mTBl16dJFFSpUkNls1oULF+Tr66tr164pICBAgwYN0qJFiyRJxYoV04wZMyRJ/fv3lyRVqVJFH3zwgSTZHA0ZHx+vL774QgUKFFCvXr1UpUoVnTp1SuHh4SpatGimnvPQoUMVFxenxx9/XJ07d1aJEiWM/8zfvHlT/v7+6tOnj5YtW6Z8+fJlat/pGTRokEJDQzV37lwjuBwzZoyKFSuWqf2sWbNGQ4YMMVqpeHl5qU2bNipRooSCgoK0du1a7d+/X6GhoXrvvff0/fffpzmK+osvvtCZM2dUvHhx+fj4qGrVqoqMjJSfn5/+/fdfSUkj4L28vNStW7dMP+/o6Gi99dZbOnTokCSpYMGC6tKli2rUqCGTyaSjR49q6dKlioiI0Lp163T16lX98ccfyps3r9XrFhAQoClTpkiSvL291bZtW0nK0MhuS506ddI333wjSfr777916NAh+fj4qFWrVkY7pXt1+fJlffTRR7pz545q1qypzp07y8PDQ8eOHdPChQsVFRWl9evXq3///vrll19SbB8QEKCePXsarR+8vLzUunVrlSxZUrdv39b27du1fv16hYeHa+DAgfr+++/Vpk2bFPtZtmyZhg8fLrPZLGdnZ7Vq1UpNmjRRoUKFdO3aNa1du1aHDh3SuXPn1KNHD/35558pRqbHx8frtdde07FjxyRJnp6e6tatmypXrqxbt25p1apV2r9/vwYPHmzXayZJ69at06effiop6Wqbtm3bqk6dOvLw8FBUVJSOHTumFStWKCwsTLdv39bgwYO1YcOGFL+jx48fV48ePRQTEyMpqZd/+/btVbZsWV2/fl1//fWXjh8/rqCgIPXt21d//fWXSpcunaKe2bNnKyYmRnXq1FGXLl0UHx8vPz8/de3a1Vjnq6++0v/+9z9JkpOTk1q3bq0mTZqoYMGCOnv2rJYvX65Lly4pICBA3bt3l6+vb4rXeOzYsUZIbllrbGysTp8+LV9fX4WGhmrPnj36/PPPNWnSpCzdPiMOHTqkfv36KT4+XiaTSa1atVKjRo1UrFgxxcTE6PTp01q+fLlu3Lih2NhYDR8+XE8//XSuP1mZWU2bNtUjjzyia9euaefOnQoKCpKnp6fNdf/++2/jPXj3lWLZIfk7VVKqAfmNGzfUvXt3Xb9+XVLS+6Vdu3YqV66coqKitHfvXqPuMWPGKDY2Vq+//nqqx1yzZo3i4+NVoEABtW/fXl5eXoqOjtauXbuMExKbNm3STz/9ZIwmTxYREaFXXnnFqKVmzZpq3bq1ceL88uXLWrNmjfz9/SVJc+bMUb169YyJYZ2dndWhQwf9+uuviomJ0fr161MN0MPDw415NapWrZqhdmGS/d9bd9u7d69iYmJUpkwZ9e7dW4ULF9bu3bv19NNPZ6geAACALGXGfSc4ONhcv359c9WqVc137tzJ9uMlJCSYlyxZYn799dfNjRs3Nj/55JPmpk2bmgcNGmTev39/th8fcJSpU6eaq1ataq5atap56tSpmd6+b9++xvYrVqywWjZ06FBj2a5du4zHExISjN/vJk2amIODg1PsNzw83Ozt7W1sb+v3MHnZq6++arM2y+NXrVrVvHPnzlSfR4sWLYz17vbqq69a7WfkyJHm+Ph4q3VCQkLMnTt3NtaZPHlyqvW2aNEi1TrMZrN5yZIlaf5MLJ/XpUuXUizftWuXsXzo0KFWy27cuGF+6qmnzFWrVjVXq1bNPHfuXJs1zJ0711ytWjVz1apVzbVr1zZfvXo11RqTfwZhYWEp9vPdd98Z63Tq1CnN552ar7/+2thH+/btzdeuXUuxzrVr18zt27c31vvqq69SrGP5utzLez3ZnTt3zC+//LLV80/+17hxY/PAgQPNc+bMMR8/ftycmJiYoX1avv+qVq1qHjdunDkhIcFqnXPnzpmbNWtmrPP3339bLY+LizO3adPG+NnOnz/f5rF27dpl9vLyMletWtXs5eVlDgoKSnGcWrVqmatWrWquX79+qt+Bs2fPNmrx8fFJsfy3334zlnfp0sUcGhqaYp0ZM2akeB9lVkJCgrl58+bmqlWrmh9//HHzjh07bK537do1c4MGDYxjbdiwwWp5fHy81e/w+PHjU/wMEhISzB999JGxzscff2wsu3TpktVz6d27d4rPiWQbNmww1qtbt6557969KdaJiYkxf/jhh8Z6nTt3tno/hYSEmJ944gnj9yI6OjrFPq5evWpu0qSJ8Z6w/N2xd/uM6tGjh/EclixZYnOd8PBwc+vWrY31fv/9d6vllq+trfdIRj9bU2P5eXb3Z2ZmWX6n3v18J0+ebCz79ddfU91H9+7dzVWrVjU/9dRT5vDw8Ewd3/K1Su31SEhIMIeGhpr/++8/86BBg4z133jjjVTfs3369DHWmzRpUorfDbPZbD5x4oS5adOm5qpVq5qfeOIJ8/Hjx62WW34GV61a1fzcc8+Zz58/n2I/8+bNM9Zp2LBhiuWWr+OwYcNsfs4mJiZa/f7079/fanlAQIDV807N4sWLjfV++eUXq2WWfxvc/V2cVd9blt8NjRs3Nt+4cSPVWgEAAHIKk6HeZxITE/X555+naGmQXUJCQvTyyy9r+PDh2r59u27evKm4uDjduHFDa9asUY8ePTRz5swcqQW431hesmyrLYAtISEhxu+3l5eXzVHlbm5ueuutt1S+fHk1adLE7snZ6tevn+HR9mmpUaOGPv/88xQ9k4sUKaIpU6YYo9EWLFig2NhYu4+X1X799VejD2/Pnj3Vq1cvm+v16tVLPXv2lJTUW/nXX39NdZ8uLi6aMGGC3N3dUyzr37+/cdn/8ePHjVGaGRUSEqIFCxZIkgoUKKAffvjB5kjXkiVL6ocfflCBAgUkSQsXLlRQUFCmjpVRrq6u+vnnn9WuXbsUy27evKl169bp66+/VseOHdWkSRONHDlShw8fzvD+GzZsqGHDhqVos1OhQgV9++23xv27v5dWr16ts2fPSkrqI2w5AaulBg0aaNCgQZKSWofc3Z/+l19+MdrWfPXVV/Ly8rK5nz59+hgjRA8fPqzt27cby8xmszHiPm/evJoyZYrNXvb9+vWzu//3gQMHdOXKFUlJ/ZYbNWpkc72SJUvKx8fHuJ/8WiXbvn27Mfq+fv36+uSTT1L8DJycnDR27FjjuaxduzbV93S/fv1S7a1uOYL4yy+/VN26dVOskzdvXo0bN07Vq1eXlNTSJrmdiCRdvHhR8fHxkqTGjRvbvILmkUce0auvvqqKFSvqmWee0c2bN7Ns+4y4du2a9u/fL0mqVauWunTpYnM9Nzc3vfbaa8b9u382OWnZsmWqVq1ahv7NmTMnU/t+6aWXZDKZJMlm2zRJOnfunA4cOCBJeuGFF+xqCxIYGGiz7urVq6t+/frq2bOn1qxZI5PJpL59++qHH36w+Z7dt2+fduzYIUlq1aqVBg8ebLMNWLVq1fT5559LSrqiZdasWWnWN27cOD366KMpHn/llVeMdnEhISHG73ey5N71+fLl09ChQ43X1JLJZNJ7771n3L/7PVWlShU9+eSTkpL6xAcHB9usMbltjLOzc7p99ZNl1/fWyy+/nOpVEAAAADmJoP0+88UXX8jPzy9HjhUTE6PXXnvNuLSzRYsWmjFjhhYtWqThw4erSJEiMpvNmjhxojZs2JAjNQH3ExeX/+vOFRkZmaFt3N3dje22b9+eagjZqVMnrV+/Xr/99pueeeYZu+pMrR9zZvXt2zfV8KxcuXJq0aKFpKR+2P/991+WHDMrJQd1Tk5OxmRwqXnnnXeM55p8Kb8tabV5yJ8/v9Fb2mw2W/W2z4h///3XCDLbtWuX6mRzklS2bFl16NBBUtJne/Ll/tnB3d1dEydO1O+//64XX3zRCEruFhwcrMWLF6tr164aMGBAqmGOJctw6G4NGjQwwqHjx4/r6tWrxrK///7buN29e/c0j9G1a1fjZ2sZ3iYmJmrNmjWSpOLFi6eYePhuL7/8snHbcj+HDx82QtkWLVqk+XPr27dvmsdIj5eXlzZv3qx58+al24amXLlyxm3LHviSrL7jX3vtNZvhnZR0omXEiBEaMWKEZsyYYXO9PHnypHqC4tq1a0agX6FCBZuteyz3Y/l7unbtWuO25QnKNWvW6Pz58zb38e6772rNmjWaNWuW8d7Jiu0zomTJktq6davmz5+vsWPHprluWj+bB0W5cuWMiS9PnDihEydOpFhn+fLlxu3UTkxkNbPZrD179hitvu5m+dmSXvuvVq1aGWHwli1bUsw/kKxUqVKpfi+bTCar99rdJ3h8fX21ZMkSzZo1K83WX+m9p5LbxSQkJBife5auX7+uvXv3Sko6GVWiRIlUj2Upu763survGAAAAHvRo/0+ER0dreHDh9v8Yze7zJw505hIqW/fvvrkk0+MZbVr19YzzzyjLl266M6dO5o4caIxeg9AEsuR5hmdgDJv3rx6/vnntWbNGkVGRqp79+6qV6+enn32WTVp0kTVqlVLNeS6V6lN1phZqY2WTVa3bl0jlD548KAaN26cJcfNCiEhIbp48aIk6bHHHkt3gtmSJUuqUqVKCggI0PXr13X16lWbPfDT60tuOYFi8gjajDp48KBxO73XXpKaNGmihQsXSkoa6WzZEzs7NGzYUA0bNlRsbKz27dunXbt26b///tORI0dSjHRev369Tp48qYULF6baVz9//vzphin169c3gtoDBw4YP5PkkcNSUq/2c+fOpbmf4sWL6/r16zp9+rQiIyNVsGBBBQQEGL/Tbm5uVuG5Lbdv3zZuJ5+wTq4rWZ06ddLch5eXl1xdXTN9tUMyJycnlS5d2mavdCkpYDtz5owOHz5sNZHg3QGgZf3p/QySg7HUlC1bNtVey5avTUbe05afIZa/D+XKlVOtWrV0+PBhXbt2Te3atVOTJk3UrFkzNWrUKN0JNO3dPiNMJpNKliyZ6om4uLg4nTt3TkeOHLE6mZdaOJsTGjRooN69e2do3apVq2Z6/z4+PsYcG3/99Zcef/xxY1liYqLV3CUNGjTI9P4tFS1aVF9++WWKxxMTExUVFaWrV69q586d2r17t44cOaL+/ftr4MCBGjBggNX6lp8tly9fTnfgSfJEoxERETp79qzN74j0XjvL7427Pxs8PDxsXiEjJZ00uHz5svz9/Y2QXLL9nmrbtq2++eYbxcXFadWqVXr11Vetlq9atcqYyyQzk6Bm1/dWVv0dAwAAYC+C9vvAvn37NHr0aCP0dnJyMv64zS7R0dH67bffJCVd0mwZsierVKmSunbtqnnz5uns2bM6c+YMf+gCFsLDw43btlqHpGbUqFE6deqUTp8+rcTERO3evdsIH4oVK6YmTZroueeeU/PmzbNkUtHM1JaaQoUKqUiRImmukzwZm6Rsa11yryzruXtSxdSUL1/e+Fy+efOmzaDdMhCxxfKqh8x+rluOZLTVYuBuls8rI6PHs0revHnVqFEjI1SJiYnR/v37tXnzZi1dutT4Pbl48aLGjx+v7777zuZ+ypcvn+oVE8ksA+Xkn2lkZKRV6J08OXBGmM1mhYSEqGDBglYj5M+fP29MOJwRISEhKeq6u15bnJ2dVbZsWZ05cybDx0rNpUuXtGXLFp08eVIXLlzQ5cuXde3aNZvvO7PZbHU/+f3i5uaWaoiXUWltn9n3dOHCheXu7q7bt2+neE9/99136tWrl27cuKG4uDht2bLFGBFbqlQpPfPMM8ZEtpa/h1m1fWYEBQVp8+bN8vf318WLF3Xx4kVdvXrV5sm3u382Oal06dLZOqiidevW+vLLL3X79m2tXLlSH330kfE7v2vXLuN3sEuXLnafcM6fP3+6z+W9997T5s2b9f777ys2NlbTpk1TxYoV5e3tbaxj+bkwZsyYTNUQHBxsM2hP73vD8rmn9r0RHR2tLVu26PDhwzp//rwuXryoy5cv2xy9bus9VbRoUT377LPasGGDDhw4oEuXLlmNgk8+Mefm5pbu1T2Wsut7y97PJQAAgKxC65hc7rvvvlPPnj2NMKdLly5Wf+Bnl82bNysqKkpS2qFEx44d1aVLF/Xt29dmT0rgYWYZjmVmBGTRokW1dOlSffTRRym2Cw4O1ooVKzRo0CA9++yz8vX1tbvO1EaXZkZGAn/LFiK5rf2B5dUHqbU6uVv+/PmN28mfl3ezN4BLi2XNlrWkJiP15gRXV1c1atRIn376qTZt2qTWrVsby1avXp3qSZiM9GO2fI7Jr4+9cxgkt32yPHGWWZY1WIb+Gfm9sacPdfLxPvzwQz3//PMaO3asfH19tWfPHl25ckWJiYnKkyePnn76aaO1ky3J80Zk5H2WnrQ+bzL7nrZcLyoqyiowrFChglavXq133303xQmNq1evavHixXr77bfVqlUrm1cn2Lt9RsTGxmrs2LFq3ry5Ro4cqQULFmj79u26dOmS4uPj5eTkpCeffNLmnAcPIldXV7Vt21ZS0smHnTt3GsuWLVsmKSlk7tSpU47V1KJFC7377rvG/eT5FZLZ8/mS2rbJc3fcq0WLFql58+b64IMP9Ntvv2nTpk06ffq08b1brlw5vfLKK+nux3KkenLvd0k6ffq00drnxRdfzNQJ/+z63sqKv2MAAACyAiPac7nk/sxFixbViBEj1LZtWw0bNixT+/Dz89Nff/2lQ4cOKTQ0VAULFlTVqlXVpk0bde3a1eYfp8m9KAsXLpzmpZ01a9bUuHHjMlUP8DC4ePGiMQFqnjx5rC6BzwhXV1e99dZbeuutt3T27Flt375dO3fu1N69e42gLjQ0VCNGjJCkbG8Dkp6MTG5q2af+XkfR32sLjfRYtvbJaAht+XyyIoDMLMsTAsmTuKYlu+vdsGGDzpw5o+DgYPXv3z9DIwzd3d01adIktW3bVufPn1d8fLwCAgJsTmqXkZ+95XNM7rNtGQJVqlTJKjDKDMvXrEePHho9evQ97cfydcnIz82eiYNjYmLUq1cvIxRzdXVVvXr19OSTT6pixYqqWLGiqlWrJldXV/n6+mrz5s0295MvXz7FxcVlqF57ZPY9Lf3fzzx//vwpRjkXKlRIgwcP1uDBg3X8+HFt27ZNu3bt0r59+4z9X716VQMHDtTMmTPVtGnTLN0+LWazWf369TP+3nJxcdHTTz+tGjVqqGLFiqpUqZKqVaumggULaufOnVq1alWG930/8/HxMSbLXLFihZo2baqoqCitX79eUlJLqrT6emeHDh06aOrUqZIkf39/RUVFGe/VfPnyKSIiQvnz57dqi+IoP//8syZNmmTcr1atmry8vFS5cmXj97148eJKSEjQH3/8kea+nn32WRUpUkS3bt3SqlWr9M4770iSVZupzJ70yG3fWwAAAFmNoD2Xc3d31zvvvKO3334706PawsPDNWjQIG3fvt3q8dDQUO3Zs0d79uzRvHnz9OOPP+qxxx6zWid5BP3jjz9uNVI9IiJC169fl4eHh4oXL36Pzwp48FlOzNekSRO7RlslB2K9evVSQkKC9u7dqx9//FG7du2SJE2ZMsXhQXtYWJhV+GBLcg90SSnarJhMJpnN5nT7lNszqjgtlsGuZZ1puXDhgnE7vZ7u2cGy5gsXLqQ7GaPlhI7ZUe/cuXONFkdNmzZVs2bNMrSdi4uLmjZtatSXfILqbteuXUt3X5cvXzZuJ0/O5+7ubvQ5v3z5smJjY+/p99HyO+/s2bOZ3v7uuqSkdi7pycjzTs3vv/9uhOxPPvmkfvrpp1QnLbQcaX+3YsWKKTw8XBEREbp9+3aaJ8quXr2qwMBAlStXTiVKlMhUi4+739PpCQ4ONkbIptbrPFn16tVVvXp1vfXWW4qNjdX27ds1ZcoUHT9+XAkJCZo+fXqaQbm9299tzZo1RshepkwZ/frrryn+FkuW2YmS72c1atTQ448/rhMnTmjz5s2Ki4vTv//+a4SyOTUJqiXL95bZbFZERITxXVe8eHFFREQoOjpaV65cSbcdVHa6fv26cUIgT548mjp1qlq2bGlz3Yy8p/LkyaN27dpp3rx5CggIMFpEJp/0KVeuXLrzTNwtt31vAQAAZDV6feRy06ZN05AhQzIdssfHx+utt94yQvY2bdpo2rRp8vX11cyZM9WjRw/lyZNH586dU58+fVL0PUwOEZL/w7BhwwZ1795dderUkbe3t5o0aaLWrVtr8eLFDu0XCuRGsbGxxuRdUuaCgbNnz2rBggX6+uuvjStaLDk7O6thw4aaNWuW8R/WoKAgq76njnLkyJE0l+/YscO47eXlZbUsOfhM7zL8kydP3mN1aStWrJgxSvLcuXNWfXdtuXr1qjGhZvHixVMNL7NT7dq1jduWLRZSY/n6V69ePcvreeKJJ4zbliMeM8Jy1KJlL39LN2/etApdbEkO+l1cXFS3bl1JSSdxatWqJSlphLfl62BLbGysBg0apNGjR2vWrFnGyZ8nnnjCeJ8eOHDAaKeSmoCAAA0YMEDjxo3TypUrjceT65LS/7mdOXMm1RMPGbFp0ybj9ieffJLm+9Tf3z/VZcmvn2Q9+aMtS5Ys0SuvvKJmzZplegL3zL6nLQcSWL7/jh07pnnz5mnMmDE2T2bkzZtXLVq00Jw5c4w2HcePH8+y7TPCst1M//79Uw3ZJeufzcPwN9dLL70kKenkz+7du43Xys3NTS+88EKO13Pjxg3jtrOzs3G1jGT9nk3tihBLn332mUaOHKkff/wxy0+gbNmyxfi8atu2baohu5Tx95TliPWNGzfq0KFDCgwMNJZltld+bvveAgAAyGoE7bncvfY9nz17tg4cOCBJ+uabbzRlyhS98MILqlWrlp599lmNHj1av/zyi5ydnXXt2jVNmDDB2DY2NtZonVCoUCGNGzdO/fv3T3FJ7Pnz5zVy5EgNGTJECQkJ9/YEgQfQt99+a/xHtHr16nr++eczvO2hQ4c0evRo/f7771q6dGmq6+XNm9eq3cndI8mT//Ob3RMnW/rf//6X6jJ/f38jFCtfvrxq1qxptbxYsWKSkgLX1MLUsLAwbdu2Lc0aLP/Tn9lAKjnASUxM1KxZs9Jcd+bMmcZrm5mJ4LJSs2bN5OrqKklatWqV1Wjuu12+fNkYhejs7KzmzZtneT3t27c3bq9evTrdn1Wymzdvys/PT1LSaG/LUPduc+fOTXXZv//+a1yN1bBhQ6sWLZbh3A8//JDmlRO+vr5au3atFixYoDVr1hh99l1dXfXss89KSvqe/Omnn9J8XtOnT9f69es1Z84cq1DriSeeMCb42759uzHi3Jbff/89zWOkxzKkt/y8uNu5c+esgt+4uDir5ZaBXVq/5wkJCcZJhTx58qhhw4aZqrd06dLGCNfz589bXRl0t7i4OP3666/Gfcvfw82bN2vs2LH6448/0tyHm5ubEZRbfobau31GWJ6oSetnExISYvVdkN5VPw+CDh06GCe11q5dq61bt0pKCo+zYgLwzLJsN+Xl5WU194blZ8uvv/6a5sniLVu26M8//9TixYv1v//9z+75F+6W0feU2WzWb7/9ZtxP6z1Vo0YNY8LWjRs3at26dcayjh07ZrrG3Pa9BQAAkNUI2h9ACQkJRhjRqlWrVPsnNmzYUD4+PpKklStXGpeNW44sTA4JypYtq++//17//fefDhw4oJ9//lmVK1eWlPQfkClTpmTjMwLuD5GRkRo3bpzmzZsnKSlo+uKLLzJ1wqxly5ZGH1JfX98UrZ+SrV692gika9WqlSLkSd5HWu0gspqfn5/NgDowMFCDBg0ygum33347xWvy1FNPGbcnT56cIiSPjIzUxx9/nO4oYsvXIbOjBV977TVj+/nz56caKP7xxx9GD+GCBQvqrbfeytRxskrRokXVrVs3SUl95fv376/r16+nWO/GjRvq16+f0Xqhe/fuKVr3ZIUnn3zSGImakJCg/v37a/bs2SlCW0tnzpzR66+/bnzvDBo0SM7Ozqmuv2DBApuj5U+fPq3hw4dLSjpBPXDgQKvlPj4+xhUghw4d0vDhw232Pt+zZ4++/fZb435yT+Jklu/dOXPmpNrj+JdffjECKVdXV7322mtWy5PrS0xM1Pvvv28zbFqyZIkWL15sc/8ZVa5cOeN28nv2bgEBAXr33Xet+iXf/dq88MILqlChgqSkExo//PBDiv0kJCTom2++MT6X2rVrZzXyN6Pee+894/bIkSP133//pVgnNjZWn376qXGSonr16laT6r744ovGSbcff/wx1ZMZv/32mzGwoEGDBlm2fUZY9hlfvHixzZOiV65c0bvvvms1QXB2zVORmxQuXNg4cbJs2TLjc98RbWP27dunH3/80bjfo0cPq+UtW7Y05mAJDAzUwIEDbbY4O336tNUcS3379k3zs+5eWP6++/n5pbhaVUqaiPyzzz6z+tsivfdU8v8jDh06ZATfdevWtTpeRuW27y0AAICsRo/2B9DJkyeNy1zTmshUkp555hktWrRIcXFx2r9/v5o3b647d+4Yy69cuaIyZcrI19fX6j/MzZs319NPPy0fHx9duHBBv/32m3r27En/RDywzp49qw0bNlg9lpiYqKioKN26dUvHjh3TP//8Y4S7Li4uGj9+vFWAnBEeHh567733NGnSJMXHx6tv37564YUXVKdOHXl6eio4OFi7d+82anF2dtaQIUNS7KdUqVI6c+aMAgIC9N1336lmzZoqXLhwpkeYZpSrq6tcXFw0YcIEbdu2TW3atJG7u7uOHj2qxYsXG6P8WrdubbOffPfu3Y02E2vXrtXLL7+sDh06yN3dXadPn9aSJUsUFBSk2rVrpznhnOVn0NixY9WrVy/FxcWpXbt2VqMQU9v2yy+/1EcffSSz2awvv/xSf//9t9q0aaMSJUooKChIa9eu1b59+yQljZ7/4osv7ilsyCofffSR9u3bJ39/f504cULe3t7q3LmzatasKZPJpKNHj2rJkiXG6//4449nekLtzPjiiy908+ZNbd26VXfu3NH48eM1a9YsPfvss3riiSdUrFgxxcbG6saNG9q9e7d27txpXBH1+uuvGyd/U2M2m/Xxxx9r7dq1atmypVxdXXXw4EH5+voaYVGfPn2s2hNISSdgJk+erNdff11xcXFasWKF9u3bp86dO6tSpUoKCwvT3r17tWbNGiPwbNeunVV4KyWd1Bo8eLAmTpwos9msMWPGaOXKlcZ75MaNG1q3bp1Ve5URI0ak+G7s0KGDNmzYoHXr1unChQvq0KGDunXrppo1ayoyMlLr16/XP//8ozx58ih//vz3fMKsa9euRh/wJUuW6OLFi3rhhRdUvHhxBQcHa9euXVbtJpLdHRQ6OztrwoQJ6tmzp2JjYzVlyhRt3bpV3t7e8vT01OXLl7Vy5UrjioJHHnlEn3zyyT3V/Pzzz+vll1/WwoULdfv2bfXq1Utt2rRR48aNVbBgQZ07d07Lli0zWrq4ublp0qRJxshyKWnS227dumnRokWKjIyUj4+P2rVrZ3wOBgUFafPmzcY8FwUKFFD//v2zbPuMeOmll7Ro0SKZzWZt375dnTt3VseOHfXII4/o1q1bOnjwoPz8/Kz+JpPSb6/1oPDx8dGaNWuM92bFihVT/F7bIzo6OsV3erK4uDiFhIRo586d2rhxo/GZ8Mwzz6hdu3ZW65pMJk2ePFndunVTeHi4duzYodatW6tz586qXr26oqOjdfjwYS1fvtw4gVW/fn317t07y55LsmeffVaenp4KCgpSUFCQOnTooO7du6tChQqKjY3VqVOntGrVqhSt5qKjoxUfH5/qd2SHDh00adIkJSQkGKF4ZidBtZTbvrcAAACyEkH7A8jyEvUvv/xSX375ZYa2S/5Pa/Ilnck++ugjm6PS3N3dNXjwYH3wwQeKi4vT2rVr1adPn3svHMjFVq9ebXX5eFoqVaqk0aNHq379+vd0rLfffltBQUGaN2+ezGaz1q1bZ3W5drJChQrpiy++sHlCrUOHDpo8ebKkpNG1UtIl4EuWLLmnmtKTP39+fffddxo0aJB27dplBFCWOnfunOrnUaNGjTRo0CBNnTpVZrNZBw8eTBGoN2nSRGPGjEmzVUvr1q01ffp03blzRwcOHDBaaD355JPG5e9padeunfLmzavPPvtMt2/f1v79+232pC5SpIi++eYbo5WIo+TLl09z587Vxx9/rM2bNysiIsK4ouJubdu21ZgxY1J8xmelPHnyaPr06ZozZ45++uknRUZGKjg4WEuXLk21FVLhwoU1ZMgQY5RjWkaNGqWxY8dq48aNVq1OpKTAa+DAgakGnvXq1dOcOXM0ZMgQXb9+XYGBgZo+fbrNdbt3765Ro0bZXPb222+rYMGC+vbbb1O8zyzlz59fn376aarPa9KkSfr888/1559/KjIyUrNnz7Za7uLioi+//FJz5sy556C9devW6tOnj+bMmSNJ2rt3r/bu3ZtivTJlymjs2LF68803lZCQYLPfeM2aNTVnzhwNGjRIQUFBNn9HJalKlSqaPn36PY1mTzZ69GgVLlxYs2bNUkJCQqqfv9WrV9f3339vjLa3NGLECIWGhmrdunWKi4vTsmXLtGzZshTreXp66rvvvkvx+WDv9umpVauWhg0bpm+++UaJiYk6ceKEzZHzRYoU0dixYzV69GgFBQUpICBAiYmJ99xa8H7RuHFjlS5dWleuXJGU9aPZQ0JCMnVyxNvbW19//bXNZRUrVtTChQv1/vvv68yZMwoODja+d+/WqlUrffvtt+me+L0XBQsW1Pfff693331X4eHhunnzpmbMmJFiPRcXF/Xv318BAQHGycWAgACreQ4slShRQo0bNzZO2uXLl08vvvjiPdeZ2763AAAAshJB+wMovdYKqUn+j7xlX0dnZ2e1aNEi1W2aNWsmJycnJSYmpjsRIvAgcnV1lZubm8qVK6fq1aurZcuWatKkiV2XhJtMJo0YMULt2rXTkiVLdODAAQUGBio2NlYeHh4qX768mjVrpm7duql48eI29/HOO+/I1dVVf/75py5fviwXF5dsn0SvWbNmWrZsmX7++Wft2LFDwcHBKly4sJ5++mn16NEj3Sts+vXrp2bNmumPP/7Q7t27FRQUJDc3Nz3++OPq3Lmz2rdvn+7EkOXLl9fvv/+uqVOn6ujRo4qOjpanp6du3LiR4SDshRdeUMOGDbVw4UJt2bJFZ8+eVUREhIoVK6ZHH31UL774otq1a6dChQpl+LXJToUKFdJPP/2knTt36q+//tK+ffsUFBQkZ2dnlSpVSk8//bReeumlTF9dca/y5s2rt99+2xiRunv3bp06dUrXr1/XnTt3lC9fPnl6eqpixYp67rnn9Pzzz1v1U09Lz549VatWLf3yyy/au3evwsLC5OnpqYYNG6p3797pTpZXt25drV+/XkuWLNHmzZt14sQJ3bp1Sy4uLnrkkUdUt25ddevWLc0+8ZL0yiuv6IUXXtDChQu1fft2nT9/XuHh4cqfP78effRRNW3aVN27dzcmFLfFxcVFX331lby9vbVw4ULt379fYWFhKly4sOrVq6e+ffuqRo0aRkh+r4YPH66mTZtq4cKFOnTokEJDQ40JHatUqaLnn39e7dq1U/78+VWnTh3t2bNHJ0+e1IkTJ4yWGMnq1Kmj9evXa9GiRdq4caNOnz6t27dvy83NTdWqVZO3t7e6dOli9Ne+VyaTSYMHD1bnzp21YMEC7dy5U1euXFFsbKweeeQRVa1aVZ06dVLz5s1TDSzz5s2rqVOnatu2bfrrr790+PBh3bhxQ3FxcSpcuLAqVqyoFi1aqGvXrjZ7Zdu7fUb06dNHXl5emjt3rvbv36+goCCZTCYVLlxYlSpVUvPmzdWlSxe5u7trw4YNWrZsmcLCwrRly5Y0J7p8EDg5OalWrVq6cuWKnJ2d76kf+L3Kly+fChUqpAoVKqhWrVpq27atMXdAaipXrqyVK1dq1apVWr9+vY4ePaqQkBBJSUF17dq11aVLFzVu3Dhba69bt65Wrlyp2bNna9u2bQoMDFRCQoLc3Nz06KOPql69eurevbvKlSunpUuXGleSrVixItWgXUo6UZ4ctD/33HN295fPbd9bAAAAWcVkzu7kBVlu2LBhxqiqw4cPpxjl8fPPP2vSpEmSkiZlrFq1aob2W7x4caOHbb169XT79m0VLlxYu3fvTnO7+vXrKywsTE2bNrWamAwAAHu1bNnSmFz45MmTDq4GQE6Ijo5W48aNFRUVpRYtWqQ7+TAAAACQGzCi/QFkOTIwb9686Y7ws6Vy5crav3+/IiIi0r1EOXmiu9wyuhMAAAD3r1WrVhkTzb788ssOrgYAAADImAe7weNDqnLlysbtPXv2pLnu0aNHNXPmTK1evVpBQUHG48mXasbHx+vo0aOpbn/lyhXjP0JlypSxp2wAAAA85I4dO6aJEydKkipUqKBmzZo5uCIAAAAgYwjaH0C1atWSu7u7pKQRQWn1bJ8yZYomTpyowYMHKzg42Hjc29vbuL1gwYJUt1+xYoVx+0HvFwoAAICsN2LECHXs2FGdOnVS165djfk4Bg0a9MBP/AoAAIAHB3+5PoDy5s1rXGZ7+/ZtffLJJ4qNjU2x3tKlS/XPP/9Ikry8vKwmPatVq5bq1q0rSVq2bJlWr16dYnt/f3/9/PPPkqSqVauqTp06Wf5cAAAA8GArUqSITpw4oePHjyshIUFS0gSclgM/AAAAgNyOHu0PqH79+mnTpk06ffq0tm7dqs6dO6tPnz6qUqWKbt26pfXr12v58uWSJFdXV40ePTrFPsaMGaPu3bsrPDxcH374of7991+1adNGhQoV0q5du/TLL78oKipKzs7O+uqrr3L2CQIAAOCB4OXlpdKlS+vmzZsqU6aMunXrpj59+ji6LAAAACBTTGaz2ezoIpA5w4YN07JlyyRJhw8flqurq831goKCNGDAAB08eDDVfXl4eGjSpElq2rSpzeVHjhzRgAEDdO3aNZvLCxQooIkTJ9I2BgAAAAAAAMBDixHtDzBPT08tWLBAa9as0apVq3T06FHdunVLefLk0aOPPqpnn31WvXr1UvHixVPdR82aNbV69Wr98ccf8vPz04ULFxQfH6/SpUurWbNmevXVV5kEFQAAAAAAAMBDjRHtAAAAAAAAAADYgclQAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANjBxdEF4P/cvn1be/bsMe6XKlVKefPmdWBFAAAAAAAAGRMbG6urV68a9+vXry93d3cHVgQAOYegPRfZs2eP+vfv7+gyAAAAAAAA7DZjxgy1atXK0WUAQI6gdQwAAAAAAAAAAHYgaAcAAAAAAAAAwA60jslFSpUqZXV/xowZevTRRx1UDQAAAAAAQMZduHDBqiXu3TkHADzICNpzkbsnPn300UdVpUoVB1UDAAAAAABw7+7OOQDgQUbrGAAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANiBoB0AAAAAAAAAADsQtAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANiBoB0AAAAAAAAAADsQtAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANiBoB0AAAAAAAAAADsQtAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANiBoB0AAAAAAAAAADu4OLoAAAAA5E5he1crPizIuO/i4SmPet4OrAgAAAAAcieCdgAAANgUHxakuOBAR5cBAAAAALkerWMAAABgm4k/FQEAAAAgIxjRDgAAAJttYlzciynu5iUHVgUAAAAA9weCdgAAANhsE+Pi4emgagAAAADg/sL1wAAAAAAAAAAA2IGgHQAAAAAAAAAAOxC0AwAAAAAAAABgB4J2AAAAAAAAAADsQNAOAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHYgaAcAAAAAAAAAwA4E7QAAAAAAAAAA2IGgHQAAAAAAAAAAOxC0AwAAAAAAAABgB4J2AAAAAAAAAADs4OLoAgAAAHD/iPDfoZgrp4z7Lh6e8qjn7cCKAAAAAMDxCNoBAACQYQmRoYoLDnR0GQAAAACQq9A6BgAAAAAAAAAAOxC0AwAAAAAAAABgB4J2AAAAAAAAAADsQNAOAAAAAAAAAIAdCNoBAAAAAAAAALCDi6MLAAAAQM4K27ta8WFBxn3X0lUcWA0AAAAA3P8I2gEAAB4y8WFBigsONO67eHg6sBoAAAAAuP/ROgYAAAAAAAAAADsQtAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANjBxdEFAAAA4P4W4b9DMVdOGfddPDzlUc/bgRUBAAAAQM4iaAcAAIBdEiJDFRcc6OgyAAAAAMBhaB0DAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHYgaAcAAAAAAAAAwA4E7QAAAAAAAAAA2IGgHQAAAAAAAAAAOxC0AwAAAAAAAABgBxdHFwAAAIAHT4T/DsVcOWXcd/HwlEc9bwdWBAAAAADZh6AdAAAAWS4hMlRxwYGOLgMAAAAAcgStYwAAAAAAAAAAsAMj2gEAAJAjaCcDAAAA4EFF0A4AAIAcQTsZAAAAAA8qWscAAAAAAAAAAGAHgnYAAAAAAAAAAOxA0A4AAAAAAAAAgB0I2gEAAAAAAAAAsANBOwAAAAAAAAAAdiBoBwAAAAAAAADADi6OLgAAAABIT9je1YoPCzLuu3h4yrlgYcVcOZXuYx71vHO0VgAAAAAPH4J2AAAA5HrxYUGKCw5M8XhGHwMAAACA7ETQDgAA8AC7eyS4a+kqDqwmpQj/HYxABwAAAHDfI2gHAAB4gN09EtzFw9OB1aSUEBma7SPQCfMBAAAAZLeHOmgPDQ3V3LlztWnTJl24cEHx8fEqWbKkGjZsqD59+qhy5cqOLhEAAAB2yokwHwAAAMDD7aEN2o8dO6Z3331XN27csHr80qVLunTpkpYvX65Ro0apW7duDqoQAAAAAAAAAHA/eCiD9tDQUL399tu6efOmJKl9+/Zq06aNChUqpP/++0+zZs1SdHS0Ro0apZIlS+rZZ591cMUAAAAAAAAAgNzqoQzaf//9dyNkHzJkiN555x1jWYMGDfTss8/q5ZdfVlxcnL777juCdgAAAAAAAABAqpwcXYAj/PPPP5KkwoUL66233kqxvEaNGmrfvr0k6dSpU7p8+XKO1gcAAAAAAAAAuH88lEF7SEiIJKls2bJycrL9ElhOhJo8+h0AAAAOYnoo/2wFAAAAcJ94KFvHlChRQleuXNHFixeVkJAgZ2fnFOtcvHjRan0AAADkjAj/HYq5csq47+LhKRf3Yoq7ecmBVQEAAABA6h7KoUHNmzeXJN2+fVu//fZbiuXnzp3TX3/9JUmqWbOmSpcunZPlAQAAPNQSIkMVFxxo/IsPC3J0SQAAAACQpodyRHuvXr20YcMGHT16VBMmTNCZM2f0wgsvqFChQjp06JB+/vlnRUdHy8PDQ2PGjMn0/oODg432NJlx4cKFTG8DAAAAAAAAAHCshzJod3Nz0++//64ZM2Zo3rx5WrZsmZYtW2a1TosWLfTpp5+qfPnymd7//PnzNX369KwqFwAAAAAAAACQiz2UrWMk6fjx4zp+/Lji4uJsLj9w4IBWr16thISEHK4MAAAAAAAAAHA/eSiD9tWrV+v111/Xzp07VaFCBU2dOlV79+7VoUOHtHDhQrVo0UKhoaGaPHmyPvnkEyUmJjq6ZAAAAAAAAABALvXQtY65evWqhg0bpri4OD322GNavHix3N3djeVeXl766aefNHr0aC1YsECrVq3S008/rVdeeSXDx+jZs6fatGmT6douXLig/v37Z3o7AAAAAAAAAIDjPHRB+/LlyxUTEyNJGj58uFXIbmnYsGHy8/NTcHCw5s+fn6mgvVixYipWrFiW1AsAAAAAAAAAyN0eutYxR48elSTlyZNHjRs3TnW9fPnyqUmTJpKk06dPKzIyMkfqAwAAAAAAAADcXx66oD06OlqSlD9/fuXJkyfNdS1HpUdERGRrXQAAAAAAAACA+9NDF7QXKVJEknT79m2FhYWlue61a9ckSU5OTsZ2AAAAAAAAAABYeuiC9qefftq4vXLlylTXCw8P17Zt2yRJNWvWVN68ebO9NgAAAAAAAADA/eehC9rbtWtnTIA6efJk+fv7p1gnLi5Ow4cPV3h4uCSpZ8+eOVojAAAAAAAAAOD+4eLoAnKah4eHRo0apY8++kgRERF6+eWX1b17dzVr1kzu7u46ffq05s6dqxMnTkiSWrRooQ4dOji4agAAAAAAAABAbvXQBe2S1L59e8XGxmr06NGKiYnR3LlzNXfu3BTrtW7dWuPHj5eT00M38B8AAOCBFuG/QzFXThn3XTw85VHP24EVAQAAALifPZRBuyS99NJLatSokebNm6ft27fr8uXLio2NVfHixVW7dm299NJLeuaZZxxdJgAAALJBQmSo4oIDHV0GAAAAgAfEQxu0S1Lp0qU1dOhQR5cBAAAAAAAAALiP0RMFAAAAAAAAAAA7ELQDAAAAAAAAAGAHgnYAAAAAAAAAAOxA0A4AAAAAAAAAgB0I2gEAAAAAAAAAsANBOwAAAAAAAAAAdnBxdAEAAABAbhDhv0MxV04Z9108POVRz9uBFQEAAAC4XxC0AwAAAJISIkMVFxzo6DIAAAAA3IdoHQMAAAAAAAAAgB0I2gEAAAAAAAAAsANBOwAAAAAAAAAAdiBoBwAAAAAAAADADgTtAAAAAAAAAADYwcXRBQAAAAD3k7C9qxUfFmTcd/HwlEc9bwdWBAAAAMDRCNoBAACATIi/Hay44EBHlwEAAAAgFyFoBwAAAFIR4b9DMVdOGfddPDzl4l5McTcvObAqAAAAALkNQTsAAACQioTI0BSj1108PB1UDQAAAIDcislQAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANiBoB0AAAAAAAAAADsQtAMAAAAAAAAAYAcXRxcAAAAA3O8i/Hco5sop476Lh6c86nk7sCIAAAAAOYmgHQAA4AERtne14sOCjPuupas4sJqHS0JkqOKCA+9p27t/boT0AAAAwP2HoB0AAOABER8WZBX2unh4OrAaZNTdPzcAAAAA9x96tAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAADiSiT/JAQAAgPsdPdoBAACAHBL231rFh1437rt4eMrFvZjibl6yWi/Cf4dirpyyWo8JUgEAAIDci6AdAAAAyAa2wnJJKSY+tTVpbUJkKBOkAgAAAPcRgnYAAAAgG9gKy22F6gAAAADufzSEBAAAAAAAAADADgTtAAAAAAAAAADYgaAdAAAAAAAAAAA7ELQDAAAAAAAAAGAHgnYAAAAgtzPxZzsAAACQm7k4ugAAAAAAaXNxL6awvasVHxb0f495eMqjnneKde9ez7V0FSVEhmZoWwAAAAD3hqAdAAAAuA/EhwUpLjgw0+u5eHim3JYR8gAAAECWImgHAAAA7lMR/jsUc+WUcd+1dJUMbZeZEfIAAAAA0kfQDgAAANynEiJDU4xezyhbI+TvDu5dPDzlXLBwiscI5AEAAABrBO0AAAAAJKUM7pNlpGVN2H9rFR963bhPIA8AAICHCUE7AAAAgEyxNfJdylggDwAAADyICNoBAAAAZIqtke+ZaVsDAAAAPGicHF0AAAAAAAAAAAD3M4J2AAAAAAAAAADsQNAOAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHYgaAcAAAAAAAAAwA4E7QAAAAAAAAAA2MHF0QUAAAAAeDBF+O9QzJVTxn0XD0951PN2YEUAAABA9iBoBwAAAJAtEiJDFRccaPUY4TsAAAAeRATtAAAAAHKMrfAdAAAAuN/Rox0AAAAAAAAAADsQtAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANjBxdEFAAAAAHi4RfjvUMyVU8Z9Fw9PedTzdmBFAAAAQOYQtAMAAABwqITIUMUFBzq6DAAAAOCe0ToGAAAAAAAAAAA7ELQDAAAAAAAAAGAHgnYAAAAAAAAAAOxA0A4AAAAAAAAAgB0I2gEAAAAAAAAAsANBOwAAAAAAAAAAdiBoBwAAAAAAAADADi6OLgAAAAAA7hbhv0MxV04Z9108POVRz9uBFQEAAACpI2gHAAAAkOskRIYqLjjQ0WUAAAAAGULrGAAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANjBxdEFAAAAAEBGRPjvUMyVU8Z9Fw9PedTzdmBFAAAAQBKCdgAAgAeFiYsV8WBLiAxVXHCgo8sAAAAAUiBoBwAAuA+F7V2t+LAg476Lh6dc3Isp7uYlB1YFAAAAAA8ngnYAAID7UHxYUIqRvS4eng6qBgAAAAAeblxfDAAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAAAAAAAANiBoB0AAAAAAAAAADsQtAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOLo4uAAAAAADuVYT/DsVcOWXcd/HwlEc9bwdWBAAAgIcRQTsAAACA+1ZCZKjiggMdXQYAAAAecrSOAQAAAAAAAADADgTtAAAAAAAAAADYgaAdAAAAAAAAAAA7ELQDAAAAAAAAAGAHgnYAAAAAAAAAAOxA0A4AAAAAAAAAgB0I2gEAAAAAAAAAsANBOwAAAAAAAAAAdiBoBwAAAAAAAADADgTtAAAAAAAAAADYgaAdAAAAAAAAAAA7ELQDAAAAAAAAAGAHgnYAAAAAAAAAAOzg4ugCAAAAACArRfjvUMyVU8Z9Fw9PedTzdmBFAAAAeNARtAMAAAB4oCREhiouONDRZQAAAOAhQusYAAAAAAAAAADsQNAOAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHZgMlQAAIBcLmzvasWHBRn3XUtXcWA1AAAAAIC7EbQDAADkcvFhQYoLDjTuu3h4OrAaAAAAAMDdaB0DAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHYgaAcAAAAAAAAAwA4E7QAAAAAAAAAA2IGgHQAAAAAAAAAAOxC0AwAAAAAAAABgB4J2AAAAAAAAAADsQNAOAAAAAAAAAIAdCNoBAAAAAAAAALADQTsAAAAAAAAAAHZwcXQBAAAAAJDdIvx3KObKKeO+i4enPOp5O7AiAAAAPEgI2gEAAAA88BIiQxUXHOjoMgAAAPCAeuiD9l27dsnX11f79u3TzZs3VaBAAVWvXl0+Pj5q166dTCaTo0sEAAAAAAAAAORiD23QnpCQoM8//1y+vr5Wj4eFhWnXrl3atWuX1qxZoylTpihPnjwOqhIAAAAAAAAAkNs9tJOhjh492gjZq1atqvHjx2vRokWaOHGiqlatKknauHGjvv32W0eWCQAAAAAAAADI5R7KoH337t1avHixJKlevXry9fVV586dVbt2bbVr106LFi0ywvY//vhD169fd2S5AAAAAAAAAIBc7KEM2qdPny5JKlCggKZMmaJ8+fJZLS9QoIDef/99SUktZjZu3JjjNQIAAAAAAAAA7g8PXY/2Gzdu6L///pMk9e7dW8WKFbO5XtOmTdWxY0cVLlxY5cqVy8kSAQAAAAAAAAD3kYcuaN+xY4cSExMlSW3atEl1vfz589OfHQAAAAAAAACQrocuaD958qQkKU+ePKpSpYrxeExMjK5duyYXFxc98sgjcnZ2dlSJAAAAAAAAAID7yEMXtJ89e1aSVKJECbm4uOjkyZOaOnWqtm7dqri4OElSkSJF5OPjo3fffVdubm6OLBcAAAAAAAAAkMs9dEF7aGioJMnd3V1r1qzRJ598otjYWKt1bt26pVmzZmnr1q369ddfVaJEiUwdIzg4WCEhIZmu7cKFC5neBgAAAAAAAJlnNpsVGRmp27dvKzY21mg1DAC2ODk5KW/evHJ3d1fBggVlMpmslj90QXtUVJQk6fr16xo6dKhcXFw0ZMgQeXt7q0iRIgoICND06dO1efNmBQQE6IMPPtD//vc/OTk5ZfgY8+fP1/Tp07PrKQAAAAAAAMAOUVFRunz5shISEhxdCoD7SHR0tMLCwuTs7KyyZcuqQIECxrKHLmiPjo6WJIWEhChPnjyaPXu26tSpYyyvUaOGfvzxR33wwQdau3at9u3bp7Vr18rb29tRJQMAAAAAACCLREVF6eLFizKbzcZjzs7OcnZ2TjFCFQCkpCtgEhISjJNzCQkJunjxosqXL2+E7Q9d0O7q6mrc9vHxsQrZk5lMJn366afasGGD4uPjtWrVKoJ2AAAAAACA+5zZbNbly5eNkN3Dw0NFixaVq6srITuANJnNZsXExCgkJERhYWHG50mVKlVkMpkevqC9YMGCxu3nnnsu1fVKliyp6tWr68iRIzpy5EimjtGzZ0+1adMm07VduHBB/fv3z/R2AAAAAAAASF9kZKQxItXDw0OlSpUiYAeQISaTSfny5VOpUqUkSWFhYUpISFBkZKTc3NwevqDd09PTuJ3eJKePPPKIjhw5olu3bmXqGMWKFVOxYsXuqT4AAAAAAABkj9u3bxu3ixYtSsgOINNMJpOKFCmisLAwSUmfK25ubsr4DJ8PiMqVKxu3LT9cbYmLi5Mkubu7Z2tNAAAAAAAAyH6xsbGSknqyW7YXBoDMyJcvn5ydnSX93+fKQxe0P/XUU8btgwcPprnu6dOnJUllypTJzpIAAAAAAACQAxITEyWJiU8B2MVkMhlBe/LnykPXOqZp06Zyd3fX7du39eeff6pPnz7KkydPivX279+vy5cvS5JatmyZ02UCAAAAyGYR/jsUc+WUcd/Fw1Me9bwdWBEAIKcQsgOw192fIw/diPa8efPq1VdflSSdP39e48ePN2aaThYWFqZRo0ZJSroMoFOnTjldJgAAAIBslhAZqrjgQONffFiQo0sCAADAfeqhG9EuSe+++642bdqkEydO6H//+58CAgLUo0cPlSlTRgEBAfr555916dIlSdIHH3xgzCQLAAAAAAAAAMDdHsqg3dXVVXPmzFG/fv20f/9+7dmzR3v27EmxXv/+/dWnT5+cLxAAAAAAAAAAcN94KIN2SSpSpIjmz5+vFStWaMWKFTp+/LjCw8NVsmRJ1a5dW7169bKaOBUAAAAAAAAAAFse2qBdSmpY37FjR3Xs2NHRpQAAAAAAAAAA7lMPddAOAAAAAAAAZMS0adM0ffr0DK2bJ08eFSxYUCVKlFC1atX04osv6rnnnsvmCh9sLVu2VGBgoMqUKaNNmzZZLRs2bJiWLVsmSZo7d64aNGjgiBKzTK9evYw21w/C88kOR44cUWBgoNq0aZNiWfJ7RZJOnjyZYzU55diRAAAAcG9M/MkGAABwP4mLi1NoaKgCAgK0cuVK9evXT6+99poiIiIcXRpwX4uKitLYsWPVrVs3nTp1ytHlWGFEOwAAQC4Stne14sOCjPsuHp5ycS+muJuXHFgVAAAALHl7e6tt27apLo+OjtaVK1e0detW7du3T5K0a9cuDR48WLNmzcqpMoEHzpEjRzRv3jxHl2ETQTsAAEAuEh8WpLjgQKvHXDw8HVQNAAAAbKlYsaJatWqV7nrvvPOOli1bpk8//VSJiYn6559/tH37djVp0iQHqnx4jB8/XuPHj3d0GXjIcR0yAAAAAAAAkE06d+6sTp06GfeXLl3quGIAZBuCdgAAAAAAACAbWU6Eeu7cOQdWAiC70DoGAAAAAAAAyCExMTFpLj948KCWLFmiPXv26MaNGzKbzSpRooTq16+vbt26qVatWukeIz4+Xps3b9by5ct16tQpXbt2TXny5NGjjz6qZ555Rq+88opKlCiR6vbR0dFavny5NmzYoBMnTigsLEwFCxZU+fLl1bRpU73yyisqXry4zW13796t3r17S5ImTZqkp556Sl9//bV27dols9ms0qVLq3PnznrzzTetttu8ebN8fX116NAhhYWFqWjRoqpfv77eeOMNPfHEE2k+32HDhmnZsmWSpLlz56pBgwbGsmnTpmn69OmSpG3btqlo0aJasmSJVq5cqdOnTys8PFzFixdXvXr11LNnT3l5eaV5rISEBC1fvlwrVqzQiRMnFBkZqeLFi6tRo0Z67bXX9Pjjj6tv377atm2bypQpo02bNqW5v5wQFxen5cuXy8/PT8ePH1doaKgKFiyoChUqqFmzZnrllVdUuHBhm9suXbpUw4cPlyQtWrRItWvX1urVq7V8+XL5+/srNDRUhQsXlpeXl3x8fPTss8+mW4+fn5+WLFmiI0eO6Pbt2ypcuLDq1KmjXr16qW7duho1apQWLVokSTp58mSKOpJNnz7d+NmOGzdOXbp0sXm8oKAgzZ49W1u3blVgYKBcXFxUpkwZPf/88+rZs6eKFi2aodcxPQTtAAAAAAAAQDbatWuXcbtixYo214mNjdWoUaOMwNjShQsXdOHCBfn6+srHx0eff/658ubNa3M/ly5d0sCBA3X8+HGrx2NiYnTs2DEdO3ZMf/zxh7777ju1aNEixfZ79+7Vxx9/rKtXr1o9HhoaqtDQUB0+fFhz5szRp59+qq5du6b5vK9du6Zx48YpKCjIeOz06dNydXW1et7Dhg3T33//bbXt9evXtXLlSq1Zs0YjR45M8zgZFRoaqv79++vQoUNWj1+9elUrVqzQihUr9N577+mDDz6wuX1YWJjeeecdHThwIMX2S5cu1cqVK1OEwY528uRJDRw4UBcuXLB6PDQ0VAcPHtTBgwc1e/ZsjRs3Ts8//3ya+4qJidG7776rzZs3Wz0eFBQkPz8/+fn5qUuXLvrqq6/k5JSykUpMTIyGDBmiDRs2pNh+7dq1Wrdund566617fKa2bd68WR9++KEiIyOtHj9x4oROnDihRYsW6eeff073ZE5GELQDAAAAAAAA2eTQoUPy9fU17rdv3z7FOgkJCXrnnXe0Y8cOSVKxYsXUqVMnPf744zKbzTpx4oSWLVumW7du6c8//1RISIh++OEHmUwmq/1cv35dPj4+Cg0NlSSVLVtWnTt31mOPPaaQkBD5+flpz549Cg8P18CBA7Vw4ULVqFHD2P6///5T3759jVH3VatWVfv27VW2bFmFhoZq8+bN+ueffxQVFaURI0YoMjJSffr0SfW5T5kyRTExMWrRooXatGmj8PBwrVu3Th06dDDWef/9943g1s3NTd26ddOTTz6pqKgobdiwQVu3btUXX3whZ2fnzL3wNgwaNEhnzpxRuXLl1KVLF1WoUEG3bt3SihUrdPDgQUnSjz/+qHr16qWYsDY2Nla9evUyRliXKFFC3bp1U6VKlRQcHKyVK1fq0KFDGjNmjAoVKmR3rVkhICBAPXv2VEREhCTJy8tLrVu3VsmSJXX79m1t375d69evN94P33//vdq0aZPq/r744gudOXNGxYsXl4+Pj6pWrarIyEj5+fnp33//lZQ08tzLy0vdunVLsX3//v2N9dzd3dWtWzdVr15dERERWr9+vbZt26aZM2fK3d09xbYNGzbUjBkzFBAQoClTpkiSvL291bZtW0lKNSgfMGCA4uPjVb9+fb3wwgtyd3fX6dOnNX/+fEVEROjGjRsaPHiwVq5cmerJq4wiaAcAAAAAAACySGxsrCIiInTmzBlt2rRJ8+fP1507dyRJzZs3tzlqeObMmUbI/swzz2jy5MlWYW3Hjh317rvvqn///tq7d682bdqkBQsWqGfPnlb7GT16tBGye3t765tvvrEKD3v16qXJkyfrp59+UlxcnMaNG6c//vhDknTnzh0NGTLECNnffPNNffjhh1Yjk3v27Kl169bpo48+UmxsrL799lvVrVvXKqy3FBMTo86dO2v8+PFWNSTz8/MzQvZy5crp999/V5kyZYzl3bp107Jly/Tpp58qLi4utZc8w86cOaMXX3xR3377rdXr0qNHD3388cdatWqVJGnBggUpgvbffvvNCNnr1Kmjn3/+2epn9Oqrr2ratGmaMWOGwsPD7a7VXvHx8Ro0aJAiIiJkMpn0+eefq0ePHlbrvPzyy9q9e7fee+89RUZG6tNPP1XdunVTbQt05swZ1a9fXzNmzLAKw7t166YJEyZo1qxZkpJev7uD9pUrVxohe8WKFTVnzhyVLFnSqhZfX1+NHDlSt2/fTnHs0qVLq3Tp0lavecWKFdWqVas0X4eEhASNHTs2xdUX3bt3l4+Pj27duqXz589rx44dat68eZr7Sg+ToQIAAAAAAACZMH36dFWrVs3mv5o1a6pRo0Z69dVX9dtvvxkhe9u2bfX999+nGIUeGRmp2bNnS5JKliypqVOn2hwR7eHhoYkTJ6pAgQKSpFmzZikhIcFYfvbsWaMfeIUKFVKE7Mk++OADVa5cWVLSCPbLly9Lknx9fXX9+nVJSScEPv74Y5vtP1q3bm20VklISNAPP/yQ5ms1YMCAVJfNnDnTuP3dd99ZhezJOnfurFdeeSXNY2RU4cKFNW7cuBSvi5OTk95//33jfvLo9mSxsbFGre7u7jZ/RiaTSe+//75atmyZJbXaa/Xq1Tp79qwkqXfv3ilC9mQNGjTQoEGDJCW9F+fNm5fqPl1cXDRhwgSbI8779++vPHnySJKOHz+eYi6C5F7qLi4umjZtmlXInqxr16569dVXM/DsMq5z5842WxyVLVtW3bt3N+4fPXrU7mMRtAMAAAAAAADZoFSpUnrppZe0YMECTZo0Sfnz50+xzr///quwsDBJSW1lkoN0W0qWLGn0Vb9y5Yr8/f2NZZZ9r3v27JlqGwyTyaSPP/5Yw4cP188//2xMgrlx40ZjnXfeeSfN5/XKK6/Iw8NDkvTPP/8oOjra5nqlS5dW2bJlbS4LCgoyws0nn3wyzUlI33jjjRQnKO5FixYtbP4MJOnRRx81XvvkqwKS7dy50+jx3bFjx1RHfEvpv3Y5xbLnvWWgbEvXrl2N1jyW74O7Pf300zYDcknKnz+/KlSoIEkym83Ge1pK6st//vx5SUlXbCSf6LHlrbfesnmC515Ztim6W82aNY3bN2/etPtYtI4BAAAAAAAAMsGyN7SUNLL71q1bWrt2rXbu3ClJKl++vMaMGaNGjRqlua/9+/cbtyMiIlJMFHk3F5f/i/MOHz5shIWWo7Dr1auX5j5stchI3r5AgQKqXbt2mtvny5dPTz/9tDZv3qy4uDgdO3ZMdevWTbFeahO/Jh/PbDZLks1tLZUuXVrly5dPMaFnZlWpUiXN5YUKFVJUVJTi4+OtHreczLZhw4Zp7uOpp56Sm5ub0RfdUSzfVwEBATp37lya6xcvXlzXr1/X6dOnFRkZqYIFC6ZYJyOvXzLL13D37t3G7fRev5IlS6py5coKCAhIc72MqlatWqrL3NzcjNt3j8C/FwTtAAAAAAAAQCak1hv65Zdf1oIFC/TFF1/o4sWLev311zVy5Mg0W59cu3bNuL1w4UItXLgww3UEBwfbvG2rBUtaIiIijFHpZcuWzdCI4vLlyxu3UxsNnDzq3ZagoCDjdunSpdM93qOPPmp30G6r5Yml5JMYyScAkt24ccO4nd5razKZVK5cOR0/fvweq7RfZGSkVZ/z5FY/GWE2mxUSEmIzaE9vklfLk0CJiYnG7eSWRJJSvcLBUvny5bMsaE+rZsurJO7+md8LWscAAAAAAAAAWaRHjx4aOHCgpKTwbuzYsVq7dm2q69szcablqGnLdieptUdJTXJbFElptq6xZHmMqKgom+uk1r5GklUQnC9fvnSPZzn6+F4lt0fJrFu3bhm3XV1d010/s69/VrN3NL3l+8GSZZCeGY58/ZL7xucERrQDAAAAAAAAWahfv37au3evdu7cqcTERH322WeqUaOGzdG8liGzr6+vatWqdU/HtNxPdHR0miH33SzD9dRC87tZhrH3EoxajnZPrce7pdjY2EwfI6vc/dqmJ3kCXEexrLdSpUpavXq1A6u5/16/e8WIdgAAAAD4/yL8dyh4w+/Gv7C9jv2PKQDg/mQymTRu3Dij/UZERISGDRtmsz2Fp6encfvs2bP3fEzLCTqvXr2a5rq3bt3S7t27FRgYqISEBBUqVMgIyy9fvmzV9iM1lm1cHnnkkUzXW6JECeP2pUuX0l3fssVOTrNsbXP58uV01w8MDMzOctLl7u5ujBy/fPmyQ09SSJl//TKyTm5E0A4AAAAA/19CZKjiggONf/FhQelvBACADaVKldKHH35o3N+7d6+WLl2aYr2nnnrKuL1p06Z09ztnzhx98sknmjJlik6ePGk8njwpqiTt27cvzX1s2bJFvXv3VsuWLfXbb79JkjGSPioqympiVVuio6N14MABSUntRNKbJNMWLy8vo5WL5WSjtoSHh1s915zm5eVl3N67d2+a6548eVJhYWHZXVKaTCaT8fOMiYnRjh070lw/NjZWgwYN0ujRozVr1qwUk8HaKzOvX2hoqE6fPp2lx88pBO0AAAAAAABANujRo4dVAP7dd99Z9VKXpObNmxujjzds2JDmJJrBwcGaMmWK/vrrL/3www9Wkzm2bNnSuL1w4cI0w9Lly5cbt5s2bSpJeuGFF4zHfv755zSf1x9//GH0lm/YsOE99U8vWrSo6tevL0k6c+aMNm/enOq68+fPV1xcXKaPkVWaN29uXJ2wcuVKq57jd5szZ04OVZU2y5/nDz/8kOb7wdfXV2vXrtWCBQu0Zs2ae+7FnpqnnnpK5cqVkyT9+++/On/+fKrr/u9//0vzZ205UW9GrrzISQTtAAAAAAAAQDZwcnLS6NGjjXDw1q1bmjhxotU6xYoVU7du3SRJCQkJ6t+/v86cOZNiXxEREerXr5/RQ71FixaqWrWqsbxWrVpq0KCBJCkgIEBjxoxRQkJCiv3Mnj3bGEFev359Va9eXZL00ksvGW1stmzZogkTJtgMMv38/DRlyhRJSZOLDhgwIBOviLUBAwYYJws+/fRT+fv7p1jn33//1YwZM+75GFmhYMGC6tWrl6SkEddDhgyx2ct+/vz5Nq9acAQfHx/j53no0CENHz7cZguZPXv26NtvvzXuv/POO1lei8lk0ttvvy1JiouL06BBgxQcHJxivY0bN+qnn35Kc1+W8wFYTqibGzAZKgAAAAAAAJBNatSooZdfflnz58+XlDR6uEuXLlbtND766CPt27dP/v7+CgwMVMeOHdW+fXvVq1dPzs7OOnv2rBYvXqyQkBBJSf3YR48eneJY48aNU5cuXRQaGqpFixZp//796tixo8qUKaNr165pw4YNRluZQoUKWe0jf/78mjx5sl5//XXFxcVp1qxZ2rp1qzp06KAyZcooLCxMW7Zs0ZYtW4xtPvjgA6vnkVl169bV66+/rt9++00hISHq1q2bunTporp16yo+Pl7btm3T6tWrZTabVaxYMZvhbE557733tGnTJgUEBGjHjh3y9vaWj4+PKlasqNDQUPn5+Wnnzp1ycnIyTlAkt8axx6RJk1S4cOEMrfvcc88ZJ20KFChg9fNcsWKF9u3bp86dO6tSpUoKCwvT3r17tWbNGqPedu3aqXXr1nbXbEvXrl21du1abd++XSdOnJC3t7e6du2q6tWrKyoqSv/884/8/PwkJb1uCQkJVqPXk5UqVcq4vXTpUlWsWFFFixZVlSpVVLly5WypPaMI2gEAAAAAAIBsNHjwYK1bt07BwcEym80aPXq0li5dagSx+fLl09y5c/XJJ59o06ZNiouL09KlS22Ojq5cubKmT59ucwLSMmXKaP78+erfv7/OnTunU6dOacKECSnWK1WqlKZOnapKlSpZPV6vXj3NmTNHgwcP1o0bNxQQEGBz+/z582vUqFHq0qXLvb4khqFDhyp//vyaMWOG4uLitGjRIi1atMhYbjKZ9MEHH+jIkSPauHGj3ce7V/ny5dNvv/2mt99+W/7+/rp69aqmTZtmtU7evHk1btw4ffTRRzKbzcqbN6/dx02vX76l8uXLW91P/nkOGTJE169fV2BgoKZPn25z2+7du2vUqFH2lJomk8mkadOmacCAAdqxY4dCQ0M1a9Ysq3WcnJz00Ucfaf78+bp8+bLRUslSsWLF1KRJE23fvl1RUVEaM2aMJOmNN97Q0KFDs63+jCBoBwAAAAAAALKRu7u7PvnkEyMIPHHihObNm6c+ffoY6xQqVEg//vij9uzZo+XLl2vfvn26ceOG4uLiVLhwYT3xxBNq06aN2rdvrzx58qR6rEqVKmnlypVavny51q1bpxMnTig0NFT58uVT5cqV9fzzz6t79+6p9lWvW7eu1q9frz///FObNm0yJvcsXLiwypYtq1atWqlTp04qXrx4lr0+77//vpo3b6558+Zpz549Cg4Olru7u2rVqqXXXntNjRo1Ur9+/bLsePfK09NTvr6+WrJkif7++2+dOnVK4eHhKl68uJo0aaI333xTZcqUkdlsliR5eHg4uOL/+3kuWbJEmzdv1okTJ3Tr1i25uLjokUceUd26ddWtWzdj8tTsVLBgQc2ePVurV6/WX3/9pWPHjik0NFSFCxdWvXr19MYbb6hmzZqaPXu2pNRfv6lTp2ry5MnauHGjbt68qUKFCik6Ojrb608PQTsAAAAAAACQjoEDB2rgwIH3vH2nTp3UqVOndNerX7++MUnovcqTJ4+6du2qrl273tP2+fLl06uvvqpXX30109s2aNBAJ0+ezPR2tWrV0nfffZfq8h9++CHVZePHj9f48eNtLsvMz23Tpk3pruPi4qLu3bure/fuNpdfu3bNuG3Z5iQz5s2bd0/bpcbV1VU9e/ZUz549M71tly5dMnzlQkbr9vb2lre3t81lCQkJxoTBqb1+bm5uGjlypEaOHGlzeUZ+jtK9v1dTQ9AOAAAAAAAAAKk4f/68Ro4cqTJlyui5557T888/n+q6yX3GJalmzZo5UV6uFxUVpb59+6pMmTJq0KBBmieAtmzZori4OEn33+tH0A4AAAAAAAAAqShevLgOHjyoPXv2aMeOHapdu7Y8PT1TrHf06FHNmDFDUtJVAS+++GJOl5orFShQQJcvX9b+/fu1ceNG1alTRxUrVkyx3qVLl/TVV19JSurX3rFjx5wu1S4E7QAAAAAAAACQCjc3N3Xu3FmLFi3S9evX1a5dO7Vv315Vq1aVm5ubbt68qf3792vDhg3GaOyhQ4eqZMmSDq489+jVq5cmTpyoqKgovfTSS/L29laNGjXk4eGhW7du6ejRo1qzZo3Ra/2NN95QjRo1HFx15hC0AwAAAAAAAEAahg8frlu3bsnPz0+hoaGp9iN3dXXVsGHD7qkf+oOsb9++unLlihYsWKCoqCj9+eef+vPPP1Os5+TkpH79+ql///4OqNI+BO0AAAAAAAAAkIb8+fNr2rRp2rlzp1asWKFDhw7p6tWriouLU9GiRVW6dGk1b95cHTt2vOdJUB9kzs7OGj16tHx8fLRkyRLt27dPly9fVkxMjIoUKaISJUromWeeUceOHW22lbkfELQDAAAAAAAAQAY0atRIjRo1cnQZ960aNWrcdy1hMsrJ0QUAAAAAAAAAAHA/I2gHAAAAAAAAAMAOBO0AAAAAAAAAANiBoB0AAAAAAAAAADsQtAMAAAAAAAAAYAeCdgAAAAAAAAAA7EDQDgAAAAAAAACAHQjaAQAAAAAAAACwA0E7AAAAAAAAAAB2IGgHAAAAAAAAAMAOBO0AAAC5iYk/zwAAAADgfuPi6AIAAAAeVmF7Vys+LMi47+LhKRf3Yoq7ecmBVQEAAAAAMougHQAAwEHiw4IUFxxo9ZiLh6eDqgEAAAAA3CuCdgAAAAAAAADIBS5duqR58+Zp586dCgwMVFxcnIoVKyYvLy91795dDRs2dHSJSEWuDNpDQkJUoEAB5cuXz9GlAAAAAHjIRfjvUMyVU8Z9Fw9PedTzdmBFAADgQeTr66sxY8YoNjbW6vGrV6/q6tWrWr16tXx8fPTFF1/IxSVXxroPNYf8RA4dOqSIiAg1adLE6vElS5ZoypQpCgpK6lX61FNPafDgwWrQoIEjygQAAAAAJUSGpmjzBADAwy4+IVEht+84uoxsU9Q9n1ycnXLseJs2bdLIkSNlNptVqFAhvfbaa6pfv75cXV11/PhxzZ49WxcuXNCff/4pNzc3DR8+PMdqQ8bkaNB+/fp1DRo0SIcOHVKTJk2sgnZfX1+NGjVKkmQ2myVJBw8eVN++ffXdd9/pxRdfzMlSAQAAAAAAAKQi5PYd/bT0sKPLyDbvdqmlEkUK5MixEhIS9NVXX8lsNsvd3V0LFy5UpUqVjOW1a9dW+/bt1bt3bx07dkxz585V165dVbly5RypDxmTY6dlEhMT9eabb+rQoUMym826dOmSsSwmJkYTJ06U2WyW2WyWp6enKlSoIEmKj4/X559/rpCQkJwqFQAAAAAAAAByxH///afLly9Lkt577z2rkD2Zm5ubMUg5MTFRq1atytEakb4cC9r//vtvnTqV1NewcuXK6tevn7Fsy5YtCg0NlclkUqNGjbRp0yatXbtWkyZNkslkUnh4uHx9fXOqVAAAAAAAAADIEfv27TNut2jRItX1ateurQIFkkbZJ+esyD1yLGjfsGGDJOmRRx7RokWL1LFjR2PZxo0bjdvvvfee8uTJI0ny9vZWq1atZDabtWXLlpwqFQAAAAAAAAByhJeXl95++2117NhRpUqVSnW95G4gUlKHEOQuOdaj/ciRIzKZTPLx8VHBggWtlm3fvl2S5O7urnr16lkta9CggdavX2/VagYAAAAAAAAAHgSNGjVSo0aN0l3v6NGjio6OliSVLl06u8tCJuXYiPZbt25JktF7PdmJEycUHBwsk8mkevXqyWQyWS0vWrSoJCk0NDQnygQAAAAAAACAXOeXX34xbjdu3NiBlcCWHJ0MVVKKIH3Hjh3GbVtnbm7evClJcnV1zcbqAAAAAAAAACB3WrdundauXStJKlOmjJ577jkHV4S75VjQ7unpKUk6d+6c1eNbt241bjdp0iTFdv/9958kLocAAAAAAAAA8PA5fPiwhg0bZtz/7LPPjDkukXvkWNDu5eUls9msZcuWKSwsTFLSm2Tv3r0ymUyqWLFiirYyO3bs0Pr162UymeTl5ZVTpQIAAAAAAACAw/n7++utt95SVFSUJKlPnz6MZs+lcmwy1C5dumjlypUKDAxU+/btVbt2be3YsUOJiYkymUzq0qWLse6RI0e0bNkyLV68WGaz2ZhEFQAAAAAAAAAeBvv379c777yj27dvS5LatGmjoUOHOrgqpCbHRrQ3atRIHTp0kNlsVlBQkNavX6/IyEhJUuXKldWrVy9j3VWrVmnBggWKj4+XJPXq1Uu1atXKqVIBAAAAAAAAwGE2bNig119/3QjZW7durQkTJsjJKcfiXGRSjv5kxo0bpwEDBsjd3V1ms1nOzs568cUX9fvvvytv3rzGeo899pjMZrPy58+vIUOG6NNPP83JMgEAAAAAAADAIf744w8NHDhQd+7ckSR16tRJkydPpi97LpdjrWMkydnZWQMGDFD//v0VEhIid3d3m2+QBg0aaPz48WrZsqXc3d1zskQAAAAAAAAAcIjp06dr2rRpxv0+ffpo2LBhMplMDqwKGZGjQXsyk8mkYsWKpbr8scce02OPPWb1WHh4uAoVKpTdpQEAAAAAAABAjps5c6YRsptMJn388cfq27evg6tCRuVY65jevXvrtdde0/79+zO13ZYtW9SkSRN169YtmyoDAAAAAAAAAMfZtGmTJk6cKElycnLSl19+Sch+n8mxEe179uyRyWTSrVu3MrVdTEyMgoODjZ5EAAAAAAAAAPCgCA0N1YgRI4z7n3zyibp27erAinAvHNI6JjO2bt0qSTKbzQ6uBAAAAAAAAACy1ty5cxUcHCxJql69uho2bKjjx4+nuU2BAgX06KOP5kR5yKAsD9pnzpypRYsWpbp81KhR+vrrr9Pdj9lsVnh4uCIiImQymVL0bAcAAAAAAACA+92ff/5p3D5+/Lg6deqU7jb169fXvHnzsrEqZFaWB+09e/Y0zsLcPQrdbDYbZ2cyq3fv3llRHgAAAAAAAAA7FXXPp3e71HJ0GdmmqHu+HDlOSEiIrl+/niPHQvbK8qDdzc1NI0aM0Lfffmv1+JUrV2QymVSkSBHly5f+G9XJyUn58+dXqVKl1KFDB7Vr1y6rSwUAAAAAAABwD1ycnVSiSAFHl3HfK1q0qE6ePOnoMpAFsqVHe5s2bdSmTRurxx5//HFJ0pdffqnnnnsuOw4LAAAAAAAAAECOc8rJgzGhKQAAAAAAAADgQZMtI9ptOXHiRE4dCgAAAAAAAACAHJOjI9oBAAAAAAAAAHjQ5NiI9rtduHBBoaGhiouLy3BLmXr16mVzVQAAAAAAAAAAZE6OBu2xsbH64YcftHjxYt26dStT25pMJvn7+2dTZQAAAAAAAAAA3JscDdrfe+897dixQxITowIAAAAAAAAAHgw5FrT//fff2r59u0wmk8xmsx555BE9/vjjKlSokFxcHNbBBgAAAAAAAAAAu+RYwr18+XJJSS1gPv/8c7388ss5dWgAAAAAyFIR/jsUc+WUcd/Fw1Me9bwdWBEAAAAcKceCdn9/f5lMJr344ouE7AAAAADuawmRoYoLDnR0GQAAAMglnHLqQLdv35YkNW3aNKcOCQAAAAAAAABAtsuxoL1YsWKSpLx58+bUIQEAAAAAAAAAyHY5FrTXqlVLknTkyJGcOiQAAAAAAAAAANkux4L2bt26yWw2a8mSJQoKCsqpwwIAAAAAAAAAkK1yLGhv2rSpunbtqvDwcL3xxhs6evRoTh0aAAAAAAAAAIBs45JTB1q9erUaNmyo/fv369SpU+ratatKly6tKlWqyN3dXc7OzmlubzKZ9PXXX+dQtQAAADnAlGNjHgAAAAAA2SjHgvYhQ4bIZDJJSgrNzWazrly5oitXrmR4HwTtAADgfhW2d7Xiw/6vfZ6Lh6dc3Isp7uYlB1YFAAAAAMgKORa0S5LZbE7zflqSQ3oAAID7UXxYkOKCA60ec/HwdFA1AAAAAICslGNB+9y5c3PqUAAAAAAAAAAA5JgcC9rr16+fU4cCAAAAAAAAACDH5GjrGAAAAAAAAACAbZcuXdK8efO0c+dOBQYGKi4uTsWKFZOXl5e6d++uhg0bZmp/oaGhatu2rW7evKkBAwZo4MCB2VQ5CNoBAAAAAAAAwMF8fX01ZswYxcbGWj1+9epVXb16VatXr5aPj4+++OILubhkLNYdN26cbt68mR3l4i45FrQvX77c7n106tTJ7n0AAAAAAAAAsI85IV7xEbccXUa2cXErIpNzzo1R3rRpk0aOHCmz2axChQrptddeU/369eXq6qrjx49r9uzZunDhgv7880+5ublp+PDh6e5z27ZtWZLJImNy7N0ybNgwmUyme97eZDIRtAMAAAAAAAC5QHzELQWvneXoMrJNsTZvKY+HZ44cKyEhQV999ZXMZrPc3d21cOFCVapUyVheu3ZttW/fXr1799axY8c0d+5cde3aVZUrV051n5GRkRo1alROlI//zyknD2Y2m+36BwAAAAAAAAAPkv/++0+XL1+WJL333ntWIXsyNzc3IzhPTEzUqlWr0tznpEmTFBgYqCJFimR9wbApx0a0DxgwIN11YmJidPv2bQUEBOjIkSNKSEjQU089pU8//VROTjl6TgAAAAAAAAAAst2+ffuM2y1atEh1vdq1a6tAgQKKiorSqVOnUl1v//79mj9/vpycnDR06FANGzYsS+uFbbkqaLd08eJFDRkyRIcOHdKCBQs0bty4bKoMAAAAAAAAABzDy8tLb7/9tq5fv65SpUqlup5l14+YmBib68TGxmrEiBFKTExUr169VKtWrWypGSnlXEf/TCpfvrx++ukntW3bVsuXL9eLL76oZs2aObosAAAAAAAAAMgyjRo1UqNGjdJd7+jRo4qOjpYklS5d2uY6M2bM0JkzZ1SqVCkNHjxY165dy9Jakbpc3Y+lePHi6tSpk8xmsxYtWuTocgAAAAAAAADAIX755RfjduPGjVMsP3HihH799VdJ0ueff66CBQvmWG3I5UG7JD355JOSpCNHjji4EgAAAAAAAADIeevWrdPatWslSWXKlNFzzz1ntTwhIUGfffaZ4uLi5O3tnWavd2SPXB+037lzR5IUGhrq2EIAAAAAAAAAIIcdPnzYakLTzz77THny5LFaZ/bs2Tp69Kg8PDz02Wef5XSJ0H0QtK9fv16SVKRIEQdXAgAAAAAAAAA5x9/fX2+99ZaioqIkSX369Ekxmv3ChQuaNm2aJOnjjz9W8eLFc7xO5OLJUCMjIzVjxgz9+++/MplMqlOnjqNLAgAAAAAAAIAcsX//fr3zzju6ffu2JKlNmzYaOnSo1Tpms1kjRozQnTt3VL9+ffn4+DiiVCgHg/bevXtnaL2EhARFRETo4sWLRtsYSerWrVt2lQYAAAAAAAAAucaGDRv04YcfGvlo69atNWHCBDk5WTcoWbRokfbs2SNXV1d9+eWXMplMjigXysGgfc+ePZn6QZvNZuN29+7d1bBhw+woCwAAAAAAAAByjT/++ENjx45VYmKiJKlTp076+uuv5ezsbLVeUFCQJkyYIEny9vZWdHS0jh8/brVOYGCg1frJy8uXL6+CBQtm59N46ORo6xjL8Dw9zs7OevLJJ9W9e3e99NJL2VgVAAAAAAAAADje9OnTjX7rUlJP9mHDhtkcwHz27FmFh4dLkpYtW6Zly5alue9FixZp0aJFkqS5c+eqQYMGWVg5cixo37hxY4bWc3JyUt68eVX4/7V352FV1vn/x19nAYGDgLII4QyVmmmZmmnrtExaZps67o1mm+OSWVlpe1nTzFh+22zTGhstLTVrzMwNzZo0E01NcV8BUQRZBJX1/P7wxy1Hds6BGzjPx3V1dd/3uZf3WS6E1/nc709ISKlvaQAAAAAAAACgMZo2bZoRslssFj355JN64IEHTK4KVVVnQXt0dHRdXQoAAAAAAAAAGoyVK1dqypQpks4MRJ40aZL69+9f4TFXXnmldu7cWeE+e/fuVa9evSRJDz/8sMaOHeuZglGKtfJdAAAAAAAAAAC1ISMjQ88995yx/tRTT1UasqP+qdMe7SXl5+crLi5OmzZtUlpamnJychQQEKAWLVqoffv26tatm3x9fc0qDwAAAAAAAABq3cyZM5WWliZJateuna666qpSk5qeKyAgQDExMXVRHqrIlKB91qxZ+uijj4wPUFmCgoI0cuRI3XfffXVYGQAAAADUTHb8GuUe3m2s24PDFdy1l4kVAQBQe+yBzRTa8yGzy6g19sBmdXat+fPnG8vbt29X7969Kz2mW7dumjVrVi1Wheqq06C9oKBAjz76qDExqtPpLHffzMxMTZ48Wb/88ovee+892e2mDb4HAAAAgEoV5mQoPy3J7DIAAKgTFptdPsHhZpfR4B0/flxHjx41uwx4QJ2m16+99ppWrFgh6czMuX/605907bXX6g9/+IMCAgKUk5OjAwcOaM2aNVq7dq2cTqd+/PFHvfHGG5o4cWJdlgoAAAAAAAAAtap58+aVTmjqjlatWtXq+XFWnQXtO3bs0Jw5c2SxWBQZGam3335bl112WZn7Pvjgg9q8ebMee+wxHT58WP/5z3/Uv39/tWrVqq7KBQAAAAAAAACgSqx1daG5c+fK6XTK19dXH3/8cbkhe7GOHTtq+vTpxoSoJXsVAQAAAAAAAABQX9RZ0L5u3TpZLBbdddddVR6Z3qpVK/Xu3VtOp1Pr1q2r5QoBAAAAAAAAAKi+Ogvajxw5Iknq0qVLtY67/PLLJUlJSUwqBAAAAAAAAACof+osaC8oKJAk+fj4VOu44v1zc3M9XhMAAAAAAAAAAO6qs6A9NDRUkqo9y23x/s2bN/d4TQAAAAAAAAAAuKvOgvaOHTvK6XRqwYIFys7OrtIx2dnZWrBggSwWizp16lS7BQIAAAAAAAAAUAN1FrTfddddkqS0tDQ98sgjlYbt2dnZeuSRR5SamipJ6tWrV63XCAAAAAAAAABAddnr6kI33XSTunTpog0bNmjt2rW6/fbbdc899+jqq69WTEyM/P39derUKR08eFBr167V559/rpSUFGM0e/fu3euqVAAAAAAAAAAAqqzOgnZJmjJlioYOHaqEhASlpKTozTff1Jtvvlnu/k6nU+edd57efvvtOqwSAAAAAAAAAICqq7PWMZIUGRmpL774Qt27d5fT6az0vz//+c+aP3++IiIi6rJMAAAAAAAANGJOp9PsEgA0cOf+HKnTEe2SFBoaqqlTp2rnzp1auXKlNm3apGPHjiknJ0cBAQEKDw9Xx44d1aNHD1100UV1XR4AAAAAAAAaKav1zJjTwsJCOZ1OWSwWkysC0BA5nU4VFhZKOvtzpc6D9mJt27ZV27Ztzbo8AAAAAAAAvIyvr69OnTqlwsJC5ebmys/Pz+ySADRAp0+fNoJ2X19fSXXUOubAgQPavXt3pft98803mjRpkrZu3VoHVQEAAAAAAMCbBAUFGcvHjx+nhQyAanM6nUpPTzfWi3+u1GrQnpycrEceeUS33XabZs+eXen+y5cv15w5c9S/f3+NHj1aKSkptVkeAAAAAAAAvIjD4ZDNZpMkZWZmKjk5WadOnSJwB1App9OpU6dOKTk5WZmZmZIkm80mh8MhqRZbx/z2228aM2aM0tPT5XQ6FRcXV2mhcXFxxg+2VatWacuWLZo2bZrat29fW2UCAADUDUudzkEPAACAMlgsFrVs2VKHDh2S0+lUZmamMjMzZbPZZLPZ6NkOoEzFPdmL28VIZ3+eFP/cqJWg/eDBgxo5cqSysrLkdDpltVp13nnnVXhMUVGRXn75ZS1dulTLli1TYWGhUlNTNWLECM2bN09RUVG1USoAAIDHZa5frILMY8a6PThc9qBQ5acmmFgVAAAAJCkgIEB//OMflZiYaIRm5wZoAFARm82mli1bKiAgwNhWK0H7888/bwyf79Spk1555RW1adOm0uJ69uypnj17au/evXryyScVHx+vtLQ0vfzyy/rwww9ro1QAAACPK8g8pvy0JJdt9uBwk6oBAADAuQICAtSmTRvl5OQoKytLeXl5KioqMrssAPWY1WqVr6+vgoKC5HA4St0B4/GgPS4uTr/++qssFouuvfZaffjhh7Lbq3eZVq1a6bPPPtPQoUO1bds2rV69WvHx8bSQAQAAAAAAgEdYLBYFBgYqMDDQ7FIANAIebxb63XffSZL8/f31r3/9q9ohe7GAgABNnjxZVuuZEv/73/96rEYAAAAAAAAAADzF40H75s2bZbFYdOuttyo0NNStc7Vq1UrXX3+9nE6nNm7c6KEKAQAAAAAAAADwHI8H7UlJZ/qRdurUySPnu+qqqyRJCQlMHgYAAAAAAAAAqH88HrTn5ORIkpo1a+aR80VEREiSsrOzPXI+AAAAAAAAAAA8yeNBu5+fnyTp5MmTHjlf8YzPvr6+HjlfZRYuXKi2bduqbdu2SkxMrJNrAgAAAAAAAAAaLo8H7ZGRkZKkAwcOeOR8xecJCQnxyPkqcvz4cb322mu1fh0AAAAAAAAAQOPh8aC9ffv2cjqd+t///ueR861atUoWi0WtW7f2yPkq8uqrryo9Pb3WrwMAAAAAAAAAaDw8HrTfeOONkqRt27bp119/detca9eu1bZt2yRJXbt2dbe0Cq1atUrfffddrV4DAAAAAAAAAND4eDxo7969u8LCwiRJzz77rDIyMmp0nuPHj+u5556TJNlsNvXq1ctTJZaSnZ2tl156SZLnJnEFAAAAAAAAAHgHjwftvr6+GjVqlJxOpxITEzVkyBDt3r27WufYuXOnhg4dqqSkJFksFvXt21fR0dGeLtUwefJkHTlyRBdffLH69OlTa9cBAAAAAAAAADQ+9to46ZAhQ7Ry5Ur9/PPP2r9/v/r06aNbb71VPXv2VNeuXcuc2PT48eNas2aNVqxYoeXLl6uoqEiSdOGFF2rixIm1UaYk6ddff9XcuXNls9n06quvaunSpbV2LQAAAADeJTt+jXIPnx14ZA8OV3DX2rtbFwAAAOaolaDdYrHo7bff1siRIxUXF6fCwkItXrxYixcvlsViUXh4uJo3by5/f39lZmYqPT1dGRkZcjqdkmT8v23btvr4448VEBBQG2Xq9OnTev755+V0OjV06FB16NCBoB0AAACAxxTmZCg/LcnsMgAAAFDLaiVol6TAwEDNmDFD77zzjmbMmKGCggJJZ0L0lJQUpaSkGPsWB+vFmjRpoqFDh+qRRx6Rr69vbZWod955RwcOHFB0dLTGjRvnsfOmpaXp+PHj1T7u4MGDHqsBAAAAAAAAAFA3ai1olyQfHx+NHz9egwYN0meffaYVK1YoISGhVLAuSVarVe3bt9ctt9yivn37GhOq1patW7fq008/lSS9+OKLHh01P3v2bE2dOtVj5wMAAAAAAAAA1F+1GrQXi46O1oQJEzRhwgSlpKRo3759Sk9PV15engICAtSiRQtdeOGFCgwMrItyVFBQoGeffVaFhYW64447dMMNN9TJdQEAAAAAAAAAjU+dBO0lRUREKCIioq4v62L69OnasWOHQkJC9Mwzz5haCwAAAAAAAACgYavzoN1se/fu1fvvvy9JmjBhgkJDQz1+jSFDhqhnz57VPu7gwYMaM2aMx+sBAAAAAAAAANQerwranU6nnnvuOeXl5emqq65S3759a+U6oaGhtRLgAwAAAAAAAADqH68K2ufMmaONGzfKYrFo8ODB2r59e6l90tLSjOW9e/fqxIkT8vHxUevWreuyVAAAAAAAAABAA+FVQfuWLVsknRnZPm7cuEr3HzFihKQzk7muXLmyVmsDAAAAAAAAADRMVrMLAAAAAAAAAACgIfOqEe3//Oc/9c9//rPCfd544w1Nnz5dkhQbG6uWLVvWRWkAAAAAAAAAgAaKEe0AAAAAAAAAALiBoB0AAAAAAAAAADcQtAMAAAAAAAAA4AaCdgAAAAAAAAAA3EDQfo4nnnhCO3fu1M6dO5kIFQAAAAAAAABQKYJ2AAAAAAAAAADcQNAOAAAAAAAAAIAbCNoBAAAAAAAAAHADQTsAAAAAAAAAAG4gaAcAAAAAAAAAwA0E7QAAAAAAAAAAuIGgHQAAAAAAAAAANxC0AwAAAAAAAADgBoJ2AAAAAAAAAADcQNAOAAAAAAAAAIAbCNoBAAAAAAAAAHADQTsAAAAAAAAAAG6wm10AAAAAAHiT7Pg1yj2821i3B4cruGsvEysCAACAuwjaAQAAAKAOFeZkKD8tyewyAAAA4EG0jgEAAAAAAAAAwA0E7QAAAJ5m4VcsAAAAAPAmtI4BAABwQ+b6xSrIPGas24PDZQ8KVX5qgolVAQAAAADqEkE7AACAGwoyj5XqtWwPDjepGgAAAACAGbivGQAAAAAAAAAANxC0AwAAAAAAAADgBoJ2AAAAAAAAAADcQNAOAAAAAAAAAIAbCNoBAAAAAAAAAHADQTsAAAAAAAAAAG4gaAcAAAAAAAAAwA0E7QAAAAAAAAAAuIGgHQAAAAAAAAAANxC0AwAAAAAAAADgBoJ2AAAAAAAAAADcQNAOAAAAAAAAAIAbCNoBAAAAAAAAAHADQTsAAAAAAAAAAG4gaAcAAAAAAAAAwA12swsAAACAF7BYZXOEyCc02thkDw6XLIz7AAAAANDwEbQDAACg1tmDQhWb6FByYgtjW2RUuLrH5JpYFQAAAAB4BkE7AAAA6kRKapYSklKNdYuPr6wXhzHKHQAAAECDR9AOAAAAU1htPopL8Vf8IUa5AwAAAGjYCNoBAABgmvTMHNdR7nZfKcbPxIoAAAAAoPq4LxcAAAAAAAAAADcwoh0AAACeZbHK5ghx6b1uc4TIai2qwrGS1T/wnL7tEbIFhtDLHQAAAEC9RdAOAAAAj7IHhSo20aHkxLO916OKHApr7tS+So4tq297VMsoNSsKUPyhTGMbvdwBAAAA1CcE7QAAAPC4lNQsl97rNj+HIsKaVunYc/u22/wckpz0cgcAAABQbxG0AwAAAIDJsuPXKPfwbmPdHhyu4K69TKwIAAAA1UHQDgAAAAAmK8zJUH5aktllAAAAoIaYQQoAAAAAAAAAADcQtAMAAAAAAAAA4AZaxwAAAKDmLFbZHCHyCY02NtkcIbJai2r5upLVP9DluvbgcMnCOBIAAAAAdY+gHQAAADVmDwpVbKJDyYktjG1RRQ6FNXdqXy1e12rzUVyKv+IPnb1uZFS4usfk1uJVAQAAAKBsBO0AAABwS0pqlhKSUo11m59DEWFNa/266Zk5Lte12H2lGL9avy4AAAAAnIt7awEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADfRoBwAAQNVYrLI5QuQTGm1ssjlCZLUWmVhUCRbJ6h/oUp89OFyyMLYEAAAAQO0iaAcAAECV2INCFZvoUHJiC2NbVJFDYc2d2mdiXcWsNh/Fpfgr/tDZ+iKjwtU9JtfEqgAAAAB4A4J2AAAAVFlKapYSklKNdZufQxFhTU2syFV6Zo5LfRa7rxTjZ2JFAAAAALwB99ECAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBvo0Q4AAIDSLFbZHCHyCY02NtkcIbJai0wsqgYsktU/0OV52IPDJQvjTQAAAAB4DkE7AAAASrEHhSo20aHkxBbGtqgih8KaO7XPxLqqy2rzUVyKv+IPnX0ekVHh6h6Ta2JVAAAAABobgnYAAAB3NOKR0SmpWUpISjXWbX4ORYQ1NbGimknPzHF5Hha7rxTjZ2JFAAAAABobgnYAAIAqyly/WAWZx4x1e3C47EGhyk9NMLEqAAAAAIDZCNoBAACqqCDzmPLTkly22YPDTaoGAAAAAFBfNN57nQEAAAAAAAAAqAME7QAAAAAAAAAAuIGgHQAAAAAAAAAAN9CjHQAAwNtZrKV6zdscIbJai0wqCAAAAAAaFoJ2AAAAL2cLCNKKg010JPnszY5RRQ6FNXdqn4l1AQAAAEBDQdAOAAAAHT2WqYSkVGPd5udQRFhTEysCAAAAgIaDHu0AAAAAAAAAALiBoB0AAAAAAAAAADfQOgYAAADexSJZ/QPlExptbLIHh0sWxqAAAAAAqBmCdgAAAHgVq81HcSn+ij/UwtgWGRWu7jG5JlYFlJYdv0a5h3cb6/bgcAV37WViRQAAACgPQTsAAAC8Tnpmjsvkrxa7rxTjZ2JFQGmFORnKT0syuwwAAABUAUE7AAAAAEiSxSqbI4S2QgAAAKg2gnYAAAAA3qeMUL1JVCutSgpUUgJthQAAAFA9BO0AAAAAvI49KFSxiQ4lJ54N1aOKHJKKaCsEAACAaiNoBwAAANC4lTF63eYIUereEy6hus3PoYiwpuccK1n9A2knAwAAgAoRtAMAAABo1MobvR7W3Kl9lRxrtfkoLsVf8YdoJwMAAIDyEbQDAAAAaPRSUrMqH71ejvTMHNrJAAAAoEIE7QAAAADtQRqPctrEWK1FJhYFAACAxo6gHQAAAF6P9iCNhzttYgAAAICaImgHAADwJhbrmZHaJVj9AyXLaZMKqj9oD9J4uNMmBgAAAKgJgnYAAAAvYgsI0oqDTXQk+WxLlPZN/WW1+ZhYFVBDtIkBAABAPUHQDgAA4GWOHst0Ge0b1TLSxGqAmjOtTQw9/QEAAHAOgnYAAAAADZYZbWLo6Q8AAIBzEbQDAAAAQDXR0x8AAAAlEbQDAAAAqP/oxw4AAIB6jKAdAAAAQL1nWj92AAAAoAoI2gEAAICyMOFlvWNGP3YAAACgKgjaAQAAgDIw4SUAAACAqiJoBwAAAMrBhJcAAAAAqoL7XgEAAAAAAAAAcAMj2gEAAADULxarbI4Ql/74NkeIrNYiE4sCAAAAykfQDgAA0FhZrGcm7yzB6h8oWU6bVBBQNfagUMUmOpSceLY/flSRQ2HNndpnYl0VYvJcAAAAr0bQDgAA0EjZAoK04mATHUk+G/S1b+ovq83HxKoaOMLUOpOSmuXSH9/m51BEWFMTK6oYk+cCAAB4N4J2AACARuzosUyXsDKqZaSJ1TR8hKmoCJPnAgAAeC+CdgAAAKAaCFMBAAAAnIugHQAAAIB5mPgUAAAAjQBBOwAAAADTNMiJTwEAAIBzELQDAAAAMFVDm/gUAAAAOJfV7AIAAAAAAAAAAGjIGNEOAAAAAA1Edvwa5R7ebazbg8MV3LWXiRUBAABAImgHAAAAUFeY+NRthTkZyk9LMrsMAAAAnIOgHQAAoKosdN1DGSyS1T/QJTy2B4fzeSkDE58CAACgsSJoBwAAKEPm+sUqyDxmrNuDw2UPClV+aoKJVaE+stp8FJfir/hDZ8PjyKhwdY/JNbGq+surJj7lSxgAAACvQdAOAABQhoLMY6XaM9iDw02qBvVdemaOS3hssftKMX4mVoT6gC9hAAAAvAdBOwAAAADUEr6EAQAA8A4E7QAAAI1BGZNMWv0DJctpE4sCAAAAAO9A0A4AANAIlDXJZPum/rLafEysCl6tjC9/bI4QWa1FJhYFAAAA1A6CdgAAgEbi3Ekmo1pGmlgNvF1ZX/5EFTkU1typfSbWBQAAANQGgnYAAAAAteLcL39sfg5FhDU1sSIAAACgdhC0AwAAAHAPbWIAAADg5QjaAQAAAE+znJmMtmTwbA8OlyxWE4uqPbSJAQAAgLcjaAcAAAA8zGrzUVyKv+IPnQ2eI6PC1T0m18SqahdtYgAAAODNCNoBAACAWpCemeMSPFvsvlKMn4kVAQAAAKgtBO0AAAAAUFe8rK0QAACAtyBoBwAAAIA64o1thQAAALwBQTsAAABQFxrLSGaLVTZHiMvzsDlCZLUWmVhUw0JbIQAAgMaHoB0AAACoA41lJLM9KFSxiQ4lJ559HlFFDoU1d2qfiXUBAAAAZiJoBwAAAOpIYxnJnJKa5fI8bH4ORYQ1NbEiAAAAwFwN7D5VAAAAAAAAAADqF0a0AwAAACgb/dgBAACAKiFoBwAAAMxSzydIpR87AAAAUDUE7QAAAIBJGsIEqfRjBwAAACpH0A4AAACYqLFMkAoAAAB4M4J2AAAAAPRjBwAAANxA0A4AAADUJ2X0bbc5QlSQlVarl6UfOwAAAFBzBO0AAAANjcV6ZsLMEmyOEEmMPG4MyurbHlXk0I3NQpWfmlCr16Yfe8OUHb9GuYd3G+v24HAFd+1lYkUAAADeh6AdAACggbEFBGnFwSY6kmw1trVzNJds6SZWBU86t2+7zc8hNfPgBcr5soY2MSYp4y4Ge3C4ZLFWcNBZhTkZyk9Lqq3qAAAAUAUE7QAAAA3Q0WOZLkFsZGSYidWgtlmtllL906scxJYRqvuEnqdlO/N15PDZ42kTY56y7mKIjApX95hcE6sCAABAdXh90J6QkKBZs2Zp7dq1SkpKUn5+vkJDQ9W5c2cNHDhQV111ldklAgAAwMuFNQ9UbKLFpX96mUFsFUP19k39dex4Jm1i6pFz72Kw2H2lGD8TKwIAAEB1eHXQPm/ePE2aNEl5eXku25OTk5WcnKzFixerX79+evnll2W3e/VLBQAAAJOd2z/d4uMr68VhLqPcqxqqR7WMrJuiAQAAAC/htenxypUr9fzzz8vpdKpp06a699571a1bNzVp0kTbt2/XjBkzdPDgQc2fP1+BgYF6+umnzS4ZAAAAMJTVboRQHQAAADCHVwbthYWF+vvf/y6n06mgoCB98cUXatWqlfF4p06ddOedd2rYsGHatm2bZs6cqf79+6t169YmVg0AAAC4OrfdCKE6AAAAYI6qTWPfyMTFxSkxMVGSNGrUKJeQvVhgYKBeeOEFSVJRUZEWLVpUpzUCAAAAAAAAABoGrwzaN2zYYCzfdNNN5e7XqVMnBQQESJJ2795d63UBAAAAAAAAABoer2wd07lzZ40YMUJHjx5VVFRUufs5nU45nU5JUm5ubl2VBwAAAAAAAABoQLwyaL/66qt19dVXV7rf1q1bderUKUnSeeedV9tlAQAAAAAAAAAaIK8M2qvq448/NpavueaaKh+Xlpam48ePV/t6Bw8erPYxAACglli8ssMeAAAAAKAGCNrLsXTpUi1ZskSSFB0drZtvvrnKx86ePVtTp06trdIAAICHZa5frILMY8a6PThc9qBQ5acmmFjV/2exyuYIkU9otLHJ6h8oWU6bWBQAAAAAoCSC9jJs2bJFEydONNafffZZ+fj4mFgRAACoTQWZx5SfluSyzR4cblI1ruxBoYpNdCg5sYWxrX1Tf1lt/G4CAAAAAPUFQfs54uPj9dBDD+nkyZOSpOHDh1drNDsAAICnpaRmKSEp1ViPahlpYjUAAAAAgHMRtJewceNG/e1vf1NWVpYkqWfPnpowYUK1zzNkyBD17Nmz2scdPHhQY8aMqfZxAAAAAAAAAADzELT/fytWrND48eN1+vSZfqe33nqr3njjDVmt1Z8ILTQ0VKGhoZ4uEQAAAIC3sJyZj6Hk/Az24HAmagYAAKinCNolff7553r11VdVVFQkSerdu7dee+012Ww2kysDAAAA4I2sNh/Fpfgr/tDZ+Rkio8LVPSbXxKoAAABQHq8P2qdOnap3333XWB8+fLgmTpwoi8ViYlUAAAAAvF16Zo7L/AwWu68U42diRQAAACiPVwft06ZNM0J2i8WiJ598Ug888IDJVQEAAAAAAAAAGhKvDdpXrlypKVOmSJKsVqsmTZqk/v37m1wVAAAAAAAAAKCh8cqZdDIyMvTcc88Z60899RQhOwAAAAAAAACgRrxyRPvMmTOVlpYmSWrXrp2uuuoqbd++vcJjAgICFBMTUxflAQAAAAAAAAAaEK8M2ufPn28sb9++Xb179670mG7dumnWrFm1WBUAAAAAeIDFK29cBgAAMJXXBe3Hjx/X0aNHzS4DAAAAAGqFPShUmesXqyDz2NltweEK7trLxKoAAAAaN68L2ps3b66dO3eaXQYAAAAA1JqCzGPKT0syuwwAAACvwT2FAAAAAAAAAAC4gaAdAAAAAAAAAAA3eF3rGAAAgHrLYpXNESKf0Ghjk80RIqu1yMSiAAAAAACVIWgHAACoJ+xBoYpNdCg5sYWxLarIobDmTu0zsS4AAAAAQMUI2gEAAOqRlNQsJSSlGus2P4ciwpqaWBEAAAAAoDIE7QAAAADQEFgkq3+gS3spe3C4ZGHqLQAAALMRtAMAAABAA2C1+SguxV/xh862l4qMClf3mFwTqwIAAIBE0A4AAAAADUZ6Zo5LeymL3VeK8TOxIgAAAEgS9xgCAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBsI2gEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADQTtAAAAAAAAAAC4gaAdAAAAAAAAAAA3ELQDAAAAAAAAAOAGu9kFAAAAeCWLVTZHiHxCo41NNkeIrNYiE4sCAAAAANQEQTsAAIAJ7EGhik10KDmxhbEtqsihsOZO7TOxLgAAAABA9RG0AwAAWMzpppeSmqWEpFRj3ebnUERYU1NqAQAAAADUHEE7AADwKpnrF6sg85ixbg8Olz0oVPmpCSZWBQA1ZJGs/oGl2lAVZKWZWBQAAID3IWgHAABepSDzmPLTkly22YPDTaoGANxjtfkoLsVf8Ydc21Dd2Kz0F4jZ8WuUe3i3sW4PDldw1151VisAAEBjRtAOAAAAAA1YemZOqTZUalZ6v8KcjFJfNAIAAMAzzGlICgAAAAAAAABAI0HQDgAAAAAAAACAGwjaAQAAAAAAAABwA0E7AAAAAAAAAABuIGgHAAAAAAAAAMANBO0AAAAAAAAAALiBoB0AAAAAAAAAADfYzS4AAACg0bNYZXOEyCc02thkc4TIai0ysSgAAAAAgKcQtAMAANQye1CoYhMdSk5sYWyLKnIorLlT+0ysCwAAAADgGQTtAAAAdSAlNUsJSanGus3PoYiwpiZWBKCxslotpe6isQeHSxY6hwIAANQWgnYAAAAAaETCmgcqNtHichdNZFS4usfkmlgVAABA40bQDgAAAACNzLl30VjsvlKMn4kVAQAANG7cOwgAAAAAAAAAgBsI2gEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADQTtAAAAAAAAAAC4gaAdAAAAAAAAAAA32M0uAAAAAABgjuz4Nco9vNtYtweHK7hrLxMrAgAAaJgI2gEAAADASxXmZCg/LcnsMgAAABo8gnYAAABPslhlc4TIJzTa2GRzhMhqLTKxKAAAAABAbSJoBwAA3sVSu1PU2INCFZvoUHJiC2NbVJFDYc2d2lerVwYAAAAAmIWgHQAANFqZ6xerIPOYsW4PDpc9KFT5qQm1et2U1CwlJKUa6zY/hyLCmtbqNQGgQhbJ6h/ocreNPTi81r98BAAA8BYE7QAAoNEqyDxWqvewPTjcpGoAwDxWm4/iUvwVf+js3TaRUeHqHpNrYlUAAACNB0E7AAAAAHiB9Mwcl7ttLHZfKcbPxIoAAAAaD+4TBAAAAAAAAADADQTtAAAAAAAAAAC4gaAdAAAAAAAAAAA3ELQDAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBsI2gEAAAAAAAAAcIPd7AIAAAAaLItVNkeIfEKjjU02R4is1iITiwIAAAAA1DWCdgAAgBqyB4UqNtGh5MQWxraoIofCmju1z8S6AAAAAAB1i6AdAADADSmpWUpISjXWbX4ORYQ1NbEiAHBPdvwa5R7ebazbg8MV3LWXiRUBAADUfwTtAAAAAABDYU6G8tOSzC4DAACgQSFoBwAAAABvZJGs/oEu80zYg8Mli9XEogAAABomgnYAAAAA8EJWm4/iUvwVf+jsPBORUeHqHpNrYlUAAAANE0E7AAAAAHip9Mwcl3kmLHZfKcbPxIoAAAAaJu4JBAAAAAAAAADADQTtAACg8aLPMAAAAACgDtA6BgAANAqZ6xerIPOYsW4PDpc9KFT5qQkmVgUAAAAA8AYE7QAAoFEoyDym/LQkl2324HCTqgEAAAAAeBPupwYAAAAAAAAAwA2MaAcAAKgKi1U2R4h8QqONTTZHiKzWIhOLAgAAAADUBwTtAAAAVWAPClVsokPJiS2MbVFFDoU1d2qfiXUBAAAAAMxH0A4AAFBFKalZSkhKNdZtfg5FhDU1sSIAAAAAQH1Aj3YAAAAAAAAAANzAiHYAAAAAwBkWyeof6DIfhT04XNnb1yo3aZfLtuCuvcyoEAAAoF4iaAcAAAAASJKsNh/Fpfgr/tDZ+Sgio8LV3ZGr/LQkEysDAACo3wjaAQAAAACG9Mwcl/koLHZfKcbPxIoAAADqP3q0AwAAAAAAAADgBoJ2AAAAAAAAAADcQNAOAAAAAAAAAIAbCNoBAAAAAAAAAHADQTsAAAAAAAAAAG4gaAcAAI2DhV9rAAAAAADmsJtdAAAAQHVlrl+sgsxjxro9OFz2oFDlpyZ45gIWq2yOEPmERhubbI4QWa1Fnjk/AAAAAKBRIWgHAAANTkHmMeWnJblssweHe+z89qBQxSY6lJzYwtgWVeRQWHOn9nnsKgDQsGXHr1Hu4d3Guj04XMFde5lYEQAAgHkI2gEAAMqQkpqlhKRUY93m51BEWFMTKwKA+qUwJ6PUl54AAADeiqAdAAAAAFA+i2T1D3Rpp2UPDmduDAAAgBII2gEAAAAA5bLafBSX4q/4Q2fbaUVGhat7TK6JVQEAANQvBO0AAAAAgAqlZ+a4tNOy2H2lGD8TKwIAAKhfuNcPAAAAAAAAAAA3ELQDAAAAAAAAAOAGgnYAAAAAAAAAANxAj3YAAAAAgEdkx69R7uHdxro9OFzBXXuZWBEAAEDdIGgHAAANj8WDN+VZrLI5QuQTGm1ssjlCZLUWee4aAOAlCnMylJ+WZHYZAAAAdY6gHQAA1GuZ6xerIPOYsW4PDpc9KFT5qQkeOb89KFSxiQ4lJ7YwtkUVORTW3Kl9HrkCAAAAAKCxI2gHAAD1WkHmsVKjI+3B4R69RkpqlhKSUo11m59DEWFNPXoNAAAAAEDjRdAOAAAAAKgei2T1D3Rpu2UPDvdsay8AAIAGhKAdAAAAAFAtVpuP4lL8FX/obNutyKhwdY/JNbEqAAAA8xC0AwAAAACqLT0zx6XtlsXuK8X4mVgRAACAeQjaAQAAAAC1Jjt+jXIP7zbW7cHhCu7ay8SKAAAAPI+gHQAAAABQawpzMkpNag0AANDYMFMNAAAAAAAAAABuIGgHAAAAAAAAAMANBO0AAAAAAAAAALiBHu0AAKB+s3hwXIDFKpsjRD6h0cYmmyNEVmuR564BAAAAAPA6BO0AAKDeyFy/WAWZx4x1e3C47EGhyk9N8Mj57UGhik10KDmxhbEtqsihsOZO7fPIFQDAi1kkq3+gy5eZ9uDwMr8wzY5fo9zDu132C+7aq07KBAAAqA0E7QAAoN4oyDym/LQkl2324HCPXiMlNUsJSanGus3PoYiwph69BgB4I6vNR3Ep/oo/dPbLzMiocHWPyS21b2FORqmf9wAAAA0ZQTsAAAAAwCPSM3Ncvsy02H2lGD8TKwIAAKgbTIYKAAAAAAAAAIAbCNoBAAAAAAAAAHADQTsAAAAAAAAAAG6gRzsAAKg/LB4cA2CxyuYIkU9otLHJ5giR1VrkuWsAAAAAACCCdgAAYJLM9YtVkHnMWLcHh8seFKr81ASPnN8eFKrYRIeSE1sY26KKHApr7tQ+j1wBAOBJ2fFrlHt4t7FuDw5XcNdeJlYEAABQdQTtAADAFAWZx5SfluSyzR4c7tFrpKRmKSEp1Vi3+TkUEdbUo9cAAHhGYU5GqX8XAAAAGgqCdgAAAABA7bBIVv9AlzZe9uBwz7YKAwAAqAcI2gEAAAAAtcJq81Fcir/iD51t4xUZFa7uMbkmVgUAAOB5BO0AAMAcTHwKAF4hPTPHpY2Xxe4rxfiZWBEAAIDnEbQDAIBax8SnAAAAAIDGjKAdAADUOiY+BQDURHb8GuUe3m2s24PDFdy1l4kVAQAAlI2gHQAAAABQLxXmZJT6ohYAAKA+ImgHAAC1j37sAAAAAIBGjKAdAAB4FP3YAQAVskhW/0CXL0ztweGe/VIWAACgjhG0AwAAj6IfOwCgIlabj+JS/BV/6OwXppFR4eoek2tiVQAAAO4haAcAAJ5FmxgAQCXSM3NcvjC12H2lGD8TKwIAAHAPQTsAAKgx2sQAAOpadvwa5R7ebazbg8MV3LWXiRUBAAAQtAMAADcUZKV5rk1MOaPXU/eeoE0MAMBQmJNR6t8eAAAAsxG0AwCAKmH0OgAAAAAAZSNoBwAAVcIkpwCAWmORrP6BLnc12YPDPTvvBwAAQC0iaAcAAFXDJKcAgFpitfkoLsVf8YfO3tUUGRWu7jG5VTqevu0AAMBsBO0AAKAUj7aJKSNUbxLVSquSApWUQJsYAMAZ6Zk5Lnc1Wey+UoxflY6lbzsAADAbQTsAACjFk21iyuu9LhXRJgYAAAAA0CgQtAMA4OXOHb3e5Lw2NT9ZOS1hUveeIFQHAFQPfdsBAEADQtAOAIAXKStUP3f0ujsTnJY3ep2WMACA6qJvOwAAaEgI2gEA8CKeDNUZvQ4AqG30bQcAAA0FQTsAAI1UZtwSFWQcNdY93RKGCU0BAA0No9wBAEBt8eqg/fTp05o5c6aWLFmi/fv3S5JatmypW265RcOGDVNwcLDJFQIAUDXntoQpHqleo9HrFmupfcsL1ZnQFABQp9zs284odwAAUFu8Nmg/evSo7r//fu3Zs8dl+65du7Rr1y599dVX+vDDD3XxxRebVCEAAFV3bksYqYrBehmhuk/oeVq2M19HDp8NLQjVAQD1gbt928vCKHcAAOAJXhm0FxQUaPTo0dqzZ48sFosGDBig2267TTabTStWrNBnn32m5ORkjR49Wl9//TUj2wEA9UqNW8JUMVRv39Rfx45nEqoDAOold/q2l4VR7gAAwBO8MmifO3eutm7dKkmaOHGihg8fbjzWrVs3de7cWY899piSkpL08ccfa/z48SZVCgDwdueG6lVuCVNGT/WqhupRLSM9+RQAAKhdbraTKQuj3AEAQHV5ZdA+a9YsSdL555+vYcOGlXr8tttu07fffqvY2FjNnj1bY8eOla+vb12XCQBoxMoK0G2OkFJ/1EuuobosVjWJauUSJtgCm0myuJy/SVQrxSY6lJx49tZ6QnUAQGNUG+1kyhrlXlb4Xta/3QTyAAB4J68L2vfu3at9+/ZJkm6//XZZrWWPcujTp49iY2OVnZ2ttWvX6oYbbqjLMgEAjUhVRqUXj0B3ZZEt0HVUelmTkrZvGqb0zAAlJ54N29s5mislPZ1QHQDgFUq1k/HxlfXiMI+Oci+vxUxVAnnCdwAAGj+vC9p/++03Y7lr167l7telSxdjed26dQTtAODlqjoCvdS2kBayBQS5HFvVAD2qZZSaFQUo/lDm2W1lTEoa1TJSKaknXLZFRoa5+5QBAGiwyhzlfl64bmnrU/rLbzfC97LUeDR8SAsFX9HTo7UAAIC643VB+969e43lmJiYcvdr3ry5HA6HcnJyXI4BADQupQL0kBbyCT1P+amJxjZbYHPZHMGlwnJZXNu1lLXNFhBU4wDd5ueQ5GRSUgAAauDcUe42P4fiUiI92mKmqqoyGt4eHK7M9YtVkHnMZRuBPAAADYPXBe0pKSmSJKvVqhYtWlS4b0REhPbv328cU1VpaWk6fvx4tWvbs2ePy/rBgwerfQ4AaExO7t+iwpwMY90WECyrb4DyM5I9tk2S8o6e/YPWtyBAv8Yn6Gjy2Z/94ZFFCgr00949RSW2FSooMKfybUnHJaVp67azfyCnZ2WoeTNHmdsyM85eNzXFrqL8yrcdSU7Q8fScc7YFKv1kQRX2q9q2qtbCtrrZxnvufduq+h7xnrNN4v2teFuGy7+/aRkRauXXTKdzz87JZTtRKFvhSeXVaFuBlH1UuSnpxjZ7rq9sJwoq3dbEN1mFJ9JVkFlyxH0L2Zo2U27ijrP7tSyQEj5R4Ymzx/pGxKjwVFal22xNm8nmH6S8lIOVbmt66fUCquvcHCMvL8+kSgCg7lmcTqfT7CLq0kMPPaQff/xRAQEBLm1kytK3b19t27ZNLVu2VGxsbJWv8e6772rq1KnulgoAAAAAANBgvffee+revbvZZQBAnfBsM7oGoPjbVF9f30r2lJo0aeJyDAAAAAAAAAAA5/K6oN1qPfOULaX66pZWPNi/+BgAAAAAAAAAAM7ldT3aAwICJEm5uZVPeFOd0e8lDRkyRD17Vn9ymuzsbP3+++9q2rSpmjZtqqioqGpfG55z8OBBjRkzxlh/7733KpxAFyjGZwc1xWcHNcVnBzXFZwc1xWcHNcHnpvHLy8tTcvLZOZG6detmYjUAULe8Lmh3OBySzgTtRUVFFY5WP3nypCQpKCioWtcIDQ1VaGhojerr3LlzjY5D7YuJiVGbNm3MLgMNEJ8d1BSfHdQUnx3UFJ8d1BSfHdQEn5vG6ZJLLjG7BAAwhdf1RDnvvPMkSYWFhUpNTa1w35SUMzPUR0RE1HpdAAAAAAAAAICGyeuC9latWhnLhw4dKne/48ePKycnR5LUunXrWq8LAAAAAAAAANAweV3Q3rFjR2N548aN5e63YcMGY5l2LgAAAAAAAACA8nhd0P7HP/5Rbdu2lSQtXLhQTqezzP2+/vprSWd6ul999dV1Vh8AAAAAAAAAoGHxuqBdkoYMGSJJ2r17tz766KNSjy9ZskQrV66UJPXv31/+/v51Wh8AAAAAAAAAoOGwm12AGQYMGKC5c+dq27ZtevPNN7V371716dNHPj4+io2N1cyZM+V0OhUZGalRo0aZXS4AAAAAAAAAoB7zyqDdarXqww8/1H333ac9e/Zo4cKFWrhwocs+4eHhmjZtmkJCQswpEgAAAAAAAADQIHhl0C5JERERWrBggWbNmqXFixfrwIEDys/PV8uWLXXzzTfr/vvvV/Pmzc0uEwAAAAAAAABQz3lt0C5JTZo00YMPPqgHH3zQ7FIAAAAAAAAAAA2UV06GCgAAAAAAAACApxC0AwAAAAAAAADgBoJ2AAAAAAAAAADc4NU92oGKNG/eXA8//LDLOlAVfHZQU3x2UFN8dlBTfHZQU3x2UBN8bgAAjZnF6XQ6zS4CAAAAAAAAAICGitYxAAAAAAAAAAC4gaAdAAAAAAAAAAA3ELQDAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBsI2gEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADXazCwAaom3btmn27Nn65ZdfdOzYMfn4+KhNmza68847NWDAAPn4+JhdIhqIuLg4/fWvf5XT6dTMmTN15ZVXml0S6qFjx47p888/108//aRDhw7p1KlTCgkJUYcOHdS3b191795dFovF7DJhgtOnT2vmzJlasmSJ9u/fL0lq2bKlbrnlFg0bNkzBwcEmV4j6KiEhQbNmzdLatWuVlJSk/Px8hYaGqnPnzho4cKCuuuoqs0tEA7Jw4UI9+eSTkqTY2Fi1bNnS5IpQH/3yyy+aN2+eNmzYoNTUVAUEBKhdu3bq16+f7rjjDn6XAQA0eBan0+k0uwigIXnnnXf0wQcfqKioqMzHO3furOnTp6tp06Z1XBkamtzcXN19991GOEbQjrL88MMPevLJJ5WVlVXuPjfddJOmTJkih8NRh5XBbEePHtX999+vPXv2lPl4VFSUPvzwQ1188cV1XBnqu3nz5mnSpEnKy8srd59+/frp5Zdflt3OuBxU7Pjx4+rVq5fS09MlEbSjtMLCQr344ouaN29eufvcfPPNevvttxmwBABo0AjagWr48MMP9eabb0qSoqOj9eCDD+riiy9WWlqa/vOf/2j9+vWSzvyi+P7775tZKhqAKVOmaNq0acY6QTvOtXXrVg0ePFh5eXny9fXVPffco+uvv16BgYHat2+f/vOf/yg+Pl6S1KNHD02dOtXkilFXCgoKNHDgQG3dulUWi0UDBgzQbbfdJpvNphUrVuizzz5TYWGhoqOj9fXXXzOyHYaVK1dq9OjRcjqdatq0qe69915169ZNTZo00fbt2zVjxgwdPHhQkjR8+HA9/fTTJleM+u7xxx/Xd999Z6wTtONczz//vObOnStJuuiii3T//ffrggsuUGJioj766CPt2rVLkjRs2DA9++yzZpYKAIBbCNqBKtq/f7/uvPNO5efnq1WrVvrss8/UvHlz4/GCggKNGDFCP//8syRp7ty56tixo1nlop6Lj49X//79VVBQYGwjaMe5/vrXv2r9+vXy8fHRp59+qiuuuMLl8fz8fD388MP64YcfJEnTp0/X9ddfb0KlqGuzZ8/Wyy+/LEl6+umnNXz4cJfHv//+ez322GNyOp0aMWKExo8fb0KVqG8KCwt1yy23KDExUUFBQfriiy/UqlUrl32ys7M1bNgwbdu2TVarVd9++61at25tUsWo71atWqWRI0e6bCNoR0nr1q3TsGHDJEldu3bVxx9/LD8/P+PxkydPauDAgdq1a5dsNptWrVqlFi1amFUuAABuYTJUoIqmTZum/Px8Wa1WvfXWWy4huyTZ7XY98cQTxvqyZcvqukQ0EAUFBXr22WdVUFCgZs2amV0O6qnExETjLpmBAweWCtklycfHR5MmTZLVeuaf82+//bZOa4R5Zs2aJUk6//zzjQCjpNtuu01//vOfJZ0J5StqEQLvERcXp8TEREnSqFGjSoXskhQYGKgXXnhBklRUVKRFixbVaY1oOLKzs/XSSy9JEr/PoFzFd9sFBATo7bffdgnZi7c/8sgjks58GRgbG1vnNQIA4CkE7UAV5OXlafny5ZKkXr166aKLLipzv/bt22vgwIEaOnSo2rdvX5clogH597//rfj4eEVEROj+++83uxzUUxs2bDCWb7rppnL3a9GihS688EJJ0u7du2u9Lphv79692rdvnyTp9ttvN75oOVefPn0knQnD1q5dW2f1of6q6s+VTp06KSAgQBI/V1C+yZMn68iRI7r44ouNnzdASSkpKYqLi5N0pi1MaGhomftdd911uvvuu3XvvffqD3/4Q12WCACARzG7EVAFmzdv1okTJySdGSVYkUmTJtVFSWig9u/fb4zseeGFF5SammpyRaivLrzwQo0aNUpHjx41gvTyFHeBy83NrYvSYLLffvvNWO7atWu5+3Xp0sVYXrdunW644YZarQv1X+fOnTVixAgdPXpUUVFR5e7ndDr5uYIK/frrr5o7d65sNpteffVVLV261OySUA+tWbNGRUVFkqSePXuWu5+/v78mT55cV2UBAFBrCNqBKiieoEeSLrnkEmM5Pz9fR44ckSRFRkbKx8enzmtDw+F0OvX8888rNzdX3bt3V48ePTRnzhyzy0I91aFDB3Xo0KHS/VJTU7V//35J0nnnnVfbZaEe2Lt3r7EcExNT7n7NmzeXw+FQTk6OyzHwXldffbWuvvrqSvfbunWrTp06JYmfKyjt9OnTev755+V0OjV06FB16NCBoB1l2rlzp6Qzre7atGljbM/NzdWRI0dkt9sVGRkpm81mVokAAHgUQTtQBcUBhc1mU4sWLXT48GG9+eabWr58ufGHaGBgoG6//XaNGzeu3Nsi4d1mz56t9evXu/S/Bdz173//2xgtds0115hcDepCSkqKJMlqtVY6YVxERIT2799vHANUxccff2ws83MF53rnnXd04MABRUdHa9y4cWaXg3qsuM1ZRESE7Ha7du7cqXfeeUerV69Wfn6+pDP9/fv166eRI0cqMDDQzHIBAHAbPdqBKsjIyJAkORwObdy4UXfeeacWLlxohOzSmR64X375pfr27cvIQZSSnJysKVOmSJKeeOKJSsMxoCo2bdqkmTNnSjrz86lv374mV4S6kJWVJUny8/OrdBRgcZ/t4mOAyixdulRLliyRJEVHR+vmm282uSLUJ1u3btWnn34qSXrxxReNnzFAWYr/hgoKCtL333+vfv36acWKFUbILknp6emaPn26Bg8ezJfCAIAGjxHt8AoHDhyo0URwl156qTp06KCTJ09KOtMqZsyYMTp9+rRGjRqlv/zlL2rRooUOHTqkTz75RAsWLNCRI0c0evRoLViwQA6Hw9NPBXXM3c9OsRdffFE5OTm6/PLLNWjQIE+WiHrKU5+d8iQkJOjhhx82/lh97LHH1KxZs2pfDw1PXl6eJMnX17fSfZs0aeJyDFCRLVu2aOLEicb6s88+S1s8GAoKCvTss8+qsLBQd9xxB/M+oFLFf0MdPXpUEyZMkN1u1+OPP65evXqpWbNm2rVrl6ZOnapVq1Zp165devTRR/XZZ5+VO8k3AAD1HUE7vMLmzZv10ksvVfu4hx9+WB06dDBGrp86dUqnTp3SW2+95TIpauvWrfWPf/xDISEh+ve//60DBw5o9uzZeuihhzz1FGASdz87krRw4UKtXr1aPj4+euWVV2SxWDxcJeojT3x2ypOUlKThw4fr2LFjkqQePXpo6NChNSkTDVBxAFGVnyXFE1oSWqAy8fHxeuihh4xgbPjw4Yxmh4vp06drx44dCgkJ0TPPPGN2OWgAiv+GOn78uHx8fDRjxgyXibovvfRSffDBB3r00Ue1ZMkSbdiwQUuWLFGvXr3MKhkAALfwVxdQBcUjAiXp+uuvdwnZSxo3bpxCQkIkSYsWLaqL0lDPHT9+XK+99pok6W9/+5tat25tckVo6Pbu3at77rlHiYmJkqQuXbro9ddfN7kq1KXiVg25ubmV7lud0e/wXhs3btS9995rtHno2bOnJkyYYG5RqFf27t2r999/X5I0YcIE5iNClZT8G6pfv34uIXsxi8WiZ555Rnb7mTGA/A0FAGjIGNEOr3D33Xfr7rvvrvHxJVvAVDS6y8/PT1dddZWWLFminTt3Ki8vj3CjgXP3s/Pqq68qPT1drVq10t/+9jcPVob6zt3PTlk2btyoUaNGGWFYly5dNG3aNPn7+3v0Oqjfiv9Nys3NVVFRUYWj1YtHJwcFBdVJbWh4VqxYofHjx+v06dOSpFtvvVVvvPEGd0HA4HQ69dxzzykvL09XXXUV84Ggyqr6N1SLFi3Url07/f777/r999/rojQAAGoFQTtQBeHh4cZyREREhftGRkZKOvNHSUZGRqX7o/FavXq1vvvuO0nSkCFDypwk98iRI8byoUOHjDCsXbt2dVMkGoxly5bpiSeeMEYxX3vttZo6dSoT0Xmh8847T5JUWFio1NTUCv+dKZ5Yjn+LUJbPP/9cr776qoqKiiRJvXv31muvvVbpJLvwLnPmzNHGjRtlsVg0ePBgbd++vdQ+aWlpxvLevXt14sQJ+fj4cCefl6vu31C///670tPTa7ssAABqDUE7UAVt2rQxlrOysirct3hiQokRhN5uy5YtxvIrr7xS6f7PPfecsbxz585aqQkN01dffaXnnnvOCMNuu+02TZ48mTtmvFSrVq2M5UOHDpUbXhw/flw5OTmSRNiFUqZOnap3333XWB8+fLgmTpzIPCIopfj3GafTqXHjxlW6/4gRIyRJ0dHRWrlyZa3WhvqtdevWWrFihaSq/w3F308AgIaMe0KBKrjsssuM5U2bNlW47549eyRJoaGh8vPzq82yAHiBhQsXuoTsQ4cO1ZtvvknI7sU6duxoLG/cuLHc/TZs2GAsd+7cuVZrQsMybdo0I2S3WCx66qmn9PTTTxOyA/Cokv9eVfVvqOjo6NosCQCAWmVxOp1Os4sAGoJevXpp7969CgkJ0dKlS41JT0s6fPiwevTooYKCAvXv31+vvvpq3ReKBmXOnDl66aWXJEkzZ87UlVdeaW5BqFe2bt2qQYMGGaO8xowZo0ceecTkqlAf3HXXXdq5c6fatGmjb7/9tsyAdPTo0YqNjZXD4dDPP/9ML39IklauXKlRo0ZJkqxWqyZNmqT+/fubXBUaujfeeEPTp0+XJMXGxqply5YmV4T6IC8vT9dee62ysrJ0/vnna9GiRfLx8Sm138aNGzV48GBJ0qOPPmr8jAIAoKFhRDtQRffff78kKSMjw5gQqqS8vDw9/fTTKigokMVi0YABA8woE0AjkZ+fr6eeesoI2YcOHUrIDsOQIUMkSbt379ZHH31U6vElS5YYLRv69+9PyA5JZ3+HKfbUU08RsgOoNb6+vvrrX/8qSTpw4ID++c9/6txxfpmZmXrhhRckSX5+furdu3ddlwkAgMfQox2oor/85S9asmSJfvrpJy1fvlwDBgzQvffeqwsuuECJiYn65JNPFB8fL0n661//6tJuBgCqa+HChcYEupGRkbrrrrvKnICuJCae8x4DBgzQ3LlztW3bNr355pvau3ev+vTpIx8fH8XGxmrmzJlyOp2KjIxkZCAMM2fONCatbNeuna666qpKf64EBAQoJiamLsoD0AiNHDlSK1eu1I4dO/TZZ59p165dGjx4sKKjo7Vr1y599NFHSkhIkHRmNHtUVJTJFQMAUHO0jgGq4fTp0xo/frwxqU9Z+vfvrxdffLHM2yKBc9E6BuUZNGiQfvvtt2odw8Rz3iUlJUX33Xef0df2XOHh4frkk0/Utm3bOq4M9dX111+vo0ePVuuYbt26adasWbVUERoLWsegIunp6Ro9enSF84qMGTNGY8eOZa4IAECDxoh2oBr8/Pz03nvvadWqVZo/f762bNmi9PR0hYeHq3379ho8eLCuu+46s8sE0Ajs3LnT7BJQz0VERGjBggWaNWuWFi9erAMHDig/P18tW7bUzTffrPvvv1/Nmzc3u0zUE8ePH692yA4AntCsWTPNnj1bCxcu1MKFC7V9+3adOHFCLVq0UKdOnTR06FCXiVMBAGioGNEOAAAAAAAAAIAbmAwVAAAAAAAAAAA3ELQDAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBsI2gEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADQTtAAAAAAAAAAC4gaAdAAAAAAAAAAA3ELQDAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBsI2gEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADQTtAAAAAAAAAAC4gaAdAAB4VF5enr777jtNmDBBvXr10pVXXqlLL71U1113ne666y69+OKLWrFihQoLC80utV4bOnSo2rZtq7Zt23rsnKNHj1bbtm01b948Y1tiYqJxnaFDh3rsWjhr3bp1xmv87rvvujyWl5enW2+9VZdccok2bdpkToEAAAAA3EbQDgAAPObHH3/U7bffrscff1zffPON9u7dq4yMDOXn5+vYsWPauXOnvvjiC40ZM0Z33XWX1qxZY3bJXmP27NmKjY1Vhw4d1K9fP7PLwf/n6+urZ555RgUFBRo/fryys7PNLgkAAABADdjNLgAAADQOP/zwg8aMGaOCggJJUrdu3XT11VcrOjpafn5+ys7O1p49e7R8+XIlJCRoz549euihh/TOO+/o5ptvNrn6xu3w4cN6/fXXJUnPPPOMLBaLyRWhpBtuuEF/+tOf9NNPP+mNN97QSy+9ZHZJAAAAAKqJoB0AALgtOztbTz75pAoKChQQEKC33npLN9xwQ5n7PvHEE5o6daref/99FRQU6PHHH9d3332nli1b1nHV3uOVV17RyZMn1aNHD11++eVml4MyPPHEE/rf//6nL7/8Un369FHHjh3NLgkAAABANdA6BgAAuG3+/PnKysqSJI0bN67ckF2SbDabxo0bp9tvv12SdPr0ac2aNatO6vRGv/76q1auXCnpTI921E8XX3yxunfvrqKiIk2ePNnscgAAAABUE0E7AABw2++//24sX3311VU6Zvjw4cbyr7/+6umS8P+99dZbkqROnTqpffv25haDCg0aNEiSFBcXp//9738mVwMAAACgOmgdAwAA3HbixAljOSEhQW3btq30mEsuuUS33nqrmjZtqj/+8Y+lHp84caK+/vprSdLOnTuVkZGhjz/+WMuXL1dycrLsdrvatGmj2267TYMGDZKfn1+l1/zpp5/03//+Vxs3blRaWppsNpuioqJ09dVXa/DgwWrVqlWl58jPz9c333yjZcuWafv27crIyJDD4dD555+v66+/Xvfcc49CQkIqPU9cXJxmz56tuLg4HT9+XMHBwerUqZOGDRumK6+8stLjq2LLli3asGGDJLk9AWpBQYEWL16sJUuW6Pfff1d6err8/f113nnn6ZprrtHgwYPLfB/PtX//fs2cOVNr1qzR4cOH1aRJE1144YW68847NWjQIG3btk0DBw6UJP3jH/9Q3759yzzP2rVr9cUXX+i3337T8ePHFRQUpEsvvVSDBg3Sn//8Z02bNk1TpkyRJMXGxpbbmmjPnj364osv9Msvvyg5OVn5+fkKCwvT5Zdfrt69e+u6666r9DllZ2dr9uzZWrZsmfbu3Sun06mWLVuqZ8+eLl8oVebaa69VdHS0kpKSNGPGjCpdGwAAAED9QNAOAADcVjJgnTJlijp37qzQ0NAKj7HZbHrnnXeqdP6DBw/qvvvuU1JSkrEtNzdXmzZt0qZNm/Tll1/q448/VnR0dJnHZ2Vl6fHHH9dPP/1U6rE9e/Zoz549mj17tkaMGKFx48aVO1nozp07NXbsWB08eNBle0ZGhlHLjBkz9I9//EM9evQo8xxOp1OTJ0/Wv//9b5ftqampWrFihWJjYzVy5MgKX4+qmjNnjiTJarW6NeHsrl27NH78eO3atctle35+vrKysrRjxw7NmjVLo0aN0pgxY8o9zzfffKPnnntO+fn5xra8vDxt3rxZmzdv1ldffaWHH364wlqKior0yiuvaPbs2S7b09LStHr1aq1evVp33323LrjggkrPM2XKFM2YMUOFhYUujyUlJSkpKUnffvutbrrpJr3xxhsKDAws8zy7d+/WQw89pOTk5FLbd+/erQULFmjUqFEV1lLMYrHoz3/+s2bNmqWff/5Zhw4dqtKXFwAAAADMR9AOAADc1rt3b6PP+r59+3Trrbeqd+/euvXWW9W5c2fZ7e79yjFq1CglJSUpMjJSAwcO1Pnnn6+EhAR9+eWXSkpK0r59+zR48GAtXLiw1GjykydP6p577jFC4ujoaPXu3VutWrVSfn6+Nm/erG+++UYnT57UBx98oOzsbD333HOlati1a5eGDBmi7OxsSVLnzp116623qkWLFsrKytLPP/+s5cuX68SJExo7dqzeeust9ezZs9R5/v73vxuvla+vr/r27asuXbqooKBAP//8s7777jt98MEHatKkiVuvWX5+vlasWCFJ6tChg5o3b16j8+zbt0/Dhg1Tenq6JKlly5bq06ePLrzwQuXk5Ojnn3/W0qVLlZ+fr3feeUfp6ellvn6LFi3SxIkT5XQ6JUndu3fXjTfeKD8/P23ZskXz58/X9u3b9fTTT1dYz0svvaQvv/xSktSkSRP169dPnTp1Un5+vn788UctXbpU//3vfxUUFFTheZ599lktWLBAkhQYGKi7775bl112mex2u/bu3atvvvlGhw8f1qpVq3Tvvfdqzpw58vX1dTlHQkKChgwZYsxP0K5dO/Xu3VsRERE6dOiQ5s2bp8TERL3yyitVeKXPuP766zVr1iw5nU4tWbJEI0aMqPKxAAAAAMxjcRb/tQMAAOCGf/7zn5oxY0ap7QEBAercubMuv/xyde3aVZ07dy4VWJalZOsYSeratas+/PBDl5HFJ0+e1OjRo7V27VpJ0uDBg/XSSy+5nOe5557TvHnzJEl9+/bVyy+/XOr6ycnJevDBB7Vnzx5J0rRp01wmdC0oKNCdd96pffv2yWKx6MUXX9TgwYNL1bxu3TqNGjVKOTk5cjgcWrZsmcLCwozHf//9dw0YMEBFRUUKCQnRp59+qnbt2rmc43//+5/GjBmj06dPG9t27txZ6et1rvXr1+uvf/2rJGno0KFlht+SlJiYaIx279atm8vEtE6nU3/5y1+0bds2SdKdd96p1157rdTrFxcXp1GjRhmB83vvvafu3bsbj584cUK33HKLjh8/Lrvdrtdff129evVyOcehQ4c0fPhwl7sWzm0ds2HDBg0ZMkSSFB4erhkzZqhNmzYu5/nhhx/0yCOPKDc319h2buuYhQsX6sknn5R0poXRBx98oBYtWricJzc3VxMmTND3338vSXrwwQeNY4qNHDlSq1atkiQNHDhQL774omw2m/H46dOn9eijjxr7SNLDDz+ssWPHqjxZWVnq2rWrpDOf+c8++6zcfQEAAADUH0yGCgAAPGLChAl69NFH5ePj47L95MmT+vnnn/Xuu+9q2LBh6tatm8aMGaNly5apqKioSudu3ry53n333VLtOwICAvTmm28ao9jnzp2rjIwM4/GkpCQjrL/kkkv097//vcyQPyoqSq+//rqs1jO/Gn3wwQcujy9evFj79u2TJA0bNqzMkF2SrrzySo0bN06SlJOT4xJaS9LHH39sPOfnn3++VMguSdddd50effTRMs9fHb/99puxXJWe+WVZtWqVEbK3a9dO//jHP8p8/a644gqXUdvvvvuuy+Off/65jh8/Lkl64IEHSoXs0pn2Q2+//bbxHpTlvffeM5b/+c9/lgrZJenGG2/UY489Vu45ioqKjPMEBASUGbJLZ0bLv/baa4qMjJQkzZ49W5mZmcbje/bsMQL0Nm3a6IUXXnAJ2SXJz89Pb7zxRpnnL09QUJCioqIknflipmSbHQAAAAD1F0E7AADwCIvFolGjRmnRokUaOnSowsPDy9zv1KlTWrFihcaOHavevXsbQW5FBg8erGbNmpX5WLNmzdSnTx9JUmFhoX744QfjsSVLlqigoEDSmclAKwpx27dvr8suu0ySjAk2i3333XfGcvFEneXp37+/EbjGxsYa2/Pz8/W///1PkhQWFqbbbrut3HMMGjRIDoejwutUpuQo+IsuuqhG5yhZ/wMPPFDqS5SSevbsaUwmu2PHDpc+9sUtbOx2u+69995yz9GhQwddc801ZT524sQJ/frrr5LOPJ+KJgodMmRIua1jtm3bpgMHDkiSbrjhhgpD8ICAAN11112SznxhVHznhCStXLnSWB40aFC57ZECAwM1aNCgcq9RluL36/Tp00pISKjWsQAAAADMQY92AADgUeeff76ee+45Pfvss4qPj9cvv/yi9evXa+PGjS4jgqUzYfA999yj6dOnG+0yyvKnP/2pwmt269bNaFuzadMm9e7dW5K0ceNGY5/iyUYrUnLE/JYtW3TjjTeWOs+uXbu0f//+Cs8TFhamo0ePas+ePUYbmd27dxv93Tt16lRq9HNJ/v7+6tixo9asWVPhdSpSsgVLTfuzb9q0yVguLwAv6ZprrtHevXslnfmyIiYmRjk5Odq6daskqXXr1pVOknvttdcaX0iUtGHDBmN091VXXVXhOZo0aaIuXbq4tGwpVvK9LCwsrPQzUfxFjSRt3rzZ6Ltf8o6BK664osJzXHnllRU+fq6Sr1FSUpIuvPDCah0PAAAAoO4RtAMAgFphsVh0ySWX6JJLLtEDDzygoqIi7dixQ6tXr9ZXX31ljNQ9deqUJk6cqKVLl5Y7KriyoPG8884zlo8dO2YsJycnG8sl245URVpamqQzLWCKe49LqlZbF6fTqePHj8vhcLjUVdwapCIxMTFuBe0la27atGmNzpGamipJcjgclQbkkvSHP/zBWC5+/VJSUowJUKOjoys9xx//+Mcytx89etRYduc8R44cMZaXLVumZcuWVXquYiXvcih+baTK38/zzz+/yteQXN+vku8jAAAAgPqL1jEAAKBOWK1WtW/fXqNGjdKSJUv02GOPyWKxSDozIWdFI4vP7c1+Ln9/f2O5eNT4ucvVVXysO+eQzgT1kmtgWrLe8lT2nCuTl5dnLAcEBNToHMXPvarHl9zv5MmTkqT09HRjm5+fX7XOUVJ1z1Pea3zixIlKjy1Pyc9Cdd7P6r6XJV+DkpO6AgAAAKi/GNEOAADcsmXLFsXFxSktLU233HKLOnbsWOkxdrtdI0eO1MGDB7VgwQJJ0vbt2422HOfKzc2tMOwtDnUl1zYpJQPZNWvWVGlU9rlKnqNVq1ZavHhxtc8hScHBwcbyqVOnKt2/ZFBeEyXrzsvLK3MS08oEBAQoKyvL5fWtSPGXCtLZ8LlkCF2V85T32pR8PlV5/U6fPl3ped58880yJ2atipLv58mTJyt8fav7XpYM16vypQIAAAAA8zGiHQAAuOXHH3/Uv/71L3388cdavnx5tY4t7oEuuY5YPlfJFjBlKTlhZEREhLFcckLWffv2Vau2YkFBQWrSpImkMyPvaxqAl6yrKhNclmxxUhMl24/UdFR+8euXk5NjtIKpSPEko5IUGRnp8n/JtW98eRITE8vcXrI9UHn7VOU8nvhMSNV7P6v7XpZ8v8qb1BUAAABA/ULQDgAA3NK+fXtjefHixS6TR1am5Ajoivpub9iwocLzrFu3zlguOVFmydH1K1eurLSeKVOm6Omnn9bUqVONUNhiseiyyy6TdGakcWV90/Py8jRu3Di99NJLmj59uvF6tG7dWiEhIZLOTMhZUUuQoqIil8k2ayImJsZYPnz4cI3OUfL1q0q/+LVr1xrL7dq1kyQ1a9ZMF1xwgSRp7969Ln3OyxIXF1fm9k6dOhnL69evr/AcBQUF5b5+JZ9TWZOlnmvhwoUaP368/u///s/lc1hyAtSSz7ssldV7rpLvV8n3EQAAAED9RdAOAADcct111xntWpKSkqo86WhRUZHmzp1rrN98883l7vv555+rsLCwzMdSU1O1cOFCSWcm7bz22muNx2655RZjee7cuRWOLN65c6emT5+uBQsW6MMPP5TD4SjzPO+//36FXybMmzdPS5Ys0Zw5c/T9998bE7za7XZ1795d0pn+3vPmzSv3HIsXL1ZKSkq5j1dF69atjeX9+/fX6Bwln/cnn3yi/Pz8cvf9/vvvjRHtF1xwgVq1amU8dscdd0iSCgsLNXv27HLPcfDgwXLD74iICHXr1k3Smffq119/Lfc8ixYtKncE/uWXX26Mat+6dWuFX8Dk5uZqypQpWrRokT766COXvuy33nqrMcfAF198UW5bnPz8fM2ZM6fca5Sl+HX09/ev0sSvAAAAAMxH0A4AANzi6+urCRMmGOvvv/++nn/++QpHLqenp2v8+PHGqOO7777bJRg+144dO/TKK6+oqKjIZXt2drbGjRtnBKCjRo1y6ZV98cUX66abbjL2HTlypI4ePVrq/CkpKRo7dqycTqckaeDAgcboc0nq16+fEc5u3rxZTz/9dJktZH799VdNnjzZWP/b3/7m8viIESOMnttvvPGGfv7551Ln2LZtm1555ZVyX4uq6tq1q7G8devWGp3jxhtvNEamb9++Xc8880yZz3vDhg164YUXjPVx48a5PD548GCjBcqHH35YZridmpqqRx55pMIwf+TIkcbyhAkTXFrVFNu0aZP+/ve/l3sOX19fPfDAA8b6xIkTyxxFn5+fr8cee8z4cuaiiy7SDTfcYDweFRWlfv36STrzBdPjjz9eqi98YWGhXnrpJe3atavces6VkZFhtL3p0qWLrFZ+XQcAAAAaAouz+C9KAAAAN3zwwQd66623jHV/f39dffXVuuKKKxQWFia73a60tDRt3rxZP/zwg9GH+vLLL9eMGTNKTfo4ceJEff3115LOtG9xOp1q3769+vTpo7CwMO3bt09ffvmlMfK7U6dO+vzzz40R5MXS0tL0l7/8xejz7nA41KdPH1122WUqKirS9u3bNX/+fKONzYUXXqh58+YpMDDQ5Tzr16/XfffdZwTB0dHR6tOnj1q1aqXMzEytX79e33//vfFlwB133KEpU6aUep1mzZqlV1991Xhet99+u6677jrZbDbFxcVpwYIFys/PV2hoqDEqe+fOndV8N86EvNdcc40yMjLUpk0bLVq0qMz9EhMTjbsJunXrplmzZrk8vm/fPg0YMEAnTpyQJLVs2VJ9+vTRhRdeqJMnT2rNmjUuz3vgwIGaNGlSqev897//1VNPPWU871tuuUV/+tOfFBAQoO3bt2vevHnKyMiQzWYz7l6YPHmy7r77bpfzPPvss5o/f76kM5+xfv36Ge/lL7/8om+//VYFBQUu51m9erVLr/iioiKNHDlSq1evliRZrVb16NFD1113nQICAnTw4EF99dVXRvsgf39/ffHFF7r44otdajlx4oT69Olj9Ghv2bKlBgwYoD/84Q86evSoFixYoF27drm8lw8//LDGjh1bzrsmLVu2zHj86aef1vDhw8vdFwAAAED9QdAOAAA8ZtmyZfrXv/5Vpckq7Xa7hg4dqrFjx7q0aSlWMmgfP368PvnkE2VkZJR5rptvvllvvPGGAgICynz86NGjGjduXKV9z7t06aJ33nlHYWFhZT4eFxenxx9/vMxR8SUNHDhQL7zwQqnQv9jcuXP18ssvl9uCZsCAAfLz89PMmTMl1Sxol6SXX37ZaNXyww8/KCoqqtQ+lQXtkrRnzx6NHTu2wslD7Xa7xo0bp4ceeshoqXKuWbNm6V//+le5o9a7dOmiDh066NNPP5UkvfXWW7rttttc9ikoKNDTTz9ttAsqy9ChQ13ay6xdu9Zob1QsLy9PkyZN0vz581XRr8NRUVF66623XHrEl5SamqpRo0Zpy5YtZT7evHlzvfXWWxo2bJikyoP2559/XnPnzpXNZtMPP/zgMukqAAAAgPqr7L/+AAAAauCWW27RjTfeqBUrVuinn37Sjh07dPjwYeXk5MhqtSosLEzR0dG6/vrr1bNnT/3hD3+o0nk7duyoRYsWadq0aYqNjVVKSooCAwPVqVMn9evXz+h9Xp4WLVroiy++UGxsrBYvXqxNmzYpLS1NhYWFCg0NVYcOHXTHHXfolltuKTckls5MgLl8+XJ99dVXWrVqlXbs2KH09HTZ7XZFRkbqiiuu0IABA4zJU8szYMAAdenSRf/5z3+0Zs0aHTlyRA6HQxdffLEGDx6snj17Vtj+pKoGDBhgBO2LFy92aZlSHa1bt9a3336rb7/9VkuXLtW2bduUnp6upk2b6rzzztMNN9ygvn37qmXLlhWeZ+jQobrmmms0a9YsrVmzRikpKbLZbGrTpo369Omj/v37u9wVUbJ9TzG73a7XX39dd911l+bNm6fNmzcrLS1NTZs2VceOHTVs2DBdc801GjRokKQzo+ebNm1a6jy+vr569dVXdc8992jevHn69ddfdeTIEZ06dUpBQUG66KKL1L17d/Xt27fML4KKhYWFac6cOfr666+1cOFCxcfHKy8vT5GRkbrxxhs1YsQINWnSpEqvc35+vpYvXy5JuummmwjZAQAAgAaEEe0AAKBeKjmifebMmbryyitNrqhhGjVqlFauXKmLLrpI3377rdnlVKpka5ilS5fq/PPPr9F5evTooUOHDikiIkI//fSTByusPStWrNCYMWMkSQsWLNAll1xickUAAAAAqorZlQAAABqx4jYlu3bt0rp160yp4fXXX9fo0aP197//3ejNX5bc3Fz9+OOPkqTg4GDFxMS4PP7AAw/oscce0/Tp0yu83o4dO4y+6R06dHCz+rpT3Cro5ptvJmQHAAAAGhiCdgAAgEasffv2Rg/2Tz75xJQa7Ha7YmNjNXPmTCNMPldRUZFeeuklY3Lbu+66q1Qbn5ycHC1evFhTpkxRXFxcmedJT0/XM888Y/Rd79OnjwefSe3ZunWr1q1bJ4vFoocfftjscgAAAABUEz3aAQAAGrlnnnlG69at0+rVq7Vx40ZdfvnldXr9v/zlL/r00091+vRpvf3221qzZo3+/Oc/KyIiQrm5uUpMTNSiRYt06NAhSdIf//hHPfbYY6XOM3ToUP32229yOp0aPny4evTooSuuuELNmjVTVlaWdu/erW+//VaZmZmSpNtuu009evSo0+daU2+++aYk6Z577lH79u1NrgYAAABAddGjHQAA1Ev0aPesBQsW6Omnn1bnzp01Z86cCid9rQ2rV6/WE088oaysrAr369Kli/7v//5PkZGRZT7+/vvva+rUqSosLKzwPAMHDtSzzz5b5YlIzbRmzRrdd999uuCCC/TNN9/Iz8/P7JIAAAAAVBMj2gEAALxA3759tWrVKi1btkxfffWV+vXrV6fXv+GGG7R8+XLNnz9fq1ev1t69e5WVlaUmTZooIiJC7dq101133aU//elPstls5Z5n9OjR6tmzp+bNm6d169bp0KFDOnXqlIKCghQREaGuXbuqd+/euvTSS+vw2dVcXl6eJk2aJLvdrsmTJxOyAwAAAA0UI9oBAAAAAAAAAHADk6ECAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBsI2gEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADQTtAAAAAAAAAAC4gaAdAAAAAAAAAAA3ELQDAAAAAAAAAOAGgnYAAAAAAAAAANxA0A4AAAAAAAAAgBsI2gEAAAAAAAAAcANBOwAAAAAAAAAAbiBoBwAAAAAAAADADQTtAAAAAAAAAAC4gaAdAAAAAAAAAAA3ELQDAAAAAAAAAOCG/wfgTmWfIJNsggAAAABJRU5ErkJggg==", "text/plain": [ "" ] }, "execution_count": 51, "metadata": { "image/png": { "height": 378.25, "width": 636.65 } }, "output_type": "execute_result" } ], "source": [ "speed_hist=lcm.makehistogram(summary_both, \"speed_logged\", xlabel=\"Speed (logged)\", title=\"Distribution of Speed across all Fly Behavior\")\n", "speed_hist.save(\"speed_hist.pdf\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 52, "metadata": { "image/png": { "height": 378.25, "width": 636.65 } }, "output_type": "execute_result" } ], "source": [ "r_hist=lcm.makehistogram(summary_both, \"r\")\n", "r_hist.save(\"r_hist.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PSDs for all measures" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (Bins_direction: 200, Freq: 1000, Shuffled: 2,\n",
       "                           Bins_angle: 200, Bins_theta: 200, Measure: 4,\n",
       "                           Bins_turning: 200, Bins_speed_logged: 200,\n",
       "                           Bins_r: 200, flyid: 252, recording_length: 2)\n",
       "Coordinates: (12/14)\n",
       "  * Bins_direction        (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Freq                  (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n",
       "  * Shuffled              (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n",
       "  * Bins_angle            (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n",
       "  * Bins_theta            (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n",
       "  * Measure               (Measure) object 'Mean' 'Std' 'Min' 'Max'\n",
       "    ...                    ...\n",
       "  * Bins_r                (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n",
       "  * flyid                 (flyid) object MultiIndex\n",
       "  * Batch                 (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n",
       "  * Fly                   (flyid) int64 1 2 3 4 5 6 7 8 ... 36 37 38 39 40 41 42\n",
       "    Trial                 int8 1\n",
       "  * recording_length      (recording_length) int8 2 24\n",
       "Data variables: (12/19)\n",
       "    direction_bins        (recording_length, Bins_direction, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    direction_psd         (recording_length, Freq, Shuffled, flyid) float64 dask.array<chunksize=(1, 1000, 2, 1), meta=np.ndarray>\n",
       "    angle_bins            (recording_length, Bins_angle, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    angle_psd             (recording_length, Freq, Shuffled, flyid) float64 dask.array<chunksize=(1, 1000, 2, 1), meta=np.ndarray>\n",
       "    theta_bins            (recording_length, Bins_theta, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    theta_summary         (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    r_bins                (recording_length, Bins_r, flyid) float64 dask.array<chunksize=(1, 200, 1), meta=np.ndarray>\n",
       "    r_psd                 (recording_length, Freq, Shuffled, flyid) float64 dask.array<chunksize=(1, 1000, 2, 1), meta=np.ndarray>\n",
       "    r_summary             (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    direction_summary     (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    speed_summary         (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>\n",
       "    angle_summary         (recording_length, Measure, flyid) float64 dask.array<chunksize=(1, 4, 1), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (Bins_direction: 200, Freq: 1000, Shuffled: 2,\n", " Bins_angle: 200, Bins_theta: 200, Measure: 4,\n", " Bins_turning: 200, Bins_speed_logged: 200,\n", " Bins_r: 200, flyid: 252, recording_length: 2)\n", "Coordinates: (12/14)\n", " * Bins_direction (Bins_direction) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n", " * Bins_angle (Bins_angle) float64 -0.995 -0.985 ... 0.985 0.995\n", " * Bins_theta (Bins_theta) float64 -3.126 -3.094 ... 3.094 3.126\n", " * Measure (Measure) object 'Mean' 'Std' 'Min' 'Max'\n", " ... ...\n", " * Bins_r (Bins_r) float64 0.003 0.009 0.015 ... 1.191 1.197\n", " * flyid (flyid) object MultiIndex\n", " * Batch (flyid) int64 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6\n", " * Fly (flyid) int64 1 2 3 4 5 6 7 8 ... 36 37 38 39 40 41 42\n", " Trial int8 1\n", " * recording_length (recording_length) int8 2 24\n", "Data variables: (12/19)\n", " direction_bins (recording_length, Bins_direction, flyid) float64 dask.array\n", " direction_psd (recording_length, Freq, Shuffled, flyid) float64 dask.array\n", " angle_bins (recording_length, Bins_angle, flyid) float64 dask.array\n", " angle_psd (recording_length, Freq, Shuffled, flyid) float64 dask.array\n", " theta_bins (recording_length, Bins_theta, flyid) float64 dask.array\n", " theta_summary (recording_length, Measure, flyid) float64 dask.array\n", " ... ...\n", " r_bins (recording_length, Bins_r, flyid) float64 dask.array\n", " r_psd (recording_length, Freq, Shuffled, flyid) float64 dask.array\n", " r_summary (recording_length, Measure, flyid) float64 dask.array\n", " direction_summary (recording_length, Measure, flyid) float64 dask.array\n", " speed_summary (recording_length, Measure, flyid) float64 dask.array\n", " angle_summary (recording_length, Measure, flyid) float64 dask.array" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary_both" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'turning_psd' (recording_length: 2, Freq: 1000, Shuffled: 2,\n",
       "                                 flyid: 252)>\n",
       "dask.array<concatenate, shape=(2, 1000, 2, 252), dtype=float64, chunksize=(1, 1000, 2, 1), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * Freq              (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.919 5.0\n",
       "  * Shuffled          (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n",
       "  * flyid             (flyid) object MultiIndex\n",
       "  * Batch             (flyid) int64 1 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6 6\n",
       "  * Fly               (flyid) int64 1 2 3 4 5 6 7 8 ... 35 36 37 38 39 40 41 42\n",
       "    Trial             int8 1\n",
       "  * recording_length  (recording_length) int8 2 24
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * Freq (Freq) float64 3.858e-07 3.922e-07 3.987e-07 ... 4.919 5.0\n", " * Shuffled (Shuffled) object 'Non-shuffled Data' 'Shuffled'\n", " * flyid (flyid) object MultiIndex\n", " * Batch (flyid) int64 1 1 1 1 1 1 1 1 1 1 ... 6 6 6 6 6 6 6 6 6 6\n", " * Fly (flyid) int64 1 2 3 4 5 6 7 8 ... 35 36 37 38 39 40 41 42\n", " Trial int8 1\n", " * recording_length (recording_length) int8 2 24" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "turning_psd=summary_both[\"turning_psd\"]\n", "turning_psd" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
BatchFlyTrialturning_psd
recording_lengthFreqShuffledBatchFly
23.858025e-07Non-shuffled Data111111.594373e-04
21219.695770e-05
31318.558960e-05
41417.330665e-05
51512.668082e-05
...........................
245.000000e+00Shuffled63863811.091414e-06
3963913.733601e-06
4064015.535780e-07
4164111.373228e-05
4264211.452333e-05
\n", "

1008000 rows × 4 columns

\n", "
" ], "text/plain": [ " Batch Fly Trial \\\n", "recording_length Freq Shuffled Batch Fly \n", "2 3.858025e-07 Non-shuffled Data 1 1 1 1 1 \n", " 2 1 2 1 \n", " 3 1 3 1 \n", " 4 1 4 1 \n", " 5 1 5 1 \n", "... ... ... ... \n", "24 5.000000e+00 Shuffled 6 38 6 38 1 \n", " 39 6 39 1 \n", " 40 6 40 1 \n", " 41 6 41 1 \n", " 42 6 42 1 \n", "\n", " turning_psd \n", "recording_length Freq Shuffled Batch Fly \n", "2 3.858025e-07 Non-shuffled Data 1 1 1.594373e-04 \n", " 2 9.695770e-05 \n", " 3 8.558960e-05 \n", " 4 7.330665e-05 \n", " 5 2.668082e-05 \n", "... ... \n", "24 5.000000e+00 Shuffled 6 38 1.091414e-06 \n", " 39 3.733601e-06 \n", " 40 5.535780e-07 \n", " 41 1.373228e-05 \n", " 42 1.452333e-05 \n", "\n", "[1008000 rows x 4 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "turning_psd_df=turning_psd.to_dataframe()\n", "turning_psd_df" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABhwAAAN6CAYAAAB1/eMNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAB2HAAAdhwGP5fFlAAEAAElEQVR4nOzdd5wdd33v//d3zlbtalda9W5Zcq9yxaY6Nt2AwQnlGhzSSGgBcm9+JDflXsglCbk3CSGUJJBCMyV0gikGg22Mey9ykW3J6l0rbd9z5vv7Y+Y78505c8oWNe/ryUPec+ZMO0fC5fs+n8/HWGutAAAAAAAAAAAApiA42jcAAAAAAAAAAACOfwQOAAAAAAAAAABgyggcAAAAAAAAAADAlBE4AAAAAAAAAACAKSNwAAAAAAAAAAAAU0bgAAAAAAAAAAAApozAAQAAAAAAAAAATBmBAwAAAAAAAAAAmDICBwAAAAAAAAAAMGUEDgAAAAAAAAAAYMoIHAAAAAAAAAAAwJQROAAAAAAAAAAAgCkjcAAAAAAAAAAAAFNG4AAAAAAAAAAAAKaMwAEAAAAAAAAAAExZy9G+ARxfDh48qDvvvDN5vmTJErW1tR3FOwIAAAAAANNlbGxM27dvT55fdNFF6unpOYp3BAA4nhA4YELuvPNOvfvd7z7atwEAAAAAAI6AT37yk7riiiuO9m0AAI4TtFQCAAAAAAAAAABTRuAAAAAAAAAAAACmjJZKmJAlS5Zknn/yk5/UqlWrjtLdAAAAAACA6bRp06ZMK+X8OgAAAPUQOGBC8gOiV61apZNOOuko3Q0AAAAAADic8usAAADUQ0slAAAAAAAAAAAwZQQOAAAAAAAAAABgyggcAAAAAAAAAADAlDHDAYkvfelLuu666+ruMzo6eoTuBgAAAAAAAABwPCFwQGLfvn3asGHD0b4NAAAAAAAAAMBxiMABib6+Pq1du7buPqOjo9q8efMRuiMAAAAAAAAAwPGCwAGJa665Rtdcc03dfZ588kldeeWVR+iOAAAAAAAAAADHC4ZGAwAAAAAAAACAKSNwAAAAAAAAAAAAU0bgAAAAAAAAAAAApozAAQAAAAAAAAAATBmBAwAAAAAAAAAAmDICBwAAAAAAAAAAMGUEDgAAAAAAAAAAYMoIHAAAAAAAAAAAwJQROAAAAAAAAAAAgCkjcAAAAAAAAAAAAFNG4AAAAAAAAAAAAKaMwAEAAAAAAAAAAEwZgQMAAAAAAAAAAJgyAgcAAAAAAAAAADBlBA4AAAAAAAAAAGDKCBwAAAAAAAAAAMCUETgAAAAAAAAAAIApI3AAAAAAAAAAAABTRuAAAAAAAAAAAACmjMABAAAAAAAAAABMGYEDAAAAAAAAAACYMgIHAAAAAAAAAAAwZQQOAAAAAAAAAABgyggcAAAAAAAAAADAlBE4AB5r7ZRen+y+AAAAAAAAAHC8I3AAPI0ygnACGUKtc7kgwg8kaj0GAAAAAAAAgOMFgQMgKYyThPxSf9Xif+65Hx7k97UF+xWcoi7CBwAAAAAAAADHCwIHzCi1FvCLgoMwtElFQ/J67hhr059+9YONXqjaL3tN/7q1txeFGQAAAAAAAABwrCFwwIxS1BLJWpsECS44CEObCQ3ywUP0uhcmuIOV3ZYPJTLX8u+hxmMAAAAAAAAAOF60HO0bAI6kKDAwstbKGJMLBKykKCmwxsSBgWTi8MHlD0lokAkq0pggDSeUBg/JDcTHuZ8yMibeOb4vf7tVerAxh/OTAQAAAAAAAICpIXDAjGJd9YKkkkkDhNCmAUEoyShtY2RlsuFBEhbEgYRs0hLJr4qQouAgCRhkJS/IKAorkvP495zbz5A8AAAAAAAAADgGEThgRvHbHYWhVAltVEkQVziE1sZ9xkyyn7FehUNopZIUxhUJSaggN8fBZNozWT98sPERVrIm3cnKOyb+ZZSGF1aKqzGifcgbAAAAAAAAAByLCBwwo4TWysSr/VZewGCj8EEuDIjDBynbFikTJsRRgwsTkjZLXpVCWvkQ/zTxK64Qwnjn865n4mqI/HPmOwAAAAAAAAA4VhE4YEbxF/ZDGdnQqmJcxUM8kyG0UmBcLpBUNthcJYQLJwK5NklptYPi50mgkFzfes+trDXuaFlr0sHT7rpuP6VlDW7+RP4xAAAAAAAAABxNBA6YUdy8BklpNODNcTBG0V/CtMIhtGllRNQ2KTomtFZGJgoiQlt1/qSKQdmQw13C2mx7pLSCIgofXP4QWimQksZL2VCDFksAAAAAAAAAjg0EDphRQisZm85I8NsYRe2V4sV8r52RvNDALfmn7ZbSdkrWWgXWZKoYkhZIfvAQePMh4uqFZBC19UIE6yofqtspmexpAQAAAAAAAOCoI3DAzOIFBJK8agSrSsXKtJik7MG9FrrqhzgUSLdHP40/fyH5SxpGuE1pRYXNzG7wDvFvNG2jlJzUJGGEJWkAAAAAAAAAcIwhcMCMkgQHjhcgWGsVhlaBkaRoVT+qMTBJUBG6eQ7eRGh/MLR1cyCS86fnlomqH0wcHLhtJq5icK2SXL7gggWrtK1SMnDapIlFdBr6KgEAAAAAAAA4uggcMKMkVQfxan7oD5G2VoG1CmUUeBUO1qRBhWurlP6UFEaDm10LJb9NkzXV1w4VhQ5RdpBWMrh2Sm4QtN+qyboSifgaJjdcWmJ4NAAAAAAAAICji8ABM4ob/hxVFbghz3EAkLwumZLJBgd+eyQvbLBeQJHuG+cZyoYPJh8+KK628KZXe/Omk8oHd7z/M58tEDoAAAAAAAAAONoIHDCjVIcH8WK9sQpDKTBxBUKoqn2iYgZXFeGHCVY2rkgwNskOMimD25b89CsZvN2V7OfNcHDXsCY7C8Ldm3FDsKuDCAAAAAAAAAA4UggcMKPYODAIjFEYpwjWuoHMuYHQmcAhCiRcMpAdCG2l0D2O5z3EQULo5kPH25LXMkOm0xkOfuWDjZOFqooJI9mkV1NupgMAAAAAAAAAHCXB0b4B4Ehy1QphPCA6tFaV0Cozk0FSGNqk8iCM2y4pee4qHuKWR1ZedYI348F6z73XZLPtmTJTpr3KCXe95L5Dv71SmojYZAf/RAAAAAAAAABwZFHhgBnFzWiIKhqS2dHePAfJxNUDQRAt4PvzHWxSjWCz6/txiODPUHCtlow39DkZ/hw/cJULyf7ucXLO3FwHr4VSvEtyDqM0rGCWAwAAAAAAAIAjjcABiS996Uu67rrr6u4zOjp6hO7m8EgLAWwyQNoFDe65G8QQhlEgECpqp5QNJ7KVCIrbJAW5KoM0aEjbNhlj4rZOkrzQw690sN4ga5cqWC8hcfMdMrMfTPo4P6CaAAIAAAAAAADA4UbggMS+ffu0YcOGo30bh1UywyGucHDBQ2BN0s7IKJrvEChawA8Ck1Q0pEOn/fZG0XmkOLRwlQtJ6yPjPfbCCqskSEgHR7tKCeUqG1zIkA6adu8nyh5MMkAaAAAAAAAAAI4GAgck+vr6tHbt2rr7jI6OavPmzUfojqafq2IIlbYrCkOr0NjMa25fSXEVRLbtkqRoiHTMGCVVEK7vURQRuEDBPZbkwgt3bPwzUzEhbw6EcvvG8yPS/SRj0h1tuqf3+iQ/MAAAAAAAAABoEoEDEtdcc42uueaauvs8+eSTuvLKK4/QHR0GNgoYSkFcxWBMuqjvKhxM+loUQrjZDWllQRjmF/2jgCEMpVJgZOO2THERQ/I4CR6SfRVVLiRtk9z8hrTKIe2kFO+jKNOICx4yrZSsSd+HpEw1BAAAAAAAAAAcTgQOmHEqYShjAlkbz2ewNhkInQyNjl+zcUAhpa2UwjAKDPyV/DB+HsoqkHFjIBQlDcZ7nDksOyQ62RgPm06eunQhbdNk4jQiN/rBq6CI9jfegGoAAAAAAAAAOJyCo30DwJEWhmmQkB0CHf+KQwhXDZGGETZ53S32h3FbpuR5mLZqcnMabNy+ya9yUHytNMxQWt0Qs95EaBd2JLMj5IUVmVZM8XuztjjMAAAAAAAAAIDDhAoHzDiV0KpFbmaDV7kQr/gH8cyGZJZDaGVMPGg6rh7wF/mTVkfWBRlSEKRzIAJXnZAckK1+kBciKA4KTFzlkA8LikIG16Ipet0kYYcrbrAkDgAAAAAAAACOACocMOOEoc1UN1RCbziz9asJ5LVbiqsRvPkI2cqItPoheZzOcc6eV9455FdD2HTf+AL5wCD07jXDK3lwraHSAgnXCorkAQAAAAAAAMDhQ4UDZhwrFzJEC/MuRAhDqyBwA6CV6XEURgMaZEw048Hmag+SKoM4pDDWZK7oBj9Hp02TCK9rUv17jvdxg6SzZ889ToIPV5FR3a4JAAAAAAAAAKYbgQNmHm92QloVUF2tIBOFB1LUTskNhPbnKCjaLRka7eY6BNZGL1gThwTZQc426abkVUQovb5kJPe6FxWY+Pq2RuSQVkfEx8ZtmfyKCWMYIA0AAAAAAABg+tFSCTNSJUxnLEjpkr0btpyEDt720Dsm3ad6aLSrckgrDZQMifbPmb9GGjbkXqvax5vlkH9jmbZKNj2n167JvQ4AAAAAAAAA04kKB8xMBYv/bnv6WhoyBIGJZj8E2SqH5CBjkn2TSgWjaCi1vCHRils22bTSIQk9vGoLY9LQwSo6jyRZ4wUY7gRexYI/H8JPQvx7cq2kShQ6AAAAAAAAAJhGBA6YsUIvNPAHKidtkWTSyofQn4VgZYwfOhilYYJVGEomiAMEE71mrZWJ2yBFeUHaYinTm6mGZBd/FkMuqPD3dOGCPzw6DTCMu7novmixBAAAAAAAAGAaEDhg5rKFD+MWREqGQPuvh6GVCeIQIT1ASmYrRLlBJbQKAlMVNCRns2nwEHpzFWxyXX/2Q3Rkuk9+hkPKJOeI9wtdxUPaUskaryVTXAVB6AAAAAAAAABgqpjhAEhVcxL8n74wTGcjJO2XpCRs8Ocw+PMh0gqDNGxwL/rXs0kY4V0juS+btGfK3KP3JB0anbuH5L5sul9urgMAAAAAAAAATAUVDkBOo4HKrkrAMe55sojv2igZhbIKFLdh8lsqWZtUGiSL/2lnpng/q/yd+EOlMz2YbLbiIjlvvFcyxNpKoTu7jdtCUd0AAAAAAAAAYBoQOAA5jb7xH1qbDHGWJBkjY7MVDVJUDVEKTDLiITslQl5AIUnG35w5v38/bk50tD2+nteOyeRbJyUTqJOCDJm4jVPgHVN3gAQAAAAAAAAANIGWSsBEueHRya+0uiAznDlXaZCvV7DeT+sFAtnz2aoAxF236Ll/nGudlFY82My9uZ5N7jUAAAAAAAAAmAoqHIBJyC7QuxZKUZVD4J4bEw2ZLkX7G2OS1kpuWLM7l7Umc958q6Pq0MFmhkgnd2JMJnyIWjUZL2jw2j8pH1wwPBoAAAAAAADA5BE4AJOQ5AVG8aq98QZC22ThPxoCbTIDoF1dgQsejKlVyWDTcyQbo9AitJKJQwqTHeWQCRfcttBrw2TDbCVF1GIpmjBB3gAAAAAAAABgsggcgMnw+iHZOHTIt0hysYLNHZKUFxgvkMiFBm7h3w8q0v3dWdz0h3SGQ1I1IT90sPGsBknGeO2VTDrkOq6EAAAAAAAAAIDJYoYDMFUuA/BDCG+AdBi62QnWq4JIw4QoHLAKw/xsiOR0mQfROZUMfUj2T1o0+ftGD8LQmyeRVE6k926TAwEAAAAAAABgcqhwAKZDvFY/MlbWwYExHRwa09DIuAaGxzU0Mq5DQ+MaHI6eVyph0uKoq6NVc2a36XlnLtGpJ/QpU+ogL0SInxsXGBjFTZmyLZdc8JCGC+mx0TwJd4w3nNpVO0gqHY7PBgAAAAAAAMCMQOAANGGsXFH/wJgWzOmsei0MrT76hbvUPzCm0fHKpM5/ysq+eBSEK00wGh0ra/veQY2VQ/X1tKuvpzNT7eAGSxt/m8m2XHLhhGw0UFrWKoxbKCluqWS8odcAAAAAAAAAMFkEDpjxxsYrOjAwqv6BUe0/FP08cGhUB+Kf/QOjGhwpS5L+6l3PV0db9v82QWA0OFKedNggSSevnBst/huTBAe3Pbxdn79+fbLP4r5ZuuyCFXrRumVqaykl8xmSGQ6KKyAkL0iIXgitlbFGoZUCVwGh5OV4sDSJAwAAAAAAAIDJI3DAjHLDHZu0dc9gEiQcGBjVUBwmNOPgwJg6+qr/b9Pb1abB4fFJ3dPCuZ3qnd2WHRBto4DBt2PfkL7848d1031b9K6rz9HS+d2K84noEH/atLwgIRkgHf80JvM8GW4dbzMMjwYAAAAAAAAwCQQOmFFufXCbtu8dmvTx/YOjWpgLAiSpp6tN2/YMqqVk1NPVrp6uNnV3tqp7Vqu6Ols1e1abema1qXtWq9rbokkJRkaHhsfUWgrS4dFxVYKRCq8jSdt2D+oj/36n/uAt5+mklXOSAdWSN6zaBQlxBmGtF0CEbh8T72+8odaZzAIAAAAAAAAAmkbggBllzuyOSQUOgZF6uttVrhS3HXrzS09RS0ugWe0thRUCQWAUBCY5lxTNVHAVBZkqhehF9Xa3a8WibhljtHXXgCpheu3h0bL+7sv36k9+4yItX9gdH5uGBUnQIDcQ2iahg3EVDkqrI5K5D3EAAQAAAAAAAAATReCAGaWvp71qWxAY9Xa1ac7sds3pbtec2e3q7U4fz+lu1+xZbUlgUKS3u/q8vrR6IA0XTO51f6t7/me/+TxZa9U/OKr//OmTuuvRnckxw6Nlffyr9+t//87FmtXR6p0rDRrkZkjHp3ezHGw0dTq6J6+lUmilUt13AgAAAAAAAADFCBwwo1xw2iItnDsrDRVmt2t2Z/0wYTr4FQzpkGcbhwE2qToIrVVQUCHR29Wu33rNGVowp1PX/3Jjsn3X/iF97SdP6tdffbpsUqWgJGiwyZXiIMJKQWDT162NZzrY9DlzHAAAAAAAAABMAoEDZpTTV8/TCUt6j/yFrd/mKLfdKAkLXBCQzl2waUWEMXrtC0/UgUOj+uVD25NT/PzeLbr4jMU6edVcybVPkuIKhijgiIKE9Kfkz3KIL22tQuY4AAAAAAAAAJik4GjfADBTuLZKroog3+5Iiiocon3jugTrDYKWJGP0xitO1sK5nZlz/8f3H9X4eCVzjJdhJNcPk+HQ3nWS+0qHSwMAAAAAAADARBE4AEeIa1mUf+63MkqrGryQwXtdktpaS3rbK0/LnHvnviHddN/WNMRQPuBw18v98q5jc2EEAAAAAAAAAEwEgQNwpHiL/gUvpZUFudIEFw5Ej6MHJ62cqxeduyxzjutvfUaj45XkmOgU6RSHKNywCsM02HCtnpSreIj2IXgAAAAAAAAA0DwCB+BIstnKA8mrenCL/V67pUxFhJVXsWB15QtXq7Ul/b9w/+CYbr5vSxIsJMOj/TQjHkydhA1K50S4KoiwTjACAAAAAAAAALUQOABHgT9DIXqQ/rBe2FCzGsJKs2e16bLzl0uSZnW06PUvWaPnn7002SkJErxAIUzCDiXzHFwoYeMHNt7BvzTVDgAAAAAAAAAaaTnaNwDMNOmMBbeQb2RNtM0kcxVMVRslSTLGZM710otXqaOtRZdfuFKd7S3ped25rJWRicILkwYL0SlNcj/+EOv0eJvuY6XcpXEcsdZW/dkBAAAAAAAAphuBA3CkefMVrBRXFUTbjDGFw6WlaOnfJmlAFAJ0d7bq1c9frSAwUSsmd0ycEFirJGjwZ0KESisdgsDKWpOGDe5+vPVp6hsAAAAAAAAANEJLJeAosfFfbNE2v7rBb7fkfvlDnlU9p8GfERGGNj0+bpmUtFTKD5W2VmF8E9l7IHIAAAAAAAAAUB+BA3C0eGv4/uJ+2nLJm6PgpQ1uux8oWC+VqG7ZlD2ftfExRUOlpVzoYOVlHgAAAAAAAABQE4EDcLR5rY6inzauPlA6V6HRKbz9rLVa/8xebdk14LVISlsoOVFQka2SSH6F6f5+q6X0ekQQxxN+uwAAAAAAAHAkMMMBOAakC/gmGs6cqzhwsx3SjdFsBrnXrFWlEurux3bpJ3c+qy27BrR2ea/e88Zz1VIK4jN78xm8x8kwaW+gdOiGTUc7Kqp2MMngaIZIAwAAAAAAAMijwgE4BmRGMMQL/spss9lKhfjFdC6D9OTmfv3Hfz2qLbsGJEkbtvTr3777iEZHy0krJv9aldAbDqEobPCrLdzshmRGhLLnAAAAAAAAAAAfgQNwLMgt6LtNRa2LkuAht9/JK+dozbLezL4Pbtijv/vyfdp7cCTbMskqE14k8xzkhw3ecykTPAAAAAAAAABAHoEDcKzIVSwk1QS52Qt+1YM3K1qS9JuvPUN9PR2Z027dPaD/98W7tXnXQEGlhM0FD9EA6tAPIuJ90mqH9IaY5QAAAAAAAADAIXAAjiW50CHzkq2uMHCtlRS/1tvdrve/ZZ2WLujK7HdoaFz/+LX7knZLUty2KTO7IU0vXCiheKZD0m0pF06EltDheMDvEAAAAAAAAI4EAgfgWJMf6pypSkhDgmQf/zAr9c3u0AfevE5nr52fOe3QSFmf+sYD2rJrIAkvwriiITO7Qe41f5/q1kqWsAEAAAAAAACAh8ABOAalC/s2M1A6eT1XAZFWHET7t7W26Ddec4ZetG5Z5rihkbI+8Z/3a/ueqL1SGOZmNbiAw7Vaik+erXZI76io6gLHIH6TAAAAAAAAcAQQOADHokwbo8ym9LE3R8Gf9+C2GUlXvWiNXnLe8syph0bK+udvPaT+gbGq2Q3Wu1gY5oZTu3kOSqsdkvkOAAAAAAAAAGY8AgfMGDv2Duqhp/Zo664BjZUrR/t2mpNJGLznbnPVc5upijDG6LUvPLEqdNh/aFSf+c5DGhkrZ2Y3pDMd0kKG7FBpL2zwtuPYxu8RAAAAAAAAjoSWo30DwJFw/xO79KHP3qFyJZQkGUnz53TqhCU9OnFZr9Yun6P5czqP7k1OhJWsUZwKGBlTHRQ4LnQYGSvr9od3JNu37BrQf936jN54+ckF8xmi0CJ0lROKKyqMkUnaLRlvwLVNroVjk7WW3x8AAAAAAAAcVgQOmBF+ds+WJGyQogX03QeGtfvAsO5av1OStGR+l85eO1/rTl6oRX2zjtKdTkCUNUQP3ZwF65oeZRlj9Ku/crL6B8a0fuO+ZPvP79mii05frDXLe9PqCCNJJg0bkhTCxE2XTFoJYa1CGa/SgkXtYxFdrwAAAAAAAHAk0FIJM8JJK+Y03Gf7nkH96PZN+uvP36VPfeMBPbRhTzRU+RjmVxj43ZeKlAKja195mubObk+2rVo8Ow0qvBO41kmhX/ngXkuu4Y6zmeoIKZ0vAQAAAAAAAGDmoMIBM8IrLzlBHW0l3Xj3Zm3dPaj9B0fq9rV/cvMBPbn5gBbM6dQrLjlB5568QMGx+M1911opfpz5WaCjvUVvvPxk/eeNT+rK56/WuScvUKkUZAZDW1M9fDqpoIh7N0Uhh0lDCLefMVHVg5VKDT4uNxsiCI7BzxUAAAAAAADAhBE4YEYolQJdcdEqnb56noZGyhodr2jzjkN6elu/nty8X09t7S9sO7P7wLC+8IP1uuneLXrTFSdr6YLuI3/z0+zUE/r0x79+oVpKUYFTmAQIUcMk64UYYWgVBCauWDCZOQD+x5XMd7Cu5ZLX76mGMCmJyO7HrIHDIQqI+FgBAAAAAABwOBE4YEZqby1p7Yo5Wrtijl528SoNDI/r4af26M5Hd+iZbQer9n925yH97Zfv1UsvWqmXXbTq2PpW/iS6F7mwwR3vhkYncyGSCge/2iEqcPCv51onhdZE/dlcUGGloE5w4A+arn5NLIwDAAAAAAAAxyECB0BSd2ernnfmEj3vzCXasuuQbrl/q+5ev1P+CIcwtPrR7Zu0aftBvfUVp6mrs/Xo3fA0C5OB08pUOLi2R34g4Vou+YFBVOEgWesSCavQmqStUr5qIQ0yqkMHpj9MP0ZqAAAAAAAA4EhgaDSQs3zhbL3lZafqj669UGetmV/1+mOb9uvjX7tf/QOjR+HuDg+bhA2urZKNZzHEgUNcBpHuo8xw6WTAtBdQyNpk6HZ+wTtp4VSwndVxAAAAAAAA4PhE4ADUsGDuLP3ma87Qb77mDHXPylYz7No/pE98/QHtPzRylO5u+vQPjuqndz2rj37+Lj2741BVOyXrSh/cxvhnVMfgBRUuiFAUVIT+YfHPMIxCCHdcGIcSfjBB3DD93OcNAAAAAAAAHE60VAIaOGvNfK1cNFufu/7RzHyHPQeG9dnvPKzff9M6tbeWjuIdTt71v3xGP7nr2SRHuPPRHVq1pCcJGkJrVbIms2DtV0O4gQvRzIbomGhotCR57Zi8odOhey2MKyiS8xZXPQAAAAAAAAA4PlDhADSht7td73rDOTrjxHmZ7dv2DOrLP36s5gDkY93ivq7MAv/d63eqUgnTqgaruK1SdpZD5u3GL/hzIJIKh+Sn/zj66e+jpHWTxHfxp9fx+mcTAAAAAAAAxx8qHJD40pe+pOuuu67uPqOjz525BRPV0hLo7a8+XZ/7/qN6+Om9yfYHntyjG+/erMsvXHkU725yzlwzT+2tJY2OVyRJ/QNjevzZ/Tpj9TyvXZJkjLdwncxqMKrKHYykpD1SVO0QHRcNkbZeMOH2CcNooHRg0lkQYWgVBEYAAAAAAAAAjh8EDkjs27dPGzZsONq3cUxrKQV66ytO0z987T5t3zOYbL/+to1au3yOVi3pOYp3N3FtrSWdtXa+7l6/M9l25yM7dPrqedGshSAOFqykuGtUPmSQ0kAi6pwUtVgyVgplZax77g2h9iofwtCqVEqrINJZEJhWcVur9GkU9AAAAAAAAADThcABib6+Pq1du7buPqOjo9q8efMRuqNjU3tbSb/1mjP0d1++V0MjZUnRovnnf7Bef/DfzlNXR2uDMxxbLjh1YSZwuP+J3RodK6u1JUoYXHslb150do6DJMlIxu2TtkgycYVDUhGRa63kKh6MNQqUjH5Ij2dBfFrYXNgAAAAAAAAAHA4EDkhcc801uuaaa+ru8+STT+rKK688Qnd07JrX26k3v/QU/dv3Hkm27Ts4ok/85/367deeqXm9nUfx7ibmpBVzNXtWmw4NjUmSRsYqemjDXq07ZYGsP8fBTViwLoRI6xCSodCKvjXvWjGFsjLxEOk0YEirIcK4pVIQ2DiwsFWtnNz5CR8AAAAAAACAYxtDo4FJOmvNfL3gnKWZbTv2Dunvvnyvnnh2f3MnOQbW0IPAaN0pCzLb7lq/I174T9sbufAhelI9PDodBh1VfLjXbWjjAdE2eS0MbTwsWqqENg41lAkmbCbQYPjxZNX62Pg4AQAAAAAAMN0IHIDJiIOC175wjdYs6828NDRS1j9/+yE9tnFf1f41TlN3nyPh/FMWZp4/+sw+jY1VvADAqz7wwgYr91p0nPWeuP3D5LkfJsgLIdxsB5vd17uf6DyH9SN4zsu2wAIAAAAAAACmH4EDMAkuG2htCfSON5ylc07KVgiEodW//9cj2rF3sPhAo8YBwxEMIFYsmq05s9uT5+PlUI89u98raLAKQxcipIGCe80qHQgduhZMXlVCMrvBq3ZwXZlClyT4MyKkbJWDdXMhWDCflJpVDnyeAAAAAAAAmD4EDkA9tSoTjElea28p6e2vPk2vvnR1Zp+xcqj/+P6jGq+E0a65c5nceQqrHY5Q6GCM0Zknzstse2jDnrTqwG9plHucbbMUhQp+ZYQ/syH95QUUYS6kUPWxYUELJwAAAAAAAADHFgIHzGxFC/pe9UFQY1CxMelrxhgFQaArLlqp179kTWa/nfuGdMMdm6J9jEmCBxc2eHlD1VDkTBGE/+QwhRBn5AKH9Rv3peGBvNZI3j5J3mC9n14rpaSFkrKzGaJqCW+QdOid2wse3GPFQQZ5w8TV+sz4LAEAAAAAADDdCBwwowXxon9+Wxom5A4w0TbjHWdMut+L1y3Xhacvyhzy07s3a/f+oeS46Jj6qUEmhPArIA5jxcOaZXPUWkr/lnDg0Kh27x+OwoPQZqoTso+9QEFpMJE9Jq1iSFoz2TSwSPf1zhXaTHVE6A+LwITx8QEAAAAAAOBwI3DAzOMt2vshQBomyAsTTKayIDBGQRBtyFc/GCMFgdGv/spJ6u1qS7aHodX1v3ymoKIhew3jXSfaN93fv1Jh1cM0aG0JtHpZT2bb+o37vDkMNlmwdgOjHX8f9zMNFtJfkjJDopMKB2+YtN+tyV0lTOY/TN/7nQlsg5SBAAIAAAAAAADTicABM4rJVS/4FQ4uTAjixX4pWs8P/IV/FwbEj7PhQHRMZ3uLXvPCEzPXvffx3dq1byi5Bz9s8EOI7L1mKyLS66b3Nt0FDyevmJt5/sSz+7MzF/wYILeWnZvv7AUL3rYwPZf/y1qrSuiHEN7shrgMwrVWwsTxqQEAAAAAAOBIIHDAjOJXMASBHzB4C/y5KgS3PQjSqgPFx7tF/0wYYYzWnbJQyxZ0Z67983s3J+esfYPpueKnSUVFphrjMFm7Yk5yrWULurV4Xpckb2CzHwDEx2SHQfvhglcZoXw1gzs4PWc2vEhDB1fl4EIJTEx+5kb6hM8SAAAAAAAA06vlaN8AcCQl8xmCNEQIQ6tSKUhChsCY5Jv8aTVC/Nz9is8ThRDpwOTkHIHRFReu0OeuX59c+45Hduh1L1qrttYgU6VgJa+CwRaGCv68CHeg289O48Lx8gXdeucbztbKJbPV0dqilpYgbssTLfu74CEdAp3eV1T1kH52VnFAEEhGJhM2hKGVMd4g6rgiIvp8TUGYYZPPOAxtEsKgtmb+XBA5AAAAAAAAYDpR4YAZxQUIQdyeKEieZ6sf0lZGaVultBIi1+bIeG2YXPVDXOUwp7s9ufbYeKhHn9mb3kv8l8ysBi9sSGc7FC+u+7MmpkupFOjklXPV0RplkckQZ/kL/+k8Buv+l1Q9pJOgq6ocvG35Vks2ntGQBBvxBf2Qwg860Jxs5QifGwAAAAAAAA4vAgfMKIFX2eC3ViqVgkyAYLzgwD1WPojILfj7A56NkVpKgc47dWHm+vc+viuZxVBrAEMm1PB2y4QRU/4kJsCrMAjdqr/3ml/94PKG0AsLwjDaNTsoOjdQ2r3uXS8zv0Hp/iGBwwTZwpCGTxEAAAAAAADTjcABM0o2bEirE0punoP3WuBVGPgVDv5g6cz8B6XnjC6mqsDh0Wf2KqyESWKQGfzsBRjZoKG4quJI8POFJCiQMhUH0YteNYTXJikZHh1G+4RJkFBdtZAZTu2fLz6Z278S2qRyArUV5jKENQAAAAAAADiMmOGAGSUNB4ys1z4p+hUtxgbGKJRNgoXQpAvofnWCP9DZnTu9RrTfCYtna/asNh0aGpMUtVXaumdQKxfPTgIKa2163qr7jYOGpIVRUcXD4VtE9oMBY6J2SDZI2yhZ90Juf/9xGFqZkjJBQnoeJUFEICkMjYIgqphwcx+i0MK1WorCiyjUYI5DMx59Zq8++51HFARG77jqTJ1x4vz0xcwcDj5PAAAAAAAATA0VDphRXKWCq2TIPy4F0f8ljIwXIKRtlvxWS0nLJVVXIySDpYNAJy7rydzD33zhbg2PjGcqIfwQwZ8FkTD5/XJv7DCsFY+VK1q/cZ+++fMNunv9Di9IUDqvQX5rJFex4AcSUiUM05kOLmzwh0G7odTx80o8yyEsCC+s3AyI6X+/z0Whtfrk1x/Uxu0H9fTWfn3qGw8e7VsCAAAAAADAcxgVDphR/HZEgbIVDy50sPE3+V044SocjPtfkhNEVRE2OXe2AsJZvbRXDzy5J3Mf//TNh/T+t6xTKQhkFFcuGCNZW501GMm4hXevqCHTiknTW+dw7+O79OUfP6ZyJTprb1ebzlozXy2l1nQeQO6CySfh/UgrFGwmNJDS8MEYE1U4GMnKeBUPrsIheqf+uZhA0JiVNDg8ru17BpNtz+44pLHxitpaS0nVCgAAAAAAADBdqHDAjBK4QdHeLISoysENkFY0zyGpaEhihuyMhfiYIEhfD7ywwQ2ONkY6cVlv1X08tbVfP71rc7rgazI/En7LJ6nGPlP/WKqcuDR7z/2DY7rpvq2Z4c7+LAfJbcgOjbbec7fNl+6bq3jIHZN9PZrjUDQIGVkHBkartvUPjCWPq34PAQAAAAAAgCkgcMCMkgkY5EIGfwC0SQZLZwZC50KKbJVD2mpJSbVE+uuEJT3q6Wqrupcb7nxWY+OV7PwGd46CwdD+fn7bpcMxRHrO7Ha98NxlmW3fv/UZPbFpfyYYyLRPUn4AdLqg7dooSUqKE8IwPchvy+S3UpItOGdcGUFbpWxAU+TAwZGqbf2D1SEEAAAAAAAAMB0IHDCjpAGCHzJ4FQlKh0Gncxui/TLHK/e6V+mQ7hP9am8t6Tdfc4b6ejoy9zI4PK4b7nw2e2/Kt1MymeHUSZCh7L5+4DFdJQ+XX7hSne3Zrmsf/9r9+tsv3aN71u9KAwOlA52rh0bbJCCoqm7wnibftJfNBBRJ9YO7Rty4Kbo2iUNd1uqAV83g9CdVD3x+AAAAAAAAmF4EDphRkqHRSRultLIh32YpDSbkDYjOhQpxSOBXQ0jZCgQZ6bTVffroe16gV1xyQuZ+/usXz+iXD26rWaXgz4PIvOw99mZPT2feoK6OVr3s4lVV25/a2q/Pfvdh/c0X7taOfUNelYJyYUO6zcsmCrnKBVft4NoppefKVjhUqHCQ1LgdUqOWSlL6+3K0W1Qd7esDAAAAAABg6ggcMKOYqsoFV80gb3u0Ldkez2dQPOMhX+WgJHzwqhCUDplW/FiSrrhwRVXVwJd+9Jg2bD6QHpsLL6LjVRVIVM128AKT6fLidct0xonzCl97amu/PvzZ2/XTuzdnwgS/yiE/uyGskRLYeC6EP89BVgrD/Hmz8x1Q34FD1S2VDnotlWrM/wYAAAAAAAAmhcABM4obGl0KXOVCdi6DCxoCt4+8gdKZtkppK6bAZEOMZHB0JpiIXuvtbte7rj5bLaX0/3rWSv9545OZb3gnoUF8fOZ8pmi4dLbqYroYY/S2V5ym1pbiv1VUQquv/eQJ/ej2jdlKhOTNKQ4ObFIFUcSvhpD8wdSuxVK6XxhG56xUwuKTQVL00R84VN1S6cAhZjgAAAAAAADg8CBwwIySzFtIWisp/RV4FQLGqBQEmRZJ2cHR7oS5tkpumxcJ+C2PJOm01fP0jqvOzNzX5p2HdN8Tu7L7uxZOyfHZYdT+e8rPfZi2vkqS2ttKWnfygrr7fO+WZ9Q/MJoZHO1z1Qu1VFUvhH4bpWhyQzrPwbVcmuIbew5wn3ctRS2VDg6OZapQ3AMKRgAAAAAAADBVBA6YUZIZDd4Q5uzshuoAwp/noGQ0g6uE8CoR4gEKfkVD4fUlrTtloS44bVHmtV8+uL1qHkNynDtn5lxetUO+/ZL/ZBq89oVrdNoJfZrX26HXv2SNrn3VaWpvK0mSFs+bpQ+8ZZ16utqTGQxVGixmp7Me0vKIJHRwx8dhg6uUoMKhsWZmOEiactrA/AUAAAAAAABIUkvjXYDnDhcmGJOb3WBtMifBWhuHEtFCqgsnQmsVGCOrbFsjV4kQBFG7n2xrpWwSYGx6zCsvWaW71+9MXn74qb36o0/eqtNOmKtXXnqCFvd1edUOxeeI2Ozz+L1VKtF7sibexajhwn8tXZ2tesdVZyXnKJWMOttbtGn7Qb3y0tVqbQlU8UoOimY11Jrf4ERDo01SIRFayViTGSLtD5dmhoOioKBGDy1ri9sn9Q/629wfjKOvzlsBAAAAAADAcYLAATNK2kopW70QJkFD2g7JBQZRkGCTddm02sEFFtFxskZBYL1j03Agempk4w3GSisX92jp/C5t2zOY7NM/MKrbH96h+x7frfe+6RydsrJPkvTYxn265YFtmtXeokvOXqKVi2b7J85VQ7j7i4ITWStrppQ3pNyQYSudvXa+zlo7Pzqv1xIpM8Oh+tC6p3ZhgzVGYWiTzzM5r3eNRgHGjGGrQwP3OQ0MVVcz9A+MJr9HrO8DAAAAAABgOtFSCTOOq2xwg6Nd6JAEEUH8K2mvlLZPSlsxpZUS6WyF4mHP7pc/i8FVIbzg3KWF9zg6XtGnvv6g9vaP6Ae/3Ki/u+5e3fnIDv383i3668/dpV/cv9U/dTq3wVVXGGlsvKIHntytJzcfULkcykq67/Fd+s7NT2nj9oNT+gyTGQC2eo6AnULikD9v1FYp2/HHBQ+V0M7o0CFpN1Xn9dHxStX2Q0Pj2f1yP4vOAwAAAAAAADSDCgfMOG5BvhSkMx2S+QzGyMRLr6FJwwVbSSsXgiAd0myUzlKwNgonQm971CIoXbB1sxisjR5fdv5K/fLB7dqya6DqPodGyvqjT/6iaru10pdveEInLO3NVDoYRW2Gntl+UM9sO6if3PlsYQ9/Sbrl/q165xvO1prlcyb24bl7kPcNeW/mgjFmSlUU6ZBoI2O9wEFpW6Vox6gCIrRWwQz/nn6tz7tcqRSOZhgeLWcqRYyZGdUOrlUaAAAAAAAADh8qHDDjBF4VgEmqF/z5DvFY6LgKoeSGR8c/XUVB5pf8c+Z+uf/lqhyMkdpaA73z6rO1ZH7XhN5DGFr95M5nk5BEkvYfGtXfXXev/u8X79HXb3yyZtggSZXQ6os/fEwjY+XJfYg2/Ya9/w354dFx/ei2jXpy8/7JndadM5nbkK16cAOj3YJ5uRLO2CqHfOVH/rWx8eKh2mFoNVqe3oHbFEEAAAAAAABAosIBM5ALBqyJgga3LfNTVmHyehwWJN8GN8nQ6CREiCsjjDGylWiug1Q9BLfquYwWzp2lD/3O8zResWopGf3tl+7RE88eaPg+7nhkh1516WotmNupsfGK/vFr92vnvqGmP4cDA6O67aHtuuz8FU0fk+cW/scrVrfct0U33PmsBobHdfrqPp20Yu7Ez5f8JT1/GM+HyMx4iPeLKiBmwvfzm+d+T4raKTnDo2V1dbTEQ9JdNc7R/RzJLAAAAAAAAI5/VDhgxkkqFVwrJaVzEFy7JBOHDe5n5pj4PCbwjvV/VYUY3qBpd6xXAeH27WgtKTBGb3nZKYXLvte+8jT1dLVltv2vz9ymf/veI/raT5+YUNjg3Hz/VlUqk/u2u/+t9q27DulbNz2lgeFoPsBjm/Ynjyd20vTc7ldobVLV4M90sDaq1JipC9VJZYlX8eEbHasTOIxkK1uSTlUF56F6gTkWAAAAAAAAzaLCATNW4K3qu6oHSclg6EqYbZcUBGmgEHjb3UBoG8cEQSAFYZIkVC1WGm9bftC0rLR84WxdcdFK3XDns8kx7W0lXXrOUm3edUg/u2dL5nx3r99Z9d56utp0yVlLtO6UhVq2oEvrn9mnp7f1a35vp756wxMaj0OGA4dGdf+Tu3X+qYsm/Pm5ygJjpJWLZ2tR36wk9AhDqwee2K3nn1M8FLu5c7vH+Z9xyBBKlYpNgoggKIppnuuKqxKspLE6bZOGR8cldcyI2Q0AAAAAAAA4cqhwwIzlVxy4CgZfOtNBaZWD28+rUAi8vCCtcjDJSm5+zoN/fb9iwr+nN15+sl507rJk+zUvP1UdbSW99KKVainVXyKeP6dTf/G7l+iqF6/RioXdCozR6avn6TUvOFEXnr5YF5+5OLP/z+7ZMqlvcCftjWz07s8/dWHm9Yef3jPhc2bPHVc4hGlbpdBrp2StVSUMkzkPM07yntMKkPT3Qxpr0FLJO5H310neyhSOPVJm4h8RAAAAAACAI40KB6AW44cQUWsla6M6hiBpySRJVoExCpOFWxPNcLBxyYL7azwHwp073pRsN67EwRiZQPr1V5+uV156gmZ1tGj2rHZJ0tIF3frwOy7VTfdt0Y9u31R421f/ylp1tLdEi/LxPbvh0sZYXXbBCt36wLZkkXjr7gFt2HpAJy2f+MwFKV3sP3vtfF3/y43J9qe29GusXFFbS6nqmK27BnTLA1vV3lbS6Sf0ae3yOZKkQ8Pj6u1qkx/DhNYmw6LdTYc2+swroZUNo3kb1Vd57kvmLxS8UHeGw0hZNrRR5Gzcn1NT+3wAAAAAAABAEwgcgBr8Dj1B3EspDCUTKB0aLa+awcaL+jZq7xN68wVcoGBls8GD4u3GRm2ZbLbFzZL5XZKy385euqBLv/orJ+my81foI/9xpw4NjiWvnXPSfJ138sLknHL3551g0dxZOmvtfD24Ia1A+Nk9WyYeOFgvSJG0cO4szZ3drv2HRiVJ45VQT20+oNNWz8sc9tBTe/T56x9VuRLd0833bVVbayAjo9HxilYsmq13vuFsdba3ZK+jbFWFq35w2462tE1W8yv21toJ7Z85tvB86fax8dotlf7mi3drvBzqxKW9+uO3X6i+no6a93XEPlmGfwMAAAAAABz3aKkE1JBZCDZGpSAeJB1tiCsg0jkMadsk/7nXb8kd5m/zBkf7+xmZzNqrf053bwvmdOr9b1qn+XM6VQqMXnL+cv3u68+O20NFVRhuroF/nIx0+YUrMu91/TP7tO/gyOQ+qHiR2xij007oy7z07Zuf0s/u2az98bkHhsf1pR8+loQNzth4mHwjf/POQ/rxHWn1hh8ouJ9Ji6XQqhK3XDraXAgy0WOmdE2lIUP+XPVaKo2Nh7JWemprv/7rF89M7SZmgGPgjxcAAAAAAMBxgcABaEJS7RAv4rvgIR80uJAgSIKIKDww8XDlfADhb09e946TcnMf3DXj5ycs6dFfvvP5+sQfXqb/9rJT1VLK/l/aSF7okFZlnLisVysXz87s+8CTuyf/AcULsvnAYdf+YX33lqf1sa/ep8Hhcf3i/q11W/04tz6wTf2DUaWEP5cgWdT3ZjtUKuExsSDsdXya0DGTvl6dg63qBw6+b9/0VHK+Sd/PsfAb0ISpVMJMas7JcfK5AAAAAAAATBcCB6AJbtE/qXCIg4e0rZIXPhgTzXhIjk2rGPIhhDt3pphC2efpdiXti4yfHkhqKQVV1RbuIH+7m+UQGKMLT1uUOf99T0whcIidtGKuSgVDrQ8OjumW+7fqlvu3Vr3W1dlatW28Euqe9bskZdey3WBktz0ZKh3ao7+4ewRLHPz3auPzWO+x1HzgUHQbacDT/P1Nx2L+4fw9ZOkfAAAAAADg8CNwACbAxKv3Jt0QhwNe+CDJBK7lUnFPeuMHAaoOJNLHJhswRJfKVUBUzw1IKyFcdYXx8wkZY7TulIWZjvmbdx7S4PD4JD+ZSHtbSWuW9Ra+9vP7tmjB3M7keWd7i/7qXc/Xh37nEv3xtRfq+Wcvzez/4IYoAMksrtvqAKIShw2V8MguKecXx4/8gnbtkMXaiQUOdT+7KYYARz0ImkbPpfcCAAAAAABwOBA4ABMUGK9NkdIKhUwVgfy2StWtkXz+67mxEZlAIFsVkW7L3ofJVUJ453HVDfG9z53drqULujP3smX3wKQ+E995pywq3H7BqYv0+29ap998zRla1DdLl52/XB1tLSoFRgv7ZumKi1Zm9t+045B27husu5BvreIZDlGlQz3TvVhcVBUwXS2VGt1r1bWTG7BNDY3Oi2ZsTKJCYxqxlA8AAAAAAHD8aznaNwAcb/zgIAiMbMVKxshav6WRqzCwyWq/q4SQsgvK+RzCHZvsYyRjvX2tO7+tuh/vEtFPGw2PDo2NKx2sV1FhtGJRt7Z6IcPWXQM6ZeXcqXw8uvD0Rdp3cFj3PLZLe/ujYdG/9dozdOaJ8yVJZ62ZrzNWz6sKCOZ0t2vV4tnatONQsu3BDXv00r6ubPLiCa1VJQybqnCohFaloHbwM1HVV7OydmLnrrW+7/4s1bu2m2URRQzVO0+kwmHnvqFM9cl0ma6Qx1o7bb9vk7q+av4RrH0M1RAAAAAAAGAGInAApsobGp22LYoW92WMjLVJxYHXiyk53C2mxvlA7txRFYOVzbZaihekkzVNF0p4rZbcvm5btgIjCjRWLJqt2x/ekey/ZVe62D9ZgTF65SWr9cpLVmvH3kF1dbZq9qy27D5BWmnhu+Sspdq043G1tgS6+rKTdNHpUbVEvcXbSiUaHt2owqFcCVWuSB1t0/S3PZsuQ7vB1i4EmsBJJrDVXdYW7pNdFLcaLTcfOOzaPyRpXtX1m10zdwHIZDMBfyj4YcsVrJWtMR+lycMP370BAAAAAAA8RxA4AFOUhA1W8dwGm6lw8BeCC/IGGWOib+d7QYALH5LwoKoKIkob0pDDyCZVDN6O1oUNNr3X+OzGSCsXzc6cdzpaKvkWz+ua0P4Xnb5IO/YO6uIzFmeOrbXwHYZWoYnChrDB6nilYlWuhGpvLVV9W34y36D3rxaGcSujBgv0+evUvGWbjQ+qjvVCB2PTxXCbvjShlko79w1FHZkKPoJGmUMz7Z/yH+3RrlgAAAAAAADA4cEMB2CKgsDkhkmbzAyHzIRoT3aIs7fNVSEksx38AdHZGQ7uIH+f5BhvjkPgBlgn9xTtt2xhd2ZZe/f+YfUPjE7xE5k8Y4xe96I1TQcV1rq2SlaVitV4ufYie7kSanS8onKlep9G1RG1ru2ODW1c5dDkMcnzwn2am9/QaL+JtFTaf+jo/Z5Lz835DXRUAgAAAAAAMxGBAzBNjFz4oGRVPzBpDBC1VPKDiXR7MhDaKLN/doh0PnTIBRhFuUayzXjVDa7qwaizvUWrlvRkzrN+475JfgKHR//gqB5+eo+e3XmocJE9DKMKh0oYanB4PPNaxQsXRsYqCkNb9c3/ZuY/5K9n3QAFKa6ssPHM5QZhgbyWSDX2dcOn868nQYPktTxKHrmZ0cnjiQQOA0Nj1TehqQcBE1l0P6YX6JPfs6N8HwAAAAAAAMc4WioB0yRTpRA/N0E8w8G1lXGLxlVhQXZBM23GlJ7bPyRtU2OqgolofxcsRIJAqoRptUMoG7XikXTWmnnauP1gcuz6jfv0vDOXTPJTmF433LlJP75jk8qV6LP4lQtW6DUvODGzj1tor1SsxsbLmt3Vpt37h9TX06HQWpVKgay1Ghktq72tVFUFUQkbz3/IXC/+i1/h4LY3HOHgzRGwNQIK1xYp34ooCSHieRHJn6UalxqrU+2RNxAHNZNdUHftmIpaJdXuGlXdIqre+Y+3DkyTGTQNAAAAAABwvKPCAZgmyeKpMUk7oyDdlPz0h0ub3LFJlYP3or+A6+9v6oQNfqumzLkzJ4leO3Pt/MzxT2050PCb+kfC8EhZ1kqnrOpLtt149+bCCgxrrcphKGutxscrGh2raNuewSSoqIQ2aaU0Xg4zFQ3lSiirCbRVilsnuQAgdM+baakkrzoh/kvVZx2nCTUX6otvKfOK1cQqHA4NjdcJBuofW+v1yfwZOpx/6vzPfrLHAwAAAAAAoD4CB2CapXMYorZFjYbjpmMeXFDhz2JIh0C7lkz+WAh/v2T/eOdkv3h7EHhBhRdYrFw0W22t6d8KBkfKOjSUbU10NIyWKzImCkB8//q9hzMVGVI8x6GSHRxdLocaL0czG8bLoayNwoaxciWZU1GuhEko0ewCebS2b+OwIf52f9jcOaxNj02CCy90SIZPJztnD860Tapxd+6wkbHJt1Sq9y4mEiQ0G2LUq4I4XI6FUA0AAAAAAOC5hsABmGb5AdDJT79SwWR/+ccl+8gLGwp2zAyB9q7ljs1UMuSv605jorkTS3JDmrfvHZz8BzBNvnfL07r+lxurFs4rFauv3/hkZptbtI8GOKfBQ7kcDZJ23/YvV8K4csImbZbGy9FrYcOwIG175QoK3DwHFxw05oUMUtWxfjul2veQn+1QPV/gmW39euTpvc3ckKS0pVIUWHgnOqwL/oft1MeG5/wbBAAAAAAAqEbgAEwzFywEQa7aQNXBQ3KM8qGAyQUXuX2SnyZ7fPzAVUq4SgbXPilw5zXp8UZGSxd0Z+5nxzEQOLzxipP10otW6tKzluisNfMyr23dPaD9h0aS52H87f9KaLV9z6AqcdXCeKWisfFK0k7JbZeiCoBKGFVGuGoFnwsw0mu4tktpyOCuWy90yJ8jDRuSC+WqGmxV+5/k/EoDCWttJqhwh1tJ//a9Rwo+0dpGxyoaGy9Xvf8J8e7H3Uv2Qe1z5ltCHWts7ue0n59wAgAAAAAAPEcwNBo4jJIhzfGCogsKrNJgwnoTcY3JLj6adGPyuqxkTTRW2uQTCHkhRLxv9DN6Yvygwbg7kZYuyFY4HAuBQ3trSa+6dHXy/C//407tPjCcPH9s435dclY83NpGi/9RxUF6jnI5VLkSZoIGSRoeLSuIP4z8MU4Yr+6HYRQeyVpZY9JFfmtlQ284cPx6Xj44kHEbbfK6iRfrk0DCWllbcK4m1qUPHBrV+meq51w0MjA0ro62lsx1rPfX5P7z95T8dRpGJNvi80zbcnyN8wMAAAAAAGB6UOEAHEbGrygomLngPy4aMi3vtXzrJb9iIvM/kz1nckwcfrgqB3/Zddn8bIXD5l0Dk3/Th8kFpy3KPH9sU7qonlYaZI+phLZqSLQkjYxWdHBoTJW4BVPhQnoyo8FbdrfZ6oMwvmho5VUZFLc8cifxKxxs7nl2NkN6X/nZDflv3PsVEPc8trPqvTRjYHi8bjun9P1M4KS1KhrqbDlc3/Y/0kUE1CwAAAAAAICZiMABOMySUMDri5TPINKZCi5AyIUGuW/OZ9ovZa7lbc+96FosJXMfTHpvKxbNzpx/664B7do3NOX3Pp1OWTU383zbnrQKw9riGQzWSmPjFVXCsOq18fEwmvkQ2qpAwh3rt1pKKxBsMiQ6GfLs2gkVtmZK2ypl2iIprQ7wWyTZ+MVcTpHsF52z9nL2/U/srtq2dH5X1YyQvEODucHRh2HFvPo9Nb//dJvwuWl7BAAAAAAA0BCBA3CYJQGCVBU8KB8YKJstVD33Wi/JDYl2MxsKjkvmOLiqBuPPfUjva87sdp28ck7mvu9av2Ma3v30WZwbbL23f1jlchQkhMkk5yzXLqkoUHDHVcL87AGb/PSrH5KBzV64YZWGDq7KoHph2iZDqm1yvvhnfgaEkkuo6FTuOv5ryW3FG/zZFpK0avFsffBtF6qlVP9v9+ng6Pie5VdWFN9P1b3lnkuFvy3Z43LvZyKO5dkHx/CtAQAAAAAAHDYEDsBhFnhhQK6zUrIxU5kQPzD545qpfEgCDZOGDt55giBtuZRv9XTpWUsz51q/cf+U3vd0a28tae7s9uS5tdLu/nimQ4PF3bBW4BC6uQ9pxYIbMJ20aXLXU7b9UTpEuro9UnqPUcWD++Xv63ZMqx7SyolMABEflLR3yvRUyr4va61GRiuZbb/9ujPV19vRMHDYc2C4KjRoirU1F9cnXERQ5xqHQ7OBha3xeQMAAAAAACCLwAE4zEy8sp+vVEjCgdw2qXisbcF86HjuQ1EVQ3affGCRqYCIqyDOOWl+5nrb9w5qvFzdiuhoWjB3VuZ5M22frGpXOLgZDq6SYWy8okolrXrIhwBuP5ukBmnrJSu/giGStGBKfqWL3KH1KiO8cCNd286WFyRhg/e+/J/uwehYNnBoby1Jkkql+j2V/v2/HtWgX+VQYxZFc62QGuyVeV/1qyhs/jOYgmM1LiDHAAAAAAAAzxUEDsAREMS9j/xAwA8C5D1XQehgcmmDHy74+2QqKLxqByUVDdHPIEhbM7njume1aV5vR3K+MLR6cvOxVeWwqC8XOOxvHDiEoa250uzmN4ShVK5YlZPn1qtOyJUiSMmQ6DAJGtLX8u2Z3HyJZN6DvHPY9LyZMCO+XiZUqL6NwtBhZLyceY/trS2S1LDCQZJ+8cDWNEjJnnniClol2YLXJ3/66uPrBxJHelW/2eoJ0gYAAAAAAPDcQeAAHAEmkxgUt0tyT6oGSPuv5csX5IUPmW3+HIeCX7mKCHeeVYt7Muf+zHce1pZdh6bhE5geC+d2Zp7vbKLCoVY7JSmd/WCtVbkcqlIJ4+oG61UnpPsmbZbiAMENd7bezAN/eLX1zhHGCYPfnqdqdoPfdinZzRsWnTy3NRfs8y2V2tuiCoeWoMHUaEmPPL03uX7yHlT8OM+6D2CCDndEUGtBfzraPTXfkolQAQAAAAAAzAwEDsAR4M9SyFclSK6ywZut4B+b7GuSn35IEfhhg3eBTOjgVTfImOwx7tqSVi3JBg6S9IPbNk7qPRd5Zlu//uGr9+kfvnqfbr5/qyqVibVsWjAnDRzaWoK6YUIzXKul0EazGyqhVaUSJuGAq3TItPVRdmi0a3Xk2iqllQ7ZgdOhFy7kWydlXrd+sJBetXjBW9ngwlqNjtdqqdT4b/frN+5L31tVa6PiY/z3kt93IuvsjfKK4oqGJs99FBb8yRgAAAAAAMBM1HK0bwCYKYz8hVl/doONf6ZBgrU20x7JvSZ3jvhk+YoHf/9ai6yBkWycMLizGknWWJ20Yk7V/o9t2q+B4XF1d7ZO6P3mjYyV9W/fe0QD8ZyAjdsPasfeQb3x8pObPscJS3r1R9deqN6uNrW3lTIzMCbFq14oV0IF1igwJq5miLYba+LgwR1jM6GAd5psGyTXSil3nElmPRTdj5U1JjrYRL/J1prM7Af/1g8NjmlkrKxFfbNUKhmNlcNMCFMqGbW0REFDS8EMh1WLZ2vbnnRWx/6Do9q9f1jz53SqOkbw3ljhlJHcbg33SM/XqHLC/0yn+lueP3e981lr4z9j0XsmQwAAAAAAAKiPCgfgCHCzFNKWRn4xQnVokKl0MH6QkB0enQkl/PP616wxE8Iodz8yOnnlHF10xuLMvYeh1f1P7JryZ3DL/VuTsMG585EdOjQ01vQ52ttKWtQ3Sx3tLVMPGzzWSuPlMBkYnVY25KoTMq2W3OtpNUImhHChQryva9WUVDF4x4c2nQtRNVBZ2TZNzr98+yH93kd/qvf//U16z//7mbbuOlRY3eCW84sqHDo7WrR2+ZzMtic3H8hcf+r8/kxeZUjjveuftWDH+hUSTZ64qWtPb/RAkAEAAAAAAJ4rCByAI8kLHrKbTbrwH3ivu7kN8mctZCsb/NZJ8o9TfmZDOrshyAca8a/ABPqd152pl5y3PHN/tz20fUpv21qrXzywrWp7JbS669GdUzr3dAhD11Ip+uWqG1wokA0L/HZK8uY65Gc+pAGFXyHhVyu42Q/uBG6wtN9aKfMV//jpszsO6ef3bEm27e0f0X/d+oyGR7IDozva0iK2oqHRbS0lLZ3fldnWPziaXEe591t/1b6oUiENX9L7r67UqH3KqS/FH+utjY71+wMAAAAAAJgIAgfgCPGrEfznfpCQNjly23JDHtzQae8kfjWEP+fBBQmZCgn3K0jPm7RVSoIHo1+5YEXm3rftGdQ/fPU+lcsTm7ng7D80qoODxZUM9zx2DAQO1kbVDfEvFxCEYa4SIcwOjk7nN6TL7emCus2EEP5x0X5KkoWoikHJ9lzGkGkpZCVt3jVQ9R527RvS6Hg2cHADoyWpVDA0uq21pK5cq6yBofGq/eoprDSY4PFPbTmg//Nvd+hvvnCXdu0firfbqv0mK/951jphvcqFCVdUeK8TKgAAAAAAgJmCwAE4Qvx2R35PpTSIyIYFyXFSVZWD/G2q3jd7jAsf0rKHwAUVMrlzRrss7JulE5f1Zu5/4/aD+tEdmyb13rcULJA72/YMJt+qP1rcgrBrp5RWN0SPsy2WsqFA9MsPHrKtl1w7pKh6IQ0b3LPqFksuyMhWVfiL5nv7h6vew8DwuEbGsi2VOrzAobjCIVDXrOrAIY1MCj6rzOdWXcGQvlZwcIEwtPrb6+7V/U/s1u0P79A/ffPBmgv/+a2NnkvS0Mi4/uXbD+n/ffHuTLsoAAAAAAAATD8CB+AI8UODfLVDuk9aZeC3Wcocl5vTYAKT7C/5Mxmy4UPaWim6emD8c6fHu4qI55+9tOr+brz72egb6BMcn7Bl16G6r3/q6w+oXJlY9US5Emr/oRENjkzsG/lFrBcKVLxwIfR+uv2iECAbDvhhgJvJ4MICP6zwQwl3QD5syN5YwcK9tU0HDu2tpeQ6pYKh0W1tJXV15AKH4erPs2o4doFaAYP13oMt2LZtz4B27RtK9r/3sey8EL9SYDL+4/uP6vu3PqPbH96h//Nvd2i8nH5GjU6br1DIt80CAAAAAABAFoEDcAQlQUKmU5LJhgj+L28/vy2S4sqEfKuk7INs+JCZ6+DNhsif393nxWcs0ksvWpm5/9BKd6+feAukfIVDX09H5vmu/cOFMx6K3HDnJv3pP92qP/zHW/Thf71jWmZAVMJ0BdkNePbDBlf1EHor52nokAsevMcuwAhdSCF3nF+54BbzszMQXPVD0popujlZSfv6R6rew+DwuEbHardUqjXDobujJbNtcHgsuX7mQY1V9k3bD+ru9TszC/lu32baEA2Plgv2abyi3+ya/49uT6tyDg6O6c6CPy/TPQT6SJ0bAAAAAADgWEPgABwlmRZLtWY3xK9lDsqfx/30Kh3cuTODpf1gIzdE2iUOfpulIAj0uhev0X97+SmZ693/xO4Jf+V86+5s4PDyi1dV7XP/E7uqthWxVhr0hiMfnI52TLm342Y4+MFBVSulpC2SF1bI3z997K6RmfngfYbZ4CJbARC9bpOwQZL2FAQOQyNlDQ3nA4c0TCie4RBUz3AYHm96kfzWB7bpv//DTfro5+/Wn/zTL5P3nbzhJhQFDq7a5XAs1e8rqA5xalZpTPAaaSVM0wckxwEAAAAAABzPCByAoyDTwij7QlLx4MoS0vDAZCoisq/Fcxncaby/ujTB5B4HfjARby2qfjh77fxMO57dB4a1fe9QcruNDI+WMwOjg8Do/FMX6rxTFmb227JrQONNDKXu7W7LPN9/cPrnP7igwM1ySGc6ZCsaXBlCEhDEIYRfHRGdyysUkD/7oKBCwv2voF2TO6aowkGS9h3Kbm9vLcXXtIUVDq0tBUOjvZZKlUqoB57cnZl94Icln/3uw8mw66e29Ou+gtCofhsmWzikeni0UrX4X3ieTGhT1H+q2lAccFR3qpr4Yn/REYXzLCZ4DgAAAAAAgOMVgQNwFGWrEdIB0sZ7Ld6zqn2SCx3ceTJtljIVDMr8crMbTOBfLw053DmC+PWuzjadsnJu5r43bT/oXbfY7gPDuu/xXbr94e2Z7fN7O1UqBXrbK09TW0v6t6BKaLW5wawHSVo4d1bm+bY9tQdST1ZorSph/C37ODCohEkUkNkvaY3kWiZ5IUKl4gcH2TkNabVE9rj8CnRaTRH9LJfDmkO29xzIBg7+0OjCGQ4tgWblAofBOHCw1urvvnyv/s+/36n//g836/pfbqxaz9+bCz4eeXpfcs+FP5PwJH2jh4bSMMrJz6JITEMFwPBIdUVFI9b7jaEIAQAAAAAAoDYCB+AoM17QkNme/+mFBkV7ZgIGVf9K9oyrK4K0nKH6nH7rJUmrFvdkXnaL/LXyhkee3qu/+cJd+vwP1uu7tzydeW1RX2fy+Mw18zOvuSCjnqXzuzOf1e79wxqttUA9Senw6HTmQhhaf508qV5IWudkKhXigdE2nf3gfwE/s2jtt1JSei63X2bOg7Xad3C45qL3voO5Coe2UrLYXzjDoa2k7qIKBytt3zOoOx7ekWz/l28/1OBTk8bGqysTnFoVBEVDqkf8Nku5FMYWfojNG646d23u1b39IxoYGlMmLiF5AAAAAAAAqELgABxDMtUOVS2UvIoC47c/Kh4onbZm8vouGeMt1ptMG6b4yORnsqeRli3sztzn1t2DycyHvD39w/rCD9arXClekPUrFFYtyQYZm3c2rnBobytp/tw0tLCS/uU7D6UDnaeBPyjab2nklr79NfBM6BA/D11Lpji0iI51rZKU/ZkcZ9PwwZ1X2etI0sGCFkTO3tx8AtdSycgUz3AoBepoa8kER6NjFZUroTY2Ef7kjY6nwU8zvx3W1q9wmGh40YyhXIWDC3tq+d4tT+vdf3Ojrv3Qj3XDHZtq7+i585Ed+o0P/1jX/PkP9MsHmxuGDgAAAAAA8FxA4AAcC3JDnJOWScabvOAe+0FEenimDVOyj7y2SSb9mRyTv67xr5MeszwXOGzbPSBZm4YV3oL1z+/Zkll4zlvYlwYOy+Z3ZV7buW+o4UcVHZe9n6e39uuBJ3Y3dWwjbrHfVTi4QcjpHIe0fVJ2tkN2xoM//yEsmPdg5QUM8csuNHHXcPfjtknS8Ei9wCFf4ZAOjS6c4dBakjFGXR3VbZWKZiu4N1FrgX5svDooyM5W8CsVop9FgcPwWLl6tkETIUMzMYSrcKiaEWGrj7fW6qs/eVyVeFDFp7/5UOH95geAf+Y7D2tgeFyDw+P67HcfaRiQ+GFVzX2oqAAAAAAAAMcBAgfgGOAHCv42/2e9Y6MHNQIDkw0bTCbE8M6TFjTEsx5MEkTM6+lQZ3u6eD0yVokWt13hRHxgpRLq/ifrL/wv8iocFvVl5zHs3j8ctS5qIB+ASNL6jfsaHtcUN2fBBQeuSiHXBsm1WHLtddKKBZtWN8gPGJLl+vT87nQ2XVDOtluyVQvN+W/o+/zh3FJa4SDVmOHQGv0jID84+tDQeDLLoZaiBfDR8UpuwHV1wJBfVS8KNmq1yMr9FlQN086rVKqHkDd6X75yJVT/QPYz/fm9W+pedGSsnAnO9hwYToKYQgQJAAAAAADgOYTAATgG+UOk0w3Z101mu6kKKNJKiSg0cG2a/JOkoYIXeuTmN0jRgOn8Iv+mHVH7o8ALMJ54dn/dBd25s9sz5+me1ZZZ7B6vhFVzCIpccNqiqm3ufqaDPyYg9Coa/PBASlseJUvGNj0mDNP5C65KwnoH54MKF7RkZj3kn9vcDIIG2ttKyaJ/UYWDCySqKhxGxjUwXP1Nft/oWPV9+OFBvWV0//0VBQ4j3rmtsmvyRevz+fDDPR8vCBzyoUw95fHq42+8e3PdY/IBhaT6gQMAAAAAAMBzCIEDcAzIj0Lw2yr5Mxoyr3uhQ9WMh4ITFw2lzgysTs4Z7RkESsMHSScu680c/8y2/qqqiXtzbY2ed+Zivf9N63TFhSt02fnL9Z5fPVel3ML34lyVw459g2qkt7td/9/bLshs27V/aEKL8fW41kahjYZHu4qGdA5D+su1S0pnL9i4JVOYtGSSikKEbMCQaafkBQyuKkLxz8E6LZXyXDWJMVIpKGip1BLIWqtZnS2Z7QPD44XDnB0raXi0ehG9f2C0MBGoFz40HBpddH1bHDzklcv1A4fk96LG8UWBxabtB1Up1x6OfXBwtGrbVIea004JAAAAAAAcLwgcgGNUzRBB2e2man9vn4Lz+c/dLOn8PAjHb6u0dvmczPFPbe3PtG8aL4d6aMOezD7nnbJQq5b06NXPP1GvfeEa9fV2VL2PhbnAYcvOgcL3m7dkXpcWz8se28zQ6ab4JQyZMEDZOQwFIUJ+hoPNHZ8fUpwGGP4l/VZE2cBiuE5LJd+sjhadsHR28rylsKVSVOHgt8uSogX/osoDfwj2SEGFQ7qYn3kz6cPM+44M1Boanetj1cySe352RGGFw9CYwrB6e5FywfGV0GpPblaGf93+ggqKscwwbcIDAAAAAADw3EXgABwDGoYKNQY5ZOcumMwxja5hvDDBHypd9JokrV7akwkwtu8Z1ODweNJS6bFN+zLDonu62rRm2ZziG/fkB0Df+tC2plvQrFzUk3m+dXdzYcVEJa2VXEWDci2RrNdySVE7JX9xPt0nrWIYGinrB7/cqJ/c+azGypX4/On8CH+gtB+ADDVZxXHGifMy1SRFLZXaWqLAwZ/1IEWzGIpaD5Ur6WJ5UfBxaGgss0g/Xq7ooaf2aNf+dKZBZu5CGE6owqFo0HPRedPrVwcGYWg1OFKuOsIWXKAocJCkHXsHa17zYFFLpfg+mhkenQZM+TZRdQ8FAAAAAAA4JhA4AMeoWsFBfh8376H49QbHSwoKyiC8woekrVJXZ6uWLujKHP/Es/uTlOLZ3AyFs9fOVxA0fg/nnrwgs+A9MDSu2x7a3vA4SVoyP1vhsGv/cFPHTUqmssFmwwYbfVP/Fw9s1b2P7VIYhsnQaCkbHLg84ZNfv1/f/PkGffnHj+uL16+PZ0FUf6M/WYCOX222wuGsNfMzz4uGRre2RP8IyAcOI6NlHRiobg3kL8AXta+yVjo4GAUI5XJFf/zpX+rD/3qH3vv/fqZHnt6b7uedoygUqNeCqKjmoVYQUdRSSZKGmhyIXet4FzgU6S9oqTTS4P0QJgAAAAAAgOcKAgfgOFAvfPCrECZyXPS6Nw/CGzIdvRi3VFJa7XDKqr7M8es37kuO2b4nuwi7ctFsNaOrs1XPP2dpZttP796ssXLjKocFc7OBw27vm/TTyVUwJM+VHeQ8Xq7o0998UNf96HH987ce0n/d+owq8aBot48rVLCy2ndwWE88eyA5320P70gGUKfnzy2tx0+HRhvPcDCKghz/pgsrHOKgob0tGzg89NReHThUvXBecYGDtRqusYh+YGBU1kq33L9Nm7YflCSNjYf6+k+fqNp3X8E1JGnYHxpdMGOh2UX6opZK+e31TjNeKX51+96hmscVVYYUVezUGnQNAAAAAABwPCNwAGYwFyRI8gZAR0+ioCFttWQknXbC3Mzx6zfuS47dlgsclszPVkNkr5v9+ZLzlqutJf3b0aGhMT28YW/BkRG38L1sfrdeeO4yXX3ZWv3eG87WW195WsP3PBnZqgabtDty7nhkR6bC445HdmRmPIS5Y/fmZgBIUUWAPwvCv3b62FZVOMzqyM5fkKQ1y3s1p7s9s61UUG3iPvN84HD3+p2FFQzjFZupTiiy72D03n7xwNbM9vue2J0Laqz2H6z+HKSoIsDt+/im/fruzU9py65m5nNkQ56i6gkp3Z58tjUW+2u1VNqeq3Dwj+4vqAxptkVYXn5+BwAAAAAAwLGueqUKwHGnie5LtY9VVMkgI8m6cCEKGwKv2kGSTloxVy0lk/Ty39s/ov2HRnTj3VuShWYpatO0qK9e4GBkrU1+zp7VpuedtUQ335cuUn/hh+t1wtIe9fVkB01/95andPP9W7Vw7iy943Vn6Q0vWTv5N9+kdD6D99x7/ad3bc7sv33PYPYb6/4sByPtOVC90D44UlZHe4tsHPYk+7tF9Pj3Ir/Qf/rqPt29fldm2/mnLZJVdmh4S0udCofW5v5R4C/AFw2NlqR9/VFbq/6CWQZ5+w8WVzi4lkqPbdynP/vnXyq00ldueFwf/+8v0eJ52Zkf9SoDagUGtQKA/JlqHb9zb3EljbVWhwoqHEYzQ6Pr/P+17osAAAAAAADHPiocgOeAZuY91D5WMoEpPEc6ODr6X1trSScsyQ5qvuPhHbrx7uyC+/y5s5L5ALWu6f+UpDNWz6va72++eLc2butPnm/eeUg/u2eLKhWr7XsG9bWCNj2Hg1+dICtt2XVIDz25R2PxcOV9Bd/U9ysc3HN3rj0HqhesB4fHs3Meaqyj54dGX37hSvV0tSXP166Yo8vOX1F13aIKh5YWI2urZzjU4ipLrKSR0eJF+30HR2RV/E3/8XJFX/jBev3Fv96u2x7arv2HalQ4xO/xs995WGF8/2Pjob7186eSN1T113wLKtWucBgbD6s/34KqklqBw+DIeP6QRH+dlkrNVCr4rboAAAAAAACON1Q4IPGlL31J1113Xd19RkeLv5GM45c/A8LElQ7G2Pi5Nzw6/rl6aa82bElDgO/94pmqcy5f0F21rfrCLiiJrrV6WW/VLqNjFf379x/V//z1i9TeVtITm/dnXl+/cZ+27R7Q0mauN0Uub3jgyd361+8+LGujtlEXnraocP9yJczMTUhCC5nCCgc3yNhVJrjqhrQ6Iho8na9wWNQ3S3/17ufrma0HNae7XcsWdsnVNvhr1pWCeQQuZOpoay5w8BfwRxq0VCpaeP/hbZv0rZ9vkCQ9uGGPLjlzSeE53JDlZ+IZEM6DT+5ueI/Jgr1qD33OBBH1KiRqHT9evF2SDhZUdozW2d/GAzqsrR8akj8AAAAAAIDjAYEDEvv27dOGDRuO9m3gKPA7J/nhg2t7FC2AR0ueJy6tDgbyLjt/eeNrKlvh0NFW0uqlPXpmW3aR+eDgmB56ao+GR8v6r4Jw4/ZHdhz2tkph6HrpW337pg3JGvX2PYP67i1PFx4zMlZRd6cXOCgNE/bEbYd8fuVCMinB+7a7+6zyMxw621vU1dmqs0+an9yrmwPhV62491Ckra25YrdyJUxCk+EaLZXcfIr89TraSvrc9x9Nnlsr/fKh7YXnGBkrF+YAQRAkB/ttpvxz+sZrDB4/ODSmn9z1rObO7tCFpy2Uagxer10hUakZVAwMFwQOBQO2i7on5dtgAQAAAAAAHG8IHJDo6+vT2rX1F25HR0e1efPmuvvgeOQSBxsPijbxYqhREEi2omhR1hitXt5T5zzSqy49QauW9BR+o94XBNWLvBefsbgqcJCkH92xSXsPVC/SS1GbpcPNfydF1QlFRkbL6u5szZ4krlrYU/BehkbKstYmgUEpMLLKVh6Ml0ONeYvgxsTVCUlZRHypgsXssMYCuZVtuqVS2QsR6lU4lAsW+nu62jQyVvx7mFfr3KXSxJbjx2u0RPrbL92TPH77q0/X6168pnC/co0/w2P5ICIOYWoNqh6rEXwUKboi1Q0AAAAAAOB4QeCAxDXXXKNrrrmm7j5PPvmkrrzyyiN0Rzia4rwhbrNkZeKB0n2zO7Vq8Wxt2lG80H/SijmZVkm1BP7ciPg6zztriYLA6M5HdmrDlgPJvkUL9M7W3Yf07Zuf0v6DI9p3cER9PR36jSvPmNB7bSh+K/l2RvUUVQCE1sqGtnBY8tBoWYeGxvS576/X+o37dMrKufrAfztPne0tyZfph0bHM8d0trekn6Gt/4kXVjjEQUXTQ6PjxXRr07ZHefsOjmhPf3UoM5E5I4eGxgu3l/xz5GZdFL33WhUKvv/4/qN63YvXpIPBbVJfUnOGQ7kSqhKmQ8/zr+X5Q6onEh40miGdr2IBAAAAAAA42hgaDSCuakgXhU3c78i1PTJywxyi/X/ndWfVPNeCubPSRVJTsGAaXydIqijSUwfG6HlnLtG7rj5bC+d2NnXv42Wrm+7dogc37NGWXQPasmugcL/9B0dq9uRv1q591cOeaxnNDVV+elu//upzd+l9f3dTYbXBwNCYPvn1B7R+4z5J0uPP7tfN923JLGjnqzy6O1tzg6mz5/WfV2q1VLJSe5MzHPzPr1b40j8wptsKWiU1s/ifnGNwNBlQ7QsCU7xgnx8YHScRU/39rlUhISmu4qj+vIuqIlxLpUZhQ3NDpal3AAAAAAAAxy4CBwDJgn8UMJjMYxcMGPc/Iy2aN0vXvuq0qvO0tgTq7W5LWiUFxmS/ge0NoS6VTDKkOghMEkQovu66kxdO6r0cGBjNLK5ba/X56x/Vh//tDv35Z27TnY/ukBQtnv/4jk363Pcf1ePP7q91uoxddSot8vwKh03bD+qfvvGgtu8ZrLn/7Q/v0Oad2bDEDee2itosbdmVPd4Ny/aXoJNh0zl9PR01r91s4DBWrmjLzkMaHi3XrHCQpC/98LGqbbXmKRSxtnjodBCkw7CbaT3UbMhRaxG/XmBRNAi6VkVEUUslm1zXJs/zr+dusua9AAAAAAAAHCtoqQQgKUMwxnpDo93PKGTIt3c5ddXcqtMsmNupkhvsW1DdEMQtaII4yKhYq1K8iJzsGrdjWnfKQv3ojk1N3X5rS5AsLoeh1ddvfELdna16/tnLtGv/kO57Yrek6Fv5X/7x49qya0C93W36wW0bJUkPbNitd119jtYun1P3OrtrVDgYSbO72nTQWyT35xDcdN+Wut+Wl6RDQ9UL7Jt3HJK1Vg88sVuHhsb15OZsMLI8Dhykxt+eX3fKQs3pbteBgaid069dflJyXFuTMxz+8j/ukiTN7+1IFv+bNZEKB0naf6i6LdPhuubQSFld/ryNWK0AQZJGxyvqVnSM++zHyzVmPhSEE1LUfuqTX39AO/YO6aoXr4mHrdMiCQAAAAAAHL8IHAB4S5zRvAY/KUiHSNv4eRQazJvTqZddvFI/vuPZZN8LT1+cHJOtboiODQKTBBdBYFQJo/DBuvMqmhUhIy3qm6U1y3r11NboW/7tbSX9ydsv0uxZbbrzkR368g2PJ9dtKQWZxeXbH46qGNZv3K+1y3ur3u8t92/NPLdW+pdvPaS/evcLkgCkyPa9xRUKl569VJJ064Pbkm1+BUDRt/WbsWPfoL57yzP61s83FL6+dGF3Mncg+n2pfa6WUqD/9dsX65b7t6mvp0MvOHdp8lqzFQ5O0YyGRsoVmwmGGvnDj99Sta2oLVS9SoB6gYHv4OBYHDhYWWuSIdD1jh/35lkk2yrFVRyjNapBvvLjx3XXozslSZ/8z/u17pQF6pvdoarQgeoGAAAAAABwnCBwACApO8fBdcp3j6tnPBgZK73ppafogtMW69Fn9mpuT7ued8YShfHitymocHCtmYLANWiKggc30NhIsnHbJSvpmleeqh/dtknlSqiXP+8Eze5qk2wURvhqtcTZuntAg8PFA4jzxiuhNm7v15plcwpfr4RWTzx7oPC13u62qkXlkbGyHn1mrx7btF9Px6HJRFmrmmGDJC2b3+Xta9N1aVs8cHheb6d+7fKTVK7YqO1SHFa0tRz+7nrlSlh3AHIzRscq2ZkV3s+i7c2GG4eGxrW44I9QvcDBHwTt1LpepqVSHGZI0g9vTyt4Qivd9/guXX7ByuTPczMzH5gZDQAAAAAAjiUEDgC8MMFmnpv4SWCMQtmqZi9GRmuW9Wr10p4kjLCVMBkAnd8/cDMbjFEQxDMiAsUhRXTG5Nv6suqb3aE3v/SU5Avf7g4X5gKH0YLFX8e1EGrGvoOjWrOs+LVN2w/WHJTc1dGq/Lt9bNN+Xf/LjU1fe6JKgdHCvlnxvIaprTq3lI7MOJ+pflF/NJmLYeNF+ez7zm9r1MbKKWpnVWsAtDNWDqveT80ZDrk/n9bawnCitdS40oSQAQAAAAAAHMsYGg0grVxQulwbr/+ng6SNyexgMjv6Q6flVTh4bZVMtEheCoxXKZEGE5l78K4r7zLueWd7i3q72pLXpqvjzIGCuQHOY5v2FW4vBUZnrpmvjvbsYvFkqxqadcLSnkxQ4IZFFw2Mzmtmn2NRvUHVReoNffb5gYM/kLru0OiCe6lV4ZDs6/1B3bFnoGq/lpKXJOT+UB+fv2MAAAAAAGCmIXAAkIqDguhxGhwExg8iTFXo4IcRRiYXOkT7uJkOQWCSodHGpAGEH1a4c3pZRdSGyVuPzVc5LJ6XfT4Z/QO1Zy1s2VW9QNzV2aqrXrxGPV1t6mw/sgVjp58wr+ZrSauhglXq/KZjZTxAM79/I2MFFSZ1FuabrXAYKKhwaHT8eNwmyW/nNF6j0masIIgo+vNUtB8AAAAAAMDxhJZKABKuxsBVJiTr+8nzeNHfpmGCsSb5xnz0etqWyQ2ElkkHRUc//TAjPmccVriWSv7Ssbsff1bDor5ZenLzgeT5CUt6tWPv0JTe//46FQ7bct9I///eeoGWeDMUJjJ4+ZKzlmhktKzLL1yp//eleyZ+o5JOXT03ehDPa8gHB0Wtd46RbKHQysU9DX//xsZDbdl1SMsWdEtKWyhF790mb9B9Fs1WOBwcHFPUpim7vf4Mh+rXarVgKpr3sGV3deAwXg4Lf49q/b4dy7+fAAAAAABgZqLCAUDCr0jItEfKv2ZyxyitbEgqGeIX3DYXLJSC9HEyQNqFGd75onOn5/CrHyRpwdzsN+Kf3XGw4ftbu7y37usHDhXPexgcHs9UP5QCo4VzOzP7dLY1l9/2dLXpjZefrGtfdbqWLeiuOk8zervbtHpxT25Qss32Aypg0p2TH8dKhcPyhd1N7ff+v/u5Dg2O6YY7NulHt29KFvMPHBrVDXc+m2l91fzQ6KIZDvUDi2/+fIP+56dv1ReufzS5zni5uMLBb7/kPu5tBYGDq3DI/L4W/AbVGpIOAAAAAABwtFHhACDDn+MQ1xxkqhOk+NvzuYMy7ZVkkwoG9037JGwIXKVENDA68AINK9eyyaYBRHyJwBiF3kLr/N6OzH1v2zPY8L1ddt4KbdhSe7aCGzBdCa1uf2i7Hnpqj+bP6dSeA8OZ/Rb1zVIpN2i5o8mWSh25YGJeb6d27R+usXe1JfNm6U0vPSW5fhIa5Bepa0wWrrVW/fKLV+lHd2xq+j6mm6taaCS00tv/4sfJ80ee3qv3/Nq5ev/f36SDg2MyRvrjX79IF5y2qG6Fgu/Q4Hj6xMaVDqZ2xYIkPfHsfknS45v2a+mCbr3s4lW1KxzK+aHR0qHB6pCjnA8sCn4fpz4iHAAAAAAA4PAhcACQSFoZeVUFcpv8Ac6uBZLb3/qBgxSGUWulbPjgZjYEcduk7HBpKQ4bZL1WTvKOVZqGWGl+78QrAz53/aN1Xx8aKWtsvKLPXf+oHn0m+qb84/HCsm9pweJ4R5MtlTpzw6XnzG5v6jhJOvfkBXrnG86WjNI2QkpbWjVajK7ZmsdKr79sjWSkPQeG9YJzlmr1sjn6+T2b9e2bnmr6/qZiqdeeaiJue2i7li3ojtsiRe/lX779kC44bdGUKhyk+i2VfJ/6+gN62cWrqoIFZ2Q0nffgFM2H8Lflfy+PVFFD2tIMAAAAAABg4ggcAGTFC/xu+T+ZtaB0IdLatAIhqUpIDo/ChsBLIFyFRBAPjY4OMplh0C6csPE5bBw8JC2bTFRtUQqMKhWrvp6OpIKiWbM6W7W8p0NPb02rHLo6WjQ4kg4jvv/J3UnYUMvyosBhkhUOpaB6cXdWR4uGRqoHJHd1tEafT9Xk5+qH0eeXHbQtRb93/mK226+jrUXXvOJUySppc9VSOnILz/N6O9TeVsq0H2rWD2/fmHkeVaRYbWqizZYkDY2MF25vNnCohNGnXqsF0+h4RcOjZbW2pFUxRTMgxsfDpLqiEdoqAQAAAACAYxEzHABkpPMTlIQC/iyHop39dkqZ/V2okFQyRAvs6X5p6JCe0mRCBnedNPyItrW0BJrT3Xx1gBR9i37JvOw36fNBwZd//HjD85x/2qKqbR2tzVU45K9X1Eqop6ut8NhZHS1JYGCtjQdG20ajG4rZ+ovW1loFwZH5R0QQGHV1tKq7s3VSxw8MVQcGD27Yo807q+ckFBkZq8ja7Gdo1Xzg4NRrwfSe/3ujdu1Lh2IXzXtwFRKZ+ygYCJ55EQAAAAAA4BhC4AAgIx0CnR3ebPI/MzMbsuFAEM9n8NsmBcYkMxzSqgbFbZayYYYfLCRBR7yDXxAwf87E2irNnd2hxfOyw6aLKgzqecfrzixcGC+VArW1NP5bar6l0rknL1CnF0K87kVragYOXZ31qyistZP65nuyqJ1bdG8pHZl/RMye1aogMOqeVfy+J+PGuzc3ve+BgVH97XX36O0f+pH+/iv3amw8qi6pNzS6SK2h0ZK0a/+wrvvxY0mwUdTuqakWUG7GBAAAAAAAwDGIwAFARjovwQsFpHQwdFJtkLZS8rKGeOizMqFCYExc5RCHC7nZDP6MB+OfP9NOySStfpxagcMLzllauH3O7PaqCoei1jb1LOybVfO1Ztoq5VsqdbS16H1vWqfLzl+uN15+sl60bpl6uoorN2a1t8bhQHbReaJBQ6N93ctHqqWSe7+zJ1nhUKQ/HgDejIGhcf3ywe0aGB7XTfdu1d3rd0oqnrNQxGVWjQKDm+7dmjweayJwIFcAAAAAAADHGwIHAIXSlka5UCAzr8Grgoj/4oKIUhAkrZTc8OdkjkNS9eBVQARe63rvev4Aa6NsRcJFBa2NOttbdPmFK9XWWv23t7mz27U4FzjUGhhcJDDSnNkdNV9vZnC06/fvW9Q3S6994RpdctYSBcbUbqmUW5Cflk47BfMfnNIRqnDo6WqTlaa1wsENap6Mz12/XlL9Fkm+trid1niT+0uNKxyqfw+bO/dUZztQPQEAAAAAAKaCodEACmRDBWujgc2BkUL3svV2jQfd+hUP/twHv8WSFM9sCJXOiFC2miIaimyT0+fbNblrnrC0V+9/8zqt37hPu/cPq621pIvPWKw53e06ffU83f/E7sy7mjO7XV2drZo9qy0JGiph/L5yC619PR3ad3Aks62nq71uC6Z89UKRweHiAcXZ69Se4SApV90Q/cwPh54OLRNsNzVZvfH77Z41fRUOY3XaGzVSqdQfAp3nAofyBK5Z1H4pChxsU9lCfuZEtI20AAAAAAAAHF0EDgCquMV/Ka1ciLYbGWMlazIL3K7ywYUM1tqofVJJsmWbVCu4xXp3nsBIxrVYssr8tJlzxz9tXClhjMJ4cXXV4h6tWtxT9R5OWNJTFTjM7Y6qExbPm5WpbFg4d5Z2xAN9AyNd84rTtHR+lz76hbszx/d21/8GfjMtlfIzJIrUChzmdLcnn+1k1VqTdtuttbKuSuVIzXBwgUNBS6VTVs3VwrmzdMv9W6teq2d0bPKBQ0dbSRu39Wv9xn1N7Z9UOExg5kNxhUP1PVvvr/62IxMFAQAAAAAATAwtlQBUyVYVJHUL3qDo7GyF7MDnbGVDOqchbZ9UCryZDYorHrwZEfHVomqG+IYycx2aWG3t7a6eg+ACgxWLZme2V0KrM0+cp5NWzNHvvv5snXfKQi2e16XzT12Y2W9lQbDha9RSqRQYXXzGksb3XmOGQ19Put3NbXD/i7a5b743234n/9xmXjtSMxy6OqKgYXZBhUNXR6tedekJEz7n6PjkA4dtewb1gY/d1PT+rn1Xcy2Yot+kyQ6NpoYBAAAAAAAcy6hwAFCXkWSNUSCbBAjW2sxsBavqigg/YHBzG6Q0yHDZQlLBICOv05KSlkrGtVwykrXJ8OhKg8Xdeb3VsxbcN/afd+YS/eyezcmC++4Dw3rDZWt16qq+zP6vf8la7do/rM07D2n2rNaaw6idzoIKhyXzu3T5BSu0ZdeAzjtlYc3qBV/RPr3dbWppKUWfivUihfjr7rUqF6ZSDXGkKhxaW6LrdHdWv+/uWa2Fn2sjUwkcpInNMigF0f0XVSj4XIcq22TgYO3haZWVnr+6WoZAAwAAAAAATAWBA4Bq3iKkieclWGNkGvT0N3ECYeKFTDfkuWyqF75NfL40mIhWzo2xMtaFGMUVEkETq7DLF3Rryfwubd8zKEl60bplyWsL5nTq3JMW6D6v5dLd63dWBQ5dHa36/V87V7sPDGvu7PaGLZPaCyocZs9q0/mnLtL5p1YPuK6lKHDo6+lIVoOtP0PDY2Vz9SbF+zQUhxjNVji0t5amtMDvAoe5PdWVHeedsjCZXTERU2mpNFHlSpj5WYub8VFrPxc4pG2U0s+/qhql4PjDOc8DAAAAAACgGQQOAKpkWyS5xCFtayTF8xSS/b1gQHE4oWg+Q2BNPHchdw2/LZPxruO2KVcF4bVVqje42b/vd119jn754DZ1dbTqeWcuzrz+gnOW6b4ndmvNsl5dctYSnb12QeF5WloCLZnf1fB6ktRZMDS6mYqGPDcTwJcZSF01LViyxi1TNx46bG0cWtTbZwLfdZ/V0VIYOMzqaNHQSDmzrbUlqPom/5zZUdBw2qo+LZ3fpW1xSPTml56sS89eqrFJhAf5axRdd7q48zY6fzmsv9/6jfv04IY9Ouck78+itdXVFnHpAzOiAQAAAADAsYbAAUBDLgxwQUDSQsm4uQ5xUOC2x1/ODkwcPtSqcPAHQhubBAtSNDzaJQ7JkfGDoInAQYqGEL/s4lWFr61e2qM//vULtXBu4yHOzSqqgFiZmxcxWS0lk4kAjsSX2MfHm1ug7+ps1f5Do1Xb53S3VwUOHW0lXXb+Cv34jk2Sot+j806JZmWUWgJ95J2X6qENe7R0QXcya6OlJZhSYNDeWlKpZA5b4FCuETi85Pzl+vk9W5Lnrg3YaJ37+NiX79O//M8rjtj8DAAAAAAAgOnE0GgAVYpasiRVCn4oUOMY1/bImGjeQuAd4wtMFB4k1QtKqxnkZjfIDZQ2ySDpZloqNX6PZlrDBiltDeQ7acWcaTl3X09HMtTZ2mz9ga16kKr6pOp8Kz4dOh1ptk1SrRkLPd3V1R0dbS164+Un6coXrNYlZy3RB6+9IFPRMaujVRefuSQZ0O3e86xJzHFw2ttKhb83E/Xhd1xSuH28XNH+gyO64c5nM9vzLboqoVUY2iSgKHJgYFQbtx+s+fpkqhospRAAAAAAAOAIocIBQJWag4ZN9YSAeJZzPCw6HQztz2ZwoUPR+ZLrJY/d+bzZDTJp9YRpvsLhSCta2F3UN7lQ44oLV+ond6UL2C86b3nNa0QvpD+bWV5uuAhtpZ5ZzbWDmtXRWrh9Tnf1TIb2tpI62lv0315+qsKw/j24oclWUXum/sGxpu4nr6010HTUhCxf2K3LL1yhn961ObN9cKSsaz/0o6r9W1sClQKjivc+yxWrsQZBzvDIeOOb8X7/ioY/19vun6LqZZudHQEAAAA8l1lrNTg4qIMHD2psbExheHiqogHguaRUKmnWrFnq7e1VW1v12hGBA4CmuUV/64cBNg0okgqF+JlrqVQKigdOu02B1zYpMEbWKG6xFF/XVTzE4UNwjNZmrV0+J/P84jMW113wrecl5y/X/kMj2r5nUC88d5kW9c1KFuijteZ0YdgtLDezVDyR77qfuWa+5s5uT9olrTs5O2jb6aox1LmnqyBwyM2nSEKFosVvT2eNUKMZ7a0tDcONRnq62tTZ3qI3vGRtVeBQS2spUKmUDRwqYajxBsOl+wfG4s/D+z0u3NPWeQYAAACgnqGhIW3ZskWVysRnxgHATDc0NKQ9e/aor69PixYtyrxG4ACgaa5VUmht9eKwN9g5CQeMkZFNqhuqD/HGTieLztExadhgkqBB8XZ3jeleYa1UQm3ZPaCVi2ZPKihYPK9LV1y4Ur94YKsW9c3SK553wqTvpaujVb/+6tNVCoxCq6hyIX6/Rd9ct7mfk5E/fxAY/flvX6yf37NFfb0dOnvtAt33xE1Vx82uMRi7t6ClUntbKWndVHT9NIDI7nDqqrl6emv/xN+UogoHNz9hslYv7UnacP3Pt1+kv/yPOxse09ISqKUUaMybhVEuhw1nY/jzMOLf+ozDVYNA6yUAAADMFENDQ3r22Wcz/w5cKpVUKpUm/aUxAJgJrLUaHx9P/v65b98+tbW1ae7cuck+BA4Ammbi1f50cLR7bjODnd1AaHdMEJh0/wJBMlTaKjBSJQkYonNH55EXXJgk+JgON923RY9t3Kent/VrbDzU//qt52nO7Opv5zfj1c9frVc/f/W03FeR/PyGWvskCj7yRtUEyX6ymtfbqTe/9BSFVhodLxfut2ReV+H25QuqB2a3t5UK9mzsdS9eo5/etbnpuRK+ttaSymZqpdEnLOlJHp978gK1tWaDhCItpUClUrYcpxJajTUYXr3/0Ejm+WOb9um+x3bp3JMX6IwT50/wzgEAAAD4rLXasmVL8t9Nvb296uvrU3t7O2EDADQhDEMdPHhQ27dvlyTt2LFDs2fPVktLFDUco41JAByrAr+SQf6w5/h53FIpaZEURIGDUZ3ZC95sCDccOjmH8YKKJMxobsG8Wfc+tkuPbdqfLCDvPjA0fSefAjfA2YUMSdhgq/eTmvuGerPfYvcHUbvHbS3Fw5cXzaueU9HZ3jJtA7OlaB7EulMWTOrY9taSWqY4NHpRXxSquM+itdT4fK0tgVpyf+bHK6HGy/VDkwNehcPDT+3R//6X2/SNn23Qn/3zbXrk6T1V+9cc62EbvF73LgAAAIDnpsHBwaSNUm9vr5YsWaKOjg7CBgBoUhAEmjNnjhYsSNdpBgcH09ePxk0BOH6li/9FA6TT4c7+v6u5kKCWIJnR4IUJbpu8AdT+42n8l8H5czozz3fvH562c0+Ftelqf62wIbOvptYWx8b/q8UYUxg4zO/tqN42p6Ow1dLWXQPRVdz91rleGnpEj1Yt7qm5bz3trcVByUScsmpu2rbKWrW0NK7UaI1bKvnuf2J3wz9frsKhXAn1Z/98m8peO6h7HtsV3cME7j2v3p8RQggAAAA81x08eDB53NfXR9AAAJPU05Ou0wwMDCSPaakEYEK8YoNkVoNN2h+517OBgBsmXfuc8f7xoroxRsa616wXNCgbaEzTHIcFc3OBw4FjJHCY6P7eAOb6521yCoB3HjfXoailUWtr9eL7/DmdhWGCX+XifwPfzW2wMlJByydrpeWLqls0NaOttaRyg0HN9Vx42iItW9CdeTfNBBilwKhUyr6RT3/jwYbHHTg0KivpwSerqxn2HRyp/fucbJjcfzAd6REORbNIAAAAgMNtbGxMUjSzob19cq10AQBSW1ubSqWSKpWKxsfHk+1UOACYkKLwINmWhA25Y0z9wMGdw1U5BCYNFfygQcl1sm2bpmpBvsLhGAkcJNdSyXtSa78pLhZbv3eSst+C91s2FQ1fLmovNL83+kx/9/VnZba/fAqDtFdMMnBobysVhiKSdOKyXr34vOV1j3/vG8+t+uibaak0MlZRKZj4P2b3HRzRhs0H9EBB4NDZPrnvCTAQGgAAAIiEYfRlJAZEA8DUlUrReov7e6tEhQOAifLaHrlBz9lAoDoIMMYoaPB9feMGRSfferZVO/jhQ6kUqBIWL4BPVD5weOTpvfrCD9brdS9ao56CtkBHTJNvzf+meH5dubrxVTPna35GRikwVW2DpPQzfd6Zi/Xgk3t05/odOmP1PL3w3KVqtsCi1jknqq01UOt49T3O6+3QX73r+TLG6M5Hdmh4tHoo9trlcwpnjzQzE2L5wu5JzY44NDSuP/rkLwpfK6rUqPXHxM+p+M8oAAAAIIuwAQCmrujvpQQOACYkrXCwyWP37Wk3w6FotbphhYMxMtbKxqGCW+Q1ocnMhnBKQdyGydimF+ZrWTCneujxvY/v0p4Dw3rfm9cpOIr/Imqtm3dQfA8TeevJt9zrHdRgdXrJ/C5t35MOAlqzvLdwUX3+nE7JRsHQe990rmStQhv9Plf8RfN613MtluLdgsDo8gtX6Kd3ba7zBqq1tZbUWqpuBdXeVoovY9XWWioMHPKtk9xn2Kil0isuWaWerraqodFT5QK2aSlYqPPnavKnpE0SAAAAAAAzGS2VAEyIMYpbHplsNYNf5TCZ8yqtmkh+ZbanYUdgjIJg+toqdXa0aO7s6t6dz+48pPuf2D0NV5i66WiJE4VDU7v2m644OfPar11+slpK1b8LfblB0kWL0G7Gg832csq2kVJ2ePTbX32Gfuu1Z+rSs5c0ff+1hkZ3tKWZe1uNAKG11dvu3VSpTpDwG1eermtfdbokFVZ/TEVU4eA+tyxbsK1Irdwp/7kfbnR5AgAAAADguYfAAcCEGFO9zJ+GBJLiMGASJ5bfAChIkgZ/RoSroHAVDv7siKlZd8rCwu0/vevZo97/vtmr17vPogHOtc9T+7XzTlmoa191ms4/daHecdWZWru8tzBMmOcCh9y5Jv1Zxse1tAS64qKVet2L1jR9aFtLqbAKo6MtneuQCRZyxxYJw9rvY/G8ruRxfmj0VFWKrtvEZ5rf5Wj/mQYAAAAAAM9NBA4AJixI1vjzi/2TmRjgjqyuZEgiCH9IdBwylIKguspiCi46fXHh9m17BrV556FJnfPhp/foaz99Qg8/VT38t1nNdkEqWkD2cwCjtPVVvZ7/jZahg8DolZeu1v946wV64bnLk7Bh3ckLkn1OWTlXvd1pxYjNdb2a0jfprZWs1YI5nU0PUJ7V0VIYHPiBQ61goVbrpKJZCk7Jq2ooHZYKhwkiXAAAAAAAAEcIMxwATJyrPHAVBtYmi/6Tbd+ejmOIHvgBRBCnDe56gZFaSiZ+zUiBpjw8elHfLF1w2iLdvX5nsm3+nE5dfMZi9fV01Dmy2MZt/frX7z4iSbr9oe16x+vP0qmr+rI7FczGLjSBVvuNeujXW3v2byff3r/RIOnfff1Z+sFtm1SphHrFJSdItrnbbrRPrcHHba0lvetXz9FXb3hcXZ2tGhwe15ZdA1XHt5SMzlozXzffv7XqtbZWr6VSzQqHiQcO/tyGonZTeW+64mQt6pulj3/t/ob7pn/Os7+RhRULBTMaktCpzp+DqcxhmMjAcQAAAACYTmNjY7rhhht0880366GHHtLevXs1ODioOXPmqK+vT+vWrdMLX/hCXXbZZSqVir909kd/9Ef61re+JUn6/Oc/r4svvvhIvoVCY2Nj+td//Vddf/312rp1q8bGxtTb26u3vOUtes973iNJ+t73vqevfOUr2rBhgwYGBtTV1aVzzz1Xf/7nf67LL79cknTRRRfpC1/4wtF8K/rHf/xHfeITn5B07Hy+mH4EDgAmLJnh4DaYNByY7EKla41kZJNQwW9b4w+qlqK2TYExqhgb11VM/Vvcb7riZJ1z0nxt3zOoVYt7dNKKORN7P95t3OvNfrCSdu8f1qmrau5eU1ODnr2Xp2OtNxrVbGqHAUmlRHpTnR2teuMVJ8dDrpts2VPV5sd7YKqvn39+wWmLdPEZi2Wt1V/9x11VgcPVl63VBacv1pzZ7YWzFDItlSZQ4WCtVK4TcPltlEpB4woHK2lhX/Xg8iJFLZXqVr/Ua7Nlc/tZV+NyZBID6i4AAAAATJebb75Zf/EXf6Fnn3226rXdu3dr9+7devzxx/WVr3xFa9eu1Z/8yZ/o0ksvPQp3OjHWWv3e7/2ebr311sz2PXv2qKsrauf76U9/Wh/72Mcyr/f39yto4r9HgcOBwAHApPgDom38aKrLlNmh01GgkBkgHT8O4sqGIDAKQqNwmlrGtJQCnXnifJ154vyG+46VK7rx7s0aGinr3JMX6MSlvZkA4ZGn92b27+1um5Z7nCorGy9Cm8y26Gf9b7c3++31onkRSW5ia+/j7qEoiCm8tvc+RsbKVed680tP0Vg5VKUSFs5o6PBaMtUaO9LWmgYR/j3VrXDwwo1mKhx27B3USy9a2XA/SapUwuSzyIcJdasWaj2eQjUDAAAAABwLfv7zn+vd7363yuXovwsvuugiXXLJJVq2bJk6Ojo0MDCgDRs26IYbbtDmzZu1YcMG/c7v/I4+/vGPJ9/+P1bddtttSdjQ09Ojt7/97Vq9erUGBwd18cUXa2hoSJ/85CclRWsmb3nLW7Ru3TqFYajly5cfzVvHDEbgAGDC3MK/jJFJKgw05V4qxkQ1DMbYqFoiSGc0pBUU0RMTRHMcxhUe8RYu1lp97vuP6tFn9kmSbrl/q1516Ql62cWrktdHcwvgS7xBwo4xpmElQK1XCxeJ63w5vV4IULi/zT52VQ/561V9S17Z5+49+vc7pZY9NbYvmNupxzbtr9rX2uidtxZUOLR7YcKu/cOF5605w6FcP3BwBQNFlRV5KxbOVldHi9paA42N15/RUC6qcKBUAAAAAMAMNTAwoD/8wz9UuVzWrFmz9LGPfUwvfvGLC/f9H//jf+gTn/iEPvWpT6lcLusP/uAP9P3vf/+YXph//PHHk8fvfve79fa3vz3z+iOPPKLx8XFJ0mWXXab/9b/+V+b1LVu2HPZ7BPKorQEwKf6wZpN7PvlzmqS6wcgbTh3/xb3utpdKUZXDkf6G9l3rdyZhg3P9LzcmLX36B8c0OJIGDm0tgeb1dladxxhpbLyiu9bv1IbNB4ov1uxi8nQuOjfZvmmi+7jgwt+nqdZLTez3uhetyTx/z6+dk3leFBx0tJWSxfqd+4YKzzu5odEmCcGaGRp9yVlLJGPU29XecN9KJVT/wKj+4av36X9/5jbd9/iumvsWTXvItK2qddwU/iyRfQAAAAA4kr7+9a/r4MGDkqT3ve99NcMGSSqVSnrf+96nV7/61ZKkkZGRoz7ToJHBwcHk8UknnVT1+tBQ+t+yJ5988hG5J6ARAgcAk+JChrQB0jSd16tiSEIGk1Y6yGuzVAqMvF0PqzC02tc/orFyRdff+kzhPt+/9RlVQqsPffb2zPbF87oUFPTssdbqE19/QNf96DF98hsP6NYHtjV9P5N+u1NaTM6FBUX71Dh/VWDQRPufaKRAcze8YtFsve/N6/S8M5fo1199mi49a2nmmkUzGtrjGQ7WSgvmVAdCktTamj3OnbLeDAe/qqFUq1dT7I+uvUDz42s303arXAn1lRse1033btUjT+/TR/7jTo2Olas+pmaGQ2f3JywAAAAAcPx56KGHkseXXHJJU8f4VQJ33nnndN/StPL/u7alpbpRTRiGdV8Hjgb+JAKYFL+qIGlzNA2L/v5w6KSNkqqrKIyJFnPTqofpm+XgVEKrp7Yc0ANP7taDT+1RR2tJLzh3mfoHxwr3f3zTPj20YU/V9iXzvXZK3oCCzTsPafPOQ8lLX//Zk7rwjEVqqzHA+EiyXn8mK5u0a2rqI647xbj+Na2N/xwVXCg/x8GvkLBWuvC0RbrkzCWF5y6ucEj/Efi6F6/RZ7/zcNU+bUVDozV9MxxOXz0vedzb3bjCYWikrB/fkQ5BGx2r6J7HduqF5y5X/Z5a+bkd0V+Tz9vf7Uixde4XAAAAAJpw6FD639SbN2/WKaec0vCYM844Qy9/+cs1e/ZsrVxZf57e2NiYrrvuOv3whz/U008/rdHRUS1atEiXXnqp3vrWt2rt2rWFx7n7WLZsmW688caa5//mN7+pP/7jP5Ykvec979F73/vezPG+a6+9Nnl80UUXVYUln/jEJ/SJT3wieX2i1Ru33HKLvvOd7+jee+/V3r17VSqVtGTJEl1yySV6y1veojVr1jQ8x+bNm/W5z31Ov/jFL7R161Z1dHRo7dq1uvrqq3X11VdP6H5w/CJwADApURgQzVuwNq5ymKbWRkZSEBjZio0HR8sNeEgWR008NLoUGNnQHpY5DuPjFX3mOw8l32Yf0Lh+ds/mmvuHVnrkmb1V2y84bZEODY1pcHhcS+Z3JWvuuw+MVO0bNLsAWz1OoerIw91qylobVyEc1svkrtngdVV/DtbawsDBVThI0ovXLdP6jfuqqkyKKiMk6XlnLalZkeJCBqvGLZVaWoLk42smcChq/ZT9TJr4zWDoAwAAAIDnCD8w+Nu//VutW7dO8+bNq3NE1Frp4x//eMNzP/vss/rzP/9zbdy4MbN906ZN2rRpk77xjW/oL/7iL3TVVVdN5taPGQcPHtQf/MEf6JZbbql6bcOGDdqwYYOuu+46veMd79D73ve+mmsN119/vT74wQ9qbCz9kubY2Jjuvfde3Xvvvbr++ut12mmnHbb3gWMHgQOASXH/gHFf2J/Wpe00VYgHRxuv4iF6LTBRVUMQGAXWJJUR06mjvUWnrurTw0+nIcLzzliiC05bpI07DmrD5gN65Jm9GhgaT15fv3Ff1Xk+/c0HFYZWbS2BPvqeF8h9WvsOZgOHF527TC01ZgZMlwl0Kap9jgZVCvVe8+cKNDu/YaL34M7v/5ksGt7c0VZK9mttKeldV5/TdFurX/2VtXUCB6/CoUFLJVe1Ya3U29W4pVKRzo7qf5Tnh35H17CFr1cdO6m78E9M1QIAAACAI+Oqq65Kvsn/9NNP6+Uvf7muuuoqvfzlL9e6deum1Gbof//v/61yuawTTjhBr3/967V8+XJt3bpV3/zmN7Vx40aNjY3pz/7sz3T22WfrxBNPnK63JEn65Cc/KUn6/ve/r+uvv15SNKPCzWmYM2eODhw4oCeeeEL/8A//IEl61atelcynmDNnTlPXGRoa0jXXXKMnnnhCUlSRcdVVV2nNmjUaHx/XAw88oG9/+9saGhrSpz/9aQ0MDOhP//RPq87zwx/+UH/wB3+Q/HfnZZddpssvv1ydnZ16+OGH9bWvfU233nqr7r777sl/KDhuEDgAmLJk7sJ0nc/76QZEGyMZbyXUxGFEEBipMr3X9525Zn4mcHh6W79ecckJWjy/Sxecukif/e7DesR7fXB4vOocYRjd+Fg51Oh4qPZ4LsDe/uHMfn29HU3fV6O3aya66Bt/tsl6sa1+zV/FrxcWTDX28Vsn2YJtRfs30tpSfbDfUklS4ZyNYW/4t2/p/G69/y3r9LEv31f1WralUvMBUk8TFQ5FSkFQ+JlbK9mCSpgiobX65s826Jb7t+rUVXP1W687s+rzAQAAAIBjzZlnnqnf+I3f0L//+79LilosfeELX9AXvvAFzZo1S+vWrdN5552nCy+8UOvWrVNbW/Nf9CqXy7r66qv14Q9/OBNcXHvttfrt3/5t3X333RobG9N//ud/6oMf/OC0vq8rrrhCkrR+/fpk2/nnn6+LL744s9/s2bOTxyeeeGJyXLP+8i//Mgkb3vCGN+hDH/pQ5jO66qqr9I53vEO//du/rQ0bNugLX/iCXvjCF2aGcw8ODuojH/mIrLUyxuiv//qvM1UfV155pd761rfq7W9/uzZvrt01As8dDI0GMDVJ9cH0rfi7UwVxdYMbFJ1fcQ6MSX4dru9Un7isN/N8046DqoQ2ud6c2bUXiY2pHgR8cHA0eby3P1vh0NfTfOBwJPif6URDhKTdkrxAwDZf1VBUATHRigh/98Kh0a1Bcq1ahkazgYONT2yttMyfzRFLWo3Fz0sNZjj4mhkaXaQSNv5cCiZieL8/Vk88u19f+MF6bdx+UD+8fZNuvPvw/EtgpsrisFwBAAAAwEzzwQ9+UO9///vV2tqa2T40NKRbb71V//iP/6hrr71WF110kd797nfrxz/+cWbYci2rV6/Whz70oaoqic7OTr3rXe9Knj/yyCPT80aOsK1bt+pb3/qWpGiuxUc+8pHCQGbJkiX6v//3/yoIov+G/vSnP515/dvf/rZ27dolSXrjG99Y2GJq+fLl+tjHPnbYWz/j2EDgAGBKjDTt5QWZgdT5a3kCV+FgssfUbUs0wVud39uh7lnpv7SMjYfatnsgOc/cOoHDgjmdmtfbmdnWPzCWHJsPHOb1dKQTshtw73cqbYkmq+41J3E7flxU69yuGVMUZKSDoqsua4ubOrUWtlRK/6XRBQlLF2RDhLPW1O79WTSfIV/R0KjCwX+/XR2tdfasrVypxOea+LHukM9fvz6z/dPfeLC545kHAQAAAOAoM8bone98p/7rv/5Lb3vb27RgwYLC/YaHh/WTn/xE733ve3XVVVc1DApe+cpXVoUYzllnnZU83rNnz+Rv/ij64Q9/qHI5+pLdr/7qryaBQpHTTz9dZ599tiTpvvvu0759aTvpn/3sZ8njt771rTXPceaZZ+p5z3veVG8bxwH6JQA4tvnhQ24hPjBSKTDJLAenp6tNBw6NJq2MnFIp2ne83PibDOk1jU5c2qsHN6T/AvHM9oNaszyqfOibXbsqYen87qp73ts/rLUr5ii0trCl0vRPoshqtEBsCyZyFHRUmvA1rC1+X5Ndry66z/yJrTHJ+WsOjc71avqt15ypv/ni3Rodq+iME+fp5JVza16iaD5Di1/RYG3DodG+zvbqfySfsmquHt+0v+5x/p/zpGohs0f2syqa49E/MCoAAAAAOJ6dcMIJ+tM//VP9yZ/8iR599FHdfvvtuuuuu3Tvvfeqv78/s+/jjz+ua665Rp/5zGd04YUXFp7PzUso4rcyGh09Pv976t57700e79mzRz/5yU/q7t/d3Z08fvDBB/WSl7xEUhRASFJPT0/dz0ySLr74Yt12222TvGMcLwgcAEzJYZjVnD2/d/p86Z0xRsbErZe8nbs7WzU4PK7RsUpm/872FoWhzQYOTdz/qsU9mcBh256B5F7m9tSucDhl1VztP5T9F4/te4dkJD345O6qRd9ndxzS0Mi4TlnVV7j4PBHTXaXoejGmz+OfTcQQVnZCMyXy12r+uNqvFQUOHW3VbZbOOHGe/v4DL9a+/hGdsKSn7vWKwoT8tlKDodGOtVarFveot6tN/YNjkqRzTpqvP/hv5+nXP/TjusdWKrbGm8+mPPU+n3w4N1mNzlJvFkf0+uR+7wEAAADAMcbojDPO0BlnnKHf+q3fUhiGeuyxx3TTTTfpG9/4RjJHYHh4WH/0R3+kH/3oR4XDpXt6av83Yfa/j4/Pyu/t27cnj92Q6mbt3RvNshwZGdHAwICkqPVSI6tWrZrQdXB8oqUSgClxi/6H7/z1X09mOBiTWdwtWmDubG+panHTzIJwvs3OHQ/v0Nd+Gg1Vmju7Q+2tpaTiQZJWLoq+6XDa6j4tyx27bfeArLX6wW0bq67z6W8+qM9dv1679w/Vv6H4lo/mwmxx46L6C84T+ZewovkPtfYrmm+dPbi4zVa7Cxxy9zW3u12rl/YWDpH29y16vbqlUvO/Ry0tgd79a+do9dIenb66T29/9RlNVUjkZzjUqjDxH1hlB3KXK81X/UyX4/TfyQEAAAAcZ4Ig0Omnn653vvOd+uEPf6gPfOADyX9Pb9mypeY3+4tCiOcSFxRM5Vi/cqSzs7PW7gm/SgLPXc/t/+cAOO4ZY+qvTLoKBxMt9lYqFQWBqVr4ndXRolJg1N5WUstooHJc5RAEUdudet/wXjq/+h+IT2/pl4w0f06HZnW06Kkt6T9kN+88pBWLZqu3q73q2K27B7Tv4Kh27R/OnzIxMDJe+/26t11nHXtKOUTBx1BzrsIEFoyLAopmj6+1X3RfpnonW113UTRLoWgxP12Er/1Ne7dPUViVv069OWSve9GJVdvOOWmBzj15YXL9ZgaZVeKwIPOeJ/LhGtNw8HStz6NRxQIAAAAAHC4PPvig7r77bu3du1cve9nLdM455zQ8pqWlRb/3e7+nTZs26Zvf/KYkaf369XrFK15xuG+30NFsx9TRkbaI/uUvf6l582rPMKxlzpw5yeOhoQZfnpQ0NjY24Wvg+EOFA4ApO9zftK93dje/wRijUsmotTVQSylQKffN8va2koLAqHtWm2bPalWpFIUUpaBxhcbsWdVDokw0q1otLSVd+6rTtHJx2r8xCIxe+4JoMbmvN6qAcIZGynrkmb11rzcwVD9wOFyfdsP5DgVTmptulVSnQuFw/fHxL9nZ3pJpkXTmmnkyxtQdNt1ozb54aLSJwpX4TdWqHFgyb5ZeeckJ9S+g4iqKPD8saCZmKHpf09VS6Ug5XkuWAQAAAEyfm2++WR/96Ef12c9+VjfccMOEjnXzByRp//76c/Mmw62TuKHMtRw6dGjar90sf7j2008/PalztLe3q7c36viwdevWhv+ttnPnzkldB8cXKhwAHPPqf5s/WvIOjJEJArW0R4vA1a1tArW2BCoFRrM6WhWG0shYWUEQBRaVSu1/KBYFKl2drcm1T1sdDRcOAqNnth7UnO52dXZEf3stBUZL5ndp4/aDybF3PrKj7vsdGBqrO1vCxC2kau1SbxH/qKzTTvGahf/C0uQEayubvOf3v3md/vOnT6pUMnrj5SdN7abUXIVDUeXAxz7wYvX1dKi9rZSZJ+IqNvz3axQFafX+fFbC4gZX1krWpH8eap3BamItlepWfzT4vZ7qjIeJYB4EAAAA8Nx2+umnJ4+vv/56vf/972+6DdLg4GDyeNmyZdN+b21tbRodHW3Ytujxxx+f9ms365xzztEvfvELSdKNN95Yc3i287d/+7fas2ePli1bpte//vXJ53b++efrxhtv1ODgoB588MG6lSZ33XXX9L0BHLOocABwzKu3aGhM9KtUihZmZ7WnC/1uQdq1WHKLwR1tJRkjtbeWZBTNgGjk5RdnBxu95PwV0cJofBkXAiyZ35WEDe7eF/XNyhy7ZddAZlF18bzs6wPD47XX0nMvFH02U1ljrTkPwd9u673W3GyHZr+h7vart1jur3LnOitlLOqbpfe+8Vy96+pzNK+3Rm/JCSQyhW2aciFEUVCwdEF3Oj+iicu3BPX/Ue2u8dO7ntV7/u+N+p+fulU79g7m9rKZv+Y//6JgJPnsJ5hS5fevfTxVCgAAAAAm7wUveIH6+vokRd+ub3bwcRiG+trXvpY8v/zyy6f93lx7osHBQW3cuLFwn/7+/mTB/2h42cteljz+2te+ph07an858vHHH9dnPvMZffOb39Q//dM/qasrnVfpt6P67Gc/W/McW7Zs0U9/+tMp3jWOBwQOAI5rbqE/MEalIA0VWkpBEiQEgVFrS5AszhsTBxAtQRJYFJ88ffiCc5clLXnOP3WhzjxxXryLKdrduz9p4dxsoBAERm+84uTk+Skr52ZeP1TUUsk7uWvnVHTBplrqTNNCb34duWrQc4Nj6u9Xe+ZDM9+in9h8iaLrN9jX2sJ2R/ltlSZmMDSSbw+WVwlDbdpxUP/8zQe1Y++QHn56r75+44aG5/U/41oVFGnoUH2MRGQAAAAA4Ohpa2vTBz/4weT5pz71Kf3Zn/2Z9u3bV/OY/fv367//9/+u++67T5L0ute9TmvXrp32e/O/5f/3f//3Vf8tNTg4qD/8wz/UgQMHpv3azTr11FN12WWXSYqGQP/e7/1eYcujXbt26b3vfW/yHt70pjdlZje88pWv1EknRV0EfvzjH+vTn/501Tn279+v3//932eGwwxBSyUAx7UgDgyCwMhf620pRe2TwtAqMNXfRm9tCVSuhHWrGwJjFMb/QO3ubNXvv/FctcRtmYLAKLAmaVXjqh2KLJyb/Tb9mmW9GhqJ+ji2t5Z00sq5uum+rcnr+w+ORPMFMq11ovY4haGGml/4tbLNz12od56k8qBxbyOriX1LfjItoYpDg/rVFu5l/1r+u8kvtjdzf3n1ZiM0DE7ilkBFrZt8ldDqn77xoPxL3XTfFr3z6rMbXte930ZDo/PHZz6zJlsXWWvjizf34TVq3US3JAAAAABXXXWVtm/fro997GOSom/qf+9739Mll1yiCy64QPPnz1dLS4v27t2rBx54QD//+c+TNkfnnXeePvzhDx+W+3rTm96kH/zgB5KkH/7wh3rzm9+s1772terp6dGGDRv0jW98Q7t379a5556r+++//7DcQzM+8pGP6Oqrr9b27du1fv16vfKVr9TrX/96nX322QrDUOvXr9fXv/71pAXViSeeqA984AOZc7S1tekv//Iv9ba3vU0jIyP62Mc+pptvvllXXnml5s6dqyeeeEJf/epXtW/fPs2bN09799afa4njH4EDgOOaq24IgmgZ3QRpVYP7tnlrS6nqm+dtrUHDhVL/pShQSCskJCVhhZGJqw5M1eq/MaaqwmHXviGtWBQNmX7V80+oCiT29o/UXExN7jf7I30993w6AoZmFnftEV4BrtfSyeQThGaOU+NZB80wuROs9AZVZ++z+XOWGrRUGhmr6LFN2SFn4+VQY+MVfe769Xrk6b269KwletNLTyn+LbJWYZOVGI3vuzpQmMgfjemsmCCUAAAAAGaGd77znVqzZo0++tGPasuWLRoeHtaNN96oG2+8sXD/lpYWve1tb9N73/tedXR0HJZ7uuSSS/S+971PH//4x2Wt1f33318VLDz/+c/Xhz/84cPS0qlZ8+bN01e/+lW9733v03333afBwUF98YtfLNz3/PPP18c//nF1d3dXvXb22Wfr85//vN75zndq7969uvfee3Xvvfdm9jnjjDN07bXXZqpS8NxE4ADguGeMkmqGFi9YcN8Mb2utXrBtKQUaD0LVW8uNFq5t+jhezE7W/E26j3Hflld20dRImjenQ0F8f5LUPzimF5y7VK+69AS1t7VodLScOa5/YFTj5Wz1hbt+el9RwGFlM681o6ilUn52Q7112kYtmQ7HQm/NQMM2bhA1ofZKSegQBTUTHbKdv8MLT1ukRX2ztHPfkCTpvb92buFcippvT41bKm3fk5/XEM2ruPn+rfrhbRslSV/deUhnnDhPZ62dH8+9yN5sUYFD/UoT78+dmXzAMBUMhQYAAADgvOxlL9NLXvIS/eQnP9Ett9yixx57TNu2bdPg4KCCIND8+fO1bNkyvehFL9IrXvEKrVix4rDf07ve9S696EUv0pe+9CXdcccd2r17t7q7u3Xqqafq9a9/vV7zmtdo//79jU90mC1atEhf+cpX9NOf/lTXX3+97r//fu3du1eVSkXz5s3TWWedpSuvvFIve9nL6v432DnnnKPrr79eX/ziF3XDDTdo48aNCoJAq1at0pVXXqlrr71WN9988xF8ZzhaCBwAHPeMMQqCqH1NyWud5Koair4hbuKqiKRKoWDNPl/hYExUKxAYIxv/TIICk+6TaeVjogqLhXM71VIKtGxht5bM61JPV7t6u9o0OlZRa2tJvd3tOjAwKsXn239wpHCwcVrgMMmF1uY72tQ/jc0GD40WfpOFfG8x2so2NbC76lxNBB4THXQ8bbzKkygsCPQXv3up7n18pxbP69Kpq/qqj/GHXqv6t6fRZzs8Uj3zw1qrf/7mg5lt//rdh/X3H3hJ8vypLf3qaC/phMXVVRju/ztV7aQK7m+yFQxT+S2apj/GAAAAAJ5D2tra9KpXvUqvetWrJn2Ov/7rv9Zf//VfN7Xv448/3nCfM888U3/1V39V8/W+vr6653nve9+r9773vTVfv/jii+sev3z58qbuU4qGZ0+12mLOnDl6z3veo/e85z2Fr19xxRVN3w+OXwQOAI57LgTIC7z2SkVKQZC0XmppCTQ+HmZKFPIVBtG37JXsY+LpzUYmrXDIXcp1Yjp55VzdfN9Wbdk1oNmzWvUr569IdjBG6uvtSAIHSdqXCxyi8xqlf/Uu4H5M5wpspkd/7n01ORQ63TaxleV6baBqhQ2TXby28UyBJARp8K15F2bYGvsUbe3qbNVl56+YVKVFeo+1DY2Wq7aVK7aqamFv/0jy+D++/6i+f+szkqRrX3Va1fH5/8tMpAImb7K5Ai2RAAAAAAA4/hA4ADjuGWMUyFYtTraWApVKpmbg4MKG9taSyuWoh70bFG1MwQwHpRUOFWvjuRFxS6WkvVL1vRkjvfKSE9TZ3qLdB4Z1xQUr1N5eil6P95vf26Gnt/Ynx+3tH9FJDSo8a3VSarbNTONmRM1rNMPhaCweF67T1xokPU0fRfOfffMXbbSbG0DuGy9XqraNjVfi/ceTsEGSPn/9+qp9yxUbDVz3/r+TDH1uFMgUvoEm21NNaKh0/T9TR6nGBQAAAACAGY3AAcBxLwkDcquPLkyokTeoFETHtLeWNDg8np7LRMf653MVBJltxhsynFQ4+HMf0l8dbS36tctPVqUSplURxsRDr6MKB9/e/uHC95lUVRS8p2aWaaejFY0bsNzsvlO5maOxqDzV8CFz+ERma9jqQ6y1yeyPWoZrVDjkjcWh2p4D1X+2ioxXQrUHpew9Ki7w8QZs1/rtKaoWmVIbpQnMbDhqLbUAAAAAAJjh6oxLBYDjg5vHkF+KDAKj9rZSzQoHKWod09Za8p6buA2SySx0u/kN/j5BvE8SHii7+OoChSSscNuVPbeMtGjurMx9bdk9kHk9GRRd1bLJ76l0ePiVEP8/e+cdJ0WVteG3OkyGIQw5SJSooCJRVII5iwqiIhgwsq6ua/jWFdOu6Oq65rCiqyCigGAEkSiiRMkSZsgMA8Mkhokd6n5/VFd1hVuhw/TMwHn8yVRX3VSxu857zzkarwim+eO4DUd9WhXXGeYNeQaUpMzaDY4n2Kv7iHDcdqdBH5oovGzej2gXUonr4SDC4+Z9xTOUlvmsBxnCFxAlccmmXLTbtfkcIhQISFAgCIIgCIIgCIIgiDoJCQ4EQZw0CDphwR3ycHBbCQ4uQSM4CIp3gz6HQ1iE0HsZCLKYoFMc5PVq0UGzXfW3XcsGmnEdPHKCa4TVixpyP+HlqNNJ68ZkFTZHOy5Negcb67LesB5xmCUbO7NZ/9Z5GSLxQmCW/cQcN4rTsJ3gwPdwEJHk5X/FF5VWcdcb2tCFZQqLMc5OQlw9RaLYThAEQRAEQRAEQRBE4qGQSgRBnDTodQWXIMDjdXETSqvLqJFFBf16lyBABAuLDnICZzmEEsfRQBYm1OGZNN4NimghoGXTdDRMT0Lzxqlo37Ih2rdoABGAGyqRQTCGjUK4KUfhZiIJS1NT1EQ+B65oYGKRtgwDFGPQKauakRjI1aOwM9wHgiJ3Pd/Dwbng4A+o2g2dNHUoJaconihOypq2EYWHTAT5IAiCIAiCIAiCIAiCiA8kOBAEcRJhzOEgh1syQ7/N5ZKSRrtcglZ0kEMnQZ2bQU4ZrRcgwqMRIAACU4kUTFNOEAQwxuASBLzyp6GQkusyBEUGnz8YMvAKhgTR4X5YWPTgH4bY4dryIzQAO1wXKfGKrKMWYmKK/6869pbmbsZMx652JBAE2OZwiAR/QETxiWpnZf06DwdOGcYAQbWnJim5bVqR27LeT7VQRVICQRAEQRAEQRAEQdRNKKQSQRAnDXpdwe2SEjJbzeh3qQz/ssig5GbQiAeCklBak5sB6lBL+vBGUIQGt0swCBVqAUMWR+Q+5Hah7iPqIxM5TjwQmOEv4woRMSUK5rVnaJ/fZ1Td6mfjR9iIqecE4y/r++XXje4AlpYbczWUVfidezgExdCBjDwXhpNy6v3iH5Mo42cRBEEQBEEQBEEQBFFrkOBAEMTJgz4Mksvau0EuAwAZaV54PS5N/gZNjgbFw0EOqQQl1pHe6yE8HEGpL7crV1OGq8oNYcwZodst9Tpojdt6cSReODXp2oVpislrIAKsPC+4UZcSbbPWGNmddW6XwyESSit8KHYqOPh1oZqYKoW0xZiMgpDz8cULkiIIgiAIgiAIgiAIonYgwYEgiJMGvbYgeyI4oUFaEtxuyVTvElTeBtB6HyihkVQCBC8RtNR/yHNCH9ZJ7cWAsLeDZp3FuPWb9CW5VaO0wOqCNUXVXjwNzpHlDpDDV8W/fTOxINb8GLxW4xlSqbTc5zykkkluCC1hiSfnYAl+XLUP+UUVuhKh46UKI2XqlWIWZooUBIIgCIIgCIIgCIKoF1AOB4IgThpiMfZ63C543K6wmKDyOAh7O4Q9FjTJn5mcY4FpxQdVgml16CZB3U6oXW2bUjwgOTeD0g4L53zgKQFmux9vY61le/EOrm/Tnpnh387ToabyZuvb1YwikhOhGmQ8z9+BoydQUFLpqKxPlTTabgh/7C3Ek++shCgyTE/egfefGIGG6UmI58WgyQTh4DojjYIgCIIgCIIgCIIgEg8JDgRBECE8bpeUwFkUFC8HQOXVgLCg4FL5h8liAISwx4PsxOASBDCm8pJQkkbr6odEhX25x7EppwDllX6UVfjRpW0mBp/ZOlQuJFWo4irJSaljRTHcx2IfNk2CbD6+aEQidYJn/naLurq/+naZE0u2BWbhrAxhhqwa0eU2iGdIpZk/7kDQoceEPyAljTYY+oGwwBbi/blbFE+MyuoAvly0C3dd05vbLmMMC1fvx4qNh9G7c1PcOLxr6N7RlXM0SuJUwu7eJwiCIAiCIAiCIGofEhwIgiBCeNwuBIKiJnySnGPB5QJEUZtMWlBmoEtChMC0nhFyIupgkElJo0P9aMUGIWREk9bvPVyKb1fsUba7XQIG92kd9myALGYIXEO+WRgpKayNcZ8ZrPUKS2cGlfHP0gSYSMtxhHoBA+OKBLLwELH84KCwWc5onsFdQHw9HMqrAo7L+gMiV2wAjPuwJ/e45vPG7GOhOiz0f7hezqESvDVrEwBgU/YxtGyajqF921hfa45HzRlsnCGjN0EQBEEQBEEQBEGYQzkcCIIgQnjcYaFB9khwCQLcofwLLpc6kbMqp4MqTJK0Lfy/OiyTtBG6euE8D4IgIC1VqwNXVAekbUrj4W3GPmvGCKoPW8SYuQG4Ls5KT6RtOMlj/bVqZsCvi/gD9jkczPIuBINGOUz+PPWbbZr1r362nlPKpDPzjwRBEARBEARBEARB1AFIcCAIggjhcbukBM/K/1rhQRtiSfW/zhsC0CaeloQKOdSSSiQI/SOo2kxP8WrGVFkdnpGuSVgd1i8MM/Rjsa/zZvs7FTJiFSHMwhFFCgvlwNAbpGXhxCzvQ7icblwWwxpz0emaz6OGd3U8Tid91yZGwYE5FkyCorlYUXjcWQ4Ja+wPFNejpy4dYIIgCIIgCIIgCII4CaGQSgRBECE8bpfKc0Gy6rtdgiI6yIl8ZaFAsUQroY4EJW+DLAq4XQKCoVBMYOEk0DICBDCBKYJERqpRcJDFDDsbK88LQiZuhtY422vj7XwQ75zVmrY5+z68XzvsP3IC+w6XYkif1mjfogH/EDHejH+TcE6ouX2IBFlwMO43A2OC5SADQVncgZwDPVybn0CDuyra8EUkK5yc1GTCd4IgCIIgCIIgCCI+kOBAEAQRwuUKeTXI3g0hDwe3SxIMlHwHsseDqq5kFGWK24PsJYGQgCGVCXsyMEAx2MpChQAB6XrBocqvyRkhCxdqrSOe4ZR4zcSaSFkdw9+yHMcA78RwHIkRMt4T3NNSvHjghj4QBAHVvqAm10Ys41DyN8RllNHhD4javA2mgzFuCIqhurrzwjjCiyP4KkU0LWnGEl3ScjJ612UoxwZBEARBEARBEETtQiGVCIIgVMieBi45b4MgCw7hPA2CUlYOpRQWAGQhQs4DIQsY6nBMShgl6PJACEA6J4eDUlbVb3jAnH1IwPx4nlGdl+uhVrDIMVFjXUaysw6OnbrcoDNaaVa3a9EgorFFiz8QtNwecl7gIns4aMoz/QKnTc0m3fVkWo4gCIIgCIIgCIIgiLoCCQ4EQRAq5ETPSrJnJaRSWCyQymlzMWhFiHD+BkEQFPEiXF5QtaNNIp3GyeFQURXAknUHUFRaZT5u1UDsJvdGK0hYGnlNmqxp6UMzAz9OMkO82uG2rbGaO6tzw/CuSEuRhKiMNC9uGN4l5nE0a5RqW0afw4HBubdKMCiG6wCaHRdN6juMtGRJOE8Hv8VohArK+0AQBEEQBEEQBEEQzqGQSgRBECpkbwS3W4AYYJKHg1vWZpnBK0Fez2AMnRT2dpCD42hzNwgq8UIWKLweN5K8Lvj84fj5a7cfwcyfdmHmT7twWssGuODsthjerx2UZusBpkb8OCsSsYRTYaaJps3bq4nwOvo2WzZNx4v3D8HB/DJ0at0Qx8t8Mfdx/lltMG/5bgTNrP/QCw5MZ3i3Pi6ShwMvphL4Hh6cNrRikvV29bpIwmudipF3KOQQQRAEQRAEQRAEUZOQ4EAQBKFCCHk0hD0cpMTPoshUwkFIJAjVUZtVZfHAFfJsEFwCXIxJs7pVgoRsLJXbkZ0gBADpKV74/NXKmFZuOqws7z9yAgUllUquAEEV6V8J+eTQlihA0AgBNWWATdQEcflYWHVXk7PVrXrW29l5uQyYhRG/aWYqWmVlIBAUcaLCH/NYz+ySheUbclFQUmlaRgqpZHG8NN4EWkRRhNlGZ3k5avaiiaX9aGqSkZ9PTRyXU1VIIgiCIAiCqE/cdtttWLNmDQBg8uTJGDt2rKN6w4cPR25uLvr06YMvv/yyJodYJ+nWrRsA4PLLL8drr71Wa+PYvXs33nzzTaxbtw4lJSVo3LgxhgwZgilTpgAAvv76a0ybNg179+6FKIpo1qwZHnvsMSxatAhz584FAGzevBnJyckJH7t8DfXv3x/Tpk2LqK76uuXh9XqRnp6Oli1b4uyzz8Y111yDvn37xjhiZ/j9fhw4cACdO3dOSH92kOBAEAShwiVAydkAAB63S8nJIAsFgiBAYEyT9BlgSkJodT4HlwCISqbosHFNUAkMcs4IMTTbPD3Vi+ITYcFh/5ETmjGe0SULeQXlOFZcgS7tGsPjFrh2ar0xz8rGqk92bJFSwFjXpE0rA7pVPW2fsSWsjoRoQikxMLgEl3LunNWJpAP1OZGW3a7Yj0fb5hlo2jDFUnCQvWy044HmdMjXmFpEA8Jhk5QwTMon8+uI2eTe0Hs88I5CvGUKCqcUOTUlrtC5IAiCIAiiNsk5WFLbQ0gIXdo1Snifr776KkaMGIEWLVokvG8icvLy8jBmzBiUlpYq6/Lz8+HxSCbm6dOn4/nnn9fU2b9/Pxo3bpzQcdYGfr8fJSUlKCkpwY4dOzBjxgxcdtlleOaZZ9CoUaMa63fVqlV47rnncNlll2HSpEk11k8kkOBAEAShQsq5EE4a7XG7ILgECCIgKJ4EupBKTGVaFyQBIpz3QWpPNkjLIZYUo5wgeSm4BAEiJBFDjtdvxkufrlOWn7lrINq2aKBYY9V2PjOxwcoWWJOzsGvaXqj11oits4hzB4Ah6twYkZRlsQsOyV430lK8aJKZYllOnTSaqTJEK+kRVMNwu1wIBHU5HzgzzdXCgxMsBQibmlzRzHH9kw/ysiAIgiAIgiDqImVlZXj22Wfxzjvv1PZQCAdMmzZNERuuuOIKjB49Gm63G02bNgUA5TwmJSXh8ccfR+/evVFWVobevXtj1qxZtTbueDNv3jzNZ8YYqqurUVhYiG3btmHOnDk4evQo5s+fj4MHD2L69OlITbXPpRgpeXl5uP322+PebqyQ4EAQBKFCCHklKMsuVVJoQdAa9EN/mRBOBC3nZpCNwnK4JEGQMkCEmgHkXBByLgeVt4Q+cbQVBccrJcEB1kKC6f6q/lp5JNga4GvJehuLoZ/bnsqw7nAABmIdjWwYNjsbLpeLsxboflpj7NhfbNt+gzTp+mpqIzj4AhwPB/U4VX89bgEqfSJchhduyYH3jN7bwbEA5NAjJhJBKR4heupSmJ+6NBaCIAiCIAiCAIDFixdjwYIFuPTSS2t7KIQNe/bsASCFD/rHP/6hMaIXFxejsLAQAHDJJZfg1ltvrZUxJoIePXqYbhs5ciTuvvtuPPLII1i6dCm2bt2Kxx9/HG+88UbcxxEMcl7E6wB8qwVBEMQpij6fgtvlkkLGuOQE0NIGOYyM7MUQzuEgixaCkoBaCaMU+kcddkkWJ9ShljJSIxAcSipDgojRgmg3y9/O6Bipl4ChuEXoHGkAETUfwTi0HSdqVrdlDgeukd3Z8dWGumJcD4fWWeno1ampo/Yy0pLAIOWFsMLnDyqJtNXignZw0p9wYnV+AUO+aYuysRAP3cv2vFBon7hRE4eSzg5BEARBEET9wePxKO9rL7zwgiZMD1E3qaioAAA0adLEMGO/sjIcsrdNmzYJHVddIy0tDa+//rqSd+PHH3/Ehg0banlUiYMEB4IgCB3yDx4peTQ0uRnkkEiyl4Ji6lelURBCdeXcDC6N6CAoln65vNyPTPPGzt3sCo5XcceuX7beYe6iw6pRhhGKPlZOqN8aJILGGUKnM05WTn4IIi1ut3GAqSkeeDzGr/Sxl3QzrGuQliTVSbZ2crTycNCPiyeCGPQnJudzsBJmnF0YjDHLskaPiehOUF3QFmIPD5bYnagLx6y+QnkqCIIgCII41WjUqBFuuukmAMCxY8fw8ssv1/KICDvk36xyzgY1ohh+h+RtP9VITk7GU089pXx+7733anE0iYXOPkEQhA45f4PLJeViEBkgCCFDkOzNYAivJEAQ5LwMCOVwCLej9mqQyofbcIXdJgAwtG6WYTq2Jg1TUFQaFhnkpL/qJNT6BNBqTEUIW28H6+1xJUHhXnjHSA4rpc15Ed8BmSffdtaPizOe1GQPPBwvgyYNjWGTMjMkwaF1VpplP36V4BBO/MzPBcATQaSyztbx6zJViCSn3iCqZdMCgnG5homX50Us12J9D6VEtniCIAiCIIiTk7/+9a9YunQp8vPzMXv2bFx99dXo379/1O0VFxdjxowZWLZsGfbu3Yvq6mo0bdoUZ599NkaNGoUhQ4Zw6z3xxBOYO3cuzj77bHz++efYsWMH/ve//2HVqlUoKChAZmYmzjrrLNx6660YOHBg1OMDpLwVX3zxBRYvXozs7GxUVlaiYcOG6Ny5M4YNG4bRo0cjPT3dsg3GGL755hvMnj0bO3fuhM/nQ8uWLTF8+HBMmDABzZo1M9S57bbbsGbNGmRlZWHlypXcdqurq3HmmWcCAK677jpMmTIFAJSZ+jK5ubnKujZt2iA3N1ez/a233sJbb70FAHjxxRdx/fXXOzgyUv8zZ87EwoULsXv3bpSVlaFx48bo27cvrr/+egwbNsyyfnl5Ob744gt8//332L9/PwRBQM+ePTF+/HjbujVB//790aVLF+Tk5GDlypWoqqpCSorxPX3z5s2YO3cu1q1bh/z8fJSVlSE9PR2tW7fGoEGDcOuttxq8RvTnRH3Md+7cqdlWUVGBOXPmYMWKFdi5cydKSkoAAJmZmejVqxeuvPJKXHbZZaYhnCOFBAeCIAgVaoOcEhIpZOgTBAFgTJnRruRwgKAklHaFBASXwDcfy14STBVCSZATTEMybndolYkLz2mLNduOoKIqoKl/evtGWLX1iPK58Hil1h6r6tSRgTIKA6TVDPX6as+M1pgpixZOqocun5jH4eJ4E6QmeeDleDg0bpCMkf3bY9GaA8q6K8/rBADo3KYR2jbPwKH8MgBAvx4tsG77UaWc328dC1IeK2OMK4KIIoPLJYs3zFBP15q9WGBDJLPDLf0odIZ5snMTBEEQBEEQRM3RoEED/P3vf8ekSZPAGMPf//53fPPNN0hOTo64raVLl+KJJ55QjKkyeXl5+P777/H999/jsssuw5QpU7hGX5k5c+Zg8uTJ8Pv9yrqCggL89NNP+OmnnzBp0iQ8+OCDEY8PkAz148aNw6FDhzTrCwsLUVhYiDVr1uCjjz7Cxx9/jK5du3LbqKqqwr333otly5Zp1u/duxdTp07FN998Y1m/rpKdnY17773XcGzy8/OxcOFCLFy4EMOHD8crr7zCFWT27duHO+64wyB+rFq1CqtWrcLEiRNrdPxmDBo0CDk5OfD7/diwYQMGDRqkbAsEAvj73/+Or776ylDv+PHjOH78OLZv347PP/8cb7/9tqlgZsWGDRvwwAMPKPk11FRVVeHo0aNYsmQJ5s2bh3fffTcu3ikkOBAEQahQG+jVng4syBRRQCoHxSopqE2SQjhptMYGK4RnsKtD8DDAIE60bJqGCVf2wtA+bfD8R6s14zutVUON4HCiIvwDSC8u2IkNYeFEsBQRnKIxGDsKi2Q9voTPaOYkKnA6o9zO2B3N7HSD4VvOl8CZcZCa4oGHI0Q0bpiCK8/rhOwDxTiUX4bLBnfAaa0aKmLA8/cMxi+bctGkYQoyM5I1goMvIIYPhUpcUO+H7CMgcjJBV/sDSE0O5yMpOVGNxesO4ESFz+kh0GCVB0J//HnhnCKd3W92Tuu7AFEXxq9OjF4DjaP+Sp8EQRAEQRCnJhdffDEuuugi/PTTT9i3bx/efvttPPLIIxG1sWrVKkyaNAl+vx9erxc33ngjhg8fjgYNGiA7Oxv/+9//kJOTg/nz56OyshLvvfce9x1t7969ePrpp5Geno7bb78dAwYMQDAYxKJFizB9+nSIooi33noLI0eORPfu3SPe1yeffBKHDh2Cx+PB+PHjMWTIEGRkZKCgoADff/89vvvuOxw7dgx/+ctfMG/ePO6M8yVLlgAATj/9dIwbNw6dO3dGXl4ePvroI2zduhXHjh3DU089hS+++CLi8Zkxb948AMDf/vY3bNu2Dc2aNcN///tfAFCOeX5+vmLUHz16NG6++WYAQKtWrWzbz8vLw6233oqSkhKkpKTg5ptvxnnnnYeGDRvi0KFDmDdvHpYvX44lS5bgoYcewgcffKA5NmVlZbjtttuQn58PQRBw7bXX4sorr0RaWho2bNiADz74wFAnUXTu3FlZ3rFjh0Zw+M9//qOIDX379sXo0aPRrl07AMD+/fvx+eefY+vWraisrMSTTz6JJUuWKILAvHnzTI+5TEFBAe6++26cOHECaWlpGDNmDAYMGIDGjRvj2LFjWL9+PWbMmIGqqir8/PPPmDVrlqGNaCDBgSAIwgGyh4MUWimcgyEc8CXsDQHIYoU2n4IxmTEgsFCSalUSarlYuxYNDOPQryuv9IcM9yw0JhuRAXwjuhD6z46EGCujsBc6D9OTgLAyNdg+Y/x8CalJ/JBKjRskIyXZi5ceHApBCAkDqoOVnurFJQM7gDGG/UdOaOr6dB4OyvXLQl4uqgMZ5AgOPr+IlCRpfTAo4sl3fkF+caWhnLYT1Y4i/tebo9zVEZZx1nH0RvBYr1m5fqKEhrogaBAEQRAEQcSLzTnH8O6czYpX8MlO2+YZuG/UmTizizEcT03z97//HatWrcKJEycwdepUXH755Y4N+oFAAE899RT8fj+SkpLw4YcfYsCAAcr2vn374pprrsGDDz6I5cuXY9myZZgzZw5uuOEGQ1vFxcVo3LgxZs2apRh+AWDAgAFo3bo1pkyZAsYYvvvuu4gFh9zcXKxeLU0q/NOf/oR77rlHs3348OFo1KgRpk+fjp07d2Lr1q1KeCM9F1xwAd566y0kJSUp6y6++GLccMMN2LFjBzZu3Ih9+/ahQ4cOEY3RjB49egCA4lmQlJSkrJNp0CBsr2jWrJlhuxVPP/00SkpKkJmZiU8//VRzbM8880xcfvnleOutt/Dmm29ixYoV+Oabb3DttdcqZd555x3k5+cDkK6lW265Rdl29tln4+KLL8bNN9+MY8eOOd/pONG8eXNlWe19c/z4cfzvf/8DAJx11lmYNm0avN7wpL1zzz0Xo0aNwp133omVK1fi6NGj2LhxI/r16wdAOid2x3zq1Kk4cUJ613/ttddw4YUXaraPHDkSI0aMwK233grGGH788ce4CA6UNJogCMIBeu8GdTJpRSyQt7lkcUJVH2Fjv2ZbSGWQbciCqrzLJeDCc9oqbWRmJKNHhyYag7M/IKJaZRhWj9Nq1r02ubTjw8BpJ/q6hrYiMcjWlFE/AdbSiEL/cIryQiqlJLs114GMnBhaSm5tfdC8ujwMfouk0VBalf4Egsay1f5gSJwAVm87Yis2GMSACD1m9K1YHmerhNNOu+I2W3MXkJx0u74Q77HWnz2Pnnp0egmCIAjilOHtWZtOGbEBAA7ll+HtWZtqpe8WLVrg0UcfBRAOM6NOQmzFokWLcPDgQQDAxIkTNWKDTFJSEl5++WVkZEg5Ez/66CPT9u644w6N2CBzww03KO9V+hj5TigoKFCWee0DwPjx4zFmzBg89thjaNy4MbeMIAh49tlnNWIDAHi9Xo2Ikp2dHfEYa4Pdu3fj559/BgA88MADpkLO/fffr3gLTJ8+XVkviiLmzJkDQDLcq8UGmXbt2inXV6JJSwvnTywuLlaWd+3ahXbt2iE5ORkTJ07UiA0ygiDgsssuUz7LoopT8vPzkZWVhV69ehnEBpl+/fqhRYsWAICjR49yy0QKeTgQBEE4QBYJBE3S6JBngTp/ApNzP2ht4uq80PqUC2ohQskdHUpSPWpYFzRpkIKSsmpc1L89vG4X0lO9KC0Ph6Upr/SjUYNkXX/miaM1+1VDlntHIZUsumahzBjR9mtI0G3eESAY81LEI8RUTWA2Lq/HzQ1VJF0HZm2F/oYK6HNA+AJBQ1k1367Yg/m/7UO7Fg0MuUYArYfEvrwThu1qDh09gZenr0d+cQVGj+yG64d1sSyvH5NtbgyrbXJ4H7OwVzrvhPpiFI41yXRCqIHwR/Xk9BAEQRAEQRAcRo8ejW+//Rbr1q3D5s2b8emnn2L8+PG29X799Vdl+aabbjIt16hRI1x22WWYNWsWdu/ejSNHjqBly5aGcmZx8hs0aIDMzEyUlJSgvLzcfod0tGvXDh6PB4FAAC+//DJSU1MxdOhQTcz8du3a4dlnn7Vsp2vXrqZhik477TRl+fjx4xGPsTZYsWKFsqwON6TH5XJh8ODB2L17N7Zt24aysjJkZGRgy5YtiueA2jiv5/LLL8czzzyDykobz/s44/OF39XVIZ3OPfdczJ8/39Z2o04Arm7LCa+++ioA2Ip3WVlZOHLkSMTtm0GCA0EQhGPUoYtCa0KiQ9jQLYTyN+iNaGqvBrWhnykz1jVOD6FPKUkeXHNBZ80XUEYaX3CQ63JHLpjb9uQ6tW2bjFZkONmwEwh4JHld3MTNjEGJqaO+htRG9nAbbk1dn180/eFz+FgZPv7uDwDAkcIKbhmfP6iMubzSzy0j88XibGXm2PQF23HB2W2Rma6drSO39fuOfKzfeRTn9miBPl2bcY3q+n3l4SiBd5zUhTphBJcfACYPAkN+jhoULBIS2owgCIIgCCIOPHBjH7z31WYcPHpqeDm0a5GBe6/nh/BJBIIg4LnnnsO1114Ln8+H119/HRdddBHatGljWU+eyd+8eXNlprYZZ555JmbNmqXU4wkOrVu3Nq2flpaGkpISBINGD3M7mjRpguuvvx5ffvkl8vLycO+996Jhw4YYNGgQhgwZgqFDh1r2LcMbs4w62XYgYJwYVhf5448/lOWrrrrKUR1RFJGbm4tu3bphz549ynqrMFdJSUno2rUrNm/eHP1go0AOaQQADRs2NGxXv3cVFhbiwIED2L9/P3JycrB582Zs2LBB2e7U60ePLHT4fD4cOnQIBw4cwN69e7Fjxw6sW7dOSdQdL091EhwIgiAcog+hFF4viQ4uQYDIJAFBDq+krislaNZ6HxgSPUMOfyN9lhNKM1WBjFStm115lT9cWdWI3LY8K14RFiLf9aipE4ZWFfEWNXjfxWbtx+V720Q0Sva60b9XS3z24w6lnxuGG70ErOaT6z0cyiv9mLlwJ0ZfdLo2ITikEEl2+PxB6TpnDGU2gsPKTYfD7TPgty15uHTgaYZjtnV3ASb/9zcAkofFm38ZhvYtG2jqcgkZ2VVpKLhFpPuURX2uasKQHutlUxP3oKUYQUmbCYIgCII4iTizSzO889gI5Bwsqe2hJIQu7RrV9hDQuXNn3HvvvXjjjTdQUVGByZMn48MPP7SsI89ub9q0qW376jJmHgDqEDh6lPds1UtDSUkJ8vLyTOtkZWUps9T//ve/w+v1YubMmQgGgygtLcWPP/6IH3/8EYBkML/22msxduxYjXigRs6jYEd9CcmqzmsQCbIhv7CwUFmXmZlpWcfJNRJv1GGQ1N4KMuvWrcOnn36KVatWca/JWBNdl5eXY9q0afj++++Rk5PDFS1cLlfUYgYPEhwIgiAcIoT+kUUDw3a1SCDo8iRAa4ITDAsIh1xiuoIqxUGAgIxU7czvsgqfzuPC2b6ohQ0rrH6j1NYs5biJBjbN1FiqiDj/7vN63MjMSMajt5yDRWsPoHVWBq4Y0lHboSJA8UnyuA3r5izNwZylOWiYnoSbRp6Okee2D+UwsT8y1f7wjxVFFHPI7zuO4qtl2UjyuPHQmLNwenspdumsxeEYpIwB78/djH/cN0QXXslCVeBQX2bby3kt4jnW2vBiiPqlp76cKIIgCIIgCCJmJk6ciAULFmDXrl1KguCrr77atLzZhD4eaqNqrIZcmSVLluDJJ5803f7ggw9i0qRJAKRZ9k8//TQmTpyI+fPnY8mSJdiwYQP8fumdaceOHZgyZQpmzZqFadOmcQ3kNRk6NZ5GZ6fI3iJJSUn48ssvHddr3759xH3x8iTUNFu3blWWe/Xqpdn2n//8B++++65mXevWrdGpUyf06NEDZ511FoLBoHL9RMr+/ftxxx13KB4MAJCamopOnTqhS5cuOOOMMzB48GA8+eST2LQpfvlbSHAgCIJwij7hs2FzyNOBG1JJzs0Q8lhQiRP6+moPCrVhXW4zXefhIMfP15SFPJs9HLqJ1174h5npXtvCm8/sJAeCvk+NiJDoSdL1Y+KHBGesSV7ph/JZ3ZqjX48WEB2EA9JvFlwC3G4BwaCxXmm5Dx9+vRW/bc5Dk8wUNGmYYjtMOYdDMCiijJNfwooNu44pyx9+vRUvTxoKANiUfUxTbsvuQnBxlHCaWRq/me6vfktdzpHA2y/9Gn2Z2rDnO7ntIj3O9WQSF5d6PHSCIAiCIIi44vV68fzzz+Pmm2+GKIp48cUXMXToUNPyjRo1AqCd6W6GOnGz3Wz4mqRly5aYMGECJkyYgIqKCqxduxYrVqzA999/j6KiIuzevRuvvPIKXnzxxbj3bfUeVFaW+PBh8nnw+Xxo0aIFmjRpElH9rKwsZVmdlJlHovNaBINBrF27FoDkOdOzZ09l25IlSxSxoUOHDnjkkUcwePBgNGjQQNPGggULou7/4YcfVsSG8ePHY9SoUejSpYtBbKuo4IdKjhYSHAiCIBwiOxtwjV+CACH0pS3nZNBvD4dUkozrekOaOiKSHL7J5ZLKqvMsZKRpBYeySp+yTT9e0x1RDeukx2of4yRsOMkH4PRYM7NBWXSSrMu/IJdlCO+iE0NskseFSotYpNv22v+Al6n2B7Fy82F8MHcLqnyRxzeV2ZMr/SBkjKFpZgryi7UJvvThnng4Mmxz2ozUAGwZ0SnBqENEaZJeR1o/wm2O2nY8hrpngq/LQhNBEARBEMTJRN++fTF27FhMnz4dRUVFlob3008/HRs2bMDRo0dx9OhRyzwO6vj9HTt2NC0XCddffz2uv/5623KMMRw+fBgHDx7EwIEDlfVpaWm44IILcMEFF+C+++7D1VdfjYKCAixfvjwu45ORk1NXVVWZlrEKDVVTdOkSDge8evVqy8TPP/74I44ePYq2bdvivPPOQ1JSkqb+li1bTBNPM8awa9eu+A3cAcuXL1dCKo0cORIpKeHJezNnzgQAuN1ufPjhh2jXrh23jSNH7EMa89i8eTO2bdsGQLpGzbxwRFHUhH2KB/HxHSIIgjgFsLIxCaoCXO8GzidBEKDXJuS6+hbUIZky05OQmZGE1s3ScXr7RsjMSFY2SuX4A7X2zKhZt0xTNMbi6IyLEdWLs/2yrthD01K8hrFEMjS5rpcTVilaAgER037YHpPYEG5LaqNpZiq/QChXhLyo2cT5ZCVS1JFTCgBKDgzlc032E6c21J4hXE+LunSACYIgCIIgiDrNww8/jFatWgEAvv76a1Oj6ODBg5VlOSE0j+LiYixcuBCANKPcSYLmePLSSy9h+PDhuP3223Hw4EFumaZNm6JHjx4AgOrq6rj2L8+cLy8vN/UEWLlyZVz7dMKQIUOU5c8++8y0XFVVFZ5++mn84x//wJNPPgm3W3p/7d27t3KdfPPNN6YJvVesWGHrARFPqqur8eqrrwKQ7C3jxo3TbD9w4AAAKZG0mdjAGMP8+fOVz/p9swoLpr7GevfubVpuxYoViudHvBKNk+BAEAThEKtwSkA4x4OZg4Pmfxi9JdReDOEM1SqhIPT3iiGd8N7jI/Dyg0Px1B0DMLRvG205VVnrwTrDqUGfwcKKG4dx1DSWQ64DhlN1d/17tVSWMzOS0O20xrZ15ShLVuPWJ46OhaLSKhSfiM8P5KpQeKYmmcZQTkHRuEMFxysxa0k2lq0/yAkdxBcmIqFWZt7XQJ9OvEPCZXn3QM0fBxIoCIIgCIIgTl0yMjIwefJk5bOc50DPyJEj0aaN9F78wQcfYN26dYYyPp8Pjz/+uBIyaPz48fEfsA0XXnihsvyvf/2LW+bw4cPYuHEjAGsjcTR069ZNWZ4xY4Zhe05ODqZOnRrXPp3Qp08f9O3bFwCwdu1avP3229xyzz77rJJgesyYMYrgAAC33HILACA7Oxuvv/66oW5hYSFeeOGF+A7cgvLycjzyyCPIyckBAIwaNQpnnHGGpowcCqy4uFjjeSMjiiJeeukl5XoApOtYTVJSOMenPiyS3D4A/Pzzz9xxZmdn4+9//7vy2eweixQKqUQQBBER1t4DLsEsf4MAQcmjEIqwZAhLIpWRA6BIoZTCrgvyZ8bCeR6EUCZZp0mU1f3JeR6siMagGI2ngsEoDP6RVueciGRodvsRT5umnaMIY9F7cwDSWMde0g1etwsnKny49oLOcLtdEDmGd6sxyONUwgcxfuLoaDmmC30UC9W+INJTvFxBpLC0Cs1CQgRjDD5/EH9791cUlUpuwsUnqnHleZ247TIl14UQ/qvZqD4+LHT/8ccY9kbgXrn2Oxkj6lA/vDEaPD/swoDFaVyWfTgIRaYsO281ytHUDyikE0EQBEEQpxrDhg3DZZddppnlrcfj8eDFF1/EhAkTUF1djfHjx2P06NEYPnw4MjIykJ2djU8++UQJpzN06FCMGTMmUbugMHDgQPTv3x9r1qzBjz/+iNGjR2PMmDFo3749fD4fduzYgY8//hgnTpyAIAi4995749r/FVdcgbfffhvBYBBvvfUWjh8/juHDhwMAVq1ahWnTpsHv96N58+ZxD7FjxwsvvIAbb7wRlZWVeOONN7Bx40bccMMNaNmyJXJzc/H5559jzZo1ACTvlIkTJ2rqT5gwAT/88AP++OMPvP/++9i1axduuukmNG3aFNu2bcP777+PI0eOIDU1FZWVsb+rbt++XfOZMYby8nIUFBRg48aN+PrrrxVvij59+miM+jKXXnopNmzYAAC49957cffdd6N3795gjCE7OxuzZs0y9FNeXq753KhRI3g8HgQCAcyfPx/nn38+kpKS0LdvX/Tr1w9ZWVkoKCjAsmXL8MADD+C6665Ds2bNlJBdX3/9tSbEVrxyeJDgQBAEEQH2YYkcVJbzOQjM6OFgowLIHhLGvkNtCSpjPKcswE9EDZg7GzgVM5wgSyo10abV9hgaD/3lKxx6o70ZpnkZ+IX5q1XrG2Uk44Eb+0AUGXeGP1NVMou3zzjGcU8cPRyOlcRXcAADN6H1seIKZKk8HxavO6iIDQDw+cKdHMHB/IQxBiWpu7m4EN/8J2ZG5Ji8MHR/49Gmvm19u46PiakwI2+ufaO61TmpsaHZHBeCIAiCIIhTlaeeegq//vqrZcLfAQMG4O2338ajjz6KsrIyTJ8+HdOnTzeUu/766/H000/X2u/NV199FXfeeSd27dqFjRs3amavyyQlJeGpp54yzUUQLR06dMBjjz2GKVOmQBRFfPLJJ/jkk0+U7ampqXjllVcwbdq0hAsOXbt2xccff4xJkybh2LFj+Pnnn7mz8rt06YIPPvgA6enpmvUejwdTp07Ffffdh40bN2Lp0qVYunSppsyoUaNQWFiIZcuWxTzea6+91raMIAi49tprMXnyZE3uBpmxY8fi559/xsqVK1FYWIgpU6YYyqSkpODJJ5/ElClTUFlZid27d2u2ezweDB06FEuXLkVeXp7iubNgwQJ07NgRL774Ih544AH4fD4sWrQIixYtMvTRv39/nH766Zg+fTp8Ph8OHjxoGuLJKSQ4EARBRID9TxLrElphwsxbQhYQwmGXBJ2RK9KfRopnhMmIme5DRAbyKAgntI1jNw4MqTUx9zlWA650LBLwY9dBWChRFOPW3bHiCvtCDik5UY0DR05g/5FSw7b8ogr06NBE2ZfNOQWa7ZXV4RiU6pBSeoO8vbePk1LxR9NjnA3Syr6rPT3MynIM7Wbr5AUGYxi6SI/gye2vQBAEQRBEfaFLu0a1PYRTmqysLDz22GP429/+Zllu2LBh+OmnnzB9+nQsW7YMBw4cgN/vR8uWLdG3b1/cdNNNOOeccxI0aj7NmzfHnDlzMHv2bCxcuBC7du1CaWkpkpOT0bp1awwZMgRjx45F+/bta6T/8ePHo0+fPvjkk0+wbt06lJSUoFmzZhg8eDDuvPNOdOrUCdOmTauRvu0466yz8OOPP+KLL77AkiVLkJOTgxMnTiA9PR3dunXDpZdeihtvvFETRkhNkyZNMH36dHz77beYPXs2du/eDZ/Phy5dumDMmDEYNWoU7rnnnhobf3JyMho2bIiOHTvi7LPPxpVXXomuXbualk9KSsIHH3yAmTNn4rvvvsOuXbtQVVWF9PR0tGvXDoMGDcLYsWPRpk0bLFq0CCtWrMCyZctQWVmJ1NRwfsOXXnoJU6ZMwc8//4zjx4+jSZMmyM/PR8eOHXH++efjq6++wtSpU7F69WocO3YMLpcLTZs2Rffu3XHNNdfg4osvxpYtWxSBbv78+QYPkkgRWK0EIibqK9nZ2bjyyiuVz999953lzUMQJxvBoAi323wWuNV2UZTCvSR53RAZgwBoygaDIgIiAxMZRMZQWR2AyyXA7ZLC5QgC4HYJCIoMHrcLImMQRabMcne7hFAOCUEJ2+MKWfuCIgNjDP6ACLfLBa/HpREgGKQkv3JkGbkvAPD5RXhC43S7BQQCIsRQXa/HFQoXBfiDYlhACDXt8bik2emhsnI9AQLcbqmeK9SPyKTxiqIkd7gEAUFR6jsQlMYdDBnEPW6Xso+KhwMLx/OXHRLcLkGTe0M+Ti6XoBw7V+iYInQMvB4X/EERyaHz5BIEJWSR/ivTHxCR5HWj2hcEA0OSxw1BgHJ+AGkcQVEav3wdMEh9e9zS8QsERbgEaVzBoAiPx6UJ5aO+hlhondfjBmPSuU/yuBAM7YtLECCGPBtcAiA7QKiX5evJ43YpBl1RZPjzv5fFzTPB63HBH4ifgGHGPdedgUsHdZCOc1DEY2+twN7DWmHiq5euhMctjcfjlq4jkYXOk8jg9UjXmFzG5ZIuIPl4eT3he9AfkO5xl6AVisTQfevh3P/yfa9HnkUvX4d61G0GgqLhmcFrCwjfB26XELp3pL9ejxv+0DWuLiNf25rnkfxMUY1FHqO8Tb1OXpaOsRA6XoJhv4KipPq43S74A0FuonL1cWGq/RAEQTMmHmZtRoqZh4PZuYoHdt8vVuMiCIIgiHhysr/379mzB9XV1UhOTkanTvzQmwRBEIQzeM9U8nAgCIKIBDtDj8V2dUQlgTerPZTngQn6sEfqxNGy8ZyhuLQKucfKkJtfhh4dm6B1s4zIwgfJRZnuc6xYhYTSd+KgT70srj9useRDCPcRnRFPMzaHw4g1f4NxDPaeC3JdJWEyJ6wVYwy+QDDqselJhNgAAIGgqPE+yCsotyyvTOa3LCGYHOuohhgRmlwMEdQxeBtE4Q2huQ+s6ptsi/ccFs3txeSzYr1PJ/s0mhoN6UQQBEEQBEEQBBEHSHAgCIKIADs7j3UKBznHQjjXglldJZSSyiysNoi/M2czft6Qq3wef0VPtG6WoWvPLGST1fit8yHoDYo1NdOWRWpVq2Ujo62IUEvji7Rbnz8xIkE8kYUNKVwSQ5VPK5okec09AkJLSn3VRs027RK4Vt+aOsXxCNmlW8MvF03bumXB4rN6QGZ91YfZ+ye5nkEQBEEQBEEQBBEz8csOSRAEcQpgZwuzC7Wh5HAwaVtQCxK87aG/zRunarblFVrP6pb75hrz5HZhHFt8EsxaNHIyWe/qtp3U1OtBTTw9HBKFIjgAKCqtNmxvlJGs5G4w2t6ZIlREglabUIkSJs2YG9itt/Pa0fbnfNyGoqqEFk5aiaCniPdLGo7JcUyAy4L+ONZWtNGT6XFIEARBEARBEMSpCwkOBEEQERDP2bf8tgTFu0EqA43KIS/qvRkOHDkheUMI2nLhVvV9A1r/ifgRrdHMbixq4YIzAd283Rh2URlTDRsgYz0LvMhOSvgku7GrDM/BoLGs11O3fypIgoM07tIKn2F7QL9PpsdD6+nALZYQ47f8l2n6s+raIKYYDOiR9Q1Y31p2AoplH5GU1++HffNktCcIgiAIgiAIgqhlKKQSQRBEAlGlYjBuEwQIQigWu7qwvE3VRpe2jTR1t+8rwokKHxqmJ0EQBEsjs1qUqG/xzs0En8ij1ccHbcz8mu2L66FgNqOem9dBZ012eMDeeXwETpRXY+bCnajyBbE5p8BZxQQRDqnEpMTnOuRE42p4Ao0tJsZ/JbpSLDdTFPkWbJuMa2tOOow9uQDlJ7Cnnj2yCYIgCIIgCII4Banb0xYJgiBOMsLGfuvk0rIHAhAOo6RWClo0SUOThimaeve/vATHiisMfWnaNu1UlZxaVagmBAmzXY9H8ue6gK0XTAJ3M9Lzp/dmaNEkDRmpXrTKysCfbz4bj487F707N43jCGNHSRrN+Imqg0HpylI7xejDEumPU9hLxGzWv7WXhGaNRbgerhOFxRr9eJzB93bgRVgy1NQrK8YC5r2aimGmVfjlOUuO6sXw8LKsWt9UWoIgCIIgCIIgiARDggNBEEQdQiUxKPkcBJ2ng/y3Z8cmhvpzl+/WtqcOx8TrTyVs1HWcjrNW7IEJ6FMvyNREnPm7rumt+Tzhyl5yZ8q69BRv3PuNBbXI4A8ac1AEguHtBuO/etlB3oFEXluOwhM5zJXATMoo6yLNYWEmJsTQrtNwTjVBXdEQ6so4CIIgCIIgCIIgYoEEB4IgiIRiYzQXhLCnga6WoFvu1ck403z11iOorPbrmqw5QYHXcm0lXI0W3nBt80lYxfmvbSIZFNPO/h/YuyUuHnAa2jbPwLUXdMaZXbIM7SV53XEbajxQJ43mhVQKBJkhHwIcnT97cUK93i6vgVWyZ2f+EjbnVec5EUkybK4YEeW1bV/P3GfB9rMjEaYu3pQEQRAEQRAEQRCnDpTDgSAIog6j9W7QGvjP69MaP/y6DwePnlDWVfuDWL3tCC44q62uIVjG7ZcSTkuGOoHVjsdDLJ4WjLGYhZV4GiprxOTJi2gDFmWYHU5bDPB63Lj9ip5wu4Rwl0ryaekY1bUk0oGgqIRJ4oZUEkWdsV9XgFkb/AXDGntxwVhD3s60eQp4uRs4IZ7M2osFY76E8FiY6pzLZezCP1k8Xkz6dyiGxCB86B8J8nPC7HkRj+cIQRAEQRAEQRDEqU7dshoQBEGc5NjZsgT1/6pE0eH8DeE8C0leN168fwjOOr2Zpo312/MdjEMVaqme2NfqS46HqFI41NA50OcncCpOWBXxuOvWTwd/IBxGSR0+SYYxQBTNfAWMS4ZjpmvLsBwHtcdKqNBuM9kPQ54I47K0X/xycdPaNA3yG7ULx2TXtHWZ+OyI7CFivq0Wn0XkwUEQBEEQBEEQRB2nblkNCIIgTnLs7MpKcmhV7garGbdul4AbRnTVrNu6pwDVvmC4PZN+7Gby1pRdyy5ptFW3dU0bMZv9XRPjdHo6+OFxIgrOEy7DjOekLno4yPg5goNUJnR16cIbMabdTzWGRNIMKK/yo/hElaq+1nOCe1xVfZm1rSmu2h5RdCxjK9wOnTdpVTL6UE2a7byDrPqr3lxe6ceJcp+hfjyM/9G2YJUQnCAIgiAIgiAI4lSFQioRBEHUQZScDXKSaMgJpKXYSPJfAGjfogGaZqag8HgVAMDnF7E5pwDn9mxhaNQsbJE+dFOsdjNeeJW6aouLxnOCgQG1FHpKj96QLZ8/J54rsnFbXZbxwvyo8NY5D4dwyCReDgcACAZFHCuuwN7DpejRoTGSk4w/f6yMxYwxbNiZj1c+W4/K6gCuGtoJd10tJdi2Napr2tHeG5HIAMp5sjk/+jrWIzOOxKotfcgh7jq7dhyNTVtj2fqDeP2LjRAZw51X98LVQzvbdqD2WJHH5/S+qD3q6EOSIAiCIAiCIAgiAuqW1YAgCOJkx8baJYdRUjwdoDUtysKDuj1BEHBOd6248OOqfZou1UKDkxjl4Zn7NgawOm28SwyJmtXM7YeZbzObPM5vm79CPU9e7qNdywb2g00gStJoBm4OBwDYnXscD/17OV78ZC0e/s/PKKuUEqvzPRJUi6oD88G8LaisDgAAvl2xRxH4agwH51RZ76Qcs6iv28Z0RnsnqD08rEIS8etaF379i40IBEWIIsN/5201Pc/O+7Nfb+nf4aA+QRAEQRAEQRDEqQoJDgRBEAnEiX1eDncU9nLQJozWtyEIAoado00SvWN/MXKPlUU0Dr0QEY3xLBZ7mwAhZoMdT0tJWBJYtZdAYnp0hF40YmAR60Tq83JuzxZompkS+8DihD8gKgecl8MBAD75/g9FLCg8XoX5v+5TtjHGk9WYZjsAHC2q0JTYdbBYZ6jnh/fhGe/tjNlqzwdrJwV+SB99Xxa+G46a5uV+UIeligTeGM0bCnmu6M7r8bJqbhVzISDiQUazKSH3fV16thAEQRAEQRAEQfCgkEoEQRB1DNOEzqqE0XI5IWT96tCqIcZcdDoYA1KSPUjyurhhY5T2BAFCKKSK3hgXj5BKNWbiD0eSqhPEa0azWairaJAM1kxpU90217SuC4mjbsgYbklKGv3CvYOxctNhfLl4F3x++9nmF/Vvj/JKP37dkhf5DtmgyeFgMvN9/5ETms9L1h3ERf3bo4laONEdGl8giE9/2I4/9hZhyJmt4jJWBmji+qhTFpieB04bvLI847vB8wFaoUCdg8TJFWgMSeQ8vJOmCm9Z9VEfhkqP+pxL26XjsXFXPrIPluCCs9uiWaNUw9hMb9cIwlTpq0nPS2fnLrK2498mQRAEQRAEQRBEIiDBgSAIIoFEYj/SJo2WTWWCxttBzVVDO8EVSjjNZGOxtiZgM7tdCNVV8gDEaOGPZGax2kgeeT+Iq8oRqZ0vEuNgvA2JVnPgayrES2ZGMq44rxMO5Zdh2e+HbMunJLlRFUpkHm/UIoPTUDtFpVW4Z8piXNS/Pe66prdmm3zIlv+eq3hC7M8rtWjNbqa/SlXgnHfjPRISAnVlrC5xo9hgvMZEkWHOkmys3paHs05vjlHDusDjcZl6RYQbVud5MAqUGm8MJj97tIKKHdbeFMZGgqJx3W9bDuOf/1sLAPhqWQ4+eGIEMhukWOZtiLtRP94PIoIgCIIgCIIgiHoIhVQiCIJIINEat8IJo3U5HQS9MBHD2GKqLRFrPoNoEjjHTDRdhupYHfJ4G/stUjhY13MgSJgZke1yQyR5nf2MSE7ywOOuGUOsPyAqYYMCwcgO+k9rDuBYcaXBG4Ax4L2vNltX5oVi4p0jbqQg4/FmugFYne9orq0tewoxbf527DpQgi8W7cKaP46ourNuUB8WSjNek7pmLWrFDPvyIkdc8PmN4tW/pq9XliuqAvj2l70RP480pU3r1txTinJAEARBEARBEARxMkCCA0EQRB1FLzDIeaQVgUFbOJTvIewjoC4XFiVU65T2IjMEKx4TcZoZHC8jWwy6Qa1SI0mnFQOxZv55NE1o4/XrZvMned2O2kpJdsPtrpmfHJqQSiY5HKzIOVQSU/8RnT6DSGEt6EifteKDeWJjVTkGjWjEGPDh11s0pd/4YqOmvGEcJrsQC+beEfJ2vbcDELAQHGShiTGjd8vevOOadtQLeu8RwzitdoIDL09HTUGiBEEQBEEQBEEQdR0SHAiCIOookiCgDaGkROW3sfVbbnaYWDkmPSGKurHkMSAjXBhbASNex4oxJDsVHJI88FgIDrEkoQ4ExJBRnWFz9rGI66ckafdBbssOgzBgUq74RDXyCsog8jwiYGWsNrdiM+jPs4khX1XmeJlPU8YXMtBbeWDovT5447UXUPghksxHrx0TL3ySk7wh8vPE6kyq9yVibwjd+TSrH86ZEWn7ERUnCIIgCIIgCIKoM5DgQBAEUYfRh09S/1WXMTXVmyWgNulMX+RkzVlaE8a8WAQTJ8c5qjEbLLlyh9b96Geaq/tXG05Tk52lgkr2uuF28TtNSXJj/JU9HbXDQ/Zq+N9323Aovyzi+lJuCa3IcKLCb99vQFQ8FnhGdABY+8cR3P/yYkx6ZRnenr3JtC3ezH+ep0MsiBb17Qzu3DBIquTTmk1M+kdqT+VhYxImSt+0vggvpFI1J6SSAcH6nrENI2W5MXJxwkmfBEEQBEEQBEEQJwOUNJogCKKuouRrFgCBhf4KEJiUXhkC01jF9uaVYvXWI/D5g6jyBdC5bSOMOLc9p1lr67acODoS1KXtQi1pk2BbNFSL1BehhamMvokcMwt12Pf0Zvj8p50I2uROSE5yc3M43DC8K0b0b48T5dVRj8UfEFFe6cfC1Qeiqq9OZi0fz9wCe+HCF7A3er81a6MyG3/x2oO47sIuaJ2VwRd8QgmHeRqROjxSKHZauIqurFyOsXA5zUZO+9z1FgmQmfwMcojz54m2TwYzD4eg3DAgGI8Z1K0wBiaYezvoc2dIibGtEk3DcFjkVaQnEARBEMTJS3Xe7toeQkJIbtU54X3u3r0b3377LVauXIm8vDyUlJSgQYMGyMrKQr9+/TBy5EgMGTKEW/eJJ57A3LlzAQCbN29GcnJyIoeu4PP58P777+O7777DkSNHkJycjGbNmuHDDz9Eq1atsHv3brz55ptYt24dSkpK0LhxYwwZMgTXXXcdxo0bBwB45plncPPNNyd87G+++SbeeustAMDixYvRtm3bhI+BOPlIqOBQXl6O9PT0RHZ5UvHSSy/ho48+4m7r3r07vv766wSPiCCImkQQBEPcfQGQjGchy5agireUm1+Gr38O/xCu8gX5gkMoDwSDJF7Y2Q3NDG/xtm87yTEgwHhMbCrUCHXdwKiMD2rDa4RtmGUtFsKLLZum47m7B2HLnkI0aZCMd+bwEy173C64XUanyvPPaoNGGcmorLL3KDAjEBSxN6806vpVvoCyLO/ykYJy23r6nAG841VUqhVSdu4vlgQHVX8hhwBDG+FrjHcewgZ/Y44HbRvyPzwPB7VAIagM91zDPGeb3jBvMlSljL5uRZUfLpcAj8u8kUDQKOxokkabqgM2bjyqOmqPHqfiCFPcUJw9ZJyIgg57dlSKIAiCIAiirhMIBPDyyy9j+vTpCOp+8xUVFaGoqAi7du3CjBkz0L9/f7zwwgs47bTTamm01vzlL3/BwoULlc9VVVWoqqpC8+bNkZeXhzFjxqC0NPzOkp+fD4+H5oATJy8Jvbofe+wxHDlyBGPHjsWoUaMS2fVJwY4dO5CUlISJEycatmVlZdXCiAiCqEkUg7EQzuUgrzeUFYyx6KvlmdtWHgWa/gQwldeESteIYuyhtpyUjYMowODc8Fdf0e9jvMQOO0OoZagdBpzWqiG6tm+MQFA0FRwABjfHwyE5yQ0wxhUjnMIYsDuGxM/VvqDG2Fxyohp7co9bV4IkOMihiBwbq61C/Mh/GQNjguJFUpvwki0L+m0Ib9u4Mx/f/7oXbZs1wOiLTkdaisfYVoivluXgswXbkZrswV9vOQd9Tm/OHUO0IZUkUVXvM6HdN6unRqT3l1n5aE9jbcgKsvBU03UIgiAIgiAAYPLkyZg9ezYAoFevXrj22mvRtWtXNGjQAJWVlcjOzsY333yDDRs2YM2aNRg/fjxmzpyJFi1a1PLIteTk5ChiQ5s2bfDYY4+hVatWqK6uhtvtxrRp0xSx4YorrsDo0aPhdrvRtGlT5Ofn1+bQCaLGSKjgsHXrVuTn5+P3338nwSEKduzYgS5dumDSpEm1PRSCIBKA5LwQDoAkqNcLxtBHyXrBwUmcc16/oU4kQ1JUTdQbYjWWRVpVb5C2C18lb7Mao1WIrKgMiODH2rfyLLGLzy9A4CaNlpNOm+V3cMrO/cVR15VDKjHG8Mpn6/HLpsOO6vkC9omL+agEBWVN6K95fCOVFwTAbCbvG9cxg0eGoUIkFu6Q14TaYH+8rBovfrIWQZFhw85jSE12Y/RF3bRjCi2UV/oxff52AEBFVQDvz9uCdx4brtq38GB4IZVkMVXpn7PT+kNkdo3qVzkRGgxChaIqaCurQ55pip2EkPBAEARBEEQkrF27VhEbxowZg8mTJ8Olm4R07rnnYuzYsXjjjTfw9ttv4/Dhw3jppZfw73//uzaGbMru3eEoA/fddx8uvfRSzfY9e/YAALxeL/7xj38gNTVV2UaCA3GyktCk0UVFRQCA/v37J7Lbk4L8/HwUFRWhW7dutT0UgiAShlFsALTGZ3XC6OQkrYZc7QsajG6bso/hX9PX45Xp6/H18hxlJreA+BnCzDww1H/59fix2NVEFE6phjAzSNYnW1u0Ca71IXGc0rltJlwcUSHJEx/BYdeBkqjryiGV9uWdcCw2AEBANuCHtIBNu47h3Tmb8eOqfc7D8gBh7wGD5Rv89bpCshYht6VzPEBQZHh52np+bX3fmthM/LJmA1q4ar9GHPhycTZmLd6FQDAsdMhb9x7WepAcKawwHR8vP4g6pJLZ4ZGfk+HQR9Yox9CyVX5oKSfYlQ+KDDv2FeGwg/whBEEQBEEQ9Z0vvvgCANCwYUP83//9n0FsUPOnP/0Jffr0AQAsWLAAx44dS8gYnVJZWakst2nTxrC9okL6rdukSRON2EAQJzMJ9XDIysrCkSNHUFJSkshuTwp27NgBACQ4EMSpiDxzVhdWST+f1hBSyR/QfC4+UYXXZ25UYrmv3X4UXy7OxuQ7B+D09o1rbPh2xGXWr5kIUENhlupCuBsz4hlqCbAPrWUXTmj0yNORluLl5hBwuQSIjB9uKRJOVPiirlvtC4IBWL/9aET1/IFwKKbDx8rwwsdrAADLfj+E1GQPzj+Lk2zNyW4y5R+UlFXjy0XZ8LgFXHt+Z2RmSEnwpNQrFjF8VItbdhdg9bYj3KIzF+7EsHPaIqtRmkkTTPWv9l5V6w+MMZSUGRN/z/xpF1o2TceF57TTtM3zWjAjGDR6ZjgNqWSGsXer4ErO2jF1TtG1bOYJ8MJHq7Fu+1F43AIeu60f+vdqxW+v9jVXDXX4UUgQBEEQRB0mJycHANCyZUtHiZ6vuuoqbNq0CcFgEDk5OWjWrFlND9Exohj+vep2uw3b5fckytlAnEok9GofNWoU3nrrLXz66ae46qqr0KRJk0R2X6+RBYfi4mLceeed2Lp1KwKBAM4++2xMmjQJZ555Zi2PkCCIeKM24qhyQ+sKhcuZ5nCANNv39x35BqOvKDL8vCFXERz0eRysB2gzdqu4LxzMvBcYaiZUR10z3FkRnoke/bEwjWUvpQ+Psk3ruuf1aY1LB3UAAO5MdZlYcjjESlW1JMxlNrB/0VEjh1RiAD794Q/Ntve+2oyhfY2zm+TkxIKgPRvS+Q0ngpbbffWz37FtTyEAYPnvh3Buz5ZompmC6y/sgtRk859w6lzGC1fvNy03Z2kOvl+5F2/9dTgapifxk0WbJJtm0F6PAY4wAAD/mblBERwYk0SUoxyPBsVTQ9cdT5zw+UWljpyrQY+gZHWXDgZTCTmaTnX3kyMvB101+XDrx+40WtWuA8VYFxK8AkHJI2X2i1dAPol29328whnFIh6Q8EAQBEEQRCTIv1327duHoqIiW/vgiBEj0LBhQzRp0gTdu3c3LZednY0PP/wQq1atQmFhIRo3boy+ffvi1ltvxYABAwzlv/rqKzz55JMAgP/+9784//zzue0+/PDD+OGHHwAAO3fuBAA88cQTmDt3rqbcuHHjTMeWm5urTCLu378/pk2bZrHHWn777TfMnj0b69evR2FhIVJTU9GxY0eMGDECY8eORUZGhmX9X375BdOmTcPOnTtRXFyM1q1b44orrsBdd93leAwEEQkJfcO///77cfXVVyM3NxdXX3013nrrLaxZswaFhYXw+/2JHEq9QxYcPvzwQ6SkpGDUqFE499xz8csvv2Ds2LFYunRpLY+QIIh4YxdOCbr1cjx8GTk2vVyjvCoAHovXHTRYipz0aUqURicB1vkMCCMJPVwsHAdfntVuR5I3/DPDzCANIGYPh1iQ7xO9YGdHIBAW9HbrkkyfqPAjwBFYAkFVKCD5eKo+A2GjeyAQVMQGuc0l6w5i1uJszFqSrZSTDf+KKAVo/lodd0Da/2W/H1SNS9uOFpO1DFwPFn2h5RsO4oF/LcG7X5klFw+3J3fHzeEQQX4aVfoL687ilSXaorxZjewDxZrP8jmzu8cifV7S85UgCIIgiLqCLBr4fD48+OCDOHDggGX51q1b45prrsHQoUPRuDHfO3/27Nm49tprMW/ePBw5cgR+vx/5+flYuHAhbr/9dkydOjXu+1HT+Hw+PPbYYxg/fjy+++475OXlwefz4fjx49i4cSNeffVVXHrppdi0aRO3fiAQwBNPPIE777wTy5YtQ15eHqqqqrBnzx68+eabuPHGGykKDVEjJNTD4Z577gEAJCcno6CgAG+//Tbefvttx/UFQcAff/xhX7AeMHz4cOTm5lqW6d69O77++msAUnKZNm3aYMqUKZocGL/88gvuvvtuPPnkk1i8eDHS09NrdNwEQSQQzdRdQasJCII0S1plP+IljVaHwwmYJI3t2bEp5i3PwdndmqN1lvXMCB6RzK492WbAqu13NbVviZg5zJuZ7dTzQTaj8myZUlLsUBgdjgGeqcrZhW6qKar9QTDGuAKBFVISZsnQL3tJaLcbjeKBgGgdvEd1AKzGs3HXMdx2WQ9jdW0TqKz2Y8NO+xi3ecfKOS3phqWbra8XJkSr48cYgkERr8/caDsWfd98DwetcMO7P2yvXZ7nhuwxYXHP6cOpWeW24HuMcNo+2R6MBEEQBEEQNtx66634+uuvEQwGsX79elxyySU499xzMXz4cAwcOBDdunWLeOLbc889h/T0dNx5550YNGgQAoEAli1bhs8++wyMMfz73//G+eefj65du8ZlH/70pz/h9ttvx+LFi/Hmm28CAF544QX07t0bAOD3++H1evG3v/0N27ZtQ7NmzfDf//4XAJCWlmbarpq//vWvWLBgAQBg8ODBGDVqFNq3b4+ysjKsXLkSM2bMwLFjxzBhwgTMnj0bnTp10tT/5z//qXhhnH766bjzzjvRsWNH5ObmYtq0afj999+V8FYEEU8SKjisWLFCeWBIRohTd6ZVu3btkJSUZFmmbdtw/OeXXnqJW+a8887DFVdcgW+//RYrV67ExRdfHNdxEgRReygeDoL8v/EHlxDK7yAASNEljfaFDKlyS6Umce7/2FuIP/YW4oeVe/H8PYPRNDM1uuTMOuN7Ih/x8e4qUQJKLMdJPkf6MbCQETweRkxZdIg2Wbf6OAZFvuAl77/H7QoZ8ROLnDSalyvACp9qrJUcwUEO+8OrwzvnWkO+AL/FePbkHre9bhhjeOq9X60LhUgJhWfiCk+8sTKVGBXSRe08HLbtLbIdB++6FS0FB3W4JC2yXqv2+FBrA5qQSGoVSJdImy+oavvVh3bS1HFwL8Z6q0Z7u+v3LZK7XK576v6SJ4hTl3iFcSOI+kLlvi0oWPBf+AutJ4yeLHibtkHWpXcjtcMZNdpPr1698Le//Q3PP/88GGMQRRGrV6/G6tWrAQCNGjVCv379cN5552H48OFo0aKFbZsNGjTAjBkzcPrppyvrhg4dilatWuFf//oXAoEAvvvuOzz88MNx2YfWrVujdevW2L59u7Kuffv26NFDOzFInhiclJRk2GbFDz/8oIgNDz30EO6//37N9sGDB+Paa6/F6NGjUV5ejmeeeQaffvqpsn3nzp2YOXMmAKBfv36YOnUqUlJSAAB9+vTBpZdeikcffRTff/99BHtNEM5IqODQunXrRHZXp/nkk0/i1tYZZ5yBb7/9FgcPHrQvTBBEvSIsNGjNOrzXPLfbBa8nbLRlTDJ6JoVCLZWWWyfWPVHhx5eLduG+UX00GanNXinNZhDL68MJrnlCieVQEkKsuSGiNcKHKtco8n7FKmZo12m3CbKlWQh7RBjHEV62yuHAGIPbJaA2givuPnQcO/cX2YYe0qMWR/TeCG6XwBVP1F5GLPSP+hypjeN+jmAhk5HqVVViynmQaxeXVSP7QDH2Hi51sitISXKr8oSYl7MybJvoSQq/bjlsuT1srNeu552Xaotjo+Dg3mYmy3HHTOkIwc+t4szjoq5AwgNBEIQECTInHwXz34e/KK+2h5Ew/IW5KJj/Ptrd91aN93XLLbegY8eOeOaZZ7B/vzbvWElJCRYtWoRFixbhueeew8iRI/H4449rJubqufPOOzVig8yYMWPwyiuvgDGGXbt2xX0/aoqPP/4YANCjRw+D2CDTtWtX3HPPPfj3v/+N1atXIycnB126dAEAzJkzB8FQSNfnn39eERtkXC4XnnvuOfzyyy84fvy4oW2CiIWECg5LlixJZHcnDT6fT8nhwEsOXVVVBQCGhwdBEPUbQQAES6O/NPdcTbLXrTF0VvsDSPK6IQiSETTJ69aEI9Hz65Y83HFVbyQluTSeaE5n4kfygqV/IbOqmojXtvrodBetIVJfx+r8xnJcXCHvCAaGBmley7JutwuA89j88eSp936LuI4/IJoem9RkDzekki8gcg0R4ZwY4fVW3h5VvoAm/4PUiPRn/m/78OHXWyM6b0m6/C/q8ciBkyQDOMNvW47gwJFSnH9WW2Q1SlUuEB9nf9VD27Gv2HIMaq9XtfDCDamk6YsfpCrssMDAmPFZaTVWy7BXujGafQ51bjNKvleHaZ+6kxrp/V8fhAuCIAinkGGfIE4OBg8ejAULFmD16tVYuHAhfvnlF0M+B1EUsXDhQqxYsQL/+c9/cOGFF3LbMkv4nJGRgaZNm6KgoAClpc4m5NQ2JSUl2LJlCwBg0KBBlmWHDh2Kf//73wCA1atXK4LDihUrAEiChT7UkkxGRgZGjhyJOXPmxGvoBAEgwYIDER3l5eW46aab0KhRI/z6669wubS5vteuXQtA8nQgCOLkgefZYFouVDQ5yY2yyvA88SpfEA3TpW2TbuqLQFAykmYfKEbntpm4Z8piQ+iXnNwS9OzYxHQM8QqHF8+XxESG6IvFsyFRw4w5pJI8TidNMN1fFS2zwnmFhvdrhzlLcxQD8o0jugKqZMduV/0yGvgtwiOlpXg0IZfCdSRDuZPLwCqkUiDI4A8E4fEYhYJZi7Mjvs6KSqtQUFKJVlnpoQBt4I5z0doDeO8r6cXn+5X78O7jw6XcMSZ5LGSCIuOGndKXcQsCmO4yEDmuE7JoGvYAsA7Xxdsfg2Agu5yE9t/0EOpUHsbkZyQ/VKheaOC1G+uVX6taKdPvYd2EDJMEQRBEtGRddg8KfvwQ/oJDtT2UhODNaousS+5KaJ8ulwuDBg1SDOuHDx/G6tWrsXLlSqxYsUJJalxZWYmHHnoIc+bMUYzqalq2bGnaR3JyMgApiXJ9YPv27cpvy48++ggfffSRo3py5BNRFBWvETlBtxm9e/cmwYGIOyQ41AMaN26MQYMG4ddff8X777+P++67T9n29ddfY8WKFejbty/X+4EgiHqOEpdI4K7WrIMUGkVNtS+oKyMgyetCn67NUFrh48aZLy2rNjYcZ8ju4xy75M3c8PLx61wTXsuOCVf2xMff/QEAaJCWhCFntlK2ZaQl4a+3noNlv+eiTbN0XDaoY7gbBpRV1EZAJXMyUr0a8U4Pz4NBJjXZw/Uk0ngtKB4KzBBeSUpibR02qKIqgIYZbs2M/EBQRMmJast6PH5ctR8/rtqP64d1wbjLe5qWk8UGADhR4cPPG3NxUf/20ngsBIWKKr+lZxUgjd1tEFAYNxSX/rnFS7BtljMk1DIUYUEfkUpfUhE1wgZrOz3Pid6nbs80QbXub7wxjDMi8aB+CA0EQdQEdP8TpxapHc5Au3teR3Xe7toeSkJIbtW5toeA1q1b47rrrsN1112HQCCAefPm4ZVXXkFxcTGqqqrwwQcf4OWXXzbUk3MlWFFfcsnKIkukyB4cJSUlSjilzMxMyzpZWVlR9UUQVtSq4HDixAn89ttv2LRpE4qKilBeXo433ngDALBhwwaUlpbiggsuSOiYioqKcNlll6GkpASbN29WVFAzqqqq8Omnn2LBggXYu3cvACnZ88UXX4xx48bZ3thOefrpp3HzzTfjP//5D1atWoWePXsiOzsbK1asQLNmzbgPW4IgTm7CORLCJOsSR+sFBxnGGLbtLuBuK6/0O0oUHNZCVGGRYNRITI1pERjwIoE5fRFm5rNeHZvcauj3am3/DuYdQSk0DRTrp362OANw4dltkZHqRe6xcpx/Vht4PW4p6W+owW6nNcEZXZoBCCcDln/0N8xIispYXlOc1a0ZVmw0zzvgD4hSrgWOMJCS5OGGRPIFRFs7jXyM/TYG+vJKPxqkJymNMcDSy8AJ85bvxtXnd0aDVG/ofLOQFwr/5Sw3v0y6jwBUVpn3faLcb+mxAWivhypfEB/M3YLdh0rQpKExXKRavGCMn+dB8qQJDS4k2+nhJ8Q2H2PJiWps21OAzm0boWlmquX+SL2G/8q2fON9BfAuiHoX/qieeDoQBEHUNPXu+U0QtUBVVRWOHTuGwsJC9O3b17Ksx+PBDTfcgN69e2PUqFEIBAJYtWoVt2xNejPyvG5rElksAIBHH30U5513nqN60dggPR6ai07En1q5qvx+P9544w189tlnqKysBGA0+ixbtgwffPABunXrhpdffpmb+CXeiKKIyZMnO1YSjx49ijvuuAM5OTma9bt27cKuXbswZ84cvPfee7buS07o2LEj5s6dizfffBM///wz1q9fjyZNmmD06NGYNGkSmjVrFnMfBEHUPQTdX2W9LtSRnG8hWefhUFEdQCAo4rfNeUhOdqNPl2YQBCC/uBL//nwDt8+KkOFQ3YPTHA6RUNPhLRLxshdrH5FU53k5WIkrkYR+MoSeiSD8iGJUDdU5t2dL9A2I8HhcihFZ3m5WFwCaNUrVCA7qBOi1gV680yOPjecFEWSMO6M/oIRh0p03JhvEw0fE1sMhJC6oD6td2CI7RJFh174i7DpYgjlLs9GiSRqevP1ctG3egFtefYlU+sz7Lqv0WybBBiSBQD4q367Yg4WrJRfw3bnGBHZ6ITXAuU54nhGaPBGGzeEVhcel36atszJU66rw8H+Wo+RENdJTvHhp0nlonZUeFhNUTRjOr3YQhm1OnyOaBOPqHB66Ns3u3/oxn69mIAMgQSSWxIQxI6GTIOorL774ImbOnAkAWLhwIU477TTbOt27d8eAAQOwcuVKHDt2LG7PGXUbVt4PZWVlMfcVCWrhQBAE9OjRI6L6jRs3hsfjQSAQQFFRkWXZ+pLXgqhfJFxwKCsrw4QJE7B161bLm/nQoUNgjGHHjh0YPXo0PvnkkxoPGfTss89i4cKFjsoGAgHcf//9yMnJgSAIuOmmm3DZZZfB7XZj0aJFmD59OvLy8nD//fdj7ty5cfF0aNWqFf75z3/G3A5BEPWHiH5DCUDPDk3QtlkGOrfNRNd2jdA6KwOvzvgd67YfBQBcOug0TLiyF5o3Mc7OTU/14o2/XIgkj0vdpMbQHanocFLGzK4rVjuTccQyPI1BE7LoFH3WCqfXC2PApYM6YF/eZvgDIgaf2QodW2fiswU7ouw5dpJ1iZT17MuTfpiXVfgM24JB0SRptGpmPmSvEeM9Ulhahde/2GjZf7nKo0B+4aqyMPo7ZVNOAb5fKXlsHimswKzF2Xj45rO5ZcOJma09HMorfbYeDkGVaLBozQGLkuHjGA4lxQupxCJ+XjEGLFy9Dx/Mk0JHTbiyF64+Xwor8PXy3YogVl7lx/T52/HXW/vx29G1yXsMqtebPiUT7DUQzX1eVx6HBEEQBEHUHzp06KAs//LLL44EBwBITZXeYZs3bx6390y3O/ybv7ra3Nv6yJEjcenPKV27dlWW16xZg7vuMs+rcfDgQXz33Xdo27Yt+vbti3bt2kEQBHTq1Am7du3C1q1bLfvasaP23rmIk5eECw6PPvqokmm9ZcuWuPrqq+H3+/Hxxx9ryl144YVYv349jhw5gsrKSvz5z3/G999/rzxg4kllZSWefPJJzJ8/33GdL7/8Urlpn3jiCYwfP17Z1r9/f5x11ll4+OGHkZubiw8//BB/+ctf4j3smCgsLLRVOXnISWcIgkgsZjkbGARACM+NvvkSyaNKFKVwKCVl1YrYAAALftuPEf3aoUPrTPTs2AR/7A0/B667oDNSkjzh0DkxjFX2uDBuEyJJCaCqGP14EoldOKdYEk5HTQw2S31VJceA6l9eHf1ndTv660L+eE735njtC/p2yAABAABJREFUzxeg2h9EVqMULF57MLpBxwl9PhQeX/y0U0mwriYQFLneGX6/aHo65FnxDMAn3/+BotIqy74rq4yeFbF6OABQxAaZXzYdxp9NBAcI0niDohQGyYzj5dUabxceynYG232v1nmP8MSMYGhd2AMnvE0rAjBNkvX354bzVHz07TZcPqQj3C43lv6uvR5XbdW/dPI8KtR/Y7kRpbp7co+jsLQS/bq3gMslh6MLh6KK9MWbxAKCCEOJxSOAsXrzu4wgiLrJsGHD8NJLL4ExhqlTp+LKK6+0naRbXl6OdevWAQD69eNP+oiGhg0bKsuHDvETg+fl5WH37sTm8GjZsiU6deqEPXv24JdffsG+ffs0Qo2aqVOn4vPPPwcAvP3222jXrh0AYMSIEdi1axf27NmDTZs2oU+fPoa6Pp8PCxYsqLH9IE5dXPZF4sfKlSuxbNkyCIKAyy+/HPPnz8cjjzyCc845x1D2qquuwsKFCzF8+HAA0g3+1VdfxX1M69evx0033aSIDS6Xs0Mybdo0AJIyO27cOMP2yy67TBn7jBkz4PMZZ0DWJjNmzMCVV14Z8f8PPPBAbQ+dIE4pYn35PVZcaVj3+hcbUVruwy2XdFeMbslJbgw8o5XSp1W3lmOK2rAdu+kr0gRglqUj2I/azrcQd2z2xyAohOL868s4PR8MonL+GzVIRsum6YCD8CfDzmmLYee0ddRHNKTYhFQCgFmLs1FaZvx+DwQZqjkhhBTDuOrQ7NhXhBk/7sCGnfnKYfxtS55t3+VVfiU/ARDOfZBIjocSzFdVWyf8dpKb4+05m3DL0/PxzIer0Lihdf4sTQ4HhMUFNXKIJsAY8ij8wSrEkoQcMsvrMRGgQqGN5OTTVnBzRsDMA0K7Tz+t3o9HXl+OFz5agxc+XmPwRrLrJ+5w7nmi5qkviS4J4lSG7lKCsKdDhw645pprAAC5ubm46667sG/fPtPy5eXlePTRR1FSUgKXy4U777wzbmNRh2//6quvDF4OPp8Pzz77bK18B99+++0ApHwOjzzyCDf00a+//opZs2YBANq0aaPJg3vDDTcok7b/9re/ccPHv/zyy8jLs3/3IIhISaiHw7x58wBIoYGmTJmCpKQky/JJSUl44403cMkll+Dw4cNYtGgRbrnllriN51//+hc+/PBD5fP1118Pn8+H7777zrLe7t27sWfPHgDAFVdcYSpSXHfddVi8eDHKysrw22+/JTwBNkEQJw9cI78gpXaWt/HK8ASHExV+pCZ7cHr7xphy/3nYeaAYfbpmoWlmqta7IeSKYGb4VYcDiebn18k2Oa4mEmBbYXvM1edRTQ2NjbffTGPQNZ8BLi9rw8xYD9TrcWHUsK5Yup4/EylW9PlQzCjgzMY3C6mk9npgDDiUX4a/v/8rRAZ8+8tePHPXQHRq09BQj0eFKoRR0fEqHC2uUASAeHO8rBpHCsoN63/ekIsmDVNw+eAOlvWLS+3HtSlbSmK/bU+hbdlAkClJrQVBMA2pFCk8L4zKKj/QMAVeTwRzdJgIaU6P8SbkjYo3VFFkUP+8fOPLjcryuu1HceBIKdq1aBDZw6aGQjTVByN43R9hYqBZ/ISamr0eEhASjjwtCKJe8+yzz2L//v3YsGEDNm/ejKuuugrDhw/HhRdeiNatWyMlJQUFBQVYv3495s2bh8JC6Tfi448/jp49e8ZtHK1bt8a5556LtWvXIjs7G7fffjsmTJiA5s2bY8+ePZg2bRq2b9+Odu3a4eDBxHpgy5OjV61ahW3btuHqq6/GhAkTcMYZZ6C8vBwrV67EjBkzEAgEIAgCnnnmGXi9XqV+27Zt8ac//QkvvfQSsrOzcd1112HixIno2bMnCgoK8MUXX2D58uVITU1V8usSRLxIqOCwfv16CIKA6667zlZskPF4PBg1ahTeeOMN7Nq1K67j2bx5MwCgSZMmeOqpp3DFFVfgiSeesK23YcMGZfncc881Laf23Fi9ejUJDgRB1BhyUmdtomcBx0oqDGUnXNkTSV43mMjQsU0mOrRuCEEQ+AY6hy9yZsWUpNe6F9p4v+Amwt4Va6ipqI1yiQ3jzh+CHLZFPRiHuxM2DOvWy22ZtGN3ibhdLqSlGH/GnN2tOfblldqG5bHDqeBQwuknEGTw8TwcAqLGwPPJ939ojM0ffrMVj91q9PrkIedM+GNvIZ7572rbJNOxcOcLP5lum7d8N5K81sb40vL4ennKIeNk/EGjuGP0egh7gmjWqj4GOfdouezh4DbuIzNZ5qEX5NTlec/eoMiUH+m8tg/ml0mCg0n70RDNI6oeaA0EQdRh6oMIVh/GSBD1kZSUFHz44Yd49dVXMXPmTCW0j1l4n4YNG+Kxxx7DjTfeGPexPPvss7j99ttx7NgxbNiwQWPzA4Arr7wSgwYNwt/+9re4922Fy+XC22+/jUcffRRLly5FXl4eN69rSkoKnn32WZx//vmGbXfccQeqqqrw+uuv4/Dhw3jmmWc021u1aoUJEyZQvlgi7iRUcJAVyc6dO0dUT04gc/z48biOp2HDhrjnnnswceJEZGRkOK6njt1mldymSZMmSE9PR3l5ecLjvdkxduxYXHrppRHX279/P4VVIog6iqD6KwsP+ToPhyuGdMSQM1ub1o8kFA6/DSE8Fo7nhaDuSD1oB+3qwy5FE4aJxXmGr92hqiljnFmODP4gEEPoeKac03A8eqaMQd2F1lvBXpzReDDoxujk3Ho9LgiCgDbN0pF7TJp973W78OCNffDjqv34YlFskxSSvG64XIJt7oFiTrgg0xwOIa8HkTG88+UGbM4p0Gw/lF+GIgfeAADwxaJd+GbFnrjkbYiVLxdlW26XwxLFE9mrgTFmkTSaae55TWQltYGeAUzgG/4VwcFEVFE/M83vSXXPeuWNca8xWw8N5X40Oi5ohBArQ5nF8zAWAxsZ5xIPHfN4UAfUfeLkoIa8yYjaJ7lVZHYswp6MjAxMnjwZ48aNw08//YSVK1ciNzcXxcXF8Pl8yMrKQvv27TFy5EhcccUVaNLEmDstHnTu3BnfffcdPvroIyxatAiHDh1CamoqunfvjtGjR+Pyyy+vkRDvTsjIyMB7772H5cuXY968edi4cSMKCgrgcrnQpk0bDBkyBOPGjVPyNvC4//77cf755+Pjjz/Gxo0bkZ+fj2bNmmHEiBG4//77sXr16gTuEXGqkFDBwev1wufzwe+P7MWzokKaoZuWlhbX8bz55puOczaoyc/PByCpjS1atLAs27x5c+zdu1epU1do2rQpmjZtWtvDIAgiBgTN37AV/3BBGZasO4j9eSfw+07ts+f09o21FRlQVR3A0x/8hvJKvxQXHgL++38jw/0Icl4HFu5PFhM4QoDpeK1C+8iT3Ws4HJHGSB4HNUA/VrswQLyxJCoEU63AVMeZF7+eE/heLt+7s/V3VKc2mQCAMRd1wztzNqOqOoDrLuyMlGQP+vVoHrPg4HEJcDsRHDgCQTAowmcRUml37nHTUFCReGbUBbHBCWUV8c9jFQiKcAkCIAABjrijNtgz5R8t+kcA71zLgkOSWQ4HC5ze20HROH7ePqkRowwSFG/DtCTqCCHhg4zeNc1J/X1BnFxQuCOCICKgY8eOmDhxIiZOnBhx3SlTpmDKlCm25ZYsWWK5vVGjRnjkkUfwyCOPcLdff/31uP766yPeBoRzwPIYMGAAdu7caTk2ALjgggtiiprSu3dvvPrqq9xtl156qaMxEEQkJDRpdKtWUkLSLVu2RFTvl19+AQBb436kRCM2AFAStaSkpMDttn4BlUUSXnIXgiCIeCMIkgF07rLdBrEBALIapUjl5PKQZnIfOHoCBcerUFkdRGV1AMGgGJHxPOJxmq1PcNJpM+NYTe67FfGMha5vyXkSZ2eeGYb2TULWGLfz29Gvb9Y4DZcMMJ+p06drM+Xv648MxXtPjMCV53VS6k64qic6tHKWD4GHx+1ylAeg+AQ/pJLfby44LFpzwLQ9daz+k4Wa8HAIqrwauEmjTUJMmV1/ZnXksXs4ORw0njw6Dx99f+rrXE60Lpfj6A0hDw2L+0mn1dnd34w5eUqal7D33qjjUOwnIkrqQ46S6DmZ9y06Tu7zTRAEQRCJI6GCw8CBA8EYw9dff42CggL7CpDyPvz0008QBAEDBgyo4RE6w+eTZuo5yUORnJysqUMQBBFX1OGKQosdW5sbWZs1SjMY2d1uF9KStQ5vFaGZ044M73IMJ2UcgnYbameSW6QzbWN9x5QcRrSN1PRM1GiEEXWdiF+srYobPBVibJYBt17SDe8/MQyP39ZPs+nywR3gcbsUo2uyx4XUZI+mnWHntMML9w7m9vf0nfa/Jzxue+8GADjMSaYcCIrwc4zXsuDgcZ9a0z7La0JwUFnpnSaN1ggB6vWhbUFOO2UWORzULcXy+OAKJmoPDdvGdU+eOBjMTgabGxkOedAxIWqWSO+7WG/TRNzn9CghCIIgiMhJqOAwZswYuN1ulJWV4b777rMVHZYtW4b7778foihCEATccMMNCRqpNbJnhBNjlvwjKFpvCoIgCDPkR5AU8ii8Pj3Vi5ZNjSHosjJTNEl21c+wtFSvpuwPK/dFMSDVOAR9yCezKnEwvMbwIhjxi3GCjTXxfMmN5DsrUvjVmKn3QjgfhFTOUFNlbM3MSEbPjk1wzfmdkNUoFYPPaIWrz+9sei7sdiElyY1WWenWhSAJcdESFPlJo4tKq/D9yr3cbSczNeHh4A8yJVSSqcFeuc6YI28AXmij8io/GGPwcjwc1PDyOOivf7NrU+RssPOuke8RuzvW6n5wcrc7fyao87aQdS7x0DGPGbIqO6ceHivesyyuXqUmgjZBEARBnKokNIdD586dMX78eEydOhVbt27FxRdfjAsvvBCBQDgG8YIFC7Bnzx4sXboUW7duVeLB3nDDDejevXsih2uKHCaputo+sWMk3hAEQRCxI82zv/7CLnj3q83KO2HjBsm48+recLn4Jv6MVC+OqRJMf/vLHlx1XiekhgQKAZKYIOfBC6VzCK9zMjLF20HK+8AbiVl86ngZ+uOdNLq2UOfdBmB6POOBaSAVXbx2rUEXHOOjdOzlsarHzKwCk4eqX39hF4y5uDuCQRFVvnC4IsYYBGY+zhuGd8XsJeGkxjeNPB3JXvt4/O4YvRDM8it8+sP2mNqtj/ASaMeKWmTgeZOYeaeo14qM4dfNecg+WIzzzmyNzAbJhvLlldJ5NBMc9MnSDesQul+V+0W+D8LleOJCIChCMuKbJHVW7YmTuP7he9LZM1A/IvM+Ei3DRkd9GKMTTpb9OJlIRO6SUz0/yqm+/wRBEARRH0mo4AAAjz76KIqKijB37lxUVFRg/vz5AMIzLx9++GGlrPzidv755+Ppp59O9FBNSU+XZkZWV1dDFEVL7wU54XXDhtHHkSYIgjAjnLoZYCqfgmH92qF7hyYoLfehbbMMJCe5IQiCMpNWLx706doMew9rc83kl1TgtJbaZ5egsnRH9PJnlTA6DiQi54IQibripD2lXelvpKKBnZHRycw9y/jwEeyrfna3U2GHgSlx8FnoP/0xkMMmSdceM9TnjUPNsH5tsW77UezLK0XntpkYeEYrCIKAC85qg+Ubck3H5uF4OKQle9CvZwv8bFFPpr4kdK6vqI30vFBIpeXShA/etShfJ79tycNrn/8OAJj/6z5MvmugoR05HBRPgAoGRAgq8crpLWNIVm0bEorTMNM9A5gIteNyNAa6iB5vHHElUk5FI+KpuM9Afd/vk2OiApc6kNg5pl9VNTB+3m8rEvoIgiAIInISLjgIgoAXX3wRgwYNwjvvvIN9+/aZls3KysJdd92F22+/vU79SG3dujUAIBgMoqCgAM2bNzctm58vJW21KkMQBBENgiBYGoVbZaVLoWOYZLySxQLe4/T6YV0wb/luzbqySr+jHATybHVpTHJKB9UM9lp6fPO6dfpdEulXTl36jnJC+IW6Bg0pLCwIMJ3tlAlMc92YNmERVkZtwFLvhT5hb4O0JEy+awCqqoNITfZAcEm5GSZc2QsdWmei2hfEhl352Lm/WNM+T3Bwu13o3bkpCQ51gGBAVDxjAhwPh8rqAD794Q+MvUTyjuU9K9//aku4PZHhu5V7DGXKK/1gjJ/YucofREZIcOA9iuVr1OoqZ+B7ODjJH2Im7PGkCl5rTsQCJ94TvH6jIV5Gab03SU30kUjiOuY6YGA+maA8ITxC911ccsk4ufatf8fUx3ueIAiCIE4GEi44yFx99dW46qqrsG3bNvz+++/Iy8tDWVkZUlJS0KxZM/Tp0wdnnXVWnQxF1LlzZ2X5wIEDpmJCUVERysulZJJdunRJyNgIgjh1UWbKM6MYEQ5npCqPsIEoLcWL/j1bYM0fR5XtFaGZvYKsIjBtZW1bgnF2foLe76J9pY3EkCaVZ3xLXgT9WcENMVVP59Uxp0KGOq6MvEoJ+6Ivaz2j2srw43a50CDdjaAY9qjwel0YeW57iIwh51Axt45hnVtAerKzn06nquBwXp/WGHFuezz74aoa7ScgWodUAoBvf9mLEee2Q5tmDdSKlGKfKq/S5pbIPlBiaEP2cODld6j2BZGeyrt+OTCACdphyB94ORwCQRH6R44aZTSM/5SI9PnGbSCCe1grKmqN2mTwSwAkJJzknMReFhwieWZIZZ2U43guOHxOOvPaDJchHYogCIIgalFwACQjVu/evdG7d+/aHEbE9OnTR1n+/fff0a9fP2659evXK8tnnXVWjY+LIIhTDyVhdEThfvTqgURGmlbgNUv06mRmOq+O/PIoTTwVNAKJaT25ThR98kh0DoeafOmM1qCovlQkYcq6DyfKSjgEkzqng/RZEVLUBjEHp4ExOUgYU8bAlHadwSQ3C9vZ3bz4/B5OCB2PW0BaqrOfTlWnqOCQ5HWheRNj0vp48+vmPGzbU4j2LRugZVPzJODfrNiDay/oglZZ6QZPGyfIogTPC6HaFwxP5tU0ypQ/DLpcJ0xbijFwPTTUOSp43g7S/aY2cDnZIWZ57ynj4rRloaNEjSgyQ6gqQ24YG8OjU8NkrM/iaEWTuIstMXyH1YXZ+HVVfIr22MTfK6duYhxf4q4l/bOOIAiCIIj6gXnygRrgqquuwn/+8x9s3rw5kd3Gnfbt26Nbt24AgG+++cb0R+rcuXMBSDkfBg0alLDxEQRxaqN4MwiC8QVW+SwoZeR3uIxUr6aomeAg1bZ48aN3woQRF/uEYhtVhSKKwpjALKy5IhO5WzSlVB/UTTFD26rPTPfZBpEzS50xBi8nfBI3pJLLhfQUr2E9j1PVw8HrcSHJJMFyPPli0S5s3VOIH37dh2nzzRNxL1l3CH96dRm+1oSMc359l1X6pbBHnDwLVT7tOVZKRGi85Hs4hNfxxA5pPPx+uN1Ha1DlNmXqZhRVH3GqbtKm2TGqLeN7NM/WGhhGwqnHO8ET4OJ0UuqCCFRzONy3mI4Bi/sNwm0t4j5O5vNKEARBEM5IqIdDdnY2cnJy8P7776Np06YYNmwYhg8fjiFDhtTJ0ElWjB07FpMnT0Z2djbef/993HvvvZrtCxYswJIlSwAAN954I1JTU2tjmARBnOTIcarVdmeD8Yrv0GAoohcc5FAi0di01V4MkneDdXm7VzM7A3htTgxkuuMfWzsOW4oxWbSqU2eeBpwDzEIhYqRmdH0qnhH8xhkAl+665M+q5oTKYeoFaw8Ns8tGyS0R+uvhGMh5SYLdbgFpKRRSyQqv24Vkb0Lns8Af4IdUUjN9wQ4cPlaOimo/GqYnoXObRoYyPMN+ZZV0HnmiQJU/GL7K5ZBfnL7VydTD91L4/uB5MHz07TZcf2EXnNenNXdcaq8IxgmnoxU/zG5y85tHIwzyFQzbNrRla+YhzXs+Kd5fXDew+jNTuq7Pejcj0eOu3eOUoOupDoTMcnyc4zhWM0/OyM55dOeovt5/BEEQBFFXSKjg0Lx5cyWJckFBAWbPno3Zs2cjJSUFgwYNwogRIzBs2DA0adIkkcOKiptuuglffvkltm3bhtdeew27d+/GddddB6/Xi8WLF+PTTz8FYwwtW7bEfffdV9vDJQjiJIb3OmSmMaijL+mjMJkJDlI9bYtSGCfVMqyN/tG8szmZZW9ZgtOnPrRJpOOKq2nBSUMWO8jAlHwdtYUAyRDrNPSSHsUQKwJwqwynGheHsDeDubhgbTiV8jaYHydeSCW3ixdSyYUGaUlompmCwuNVpu0BQGV10HL7yYrX44bH7YosyluCWLL+oOrTAcP20nKfYZ2cu0HkeDhU+4KmBm8g9LwIPTTkZwczFGRcwWFP7nG88tl6JHvd6NHR+LtYFiGcHmIG/W1lcc+YekiYC4jm/hbWY1IjMgYXauqZFrv3RVTfY1HWO3mpeQM9GYrrPzVx38T4BHBQJCy01LXvPoIgCIKoDRIqOPz888/YsWMHli9fjuXLl2PTpk0IBoOorKzE0qVLsXTpUgiCgDPPPBPDhw/HiBEjNAma6xIulwvvvfceJkyYgJycHHzzzTf45ptvNGWaNWuGDz74AI0aNaqdQRIEcUriwKGBWzYjXetpVq4LqaRPRB3NuMJtmW8Lr7PP2yAbvGuCeDdb2wmg1UYYjUEmHvYf/a6pPgsIuUIoL+L2MdkNCWhVY9RsMtTlGFXlfdaVVn92GlLJ4xLgEgTce/2ZmLU4G7sOFJvuBy8u/6mA1+MCBAHJXjeqfPVfdAkEGZgo8r0fZC8WeROnDBfdw4UXrknmrVkb8eajwwzrg6Gk0vomY4l3rn6+K4scy58mHYvKeyPyPq08J9Sfrdq1O+Z128AtiiJcnAT1mvZNliOlVifJ14EZ+tbUtoXY+XUaV0HF8Q8d3ficOjbFoZh06ZhfP9L3vtRKpMclkr0nCIIgCCIyEp40unv37ujevTvuuecelJaW4pdffsGyZcvwyy+/oKioCIwxbNy4EZs2bcJrr72Gdu3aYfjw4Rg+fDj69etn+6M8kTRv3hxfffUVpk2bhh9++AH79u2D3+9H27ZtMWLECNxxxx31wluDIIh6jiCFLFIC+zARRp8EpajyV37PFEL1TXM4cNJAmL3TCYIqSbCuT15biUY2itWluMlOZvIZo2SZzDQ25Dqw7R0aS7462guvDKe2cQyh9YypaoaNkmHhSlDKKyUc2Fw0+Rtkbx2zsuoONJW0O8AVFyzWdW3XCM9OHIQX/7cGm3MKrAccBW6XwDVwx4LX43IUeige/QBA0kkiOACSN0GQk//jswU70K55Bto2bxB1/gbGrEXT4+U+BLghlZj2OjZEDgo969SrIhqZQyxyJIRFTYfG5iiM0rzu7Q2Ytfj8ryOG95rwAjAPv1d3vm/jh/xbItaZ+DUviJ2MhMOmOS0cYeMWv7EcJamPsDxBEARBnIwkXHBQ07BhQ1x++eW4/PLLwRjDli1b8PPPP2PZsmXYtm0bGGM4cOAAPvnkE3zyySfIzMzEqlWranRMU6ZMwZQpUxyXT05Oxl133YW77rqrBkdFEAQRGYLKEGv+PqbdmpFmEVIpVFTv5aD+bJqfOsJx60fovG4chIRo3wvtjOQxNF2TjUYyK1l/bEWRweXiew847x+6+gxWFlLzBLDGssZC5rZgN0dccHFCKjXJTFH1x5CSXDM/oxplJKOw1DpkUyT07NgUt13WHU++szJubZoRFhzqzgSRWAkG+R4OR4sq8OQ7K/Hu4yOQnORW1kuXB1M8bITQDFwBak8fKDlQ7MQlntjBW2cFY6Gk6aFrXe8NpCnrYI2+bfXfiASGBKB5bqi9niIwBkY7UqtnrP33lbZu/THcW3yv1IjgUotG+xo+J3XJYF0b4cH0ZzaRxyOqsJt16HwRBEEQRG1Tq4KDGjmU0plnnokHH3wQ+/fvx2uvvYYFCxYAkL7Ajx8/XsujJAiCqNsoIWRU78BKYumQQd5MhGjRJB13X9Mb6akepKV40SAtSaobhxdq3gtYPASCeL7q13bIo+iI1tASTwMNszX4M9W/yrqwdVL1P2d2LGdJ/qicM9m4qwvDpQkjZdKwlf1rwpU98fF3fyifr7uwi2acSZzcD/Egs0F8BIcGaV6kpXhx88Wno02zDCQnuVFdw14HcniqJI/bpmT9wR/k51kAgCpfEBOeX4hrzu+MY8UVSPK6MWZkF7RunsQtz5gxzbxZ21bbg6GQXeFHKFM+W+qIFh4JRs8mlXiiNnwjlJsiXAyMiQBsQgPpwroZ+od1rpVYqEm7sJVh0qzbcCgqhx3Yu345aCiCcnWEaAy4NWkYt7qGCWc4Oaf6eQeOHaRqytbv4FxL+xVeJgiCIIhTnTojOFRWVmL9+vVYs2YNVq9ejW3btiEYDNIsAYIgCBsEhEIZOYilrX+mqvWE9FQvRvZvD0CaPSuKqlm5MHpL8ML6CLB44VPCOam9ImouvJFZ2KFEI890jub7zO5Fmx++Qhs6K15Ecppk4yOThQTFiKifscurbC3+aOPMMyUUk3nQJ1XYi1D3Olup6XG64Ky2KC33YU/ucQzu0xrNG6dBEVgAuN3OD/B5fVrD5RKw5o8jqLJJKJ2ZwTdWR8JpLRvgpQeHIhAU4XYJ8AdEruH6jqt64USFD7MWZ9u2edGA9vhptTHZshqvRzreSd6TR3AImuRwUPP1z7uV5aOF5Xhp0lBHbTPYezjw8oEEgkx7HesN+Gad2Y1Hd39Fgyxe2D5/OGMW9H3azIjnjlH9HcixQtbGTG1zIjvGsXxd6p6+cGbGjb4H7fo6jpXLj1n5GC4i5TdBVB4f8Z0wYFij+n2mWgnt1aOXTXktW5cJh1h0Plp+P/anzvRZ5kDM0+aVclaeIAiCIIhaFByqqqrw+++/Y/Xq1Vi9ejW2bt2KYFB6AVd/UXs8HvTu3RsDBw7EoEGDamu4BEEQ9YTw81Ofp0GNWjwwyglhYxHvvUnK4eD8ZVcQHLwa16Dxp66+/DkRRKyEikj3ituOE2NH6P3a0tklAltN+HyEvBGsLKMaLwhBu4orXvCNAbLBwFHoJRVutwvXXdgFIpNmuIdDx0jGXncEeaXuvqY33G4X8grKkX2wxLJso4xk54M0ITnJbZglyjNsD+/XDst+P+iozcz0ZHQ7rTF27i82LeMNeTacTCGVAgGmeBQ4Yfv+YuVa+WNPIVZtO4JeHZtgYO9WShm1kczOw4GXVFoSIZzMuuU/AzXXhr21DtYPCr2EZ9cc44zBUVXHMPmGt/yuqgmjO68PmxJmz3nD85nfVl39josXtR6mJhHH1+67OMKJC7V+zGoQjShq+hsisvbUxyqW5vSPnLolbhIEQRBEYkmo4LBq1SpFYNiyZQsCgYCyTT2bonv37orA0K9fP6SnpydymKcsn332GWbMmGFZprq6OkGjIQjCKfrZ7NLjVA6jpH1XFnT19NvCYZfk2bO6+NEO35zMPBe4XhFOozbZ2IbMQkVFQrReEbXmTREHe5nhZdtKVdBGU1bViaQ/+TLSiwqAnXeDXV8MIsBc+uHZtsdJ16Dth/HX8/I88HAJAlwhcUId69+MzPQ4CA4cDwOeYZuxsEhgR2qKBzeO6IrXZ27AiQo/t4ycw4HXf33Fb5LDwYpAUMSh/DI8O3UVGAMW/LYPfx5zFob0aW0oa5ePgZdUOnwujV4CZsj3ltrgb+YhwFS3p0Gns7gJtbOBDc3qvqeiJzLja2x9hRuJj/irbpJXtz4Yi+vUGHXG+khyE0XRGQy/iwzDieexSYQo5gzH13mNHhPr4xHPY6/fi7C3qmnn4aEx/aY6dL8QBEEQRIJIqOAwfvx4Q9xLQRDQqVMn9OvXD4MGDcKAAQPQuHHjRA6LCFFUVIScnJzaHgZBEDEiQDJQcRMwyzGPLCwwWlFC+3JnCKtkIxYoL1iC1igvCIIzocHk/UxvOIuE2plxVjNGAycii5kDQaShU0xtOEy7YBqZIuQVEBbIVBeA3vKmNvCL4TLGdhnABEPoBqZSESwFirALkHkhk/0AJCHBCbIRnjFnhvj0VP7PsxHntsPitc68EZK94TbMDoEsmHgd5qJITfaga7tGeO3PF6LKH8A7szfhj71FmjJejwuMMcdt1geCFjkczPD5Razakqe5/v4zcwP692yh8oyRDFh2bfv8xhBc6jBLsseNVkjQ1wg/M/VXrVRW5BjYLIelahWACMCtWqd6YKj7NE38HqriJIWDIQk0t4yZPJpgE64oAm7ePR8ahZNnr6OOnF6f8VBfzJoOHXTDwY9pzjjidbbsjb6RjrNu7FedxXbygsUxYPr7w9l9EvcgYfr+LcXWk/6MEgRBEERE1FpIJUEQMGTIENxzzz0499xza2sYhIomTZqgS5culmWqq6tx8KAzYwdBELUA16tA3qR62VaMvQIEQecJIVt+uIqFYVH7Am/ytqUVG8J9xSkntaOZYwmNOqHrLNqXULVXiX4XzWZxRjOLOByWQD0pgB+ayKxPfYtcBcGJjc1m0AxiuBFNGATROKtQKSono2XSf6o+RCaa59eFlMNEuTVUu88Y4Hbo4SAb4QVBQEqS/U+v1lkZhnVPjOuH3p2zEAyKWPZ7rm0bek8KnqHXHTp+HrczcSAt2QMwqW2v14W0FOO+eEN5LaLJ4XBuj+ZYuz0/4nqxcumA9lhgkZsiGBS5XgZW+AJB5OSWGNb/tiUPw/q119zbdt4TJ8qN3iTBIAMzNWarMUnErNqfQFDE7MW7sG1vEYb2bYOR57YzNe+FRQ2GIwVlWLstD79uPYLjZT5cNqgDrhrayXgP82Y8q1Yr4Y+iVIN5IVFqwvBXN2Ypx/BFphdqON8pNbZ/Nl4x9kKA83El8qs+cb3J36kC99zF1Kx+FTNONuEWihsqMTSa33LMKJbqK1hJH0YvT9shmHel/KP6TBBEvWX16tUYN24cAODBBx/EpEmTNNu7desGAGjTpg2WLFmS8PElmieeeAJz584FAHz66acYMGBALY8ocXz//ffo3Lkzunfvrll/6NAhjBgxAgDQv39/TJs2rTaGV2dJqODQsmVLHDlyRPm8cuVKrFy5Eo0bN0b//v0xYMAADBgwAJ06dUrksIgQt9xyC2655RbLMtnZ2bjyyisTNCKCIJygflESBEEyQkmuDJBfd5S5ZOqy0L4MCQKwcdcx/Lb5MApKKnDseBVGntsOlw7qyO3P6r0wXkJCLMRjDLLB2UlfMfXDfenXG+1rC7VYoB2PevafXSgkdR35eImMQWvq1s8iVH02SZCtLsYL5+xkXNyQSpqQUXrvCumD06TRHo9LuZachFTq1qExLjy7LZb9fggA0LtTU/Tq1BSMAbdd1h1BEVix0Vp0SPa6wfcKCaN4ODgUHFKSPSGzl9RoWorXUCaWkErNGqdGXCcedGrTEGkpHlRUBbjb/UGRm0fBiqNFFdiw85hhffbBEgzr1x5A6Ppl9jkcTlT4DOsCNmGYoBPWpP4kI6Le7vzzhkP4/KddAIDNOQXo0KohOrbJDLeja48B2J93Ak+8+xsqVQnQ//v1VpzbswWyGiZpkj8rwgIUSRPxsJjae2aF9te8Aem+VHlfR5szJxpjvVrI5AvKxvFatxS/sdUYCflhUIN9WLnOOG4iUceAf13XxvVgdo1H5lzIdMvGH3lONEsnAoNZDhizsnbHs07dgwRBEIRjDh48iMmTJ2PlypX49NNPa3s49Y6ECg7Lli3Dvn378Ouvv+K3337DmjVrcPz4cRQVFeHHH3/Ejz/+CABo1qwZBgwYgIEDB2LAgAFo27ZtIodJEARRr5GSNEdn0DmUfwJLQ0ZOADhSWBFqM+z+btaqITG1RgjRvqgZnSfqxotYRC+/hsq6huIWKsO8m4gNd0pRprKbaA2DLOLZk1qRwLExhffiHqqv3sSYKnQMR1FgZp9C9oKwR4LFuCI1BISaOqNzFr77Za9tcSWkEpitN0GDtCR43C7cdnl3dGzdEFXVfozof5qyPcnjxsRrz7AVHJKSwv2Y7brbJYWjchr+KC1ZFaaJMc1nmXDS6MgFh1QH3h81QfPGqWjUINlUcAiKkedweOzNFdz1uQXlUGb0Ku3bCA7lRsFBrqM25rPQP7x8O7K4wURAkNOchCq//sVGTdn/ff8Hnp04yPL59NnCXRqxQebbFXsw4YrunBrhcfBuN1FnTLQyzqk95GBST/9kNIRhisH4Zxgfk8PFcURRzuDtQ0JpR1/b4j2f+IhGNUVUBl79d4vShoPvck1/kR0by+8mB9/HCTNmK4KlE6GvZsdgui2C4+D0t0r4N4jz/TaWrJM3MUEQBGHDvHnzsHLlytoeRr0l4W92HTp0QIcOHTB27FgwxrBlyxb89ttv+PXXX7Fhwwb4fD7k5+fju+++w3fffQcAaN26NQYOHIiBAwfiqquuSvSQCYKoo9CMISeoXpYFpvN7CNvFpXUCmjXSzjAuKKk0bY7fm2AQGnjLDpqKHEEWLmrmxS5So4/m+uTUrbHIFSbGDnk8zl6yZaOYTkRSh2bR2Pb5KgBPvFB7jSgzHDlDEqvL4duyCO7UDLhOHwq43WFPA7VdhzOe8PgZwjkejLke5HqntTCGMLKDMYZenZqi22mNsXN/sWVZr8elGEPswjA9eFMfAAxulwvD+rVD0O+HN8kTMnqEZqcL9ueQ52HQuU0mduceVz5369AYgPOQSqnJoXEwqe20VI6HQ6itTm0aOmpT335tkNUoFUkWoks0ORzMyM0vkxZY+L6xa7uU4+EQDOo8HKxC1jhcJ1NyosriOSHdW2v+4Ie+qvYHQ98ppjdnaBVT1mu60oWJYqIIwcUXr0zzQVjZHa0Mlqp2nYV2cVDO7DjajqOGDJTM/Lhxi6u+N2L7vcUxwUZy/OLwfRmjzsRvkNl5GqmLx3gMdd+nNfUbOFIpKepxhMQ3S73FcsnZ2ByUsigfekaZPKM1XhcABMbA6L2EIE4Zdu7cWdtDIIg6Ta1m9BMEAWeeeSbuuecefPLJJ1i7di0++ugjTJw4EX379oU7ZFzIzc3FnDlz8Pjjj9fmcAmCqEMkxiW9vsHCf02OjyqHs3qtspSlExw2Zhfg0x/+QGl5tdYLQdWOIEj/F5RUotpnnPFq8HxA+Pypl/Vl1YbpWDwg9EZ2p6F/ePDDXHAL8hYTjuP3XkdjlI2DZsbDkP1FNvYz3vUov7zzBChtmfKvp6Bq1SyUL/0Y1au+5I8oZDxjqn3Qn2t5mzz7mzPlGN1Pa4yOrcMG8nuvP0M9FM2+acYsCHj0lnPwzF0DcXa35twxAqqkzIw/C/qea8/A0D6t8MD1Z6D7aU2UfVAbOcL9O7ugkpPchpK3XdZDOe5ul4DRI08Pjc+ZN0KKLhwUN4dDaF/792yBVlnpmm2tm6UbymvaryXBITM9SZXI2UggKCJoG8LIGUWlVaisDntSMMA2P8SJCmMOh0CQqa55Yx35FrR7ZNk8vqRl3b7bOwtpCxhvOePAeW2Kun71n8N1rCyW5t+H+n7tJprL23kCkXnd+H4B8B0inPVhXyoRX1aRyl/RdOH8OamtU4PltZUjb4fz/WYcUuTt8n4PRRbOKwosxEzjksPmLMen30vjc4dpPI8i21frrpl9GYIgCII4Sam1pNE8kpOTMXjwYAwaNAhbtmzB4sWL8dlnn6G8vFz3Q4AgCAKIfB7WKYDGeK+b0ad/hHLC/vBiqC9cfQCV1UHcfXWvcGoIDh99uw05B0vQt1szDOrVEn27NeeGVWGMM8s8RuLRXk2HdXI8a1a9VJNfe8pLun5Muk4114ixvD42tKN+bfoSiw8jmL9XWe3btABpQ26GPDtXCZsSDMC39SegvAje3hfBldHU2J1yvVkYCQXg8dv6YUtOPpo0Ske39o01RkX9bqmNFC5BQJd2jSzzD3jdrpDwwcCza/fr2QKDejeTjMhOYMC4y3vg0x+2mxaRPRxEJsIFablLu0Z4asIAbNtbiD5dm6F1M8mzw+MwF4VeYEg3ERwYk9q8+5peeGnaelT7gmjeOBWDerfCnKU5pu3rBY1EYhVWyh+IPKSSFYeOlqFTm0zlkrTLD1Fm4eGwYNU+fL18D1o1TcWfxpyNJg1TjLdpSJQTRREic2lmG9mGc9Es2h8DSeA1KRma/Wt3tan7shZMtIZl7fcdi/tPBJ7XlzwKQRmvk3juoVaYhYSoF21jIrIDwd8HXRu62fbKugj6qDGibJvrAWfblYlxXhXKz+nsf16uBYejgNX5NXp2hEIq6nKXxN1jQjf7nzsumO+nMQdN+L4xuyc0v1ZiEuJC96Z+DI5arOHrmyAIgiDqCXVGcDh48CBWrlyJX3/9FatXr0ZpaSkA7Rf2aaedVlvDIwiCqPvIXgOCYIy4ItgY1AUp4EzD9CRu8tT1O47irqt6SiGTQqFp1AQCInbsK0JQZFi99QhWbz2Cf00aijbNM5QxGV7AaiiztD6sU0R1DbPunbUjhI6vufcE3yBgtNcw/rK6XZ0h0Uov4B/eyIxCtkfAYF1k4T8CU0SCsAVGXmTh+rpBi1VlJuPWlvWtnYPAxu8BAMHda5F687+0faur6mxlutHC63GjX/fmYIJHs80u5IOMVagkKaSSNAarmfTKsTLTgYSwUWzIma2xdU8hduwrQrNGqdh/5ISmeLJXm29Btl92O60xOrfNlO6R0H55HOZw8HpcCARF5ZjxQiB5veG2Tm/fGK/86XzsO3wcvTo1xeJ1By3bT/bWnuOtlegSFFlcBYc9h4+jU5tM5TjaeTj8sukwd0xFpVWY+vVWiAzIL67ArMXZuOe6MwxlGYCVmw7jnTmb4BKAh8acjf49W1o/Cpjmj2q9ubEP0IdSUtcxNs80fYh8/dOqGc7z0tTQx7nn9cuRY2/INw6H2RWI3mBuYTDWHhftsbL+voyDahPh/kRs+LYx+iurIhqFXCP8nLQ2Ikd/nBiTQvHowyXxluPVZzQo8pddGCmLw8QTEZx2LiV4N9/ryI8G57nEuT2VX3YaEZCDyMBcoXNp1zBB1DDDhw9Hbm4uzjvvPHzwwQd49913MXv2bBQUFKBJkybo3bs3Xn75ZWRkSO9pfr8f8+bNw8KFC7F9+3aUlJQgPT0dHTp0wPnnn49bbrkFjRo1su332LFjmDdvHhYvXoxDhw6hpKQEmZmZ6NGjB6688kpcffXVcFn8Dt6/fz9mzpyJ3377DYcOHUJ1dTUaN26M3r174+KLL8ZVV10Ft5s/OeWJJ57A3LlzkZSUhC1btuCHH37Ae++9h7179yI9PR1du3bFY489hjPOCP9OKisrw4wZM7Bw4ULs3r0bjDG0bdsWl156KcaPH2+7v926dQMAtGnTBkuWLNFsU5+DqVOn4siRI/j000+xfPlyHD58GIIgoF27dhg+fDjGjRuHxo0bW/Z19OhRpf7Bgwfhcrlw2mmn4ZJLLsHtt9+O8vJynHfeeQCABx98EJMmTbIdf01RXFyMGTNm4Oeff8b+/ftRVlaGRo0aoUePHrj44otx7bXXwus1hmUFjOexqqoKn3/+ORYsWIB9+/ahsrISzZs3x+DBgzFu3Dh06dLFcixVVVWYOXMmFixYgJycHFRXV6NFixY4//zzcccddyjne+/evejfvz+mTZumGYeacePGKcuLFy82zTW8bds2TJs2DWvWrMGxY8fQoEEDdOrUCVdffTWuv/56eDx1xgxf49Tanh4/fhyrVq1SRIbc3HDyQ/nHiMfjwVlnnYXhw4fjwgsvRMeOHWtruARB1CGU2OwEH7Vt2mDTF5R1sjFZvVkQBAzt0wo/rtYaBiuqAigt9yGzYQq3yxMVPo1BrkGaF22aZ1g4RITLxmtGXTQG/0iQXnZr/uW+JnsQoL4g1IKGrl/GWaupJ105PL1DKmHywq2yKFq9u5vFbNf34g+JDQDAyosQzN0Gb+ezw0VEFgoeGR4R1/6n10y4vTHluOjjNgPWeRDkkEUM/DBXsgHHPPCDpjCYIBn7/zL2HIgiw9GCUjz61q+aYskReAs4TRotixTy/vNCKukN91mNUpGZngQAsElf4Xgc8WRAr5YAs07mHQyKccvhAABL1x/EyHPbA5COZTRiRlAU8cvmPKirLvhtnyI4qOU5UWR476vNSsi7/87bIgkOJujvFfm6tPIWkhEEIWyc0zcor7e8DvSGVnN5QBmfIc+D+tllUClMnzsWT0Rtf5Z1VetC4qEzrwd+GUczrJ3O5NacE855MDk3cZ2trR+CSX/x/q515n3nxMivlID6YCn5iCz7lAUM9Y5HcWxrWoCwU/7i3K+yNzyRyNHhCX0nc68Zxh2e/l5QVw9PMhBM74nIxkcQtcsTTzyBb775Rvl89OhRZGVlKWLDzp07MWnSJOzfv19Tr6SkBBs3bsTGjRvx8ccf48UXX8RFF11k2s8333yDp59+GpWV2vx/BQUFWLFiBVasWIEZM2bgvffeQ5MmTTRlRFHEm2++iffffx/BoDY879GjR3H06FEsXrwYU6dOxZtvvokOHTpY7vOsWbPw1FNPKZ99Ph82bNiANm3aKOuys7Nx9913Iy8vT1M3Ozsb2dnZ+Oqrr3DfffdZ9uOUZcuW4dFHH8WJE9rJQTt27MCOHTswY8YMfPjhhxoxRM2vv/6KBx54ABUVFZr127dvx/bt2zFr1ixMmTIlLmONlR9++AFPP/20YV+PHTuGY8eO4eeff8bUqVPx9ttvo3PnzpZtHThwAHfffTf27dunWX/w4EF88cUXmD17NiZPnozRo0dz6+fm5uLOO+/E3r17DfU/++wzzJ07Fy+99FLkO2nBG2+8gXfffVcTBrSwsBCFhYVYu3YtvvrqK3zwwQdo2DDyXHf1kYQKDmvWrFEEhj/++EM5Ceov/czMTAwdOhTDhg3D+eefjwYNGiRyiARB1CdsDRenIhaGII2RPzwBXJvYWcDVQzrgp7WHDAa2I0XlaNQwVRIspPy1Cid0IT9OVPjxz/+tQXFpFZ6/ZzA8oTAroYGEhykIAORkuEYBxA5RZHDZWTHjQLTvlHZGC878Omg8ARIirIUFBJNBGWuo9ivyo89C4o1LawhQGzkEo/HXicGIVZaquwk3i/D1rp05qiusGFaheDdAsBKywrgtZshLYYYkg5Tp9cqYYWyCoB5POKdJuIy54T/Z6w7tH+cqC62XjXpODf16bw8vx0ivjFCxeDMwiBDgsr1X4y04JHvdyEjzovB4lWmZG0d0AWPWgtGUT9fFNI4+XZthU/Yx5fOuAyU4lH9CCWkVjZgRDDL4/MacOTwHmcrqgCZvROFxVVJop8ZquWzomnG7BK5QIoSe5YqorQrbYmWW1Ho7hJfk+1BjgDYRM8xkBtPnbMS/IVQSjP5eVZ4dIgTBXOxjIcFQfuIy+R8LA3tY643SsCs/xxjABHuvNe4+2BpedUqCdqt5WQdtJwaH92DEFuZYjPHqu8FC7LM7N0qx0H2klNfPSFH/FrBwnZT7jGD8+mvX9rcR57tLfRuof88q3/GcfjSFwoqCVvDRTabQP160kzGY4XlktSfMQRmCqEnWrl2L6upqtGnTBuPGjUOjRo2wevVqnH22NDln165dGDt2LMrKJM/is846C5dccglatGiB0tJSrFy5Ej/99BNOnDiBSZMm4T//+Q8uvfRSQz+ff/45nnnmGeWzbM/LzMzE3r17MXPmTBQUFGDTpk2455578Pnnn2tmef/jH//A9OnTAQAulwuXXHIJhgwZgvT0dOzZswfz5s3DwYMHsWvXLowePRqzZs1C+/btufscCATw7LPPIi0tDbfddhu6du2K7OxsnDhxQhE6Dh48iLFjxypRVXr06IFrr70WzZs3x4EDBzBr1iwcOnQIzz//fMznYO/evXjooYdQVVWFCy64AMOGDUPDhg2RnZ2NmTNnori4GCUlJfjzn/+M+fPnIykpSVN/7dq1mDhxIvx+KZ/XgAEDcOmllyIzMxM7d+7El19+idzcXDzwwAMxjzVW5s6diyeffBKMMbjdbowcORJDhgxBgwYNcOTIESxYsACbNm3C3r17cfPNN2P27Nmm51EURUVs6NatG66++mq0bt0aR48exZw5c5CdnY1gMIjnnnsO/fr1M4gXRUVFGDt2LI4cOQIA6NChA0aNGoW2bdvi8OHDmDt3LnJycvDII49wvS1uu+02jBw5Et9//z1++OEHAMBDDz2E00+X8t81bWoM4/v7779jzZo18Hq9uOKKKzBgwAAAwIYNGzB37lyIoogNGzbgn//8Z50RiGqahAoO48aN07x4yHTq1AkXXnghhg8fjrPPPtvSzYogCEJNTcyEq22i2SfeC7yV8V5fXt1biyapuO2S0/HJ/J2aMnkF5ejeIUvbkCBAYIyb1HTr7kIAwKbsAgzo3dJQzxS1MVs1GTDamWRmoaitzV81QWL6c2CWN7xgW1wpjvsUzNrRGxqgNgyYFOchBgC3B0xkPD2CO15DU5xToA7FwmDcDt0MSTnqevi6lJZ5xncZyZgtNTLkzNb4SpXHYMiZrbnj0S/rd0o2xDLwcx8oxn2N8YSFDMLa2ZxetwuZGUk4XmbMFcBDDpGVZpLkmReZnkHKd2FFtILD8H7tkF9UgePl1Th4VHppbtQgGa8/dB4CjOGhf69AWaXxGfWvSUPRumka/H6/pWDE486remLqt384Ktu2eQb8gSD+2FukrNude1wRHKLycAiKXJGEZ/iV8z1o6oui6T1rGtpNZQT0uF0IihzBg2Nc5u2dLIzIH3hmT2Pb5vD0E7VIZ1rXgSHU4Gmh7sx2hr529rQjlApOrwvt+YokD0DEY3M4Dmdl9R4C5mJsRKOw3Wcnv7G4V632U5wSyWvb1x0TxmCMkWm/j6Y9cOtF+NskTtP6mdJW5Nc5byz67zXba9tqPxgDY7ygmg6EkpPrlYSo51RXVyMrKwtffPEFmjVrBgC49tprAUiG+YceeghlZWUQBAGTJ0/GzTffrKk/ZswYrF69Gvfddx/Ky8vxf//3f+jXrx+yssLvg3l5eXj55ZcBAF6vF6+88opBlLj99ttx8803IycnB5s3b8a8efNwww03AJBC08hiQ8OGDfHuu++iX79+mvoTJ07E//3f/+Hbb79VjPNz5szhPstFUYQoivjwww8xcOBA7nH5xz/+oYgNo0ePxuTJkzWhmsaPH48///nPWLp0qfUBdkBubi7cbjdef/11w3EZPXo0rrvuOhQXF+PQoUNYsWIFRowYoWwPBAJ45plnFLHhiSeewIQJE5TtV1xxBcaPH4+77roL27Zti3mssbBv3z4888wzYIyhUaNGeO+993DWWWdpytxxxx343//+hxdffBHHjx/HX/7yF8yaNYvbXiAQwL59+zB+/Hg8/vjjGhvx2LFjcdddd2HNmjUIBAL48ssv8eSTT2rqv/baa4rYcMkll+CVV17RiDm33347Jk+ejDlz5ijHV02vXr3Qq1cvbN8ezpd3zjnnKCKC2ZgbN26MDz74AGeeeaay/sYbb8TIkSMVj5lvv/0Wf/vb306JyfUJt+zLatfAgQPx5JNP4qeffsIPP/yAxx57DP369SOxgSAIBzDdS8bJN3coln1i0L9lCaF/9S7/Jn0w4PKB7XH5oNM0q/MKyjWf1T/yTpSbGypXbc3jvn/p18UkHOn2i2cztm1CMB9DzNeYrr6TGXHxRgi/icfcFn9ydOiDGPpfY1FUbde1ob6fpRAkTBIX9H36fSHbP7dzZZ1+ZqSoizfNNSgaW+K0bb05PZUfixQIG9IZY2jWKBXXXtAZLpeAFk3ScPmQDqFDoDsOyniNBnytUZl/3QY4RmYeDFL97qc1sS2rp1njVLTKSlM+9+2qFSQVY3PoH1sPB44B3SUIaNIw2bLedRd0xt/vHIBn7x6IJ8b1wy2XdMM/7pU8q5I8bky6sQ+3XtNMOUQcsxSM9AgCMPycNrjg7Db2hSGd/zYhcUGmQiWAROPh4AuIWLfjmGG9nH9HfY1UczwhFq89iCffWYk3Z20y7YN/nwNMNM954fOF+9Lc44YlfWfGrRoRgne/K57SuqbMvtqU+8tsDMYd1vQrig5nZmvWaNti+oL6v8b2rPrk5WYwKRnerhh5HfyOYgy2Mfvt0IlAxi7064zPQfOyug6sBhHpd598vVi1b3aT8MZlJrLryjoNGxp/wUM9HovtNgWsjpf2cuUnZua3qL92gcDxYyhaNgMn1v0AFjQai5yMJ1xG/Vflycf5TubVc9oPQdQGY8aMUcQGNT/88AP27NkDQJoYrBcbZAYMGICHHnoIAFBeXq7EuJf57LPPlFA/EydO5HpANGzYEP/85z+Vz19//bWy/PbbbyvLzz//vEFsAICkpCS8+OKL6NGjBwApRv7ixYv5Owygf//+pmJDTk6OIiR07doVTz/9tCEvREpKCl555RW0aNHCtI9IuOmmm7jHpVWrVppwQJs2aX+TzZ8/Hzk50iSlK6+8UiM2yDRp0gRvvfUW0tLSDNsSyYcffoiqKsmj+B//+IdBbJAZP348Ro4cCQDYvHkzVq5cadpm165d8cQTTxhsxMnJyZpwV/rjduTIEcyePRsA0K5dO7z88ssGzxGv14vnnnsOvXr1criHznjiiSc0YoPM8OHDlWMSCASwY8eOuPZbV0modf+aa67Ba6+9hlWrVuF///sfbr/9drRr1y6RQyAIoh5in6iPAKAz8GpDPqgNkhrjJM/AHlrVqY02tmBeYYWquPa460MqqTlWXKHt1/Rdmn8uBf2MP5N+ItUrnCaEtkMedVxmiToRRmpg+pza2K2cIDPDGZMn3zo1dHEQ9a/3up44M6cRqLY3TOjGpMRQF0WulcRoyLdpXv0vY5ryGalJvCoA5KTR4WM6alhXfPbcZXh50lC0bZZhpp1wjbDh7daDbZSRrDFw6md+6zm9fWPL9uR6+mP20I19MLBnc5x/VhtMuKqnurChX7trl+fh0KVdJt7663BMuLInp4aEHLKNMeDMrs1w0YDT0CAtCbIA1btzUyVngrY/N+TcHFZJo/W4XZKXiFUeBM343C6kpWgFqbLKgCI0BKMwHO7OLcWugyWG9SVlVdAKVgz+gLH99+duQc6hEixZd8i+M6b5AwBwmwg01QHR9LrVG/G023SdKOKj3lCu/grRfZnohDq5iP5RxTUNR2CM1tpMeQZnximpKmZmvDQddLhdK6+n2sVCJDA+4a1binl/7NqP4FybiCH6a1wjDOknGFjOpHc4Nk3bNmWcYCrYm7dhFPb4+xsuwF2MEP49xJiIw58+hdK13+P48s9w/OeZJtX5+8QV0SzPE1MEdOMloT/f8u8D/rOJIBLNueeey13//ffhPGhmMfBlbrzxRsUorzf0L1q0CICUf/XWW281baNPnz54+OGH8cILL+CRRx4BIBmG5Zn5HTp04BrlZbxeLyZOnKh8XrBggWlZs30GoEnuPGbMGNMEvhkZGRgzZoxpO5Fw2WWXmW6TRRRAypuhRj62gOQdYEbr1q1x1VVXRT/AGBFFEfPnzwcAZGVlabw0eKiPq5VwdMkll5hOBuzevbuyXFxcrNm2ePFiJXz/LbfcgpQUfg5Kj8eDO++803KskeD1ei2vYXWOjmPHjJOGTkYSGlIp3gk5CII4tTgZPRlk9CEYIvHJNp0NJ+gDZqhFB21PUi5YOe2yZNRv2SQNbpeA9FQvMlK9SFfFidd/91sJDhXV2tnq6qqygCAyphMzHOy/8sLPcXpnkR1DwHGUJ8d1jI3AwZCkQso5jfM1H8r5q+sy1E+EO6PElBac3Jsqw4woWnizhF7SeaFaAv5QKCb7fsxPPwv1oV+tN2hqLSWG5pi8/4JS3omHgzwCzT2gG4ZdKAjJ4CE/L8KRvUcN64I5oVBN7VtkoH3LDH5lQ6QW6cPA3i0xc+FO+K08I1T3nHzIWmWl4/7rewMeL8SgCLCAqn2tEdnOwyHJJKQSYwxuCw9Y5fjyLckA+Dk23G4Bst3JE0E4J3k/5GTgdnjcLni92vbLq/yK8SqO+ahRXFqF5o3TNIZ1noeDFTzbuSiKoeMlrfCaeTgY+jJ6HSnrZQ8Ffd+Mha5TZlzPG6/Jshlqwz7PPG4sL3mhKLss36O6OmHhVntszMak11hMx2uz3bKuciidf6cwkQEuO0N5jMIHCz+7tKvNhRbHXpAW14pj4vXdy3uYO/y+ZSLjiPvKVvt+Vcv8e1A9HpO6uu4YYyjbtgK+g9uR3mso0jryE5zajc38nlB9t4m6e0kUgdCztzp3FwKlBcqmE2u+RaNh49S/NFTf0OY/v+RdNf1ONlTQjtXYnghBcKHKF4DbJRhmTRNEbWCWmPf3339Xlnft2mVIrKsnKysLR48eRU5ODsrLy5Geno7S0lKlXufOnQ3JoPXce++9ms8bNmxQlgcNGmRZFwAGDx6sLG/cuNG0nFUyYnWfPG8KNVbhcyKha9euptvUyYP1oX1Wr14NAGjQoAF69jSfeAMAQ4YMwRdffBHDKKNn165dSh6QjIwMSxEBgBLOCjB6J6hxetwCAa2tQT5uAEw9XWTU11SsnHbaaabiBgBNCCWfz1kY2/pOQgUHPTt27MCPP/6IjRs3oqCgABUVFUhLS0OLFi3Qs2dPjBgxAn369KnNIRIEUSfQvSpwZ5ydbEFTrffJ7OWbhQwKsnQQNl7LbWoasXgDE9C5TUNMe3ok4HJDcLkQ4HwxCgCYIHBzOMiok5UKgoCyimocL/ehTfMGkE2mykDUb4dRwzt20TUoxZ132RrUYwkHFVseEt4Lr40IE4kYEzJSKIY0JQ+Tqk2VEU5nXzY0xd1NXmHejO+Agx9mTIQohuJeK7Y/BwY2ldAg6NbxhsmbuGglOHjcLo2NiSmDkz/ZGY90nXLGfs35ndAqKx3FJ6oxuFfzcM4sTlX5WpDFCwYBGWlJuPmSbpj500543C6MuagbPv52mzKyu6/pbTE8c88jJVkwU2xFpnhMDPiMWYsVnpAYYcwdYX2tC4KgGPs9EYT0dLukZ4KZQGIYn0dAslf7k7us0o9fNuXC7xdR7TOGEIuWUk54O15yaWfovjtCF1LQX4X27mOoFJJQwbQvVkpfLPxkN+SBMHkOMCYaHhJWd4bpV5jSkfqZpb82+PWMXnSqdL2MI1by+taNUUYUOZmDzIy7IWGQOdLeQ6bV0L3Mf86GRRFBtY6pv3RNZ6prj6P++8rUC4BT36x56IoYnr+hfmXDeWzfubyVZhssKtp9tUQqWoSOk/ZwOfu+TtSEnIqdq1H43VsAgBOblqDdfW/A08gYcsTReBgDmGh/HPXVALDqSpMtMb4LKL8FpH8VoZNBEVvDRRl37D+t2Y//ztsClyDgwRv7Ylg/iuZA1C6ZmZmGdeXl5Rqj75///GfH7THGUFRUhPT0dBQUhIW/Nm2chZlUo65/2mmn2ZZv1KgRGjZsiNLSUhQWFpqWUxujrfps1aqVZX8dOnSwHZMTrMajFibVz06fz6fM3G/Tpo3t956T41dT5OXlKcv79u2LKIF1UVGR6Tar46b2TNF/5+Tn5yvLdtdl48aNlWsqVuxyMqjPoVhDYRHrGrUiOBw/fhxPPfWUxkVITU5ODlauXIn//ve/uOCCC/Diiy+icWN7V3+CIE52TISHOCaNlr+wEpmI2jZJrEU9eSaY8saubDSWdZnOlJNQ+T+EDRiCACYImnEJgjQrWH2ILD0cqgJydl3sOlCMf01fh4qqAPp1b45Hxp7N6V/+LPern3smqAQV4z5wo0QJgt50xsXCJGJRKwIcNJOIK4+BSXZ4M8OU6XUiX2cCt3x4FiXnaDs0/gPgezgEfSZnXTc8zUeVQS3WGbnc7mRDLJCeav6TKuzhYDRY6A2hsoFDZKJJ3Eu+B4QgAP17tUQgIOqOn75P/m4zxjDi3HYYdk47eNwCgiJDy6ap+G3LEXRo1QADe7cM251FKNqVcjUwnrFWP0brq9vLO4SyIGAR8sjtdhkNvRBVp9zamMoQmYeDOyR+JHGSdfPwul2G62P574ew/HcH4Ywi5N+fb0DrrHT069ECt14muen7OCGVIiN87ILVFSiY9hTGC4dxIjMFb5RegnwxbMzw+YMa263z7zSLZ47ue0AUw8Zn9QxwbSmmbYPTlZJjBOaXB28jU1dSHi9MW0Ael6FduU+eUR6qPbAYv649OwzhquRlxTvN2tNAGpp1uB21gGozGNvxOm5LKS9XU/0m4rRp5lFh+3uPN5NdNkZzBU7j08hwjEPnmx9uSrA9Tk7EL16dWH/b5n/zhrpFFC2fiebXPARAMp64XC7TsSvis6p+aEP4L+cnfvg4C+G/ScYZpJJ3gfGZLHvsmCL/huHeeroRM8kj18W5lkRR+t5+d85mpaH35m7GsH7t4vULkiCiQh+7HoAyGz1aysulvH7qEECpqakRt6Meh9P6qampKC0tRUVFhelzjbfPMmrDsl2fGRkcb+EoMAvbZIU6TJDVrHmZaI5/vDhx4kTUda2uxWi9xKI5dvEQHLxe88lnpyoJFxwKCwsxZswYHDp0yNFLyPLlyzFq1CjMmjULTZs2TcAICYKoi5jO0ldtT6RIEC+0+6UyjMZjX0LtCELIuKx7iVLPgFZt0K4xmWWo50S5uYeDPyDCHwjC43bh+5V7laSm63bkY+eBYpzetiG0b5k2hkmeoGDh0SDI+xHBIY3kRT6Sa88u+Wd4NrBgmE0XP2QjmN2Yw9ekYf/01xO3rj1iaT78hfvBmncBMhqHX/w5SaNlDwfF0MIdPs94E14SOEXC4UO0eoj6vGrOmy5GAwsZ4px4OChGec7xlGyTsimSaTao7VpMGWg4HJDaoCjPujSb+W1Yr3suhMMEMXQ/rQl6dMiCyIIIBuUOwvttdiIUg6rq+cYg2IZUcnO2y8fCKqSSevy8+ub6WdiIGkkOB/n0OU007fG4kZ6SmJcQUWQ4lF+GQ/ll6NWpKfp2bQa/P3rBQTKuQTGel21ajEDRYQBAA1cVLk/biP+VXaCUrzbri6nD4jCNE5NGuNJe/eEyus+h0YXuW8Y993Jrm3MKsPfwcVx4dls0yfCoREjoDJwhg6iguk9Dzxu994zIGNxyD8wQAcqw77JHkWF0PNs4GPS5i8wOgt77zAzzrx6r76TIRHgrTyfLwejEDUPLqnV237d237FKg/wS9uvU3xGqKzWyb2vDhWfycNbVUq5JXVlHv290v+tMjiFjou58aMN9Mn+1pry/INdRn6LIuM93fh2tgd84Rv41IFZVQEhtEK5ocUyZyCDwvm+U7wTdFz33qWRcU1ap/S0s/96NWB0iiBpGbYTt3Lkzfvjhh6jaURu5Kyt5nkfWqBMdO60vix2pqalRvfurPT4qKiosxYnaDHujPrZyUm4rojn+8UI91ptvvhnPPPNMrY0F0F7fducYgJLsmog/CU0aDUjuWgcPHgRjDC1atMBf/vIXfPXVV1i3bh22bduGNWvW4Msvv8RDDz2E5s2bgzGGw4cP469//Wuih0oQRF2B/8Zhvb2+I8/YNLdg8FcpMz3l+PrhlyaevIBQGcMPNkHZpNQVeOVCWHk4AOGXrnXbj2rWL1l7UDV43tBMwkPINRzYB8wwzhCPXuThOwqYG7/5nxE2LkP7ws9rO/4hFKI5mFrDmFxDP7bAtiU4PvV+lH32V4jH9oIBCBQchO+rp1G16B34v5oMVlkaqs8Axs/hYDsmUQSD2pKpa8PhzFnt+I3ngmde8rhcSE3mz+MI5xhQCQNym/JwebOQ1XAmyPL+2iEbTrWPUG1lQ1ui2ujJMRYqq3jXqlyX2QoO/JnWCNU1r2XIuRG2TqpXW+JxZAyTkEIqAUleh4KDSzC9NmqSD7/eCgDwBSLN4cC/HhiA8i3LNNvOStqv+ezzB7XXCke0NjQsr+c81CN+zOnu8V825eGZ/67CJ99vx59fW45qX1AzhvA9HaqqGrN6m7I7mjX6rjn7G/qruM4zo8hg/KqI/tnO/ZZR9se6L/3+OushgmePyY6bHE3Tz9yEv4Z+zAelebozORmws50wu365n2PEsrX/Z++rA+y2rvS/Kz2YN+TxmNmJHXDAcZgbZm6om2yaYtruttvtdrdpStt22263DEkx/TUNN9Qwx2E7ie2YYoiZcXjmoeD+/hDdKx3pwYCdWF8bz5N04ejq6ko634HAxYwYr9A6gEi6RvUcOj6hyyYHN42Qa+XRd+1dedz+xHLc/th76BNCc9JEnPCb8II087SFbaApYX0yffej+9PJL0Md852HCJ3IgTRU4a5ixKgGzc3NSKfTAIAtW7bUrFgXDYLFsDphWLlyJZYuXepaoI8aNco9tnHjxrBqLtrb212L+DFjguHcKsHo0aPd35s3b44oaSW13lNoampyPSy2bt1adi3ZsmXgvWYrxciRI93f69at22NyOBBDZZUbl97e3gHxbohBY0gJh9mzZ2PevHlgjOHEE0/EU089hc9+9rM45JBD0NjYCFVV0dzcjJkzZ+ILX/gCnnrqKTfJx9y5c/HWW28NpbgxYsTYwwgoVct+YA1Ir7CUcQPTdmXtVH5OpCKU6IP5rN8q0aW75IJrqlm5XJUSDjdcOEPany/p0vcqs/9H9hsSKYHEAE+N/nsbRNUv33ZYIs2KerbrBgmVMspAb6fcV+Sc5tIfcA5ezEF7+wFAL4L3tkGb9wgAIL/gCcCxliz0orToKa8VIq4l14vSEkApHAC5Hkfw2nm6BjqYhbi3LOEn/OEAGkO8HJIJJVKhxYUfYpimoG7IUxx6+3yN+OuYptumV78yFZ8jT/goeGtlVBkHSg2hS6zdlVnHSrXFqVBmHeacI1GhtwJge2JwjhSRc6KZ5XBRZiE+kl4BxRYimVBQXzf0hMPOjhz+8MgSrNrYWXVdHrZR5ho64ZvEuVrz89Sv+BdvFHCPrHMUmOK9bf/+6b2L3Oa6+0p4aYH34emQWVKXCMobzlNWslbQ5cl9vnce14socsmNvqfD+vLWGfEv/cz37vAK+gh9hlQqWbCeuH7RIygcj+grUrSBVAoHHizh72qUQKH3Xi2ihPU5UOfLxHXTGv/i9nXY8fcfYeMvPoEtf/gySu3bpC5Fmf73zvl4/u1NeOy1tfj1g4tlEcP65AD0oBekkQsP6RF9uv55Ja833ryz7wEeJCMcFEvB9xLDrD78VYwYgw3GGGbOnAkAKBaLmDNnTmT5UqmEL3/5y/jud7+LP//5z26S3tGjR2Ps2LEArLDo3d3dke388Ic/xFVXXYUTTjgB7e3tmDVrlnts7ty5ZeV+88033d/lkiiHQUwUXa7PefPm1dTHQIAx5o5PNpvFihUrIsvPnz9/CKSiccghh7heBAsXLpRCbVFYtWoVvvjFL+J///d/8cQTTwy4PEcd5YVsLjcu8+bNi4nhQcSQEg5PPWUpE0aMGIHf/va3ZWOiNTU14dZbb3UZs0ceeWTQZYwRI8beCko1GLVdQw975GHjfYhW1X9U2VDTMB5QFgWSNPp0CqZpoi9bwo72LNZt7UJfrugSE059BkiWaQAwprVe2v7RHe8gm9ew33g5+dO23Vm7HQZ/loWaHA5Y7Z4KYbWiElmGwbGw5Nz/KcvJn77aFfVRMyIVS5Qm2183sDNyOhrbVgKGNz/MrcsAcJRWyS/5+irrI8Ls3A5zF2Edo2ty/xThQJIQloyVorJkl4JiUlBa1ofkcXCU2eFD6osH7lOEBolXWllqupa6QYVJ5MkE5KFKSS1HTmXH0lsWm8u6KaqPCDHLhlQirHe5kJA07P7mtqa6GsJBUaxsIqmkn3Dg+Lfm53BuZimubJiHizMLAVjhmoYqpJIfL83fjH+8urbm+nK4LqBc7BZK4ea04xAB3CRoLNLyWuxfvL5c+Ne3mpV5lm7YHqKU9K2L8v3FCdJZeHaH9ibcN9w5J3k8yz0GnDWmpBm47eGl+MyPXsRP7pqPfFEX5IwG54CR70P7Y7/A1j/+GzpffyB6TSAZzQognJvvQHQf8K4zD+mTE7/KNEmViC4oTyR6TQkQNSDLSW2U66cS+EgLee54slZGuFg/cmveRcer96GweYUsb42vIJwDu5/4Lbbf9S3k11prn9axDV1vPgyXARbk6+wpYu1Wz6r07WU7o6+98+wDqvJwoOZapadYSTlxXAshhEOMGHsjzj33XPf37373O5dEoPDggw/i2WefxX333YdnnnlGyklw5plnAgA0TcPf//730Da2bNmCBQsWAABmzJiBESNGYPz48Tj00EMBWMmGn3322dD6mqbhL3/5i7t91llnlTlDGuedd577rXj//feHhivSNA333XdfTX0MFC6++GL391133RVarqurC48++ugQSEQjnU7jtNNOA2CRU3/4wx8iy99666144YUXcMcdd2D58uUDLs8FF1xg5RSCdY2jPHj+9re/Rba1LyZ6HkgMKeGwcOFCMMZw5ZVXVpyApbGxEVdeeSU451i2bNkgSxgjRoy9C7ICwNtd48fw3oqqlNg+BYzQRkBZ7G+XMXLsotRHP713EW768Wx85Vev4zu3z8OqzV1ec/Zf0+QoavJHVmuznKCprSuPl+ZtwpSxzZICf3t7FoWiFrjMUTIxiGGWqLEL7quYNKgqz4PcTjWJJ60YxIEC5M/w+6ACISss6IUCiS4biFlNDr/XDgOTyAapPz9KOZTem438w/8NY/nsYB295CrtAACUF4She3oNcMlTQgyn5Fc4BBXzwlj4ZTUJJRTnME0TjRk6RmjSl5BY6humbzzEeeILD+XquLj7N5ijoEx4kNBlVVRfyWFGOA8qi/wnEuo14YwXLx/fnZLbUbhG5o2Trh8xHiEKJlG5WU0OByc0lP+6TkvsxCjVU3qdlbHeW9WEgswe8HCoFZKi3080RWCc2olPKo+h7a6vo7B+UchUo1TH3rMtTEdLNUHOlzJVRU8Z2sdJPn85PI8V354Ta4DTXtA7wrddVkr62ML3d+G5dzaju6+I1xdtxeuLhNj51DuB3be1BnL0LnwehdXzYPS2o+uNh1DatSHYM5d/+BW9YWIGDlX7XkaF3envux2n5aDCB4XyD6aJjtl3Ycsfv4xdj/8GZqkgv0LVIC8pU1hhU153PY6jhrHx9ZvftBw7/v5D9LzzJHbc8z2Udm+iiknSkacovFdqXbvQt/TVQJG+pa/4WrKglUtmH0UMEM9/s9Dr9uKu+aTQxHPeLcvt/xPPOu6bK753OgCBd2EAMAwTEVc5Row9hquuusoNabR48WLccsstpGL2nXfewU9+8hN3+3Of+5x0/MYbb3ST5d52222SF4KDnp4e3HzzzTCshGD4+Mc/7h77whe+4P7+9re/TVqkl0olfOMb38DKlSsBWITFeeedV/G5ihg3bhyuuuoqAFaoov/4j/8IxPA3DAPf/e53sWrVqpr6GChceOGFmDRpEgDL+JoiQLLZLL7yla9IiZL3BG666SZXyX/HHXfgnnvuIcvdfvvteO655wBYRMWNN9444LJMmjQJF154IQBg/fr1+Pa3vw1Nk79HOef4xS9+UTaKjphnJA69VD2G9Ounvb0dAHDQQQdVVc8pv23btgGXKUaMGHsfyDi/lDKXQ8jrxqtSFkd0bv1lTrf9bVQUsor+Q+vL21Ef9mJuQW6XjVTk2+fq5GvgJgKJcJ2EeOK4lHwfWOmkSibQvf+FVbj0I9MwbkQ9trXlbPmBrbuz2G9imhZKON0o+Znj2SApjP2/+oMqr2M19UMO+ZOUBitVKQEXZ0PUufg/wO0cxYF7rEIFS5jXgZKQk0NzjsIbd4c3pMsfQZxKLG04+7xzjAqLFBZT3PnJmLgdvB7+PbkCnTy9Pq0Ghl2cqpY+zwwncZyCTKgkCcFh5a5QKlZ8mWYwpwLnHCY35XVP7DI0AacJpsK2AueSOYsoTdkcDhGil/NwkJSjzh5B5mENNBnkKJbUKggHJ6SS//HQpNBJ5xKqUpUHxS2fOBb/e8eec+On4Twbw8fpivr5mKLugtEOdD33RzQccLT1LAnV6FLPe6KY6cwp+f7kpgkoqnsPucdd/XiwMX+uDnoNCK6BtGDO/KHJfLeMs5ZQ48ClQoJymdvJ2S387uElUrXfPrAI5x4/hWiPvlG7X5etTrvffATpK/9LPv+AMjUkcUyFt4pFrAsnAXkMuFw4qiGIq60zH9z24a3PnHvb4ekF5L6MbDe6XrsXXCth+GkfQ6JlDACOwqb30P22Feqhb+mrSI8/AA0zz6xMZrrjiCOCvMKz1p9AvNL2xDGnLmX7c7dLhdtfvBNjr/l6eLsh5yo+KzSbtKgIvIw3htMn8cw0TROcEyGV8n1RHYLOEeSbk87zlDP7traugOyZ5K0u/lMoER4OusGR+ODwzTH2IdTX1+OXv/wlPvnJT0LTNDz++ONYsGABrrjiCkybNg3d3d2YN28ennnmGdey++KLLw4o+qdOnYqbb74ZP/jBD1AoFPDpT38a5557Lk499VSk02msXbsWDzzwADo6OgAAp556Kq644gq3/jnnnIOPfexjuP/++9HT04MbbrgB559/Pk466SQ0NDRg/fr1+Mc//uHmW2hsbMQvfvELl+SoBTfffDPeeustbN68GS+//DIuuugiXHPNNZg0aRJ27tyJRx55BKtWrcKIESNcHeaeQDqdxg9+8AN85jOfgaZp+O53v4vnn38e5557LoYNG4Z169bhwQcfxI4dO6CqqkvoqJFWOoODmTNn4itf+Qp+/vOfg3OO73//+3jiiSdw/vnnY/To0di1axeee+45vPvuu26db33rW25IroHGLbfcgnfeeQe7du3Co48+iqVLl+KKK67AhAkTsHv3bjzxxBNYunRp2XET5fv1r3+NQqEARVHwkY98REpAHoPGkD7+nAtYbVIap7xSzp0+RowYH1L4P9Q5Ah/G/SYG7Hb9e4QP2kEBZdkcej4Rig+nnv3RzZhi/YXzceSRCRHCCB/2VoLoRl8YkFxOE/qxkEqq+OW/n4ZsvgRNN2CC4dV36QRNnHOMafUIBwBo7y5gv4lw22SCbZmnq3A+uqM/8BkYmDB+Xs7sCpJCVqpEkX6xkLqysrNcS1V06tvJgj9DmpWcQuiNqkHHAXfatWESHg6mCZbOgIeFQKD60kvyNSRDKnlKCG6aPkWLbenOQhRpEcNA6UQlLxX7+LiRDVi/TbZ+GdFch4MmtTgSSLd3hB4WCBHJVbpwE6pPk+SFEHLuGu+6WPVYYCpZJAGTz8fpRyBcHEWLq6AJDGGE8sg+ZyVikD9yxDhyv9NsObKCvEalPEwOKPVNOOPoSXjklbVueIurzjxAKl+Lh4MfwdG1kKyCbACAmdNGli80hJDXzvBxOijpJYw0cz3Qdm9CatRkqw3hsW2RBV6bpinfF2LPAofg7XXKOspBSSrvGUZBVVmk3l9U8rvKf9PrxysbvWa49UXlJFEt7L4xObc4FmY9u8yQsCz23Rv5GkTV5P4QdeEnEXgnKUvSlG0yonw5mUwzeKLUe1SFfXIAbU/dhvxaSwFS2r0J4z/9MzAA7S/8VSrb/tztaDj8jGj5AIE8oOehRC74ZQx934wYF7eb6LHjwg+tfat0rLhttdtQdR4qAuETETNP4rVMAGp5eekG7P6M8iGVLJk8b0znXdglJwPPO7rrts48HnllLYY1pnHJyVOQSdLqE25yFDSCCDHN8MZjxNjDOPbYY3HHHXfgP/7jP7Bz505s3boVt956K1n22muvxXe+8x3y2A033ABVVfHjH/8YxWIRzz33nGvFLuKcc87BT3/608C39Xe/+120tLTgz3/+MwzDwNNPP42nn346UH/GjBn41a9+halTp1Z/sgKamppw//334wtf+AKWLFmCLVu24Be/+IVUprW1Fb/85S8lb4w9gRNOOAE/+9nPcMsttyCXy2HOnDmBnBvTpk3DpZdeil/+8pcA4OZTGGrcdNNNaGhowE9+8hMUCgUsXLgQCxcuDJTLZDL4xje+gWuuuWbQZBk5ciTuuOMO3HTTTdiyZQvWrl2Ln/3sZ1KZpqYmfPOb38TXv24R7tS4nXzyyS7xtHbtWnzta18DAPzxj3/E6aefPmjyf1gwpITDuHHjsG7dOixYsACXX355xfUct6rBYr9ixIixl0KwKIowVYs6WFt/ezMCH6Q89ENaLMPgN0636vitT0WLbgBo8MWkdzwcRKgqw/hRDShpdVAZh5pIYN7yHaQkDMDIYb5wSz0Fp3OJUhAVoNQ3eOhVD54sXayf04bZjYQpgqyeCS0ocxSTIQocUSvn3xcpTKgQ0Y24CmSfJWo5eopzScHqKdEFkq6UQ2nu/cHKhg6Wqo5wgFESlAYgCQdu6jSx5Nfy++aHo2xwFRGckUNAG/16SpqZ00dizhJP6Tpz+kjccOEMpFMqDJPbisFgbgNHG+mFaglZ+4jdXDxQBn4CVUrGG2iUe0MRoqAl85MwZ27AJli80BdhdiPHzhiNj562X+RZhCWNPvWI8S4B5EnCoa1+G7nZfwYMDfUn/xMaZl2A/7xuFl55dyvGttbjopP3cwYBnPNAeKQoOLL4xyXsjkmoygfi8eJgd1cet9z2Bq488wAcfdAoOOG1HKvfSuGENHNJKmf+Mu+eEYkIbxJ690BEMCy6zzLj7HjKcHst4T6lI4fz3OHCPq9LUhp7vnvEnnxGlIyc0WfmnYQ80KSTZ+Bkqxsr/3HvPuLuX+8Y9y1F4R4ELllSsQw+OcgFp3wb8hLmySdap1OyOGQDAJR2bYTR245EYwtMrRghpbPFvd+kjGGLeJnzca6DYaDn3edh5LrReMxFSDY0y+dUtj+qXWK3/TytepkSJ2aUYaA1uSTpqHcn0+Tw87PS2u5eSyKkUr7X+2ww7RlQ7t0ocAv57gfTxH//ZR62tVn5xnZ35/GvVx0RaMjI90HrakOpGOzQMKyzNk0zNp6MsVfimGOOwQsvvICHH34YL7/8MlauXInOzk4kEgmMHTsWxxxzDK655ho3yXQYrrvuOpx55pm455578MYbb2DLli3I5/MYPnw4Zs2ahauuusqN8+8HYwxf+cpXcMUVV+C+++7D3LlzsW3bNpRKJYwdOxYHHnggLr/8cpx++ulS/oj+YOTIkbjvvvvwj3/8A48//jiWL1/u9nf66afjpptuQjod4oE/xDj//PNxxBFH4M4778Rrr73mRn6ZOnUqLrroItxwww1SDoc9aXl//fXX49xzz8X999+PN998Exs2bEBvby8ymQymTJmCU045Bddeey3Gjx8/6LJMmzYNTz75JO677z48//zzWL9+PXK5nHuNP/vZzyKbzbrlqXEbNmwY7r77bvz85z/HggUL0NfXh9bW1j0ewuqDgiElHI499lisXbsWjz32GD75yU9i//33L1tn7dq1ePzxx8EYw3HHHTcEUu67uOeee3DvvfdGlikWgy/fMWL0B7QHQYRiVCoV5Wreb8EcjfIAtVNhWf9vqr6oII5si3nKTCbulyF6AVhlGZj9s9EXGikrEg4R160+Tbu5fvx7z0E3ZBnauwsBoiNggS8oCmS+xVGMBDXDMplCfVkGwST/Crm9ctexdgLDkp9xZn9EC7SLrI3zhbopo1hwi1FKKQZ9+2rk5/0DLN2A5PHXQh02QmiXklFqtEynVo3SS78PhEICAOhFsFSmrOxSk/52CAtHRCS8sxoR7psK9D1MGn7u+xsc26MOGoWLT56C5es7cdTBo3D+iVOhG0IdQnFCx3gnZBGbMU0wvwLedM4tXAnpKlm5E4KJcLnmwTvA8YKgEHlr+NYphbhJPnvpIThl5jhwbqJUoq8f55wkHFqb0rj01P3dMm6fAHIv/8Wde7k37kXysDNx8JQWzJo+0kqM6CMB1KqTRlvnfcoR4/DGYotkCvNwqLbtwfKqm57YgUvq30WRJ/BQ9njsMsM/SNdt68Gv7l+IP3ztDDTWMWEtqoZxQEWKYqusTWpQinVB+S817duS9cocppaHXwErebJwTv303ULBue/Eh3dJGKc+9+Tw1Qje+9RSLq4J9vPLqRU5J9z6CLTLITsqeVWoePTcbY7uoAIEBtJ/okTrlb4m+ddK6f1ReC9yiKwyr0lUmwBgFvNAQ0tFzzniZ0Xly4jgFu+cfRd6330eAJBb/S7Gf/onnr9qJcmIK3xPID0GnfqRj0ybWibf5aOhGcEWDZO7TyUyJ4othUk864NJo7n/1nN3A97k8E5RmP/2c3LD9l6XbACAl+Zvxb9ceYTUWHHLSrQ9/H8wC1mMGDUTwCypO90wQS5sMWIMImbPDuZCi0I6ncZ1112H6667rl/9jh07Fl/96lfx1a9+tab6U6dOxS233FJT3R//+Mf48Y9/XFWdRCKBq6++GldffXVomffff7+mY5Veg+OPPz6yHQfjxo3DzTffjJtvvpk8LoZ+GixlfqVjPGrUKHzpS1/Cl770pUHrA4gefweZTAaf+tSn8KlPfYo8LuYMCRu3/fffH7fddht5bOLEiRXJAaDmMfkgY0gJByc2m6Zp+MxnPoPf/OY3OOyww0LLL126FF/+8pdRKpXAGItcCGL0Hx0dHVizZs2eFiPGPgDnA0KMvRv1oRKwpOPeR4e7Y9DclQezbbEb8cvY6pMMWyApTAUFo1uUuyIzxtzPp6qJGbvvxnof4VDQrPao5jgAWyl46qzxmDGlBT/8m5z4y082AEBbNx33nEKYQg+IViz05wqGK1O9g4HxrUT70B+T54r0DPTctRKJ6uh79rfgeSv8j2maSJ33r27DLKC1chRFglaLkF9KKm2aMLcuo4UzSpHhF8jz0TX5rqcSjTohlSQNVlDZ729XW/Mm9K6d4H3tUFvGIHH8FQDzrJpMblpT223KzUztKfHBoTIFV54+DdeenYDJTVehIsd/Fs8B0nF523StpsVz8uvz/LHKvavu14aVW8uI49xT+PgCyhB1qd+e6AAdikhV5Lv66INHY8HKXe72yYePtcsF58uPvnA8MnXOdfIpMzV5XTF7O8Gag6GKnBrJcvklBIhJ668750CMHFaHtVt7wDavJcs7Su7TjpyAVxduJcs4CPPkiJKlkqWEwcQNja+jRckDAD7a8A7+0HtOZB1NN/Hu+7tx4iEjkEgqNt8QlO+MumV4uXBIYL+blwT21XEEdZXrtuKem8HnPGBbKdu/7R9W/HYnXKCPoLVhFnLoeuxnKG1ZgS83jcLve89GCdazTFUV/0yRJHY7M004ptbkWuOcj3+d8bUXDGtGK1BJ5bxgDEB7OFgW+R3P/AFmIYvW069H3cEn0gVDIJ1a2BpJ7JE9iux/QzXn3Hd6VuJty3tKUPL6+wphDPw9y1XCztUmIUxTqsWp5MMle+0gjvk6i97vPjKJdznKkMQuK94JDtkAAFr7FpR2rEN6LGGwRyjmmbDllAl7Cri5lnjwOkaGWBKU9tU+08M8HEL7FN5pqNxQQcLBwra2LP7f0wuh6QY+dclh2H9sg7weOfQh53hzyXZs3N6LM44cjzGjm5EvRBgx2POz88W/wixYpETz7iWYrE7EJsN71hhmeGLyGDFixIjCHXfcgblz52LChAn47Gc/i3Hj6PCjAPDiiy8CsMLQH3rooUMl4l6Jd955B7/73e8wYcIEXHbZZZGG6y+88IL7+/DDDx8K8fYpDCnhcPDBB+Oaa67BAw88gO3bt+Oaa67BiSeeiJNOOglTpkxBJpNBPp/Hxo0bMWfOHMydO9dVRF599dU45JDgx0yMgUNrayumT58eWaZYLLoJe2LE6C/CE0n6PjKljwJCsTtQfICrDBl8gkEOoVPLl0i40tcrwcGYZcfMYIVzYb6ks6T1u7DP7+HQm9NCx4cJH9bjRzViwqh6HLLfCCxfH51sq73b85xyySgwV1YWoVETQy45ISD8XgqylboMx5KUMVaTtwylSxBaB6FVgOex4d9fQb2IWgiVhUt/AMDYtcElGwDAWL+AaChEw+Xf5o5yQyhf6IOZCyeSuFYCdDrBcij0knuNOefhORwoxbzUOaRT0168FeaW97wmABTAkTzhn6qTj3s/XGtn01NKRiUx5dtXoLhjBfi4Q4HJZT4SuGdVHejb6Zf59ksF/dba4g8h2Je7xDhK06CVtycPpHzWXp4HJig0OamP8idrvvLMA7B6cxd6siVMHN2Ik48Y69b3I51U4c6HcrdviJWutvk96DvXI505qEwDQh3dDhUEjnRSwRUf2Q+bd2fx2F/fIcsnEwo4OC48ab+yhIPfC6SR5fFPDXMxTu3C68WD8HLBmx8qDGTqUsgUO3FMeh226cOxWJtCtjte7XTJBgCYIeRbiMJtDy/BbQ8DU8c24fwTJoP6FLu8fgE26yOCB0yPmPN+MFvJS0wG7k1heT8vt9hKZQur30FpywoAwP7J3TijbjmeKxwBwJtvQQ8jh/zwEXgc7pw2GYdCyeZ4OYin6fwVyvfMeRi9859ConU8Rl/2b+BNY7z7RRgEx7OBudb6YYQDR9cr96C0Yx0AYPczf8DE6ccAalosFDVYbn/SPuc1QORyyox92PscGQ6nTBucmAQRb4H9A6W4LuZtOSIIh8Cz0P1H3m1yMEVMOiy/g3nPq4gk1wKMvi6pHWd9ZuR7hb9LTr9+ANY41PIq6jOUCQN37y97i3PoenB8DcpzkQPWm6zglUaQQaIXpOsVBY4/PbYc763vBAD89O75+N1XP0J6Oj7/zmbc9rD1LvDMWxvxh6+fWYajt8ZT27le2n1Iags25QXCwRBCy8WIESNGFaivr8crr7wCwMpr+4Mf/IAs95vf/AbLlllGXqeffjpaWlqGSMK9E6NHj8bcuXMBAO+99x7uvfdeNDQ0BMq9+uqruO+++wBYIbZOPfXUIZVzX8CQEg6AlYm8ra0Ns2fPBuecTHriwHkwn3baafj2t789lGLuk7j++utx/fXXR5ZZvXo1Lr744iGSKMYHEVHeCt4x8cPEeQGXlQAI+yt+HTjERMiHXs1w2+UDQj5U5WpOnadcAPLYOYoO7ip/yfqM+MqkuvB99Db7PBx6+qoIq8Y5RrbUlS3W3l2w+iXGiHEnxEbUx3iFKogBmCKyjJUovyBZqfrnk6iYlSpIVoY8WMe/v8wQiEpvAFJy5cqErxzG1mXgz9+Koh4xV/QSuFEd4aDvWgel0AsotjKNOAduVHJecLWIPNclkQ0OioueQfKEj9ll4f6lQruE9+Hvz+tXXO+MnWthPvdLyxZ88TMwPvpdKK0TbcWQvz9PiQIOOgE2ea6O/M4uE+BKiBWp065vOWHeHk8ZWW6NlK26qaTRXkx9q+z4kQ344RdOxu7OHMYMzyCdtOpohHLKv2a4p0oprUzTDg3ljWFp3ULknvw5AGCUmkIDuxxZXn7NKmlGYBKMaqlHOkGPRcK2lB8/qgFXnLY//vHqutC2FYXB5ByNmST68hpOr1uBw1JbAFiK/aWlSWgzm/GR9ApcXj8fDICS8WS5q+8UzC8FLaDrWIX3Rgg27OjFHx5dhv+ZUEQzcfyq+iDZwg1dWMYc8oHTfGrIVOamCXDVLebuF9ZIlxSwmyiulmW5sH6xSziIhE74U9bLyRBSwFOcEpbqXpglTz6jayd63nwQAKBtX4Oet59A41mfJhr2hgMAOnuLeHn+JrR10QRuYf1ir3Yxh9LO9UhOPtg9v0q4OPochXMT73dHtlCCwRQ34ISGohxBxPw3zE96iB1XchLSs9QjbULbc/YQzwyzmPXeqah+xHZ5SLma4Hu/I5o1tZJQlhizSi84U0DlQfCeUZWiyncioaxBkAaGyaHpJjbv7MWophTq6qmwfwDnRA4nnxekM5wO2QAAO9pzaO/KY8TwenCfvA7ZAAC5go5XFmzBpFH1AIAGVoDOFRSRksaNuvT+4TcqCX0VI0aMGATOP/98/PrXv0ZbWxsefPBBLFu2DOeffz7GjRsH0zSxfft2PPvss1i5ciUAoKWlBd/97nfd+m+88QYKhcojCYShpaUFxxxzTL/bGSpMnToVp5xyCt544w2sXLkSF1xwAS655BLst99+yGQy2L17N95880288cYbMO1n0Y9+9KO9JmfHhwlDTjikUinceuutuPvuu/GnP/0JbW1toWVHjRqFT3/607jxxhsHLZ5tjBgxBg+Uol1WloYdc3fQ7YZ9CPYbhMaj5q/1SrskxsOn3AuWReC71FcQktJaIiKCIBX9jAMm0NIkP3i7siVPi2Fj/oqduPuZFUglVdSlVBx50Ghccfo0MMYwclj5OP3d2RJKmoG6pAq/MsCVhxQ85Hz6YQPJmB0K37efnG7M+s/1spBrBBsQx5kUL+Iicb91eLBsMISSX7lCKXSEPYYGpvo/7qm6pIBu39rrdwJRZAMArheBKgkHs2MrCg/cAuWcfwfGH0iGVJCsVUUtJLxbWbp1BC+PaIE5zGwHtNf/Bj3fDX7EJcDBx8FVXvuSPYelg/Wp8QEA+spXpX2l1/+Gusu+GS6Kb357CZqd250Ty0e0XNFwlKZ0WAjntH023kGZOSNDKikiEWajoS6JzNhmmLYykHMTY4ZnkEmryBetazx1XLOVy4JZ3gPcFOY6Mbe4oQdCjPS+9Gd3DzNKOLNuGZ7IH02eg4iSZof14c65WV4Mxx08EtgQLO+FSeJIpwgFmgAnctSnLz0Uv/77IpyTkQmxs+qW4dHc0biyYR5Z/4bGNzC/I0g4pPpJODjo6C2hmfh6GKEGw5lw3QrBR6437g9fqCFOlBG2nBwkksODr3lWF7Riy7Ai8jxtfViGELrcNN2wgJ58piyfLTMchTbzdol/rRrePdO70AuNY22/YBEOTjvSwmQ9LzTdxNdufRMdPbSygHws2Wu4P4l6sLLn0WGtjRwmt+Lnl329Ett2rp80PMErJ61b3EosTb3nhPbN5fG3QgNy0lGGrk+3TxEOhh0eh1TI+xuM2CVRHvZFLv9mwl1Cl1xvJSv+8uC+xtxXEUUFDOr8vPtMDB0XBsmGiCJ6I2AQITazBR3/fftrWLOlGyOG1eG/P3MCxo1o8M7FuXWJ5z8nvCa5riEJHZqg8jAdg4OQOeFgW1sWk0bW4/y6xbigfjEKPIE7+z7iylLp01Q3zEH8bokRI8aHGc3NzfjDH/6AL37xi9ixYweWL1+O5cuXk2UPOOAA/PKXv8SYMWPcfd/5znewdWu0Z20lOO6443DXXXf1u52hxE9/+lN88YtfxIIFC7Bz507cfvvtZLlhw4bhhz/8YWhC8xj9w5ATDoAVV+zjH/84rr/+eixatAiLFi1CW1sb+vr6UF9fj1GjRmHWrFk44ogjkEzSiUdjxIixd8MfrkBK7OeV8j6CQhWbodoHtx6T/P77AQ47LsjAMAz+UALh1vGVyu4oTyso7zsNV4USGGZRCRBUhw7zezhkSzBNDsWuxRhDT7aE7e05t8y4kY1u16NbK0sM/JcnVmDS6AacffwUpBThIzZwUtRYOucoaD3sj2VZURS8thRZwAChbj9AWvLa4Q/884w78tv1qPBJnENhDI71LddK4FDA0gmhGb/yhpOK4EACZgC8lAdL1QX6l4faVrSRCRBtJVDvbuKYD3qJlKEsijnwVa+Djz+ADqkkKI8quX4Ve0QA0N/6O8zNS6yN1/4CPu0IQE2FNOxb19wx5eJhcHCYW5bKVXev9+owyO3AboIgZ717nLsXLeQWCtnpyGYp8QMlHGtkk8OJK8M5h5nvgbb4abBUBsnDzoa5ez1481ioLSNhqzF9MsoQczOIYTD83m7JhILrzpmOe55fg7p0Av98/sHeccaEuqDJLHtfcfU7yC97BeroaeDZLqnI1ES4EYyIku7EO4d9T1sLxoGTmlDc4C/NLQ8HewqkEmUIB3vxO+bg0TjzqAkBAiPJDExJRIepo5DCwBAOoe0zQgFoj7k77UznWsFVGnuFuX+xcWu6M9SnAKYfhxxKpikgy4GJHVisTQlaG/uVjjwYQMwK4cbt94NwRaN4X4ttuW0Ea/jOUG59ztIdoWQDANJLjDM18P4V3kBlhyhbEGpfVI4lr4y/F+YeC74fOoe5dH0iO4jqnygW5uFglaNeOuk+wrq2SJxwubwwgaFF5PKan8y38iUxVfXeDagLZJUE49b6wlQ1MH+o9wfpXAhCQZxrZgRBIxHWtnw64eEwe/5mrNnSDcDyfn3gxVX48rVHBnKNkAYHRsmTiXMUt61Bx6M/x89au/BC/jA8mT8KgENyA+u2dePVRdux/7gmnHrU5EBzCYWBlXK4oN7yIqpjOi6rnw/OHc+kyi6a7oRUqqh0jBgxYsg4/PDD8eyzz+KRRx7B7NmzsWrVKnR2diKZTGL06NGYNm0aLr74Ypx99tlIpUK+S/ZBtLa24p577sFLL72Ep556Cu+99x527doF0zQxcuRITJw4Eeeccw4uvvhitLa27mlxP7TYI4SDA1VVcfTRR+Poo8tbk8WIEeODgjArWFsZLB4MUS7QuyI+2FzSoX+QyAtOyVdLo46SgvKckBXOkBTmQjmpDORtBsFyShhjMbG0pb6DYucq8KrTCkDRYi2ZUNzQHk5zc5Zsw8xpw2EyE0++sQ5Pz9kg1XcseBmAsa31IQMj440lVjzxLW053HTJDEFRTowDIOd1oPXzvrrlr2XY5ZYVLwLZQUx0ah6GxrY2OVggIDjRb6A+h7b5PRSe/S0KxTzSJ16N+mMuC22HajWotAB4KQdgeEi1qE9lDq4VYfbuhtLYUpkYeom0RqwE3ElEHZbDQZDL/WWK95tz4/BowsFHEhjrhDAtWh76hkVITDvOblG8b7lvzjjdyZ4HrsXrsHHguW5f19zLicL8yndPLicWvqumJ4kg96oL64p3xPmfVF4IPRQcExmFZ38Fs20jAKC04HFrp5IAu+BLSE6ZaW3b40/kfbYTKtMKMj9OnTkWZxw1EVAtIlQrEspYzl3PCGm3ocPs3oXcM7dadTcFQ2mVU5g6KGlE/HAO0sKXgSOZsE+cA6lktEl2rqC71/nc4yYGCAcVJpKEcr8cBsrDoRqlGScIQCe8W6Gko7e3gHEZ+RnhWA4H7ilpkwteReLj0prjlCK+Ve0DNNGqOsJjBzRRGygL6/qGvYE4bXFvI9gAUck+DWzZnY3s3yzmiJ2G0HdYJ7IQcsgj713CkkNec8o9S7npnIPVr2kayG94D9CLyBxwbIjhRcgz0v+OYo9N8NXRV58T+6RywrOBIL7NQtYqS+UJgD1HTXHcQtbLKFR0fYLHTN1Rqpct6l0DP7kH2J5hVJ1gW0auB9mVbwHNY4MVTANOLizymSw1bAnqkA+Uh8NTczZK228s3oZ/u/ZIrxnnL0V+6JrwrAS6X74Lhk0qn5N5D68XDkI3b4DBrVBlX//9W9BtGZgaVImoigJ0ynkLx6g90HSOVFqWR4T/OUKdZ4wYMWJUg0wmU1H4cz9mz549SBJ9MMAYw9lnn42zzz57T4uyz2LQCYfXXnsNDz30EJYuXYr29nY0Nzfj8MMPx+WXX47zzjtvsLuPESPGHoPzgi0q1f0fhZJaTt7vxOEtp/Qivz73TgQs86kvOzgf2aILPA8c85cJA2OexT44/QEv7tB7dqP71ftglApoPPFKJEdNxfCmtEs4AMDvHpEtsv2oE0KGjB0ZTjj85/VHQTEN/OS+xe6+1xdtw+cuneHxPf7LK45F4NyZMKbMPn/mDnF/Qi2FjnPY9BM+9OWwBP2Zq54MuVfuAGxlU3HuA6g77GyomXq5ff/tJY5dKaio5cW8bwePENlry+zrROnR/wV6K7MOB1BTSCUXqYzVPRVSQVRw+u8te65Lp1POw8GuQ11+M9tlK+bhKkW98n4yL/zas/phgVXA7NkFtWUcLQt8l5aLtIjYn2+d9ASlZQolxgDLspz5ll4OY/v7Ltkgn4CO3Kt/w7Abfu52aXn2EJ5J1P3Nqe0yijlXOYqQkEoaSvMfi2ijcsLByifhyebGoieUYCq4m8OBcyCViCYcDJO7TSfU4HipzESyBm+FgSIcaDUlDa5rlmLWp7jdtL0Hz991F1r0XegYcxxuvPFicHDkVryJvlfvgZLOYNQl/wo25gCrHXeMnQ1rThZXvw2D60jPOBlKwvPGo4hEFSYOTm7FiLYerN7QgkQqhQPGNYA7OUScfxw+2eSAnU8DXglbBCHptbvWm1C4887Cgs+nMoSDY33uKGNL29fg0LV3oqHRwKO5Y9BlBsNEBdZs/7m7Sv+w9ygunXsFnJ8jqd0Eta7I6J7zCLpe+zsAoG7K4Wg++lwodQ1Ijj8ITlws+TXEJ4jzHAr0Qb07Ifh+41vAA+GeiFxAphNSiehDa9+KnU/dCr1rF+r2mwkj2w3GgGGn34DExANsUlmYq06bnEMR9ovvYmQYvJA1j+sl97zKJyHm3hgI15oBAKFgD54zh6mVsPUv/wWjt4MuLc63KMKBQKW5DV5ZsBlnHzlGfi4Q3hR+orG0bZW0PSO5DW+VDoBpcrw0f4tLNgDAr+5fGGjvsdfXgR1QxBm+/ZpuVDD2HnTDrKp8jBgxYsSI8WHBoBEOpVIJ//mf/4kXXnjB3cc5R3t7O1555RW88sorOPbYY/GrX/0qdmGJEePDhAABUOVLNvGh5ioYSAWiG3OkgqbLJG92PsyYsO0kPKyJ1IhQ7kUVCynkDzsVsKsUdHNM3HCUze7QEhbNtvKh84W/orDBChujd2zHyBt/gpbGFDbvCpMriHTSe7QMa0hJcddFzJzeih27+kJaseUTE07ac4K5Cl07NACjPry9bSpftlQsQv/nt1t1SIugQpmoK3UsXn9KGkFeaTctudm9U9o2dm+AOvkQVwHOWFB1ysT+iRwLXMt7x+2yZvdOmNkOJMYdKCvjhLmYe+3OqsgGAEApTyoMKoJiza/wHA6WkoWX8jA3LAVrGQeMmAQz3wP07gRap1iho4CyuSaiwAu9rnKRPO5alfq9m3zLm0aEt2rbBIiEg5874HYjAtMgKewCCkG4+UkIQb3rymElb2VifT91YjWiLZsN/Y2/EQ3a5fo6BIEtECkcSKV6FPxhwpzhkMQMCalk9kXP02pX+cAziVBeKjCtRNAmB4dZlnAQoVI5L2r0cEgziuCLeC6FyYTK79tgyBYL8x5/GOercwEVMDrXYdXqmZg+eQT6XrkTXCvCKOXQ+cp9aD7ns8ivegd146chvf9R0n3Q88pdyL37DAAgtWY+Rlz1DTDGsGjVbmTX7sJUnyxn1i1Dg1ICNgPr17+BX/VcgEtOmoJ/vmCGX+rAeWzY1oN8oYQZU1vKKOXtOSkYC2TzJWhIwh8Oyq3Bg7kIuGmg/YlfY3i2E8NT1pj/pc+v9gTMUtDDgTtroKhkDvH4cZT95HlILACXh0VS4DsleMCZ09Q1l2wAgMLGpShstAwWhp1yNZpOupLq3O3SbxDhHfD/CpFTOkIf4Ho44eAnygCg5+3HoLVZSdzzq+e7+zue/SMyn/6pVY/yuChzm3Fii5ozonei+yyQdgithV5fBMLmyfWdnxx9S2aHkg2ANY8cL9uwOe6JJMpquqGGotDE8jCe+wU2zs1i2FHnouWky61WCHLX1ErI5jWkEjR1rNs3mWaY2N4W7T3kYNWG3TjDF6FNL+SA5oz1CkNEyPNfat0whQdVjBgxYsSIse9g0AiHm2++Gc8//3xAEST+njdvHv7lX/4Fd999NxKJPRrdKUaMGDUgXBEvaNSqUdSTFoAVMQlVEQPliQd4YZDID95qwF35pL59xIzf80CwIYQ0nt5JCG4LxAenq/Awg/Wo37ZsDtkAAEbPbhjduwKJo8uhLqVa52iaYGCYMKoR2bwm5XkAgJSqkB+cpsmhiGFWRBeNEPivpxuOpgIvkGBj/h1R/QqHSRnDtB8+ZTGDpA2OtIajDoXOZ0LxAdAhlYo5aay0jYuRf+5WwDSgjjsQdRf+p9wP5zB726BtXBxoqxx4sbKPfRJ6EdbcoGI4a7YCT0P+kf+x8kkwBezYq6AveRoo9IE1j0by8m8DqWbL06ISeYnrwfO9AA8htLh43zr3vulzQLGVYwZBOPhCLPm0fcSUdhQ+3HeLe/utIp6S3v1BKsOcey9EYcV5JNngl9rpgkoarSrePcptmZ2//j7lzfBtMkeJoQHEvBdh5VapDRwmuBlU6qtMHvNkmRwO+41vhjP+qhL0J1DBQ8iDaKQJDwcFHCYY9k/sxEHJ7VihjccGfXRkOwlWDeFAh1Q6vu8ld7/KOHbPeQL7NZ4rrUvFbavRds+3wPUS+gC0XvE1pCcfDsc63CEbAKC0YQmMbBe6tDr88G/zcWN9FlN9j60GxZsT+yXaMEHtwBNzGD52zkHy3LGFdSzOn317M/78+AoAwCmHj8VXrj/Ga9Q0AUUl5yrnwEvzt+APj60AGPCtGX3wm1cFnhb2/VrauR5mttPdPzO1GRSMAhFSyZ83g/PQ+8GL9e+/l+CtF8Ij1AoDGP1CVNy5HvnNK9A4/SjoEYrq7jceROMJVwoKW+I9x5XHeQ/zrV9REEgfqR1fu3QOB3tcCQV6fuVcsju9favlMZeUJ17UOmY9qwCjlENpwyLUjZyI1KhJgiDE+5Ht1SKfF0IJFbuEVcYEmGI/D0I8HPzyFreujmjXd49XQCBwvQQj3wu1fhgM4vz8OK1uBQ5JbQMKQO+cB5GZPAObzDFIdOfgz/LITB1f/sXL+OHnT8L41rpAW4ZNOBiGWfF7bYYF751SLgtgREX1rf7k9/IYMWLEiBFjX8GgaPkXLVqEZ555BowxKIqCyy+/HBdffDHGjh2L3t5evPbaa/jb3/6G3t5eLF68GA8//DCuvfbawRAlRowY/UTt1v1uA9ZfMeZ+IKxQoJL0x9sWlPT+D0eUtzgP0azZzQnK7SrqVQexHUcZTnwo+sgIaT9QXpZAfYfkiPq44zDzQSWwWcxieFN1CaisHA7c1b//4KbjoRkcN3zP83hLqAyqqqCgBT/2C5qB+kQSsrrSgn9YGGN4/u2NeO7tTRg3ogGfvewQjGhK+T6a6Y88KQ9EGTAW/Jy39Ii1fUBKic5tZbCkoHYVQQ44EBKf3xZQEsXNmeL247tfNCKkkmgtyzkKL//FDZFgbF8Fc8cqKOMPkupoi5+NPM9QULHHK4VegrHmLWjzHw4es+XVVr/lJa/mJvR3HnCL8J5dMN5/HTj6orIKaA8E4VDo9WYoNyEa1/pvQSckEanco5Jn60Lyy7KS+RJScnuuUOuZS0jY1sgCnLTLweWHu4rGaKVWmHzeD0pX6ZEQ3JWv3Hn7E0q766g9vmR+ENOomGCqCIICHdwKsaU5OSwEKDC9W9nkkTkcGAPOP36yO2aqqgSCJynMRKYGwqGOqKOAY7zaji81PQeFAefWLcXPei7CVsNTjaswcHRqPTSoWFSaigQq967gekmYMeHXtBl9MKkwWMK90fX8nzHmM7+x9hPzw8h24eWlGjgHEhV4gAxXsthqjEBRM9Bktyk/6a0th2wAgDeW7sA/d+XRkrGSp0sUsykouO0b6Pf/eA8qTGhIYPm6NpwS0IFKi7Z7/1GEMAVOrKNOWC8utNfTE3y2S/eIxygE5ROMFxhT4BARTHjGOEtNcddG7Lj7O4BpoOf1+1E3+bCKzkO0/g48+ojnlySzKCcH1m7vRi5XwCFTW0OcF+UOqJBK3AmpVKUXnjVfGyqS10ombV2HXXd+A0bXDkBRMeaqryE5dRbAQOaiobxa/P14zyF7PSReYZlCE5/WtfTW17IrvqF77ZcJqVTavQltD/4vjN52ZGacAmPcJeVaxzkZOdfOrvu/jzfzh4KD4ZxMsHy2r4Bn527Epy46KHDM4NY5GybHsIbK3mspwkHP9UkEqh9+/wrXwyFGjBgxYsTYxzAohMMzz1hWR4wx/PrXvw4k6Zg5cyYuuOACXHvttchms3jooYdiwiFGjH6g36RADW2KHzKBcpSySFSAUsQD1YekqqAshGolAkLktvthflKglh78X81hJALRfxWd+AgYp3Y5uX0f3PZvoy9ojWjmujG8aRSa6pPozVWm5EqnVKl3BifmuYdU0vrwo0ItlTQD9cKHJGV87SRbbu8u4N7nV7m/H311HT598cFeQZvUicrfwDmHQoUX8IE5ihXB0jJQJsrRwKnHWe38VYijhxMeofz8sZW6ZNLovNWCfal4QQ53Zexc6xEOJgfP90Bf+Xp18jt9FcNCaVWAbAf0l/9Et2uHCTG2Lo9swlz1pkU4VKCADg3FUQg/B/J2D5uCpPVxCZybMDcvAddLYPsfB6jiPSuU7dkNo3MLkpNmAAm/BqYaLYc/lrq7tx9NeifNs11Izrkf/9q0Gc/mZ2KtbiUgdcIGBYkGYv0X1y6XXzDBuSoTPCE5HMoRTFXlJ+CQLJCLz99GlvOHIHLWPhH/dM4BmHXQaKSYgREtje45JhQECAcVJqkEKweqjgITl9fPd4kghXFclFmIP/Wd5Zb5ZOOrODxlhZB5pdBWg4eD/KzR9WB9zlQ8PWcDToloy8x2erOgFLyOXe1d6Oq1PmsSFYR90m3TesnLjtuJgJ3HKjEftrVl0TKxMbxhu0rfqvn43+H3Q4WJf+SOjVjy/Qp1ToeLI2ASORxg6q7y3ZlHcxZvwRH+XiUyKHiuUXeCqJR2r4lpomP2XV7Sal1Dft3CSPm9pPfBnDdcvNcBIV+OsJByORnyU3M24i9PWgTRabPG4d+vE7xR/CSlA0KpbxSzFPNRFqZGJHTmTghLqi2O3Op5FtkAAKaB3U/9DuP/9U9WHZ0IG1TMI/AwCfOiEAkDLhyz3M0iz4WLdaLKOYQN5+CU16GA7tf/DqO3HQCQX/EGEnVBQqqSIT87swwLilPJY0lm4Mk3N+ATFxwQOGbYY2YYXMjNEo16Yt00nKTiCImp5INu+owCYsSIESNGjH0ElQeSrQLvvvtu2Yzg06ZNwyc+8QlwzrFixQqUStV/PMWIMdTYF5J+ceEDziMVgv9Rr85lx8f/BUO141fSRzdWwT657XAZPVncUCeVtFkOgepeeJPAuYadku/DV1IMhNThbpuOp0HIeQgfpnpvZ/BwrgcXnzQFf/n66dhvXFPgOIW0q1DzxrRUkj/qUwkF4Bz5YvBjv6QZYEyO0w6BABMVi8vXyyTJy+9ulc61PMdTyRd1+WPkvHKvc+WNO9bn8nEulSX7Ygrdljh/xHlEeDigmIuWNZGU2jC2rag58XO/QipFwVF+lEsGrVheOFGWxBLhSV3efC+CMauFNaQicJJwgF6CPu8RlJ77DQov/QGll35PV92xFqWHv43i879F7wP/DV4qEn0780bYE6EE49yn3OPB8zGrsPx1nhmldx6CsnEeDkzuwKcbX4FqW8o7CZVD5a1I8edXIlLW8lpZD4dKk0b7laOmpsHctY4sqzA5Z04ycL5AKqFiQqIbmXfvR37eo671OWWEbBEO1XtqUB4OKuM4ICnngzkoud39Xc8KLtkAAKfXragqh4MT4syCdR3buoNK8rXbs1j8fiWJgqyRNCkPrUIfWpotF4JKPBx07hEO/leRqFlgGJ7HCgfH1t19eGbuBqzb1uO2wwF0vXYf6piOJDNxTcPbZBisJ19fh5/etwSz51tj7Ex1ytuDglmikkYHz/3J19YEyznKcXGf9DwT3onsA6KSVq5nbRW3rKxIbk8GT/6KXq8DS5u8wyEbAODVRdvR2VsI2qhAPE/Q5CQVqqoS8SKuGz1yQGHr+9K2me0W5CPIOemay++Qobms3N1W8mLLYy1EUR7WRgjcXCCg557YSmH1PGlf0453iXKVrcEHJHeQ+x0PLJMgJZ313TBNkvj0Y2ZyIy6qXxTYr+d93o0h/TgwjSjSKUaMGDFixPjwYlA8HLZu3QoAOOmkkyLLnXHGGbj11lthGAbWrVuHgw8+OLJ8jBiDgcHwDhh6cFRrMl3+vMu06XopAAFLq5B2Pe8B56XbH17I/3EET2lL9S32VzEZZH/RC3IHvBECVXjtFumBdmQTdb8Vn1Q2xNo3pHGAc+Q3LYe2ayPqDzoeyWGj3P1um+JfX9s64eFgZLvBGEPv24/jxsIz2NQ4HPdnT0COB+PjOrA8HASlP4CS7+Ouq6+E7/z5HazY2BmoXyz5PlpDhp8xK1+EH725EhozSWEKOx96DmHhy/cA5k5Zf5ilwFRm4ddEjkLjHSsufRHFhc9AaRmD+jM+BTT4Y/+G3Wuiwlu4r4g48a5FrFvFue5MOl9u6CgufxWllW8E2whTZNhgqi9icn/CIg0a4WBb1xJhMiQwxRrTKAW0aVpWoGEEa6HX++3m33BNo4PrlJiZ2SkDhBMO7z3vbhrr5sHM/ROUxlahL6A49+9ufbN3N/QVr4AdfBqxlnr9O+ud47EjFSEgKoy5rlnKuPoIC+9AXeue0td4sc8blBIOTm7DMm2SNz/J/v1rvX8/cV04QpJG6wCRoFtEishzAADTJjRj7dYed/uTFx7k9s05J+9JB46C3jRNMACpRPBeTykGOh74kec1o5dQf9I1SFBJoxlHRhmYkEqMIA/EESVJiipCKhXzRSS3rETfgmeB5tFYM+Ij6C0ChxKyXdXwdsXtUrkLWLHPNdquhBRxzlP0vhMTknOTgxNmWW48dnDs6szjv257C4WSAcaAH37uBEwf12TN990bpXqTEu2Btu56zvLOe3dVO6aMacDBk1usA0QiY4agH4BJhlRy6nL3/qaSjIvKcS7eWyKpwD0FsJMg11WcumtcpTRdEGapAPBG6QnorrYCe8NNHlg+PRlNmFoJCqFAb+8qYESzz+uLu/9Ym1TS6NDnU4iLodtWSeiDEFaUwfEqStK5BMLzSwg5HBzjIH89MDDOwYUwjH5JwgiHwobF6HzuT4BpovX8m2j5xf7EHA5RnjnEWFBJuSudTGGv5AlmWHOGMCZQHcLB4DDM8h1dVr+A3K+++ReYR51g5cEgvFD8XmCVJMeOESNGjBgxPowYFMKhr8/6YGppaYksN2XKFPd3T09PRMkYMYYOe4qA6G+/Ayc3pfwMfKqQ/ft22EX94ZbI6uUJA1Ipb7clkg4S98FtEVhQPrIPoS2frNWPL0GKCL/l0E2Qy1Ykm9eOU6dn8Wx0vHgHAKD7rccx8XO/hJKuL9O+t02FVOp76xGkJxyI3jkPYDgDhqd6sU0fjmcLR4ScN1CXdj5kmZs7QtOCH1wU2QBYhIMzMtLnvWQpa5WgktBu2N6Lw/Zvdccq0mLOJSQ8xbx4XcTrLs4hh6Tw+CPuK2/9NXrakH/1TgAcZs8uFN59CnWn3gCJYJPO1Z3UQVmd8oQSopIQHIxxZF+9C8VlL5PHPcIhJFGwQDhwAJxIdlwpBsvDwVV+lPNwcEJoRYXYMXUA4XGeHcLBuW5c0ogxmTPkEbOQShpNECFmbzuURjHtLIexU7Zc1jcuRPLgj4RJTOzi0jy0zoJepM3unSg9+0srhNOkCmOzR9x7jjKbUqr7q7ped1oBhRXvgNU1InnACZboJgdT5FMhczgYOshE4wL8VugHTByGAycNwxWn7Y+l6zowZ+kO7De+GSceNkYSMMqyWXGUs/ZJJRNBLXZr10opRFfh3SeROfEaOsk2TDLMRznUEXVU4vqYZZyfUxV4Dzh4+91VOHLxs+4cX5HfgqfzR+LXvuzJx6TXV9Sem1Q5FwxnZuR7UdRsr5kKwj45pIRumILtsXPvWluUFXTB9tbjAJ55ewsKJSdnAvCnR5fh/75wPKj7Z6TSG9h3WnoFpid3YElpMp6eMwYHTZ4FcJPMsZOAAc332cYpDwfTk0+sGyjnKse5/BfCZsi7msz9BZXelcKVP+q9J/jThd7bjrZ//AylnRvQcPAJYJgOLszfQB1OZPuinqdaESb1TFWUyDwF1Lojvb9IZA4H10oobFpGtQRTK6Ln9fuDR4hr7q+rdWyH0dOG9MQZ1rPORzww51z8NTlH18t3wcxZ3+SdL/4VdZNnRHdn6N69E+XhQJAL5L7o3soi6Xg4EPeQaq8LusmhlSEBGDhGqnTYRGZo6Jn7CJpOvoY0bvDfb7yQRX7lapijJiI5pcx4xogRI0aMGB8iDArhoGnWC1cymYws19joWcjlcv2wlIwRo18Ij+fvlhCUiOXKfjggKnYFJXfgeFg9cReheHe/eARFjGC56irjQ0kCXvarhPs+7NwK4sd1aLx9bofZF2WtDSSxICrOfWXp+lRdSNdG69jmkg2AZaGXW7sIjTNOcrXigVPxKRoMIqQSALQ//GNp+4L6xdGEQ0qFpbSxEuUVSgZWbAySGX7MmNKCKeOaMaY1I8gGb64gOBVNwkpt3fYei3BwKofMFYuroucBY8yySK7g2jOPdfCBQ1v9lnSstPRFi3Ag+w0h1fy7KOWmHbc7eMvI90EY2QD4FBmEEtzYshzqASfAisbIgHzthgKDHlKpnIeDbd0ZmZzVr1hiqqyw5tyy0E3VA9y2QDZNQBUtfsULErI+RiSNloraiUydO5mc1pxITslhe0WINsR+MfykAwsc05e9BN5jJeI2NsuJPGuBky/BiTDkroqmrYyDGFbN+pV97jYYW62wKeld65E89kpHSJlUJqyWo0gBB3U+D4cbLzwQk0c1Qk2oOHbGGBx/yBgUNQOmbnjW8JyTVtIOVJ/ym8rhkDKJd2DOQRnqV5bDIfjgCksaHazp1aNyIVSTw+FIrISoezsvsxRP54+suD4FzoFSPjheZqEPJfuerSSxtWp7q+mGPAZivhCHwBCRL+pumSVrZK+FDTt6XRnD+hPx0QYrxMzM1Gb8af0IcH4E8msWILtkdqBsgpnQfE2QHg7OXBSe76SHg15yn5+uI5J43HGPYM5zkrl/rQKmV65GNTGlFLYahXt/9a16Bx3P/z8gkcKoi74AdewBbr99C59HaecGAEB25Vs4JFmPZdpE6Rw4YCe4FhoXZQgjpylioVzesUCYKu4SDaK3gTOwO/7+Q2i7NwW75hx9bz+B/Mo3A8f0rp3Y/Lt/Bcs0o/X8zyE18QBY3yaWV05uzTzsfuzXgGkgPflQjL76G5SkpPy5bB5GlxdizejrpPOEiC3Z42edZvjaQHlrkOUrnEphfjVOOLVsX/DecL3NDLOs10E5L6metx9H00nXkISVuEaqMDB5/i/RXegEGAO7+utoOOCYQJ0YMWLEiBHjw4hByeFQS5x7I8IqIkaMgUTY/JTiDEeW7a/9TRj81mVR+Qb8VWv54CursY/eL/wNJPqMGDcv+TN1XFZ6QfoP7ocb1W6IsPLxSAKDVvZX1k9YsxF1QqwKA3KRsgjH7Gvfu/iVQG29k4pxG35d9T6acKgWVoxyr59NO3tx+xNebOXmhiQ+ccGBUp2jDhyJ737qGNxw/kEY1pCUw2T5vvGZsItyi9+yK4uw86yZLHTusTCCorZWQ+YBp8s4IK23nWdo2P0F8DIhkETCgbKi1Ne9g+Ls2y2F35x7oS99PlCmYkQkXO4XKvRwYE6opIiQSp6C2hlMQgFRzJEX3792c++AtI9XRTj0QtuyAtrLf4Lx7mOehbIAc8dqFB+4Bdk7v4TSyteJZ4igYAysb56CO3CcA8ayl4JyloHhCysjwlEMWelH5Oev279wf5g9u1yyAQCKi56hG+acJhfKeb0ASEGTFFkjh9V58nBRFW1CGp9IDwfTjX3PTZMMqaQQeR1C22O8LOFAKctIwoEgD0xBMVoNuTAUcK6Alg8SljzfJ3g4VEA42GNU8ofwszoCB0IIB80t05ihDasqTfos4oLE2+h+40G0P/Eb6LvWB47vn9iJy+vn4ciUd4xSBrseDsL0pAgHcG7l2CDeLah3T7Fccesq7Lzve9h217dR2rmhpu8+AOClIvIblqJ79t+QWzk38D7JTQPtz/8FRrYLRvcudMy+U5K/5+3HpfbOzSwJnKJ8DsTfkHuXUpCXW0PMMjlixM5LO9ejsGk5XUQroHvOI6FNGNlu6G2b0f3GA4FjbU/e5pIlxU3LUNq2ym6UOxyRJQLhXXDHY4sIYcp9K5gW0c4R6f1BeZeNaFuE/RKV5G4JQgkhBBwPh96eoEeR59XEoevR51XJGgLQhJVIeB6bWodUwX635hy7H/9tRe3GiBEjRowYHwYMCuEQI8YHFVEfTWXj/A+gDP2xGKu0D/Gv9Fs8T/8+aT+hyAooTYX/QjgIDjmhZnWn7f98FM+pEhLE39YAjnkUCVP2A45QOHNxPIPQ2rcG93Xt9PoOXK6gYtoYIMIhk5Kd5+rr5O2GuiQmj5FjwDthKSRVnEgOcCskkDwWQHN9UOHjtOUvG0o2CGMQLCLWF8pJhI9YV07SzfwhraQ6hDYkgndwd1MKEm5GTyvOYfa2RRQAzHyPN14hYRuM9fOhb34Peg3KZ1meQVJkOp4eFYZUikwibCfDBGB5CBADbOpFd3nzUwyce0o6XugDz3V5R522DC1QEwAZUsXo2oHskz+DueFd8CVPQ18SQvjkuwGtiMKb98qkhLvu+/rivvuDjG1d29qYfeQHMLa/T1t828qfhKJAWzsP+ad+Dn3ew7bXCaEEzdLrk/dYEkgAKhFsBYSDwoB61Vo/Lv/IfsJaZlsQh96T4W0fP2Ok2wYAMqSSQoQ3CRvzSjwcKAVzpR4OpuThMPDGQGGKwkpghazh0IkcDrzY55IHlGdGmBy6YXqKbt9wlIhQgLmC7pZvJJ4/AMp7WBEYxnvQ+/ZjocdvanoZZ9StwCcaX8cRSYvIo0hkbuheqBpuJQlOhlxHU9OENc5EcfNyFNu8JOEckHLGANZ60fns71HasQ7FLe+j4/k/AwCK24KJqctB27EGO+7/AXJLXkLnU79Fbs27bh8AYPS2w8x2u+VLO9a7pisU/POZJE58i3UYOWRq1edJgW4lSTeLWZi2YYAngtyx1r4ttBmTCBlGobjBJliE93O/115x+1pp210vCcJhyfKgt0Ul66Yz36OItrBjn2l8uaq8MA7SIfl2HKKgtydISjreZoZpwijj4VDJ2sfBYRLEv0j47peUCRVzsIwtYsSIESNGjL0QMeEQY5+Fq9gnFLmid0FA+V+OlOinTHI/1MdSkCyoSQahnXJt0vUhjAVBPgT2cOl3wKpOPF2X4KA6df74FObiBxdFnkj1KYW+vyvns7YSsiICJNEQVPYHZSXqS/vlBjTBDd6B3unbF7Cml9s2cv3PpXPw5BaMGm4laWTMIhDq0zLhkCvoyKTlsCL5oi7NPyniMOehkQwO3a8V/3X9LGlfSTfBmBAUpsw9bOViEBNHC337IIXHsuXycm0S5dVgDgBaoVomWoPYNhUnWlBw+dtxwkmUJRw6t7vKiqg40fnZf4pspyYMGwvWPMoNdRRApZ4pnENfPScyia9d0PpTaUilMILEJixI8taWx9ywAPm/3wLt/q+h9O7jvvohimNi/IsLn5Jk0uaHW8A6bZid22AWeqzQT65MRFnu/yGeT3Q30eAovfh78ojqeAlsW4b8i3+AsW0F+PKXoC19juw4ihzyUtvY6zaVFyMsfIsP//upw/HzL52MS0/Zzz4D/7Xn8r+cg0fMt9OOGAMxdAhJfIbMbyrMXiWEg19ZpsAkFXSUJ4REOFSRr6FS/FfzkzXXddYlvUCsT4XqPBxGqz24tn4uUksflUITmdx0H69hIZXMQh/MUgFNmWBkWt0wKlPS+lBN6uXrGq1QO2S4m4BilyMZopz1lKUcOx/+CToe/jG2/eVryK96B/73YqsYh5HrkcLulLathtHVht33f79i+R30vHaftN32zB/FrkDmCzNN4bgM/3w2xXXbfg8IeJ+FeTiUKlsvpP60Inb94xfYetvnseXPX4HWEfQyLbVvw85HfoGOp38X3k4+aJ0fBs4NZxkiZ1C+pOOuZ1finudXI1/0SHTqmdbAgmssmdjZX0aziEA/OSXiy798ndzfqBRxQNIbJ7PCBw4VogzwyNZ8X5BwUOw5rXZuQroQTOIutV9p4nkypJJ3D5q8wneXGDFixIgR40OIQcnhECPG3g0OLykrkZuAc+Hjnyrjq8H7n6zZJT7ClA4RfUjK9QgxouUsf57BGjygfJWacr4FAkMcfp5Ou5JcnPmU8ZRSXuyHOJcQ7wJ/3PKAfIHfoWKHoDryJ7BPVAI621T4FsOE3hNUJmtdOwVlO0FywL6OdhkqHnQl+NI1R4AbBvpyRZxx5AQAXJoCDb6wE7miHvCCKJQM5Ao63nxvJ0Y2pzHrwNFWO7640oHTZ0DKF46kpBnS2DEWVOW4iZ3LXVT/chBVUCpjtU1Z9fF8D5Aa5ZOHJizgvx8gkwsuDMGrwx19cc5wmMQckbsyYbRtABs9LToxZY3zJApsxBQ0nnEjNN2E9vodMNbNlwuk6oEK8z5or/21bBmulayhigqp5IwzBxl+AgCgl6zr7FvWOAe4rRAx3rjDtbjXFz2F1BEXAvVNFtkb1r9WLjFoZSi8cTfMHauBdAMy53wRsOOfu0K665v1jzd7OGDaXkVgNSlQPSF6yZwdKjNxXcMc6C/IFrildx5C+oiLBDntv2HkjL8cQHs4FCpT4tWrBlhj2qpjk4oukWGa4IaG0tsPw9i1DqkDTwTf7wSwiPFpTMvrE73cV/5waVXL3wdJZkjjkQQtH0VciGRFJZ4C1WJ8oqvmuqZNNBjEGsSKfSjZFsyVyH1J/ULrx7rV6HheR9P5/+Itn7ZiulgMjtvUbS+i/fY5YOl6jGq9DID8fOvNaRieqME6vgo4uUaocfDuVW8ChFlr97x6D1rO+xxKO9aisN6xljfR/eJf0HDw8V6bnFueT6pCKpX75j1OKrCrhZHtsh9bHHr3brQ99utAGa6XwJU6UA9kf74UXXfeHbl3Hr731rC1zSybnDmI/LpFyK6YY/XdsR1dcx9Fy3k3SWXanv0jilvej2xH79xecZ9mIQsoaXsrOCYvzN+KJ3ZYhg9bd2fx1Y/NBExOvptQhENkmCSnV1O3CDMevg4WCiUgQx+rg3e/aESi9mrgzPV8lnjmwMQ/N7yJce+swxgwbEudjAWl/el2yoWTS1kns3rjbgwPyODV5bFtZ4wYMWLE2IcRPwVj7LsQrZ5J63JBwe2U7acHQ1AEKlauLFeU90HZEFDhR+W/hJdH0Bo+qrmIMm7TXG6Tc897QPQmqAah/YZcK78C399WNR4G1YALnQtEAnm+IZ4tkkJbJHcAGH0dpJWVme+FURA/usTrLvfBuQleJjkghZEtGRwzYwxOmzUe5x030U0YLZ5POqlCUbx9mm4i4Ytjnivo+Nbt8/CXJ1fi/+5djBfmbXaPeYlj7b9ifgfOkUz6CQcqESEPtiUmKie8HuS6EfepsM8h9dzmCHKAO9aL7ikFr7eZ7URhwePQ1rwdtMCkFCSmTrbjhHkCAKOMhwMA6DvXWcVrULb0C2oSTFHBmAKWSAcOs1SIpqJW2BbwZUMqAfalDlG66CX45wIX1zsOwKfoNru2eeVDlOiRhE8VMHestn4UsyjOucdp3e2fzF3klwUmUIXFLQVj06LAvrFqN45Prw0WduTyL9GENwo3Tdd7wFpm7XOjCIdKk5yXIXu0lW9AW/IczB2rUXjtTvCOLWSSardfw6CXCxGRxgDVw69wDws/0qgEx7SOeXksBsPDoT8wS3lwk4MXg9bnrJRFsWSdZ7VyZ5e95rbf9eRvsOv3n0fnM79DqSiPTwMr4MBuS6HMizkcsvu5QFudvcWaQirVEmrKJOY0NzxvQed2IHM4ACisegtGTxtK23xhd0p5ewnzmYBwThLepe3Vh1Mqh44X/4rSrg2B/U5iZgp+q3QjLFySSI6EEg7VE+vZZbIVf3b5G+5vh6gpRzYAgNYWDG0UBiPb46zm5PHdnd69MnfZThTWL0bbk78lc3zVE+sBaeDgL2No1nyJ8Iagwre5xwRvBSovVzVw5nohF1zHW9U+HJte58rz8cY3AmUclAuppOsGFq3ajdv/sSRwTFx/BvarMUaMGDFixPhgYVA9HJ5++mmsWLGifMEqyn7xi1/sr1gx9nVUZKkuKIdlzSMA5lNOihZTPuupyoXy/fX3Lfbr9CH0ZZetJCRSWBJRul9hX7AhgNlKIaG8ZTFf/RgEQyzJCjDm/yEpykSLbkJ+qT3/taOEoa971Qg0b1/HUEKhXI4BYq5xDq0rPOme1rUT6dFT6fN3FKOMwyzkKIFD8ePPHYuOrI4Z+49CfV2STmQsjGNDXQK9Oa+M3xiyO1tCd9ZTJKzb1mNNL+FDNJDfwT6UTspheEq64Y2xSNBAnv+cO94dTAqN5BWANcfBPC+JsHsszAOHIhwiLa05uGGg96H/gdnXAQAoLnwaDRf9B1hjq1WCUqaWsULkvHxIJQDQt66AWcyjtLD2sCe1gIkuMUmacBjQD3dHeR2hvJLGNIRw4FpRDsFlr4eOrwC5Jgvx+gfbw0GE2bEl5Ii1dlpLq/DsEwOb5bqJelX0vWFBYN9ByfAY5uhrBxpaXTk452RIJM4NMMhJ6gGQ65HZEcxzQ4FrBTBwGJ3bUVj4JJS6BtQf/1GX9Cq8dqdUXl/2ApIHnBTeoD2PrKWWQ9+5Dken1mGZNhEFblkeD28Kznlux9+vBX6Fe5jCuZEFx1RhQAo6ikhWFFZkKMFLeXBADhHmoJTFqh1tANSa5O7NlrD+lecwep2VR6Dw/lzwxgOlMpMTchiWJr0j0E5nbxFmQ/XXLRVCCoWBgZP3pWOx73oiRBAOAKC1baYVxa7nrciWhXgE1JAkOxIcKKxfTB4ySnkk6pvJY6PUXvxL0/M4ILEDCgOKr7+F4vDPITN6gtuuf63gBi1758t31yp9KKJCDonQ28LW6iCMbDcSTSOdFTzYp7B/uNKH9kfvQti7HhlSKWR85DIOOR9BOER4DCgwcUbdMlyQWYxuM4O/9p2ObYbfb6AyOGQrJ8j8sWpX2foNrIDL6hdgZjKa9FEMDT+/fzHGEmuNuP6YNX0TxogRI8begc2bN+Ouu+7C3LlzsXXrVmiahhEjRuDII4/EtddeixNOOGFPixhjL8egEw7l4FiDVlIWiAmHGAOD0ATQUfuiQtrUbAUfJYOoSBe3/f1VSn4ICS/FcxKtxf0ySARHBaBCFEnHfG2F9Stu++PpC0WMXA+U+mZb4eePZyIUdKzOYYfQcRXJcr8OHRB2vv0KnyVdGu4be5qACE+mLZfXu4P5GxyYOUu5zU0T+U3LoDYMQ9346cFyviScauNwK6dDiCJhytgm7JdIQkkkwBiD6ZA9ijWGYhAjBkhkAwA89Or6UJkBIFeoPCTFyGF1+Po/H4X6OhUJhaEuZSt0ObeVux6pEG5pHHpzQ76nnDwOFgnhzlGhXTckTUi4BtfSmgnzVJiz+vbVLtkAAGbnNhQXPY3MqTdYO0hyx/TNlyD5ZvZGxywGAH3ze8Dm98qWqwXq/seCd2yB2RUMF8GFRIqUhwNSRPLtfoAbJctSOsrDQSSLwtZ4KYeDn6gFaWkPbnpzMCxM0AB70wWbD7bvWDTDNMHsMGXu9Mz3k3DYvDSwrzkNIOQ2Nzu2QGkYbvdvC0GF1DJ1QEnasnLPKpuyyq0wpBLPdYNzjvyzvwa311aztw1NF37Zk0UsX+ijr7MjYu9u97e2bgHyL/weH2/k2G004Ufdl+Gkw8aisa4dgVSihh6mFyyLUw8fiQcWeWt3mIfDyDpa7jqmociTe6WHw86OHFau3Y7jfMuEAo6pid1Yq48Oje8ehf/8zav4XkrOi1K/7DEAlwIARii9uKr+baKmfO939paA0dW/JyTLhXDxoZ4V6eczMfejrLUtxWyZvjng2JGQz7QBJBwiiX0A0EvW21FIkYOEfADptlXoeO5PmHDD9xB2M4XlcNB2baxM4HLgHEZfF9pf+CuMCkMl6VV4OHS//QRaL/2yNQMJQkPcMyu1EVGLygnp1cGdFVxb09ChIpqciCIBW5QcLswsgso4Rqu9OD+zGP+v7/Sy/VJwyDVGPNsNHgzscEX9PByW3AyNq5hf2h8HJrdLcygMCuMwNB2JRPC8xHBMYURQjBgxBg5rNnftaRGGDNMntQxZXw8++CC+//3vo1SSv1W2b9+O7du34+mnn8ZVV12F733ve0gk4kj9MWgM2szob/JcCv2Nkx8jBolKLfphK6xdi6+wtsp1RyitJQW/25msoPbnFQgo4xl9LsRZBNoJLUe15ZcxWC7cgh/hMkrnHqoVBgCYWgm7HvkZCptXIDlyIsZe+02o9U0+kQXCRlRaSfyMb1zDUAXnIp+DU13MFRAkOgKdkf1x4Zoz6bfetdtf2IWVl4Fj1+O/RX6tZbk58vyb0HjYR6SmjaKs7lLSDeCGQYZsACwCg7nXSZxH/s8relw7e4uoS6kolOgP1JEtcggd7l5HuNeNMYadHTns7ioimVBhmhyNjSmMaEo6huYwsl3offVOGLluZI69DMqEQ6U5YpEQtIyW14MY496Jae+dWvAyifMcIaGuZOWtEyffnSG5rmAdMSl4WEilUNLE/lNDEsyBBEvWof7Sm1HYuBT60uesUDQ2lHEHeetGIphoe8BDKjmeDZGEg+GtRWEx+vWSRSzZohs7VkPfshQYdzAw8RA6UbFIMpTLSzAI4CLhUfZdjVtKrH4SDhSUqPwZHZuBSYdLcnAqd4hpWMc4h/7eizC2r0LpoBNoUq5CGB3bwPraXbIBALT1C2F0boPSMj5QniXrIkPoFF67E2z4BCTGH4z8q3+FM/ij1F78zzkKxs6aDr4mSBpzXQMPS6JeBscfOBzzdxWxbptFsoTlcBjbaALEFJ00XEV3x+DkcOgP9N0bcOd8FYeFECjTkzuwUR9ZU9v5vhzQ6tvpWmxz3Nj4GkaqAVoIaWjQkEADK6KPp9HZWwQ3Bv/Du1mhvaCskEqe4t7kPNLDwcz3SiEP3Xaotk1rXgYbGTjCofOJX0Fv2xx6nAqtFoXS9jXBkxFeB/uVn6YCcA50vfUoCqvnVVzH6A5/p/Mjv24htv7qE2g+/lI0H395ZNkRSnD+ihitBknZSsYnv/xNqMdfGjkPokIqHZNaL5GER6QqJ1z8SNrkGjOCz1bKi+j0Oi+6wiWJhaHtrtVGY5zahXrFazfFdKgEmScSfLGHQ4wYMT6ImD17Nr797W+Dc46mpibceOONOO6445BOp7FixQr89a9/xcaNG/HQQw+hsbERt9xyy54WOcZeikF5I469EGLs/RAUpYFDlSjt/eXhtlWpFXwgUXQ5RTxl7R84HtFvJBkisRuQCrqkgq94pJw+IsMJf1PNe7ejd3fGySUNGHoWPIvCZusjQWvbgu63H0fr6dcTp8TlxmzZ3LBPrrLZT3A4nfu8Jhi3m6jsRNwcFWJi1gAhImxSA8u5FVbIT0YJG1TCaAdmKQ+tc4dLNgBA27N/sgkHryG/h4OSrgc39HDCQS8CyVSZS+onIzzohonD9huO+e/Tso9stpNCSuNiEw32ntLGJSg+dyd6u4EHc8ej3WzC5afuh6tPn+Je17437kNx1VtWn7s2ovmTvwVLigptDsbC0hkJc1r86yvDuUOyEMfDcjgE7kevDzNHxOW2Y/ozMNoi07UsDMrg3kc1xBUfUCgqWDqDxLTjoE44BKWnfwqzaweUYWOgTDnKkpVz2sNhoJVCjqI/IqSS1GdIqAg3bAMHzPYt0J/6qbWx+BmoF98M1AVDfzgKM86rV571F6QxSASJPlAeDnTj4cpsToT/4lRMdXve66vfgvbW/QCAvo0LwVrG1iyW0bEFCYIIKix7FZmTPxaskKwrOz+Lr9+FxLU/DORGGV7cZnmIUZbBhlbdu4iA5joF3/vU0bjhB68ACPdwOHhMEiAMuTOqDiC913k45Oc9jlP1ETAIBTkAHJDYiVfZjJraTjMiDBezPpPqWRFTErSH2Gi1B9c0vI3JiXZs1Efg/d4bwQ065M9AoomFhF3zzaVSPhepwDULWSAdJHS5oYMxFQBHfuUcmB1bkTnkVHKuD6SHQ+H9uZHHo3I4kDANad3jpvw+FebhMJDofff5Qe+j5+3HoTYFyTbR27QcvUyhkhwOve88jsLG95AYG/SedeBP6C2ijkhe38jygRBmlcBZsxSCcAhbByuBAQUlqBB9LZPQyeTS4rpp1hBiNkaMGDH2JAzDwA9/+ENwztHc3Iz7778f06ZNc4/PmjULl1xyCT7+8Y9j2bJluPPOO3H11Vdj+vTwZ0CMfRcx4RBj34Wr7xUsp8M+7sNC/wTi/FNVLQLCIyL8iuaQeq43RRkSwb9tK8UdhWZt3kautj9UNklhKli3R8rptCmOd+hYhvRtaOh640FpX8+CZzH8tOsAYczk8Ek+2emT8ogNyrOkn5Dkka4JDx022UvCma7O+cE9V703GEfagVnMQeso78ZvFuRwJSxdDxalRDNKnuzMGzLXAcN27Wd+8smGppu4/uL9sXrtVpyeXAoO4MXCYcjxOgBWmCRXFirql6Gj79lb0VzoQ3MKuBzz8Ze+M5DwGQQXV3hJHHkpB33Le0jud2TwsnJYwcvtDY8Xopkya4bZ52iTKowxuV3GSIs/N+62Q3ZxgAlh6KnktlIS4SpyODDhfguzVGT1LaRXxYBDUb3bPd2A5qu/i+zu7UgPHwnNULyLTHg4DFQSZa9Bw1L2hyWDhjxeockwBcW0segJiDdz6e0HoJx4HVFHg0NUDbmHg6EBiQTNGUeoo3i2c/BkomBogecK5eHgzPvSqjniXnAibFelMDu2kkpIfctykiAy18yFSSj65DbDY7JzDpqYNDSgRg8Hv/zJEEVbum0VedUbFKt+JR4OOTOJemXwlbYOopSQ49TOmr0y6iIIh+EKEc7Lxql177syTUm0o6ttHrh5Wk0yVIMwD4fckhdhnv5PQDIDs5BF133fwlg1nDA0C32gOHeuFcESafTMexrdr9tk3pKXMOIi4htvCNcxrhVsO4QqrjM3sWlHH15bvB2rt1jP1xNmTsC5x08OEDQDj1rewWtD54v/L7BPVIgPC5kzUSA9WghoO9dJ+Yn8iPJwoDwPbh72BJqV6r0yHQ8Hg1irqXu8UuhchcZltUma6WSoKHEfNSLc0MHUOPxIjBgx9k7Mnz8fW7ZY761f+MIXJLLBQWNjI77zne/g2muvhWmaePLJJ/Hv//7vQyxpjA8Cwt8MYsT4sMK2mCeVK2HEgrXhKQj9x/3lyopAKdR97YZZopJyOv9wX/GocxTaKic+9/9nK4AkZTkxLkEhyyMytBF3PRv8+zXRBV/yioCt05XlC17/cLKEOydeJtSTM96B61sR6SOQMn4Swh1vupoRQTjwUp604OfOx7p9fqYvPrpSl4FSFx43v+32L6Pn1bs9Rbf/HBlzPVsozqahLoHJoxvxjSnzcGZmOc7KLMfHGz1yYGRLOpCM16MBOIyd66S4/zNT1vVXFYV0BHJ3CSFunOTmZMJo95ys/8T5QiVep8OkheRwCCjPffdtCOHAnHlQpYWpa+EYQjgo/bAGrwpMyK0BDqaqUFrGgvmUqoxIGh0VI79WlLXad8NU8fBQEXrRJZL5NnltMnevJ4kSbfEz4IZmDcNQEw6uEsb3jAl5VrirXz8U+DXB0MA5t8apmLUuA+HhoC16GsbmpTC2rxqwrnlfu+WF5IOZ7YKZpeeMtvCJChoOJ7up2OdcLwFhRFc5GLqkjw1LSMyz9LOjziEcKvBw+HH3pSjVj6pexkFAkhk1e2VQY2TYhENrBOFwfHqttH1E7+tDYjUfRjgAQNuTt4KbJgpr54P3hXtAAgAv9MEsBtu688ml6Nu90yUbAICXCsivWxgoaxL3y2CBa6UK36k8tHf24qu/ewsPvrIei9a0Y9GadvzhkSV4b217RRb8/cHQ0Q00xPA+zWFeMRGgki+HQe8Mz32gRBCBGcLDoRayAfDWrGxv8J6lvJgqhQ4FRR/hkGQ66blhkTwc49UOMlF1NWMaI0aMGEONBQsWuL/POOOM0HKzZs1Cfb2lK1i9msgBFCMGYsIhxr6OKMW4X7lf7quBywpJTynp/1tJO9FKd39/0fUjCBKxrEhyhCnqyyGsaNhYiOGM/CSLSwzBKwOEWuvn3I9gn/LWLxSlZCO5h/DrULHXiO9acHlg/R2G9Eddb3GumdAjkgGbxQKpjPZb6gZCKqXqoaSjE/VmFz6H4obFgXBClML/s5daYS7qWQHj1Q587LSJMLUC6jrXuWVmJLe7H6XzVuxGtqC5jTDGYBaz0Hs7IsffMbBjQIhVeljeEu+epUJmibuc3+K+QklHW2cO3DRl+aixt/NqBCIqMQa9czv0HWuCdUTFNaUg4SHEj9BBmGJlyAgHRQ1fI8TBIEIqJQ8/d8DF4eWSCJuGdT1LeYR6QjhJowHaGp3I4WC2bUDx5dstZX5UDolBgP++59y0PHOEkICuh5xbpn8eA7WA6xp4+yYUH7gFvX/7Cgpv3E2GVNJXvILic78ZcOLG7AjGkOeFXphEqKcBAZmXRYtMwhoFv1VyKiSHQxjqWeUeDt28Aan6xqraHywkYAyoh4OVCjeacKBgFgc/X04zC++jsG4hiiWd9H5crY2Rts1CH5nf5/3la7D62fsD+3Uqv0A13gb9RC1r5lNvrkdJC8r41yeXDXoOhz0N0cOhWSHC0pVBNWH/WI0eDiER0mqCQzhQBGJ/QipRHg4pRq83CRi4sv4d3DzsScxIbQscN4c4lGKMGDFiVIMjjzwSN910Ey677DKMGzcutJyo7yoW43UtBo3Yny/GPgi/9TiE0EFhCYS5r7x4SDDhriQEj1OGVHz7FeB2Z2TYIVEkOnxQpCLbKcuF+lSbvm03nJL9l4dZh/s7c8/bkTEgrFw8pCUj20XuL2xeKYy/I5tDVwiERmTCbue/crkwgvk6HK8G7pAVgSnE3VBP3mWJONGQvrmdw8D5beR6IhP1maU8TErpWchCSabdPBZmkcjhUEFc5u5X7kbdtKMBWE25l9aGM5KnHzkOXRvX4JjNf0cdilCeeRl9s84JtNfAiujlGfzj9Q04eeYY1NukR2nr++h68tfgxSzqDvkIMoefSUjDcd+La/HKwm349g2z0JIOfghyLU8n8A4BY4CZ7UH23afBUvWoO/xssEwC7pwGw4ZtPfj5ve+iO1vCMQePwleuO9prgAp/5LsenHOYhSz6XrodpQ2LaEH0IripW7dQWA4Hidjz5OMOuRFyPdVhYzEUAVH8ngwA3PvSJa047eGgTj7MipVPJWGuEaKHDHk824nsP34As20T0DCcLqPbocUAkHFJQhJ1G2veBj/7C+BEvo5BhV5A8AHo/2nPG27C7NwGmCC9CwYVhgbjvefdZNXayteGtHvKwwEAjPbwZLY19xVCBnJdA0/UGO7Ft0aEeTiEwYmpXqm3gFrfRKROHXoorHYr5gZGhMuyV6ZWIll0FLqe/HVNMlSDpjLhcf7fE8tw/Sj5vi3yBJ7KH4l/Tz7r7jMLfaRxwb81Pw8Q/FqUR+VQwFHWmlV4OWza1knub+8uDDrh0P3qPYPafjk4Hg5HptZjhFodcQagqoTg/vdISY6IHA4DCSekErUO9ItwsHM4iEiBThrdoJTwkbr3Q9uKPRxixIixN+PEE0/EiSeeWLbce++9h3zeehcZP378YIsV4wOKmHCI4eKee+7BvffeG1nmw8NeOspngliQ9M3eRsWW/pzLvIWzLYXxIazWBUW8P/xPZF+S6NyL8y/Jw0LIkKBMItHgKOqdnGeRYyAq0QOEClHWn7IhkJg62BcHYISEtChuXQVuGsHwQWEkkHv9mX/Eg73ailtxXlTp0R9sE0Awl4dHlgRILXHQhM6NiITRAGCWcqS1Zc/CF6B1bEdmyqFoOuq8QA6HSgkHxzIy4AHgcmfWnEwnVZzbsg65zbayoJBFz1uPBtprUgroNawElvmCbueAAHpfv88KrQKgsPw1pCYfFqibhAENCWxvz6NQ0mGC+PglQkc4c4RRZBPn6Hz859B3rQcAGB3b0Hj+v4gF8ODs1ejOWh+Q81fuxooNHZgxLm3p0qlQKaVcYEpGkg1OPa0AZBrp3Bph18oJw2SaoWWUljHk/mqhHnASjNVzwguEJuf2gcjhgEQK9Rd8GbnH/6824QhQoatEaMteAu+xrXnDchi4igNOnp/xejCmttdBCbxnVwWSDiC0ontvmrlOFF/7G8yeXVAPPw+pGae49yvnJorP/BLGViKE3VDA0MDXvbNn+gbIfBEAYLSFJ98t2yZFFJoGAE7fm7pelbJP6kvXwAXL21SVYYbqbOV7ogIa4WNnTIFSWLdXEA5A7XHahxPKWGZfs6gcDnsK5QiHOYu24NAJW3GAsO+x3NHImjKhaxayMOsqD7VjDJaXT4XgesnzIlTUiu6RugTHZZn5OCa9Dhv1kbg3ezJyPI1kQimb8L2/yC16YVDbLweVmRiu9OETQsjKQUPEWFaylgwEHJK0ngjT1B/oXEEp4OFAJ40uB7P0YfmWjhEjxr6M22+/3f190kkn7UFJYuzNiAmHGC46OjqwZk0wlMeHHVZyZidGinzE/esQB+6hMI8D2jq+fAieECLC7w0gKZ59CugwrwnyN0FISNKEeAOI9cVTFZTLgf78dd2swpATSPskcP+6jgfWPj3Ew4FrRZR2rkfd+AMh51wQ5RHGkQnCUx4t1JwIjIlvEPxEQaAITfpEk0oQyBhn3F3XlGDIBN8HOC8WAhb1ANAz7ykAQH7tu1CHjQxYpql19eGJcqX+FOkcLH5AsFgXBkQvQ44AwGl1K3Bf9kQADIWu3chnlyE1/iBX4e+guH5RoG4d01yX95JmwjSCVsoOaeFI5XllcGm/c52M3jap79KqOcD5/+ISIQCwdK0c0mru0u2YMX4/a4MKYyRavdv9ljYvC5YLyG4phegcDt4+j7uzxj/KuwEAWMNw0nsgecjp0Ja/UlYuB6mTroM5YgL0XRtgrJsXLOB6OHj3pUu92aQsADKZImMKuJosI0AGqCK5dLmQSi7ZEAUxNEJEOAkKZikHs5I+BhBcL4Lne8F3bkRp/VswNi8FAOhv3gVzyuFAXQs4OIytK/Yc2YAQ5fxQ9l+kLdr7RTgQ3i7a8peRmPER+p42NNQaqsa0iayDJrfg/U1d1Xs4wCYcyijTlg4/E2cdOQb6oqaa5BwM1Eo4tCrBa66YpdBjexqZMueZYAZ6uroBgV8o8CSyXCYceKGPfEcIAzWPhxJiiB+mqBUZRhxWeBcHZ5YDAA5PbcGJ+mq8VDgMyYTSr7XG4AxqwHBp70ICJo5JrS9fcJARlrh+wPuBgSOSGzEpMbCeODpUknCgkkaXbatYBJGpKkaMGFViyZrd+P3DS7Bl1973jB4sTBzdiC9cORMzp+/Z3FnPPfccnn3W8pacMGECzjrrrD0qT4y9FzHhEMNFa2srpk+fHlmmWCxi8+aBDykwpIj6NuC+HwFLfCtMD5MszUUlMIT9Yt0QQkEQJhDWyFVe+5TlUn3vuKTsDwvrxEP6KxcWqpyXhRMqSD5g/2WudBWFafVfAx/CknYCQHH7OqTHH0DL64YvEskWyGSAQ2xI1xI2P+IfL+cnnTA44KUijq8Y+ikKITkmxM3SjnXSsWTreCmBtlnKwyyjhM2tmkeGVGJGBR9STHFJBprkgXt/8EJ5K9ET0mvQZdbjneI0jHztJ+gxSmDJukA5hQi7k2K6Ozgl3YTJg/2JlstMHEkh3Jd0+SnlinR7B6+hFOqBCpVSyoOb3GUGuKFXlhS5lLfIS8qK0LAtpak1giOyfSUzDCyVCSidlJAwQmFgioL0zHOhlEoobH8/6EGgqCg758EBhXg14TQRIfWvpsBRBeEwAIlOuSEkMK023n4xB7N3aD0ceMcW5F+5HTzfI0f15xzamreBw84DOIexliCMhhJ7Oq56iIeD2Z9cFkV6/Ss882skpx4R2M+1AnhEfp4oaEufh3HIR3D1mdPw2weXIlVlYtw6bil1o5RpKxqOwSlXXg3OOfS6YTXJORiolXCYpAbHOq1nMVzp2ys9HOqJEFAiEjACY1HgSeR4Cib34ubzUj7g4bg3w8nhUI2X6cHZBdL2RNVSRluEQ+2W9wWeRMMAW9IPNBIwcKwvsfmeQLLGZO7VYlZqI45Obxjwdg2uQmP+pNEGmTS6HDTK0zZGjBhV47YHF2Nb2wfn+TUQ2LKrD7c9uBh/vOXsPSbDkiVL8PWvf93d/uY3v4lksoxRWIx9FjHhEMPF9ddfj+uvvz6yzOrVq3HxxRcPkUSDB085Tx9z8yGIZcp93TgKa0qrToU4ogUL3898vyXLd0q+AFvibQf5DrrvMMW9r4Ls2UD064R1AcBELwAuxt4hCBvhHLm9PyykEmCHWwp4h1AgxqIi+BqVPDYoRXt5okZumcOx+pbGySG1AGkemHoRvUtekZqtmzQjQDiUs0gsblsDtUFWGCnpeihm+cFhiuJTwMuDzsBt5xsOM0Th5sf5mSUYpfRAMawPecr6kop5LsbsLekGVq3fDn+wIF7MWWNMkHZSOe5dC+pYVJJDyYmIUpxy0wrFk0xaBStMgsk1x8OByAvhWEL7PZjcLumPfdY8GkhngFR9IGwQa2gJlq8fBp4LvwetzgBQ3ggVegCwsGTlFBFRQ/sOzLaNVZUnYYdU4iavOs+B2bMrVLE9WNAWPw2E5K4w2jaB7d4ANnzikMpEC7OnPRxC1qoaQxwBgJnrovvKdcHYuS6wP/f872ruCwBKa97GtOM+il9+6QT0vboaPDykeABpHh1S6Y7C2bj04vPB1AS4rkHJNPdL1oFEXY0K4P2TQW+jNC/guy2P9FekQUGTEv1cTzKacOBQkOcpSVGude9GdavnnoPo4VCJdwOFUapFhicTCu2FWCEKPIkG7N2EQ6vahzHqEOcKIpAcopBKg+VxokNBiQdzONSSpL5U2LNeQjFixIhRK5YvX47PfvazyOWs75dPfOITsXdDjEjEhEOMfQ5cUIBbO7gQPYdWQrvW23Z5WdGPMmF3fMfI33Q5KdyTRDCEvFBXHLoJ0nlX146g/A49Vx7cihoXX2keaMLa6F36SmjSaEDI70COs08RK3qr+EkUSdlPeHv4Q1CVGy8fSSHtIZTa3DSEBLvhRFJhw1KYgvKQpevReOip6F34vFe6mIdZhnBIjhgPrX2btI+lG6BW6OHgZ65cGonL516Jh4ODchZqVFuWcoXjjLrlaHr7beQ6gxarvJj15ZvwZGQOuWV7rTDGwKiQJqYBqMwjGf19mN58CrOezL5+N3hfO9IHnQh1/IyoU/XadRTaZCx4z7uD9IzRg3XUEZOQOuFq6zxTmcCpUB4O6oQZMNq3gHdsCRxjsG4NxhhYMh0cGiZ8rIsEnW+esHQD1EmHu+F+1ANPtTsv88pSaY4IG8b2KjSwYdBLMHauAd59qmrF1YAQHtUiIlG2vvYdYO07UA89O+i1NdQw9qwSL5Rw6E+bIYQDICtRBwql915C+tgrkFAVKGapKnVfikeHVPr0R49Gy8Qm6PZaxzJ7T0ilqxv2XO6PAYU/TKYP5RLghhEOANDH6yRFuWLuWYKvGuSXvIT8+iXIjN2vZgJQtxXHSVXpV9LoAk8B2Luta5vY3qHcHioPh8GCDgWMCqlUw3lpRF61GDFiVI9/vfoI/OGRJdi8c98JqTRpTCM+/9GZe6Tvd999F5/73OfQ02OR2Oeffz5uvvnmPSJLjA8OYsIhxr4LW+Es7XLICMbcbRZuJi/VDOtDyq/ghtEhiA0pNFFQLjGRM7MTHTOSMeCebt2Jiy6EiwmTLzRtcqVEgf/DOKw5ONb7lKcADSPbjY7X7kdu5VuRIpg5wcMhynVBugTOYMmeFJ7CTSCoqLBZYbL7Lei5XN/hj/SeNuQ3r0B+7SIkho+B1rYF+Y3vITPlMIy+/N8t4kGyWveUEKXdcjzxhgOPC3gqmKV82fjMvJiH4csFkWhqBa9k7jPFupdsEZ1TZMwaF7OQh7Z9FdTWiYGwTf0B1VaK6Tg1vRKX1y8A2oBgICZBaR9gFd0bRjjOwQlFPYwSoBCJjd2mhLZDlNCl998EAGhbV0JpriwOZ/aZ3yI5+fAQwkFQDPo5MsYDlqBKYyuarvgmDDdvQtAjgRFWy0xNou7CryJ/91eCMohDSiR+Zooaep8zKdM4R+q0T4JvXAQwBnPSkXbfA0s4DAR4rhvFp35Wk0V+gHBoGQ9070CtcfsHCsayF5GYfsIelaGaXByDAV6lt0pFbUaQ5eYgJA9XWsZ5fWvVETgp03pmhHk41LfYZKRzz2caq5YvRhkk64FS7cpsOqSStS6v1UbvFVbvNaN3N/K9tee/ySglZFgJZxVfhN5Xe+66PN/7Q0hkBinkU5En8Kx6Ji4zny9fGEPn4TBY0LkaeCe+pH4htukt1bdVjJNGx4gxEJg5fRR+97WzsGZz154WZcgwfVLLHun3xRdfxFe/+lUUbA+t8847Dz/72c+gVOldHmPfQ0w4xNg3EVCOBxXK3FVAc8+SWQxrRDULn8W8GLc/0B+X6kl9CeW4oBiXvB2izse/3+dNESAXwvI2BEgBJ9SUQ1QQ/QdE4bIcUq6IENmF8TFLJex48MfQO3fQ5yjAyDkf0OJ5C9bekiU1950TUNyxHr1LXkZu9Tyo9c0YdfEXkR67n3xuTqLrUPKB8JiQT8791fHKveiZ/zR5Lvl1i9C7+GU0H3l20NmDWT+0DjmeeGrUJLBkEqLinOslmIVo5Zne0wauex+lLJkGS9cjVdeA1OjJKO0KT5TKIl40zFIeu+7+NoxBSIxLWSC3sByuaoiOPW8RFd6ISmGghDntXl0qfJGhgSVToR5C3LlHgIqs3qtJHFxa+QZtkekSCvKcc2e7Xw4xnwLn4DyoDKBICKgJKKkM1P2Pg7EuzJKYk3k34JJn5cHUJJIHnwyul1DS7fu4TNJoVt8CXkYJpUw/EeaauRXJUBHyZcJLRcDvJcJGTwM//fPAo9/pr1T9Bg/zMPAlpR8oKGMPhLljlbejiiS2g4JBIDx4P+ZKLXBCk3HOKw7b5iDpEA4h1rusrkly8KLIyRj9A09lwPpBOFgeDvJ97Hg4LNGm4KS62hXtH3SMVbvx4+H3A/107HAInL0Zw1X5/a/IE2W9YypBiSdgqGlUGlHog+/hoMIgAo+NT3RV31bs4RAjRowPEO655x784Ac/gGkbt11++eX40Y9+BFVVy9SMEQMfmJCdMWIMHFwlNA8qv4RQRuK2vUHqj3lgf4ANEP4L1g33LIiS3/rr1RcU3KKy098Q2abvXKPyNgTiwxNjKLUrHi+jaHS8LXx9dL/9eCjZoPpizBu5bgTOpxzs/rrmPoYd930f2WWvg5cK0Lt2YfcTt4KbEV9TVOimMDLL+W0TH0a+Fz0Lno0UrXveU8KYWDAKfTDyvShsXoHcKlnhmxg+FowpYCk5oXJUGCoA0LtlJa3aONwNjTPm6lvQcvJHwyszL4eDSCD1Lp6Nzbd+flDIBgAo9AUT/h6fLq9A8UJQMTieGbph4t7nV+Hm383F319437rmzuWkPBx0K3STXSJw+NRZ48FzPTDzff0K10ChuOJ1mgQxnaTR0l7vj7+Omw/BLkOFfiJyJrgkBOX1JBCQjEjqzSM8pZhLCMpiSb+jrGjUJJIHnRJ+HFa+isTxH4ssM5TgPqt2lhkGJDN7SBoZPEThzppGDnhf6dM+jbrzvjTg7e5tMMuswwMNfdNSFN5+yCIT9eqsnF3CIUSb6Hkb2TdngvIni9Ef8H6OaVjSaABYpY1F3tz7rfP3dvhj+n8QMFBeGUWegKkGn/NhCCMvPyjQuQJtgK537OEQI0aMDwpuvfVWfP/733fJhk984hP48Y9/HJMNMSpG7OEQI0YY/ArkcqGFuFhG0MBKRvXc2yaN3wUFIbE/POeC0Ie7S3T+5ZDCQ1WiHJfIi6BXANGlQBbIoYP8bTqKRybkL2C+YoDlsdDz7nP+Xlw0zDhJ8hAwcj1B8od7vy3Jgj1y00Qv0Y/evQv5tQtRf+Axdk3by4T5zl4iVYiLxCHU4Xbbu1EudIrR0yb10bPwBXS+ej+CE8RCcvhYAICSysAQ8jboVSq6Eo1WuAwGy9th2PGXgifS6H71vkBZS6Eun7/e24mOF/9WVZ/Vwsj3IeEb6imJNrqwAF70KVIZw/wVu/HkHCu8zcYdvVBVBVeePt06TnkoGLrrGcMY0NyQQk/WU+iN71iA9qfu6VcyylBoRTo8imR1HiRH/YmmmZqQyU7Kal0lFPw24RCW2NmNUhcWUgnw1gmR/I0El+v7oEw6AolDz7RCtYWBKUie+E9gagLK5CNgblpcps89gPph1viWid0+JMgHCT0AYA3Dwbt3Dlg3qcPPRmL6ceCDGEYqc8W3kX/sR4PimVENyiZbHwSUFj6N1OTDwav0cFC4gWmJHZiR2hY4lphwMBxvQc65nbul8pwfOTOJ2YXDcHH9wqpk2tegKWlUrs4Nop6VpAS6GldgwFpDDah4Jj8LHy3jERgjGr187yCIq0HeTKFF6b8HV5EnYSiVz1A/+fVBgwHFzf3R77YGIWdPjBgxYgw0/vSnP+G3v/0tAOs977/+67/w6U9/eg9LFeODhtjDIca+CckpIMKin9oWPAucEEiS4o4jUDaoUxMU/j4L9mgSwKntL+8o8EB4XMj1ZO+NECvtQJ9e+8HjJHMS3C6jPwuoHjlH7+KXLeUugWHHX4phx19ih4axq5QKMAMv8qGD4cIs5kLzC/QufTWkvl9pSjQecc5mjlbo+aHbyZG5oaNr7qORjaqZRoBzKCn5I5hXmTtBbSSSBdfRMbodRZaob+pb9lpZMqW/oEICqKzMJAPATM0OH+Vdt7VbZUXgQ7PXoK0rj5Km4+33tgfakMLNcA7dl1zbXPjY4JANALhWhLZpSfCAo0zlALOpRWaH3gIAnvUpO0OU9yLokErWvuRhZ8u7Z17g9gUG0sMhss8acxSz5tFIfuSTUMZMB5JBkiP50e8jcfINSF70NajjDrTq7CVeBAFkhkFhDKDCUQ0xeIFen5SG1gFpnzWPQvKCryJ9wtXWNmOkR81AQGkYjrozPws2bKw1T/YQ9gThAACFhU9X7eEAAJ9ofI3cX3/8FdbqaQrvLpxD3f+YyPZeLRyM3/Scix91X46V2rjIsjGAVTv7p6BtVOSwLf7wP68WZ/Sr/RiWx8iz+T2TvLNW5AcoDFSJJ5A1Kl+zT69bQe6vJQfCnoDOVZT4wDyjgt8pMWLEiLF3Yfbs2fj5z38OAFAUBf/zP/8Tkw0xakLs4RBj30M5y9FIT4YQC3b4SQCnWFARHZqIWvKOCFOSlycj5GNEDgZZHAsB9wLRo8F/nChaNgyTr5LbL5Hvwi7DuYm+94LKfqWuAaMu/iLqJh4EAFAzTVLIIDPXDWXYKM+pwO3TR+wI/UWFHNLat8Lz9AAsBwcmjIlA2sidukclfw+boDDylSVs1HZuhDr5YBj53tAwJwCQHn+Ae35KiPV5pVCbfEpFDqhNI8iyXCtaMcKF+VUuZ8SeBi/mgEwDAMtvpS8fVOwsWduGZWvbUFi1HTP8XIuuSVPp2584ClpJBwdDqVgCf+bOQZPd7Gsn93PT8O55NxeFZX2s71yH7HO3SuX93gLJIy9A8YXfu9upoy4BiCTNVigVDqVpJFKn3AB9xStQh0+AcsgZcrkEQThQSZ25uAj51puAxxW1zljlGQMZsok1joDSNNLybHLu49QQKPRr8FJgmWHW6SQzkff6UIBHeDgMBNQx06GM2t+2jLdDmKmJwSHqkmkkpx4JTDwCCgPyD30bvLe8N9RAw8x1DXmfAABDB69BwdWsBOOM11/2dSRGToVuBud26ujLUTR0INcN3rsb3A1fZyHPU1irW154zdiz8/uDgGw/Qx41+a6fWhckWjfrrZiU6KiovVXaWByYLJ9La18CA/B0fhZ2Gc34eOMbe1qcijBQhEMRCfTp/bf4L35AVBE6FPQMkEeLWYoJhxgxYuy96Orqwre+9S13+2tf+xquvvrqPShRjA8yPhhP+RgxBhSOQt+nVAciiQZJcRyhSPISR3tlnVBIXkgjH0HhF0/sh5KJJB4chR2PCL0U4pkQIBUIosFW1okeCERMpQiCgdvaelIC71w5BzcMFHesg9HX6R5myTTG3/gjKJkmKImkp1yvb5YIAyPXg8SwUYF2g6PtyWpkw5X/XiJqqr6w6R8rzmFqBWjt25AcMR6Kz2rZDFHo+dH55oMYM+arZT0i6iYf4v5WbGV6rUg0Dg9c/wAJYYNrJTAGaN270f3WY2DchN6+uar+1MZWK//GEIU88SvfTOJ+zhU0zHlvJ45LBWXihmYPjVVv8pgmgJtIJlRouV4MlCpTaR4NbpTAKwmJFRg7Ds45SmveQe7FPwbLqwl7nTLBGKCOPxSJKUdA37gYyojJVj4ERrwiCCRE8sCTkT7oZKgKUCjqsDT/Vt+hSaM98Uh4y51XIFpt74WOY6ngvKeWQlK2gcaYg4AdK6urUz/M+puqA2rPFTswCPFQYo0D4+EApgrPEvuvmhz4ZNGJNALOvBTxNRQgEt33BwuLU5BiOg5NbQUAJPY7Gvr6BcGCiTQQlgS8WigJi2C24T22OZSG4Uif+TkwQ0Np7v3QV8+RqorhSMxaXZr2IfRXMdzI5HupsbkJ8PEFxSri+W/SR8SEgw/Om+U2Y2CI2KHAgBEOPGkRDv28lQsDlFNisKFzFd1m/4x5HJhUWMwYMWLE2Etw5513or3dMnCbMWMGTjjhBKxYQXupOaivr8eUKVOGQrwYHzDEhEOMfRiEcpzaZjJ5EIjfL9ZzldsW6UCSCqEeFDJpAAjdiHUExXw5K1qnf9Kjwu3T7kjMH+EvLp6//wD3lIxUFTLaUGAMPctsrWMbdj32G+idciibzNSZSDhKb0EONdME0T5dz/Ug7Vrc+70svMwWoteBEWF5yjUrTJOSSrsyWqfsqJz9BJP1x8h2Y/vd34He04ZE80iM++fvQ61vdts1KgypVNq+Ftvu+g5az7g+tEz9gcehadY57vVW6poqajsMqqP4dMGhhlg2c70IbprofOEvKG1bXVN/rK4ByYYWaDvX1VS/WnBDk+L9F0tBUsFSoANJItEh1zXondtQWrcQqQkHuqF6wDmdX6EWJFJIH3cFEqP3R++9N5cv7yqIvfPSN7+HPEU2AFb4GuG+YIqK+rM+C27nVWFqEoxSzLr7iLWSCx5cRA4HKKpn0e706zTLeHCtqATCdVSaRkAZvT/MXdY8Ug/+iN24T9zU4IdUYqOngVdDOKhJK/EuA1iirqahGAqwhpaBaYgKr0V41PQXLFUnp1UCwJiy145vOSgTDsWDuw9EW3s3VmjjMXlkBsdd1AIj2YBkyyj0blocCEPIkumaQipRcJJF80C+KgGcA0QyQV0gfgweR3Qth/7Giw94OKQyaKhLIFvw5kexihAxm3Xay3FfhkM4DFRs/6HAwBEOCRQ1DtaQrsmDysF6bTQmq+1oUPZuJbwBBd3mwLw7VJtTJ0aMGDGGEg899JD7e8WKFbj88svL1jnuuONw1113DaJUMT6oiAmHGPsebOvzsmGTpJBAvEwdqhUOPxHhfJtLx9w+4Crt5IZ8bYRKTLQnyuJvs6zGxSYTBCKES20T3hI8eCRYzu9ZIpfufuvxANkAAPXTjyKlFJX4QDA3guSRIR2wCQlukQNRMHLdUFKjffW96+gQECLR1Lv0Feh20me9pw19S1/FsOMvdk+5Ug8HwEoe3bvwRfJYomUMRl34eXDTBDct6kWp65+Hg5IJ5mtghAIJAMA5jO5dNZMNgB0Cqop7q9+wlW+MMTCYKGpBUsFRyiQQtPQ2u3eg88lfAIaGLBiKZ34FT6xSYZgcqXw7ruineOnDz0bdMZeAq2lArzCOt+Dh4AylvmlpaHFGhUtiAJSk0BaxSNhKRgZFWlfkiGgcCpXDganBNv2h39x94iandpMy1p3zLyisfBOpTAOw3zEipeqWHQoPB56q0hKyvgXM8cQiclHsLWBpOpdL1XATiHt/mJqMfiyptmdbFWGXyNBeygdY2c0UXHzJqfj7q5swUzdx9WmTkRjdBK4ZgO1ZxA05lBGS6dBcSNWC+/JsyLcv9/4l8nEcf+g4fOSwIzF2RB3aN20CZj82IDJ9WFHP+qeU9Hs4sFQGLQ3JsoTDitJ4Mln4ZmPPEw5ZM7VXKaadqa9/gFIiDpRHQYknUNQNsFSmX4RDCQncmz0Jl9QvxFh1z+S5qQQ6V2FARQFp1KF/92Y+9cHxiIkR44OA6ZNa9rQIHxp0dHRg586de1qMGB8ixIRDjH0K3K/kd6xqmayWik4aDVk/5icCyJwERDmqH18YESZ6PYiKejHeeQTRENqv2JcUGslpN8TFgdT4OQSN/wx85SWLyHB5itvXBI6wZBqZqTMD58I5h1InK/bMYtZTewSuoyAh88azHOFg5nqAltECcST8tc/NGTJHCdv1+gNSG52v/x3DjrvI3a7Uw8FBYdMycv+wYy90z8OBShAG1UBJNwCwQvKwCoiAwrpF/euvrgHcGJpwSgAAw1bi23Of8nDQDRNTxjQi0R08ln/3Ga8NcCiLHsPra04GAExQOwC/g0iVUJpGQEnXw9ANsETSUrQa0cQDF0kCe16a3btCy1s5HAQFIUkEEPUgekz51k1ni8EOZePvU1TKyO17jg9Ra000OOdAsg6JQ89GKqVCK2nggfWGA9UkjU6mgSqVKeqsi2FQCbcjwDLNcNdGSkm+l4AlalBWDZ8AdG6V9wkeDu6VjkganTz8HKSPvBClxc+htPjZyvtOpgH/s459cKyRA1AUjBmewb999BBoxSLUhOoZRQD0fWOaCAuRJaF+GFAusbWdw8V6PjBwbgIC+chNy2uJEdfygCmtSIxvhMk5xo5sGJKoYSZnUFj1a4kfd/adgqsPV5BZTyfSHgxUE+6IQosi51JiqQyGNSaxtd3Ln1FCsA8qTr3OFXSYjSghgRQGIc9Khbit91x8tH4epif3DmXInvRw2GU0YbRa3XskMLAeDvmigZyZQn8o/A36KKzXR+O97sn47rCHMFwduBxgA0lQ6VBw/Vn7wVykVmCwFY7V2hjkRx4zIDLFiBEjxkCjtbUV77///p4WI8aHCB8ck4wYMQYMUW+Kfst/4UM+6CcQ3bbjrSDGO45SCnCve9pDQGjLT4CEeUaU85yIIDwkzw7yuCAHyUMEz0SuG/xtakXo3XIEfJZIYcQ5n/KIBdfa2frrT5BsFnxqDPc8iVG1z6Gsh0O2O6iPJQmNaGUpd4/xqjwcKLB0PcZe999oOPRUuEmb7dNU6vpJOAgkDifniYz8+sX96y/dADaEVt3c5zVQ1ILKuELRQF1KQQJBwsHskRX5yY617u8U678yhvm8A1glScBNg1ihIq5bSPiasvQSmfjZ+ocJocbI60mF0ankyz3CI4uXmZuAHFKOgUGpImk0q9ZTQU0icehZkcpzsh/bc4AhxCp/b0EVRApL1yNz2TegnvyJ4EGRcHDWmIi2WarB8kwh51CEDII3ixtQj/JwoDxy9kYwNfDscX9xDk54f/AKc0coTaPLlqGIBLoxKv9L0pLd5EPiZfKDrsux2+xfeEEA+H3vWVhQ2h+oay5feICQNVN4Jn8ECqx2Ve4IVfZ0UZpa0dIor8uUh0MPETamxFVwMPTwAfJwqhE6V5AbIIX5QIDZZJaOoSccns7PkrbV5pEV1asmjFYUctxaMzf31O6dukobi/W6l3Otjw+s96EjYxiMkIR3fQTp9tlLZ+DcY8aD9yMHUJEncGvvechj8MM6xogRI0aMGHsDYsIhxj4Gz5ZX+lwP6KMDJXzNiGRCf6znOIjO/eKGKPSD0nkK7bDuPJm5fz/duK+eKHOIwBLR4avrWF4Xc+hdPBu5NfM9hSEH9I4dgbYnfv43qD/g6NBT8isEzWLOVVAGpQyON4cVMkmEZeHvQUocHUrAeOdohsTL5kLYGyPvS0ZdZUihlhMuQ2rUZFJB3O+QSoSCm4FDaaTdwIsb3+tXfyxdH1CyDyoMTbqOfg+HU48Yh5MPH4N0UkWCVWAZLGAgrD+lkD+sQqW3lDSaW05HxQhLQUV1pxzjNlnghlCLEs515aFzw9jjyiilvqJauTPEpYTJ9XyNRcsi9lfN/VNhDgdlzPSq52XylBtsxXiVSh1R2U7lvxAxcmp1bQ8kKsyzkL74ZjRd9d9QR06i61AK56i2HQKrSkW1dy8Jc4lSGBEJx8tBGT0N6dM/A/XSb1Vdt2bY5x9q/kCETuKlyiyGWfOo8oX8hI/vtYDbnnFUDgdxjvdHaVcpsjwNsx+5IkpcxVvF6XhfGw8AYInBdwrPmiksK03An/vORAlJvDnqGvSMmomX84dgbmF6v9pWG0egNUA4BEm+bISSdqAVwtVCh7p3EQ72X30P5CTZpg/HX3pPw9LSRLxiHInmcz5TUT1tgAgHZ57UPCcSafy+92yIZg695sDOr6i5DAA/7L6c3K8RASBGttSDg8OIIJc26iOwtn4mElOPQuqoSwLHnXlChfGMESNGjBgxPoyIQyrF2Pfg9w6QjpXb5ohOGu0/5DQghGryh+PxlxX3B/Iqc18f5a3PfQ1QPco5n8WwRdwLSMKJJiyReIhZNC2Xke/Djvt/AL3LcolPTzwIzUeei9SYqSj68gDUTZ0J5sTtFi06BfJDScvKw4CHA7j0R2jEDaXl93BIjpqI4hbPnTC/8T00zTzd3hKvpd2wT9mptQfjHwOA3rULyeFjYRSy0LtkK/kJn/optO5d4MUcdj9xK1lfhJIRLTdFIoWRORgqBUvWWeF2iLk+/PQb0P7kb4ha/SHdYFmclwkZNJDgesm+ZJbcHz9/OnpyOkqaiaJm4rRZ49GYSWD2gi1IEh4OFBSYMKEMjIdDIgVwIaFyJR4OdsgUBgZumsi9egfMto3hfSiJCN4w7J72DitKcOlxCQxwOiyQoGRkAnEhtAyr42pIHl/YL+6EueBgcBK6yyRjRTkc6hqROuEalN68pwpZvGtXbQJkMadGOZJDPfw8GK/fAQx14smGVms9ppCqB0o5gDGkTroeysgpUBIMJgftueCG9PKeiVE5HJxrVrGFvQM14csvApK0YKl68GxHdU2P3h+JqbOgF2tYuyoIk0YiTFHv3LNmcL0ytlXmGs+ay3s4cCVp304cnAn3lT90I3WdhDWHTEgPQBm1H8zd6yuStxzyPAmlSsLYwW97zsUafay0TxmEpOYilpUm4Pa+M2AKtmBmywTsGj8Dj76/AhdlFvarfaVpBIY3lfdwoEgIxb60fWa6rKla3kwiowzO81znKoY65Xt+6inIbHyTfNd2QyrtAQ8HDSqWaFOwRJuClsYkrhm1X0X1SgMka860nlNZszZjEWXcQTB3yZOpHHnxWuEgLC1NRoaVMCXRhrMyy6BzBQpMd47KMkaTU71mneVw5avbgWEYDp8Xsn3/6xGqk7XaGOwedjZmnjEd5o7guuvMk5Je27oUI0aMGDFifNAQEw4x9k2E5jUAXKUXF7Yd5TLzlfH9dNsG4CaJZr59VHf+umHHiZ3c/peLijWKHRDr2QoDR56wktE8AiUr9x0Llul46U6XbACA4pb3sXsLrRBJjhhH7hfbDoRUKuYgKbLE8lw4K8Gy2uiTFU2pkZMlwiH3/tvIHXoq6qfNAjkyvvmktW0hJdbat6C0exPan/mTfIAxqI0tSDSPhEYkzKagZppoWcCh9sPDwQqnJJmew7kf0lMPRdNxl6L3ncdrbp/uNDGkHg5cLwHg0Nu3IP/+mzhwxETUHX4yVEUBU6zkzwBHOqlCZZURDsOVLNrNpgEhHPzhXZR0fXnawzSg79wAJdMEvX0rSu/PiS7vt0B2FJZ23g5nXfGDCZ4R0nF/0XIhlXxrndens4OQuRJy1WZHxepu6hjn/q+AcKj76HehZhqrn5eOcp1Kyt08xrI2LxDh1NSENfaKAqjRShLW0Ar1oq+DLXsO+pq3vAPJNMA5lEPOhrn0WVQUt78KJE78JyAkh4Ny8o1INjQhka4HbxwpPyaoMEhSSCU7NEmEQtci3Xj1oXhExTaHpSwlPRyqD3GhjJhkdQFA3e8YGOvnVy5Wy3jw9nBCMLSeolghiVTrqV+OHKwKDcOt60KQFgCg14/03hl8PD53yT17B0E4OGQVBwcn82gwJA88EcUqCYcSV5HyrdMFngSHUrPHGaVAZgNEOKzjE7E/C74j/KnvTPgvZlN9AvVpSxYtJE/AZr0VswuH4sbG1yP7VZpHYniPvIaWCMKBSirsrM49Rqrsl6NaI8lTCTQoaFWGIvuHh4dWpvDJS7+MJx+bjfMzS6RjO41hACx6W+dK1R6R/YHoqaAb1nM7NeFglLaurLhef+B4mtTq4aAQXlB9ZTwcTChYpVvfBYu1KXijeBA0ruKCzCKcXLc6UL5cSKUwomgdn4BpvnvUuf/fSR6H84vPkPV2m80YPyJjPcuJ9wzHw6EUezjEiBEjRox9BDHhEGPfguTdYFtwUx/sUSSAq1zmRAWRiHCU3ozweqCse4M0gsBY2JtM3scpTwlC9gDR4R8Hrw8O22vBX9q1Eg56STCxLz+p4XbDoe3ejNzqeeHy+pAcPt53OsEkxsEcDr7wEVE6Ss5h5Hth5oVYx2oCqdGTAkV7l8xG/f6zZAKJ8mBhDHpPW6A+AOTWvIvSjvXgPsvWxLDRrsUnFc6IQsCLQRjrsBwOLF0fHWanTP+MKWg6/lKUtq1GccuKiuSsBExVy4eRGUgYJZiFLLof+h/ACX9l6Gg4/Aw417QnW8S6bb2YWKGHw0iltybCgSXrwLWCb5/zkWzdXZV4OJjdO5F99EeQ1ouofkPzKdg5GOw1IbHfUdDXv2sdVpNITjrc8z+QXKPsXfY+phIf+orqd6eKljUsfBlRJuysXa8LZie45ZV5OLgW9dXmU3DmMaWcTCTBeB14GOEAWOdTjuRQk2ANrUif+nGoB56M0vY1SE8+FKlRk1EolGAaBviaN8HLJQCuAslz/x3K+AMReCY5UBJQR+0HRWEwdN09F84Zbe3OEkE9eUQOB2XEJGtOVuvhQBEUBOHAEuHeFa5svnVbHX+IO/+SJ10PtE6EYmhgqorSgmhSVmkZB6MGwsFNeC08Xzk3K8plUhaKCqVxRCBHDQBs14chc9hHMcz/LmFyyePQBTX/Vc/DgZQ2kfQllq8MBZ4MEA5FZt1DtRLAVIgcluxfEmcHmzEWk/g2JAPK6SBz1FyfREOdNZZaiHL0lz0XwICK+mwRVze8Q3fKGJSGFgxvlJX1lXo4OIRDJSFv/NdiIKFzFfOK+9ecNHq1NgYHVFm3u6Ti5kd70FeYhXajEdc3WmR+n5nGO8VpnmxQkKjKO69/EMki07SuT+Mp16LriV/BjFj7SwOU4NpR5tfq4cBUFYdOHYZlGzxZe8uQF4bvvuwwrXfdUkiC9XLht0wopGfEOkwE8La80/bQWs8mY502CvsndwfqaUodzpg1BgDIUJiO32pL4wckZ1CMGDFixIjRT8Q5HGLsc6jus1yMlFxGOUb1Epb3IJD7wSY+yJwQlCcBsS2ZHPIgaeI0LZWNVvZxqYyfUOAVtcO5CdO2mOxZ9EJ4f34oqqXgJ8fdO9dASKViVjhH/5g7db1jVt4ID8mWMVAbWgLiFLetlbomr5/djynmfBCQXf4mtI5guKXhp1zl/lYqCfcCQM00SpeEC3MozCo72RrlMWL3T3hHcDvGv4OBTvDMhtzDQUNh5RyPbADQ+/IdnvML41i5qQvb2nMYl+iqqM2RqqVErtailiITJCU355WFVPIqVFZMTbirG4etmHf+E+7xuqMuQWLcAVCaR6HulOvt3Aw2WRvo2ttHKfUpkoOF/K0cHMx/zoF1ybs/GUPlCYJ5FWVtMMc7gVKeq8nQ9sRQRawM+eaW5YA6ej8oM86E0iLf26x+WOVCl4Fy8TehjD3AapexkJwMKjjnwhOzjPJZsHB364QlMm9shVLfYv2uNmk0C+Y8INsoY72emHqkvKOuSbovWSKFxKFnITHzvLLXDwBYU2UJXgMIUcg7RHz6mMtqaxfWuChNrYH9RZ7Aj3suAx81zSaRPKUqh3yvuf+W8WohvUzUFFmv04he//KEUrEEa1+yRuU3ZfmsVJEwPbJtlsRqTQ7XtE6j82c01yfRmLHmJpXMudOod2PKR1lzKw0tYGoCLU3yPC8ieE4FgoRwwhhlBzjGfrXQoWKJNrnm+tRcqaROX8F6rr9Tmo4/956OJ3JH4qc9F0ux/vUBUuRXCpGAyhUNtPcUkRwxESM//n+R9UoDZGvoKPPL5UkIBVPx+Yun44AJXnjQch4ORojaohhyTuU8HMLQhuFgDULOsmTaXe9LXMFtveeS9T565oHIpKzrwppHBI6PVPswbkQDzj6u9jkcI0aMGDFifJAQEw4x9lHYCmcuqCEkJZWgrKZIAKmIT7kvBRnwd0syBURdSqYyclL1Q0gS7i9LeTT45RCJihClpjMcWscO5FbPR3bVPGz72zex+dbPo+PVe5F9X7YYSraOJ9sBgHHXfy9U+S2KoFBJowNShcurdcoEQHL4WKTHTQuUNXPddigeu6YzHkTTRgjhQGH8J/4X9dOdpNgcLCRkiQiWqnO9GPzjAVjKJz9pUjflMDsMUzSUdL1MXrmX3LLNYqy8QrRaJEZOoBVUgwW9JIX1olDSTJyaXoGpCdpbxY9hijXvqrXuVJuCH6WSIhMMrIZwL2UhjLdfye8FWgOU5lFouviraLzyO0jufwzC7iUwJudlIOaIP267tOy4RAd9T3kN+3fJ+5jzT0R4umqsqJXh5Uk6qf9k2upIoQmHUI8JUeFd7v5SE1YIIhJ2AvBMcyXilgWbeDgUydOMk2SKFW4iRCZS+cwC6yejxgyAMnp/u+sQRXYUhDnHvQlAdBK97iYOOlXqO3nsleFdVnC/MoLUlo4PGwt1wiHEAUVQ8rv/uEgdcDx9fhVBiQwDogqmwOE2Btw6RpI6SbhJpYmQSiyRJPc/lDs+UmoqBFCWWe8OtVrbUx4OygAljT5k2mg8nj8aBrfG0+QM/8gdS5ZtEjwcuszg+5B47lEKb6VpJDgHhtUnJYvuSkMqOYRDX63K5QHAZr0VJhTkeBo/6Lq8pjZqIRz8Hh/vaZPxYuHwwPUYyjwOHUaDlOsDAL7423mYs2x3WTnKhVRy5mU5OJ4N5UiCUCgKGuoS+Oezpri7us1octHv4eCA8tQBrBwOUQm961JhxxjqTvonsHQDwBRkjr3CJfrrkip0KDCJNXB4q/Xc5eBgTEE+NTxQ5ravnYGxI2oPexojRowYMWJ8kBCHVIqx74FURBGKfbIughGShGNueCI/ASFW4oCb/NlXXf4BWXEmhX4SyQL/D7u/oHAIjx3FfP34wkKFBizhdne2ilLX0PbMH5FbFXTr71v0krStNrVi3A3/A17KY8eD/wetbbN7rPWcTyE5fIwkMydDOhFJo90cDoLIbugJLtS2ronWIedMSLSOg5LKYMzVX8fOB38sHdO6diI10gm35LsGzPtRKeGQHDUJyZYxFZUVMey4S3xWukFSpW7KYcgufwMAoDYMw4izb0Tna38v23ZkSCc7Bn6/CAc1iaYjzkTvu88BAFKjpyA94UDoIXkvBgO5XB69OcBPvzw1ZyOKGke+qOP5eVvwf42Vh/9yQndUE8Kj8dTrwOpbUNy5AYpphWvpaZiMtNIA6yrY4z1AlrUiGJVPwc3jQNXw1go5Ca+zXnhtMMbIpLCcyingI8rc33JF/54gGOBPch62ULMqbC1Sh52F0oInKi7vkgVkDPtEuCW9kgCz1LHlwzg588Ed9uD9r2SaKwwGFo3EKR8X/WDk/qUOw0J0hV0GLj8TOULHRhWTofaDcIhso4yHgzJ8PDLnfQnF1W8jNe4AsP2Osg4IJLyVw8CEuv8xwNy/Sx5UAWSGRfZXf/k3wbUCcvf+l3zAVsjLrxieZ4mSaUbqsm+h9Oj3I9snwRRybXcsqRUl4EskvzOZ9vwF6Bwm4j4qn5WaJK/XWn00Gi74N2jbVkFJ16PwziPS8QKhRO5ldFjBSkF6OCSSAxIwZ/L44RjZ1oxfbboAx7XsxvyeEdhk0B4vTZkE6myL6S5CGVsQPBTCQi4BgGInBFfA0NKYQkevNTep8El00mibcBhCD4fdRhNeT5yEQ40V0HQTj+eOco+1m7Vd39o8HCp7/kYptgcSWTOFh3PHBfZzAL/5x0pMenMz/jOiPkUyiTCgQK3g6eF4StRKQjnvCEx4Quw2o41iwjwcws6pl2dghIS6eiZ3BC46fgKwLFiPcyAxcQaarvs/mKaOZKoOumGAg+PykyZg+aYe6FCR8o2Tn7Dd3TQDk9u9fFpZM4VkYugTjMeIESNGjBh7CjHhEGPfhKBnpw4FchKIZIK/AV/CYNnhQFTeIxiGxO+5EMJ3iGo0Uf0vWiJDSnTtr+UTzumLlSsr9FoBKdO3/E2SbKCQmXKYpZhM12PsNV9H19zHUNi0HJlps9Bw8ImyGH6yRbDsZKk6m+Sx1WJaEdzQPYVqmbBRmj+k0nAr1EHdxINQN/lQFDZ5XyNa+zakRk6EkzvDGiE/IcNh5okY7QQSjZ71E5WfgkLTkeeg+ejzANMEfd2s8xp+ylVIDBsJs5BF08zTodY300oeH5Q0ZXnlU2bWmJS65cyPIzVuOupGjEdmv5kwejtQf8AxMAM5UfoHpb45NKwVAHR29mHOql5c5NPfPPL8UvRxh8CqTp40KiAcGENq/6OhbVqK5ISDkTnoRHA1iVv1qzAub4XseqdjGn6Y11Bf5z2eB4VwsJMUB+ZPFUloLY8XmiRgDFBGT4O5yzov1tAKlhkmKRecStK8D+U2w65H5dfJTWwLIHngydBWvRlR2NKCs2QdEtOPh77m7fCyAphq5wIgY9inQu9B5xpbIZ/KKMVchbmtqSfYahaSx6UaJGddBJaq9z0KeWhIJalMOStZm3ySLmvIPFeGTwBgz69qY/yTORxoZXdq1gUoLaKTgUJNIDH+IOgjpyOZSkDXBG83f/OJFDLnfQmFZa+Ab1hAt0fE96ZkCsA9n4iXhVRtSmGuMC8kmADHcl51Xwc8CiqMnGRUrg01YRkOcNDPOkUlySCdq0hMOBhs/GFAz3bARzhQCuGsUt6bLwqU8jiRSCKCQqoYiXQdvnLVIdC0A6EowDO/WwDoQQVvS0MSI4elYdprFmX9PWJ4E2A/6sKSSgOA2jIO3DbJbm1KC4RDZTkcHJRTLhd5Ai/mD8NF9Ysiy1WCDrMBK4zJmJcfj1xRHh+/dX+lyJvVEw4UoUUhavwHAhpX8M3Oa1BEtDybd2WBYGQ0r50yHhAGV4CKPIOseZmtMWm0c6+rwqWkvHgk2UJeTsKIpB4zY3lsENWeLRyBjyWin1MskQTT5UVu+vhGXH7yRBgrEoCfmEnWCc9LDu2A05DbPR/1inW/zSkeiMMje4wRI0aMGDE+XIgJhxj7Fnzhkbi9j/s/fiUiILJB6a+svvNZDUvK+ygFKyd+OXyFT+nv5FEISwodKnvQOl/qtVrdry1PYcOSiqvUTZrh/mbJOrSe9jFbGRFhQ0joRxljUFIZKZSSWchCyTRJYUeCHhLWMb1bTpDpEA7gHMnWsTLhIHhDyEMkDCKrPKSSE/aIV0DmOEiNnEQQS8G6LJFE81Hn2cfMkHkShOwxIpBZAuGj1FWTU8BCy2nXoXHmGTBLJQAcdRMPBrgJpqgwDXMg+QaozSMjCQdT11DHtMD+ViWLPsM6f7VKW9a03V5kDgfO0XzOZy0rXkMHU1Vw00ROacIbxYM9+UyB4AMHG6BQHhL8CkE/0SDllPGtRJSmkSibPuEalN5+ANBLSB9/pRdGR64IwLJyrGD6w89IsHQDYBMJ3mxlvhrB9bbuxKuB+mEw871Qh41G6Z2H6e54BR4HYl+JlE04hIRUUkOUPWJIojL9KYpCrD+CDHDGpX9w86r45gZTE8HbtdaQaEwIPRiSEJoNG+39jkgazUZPAwwNvH2Tuy8xZZb1gwPcNMFURlrRMzWB1JEXQuvaRZMEzOvXzZcj7JHAAXXUVCRO/ST0XWvI5N0slQEbPhG8M9yzi4eMKTd54BScR50192r0QGMKGdLPsfa3Eq877zryX1c2Zwmg8rWoSSvPR9h9zlTy2uhQ7GXHhEKEq6IUwlllEDwcBihpNJIpMMaQSavQdBOqoiCguARw2ckToTDmJgSmZBqeYfinMybjvpc3WeMUAtUNDccxvNE7D4pcKPAkTM5crwYR5TwcSjyBt4vTcEFmMVlfhM4VJOzE2VQyZ4OrKGkm8qWBS0Jdi4dDpaGSBiKkkn8cnsgdiQOT29Gi5PBcfmZZssHBU7lZoaRPuaTRZohSPwy1Jo121gjxli9HJFHvbUA4UdFr1pFtbtKtUJYpNbo/Lv/jrn+XnzwRPZsy4NmiVJ4lM0JF4IhDJuI3b56PmcWl2G02Ydwpl0b2FyNGjBgxYnzYEBMOMfZRhFiG+0oEywqKhlDlragA9iu4g3VoesFfiFAWB0IfMU8+x+Jf8rwgSBTxL+ngEE4+cM7tCCZWZc5NFLa8H34OPqQnHOS24/31yQ9RLkIQJxFsuh4QCYdiDkogXwFVHzD6OqVdaqNnGpb0xW/XOrZ5yjdxPIUwWtzklRMOgodDpVDqhfPyj5UY3sa9pqKc5a0ClboGuGExfHCapL0gaDTMOBmpURPROOusaFKhAjKkUiSaR0PbsS70ONdLIYRDnx3aguPYdHh9Ck1KAZdkFuCkutXlCzNbOc6s8ESq76NXN3wK5EHxcKCVDqJynrpcAQ8Fdz/ce9VR/aut49F86X8CpgkDDLptxWuVDV1YELU+J0/9JLTX/5+3fdzVPjmEulxIdu7cs4oCBg5FTSJ15MUwDR1Krp0gHAT5CI+D5KwLYbZvhrF5qXxAUa2qYXkOQsKRMTXhzgumVqbA4dKa7pszlVjQl4FLnvjhb1tNAvWtFlfsTmWHhKWvMzdFQs9exynPiUQKqBPWPCLGf+LMz6Nuv5nQihr0to0wX/kTeKEP6pQjoI7eL1CezimRAGMKlOknw/ATDkxx837IpyM/H/2OWpwDSGb+P3vvHa9Hcd6Lf2f37acf6eiodyFUQBJCgOgYMNXgAsY27rmxncTOTX65TrOT3DhOuUmc5uSmOIlvHEOM7bgX3Au2AdN7FQgJ9Xr6edvO74/dmZ3yzO6+R8IGtF8+6Ly7O+WZsrMz32fmeQDYCgeUKihuvgaN2z4B1Metx8IGuIWgrYSxRIj8I8xwas982qRSRFAGZFtSikc4TsGIe45331KkidBxPTDC5B/lc2CEH6PCwSBlGQPtbJx5SNwkQYAVysqUjcP37bFu6XANF5w6x7pvgjencPqqgVDhkHTCYWAeRL0P9MTjC7VjvAnf+alO283e4AWM8C780chr8Af9n0sM+2cj12Bd6Xnsa/ehwhq2wgEM49OtjIrobJicgcIhK5IUPlnw16NXYE+rD2/v/iFWF/fgkeZC/GB6Db493fme+G9On4J97T68s+cH1rM0Hw6dnh5pJlAJ07zoVFjMVQQAAQAASURBVBKIsVz1DZOGAW+CvH/EpXDgFdIMk+j3xQJd1mo5+o5zwhefnF8TceX4Gb7gvsfwi2+7DHc+vhmru4s4/aSEoyc5cuTIkSPHyxC5wiHHCQZ1dS6UBwoxEpG31omHBLI75NwNkl+ZpOrPQYaJszDYCkOpwA1FgnMdptrAAEBrE8R9nRjiQYDmwZ1ghTIKA8M6AZlg9qd5aBdpSsir9lj3u9efD7+rj5DbVSAzjKL44RxeuabtD5R+HDiPFRrtFuq7t2Hy6bvR2PssqkvXo/eMq3Un08zTCP3CoKlwUP09KHWoEP28MaWRQgBQO+kM0tRUrHCI0+Ipq2u/1ge9nxgBqJ23XNjWTl/Ymc6mdfHC+H5Gk0o9m16JgQveGEZjDEE7tjVutk/tpDMwctunpPzVk87E1JPZzNiYKKT4xdi+6wjKRFUM+BNAE7io8iheXXOYQnHgpOJenFTcmximtFJxfqq8RwVjwd1WFA6MIdW+/IzgCRM+zCDMiP6DRN2j0ueUo1KaQs6tX1DDMcQKD6Yo9RiLyVxvyUb4Ry4B9j0Nb+F6eHNXSQWHdOEg4oGwOU/A65kNb+lmBNvvARhD9fy3ac8pMzNgHryhpbbCQSZKkH+FIlBMdhrNGEt2Gi3Idzn+Kc/UujwOJxxQqEAdQ8Tw4S3ZiPaBZ6U8lUt+GS3KdBHVF5SH1nhEjD2sZyjqo1HjUjvnq71gzAtPu81ajNrr/wj1o4fhDwyB8fjEUbxXgJA1sr1N7fAP20N2LqIoPKoe+xkrVqi7QLECf/7JKL/+T9F+4gdo/fSzVprkSxO0FBkc9dqp2SkpFiMVZYLILkTEePzpTRgUKKWHV1S6hE3SM6/g6CtQxgK7bAFhvmuEHaNJJYOkdJKinge0O/TsEPWncIrHyTL/1nWr4UUKSIryFODNOqrlsK6TTPqwrnCuwQEM9MTjC31igaEFHz5xWq8FH1NBEVWPJpGFLf0sxP7hoAvfnw4do68v7rSet+FZyvdjRdoJh4Ptbsz2bQVgFiQpfNTTHC5sbw0BAP55/JIZ5a+D4YHmEnxraj0urT6sPUkzqdTpCQcA+PH0STin8qR1/y9HrsL60k5yPsWiE48lg/SfDIqoOfqXC2edthwg9ohM8TI5Pgjn0y6Fww0XLNKuOfUtC+yTN+EpMP1ed62Iy89YiGaj6R7fcuTIkSNHjpcpcoVDjhyARl5b9yMyWTocNh2TEmmQU0rtxAG1KzBBBjMp7bFQAigKECIGPc3V7/IgwL7P/h/Uo5MKhb4hVJasR2P/c2gefB6sUER16ano3/oa+L2zwIN2tPsTmH5O97xW6B9G31nXorLwZDQO7MDBr/0TeNBC97rz0H/e9YakBoFCTcqlQsYunenoOJjSF4y83cLe//ojNA/tkvca+7dbu+r9Wq9GaEjzShGah/cg4FyjImKJQpnbhvkMv2cWhq76ZTROvwJ7bv5D/RmldAHQtfZc6fDZTKs4ON/KWVPAqJKZ9zKcIijPW64kwe04nCf6cBi44E2A58ErVdG1+kzjqUpexukyhHXRe/Z1GLvrK/B7ZqF7y1WoP/84AsIcSRr8gWSFQ5G1SZNJg17YbzpVNiShtPpstA/ugFfrR/WMV0dKR8i2OTLewPZ9+q69dqDLdkxOuh3QSEkxfPDQVItKKKqHZGxlo34ShoGFYaL0qO6mmjmDosB0ULmk3MWNV6Na9FAXbkxMkz/GqSjpjJkRKreoPQrnvhXe2ovQ9EoozVuMRr0B8Y4x4oQDYx54ErFLOo0uus0leQUpG3MpJQpllLa+MSqTUgDjpAMDhzdnqZ73rMWaqaEsYEXzhEN4VVhzIbzeOWCTR8EWnQKv2gsmiUGaBLdvceVnWM/egrV2uHZLi0OdzGG1vuhbHfZRVqqAdQ9GigpCHKLdWKkapkG0j9evK57NzQF2P4++xTyg/SmUKjpxXuzgNEqbMC8jlRPHdkqMeb7DpJKHhbOrGOwuoS0UPwGg85ZG/i7H3EbdafDcpwWUGZKdLEHkjgY1AHXsaM3C4sIhR0wanPkw69K9C7vzOldNtHFwyz8BYBOhBZ+R5Dsr1VAphnXtMukz1TUPA3JPCcegonBooIhvT63DJdVHEHBIZ8T/NbEVb+++TYa7efxs+ftTE1vxpu6foEz4K6pHy0rKN4QJdec55fS3/QI4YU5TONzfWIKNpeek0uG26dU4c81s3PnYwdS0kxQOfz5yNa6s3Y+Npc7G4GPF061hXIqHjbvJfZYi6E3sb/dq15+dPAMV1sDm8nbt/oGgF9+bXofLuh5DlU9qz8QY0VctwmNAZDkMdzeW4/wKfVr6x/XV8vc7LlsOzgOUigWctWYAY4TCgUfKMxPiFEeROF0EACct7EGoFEyYlbRd5jOV9ZYy3inTvhw5cuTIkeOEQa5wyHFiQm51dJEjYnVGE+DiiolnLh8QYrKZqGggfpuywEhPmk+iyyXli7d00vlqcjFMbbtXKhsAoDVyAOMPfi+O1mpg4vHbMbX9QVSXb8LUs/eD+SXMvvLdmNx2n5ZF9ykXoOukLQCA6pJ1WPiuvwZvt+AJJ88QRFNKHSTO0MNnfpe++GlpZpI4Jp+6W1M2CIzc+WXtOtzdHxMnXq0PrFQNTy0A4M1ptMePgPUMQl1RcIXINM0p+bVeABzF2Qut/P1qD7mg6T39ckw/9zDaE0fDG56P6pL1GDjv+pTdq6HsdpeM+koWk0qlqqHLUBKLZE0yqeR3D6Br9ZZQ8RYE8U511Xk6KTfQc9or0bv5cgTtNsAY+i54E458/R/jtHuH0B49kFqGtBMORbRJ585DfjZTWJ3gjqnluPyGd6LdbIAzzxoLntpp59lqh0w6i04MHKtJpa6L3o6J7/0/4y6hoFJvC2WAQQBKUlzYc7c6G48VFWZ6Sdm7b5J5izTlXS2qrWzTT2jY+XjMgz9nGVqNlhGEJqFDr9iUiZUoLkVQMpZ6wgEAecKheOn7UBpehsAvIzBIeApepRvFTVejed9XgUIR/qlXoPW9f6bzdoGUNaxIf+Ep8H2GdqMu73ObhXaCC78y6qmMSjfQPQiMH47LsWSDHc9EWZjPUYh+69seKZ8Y6HFQ2N4mlEv+8Aolf8d74wAjfA6E9+J43qwl+vNqrzNpztvih0OCpP3wKXCcrunpruJXr1xpZhtdqzfiXfuUFIyx0HGxS0DPh+uhnCsQr1WBUB4LO/VfmDwdv9zzLRRYgLGgggproJiy05wTykLfY3EfUTEDU4C8UArrKeqj9aYtDzfIzkrJx/hUC58cPwdv7o6d3VdOuxK+x1AteeAE/xlwhiOLX4H5yj31hAMAfHlqM+5qrECLezgYhH3vocZi3F1fhrXF5/Fkax7ua8R99P7mUjxyZCF+pfebWFbQiXjhwJtnMssT1x2loKBM4Rwr0hQOU7yEfxq7BOdVHsd4UMH4svPx1kuXZ1I4JMm7L+jHPfVlP3OFw1PNuTgaVNHvhXPYRv8S4HByHKocu1v96PGm0eNNow0P7MwbUPhOQyrBAnj4Sf0kS+EgUKqUgSlD4cB8cB6+WwM9JRwaDR0r3zq1AVtKz8hTNKzWDzAP3zi4AM+1Zsvo1ZKHrWuHQqfQCWbNkkwqmacrBDx1E1dgj/kcAA8S/HVZMeJ/c+TIkSNHjhMJucIhx4kFancdh01OUOYqXMlpwVQFAbPCySfWKYh0Up2UT+77Y4rMSQoQx7Nocj257d4EOWIE0xPaDvx9//0X1m6f6jKdKGKeH9vo1jQ2yDAP10kN/TQJR0HxuwAArbHDslqmnnsYh77xsbQMAMQnDsSpEcYYigNz0dj3rAzTPLQbBdP3gkIMmM6K/WpPSLZ6vmZayav2hA6g1eJF28mLA3Mx7y0fRnv8MAp9c6Tdea0dlXajzChRTZ10MgEIT1bQUNJnzDpRoufhfsbACd0bNwNBkJrlZZvQ/4q3or77KVSXn4aJJ+9IVTh0bbkahb6hxDBF1pJOnlUMe8df4fCdh47glVc6FsOcY2LaXrS2A+N9PQaTSl1br0d55RZL4cCDNuTJgCg7xsKzAPR7qYw1iMOrY5Gl5JLhbLmk8sAwy6IlaabB47hqmvopixQwQTrD7nrMIStlZsfzaFNLhtxm5qxI20APfThEF9SJlmIlvG8qy7l58iRuv9LGK1E8+XyAeWh7JbQynyOJsiiUyfqkTLPRpvb0/qKF748pUB5wwAvlKp55A5rf+ScZt7B0s55Ws24mpeRtkNmKsouru3Ypp9FCMUDUvT+8LCaMBPHNOVSFOeeRclWdDXAOj2hvmVekMGPdgyiedA6aT/4YYB6Kp7+WnpcA0XdWGR/izCCUzTMFByP7+vw5vaj0xGalQqWPummATs2+E8RxqGieT5B7enocgL94A9o7HgAQ7or+xtSpWF/aib6IVC2uPBPBtjD8ttYwPjJ6FRb4h3Hlta9A5ba/BRop4zyhSPR9Bq9nFrxZixFEJ4VKJ21F45nOT8NpJ9aI8lZLiuIl1EygGikc7m0sxdLpA1hd3IMFG85AYeE6AMCaRb24f1sLY0EFPd60TOtPRq7F2+avj5IKwDnHQLetSNzb7teuW/DxnxPnOcvQRAGH2j2WwqF3eD5gW9a08NP6cu26QSxHX4gTDtO8gIDT+mAgNEt1IOjF5ybPwMkLe/B7r1mHrKdY0kwRUWV8odGGj/8YPx/XVO9Fb28N7JTrgWf2J8ahynEo6Mb/HbsUq4t74A0swHs3bMAfL67jt/71ARnG5cNixfweFMtlBFPGA+EXBxyDvWWpcJjgFfzpyLVYV3oe1736XPQtXgGv3cDBrz4L/kSoLSkVPGxY1o8ogRB+gTx10CL6UZBiUgmIp0dCRvMeddqMG3Mq+/R8rnTIkSNHjhwnFnKFQ44cTsRkQnwtTDSobC6hXJC3DGa9w92RZtbab41rIHwrcH3nPWk2SiXueYCpZ+7PJo8JY5JfnDUfxf45RHlhM+EWuWgWUCWT9LhiEeD36AqH9lhoQqE9NY6DX/2nzMXwu/oU/U1EgA3O0xUOh3ejumQt9LaLZWob5py8akzyD17wRrBiCcHkGHq3XAVWKGqEjfzNAa9chVdSTHkEAWxnzhEB5OqrMt3wqufUCzB23zdlu1SWrMf0zseAoA1WLKPvjKsS60e0SpIPB6dSYwbrLMYYamvPRW3NOQCAyW1ucueZ5hB2YB7esOkyp0NkgSJr006j/QmU0Jnt4DS0WBGex8K1aURQsmgnMACMJykcIhzLCQdWqtDvoWF/OBw/gqjrmCeouMa52HRE/K6ykLbMuPHX3SlcCgUrXiSqVCJYCUEZToTfACO+TNYxXhEnHBjz4C06BbjzM5FdfcBfehopopqZ0zyW8OEAFtq2NmOKEwFKCSgfFVwqIMLxkpW7Ad4G2jwkUjvZlVkoa3WiWu1hzPHdiYXQrv2tb0L79psBAN7gAmB4NREP8OauQvWyX0H9uYeA+evg9c/VAvhDS6JyhP3Xm3dy8tiiDYvRBXVKLNq9zjz7XfOHliXuQXD5NQKH9A2hhVdPkUUJl7a+AcW1F6BQqqBVHQjLR+kpg7ZOekXlEl/6YzLZwTx6rCF2/OteqiDnG6lIks/zScfM15w1P8oiVHQUT70MwdE94OOH8dXxUzDCa/insUtwaeUhnLFhCSpbXoX2U7E9+d3tAexuD+B9S+di5K4ygkaKiK4TDgCql74HrcdvQ9srobbhYjSevS+1yFb6fol4c2PUhMNaBb21Ig6M1NGGj89MngUA+PhZW8GCNjg4rjh9Du7ddhSfnDgH11bvQR0FfHriLBwIelEu+tKcEgD0dx8fE30jgX3iY9aCRcDTdPivTm7EKK+Cc+CuxgrtGWlSKeWEw/en1+DCymPZBY7yacFHCYRpMuhmqYpFLxxGMx5iMWdnJpJ8bLyQeKY1jL8ZuwLXnroIm/oHAKQpHOx6n+IljPEq7m4sx6pCuIlmwWx9c4lLQbRifjfYCDGuMA+iT87qKeEp5dEIr+Ho3C3oXxQqe3nA8dpz5mN8uoUjYw286qz5KJcMx+6FMjihcKBPOAiFg2vsNufU+iPGOenDIQwQB+SMgQUc8BVlRI4cOXLkyHECIVc45DihwM3d4Kp2QOW5nR5O1cQo4h7KhJJ4rjqltuQQQQ2CRw0jbyh/dW45vmcnLe9QRFFjz7MIpnVb8oMXvw3B9AT87n6U56/CkR9+GlMJpK9AZdE6N3kXF8Yoh3uCbzMsOswTB62xwwA4pnc+Bt6cpiMRsB0mcxT6dT8OrSN7CTFjVUBg5Kea1PBqvZh1yTuMspp1AuM+yLqUTqCV3aJ6MKOvg8Ov9WHWZf8D4w/9AMXBeeg/53q0ju5Dfe82VBevQ6F7INz5bhbQIBBZpQpyizgQ7ujliMuYWcmmhg3zkvb+o/yTiPePjb8CvQN9eGOhDPAW/IF5aB/ZQ4ZdWnCbRxjyM2zP7ADqjmGdRA97DHXCodUOdLLfUe4HG4twasl2tqnlT5kBAeRiWRuuUhbDYYsr72648k6JaPYl454yyPEsQmjyOJysS+WC6Ipc39Equ6b97olxkStjK6kk8DywchcKZ1yP9oNfB6p9KG68ylZomHCdcPCK4RvtKr5JpMh6TBpnjWvPo0lsB1ih7CDYEyJpiu04oLd0M7z++fDGD6C05BRMBa5EGIqLT0F79gq02zaFx0pV+JtfjfZ9XwYqPShuepWSt9Dzc7vbqSY3KJNKkZkjxhi8xRsR7Lg/vD9/Lbyufn0zq6vPeUwZ+4S8hMIheie5rCMOz2PA4AJ4jAOthP5DKIxkTSd8QjOBeQB1akc5YRWeUnAoN+QpiwCsa5YjkwQBPT90Aq+YMdzffwquOGOelpfXNxfV1/wewANs//TjwO4J7G4P4Mf9V+GCczfAQ4DAdVIii/LWpXDgof+i8unXotFoASmKbRcY88LxJaDNX9UqYbrq+PT6i5bgT2+O/WS98RXL5EkRxjhWzu/GueuH8KOHgcebC7T0ykVPJANwoFQ8PicHQj8ZOlj/XACTdmAAlSLDN8dWkc9monD4ztQ6rCnuwrA/ikPtLszyJxLDi3za3AMYTRarI47L3I4Lab4Pmgl+Lb4zRfivOc4oFz3p+D0JVDlUU1Sjk+GmDM6BrWtn4fZHow0+DnN63dUiMEEpMn35mRjstZX665b2hz+i12Cor4z3X38y2s0WAhaPQWL8q5z5Okz98BMy/hcnww0AlCJEnOJIOuEgMrfeUjl3SBjLjO/fMSmCc+TIkSNHjpcwcoVDDombbroJN998c2KYet02Z/CygMn9yvsKOc8jkjeNYJMTURZPOsUBB2fetBJAD0QccTBmsZrtdBeB7Zj41vdu065rq7ag55QLwiiRE9uB82/A1LP3u3f2RCgO2f4KbJhKB/OZK5qhbAF1wuEweNDG0Z98LoMcMUJ/C6psQKFfN8/TGj0AixxXqjlo6AoHr1ghSFEeFYVQOkgikdo5DJ3gVcwq6boFXUGg1mdt5WnoWn2WlL80tAiloYXpKyKVRIMHVqmBT9sLfI9ykkqkQbFjMWnGwTxmc9MJpoVWLupHX1/o6A+co/f8N+LIVz4KtJshydTOdnJhrn80U7isYMIMjuIcOYbDpJLiHJQxBo8od4P7uGniHBxoP4Qya2Jbaxhv7b0dzCAkWamSMFpxKZPszcyod0e3MN9c1cyOpliRtpCyrbiVwwgJ1D135idjmIXmXLvNlDGUMU83p2S+C4TCgUW7M/1VW1FZdz7q0w14hfCeSMuSnUFzGKtBaWOyvco1u74dSl2VT49HC47S1jeicdt/xEluvQH122+h5QEI59U8/g6aYakEuLIPngP+0FKUhhaGfmga7j4hxwFHwoU1F6K24VJM1dvwWVueCMrkvYBzgBGOp0uCPOUonPNm8OHlYACaS8+Mv+MuU0AJxx/idBUYfh2E9LKxxMkHaj4QBArhpaYQhu9UaafJ4VA4MOnsWc0vNN9k6bbEmFLtRWHlWWg9fQcAhsKZ18kiCtM+REbw/CK6Ln0Ppu79GvxaD5Zufg24F5uK4kqfYpzjf7xyKb529z60OfDqcxbLpMxTYlpZ0kCEESccrL0PHfpw8GYtNu7YctbKBe0ZB7Bibheu2boQtz96ACvmd+OCDcNW5b/z8hUAD/CjR3Qn2eWSJ2UXWL+kFw8/d2wmBMe4/a0PN2gQ3nsBXHXJRjR2zMKt99g77CkfDkGKSaVRXsOfj7wKvd4kpngJfzbgHssEmvATFRlqjcYOhbN9u9JMKjUJQv7e+lI83FyIexvLMuVxLCgXsikcqPpRFQ77jkxD1Mlrz16Aw6MNHBpt4KqtpwJ3xX7RDrR7AISnc6jNIsIfGecBZvXY487K+T3KFdd/iX84IN7I4tKNaDx9J9q7n4A/ezHufCr0O0M5jRZKCJdSSSzjzP1j8ftP9AlxOovH4yDnARhU3zTZ+lKOHDly5MjxckGucMghcfjwYTz9tOMs9MsFXCd6WaLTaDUaTRjHwQnynFigy9/aZDmWK2g1Ud/1JNpTYyjOWoDynMV6GmLnkSW2STIos2OAIJ+5NL0hFB31PXrbl+etMOIwFHoG0Lflaozc+UVTAA2lWZTCgSBCXOSNegLERQIq8HuMEw4j+7Hvv/8CraP7rLC9p1+B0bu/TqfT1afIGeZX6NV3arZGiN3xCkPL67qhWnuHq7VyoRER7/FlloWKUsdy9zniayV7s/K5JpdbMBbJ7ZW70CYUDtSOXpkfI4qsNDPZrSHpLXLnqcAfvO00eL6PZrMFgKE0dwXmvPF/ozl6EIVqFw7c8mFnXBXnLWwARzMFzQTPIG3D9y0mWyembeVdux0pH8W4QhBgLe5hmpfwpanN8t5bh/YD+57Qwon2KK46E82n7oxueigv32xtdmeRfJST+1hupoU3gmnPmKFk0RUR0V/OrGdJECab9B2EXJcnllpRTNALflMBFPK9yvjDOe2rgXkaJ+5WFuu5OX04eIoPB85RXHcxmo98B0BogsgbmC/Lo39aiHc8LIrB3AP+4lPhLzoV7ecfAhs+CcVlmxMVDiiUwjQEeWuYSQJLPc9hPOXQ28HSCkGw2Dqpw2M9Plfvu/Jx3aO+hxGU95R5BRTXXQzm+WhN1fW4MD5FitLYPHHDAXhzVtp5tRowvzOmmHLMM1Eo2p8OS56ZKRzg+Q5/Jcr4I16LIAj9g1On76JbpXNuRHHN+eBeCeiZFT1z9xZhTqkwdxVqV/4aSgWG6XpLbnig+kxfdxHvvGwZmgEH8woQZksCx6nAYzrhYKYFRUGbAFbrB3gAFEoon/laiLlc6OvWPnJULftWmowxvPrcRbju3AXh7m7PR7vZ0l5x32M4fdWArXAoetb84bpz5+PoRBNjUy2MT7WcCprNJw3i8R0jGOgu4vmD+maKOrfrsiA3bQA3j2/Fm7pvjwrVh9LyTajt303m06R8OGToxy34OBz0gGU4utXkHjg8koCmUCrGY/yc/jL2H03ecJVmUok6xfGD+snY3pqTSZ5jRbnkoeiHBHubM/iOk9wHgx4sMjxLTwX6N1BsbhoeqOCDN65Hu90GY8A3Hr0Im8d/gCb38bnJLQCAnlqR/IZyRfFbq9h1s3xeVzScGd81ao7Kw80dtct/FWg14Hk+Jv7q7qis1AmH8F6BMq+XCO6eunu6olCuCdTYWabxOXLkyJEjx8sIucIhh8Tg4CBWriQWxwrq9Tp27kw24fGSAzV7lFxTJ7NDhR21SDuxm9Ydb2r7Qzj87Y+jPTEin1SWnoLZr/yFcOe9yxG0oCZUQtmxuzdpZVzfq+9KEwoHcxd+7xlXo7JkHXizjrEHv4upbffpCTGG4uA8JMJUhDjN74icCd8FypFmr1TVzDAAQGOPfmIDAPrOuBq9W67CxON3oD1+xHruG86nwYFCr3HCYeQAeBDojkoV6UyTSuGO/4SKl9XL9fpwOWEV/VXjW01ST7ni5nNHmokC2rJ4lRraI/o9VqzInd9kVCkLkWb0eojlH1eJs+gnKyR8sjzfIsr9nkF43QPgk0cTyqdjsL4rc9gs2DPSwk8eOYAd+8dx0YZhDPVXVBEdTqN18oQiAUnShNqdHPlwqGy4HHz8CIKxQyhvuBRepRuBYidG0GbSNj/CdqR6hkaxGa/yQ88cwfzBEga7jMV3fLTAitsZ4kjh4QkiQeLVYdGucfHXJsId7xtAnnDgjEXKNweBTu7ghmZiTXtUKIS5RcrW4mnXgPUNI5gcQWXdBWirXrOh5iuTTkA4ZjC/hPIr3gXGGJqtFliaOYlCUXNqm6hTUetX5Kkoe0xaNv4EqAosNVSodHMqNBynO8AcjpPV/IjTTvHO97ROGfYd9TRhLKMd1x9aqoUAAG+A+D4KYo3rfai45TVo3vV5eatwyuWkVHr3mCmrxcI2NyFJejHXUIVOTA3e0DIE7TYCtc5d0Qj/DWL+wY3+ZOuS1J7DX7ATDuHnMvKnklG546/YgsqW18LzWFgXXCgOQxmLBYamYkZr2VzjVIxaNjmdNEhNHpp+8Ykd7OWiD/DQT1cYg2PxUA0feut6wPPwX995Ft+6j7br/6uvWQOPN3FkZBK/+s8Pa8+2tYbR4D5KkXmi+vA6dCv539lYhbGxKoa8Ubz5+mvB/AK6yjTZT5H1ttrFDZ5ifgmITRpRToQpFKMTa5wzvOOyFfg/tzyaGD71hAPhwyHtFEenePPFS/HJ72wnn5UKvuwf3DGytriH702txabSc9p99YSDAOdiHBTzd2D/7M34zZ3zwcEkqd9bK5DjijQtBmDpXN3319K53SiXfDSbbfDAPLltyKD0HsYYeLECBPF4QzmzFsqsUsFz9DJ7jmPdKNWARmw+jEXrB/Lzk2ENliNHjhw5crwckSscckjceOONuPHGGxPDPPXUU7j66qt/RhK9kIgWaUEAtFvgQQu83QLzCyFp6lFHYMWEUbCfaloUi2ZMWMm1SBhm4qm7cfBr/2gRBdPbH8K+z/455t7wu/DKtXihKe2X8tBJGvMjR7nGpFaSUowgI2IzPGOP3Ib2qLIrzi+gOHtRLKNy6oAxhvLcZQDnaBzcaSkcCgNzo0W9MbOWcjuIOYVYcLvQiONyo34LPYNoHnKTxX7vbPSdcTXAPJSGFmOKUjj0zTazgVftBiuWwZvh7jbeaiCYGlPML4kI0S/DpBIrlokuQvUpcekoPMWvwb0Q07KxEuJ2AKuP2p1WkNHg4QkHE67TDS6n5vRWMbGANPLmyWSRJwgh2cXiBXUmkilC3xTdh7z+edh3tI4hY+dfEhrcRzsAPvqF8NTBoqEahvorkYjRu+90Gq30EWLHbYsiLwolSw3BCmGbeN2D6Lnyf4K3m+HO9ITxiBqsRHvIWhWnxMR9BjRbbfzV555As8WxZKiKdcv6cd15i1CiHDMqp8y4klaW9TgTGSaF4SxM2yyaKz0RhhljCzht1555ZL9mUSIUncOYZ5nTkYhIXUmceB6Kq89Bu1EHK5eBVmAIC6UNdHmZZlPJJjqyKNLZwPwonbaVL2MsUgZkaK8MDSpIozBtR/zou6uP+8r4YZTJ3T04eMuxU9lFLJlZSLJZJcOJ5CKCt3LZ+zD9zb8HouvCkg1RraYT1oVVZyM4sg/B4R3wl50ONrAA0hGH8e1UP/OFLdehdddnE9O2wDzSaTb8AjWrcTBrxiWP+5veS4kd6cwHzPYVCHh8wJMjVPZZ+SrfYdeG9+N0wiHMjaeOQwDoE1IK3nLxUvz7N54FAFRLHi7ZNCy/s2aOgtgVh7BCM59xiAIhZ1kqFo3+HCaT4tMh7OvdVbtOpngJ/zVxNi6vPoDRoIq5665GwdfTerS5EB4D3tndB86BSim9vgR8h5+FmaIRfR0zn3BQlCcnL+nDe689CX//xSed4VNPOJCnOI6vwmF4wG3SslT05I5+Sjny4+lVuKO+CuOEqaxJReFw7VbdR4j6PemtFS1fDj21YuQbzIBQ5HNg7kAVZ62ZhTseO4RigeF15y2Oh1k1L7jH23g41Z9T7S0UPYUC4Da2GQugKj5FFrXz3ozJ7/yLDF3e8lolqv3dzZEjxwuHp3ce/XmL8DPDykX9P9P8du7cif/8z//E7bffjl27dqHZbGLWrFnYtGkTbrjhBpx11lkdpXf06FFcddVVOHjwIN773vfife973wskeY4XC3KFQ44TAjxoY/zRH2PyiZ+iNX4Y7YkRtCdGtB3xKlixgkLPIAr9c1DoH0Z5wSpUFqyGV1bIIoOTCv9SrLAg6xHtYFT+AmiNHsShb33cxQ6jeXg3dv7j+9C9/nz0nfUqFLoHAXBM734KR277DBp7ngYrllFdsQkD516HQs8s2CRGTBcEjWnU9z4D3m6hsvBkTO96Aoe/8wktdGX+qnhXdULZyvPsEzGVBSeR5dATs6/NRYQkoDJO2IuzFyYqHPrPvEamXBxahKlnH9Ce+1398Iolvb0i4rvQO1tLuzVyAF41tC9rkp+B0ac81YRKR4sPqoOJS0F2RbSmSjw5GTvjuRInNqeUXUi/0m3d8yLHqySsXeWxfHKdKEzwMEEtMq2sLMGkUnzyI85D6h6ykEwpqK07D63bb+vI6a65o/H5g/Z4MzFFOY1W2oOBJMkoe9CBZyscUCrHCkqo/SaCWWcc0tRaLAQB7TBJGObxnWNyp+5zB6YwPt3GGy5cbGy/VqHka3ZbR5Tpehv/8a1n8cTzY9iwoh9vecUSlHxI0yZmOkyQgkpaobmqSMFBKI5NvRsruu1Pq+JqTCAAb8VZCLbdIcP4K88KldmFUmRWJ4bn+XHXYpbIDpiEeGAoQAiL/lq9cnhLT0Ow/d74ea0frFRF4Ywb7LyUEx2xslN93yjCzXweU0ZmfCtOdGIicUc9ZTrH1WWFeM000yidDNQuZUP825+3GqVLfwV892PwFq6DPzAf7ab93mtpRmM1K5ZRPOsGeCwISTJj/FZ9GohrgME/aWuoBPxpB0oHj5HkuKWwzVI9hH5Z3VRAwnTCrJDuXL2HSCnNOTQTVooSu+2Yi2VRPlNhLB8OMvkMCoee2VFcoSgCuDCrxznOXT+EapHh+YNTOPOkAVRLoWlAUWW6mS4xxsT1qJaUKrfnqS+RTqCC89iptAPkvoAI9zaWSf8Df94/B0WCy5eOeTknFTcu+Ckf2+Vzu/DM3nRH0QLihANlYkdgIojna8WCp73HG1b0J6af7jSa8CVwnBUOSY6uy0VfKqRGgypm++Py2VRQxKcntwIA+phdp+KEw9nrhnD5lnmwNHpRt+zrsr+VPdWinC+r8IdXaH333VevxGvOWYCuShG93RVwufGC2KgEYRZWFyH8ob8DVHuLei/6DHz+yWjtflw+84ZXihyseOr9wtKNKG+6Cs09T6K0eD28OVF5LGWDeOc6/bbkyJEjx88Xn/nMZ/ChD30IjYa+ZtmzZw/27NmDr33ta7juuuvwh3/4hygkWSFQ8Kd/+qc4eJAwT53jZYtc4ZDjhMDYfd/CwVs/ljk8b06jeXg3modDe7Nj934D8AqordqM/jOvQXHWfMjJL0nyhvfjHbAW7SPDHP3xf1uKD9M8EMAx/vAPMPn03Zj1yl8AABz4crxjkjfrmHz8DtSffxJz3/hBFLoHomjxSQwetDBy11cxevfX5S58r9aLYNJwHOgX0H/O6yAm9JTMAqW5y1BZtBbTO8Oj5uWFJ6PvjGvIsNq9JFKcglZ9PCYi5M5OhsrC1Zh84k4ranX5JgxccAMKXf0QpydKQ6bjRqDQN2Tdk89MhcPoQZTmLifLZJ1wKOkmdKxyOR84+hRTdv/LYNyua4UclNdavq68peSmoNo9r0opHERZBRGkE0IuxQhTnlsSqQSpgywqyNM4oawMUf8QO/k9D4T1+47AimUEhTLQSA8rYCoFdu6fMEzOAKNJCocIHmGOoLurAozo9wJid7JXKMNqZwcjaxPAjvri9MXD249qwdYt7cNPnziEZovjjNWDsd8GKM3PYJHkmkDyFQ9Z+LuePIx7ng7z+fEjh7BpRT9OW66eNjJ2PxtmtphgsLXd4XEYkjSnHD0rDiJl2kZcf90lCI7sAsYPorjmQnhdA0DQAivVwA2FA/yCVITIsY06FaTs6Jd6bJf5Nee9yBwUGAobrkS7MYlg4ijKGy9HsOR0sKANoTihFL6WLohSThnxbBGJfqaW3YUk9lNNz2pjJQvXCQdAjhLWWJFlxyqRn4jkDZ+EwvyTEAQx6ayfzuAI7fpTY7YglnmkiFXKI5W0en0y5qFw8gXgfhmt229KEDoGYx7gE4SlUPRG5DfnLPZdkHpaRiHL1fqhojGfMM2u1wdXSF0rf5F8ECBwmFTKdsLBJoY1olxtF+LdK224HI0HbgUAsGoP/CUbpIJOV5DEcbacNIjNq7hm5o4ER/xdtzlYtFpEuR19V9wmT6ABeMdly2IlDoDXn78Qn/7h807RKkUfFN9dVMzWeBkUNAIFlqxwyOY3J8bRIDRV5SL5x4MyHm7G/scEeS/azFFNEmknHKh8j2VOQqHgM1x2+lx84+691rNykcGP3u9PT5yFX+79tnx288Q58nfgcBp98uJevOvqVWBBALRDPzTxcBx2xmrJjlsr+2gYZuTKm66AV+tFm8fKZ8aBhbNrCKzhofPvgRqDqndxwqPgM3hnvBbjX/yzcD3FPBROu9b9KVOHb89HaePlKGy8Cj5vod1qh6OzpiCEMUYe3/bOkSNHjhcK3/3ud/F7v/d74Jyjp6cHb3vb23DGGWegXC7jsccew8c//nE899xz+OxnP4vu7m78zu/8TmqaP/rRj/CFL3zhhRc+x4sKucIhxwmB6d3HwRl20MLkE3di8qm7MXDudeg57TLDjn/0iyvElgYxrQ6JBYCjObIfE0/8VAvVf94N6D7lfOz/3EfQMPwqBNMTOPClv3OK2B4/jANf+jvMfcMHpQNGcIC3Wzjwlb/H1DP36+mZygYwzL7sF1GeuzzejZcwQWaMYejVv476rifgVXtQnLUg4r/bFhGhm0qK6oIkwRGSLhbHzQ0STp/EVxasJmXsPf1yFLoHoNovLs22nVr7vbN1Al9ZxUhTSxGaIwdhCRhdBg2XD4csi2Nu1Vt8T13EKHWRRviY59KJdnErPmz5xOKS2rHmsk+vlZ+phJFqB53Hj9V+wmL7766TCt1bXxf1L7qOGfMQFGvwm9l3Q5rwCuWQeO5E4WA4iTw4ohOdrXYbU3WdYHrl5mEsGa5J4zEMAAjHhrWqvROZslvOPB88aEVKGAfjFF7o71fUDmqNSkLd0ZWfPzipXT/4zFH88KEDAIA7Hz+EX712lU6wmH0TEekJc6d+jP/8znPa9U3f3YHTlq+Py6AevRCviMVg2O8jY55B2oqxnDsVXeAczNPHevmLcbCeWShc/hvorhTQnJ6OT1OUaoDpV8T3ZX3H3V+tH/Nv9JMLJ9pKWVQlivgeISbT1aJ73YMoX/E/wVtNMHDUXUStkTcDoXhQ8tOuE5QEVnmtvNS/jkRkPQnfIylwnHAQJ6tEFVhFIeVMgDmumm2p1Iv+aeOw2lkdPjMN1YJNts2j+Cu3ov307WQ00vyP2v+lUpjF74d8FM5tONmO0ZWsXADlLqAej8lszjIrnqqk4CwAHOZwzN4xd7CK5/bFac/qLdllcYAVbQWj70Fr91gRQigc1l8Cr6sfwehBFFadFeZJdF+9G/EoVaPtifCq/wczYer1DUPoO8LVvKld8e+5agXOWjsrToFzXL55GAuHahgZb2C8HuCW7+/Qy11k5LgtLBNxAMP9hPLWgaQTDuWih3ozWTlTWnshDj18B3q8aTS4j+9NrwXg9uHw0bHLNLK96Iv5VdinTXNRJlw+HO6tL41+2c9dHgRmCt9neO05CzBVb+GHD+k7SEsFH4WoMZ5szcMXJ0/DuuIuPNGch4cURQvlrJsXK3jdeeHGDo5A+Y7r31aqLzEGlFaciebzj6G9bxuKSzeifOpl2oyWUopxw3RcONwEcL0frnSo9hanHhgY/MEF6Lrq/0Nz9xPw550E3r/ACm9nFeUlx3hdEayFy5EjR46XGNrtNv74j/8YnHP09vbiU5/6FFasWCGfb9y4Ea961avw1re+FY888gg+8YlP4Prrr0/0BTsxMYHf//3f/1mIn+NFhuN7ljNHjhcputedCzBHd/d8sFIFXqWbXGhaCNo48sNbcOQHnwJtC5tYbDt2mI0/8D2oR5OLg/PRs/EV8AolzHn1r6Nr3bnp8hho7NuO8Qe/p+U/es+tlrKBQt+ZV6O2ajO4OpFOmTAzz0Nl0RqUZi+MF5sUqW39pogYG257rfr9Qu8sFPrm6Pf6h1Gas1ivfw744gSIkZN1HREqhR7dmXR74ggdBZRJJbVPqWRCykLEVScGSeE+om22HZE3kQeXjKTdPkxhx3xC4eBFDooTBJ45GCNNXdROvxqlhWtpBZ+6s3kgwyIyKftiKfNxUQHThEKjFSu9AGB0Uj/d0Fsr4q2XLMWaxX3KXcL/BYCC71nkzS6D8BfxXYo9ktTnnNi0G99TnzGIHfmhjIdGdG3M0YnYOvL9245iZFK1lmz33ORd8DTETmblwIT+QykPg075CBKelkHpO9S3o02YxCHep9hJtfjLSV8nzPO1MtgmpvS9sCwD80zunhXjOosIdvMECFEOBuPkB4vTUe/r5i1SyBduhLHIVwoqua0JKJ9xYkzkUV4iWNIJB5mkCJym0808vDmIZNEnlDLYMR2ZRKbPLPNn6jteJBQOSzbCm7Pcus85p51GW6bs4rLYc6Ckb70ep3DmG+S8zJu1CN6Ctc60tHuKIo5zI1x0/w0X6KcY33zxEgBupbUKRpgLlCaVTJmIIZR5HkprL0RlyzXwuge1pudKHejzIh2cI1bOyBuqT5o4vhpu9YJujfTduGJASZ+el5UJkvjM1bMUMjWOsWF5P85bP4RZPbZiqlR0Kfzj+wuHKli9MJ47MADdFfq76jkUDr7H8O6rV2G6oT//3vSaON1aHypbXo0/m7oBf3L0Gnzo6GvxSDMkzCmb/vcVNmJvu1+7VzTqxUsxB3XGybOteyNBFV+b2uiMk3YqgsIpS/twyrI+8pnvMVSKBfzCZcusZ8UCg8fCMBwM351ej4+OXYZvTp+qrY+oEw4ffMcWrFvaH8sdd8T4D+c4aWE3Ksoph9NW9ochShWUL34Pet7056id8yanaTM5UnCu/bUCyXGPHlPVeEknHAQKQ0tQPvVS+ENLSbmSBnp6TaNci3c3kjtHjhw5Xuy4++678fzz4YnGX/qlX9KUDQLd3d1SgRAEAb7yla8kpvlXf/VX2LVrFwYGKA4mx8sZ+QmHHCcEass3YtF7/hbTzz8JeAX4Xb3wa32hQ2DP1xZEnAcIpifRPLoPraP7Ud/zNKa2P4T2qL5baOy+b2LyiTtRWbwGhb4h1E46A8XB+eEck3HIg8YRGaftSGcAb7cxYZgA6tl8mSS3vFIFsy99J/rPvR5jd9+K0fu+CRCO17pPfQVaI/sx/dzD8t7Rn3wBXSefDa9SQ1CfxOjdX0+to0LfHPScfqXjqShPXK6IMdLKKu7TZBPIxXkcXglgRY/JJGYSVUoq/We/Bge/8a9A0EZ53grMuvQX4t2AygKJMQ+FvjlojeyX8SsL11AZAwD8Lv3j2B4/oqwluEb+2U6jBeGjL9zlX40sMre7Ks8TFBDpa5gEIkssjLQttlRg/blfo044qOQWlSZRB0K5RewS151ig1ykuuyLC3pIXJfnLEZzP+3w0esZRDCW7AyaFcoWAZEG06RSoxVo48CYpXAowHpJHG3CwDG7t4R9R2Py9OhY3X7HuFIXMkm1rlUiWdmdbC74jV3qTOurIel1YFTv+yb2HJ7C7N6SrrQQJmEccdL6tkoCJZvYUMqv3TPGNKoCKemCVvRUvID6OKBFk+R16GiZNOsi+q0kuLleJpVElmahovwV8RPpK9n3jARdxK761xg/yWZJaitlSNPT5US8yEa/dG6eJJ8OkYfeFfSw/vy1aB3aGcfRlJEEkaW9AzbJ5TbWZozdkiijWinqMFqzcL0ZuBle+a2YT9QiUafO/IJ7AwZ5P05PVk3SpwJifODO7xMH4C9cB//VH0RwdC+K81drO6vjPQ9KYxppxaQiA+c6hbhiQTfec/UKPLDtKNYu7cfqBd1x2VPglQmFg2/IxnloXoUYc7Tvlja8UH3dimyXUVGuyG+2UGQqGXEe+mN42yuX47O37UBPtYDrz1+spSteQVXxQe3cl93AlDdSdJQIZw0eM5y6i7SMd/j9152Ee54+ip6Kj3XLBuD5Pt7yZz+x4lEmlc5ZNxvXbl2IuYNV/Put27RnX548DRNBBScPcWy64lVgngfm+dgX9GvhqB3vHtEvSgUWHXAwvoUE3nbpMvRM74CpyvzTkWul/wMAeLCxCKeWwrHnSLuGPYaSIw1vungpLt88DN/z8Mc3P4JHd+inlAu+Bw4Oj+qXUf8p+Mw6DXnZ5rm4NTLDRI1P1e4u+5Og9tPob6Xk4xevWI7//tEu9NYKuOF83dyl+JcB4V4rRaErznRyY66hRRJQph8cIh1acdt2+M44/5QhxG9D/I5JWRzjlnj1VP853KhPNT11z0GOHDlyvBRwzz33yN8XXXSRM9zGjRtRq9UwOTmJp556yhnu3nvvxc033wzP8/Bbv/Vb+O3f/u3jKm+OFzdyhUOOEwbFwfnwuwcQ1KcRszseoE5yw5vwq93wyjWUh5eia/UZ4DzA5JN34fD3btJ8K7QnRzDxeOgUdOTOr6B7wyswcO71YIXY5jEcxMfIHV9Ae+KovGbFMmorN2szU845/Eo3+s99HUrzluPgV/7BKlf32q3wqn3Y88nfA49MRQT1CYze/230n3UNJh6/HYFqtqBYwdw3fhCHvvnv0mQTK1Yw+6pfhlcQTpOzEzzpz9RQxkrFItPiNtB/I3nGHpES1RUbseAdf4agMQ2/dxY8rwDOhZJGqVdEyomv/zOA0JdFdfkGKz3x2+/q17Jrjx9R0mRS6VDfs01rU0DZ9U84a7YIIseOLa3erI3Njnox+p5Z5XScLO0YLs48h9NoU0lA5SEJqUjGkAMVppNixlDj9jjt8FPe4+IfhdxjEP+ge94SHHnYig4AKM5ahHqKwgGMoa+nAqQEU2GaVGo0QwJlcrqJj3/tWfz4MT2xnlpRiq7B0S5vvHAhJlseyj7QbjZx6LYHAdsvNQGDOciYnx0/xthkSzqMdmHPoWmcurQPgKn0gPWbQSEtjTAqPEZ4XZDDiuLYmLFwXGOIiEIzgtFvudmPdDTrDTy+fRR+oYBKuQCfcayYF78TsYkwJrMQkrIC4ZiXxTLo72qWd9J40TU+TTK3sd19KgWTFWHKfSIb87mpuGIpfShLH09SIOnBMxC5Cgonn4/Wk7cB9UnA81E68zq6g3HHJTcfCjaXmSH1yKrtffMxR2TSK2bgwihG2hxa4c06MquRNHPnFVI+BJTwritXFC5JQD1upHSNKtzrnwfWNRiWQ91QkdB/9CmK/l1XS3XG6kFsXjkA7vloN6PTVVl8OFRq1i1XX2TVPnDtmx+NOFx/l3U5I5gnGDKCqyNbIJSU8Rt4zrpZOO+UOWi22vB9Bh64TRMBpMW+WNBYt6mhQnmHdmxaiPUtHAg4imUPW9fMQtAO4vsEVKJeYN2SXswZCE/2mSaV2vDxrelT0B4cwuk9sxEA0meBHo5QOBCnF5McMFvxPUYq68wyfHbiTDS5jwpr4mtTG9NmSxaKfrx9gHLAXVAUY0uHa9i+Lzz1WCv78kRkwWeoN/V482bF40QdBRxsd0un0pO1eegTZVNO2oT92+jDHDh91QC2nDwHDG2026EiTJo1tDYomeD2z8zvRjj/C4eeOE6LaO+1Swdw0iVLjTEjuiLm4dy1eUbdHGQgyynEHDly5HgxYtOmTXjXu96Fffv2Yd68ec5w6inbep0+PdxoNPDBD34QQRDgLW95C0499dQXROYcL17kCoccJyYkoxMt7MkJbTyhZGDoOmkLioPzse+//8Jy8izCjz/wHbRG9mPo6l9RFrb2BHvkzi9j5Kf60bPq8k2h+R1LlvC6smgNKBQH5oEVSug97TKM3PkleX/s3m+gd8PFmHr2AS18z6ZLUOybgznX/k+M3PklBNOT6D39ChRnmSZnOISvCXVXtrZbj0VhtHk4j4tsEePMrg4xuXftsBcEi7XjSZUpfuhXe+DXesEVU1XWwoUx1FZswpzX/i80D+9GdfmGyPSRWGgwrR0Khgmm9vhRNVEADO2pcez/4t9a4mtmurIsnCyChuoPivKCGW1EpcfNa1HnIj0zffNOTPqKZqVOOAin0foiVEkjQ/ktEzeqRBRZ5BVsmRVCS6BIOAoXKMxehPr2B5zPRZpd1SKS9/DrcJlU+t79+yxlAyBOOBCvDtEmR8cbWD63G9wvwgfH9NQUjhDMEWOWgRm4FvxqO0P8MklmQfAb/e3QaLqZmt2HzHEzXTkFJHOjkuMxK83sazxD/3MRHES88Yk6/v6bz8rrWT0l/Pn/OAU6+a8mKmQAQNnJV8Jl4YIZNSZmQvg9Ewo/U+cpT2OovYDpMqmfAmq0sGSVAblxP+5fsu8xIVe0CcD8lliKWmUcFt8KR9WwKDyr9qDyqt9BsO8poHsI/uB8tJW6UctipUN8n8WhkZlRS5S8rpTi+3aX5Mr/SmqEwoF5PkmOOhEodZxlA4AqJKF0mDGc7yVR+zycm1g5W+ahbLByF529SjJGqGx5NSa/Hn/3C+e8Of7myXpjsZ5IrUKzKEJDwQGOAPqua6NdtbpQ+oX2PoRxxCkJbqURXlLENYiyqgIvnduFroqPienwzVmzuM/ZbelpiSydVB59bmILXtt1lwzzralTrHilotgkBKeCWy2PSsALUCaVKHOJ4YnGuM6S+jFjyPROjfAaPjFxPvmsVPTkpgQX1NMolKLIE32IMVx/7gL889efRb0Z4I2vWArf8xCAo+B5AHRlzdwB9XQqw80T5+C1tZ8igIf2ymswL+EVdpv1jJ5r76m4p8eX83WO6L0lviw8WnIg/dOnpk8pmFYs6AvrUmxIcg65aWWDQ5Gsjsfxw/ykQ44cOV4K2Lp1K7Zu3Zoa7uGHH8bUVLi2mz9/PhnmH/7hH7Bt2zbMmzcPv/7rv469e/ceV1lzvPiR+3DIcWKBID2Mh+Evx6ywNHsh5rzm1+HVep1ZTG9/CEdu+7SVZpQwprY/jKM/+Zx+3/PQu/FikswQfzzC7jcAsMjmcs+GV4CV4515QX0SI/d8HdM7H9fC11adHmZZqmLg/Ddg1qXvQHFwHmjKJOPs2Jxbu6LK4hgLEHXXf9Iufx7/1eIpMkhiW7S1KyyAyoJV6F5/HvxaL+IFug3rhMPkCHigOsbmmHziTgRTY1o4VigqzrvV/F3lVsqqPTcEV2Wl0jXD6InY96xtsURQA7RJJYXcSus6Rp6WA3bZDxTH0o4TDsyKpwrAwTiH3z2AwpwlpChFwom4ihYroji0lCQUes56DUqL16O8fJP1jDKpxHmAu56wlQ1Lh7swf5DYWeuQ6eh4A2/687tw45/+BP/+jWfAGLBv9mYtzEPFU/Q+dEyrXa7JY5LQB8fSvWl/5/79+JV/uA8f+ewTxKmEbOz5jRct0q5PXmj0Q3VbsdnHBNGOWHlm5kr6AiHQMPZrHJLlj/PW0leUJ4yykw9b2SbkdMsRpymvVcUAoLoIgvN9N8cQQkGQRWGYSDwJ5b4kXnjCKQhuFkUT35SZyREgkRXTxekaQGHFGfCk3x+DzCWTsokzVya2819u1Cm3soWZraV8Jn6p3w7q8035pWI+6ZfEaRNduccMBQL1pRbhzPtq8lJuhWBXE9Q+icq3PbmN1QyM29GPLD4cUKIVDnFJRJoc3uwlKG+9Ad681ShuvBLe4lOdcxhNMAcJSw/T8bsTKx/MLHS79dZj7YHe0VJcE1h1zoPQLM87X7kUc/rLWDrchddfQH9bgViJaMuky/GT+ip8d2otnmoO45Pj5+BAYM+ziymOmwHdwTelTGkTJpX8IqFwEKcJ0oc+eIyhuGKLdu/BxiJHaBqrFujfsvmz7Dm/qkChfEqEJpXC92vt4h783Xs24h9/dTPOO2UOmYaArnAAtrWG8Rejr8JHRq9Cq980iyTeWR5fcuLdJCuNeDc47acqHttsk0dxatzOhof3zlkTbhSiTGhxeWJDiMOlEj5R/kxzKHt8PJaZV44cOXK8WPGv//qv8vfZZ59tPX/88cfxb//2bwCAP/iDP0BXV/L8KsfLE/kJhxw5OOIdKnJayIiZLQcYQ3nucsy/8Q8x/vjtQBAAnoexB76r+XgYf+C7qC49FdUl6+TEnDEG3mri8Pc+qSXLimXMuvSdKDnI0HSOI5TLK9fQu+lSjNzxRflo9K6vakH9rgEUB9xH42RGXCk/9Tey1evaJ518jyPc8WfuJjWjEISMlWbWLb667FYe7pU5AAZWKMCrdCOYHpfxmgd3oTS0SMad2vGIHZ3pO+T0lOn6S9stZpUhFQnkxwwWVGKRSTkjZR5LrEtJwslTMUbnVogh7fRKtCBlxO5UTQnBuW7DXakrxhgGLvkfOPiZD0vTYwIF63SPjqeHL8WCUokk6bo3XYrahktQf/5x1J+5T3vWMEwqcQ60A45aWVdEvPHCReiuFhFw4Ot37cbiOV2hg0ZDB+NCPfINUa/MwtenTsUF5cexL+jFxMrzo3zjBA6NNvBft+1Go8XxurPnYcn8ckQIBRHPbL6rIfYfreOW27ajzTmuO2cBFg3r5MjBkXSFAwCMTDTRV1NIP1WDIa+J/h3dMskSrjyTSSFucxb5pdBiGWYd5Osktk/CVjwU116I5qPfDy/8IurD6wDs1MKoOrPYlJJCwIrnlMIh2o0szR6pxCL0Tf5mlUklAaJxRRIsaTSHwaQpr6PqOyKu1/ivJgPX65PK2xrrCdHkSYhIJi53jlosk95Plbpi1PgufqcRqwRRLNKzxusO01YENeIx4yf9/tF1TIgivjdSwUUQtEGrs2+IaI8MfGIspbvv8YSyUGFNUUPlhOn2Nc6SaycyjIcZfDig3AVgUrvlanvGgNLJ58I/6TwAAdqtFqA5O477sPnF1xUERp0kfLbDuIESUOmCxrgB9Z7oGtFN8Zey9x/rAsIxggcBmO9rbX76qgGcsWYOeBCgEQCct8g+ofHiCWVqooAvTp3uDgBxwiFMaO3iXsuHAaA4+OacVDhQJnYKpMLBQ9Z+yhhQmLUIpdXnoPHEj3G43ZXoLNpjukUtAHjDRUvwex9/UF6/8fyF+Mjnn9YDKf3CJ9ot/D6KObah8I4UP5TCoeJw+B2m6cmsRVI29HdOnk6TcbjspOKdtswUyWkg0X+N18P0ISXC8YCDsfB80OWnD2P7/im0J+0TLWIjkCqLZZ6OKqb2UD01RI+Rjog5cuQ4zvj1v/nBMcX/61+74CWT7pc/cu2M4x5PfOMb38Ctt94KAFiwYAEuvvhi7Xm73cYHPvABNJtNXHnllYm+IHK8vJErHHKceKAIFjtQQvzQ5n/f5suj5AJ0nXwW9n7qj9EeOySDHfn+zajc+AdghXIUjmPiiTs0R8UAw9DV70Vl4Wojb4q8Yeg/53U4+uP/lnf7zrxGe96z4RUYvfebDpNPQGXJOpqQSSEAhHJAKgk05QP0uK7dferiQs7RXQ6mMxBlrnucGUQVRVoZfgTU5w5C3+8eiBUOAPZ+6o+w8N1/B69cw9gD38XUtvtsiZrpZmbIYqjEk3OVpxNLWr8hdk9a1w6FmlMmQGM9yR1p7ZYRwYycADV7oSQww1AnHDyT1FdoHaG0iOrG7+5Hz4Vvw+i3/kWLU+iZBVaqWA6/AeDLk5swPLwJAEdxeCmmHv1hnLdii5+yy2+aVAJCPw5Tdd2cwY79k/jxo/HYcfKiHlRLPpYM1yAdBCfgBw8eQK0IcObj1qmNuDUiOt4UOTsXRCZjwH98Zyce2B6exNl9aAp/8a7TFFJCJQv12v+3b+7A48+H/f/A0Tr++B2narIdynDCQUA6YE1TKqose3RdMAikVmSmygyqJxM+pe2wUzIo7w8HwDgqGy8Hgjb42EEU1l+KoGW3d9Dm8DW7346xnNhlzTlHq80RcAbfi0YnI6pG5ApzdsZ7m6TEDbsBJ27G6bodIFMI3y0zjpaP9T5SebvSFn8Z6Lo0g0uKFLHhlWzQ9F1RKtZJR0eCTPEjkjzK6eRUnKb6zaTed6XsUXTXLniZHtNva/J2DSYrHKq9wFRM5LKhZTKtWMdFCpGIrPptck7AoPP4egTnA3Vqw4FsJxzKXQAOEMlxW3lJyBJnqezMRtzyMUeqCx5WqT1/kq8459qn3eB0NRkg0nLUjXp7+Vz9ZN3yeV1xCB6/s/rJlmj7ABfviei3du3oXU1JI60uCZQKXtgPfOC68xfiQ5981AqjKlAohcM04RuCMqlUKiijAUeiLwyRZ+3s1+Nzk1vwnQcOIkgwIrBoqAqA4bn9oWLrog1zsGioC7/7pnW454lDWLmgG2sW2yc8ZO1xTp5w8JTnMgbXBwPqO+h5DFtPHsDtjx+xnpUKnjUWukZjLjq4A+Q0k8NyumxtdFJuC1nid4oep4f7y/jQW9dh8oG9CO6/Rw9jKmJFF7YldpdF+KXgjpD2q2x/U3LkyJHjJYgHH3xQc/z8gQ98AMWiPr/6+Mc/jocffhh9fX34wAc+8LMWMceLCLlJpRwnGKjJnjJbVP+3QpkT35iQ8Gt9mH3lu6FOwVsj+zHx2B1xbM4xet+3tDS6158fKRvMTMWKUZega+258HsGwzy7+tG19lxNLq9UQddJ+rFuFeX5q/QyUzAWvPGCOWmiHFdcsgJBISlUPinLJFwQKVR4Mj43nstVuBIi++Tf7+637k08eSeC5rRiQisLojri+rW9Y1eVL2oDeWn2lcTsSHJoZoqe5Px08t/o09aaML6hkaRiZy4hE2VSSSohqD5B7I5lXCf7w3SL8Kq0mbQ6L8gdfpVVZ8BT/Hn0XfjmSFYO5tvKBdOkEhDanZ6Ybmn3eqp6uR7fOYbf/8TDaLaz98+RyRamDSeaZWnvOk5HKBsA4MBIA3ssvwogFT5C2QAAzx+cwvhUS2vTeortaRUqCdRscdz8vefwoU88hB88dFDN3pIBAAoF/U4rUPu2ODUVkveP7BjDnsNTUAcbSSxHHskt8sNiUSInlKUqque8EbWL34XigtWguKd2wK1+ripoZV6Ecuqdf3Mf3vP39+ET39kemasyGHD5XmiUeHjaINE5JfkxS+CbufHXnb/5zM2AKvnJujDSMd97Mi/6HVefZdJhqDesDQiOAqh5uTYtmPe18ZpO1yyjRji7PnPOYYFrv8QYWzr7TZJgK550Nli1xybclEwKW66XSjG28FR4A/Ohftudnx+i/TVTXIIo5IEx27FPBPBozhR+KqP/eGBvC1eyFtHVsoebGuJAPIMPB5Rs03Z6mfR8OdVvXHMUsxEpBUPqNz2lI1B9UptyiHYI/9YqBdxw4RJ4DOiuFvCGC5c4k7Nv0n1VhamQ1MSPyv+Wi5fYEQkUld35K+f34Ddfb/s285Uw1G7+Z1tD1r1S0VZEFQuCzk7/DjMvCssBv1RJVDYAQMln+F/Xr8FrzlmIN1ywEG++eAk4gHVL+nHjxUuwcfkAGa+t9H+X0+hkk2cOcOC6c+dj/VJ7HmTXYdTpAzFkRrkEar762CoVEbLbq/2Glpfr9gARn5BVx5SkMkUPCWcX3FPnZtnnWUQm9qU5Ds5AOZsjR44cL1Y8+uij+MVf/EVMToYK87e//e3W6YbnnnsOH/3oRwEA73//+zF79uyfuZw5XjzITzjkOEFhLvpcz6nf4lrfrV8eXo7u9edj/OEfyNCj934DXevOBfN91Hc+hubB5+OkGEPv6VdkkzXaFuRXezDvzR9C8+AuFAfnwStVwCWBGsrVteZsjD/8QzKl8vwVGfIz8lbKCA7l9IC4x232StzLPMmOJugup8eZkojCMUDs6FIXPZqYYieesbtXk0cQhVG5Cz2zrCyPfO8mTG9/ODRRkSQXk4KlFSLOW7uXkLb4a0ZzRA3FSSZHEk1dKQj7e9TXmBf5B7FJJymMya1ws7xGITgHPE+2o8uHg5WPS37O4fcPk7J51R60tdNHIZq8IE1CML+I2df/Lqa3P4Ri72yU5i5X5LAJiya35W20AkwaJxx6avSnuN5so1b2ydKY937y2BErTLkYL7Tp3f2UooDL8OEOVIZW22bXhQNsEa5JhHHh6d3jGJ9qobvi4wcPH8I379kHAHji+VGsnN+D4X7C7nyEgmHDu9WOnCBruxM5/uzTj+OpXePwGPAr16zEacu6leFFf7+e3DWGfUcbGO4ror+3itk9JfAggEcowsStNkF8ttoBSkVBZMTPmaFBKSxYj9YDt8pbO1rx2KLXo+PdZwDj8XgSKh2i8FznknWTD5EiSZiOIsjOsB5NclSVJ0yL6k+uPpYYRiPplToTeZmnIlRRZGB7zBTlEGOHacKP82PZcWOOl8bTIH6HyGhqtyIHaeMHR7xJ2SWPNFNni1hccSYagytQ4XUUZ81D6GuXaL/orzf/ZLArfxtl3kCzSyVndVlTfWZoUyTFEaz4vCJph7uVQIwAWuPFszmj36pzCvH6ZjjhQJqhcs0JFf7TPDxqf44NAjVI3uEvX1Htnvo+mxJZWxTiWOQ7xKNTIxyXbZmPK06fg+l6C121CtqtZkQKU98HOzGxAYfawS2bgIN8zjlw5ppZeHLXOJ54fhQnze/BT5+0/RwBQLmgtw11CkAl4n2CbH6qOddOl9kn9IoFT04V0+CJOR7npJLDRKngoadWxOvOW4RWswnmeQgQjVlB4Hw31Ppz+nAI2s65OWWiTGCgu4j3XbMS7/67e7X7RT92PE4IpI8DShtzzsEZi8ZDPYw+BqplC+uQa6++eMFSxhzZN405qEeYVGIe8f4p8Ygpsm79KZDBxaeXfOnNRNKWADly5MjxIsa9996Ld7/73RgdDU/BXn755fit3/otLQznHB/84AcxPT2NM844A9ddd93PQ9QcLyLkCoccJxYSyWt1geuYdVrh1d8cvVuuxPijPwKCkFBsjezH1LZ7UVu1xTrdUF1xGgq9syC9ekpyW0mXqZP0cMbrFUooz10a58ujv9FsuDR3BfzuQbTH9QWbV+1BoXdIz0sjoujFhGsCboVTq8K8Z4Xj1r/ac1KBocqdAdZii4NzFu1sVkkIm0RhRH10nbwV4w9938pm6tkHnCJUFq9NE1L/qS4SLSJOX7Rl1+k4AljtlLSyNtMIr3tPvxKt8aNojexH76ZLYufbWlC9TdN3VotogiSMo5MnHAoFjdgTzauWRk25OGshivNPQnP3kwCA/gtvBBC+HxTqKGg2qL1yF6prz4MXNMP3XDRLxhMOjZZtUqm3RpNg9WaAgHN87ofP4wIyRDLKpYKyk5Be7DbbQao5d+r0wnQjVnQyBjRb2RUOADBZb6O74uOm7+/S7n/uJ7vwS1cut8KLXbLT9VZ0HRJC2i5PBuw9Mo2/+cJT2HM4NI8VcOAfv7INH3vfhjicwSL96OGD8nSFwEfffQqqpbB8FAJC4SCUEGmnh/w5y+AvPhXtHQ8i8Er40uRp8lmzxbXOa/s+EO1Jy2b7AdAiy4TNJmem3Ex0bR6lqyg4GNN3dUd6AS1bLQNu3aIMN8X6X3PsI6k3+1KQUpIBJqJlBpG+/GEqRRWynxnBxRWHPjipT9V3U7afqFA9MDWmxX4b1Cd64VnPbHiFUNnEI0VuEli1D165CNZsQVUUdGJwCwAQ8JjkjNqokxTUaksPqf8iv40ZfDgkmT1JN6nEwVnkDycA2dacUWmY9UKE4OqUUDkZKTZYcCMwPeTHYYWvgyig74fjqbb7XM5Juf7Oi98c0U50d3+iHYjrPbla8vGeV61CqxWg3mg6FQ7FglBchXmbCuiwbLEpLZ8g/yc44Yy5ZN8rCuVGkN4B1c/QkfF084JFaabIoVQD/R1hkf80lwc0fRjmRqdJf/NcSgwtVccpIw2yryhfFmPdwU3NoRrXsSQR0Oa+5jsWhHlxzsPf1Kkm64SDPWMU74G2iuBJc1gTnY11OXLkmDlcvhJOtHRfSHz729/Gb/zGb2B6OlxfXXbZZfjLv/xLeMZ88pZbbsFPf/pTlMtl/NEf/VGmzUg5Xt7IFQ45TkyYM9WY6VADGXGgz0eZ8YBzFLoH0LX6TEw89hMZbeSnX0FhcD6mtz+oJde78RKFPEqYlnJCJtesl3MwD6itPA1j939be1RecFKC6Q2CRBGLWe15vLhNJrU6gIvwtpQPJgmVILsZhdqCqMbL0A7luUsx5zX/H/Z//q+SyyPAPHSfeqGRn5CFuibyT61Km5CST5w72bhsXzujLAvJuB4L3f2Y86r3gvN26EBdpg27zjsom6uXkiccvALMFSpFpsYJc/Rf9T40n38UhVpveEqBAYXeWaA8bjR5IbQbrQgdT55iJYpHnnCwFQ7TjTamGrrC4d++sZ3IOST6H352BJ/7yS5cMGiWKb2t/vbzT+Jj/3MjaspJh66Kj4npOP+WYrZJd9ob12Kt5GPeQBl7jsQ19MzeCXRXi+irhuEaHSocXCaYxiZbMSlhyMHA8B/fDh01cwAB53jXlctwz9NHMVI/irNW9+GvPvck9h3VWzIsoxino5vKO3hw1CaIphptVGtKHRudijrh0A543C7aeGOHrVz0P/Dj2x/BZ+88jDFelfcbST4pZHLGWGiR+45xTr0lkzSVm3G6DDB8iKhOMgX5ZSL+HmqiE76DtPcyA5JNhRh3qM+j89tHy+cWJPkxFTA+bad/hxLXYdz80eGiTew0tsZHliHjlLysbwghYXQ6JXWXuBxzxAURgHtKs4hvS1pDcPsTmcWHQ1J6jOmmYzgiwl2Q9wlxqW3djqCmv4SYXjYnokZUx7c9fWOCM0UiD/q+q1C2yUS9P8qTH9GzUoE5N1OIb3G8B8UOpH7TTJ8/Al+cOh3XVu8O02IM3vyTAezR8/KNUiVUosiGg2O6bpttNFEsCCWR2peMOSnnOHP1AO584giA8OTG5lUDcXgiXenknsft7ppeqhCPSDNNBXpsNJNzz0bjeEmEvZgyZoM6tiVLwakTS0yZm6W8i0kPeRCAeaJP2u+dDJe9YDly5MjxosNNN92ED3/4wwgie7KvfvWr8Sd/8ifwjc12Bw4cwF/+5V8CAK688kpMTU3hscce08Ls2rVLCy+eL168GF1dXS9kMXL8nJArHHKcgOhw4kftNBdmHoh1X++mSzHx2O3yZvPQLuy96Q+0JEvDS1GatxLxajxOX7dt7yoBj3b9Mf06Qm3VZl3h4PnoO/0KXVDt1IBjdZdaVRSZ5Yhkks+KqYz4UQaiI5tgznjhKQclHVPZIBbT0U4yWc+co7LgpNQcuk+5EJVFa1DoH0ZxYNhKI6ucpjKEC1LGVNC4FDZQFkCkwkUmKn86OSiKDEosi+sZsSRVMtYXy2p/AcBoPwnMLxg7o2OSiSE80k/FqSzfFO2tC8PXVm/FxAPfscJOsBq2nDwYyqb0WZUuAWhzT6ZJpYs2zIGD/yDRaLbxhZ/sSg+YgOlGIBUOjHGcNL8L9z0zqjzXTbJFAY1LhoHuoqZw+NjXtqFYYHjvq1Zg04p+1MoF9HUVMTLRzCRXvUmTMgGhKBP9IuA89NkQodXm+PZ9+/HZ28I6+uS3nSbe0Wpz+L4nXxFBpnMOHByxVU3TDcLXh7I7m1Q4mMobRXbtFE7UeSYqQxjjug8N7aQIUZYwrjKGMYXQ4/E9K4GIzNJ3SCtjsJqJYxgWp0yY9pgMSAtvhVEuxfgovklI4cQ1edVvph5JNeVjOytN/9ZaFp24SXHRe42pnfB2jVAEVVgPtDLHnZhmgiUaL/XATjUsDVFuhbE8FsosrseMqchPlzIecD2F+Lb6DRfjPycUcUg94eCtv4wmchNmY+RdTiiFxautvINUvPi3NUGKyWNxesKhe5A+MORVEF5bdp+Y/EkXJA7KxekY1zTNkYZ4tbnRHq4pJ2MM1ZJvmR4EgJKiPA/FJ8Zh1c8BccJh3mAFF1/5Gux/uB+z2/tRXXMueN8sWAqHgklUu/uup2zmOf/UObjnKfqERpy2oXzSNXVy/Hj12fNxYKSOI2MNvOqsBeiuFkOTSVD6hyYhpxLU3oekaZ49wzKVNmH6MjlVkRC9e1q35FnJdmUuCmUOaAbJ9E3Qx2jm+XbLeZ6c6otpdKJOVHyalJRMpSAdz7x/LKNojhw5cvzs8fd///fSHwMQ+mz47d/+bfLkwjPPPIOxsdBX4Oc//3l8/vOfT0z7lltuwS233AIA+MQnPoEzzzzzOEqe48WCXOGQ44RC8gLeWKCTE0VzEm8nUxych+rKTZh6+l77YYSejZdqJigkIZJG3qukuCRmYrkE6VCetxI9Gy/B2APfhd8ziMFXvAWlocWyXO4D2UriFsl8DBNla0cu0/+a+VtZKe2RuE2LSFMseBIW21o+lMyI26h20hZMPnmXU4RC/zBqqzaHIrcbcK7MedwWeg7UFY0ZbZoSeWo764wwaUxftPoK+xHVXjOEsShWN/wBIO1vs4K453DuqC5cOQf36BYpzF6IuW//Pxi966uYfCT0S7Gdz8Nll2xGT7UguSJJnEXtJ4lB4th+Q1m+/9LVK3D+KXNw4CjhpNmBejPARRuH8fjOMdzfWIyNpR3y2Z2NlZnS+NjXt+PwWAOnr+rDa85ZgEpJJ1Ee2zGCv/38E/K6t1rAr792JebNqqFa9GRXqJZoB9hf+MlubFrej/dduxKeX0C71cJb/+KnmcpGIYmcMM02FXwmlQ2AW9mwaUV/6EwTIZmldu+Ac+cJB8YQkoQKqS921SaZVJKQSgeAGlvahN+LuIxh59eUo9q5Bz0v/fBW/OKwyLY/iztuaJ7McApgkilQrymumuCDmPbbTl8LQykxDUUws8IrFC41fAPadzUdytgl68xQ3siQhgLB6KeMahtTiW2kB4O00+Sy0lG+a9ZcRf1tDJpC0aQSZeCpJpWcH5cM5JzoS5rfBkTko5mNTg0iufGyNazmK8IE8Q0pnPUG8IPbge5ZwKrzXdRhilh2h1S5+USFvkiDmH5w4yY335uAx6aR1H7gJED1tpPfMnDCVE70znEzgvrapH//dYWR8R5o8nA5iFdLnqVwYEyYOeJxnRM9USh+Oad37PsesGBONwqXvw5TU9OAV0S1OW2FK3jRrIJzpHl8CZ1Gh3mtX9qPjcv7cP8zI87woemqAJyrpydZ1GfiSp47UMHvv/FktJrNuO9SwwCgPUu4QUKaDyI6acFn+jCTJT2ECnoIZ9riWg7QzGg+e/yUygpT6asLrind1FFOVhDhw8E2qQR7nD1WUAqh45R0jhw5cvws8C//8i9S2cAYw/vf/378wi/8ws9ZqhwvNeQKhxwnHhSS1yY/7Gu3GQYWh1VYDg6OwQveiL37n0N79JAV0+8eQG3lZiV1Iy9yDaXedC2H9VXIwPk3oO+sV4MVSuGRXx5Y5YpNu7pWw1x/rk3uuXJN7aYyJ+704lAsjvWTB2o57Ti0roBoQ9N2baqyg5KOa2RN35nXoHl4j+4AXEGhe0BvIpVxQFJ/AtG0CX0vlcEw42Qo67Eshhy77TQyIU1ZpIZVIPdVUyccPD9eKEah9TSIOlKSZ0JKzlHo7sfARW9Gz+lXojV2BHNmLQx3LhptqCcSlot2Gh3LWyyELoipXZsu1JsBuiphGl+d3IQVhf3o8aaxt92H26dXWeEv3zyMh7aPYtehWKnx4PbwNMPzh6axZlEvKkW9Du82bGWPTrXwhzc9joHuIj74xpMxu7cEAKiUacLl2X2TuOWHz+Oep49i7ZI+vPXiJfA9Rp4AUDEdKRyuO2cePvvjeGfpyYtUJ6BxuzLYCody0UOrnV6fYperUBbItDnDRL1NyjptKEQUvh5gDJQFqVY7cvbJPNknVV2DuWs9INJotCKfGsykiVVwhZ/h8fsBxPkynewVZTATtA49UTlGijUzuqSOwqoENDl0cWUEIm9Kn5EKa7xX03UwVEnjj+QLdcH1ejT/ImqnOP1kh8Nc+Z8Cl5ys00l05iGfbsf4fUpIyCEeD4KQrU0KR/Qxy1a9uMfU52FknTRXvh6WOUQOfdxPgvLtpczyzV6K0knnoB0EaDeb4Cljit5DjI0CPHBPc1Q9EHQfRa701AwTNyHYL3JI2AYyMiGSvfFE3VkeTjFcnUGdq0b28qN4F506hO89eEAGvXjDLClCPIYqaQmLSnIc5uiqFHBoTD8t11srgoEhUOuYEE89BUcpHDxDQcPBUSp4WDW/C0/tngAArF3co2pK6DpQ01SCFgsMv/aalQhaAd75t/c5wtOKzaQxiovxQQZ1fB04wJnePlp8xyvjytpjWseN89CECfs+h2e//9S4SWTM1TIZm4biORjksoM8PWEMrxwBOOXDgRynSeESn4WvC7fk1RWF7rbKkSNHjhcrvvvd7+IjH/kIAMDzPHzoQx/C9ddfnxjnzDPPxBNPPJEYZtu2bbjyyisBAO9973vxvve97/gInONFi1zhkOPEQsZJH7U7L74fX6lPVALYr/Zgzqt/HQe/+o9oHop34LJiGbNe+QuxaRhjwk5zpVwPIHajaWViCvsTJ+mVykSiejjXaQdzF3xy3RHhnIQHVVA9CjOdZZP5OVb1HMbimxv5JsSVv6I6IUQt9M3G3Bs+gMaB57Dv039mpeJ39+n5CtNMRH8iEVeCcc/ob0YemeDccaV1uOzxnVXpansjeGqfYlqL0WSeuRjm8cKUSpPHz5mnxFHq1O/qh1frQxAEGuXLDJnjd4eRO4avPWcJLhhYCc58LJ8X2qWcnG4llFlHvdnGrJ4yAGB/0Ic/HbkGs70x7G4PoEl8vlsBl7ahKXz7/gOY3asrRly+F46MN/G9Bw7g+vMWgHOgWqSMLYT46l17AYQOm1ct6Ea5aO9OtcoWmSwyzVaE8jCNHGfasxhexn5fFGyQGjwi0BuOkxamnw1wDnhRH2a0QkW9x4TWQPZjFt9nIQFBpaGXMc7PFF8lOdym0MQfYzxlgpRX+r2yw1WOBlxLJhqXGVTVA/XlsFhnbThOiGeNZY7xQY7JXPuOhTIS0VzDjCt5Zo63ahxj0FMurTIx48LaXk6N6XHUgLsdFFvSKQp20n+PdU05azW/xzGhNzPCLGwMdcTU5U2JTo7htpz6I11mrSU5p304+L7xbU4ua5a64NC/HXGqxjyMm7VjvgfmN5fLPxZ5HDPoxn3jOagqdPV3JawcKqg5B5enE644fRiP7BjF/qN1rFrQjTNW9GnBrHQJDHQXseOAfhqwv6uIuJa41bsE2m1B+LJwd74Bn9k+Ijjn+KWrluMrd+4B8xiuOWu+FY9z4O0XL8T/+4692cSTc/U4UdX8Ex0+Shfm2K6n5Zo3Ujp9dTqn76vJ9v5ycPRUfYxNxd+/WqWAZKUBrezSbgWRI/UkB+NGR+PGp9mqp4QPn5Z9wgkH6WAcCf2R0l9z7j5SmSNHjhwvcRw9ehQf/OAH5fVv/uZvpiobcuRwIVc45DjhIE8RADGj4iLcwx/RX2WRRSy45ImBaFZc7JuD4df/Niafugf1558AK5bQs+mVKPYNyTQ0WRKFjvNzktbOdPTZNBVft//MyV2KmixJcjqEcvHicocQA8x2sPknRyJGXvLaKauydDEW4fEy3yBHDGapOHsRvEo3gulxLWW/eyA5b4Vk0NrfIqMcomtppQRSCQh1tZbWVzqBpQyh0nQzS5SpElqp50ZIMupyxLu+udKdRftxuUOOoHKJ3HU5GEH0esROysXDXVi+qB/FUhHCb3FnCof4hAMATPAKJtqVkKQnFAWtdoBygmKg3gpQNUwqTSUoBr7y0724/rwFAGCZYnLhn7+6DQPdxVSFgyD6TQWJeorB3KluKhzGpvS67KkWrHtAeMJBUv9MkCGMTFNgupHsS4HiGlrt+CSACrs/h38phYN5ikMzRyKIMnCMTDbRVfLgC0UXR9ivLQcYcVnV3EKzSkbRoqBMKCRgPhQqQLM8UYIcRJj4XtLbzORfD0Ab4XvLlG9RJByPv4O6fwxjHEohtM0xgyWOBBmQ9M10IB774nGayx9cfaL1WSol++Nhk4C6vMkldT8NTcEoEwYhYDytUjO1SO6Zk3Tc9T3rAJSvnfiUXIb4zEPsJJpLcpVR8xdw5RsjBFdE17qr6N9GhupRF84JMbn2r6kcSISjDsVrQU23zP7IDVaYc2B2XxkffvsGjE9Mobe7Al6fiqqLEsowKxnV5UC3rRjq69LvcR6QmxDEuMo5fcJBU7aK+Rjn6O8u4i2vWAAwH2AeglYj7sJRMuecPAC/WMa/3brNTlOtJM7AGf1tAYj5gqY8ZXqmMOajMkp6I/OUcdAIDQB40wXz8R/f2YVWwPG2SxaH5p+E+T9FoafGoroSeVohi+zWC6KM7cR80xobjbGTVDhY/Ub0Z7dYyTJmjDbz4S9Hjhw5fmb4xCc+gUOHQisda9aswVlnnWU5fzZRq9WwZMmSn4V4OV5iyBUOOXJYSJsIi4WhshrjnJxxM7+A7pPPQveas6Mb5rYqI3GewFqYigAtP4pgEOyPQ1HBQR677gSaWSYtD3daYTG4UhdqWIUc4+o9KhFCScSj3akG76S1j7rtS0mDXgjwaOHIteuQlPPQs+EVGLnzS3FwxuBXe+00kHK6wbUwk9sYufydbcHSSVsqeaueaGHLJE+9aLsvjd8q46E8znS6w4CmBHOsmv1ab5yyUTnSXBJigjFeZ/L4T1TcmBNxvGgqaaL0KfM90EugP+jEpFKj2UZX1f5Md1V8NMYphQPXnVEa8BnD527fp91LMn1UiXZo3vH4IXzlrv1ZxUY5YWengPDhYJ5w0Hw7cJ3IMcl4E31dRUw12mi19TJRu1wFWo40pxrtaJwieh7npP8F6TRaUUip5LvWGzhd940WV0LbCDjHR7/0DB54dgSze4v49WuWYd7sgpptnIECxpWOrqZNvPJxfFrpEJZNUTAw+13R1Qtcno6QrxKLzU0JdVDs/FofwMn7JpRnmtQpZFviTm/zmRmYGX/BddNb7kykAoaymR5/66l6TVDcJJTFzMWbvQR4+o74hkHEa35HOmCxWRxZ5krFCkukfDtMG1JcJaLjexTccwWiD3vECQevoJCWSX0MSv+F/sNUhjgiJ+qNREg1LWdykRkjanyScUJhXM6FmZqX8VsPSZ+0iadUYo4jRUOp6GGwp4SAMbSVCKHZHWXWIOeAnsyLI0nhIOZ3jjkTgIHuuC+7fDikQ60XQJ4eArBl9aClcBDZcKjt5+5LQkfKjXx0R8TGD9kfhAkrQmpxP+lVdbyQIsWNy3tx1rphtLiHAm9FZYLm48M4O5SadufQ89LVqWKzlqu/GiBPOCjOxyGmtTMSVMmaeFe0xjjG9HPkyJHjZ4TPfvaz8vdjjz2GV7/61alxzjjjDPznf/7nCyhVjpcqcoVDjhMMjgWsaodBEImZkhOT7jB+PK1UnOkyM7zyV2U7XfLKSayxg4e0U60uApgdVmblXvXG5KlO+lgkvXMxRSxuKULAVBrI69CfgxlfdzxI5Eo0bUyacO1mnJVjIWC1myFEVD89Gy/B5LMPoLn/OQBAddkGcgcl2czOfpaBkdAKmBSep/xOXxiniuFc+IkArr5qyqTfFyUzSZDec67D6I/DiVBtzbnwq13gQUAtMyPYdWORfIriiwEIovdXckraLkEpmSM/JR+PYdehKewbGcM3792HI2MNjBM78F2YagTYfch2ZtlVKeDIeNO632rzyKkmjTQ/sSZqlbAvJ52CoHDB+llotjiKpQJu+cFOMky9FaZpKieSlAouR9MCfV1FHBytWwqHh7eP4t+/HeAVG4awYmHok0Iwf5lOOIQRIgfMHNxhUqkldj4zk6DjVjfkPM1ptKGmjOLf9/RRPPDsCADg4GgTP3j4MN5wYWiuS/pEkP4WdBmZHHOUMcNS0tnvXGgdisEqhLhlF0/LOdFfgCMWiyl5KxTL8A5SPD793tvhNXN6xDfWfcrDlFMNI76fRhwlCCfScKKTMZvHzu1FHfjLtwAP3gpMhn2pcPabdakc6cvvZmb/QSKevRs+ziqsHzJJcne0OhxTChv5TzSFUt5H6vtM3TMQlztj1ad9Ipw7tc3fUTh1vsD1d5sy/ZkF6tvFAw74+lMzlTj7AEbghPJG9DAxxeJSArOswnySjr6uIqiTPjdeuAA3fT80Xep7DBedMlu+TAVCu+B5xnzfFIwZbSHmI1H/9Z1pGukldBTVHKBpoip+T6i+Hyi/HYkLBZD4m6JH0+OGfwq+B3APaNFz6+Qk1H6s/uWkzpsy28ZlH2dI/H4YcagPESdPOPixrM7yKJ1a/wNLsaDEofUgM59j58iRIx0rF/X/vEV42eDw4cPYt29fesAcOTIiVzjkOPGgLkbVHXbyl2uZ1UkGKtVgOPW00uX6PU0Joj4xFQEGaZS0/jd2zMV5Mf0vFScJHHL3vyRphDjWwtxcXDoWaJbtYpPcce/nCyf7zN6pJOuNSoubAd2Q9Rymw3wfc179axh/4HuA56Pn1IuUMjlXgynX5n2XfK66oNPSSBfXojjroogbDQ2j6VJksS7VxSfM/qCn27PxUtQWrkZregrF+SeROUgTK9R7IQkoHhOpHO42sxRN9nPXej7gDN994CBuvSf76QAVn3aQ9S6lwh2PH0at7DapZJPoyeiK0mq0OlM43HLbbnzyf50G7pfw8PYRPPLcKCFLmOZkXZcpdJosnWuEiKo/7YRDb60QEUx6uIOjDfzw4cPo6yph5cJ+LV23wkEvsyBsb/r+8+CsgAefOWzFabftXhCT9Yj5lmg8cplU0s2wKODA1+/SFwHfvP8g3nDhIrVIliKXMc/qn8weoG3CnEW0SmC83PL16uDbGA8+VjpaMA/KN1B5YH2yjZMPoZ7HMsMVR842VpI+ILSkspWZI9C+uzSZbraX2DdMtP+MCSu67MwvoHzFbwD7nkC7Nhv+7CVoTU1YcUzyMO6/jqw0Wal5hyNOR8/0sdolj+6sOvpNOZCFb8wLU8A5mBc6x83eKtR8Q8nTOCWgZxd2oKS5Kef2/NC1n+KYQBD0+uxZNRSWYU7pmJYNdpesoH1dRX1aFIV9xamzEcDDzv3jOH/dILqjU4EcnDR1GOsGDM8dXGkhOScwi8Bjn0AKdGVmusrH85jW5VKbiJyaJMQi5/whslgl5WbFqP3UnqJF6xNq3m0m7BY5TlMdaNR1hdunjZ2x+r2y50SMUkIgLocqpvXWUuVSrsSn1WoeTpzaypEjR44XGQYHB1MdPx8LVqxY8YKmn+PFh1zhkOPEQmYigVgcch4yGi5mldgBb2dO7LB0+YWIZqxyBw6jljHcTsPJ/BqL7wR2mKuLC1WeBEiqhIgq6sK6pxIUjnqws01jO9T0xaxfXaXaW6zk7isrSW41mZY2AK9YQe/pV9gLGLMtshAajjom64DoTpSYx4Ysq0P1t1LPst6ZcS8darF0AjCkGEtzlsBvtcDFrmui02Xff0u9ly5plDy4oIfcOb3/lr04EJgmto4db71kCf7wk4+Sz5JMNk03O1MciBMOaScLTPheuNO0BdqkhZrmv39rh3Z/p+IoVG977lQOxOFZ4gmSL9+5F7c/dhj1ZoBLNg7h2rMXoEkoCYDwdAnV/7/74CHrBEXRZ/D9uB8KB/GqriFkxPW0AqI4HOFJlbIf5y6iMQaMTtonW0Q1cQdJZudgK9plVUePYhNM8XO1PEbTEDLF76QaR/9hxFGUCSJRMygVNeu7zpS/0RfJzluD4/3mIh3b5jdjsJydxh9wMQZSShE1+PEgpYhvt5EuK3ehuPpc1OsNIAj9Zohvf1gObiWVCYZJJUok+7edCTWDSoTYKa3MLaw4VEP7wimuIMptyBMiKXlnRtpUAGpr0CHC7kQrp8TJQ5fpHShdUY0XpRxfM+4oN4+mNyzKByl1ECtMbIfCEBaVJPoJk0rxqYc4IOcheX/JpmHwRh+Y72liUOaTXN8kupRKdtFfSomhOSqP5o1J1eFpY53607yGsxc4FW2ZJofp4GINIoYrcxMUV8JB/R5EfVNr75mOaVwqAcAQzvlEX3PpliOliOxnaT4cZqiZ0/qxObZCPbGV1RRqjhw5cuTI8fJDrnDIcQIiJj7l7nxyIUwv0uNn+u5KeVsjzDnk6QHT7r2DBEgSOzG8OfkNt06akV2J6sW2V7CQNE0Sy6RN3CmFjZmwq+zG5D2bniRehFgmmQSJwmQi4Z+MCwGrbs0teeJv0tYxdaeluDdDkuJ4rF46Jkhmlqfc70juFtTUPlZMO3+bKA0h3iedRQmbOw6r2SVX9U6MR/b2o1vaNkcbOi0l8mUYuPBGHPn+zQA47m8szqRseM3Z87B19QB+8+O0AoFCT62Aob4SDow0MscB7BMOi4eqWDK3G7c9dMARIzTFUu9QUSH8MjC4/Se4lBgunxKMuc0fCfzk0UOpsh0cDets+/5JbN83gb/54rNkOPWEg+gPAQssZcPHfnUjqpUSpuotFFUnoQrZAE6PDG1K4wCg2Q5QNu4JFwiUwsHUZZB0ryD9xecIRu9OeL3pFoxPGDDGIxmYEt717rh3cX/hjj2484mjWDG3ijeeNxddBV/KrI2yFJknlT3hP0wdLlxQiCvtnjk2u8ZdVTFinqzgIPwzcOWZWUOcKpRWCMaYjOc06SGz4EafoGSfKdyVa58wUdolIbUZycDdl3KDCIVTrgAe+joAwF9zUWgGketj3OWb52gn0y4/bUhPW5GcagsO2Du+4yju20nzO6k8MAurpEK/XHSaCaAUFZpjazGdke8K4cGLi1cscE+jEZ1KVe8xhw+HmnmPK//q98U9SrngRX1U80OgkuamJiZD/QXUdyshXmjViZ7Qyr0q0OcvUjauXJMQ56rjgGkl4EZdUqarhDLFqQYTyglNZsfXg6wubjRHLINTfmtjC+yhlHAsLpUQHJEvO8cIlVLXlElRbblgmh/r0BRdjhw5cuTI8VJHrnDIkcNCwtScc3AmiBZ11WUTBVYqGrHhmLSbE1d7Fe3IzymwLiPlM4Fa9AgixlzRU+lbE3Vr2Wk8dls/NcMBSNhVlUX5YJIscDAfyW3uvOdUPmR0Ep1BDrnwIskoN5hpgiFhN5arrpNg0wtuEsrMKjGUXGgmi8ORQCgRSO4rSjiLBozfQcaMVo3aX4wGXSeficLQIjy36wj+31cmE+V54/nzsOmkISyeFZLVnWByuoWeWnEGCgedVOuq+GgRfgQE3nPlMgCdn3BQTT4VUk44eCy02CNwwwULMd1oo1xk8Dy9rzQ6lCMJ920bwe7Dtn8MgalGAAjSB2H/sZxReywkjiTZbpC8DsJZ3HcpV37l/z6I33/TyVg+rxvm2xI7lQ5hEWqqtkFl/sTfcEuwkaw6ntm6cSvtYwGn3+1teybw5Z+G5qL2j9SxZKiMV26em5BM2gcgVoAkCJMQWbEdbibDqLZF6OODTD9lrDajCMZR3hNl5UockxBN+eYkwNwlrzW7tqGis28QQCj0OyC/s5rssvwZpH1e11+O4vzVYOBgs5fGz5RKuPCUWdixfwI7Dkzj3HWzsGCwrCUjWiPmZelMXZunQ78GCR9DrrQxV0+OuuvE/HKlIa16Kdv6yXE59SdDRG79qpZskrin5uszUh75JgkCcI+apyHBh4MtYNwcernV9nZBL1bUtgkxWLTphVDT0OmTeWZ5P7LPz6zUzDEo4dWiSXelXSNlhKxf7lYk8GidBQ4wj9IuC3mpccs0k8VpM2qelzBedt4WpDya3EkvRY4cOXLkyPHyRa5wyHGCwVzIR3+Zvmh1L4pM8t5UHNArmDCmEbejiWfSYkF/Jk0iqLvBk8hmVSaqDKrIPNrNCpZOHJN5ua55zOpZ8dLKbuRl7Vi1nb/KdlF9V6QRWC5SnlQ+iJWZUY6UBaKtv0ipv452LqryG325Y6QyFY7FnCMeuWU5Of24FPHevLBZ9ffbrk/h0J0DngfGg4g40mVmnEt9Rnj6IXqLrS4iqScADMWB+fDbs8HxoFP8c9cN4lVnzEGhXANvNVErF7BmUQ8e2zmWqfgT02188I1r8PAzR/BXX9iWGPaUpT24cvNslAse9hxt4mPfiE0YPbZzHMC4I14vZveVwQO3nwMXSoW4knzHCYe7njyCe58+iq5KAWOKGaR/+dqzAJ7Fb7/+ZKxb3A0gHsuaDoJ+pth3pO58RjnKNn1ItAKO0ckmhiolqH1bNQ3EtN2V+hjrUjgAwIPPjkYKh2ScsqRbJz+J8bKT14uJoYuJfh+Zh2KARnKrQyciJbxkX/XMKHJev8/xmdt2ac8+9aN9eOXmufr7xpKGaOGTBboGS8vQjpMWTueSY+v0WnCZsQhljtNU3uIZ14JlRTbFbZw3k/MV8ZfKkUhxBq9chq+1flf2G+N7fBw0XFxpu/A6HKu9WYukGUQ5eisC9nUV8f7XLMPkZANeuaLZP7PMGDnqqNMpnnCubBKm1v4A7cKel2g9igPhLmva7w+ZLjWlNZtDbR+u9HluhJEK0EgxQE0/YW9BYYxh68kDuP3xIwCApXOqGOyx/TqkgTKpJJUZXBeR0ipIE0mGvCYCdR4oFIAJHWDD0h7iLieniS7FAl2XsMqQGWqCiuki19pI6FSc+jZRJeLdcVWIy0RthyJrmao//SSn0YDel+2yyimzpVShXhTEcZRHx3fmkiNHjhw5crx0kCsccuSAsViaCQEr4mmEecIkmtr9pLL6VuLRX227HLPjUnmpO91dO9vVbFzyUTKZqzXuyIeM75jgH8cjx7IY2g7RLLteM8ggEyfq3fQ8amsSwluZykuuQKPdfa441Mo5JY+ZkDtWeztWwAkkoZUW3GauGFzVlZC/JJpS8nfcUek5q5zm+x49d5kREli7uFfLiDF0ZLZocrqF7moBc/pNwzs2ls2pYsOyXrTbHON1WrlAYUAhd0zZBrqLYIzh8Bh9wkKYVAJjiXXxd19yK0ua1skL3vFJi2MBlX+zZfeXvUfqGBroQjvgKKqWEzR6noN5+tkZBk76cBDYvm9CDRydruHwPUAV7V2XLbI/G8oGXp2GZPEN2YVZ9FdRsFnSxImHQ1v8PnQyXMe+HJhG0rv6P4vYV+ZR7yWTihFDxLDmM55+YknhBPvL9bE7jhuTipmqIem7aJFXRp2L36B/p4EZaYbpRt8J5wYL+75LeRTGMCvDns905GiclAlxu6S1sRyuXeGMeYhZNKvTKZ1NeQ86KZEMO9N64K72Cp9pTxKCxnUJ5R1yp6tfxpGknw+FVY1Dc+W5nb+sPev9Cuesb714MeYPlFFvA5dunK2ZxLREj/6a5DdlUqm7WgBjDPrwK+YJkfKU6uYJbRY4N1nYOOOkfiwdrurdU26CETVDzE0UbVRSN4gDRD/V+GC4bPMw/v2bz8mgF546hHDexbU8OoWUnSG1Llx7rtxrMeK3aywy3wPKh4NHfFQylJsHAeAJ0dLnnaZcOXLkyJEjx4mGXOGQ48RC4oJf/uOMqnHW3JjNi0AmiSx2O0WmmLTdoGmEs7oiIXf/q3IZ8qiLSGtinZCvuYMpStT0i0ALm7IKMhcQqswuo9s8Ypy0fBLSt9g3IRfiVY6oL7XeJByKGxFRXfCa1ZhZ4eKQPTkA4p2VWdJ3tRcz5DbKN9PdZla/d6XRQd3wyGSCeVu0p6v+nUJAqRalv3GljwC07e0odCDawKkgDJ0IJ+HUpb2aPXagM7NFE9Hu+3kDFXzgDSejXAC+/8A+fPehI1bYRiuQi/dWxhMCG5f34ZJNc8JyerZsb7t0KVYt7MWvfPQeMr7vMUlMUiYtsqDVtne9micM0uB7wFB/BXsTTCe5EAS23rBF5P/gs6MoFgr4zG3P4/B4A8vndmHp3C5sXNaD2b0VPbAgsyK0HQ6rAWGWSjcKNDbZ0pQNXWUf5aJev2oMa9ewQs+FzcPAeZBAIuvymcGoa8aMESri/gW5rRNN4h1kiac9KLn0UxWaFE75rFDifTfjqDtb1VyVvKikyfw05b2jjNz6MXMQuuYZpzPTqNS46DhBmTDjUohYXZjMSgtKiSDEUZJmYOG4TtrT53GEtLwU2eNfPPobgBmnDBTePi3xODUlG9Ukk1ZN0UUnJrZM804ddyFz/qwJKuohnZRXlRmlgo+rzhgOfWwEAZoyjyRFmQ7q+9NV9iEnEMRoJK6sfR4JsstN/PIfkPL1dxXx7suXALwdzWMydACuywiI/tvZmClKd8aqAfzokYN4ctcEhvtLuHzzsDOOmGvFQ1TGuaApcDzUa6CHK9c4SaxblDUE5wH9OjFb4cAovw5JsNYhHUYHD81HzjiFHDly5MiR46WJXOGQ48SEXESKySpN5rvuSbJDuWMuVzqWRUvLQfrqq81ERkU3reQoF+l/IFFYRfHQ4ZJUJWid+UYrm+N2ykE/Ds8dpII7utmu0e+EPqESYdoaPgsZTqTaye3sMPpYR13XJFbs/kW4mwz/nbHcyQJqCjc1lkGiWJIKYhF2GNcuQxFKGwMYi5QU4TW1q1LF7N4SeFv32zDdgcLh2b3h7vdq2cf6pX3YsW+MVDYAujkk0weBCyvmdWPFvG5pCqFuOJsuF7xERYJKHqfVhQvSt4QS3fMYusq+VLik4RcuW4qn90xlVji89+olWLNsCH7QVIidSOHFOXHqAfj6PfvxdcW57IGRBu584giWzF6J2X0VPL5zDIdGp3Hash50dRWtHuXihyn3D+aJEsqxavwe6Cmr/gWSWkT4KVHNiYHrpsRsx8ByvzPUP0kIuac4Ubo/xe8Un+n3VcuTqFTxTFFsitMpZLtoz8LxpSOpqEHQrM6kgVJMVbTd/VR4henjCC3qKFXIgwC2KjclyUg2y8SMcqkr6bKRwnFcvSJSd+fLDNX8jC+2+AQ4y4Pok97Bx8kkogl+NbxPf5OYfkPGtH+Z2SZXjjbXkUNVVCfklNKc29hl0PN3TJFNql7sMCdfWYLIt29YjZbUOklNR59w8PV8zezBwUxH1ilyzOotKWHCglPtP7uvrJ2MshKXNu0MmYi0Tl3eh4efG5XXi4aq1v4iSt5iwcP7X7sC4w0fVb8Jv1hGu902ZDFGQC5GPLVN4jmSa3Tmyr/JUpnvEe3pza4Hd2/lMrz9beGA8t7r/UxV5mRXCiaIkiNHjhw5cpzAyBUOOXJ0As4hduK7F39yygrl+AHAmSQJdEdxyoLPWoSYC3axkFFJVpNgUq+jvLgqC1Ume9cQlytGUyYivv3TGUa7p+56jz3KJSORGBBsg6HYEAs5s13ItOgVRpycko7RPpl2FHJjwe0qj729znVhw2WaSyzko7/GWsvO3+oXydnKaJkimX1bSUAgUTkU+WKQbe3MRSfWNELWFJNqe1Me8T4hIhvtVk8S+x2XLiHvZzWpxABcdtocedFuc3zk809b4RYNVfHWixdjsBYL005wEO2Uhds+HMpFT3MMbULoGxjSzUu5oCotxEj2qrMW4LJT+/E7n3gC+466/S8IlAoeZvelm51Sw/dUi0CrjWbblruV0eqVx4BFs6v4yWOH8PFvhj4zhvtL+JO3r4PYa8kYx7uvWo6iF+DoZAv/3788pKVhmm9iDBiZbGr3+rsK0TOdqNX8BcE8VaCDVIgSJCENNxuT7rBZT2f9kl48f3BK3nntWXMyyWDmI65iPj5BDpdWQcvT3IzgiCB1jx7EPlaL0E8bw9Vx7BjJK6teVMbZ9f1RBq6wajoXQu+Lx2vjgA3h7yCpbydEjohd4xvXiXIkGvlpWtRIj5p2WQZ9MjCbXPtDKgDSFCfmp5IjrgNqTpuuiOHxVAh0W7inGNG82JFHyp4aaBXiyIRSOHRVsi1943pW8wl/v27rMP779tDJ/eqFPVg0u4ZO+ntStXKRZ8LpBw6O89bPxrfv24/9R+soFTy8+aLFpPxU+p7H0N9TQtBoO3fdz3SDiDDLxOMbiAdbRqQdn4hU09C+TZzrS4S0jUOp3ZaYH5tjpus0RWom1NybITrqlyJYjhw5cuTI8fJCrnDIceJBZVnlgpDR9zSSQSUfoMxNO5yVy7k3TWAkLbLCZ1knuY4ENCHM+460tNMJrjiOeNozk+HhhNjEwnumk3RNqZEhLLGtWK1zXelgBY31GRnth7+g6GzbLZIVPjQZEnaHY+iPEhTJ7yAyGQMP3ESTuUi0yBVAoYqiMgdmPw1DSSvPBBdL7lKNFtolgoy/4YJFGO4vY+uaATBuUnkMH7xhNRptYGw6wJ2PH8QPHzoon56ypAdXbxnGQzvGcdrKfiyb2yXL+tSucew/avtSOHvNINYt6QNrN+TiPesJh6/8dC8e2j6KerOND791raUMKZf8RLNRgWLX+3XnLsDOA1N4RNmJmQVNh6ydKDFKRQ+VEmG/2YHFQ9XwB+eaWRjRp6gTDhQWzK6iVPTw3fsPyHv7jjbwk0cP47y1g1YnmmrY6UozViwmj01H1qFpEBGI00MnKSHV30VeNsjhl6nPo4iOb6J5YkAqCqO/jDGrvUu+LYxuLkp8kymiiJA3ERnjG9+ScAxyvFPWJyyjUEkKaJculCthjgGqjDMh8sVpoFjh5fi8Z4R7p78zQobYNsJXnTb3o34NnNnyRKm0hLRmJNqUJN9n0KyJmwlSI+uZcoP7Nk8Oxt8/Kin6WeZ9Fpy7ncCnRQb9reiu+PGGHX1bvfybNod75abZWDyvH+MTU9iyRjFJxJE8ibdE1s2VuuqLQrno4w/fshZPPz+KOX1FzOqrmYkDjEnFjjZ3F33ePdWSYbmQU8R1nWUg+oX1Tslvqvtt4xzuDgL1mWtANINzeLMWgx8Klf+s2gNW61eCR/k641NZM8cDAVuB8gLqX3PkyJEjR44XLXKFQ44cALTVoLxlruZdUXUyWidhlRU3Q7xqixZ02iZ8g9gWUbTETXmV3UIyb3MyrpFARhqcuu9YQHdCZsx4a5QlChEgLb5ZrmjlHS24QrNKgpxTKp9KziAwAKbVdVThtHjO3ftGH0syi+UgkrKbf1B3lbniJC+UsiAWR2hcyIcJMrjCR3GixVrygYes5aOWuaKPcGshySDy1UkB8Y5TufbVilizsBuPPR86aT537Sy85pyFADgYD/T0o2SXz+uC7/sYmWxj+17duXN3tYANy3px6opB+Iavw2f20I6gqyUf+45M45+++jRa7dD/wY6D2X0ZPLd/EgDQbAaWD4dy0YfHGHyPtr0/3F+W5GVPtRCeGugQrXagdVu16bMqHMoFD8VCtndl1YJuzO4pIQCnXxcW1kUWjE628NjOcTy3f0q7f9dTR3D++sF4eIr69eR0y0qj2Qo00peDo1r2sWy4hmf3hW2z92gdT++ZxNqlZV1eZo79YQqWMo4lDbkKSWUpYNyvm1l1pnEiV36mf4xiocNxiBDI9MFhfOXkeCz0JVaTJ4nA7N/p45P63qvKk1ikTEhlCRG3VTRaJX4CjiOsOk7/qKcgXXHAjW8M7euIy1dOffdSzig4ZYoyzhAuLXVi/pD1U2Y9V0uT3tgxd0qQo2R42xySJZrSNy2/EB35aUmBVm3K+RDj3aAstXVXCjHZrkJsZjA/OmKHvTG1Wbe0D6xdhVfwFCkgyflOp8E8yivJZJlIXyjAqiUfpy7tQTsIz8xwiPhKG7gyiwZIapqZadNMR+XrvO058SsxdNwJYA54/mmvRvv2m8CDFipnvi50Gt2pPJQY1r0EWX8G42+OHDly5MjxYkOucMhxQkG112lz+OrimCBpBbksSUkzLhwzUksKQ6aMOyoj+XTnzbE8iXxrJwtjlRhxkea6liT+be4YSyqPyJaS4YUCVdmaYonaMWXa23U2FmLCneo7lMLIjO+QWXtO1WGUt5ZHp3U509VQJ32fwIyaPOpnnqNOE99HRelHbtQT7zfXr8VvanUuw8XJvv+6Vfj6PQfgszZedebCkNQJYlLVzDrUaTC862/v1k4ivPkVS7BgwNcjKVk/8fwYUQigUvLRanM8sWuSfE7hf71mOf792ztxeCw23VNvBphuGCccIkfFpYKHqYZtZ6hPmvoJr7M6q1YhHCpTO4uzKhz+5evbcWjMPv1BoVwMHYlqTsqN17gVZFM4jEw08Y179ln3VZNBahb/8OVnrPvCjJVa/o3L+7FwdhW/+W8PAwCeOzCNj31zJ/76XQN6mqpe27gnyEVmOJXVFAqIfTaoenILHGAOHx3aSGTkTcE8PSIUDp343FHryuYSE7QkEOSo+W2lvgd0niKkOjUIFRk2geiUISWvrDFF3slhzO+T8ahDBQVjimkclWQ2WVoKUpFlzqsyxJvB6UeXb4Xws2KMO462I+vXIS8pYUJzOG50AJVwFqf0zHmn3uY8gFQudpSTJO7d/dxUGWnTWlKJweUzpkUy527WD+vznOTDQU6fTaGMxJxT60ix5d69bn8vij6z9p1IgY9h+ss515QEXPRlU+uamo7yO+D2+sGh7FTFj6dOnI6jXqZNizsFF31H/+3NWYbiFb+GdqGKYtGLHJATohmbTTTIulTHBrpP2ssM0RYv8Bonx4zRmRP2HDly5MhBgRpLO1fx58jxMkDyvEIndmkOPYkwNlcT6sw2/m3N4V3xLcLZjk+DQ00rzoMrZXKkk5Y4RbaSTIFaZuNRktgqOiIUxKJfWSgRi9I4bIIw1IIweqC2l94/uB08k9j0Iu6YQS3qyOyThU3f2dVh3Sbn1kFQQQ5lzM4gHeNyk5oHkrBVn6u/ZVVH/a67UsDrL1iEa8+ch1KR3kFq7gZmjKGnqu8D2Lp2FjYt7yNfg1Y7wE+fPELIDlRLXsf+E6YaAcqGOah6M8DEtGHKJ5Lx3PVDZDqmSal2RqJehVC67D08hYe2j+A79+3Dp77/HManW4nmnFRkVTYAwLY943hy1wQmpltaH1Hr3fSrkATq5MfhsSZue+QQhP13gUnCCXZT7vjX0zEJtHZb36GdRXmtBUno12paLLrWze7oZLuVhrOZFIfrUTBL4aDsQGWxxsJKiRl6VsYYcc+STKtWS0/7syYe0pTRlDyp38VjKwMT2lNnAJvIJEWa8Wcgm0kkPU87hvVtUxRuRKadZNZRVGRUPIUzAeqNovpFYm6w56JZ4xtzzQwtkfCqO253NEFSxdF/G4oiEx7lw6FcsNJSc7GnfmEd0DJHih1FSaLOeV939jwt9GvOXkCmTcGYgdDChomQ8RMixNECbj2lb3TQZlypjQxKW/MpV9uTx3WZNX/OA/0dcn6nOoNz2ZM4j1TD/4y/KzkywYvmGu12O2+jHDly5DhGtNvhmtZT1nH5CYccJxhcq594Ap64ptSeqxNieqlCpsfNiT+jHxkLBLm40nbjUGGp/GYwiTJXdq4FunOlkpInseOuk92s2WCWPapvrdo7JLiTHWxYbaQ9dyl43BnKvy/URNhM1+mjIguoXcQd75hMDdGZSM77oi/Y70fawtbiJ0UcdXHtyDjJNjpjDN3VAo6MxycMxqdamFX1lTBxeN9j6K0VMDppm+QJFQ6d7SmYqrfl6QWBkcmm1kfKRQ/lggfGgF+4fBnueuIgjk7o+ZeKntytzFh23xEqWu3wZMUf3vSIRsivnFOSfaRYYB0pAZIwVW/jQ7eEzrcrJQ/loo+/ffepADi+dMduPPzsUTy1J/tpkVbAybb5t2/swBmrh+CzsIwFqMqFGKqjbmlvHITCQXL7rp2sxvudIDODF74VWlIp70KGIZscU4QOIfoxaSi1/vlbu7D9YB2nr+zHysUDSlodfiMM3sntcyYDlDphxn4d1Z+DMHli04XKPEORTd3FTcVTKcGZfiPN0wOhDLzj8Tk5fFQwzSa9FKCD9FSCWZ8jJYpmti0pakaW2aR7mSNYkjxZRLfZbfuW9liddybVKdEGgCNOxj5lkL7qxhUta1VOUjYzWVfYFGGMX1SNmP6HAMD3PUeJ1UZObrzEabUyLT5n7SB2HZ7Gtj2TOPvkPiwZrgGwZVLjygyY3oaZ33y1jh3vnX4iQYwwCfNTauOTmRdRIbH5yYS6JLPLtm5Jzj1DRlBel9SxMIP5NfkdyPdzvlRQKpUwNTWFdruNer2OSqXy8xYpR44cOV6SaDQaUuFQLMbmlHOFQw6Jm266CTfffHNimHq9/jOS5oWDtkASE0yFKCVugZxky4k81+9x4Y6WE0HVfBMm8OSOR2oZmoH8Nm5TcSX5QObjkM2ZtktMniFwSr7UYlUTiUdkWUTTULZAxEqLAzDs1JLKIXMnp4wfrbYTnXintI8M00l9uJhs9TdTbipyONouvEUvpuIde+6FqwhJI6FfcX2ncxrMncx0Puq1i1wK61/u2Cbqw1Fa+b+5+9tVemYKDl3pwI3Hob+D2PTO2FQTQKxwUAk4xhhuOH8hPnbrdivfaslHibCD/+E3rcA/fXM3ad5nqmkrHDyf4d9/Ywsa03VMtDw0mnpJKefORUPR0Z6BwqHZ5nhsx5i1+/9vvrRd/l6zqBcD3UX8QHGwfTww3Qi0LrH/aL0jZQMQnnCgFC2zeks4OtnErC6GP/6vx7H7MO1XQ5pUkq9u6FzZUjiIkxRUH6aq3cE3msRuEq9N0WCaKSplWDOT0X0XxL8f3WmbBvvGfQcx1FeWCoc0M0EMCE08BZysjzgUJZj2xwkqmnMM4wDtHNUFZfKRQRaFMUxOlXN4JpHtUozLpNNJy47hHGe5dhWxdqmKXzWGlQ0z41PzLCumnVZS5i6ylfrUkWSlESe1mt3vuvUREvPcpOlcwlVmiHZyzCmoMrkcCce/Wfx3hhDRTUWmLkeQ0MfiFkvh1BNk4KiWCnj3lcswWeeo+S00WegnguxpUX3xbJ0hJW9dWHuOYvuksNswbld16gQPKV3L8S6k1B3VH8la4MaYS04tlbE0mrbR1qX0+alWBUz/kTQXNrurq/WO00ia4ziit7cXIyMjAIDDhw9j3rx5L8Dmtxw5cuR4+WN0dFT+7u7ulr9zhUMOicOHD+Ppp5/+eYvxc4Qx3U0lUmFN0O01l7J4oiYwJoHvIBDk6QbrmXJPEKdmOG1nkq7I4NLXgCEbpVhI29lILZ5TF2dZFlbHMvEzF7YiOSVfk+uYUTZEnbvaXKKDDK11YFLanSowjgM6OtWQVm5y9ZievnHPRe6EZnTVOlIXrmG9CrNIcYSklHn8R3Qpk9O0ujnJSAEITzioGJ9qArB3XAlFxis2DuHR50bx48cOa8+rZfuEQ6XkYemcKtYv6aUVDvW2ZQ6p0QzgMYauSgHdxRJ8z0Or1ZZCUKS6qrS47eGDeHTHqBUmDe02TdirePDZkUxp9XcVcXSimR5QzT8IlUpT9XbH+lEglL9hnFz4v798KroqBcAvoN2ok2aXBBoOB9W+YU6q3XYyKToMMkTwMNIHgfhMOVJiBq2u/p6st4F2C+IgjmuxnvTmu+rY9x10PudgnhcS+jSvmZ63lWmkPmAMsdkrR8rE7VTlqcqBBWnKVuYc44/f6C5INuJ7wpAyjtPQT2gkE8fqE2YRmFnmXWEK6q5/x+hsxDOei/eHZ1T0kPXilpduRkIW5zcne9pCcXhsfST5JCbVpJxxo8t0xC4raZvMLdM2Q8T7PWIZk05/Ukr3NLGOiWrksbk8XY/Ctb+ZctSCMucj8jCAOq0xvzNyysLpvswczSO6J3STgDOGs9jcqpaZ5ubqGmJb2IzTYynfhxwvGXR1dcH3fbTbbal4GBgYQKVSyRUPOXLkyJEBQRBgdHQUBw4ckPe6urrk71zhkENicHAQK1euTAxTr9exc+fOn5FELwB49I9c2MUTxnhHEDP+irgcutNoe1GhZ5RIr+jkdKq5gZTVn2OXtpVMkjxReXV9Breehz/jsIwp5bC2TKlxXcU4vuS4OOWQboIoJhhomQwiWs9ESSNtYZ2hDVPjxkgyyeMM7CQkM/RBOmE9vvEzvncMZc5kqyVLfXD9t0tOTWlEKCFkUkSby/pLksQ8FWGEjR6aPhxUkzwuq2ZDfSXrdlfZt3w4CAK/UvKt8ABw77ZRzDHSqgvi25G3aQ7o5t85C37QlAGoExBZ8JWf7sH8WcfnaPvvvn4VPvCJxzqSJYhImr/54jN4/PnxjvNsEiccuiq+dhKgnfB+aMqKKNjTu8ctJUaS0gKwmy3xrYoeivFT7OvUTyXEmJhu4bsPHsLnfrIHjAFvecVCXHDqsJ0Z06kdzjke2zmGgudh/dJ+gAHvuHQJPv6t5yyRfvToYVy0cR5NDkXsY1ZCV943NRQdkgqq422RZrpLBfN97yT/eExK01uS+Ya2pIzhusPvUspYnjT6qSZVOAhS2c4sjptWN46M6RMFaljX91Drqe5iM5ZQjrCPhQ6b6bG2E8zkM6pFdprVoedAGlGemDQDR2BM8Y4fCcuhiJ9Bp0phy0mDuOl7O+VYfPnmOUQu0Fh72XcS+h45zRUIOLjP7W98BtBEfmfp6P1FrG/0MS9sJVtC5+kTrs+ps0rEGQOz8qZjh0ok17hEj5+mMso5pQcle9a5u+gTLDZvqM33uPYnlM9UEEXvRU5gv+jAGMPChQuxY8cOcM4xMjKCkZER+L4P3/dzpUOOHDlyJIBzjmZTN788d+5cFAoxn5ErHHJI3HjjjbjxxhsTwzz11FO4+uqrf0YS/axgLzK1v6lbvRLS1U4tQE427U1O7rys0w2qcsHFQIYP9WtNFh4HISfPKYtGQ85sCo0XA6IFJecaWRRCrGph3BeXKUR4BtLeudsqNWqW+lMXMzNcncu4HWY9YxynxC0TZHRlSlUi5+DxFm+ltpL7vUu5odpHN2uexZoKRQpT7jjf7loRKv79G9uxdVUPeuV9W8aeqk5qXXn6HPR3FxEY4VrtcEFfLdMk2I4DU1g0Wyf5TYJbFZtz6XNSwveiEyJRnWR18Exh9yHa3FCnKPgMrRRi3kQQESymM+OsmDZMQRV9Bo/pBE+SqamAh8oEtaX+37e2Y5dRJ0LhoA7hjHlS8RpeR5QvYwljNdc+LRaMjn3ftiP4hy9t0/rHx7+1ExecMhym5SwZcMttu/H1u/cDAF6xcQ5ef8EizBuwlWYAsM0wZRXrMIgcmMnjM6QpnuMRk1lfTsaYPRwQ+TLXM7XOXBViEHEsSTGREZTukyIXE4VRfstTMF627wrz1HFtRllmjp4Ki790aSgEBZuUbQIBHaTYd0/iT10cK5GIQq8myyM2xKQlqaQ8YxB8a8YoYb88Jo1KlJ66aQBxeWplH796zQp8674DmNVbwlWnKwrRxOl7TH67iHYeB9WUq5ZcMqz4150xJzft6C0fJ8UBeJC+FzKQ+kn5Wib4IlFCkai1iZBJf2tUXxCqLwatDFzEO3ZYSi5On3qzponir0MMS8mtlsn1/eGOb6xor9y1w4sStVoNixcvxvPPPy/tj7fbbfk7R44cOXJkw+DgIAYGBrR7ucIhxwkGY1EiuXYeP5O2JUz2RTw3Z6hEGICekHa82z0lnJzMJy08O8kPUFYKsQwhS0tkpZplUmWmds25nUInLThjTjZpkZY9PSNk52HUvkEV9di2IxIJGvlSUPoqRRqGxJujz2boQ3Y8M/4xINPuIUce3JBDboU0olJmy6ysE95N2eZRPKWuAQbwIApGMy+dLKt7q/ZnWe3POocZXplxxqbCUxGeF9r8V3fBtwOOSsm96i0XdWkbzUAns5Vn9zylm3ECdCKY8jnw84DvMVx1+hx85a79HcVrBzNzeA0A080AA91FNFoBmq3AMlUVpp+c9lfu3IN2q41LTl+A7kJo3slEwIHP3LYLG5f3YslwF8jeZvQf7W+sd0sdDVVi/T++9RypjGoHHJ5RVKb8YIBUNgDAd+/fj9eeuyB5FyH5SDeJYZNLLKK3lLKr775C3JpKCroi6NqxdQzZ+7s1/gQZTkpkTltRMnUYT5Op0zy1Kqc1CFll4khX9oWKlCBOPkP9McSdXvNdFA+syXJ10D/suO7vzIy/pJK8jWjh1BMkLtni9BL32VgKReVdVLLutDwm1845B2cc4Cz+hBvEtiY37DAAsHZxL9YtG0S73YbPW2gFiOvIJNPJz3/GjhFFl9VoTovNgIimDjNRtIn3TFMyGCsTKk8jvCvbbHNofS3lTFikl7VLyiVIghJvZl08hlz3ZUiO1MRnMLuX4yWFWq2GVatWYWJiAqOjo2g0GgiCmW04yZEjR44TCb7vo1arob+/X3MWLZArHHKc8NCVDiAmlomRiHtGMjw6jKsqKqg8nKcXOEE2uye5WnRld1Fi2bixhEydQ1srQyViBhyHHW3OdNV6IusZMdGQxO6QqzWAVDZJhZUZ1o2ZEktcJdCTEtSuM2R2zG1CvBOucGlyMGI1PSOkta+xM0/pPowBNt+lyp4sm84lKottggxSr7oq9meZuifjMmaZYRqfis0wFX1d4dBsc9TK7vRKBf30w9hUCwHn8GArE77wk120TIhryjTr9PNAwWd4w/nz0d9dxi0/fD6zaaWAp/uRcKFc9PCRXzwFvsdQYgGaQTjm3LttBF7Bx/r5ZWm2ScB8ez7347B+73p6BB++cRUmG/Ruuy/esQdf+ekefPitazHcE7afJHUTyFHGGOrNAF+7axdGRqfwys3DGB6oakOdRaBHl4fHGqQs9VaAWsnT3yMWl61FnBiZrgfwUhRTGS2sEWGjnDvphpoJqbSI5reZeK79VdON2yFNDvq5nq6bCCdKYShRyayNqUgyZ2iUzyG78wsx02GCTND9zobcvOs58c1xfGoZEB3vcglOKMMc0xHHozhM5iEoroy0OPZBGlfLpCUU/6BqIhNtbU211I6Wde4i5mBKmyjvh26uxyXgzL9VYa7mGmIG347UdgsAcOMkhN4P00838JQOp18IZ8mcZyRfOadlyNiR5XfL0J6Ep2GQoZnU+tfzdPaDrHLpiSWOoWqkOMjPfz6Uww3GGLq7uzVnpzly5MiR49iQKxxy5ABArwTTV2yqUiH+mzYjdisp3PnzeBZOrgmVmxqx2eGq05KTKfnOaNufjRnPtzOWhTyd4oqiLgNYRi2A0hZqvqacSexMhwseaoFEEzoseiaqIdrly6Kbzr6GWFYrTAZZO2I3snaABOWcCMGoeogINnIXbdiPye6hNVtIXMSkm75cJO2DU+09g7qkiNeCz+J1bdRWaqjVC3vwB69fhr6eKrpKQH9vFUFUwILPAMVf8m9+4ikcGW/Bhb6aPi349G278JnbdqGr4qO7VsQN5y/GmScPpJaDMQYEHL6f3YbA3737FPzqPz+UOXxWFHwPDMBlm4dx/oZhfO3OXbhv21HsOVwnd+kLBMdwwsE0lxRw4K+/sA0PPBs60H7lxtnWCYcrt8zFrffss+7vPjSFnQenMd1wy9oOgB88dBA3nD2s92+FXOWc47ZHDuPebSNYOKeGSzbMxld/uhffuO8gAOCebaP4yLtOsaw+7Dgwhaf2HUGr2caRiRa+ec8+pxyNFkeNto4EAKi37PqcarSRTS+VMkY7eXsHKaSO/yrfygDGaC8ATGTDDBKfev0d45C2r4D8pBsKD56glBCZk7d5ehhHesyQwwWNF6aI/6Q5jgzmmn8lKYyNbDIEywryy5rY75L7JJ+BJOI7o8GVhNq/NQUAS99YQSDrCQ55wkAS/dlLSZO+WeYa1C3qe9ypDOKFpOeOSSbJnPezTpuZ1Z2JDJjejlb/Vwav5ByVvE0zTeL7wqywcf/qdO7quEmKmSHtxJPjHcCa/xFpyMHeCDeD3UJhO6eYXcuRI0eOHDlehsgVDjlOLJinGKJTB6nzVbGbSBK5YjHMlYmquUBWF9uh6SHGHEoKx+mFUGdArrAQsRB2tkoaWl6pE2UXgW6nqy0+TNGNMiSm84JjhovapKTUNZ8gpa1dVClpzeh4AwdjnpUXkTgRxshvhlWQlEfi8ffUtI4RnS44nWSWu1+yhHzUsjPxvncmkUQhgaAn9Rmco6dawKp5NfilMnirCY8xqXAwfSgkKRsu3zwHS+bYjpo5gPHpNsan22hHR8wZkGiaSYTp5ITDdHPm9nIHuou4dPNcfPmO3ZbpoYe2j8JnHI2AoVgs4Ppz5+OJ58cSlQ1A6NSZ2pF/9trZ2LlvHDsTfEy0g4jo4xwjU0285/8+rD2/7dHDlmLhNecswFfv2kumNzLZJO+r8D2Gz9+xD+MNjstPn4vh3gKOjjfw/JFJ+CzA8wfruOW23QCAe58ZxQ8fOoijE3F/GJls4aFnR7BhaS/2H23goefGUCz4+N4D+7DjQDZ/Gq2W7ShXvhtgqBN1Pllvo1robBxgasKC7TdeX3ok1O8mnWDQybiMb7SphVDySd4Mm56+KLMkHskoSvm0E0lGbuJzD7Ezm4FZprCUtDKUPw7NrPvxr8C4wxTZsvcBp+I3IYJue10pj1TKEd9xUhfC5fMsh/riHLN+FYS6x0EwO2/N4Ksju5LqIFvsUD/GbzNdrfFj7X7Sm0HIYvbHhKLrJpNEj1fSNHh1+a3vdJ4md88L5YtC7huyKrP8TOk62yKl3JlOaCFBB5ARmZVjChjz6Bk6N9vMnWsmHC87dVmydp2eOuYTxDly5MiRI8dLE7nCIUcOXZNAPE6a0TtWgYnzW278FjuquEHYm8F4/JfM31zdiTTJFXP4lyWUO5P8thxyRx1VbQQplA53YOdOPKGQeSGgtoW8IapRLNpfqMVFJxXHOoiStKBNKctMF1IzIP07zACx7YsQTJBO4nnU93WlEUVuiPjCLnxCrkZ9iIMlkkJiYinN5HN5xRhOXdar+V04bWW/LhIzCgTi9VXev6yE/6u2zMHrzpmPqbpbIQFETq0jdmKgW9/K/kuvWmWFL3TgwyFJGZKGq86cjyu2zEN/rYB/+fqz2rO//0p8XS37OHPVxsTTAgJBm5POpn/y6MHUuGq87ooPj+kOtqeI/JPMCv3frz2XmudX74pPHtz/zFF85J3r8PSeSfzTrTvJ8KqyQWB8qgUwhu37JnDz959PzdNEj3pChviuNJp2uafqbXzks08503SSZgbjq75LaZYuBOUoI6gyC86QuT+N9ivnajtDCvHOivGJSMuZjnPzAS2f+FTRPZ0D8BTNQ5wQVTZuXCflbadBB3SWm2t/EucS5Em2mbKnyjibpAQRZsKsPMkEZyADkvpTlnyVrDv4PvNO8nV0AdpWfwfz6oziJnLQjArIHZGyEP6dxeJqB+64H1JzegLRZoPkpJKfU01Fvkv2EJEgF9XWyFQPzvTVKT0zvweqAtEcOJTnauLHNGc1stWySqlNToXJkSNHjhw5Xt7IbusgR46XC0zCUfymwpiEflYTBebkVtkRF/9w5U1dWMs643cW+ROghbfjiF1bbtundLysyLLIzVaUpLoEElc9qSIklJFss4zoVKElYSsUbJIkYkYYgxX4uELIeWzpd3wuwPX+miBIejVeTBBw5ckxEkhpvkRA11ZPrYg3X7wY5aKH4f4ybjh/kSEC4VhWK1/8hzGGYgaTRgtmVfCWixagUvIx0F1AX5ft8EnKp/iLMB0hN1uBrkBBZwqHP08gndMwVW+DseQTIoDYgR+a8UlDO+BoESaABM5d0w8gVOqUjB36rTbHB/7fI/jSnXvgeQzrl/Sm5ndotO58lkVBouLwWBNP7BrP7K9CQHTVRkK5XVgxrwuVomfsJNe7J6VwaLaD9PxY9J0gTcOl9zFGspC0k2Ymp8Z6Z+bgckjVwjtOnomgprmmOJ5kzWXFMxkhQdEii6HnaYVOPa2YDbGuk6V+iBkzTL9AN+lDf+vdatzEGlAIU2puEk69Au06lIEIqOVJKBnI7In5oCmHUIhkIDxlGdT+5swvGS5uM0sa3CTn03htQimbTdQUQpwIMaNZZkSa22Wn+0x6enYf4WrnIvuiMdeQSen9Ru8malj3myDJ+Cwg6yHM+Hhw8ulQFQRqfpSywIiCqN5nStrLyQlzZhXLEymF7SfZ7hkyHp8Zco4cOXLkyPHSQ37CIccJCsfMOpFQj39zzohFK4ydVdy4Gf3W8hOPORlUk0msKjgVSLmVuk1ILY/cB5mx7JbgTlF+buCAwzC2EY47F4fyuSsDrjBISp1yzuJ0Oxc6JQxTdl8mKU6MMsld8OS2Pzp+VrhsTohnSlqdL2BfoE7FhbkIBzHNOZgXUYV2Ncow5G/1XkI92ruv9bBXnTEPV2yZDwYe7oxv1KH0OE2gNJIx6YTDr127HKedPAdFtAHeipJlWDZcw/3PjJBxqqXYKbGZdtM0P8SAQgemckwTQ51gNDI5xJwOYUM02xw/eewwjozrJor+/r2n4X9/4iEcHI3vBwF9wkFg/eJu/OLly1AqMjQabbz9bx/Qqn/34WnsODCFejPAu65Yivf+44POtDwG7Do4lSh7p5iYbqNNmIQSuGrLHHz1rv3aPWEejDIllYayaGsmNuDadVdv6vdWzu/GmsXpypgZjauCz1evU3QVLuWAM/2k6wxgzCZsVTJTKzKZ/gzfGTMaochRlR+uNDr1DwDoxTDzmLkxurQ8lToln8UKHzfpK+6JKUaHss7oGzizROKTBinh1Wkc181dGQkq2jP1u95hoUwi1ohOfvXTFB5UnAxIFj1Dqg4lgpqC6z4HT7C+k8FUWCJbnhZZBHMEJJYVnaTHOZWyNYNJSAdgWRr1mLUi6Qqo7Elx8dHrIL8cOXLkyJHj5Y9c4ZDjBIRCFDPzvrb60sLH9vqVx9ZqSSVgSe2B8dchm/YzeQIbLk7smXm2nVqU/DNEpqjZFx1xFGbUaycw4pjZd2JuQO5ANToBtajvqB6PwyLEJH2kqayU5LnZV83fnYgwQ7bNxLHoGTLWO2NMHjxSqS4zGcmv8Pg+8wifL9whdBbiICuIslEbvc12SFI49HcVwxMIgSI953j9+QvxqjPnY7i/iP990+M4ONqQcYoFT+ZRNE44CJJazbETHw6VoodpYgd8Fpy9bnaYt2mInsDHv7UDdSWfctFDT7UA3ziN0Q5oHw4Cal0AYVmbxk79u548inc8eRSnr+rH/MEKdh+mfSH4PsOOA8dX4QAkK3EGuu2TLKKbmeXIglKRqntl3GSwfDiUih6aKb40Do7W0R+5FpHfOpY8TGTtdVQ48tQDY/rQZCh/taGWczDPA1c0i+bJH0sISwEQh850oMOlPFfTJnbfA/QpDy28SCNJiYrQSJXMTpGfHLvMclDDZ6D3C0ZddDzAUpV9HBTwGV8XZn1QkrLNkLdDPtcniUgAqvCafBnyUXK0zFtlmnp0KJ9M2EiAJ2YYnc6lpuIp4I6EExUt8n3r/FsWHza2566Czz5uVnmoeiROWojAliJOleM4HYkgk9GWRcY6TFEXuvt8FEI1lwehGDkeSosOGiQ3qZQjR44cOU4w5CaVcpxg4Nqf+LapCOCO8Nwgv1OWVxS5bZ1YMANrM+Ioy/BevBjgZHZW5lz93yGjlXeHSFEGZNsBdzwn4Wbd6Dvy7LxmpsjgZp8R9ZBa3uOkjDCZL+t3dEcxp0SudTpcAKUtCI8fOpCLzF+Jr+3KNNrLmWZnhWLW7jYz/0ypaD8pPZIJ6TMlvgOmyPGuVy7Eh29chT++cSXKhoJAUwZIRtTDqgU9WLekFwM9JbQMkzzCRBODbVKp0VIULFF6nZhU6lTZ0Bv5CzhlWR9OXphhl3yEScOpdG+tCM9jlsKh1eZI2uj/48eO4KdPHgkJZsaweUWfM2yp4KFMEvIhCh5LfD4TBBxW+6mgzG1JhcMMTjio/UEblRQyvG60cbnopeb1e//xsOYI3BzxwuuwDTK/aUJhwZhUYsj3iEpEeafMXNJeb3rcJb5EGRTrzBgjMiPNvrhKnBljT6df5w45XXuntzYszWxuQG7CUIl1isTWrjl5O5bLfMjtn5yDKcusRAIe+rdDLTfX+h4pjTvdlEYgHVNnarhOSNbsQVVwbZ4VveVWs2Wfb9lvV8L7NsM5Deeadxh7/mtOEawpgxpXTdeWXCfOM8hm/UsIRZzeVPPOOi3inKf3PbJfHMNaJCXx5G6YtWCJmabklqDczZEjR44cOV6myE845MghINfbLmVDtjjyxAEzw0AhpYl0rLxcE29iYWvdTxFaruG4IZ9aHsdOt2NBBhvQTmRWXCQtWMwyqWGzKSJ0gjdekHW0jpiJqSGNNO+QwE5lxBKepQnofEzV6fFEnH72RZzR/sqpFecCnMozpU4YQ2z5TDJTBgWWpkxQ+T+N/Mu+J48BWDqnCuZ5aLfaGB4oYceBeIe9rxLOtp0nMMYsMriomEgan9LNEm3fOx5nHKWZ5lNhpigVGP7qPRsxMlrHvKEueJGj7a6K33FaQnFhKhxMctzEvc+M4eBYC+dEvhz+5zXLccdf3OuQ10Ol5K4Lz2OY1VNyPjdx7tpB/OjRw4lhphvtRIWD6XcCALaePACwYznhoMQjzPSYJxzKRT/RTwYQKoh++MhhXL21iw6Q4f1/bOcovnz7LvTUCrjh/AWY3VfRk4j6P5PvQTw2qO+cc6xhNMltvefmY21ukByWLChX8hUnFkwFCkWAahsflNQt8p8uMD0E2nMVahYR13EaQeeAokSfCaw2ED+42KVPFI7mDzsEl0mlRRVKtPQkj98cLZ7+ZZPO/dj93PZ9YMggN9kocyRHWvK+Soi7wh5DPenT7Gj80rfZ2y9PQn6kmS7jd8KwkBH22HusyDLzkHMpl+CdFsj5smYDPUxxeih1iSbGq1xZkCNHjhw5cnSMXOGQ48SCudNfnk8mdiFRq0Jt0qkT29ZOY7lbkSn5mIlZiVrpJDtqJvYScmrpIhQdxLZKGX3mC4HY3NQMZ+QusmbGMFkUxAzwcchG3/Fl1v1xXpm4V0GKAsdgpqntqS/oYumFWI0lpGmSZo6+ywhiKt5Nyx1hTRni55J0TBgY7HQSyBfH62jG5iKw9Sw+tcIMspzqCg3Dfn54wiH5vTeJZ2E2hzHguf2T2rO7nrAJ8A4sKnWENQu7UCsXUB1k8Lx49BjqK+OcNYMoFhgKhQIWzK7iP761PTGtvq4SwLmtcGilO5becWAKj+0cx/K5tcRwpaKHSsmtDCl4DGevnQXmMXz0i0+n5utnqNipRjvRpFKRSOPWe/ejXEo/dQAAaxd349Ed4/L6R48cxtlrZmHt4l6t4zHEw6+lcChky+vgaCO298/jdFUYb0BkXquFqWaAf/rK05iYDtuTc+C916yA/t1UYjIWZaOmGBL5+i55+6tlk/w8JLI995uW9O2L9cxqfTJL5o5gfgpThu/Eema6XJp7I6nQJdJkxok7vaplfMaIcou84Kg7ziEPcHNEigTIcjKlD/1s8bNjLMUmiEzFjOYQWeNoGyyInfeppeQAR5CqfABH6NzmODWWfdogPd0ZKSrEyYAAkfwgiuBQ5FEbKcQOBivKzOpFjGNWdSunSUylR0c9lzFwCLOVhIxpCcr1SHquwoxbhoByMiVjHEdlXY4cOXLkyJGDRq5wyJFDNU/knH+6FirGpFgjM1lCGFNJYSzaLDMp8cQ9aQGk+ZlwkbGackFZhVOyvhCwFBMzz491um6gTliIIquJMcJOP4UXw64nh72OcI0qiBWVeKcKFikv0hjwDhZopI4tNVLaQ6L9XCDsp2cnX5IFO9ZmD1uBa9fmc4u4NCrTGB2iPyKsoQRj4W73vUfrWqzBniKpaBGvKA+4fcLB94AgmSAWZCBg+3Do7y6iv1bEdkNZkRWVkodGM5Dme0xF58JZVfzSVctQ8AAUSvAYS1U49Ee+DH73+lVg5SqCyVGwUhXNDAoHAPjQp57Cv/3qqYmdolxMPuEgFAjnrh/KpHBII8Jm9ZZw+eY5+OxtzzvDUG3/hTv2olz0cN66wVQZXrd1Hiamd2mKp31H61i7OPzdbLZx8/d24PGdY9iyqh+vPX8Jzl83iM0rBzBe5wh4eMpm3xHar4WK+bMqkcyR7AT5DOIZANz+6CGpbACAe58+Gj902TsiRwv6nXWlMDNbS4py03znSeY/QSGtyMaQ2D3Dp0y7glCyaDD6ncaJikcei4YgikF2inrc4TYhKMYvhYXUlMtK+ASR7a9CsgxZyGsuFCLHiVwO803oirbGLFtAZaMDj67NmB2f/ExEFvJZhFS3BPB46kCFljfjHp8Fzg38LLlXmFy7ol4w5E7PX0snc10LZUgAmPrvVLNrhBDpofTkmHpBfA85JUcsXqTCtCJlUgzJJufuIV7Lj2oPUWYiMrN+xLHUdWaOHDly5MhxAiFXOOQ4AaGS/eqCE3ArD+DelZOgpNAUCpbOwlQ6JCRmrhhdJzM0oVg6M0rI1LHN5DTzTYnIqnjohGQ221OwUMd5tm+ebFBNVKRWIVOaNJtcTDVPQqSv7YajH2TKR4CDJ1fZi2l3WNIpkAy33GD6a8Y5mO/bhLvW5iweK0Qj86DT6oerX7hrPewfrtd35yFd2TB3oIyuSila3zOpc1NHonag22H2WGh2KNRBODKK+ql488yTF0fHm/iVq5bjj295wlkSE4M9JfzDr2wAZwW061MoFX2woIWWzDKsc9VXQCxPevr9XSUwhKaVWLmIRttHUPQx1WilxhUwT0eYKBY8VIr2CYeB7iLef91qLJlTySougPTXr78rVCYlOb3+6Fe2k/fLGRw5A8BQfwlrFvVoCodGM3Yc/uPHDuG7DxwEAHzxzn1YMq8XZ6zowv6RSfzV55/BVD08geFl8PPhNG9lDndEkEd3jDlSjegrBvyvf30I+0fid+TGCxfg0s3zicy4bn1M7OJ3kYXmzRkwsGra5vstdYyMWaRbtpFfVaLopwbEXCDOm3i/LEnFKRBaUUoTfRnIOGp3uqog6Xj3gRo35WGoPRIZZ0iwYxESoatDEsIRgcgd4PK7FF0bJz60jSsOxNWdRTJinscRnfrJoISxTu2az18gnZV6usZSWMR5x9MDU9nQQV80TJyZ08gkdQJXlXvaQEHHccnlXE3waExwnJZjxr+dvoGdOW4212i6v5O0KB09UxA6q043D/kC6U5z5MiRI0eOlyxyp9E5TiiIiW2ymSJ1eWeGV4hlVzpc321jL5Y6nI5bJnuMdLm5u0eXXWTpckgZ3z82Ajlt4Zm8KHAQ6Um2gDOQ+tnuJ/UF6nbS4pcTYY4XDPrIWAyTQa0k0iqNIm0yEgJZVloWGdzhjjoyzSyBzD2FcL5XdBvaLJ/ZnzWFEGLSrlNkbKFsaUmOjGP7/int2fK5kT18TkUA/u3WZ/HOv9b9ERRVp8AMuOasedrzy7fo1wAw0F3CuiV98vrijXOwdnE3rt1qh3Vhst6S9S0UIybB7zLbkuVdXLmgOwzvAWqF8ICjVvYt59gU0kjzctFHhXAKfWS8iVt+uFPL49qtJtFtI+WQCUYmmthzeBoZ9Aa4aMMc7Xp0soUfPJzsHwIAPn/7Xtx6zz7tnqqouPepI9qzv/vCUwDCUzLjUy1p7ilIMPsUp0uEYbHCAIjfOaFEE7uwn9494UxXtJqqbACAvUfia81CHTXuir6Z9v2yTik54lnB1XHZDmyajsuyZ4BnPIKmmz0yy86Jeylp2IIgVk9mh2xz8hMSE+baXEzEtWSANk7oY3sC0Zu6A9y4JKs86zwlGZZ5osx79fVYmeKYn0PnBRFVxk2bg0dXWaZhWTKnp+qp6HRGwsC0uXmclz7foN9klwwdzlGzppu0lplB0kzxuwHHeiMMmFUFSj9knqddJwZX127G5q/Eae4M5rOZ5nwdH/vNkSNHjhw5XtrITzjkOOFgE+MxYU+R4tqEVX+gpUGGUR8zdaJr7IRynSxwmW6Krp0kf8qMPam82UAQuJnjRMSs3JqZkDSQsiokdh2qW6wEO0mJmURGpq1E1cWUqrDhIqMO6jWr7aHMSdqKCWleiQptHBtXF03xsngmC9tO4mQPSy2+9TK4OpQgrG3lQedwKJpigawYjEVDAKPl0BbRRH5M/utIX/6yjcU/+NwYPv7d3dq9pcNdRjwFXmi2yHQ4XCx4WvjTVvTj9FX9uPupo1g0VMU1WxeQ0v/um9bhG3ftRqng4aKNQ/CCNm68aDG+ePseMryJ6UYQktKyikyCUrGtr5LDGdJeMKuC01YNoNVsQ20VxhiG+sr45/eeCs4KOHx0HL/2r4+RafheGF71Z2AidBpN+3B44JkRmScAvO6cefjeA/sxOmmfsFg6XMP2fZOW/wwTB0cb+M2PP5oYRuDOxw9lCifQUy3gna9cir8lTD999se7USn52H9kSpZLxVQjQLWc7ti7v6uAoxNx+dMceKuYbrRRn2yir7+MerONoxPNhNA02d1q27u8mULjaroAx/ZnLTqhoHTpPJmnPxOfKql005Kixy8pkhi+1WJq3y9FPKm4iSJECeiHKY2dvsqQFH/SWXh61DUtitKb+fzj2BDzjsb3zSVO0iddVLTrMzKTMhrTqxe8lriYTyaE6XgaMDOy2UpC/XwbJ+6ckdR+Lqd/PGFjRFKi2QpN+QWR08E4lDJnZ5GIHCySy+GuXIZ1Etqd6CMSi0NrkjiSm22mao/EQ9rIrP46PsJYyJg3t37E7mO4GWwGJ8hz5MiRI0eOlzhyhUOOEwsUTziT+Z+2e0chnuUMmiYgVLJfj0NhhsQoNQGWSSRMeI/bRF2F28RL9Nj4nbACSVAQkIQ6ka8gpm3zE0mrKU4ol1wKlxekEiPEZJNGPBsL2JltgScvjx2JJ4kSkHmrbIqiL2aPDeWd+KsoV3hUqxnGhMTHMhEzLK0ocYIaG8xdgw6zCdqObABrFnVbYVbM67JlUZIuEbvxi74XJ4rQP8NvXrcKY5NNdHVV4PkFKZ888MGBatnH1WfOj4oQpO7OpzBZb6NW1acs8n02wurVZtdPb62AX7pyGfaPNnD26j54kdmUpPbYc6TufOZHTqtv/r7bX0IpxYdDvMmfoeD7+D/vPAVfu2svvnxnrJTZumYQv3TlUnzkc0/jgWdHyXSuP2cuPvPjvc58KEzWs/mqEGi1A3RV3EqDT35vp/PZI8+NYfWinsT0Ny3rxvrFPf8/e+8daMlR3Qn/qvuml8O8eZOjpJmRRjlLKCEhgkSwbLIAY2OD8WJj863tz2mN7XVY72IbnLAxNogkDFggkFBGozBKozAzmpxzejnd1N31/dFd1RW7+74R+xmmf6B5t6srnKqueE6dc/CVdXHdG14g8B7jdYUGFMcn6ugq+ehoK2Dv8Wn81bd2YGLGw5Wr+/DeG5Zo+ZcK6VIpbooqbVmJ1StiSLflDXGl55S5KzlIekfVSElM8qSMBW2iNJiSZtmqREMufcVMn2z5L1NesZAm45qvZkRkRq/pAkqm+kq0mn10JFAVF5JpH8j2LMZNUJSNYIIuWrNkAUw82igNrJTNymxQCumzQci4n03GosQtsYBWqEkISR7v1lIS6KNiBoY8E4iR8xW2klrvSag/34YIa5hcTkKd1e0MhL2YqVxln6LRaTh6ERZTEKAYaUlCps+fRcchFzjkyJEjR44zC7lJpRw5WjovaVdW7O8NhWSzd6unax0Znai9Fsi4EZeYv9q7jEIXHmxmtMrpTMydhPxb4rZTud6cF5DCAJ9NUZkgCCAQHXskMxumAmVBBUls09mwnmzlJaSZlcaOKdsM9CZealQZ+2L4bGFvWxODXDXXoqXKKpMBUC64+ODrYzM9C/srWLusO+oasnCEJTcKHAp65oQQtJddFJyYSUn4P+KwP735aKaWzhRPFjzEoBS47Jw+3HrJINqF2/aEvVTSEgIcHbELHPq7iiBI9kVQLji4/Jw+fOqOs/GGSwa19+PKLfzejiIGe8tS2DPbRvDynvFEfxFXnNNrffdaoeFRlA3+KLJgaKKBcoqJqlLBwaObZJNOsYZDXHfP8/EX39qF3/n3rfj9r+7BsZEavvXEYa4Z8vyOUew6qmudNP14/rbZ5W4o2j2nM/aZjxQtnP0rjXUiveXzgzCmhIzlv2KuSVOgyNBTuOtc+9GQT9qsqpMnGrliebTAZLfmLwsaWoWeJunbEulPTEva/sNYUKYbzgRO8o1y1nfZc2qOCWURdq/e3m90mkUmNbtgY6fCdFM983BKmMNbTJIoBrCWkfWej3DxI5ThpGiM/BdAq/SZrlFImaV2bXYOSi0g8cWst8/WCytKNEv8zOWasv2v3hly5MiRI0eOHxNyDYccOWYN8YYbO5mIV4OocDI0XLsRb6y1yoHOcAub0cVv9kXlmAURhltOSUgtO4W5bwxTGS1qPANh8ZWo6JGAMqfRRjMXpnySBB4GqG2vyBgkR+FitNM9bxCBTpUbKkY73RtUYpueDs2ZDqAZobV3QtuKbWS8ycfYfEmnXplZrkdJH7N83Nn6YYa2Sbupr8U3Du0w3ZsvGcBgbwUnx2q47sIFKBYceH6gdOewjxGEQgoVxYJj7oMp/YQzrJRh8cFbluArj9pvw4sYnqijv6dN0pyQyKb6mLOBQmZestvW/3DfPry4exyeH8CnwH+7fQUuPasHAPCVHx2x5vf6C+aE+SYUXyo4WNBfwfz+CqrNAI+8fFJ6PzReR1d7SapTw+CAoekFXLhjwm9/abudiNcIfkCNAqks+OrjR/C9504kxnlul669YWqLF3aNY1fkn2F02sNXHj+KLYpZq/XbdHNRotLaVNXDv963S4vjecz5NZGFZVrTyzMu61dPbxnCDzecwGBvGT9/03zM6S3aVjE7CDFr8diYsElZRf/TCJWKIzxudhrl36yO4Rg1Syu4QDyBjy0j4YZ0EkRNEy0XKudMIiaxtfxszMpUKDTJYJIlYW8J81IjlZ5mm8ZYvoVpbE2XZcFKKTetHkpWlAYg/F6cXseWRFcG4YFRO4NA2bfbe0QW2Lb9akC4Vcgg+DJlpCVTL5Ok0ykVoflzSBPMqNK4tAJgvLDzmkAeGKHQM4OzZxWSkM1m+05LY0gr0XXau/McOXLkyJHjJw65hkOOMwyv1c5WEBYYX6sMUeW0k3YTnh3OZkVuktPq5HSvKdjZmTMX1EOJ8SpdwqOB0am+sz0abuipTn4zSnCiv/wfS7z0rLIhoa6qRoLICBZ+i7fNbXlx7YYM38SIrOapzImtdJ0+kvpM0kcyCKXYGFaZO/H1Y7uWiDD+k2um241X0VrLxPV3XAeXn92D2y6fh56OYhzDUqDKUL754kH80Z1reX6pJSdpZkS8xuvXzsHFK7tRLjq46aJ5ic6Z//jr20PHvob5TL4YToSyzSbdKGfyxCbmCIBaM8Bk1UO1EYQ+LDI4Mz5/WRduv2wuACBImGvndMfaCssG27X3v/flrXFdIlxyVi+KrlyBph/AdfVKve68fnz6A+em0jtbnLOoE7ddPoi3X7MQ77p+cSZH2jZMVnXfFGngAgcSsoUJgebjQhU2AGaH1J9420r++/Eto9i4b1yLo/ovCcuWH0cm63hi6xjufuIw/u67u/C9Z0MTUJNVD3c9ehAnxurYvH8C33/hlHGJkvtmhkUjE8+XaL9l5pmaoYETC3CBQPqNfKL8NUwPRPthz4bTyebLWFQiO+y2zLVilgZfE8ZHCuO8EudjDuf+PAjLJHlqNCqkaJGSg0/bBrzYhiauryF/cb7UX4rUyVkZLwJA/YZiMoNmLk38NFI5fIVNEMjqUofXeN+bBMN2TQUV+mKWPh6lakGq0IpgKlu+9vGRHaFgjcRFZ04YC2ysKinKlq1l7W9t3oG9aYRurh4xWjI/liNHjhw5cvyUINdwyHHGIr6FPosDHE3atIZ5ykIGk2YBleK3TMBrCQLQDMw1XnTioToLV4TEpwqbICLtVG57H+Ut+muQklk4D5nOa4QAmj1j1iCUXVNLyEC8eZbFASIrltU3oc48X2smXABh5Dkllp/01s6oeM3A6bOVodJgrhR3Lp0gKIxtWwMpRqHlfqxlZWHiJbaXhUkfMc5ZcjEWz0YwaaRlb20XEttLZv0req0KHMpFB71dJSFlVEfH4dxTXUAYh5m+SEelgN97zyr4KKKtrYyXdg1LN9kLLpEYvwWR0U5YnizMIKSNnud0FTE8GZssUk0VMaimiurNANM1D8UiwW2XzcX9L57ibfGeGxah6AA3XTgABwFAiJG5zTDQW+btvGRuOxb0l3EswUwTAMzvq+B1awfw+KZTPMzzqFHgsGhOBf3C9xFxwfJubN5v9vmQhhsvGEDT8/HOG5aivUDhlkpoLzqYmElyxPzao94MdL5mhvmzVHRw9oIONAMK36eY11vGpWf38rngu8+fMqaLTSrJgkIiMFgPnJjBvz8aa75MVTtxB4BdR6cgdoVX9k2mEyqCEEljx+SQloPGacT41iQaY18QTIhZsf+U8aTLElLWuwQGuk2jwMTDVtO29iJ7XgQElAhMTMvaOtvVzrqCaYvyLDMjtr2pxJW3ZkAQ7gWJOsdIa53sCyza8drpNgSfLp8/jXmrUqOyoilsAySGOobM40nfT1LTxQGTLwIjDO1oGczU9EkzgCaWD00jIEv+hE8Y1gjx1ieZsgRk2WsSWC87mLIj4tvZd8rcYlKOHDly5MhhRi5wyHFmIcOuULz1mnhwU39Lm1fL4ctwe8t8aGSb9+zeVbVbaLMx1ZSFtpBAJF49I7bfCoNWEgboR38937T6sEODyi1RmKFpZ4vUfqK/l8xqzRYtfbOIqaSZOmBcaZFrNGuCUl5bhGgZT1+z655ZT3ZE6G+nd5i0ZW/+TfgfqatryVuofIqGSmLclEaWmIwCp0+1s99oBloaY37EEsPSt6OZFoB+Y129ZR46rfZTKNDf3n7lfNwlmG5as7hLisziqwKHf/nhfi3vy8/pwW1XLcH5S9pQrzdRcBzQIHRonCSz7W4vAL7P6evtKOoCByJ+j1AYpPrN2HV0GifHdEFFV1sBlaK+pfvQTfNxy2WL8Adf3oLJqoei6+DURMNOqIBbLhrAh29dDoIA+07V8akvb+HvFg+0ZcojKz79/tX49Nd3WN8zQZTITKo20/16vPO6RVjYX0EAFwUSgBCChhenu2h5F17aqwsEmNNoracJ5X/2np3Sq93HQvNOqvbHkoEKZ64Twj5yzOTPNDMZxw5BqlBUTc54duLN4KR0lkDiOKB+8v7EurWBvlLG1TPUU+W3EsKFsMmg0TJAQGgsnBQ1EsJflMlMtXIiDr4xawrRO4FlQTBpDCRwXuMtZMa9QBLjOi09UUiYxRLJl4w0KVErgnkhH3lbre69s6+hNLA7vtYjp0eljBxrPBqZbotpJoY+Eu/bWzW3Ews1qNFclfpxs8C0bke7SMIuJ5j2vrZ8zGPZVlQWWHaciTCZOE2Mr8amhvGaQH9i1U5nO54jR44cOXL8BCIXOOQ4oyEKF1pMqWYk/CV6NALDYSkjEm5Rnz5ay1duK1O7UcP7DLeSrHUk/D3hGgEWJmsmpqxJ6mC/ETV7tCp4SGsbIU5iOybll8TIIexUmUxLBpxWU3JTAsldvmVZmsQQydDnKf9HLigpKQFMdq5VlXorE44JKljcqAvFpMvjydRnk8/VjNnmpByIQ0ZeWdFwYE57T3ukGOjLmifXcCBEaWsnYoRQRcwYwlcEFyyfkBEZt2eSbwQAuHpNP379bSuAYhmgTa0T2kwqDfaUYu0aAADFuYs7se1QbAKoVzFzte/4NP7h+3tweKgq5fXEFt0nAQA8tXUE+05UtfC+jnCb92cfXAPXdYDAxwf/ZmOicISh2vD5tCAKnABIzrbTcOHybmxK0bDobi+gUnJQa5iZ2HVR4BU1e90Sl8cDMLe3Yn1HYfYNAYSaJKL5DClhhGLBkejyfIqvPHoA87qLEFFiQiOte+kfgc0CXDgBmVGZFMZqRShjtLOVKJ5X5cIIQo29KA8npjNcCRwlfx3y0ivPYXGoJT0rzonrJI4RiEIFIYvk9do+P1qiJiNxW2Gag+MZSK23vPswa3ew9OF8MTvHw4kaMRJavVCQJUeqbXdl3yHyzywlUJOmDL9HIOg68O6rf5esZplsHzy1y4nvZ3FhpaXyTid308sUbe2WLnkIaTUtlBbrFM5BhnFC1Ee5XPkNm0My1CHzuDkNvOZnjRw5cuTIkeO/NnKBQ44zGK1spKMULZ06TO8Nj5l40yLXUt3EGxKz2/az3jwrB2ViPjCaC0ipDGdqJxAnmmexMVbTylA5j4lOD1VGe1rDWejPKmdoWYiU5TanJY3EESLQ62pi0ep5hX8SiHitDlKzyUZpS6l5M9JlHNuZxmWrwqWkFqdavIi49DyJ/o1ihqOQIdfAICCOKLyRy1BvadeF2+RxEeLtTX1E2AQgOtM0/Du3p4xT43YzQ0XXkZW+iKXllfqcs6gTb79yHppBKJJYtajTmL+q4aBCNOkkOcKO/ppMKrkOsKC/AkIcjFeb+MIDB1Ft+BidlLUMPnjzEuk5CKgmbEjC7qNT2HFY92HQ31lU6CSZhA1hntOYrDbR2SYz1gGgUsomcFgy0IY7rl2QKnDwfAonoZ83wo8XIVzbVJpUVEpulKew2BIHYm81+moAUDcKIuTvb/reD72oO8QuFhx9jAnMLc0ciWHcJNtzJ+Ln5beSKSumZWaqvt7LMg2zoIQSwm9CJ5co0woly/Am9eznbaL8ZYJIUxwzTaY8bQ6lDc2RttVQy7MRogq6kq/Uy9mH1+vt8ZgAlCAy3xUjyVxRyMDN9m2MPhmkCMl7wDAKlb6jrf6UIlXZx0RLPDSUNuD90rTPS2PEEzlvJbwV2NwS8HeJ0MuzKiUL3U29Y6HWVv38RkHmrJB2o4P9Sj0AmIM0urQBpqRpoR7RPpt9efO+5DXaI+fIkSNHjhw/QcgFDjlyZIZw7Em8EaQ+Ryf+1vmSBhKiDBKZ57NAyq1ojfg0jQSVoS0xSxiXReIKh+FamC3/BHIF5kpsvkKgm9WF0JiJIHJrWhQEGDkmWVKSpH6UUhYXyBAQIoQltE/iWcfAcNbKtGVqZGZlFdiIrIQsN9BY1gmDiTPU408S2uUmcfpZazapxCjPGcybzPYSHZE/v5UKK1MR5nK1fKPfqoZDU2HA8v4HmemnMuYy21MG8JE3L8ff3rMLtUaA26+cj/tfOC4Nq4JL4rnPwDwgUS0J+/jR37XLunHu4nb4cFEuldBsisz+mFFg8o0gouAKN76liSPEnK4SjgpmkgZ7y/jML54HT9Ci2LBrVErzzmvn4+zFPbhyVS8aflyvVp0ylwoOqsqN/wX9ZSweCP1VNJoBNh2YQNHJ3vtOjjfw8X/YiM/9yvmaJoDaP2zw/AAFl6T6rGh6QaLAxyQAqDWSTSq1V1zhxrmZid3wLAIHptEjzTkAc1gdBBRNi7BCReq3lAY0699C71IGbyrDLeG1dNuXM7HlR2KZLbhZqCwsP2HtNe0KYrCxwYZUxnmZ6N9zNkIKUaPAElkPAiAJW8XtjQXx+tYaszTUGDD3M1tOfF9hbUvWl200GuIr0mNRO1gVHBs6rNKZW0fsryDjPkFMy/81f0v927O4hMfRCWJvqBLBMJbFZMz8EWE6eSy7SKslq/CKJnzeRMjf0Ayibc2t4OMnjqdqoLa215rN7igFs2go+3wgP1LbXlQIpsLS1arhrBw5cuTIkeMnHbnAIUeOlpBhs8iiCA72JLwWggdTscrNwqyFZd1YGxJmuqGm38oj5nimwomaTnHGZyjD3A7ZmME6DbbrYOorhXneCkObCw/S46Ub3DYwwdPiyJx5ISXJdrjXsk07oUZIrIuBYZGeYaaI9u8i9CWSTRgUMsmyzAkZvkN6Ji1EFdj+wtXkbDcDw75bLslM0lf2juPfH9yHX3rzygRa0r+98TZklOSSs/rw+U9egamZJrYfGte6iOtEgiOV0UjMZRMgig8pTcjvifxBCK/TNByKLjFUnTFMHczrK2PzgdgfwFsunwfHIegsFhAAqBT0/B2HYNBg9qdVgYMqbFg22IZfeuMyTFYDtJEAMzUPn7t3b0t5MkzOeHhqy5AUNj7dxIduXoy7HjucmLbpU6yc34G/+oXz8OG/eQW+Rb0i1HCw5/Pb71wVfjeHcGcZB04la4C0C1oY9zx9BEPjNXgB0PR8vP+mJZhT0QVpDKIwQ+6zJKpXdv9K4rdkpnLivkn1i7XCrzAe+ycmiKQPNYGJL9ZDWVuFqZ+zV4lYRixmUGOy+nCadAqkJwpm7kllypoYdvJ8HgqNZdKNdVVK1jSRBGpsSBboZoG+VxHXU9mPhzJnCeRmWSI405+IZrhOf4NJRZt+Yri2n50dgzh9fRUKjjoxiR/S02QpyxQ9y9YlPUoLEbMIUMRvqsc3unAQ9jPEaW0t0QuA3g+EYZx4R8gEoc+qwq8EGQiAaG9vmvyMtykSyBIK0hWH9D1D+KTOzEpx6h6divvNH9PBL0eOHDly5PgvjlzgkOPMgurF9TW9TEPse0p2Q1LVTMhKg8YkTktAld8tbnSJ8psSgVaiM6lNJ33+TKSfwsVjA+PB8DeVQMNbwmg0Hb5F+hnxJPV0zx2Jq2TQ0+hIRuaLqXDthx28kdUMiP6txINV2g3PqL2yMEEy8yCShE1JdU114iBU1CC0ag0ZOXtpqguEpMi81PqE7a0xS6y8E3N7cCai2h4W/gXr5yaGt83evSY0I3GpUINNgjohYmelgFKBwDMwpolDlNnMdOiPc2ZjwUCakDoOKGTUcJDKi0U7msBCZK4ThKZ1XIdI4f/x1DHM7WvH8sGKRKfqLLpVHDhZxR9+dTsA4LylXbjynJ5Z5/Xwy6ewYeeoFDY23cRbLp+HJfO68Wff2GpNy0wWOQ7BVWv6sX6r2QeFF1A4FonD0rntWDRHd1JtM4fE0F4pYHzaw6mxGdz77FHp3duvXoCBCsGhYbPWRa0ZIGntbGTUbgCARzcN4wO3LI3HqGXeC4VgiMtMWB7FCCQafyb/DtCj2yGNW3mwxrOyuhcJn0+O1fHPP9yHU+MN3H7VArzpsnmJxVDGjIuWKptptzRosUVhv2W9s5Ug+1nRac5OlI1haQc1rldi2jj8tXLpZWqntIsSs7uhnWGPY4GZmZ5QDAVCs2nZBIK2ppx9Eydc05CaIcN+U9Q+IpHAztAPmHaEIRONllb7DhX3+VZa5QJiwZcWE6aWzdKnNG3lBKhDnhB22cIgLDO2m5KZYcozN2OscZxFIJwjR44cOXKcCcgFDjlyZIVyI1CGKXD2jE1V5T+r6R0t7uleqlFv7GTNS9nEm89WYn1ZMcm7dBNT05anllALNjGls5wSTo8ROLt8NNYtuJCAC25IHE8Mb+GAn3jwS9MMaKU+pqjWk7/I9ErSuiBCO4jPQCx8miWst2StgadZlujMeBbJE8KILU9ljJSLuo3+Ine2nIFJkEC43GPEuS6Ok+WGPyFE+Ky6lfU0GZBEDyGZfDjExelxVYGEyhAnhKBSdDBdl00BSTfgWVhGHwlZsPXgJLYenEyPaMHjm3UhAaPz3CVd+PlbluDLjx4ypo01AQje9/qlKDjAxIyHjXvHpW/zp3fvtJa/dnm3FnZ0tI7hyWYi3TsOT+JT//yy0WdF+G3s35tSYMPOUVx4Vh9c1t9JfGvdKnyzwPOBgiIMNs6cSVONAqaBoL+A1j/jnquxIKVCGQ+OMfjkJZfEy41C+/efP469x2cAAN9cdwhXru5DV3vJKu/lU2rijWahXGXt1+YfSs1zHwxtaaDfTAOb74RYlAKORYvSnElSCRpn0rQVsZqli9JqGpWtLhpJ4KS1nqduaik7w9saLSF9a+4iTHsqCO2udhI5A8rDSKhxlXG6powY9RtlPldkeZcBJuGaQFN2E2B2mL+HcURGb9I3hqFwdfZ1TxT0CJOMfTSLbygIFCGxqNkUbUByuUOOHDly5DiTcZp6ljly/KThdDfpWd4lbZoNzHslOleJz3hoDDfg9k18epVFhp/KHTAWmPAurSxWraRyDLforXTFbUWIEsZ/KgdxE7ekZQgfr+U8jNzehOgJ9CeWkoXLnFZetiRSuszMjuSO+drxTDIyYkzMKqFfaYIO0zhQBIXiXzVapuq1wESyX9SLx4I+T5g1EwCgZLDRX1TCWLdqqZsoZWnTASvfInDg8cW2VrIwdWFW9ySGg+2GPQPTOjBN3YToGhLcqXB0c50AqJR1zpTW1qR1k0r/t8GasVRyceslg1i9uMsYb7rm4/kdo3hl7zj6O0v4ldtW4rffucooRDChreTg7VcvlLhXDS/AX/yHXUAhwuYg2wvSzd597nu78eWH9kNV4SMAmhbfDzZwwYsmCDCAL2skWmJYD5/d/kU2BcJ+EIFZZk2oB1ma7KktsVCKUmDL/gmlXNN8aNkEpWhxaU62f1ygEMzRZNByU9JaoxrDLGt7Vg/vvFxhr2ksKInhmtCqWdbO1E8SR1B5+no029qaVEjSuzQGeji+4tZOl4Tp5qUSirfl04ppqaxIzJJmK1bKJhSQ2C4+pTmlT4WQjJsdAwz7/swdTY5n6buqxkh2ZN/jqmsMSdlj5MiRI0eOHD9t+K99os2R48eE18rOLWDbhLe4Ic4anHToig5prTg/NGalHfaIIUyMTAShhyGedPveWJj5t8rAzsJ8TbqJrqZJYAibi2BcIBO99qBEZO2HiqCBKAzX5Px0pg//nmLbWtrHUEjiYwjDmDCNE1sddImThZY0ZPSPYQM3u/XaHRK1A6g1a7uxh0y3WZU4Wfg1RPjXxPAuuo4QIwNm0/hRmorhhn8yg9huqsHUtTkz14lrXUjVcHAQt5KeqZpe1HBg5VSMmiPMGbUQ9v+jwOGys3tS4xBlnP7Cm1ZYNUQ+e+9e/O1394TporBPvuMcfPqDa1PL+V+/uBbd7UVpan9p1yima8kOo9PgZdRQePLVU5iciTUpGP2tajg0vUBil0mMJya1I+KzvB6KQeI4CCjFnmPTODFa52nV/j4+08SxkZqirKgwPKXiiOHZvn8ghOCiFbIAqbMtVqDW5jzlb5K8IXUKSVym4nrE5qZSsmtFcG6X8mq/jdsY43hpbc5szSFvhDQrORTKet1CGS1WSSpGufgvKetSRlhMoi0jtTgrc11rB3VtnuW6n9SHlHr8WGEsvsWyM44bU9Gi9qGYlyluwusUURFJJa7lHZxlK2w3uCaU08L0kSNHjhw5cpwJyAUOOc5QZNkNimruxBhuzjpL3iozgW2aTVxBWYgg3pyWmfymcjMeLhgDLv04LvxM2+UnMCmgbszNjMHE8hOFNaxsNY4tDTG/ttXhtThVGJnqLR+N4npKyWS6SabvRiz1IsafUryUdkizT81itQqJCcFvsEP5bqb2MWYkwW6DWI1nYVK13DWS6m+aF3RCxLEk0idnE7WTJF7Q4/d2FHHLRXOldyWrXwEmdLSTZypLSK2FrV7chQ5BG+DilTETnNUz/NRKWnZ1toWxyaKmajioPh6Ux93HpqXnnUcmtTiVkr7tkp0Khz4n0sw7/ThhEvaoEPs9cQiWDrZj8YDuZ4EhFKBQPhxLRQdeBsfLgz1lqUwg9Bdgwy0XD6bmCQCf+c/d+PyDR7Twi4R+xvDqgQnhKRynjVY1HLzAvhxZpih1SldBCPBP9+3DX35nD373y1uxYfeowFgP/27eP4nfvWsHfuffX8W/P3wwShevdzJz35Heq1pyZpl0GKhq6dSb+reVNB44DbI5IaNGmPrA2stRzZlYoLQtEfdcGbY8fEkx1MOcpoW5R+Nusj+KNpm2rp1e0cQxXBLJYK9fej8bZxK8Dq9BG9oWLwOs1n2U37FpT0sxPF5KW6UUqCZLY2XbNCXtYpYM/dRmZkpbU7PsTVqAtc3M9oe03mJJbxKmEJBsWgXqftm0rYA8D1izSi8tR44cOXLkOGOQCxxynJmIDm/mTbx8KM5kZkjlGCQydcXfpnjm8iSBQCaGcNZtryWetYwkxmJKfThznGhFSDxbjYFriMiCifTDQHvSs8i9SGgvSSAh/lZokxPZ82sVmfqg4Z31dKS31f+tG1khSYbvqseU+wKE35SehuPMFr8XLzPhHf+dnRnUyu3JtKgik6S1sWmH4xC0KeZ/2E381oWqYjDRh58hTano4uO3LceC/grOWdSJD92yRIonDwnhBrgyPFuRDaY5jS66jjAFE+3zX792jhT/opU9ynpiZuabzFcloRzF/3HJJEzZqoz8t1wuOwUmILhgeTeuXNVrzLNYkNlfhBDUGukChz/9xg78j7texe9/eSsOD1Ut1MV4xzULUvNkeHbXhBY2PNHQwrbsn4iXiGhOalXgkE0jIt5vmE3sEOnX4aEqXtozDgDwg1D4IMUmBF9+7Ain9amtIzg+WuP5EKEvx9BtpptIkM0bxX2Sod70QRLZoUq2EgkWfwwkemdirqprhalc9TUVwpS5Od5vzW6Q0aS6E/v8rwplZr3MWRP+OBf6tDqpRBniJ5qiyka72rYUVGNmn76Ws57WSnrK3sFeZcv4p6xO7LGFPU0G80di+2WVQWnCMOaDKiG6RpgSaDp7pewYTYHZv7K6p7dtGjJeLJEpPn2fGDly5MiRI8dPInKn0TnOOGRj9OmbRLNsIDyYZnXqLKaLd/KGg4+6oZWy1wKUPJMPEoxW6Va2qj6vctIkJ3cmBrASpjDZ7IIVoQ047VR53+oG3d6W4Y0wC7MiiT5DtsZMKFL7g1yWwFS3phHbQTm0qOmkbmv4JuKVuCShGLeja3DSbOgvsXmVKO3spQEWkmbHHLD3vVnkkxwjPY9Zl21hOqljLCGOaP4mvO0n9w2bPEu9gS6b+aFRlzI4ceRM03QQaZjJLIMrV/fh0jVzQYiDArwwGuey68wYs7kINUzmoIglpmkV9HQUtXKJkM+Vq3txyfY+vLx7FOct7cIN5w/wOGzeMQocCvFNbSKM08vP6cWGXWNa/BsvGMDDL51s2bx7Gm69dBA/d8083L/hlPbuHdcuwqa9Yzg10cDKBR24MaqbiPfesBiEBABxMDxexa//8xb+jgurEDv3bjTTzSJtEZxdT9c8AMDEjN1ZdJqWShpOjevaE9sOTgBYFD2FHKxmiyaVGk19LQtv2otWyykLFXceUTK21lIe9cDJGXNhwhw9Ou1Jr3YdmcJV3RVJEGcuNS6HqOuGIMhm+4hyQe7XDUHDQZtehCoRquw31KqwMtX6Re+028i2tcc2bZjiaOGGPQMELTFiTmqduw1rdjwPEuVZjpo25IkpoSWe/F2EJ1UQozKTVaJMsNzwT91/n/5ybQark7V59PXEjExfgRdpIiP+zpIEJIpg2PMmtJl1r9nivketlXnnbTl3gM0Rwv6UID4XEGEOsZwjZCGHQTBFEK796vFArID6YLxtQMF8KtmqpGUXkznr/WT2HVGOHDly5Mjx04Vc4JDjDIXKNBI3wUlXrIj9dSamsRJfjUOUAwgR3hnztL2OmVZQNsmJThs1klqsk5G+tMOTqR1sh9ukOhvKF7+rdiBhQhTxJiJjEKi0G6+oxTRlPvMlEE1Ehk9CWZbXmpPo6FCr3/oy5GG8zcnaKInuVg9QYhlKWkmolQYad03RdnMCrQQElI2ppHGsJWRxbfQROarU7gkdQxI46rlRAIQxF5PIY38dlmeGNozaIBYUmNOot7hVp8mvhTDHVLbN5nvm3JRpS7ffTpTwUOiSJnDo7yrpBQloKxXwe+89F9VaE0UX8AMKlalkEjgULaaqPnrbCnQ9fgQ/2nhSCj9vaTeuXtOPp7cM4dR4A5v26zf1Z4PBnjK624u4aEUP7n3uOA9fPFDBYG8Fn/ml83FyyseC/gqKhEoCD2nmIA6aMp87FqoIw6JmMLuThEYzABwHo5O6FgLDJ/7hlZbyVKGaAlrYX8Gvvu0s/szm01Z9ODT8AIATpnfU/m1Zf00Qxoapu4ZDmyQIKrUf9ijCb3E+M41PXcMhiOOaBJB8oqP8Tzx29WsBYn1Spx22HkZ/Q6GGmJdYJy1pSA/LxxE6LFs7CKxzrezQurX5MdEsXcrWQMmopXJT9xc8Tnq+5lvmcljq0ttyI4jpDGtmaoHChY6obNO2t+WLRS0gKW9ZIPlfBa2v/bEwLHta81ZNHsxZhWzWMtTURA4NuwS1CztUsl6LbVGOHDly5MjxU4LcpFKOMxBEONAQw+ZQPW5n2EWabu2Y8ktk3prySyDNmKd2TBeyzLgLNsXTblMrcVgzzfagq/1OCovL4bcMxbKTbuOZuA48PBO3Rzjg2OnS80+Il+GdWUbUQnvFH8ieoe2auxRq4tpY2i5LX0jkbyj5zpa5nYkOIv+nEZelfln7UAvI0qez9ANiEjyZy2CmJgj0W9zxLfXk4pLGbdYWItYHcCYmlx/x//RvZnVmb8AtF89NfD+nuxQPF43gqDzHgeM6cIjYI2IakjUcWFZh3braCvj4W8/GmiWyQ97u9iIuWtmDj7xxKV5/UTLNNly9pl8LYwKXVYs6cOHysMxigeDOmxYDCLUzFvS3Sc7D4+4nc2CavvzMtWNY/yJArdGa42cmoBibsms4vNa4bFU/Fg+0ScPMD6hdu8CCUEBh74NEnT8I6+f2PI22yYUxZkoqC+KUlw7ByGQD+0/MYMfhKbx6YBKjU2bhDp82HKBaa+L+DSek96HAQRUGM7rU/YT+Nw3SPoRvAdLaV0VsninVsXU2goSsI8aqaT0Xcrfmzz89tWUTzX/xC51pmkBuWpwEIYGV6szaRVFfb2W5tH1bano0N5jG1M/Io5b6RtKFGS48S7s4AmvdqSTcUlLysvX8jfxvU5cU6DOnIVIcPp/wvFivTG68zOavYuqsNKkxzc9JZaj7luS0mca+uq/IKCSbtSPyHDly5MiR4ycUuYZDjhwAdI5W9Nd4AZhAPX7JB2tmUofYk5iKt966JgAx3K7R9t9EuCaYVBDiDPieOY1AMV4WJB3QhHD1YMbDiKGehvgJeUq3zQlAudknw8FFPVAayzEwDkzfOtPHDsvJyguX2iMOlMtit1rVrqkWklamWo6pLYx5kuSqZ+UoMeZ6K7cJM3+zrP1X/KZZ6BV+I72KctzsdSWGX+yZ3UDWPo0jMHoNt3KlT6YkVpnGhYJjiGahKsM8RIgTOi/142CitKUxowzTlc4XMhMkMm3TzPH0dZUQtrHYl4XnJFqc8IZ2ktNoWzaqCaGejiIXDBULrd8b6eko4N3XL8Kz20ekcMZEchyC/+dnz8L+EzMY6K6gv6vA62ij8dFXTuCRl0+g6QVoehRnLeiQ3jP/GOzT/edTh3HP+qNSnLdftQAfeuNKVCcnUSqX8Lff24Nnto/y9w0v7Cg2JviPA32dJYiL71TNwx/fvQunxnUafvX2FejuLOGV3SN44EXZLFWzqXbYSMOIIp5D+HpFhN8sSnTDn8Tv7d3V3h+Ns2SkUUBAcM8zR/HMtrhf/MKtSzHYE5vPMmnMTVUVdRaEAock2W2oVeQAYIIY2z4hjMvaQH0Hovp0MNyHlrZiqgk4cf6yMCal7YF5HyCWRa1xKLhjbvmfRJpFcqxfVjKVSbVw/pg0VbWw5qYJclP3IeklyPnB7qkgEVT6Yy9C/BZUfWnKkySsQcRaZ0ppuOZkWMNSaSDRj9lc7jfsJ3VNQEOZWj6z+ipSKUQpX30vzhlxkYb2Z31faVti+RZJlzBUM5HxtsRCIw9yAMiCdJphj5AjR44cOXL8tCIXOOTIITFtkXyIYL+IxU6/sHGObwiJaYm08TXmQ4gcXyrexOAi+maaM8SUsrW6EIERqZbH6DDXkyDZca/5wByVJf7VaCZmktWsEg4L1uCo3FgAYRJ4xL+ZEQdqEnaoNyKNfUKNY65JdpgOOJbb61FdqNcMD7kFw3RPVGFMC+VC7DoJ3+I1sjFspslyXZf9FhnsKQyS2ZEQ189cHfvtNnkIGxJTmiJAtHzzLAIdW3oFKkP6xEgNMQdHnIfU+UcoKgszxMbMZsy5qC+H1bMz2JOYUmpz681EUE9xYlwqOPADwVQMy5mE8xElwt1lzuSQG6DNaFIpWWigChy62wtgM6ApbaXkGB0yl4sO/sf7VmFeTwndnRXt/eKBdj6qHEJw9sIOFNwCgsDnVY0rF4MQgrHpJvYdj2/993TI800oVKHhN1V9fjD6So6UtWampxEgCCjGE3w4vNboE81oAbjn2eNGYcObLpuPa8/th1Mo4Jx5FYxN+9L44SaY+NgQ/CmZCiaxsCEOIvGcRsJvZAQBQM17lJXzO9hrI1Rtm4YXRONF78tsDJhMY6mmqVidhDvoEcI5Lp7qzGutPH5NzuvFvJlvpJhkzqQUotuczLJ9EY32KOIsR4X9Vpap1r6dVOuZ7QJCYmFxTvZo2g/ld1L+xOYnJy0zOWw2pomy3JjnQp20uFpD65dGKILXcqeQTA7s/YRKHVgdF2m5mjKkxu9IHALVKZA0RrN0GJNAUI0vnW/0qGLfkJOz2YGAX8CSsknYa5M4IL0f0Vi4a0E4v8hx1K9DTINfoCNHjhw5cuQ4U5ALHHKceRA3fWlnIxNTUDrEJmweiRRROlzqzptT8tGuiyUwcdMu18u74oTISvmcTqK/y3I0S2urlg6hMg2xAojy8Qg7xAvMCPWbSoKhFFolBmtYb0IU5k3S6VGLpJajHtg4dwrMgbPuZ8Kc1hs/hZEffQ21Q1sB4qLvhnej64KbEkgy19noODotre1QqZ1HW+g3mQ5qRI7HhFeioEmMm5JVdhi+hcaUxexuIsJUdcN80AI/QhV8ZPVhE/DpzMQgz9hgyudMsqOenE/Yx4hD5KkWQg20+U78qZdcMzgxvnJVL8olF9ed26fwN9KZepwugUFpNKlUdKC2e9htw7F3+ap+jE/WMFH1MTnTjAQOUVqDwKG7vYhaQ3d+3FZysXJ+B5yI4d/fWcSIYJ5oYb8uhEibtpkpINXc1s4j09JzsRALjygxa2ZUilHbRMNHFThUGz6Oj1Rfc5/0Sehpjx2F+wHFoxuHpfdL57bhN39uNUqCU2xQqtXvn+7fh8cXd+Dmi+fiitVz47jCr5lGgCe3n8ScThcXrOyNExOzT4ayoS+pg6HoEklTabC3okUP2zO8fb9u85D0fnSyKUcGoCoImMYN00aJRqlWpjitUx6WzOiTB5aSIRD6atDGUdraJWdtK1L+lXVxsKyphO8e9DqpexaqMtAN64rySh4f9ppp9UhbErMyS5OiSXvvkLGfjox7TMC4V6GUgtLQmb2UT5I83whpAeEaR+qEFDcTgfnTtVYq38dAv6cjZq75PlE2HWrb6G0V90EasH1t2saFiCmlYJHW2Mm6pa0yzOlpeyh9WogFkzp9yj7R9E2081QykVzLNOH75vKGHDly5MhxpiEXOOQ4A2HaWNoClJv/fAMq3EZj/D4imtcxcLkY45OFqlfwoL6HoJpvYGiqjH8lL25ehyg+B6wbXjU/QwxTsHrgIkSON4sdNtuwU2HzznQN9INClgzVg4PIlCHZ8uFMQ2MjZKNDYkQr9cjCRTN1A+0yaBhp5LGvRsIGgmL/fHijJ6IDqc6CiRkcjEZqPluxtjIJmQiMeRvzSI6gxzWlkdqshT7WSn8UBRb6S8gfQO9HxmQpNMfNK44jw1whlmGaS7R4JLpFLMwxEh3E9EfCojltluZT6q3OT5mh+0chhPlpEek29zOifA6dRtPL+LeqFbCgv4zfeMdZgOOgSALe73mLEcZgMNdFFm+FNJcKOpO4EDn8lvIVBNIfe+s5oPUZBE4BLqEoFGLGvMnh9B3XLsZDLx3HvuMy05+ZNWIoFdXb7LHgSp7CDYZ09Gk/EaFgJI5kEjiUS67Afdbp+9qPDuFrPzqUXNBrjIGeMq9o06forLiYqsUM9vl9ZSyc0w7fD0ADj/dfkyBo2+FpbDs8jS+tnis3ICEIAPz5t/fg+GgoKHr/TYtx22WDBjFU/Pe8pbJvD2ZiSfwUnnJrueg6kTPzGDTwUd/0AKrHduC2tl48UL0IQWReaKLqGT9uPDUT1Bpmk0oaWtDYCpmF1MimjFuhlb2cTHf8VtlzSXsDYb3X9hBKCeq+hCu0xLOsvmpGay1P5iSOI20etDFKraEh5FnMXpqhdP6mpdldapqU2+NR/eRLHOyyRUoxjgMEvpS/yFSXwoWxF//NIsmM5vw0SWxKDhERMPyM4iRc9FC3Z2kNo2p6WrVOU/IRIyWurwmpDZ1HNOWk5UDFwsSRr+zfqTyOxA6kdKVk+qJ/bPHkPRPk/qOukwlzRo4cOXLkyHGmIHcanePMg8TwVXeWpqNU+i5cP3yoG+TW6LNuim2MV2s5xPjTHkdui7ipTPWx0SK+F2kT2zvbYU2/gWejNX6W5Qf27yknt9VLrYMSRWqfmFlKlENIIpKY6ho96e+bo8dx4rt/EwobACz+pb/G/Pf8Pnqve6eFLuEAaevH1k9mqF+WerQidEgMAyySEUN8IYwJErRkCQ7BWToYxrvWlqcx/gFkvtGpjYe4jjo/oJU2DfHRNy/nvwe6S7jK4Gg4mTw5f9kGMyPZPN8RrS2l0JabNhbM2N+rToz5jXtWrmF+Cy9RxiZcrFN0lOb8FT2G90Jd1duygFa2OIWWi/K9kUVzKnjDpfPxW+9azR0/M0zVGGM4TFwWmOLFAtF8dmh0Jr1LGdNFZlIpyqVgcEBeKTngl1IBlIuGG/wC/uJDq7F4oC0xzunis/fsxB/etRUHT1XRVi7gY29ZJr2fqbM+I0pfgJJBECSCNVetGaBa97H90BQXNgDA1x8/bJ9vEPZI15FXyIACQRAyQwkh8AOqCYUch0SSiTilf3Qbmq8+An/4EN7UthnnFmPfGiy95wd4esswNu0bV5j/xGi+yyRw4MJDcV2harePAiE+h38yjX2TAF5tvwS5BxH+Z9orJKULx7+yziB9bNjzttxAT6Il69JqXdNTMtWmiPQ6ZhVAx+Jgfe5vRatETBXmG4dIJoqy7mks5ahI0ywRxc/JzjROEzRgi4Ylgqjdon8bqa0J2xoa4tgWO33zYd6OGUmL+pOjfHthf2+iUzXtSazfVylK28qpa7yZXvN+wBBHC/sxfvccOXLkyJHjvyByDYccZyhSdr9sI0mFZ4mJmMAQ1M7LkTkEze6O8Fu6pWPZxEs3spTbWWKURKapSJxyYLAyvpX6aptsq463ECWlTlI+GTbkRHmQyDByMZQ2VunPfiiWD9gsvilPS52FKEZmvY1uFi7kwW/BRe/rJ/bhxLf+F6gX2xg//IXfRNtZl2LubR8ztC9R/iq0ZLVdkpGhoh1uk4QVxvKznCDl5/i7ptRlVkKQpCjCeEnmQmeGLEhTikHCrUdCuBaWNH+kkHTrZfPR213BsVPTuOmS+RGT2KJlwctJng/Mr5Ln4wSSo/fCrVnWx23xmSaBQdDx3I5R6XnfiRnhiUT9yX47lOjTcvQq9u/Q01GU3nVWBKa60v8JyxjxyFHn9qKiBdD0wvbv7yrj6jV92LR/gr+rNQKpMf/wvatQKLkouA6KDhDABYImCHFAI8eXcnEmzaYUIR2jk5lUiupQcvU0h4eq+PRdr8L3fTiOg91Hp7U4ItpKLub3lXF4qJpafivo6ShifDo0JbTn2BQAYKrqgVKKXuX7jU17IAAe3HACm/eNwqcEjaaHuuYkOgar+YbdY/iX+/ei4QWY31tOJoqEpgPD9gMfz65L4AmCIl/g8/uKAKnoqto5IRrP3C3Fu7PjKfze2HsBxDfB//qeXdhxOGyLn7lmId5+zQKel8mkUr0Z+/0Ipx15r0L4mpsB2hzH/FuIeTqghAIBjZdLaL2Vl6/3Y+W3sv6IQVx4mWUON61Jhq1UaGpM3BoIZhQTlkmRaLbWmXyD2AU6YlhCfcSlO22/lyHQnoOBDsM+hGlrZqfhNYKQdxZfFESUoNrazbSNlMqyVajFcArrGYP7PQFMfo+N5GWBTbs6vpwjvCNKGtN20UaEYVDJF2wS0qbuP0RaCZ9brLmRH28XzJEjR44cOX6SkAsccuQQkcpkNXH35IO0/l6No2zAtRt5cnw9c/3ZysSTCNJpkQ7uGiPelKXx+A6d4W44UfOoGU7PXOCjCFmUW0z2g4JwQLC1G43+mQXjl9dZEgQh+TCk68wrfYgmx4fAoLF8h8lXHpWEDQyluUttFdHz1l6ItNgPuKqd6Vk1K0xVi/qTLb9Ep82ZODVKuHwCTjTfI45VY94ZNBVS5xwmUDK/bQ4fwfj6e0ABdF99B8pzFgh524o0HcrZHznR5av64S/vQrFStjIrTg9E/5XUpNELwoafhXliMkdhvCkuzDVrl3XjO0/Ht7sX9FcMU7nedirjMA4jWvuWCi4+ePMS1Js+Gg0PRUlDIZErIsSJaRnoLuHv/tulIKBwQNFeLnAG7wLFJ0NfZxGiuaaOSgGkUEAQBMKsTTRZqPqF5OFiCDOg6DrSlyoYTA59d/1RLSwJTT/Ar771LPzrg/uwfutIeoKMWLO4E7Wmj417Y2HN3U8ew9HhfdrXYYKJAyen8dKe8WwFRA3xxQf2cU2AY6O6zw0mzLEb7SJ453WLQAOKSskFcQiEi8Gh8EmA6zrSIOOCumZNitfhxGsIpcCR4SoXNgDAd585irdfs4DTaHK23mgGwnhL2s/E6yeRwg1R+TPh8WOtCZumG+vRhn5r2HvRaHyEWxVdmDY+3cSDLx5FR6WIWy6ai5LiR4PFDoR68b+SACOk2b6my89cvqENTEvfsLS9ra2MS6D2rJRt0XZIQjg3k6hvh5xt41yjXNrJZFJJ3POqe0i+dNn2BGwGpFF0wudKthOQKEwkxrQmMYgVS66VrKvB6hNmRrOuxbPdiNnysWXnEKS644jyoK1qd2hdWVyDTZ9V3i+ZzYgSbV3XfGAk0WKgjWj0ySsnyY1K5MiRI0eOMxC5wCHHmQfhhhk1bcatjP3WNsmmDW38G5bfFnpSb1JFByVDXOuhP+UgEgs+zLdaASTfQGeMKMb8U+uscbUMB5rEQ3gLbcXD5VNyGKzWTYjC60C59ojOFVUOo2nXn6ywMU2EsqQQpb0I0BwxM+zaz7rUnh9rm4iDzn1/QGWOCGkJ9HbgeaWME9avkvofQZSPjQZI6VN4NZYCUhKZ+qbhICnnEI15C2Nb65qqHek0GKKNPHoXmkOHAQBjtWnMf+dvKbJDOZH0yI2+28cSUZqKGNtFJzTZVrflVrxhjoxbXGYiGBmGSU8OiW9tmr4PgAtX9uC8pd3YenAC5aKDX37zsri7E21a4OkYc0o162CqX6Xk4q1XLoDne6DNJtxSyUqTqXbRKOWhRdfBwjntaHoevKYvmSpavagD5y7pxLZDUyAA3nfj4qgouXEppWFf0BiwSkU5c9dGnR3Fgpx/0aDh0CrqzQCVkovxadmHgENiB+cMywbb4fkBxqaamK4bru8KcB0imZsCQnNUpv48WfXg+QHcFutDAyqYYzJDWxEMQ+9Nl81H4Acol1x4AeAFAEjI7PcCmQNYrft4YccILj67P/MSRSnF0LguxBZhMp8khsndTe7BzIl4HFnOJ7bvHo9/fb6P+1b4ShTe63lKdCl7Eb7tUeNF+Oz3duHQqVCj5vhIDb/wpuXmzA2EpmkCiWZgZjs6RIGijR5K2XrU+kZFW8KQTmtmZ9PmxNY5p2XYmqVFOTqloYBWbT7tzg5RKLfe22CriBqiRrN92zhl2hIUjxV7JPlzEXnbayjTnEfW/OUA4/Yd+sUCeT2HNuTFX2GVs39gtnehtv2nUB6fl2icNukA8FrJf3LkyJEjR46fFOQChxxnIFIYXaZw4RYnZRt20X4Gc4ZIxRMsex1vSHUnf2JcfvJNp8t0IpaYxETPWqQ1EeohWeBHqzEzZWfbgEu7dn4IynK4JGobqSaIWnAmHH4eolcw7eDOGsWopSB+5+i4ROJ0iQ4B5dOLIVzIWxKkAN7YKS237ivfiuLAQiAIdLokZsssTkIiU9r6XqiKxjxQ+8ZrcBoTBREi98bELbbm0VKBGd4ncgAgjnvOqCfqex3U97mwAQCapw6Gt/QIgeyiicT9jweZ+relLP7tZiVJk4rkskeSxpQw3+zWk5xmnxGSO4TgT37+AmzdO4TB/k70djigjGmbOhfrbUf4WhHdKxbWEZZENTXDGolHcQjg08Rq2pcvgt/+2ZXYcqSGga4iVi7oRBCErJSYhlBYQpSxavzSotNfPpdFc2jKuOJOlKM5QHUavWywDafGG6lMeBGN6Ab/yTH5hv7HbluBgd42EFAQx8XC3gIqpQJKRYJPf3WbdFvfBNchcByZvumana5f+MzzmK9ok6Rhsqo7WhbRXo5vzrM1Sl0zqg0fU9UGHFBU/CJcQlAouHze6+0o4LbL5+L+DfG6cODkDC4+W/XFYp9jnt0xim2HJs1ERv2mpnyzrrYCfva6SLgliBlYXaQAG0z7hoTEhO+/4vdp8wtf+6i6B4h8swQsj5COU+N1LmwAwrYJBQ5sPOkCZ/Psn23NS53ZLBoG3MyikIlUXds9kYS9Wmhyx95P9G9jkQaTlG8zy+lcEh5lS2F4VPd0toIM6VPpyxjfUD5NMFOYfLkgfjbP50rRfM4jiP1AJBKb7T1b8MV+aRRhWDQA1E+l7YuFuxsQt+Txems/VxBwW3Xa8St8p6Y0fQoxKeuLxhJziUOOHDly5DjDkAsccpxxiPd74kmsNSZ8+mvx0JWFUWWgiSWXkhhoNTKlTcUpefOfAq0qQ8yUh6ZBofP7bRmEtsxth22RPv3QatSUkNIKdSdEIYxoUXQhjVIpY5h4GhHLNVTHiPgUIrFvTZ/N8L2SDitBbQZBXbB57rhY/NG/hVNqQ1CfQf3YXjRO7kdlyXkoiWZ3kmjl/TfD+LD2q1ah1JdlrdU97VkiRgui2rgxFx9zanQ6VR8a6mHa6ExeYbzozRvZIFduoZrY/TQwMEEDH3BNSzsx/BJp0YVcGmlJPCKSQVRoY+akHMLlT6W2sY0WdVJS8wj/U0t2HYKzF3agUCqD+k3l0yhzX4ZqaBpmJlos7aJHlU2VMfvgm/eN4alXT8ElQLnsYu3SXlx0Vi+A0DnzJWf1oeiaxzCxPghtqbQ/Y32L4cvntRvzZ+jvLvOxTClQUDQCutuLGJvyYDQebkHDC+AHFMMT8g38K1b1oqsj0vqgDlzqwfMpCHGMt/FVuC6BS2Tml5fgULveDLBsbjsOSD4/7Jhp+BifSRY4rF3WbQwX56Tndw7j3x/cz9/dcP4Afv6NK4S4BF0VeS5gjsGJMBUVL3wTmpse4HFeacgm+Ey0xsxnEvtriPC2axbjmnPnwOdCbgsIuADevq6z8sCFBISNb0cYyrZtDyLza46tDEMaHi/WxCKEoNpI75vMZJA6W6sksuYzTn0kmVZpayAu02aC1B/xkzjXJ+2BrcIBw4Rhu1siRFcFZ9o6qaz3/NsrZbPZW9pH8YJa3X9oC7+dPoUORkHskNoWVRHaUNi3VmxNMN09sdKsh4uhiSaDIrK41ouhQMmMk7qPkX6by+fmvKQ+yWgyU8Y1B8VvkjDe2ZZc9EkhpmEXOpKyACH6N6esW+pCEXm02/teLmvIkSNHjhxnInKBQw6Or33ta/j617+eGKdeN9gZ/olE6zu/1A1/YlHqRtzEAEsoIzqYa5FsjPEkLlJSWq1cItxg5zt5aNt1G/3CIUG1R0sJFRQcbDf+peNSAlOihcOyJjRRD+ImWsQyWug7rZwwpGooXAQbUwJxu3oTsnZDsXcQTrGMsWe/h4nnv8/DqefZBQ6cMaCYW5D6HAAa+zaQhQEmRom5rye1o9o/bPmFZ3I9ktEuNhcImMdPzAzR3/E+nEi6oUxLXDae7FouNiiZGQQONAhAXC3Ykps8JtN4g629EIlKIoIITJXw+7BDfVIPsedlLyzRabYt3BSN/VCZLsq3T2oas8mpiAGTxlARyiKEYN/xaTz80gkexfeBi8/q1YUdsLBDRCGsVhwRXttrdN7Sbpy1oAN7jumOnn/5zcvxxisWwms0eOlFV3d23ep4+Mtv78EbLpmUzCf1dBRRLuoDgNHesAgcfrP7fix2RxCAwDni4PmF72uJlt7OUua4H//cS6lxNuwcxT3rj+LkRBNTMw188o7VUt8mRHYQDQCOI8xT0W3/QkH+Zk0/kORbhBCQti4pTo3KdVFNVLmOsK4ToKr4cKgUlfErdkOCkPkPB9pNZYJIcVSdl5MnGTkXcd0kSDLxZxpmxIGyDsd020wQEXVUSRUnSiyxbmr9idxWNqLVoAz7DHZL3ibz1QpJm0v5vMC+W+JsJ2+fUr4JgDhv9S8x0cTmdQMiJnloNo6VL9eDECqUl3EOyrS1k9dU6fqMcf5XaJP2Vi3Qxz5y1j2+sJYY68X9ZNk7T7LWbirB1jyjgoVA9jscqLo/KlHoE78LZiZAmtNA17wUSuI09u4vfxM2VMznH6HttLQ5cuTIkSPHTz9ygUMOjpGREezevfv/bzJ+zGhhww4IG20xPaCf2lg4ldLqJpSUR+FQxw/WlptK8nU2kSiRJpmRnrzJlZkGVPitx0vjfiW3KbHSqJCWgblgIdJMG2cEQmCSU5kOkREtfVe13tppIuJnRPGkPJXPw5goWpYpjBStPYj0ByDwpkZACiUc/8afSmmdSidACAo9A1J448S+mEaIWhZGTkYG7Z/EGmQMU8o0prGMC/FZG2+swVtgJEj5Z6FVvF5qOTgnZNOKoSJuZohNMYHOPKW+BxRLvFwxvlqyytYxcaOUo3VWQrO9V+c0SzwS/abqO9HETzpRFvoz0EpY2yvxCUHI+AiUJOJ8F9aR83KEJiYkcqQcMcXi8a6bKZPKV16qzoGLgvki4U+Yh8CwI44jtLFaLzPkaS021UWIgz98/xq8snsMP3jhBHYfjc0WNQ3aAaoPB0/lnmfE4aGq9Dyn2+4TgwD4iw+fj4/+3UuapkMBPgokCqN+6HMi48i8fFV/xIR/7UABfFtwYF5v+lyQwr5VoDiqcAWBA5u2VcGOSVODOLKAxlH6c1d7kTvHBgA/oAgC5vBY13BoKxsknuKQF/4i6j8210vi2BOWPX2eIuD+mMRlPx4uQiKxq6fNCcJPw3Sr0Qo25FmgcVkVhHiOHC6ybRndopaFfJM6/kuASJCj18Por0Fa27P1Xe4fIqJHosUoCBDSZRxL+vfIljZsu3g+axnK2pc1m/SZMj32afm4CDNASHxGp8RZyktdWy0vTWce07NpXEhLqRxBd6BtuYBlGHPe/pdRffxLQODBXX09ipf9TBRdiUvkH9K4SaquWjeVRmMtcuTIkSNHjjMDucAhB0d/fz/OPvvsxDj1eh2HDh36v0TRjwvCidfKxBejywe7looRbnJJm1vtxo56Cpdfy2dFeSOrZqU5Lk0j23jwtLQDK5pzzhK0EtSDh8jYMjELM9CpnWlNNAGJGgyp3zuBmFjAYEvL6mZKSOU4gDkeJ089YJlpHH3iP1Dd+7L2NmiG2kjl+Sul8MbJ/XLTn/bNK1N6YmifDPlrZzSlraXOHv1NuJ0MsuhoAAEAAElEQVRtoksX4qhx1fqQ6P8pggHlsGqsvzgnpDaPEI/dVlTHm0HDgTEP9eKjW8dqPpZ2Ez+FyYyARKOTjdHBbwIT+UBunHbZW6K2V3I/Ik5UP3UeFOOIggyt1JA2WfbIaKZQ7cRzEnU+hzFv44cXGXkGs01WbYjwJb722H7pzXTNM4wZuVyp/cVwwrpb9vmAxS0VXFy1ug97TlQlgUPI5JbnM9WHQ9M334/9tbefjb+7N7wI0e9MwgXFqSA2N7Rd8S+w99g0/37HRmq465ED8PwAV6/px84jk2h61GhWyVFKLxZcAMlmj4CQyX/nzcuxfsvJ1LhJYJ9+oTuC15V34oTfgyfrq0EjJuLQRAOL5rRFkQHQkPEv4tFXTuLWS+dj8ZxYQ0HXJInMHInfVxE4FBSBgyhsYPD8AG6Uh9qe5ZKrdzsifn+WB0W14aNccITX9vmIdU5CHFDq4fkdI6g2KC5fPQcdRSLlITqbJoTNMxJ3X2Ewi4x94QKGMG6DDMJ327bAHM+8FyFCuZIZHGVPxceoIASIzU6xvIRwZf20WreUypNFHPrspDwTZNBWVetiWgzT5p8s+4mEOIn7gPRyY/NAKR9cFPoaxoK+XTCtSkRv/LT9FdtrCMFSX6LRs0Qq32xYs9XJjx+UlJIZM+3II5QpVYU1qTI2KRPc6+RAX3yjuJHAoPr4v/O9kr/jSRTOuxnoniPFlzX5wjDp7pGmcxN/UHnesQtqTleulCNHjhw5cvwkIhc45OC48847ceeddybG2bVrF9761rf+X6LoxwDlhpYULgcg3vkqh5+kPPQLLXE8efdqIi6J8IQwEtMkhofc8fi3SDtMm19Lm2j1SqElLSp/Jdzli+giWl2yIOVEBACUImjW4ZTahDQWoYNYvvjO6Jg66cCshCecNmJbxMJtKk1wZG4Xb2LImGfHOZcDICj0zQcplkEjAURQm4Y/NYpC71wjJZIwyNBndT8cct00xryt3imHL9Y/9PwS8lTLNQrTdCJMQghzEcqYE4uyHoXNzGKjtk0atG4tCxycjl445TaAKjfuVeaZI9+A1w/sEOIZ+rBo2sU4BpR2sg0TLX4rCPNNuFibLf+kcWn6NgKDhBAHEL6BfAsziptEUerQYP2M9RNzKyflYA6S2SYsf+a0UxNwcGEVe2bMH3Pjq/4ZPO43gDWcKY75+viy+R0AgOvK2/Fz7c/DIcAPqxfigerFAIBLz+7FS7vHePxffHMsYP3yw/uxad84AGDLgQlj/gwLC2PSc1/jKIBBa/zXXzQIgOLqcweweG47XHc2fThGe8mFV6/iN7ofQJmEgg6XBPhRbS0A4NRYPRY4RDDduN96aAI/eG4So5MNjEw1cWxEdqht1HBQfL64JF3bxPMp3GI4BhuKhkOpoGo4iPNP2IlPjNbw1/+5C8dHa7hqdR8+etsKeU8AWMcmAfDdZ4/je88eBwCs23QK/+P9axLpFfturHHJgsQJSt4biGuDeZpW9lgANOe34rJK4hIITx8zM1VNqiQzNjzvLHtHVeCQGl3UitD3IQSQnBmrORuFDmyOkZaEeBaRtDAMe2y2H7CQzCdVOU7Y0iozXKJJaGLuM8CCVH8O0j5RLlP2oZDUp4T8LJDcKZze1MO/gahJo+5vOBwhjpKLJpzyPWmS0k02xWubvXva5wAtOy1S9D2UixnB+Am43XOUAk05tdKwWeOe5sfKkSNHjhw5fsKQCxxynHlQdrV2fp+8gY4jJ+cnp096ToB4K0otniovtE2zSeohxJOYjMSQrnU6bacm3QarfELQ1aSVvK2nMUZ7nD+h7OAqM+0nXn4EExvuA23UUOgZxMBbP45i73yxMeN8klh42ucU6pbgaE9kouh2blPa2nSiiurKYBI4kEIJHWuvAwA4jovSwBLUj8Xm0prDh1HsFZlpaSc3gR7xGpgmeMmQXsvPzJjIxoS39C+xb0BgnkhDxUKzdWyZyw+/qeVwrtJpz8ZMhkW4yceKcpBWTaPI+SjEZfzkrUDmJWTtExD4e0SZlgz9xZDOVL7REakpISsziUQSp1HnqbBp1e+jPxBCDCZPDOWI9rKN5Fv6gpYXe6fWz1JZsY8IghLiECCIipU09hCZVAqFUnyqIkBJvVUvChOiclQHvEeGa+hq17ekE9Ht+nd1PM/D3tK2CY9UL4AHF1es6sNAdxnPbR/Guct68PqL5uGJzcfx1NYRvLp/3Ng2WVAOaonvb710HlbM7+DD83RNKk3XfVxeOsSFDQBwdXk3Fzh868lDWDavA4N9Lu+PqoYDELbXU1uGreUwHw4SlHnDRbrAoekFKBdDPwyqhsO31h3AMwNtePvV89FTYf1J3m/84LljOD4atvFzO0Zx88WDWLOsLCzFwphj0yB4ci5sAID9J6Zx8NQMlgy0h907wxKb/WsxzYcEDQd1GZJTg4sXDNJRSbgn1NlUBoEwF0VjlCfhQkADYbZnayOoc1z07VRnHjx28pzE1t8sWlPJUUwvBWFNxDhPllvYC7CbH9STC58hCqNWDQTC51N5X5KFJoDEgoqM30tZCZEosZLOAMl58z2UNSprHKDx0vfhvfow0N6L8g0fBpkjOqJPoUmiK3p07O/EXMUzh7UU0xlQy8n8HNJhGuiEj03Nr0S6DDVHjhw5cuT4qUVGg485cvy0wrSx1Dej8vWb09kxqgc+opdhuDkkBpv5oeZ8dVKNie1BYqGcaSAwsiC9Vh7SDkGGdyxdEjM4AxOYIfCbqO55EbQRMja88ZMYf+77yaQkhot9gGjto8cxZMZPjuaDp3r7kudvQFCbBm3INsz7bngPFnzwT1Do7ON5lAaXSnEapw4L+SYdsjP0dYtQxJrc9A2pmZkrM5/UV8m01Y/uxsTLj8AbP6W8SelfWuHpZVkyS3mtzDOzKIOqJpUkxqFlTmipnLh/O62clDNHTYjoKOZPtJ/2+hCLiSeN4W8VShgCBSZ8/eguHPvq/8Cpb/wR6od3yIwOMWtTd1KmCutnSWCqmDOLQQ33eRk5sWAg9r9gg5FBmBpEcHxMZtYfPDWjyYpWzOuQbP2fv6zbyB0am9HN+QBAhTQAAO3lAn7pLSvwj796IT55xypUSi6GJuqnJWwAdA0MFaWiI3HBC+7pb6ebVBa4nPB7+O/DQ1X8wZc3Y6oaCyRUHw4A8N31RxLL4BoOJO5JjZd+IMU5v3Q4nVafwg8CDE80cOul8/H+1y/j7/Ycm8K6zaew++i0cXkACJ54VRaUP77pVEyXkODUeAPVuh+9UgUXMcamm6atE2JGoEIHF7DFz/wnNwUjZ2hobuVWvpYkGeq2gNElbKHkW/FKfPaPwUSeaD7KttOI/xKoezqpJO0bWikyv7NshcT840di3lNYkbCei6DqfMbaqbU8s5FFoqmBz7ot77FCU0NOUhQAaRoSSfsKos/vKVsEo5NzQ750ajgUNgDAzBiar9wnlioVIvc6Egmx2bchSkwtQQqdCWsbo0RZqEUtPCLEVfNP7XImAXQrc0OOHDly5MjxU4Jc4JAjBwBt28v30pbdbRpD3CQ8EA62iXnIkS3hhuSi8GIW0AUGNpqyZKLmldCGFiavlN6ah3oIil8QEDiFEgbe+qvoOPcazsCsH9oOSg12rE3fyvT9tXZOqpshTKGfv8siSFHieJPyTdZC/wJ0X/4WFPvmS4UX5yoChyHZD4tk51kjTiVDNFnQ4tgQ0qQy2C39IwtjvnpwK058539j7Mlv4tg3/hT+zITh8C0yXA3tb+3LyjttrNvz4baYE9tZLkNsa7Xu1Fc1HBweNylP9R3na8Q/5PnAhqgurF6JtruFtmMH/XiOTSgiqpNJ4BEzDPTyvPFTCKqTkDgHLEb0vay8C4EujXlBCMYe/yqaI0fhj53A6LqvC44l5TkkoQQxkka7SiuxvFOLNOVDjBGVOVVtHyVM7/Zqn5fTnBiVBQ4v7BiNv3uEUsHBL79lJfo6i1jYX8EHbpbnKIbQf4DerxgbtlxyecGshKzM/zIaWF44hbZIeCGikDLNlArCWCPkNXEaXSSyz4gmlTUPpqoe1m0OfUUQQowM8DR4vnA7PvqX1qdbzmd8pok/u3s7fuMfN+D+54/iugvm4varFkpx9p2I8xV5yKY+Wy7K3ywIAvzTffvwO//2Kn7n3zbjwMlkGqXmJ/KsqUKdd/QxEo8LrnUFwFfMUZ23tBuOKtwkTjQ0iJANMYwh9izPizpV8pyihZnWLin/OMVse6jclmxeNEQyzJVaDuyVYN/fNNfIyVuknLd/9nS0PoXms3fDe/orCKYUDSFxmk5lZsvtozPpU5LbcpX8j4j92jCHJ+Rt0k+Jf1kE9Ul1Nmw56OgxKUpwfJetyNagjHH+13A+YOOPCSvld3afVPGegqQNrXBvIsTV9rjKN9PqkCNHjhw5cpwByE0q5ThzITG8YtV9U7yUjJBJPVg0aaKdqsTdqGLaiBDlGpN2YlXKsdnXt+x42fs00zgtbZTZwcrYoAbaYGjntHZV2o0I8WMbBXDLnagd2o5Czzx4o8cQ1GfQHD6K0pzFUQzlQE9tFSX8OyYxSvXPReK2NX0fY0mxfX+daRmfglRzSoWeuXEc4bplaXCZFM8bExyc8oOSqQ+aKmQjWuxH1kgpzyItWexi6P2HgGDk0bt4etqsYXLjY+i5+u16mqzIIohh8TLnHwsQ0oUoxPgzi0klMRllNHIY2lhkIhNY7sqn9QedASCVIYYQApLNEUNUMonmblNeIWkjj38dU5vXgRRKGHjzR1FZvtZavphWuz1t6PtBsw5vJGao+BND0EylEPMDIY7kdN5qYoT3ZTWIaN/HCHEJEeeqlDHKoor2vB1CEPB3hPuRSJoW3n7NImw/tJ0/v+3qmBEdZhGuD9efPxevO7cftYaPtrJ5Ozox7WkOnUWEAgdwoggBChmY/12kik91349+dxqjfrv2Ps0ng+ingCBdIyIL2guyaaLLy/vwaO18HPX7eNir+8bxlisWQjSptKpwFHd2rkcBPgrEx47mQvzb1E3GMkKn0YzqEM7AMvhHtvLnL01dn0rrj145iYMnQ+26Q0NVPPLScaxc0CnF2X9ihv+mlOLe547h2R0jWLWoS8uvUnKlmWX30Rls3h/63ZiY8fC99Ufxm3ecBYBarPAljG2Ydz+i/4CN+yfxyr5J3HrZfKyc1yElZuNi2bx2/MYdq9BsNkCIi0rJYMIO4uyojFc4hohCCqKk5s/strsy36cwm4kDhINXnDOIKapeD6L6cEieO+2Q7fqzdY9GZrvYGhNGVQzbENmfAhfYmwgglnepS0uYqP7UV+Ef3gIAaAwfQvm2TyGtzpldjRGAGLRQrOQIVkEBvQXDMArtziBbNwQhNWX5aeZDhZyF5UG1PKkdF3gepn1AVGpJ9jXjDIZ+dTQ+v7xN1SGQa13vEwL3HZ/GZ+/ZgT9Xp3dTXg4A3+aViCWLBpRlC8TWSCKN4bBNdx6ZwvM7x1BpO4G2SgnnrZiD6y9ZlFBajhw5cuTI8dOBXOCQ48wC31ETLViJCNtJRTpoGJlT6gEykSBD3GyMOukITYD4ZCGeHmRGk0Z7kiBDoY8x4wgXnIg2a2fDbEnYtScx/bNmHdWPOA5Kc5eium8jf10/shOlgcUJ5TBmRPqN7bgZlfpIfQPC2U47wSnPlpOs2lyEwJ8el6IUuuYI9MXx3HaZwUObNZhuhWk0aJ9I4WxE76125GE4wNm+r0HgJDFUEoVTMnn+1KgUzP1XzFbYkMZZMLUlEX60XK44Fxi5awAAqniNbQ4fgTc1GprTsjnLJsjoaFkuy5iXRnMWW9GJhUVRI/vLWdIocbyJIUxtXgcAoF4Dw49+GYs+8lfWPpqFPsL+oUCgjDm3oxeEmHxnODYD0sov9m8ogKDSOqLmqYwBi4Crt7OoV0kQNnDGDxGFXjp9RsaMtnYS+TcBLj67Dxet7MHGveNYNtiOt18j3nxXaY6F/abmuufpwygafAowPwOVonCjPIJJw0EdxrdUtqDfDW/N97kzWvx0gUPkSDu64f5aCBzKjn4b9//pvg+fm3gTDvihMHnj3jE8u20Il62aw00qOYSi14nrUCJmM1SA4k8DoSDg8HAdC4Qw4hZTaX16q3wL/D+fOoy/+fhlUtj2Q5P4xOdfhedTNAXtgBOjdS2/zkp4HGH98dkd8hz+yt4x7D8xgzldJbS5+ngLxH0N+2lw3sx/CgJqIDTdtH7bKNZvG8XZCzvxjsv7sOLYI6CjRxGsvgaVc69HT3sRF64sw2/WUSqV0PCCbEuDjYZoDZUY6QQYHq8DtIl5Pa6WRsxTlM2z+QOMS2vqjg6R8hCXCO3OAWsj1dM1bzuDcIDVir9WMlSbgi/vCWNHLCtliWHzodrmXNvBVEwUmQkbAICOHQVqk0BnWSOfwJHMaPF5R9lKq1O4qCljolkLYuXy75tUdbEvJ3DwtTESE0SIEp4CLgA3lqPM14b1USpYPEcQat6fEOlPAlGxD4V7nj6M6ZoHqAKHSqdUrLRUs+FDhf2/dDlA0ZMlKW2O8ELFsdE6nt46wkO9gOYChxw5cuTIcUYgN6mUI4cVBjud0kadxEHWU6X4lyjvTXGU1/rJSSZDSabTaUZcjbSDXsY4plfGepnaqYX3SjzdsapidiZqw/KiVVK8xvH90nuVYSwzuk10KfRa2kqjRU2u9g+ivtPbozl2EiM/+hrGnv4OvAmZ6eO2dUl0cKZKsSLFC5qqCZGUA6Z0KBb6vsY8FPJK6z+m8DQGs/gtWmDik0IJah3jA7NhjBmZuZZ4xrFsYfZDPa5qBCVDieNU9JvZ3GeF2E78e6nZqW1in5NsfhGkOKZxmTg/mb5JUkJLqJCwOSzbsA/NKpkSE8TMKfM8ayowqE1Iz05Hj5DG0J5EfqfOEJYG0n8R9Q17JvjgG1bE9BDg9qsXZR8fhrWAMfDjr2ToD5DjiygVXPz+e9fgi5+6Ev/zw+ejr7OkT6MCYzDNmWxgHEfhGCsXY5NK7E/BYA+pt6OIbsEp9Y2VrVocEYUUmkrK7fYsJpXee8PCxPdlogscCiTAzwkOswHg8z/YjYnpBvyI8amaXjIJaBg8T7oyjo37JzEyIZvAmtPThtlgsLeM9rJMS7URSMIGGxqeSDMxCnD+6Kvb8Mdf34aTY7rAoukpZWhTmTrG458v7hnHt56OnVDvPjqFTQ/dB3/PcwhGDqH2zH/AnzglJVL9Qkj29qOxY+vXzNG6CU9vGcLvfXET/t8vvoK718m+NLgAxcY8t+VqCbTIK0VCkx4TAuVy1XEflQ7pI1GRucsSE1CvAW/Lo2hsegjU0797VtrjYKENE/Y91FOEdsoEJprCkvJJ+ACycKrF9T51PpfXjFbuFGTKlzCNF3muJQabSrpvKUcZC9nWJl0gxfqOw8OStAS3HZzggumYFhdO96CQXeIASF2h1fexYElOpZpjey0E1Dly5MiRI8dPAnINhxxnHqQDQnTist0GZvENWhH2vNWkdmYWLzrhAKnlK9IrHN6Mt/HFWznS4U/d/KdoZCSdTKX2E7K0CEusadStu3RqMhyhtCrEcdTNfmmOfJPInx6zM35tkIQ9evvFVUo+wGQ6CvI+B6ndKKUY+sHfozly1JjMaRNNWsTf3lEEDrRZD51REuGGc3y6A3OwSFWNnWSiWe7pUcRnTdtBf06+AcmKNHE3oqBiWc6bEE1bxJZWoynp9JkUic074tg1aUpFcESaLJpIxd55qCxdi9pB4XamL9uBN4Kw7wvot/Oi/uA4unkFW3ZOlJ9qVJ7XUWJPIzShZNR/MZHK84qbLJyPHEIQKPNIcY7M1HW7BxSGRXwDUqTM6kBTiEdh0HBo7zHM+yQeR2p9+Gd1QIho3kY3nEHEBAmj4G1XL8JM3cPBE1N4y5UL0dNRgueHhpBCk1VEaG1hnuS0xH0ty2gnDgktSyh9lM0ZBGH/ba+4EfMp6vu2YZaytvpwMeJ3cI0EAHBJqGFXKTIGNyuDGDUcSgUHbeUCJmbC8ZEmH+hsc/G771mNQsGBSyg+/dXYRBQhkYCBfz4nk9+I5YPt6OkoRn4pdLQ7ui8JAFhWkAXLnk/x9JYhblLJUwQOBYPggqHpB7xPAMDOo9NYpnzxw8M1U1KO37hjFb7wwz2YrikmfghBd3sRMwa76WlQBQY2htyp8QYe3HBcC296ZiFLbAZF0CDi74DJahP/dN8+Ld1b21+RnuuvPoq2a94bjRHzXoaNM9vWRxO4Qd67EQAv7BhBR1sBjWaAR185ibdcPh8dFcEBtLSni/d/egFsHhLHdsu7HiEfGCzoZDAGyPdNJEmhNN5fKYzzYP1XQQ9vAhD6Aijc+Etx2UrDynOcuDWwF6yabeIQbulLNHF5RYaWlOJEa16WdEK5Iv3qOqaVwV8b+oH1Fj7h4er4kGgQ80lbJFQNB+mygo0Gm1oDWzeytVusaRPCJHCwpAz7FNuvOGYBkf799D0Ff+MQkCA0AacLHPL7njly5MiR48xALnDIcYYhaWMrPrJDpMwoYXEJoYoggf2mfK9PxUMKicyDKIdd8a9SgsWcj1CeiUHGhBeiiR/tZhELU9rCtJHWmive/Es0qsIEE136g5yGczcJuE51Fu6Xmg8A6vsI6jNw2zoBELidfVI01RSRTGwrZUmsQY0O9k5jUMSdxMACsPdRf+KUVdgARBoOpoNPoRAetNjNMxqEv13xtl8GxCf4OB019EUen/A6MhZzSx9VGiSm8aaSZw53iiVj7JTChb/J9GrfN2v+rbS7IU9CCIirLOPq7UIhLjWMe782FfohcFw4xQpIscJNcKlyGU4CH54J9JvmNWIysUVgNu5sy9fevgRE92PB2oP3VWgCWxOd3LE0ny7DuP6MPHe4HT3q8tAS4uokMcXYekKU+oe/iwUH771pGZqNBoqlksSUZBkQ8bux9lamf8LpIMISKI5Bmmo1SzTZR1h0geHElyklHQFw1Zo5ePTlEzz8qjX9aK8U8aNXTmhaDrGGg6PNDCZmdbHgoL+rhP0nsjlILhYcXLiiBwEFqrUmHBKb7SkVHBCHSHy11Ys68d/fuRr/59s7rHlWGz5qDTszvotUjeGHvH4tbGi8zhvRgyJwQJLAQf6Ax0brWEFkplyQyBkO/WYs6G/D7qNTUvi31h3E8dFkYQVDsUAkIUND8S2RxJh9euspLayzrcDTEcryYDmGvc7zAwxNNjE6XsWJkRmct7QbW45U4dsVQjio1wQI8NVH9mNqpg4QB00vwM/fuhztlULCEpi0bhE+aJi24PHRGiZnYqHx+EwTnZWKwlwXhJORQIWNIb7XTIE0VmHW5iXCjXbxrrdmUomIwmClftGf2iP/BAoKxy2AEhfuNXcCCJ3uxlOg6DcivGDBhA0A4B/ZCjcIoCrnEyT0F6K3ugaVQQ4AVsG9TKM898vfUp1P471f8qWMWAgexZSiy1oG1Cgt0WkS87bdi+FBjrBeEOFdlrWNAKCqb6kC+2G4PBFdfGHLG08jCtmg/1YIkvtfvOd2iS5wiC9SxGbIWPnh3bJYSClSKq+rVCFBaRzl0VMuYxQLucAhR44cOXKcGcgFDjnOQBDpj/5aONgZb+Gb8ku4nWM6hhk3+2FgfNtNuN2UcKgw37az0QnlnaGuxhs89tLtQgvTKcGUfwr4wUfiZsl5Kc3TOHUIJ771F3A7elAcWILS4FLpvT89FmXJvpHhVMpvO7Hs1XqqbSc+G+pq/Ojyt1W/JVHyUZmdKpiGA4HAdIj+OMUygnps5ztoNuC6Rb0ucQ7K3xaQJIyJCTQEtlJOC32KOJh85REEtWl0nH8d3EpnnE7sX2K+SXwBrZ10kuy0WCee5Hy4UE55qdgzN2s42K071w5sxvCDX5TCFn30b+CW23lfJkpfagWt3qllzCxi43BwzrglvSJw0Mw7xITF+XFGuqEPKu2tCivdjm4lQ/YkmwbR5kZi+JY8H5GhKPcLUfgBkPCqvtEvUTb9EbMZLaUdIuaPJDrV5mPD3EyE/MU+JAol2OckBB9/2zn4xM+sRnV6Bm3t7Wh6HiarPuoND4UTrjTU2M1V5jSaCGUYNRyKDlYv7sJLu0dTWwWAVFal5OLfP3UZPJ/CcV00Paa1QXmL9HeXcUV3GT93/RIcOjmFWiPApn1yX5mp+6g37dztRtMHynr4psZSLWy65nFH23McmfE/6E5o8Rk8RRPg+Ghdc8ptMmElolRw8K4bFuMv7t4uhd/z9KHEdCI6KwWMTsWaHkzgwLpJQzWRJODmi+fjwQ2x4/bFA+24YHlPyKCmBJNVD4eGZlCre5jxHPS1ubhsVS+++MBevLAztqP+4Tc4CIJsjD9SKAIgeHHXiKSh8r6bA8VEfGQuSZ2/orn01HgDfV0lFAn4HoQI6097uQAgNh1UFbRFRAGKNq8ahIzanMQZ3gJJrJmF+U/NO9ZSgA6iPJjiUOYjgfL75u61H5Qz5FoiLB8CmEyDUR8g6tFVbmeTZh6J6iHOE7xugbJmugW4fQsNOmemItkPaoxrqCL09QCcwR7u+8N2pPUZ1J/7NoLxE8D5t6Kw4lKdDl5k9L1pkGErJa7ntn2H8C15PMH/D1vDxIqyCtZkoa5/aLOQNas7O+eos49p/aORxiUF80FEFP908e9o7yCosO3zBlBAgDkdDjq7O3kaXcfSIHjj9Rd+29pWWJ+5/5Moz7dcPoi3XrUQpe4+FEul3KRSjhw5cuQ4Y5ALHHKcmZCYTYg3y2ZJgMwcTRQAsN0olbK2FB79FRj9NGZgZEKCcMQoQzDRLu/vLeWY6LZnYFbxV3/rN50YA0wz5WOk0ZY3UD8a3jL1p8fhT4+DFEoghRKoF5qsoF4DtFEDKRtsVRsO6tI7ftCJwozd5jU6TCi0+KI9egNcyaQSxA8fmhUSBA60WQMqHaZCkV2txHBYTWLSZxFmRQc2sxmhiCGScCikvs5gZk6EAWBm78uY967ftXyhlO9GVNM0cbjG8OHkmupIhC5PlKFAsjc/EN8ejEB93zL4ZbLCVwTU0wUU/tQY3LKpb1ipaDGOyvBgzCaiR0XMn1BtOYffQxGjmDQc2DhS5n0SlUk1Ia29g6kmlSaevw9tZ10Od8DkAJKgeWIfGtG4o40q2lZcCJ1hJpdJDEIEgUfD0Tx1ANNHd6Bt8Rq4gysgR+YVlLLnU4okZNNpgOm1Gjuhb4kh8fLR2rxIAPR1lfAbd6zCyW+0wR8bF96FN0yLLuF5M+aU6fZoqeDgLVcuwHfXH0a1EaBBXZQSTA+xm7cQ1qKCS1Auu2grhTbJmVBXFI699/XLMD1VhVMo4uuP7ccDgvmf0elkc2c2EZFncLc2VfUi/xXABaWDcl0t9ZrbU8b7Xr8MzESO5wc4NV5Hd5esWfGJ7ofxyZEPWeksFx0UbJZJMkIUNgCKSSRCJEa7itWLu/DWK+ah7odaJ3WPSnPegZMz+Nvv7ubx1y7txmWrejHYJ0tzHn3lFA4NmTUynqytxvWVWFvF7VuIF3aMaOawAq6YYej5StBdjx7EuleHUHQJfuud5+DshV1afM0HhtQOosAvHn9EDNRoIJB8P4iFiVOMcWia5myhTFN3NUm1aSBHJiTRJxCniRrGSyDvC4yKxmw9pjSmR5gHtT225nOgEK0thmmStZu4hlknSqFMxqmvTSKgHkhjBrQxA1rssqQFGpsfgr/3BQBA/ckvwR1cBlLpVmKZZtxIeMEZ39YiUvNiey5bPKkfsH2NbzAZJ7Sn6Z1+htDpaWx/EsHYCTThg3oNFNfeCnQO6FQRcNN+VVrG307cBgD41w9dCKdURCNB6Ju2RdVfE2ks8jAFDiEoFRy0lwtoazdp3ObIkSNHjhw/ncgFDjnOLFh3vLAzBi0ZEeF2D4sm3WBiLyRmlul0R/RybAIQLTxOGzNCqZLMUq+ktmBZg4ASixDEKniRD6ZS+erhRM1Po1kh0nga0BnTtcM7pViVxWvQHDoUO9RFKIxwyvLdRKs931SoB3/lQRIcKAc4K/c8JoR6TUy9ug7jG36YSIVTiQ+vcQ4RbUWZ2UK542hi7CIWUmKaBTV9ewaG4xmRTRLYy23loBynEbU4TGieOgR/YgiF7jkabazYiFJQQi3jRGA2wECnVm32zc391Yg0bi+LpphUouptTZE5b5CCmjQi/JlxIPJ7kkWYFh+2RSFH3M+5yQQ9oSEwGZQGCBo1OJW2iJki56G1h6/eEJYmIWs5tunRpGVEmwamZdR/x9d/B41je3jwvHf9Ltw5izmfKrEFlPYhwjzSGD6Coe/8JUApJhwXc9/1e0D3PKvMPGSMCIJc2/giCC1fBHoeEh2mPh/50SEOAfVt8xp0Hp1p7tfGkMygdEmoC+hozhjMDoeLLkFHpYD/cedaPLDhOHYGt+P8Y/caaIxA49lTsvvPnq3Cljh83/H4pu9idxjDG7ZgkbsAR3zdRBIA7a4vg+oUGgBOjdewdLAVoSDw+osHcdWaOWhG1kJOjTcymRNSsX7rMOb1GlQxTgMNX1xPgIFuO1Ouq6OI7o4iQFwUSy7qTcpvqRMAL+0ek+JPVpsgIBjslX0Z2YQNAOCrQh7fx8kJ3WlxEMTGM5WVXcKp8TrWvToEIDRr9W8PHcCff/h8bbvUXpHnr5mGb50m474Zjxlx/VIj69N/hvmXAMHMGOjkKRQGlwGkyDOLTf+wuUXMksRbCxND31YfMcBweUA11xMnNq1LYIZv7NssAt18kiC0jveE8ehO3pawSgtrrPDkPfRZeNOxlk3h9t8FOmTfWyxu89VH4+DAR2PTQyhf+U7zZxPCaHUc9Se+DTo9Avf8W1FcdpEcVZy/+ULEVon4mya6rrPs/1MvTFnWJ9t7sXxv74vwj+/iz4WVVwKdA5C0+aJ1qN4MeNhidxiDxRnQw0ATAPqWA32xoIJ3BLbf4zWxiIAN+yg5L+1KirFuOXLkyJEjx5mAXOCQ48yDyFAErBvnOHrkq0DhqcUR4vTmzbm8OdVvS1EtTvwuSm9Mg/QNrEEwAfWAilhQITPGM0A7fSWkTT3kplYmLkw84Yuq1ZSCUor6sd1SyvKS1ZjZ9YIscJgZR7F/gZCfzriKg6jwLeTDdnzQMfUjovy11MkoYIp/Dz/6ZczseM6SRwynrSuqit6v2lZcCH/ecjiFEuAWQEoVA80JnA31vW3csD6k9VljhtlhJU0+FJJiGV0X34rpbU8j8BpGW8yhUEIVOKgZi+NM+Z1EJG9y233ltD6hx5d8XwjlN8dPYWbXBjl6IJt70X3ByIPWKHCIbvGn+5KB8L1tH8jECBL7EWNSxONAmrYcRi9B0Khh6PufQ+PITpQXnI257/gknJKspRQ0ZCaiURiQArXriiHB9JgWn7ehqa5uUXr2p0bgDixRNDakwqx5iRh78ptxnwx8TG16DF3Xv19Kl9hTiawvwphQvLX5kJVpSs4zQZ7PfzhgzEmZ0STSw/p7GOJPjcAfVX3XGATR0U3ZgsEj9A0XDAAgWDG/Ax+7fSWaB6YwdkyLZi0jCCiqDR+Vsvw9zcy/iO7oNvYidwSf6r4fLqHwqIP/PfFWHPd7tWQ2Z8+qjwYAWDG/E++7eTk621zQF7PNJUVJEENwLPK3QC1ftVx0jCag7n/e7kdIRZ8zhevL2zERtOOJ+hoEBm0NAGg0A6n//Mw1C3HgVBUb947LdYCHLjoNRIaMJqab+M6ThwAa4PYr5qOnzcETEWOf4fBQqMHRipDEozKdNPAiR+wy4mUh3lNxCGHHR+R56PhoHcdGa1gyyC4JhHHbVA2HhuC8mM1FJHZSS6R3MohDkODOQ6ZTucrPBO7+0CHUH/5HwKuDdA2g/NbfCfcOPCnzwxCtASb/H6qQwG/C3/0simuuNZLCfwcmk0p6mOw8W24XKZ7hF0CyCUTEdZ2Kn9Y+9kTn7ADCtVTV7KC+1E/i4vR8g5P7FIL0egOAt/lB+Ic2h2me+ioK888BKm1aGTq95rL5t41qIdOQTLMSQegfJGofKrRnvNZrGg+EAMpa6h3dDjQ9NB0Kd8kFUrq64Cvn5soWXFbej5knwufi6z4YChyU9Uwy52jaNhnaJ9z+qD1L3t+0fHEmR44cOXLk+ClCLnDIcUaBMdDSN8YEyqnC/F5jHlquBDFmjvEwypiU4s5U3HjLNRBvEUssI4KYZhrTY78dpzATkxhc6jv1hpSRcWY78jGuVvg7qM+gfnQ3igOLUOwZFLJQTZ0YDiBS3PC3Pz0GKjAdSakNxf6FcDt6pfT+1Ji5bnGlDHUyvDeEm4VKhiJoerzGyQOZhA0gTqSxoeQREdN/4/vDZxog8OogxJXe08DH1JYn4E0Oo/OCG1DsmgOjNgL/toxpKI4BSz1tJKs306T89DrE8fU4LJ1TLKP/pveh78b3IqhP4/g3/lQSNAGQtCAkgUUq344Yf4r9XRxXKiNAolaqC1XfxvkmCEPrR3dzM2EMGvM7IsEmAEkSOIS8rYjZYGImpTDFTwcmcz0ze19C40iovVQ/thvT29aj66JbJHpqh7ZlLcH8W2IsMGZszHAxmTVTtUokpoPCJBn64T+j59qfQ9sFN3OatcQJw4hpORS6B6T3pcFlcnyiZMMDqNAvqbGdw/LEfqxSIRBKSMjYVEzLsGWBkNCcBIWq5eLwfFQmTf3QFlSH9qO09AJ0LF0Nf0b2SXDQm4Ojfj/uuHZBTIvDyiQoKCaVzlnUievWzhWWXKI7Wzfg4MkZ/OfTR3FkuIrjIzWsXdaN33vfucISTDjTKh6q8VzFHCu/vf1FuJGQpUACvKNtAy7+yO/h+88clpj3RYvAQdVwWLmgE7/69lUgDkE5qOGK8h5jOhWq5gdzTmybYno6ijg5VkcZTbyusgNN6mJ9fRX8SADSQWpYWhjGYa8fk1Q3T0gQ4JNdD6DPDefbTqeGH1TNduibXqCNhcUDbajWA+w8Eo65uc4EPt71MNoemMbU4vPQ+cZfxefu2YGtB8L56tDJGfzOO8/W8i4Vw87x5Ku6s2kbNCFP4GmOXwHAF/pCQAM0tj2B5tAhFJZfBnfxuTyeKDhgeGHnKJbO647ShwKqXUfk+UUyqSQ1DzE/s7k6ZTGzCsRZHhEaL/8A8ELNDjo5BH/3s3DOuykxdy68ZPkYBAd08pREh7aNotAFAUAkvIgnaDbmo8wEwY+ydgrB8b2VaA+tCRyceB1Q92iJsO0liVC+oscUCVDUT2lCMHokpfiw3f1d64VEHrx9G1A493oDXfH+WlsHLNJjUcuO+wRhcu/Ro2i+eA/8o9tNCcH3/kBo8si696bSOsx+BCOHpZiNTQ8BeAgA0PmL/8BpopDHmybkjC5lEDANIYOmBok1W4ioXUf09mLraWjKKnLgLoyBXN6QI0eOHDnOZOQChxxnLviG0cbh0Q8u8m3qjMw2qRw1TIus/9bI005B9lc2GnnWtjKid4Q5LrYc4pKqwPlRqmAj/jm98wWM/OiroPUZgBB0XXQz+m54L3f8qjO809vcGz0hPRf75oEQB25nrxQuO381nXYtEPsFDFH5AcxAKxdk2bNXMfHyw5niOZUOSDbHE3gOOlOfYPyFH2D8me8CAKa3PIFFv/i/MzHl1HwS7zhbhXE2zRCLAC+x/Bjjz3xXEzYAQFCTnazGQj8xD1GzQM7anx7FzJ5XUJ6/EpXFa4RsmEcBvX1nW4fE95r5pFBwJH4GiRbxE7B2N9hb9qfHWmv3LFGNcRLmQUWgCEoxtu5uKdboum+g+5Jb5aSGG7CUItKUIAChXHBC1MvWYp0tfdUkoCFWx9QE9QObteDx9d9B5bwbAcYAVsoipjHB5+IQKhPebe8BY36L8zUR+jaR+rmZXkKYdwQhH2t0hW6EN1bD29Gwz4NKKnHOmN62HuMPRU7MX3wQxTv/SGvfuX1t+M1bzsbVq/uk9ZVpyqhOo31fv8Ov+j7RQCn8gOLZ7bH5kxNjkUkdotgjJwQnRmv40sP7Q58CDQ9zeyp41w2L8eSrp7CmKKtSrCkeg+sQzO+TTfwULdfRLy/vxYbGWfy5g5vdCXD+3rtsFYDa9oWCi5mGj/919zbUGk0cHkrScKBc4PDLXY/hnGK4ri4ujOIb09eih0zjt3ruQ5dTw1RQxmcmbsNIINujX1U4zoUNAHBr26tWgcPcHqZ9ENPy9qsXolws4MOfeR4AcEvlVcxxQzNV3uGtOLLxeWw9EOex/fCk5mMBAGqNAN9/9iie3jJsLNsEVcMBgQ/f19c25sgWBGjuehbVZ78dpt/9PNx3/TGc7rkAgKmaPm+s3zqMO66LHIITgmrN0zQhZupeilDQvqyyOGyeI46jzauUys66VQSCCRsA8A+8jOLamwQa4n+tMMyZ3J8UkVdL8YkaBQ4BT6dCDyKCOTQ29+lzJFU0MIjjggaBtj5QUfWLRLSr+wYi5y2ypQEKqpnko8ZtOwFAKp2gyl6FBgHgulJcS+U5nJ4FMH0j0Yl4OHfr2dpgWqPqT30FdPSwOX5SzkR9NJVOQKsThvAIfjMWDBCgJmg4BEp+lAaSIIDfpaCqUDx+aHoBXto9hPayiwtW9nM6Ja0IjWTDftigfZcjR44cOXL8NCMXOOQ44yCpB6tCBW3XSOR0GXLX8svAME/PW92RM+ZZQjqJ+azQZYwXlWPlARoOLLAJE3QmrfxAMLPnJQw/8C9xEKWYfOVRkEIJfde9y1A+RA4Pqge2YGbPSygvPAcdq6/kEZrjssCh0DsfADQNh+b4SZ2paSk0Nr+F8GSi1cvGMLe9t8WF/D0IQeP4Pj2Jgr7X34nO866TD7tK7lT7NvFBnNKACxuAUBhTP7EflYVn6/SxLHg7GMZMpqFiiWT9Dq0d1AghmNz4mPmleNKUSkm/jRbUqzj+zT+PtGgI+m58L4p9C1BasBJuud08xlgVWrrqJp7+CU9PhDY3MmRMdq+VbMObeCExJga6NyGYJDEww2mq4JEINFv6NyEgfH5KEFIJ6LvpTgw/+AX+XF5wdhwtotPkNByBr5uyUOkxhfP3Sl4K4u8g99Ok6Zl6NcBtS4xE1EyE377ivNrpYM5EbeNHz5M4St6RgNjKOzHkZyc8IT1xQITb/KxMf2YCw0zYAACgqO1/NTRBJaC7s4LF5/SB3bZVSSsqN/m9+Bo6j0/SvB5TH4M9sgmeYyM1fPCvnsel5/ThN9+5VnpXa/rYsHNECJnAe25cgv/+zlWgj8lN4hCK4YmGpolRIman0ucqAotSwcGW/WMojezG3Jr51r6LgGsiMBQcgkLBxe6j8i1609d0QHHWgk6snUdwzv54Tb26vBvfmL4Wb2rbhC4nZI53OnW8qW0TvjH9OikP9j4Nf//fLkVnW1HbigBEYhxeU5FNJR5+9iEAb5TC9p0w+/C5e52ZEWpCAT5WFOR2PXZqEl5JnyeCgHJaq09/XXhD0dj8ENqu+wAIASar+rc9OVbHlgMTOG9xaB7KP7EHl5f2YHNjKeoINaO4hgNnDCOeWxUYTeEIT+xfmUluX4eN0zLlI4hnJm4DjLOHac5sTOnxHLUDGAiwCHe1umfcgxDoGmp0cgje9idQPP8NMRXaOugIS0S8RiTuIepVri3CoWh/iPdVSLlDFzhMnATmLAQv0LR09S4AFezFMd9eMXM86dzA/5FoMkcUCg88q7ABYEu+Y902EITrEfEB5msobGMnFggkwWsChTKnqxrNG2U0sMRVBI2iE/OovanfBBw32huxdopr+Pf37sHWg6HA453XL8HtVy5QtvnRg+MIfTRuo3998ABe3juOguugWHDxa+++CNdcsDClUjly5MiRI8dPPnKBQ44zC0mMMpkjAlH9V74lHDPMtLSz8zicwHw1PFs1GAQnZ+xwauEcxfZ2W9MeiDONCcnCpI2TxhoLpfkrUBxYgubQISnOxIYH0H7OFSjPW87TAODp6sf2YOiH/wx/KmTuTG36Eaa3P4OB2z4Op1C0aDgQFCMnuABCp4D8kJlWbxun2MBIbSW5gRkQd7jw21BKw9vmAgbv+H9QO7QN09vWw+3qh9vZh0LPINw2+XZpIjMBIcOSlNrglMpoHtOFGmq5IVkElMrfn9GraqPE9whFOoiQj0qtqU9KyXhZ/D/2LGpCkDiftrMuRXXPS1JWfde/Gx2rr+Q3OzWGsiLw4eVHvPHawa2CyS6K0XXfCKMUy1j8kc+AFLPYeBfyhvLbimyMHKlfE7Xddaa77mQaaJ48KJWXYrlf+C8pFtHrKc5TsURPY5sxOG2d0hsYNHBMQhga+CAoKd3IPG+NP/8DTLz8CIr9C9B3y8/D6RpQhDwGDQrfV+qWbS7V6ymOLaH9hS5OKUUwMy4LhQC47d3SM4gTj0Bi/z6iQ2/pQqbIRBMFE8bhGWlFaNOCA0piUzmExzWP8eq+V7TgoD4DRzN54ipCBhLe3o5QVDQcmO196b5xoYT2i94It7MHpFSBE/igbd0ot3fBa9bhds6BU3HRVnYlszb1ZiDddGd22lUhBwDc/fhBfPxtZ2Fy/HbMvHgfD9/YWIr+ybpm4sjmSyGkPYjiOHhx1whe3DWCmypbcUe7Of6cDgeBW8LQRGxy7d8e3Ks5VAaA9jLRbP27CLB0sB0L3AawX8//mrJ88/3q8h5N4JB1X9DVXjBvnQgwVbMLUE0aIXstAodW0OPMYG1JNl9TrzXgufrYV29PS+8ETbFpSz1e2DGM8xa3o7nrOThP3oUPdgLHvFfxvybeDgqCmajvEWWQEnG6UOZ523pimtFtW5Nw+BOg3AHUY+fnKKf4ApBoiYTa1KbhoNAt0kcI3O5BkPmrgOnR0O8UIQCJtV/1zZW8xuh7DJ1O4iDeC4gwabKxfY4hH3/0KOobHwLK7XDOfyNQaYf4PQgBqGdoB6qbEgvrb16bqaAZIuUv1s5kIorn7MQCDblIiVY50BRReTb525BiBQAKcftxoZm6J4Ag1MoO6jVAihUAoU+I8qENeE/7ZkzRNiwsjMmRhfahfhPVdf8O79CrcAaWoeMNHwW65kjbwOMjVS5sAIBvP3kIt1+5wL5tE5ZCdj7yAgrPp/B8H7WGD4N1thw5cuTIkeOnErnAIccZjGjDrp5ZhEOQaOs6Fiqo2SgMSiLkK8QhwoZe+ktl5pJOZei8TyIvOYklI8PhjjOCDEwgsR6GQ2QSvXpcwbZu9L7Q2Yt57/odnPr+36F+eIeQA8XU5nVc4CCiOXoMJ+/5jGa3vrZ/M2a2P4vO869Hc0zRcOibBwCoLF6DjnOvQWXZBWhbfn7o74B/NuGbmKoofWOtwkogMcdjgaZDlgW0Xg1vXrHUxTIqS89DZcm56LvunWEcGmiOaeWmV/tt2EdP3vs5NE8dBCm1gTaqetmGsDRo5rO0CNYHS7xWO7mMvpvepwkc3K5+Q0HKgV0QWqi+RHzVHFME2qzj0Oc/gfbVV6E8fyW6LrpZGOsJRBp4JsnjTWComJjfgW8czxQUU9vWw58cQef5N8KfGgkZLUYfDmPwp8fgdvZJ5eo3I03Pp3mSJvY8VKZ6oPlTIGbzHYqZqZhP4/D30zueg1+d5Jo+jaO7MLXxMfRc9x7EV30JjCabTGUSAmowwRK/d2KmnjWOPK9QSjH80Bcxs+sFLarb3hPak08VfAhzncmUGZufTO/Vb6MKMqQhRKT5VaungTT9ewLUa+omq7jJPSXLyPT6nJ4yPv3zF4fCN7eAjqKuhTf8/c/FzDsACz/2d/BIAe1dXWhMT0W3Xj0MdJdx6JTMxB7sLcf8sihT1YwTAPR2hUKugjSOgCotYWi8huvOn4u/+uWLUCgW8al/3IDPTNyO3+v5Lua5uumQv+3/KjZ3X4+tHVdg/ZZQ2NRGGlo8hivO7sZ7bl2Nugf89r9uxOhUE02P4oUdulmhnjYXUKY1lwQoFRx4dVMZ1GoZ5Nyl3bhm7SD+7Ye7k/u2AMdxwP0xE4KAUmw7OIFSsYAxg4kkhgLRx+Le49OGmK2hTPQyPa8Jg8/oUMMBkIVyEcQ1xaThAADj06EpmNqTsWmsBYVxrCkexbbmovCmtjBfiX3OxPGsNQM8v3UIzaYP4jhwCXDDhYP6WOWQx3h8wztE6fw3oPHi9+L33YNC5JS5hs0XJiFwJMTQNRMcae4o3fprINSHWyrDDRpoeEpRwgUEeUyamse0HhM47X0g7b2gM2NxsLDHJIp2HEXogSa+DBOg9sjnubkk2qgCN30kjOzwj2c0X0gMkjZusE3R1nN6FwDFkjItG/YK6r6AyJpOzS2Pwdu1HrR3EejV7wLaFGG1mG3keJzwfQk1dyND3UR4e55H48RuFBadB/esK6S9BPN9YCNB/W0uIPxe1PfQ3PwoFuy+Dwsqlrg0FBMSB/D3bYZ36FUAQDB0ADPbnsZ3Rtfi6NAMbrlsAa46pwfHRvW9MBv3bMn8h+/txOhUEwWXoOAQ/OKbV2J+f0gAIY5mjk0ViufIkSNHjhw/rcgFDjnOMNgYvq1w8RlTOgtjzcSgUaMYGECWm94xI1HltJjj64dSoSyVRn4WS2BSqb9FoQkR0pqEGxK5IfPMKVUweMenML7+Hky8+AB/P7N7A/pv/gD35cCKGH3865qwgaF+bDdIqQ21/bLN9GJkUskpljDw5o8qTFrL4clItK1tTHEN7Wz79gn+CzxFy8Dt6DUz9U23xSTGNdAYPormyYMImjUEtWk0Tx0MS7EIFvxaxBDgh2r5Vi8FjfTcExgtovYA7xJqneUxEjQbqB3ahkLvPJTmLEJL4xLA2LP3on54O+pHdxnjlAaXRTQkjMUkpIz7mR3PoXZoG7ouuRUhh8qWZ9aOx0jTb+Qbb/NrzO+wnIkNP8TYU6F98fFnv4c0NE4eQFtnH3Qhg5k2tcjwq6b0f3uG4L1ISOZUZA2HLA6cwzDZLIkq4Tl13z+huvdlLd30pkjgwFJRM/OMa5WozWCgBQg1NZxyO0DY/WiFU8ZaT8mveeqgUdgQ0hCEjCVlSLJbuQplyc9MEC1NJ6a1IzlXMVC0F86qq/JATeawAq+uhwcBQuexRYjZsswrJRczdQ8v7zwFt1BAuUBx8cpeXHxObCJJXUdIoRR5ACZ8jiIEmNtT0gQOS+e2Qy3YNTCQ+rtK0UtZIFyAj2MjVXS1l9BRdgCngO72AiZmPPiq7wABV67qw6plizMJHG6+cA4KrgPHJVg00IbRKTtDsP66X0b5wb+QwlwEaCu5OKe3iHElvmtgDvrRfLpifgduvWwhntx0AjS7j+YIYR5TVQ+f+c/dKXHNGg47D5sFwq2gYhA4BM0m15QRQSmFdV9A4jeqhsN5S7tw5eo5mD/QYUy6wB0NBQ51OR1hk6Jl+ZquNvGlhw/wsP7OIm68KBISEGGu5mPcEfwcGFAoyc/NOs+D8IEMvtbrcwY1C2ktgnshcfyXigFqBNOjPleJ9v29fS/DO74Dwbw1wMqL4bR1oXj+LWg8/52YPjY/KFt4XlehvODUAck3Q7D/JYD8kjZXGgXTiv8I8buoPprabvsNoGDjogtQ1qjQfFyYbzB+HM0Xvxu+mDiJZt8CFC6+Hdxnm6VvAXIwUUNMWiwC6utDTdDmnhfQ1tGHwoJzAABHhmZw9+MHQSnFO69fjHl9gvN5cc+Ysl+ifhOkPoOpBz6LYPRoYlxRIONtWyfTuelh/Gh4AACw8/AkVv3KRRib0ufZsekGetsIF469uGtUev9X/7ENf/0rl/DnpiJwUM3p5ciRI0eOHD+tyAUOOXJwKMxa8bZoK8wyzm1LYL4n3WCWbv7EzA8xjEI45GpMbWrXk5fIMJRtq48xSKBLOonIhwO/OonhR76M6p6X0HfDe9Fx/g1wSxUehzgF9F77s5ja8hR35hvUpjG983l0rrmG51M/vAO1Q9uMFLqdfZjeth7T29YrdDoo9M3XySey+R9ztVXGuDlcNilFlWaN2sLQfySzQiofNMojUBwMuh29/GAf05ImBgnfzux8AePP3JMYU0RQlRkCMaPCXoaSQP5rimc4kB//5p/BGz0OEAdz3/YJtC2/QGGYGsaTkH7ylUesQpSuS96AgqThYKFdeFc/vg+BV0d5wTnhtzYxDRSUF5xlEBCE/YD6Hsaf/wHqR3ahbeVF6Lr0VoQ3/ADbeKRBAG/iOJz2brktDMzvyZcfRrF/AdrXXM3nCEIIFzZkRf3EfrStvDhMr93eFTp5K1MjSxDNH4RQ3of1sQOtf6hmw4LqVKjhI9zKNX2fkGFtJtSfmTAKG0QaCBdMms10iYKf2ESRva8Qt2QMlyNpP1Dd+bw1+tiT30TXDR8wZSA/KmsPscUXk3JTXFEaQmQmpUOEC6rpzKH4M7M1IGyz9rMuQePoLtSPxBpvtNnQ+nnzyHY0Du9AacX5Ao1yTbYfHMfDL8Xabp2VAi4+ZyBctgJds+fIP/4KQFwQx4FT6cTgh/4cADDQLftxAIAlg7EdI1auazCp1NcZfmdSkLfaReLjwReOob+rjHdcvQBA7GfCg923xJGTk/jjh16BCx9Xlffgxsp2a9x5PbGQo6cjub+N+B1oD9rQ48Tz5p0dT6OzuVgwHyfTv6WxSDI7dMLvAQCUIt8Y1YaPjlTD68DSwcj0DIn7Yq1hNs9yyu/CXDcWMtYNRxiVsTcbmHxp+J5ndBrtMw0HU1WZ4JRQTcPhndcvxlkLuuCWivAbBo0KGrbj7qNT+MQ/vILeziJ+5baVWDwoC13Duc/n642r3Mb3A32enphp4pvr9mJ4oo7brpiPC8+eg+OjNdz77HH0dJbxpsvmolgshHz1gtL/I0a8qhFIdGlDPJeb/OrUpw1TlGyIkRrmLBLNw1I6yx4jugcjbEUIvKM70HjiS2HAzmfgd/8mnLnLgYIsFPS2rwOKZRQvuFUSGKq7LUoBYrndL2/NLetBgiki9UIBcQqCyVRh10kp4NUAtxh6Z1KFGI4bfksK1Dc+IL1qbrwfhYtv5zTyskTBA9/Ts3WQ7WsRx7H41jCh9vTX0PnOTwMAvvDDPdh7LLzcMjLZwB/eeZ5CC5G2vFZ4DTR3P5MubFBodReuQXBqH39+ZOZc/psCePilE3BcfU4emWygr11fGxhOjtWxbuNJXL12bjhCle+cazjkyJEjR44zBbnAIccZCPlQZHV8TJS4ViaKwiVTkxhvZykFEWEDz18TgadnYB6ZtBTEtICsMcFjGupk1K4gWrQwT0NVDAhq0xh66Iuo7dvIw0afuBujT9yNrktuRd8N742zdwtoX3UFpjb9iIeNPfkttJ91CZxiBZRSjD9/r1ZG+5pr0P/6O0EIwbGv/TG88ZPS+8rS8+AUy1KdiHoo5r/Z6ckkICByHPEgZmkPoxggOkXL5qXsDappOHT2xukShWBE+DeEU0q4GUccVJadj9r+TTwoqE2DBj5mdm2AU+lEZel5mW67Z+ZAa01MUN3zSihsAAAaYPTJ/0Db8gsT8hDZE0D96C6jsKHQtwDzfu63UOjslUxUWQVl0e/xDfdzIU3nBTei/8b3ZRY4SGQKBc3seRkTL9wX0bsTpXnLUVm0OqozlekAEHgNnPjWX6I5dBiFnkHMfcevo9gT3lg1Oo0GMPLoXfBnJtBz+W1Rtq0z4TQfHsldlZNM/SbGN9wPb/Q4Oi+4CeX5KxUmt5gk+n7aOBLmK0LiogkBKVZAmxETlAYI6lXZ1JLJ9jZnGOiVsLWhqXLEcTDw1k9g9LG7MLX58TgPX7XzkZw3KRTDuhAiO3MnUISh8TimJNTSsoIG8tQEgDqsfcXCFXa/wqgXjYtx5pak3STVRMpXK4cLk1jmJLSZHujlAgSlweXouuQNisChbraxroSp61rDUxg84o1SE4OQUoB6oEF0WxahGaiejqIWdfHcToHuEAWDU/L+rpAhpZq8K0Q388Ub81zgkKDhUI527O/reAZXlPda4/W97Tfg9s4LhTXEXAcRAdXLXVs6ghrqxvm0CB8bG0slgcNBfw4AoFJyQByCWt3PZFLp1ksGcfDkNGpNH6sW94A4Dqp18xz7QPUifLDzKf487Hca450uKtD7x9PeWlCD4fWAMk0k03of36q/YHk35vaUMVXzMFXz0N1e5PtP07rqI/4eM3UfM3Uf33n6CD75s2tipnwkZCCEYLrm4d6nD2qaLL5AMxsj311/BM9sDbVk9h6bwt/+tx78n+/sxvBkKEwYn67jl94SrWHqnKM6Pbbuc4QyTfMgDSLhhSvttdU5hBiFVqIQ2EIOE4IowbVnvilFrz9/D4q3/yZgEAJ7mx8C6lMoXfVu8Ms/JNJqIQR06ACaaJq1JQVK2d/AKJgw7NGjubT9hg8haNbh+36oVVQsA74fb88JQL0mmo//C+ixHSBzlqDy+o8CVdkkm39wEwprrosqpQsQzVpw6RCTZNkX8bgToepTs0m5sAEADg9VMVXz0VkO+z6B4l8oKU+vgcarj2UkQBjHyjdpUlm4UCw4OD6q9nlgeLKBs+ZV+Nd1HSKNNQD4wg/34qktQ/jkO87ShJUmE3w5cuTIkSPHTyNygUOOHIB2cNFtoCtMan4jV9kNR+r1xrRJN72Nz1TfZUvMH8Z0FoUGCu1aeshppFcy88gKfjiMhRKqtgUFxfCD/yoxsEW4bV2alkH3pW/C1JYnOcPQnx7D+LP3ou/6d2P8ue8rfh6Aee/5fbQtXh3WNPAx8Nb/huNf+yMpTvvqK4Wb+QqzMyrfr05h6Pt/j/KiVei66GbZbr3Q3rrQR8xXVVUwPbbCiA/j+lNj0iu3o9eSJvqmys03sVhStN/GKs5ZiM611ykChymc/O7foHZwKwCg66Kb0XfTnXF5lADEpk2jFM7JTG6DalQWAxc+xBnIf3kZIRG1Q5YbvwQodPeDNhvwZyYRNKoo9AyYyRTGh6gRMrV5HXqvuSPTwZoLEASmEMPwg1+Q4o4+cTcWvO+PtLIZXZMbH0Nz6DAAwBs/iZmdL6DnivBGYtKtwvFnvouey2/D+Av3cc2hlhB4rTMhCDDx0kPcZNP0rg1Y/EufgVtIZnhqmYDCn5nA2Av3gRTL6L30TUChgEP/8Cua1lFQm5IEDiqTv/f6d8NlmiGUjXuhLPU2aBYKVZ8pvEz525nMOwEIzfew3+bp2JLO3o5yvQWhgpEpRyx/kTye097zSIgFF1btJHOlHWWeol7d2I4kssMt5++ARHb9G035u5YKDqeKesn2xhljESAoFfXbrZVSbKKEVUd1AA0AfV2lkDzFLE2RhLRR4TJCMxI+JGk4lKNXScIGACClNkELhaA7ReBAqczgZqh0dsDt6NHCC8TX4hciFRfWXtWGj43NpVraL/z6pfje8yew99gUth4Yxxcf3A8AWOaewu9/4CIUK2X445MQ9xcMNSrXw2T66HTwpx86F3941zaUFQ2H5+pn4YXpLlzcYxA4JPnK5f0IeNf1i0BB4LgFOA6B73n6nkKASfC0ce94lK8e/4v378bLu0e08NDptjzeHt8YX8zwfIofPHOECxsA4JltI/jl284K+2dBHY+KKTJJRCm+IPFcYVmnaKMKlCPhnTAHkWiPTb0GaL0GOGxPGzoeFovg9apOwK9PgvbMB0hR1gIVy5yUfZj4o8ciTQ6zMNfbuR6lq96jhTe3r0Nz/d2haErVAmEVUdrFOO9IbSP3r+LyS0KBVgAUiwUg8OErZr38AxtBj4V7Yzp8CN4eXQtOdF6eTRMhpl2fYyF9W74ktKDhwOAY/J4MTzTQOdgG+fxDzEJnEV4DhcVr0dz1TGq5lPl0ApF8dQBAV3cHIMhkLljeg837D2l5jEywdCGNlZKL6Zq+Tm07OIEvPXzAYFKpdQFPjhw5cuTI8ZOIXOCQ48wF2zSrNzhNTHSbaR1TGCEhH5YIwgfOn5eZFIYMDL+JEkxAUpk+Kp3EUl7W9CJN1PBbSoDq3ldQtQgbAKC88Jwo67iNi33z0X3pmzHxwg94vImXH0b72Zdh/Ll7lfRno7JolVRmsX8BSvNWoHEiVI922rrQvvISmVZ2yKAUU1ufxvSO51A7EDqMqx/bjZndL2LBB/80ooulUb6/dBtbb1T1Xl3I4ExofBvHkRDtlnmhsxdqHyGm9Ia+qTLyRLgdvXDa5Nui9eP74E/FDIzJjY+hNH8lOgQzV7wsArmdJJqU8aT2IeGbmGw9W2FosvqxPcao3sgxHPjrD8dJixUs/tjfJGZvEiwEzZrZKbGCqS1P4tQPP49i/0LA90C9Jua9+3fB6ynmOaM7iBUxvv4/5ednv8cFDiab9yImNz2eyV+DCda8RSY1u2op9DepPN/D1Nb16Ln8zcl5GXDy3s+iEX1Pf+Qo+l//AeM87FcnwdmQRP9uxBW2OYbi0tqQOCS8As6Y1QCgmFjQymQ/kjQc1DDlhiu7RSvFUQUdIoIgHkvmUmEVjKqvQyKMDk3lBJDKtGqPWVLLTMZwLlBNuNBm3ex0lgYhjWq5IKgf3YnF45vQQfowTUPNLlFwkCZwCGYmMPH899F12W24YnU/vvbYAf7uxgvnCkXF5boG5llfVwlAgKln75HCVxWPA6AYmWyABgGmX34A72rbikF3HOcUT2j5MJSqpwAsSaQ9JKaIP7lrE0Yn63AdgsNDZhNzDD/aeBJvoAbBSkcHSnPnw+0ZhC9oDxahCxxWLexA15EiykUHlNKI+UbQoC5KJP5+7SXgzjesBCjFR//6WUxWPRBQfKrnh6h+/4eoApgHAPigRo8qcFAFA7PFu65fBAJgoLuCj7/tbPQfGwYEmU6dhnOIyuwFmIYDgWlTJvdly4AgRPErFWKCthsihwKCX/wbmal84cpebNo7Zs5fLsqo7WY2Q0UA6sPbrTCwm3UQAgSNKprb1oM6BQTlCmilA2TR+cIMQ6I9sF3TizZmuMDBVHxwZCsaj3+RBzldc1C6/bcBt02KSocPovrYPwHNGpzBFSi95ZMgxbJcV+u0GL2wCBxkkuL/1dbfHb/QtD6AxqYH4NdnEAQ+0DcPzllXA4E+76iM9EYzwFcePYBthyZw8dn9+MAbVsQmfSStsHDvx/0xRGi+/H0D5YL5JcN6x+6piIIjTWsnisQvUMjLfqZ9kYqC6+Dis3rxyp4xHjY0Xsfyee2YmGlg++4xLJ/XgSWDndEanIDAA5K0eAUQGvvWosq3q/kyW6RUcDA6qftw4AK6qO1McwPDy3vGMNCtCJ0LdsFyjhw5cuTI8dOEXOCQ48wCY5IRmQmqayUgkRkmxwP4DjyrvSFTHgBSefk6V8gSX82QhVqY0zSKKzLa2TtTmhRMvvSQ9Z3T3o3SgpVKaMhs6rn6bZjetj5mdAc+jn/zz+SYpTbMufUXte/juAXMfcdvYOzpbyOoTqLnittDx6xiGUJ7zex+kQsbGLyxE2ic2I/y/BUW6sX2iRnorB/FcogEph9RDsEKky/8E97C9qfHpdRuRx9/n+mbCPFI0X4Yczt64aoOeaf025KTGx9D57nXigVA62PKjX4rXRnsewOR+QIpLf8n/ktC+8hM2JSaZ7MG+L7MjFbyDOq6VgD1mhpzuTS4HI2T+6WwqVefAAD4E8JtysAHN7Eh1ccxD1dLH3Lau/k8lmYOaHTd1xPfJyFRk6OFqS6oMZvr4lxFOJM5fJSFUUF9mgsbAGBm1wb0XHOHOX/FcbTG/HcsB3umjWXRQgCA4kDE3FXM5cj9BqGNcu1z2X04NI7vC23jV+xCQJmhn5wfEDP1xKbkZkCEPPnoZEwmw+3VmKmkrAeKOFX+pGFcQtj8Fa8hzdHjaI4cQ3FwBVAqCTJbwuvlzYyBBh68cZnhHjTrxn5OBCYtpcD05sdQP7QdaFZRP7oLlwFY2d2OPx2/Az7cWAOBENmsmgXecKhVNL+vgvfcuAQ/eO4o5vVV8K4blsqMv+h3qeBgfn8Fx0fC67FL5rajUnLhNQNhDMT4bP9XsHfsCvzWFy7Brwf34XWVDLf1R4+A4JLUaMRxsfXAeGo8hg27xvD6bn2+CTbcg+YVb9UEXR/pehxP1NZIYfuOTWBypom+rjK+8IOdWD6vA/tPTGNzYwlcQnHV2vmgyvzHzFw5ihNqSmQNktWLu7Hj8ARn/DNUEpxmZwUBMLenjKVz29DVXsT1FwxiplnAtCRwCOtvYsyzoOMjNTzRvBRt/iQ6ywQXL+9A+bK3YqraxNR0HSNjMxidbuKa8+ai0KjCn56AUyqDeBXQhlyPGi1gW3ORkV6TA1sTQ1SvaNie4zP6HFKyObD1GvD3vygFMQYtnR6TmN1e73y0LTof4nxBKUVz3wvwtj9hzJ42qpHAMDKhQ2L/NwA03w/B5HBob3/JeaxSYfiup4HIzF5wch9mnvgy2m75qFaeaUl12rrRPLoT9XVfMtIIADuPTMJ1CygVgBWLszG1my9+L6a7ow/tZ11tN+VGYlb/i7vH8fzOcO/15OaTuHBJGy5cQOAFDaBRReAU4Qwsi5Nn0SxgeygCzRE1g7QEAwjGT8B76XuAWwh9PJTmKnuv6DdbRzPMqTxVew9PN7dXbs+hiQYmq0380Ve2YarqoeAS/MH712LFvDZTVhybd53Eue0ZaRC1GhUNh1rgoIQm3ty2Cf3OFEoT3fjsr12J+9YfxN2PH+TxRibr4Ht/QhJ9x9Sbga7hYNCIy5EjR44cOX4akQsccuSIoJsUMm8I2c0tmWEq7tZJzFdmt4I4mybm0FjV6XXuqiGCaM7JEJ7FZrvp5n5iufE7zemycFppjhxD/eguKcXAbR/H1JYnETSq6L32Z+EUSkrZ4R+nWEbP1W/HyCNfspbee/U7UOxfEJ19wlu4LCe3rRNz3vDhiCSR6QWNQdp14c2o7n1Fy785fCS0O68mjOp48t7Pwim3o23p+agsOxduW7ecgaA508qNX1NETeDQqZu34EnUE6MCp5Sg4dDeDUdxyKui0DsPc9/2ibhIoX29yRHQRh3FgUV69+XjIK0BSGybXwBtVgG3k8exYXrHcy0deoNGlTshlsd++Mev6gIHf2YCgWLTvOPca9F9+ZsxdP/nE8ujXtNs1sokhEhAoTsyBUXIrMwYmECK5fAmuQCdkZE8J1jny6Rb+RbQun4juzlyzBjXn5mCaLpEaxO3CM4YEeaAxsmD8KuTaJw8YKWjvOBsqJMHIQTEkbdOTGihMe+tAgIKb/Q4SguWgUjf37YmRAImL4GxSAOoN1SpQBMNAgw//G+Y2fEcyvNXYuCtvwrS1iOXShzOjMm6jNgpD9uteWw3hr/7fwDfg9vZh4Uf/guQQllabqY33Ifplx7QcgAQ1tlwK5ci4HNedfeLmHjqP7Q4fe4MLintx4bGWXCi+ZiwPNMQMJ8YBO+4ZhHedvVCOISgVCrw7i5qsjmug4+/7Rz82wN7QSnFL7wp9uNCiFnotXL8BXjjCzDa2Y75bjYBwaL+JCFViL/81g60sr3/ne57sbAwpoU39r+C9vOuAxQB23x3HG9qkzUYC/CxYn4nLj67D/90707uJPmu6Rtw1oIOvPFNV6BZq8IpMGECRTnSOikoAgdCfZTRRD3SXSqXwjFST9Fw+MAty/DVR+3j2QQK4B9/sBcfvnUpFg92hAIppX+wclWTKbdeNh8r5odr0z3rj2L9ZOTEfAZ49/mLcFtHH37/H1/E+HS8Lp27tA+HnnwAy48/ysPc5ZcayzNheEK/TZ82TCnzpkEIhsf19CZn2IB++xtAyKA1tJGoIcC87vjbn0Dzhe/YCWtU+RjWhQHEzBwPIhNRwsUNOiH77mruexnl6XEQYU/Dsi+efSWagtaGO28lph/8ByuJL9aX465v7wQAXHpOH35tcb+9PhbQ6VE8v2MUXUOj0IyMKRoOX3p4v/S84fEnsKqyLqZ32cUo3/QRAJFpwASBuVxGtI6ZTAjyfVr06DhoPPFvoCOh0JXOjKPt9k8qSRxAHLdZ6IhQvuxtvFxV4DA8UcfDL57AVDR/eD7F//nWdvzym5dgeUKeL28/jjUXZdxkR2slISQ0NyVgIBjGm9tO4Ja2LWHUp/8FZNVfY+WCDgBAO6nDow5GJsIxTWmAxpbHcWfbi9jQWImtzcXGIusNud1zHw45cuTIkeNMQS5wyHEGg93OUbjREpNYeM/CZ6nEYCdDYBRbb78TQ5qEzbUkhCCxgMNwmzWFOKmYNCY+pRSjT31LCisvWoWO1VeiY/VVLFYC0RQdq6/G6ONfNzOF3AI6z7vOllSnMQyI4zDdcQpUlp8vmWBiaA4fgYwwDQ0CNE4eQG3/ZgDAzI7nABAs+uW/hltpV7hzajubqss4VrFgQmbwERDHAXGLnJHudvRqh8NURjA70JfsN8Sctk7NpJKKrkvegEJnH2KPhSGxM7tfwtCDX+DM1fLiNei99mdRnrfcTIvYb1T+rMG8kD8zwQUDNvjT4xh94u7EOCqC+owxX6btFBgEDie/87/1+IUCOtZcjdrBbZh6dZ32noF6DaPAQb2BL2qIBLUZLb5oUz2bw+Nk9Fz9DvRe/Q74tWn4k8OAUwi7jKARE3c1A1tZEVS2r7kaM9ufjent6o9zUJOb5h8CBA293s0h3Y4yEDo3F5FkUonPtIRg6KEvonnqIBIhmE4ihMD3Gqjt26xpRknfIbziH7ZL0veRGD/ZGCVOOeGWp+9F5TqacAUgqB/agpntoX3r+rHdmNryFLouvz2mWRRWIBSTy5dZkwSoenh4W5li/PGvxn55pkYxtXkdui55o/Ttm8ftPglsJpVEX0ZD9/+TNf0idxQbAEgWjzIIHGjgm5Y4XqbJv8na5b34m1+9EtMzM3CcQsR4dTQNGRGvK+/AaJBd4HDHNQsAxUz5Ma8Hw0EXCsRHAQH2jXpoZXs/4OoaGAzU99B13Xtw8sUfoXjwBR7e48hCwbWlI7h06QuYXrcZd1x8Ie56Jsxz8UAbPv62s6NYcpuVimG7uET/vgPuBI5EjqiLrgPXITi3KK/Nop+Dgksw2JsujLGhVHA4dargm2lWMO2CbjKDXmcG1y6dj56SD/g+Th3YhwppQ42GjPcXd4/htmuWorezJAkcxqcb2H1oDMsFmYLvyXOWqskhYsSizdDXWcKoQfsBCB1HF6Mb1UMGgYXq7JajqefHNRwUYUQoRORSToAiWdgAgDZm4Ch9goCEgzWAmTkumW1ENFnp9NOx4yDt3VGXI3EaZS5p7npWSytihsZ9ioqaAi3iX364F1eVjuL96jZLMac1p7uE4Ym43fv7ewBxORQuBhBCspkyEurszjsbQaS9FUM4JxCCoD7DhQ0AEJzYrUTV1xeTUNgGtuchBBjskcfs0EQd8xUhxFTNw+e/tx1/2WfPs0Q8IKPMg5swIwg1ZgRc524EhGWW+A3U97yIOXMuwDs7N+D60lbUSAUvz/1ZEIeguecFVJ/7Di4rA5eV9+N/jv0MRkkPigUH1XrYJmuKR/DB0tNwSgFcEmBncz6KhTdmIzZHjhw5cuT4CUcucMhxhoHAeGLQmAdJzPzIRwPXWYg0HgzxwreM0SoydIgUR+HsRH+z2s+OfpCIEZwmENGurmocbIG2SC3+xL7QVnr/QhR7By15EdQO6My4rgtfrxfFvgM3bxULXZxKOzrWXM3N0ogoDSyBU2mHyEiXSSbxXyLd91VjghBg4E0fwdG7/kB6wwUOArfJr03jxH/8Bbxx+SZdce4SuJUOTLz0AIoDS1BZIpuZMNFH+Y1/pRADnfPf9f+CUgrarMGfmZQdHZsL0A/kEdx2i3YEAKfSAVIowe3sAymW4VY6NH8Ixf6FxvqMPf0d6dBbP7wdww99EQs+8CcJo0hk2MVtYBY4TAFz5LR8DEdZ1I7s0G7opyFo1OJxqVpTA8nsaJnd4Cdusk3ewGvAYOY94oSax3xTdZoNyAyK10DgwPqFW+lAIRJmUN+zaDAB8nwlCB/ZW0WjgXrZbz4yBAYNh4ZFOCAzvnSzQzO7X0Rl8WoUulgniubtDI6siVvgMkog7J9D98m3YQd/9r9HvlUkMsKSkpgw7KYuT6Mw3ohimowQdF12GxrH9hg1s2oHt2D8R3dh7ps+Et0+FeZIQjD6pKwBML7+P2OBg0q8qi2VqP0WChao6RVi00QM1f2bQoGDgDTBmdHsVdQ/fUXgpOLmtq34Ue08OEIdMmk4KDePCRD684geKKWo7nwB3tgxdK25GgWmdccThE/NkaPwx+x+GVxQjAcd6fREeN3aAZxQBA73zFyBtaXDuLS0HwX4+JPeb+Mb09diQ0M1XWiGk7RpoAFKC89B3+hxTAkCBxPqu8P3b3z3TVh97kpQCizsK6JUdBEEFJ4fgBCCohuug8yUj6rhoIa5LkGxQHBz21Ypzj9P3cJ/ez7F3mNyX+jpKErM/iQcOFnF7qNTWDzYjUA0hQfg5zpewIA7ia7r3oM5PW1o3/8U5u65H1h3P5io5hMVYKzUjs9NvAnDQRfWLu2OaCgBiOl6afeoxkj3vabkKrxVDQdQCjfBREvAtwQEQ+O6FqFR4GDSYgBQXPW68IcijGDCdGkPlgLaqLGiokwgbG0tJuSkcWkXCWoCkUhgGrTAGAeAKo01N4LY9P+sUDAI1kJn1XEDrF3ajSdeHUIHqeFdHc/hPG9Mis+EYbzNsmgWCO1YXHEZmlsf58/OnKUgUOZ363xMeIsT8TsBZuGQDU4BrM5zFYHD8HgdV6+ZoydJOdjMaSegfjYTa4HvwfMDoxzYow4KRDHx1qii25nB9aVw/qnQGl5XfwLAVZhZ92Up7m1tL+Obzdej6DqoIhI4FI6h04nH3QWlw7mGQ44cOXLkOGOQCxxynLmwn1Wi93ZmEE+vBYkMTNMhzpK/OUIcz+i02k6I5JOCCHRpgo7kTXxj+AiGH/gCmgLTqGPN1ei/5edjhp3AsK0d2ialL81fiXau2WCARVOj84IbMb39WbQtvwDFuUvhFMtonDqEYt88pcrpdRDrrMYu9i/Ewg/9T0no0Bg+Ip7/AAqMP/8DTdgAAG1L1wKEYHrbMwjqj2LBh/4MhDuDI8q3Exhe1H5yZcIS6aZwuSM0eUSpUgOFMRjVU3uN0GySDW5bJxxCsORjnwUcB9T3cPDvPiYx8UOBA0HQrIIUKzxrU7t4YydC+8wpWhMyoVQzIbXgg/8Thd55qan9aVlQUZyzEM3ho4lpNLM97Fsx8zUZNZk4g91JXk5tTE6nbGM2Enjjp/R8/NAZa6h18xoIHDqifmFjECUxjkxCP4WRT/0mprY8iZHHvgJSLGPgzR9FZem54NwlIphFY23fMJhUGlJvZUb5C+1KaYDq3pel99XdL6J5wU0odM+RFdlUPwym6qn+HxRmVaFvPiqL10TMFkM7JTCD4m9nEPQana+Hv4pzFhkFDgBQ3fY06muvQ2XZWgBA48Q+1I7sQnP0mEFzSygvCZpMVFzjTGtPCogL7oOGCQ4t/XjgZ34THSsuxOjThpvSEYOrdmBzWon4RPdDCLjvA5LqNBoAp4kJGhg/jdVvett6jD0SOrSd3vw45r/nD+D2DErN6c+MY+jbf55YzOsqO9NpYSRNj2J648NaeJl4KBMPXQJTy8TctCGJmTf90g/hjR5DPUM7M5BiBWfP7cbESw9i4nvfQZM68KmDdfVz8cPqxVg40I7PfOxSQcPBIHAQ6HcdB4QQzdeDT2Wm3dyeMt521QJM133UGj4mZjyMK2sKAAw4EygRD0f92DzOgy+ewIMvnsDvvHctlozq5tvWFg9jX0Bx8dn9mBmhMIm3e50ZvLltI742fR1qzZDWnk7ZWez3nz2Ct7fJa4F7XN43JQsczH3X5LScwQ/Ax/mpsYwCB+hMe3feSpQuewcAojlK9g69GgqpkyYAtyAxv4MJeX1T5ZrE4Ew79IUEeU4yOWOO1gVmOodn3oLpHwCoRt+CIMCioWcx/eA6kMUXaPHK130IweRJNDeaTcMBobN16fncG+EuOV8KY7b+K6SJS0oHAGXbIGnfEILS6z6IxlN3xWFuUfMVQX0vWmodUJXLLghwuODBMB+H2h3CWqQcS9wBzViUFWxd/da6A9h9RNauGptuGP0buAahpIhF5BToVLZv+42NFBtfeQkfvW0FVijvfDjY2xzEqmJ82aPQOx+NUXk/GYwcBjWMmwF3CoXA4f5pwjz1+hRtflNy5MiRI0eOnzLkAoccZxYIwByWxgH8hRIW/eavEhhybDNuuglq1GJQswjfsVvFmlkgpSy1DFN8xb2nkkaoK2P6KOkpBYYf+BeNUTW9/Vl440MY/Nn/rt3qVu2sd118S3i44AfRbEzN0vyVWPyxz0UmRKgswFHbhZDom1oEM1EciW3HfXBQFPrmSwfhYGYC/swk3PbQ3A4NvMh8ko7i4DJ4U6O83t7YCZQGlhiqGQfUjuzAyEP/jsCro/O869Bz1dtAnFZMQRiEDMb6yt/fKVdAShV+q1CEU+ni6QDAnxyRhA1OuR1uWxdOfu+zqO59GaXBZZj7tl9D/YidWRY066GQRBJuqTQLQhivKTOaiYPinIWyCSc2pJR8/BmZqdSx5lqMPf1tK20AZF8MGo0UHauuQHXPS5jZ+bw5A5bUDW/rpTGwGZOzOGeRNKbm3PLzhkxJVI+r4bR14tR3/ybOhzNMMjJOBfTd8F5M73hOMiPGnVCzcoU5SKNJGV5BbRpBswan1A5SqoT9TtVwaNYw+tz3Qb0GqNfA6BPfxIIPfDqRzrblF6Dr0jdKzudNwpfuy96M8uLVjECMPPpls3kN8fYlIaDNBuqHd1jLd3vmojRnUTg3CPMlVW+9R4wTb3IE0zueR7FvPtpXXcF7Z6JASGCmxQx4vY1V9F57B/pe93OoHduDE9/8M+39+HP3om1ZKAidfPlhVHcl3Eon8R9CCAiN15PE+TRK5demMLnhhwiadXRe9maUupWbqSaBlCOuvyFs7cSEV5XlF2Di+R/IL4PQvEmhdxCFnrnG/sHQSWpo+FOcpCwaDjQIpO8hV4Wgtm9jHLc+g+EH/wWD7/4Dadkff/bebCZPWsDUCz/QwkqkiSaV12KVuWkHhWPS0IzgDR2CZzFnZgOfC3wPBBQl4gPERyGiqd7wQZyYKZeq4eAQ+D7VmI4+ZKbdhSt7cd3aOaCEoFx0sW7TSby6X14bri7vwnvan4VDKNbV1uA/Z66U3t/39D780vQpbaVyEWD9q6fwxisWI5jQhewMV5b34jszV4LWp1CvznA/FSLaiD5vb1r5IWzetBsdTh0+ddDnTOHSi87CuUu78fffi31iDU/o6zdAkHRhOvTREGp1vuO6pdi8dxSnBF8OnsGHA6VUMz1GCrEWAzWYW5r6ym/Cnbscldd/BKTSBWfOEgTDcd9pu/a9cNo6UX3+XgRjR9Hc8hiceWejuPQCvgbT6iRmnv46/NHj5rFjEtSa4nHaZYFtq1qBTMPh4tIBvKnwPLwDAA5s1OJR6kfaCnaoQkBxrWTrreeHfdzKYGcChyi+O+8s6TVp60b58neg9vi/xYGCQIa09aB40VsApwCfUhQ7eiIZctiB/OFDwKRBs5LvjYRFI9L0DiaH4LR1oLDycnh7N9gbgCHaK209MIE9R2WBg+9To8AhaY4CgBX0IILR9KIBYIkzhC3Vhbjr4QP4I+WT+ZRogkxKKWDQiNy6ZQ+WqHQiQMEl3IQZoM9TQO7DIUeOHDlynDnIBQ45cnDYmaFmIQQVQlLvdcrxNG0DIU4kPGiOnYA/MQSAwO3qR6FnbsSzlxnORHlWOVa6M2y1PiqRkQCFEHRdfCtGHv2SFqV+bDdGHv8a+m/+kEy+4nOgNNdy64kJfgykhHV0QDS/CGIdEgQ4nFEmM6k5oYqpGEJclOYskpzHNk4dQNuyCwBQVA+8ajSv033l21DbtxHNk/t5WPPUoVDgINZG6Suj6+6GF93sm9hwP7zJYcx9y8eMbaFlI97CF97xukDQbOEJY8ah29ELr6EfJlX/DQ1FcFTsX4jJTT/it8cbJw/gyBf/u4XYEBpTTzGh1Bg6DH9mApVFqwDiIGjWUBxchmB6An419NsQHoIDIR24qRkijCFVw8ER/BzYEJrtsfQjZhaiaGcgLPnE56NxEh0c1dvwClh7qEICUigJAkN9LlCZGMzMxMSLD1iFIcX+hWhffRXalp2P49/8M4AGIKUK2lddAX96LDRPFDFe3PZeVhorVCRAyVl+nt7+rOQ7o+uim0HK7VKc5uhxBPXYpIioLWXu76HwprxwlSRwUNF92VvQd+N7eDaB18T0lieNcWd2vYiZnRtQXrwanee9DkGK+S1ar4L6TczsfB7ta66NX6jMKscF9Zo4/h9/gSAyB9Z5eDt6b3hvKNhIYjbTIHEaVhGaBBGe0/pb4IfaEAkCh7TiifpLtC8FYOSRL6O65yUAQP3obiy489Mw9iMB3sRwKGAQX9sEDk7kNHj+SrRf8HrMbP5R/DIS/lQWnIW+G96LU9//O2s9OpwGli0W/LVkcS5PA3nOVSAKHACgcWI//No0nM5uXrXawa1auh8HPtj5tBamMjcvO6sLC90xrN89gyotokpLoHBSTZXMCtG3UQVJl5f3Yl3tXBQLoZH0YqQNmKrh4BL4AYWrMB3fcPkifO+F0PzRZef0o6ejGGpIAQAIt6Eu4n0dsT2qGyvb8UD1QszQ2Gb8yNEjIIblo0AC7D0+jd1HJjDPoIEl4n/13Q0cA448cgUe2Xau9t7EgJwzsx93dq7nz93VKrYdn8+FMuWig2t7T+LKYAP6yh14qr6GuYIGBU3RcKC8T87pLuOqcwfwzNYhFFyCgkuwaG47brtiHvyAoFBw0d3uwnEc+L5qNklYi3x9DiWBh+DEbjQ3PoDKNe+ObeVHcAeWwWnrQDAW3xZvbn4YpWWxxkD91UfhHdpirQvrW0T8RxnPpL0XpNwer81i8pRvp+Ky0j48Uz8HH+owry0Mbv8S+KKvAwOKqtaRqzOxmfBHNevDoPoXQUG5rOI1dHOBwjrktPegctlbEcBFo9GI+lfYOxo7nkbt8S+ZiQ8CgPdb4aJIfRoz3/m0OY0FzV3PgnTP4z5RJFIDamTG+9TB7uYguttcDHq6BlIreF1lJwbdcfz95JsAxQd4AAeBsjL+871bQYmL9ytL7qPrXsGHFSVeFxRFV9FwoLnAIUeOHDlynLnIBQ45zmDot3rjVyIzXGFcc0tFpgMeieNr5aTQgZCpNLn5cYw/+z3NvIzT1oXKkjVoXxUyEkmSSi5RiTUwVpO4TcIN6/Fnvwt/ekyLMr3lSZTnLUfn+Tfycgbf/uugAYU/PYbmyFEUe+crAgBTuRm4blqTyresrDeDGeNYjMyZ9nGepcFlssDh5MFI4EDQOLFfy3buz/wm2pdfgNGnv4OJF+6L0w0dRgdEwRI0ugZ/5jfQHDqM+tHdGH/uXszseB70jR+Jb8hn0IiR2kA0qMtead+aAJSEAgeDXwC30ikJBFStFuo3Mfr415LpURAYNCkYprc/g6Ef/gsAisqSczH3Zz4Ft60LC9//R+EtckJA6zVj+6mggY+Z3fKtOjeTwEFxTMzGunDD3+TkmcEplGLNGELSGcBeAwBB/xs+HDLgAx9BvcpNGtm0mlTNCSawsGlwFPsXYsEH/iRiaDtY+ME/Rf34XlSWrUWhoxf9r78T7edcjqH7Pw9vYggzuzeg2LcAhb5BOMUyCj1z9TlRZNwQJvShGuOeBgG8IcV0j8J0dLviW/BBowZv7ASK/QtBHFf63AX1trzaLgVmBzpMkSREYIKI6a1PwW3rtAtCGV21KdQObAlvoBLCHZuqDDTiuKgf282FDQAiYWsYvzxvOQZu/1Ug8KL+HkO71S/JtJU50xQxwRExAExufAwTz37X+r7YvyAxvUaTAkrp/8fee8fLcdVn48+ZuvXu7UVXvfdmSbYsW5Zc5V6wMcbYYMBAaIE3eUNCeIGEkISQEEJIIQmYbsA2xRTbuPduq9iWZEmWrC7dq9vv1pk5vz9mZ+acmTOzu5LML6B5Ph/p7s6cPmfOzD7P+X6/rtgA2EG9zfwIoDmCk/jmrfTvx/7//Dhaznkn0nNtd3thwoxjPWR/EbgCcYXB2hZisuaRpfXEe3Guj+PdrrpH3B53QqD1zEDJ50Kw0r8PasZ2Z0UQtLz6XeKq1ItQYeL+4iJQEMxvt7Bixw9wfrN9vt/M4AvD17wlgkNh+9PInnZJICBus1RAlzyMotIBwIvhIAusMRRYmKkcxlSlD1PHD0DLVZDwWQZct246Zk3vxljRxLY9A/jb21/DeMHAWNHE525aiPFi7d3srdI48qYnOEzUgnGEAFsAuXx1L17fPwLj8BBqO/oDTENcf1oL3ljbdvejlwlWq8DCrkNj2HVoDP/nbXOwuKOC4Z98GxgDrk0DRarh+bK9u/3YSAnFcrjLGdN0JBh7Hl+7djKuP3c6YBqwLApLkoFKAWVDQjqtI1+oBob2x2lQEnDuR5GFg4PK1keRWP32wNqff/C/Am6U7KC9dpnPbhvAlG0vIiqiicgaivoEh6a3fQZIZDzPdPBeq8y+PRGlBzFLPQIJVBx/yQWB1NoLq393RBqKElUwijSyOgGsiifgMGUbVTc9onsCAFApVV1XVdc+/4YEoxQ8VrWKFBlgwxGyKZAPExsAlEoVQFMBCe4mJyJJoJXGBBwAqOx+CeXOeTg2Enxnsigg+8j4LCngPZlHMUEewpOjs6FkZ2K9ES0A1cIs9QhEzycTUkAQzBfKsCABWT7tRHkgkJ8QWzD5xNvmwrIsSLIM6ZV+4NVNvnQn1PwYMWLEiBHj9wax4BDjFAOBG1y5Zroa5wNuidjTDqntJ+4ERJI/rxX0ZQ8AVmEU+defR/715yEl0kjNWonE5PnQ2idCyXXyzEitflHfMSCYkQCSqqF5zdsASUZq2hIcuv2vYTABMAcf/RG0npnQGPKKyBLU5k6oLZ3cEAX774kh3BD4fZgHYiD4O+jRQS5pzOk+LPnPpGVc/GhdUwEmSPXwUz/F2OaH7SDKPiK59bybbZclCFpwlENcT7DuaZR0Mw7/6IsMSWlfb7mpDVYpDzlp/6ox86MANe2d/qzLpTrcrrj99Vk9hBHxnoWD3U5//ANWjKkXtFyEFyQbYF2Z9d/zDTddcd9WGIOHOAKUEAkkwdAOIYITpRR9v/x6wKWCUo/gwO10ZCYMM+cy88+yxT1ZwdGf/hOTXPKa5f7wr+FSqUqKJKougCRFhWVUQCRJsIPaGysl24rms66zSQRSDfIcRQT7fFyrbROgtvfa/pur8R/yO56HMdIPABjyBRNWcp0gsgIp1YTu6z4FSu04CLQ4ivTc1dWg7dW6fFYslWO2kMbCKvHCk737ksDKj+DQj78Ic6QfcrYNPe/4DCcy1BSN3JgZRNiWMAw8+F10XftndaWlbGBnQgIEGpFkmAWeoLTnvZ13fNvTqPTtQ2bROUjNOZ13zeaQsUIB1vfMqKIyeNgWQLRkKGle3LcV/fffhvHXgrveuVpUhzwMeRaJ1mo4VhYk4MMdqI6XaWDwubsxvu0ZqJ1ThXVbxXEce+A2JGcss++lMDcn1bWXgNj3ia8uZ2ikOgQHRzwCIZzFTSh8EWIrfXtQev1Z6F2TkV60Tmj14A9uXe+cfKtwaWoj3jTasYdMxLp5WYx5nnlcVzH+uAgnBU6sE4G/fIVY7u7fSJdKxMRydTfWJHYAA8AiwZpHCyNYObMVBtFw77P7sOewd13HChV0tSYwb0oOW9+MFn6aSB5lKChSDWNyDo8V52CiPIDpqkeOJ2WKd6yfhv/5zU5MKBTq+vVkhgQozugAfEY2mm/3+/rka3itMgGvGxPQnNZQeJ4XmK9MvegKDmKxwbtnTfdVR7S2UP4o+zrrv8dVzXvNFNz/XO2lfOC+9osNblpKsXHXEL7xmzfw0WwCs9Tw60Xc+FdMP3zPf6KormUS+7pkvrkxss1hCHNvJE9ZBolQGId3ofziL1DZ/kRkGQ8VF2J38+n4yxsWQNKSIJUCKo4oRexYE0cG7edlmIUDqGX3tyoqBMRWoxK0tqxuigC1rQsoNUGVBChkYMp8QM7U/EX0F9/ciPdcvhCLp+Xc9obFW6oHfX2DANqF517awRP5FyU3Y6ZquzG7IPkKnh2fATTihTQEonXHpEHBQSIUJg2OUI88FDgmV10qTWhPA5SiZFgYoMF6qFGuSyiPESNGjBgxft8RCw4xTlk4rlnqSywsQHwshPR3aHWrUkTp4C6UDu1C04qLIflckKTnn4mhp38KqxhOiljFcYxteQRjWx6xy1Z1pGYuR9PKS6E2d8GJeRAMFs32h2+oMdIPq1KC2tzNkaeZ+WvcvnZc/jEc/tEXXLKLmhUM3H8buq7/i6r5erUurp4QRJ5jAzwH2OZ6bCL4StwxoMJyNQExZo4NwhwLOoVVmrwfSX7BodK3z5MxOKsSXkCRtATM0WNuvgPf+r/u55ZzbkB26fkY3Xi/az0hJbNoPvMaNC09j2k7vDJRHZWIuUcJEZK4REtwu+itchHjW5/i0mSXnIfRTQ8GC46At+Ocn3uie64ywAoOXnpKbaFNTueYOevBGD6K4t6g+wU5Va9LJTA6A2E4xKoQ1TEZpHu6a3XhJqAWKPUZ3te0cLAZJteSIWyLm++4nM4ht+JiQFbc+BuUUrs+4W5PP8knmCsRvqadIOBylUgYeeE3GHriDgDA6OaH0XPTF7xZLbBw8MMq85Ykjt/qsW1PV13GAeboMez/709CbetFdtkFtmsmgbsJrhy/wFOPmxzY97V/R2woqAXKkBL+3bWlQztROsQLLFLVWmh008OumDO+7WkEQE3fShYMau+eIfbK1Xf31zjBNwxjmx+umYYbA4fMtwyMb3sGRFGRnLHc1wj+q5kP7gSnRhkDD3wbpTdfsdMwa1wApgFjuA8k1xUaw0GSFXsdIUDQwoGCUgvGwKG6gqezaxxRdCi5TmHAe7d4y3TXcKswhqFffAWwDBReBQo7XoQ5GtzdCjegOwm/vyNQoRLUMKLxOPHOrm1IX3YZkuXdYB0DTlIG8HfNP0KJvgU/A6pzS3RdFJgYKhogIJ6Fg8ilEkxMViLmD4CBH/4l2q76PyATFiCT5NeLsaKJppRaU2x4W+o5TFf7kLdU3Da2DgPadNw10AwCiq+2fs9LaJn48o9fwcZdQ1iTq09ICrNwSKoQCA5BceamzBP43NC1MCwKg3HdCIALEO7g07mfI0OKtqgDE38+eD1K0Oyg0Ox7AnyvJ7akx7y6VU/4RIXyjmeBTAf0GStqWgmZfXsAAdEqhFHCt+61rQNqBQd2y2Tdu/mFPVllVtOqID1yBMZjt9XXHh9kYuGNSgcvQK17D8jUldATCRQ2/RbFp38cWYZGTBSoDI2WQfNDsErjIOU8qKwDmuZeiqND9rhGjkOl6AkOsgTl9Osh6UlIigYiS8F4Uqbhjkdp0z2gY97aRa/7K1A9jSdeOYrFEe0vlQ0899izmHWgH1LndKhzzkTphbtR3vJAZL/D0D84jjDBYete/tlydoKPt3S6vuu46vQjSYL3sUQs6ODvRRkWNlYm4l9HLsTHmjw3jzox8IS6BmdVPHF/r9EGJSm573kDIyXc/+JBbEhyRdoWQrHgECNGjBgxTgHEgkOMUxLujnPnVxcBv6ue/SEW4l9dUCqfhiG5K8N9yO94HoU3NqJ8dK/7oyk5fSkSE2Z65ASlkFQdmcXrMfry/W48gHL/vsgfeLRSwvjWp5F//QW0rL8RmXlnunWL2+ntfqOmgYGHvofx1+zdWZKeQvslH0Zi8twAaaK196L1vHfj2L2ee5Dy0T0Y3/ZMtU5mbLnaCCMiBM8L4yuEthnMNRKJCM5f8W4+zwWT5yZL65hkE1p1/EBWmjrcepSWThBVd6+NVcrDHB2Akm1B/o2NKLyxEXrvbGTmn8VbOeTaA26LHAw+ejuIrHDuOKzCaGCHb+hs5MaGHwY53cwlzS69AM2rr3RJBmpUsPdrtwbGIbtiA8ziKPLbowMoswibr5aAqCSa59KCUopjv/0mKgOHYAwcglXKIzX3DGTmnYnktCVcPkNA+im5DsippprtY4lwalZg5kdgjg+Bmga0rmnIb3sGavskqO0TQSQ7GDK7Y5maZvUJSqp8aK2g0WW3f7BMWOUizFIehEiQHWsO30WllmnLhhwvTWxiM4xktRzSE4ElyS2ijh+6zu5+R2wA4LoC0zsmo3R0D/JvbOTyVPqCFj7U57rK8S899ORdgbSVYwcw8MC3kZy6KCDEBvrAChJEHMQ0DA0F2zZNT9wQCCp+ONZCtVyQiazYOFTvY2pZyO94ERTh99TxgJqVACd+7L7/ca0w0gvORuu5N1XbAv4vxIJD+chuV2yohaaVl0LJttqOQ0IEoEPf/TQAoPPaPwsIerRcQN8v/iUy+DcH5v7MLL8IuZUX49APPh8eEJlZA/OvPMLt1i/tC4nN4KTxxboQITnvTBR8wu6fDr4L7888hEXa/pBcjaNNK6OtPQVzd3AnckoqQ66XFG4ArpglEhyIiUMDdluiLBw6sgp0wa5iP479/CsAgFsB/Le6Dq9U7E0A9794CB++YjY++faF+OefhM9Jh0ROSRVcn34a39Nm2X0AgUkJEzeC4tXdg2iRxtEhj4aUxsMSuArryOmgRvC4SHBokorokYdQLJs1XagBQIqUkZa8dVAlJqgsIaXLvLhZvfGtcgGFF+8BNQ3oC9aBJJqZJII11Sij/PxPobRNDJL8Pph9u0GL9Y0TLeUxkrfnTK3gwLZlEwFh3sn1iz8ByTIgUQtmuSgU/61X7q+rLSLIsFyLIAeS7jnvpyGWLCxUGChAwyrzReR/8k2v7NlrIK+4AkTjn3f+GCwsqFGCRAjM/r0oPHMnoCaBZBZS+2TIM0+HuW8Ln55dX/3XTdHw8MuH8b37d+FffPEMWLRLo3hb5R6UtwPY/iRKL94NegIu4waFwc9tWJY3B5qlOqzRjhMUwNiGzyNz7+fdYynJwHyNfzd33M75LR9kWChK/HVbOK0Fs1Z6gbyb0qpQPLIthLKB4zFixIgRI8YfGmLBIcYpBX4Xc+Akgu57aqVzDwa+U7OC/I4XMLrxgVCXNJX+/bbg4Cs/d9rFyJ22oUrEElDLQOnQG8hvfwb5HS+EWj9Qs4KBh76HRO8cxj0JK6740lOKgQe+ze3AtUp5HPvtN9Hz7i9C1pN8GQAy885EYedLnN/8gftvQ37HC2i/6FbIyYxgPHxw7dx9O70ZkQbcJ6bICI9K3F/nnCsqkVAOiKg6et/3ZcjJLEa3PBpBFhIo2Va3rQQEWvskbpfz+NanobZ0ob8qyoy/9iRkPYXUzNPcNHK6JaR8GwMP/wDJKQu5Y/Yuf/uHdh08jL/ZAAW0zslIzV4FKZmBkm6G3jubI+eJqkKfMBOlA6+7x6RUE5SmdrRfdCv2NiA4BGM42I2uCHZosz+ICSEo7tsKc8Tb3Zrf9gzyrz+Pnhs/D62t1y3LHxRSnzgXPdd/2h4nLcmZ+8uZFs5ihVYtHA5+9zNcXAs5nUPXtX/uXj+iaNB7ZwcFB6MCMO6FiFzDwqFSBiEEpcO7cfhHX3CPa11T0XPDZ4V5+u/7JvLbnwVRNRBFQ+s6O/6CFeHGwCoVQKllk9OSZP9ElhQQxwk1QaSFg4sQv/rG8FEoTW048pO/C/ZRQBy7liRVOEIBIRJoiJ/qA//zJ5AYK5X0vDXI73yBI9zHtjxiu8zQEmhaci6sGi4+uHY2IDh4VhsElNYmluREOiC6ijDy1F2QqAVKKbT2XqTmnO5UA8/1G3Ds/m9hnHH3drJgj4EnrlPT4Fw+jb/6OFrWv0vwZLPhFw4TUxfbcU180KcsRPfb/hQHbvtz7j5Lz10NSU/BrFQi470AwOjGB1HY+SJ3bOyleyPz+FE+vAvalPnuBgIifI4zYMQlcyx6p70DlngUCasskrNWcoLDEbO2SHo8IFoShJBgzJoqVIjv8xMBNQ3XrZgfCizXsiHKwmHD8nZYu1TQofrrnawccwWHF3cMYjhfwYo57Vg9rx1Pb+1HLRGoXR5DQvPWcQMyZGZ8ztc3YUNyc93tEfX/unVTYD4ZHPNlmvgdUYKFf/jJVizQluIDGc/KcK/huZ/TUMEfZR8IWD2oxMT1501Gc9aJFUDwzV+/jrFCGZoqY/XALzGxYAt2xqHt0C79MximhZJhQlblcJdglWJNl0rW2EBNUcIFI/4rYbEL3IKd2C3etVS6ZkKSCGRFgWQYwvd8mh+qry0CSKBBAYANzFzHc0EjBkARCEhsvv4kxnc+g8RZ74I8a5V7PNLCofpstsYGYFUDVVsAUBqF1DkVhUe+JUxPCEB9vx1Km+/H9x6fCAA4ZmbQJo9BhEtSG7k4FiciNgCAWbGfwZPlfuikgn3SBExoTYEQYEnhWSxp3oqDRiu2VeqINXScUIiFogGwcZ81v+kRvPXJ8r14y8RCyeTfQZsSQG5CFs5vjoSmQJOC647VwAaJGDFixIgR4/cZseAQ45TC6KYHUTq6F5Kqg6g6iKJC0lKQ002QUk2QUzkomVZImu4L4MpaOnjHAiD2i+T4q49j5KXfRruUgN/nv0f+SIk0AOoFrpRkJHpnIdE7Cy3n3IDim6+iuG8rKscOoNy3D1ax+iOBELRfdKsnNgSixHlbVQkhGHn5fqG7D3N8EIOP/ABt598iJFJbzrke+d0bOVLSHDkGMz9UFRzqhcASwWllaBwM97/GqnE+cOXCrVvN2YEsRUGVHciZZhBV40hIrXMyJzgMP/uLQL7xbc8gOWM5zLEhKNkWyJlowUHOtgQCjvqtE+zqGTGJsfjwjvHjm5q+DKnpS+2di5JkW3W4Zdh/Ws+7Gcfu+ybKR3ZDSqTReu5NNrGvaCBqArQSTQ46cNNx14vAGOQFB33iXKitPcjvfAlyphlyOgcl28YJDgAAy8Twc79Cx8Uf9OrwEdysK4HsknO5gN5q+0ROcLBKBVDQwPU28yMo9+316jDKtr9dn5uC/f/5URA9hQk3fQFqc2dNl0qW41LJdz9R0wyd4zbhQ0ErJY9sJ9EBualRxr6v3Ro43nXdp5CYOM8uQol2V2S3K4SIpLZQWi/0CbOQ3/E8X4BlgihapGsji5n/mYVnA7LMEe+VYwcx+OjtUFq60bTk3LfOwoElDeuxcEjUv/4NPf0z93ObUUZ24VpuKaTUihQbJn/iWxh9+X4MPnp73XW6ZfvGgCiqfU1YUa1Ssv2hs0J8da3xWzjI6SZh4G7nuvjnE1FUm/Q3zXBrnSr8YkPTmusx8mS0CxM/HPdd1dqrbQqfB06MCEoBSYu2tnHzmJ6FgzUWtL7i0hZ5Yq9cdW3kkPAnC3L1uUZLYpEyOhjuccIZB2EMBxOXrJ4ESJ6FgyhArmUYdd1vLBar+/CbwjL3eyahggBIJ5w1l2BXpRMz1HBXWmlGMzOoBJ0Zn0bEBgCYUtoBCadDAoUBGX902UyctbATe7bIwFB9ZTjVlyz+uVFmXGFdlnqZc/fjQIEFpRqAl1Q3X7y88xgGR+178rpWzzrIGjiAP//XRzFo2evXlz+wDJkQUcEqjdVebxu4dpSJqSLXEIXeODCMBd4lduNTwazAHD8Gs1QErAqg6pCaJ9ie2CgB6hCBwyBXXVSxsIYOwTiyGyYoSi8E3/n8cGJ0WBDc35aJ0ot3I8EIDiKrHy+9gcqW36LyIl8v0VIRbhad3y/8+Ja3PQHgHQAItle6caa8M5AfEAdIPiFQE+cmXsGVqZfstk8/Hc3nvQfFvn0o/Nw+1qwdCFgbRMGcshLK0e2ghWix14EKE8VK7XnhiD+B2A6wAvclNSruXHNKTghet06mtWKMGDFixIjxvxmx4BDjlEJh72so7Ho5OhGRoDR3QG2dAL1rOvSJs6F1TQORFSYIbnB3pFkcx9iWRzG66QFYBfEuIQdKSw8SE+fYLmJ4Z7q+z4HGgcgqUjOWITVjGUCITUq99hQGHvoeWs+9CalZKzh3EFalBGO4D3I6BymVgyRJMMtFjDz7S4y8eE9oTcX926skqieEOL9VlFwHmldfzbtbGTiIw7f/DVrPezcyC85imsy8fIf+6KvHIkIgOvjFA/e4d8yp3Q6e7GWnTGGeHkHswKwhcIQcwlwz1RfHQYTige0wBg/j4Hf/EkquA8awOHiig5azrsPgoz/ijsmpZj6RO5b82BGhHx0SHMMQ6N3TMeGmL9hEuyR7o0YI5FQWxnB9gsPQE3cgv/MltF14C9SWHnfXt98Hvd49HaX9OzDw4Ldrlpnf/ixwyYfc9gRJU48t8u/OVHyCjVXOi+9TSlE6/AZ3yBg6EhCAANsVhCMg1Io5QI0yjPEhHL37X/njAdKTcHm4M1WhgNbYES4E8X4YS0p9LpWEvvGpBbNQn6uMlnU3QMm0cYJD6cDrGNv6VOiOaxEkPRlucTF4GNSoNBSgd+DRH9adliVw6okVYIvFjZNbAw9+zxYcuLqj7lcComhBf911gp93dnulRBrmmDeOVnEcCLFYCwgOqRxoWSQ4FO38IeLg8RAv5lh/7UQ+jDzxE9t3uZ5GetF6+2DUnGGeoaJ7Xwg37oPtBi8yaUEsOMiLLwZe+UZ99dUBZ12qK1D2SQI1bdJNdL/M6NRwzgId1DTQlLLbJiJXLbMCqY7nFYseZQjnJV7Bg0XbOjCl22teKundI37S0I8W2VtbDUSLyPXg40334o7xM3DAbEVHs73uZhP1h+p2XC1VKN8Wdsf9Co1/XjlQiQlFZjYlADDM8DFlr4NpIdSKgRbHalo41B2/Afau++aMiqGxCnqUoci0R/e+iXlOTAoC9/3POLgdhfv+zU2nTFqA9IaPMTmPX8iTYQWCOJv9+1DZ/qRgP7wYKgw0kTzSVPzco+ODYNfZKJdK1mg/Ki/dHTyh6ELBIbX+vfblFwisxCxjtf46Npcno4zwdxg1oj3HBcvClalN7lf6xrOw1lyHyiuNxQpjUZ62Bnr+KIw6BQeFmMjXYeAlhQgOE5VBpKwnQfQ0Eisuh6zqUHN2XIo7H92Dp149ioPHCrguVQESfJk1758YMWLEiBHjDwSx4BDjlEJdu2CpBWPwCIzBI644ISUzSM1cgeySc6G2TgBLVxtjgxh9+X6Mvfp4BHlCoPfOQmrmCqRmLK3ucCc8j+O60uDz+c3Dibtz3fkuI7PgbCSmLISSztkBbuGQywSVvn04ctc/2IllxfabnR/l3M2IkDttQ9A1CPO1acUGVI4d4AIMU8u0f2jW2E1md5XZWe83HHGVAZYk96kegQxBSwkvSWCgvTawsTuAgOCQXXYhrEoR5cO7obR08/WQYOBoPyQ9jd5bvoTxqjuiWmIDACjN3QFCT8o0wZ0zdfpUIn6hgXMrJSrDuyZEUe18jsAGO3g16mi/g/LhXRh68i50XPZR95h/fJWWrkBwbklPhRLSVjHvEe+CXdOOgOe/Fx2rEqLqkPQU5GRWGAMCQCAQdaS/fVkFQEDqCBptFceDVk+OGxaByy8/iT5YFXHS88+MrEsEPmhuHS6VLFPouskqFyBpSUGGICQtJdxFbltI1E8mEj2F1vPejdZzb4KZH8HB2z7FnT/4g8+jadkFdZdnDByqO63rRomgrl27fXd/DUnGfVrd9bCWQ9V7PErgIKoGQgiGnhEQT/XUVy5i/zc+jvT8M9F0+tUAbOsM3u1YuCjkX5/yO18U3gNu7BKBRQUAWHVaTJ0orOIYhp/5OeSmDqQXrbefq2FWPPAsHEABa3yozkqqrkuAUIGMbQ+LKRNb8eW1yzF9Qg4D0k7kNx8/8caCGmXbKirEwuGtQGH7M8iuvkZIfq4cfwz5nzyGUnMXLrju05jcsQg/+MHBQDpCzbpc1fhxlr7dFRwUVYZFCNK6t/YZNJp4vmDgdmyX12Kf2Q6zRtp6ME3pd+tM6famFYlajQsOPvFDgzPXLC5uA4v3tr8EObUChCFKDTO8ZmdFniz3Q375LlR2PytMNzo4BCmfj/4BWYc469ZbHseEtg4MjdWm70/T96C0/Wkk5q62q6HAwxsPY/zNPqxnE1bK3Huf1NID86h4934tyAhaOFT2bglJLYZKTNyceRyzaNClpAvL63+USyVzx9PCzSNE1arv/x7krhlQuqbZ6QUCKwHwjvQzeEf6mcj2V6gMXRBn5LghuLdpcawuUT8MyUe+2pCDuDX661COtdRMJ4MiScqQYeFochoODFuuC7RWDIOWACnThsSM5VBlAtM0MTxewcFj9porkhdF1oAxYsSIESPGHyJiwSHGKYXjNWO1CmNVn+EWWs+92T1eGTyMQz/8q/AfV7KCzLw1yJ52IdSmDob8ZcWGEFJfQCpH+QZXMs0+KwC7Li5ArGnAGBK4E5AVdFz2EWgdk5Hf9TKUpjYkpy6O4PAJCJHQduH70LTiYphjg7CMMpRcJ/QwAt5nZsyWFaiEcQUUPCvawc+cYywbKFOvnY21iOCFBsD+EeAnhMe2POKSZq3rbuCbAUBr64WczoWS0tnTLoKkp1A8UGdwU1QDzzI/HImesv2jcwPBiAPOmLD945ISEEo89wNcCWJLCecIK/cEgjHXYTVR2PUy+u/5BogkQ+2YhPzrfBwItaUb5SNvcsfSC8/G6Iv3BQsjBOW+vdB77KB8URYO2WUXIDl9qe06yTKQ6J2L5jVvc+9VSu1gvCI04jLI2UGsdU5Gy9nXY/DxoKsXfcIsZBevF1tJmLYvfVr1589eE79AagwehjF4GMlpi+tun9tOSYZjAUTUOgQHAJZgp6BVytcnWABcQHU/cquvxjDjUigKkpa0LcxkuSrw8Kj07cX4djE5dsIwTVhGuTqP6iNDjIEggVo/mLsuUnCw13XrBHxpW4VRjL54H9S2ycguWOMGvHbPM/6+HaHbeQb5YxSEuaJLzrZjUwTEwep1PNmuJVrOuSHSxRSRJFSO7gHJ5KBPWYTCNj5ws5xuRttF7/OCTBNSt+DAxnBQO6eg9dybYRoVGMNHMb6JFxBGGXdvAJBKp9DUlgIhBC3n3YLSnk2cGyhtwiyUD+6oqx1cm6pr5O/SwgEAjt31JSjtk0LPm0NHkH/1cZRzK0FBcNBoxgRmd7suUZQbtHAAgFaZ7ychQDrBCA41rBYS5hj+NPcb/HBsNYwT2BXPwt4ZTZFyhA+Bq6kweBYO/M81hZh494XTMCFtAY+K83ZUDkB98z5gys0wTAs/f3wvCqWqm07Bi93/a/45xiwdGakE7K4elOTAOrT5tX3Ya0zANekIdzcRa1ceSaTgCWC0OI6KYe8MtygJBI4uUYUjuytPfd8VHJ7Zegw/eHA3JstjWO+F/QE1vWcnAezd/8cJidCA5UW9bnsA4M7xlUiTEmapEWIDbIH36jUT8bMn9wvjmrjpQjYLlbc8ALz2CH/QEYFJUOSsF8+UZmC6chQZnLy1mlgmjphN6JK9cTT79wI1Yt+cTJyd2A5sq/1eLhELFyS24Lzkq0AB6BS8/ozf928oZNvQ9rZPgaRaoSne+7QsCITeiAvIGDFixIgR4/cZseAQ45RC7vTLYIwOgBplWJUSaLkIszgOa3wYZn4E5tggzDBygRA0rbiY2WFOoTR3QeuYjPKR3XxSLYns4nXILr3AJmnZ3bGB3fYQcb3VpIT7G8jHfqR+ergaeFnz2fL6IGda0L7hA9B7ZwOgyC6294kRSWLKF1kX2OST1tYL2trjHmO75IR+DjgzcsQQQXciLRW4ZL5xFJHfbIwDUX6f6FDu44lmJdeBjis+jsKujdB7ZyMxcY6veTZ5277hg54ViQ9yqgmUUpT21y84+F3myGnml7TIXZLoKyGh88o93yD8gkNm0ToUdr0Mc3woEKSZhSsy+OOFSDK0rqkYeem33GG9ewZEDkkmf/QbjO9/Iojh4JHRetc06BNm2sGaFdUWcCjvFqde10BRcAWHtl5o7ZNQPLgDhV0v8f2ZNBflo3vQf9//BPKb40PY958fg1XKo/3SDyM9y94dTwgJdRN0XMRBoxYOAMx8cHyGn/45mlZeWld+IsuwCgI3DqqO5jOuBMwKhp/7VXQZVYsU516VQuJPFN98pa42NYrKwEH03fldGCP1u/GxGokRwYG/J2mERYWkaMd1D4sw8Nv/RnbBGsi++BNmdZ6Jnj9+CwcR0ovWobDzBRzc+WJgLh+773+QXXZBI55XPEQ8HmyXVuGwSnn0/eRvQs8npy1GcspC29+5ZQKURls4VdF+yR9B65ruXkEl14lUaydM04RFZJjDR1HcI94VTdQEJL3a7uq67XfRdjxiAwB3V3MjFg6pBWtRObwLlWP1+0/3wxw9VtO1ZPnQDmgDZXyk6f7AueGRPDTjxCh/Quw3jxVzWvHstmPYsnuobquF/WYrDHriLpUAm8i/v7wMubRNksvpZhh9b9bIZUOHAQVmwMWOChP5koW506XIcBCV15/CkcVvx65D4/jp42+iieRxY+bJUJ/8GYknlUkig8z692D01//iHktLRTxamoe56sFQH/uUWki/88sY/+H/DZwbNBJIKYzgUMqjYtgLQZEqSBF+/fzG6Ln4eBP/juDgV799CX+cfToQ7NgTMqubMRrZOa9onDVAZADnEJBkE+Tpq3D7cyMYtNJ4f+bhmnl+cv9WrFi5ED97cr/QzZjSMxuUSNHCt1/YrQoOlFLkfyl+R60Fk8qQGrBIrAcyrICbsPGHvyVMe29hcWj8lH8avgTvzz6MnPTWWXDtrHRjhS52W8bCGj2Gr/7kVcyaMxWq6q0zovkTx3CIESNGjBinCmLBIcYpheS0JcwOcAJq2cHUWAaDVkqoDB5G+cibKB54HcU9m2GV8kjNOR1qc5dnslzd7dl02gb0/+Y/ANh+rLPLL0Rm4VpIeqrqDqnq4sjOBFYUIAHSXBxlgENNkonPK2kJKC3dMMeHOUJYSmaRWbgWuZWXQNKS1YBybHBsEigr0rJA8C2S73b6ErlLnrFyYPvt/ywSGhhrEi6uASMYsb3Lv7EJR3/6j1wxWudUaO0TobX1wo4+KKgfQGLSXPS8+29x6DufDvRATmZhDPdxrkqIokHrnIKSgERScp1C/+ju/HBcHNWpy3AQWUAQ33lCePGKEPdasWbgclM71NYJaD3nBlCzgtLBHTj686821BylqQ2SosHyEXpyOofW9e/CwMPfd4+1XvBeSFqCI2EDFg6q7d4odGxYKxeQugNgh4JI9q77an3GyLGA2AAAI8/+MrIYx9+7342UFSI4iISAmk2Vq/FYUL/gEHZfjvh2Z4dCUoQulaxS3m6KVPsVhFZK2PvV9wIAtK5p6LzyjyMtJ042Rp75eUNiA3D8O0k5EIBEsPFE1RuKg+EPCO1HuX9f4Lx4V7w9h+qKa0AtGCGkdX7ni0hOXwpl4ny0nn8LjKEjGHnhN7XLRPC+ZyHp0QGea8VWcMRMZwGximN17UhPTl3k1k2NMqzSGExNwbFHfghqWaFiAwB0v/cfYUg650kQUmNU+22ja/H+KbtgDvDjTY0KiCRFzpW2t/8/SFoCkpZApVxGom0CKm9uRN/P/7mhNvhRy0+5VSogt5t3CWZCwoA+EY+9VsHFySJSx6E4EFhYv2yC+70praGnLYk9ew5hmV6b6B+xEjhottaM99AIJjYTSJL9ftV8yUcxuOUJlB//Ts18GjGwVNuDmzJPcsdVYuJnT+zFJVNaa5YxsukhPPnSKJpIG85Nvoq5agMu5fLD2HrYwETmWIbY1zVSDrJMkGQGREuBlvm5d8hsRs/ELuiZJkBLQemZhcpr9lpHBW+NfvLdbPYsZzKkKAyYDaMMwpZVp1WJ1NwNKBqs/r3useMRHKRcF5TFF+GJx7fgr5vvqCs4+7ZdR9APO06Y38JBX3IR0me+HZVSEYWn74B1uE4Bsio4WEOHo2PWRMCAhB2VbrSHBJQ+HsjE4gKfR6Fghb+vLND2v6Viw+uVbuw123EG6hvv7QfG0dRZxGkjD+JDmYNIkAqmCeZnIzGnYsSIESNGjN9nxIJDjFMMDpHgUeJejITqDlotCb17OvSuacguWQ9qmii88TLUtl6I/KwnZyxDYupipGYsRWbeGs+/fNhue86dktOUEKuHEHdGwY++xAypLqdymHDT34DIMszCGMz8MIikQGnuAHFI9EjXBTWsA7jDJJjebRoJ76to1757qaIEiWC7/HYe4aqHJzoAYhJW654WbCo7hxhyXsl1YNKH/wNHfvpllJmgw1KqKWDdoE+Yic4r/xgjGx8EkWSMbn4ExqBNAqRmr8TAQ9/j0su+gMdcmxzxQWgFwwgvgikSEG4C+flxT81cbgduBmCODUJONdm+5FW9piWNCMbQUez7j49yrlsAu7+J3tm2tcAbG5GcvhSZhWf72gaBhQN/DVnbGhHq2bkcBcLuticEZgNuFkTwj0O4hcPxCA6ei5h6XSopLT3ouPxj6Pvlv9ZOLMDYK48Jd0k7FjxEaewVpHxkN4af/7VNnjcoOGSXXgBJ02taVATqPBzc2aj1zED50K7QPMcV1BvAyMv3ozJ0BEq2DU3LL4iO4SCr2Psv76+r3MTURWhecy3GX3sSWudkjL/2JIr7tnJpBh+/A8U9/C5S/3x0QCmFOTooPMcjmrClZgVyMgt9wVmAZULrnAyrXETx4E7kX3siNF/+tceEx4mWqBm8vRbGtz4NSAraL3wfKMyAGBoGapQxvOlBjLxwb+Pui8oFIGGvn85TidQhxjnYUenCxspUqB2VoOBgOhYO4YKDnGmGkmmBpKdAiwWABGPgvBUQuWzbljkdxxKTMHrsEFJSUFhSJy+q6T//zLkteMe6Kdz7iGFSZKT67stXyxNBQU6ahQMAdKcpiESqzyQKRU+gHspRI4bwGaYSE61ZXTiGfrTt/DVuzR6/b/xvP3QQn2GMLDPEHsdIaxHLskdeSwA+wWFzeTLmrL4KrR1pmKYBRZZQvs92byhyJdTqt15g7vEp7TpEA2mNDWD8gf8CNQ0k1r23ZkwVt+xKKRCjSIpwbxQG8/AOyEOH0NWSQI7UR4inSQk7DtjPdsUXM4KNwSQK/hwGIssgICcUw8WgMu4vLsLqRP2Cg99dkh8pUhKKSyI8UpqPfiuDW7OPBM5tSG6GSYnQbdHJgLMG1Bs02wTBxOLrmDDwAiaEvGZtr/RgQtsE8ckYMWLEiBHjDwyx4BDj1EOohUCVYPW54SGygtTM03yCAfU2gEsSOq/4uE3kSR6BT5j0/sDPwrYIxAzvOOP6J9B8RqCo7umi/vgE1T9yIg05mfHtcq9SHK5lQ1U48O98r/aLK9MfzJqrswaZHXoZogUGLhtDpjOXRFxXyO59Z7c777bIht41Lbz9PkiSDKqQwA5aOdWEsc2PcMcSE+dC0lPIrbwEoBSZxetRfPMVjG5+RLh73Gsb29GobfxicAS87/qLM7DXgiA9exXy259DftfLSEyah9QsLziupCYBIqFRHykiUlNO50AkGZ2XfQSWZUBiyTemewHBgRMAmD4wB0ZevA9jW5+EOToQSqjWCyIrjs0AABq587oeOBZIzr0UJjiEWTio7ZNQ6d8nLpwJ6CsJBIfuGz6Lvl/9Gx/DxDKQmDwf7Zd/DP3HIToUdopjZNjBqAnU1glIzV6JysChuuNmjL58P+RsW91t0CfORXreamSXnAsCQM60YuCh79ad34+u6/4cWudU7Pu3Dx13GWEYeNDe8az3zkbT8gsgp3PovfUrOPDf/yeQVmlqQ/nonrrK1XvnQO+aikRVQB3d9FAgjTBeR3GcWwPclYJaaDnnhtrjWMMaz4l5QAgBFBWZhWtBKyVoUxZHCg6h5ZWLGHnxnobzBcC6XavTmmjgoe8hHzLfa1ZXLoIk2aWWNGThcPv4mbhw1URkVi9GcTvvto66LpUi1jrnOlS3YRMCYcD4kw1DIFotGHsaGHsaazOCDACy627G0J1fCMQQYfFHl86AluZ99puWVfdO9fuLiwAADxQXIl0q4YrVvdj94tNYpNUf28ePZo0nvFW9dkyBI2YTSlRBggSfKwpMLJrWDCu/W5CTh3QcwbdZjJh8W9NVt0uRFiDVe4ioicDbhQkJxbJpCzCWPef6hu0yRa6EVmq86GtK1ecXITAifOFX3ngBkGTPPZoA1L8twShzz0rADhr8y/wyXJ56ObQuEYiswmL3N9XA2sQ2yOou7Ei2YFN5CvrHskjIFLdcPBMaGw+lgd3xlT2bMPqrryCxcH1DbWdhQEJZIL5p89aivFUs/tayXmjsXqKQI7aOvFViA+AFaxfNSxHekX4aqcrUwPF/GbkIbxhdAICZE3O4eOLck9bGGDFixIgR438zTp69cIwYv28Q/ggQ7Lh3d4+H7CB3vtXj6ojZJe/tvvNbBJCQ+hpEoCsEvBgQLD/o4knQNq5Ih6AQpxO2p9Yx0XnCOEWKGGfCpHXbX6st1fNypjmQTOuaCsFAcm3irAtIMC7AwEPfw/hW3h1Cwv2xYeeTVB2pWSvQuv5dPGlehZzOcf2oNQ72aWYeuX+5EsL7FizMzi6r6LzqE5jyiW+i69o/q1qF2Hm17umY8snboPfMjC6rBois2v7Mq+0nYa6sCEANn79iRYV3UcTdMgujqPTtO2GxwWkr26ATNZO321QVGygNDSwY5haGRJCURFK8+1VgzaP3zOB3UQKgpmG7hguJm3C8sIrjoJQiPXslOq/8BLqu/bOG8vsDu0ehtH8bBu6/DW/+07ux/1t/FgiM3CgkPQVJT4DUcN0jgtoxua50xpAdXJRIMtRcp/Cekpvawbl5i4A+cQ4AoHRoF/K7NwutNkTxTFzXUL61hsgKskvORXb5RcL6et//T5j0sW+49YaCIwG9OkTib70o7n2tofRKriN40L2PSN3k3vGKDQC8uFGEIf2l+nbX92dn4/ILl+OWi2dDae1FyyUf4c5TowJCa8Rw4NzN2PUnpy5Cy6UfQduGDyA1b029XWkMRgnbK90NZaGmASmZjUxDqv3xYmBJMExal+AwvvxdOGbZ5b9SmYRnyzPxq/7J2D37RmwpT6yROxyKWbWuqF5jOdmENyqCuVdFv5nB3w5fhSdKc4WCg0wo3r9h2gkFja8X/p3oGjGhoYLdRnj7KbXs57fgWhmQUCw714J/F5UE12iGepT7bkqquyZZtYLvWiZglEKfzQbl+0YrpYDgsGJmM1JT5kfXIwBRNKQSCkp1uA4qUQWz1cOYgX3YkNyMKUofNpan4vnSdGhzz4bSNcPtM23Qgso8vANSIgNt0fkN9wEA1uivIy2VMW5pkNItkHJdkDumIHn2TZByXcI89bpLqoU7xlcBIJFWJk8UZ5+UukQwqlY8/hgqYVik7UfSDLpUTDL38LsvbXwuxYgRI0aMGL+viAWHGKcWHDLS5d1FhHnYZ/a7n3wOq88fpyEkbRh5zBLGnEDB/ONI9Vp/Qz4LSXnvnLh14rwCGcPXjpD62X6wpPGJCi9MGVxPuDps6w+W2EtOW8KY1jNtEVqZ2KBGJeBOJRDMVlah9UwX5ldbu9F55ScDYyokxfztArhrFpne13c+BS/SiK6nQ0x7+oU9z4kk1XTX07L2HWi/+IOh56Vk1iXd3BYw7TGG+1A68gaoZQYsHKRasQkIgdJU/+74mmAIemN04IR2zgM+tyeWGWotIiKH5UwL5Gy4P29WTPC7nhKlAWxyD4TAGBb4yK4TRNXRc+PnuWOlgzuw95/fg2MPfhflvn0wx8W7lWv54/ejbcMHuO8quyuUWjAGDmF004MNlRloUzUosRp6T9roftdfBY6l55yOjqs+gbaLbkXL+beE5jXHhzkiTXRdlaa2iPvcA9GS0LumA4Tg2IPfDcSpcescCYo4ZpGZj4LnmH++OCi8+Sry25/DwD3/Gdk29/71lS0pWs3gz9WEtdPUgB0fh8f4q0/g8J1fAjWNmnEITgbMsWO+x6JUtyDa25HBZWdOhiJLIATQJvjIN8u0SdQI91zUsgLvQmpTO5LTlyE970ykF5/bUH8aQVJApkeBmgakdEt0GstA5cgbGHroOxj+zb9i8GdfwrzhJ4XuelgUc1PRsWwt5k7m54QiS3jfJbMwpbcVBmSMWxosSvCz/Ap8fuiautptHt6B4paHUNj2JEr7t0LtnIJ/Gb1YKGLsM1pxyGx2v/sFhwEzjTvGV+Gv/vFOFDeJgymfTKxPBEW8L7fejpWCQLp5OYPUuvdAX3aJ/U4gcAd5+vxudOQ8qwlCCG69ZCYIrLpiHZhEreaTYNYSHFB9joWITRYkWKzoIIj1sGFlN65cK35ni0Jl8734i/lvQCfh7pzKE0/DHw/cjE1lXox+R/oZAIBpUTd+GaluVGpUcAAAKd2CxKpr0HSTeP2PQkYqoV0axaeH3oHmm76Mpuu/gNx1n7NjkiTEAv7JEhx65UFkSCEyaPWWyiQMW8nQ8ycCz8KhfiuhpBEUARPEnqcfvW4pls7uPDmNixEjRowYMX4PELtUinEKw0eUOy6FnMM0hJBlXA955XiBqKuZI+r0O9JnzjAWBLXCN9QsmzkeaX0RWpk4Wc3fgwHxI6wwEiSx6mocK4TY/pBdUtoJdu1cCtE1dF1XBa9D59WfxNCzd4MQGU0rLuY77XP1RNyyvGsuIoL90HtmQFJ18Bff+5ycuhBd1/wJjt79ddBKEUpzJ1IzlvFtj7yeonkdbH9Yeu84ePdTYRPF52Kr+7o/R7lvHw5+9y/Fxao6pEQ4kSw6V9i9GcMv3MOJN3KmJdhXn8WBSBSLIuVFaFn3Tgw/+yuhyxkiq65QZhXHUDl2sKGy/WAFhyhrCbWtF0quE8W9r7q+1ts33IrRLY+GF866VEqkIekptz4pkbatGPwEcpWkNIb5HaaNgBqVUOFg9OX7UTzwOozBw8HmJjNQmjpQPlLbZYiD9OxV0LumYvjZu0H0FFKzTsPRO7/MpfHHU2kUTl+U5k6Uj74Zmk7rnBo4Vhk8jMTkeZBkFVK6GVIqF7pD+eB3Pg29ZwbkRAZKrgOJqYu5GAtKU7swX9sFt2Bs69MoH94FgKB13TtBFNW+Gxq0VPGCXzPPOHbZChEcBh74dl2u1ahpuLemWRgBHTNgFcdgmjR6jQOQmnM6MgvPwdG7/qF2RyIgpZuEx0v7ttqiZh2E5omifGQPlJlngEiq+6yuO/C45AWDBwAplcWED3wNkpYABYUJGSjmoU+YCbOYh1XKw3IsKhy4LncIzPwwSns2QaIGjHIJWrYFEIgyjUKdMBtKUzsK257ijidJg+NrlqE0tQvjH+gzVgBEApEUmKMDyL/irYe0XIKCaMuI1snT0JLVsX7ZBGzb692XikwgyxJmvv3j+O2Lh3Dbb7wAsuclXhEVJcT4Uz8BAEipHFqu+RRyZBztUvCdoURVyLCwRt+OJ0tzAoLDbwpLUaYK/ihri6cWBf555BK8Lf0cpiqNBbivB5ekNgmPi+ranlyK3tmrUR45hpHvfDIQvwEA1i6fCKU1Bfb5PH9qDhIo9hptkGGhVwmPEZPqew3mwAGQlomuNUsUiFWBetrVKO3dHDjXZ2Yx0VeXnOuCnGkFlRSM5CsYHFVgDBcQLTEHYb7xfE2amlSFECcuhgiVkWMgxWHAKMMo5WH1hz93wkCNsv0M0NMgqWbQ/FBD+T0LA/4dMHfNX6JSGMPotz/OHS+fJHrhzMQOnJmIDthsUBl7jA4s0bxA32UqQ6vTKiEKZ+i78Fp5Yt0xHACgZSz4zuLcwzN6T3wtjREjRowYMX6fEAsOMU4xCGhz1ke1Kxr4BQUmC+UPC774SHc/UUMYdzcIECvsrvEgv0sQShxz1TsksyiQtFNOaGY7BkX1q2AkeHKfD+TgTxlsu79Twv74+8m01yH/RfnDLDXcf2z1xB4fZkzkdDPa1t0IajFkGeHLitJnrEJtkijB+m5lBAu2muS0JZjwnr+zCcqeGYEghl5eNrYHO3cjRAe2Nv9w1WMZwU0I8Vx0dw3LSiBYI1E0ECXcf7VN6PLtsEr5gKWIORYkJGzrCn7u+HukNOD/HwD0npnoufGzOPA/fxqsjw16LJ94gFFWcLAiBIfswnOQmDTXC1BvWZA0HWOvhvu99+JN2J+b116PgQe+A6KoaLvgFntu+wjk0pHdsColFHa+dPydopZ4/lah5joD8RvaLngvqGVgfNszDVVl5oehtvWi49IPA5KEgt+66IRBbEGM0giroyoEO8rHX30c468+DgBIL1yL1IxlGNvyiDC7OdKP/IhN6BFFg5Ljd0Uq2TbhvWcM96PnnZ8FNcqwyiVIrpuxxgWH8qFd6Pvl16G29SK36rLA/BC55moE1DIx+NB3IckyxqLEMlHbDr+BgmD3dKOQI8qoDB6C9TuwcBjf8gjk3nnQp6/wDtZ4xjsgRGJC5xBIkgwpnYMkSzDLZcAC5HQG3W//NCoWQImEo9//DMxj3j3X98PPQc51gcgKqGXArLr0Auz1r+PaT4FoSTfGzPGgaf27oTZ3gwDIM6JDo4IDNSpQmoL3Xu7890OftQqUWiBEAvW5TsuQYk0LB0eUNSx+7BXZNgYnhGDhtFaoioSKYZe1RGuc/LXywzj2/U/jr1vE52eqVZdqhGKq0hewJChRFdeknvOaTYDZ6iFsq0x4SwSHRlA0Zfv91jKFYkOFypCae2AOHIA1ehTmyDGUC0NQJq2ECRn/NHIpAODMzF5crz0irEM2izD3vYpKqhtqPTvPTQNSNjhnylTGv49egL9t+Ql3XJ25ClrPLFiQ8OG/fxzY2Ic2aRSfba5dVaNwdu6XEL42l199FMWNvzyxiiqMoNFAfBgHq7RdkGGhfHAHSCINuX2y91NI0SA1d8Ma8jYOsOT/Ww0LBL/ML8cida9rIfOG0Ym56qGTUv5c9SDm9KZhHf++C1dw0LWTF4g+RowYMWLE+H1ALDjEOPUQvXGyrrR+KwdXqBCx9KH5vMK93fIi4h3BnfoClzfUIfNrBqf2t48lk33igSM8OPkF5/kCQwh/4c54QTu5cmu0tyZsQSHMIVTNxkRmYwh+t38E+V21gwomZyytoz2Akm2F0tQWPkyohjpswNoh8rjPUkF43rEAonygRU4yIQDRk5j0oa+DJDPo/+XXkd/5gptW0vRIolJKpANiihyymzvQRLZcztWYB7V1Aoiqu5YBtaC2dEFO55BdfhFGX7qPr09W4fa+Tp/rUbBKeYAQmOPDGHr8jtB01DIYgcltTeiOc7ut/LmmZRcgM/8swDIhaQkAQPvFH7TLpgCRJAw++mMM3H9bQ31ITl2M4v5tnIWGKwQJJlZq1mnI73ieO5ZZvA5EkpB//YVA+iiYY0P2zn9CQKmFIz/5+4by1wbF0ON3QOuZicqxaDKDmjV23pomWi+4GVY5j/z25yKTSnoSes90SFoCxugxr58CUjr/xstoWXeDHQtFtdvsPEeORyDI73wR2PkiiKKhefWVdt/KBRA1ET7fhGK5AKaB/PZnAbPxYOvGcF/gfjweyCmxhQMA9P3sK8ictoE7lpyxDIU61vlGQRS9ulyFCPNhkBwyvEYO5jklWqvM4SrJrSa449QyQBQduQtvxfjTd6Fy7EB97fKBVmyxwm9hJopPEIXhn/09NEHAVaLq3iBQCinJX9e0VIJcg5wmkk2WGyYvTDiCAwC0kyFcNN3AcztHUaJKpLucE8W8EMK0SFUctXJokT1CPysVMWolhOl/l8g22a7QaIgLr/20Ex2pLMYe/TaMPRvd42qul0u3vdwFRCxXtDiGQtGsa+c5NcWilkbMgNgANQG5YwoAQJIk91XE8eV/sjE8Zr+HiOI8EFi4+IzJkJQx3iGUloS28HyUX6pfhKCVkuc2TYoKwSzGafoenKbvwdjdD0Nq7oZy6R+jtH8rYJkwK2VObPhdw6Ay+qwm/M/YeqzQ3sCglcZ5ycZi+UQhl5ZBzHALlHpwReolNEvjwHNHYZxzzcl17RkjRowYMWL8L0YsOMQ4dRFFxoqsA0IsH8Tuiqpp3Sx+cpAt039IQO7XanOg/dTd1e61j/B9CssbVU9dx+vZPc8KJo5bpJByo3Z6uhyKZykQEGgI+8HXP/91FubxH+LbxM6K7OJ1GHn+1647kfSCs1F4YyOswiiIlkDLmmuhd0+HSwBSnz1CLfGAUl9fG1HPwvpYKx2JTCuc/YSAJG3hwL9DmKg1BAeB+x0lwg1S99s/DbVzir2TXhUTLu4cq+7i13tno7hnS2iZXlsTkFJNCCPz+bgIJx5Y2SqOg1oWDv/k7yLdGPljV7jzIEJwgCTbu06ZOSNpCXcnPgGB3jEZFBTUqADUCtZTB6hZCbiD2vcfH4WkJ/kYFU6b/H74iQQ3UHiDViNWdQe2MTaE4ed+1VDeesGWqzR3oe3890BpasPB73+Wj99iGlByHaHxL8a3Ponykd2oDNR2w2WOD2PslceQnncmum/4DECkUFKaEzq4JOSE5ujQk3ciNWMpyv37ceyB7yCzcC0Kb2w87vIAwMyPHJfYcDIhiuHgwCrlkZq9CqkF6+xYDpYFUh5/awQHNeHem0G3jRFwXCr5XjEotWxBJpmDkmmClm2B1NILCiB3+ScB04CsaSjt2YLh+//ba4eigTK7oR3iODFjOZrmrMLef36P0M99LViFMVDQgOCg1LI6AFDqWQL90Cb3e3n/tkAaotpWc2NP34XCpvsD55ukEmrRrI4QY5peutO1nbhwx/dweId37AIAFzTXbPZbhiJVsbPShTmMIGFQCY8X52KP0YH3ZR5GSjq599VRM4vXKz04K/F6ZLqVC3rseRwiOGToGN79xUfxtlQBa5nH9R33bAKwwP0+UNEhd8+EeXinsBxaGkO+ZNQXzNc0637dSZ79LncuAYAsETTREXww+1CdJdQPKduOArGt/0o0uDZ/pP1pLKA7UX6Bt/RTZ61uWDymhnNPk0BQ7GEriZxUv/WSNXQYI7/5V5iDJ+ZC8mTBubqvVibh1cokpEnxpAoO0+k+UDNcmK4XaxPbUdm8HebK82PBIUaMGDFinDKIBYcYpxZY0tb57loRiPxGEx/B3AjBKyaFOQHA+V9AHHu8uScUCGvnBIrQ7en8Z+J3KwWOmOLKFhIffkHFlzVymHzls3/9xwPts68Ha7UQKVg4LEyYCyZRnf6toqL03Lzx+qRkWtBx2UcwuulBqO0T0XL22wHrJpT79kFt64Usil1AfGWKXE4JLRL8SYlYZBKAk1E490OCeULZHKILHk3i+H2gE1WHFBFY2hUcmKLliCChZn4EuqICkgwiy3XMJYLExLl1CQ5qa7e3K1BA1hLHXQ0J/og/HphjgzahVwPUEBB+hICEBdGt7tyNHBvBfDL9vt5rQM62QkoG/aRTo2KLGD4kJs5B/6++zjdJVpxFMdJiQwQnwG/p0M6Tsvu9FrJLz0Ni0jyAWiCKzgkO1DLQduH7cPRnXwmNx1GP2MDCGLUDOxNZQWiMhIjgwCfqAungdz/jfo4c34g2sDAEgapZTPmT72Lgge9EBvqWUk2w8iOeoNYg5HQNn9rUqrrRStluyQpDDddRDyRHXGtQQyaS7K7h7loFgBbGMfwEv3u76ex3ILP8YiiZFlCjbNfpd7Pl+17p24eBB7+N5LIN0DsmIL34XIxvbDxQ8dAvv9pwHges2OAgMXMFche8Dyo1UCqWQJIZVPr3CcUGAEiSEtJKjflRdTvHWjj0KgORAWv//0CRqhihvJu6rFRECSpO13eeFLFhqG0hmo95LunKVKkvEHDVUodYwfXpWboIe4t2u4csPih8s8SL0ZQCibNvxuidfw2JBp93tDiOfKEciH2Ql7NImXxcDGpW6rJ0HW6eA33amfjy7RsxVqjgzSN2cOaUXEa3PFwzvx+jVgJZKWRnvJpA8vRrYGx8BecntmC+GrQcmmXtQnnHrsDxyqsPQe6e1VBbins2Q+7fDyKrAWuE/xg9HwBwa+ZhtMn1xY4JiA2yivQ5N2P8oW821K6TARP8u9f7L58PhHuXbBhJaxzWUOOBusNAToL7zRgxYsSIEeP3BbHgEOMURMgPj4CLkrDsIQS0Iw4ADFHLJqlBhMJXRliA3pCdjx5fKCKthVl8ZYaQydy4eEIFx7ULrTjY8mr1nc0VJiDUuDacKEG9frG7RsP8ToRaqYjS+cbJd01SM09DcvpS23e6JAEU0Hsb+3HoCSuC+t3A5s51qJOh8rkp8p/zyqlVHjsv4PtLA+6//K6LJDVRw8KBISIcbUwOd2dg5oPBnMNbbjuC0ifODpxrXf+uqiufKpkrK9VYHtVxFvi/Z0WIULIfQG711UjOWIrD3/9c3W1lkZyxHIVdXhwF/y52x0gnjKCv6e7JL4oCsCiFMcL7A2+78L3Qumdg4MHvonTAC77cfumHkZm/xraIsCy8+Xq0iyAHQv/5nNVIHRYbDPru/ldkFp2D4t6tddV/orDJXti3g6+tB7/zl9DaJ0Jt60V69ioobT3o+/lXT7y+GnDnBiM6Eil8DochMXVRXaLcicAMCZjtgBAJLWvfjtScVRh+/jco7g4Sz+0XfxCVY/uRnLYEB2/784bbECU4SIkM5KYOgHmUNCqC1Quiedu9SS3LPl8b+YIICKEwBGKhwQlctkDtJ4ZFa/P45oeQmL8WAJA7550wBvajtPfEdhC3XvFJDD/6A5jHGZCeKBpkLQlFVWAmKKhlIP/ibyLzvHNNO8yIcDS2YA388AGP5B2xwuPP/P+Fv8jdDclnmdlE7B3qJ8vF08v7Laxnut7V2YIiWoGB6HzFJ2+HtedlVI7u4Y7vNdqwsXU9XhscAgAMWfzmi2YpSOga6U7smH0z5mz/VvDcvi3o3bcFvT6jxm3plTg9dwSVfa+6x8yD20EqtUUYCxIkCXh1zxB3vB4rHD8qVMKAlQ4XHCpFjD/wX5gCYIpgH0otmIejAyn7UdqzBXKFH2PttCtw9zMHMWolMEaT+MLwVfi75h8jeRyCldI9E9rMlTj08B1oovW/k50MmD53V7mmdEjK/yWIeFeMESNGjBgx/tAQP/VinJLgKH2CKknK0PUMMW2f4XeCA34BIWQHuitAwMflE/Fn+NKIYhfUQ0b4djzCbX90Nk/0YOsUmTAI8nEfQ8hroViDGu2KSuAn//2na5DotcakSvpT3+5wwv3nkO+eyOTtJg8RJ5y8rvsudp4IhChECTGCPrkfo4QegtC5568/zOIk7NIwIoTQpZLAwkGfOBdWKQ8l1y68LslpS1BgCMfsaRchMXEutI7JvoaFdcM7p7b08KcUDdnlFwHU5C+XE1yRkABZm5q9Ci3r38mUEf44TU5bDFk7DlahCknjWZX8689C0lOQtASIqkHvng4CID1/DUZeCJJukz/xP04rg/PI53KNUFuUsQqj3I59omjILFpXJYGvhzHcb8d7AKD3zmbKqFMAAyBnmgPHOFI94of5xA98FcW9r0LrngE51YT9//0J0FIhNPhwz7v/Foe+8+m621YXmLb6dy1ahVEU99nCR+6MK06Kyy3PfQ4RrBM2zLFBDD//a+RWXgr/DdqI4MC5h3qLYBVGa6aR9BQSvbNhVipCweHoXV9G63k3Q+uYjKbTNmDkxXsbaoPS0g2tayrKR/YEziUmz4MkybAYa5KTch19UNsnQm51fNg70m/ttT69+Dxkz7zWfUdx1vvDP/4iSgeCrm/Kh3bCzA+DalVCjtixTliEzRFJS7r3tshaqVHok+YHn0+NwLkXAFSO7oYx3Fd1fxcOrTKCKMcxItF42Dr+dfutgl9sAIB52kFclnwJWoMxMcKw3ueSJpVNA4XaFlJ0fBCl158OHDephNcYIj8oOOShwkCPPAQTEkxIKA31wQhZ58JQoXLgHi1vqm9NMEGgqTKmyH3olochEQsyrJquuABAW7KBq6dCZQxYGUxBtBVXPZCae2ANnVgAZL/YkFh1DdRF5+PhR59HkdrCPYWEMZpAEscRU+fAVgx+6+MYk7vQZPyOBQfwgoOm/e+mNmILhxgxYsSIcSrhf/dTOUaMk4wgPyogUrnvIsLc+esnuoOiRLABznn/vnQSQt4KSOlAuwj3R+z+J6QPXD2sVQbbnhAxhTdvENclOs6Q0c4oCHfph4kTrjWJvy1svpC6US9x72tvpEstgehRQ8QIratuBK9pY+QNK56EjD0gnkuiwOYhFinUMmEM8TtYRTEciKKh+9o/c11aiMrNnXE5Sgd3wCrlkVm4Fq3rbgQ1Kl6/RW6zOCEH7rSWEhkQLeGSqtQow8wPQ07yu4W5aeYjMeRUE7TmblDLtMcwghwnsgwl147e938FxvBRlI/sweBjPwpN74dV4smCwu7NKOze7H6f/HHbB7vW3gs52wZz1E9yNCYEGCP92P/f/4c7Jje1VYU0QO+ZUY1FAnvHtywDlKLcv7+heShylUXqtHBQcx1QFpyFutZdAFrHJEjJDKxCfS4j6kFdbSUSEpPmoXz0zROvT6ovcGl+x4tVwYHL3ZBLJet3ITgU63VTQeDG9RBBUgBCkFt1GQp7X0Olb2/dbZAzLUjOWC4UHNRWJpBt9dnTiGhTC2r7JCRnrUTT8gtQ8a1jiamLUahlKVQVC/wWEWEuvIyBgzjy3x9H9qx3ILXkAvtgwMJBF+QEiJ6s3tukdkD0OiCpWk0LFwC4a3wl3pZ+PnDcDvBsfx579hco7nqxZllWLRdxApd8J2LhUKIKDps5TFFOnHSuBzkpDx1vVRBrgqJ1/D8Z/aSwyKVShzyKP8l5gjl9+HkYnZc1VE8eOnCcruPePFrEI7/aihX6G1ib8Cz4XitPiMynL74Ayvx1nOBgQMaQ2fhO+zvGV+G6NH/fy53TTlhwCKD6PmNa/HNzxEqiQ64tBItAZAXjJAcY+064eY3Ab+GgaydfFD6ZiLKGjREjRowYMf7QED/1Ypx6EBGU9gE4FKOIQvWOiUh2P9nJfvbHS/AR5v5aXOEgWH/oyUBDHcHAc6ETTgvXZ/jgVutuiGb6JooxwY5zJBlYD2Hv1VG7kcHjoTv9w+J2RJVP2M923zz9RSwE1Wy632TAycOR6KjuPnd8ezQqVBDhx8AxTrARXAzRnBaUPfrKoxi4/9uB/JKiBSwcqFGGv0S3MIeAmzgXEz/0L7AK45BTTSAALKMCKAoIkYJ5I6xbiCRByXWg0uf9MDaGjkJOZuEIb24uxyWXj1D2B1SO2rVGZBVEkqE2d0JpaoOUzACPhSYPQJ8wG4kpi+w2KSoG7r+NL98dTyImGy0L8DfPL6QRgsHH70T+9edR9rnDAAClqR3+ee8f2kM/+HxDQYCFFg6y4rYn0n0NM18ppQHXXSykZAZEktG+4QM4+rOv2McS6QYI77CCJbet7Rd/EKDAyMYHMf6qd3H13tmQE5mTEuPDISoc12ByOgdzPEjacsICM071Cg7peWeiKAjM+zuH8yxDtNjizBMl144J7/4iBu6/DaObvCCvzefcgKEn7gD8RLmsQlI0hMXDSM5Y5jbDfjSThgOZR6HptA1IzT8LsqrCKJRsSzqn7unLagoOVBCzgtRxnYmeYu5d3xaMkLxyNbYOIeGCRiOgRrmmFc03Rs8NJfw5sa9OEahWTBr7/uIXtWF6ApZpsDBmJWonPEkYtxLolQffkrJpfhhFs7d2whD4wzb7LUeaSAEK+PlsERlD+cbisgyQFhDl+MbAoASPbzyIq1L8WpOoYTWS3/ky9FHe11SFyhinYvFOnb4ClTdeCBwvUQVPlOZiibYXs1UvzkKJ6KhPaq4fzv3DxisBEIgN0ghMKuGNMR2z6uT7y1TGM6WZnLhzPDB8o6OGCA4vlKZhhb77hOqqhSJV0HrRB6CM92HkyTvEiU7Cu0CMGDFixIjx+4JYcIhxakG085nd3e8QuYHA0l7y+jwrs3X6vzB+9F3CPiQj69InrFBGDBC3j9nDzpQXViVL8gatBwT9CGl38DDvzoWw/Wb5dl8eu28eMcq1nL1WIbvxObHBEXvquYiEtb9g/Gux/XT/EM4tl3s80C6mo6xblBrDKdy9z/bnRFDPeET1QxhEmlSDP/MFN591HaREGoTItq9+hgCkZqUaMJiwI86VK2kpSIruEm0HbvsUrPywbRkhq+h9/z9B4Uhs9t4l8MadQsl18oLD8FHoPTPCh8BHarluRZxrELEDm1R3YDvX0hwbCk0rgto+EenZK0HLRVimyQsOssrt/haReNQoQ1J1e7pFXG9zdEAoNgCAkuvgDzD3rntfN7jzWcm0BA9ybopqvaLYjaBGmZ+fsoLk5AW2Cy5ZRcelHwFgx1fpvv4vUT76JlKzVuDgd//yhEQHdqei3jMTRJIxtvVJLk1yyoJq2pNAMrguvgAQAjnTEiI4+MfNHie/ay4R2i/7CNKzVuLNr763Zlq1dULDga/rhZTMgFILtFKBVS5Ektwc+UykauwV5ryqQ23uQuUYH5hVdgKcCwLcZhaeA71jEg7f9WWUj+wBUTVIio6Oyz4S2m+1rRfdN34O+772gci+tZx/Cwgse86w7WQ++10dCUEt7/nDus5SxUSng7Fnf4axp+8CtQxQ3/wXxqpRVE+IIASow6WS1jkl0qrHqsO64bVKL96TCVFmnfuJwN2tXQvmSF/keed+ntaTxe5D9i7vE7FwkEAxaP3u/MlPUo6h9zjJ9looDPZjv1HGiuPM79+FbkDmgipLhKJV4q3P9vUX8eSefpzTXF8dFiV4bh8wZegYVhwHQ29U2xjYMR8RF+OF0jQs0Qdh7OYtbHRiIE+D4t1R2oLJ7ZOFgoMjuGi++sZMDdHOwgAp2w5rtL9GKg/GoddBtRTmKftRpgr2m60oUu2E5vtYGdhZaMFFdQsOSqCvxwOrKjjM6M3i5gtnojWXgP8u2F3pwKby5LdUcHimNBMPFBbi3+etgpUfCRUcYpdKMWLEiBHjVEIsOMQ45RDKz4o48rB0HLnsEewu9898d3e+C3e/+44RlnUXEOiR5DArZoARUMLSM+W61bGWCtVCiHckfLe70/5gZaEujEI1FEdoicrnH0uGgBf9FbQJcC4jDaaNtB6oxdDXHPDw46RG2aGnw2iriIJ81hOhhTuEMjtWYVUz8Adj1XpmoHn1Ve6ckRQNFis4VMogWtIrMMwyhm23s5veNEBNwyUeg3JasG9qroPz520M93l1isRGH6lFTcM39SLG3Uecm2P8z2El12HXHwJzfMhti594lRiCkFpmwPICAKxKGXIoj8C0O4Lgl3RnR6qXnlL7P1oqwCwX4BeftJ7pKB/aBTEIpFQueJQlj+t0PeAXWSQtga5r/wylw7sgp3JQsm3VAgkSk+YhMWkuQCTI6eYTFByCrBYt85YWctWv/MkQHOwg1cz1CinTHTffPaR1To0sX2nuQnrWSpiF0dBd/yzUjkk1BQe1bQIqx4Jp2i/5EEY3P4JSiCVFYuJcvPmPNzPlhO+uJrLi3fOECKyPFCitPa7gIGdb0XbBLdCnLgUhwTgG6QVnof2i94GaBmi54P5zUvXc9AUYo8dQPrwb/b/5DzcftUxbFE1mI+NTZBath6ypgGWCmuwObmbdqUO8cy0cJMJZ0dW0ZCESrLyYmBblJXrKnXaFnS/AqCPQM9GjLQNqCQ5PFWcCIMgQsRWEI04DgFQngUdrxQyRZRAAf3T1Anz6G8/CMCmUVCY6T1RxhOKO/OlY1ZmHNrL/uMoYtpLQZSAhGTWtxxreDNNIO0oER/MUyB5ffr9LJcCO48AGVW6TecHBosQlk6PwryMXIkOKaJIKKJoEY2UCHIdhidNGf1ujRByVmCACS6OMVBIKDm0YRvG5nwrLkgkFAYXmc4tlyLWtbBoRGwCgsut5VHY9jw9Wr+dXhi/Gm2YHRk9AcDCohB1GDw4ZOfQotQVFAxKSdcQcua+wCBclt0TWCwCTOzOYM7lZaIXW3pyAcbi+dSIxdw2K256snZCBRYHbx1fjvZfMsa3MtPBxjF0qxYgRI0aMUwnxUy/GKYwgae0S7QQg3K56wS76ul3a+Ihd6pC9LKHfQDm1+O6w3ed1+Dmvm6xm6+Fc8ETlYgQTrv91tCvg5ickjb++0LZH5OW0JMIPJ5vI77KHs1gg4Mcp2D6+bv9F5QUBd3e+X1hpAA4hTkOtFby2umJP1LUh4M/7XC3JPjLZHB/m+6RoQCnvfhfuYGbH1H/tCAkELyWKytdBCB9btzrWBBRmng9sOPzCPcidcSWEN5jAvU8gcGrENfHnPfbbb3Lfo8QGABi4/zakZq+CrOpBMlXR4IyNFeJWKCBCOIKeb2yiLAq09okY3/4chp+7G2ZhDFZhzI6lcd5NePOrtwQzyDL07nDBQUqkIQmCh7PEfGrmMlSGj6KwM9o/u1XxCw5JkGr97r3LuoGqXl452xrY9d4QXOKAuGuF/xoQLWnXWdNao576nLGpEq16GlIiA6voi0sRELLt715wbzGIoqJ0aBcO/+gLdTUnPXsVzPFhoWjQvPZ6ZBeuhZTIYPj5X2Po8Z+455pWXY703DNQOvQGlze94GzI6WboE+eAlvLI7/B2AUdeJ5m5DgDGX+PJooH7/gdyUwd63vVXUJq77WDrhMBy1hSfhcP4q0+gtH87aKUciDNAVB2SloDWPhH+NckYPIyBB74T6TJDbmqvtpSx4iLe/HGFRQGJ6YcxdNSOtZGw++OUV+mP9qEupZpgBeK8VJsiEBwkRjwYe/n+mu0CAEtgecPi6E/+NvTcw4X5+EVhOQBgSjMAgSbICXgNBPJWumfCOLxTeM4hAudMbsaXPnQGduwfxspFvRj7928K09cDCRTacQThBYBf5ZfhkeI8VKDgw1fOweL9d6K0++XQ9FvKkzBLPXK8TY3EvYUlKNHjX8NEwsGQlcIkeK6I2nwWDgbkgCsmEQatNHZa3V4+enwOiJw2WnXU6UCDwW2ccPBocS7yVtDSqAQVKYS7/1NgBnb9V5TjFwHqRaXqc3HkBFyAGVQCBcHXRy/EF1tC3Alx6WUkSLR7tvsKi/BMaWao4LC5PAkVeO6h3Me7ogHMO+XR+dfDOLQxtN0KYZ8BjUt3EgFu/+x66LoKy7QgaRpar/gEoCQw8NO/5xPHFg4xYsSIEeMUwsl2Cxkjxv9y+MlLntgNkIZh3/18DvzUA5+e/V/8iQjqqp51XPv49RG3UqZ2IelJ+Ha45QnqDBz3n4+oxyHOa5HhhHWLxP4NjmJANKhhsRBII74oXje4K+GvmymAHY9alhAi6wr/vAn7LmqrYEz4YNuNBowWtDXyBzaJPi0aewLIad4JgDk+DMqIF903fAa9H/oaWs65Ae0XfxDG0FGUj+6tCh0h7az+HXzsx+i75xsC8j2CePKVmV16Hvc9M38Nn5jwY++vq/DGy8x5jwAWVu3EJQiRwerx6esEtA6ILAxpX9glJqMOfPNPg2sFV4j9X5jgQNQEkjOXw6oUUT6yB+ZIP2iliNGXf4v+e/5LnEeSoXdNC+0PNQ0xCc8cS0yah663/Sm0aoBqf6PHX38e+/7jozjwX5/0tTfapYxzfZVsa410QG711eGlSFLg3qWVAvfdcWNUj4UD0ZJoOm1DRH18GT03fAZTPvmtYLqQdVgk8HD5ZNW1yKgHRNVDXR0pTW2Q0zkQWQm5Twmscp47npw8Hy1nXYvUzNPs2Ar+9usp4VyoZ8eoOdIHtX2ifT1864k5PoTE1EXQJsxy0xvDfcKgxkTR3Gek6H4xxgYi72cpGbJF3P9MqsPCoXJ4F/Kb7gd7T5OqFU8U/GIw1wwBGSY5lmc1REkWmUXnRCeIEFSeK88Arf48Ge4+TZyoIddrNtTuGUguXC8+1z4JUirrzolJnRmsWTIBbU0JNF/4/trvNSH48Kx9kMxwglnUDlNN40fjZ+D+4iKXTFUUCVYx3EKj1DwN2yrB4MYnIhLsM1oxbCXxXGk6NpanoHwCZZk0OH7+wNGtvmDFJuqzcEgRfnx3GV14uDgP9xcWYrQBAj3MpVIUBq00NItf858rTcev80uFFg4lRItjMrECLpwq8lsfB8Sg9v00egIxHCrVcRujSfzN0FU101sgmMPEqvCjRBX8prAMJRocs6az3o6+2Vfj22Nr3WOGybiV84mQkp6CEQhkZUNN+Pp8vKZCzJpNCEFy+lKoE+cEkp0U94oxYsSIESPG7wliC4cYLn7wgx/ghz/8YWSaUqn+H07/ayESDdwDNYjXUL/1fNkkyhKCCI6xJ10XR/63XjZPiGsbx3qAAnZcijoEAGc/u8g9khtjgmkTt/Pea01oLQG1pFZzCIgb9Do8Dfc51ELCR9YTwhtKVMdLEK2hRhv9baHucIvqjWiS4HgdczBiDp2Q+BBhacK5VRJaSfjSq0kQRfMISbMCWi5UA5YSqC3doJRi8NHbuTZM+ZPv1mzq2KuPB13hyHbwaGFzBH3TemYis3Atxl55DEpLN5pO22CPriAtAQGthOzEY8c7jLCXZa5NzWddZwexraLlrOsw+NiPxOVXIckqLNMIELzsjuTKYMiP9zp2S9vt5NsvZ1qQmrEcmUVrISezrujBYvzVx0PL0rrDBQcpmREShayrFPdY2A90asH0BesEAEllCJqI20HO1BYcQM1AvBGvgGD7LZ9LJVJti6ivyenLIOlJjG97BnrPDHRc9Uko6SZUBo9UBS0fJNkTi6PACMxu/J/qvG5eez2GHvuxOJuiQmnuQGr2KuSrAYsTk+ahuG+rML2k6qHBul03VkBg7JyxCLjCqq4NhABEFcQSUHW0nnsTDv/wr4Ll1bHsDT5yO5pOvwJqtsXOV81T2LMZVmEsIifTRkbMEpHzRNUjCSW/WyzWLRe7dhf3vlZXe9z4MPCKTU5dhLEtjwrTy83dUJq7wvdYC2LRuCILUEeAZoLc6iuhtgXJbwdq5xRUIuI7sDvUj7UtRffRZ2H6LDLM8aHqeJEAuRiGyuFdqBwOWlxlVl+DpqXnQUrlfK8W9pf0grXQembBKhVw4KnfQN8bHcybxcLcCMojhdoJARA9ha73/AOe2rwfT/+I39GtyhJoWVwO0ZLouv7T2PDoRsBnbPSl4cuxRHsT57XsR6ZU2xUWi++OnY2jlidOlU/gJ2OYhQOLdp+Fg0Wlusj/lG+X/GuViXitMhEAMGYlcHU6GC9BhDCXSlE4M7EjcOyO8dNRhoq8IGh0iaqRa9WXWoLvAWXprRccHAuH4TpdKgWtAjzBAQD6rCb8Jr8El6Q2CfNblOCrIxfjb1t+Ijxvl2e3SXQ9iKIhP3kFzGde8dpUFRwIIV68oyoUyRNVAmVpCdAS+z55nIqDWQGQZNZ3Yr8/8LXFgkOMGDFixDilEAsOMVwMDAxg506xqfkfDFjNgD3EuYZBlahnEgh/IfC7JEOtC7iAw0wD3FO1GGjiEcyhrnDCjxHm/5qkTCNkNVtV1E5/riE+wjzgjqeOtgnrAjPMxAuOG9auyLrYINvuf/ZfPxGKkDgTgfqiaqvjjPD6nwBcQpIEr0NIWu474zrJ+c65YYL9o09O5zh3QebYEBcLwE9WElUH8RNe7vB714FoScAf7LRKOoXYEPjKs1O1XfhetJx7MyRVs0lLEalcrTc5bTE3VmnWIqIqkokISKWlO+DyI7t4HYpvvoLiwR3IzF+D9NzVsEp5WGXbasAPrWsa8rtexuiWR1B88xXunE0GVr+E+PjWOqdwfXHcmxHqCGzVAnykeGbBWWg5+3o4E1gSCA6hkGSobb2QkpkgmSvJaDvvZuHOdNGPca1rKkoHPWInt/oqO22IJQPREvDfWaJ7tR4LB2qakJNNMMeCwgY7VwefvAvFPVtQOvA6lybKwiF3xhVI9M5Gx6UftgkSQgDLRMva64WCg3+8Sgd3YvQVAbEcYanXtPwiFHa+xI2ng9SslQCAjis+juK+raBGBf2/+c9g+U6xqh7qxktxxBwicH1WvR+sEm/hQHSH7CLiWAKqjuTURdB6ZnCuutyxrfH8Gt34ADKL1gHZVrCLciM+tYnGCg7BfO5aEpZfqoqPzPOQC2jvPO4Dgb9DIEmc8A8Aqdmr0HbR+1DcuxUWpShsfwagFGrnFLRe8UmMb3wgun3+Y6pebRoBSDhZllm0Dm0X3AKianbAdlH5agJtF9yCwz/4fODcfqMFCrFc8hMApFQOPbf8PfZ/7VYurcHGDqlh4cAJ3wKMPf1TqMks0ssuYDLxadTWHlDLwtiSG/BfrzQjTUp4b1Ys6nCgCBXlAqiOvSwF57EiS0ivuBTD9wUtytTeOdB1DTOm9wYEh2NWFg8VF2LN5InI7Li7vnZU4beOKIcQtvXAIY17O9I40Gc/u4d9gkOLxD/TDch1bQFJSqJra8dCyEl5wTkxrCphbh2nSyYHBmTcevkcfP9XmwPnioLd+rVQIr8Dl0qOhUMNwaFgqfjC8NXIkCI+3czPJ78rq/uKSzCzJ4nZw88EypEIDRUA/G0SiU5EkqDK/H1imFUBhNikPjtzetsSWH/6tMD9AdgiMiudWIJnvYPE9GUoijYDwLbaJMyaTkED7vpid0oxYsSIEeNUQyw4xHDR2tqKmTNnRqYplUrYty/aP/DvFfw75QO/8/wHWBVCZMUQQuyHkc5+MjWCKOK+s9v0uWM+Al9Iujv95PtOKPW0GLZd9catYMcvIp0Xj8CXTtjOaovCCPFG2ucvGwAIta0pnEOBaxQhBnF1Ee5T0HuAYN4QIkrInBNcP+LkZ9p8vBYN/vrYz84kYC1eGtr1ZZdn+XZkDj15Fzqu+Jj7PbDDWXXcnUT3SdKSCOwb88Vv8LfFf82cn4WSqgWD/zr9Z4qT081oOecGDD97N5RsG5rPvi5wXzevvhrUKKO4byvyO16AnM6h/cL3MZYXdhvkdA49N37eJqKqdbecfR2oZaLtvJthlYs4+ot/RvHNV0EUFS1r34HK4CHktz8b7B1DzIYT8Eww7pDhIYQE3OkYI/yuYpGFQxiIrIBIEtrOew/6f/tNgFpov/iDkFM5yJkWqC1dtlslH1x3L8w8cEh7B5Jm7yIM66+X3ne/cMfsGA61QC2Dc1sV6CPs29gYPBwQGwCHRCdCNzuuNYdPSNbaJyK36nIMP/dLX8cYF06EoHzsAEZfvE/QsGqMDkGbZT2F7hs+i/LhN3DoB59zj6ttvcituhTUKNtueXpnV2N1jAhKqVYT4VLJHlu7rf7r7OySt0QWDm6a4JjTchHG6EAgILMt9NW3Dgp36DcQX4MoGsZeexLm2BBMwdgQNRG41lrPDORWXQ61bQKskDXfb5nWfPb1KO7eHEmU2/mctcubF4RIyC49H5mF58AwLZirrwbND0PtmQUqKdGkl8DCQVL1mo92oupoOfs6V7DQOqdx5+VcJ1ouuhWJtglQmlqhT5zLxe/IXfFJ3L0lgQdf9GJ1vPfS2bhw1SQ3uhYLdre/VEOcab/uL4FKAaVjByG19mLorr8LlmeZkAjjntB5XLjrkL2OmJRgp9GNDKnPagEAsqvfBmOkD4VXH4tMJydtl06yIBi9qkjQJy+HNmE2ygd960z1/po1YyJG55xhC0wAfpVf6ibRFp6LX27ah8tT4TEg/PBbNJyYSyW7T2uX9WLLjn68snsABZ/LIf+OeZMSoZXAc+WZWKV5m6P8Fg4AkCAVobVAFIzjsHAQwQJBoWSiSDWMWgkuMHbeVBDi2UeICpUwVAiu5PqyS1DaeC/gC3h/vHDI/TEabU2RlCoYp4lAnAlAPG59PWsw19gOa3wwcK5WrAznvCG6HkSCovAD+fKOY/jyj7fg/75rRWAN7mlNoHvNTBwVCA6EsYjUJi2AORxuCdRy/i3Yc9su6JXg2j983zeQvP7TXrkEAcEhDhgdI0aMGDFONcRPvhgubrzxRtx4442RaXbs2IHLLrvsd9Sitwo+cQB1UhXub06eIIrM63Dm7hcCntWn4b/ghW2uv5kBsi2MM65FtIe2zyNwCfWTek4K4v7v9d/XZ9HO/VButIbg4eSNtBwRFhxeXth3ri74BARGUIl0jcXmDUsvyFMHKf87geh6MqD+YKzbnkbHlR93+xgIsMsSyBEWHX4CGvATlFFjI5rwDFlHBTYr1dNNp21AbuUloEbF3u1PqTcnCdC0/EKAEGSXnm/vmqsUIamJ0F3gADD01M8wuvEBUKMCalTQfPZ1aFp+ETqv/lNU+t6EnG6G2tKNsVfFxC/b7+zi9Rh++heB/nG7loX3m90HtZV3g1JxdxHbBLaUEAsORFGDu9irdabnnoHU7JWglNoxBEzDIzUFRG/75R8VlM+TTlbVvVVYTAKWQBDfb/YfpR6XSqbJudHhwLrDCSESHPdOojJEpLrbPpG7Kb9oEebnn/Dz2ZmjzswmhEBK8LuLvTgL3nhJWnQsDEnRg67NnHOq7gY+tko8OTvwwLcx8MC3g3n0VPXRKLZwMMeHsO9fPxA4HrAoCLNWQlVw8KnC9cYAIIoKIkno/9W/RaTRAtep9bx3Q++YBKKnYFYqPimIGW/ms9o6Ab3v+zLyu14CFB0D94pjpYjjRfACm9baA6m1GyatPmYiSC+RCCenw2M+ADYJp09ZBCXX4bqekxNpNK25DiNP3Qmi6GhefxP0CbOh6PZ1za19B/p+8regRhlK+yQkJi9C5eXtXLmH+gvYe2QUvR1NyK64FKMv/No917T6Gq+XNVwqEVVFonsq5AlzURnpFyeyasfMIASwLPva1Uu+E0VF9oyrQI0K9MkLMPLI90LddzWd8y4QAihycM1SZAlyIo22t38Gh756M3fOFvTsPC0X/xEKPafha3fvxC6jy03T3pwWksRR8PexUcHhS2NX47QFvXh80yGX0NYUGYpit7VQY7e/BQkGZPwyvwyXp16GQSXcPr4aE2SevL4+/Qx2Gx04ZLa4xyrHYY1xPC6VxCA4PFAABcFDxfm4MvWSe6ZWDAc/ylTBkdGgqFBOdSBx4YdhvPowpNZelDcHLSMbgRPfwIKEHZWumsHHRVYHpmDMSySJtnf+Db77/XtxcYEX0CVQ7DHaMVUR35Ntsv1soULBgQjvk31Hxuy3FZ8lFrEsvPjGCCYK6tEmzkPlyBsAgPK+V4VtAWwXjHK6CSRkrTDHh5zavL+W351gbOEQI0aMGDFOLcSCQ4xTDDwJ75AbHmnqEDU0SB5zxTCkpZD8rfd7BPHuENj1kOFssdQvOrDVkIAOEdXK4wLrtiWsjScdPInskcBhbageZ61CnGKIJ6R4+YNWFIE6/O6laghJtusnryzqL++t0hPY+V6X1uW1x7EGoSKhzM/jEwJJ1WGWQlwaEAJq8ES8SEgIii2o7tjnISXSwuTisursO0TzmDD/xMR9oL7AGuF9pkYZ5vgw890mfokkQe+Z4SZ3++evQdXc8pRsK9ov/wj6f/l1Po2QUCVg1FAACPhdLx/ZA6uch6TZ5HRoUGwtBWoM8wclJ1A2tetn40hU7zMC2IQpe84yXbK9dPgNHPr+Z4MVVueNX4hwqxYR5YLLpeTaoLZPgpxpgZJpgdo2AeX+/VxcitGND0BngglzRTLEbZgLHFKd00pTG+RMC8yxQSaP6rXNn08UH0Did/L7LYi8fnUIj7t1keCcoCZ7few1sBYRT1RVGB9EnzSX69P41icjy2le+w7QShFyurlavVhwCAVLOBOCjks+jL5ffV24+1di0jqWTfULDjpqLRySqgdEgL6ffxVa11QAQOa0DUhOmmeXw1qywSmauMuI1j4RSrYVpf794W3iYkIwIopwA0D10ob0V+2YjPTCtUBpDCPP/co9rjS1i5+FVWQWrwdlLCMc65qmVZcju3g95EQSlqRyAnRiwiz0vv8fURk4BLlrBixJQUKT0ZTSMJK3RYt7nt2He57dh7u+uAHZ0zagtPdVlI/uQWLaEs/FHakdw4HIKiiA8U33o/i6OP4CtXzzP/hAA0BhVgWHSr0/nwhxi0vMXIHmhWswvmsjxl55HGrHJJhjQyjt24rEzNOgTVoAAFBErvmU6lwVWD+wFkREkrBbnoJdBm8FlEtr0AJ2gdEwfVvx6+6zk1/SMIYUFxxaVSWost2HQSuDp4qzUKAqTtd3ISPx7wPOzvYHiovwXGkGLBCM0SSuSQWvoV8MOR7RwHBdKp34y9cDL9iWOqpvzEUBkKNQpgr2D1nQpi1FefdG9/g37t2L3dI0fPTq92DejPYTFhzY8bp9/ExclnwJMqG4t7AEf5S9H01VK42f5VcAAKjAtZpzvXRVwp++YzEgKehpViGl0jiUmIH8uIYU4/5KJhZ+OHYmPjHjdcgSsPvgMOaqhwLlrlnUDRzgjxHYIpwfqlp1wzTCWymMPPcL/HrHTHzQl16bthTJeWsgN7Vj5PHbAaMMpbUXudMvw7F7vsGlbTn/PSBEFlpm2qAgEnFj+I08/TOMb/e5k4rjN8SIESNGjFMMseAQ49SD+9u8jh8VxPeFVIMDhyUS/Sb3u3EhDNEgrNMhsMW7IMPbKhAsQrMKyE8Choj2k7y+Nrl1scKLV1aA7Gjk9xu3298ZM/DETFhWv4BTV33shzrjSHCZGbdDUUKQk19k4VG7cYFjdgDsk6BKCFy6BNoWNV/DyoTj+ihoSg8Ag0/cieGnfspnU3Vv/rEWH776JT0oOMiJtPj6i/5S3gaIJ+yq85qZz968FvXV/903DwLaBt8eP7FKzTLf3mr6MMFB8rmIycw7E4RI6Lv7a0wiOdAmEeRMS+DYkZ9+BT3v+IydTVZsNzo+iw1JT8LK84JD6E4+39wnssKRfdQ0XEK0MhgkHwDPwiHShVSU2Fi9/pKewsRb/8m2LrFMSIqG/vu+GciidUyGmR+BMcTv+pSSGTjCcLiFg+62hZriWAbud0eEpFQcWFLmSd2BB8UB1l23VE5/BZ/9dfvb5tQSBaIloU+YxcWDkNPNaFl7fWQ+FokpC5BbeYltPSAroFVriEYEB3+g8fTcVdC6/xFjmx7CMEOc2+UGRZ5KBKHP5Q2zdPGl8V87c2wAhapf8OSslQgb17DHgui+dCFJ1SnoPReCRDm8NRUIuFTKLLsQLWdeBSXXiUq5HIitISVScFwNSYks32Ytgai1RUqkIWkaLCNIditN7ZDTzTAsABbwgSvn4/1XLsLbP3MPl06WCUimGT03fwGF0VHoza0gpgnq3Me1hDFZRf715zD66A9C05QP7YJllCHp1ZgrVaGAACCSfU9SCrQ22XOAgqBMZWikBolPJHdQnDFMTFmExNQloJIMUMColDjhSBbs3NYU28pMNHO8ODEAQJAvBQlRSSJQG7Rw8IOC4NnSDJiUoAIFZapgoboPPcqwMH2pbGDbXv6cpsqueDJgZfDj/GoAwCGzBe/K8MIku4t+hHoWWQcM3gpn0EzhmOXNy1ZpDGfowRg1UXi90o3HS/MAAL1dWUBsuFUTx0z+Oe23Kmk0hsMYTeDIQB5mE9+gAtWQLxn4wQO78MWZYoF5wExjU3kK1ifrCUDvzaxjVhbfGT/H/f5PI5fiDH0H+s0sXihPt/thyShSBQmmf45g8/bzZmHhjFZULBm6ZICCQFEkSD6XWSpMHLGa8evkFXhi82FckXwxIDj86PPnQVEkDN6zBnlGuJZU3bWUYaFV51b27Hdi9PEfeun1NAomv+5RWUPL5Z+EJElQu6dDn3sm6NBhKJ1TUHmdFwr0WacjOX0ZAEAJ/gi0R5BdByUJlYEDMJl3GLVnJnJL1gczxogRI0aMGH/AiAWHGKcWwlzAcNYNzg9z1oKhHjc3otPVvAQhdR5H26MTBQhVvnENksai+gLErZ+c9VUZGHOGkGXz1yWwhBP+3k9xAm/ntqActl+ccFKrLucoH3xWFIyWRJwTXvdaU8EvUjU6d7iqQgJdc9fbCX3HuAwKK4u9dr526RNnM255ALmp3a3fKgV/0bOEXujYUbElBOfuR3SvUVoVacBOG0ZvqiGusMuASGQQ7YqsdZ0Eu3OpUQmIDQAi3BkJSFDfrvPoHdzMKiFor1Ucq65f1XboaZh+wUFgceIF8o2uMzV7pd1eInOxEgiImHQHQCv2bsswV0cS61LJz+K6aw4RLodKU9ClTNsFt4BSCyMv3AMplYWkJaFk26C1T/TEF9EYyyo39gG3Uwq/M59Fet6ZUNsnumKInGqC1j5R/CzxwXUbxIrJAN/fgOBgCNshgto+EV3X/l8QRUX7FR/D0OM/ASwLzWddCznT6lmY1FGWHOLWqh5y300rGHutfSIyi9cFBQdZBX8z14+wWB5cGkWLdpsR4o6DMP/7zyiZFuTOuBLDz/wieFZ0n/nG3S9CBMQx04CcanLvf7+bLNaySU7zcV6aV1/N1BHoUPWjrz2+NxMnLyGA6XPDp8gERJJATLuvUiIFiUigML1+iuJyVJGYvgxES0BOigVbB4VdL6Fy7ACUCTPCExGCaT1ZzOzNYueBUZSpUofgwPbVWUhJ4LR9hewU4qDRcjUtQdPaGzDy2O3uufTKK3Cwfxx/dduLMC0LYwV+jp0+zyakf55fgXsKS3Br9uFQNza18MPxNdz3+wsLcXpyDwaNBK5OPe+6wQEAGRYOHeOtsDRFwrplEzA4WsLwWBmHjtnilsi9UpiVwubKZFxpvYC0VEaZyvjW2DrufIc0gouSWxrqV5pUreYIMLknB+yskYFBnupIVfM/VhUtHKjEb+HQ2M/ur4xcAgtlbNs7gimKJ3AVq/EvDvTn8fzWo5gtyPv01PdjdFsw7pMISU1GoSyey0NWGvcWlnLHKlBw5/jpnEjkuGWaO6XFPea8c2mKxIkTAHBL9hF8deQSJHV7TJ4szcZ5Sc+lUXrROm9d9FurSTI0NTiW2aQKSD6LJdjPiIIpw6KAc3sRs2y73pPtsSSqDqVzii18+2PnKKp9j0oSZGKJ3x26pjmdtpuo8u+pqYXr0XTahmDGGDFixIgR4w8YseAQ49QGazrvJ3JEJDpDC1D+EP+FhJPnYv6NuOQt52/baYOQNGP7AIE7JgE76pYnItkFwkEI+RpKWiNILkSi1k5/EckeZX1xXDw8EX8WXFcSeh7MpCC+IsXEJpe+mo4fV0F/G7U0iIAweHdNgpz5IIxNQblvzWdeg7FXHncJ8NZzb3LrEZHFkpoIzh+BVYnQpZIb0LjeSVC9TlHj6W5xDY6Pa2ESEBp8JK8vX6AKH1k28vw9SE5fhsTEOUw5UfETfLErCMHIS7x7BTfYdNSu/xDISX5Hs5RIwazu1naP6Xw8ADsjExA5ovzOyz8GgIIaFTsWBKXuNaEh18ZxQRW2C95xY8TXKyKZ2XvPRnr+Ggw+fodLcLRd8iE7iawgt+Jiu1+WZdfhkCBE7H7IL4wFBAd3jAI9gNLUDiXTAigqaKXMkNi153e0ixn7vpf0JNov+RCK+7dDUjVIiSyKB3dAd0iTar9E6L3lS+5nNdeBzss/avvulyR7bHykaut5N4daY1SOHYA50u/FD3HImkYsHFzSnW+vkm0PJmauU0PPKlStjGqJPQKXSiyoaVZfO3zrhCMqi9ZiQgLxcNxTjJutwHQnBIR5F3GL9rn/oj4RpOWcdyC79FxY4yOwykXoPTPcsRIKpCEjyeoczkciaiuAfKGC3UdGMZLniULDjLAEdTZWCOZ7xzV/gvSsFTBMCmqUQUPWT65IVogJ2WhCCMHn3rMcj286jMRLKaDoia9KxxQYfW8G0jt/vVeyiPcIiF3FODu3ASC54ByUjuyGcXgXErPPgDphNshgEcPjweDJPW0pXLd+OohEsPa0KXjwxQPYY3Qct+DgRwkatiWW4OhgAVfiRe6cJBg/RZawcHorFk1vg0QoxgsV/J9/uBdJErSwChMcClTD3wxfhfnqQewzWnHEaubOG41EZa4iXXUZlNQVTOttAW1AcPhlaRX2lbIwIeMgE0cCAHZWukBBoMKARsyAdQYA5EkKKSp2PZkgZeRpAl8fvQgAIMNEkpS5YNr/dtcr+BdfsWOWDqpncfqqecCrPrc+AuQyGgoDnjjUJQ1hunoUMixIoDhsNuN1o4fLIweCfEvIJFX8211bMDBShKpI0FQJn7tlJVQleC1729P41OVLcHTIvoeOWVncW1iMcxOvYkRqRvcKL15gQECQZExoS6E9l0D/sBeU+5xl1eeIb00rm8DhgQKKzSpSzFyjlSKgabZFE7NK+d0mEVkFIUDx4A6hG0G7TUq1nOq65H/+V4qibDFixIgRI8YfNGLBIcYpCOFP4zqyMeRZvTvNmR21JCx9nbvV3d3kdaSE42ynHrLa+WEdsXvbFUEYQYSpCg7RyR0PtMnXHqedhPiIRT/hDBAaLnAI++NenrBrXXuHf/CYILtLShBXs/KShQgZgXPB85HCgv+aHo+1Q00iVtS0EAImTHwiBEq2DT03fg7jW5+G3jsbmTmne6cFO/Ntl0qiJvBzX7SjXk5k6rqN/SVzfSKoTvKwgojvL3Oc28kqSBUirgVJc4r89mc9waEKKWSHrr2bna2bwBjuC2m/rz0Corv9kg+h/zf/6R5pWnkJl0ckLohcXPkJeG894sfKLIzaAgKlkLQEiJ70rq9I7FR1tJz9dvuzwNKl9bx32zEE6oWP4FWyrei58fMY2/IItK5pyCw4G2HEI62ufcSJU8EgPf8s5M64wr2XKaXovOqTACjMwhhAzWqcC7uMyLYJz0Wc4iwn2I/MPCESzPEhjG1+2D3WVCm6goO7sgkCgjcEQpBdfiFAJJSPvsnVBwDlw28gv3szcq0TvMYSUpc1gVuFwlvGOK7miKxwwYabVl5q+8APE4FrQO+eXjONpOmRFkVDj/4QTcvO9/eA/yayUAghuer3Ce49b/0WDuOvPAZj6Ahyp18Obdpp0Np6obVOsHf5EglEkt013i+Qil1xifsifh7bONA/jr++7UXhOeJb31D95xJ7IrFPTVbz2pc6zCUdV091LF3ikUiu5kyZZ3JSV3HBqkkozf4MCKVQVBlKOgeDEhz+2i2+hjACF/Ng8N7SvHdJQgggSSEWDsR9LZC0JHIXfQgJRULFIrAsC5Igz1/etAwLpjVDrsZ9uPqc6di6ZxCj46JYSceP5oyGo4MF/LqwFN3yMEwq4dLURrwz/SQKVINCTPz76AUACCqGxV3PZELFl9YOorQlGOdFFJjYQZ4mXPc+fhxPwGjbwoEiocnIphMYEaS5c3wlrk0/H2ynYWCfKRA3AWyuTMHmyhT3uwqeyK5AwR3tH8J1R77OxTdwkCQV5Kl3vUzIGKPB5+2wlURO8gSDZ0szkC9WcNqZczDAxEEeslLYa7RhsbaPy394gLdEma4exTvSnlDxVHFWQHAwqYRhKwkFJmRioQwFS2a2YtveYRTLJopViwlJ9uJ2sEgnNUyf34XHNh12j91TWIp7CkuxdGYblrV2o2JURQ2/4CopkGUJn3vfSjzwwn4YFQtnLurCrIlVSy2fYFCpzqUi1ZCCt2ZZ5TzktOeC0JmZqRnLQZp7QIujkEBhJlsBAoxteijQDzcvJ2YjYKUXCw4xYsSIEeNURCw4xDjF4f7yA8/M+AkJASHsHq0hYIjI9ygi2/1LPPLT94PbJV+p5/LGDeTLxVbwE/0hdfsbG9htH4FQDiGK/RUR7gKrDS5FkAjmxIpGyGa/VUioMOHLQ5jPoSQhvF3vNbh8UTXCdgYKOw6RQViMnxpn3A6BJygjy3Lvh2B6vXs69M6pAZczIpcpnNUDQajQJHSplIzaweq/EAT+YCwefcXmERQDRBLB7PgFxpedQ84ZgTsQIqtVgt47JimaHfyUIfi07ulQ23oC+cNISPceYtUbRlAEgPTCs1Hcvw3Fva8hNfM0JKct4RoiIu5ExL/nDilirEAw9srjGHjwO+6x7Gkb0Hb+u+0vgqC/vbd+BVpLNyi1bFcrvjHJLDpHKIA0gsSkudC7p9ltl6Sqy6foe8FPeipNtssl9zwhSM06DUTRYJXyNonrDz4pWC8YWtUpCFFj2nbhe6vEuDPX2PS8tUnQ4kINyKR6zywU93k+wDuv+VPuGcJbMwTXaLvJEnIrLgatlAKCA1B14ePPVieRnjv9crGFTRXN625EesYyUFAkJs8Xzql6kQgJHs7CKuY5ktkPm8QXX79wcp6gZd07kTvr7ej7+VdQ3LPZOyPL1V21VWLdvzOfVMuqWgASBOcqAJT2b4e1cK2oUfx3QewPwqUNrnFOf4TP6ur6I9rV78/vvAv5X8/kbFsgPb8GEFuQrgEiy+5aKHpD8Z7tdu2SqmHsqTshyRIkVQdJ5ZBZcj7GNj3g5lO7pnrPBPddrlpGdTc0qQobTp9SieD1UZSqJQvxbWWQCGABikBwSGiyfb56qrstja987EwMbiyi/NjLkWNx5/jKyPMscmlb8Hu5PA2fzf0UbfIYAGC66gnf6/SteKQ03w26zYIUhoXljgqI9XpQibBweKw4B+3SKOZrB7njKrGgwUBCUyA3tUObfhrKb3gCWJEqeKo0GxmphA3JzVxemdR6yfNg+WaWBAvFkhmwFnCQJEERQoQBM8MJDpsrk9GUr0DNdsACca1NmqU8pNZOYKxGO327mxZre3FvYQmGmVgaz5dn4Pky74LswzPbsOUN3gJSVSRhvAVn3RYJbOmkt87Y65dfcLDXi47mJN61YS6McgWqIsGyLAAkYBHhxLr69thaWCAoUg1f+NCZUHKdbh3OmkstC+OvPGq7dzNKIERG4vSV7jobCt8zK2DhGAsOMWLEiBHjFEQsOMSIAYTzN/4f2/5fuqLzQIQlgY+05kgbhrANcAYhhG6UBYNDboaR6X4y2yVgGdIjTHQIEGPE9zHitTxAovOfOdcDwipEVIDIfVQdpHzY9Ys8FiSv7SPMmBL+bJjzqeB8YooWkvgCRvIEwVuYsKRmcD5wxGWwIN+89/WPGRtCiVA0sIlrwbi4JdpjKbJwcF0O1brubDsDc1U0viT6XIjlQvCeEDZG6BaId3ni1SEl0jDHh9wznVd9Ampzp+duxVlipGjyLrQ1hEBSNLRfdKvAFVJ1PqoCsUcgHoX55vfD786F80kvWAjUHB8gk6gaJzhQowQSQkC765kjqAqvV71rl+94SEwEbg2NWG+Ea7vIBUsE5GwbmqrWBDVdr5Hg7nQvoDLTLs13bcN22pMa8z2iD0NP3IHExNnVoJzec6Ae8KJJMA+RZSQmzwW1bIEKphVInpq9CvnXn6tZl16H4ECpBa1jEoo7X6iZlm2ES6i7R/lnCZEkEFULWg65RJnoOeUR224MG4pQQSQ0UDeBK1IGgty74y+qn21KsD/sHBURkmEgrtmB/T0xYSbkTCvn6k3SHPd8FISEuH3zl1sj+DSq72/O0mTlR1F49VGvDx2ToXfxu+5dd0/ctQ25HarXsbUpgXRSwTgTi8FxSWPrxQQSvGtOAMgCwUZXJa5cAFAUCclcK8Jo7CEriR+Pr8ZrlYkhKXhMaE+hLec9E5SQmBaZqssiXWXcn1GK0s4XhPdKkSp4pDi/rjb4YURYOJx76flQ23ux/4d/6wojbBuTugKlYwoy627GACM4lKgKEzLuKSwNCA4S6hcxLZ+bKEIpXtszCKVFPG7LpqZBRzI40BetEPhFFhUmjg0XQRQVQ8ihFUPuuUzpiLCMOZObsWRGKx7ZeAjWON/OjFTCDPUIXipPi2yHZVGMjPPPFU2RMaE9jZ9uWolrGAsRqanTnrthggNzk5hjg76K2PcGZw31Vp3KIT5oOCmPA0jjTbMDS7U9kEBB+nahlD+ExNQlABc/Chh++mdc+ckz3lb9HP5cddcP593d955qlUv+LDFixIgRI8YfPI6PlYgR4/cV7I/6AOEuOi4sJLzssO++ujwygd1dXIso5f+G7rXhuIf6f8R7+fk8xPmFTZjPgUbVKI/b2eccroMY5qpp4LrU2IEeSO+vt8oI8MEtfZ9D63AHy1dmWJ4o0to7Vq9ritoggesYWZ5oxzKY60n4sWESBctg6xS4TIkKFMveM6Id7LaFQ8Q4ho29/5x/igvSB6i10Lpqj6vI/7h3jM/vty6wSnlRa9Cy7p3ckdb17wqdV4KDkWuhSFzw96H1vJvRedUnBGULmhvwlcwEWa4jXknQbUEIlRZ5v4rS1jm3nUM+spKaRs1niV1L1HWJyk+QW30Vd6T5jCsiymKKc3ZyGvzYS+x1dHaT+8bXcoJkR61n/nrZsyHxJfI7X+LX5jrXOMsou+u1W7273gpbEGhfy9rroTTbO10Tk8Ukp5TMQmnpAgC0XfT+0PbIqSyaTr9SuOvebUHIehHWY0ophp+9G8NP/RSFXS/5Gsa78QjUJfgQRqwTRa3uiA+OvyNiGgOHuOPj254Jv1ZEqvtZVcvCwS2S+UsYotLy7R52hFEvZkTtdrhxUpgp4heCuEYI/MoHBFTWzYp/TH3vNewbkmHwa5+iSPBfYbY4RSAy61rQYoMQQEo1BdI6eLw4ty6xoa1Jx9zJzfjQVQvQ1eI9jxWIiXMZFpK6jOVzO7l7Nf/yb4TpE8RoiMhnEWXhkM2mkOvqxT+MXhk4t0LbDV2rWpKU+flUFAS1dtBnho+nHxQErJGHRCgIrFAriWtWtWL1ou6a5Ro+91MKsXCgbwz/cscWHDRy3DmpwrtPctCWS6JUsXB0sBCwxBDVsULbhTX6dpyjb8VfrR7Ax66eh4QWHHtdkbB2STc0me/jsVH7XlEE8R0ySaW6lNvtKB/ZzZ2vDBwKvBcDsN8PCUH5wHb++LDntuntqWfw7szjKD74DQze/c+gRonN7lpPeKDepoGoVxJqeWs5CQaNzm/8LYafF8/3GDFixIgR4w8VsYVDjFMTHAEaQrBFChDOXusQYoTN65omRzCZHL/k/UCkXDv52t2/zjmhVUA9pGuwHW65US/X1R1qNcdAUAkbSJH9G96oOnf1O+SA61YqqszjQdh4Ut+1rtVGURlsLQ3ErDjuvvib5Y+lwZ20/9axa5pLH5VUtFOes3ogcK1ufFPAv3NM65padf1TZ/2EhBsysGn8a4Azt+olr31sH+tf3j0jEl58cRkc+HfpWsVxQRMIUtOXIT13Nca3P4vEpLnILD6HaUYEgVbrGMTurPx94Hads0IZfO6mCFDYze8WZfukdUwSN4Ipzx8LpNy/H2pzVwQJSsTzOHLKEL7t/nw0SOIKr42/HWGfKUVx3zYcvv2vuSwdV34C6Tmr4Lhoya28DPmdL6HStxf6pHlIzzvT15FguSwC/vcD844ESJP+X/87+n/970hOX4Kuaz8VbL+/m+Djr7SedxOO/fZbgXRKBEEfhXrjS4hbaB/V2nsx8QNfhVUYA5EVvPnV97oppEQG3e/8HMzRAXeNzC48B5XBwygdeB3lI7vdNkipJqSmL4dlmuGWIFz13jWJ1nwJBh/9MYTPQZ8bDyKFWAgySExdjAl/9G/o/8VXUT7o7QQmsn8tCjZKTvFB5G3LCSceQfS6G/qcJ0RIOnJJJEksHhD74UDLPIm6/z8/BiLLSC8+F83nvLOuxySRZO8diKtC8GwkJBCYFpIcEFAhK8HHjfC9jFknJYKKyZPtbNBdwjwPSfWAaJd4QpNBiAT/vJFSuUBaB1HWASz+/MbF6O3IAkTCvCkt2Ll/GE++cgRKiGsgmVj4i3cth67KeHl7P774bduq4UPZPOaFcPkfumQq7ny6D0cHxQR5GCJjOMgKKKX4+z8+F/jOD7hTB8xWJHUFIOBIaMC2cHBw+/hq3JB+GgBwyAgGU66FISsNAgoTEiwQqCEiDQCMPvI9JJb8ZWR5rU06DCto4QDY1gVjWgdgvVmzXapC7P5DHLDbH4z7qtSLyFYtV7AdmHnOJXjtQHAXvywTZFMapnQmwQbHyFeXblkglqWT7FokuHmpBQnElaRqCYoSU0fAfZXAagySzK/h7ufwxbWw/Vnggnc7hQhdTeZ3vogcGxcrRowYMWLE+ANHLDjEOGUR7uomLINDNuLEXOm7uxkd9x71pXcJ2LBkAZI6ksEIfqbufyFF+Dsu+AEeJUCgBqkd1k5fnAVRQE2KEBKxdgWoJWYExpXUzsOV74sVwJYTjFEgGlf3P6+vbxH8fsMD801I1oaNB0vSA95Obju9cKe8qgfIFzHxzgsORFIgh1lHVHfBOfMu4OLGF88gGFtFwEC5pxhyr1Fxz26M2MJBENcBCFo4GH43A848kRV0XPExdFgftn/0+gMuNgL//Sa6bloSRFYhJTOQEmlIyYxLigfL4csr7tvKfTeG+9y5ovfMQGLyAhT3vgqAoP3iW9kCAQBKrh3GoL3rWuucAqsw6uPcw0hOHId3svCylOYu7lBh90ZQ06heS2/N50oJtM1rlG29wsMqF7g8SrYFE9/3DzBGBiBnW3myM0zQYOoaffl+7og5Puy2053WAtIEAEqHd3tN5uqJEHsJQdPyi0CNCgYe+h53Ss62uuU592tmwdkYe/VxN01m4TkYe+VRLh816vNxbhfN+NL33edEVmxBz9d0apShtnRBbfF2GRNVRdt5N4OWi6CShOGnfw5jpN8OSi0rgGkGfIgHG+NfV3zfvUkCQgiIogjFFTfQsYjUZp4dbPlE1SBrekD0DVt32EHJLt+Aked+6QpqHZd/VNAvwVrpzinnHP8SJSId+TJFbbLLoEY5uG5TC9SwMPbSfWg+50YAQGb5Boy9dG94PSzxSAgI4Z/7hDru2KpV+PzKE0kJxGWxrYaq19B9jjPPQ6Y8+yuBZVG0ZnWUDROGYcGwKBRJCtnPYF9nWRYIDqozN4i7JwKUQPKLRgyMOg3fK0a1wGr7+4Zt4jnMwmFGdxrzp7UAAPYe8dwDRdW2Zm4zfvyE2P1PFKJEE0lSQAhBe3MS+fNuxfgD/w2JAIfNHLZWJuDMKuGudE1Hx4f/C7f9fDNeeu0AV8YzpVk4ZmbQKo1jY3lKw+9kfzX8NvdzpzSMBdr+8MSECK0GWLTnkjAG+DSOa6tUQoGVao6M2fBgwbbqUmTJjR/ij+EABIN4m/5+WwYswXu47T7NQtK3vDz/+gB++4ON2LB6SiBPOskHW5czLZxbpcQkxhKt2gyJENCQ30jsu62C4H1LnDTObSnJ3BpO3N82wuJtVNcDdyOV4F0pMXFuRAExYsSIESPGHx5iwSHGKQWHbOR+4vkJNR/BHB7HoPaPDM5POPvjv/pbOdAOZ/c05ev3sToeEUyZwtwymB/0/h3ZNUl5P9khOh5B0EYSA40S5Q0ygvUQvG5SOy31iRlOEG6XlGKbQER1+PrGpQkjXaKtCGoGahbkaQisMHQ8qOb3AhDXIZp5md0/IsFBdCyQnQZjOFjO7la/YCKq2/1KmDPsPUbqGNdaZK4/uaju6vUOCRodJCCJ6/bFQX7bM8guXFvnPBC0QWCtERAKfH1Iz12N4Wd+4R6WM81oPvNqtJx5DahZAbUsoRVE3WDjiRCC7us/jfwbGyGnm6F3BYmJpqXnobhnC/Te2VCau1A6tAvpuWcIg1sL9TziJ0J9n+uc28kpC7jvVmEMR+74e/Tc8P8ichFhmwB47l3YY+xudme9kFXI6RyIJIGKuL6AiBjRIeL7QniiJrzcsPLEafTe2YFjcqYl0JimVZciOXslaCkPahpITJ6P1OyVOPrTf3RTlQ7trDacCOcuYf73f+QOMiTRhHf/rSt+OqSR7b4nOH6SoqHl7Lfbrrwkxmd+iOCQXXaB8HjNayMpPR18AwAAgH5JREFUAIKCg+RYFxFnPQ4vwq3JWX7CYnhEPHvlZAq97/0SRrc9C61zGtIzlsAsFwXLbdWeyxk3X3l+yxc1xMJh/fLe8D5UK7XK0cFYnWqazno7SLoZpDAMtbUnYGlDArEtgmsmgf2ORUAw9tSd3OnygW3QJ/PrQOT7EfsMql46QgBJIvjWZ85HpVREsWwhnVDcRzZ1YmmA2GNbnWaiGA6aKrviBhuvRgpxawaEk/U3bpiL+VOaAUqR0BV0N/NlHBsuYp3+WqhrIIcAt0UPL40GQ5geAGipAMNsfCNJCSr2Gm2YrBzjTyQykJjYQsmZq/A3Pz2ANnkUr5UnwoJkW4UAAJEARYOhpnHMCgo0Oxq0agjDan0Hzk2+Fno+u+4mJCrh1wsAXt83hFtb9nHH5qkH8FJ5GixKcdH5KzD68we486kz3oZdB4bx3PYB7DXaAdiilWPh4I81AQQtHBKEv3bUqICKJKTqHEz6bi8DEjbuOIbL1kwNZMkkVU67bLv4Q/a6b1aQmLoI+sS5MA0TCFj2EOEyKkkEZy7uwVObDwascETPWr+QXT60E6lZS6G2idcjpnY4a53oPSg5bVFk/hgxYsSIEeMPDbHgEOMUB0PqUUaIEO7kBvOrEAzZ7zsPBIk8lghgBQjPv0iwPU7QXj8JJPxKENhJ7xZZH9EYuuMyNOSxF4D1eMj+Gol87RUcD88MgNZVT7jFAB+/QVhHqLhSq3WkdjqGTKlVx3HFcwibG8K0AsKI/coJZc5J//gE6xHt/pKbGLcq3D1Ynd9OumwbWi94LyRFA9GTUNLN/tIj+oO6SDk7qeC+jBBYasYHccpgv4YKDsG06TlnYPSl37rf8ztegDE2xLs5YXbpBdsiuA71zEcGamsPOq78BGBWQCkFkRWbCK9aUfBV+66h277w+vyCHJEVpGYsq5K4ViBvau5qpOc/j/HXnkTpwOvQe2Z6P/QF9QhFP9F6z96D7JgGt5K77dQ6p6B89E33TD1Bhn3FeBAE9iWSjNB1KbB+I3Kc621IfseL4lTET9KEzH3//UZINd4KD6WpPXBM75oKrXOKbbkh2e5hXMuKKswRH6nItUM0FhHjQwBCJGhdUwHYBFqoACfKz0Bk4aA0d6H5jCvdynhrq9Ci7PLKQbcyRE9BD+yY9a/VYdeDBCwmiKIxrxThzx+lqQPZ5Rt4AQwIjhXxX/xwiGI4rJzXiXdfPMdXB9sSe/yUVBY97/kSzHIBR3/0hVCxh8gqMisuhS4DhX3bAuclxhrJhmcVZ5P9fF+swmigDL9wprQwxHTY65uvb/6ZaouJFp/IB1WRMG1CE3YftH3WzJ2cgyRxg8VBmzAb5YOvB8oxIOOac6Zh96ExvPx6n3u8py2NedNaUSxWIMsSZGqibNrtMSwLA6MlLMrsC5TnQOZ2lXuN0Ui44GCO9sO0+DE/d/kEtGR1EEJw5yNvhOb9n9F1+OuWu7hjqTU3QGnudONsSBLBXrMde01v7VFVO4YDsTflC11VnUz4SXwXsoLEtGVIzDgNid013PMB0HzBuqcrRwHYj7XExNkotE+G0b8XAJBcugHp1dfi0BM78NCmnW4elbVwECxI/hgOCcKvH4VN94N2XxjaxulqP1iHSxmpCJVIUJTgGExotzcMOFYHyWlL0Pner4CO9SM5aR4kApgRl0ZpnwSj35uPyWlL8MeLluGs1BuAb9rbbsfY78HyrFE7IH1QlPTQfL7nis+2Sgu6y9R7ZoY3OkaMGDFixPgDRCw4xDj14P9VhxASik3LpWF+RIeROxFEhEfwOcRlnT9oAhYQwR/z3E42yvcrrI+14wUwBGGItYRDgrkChKDtxxfoOGSso9LVTBtRRqiGICZlA4KFUNyJrjJAgAlcFp28gNGNojEyOsioMP3xnZI0XnBQmruQmrKoms1xVSSmV+VEGk3LznfdV7C+t72qq0RwA+S/1+bj6XPo5PHKFfBvoh+ldgyHYGl6z4zAMWP4qCc4BPoTMmfr4gDFYyNpOtKzV9oCray4a4dZGLXdIREJRE9BTmU9USmE+K81t9jg7H4XeITYO6QJIei47KNITJ4PqzCO7NJz4Z+34jkgED5qXXYS+OA0BiAE7Zd/DAdv+5RNeBI7lkbkDBQNcXU99pO5AAL++ustr14Eu0fQesG7cfTOLwvaUr8LHL9w549FAgBytiVwTFz0SVr/Gi6HhHwOJjOHjoJWeF/mPTf9DfTuabarDipyceas8w20yzKF4xGwnAw8s6v7cEMtHLy+cPlDGPK6xH0J4nWV2K2RBILDn924DHqVAOba49TtuMmTFagdk6BQCn3yApT28HFh3OcsgZtHEsTO8ftyD7aVeGYIgPAezZ1xFcqH34AxcBCpZRugdU0BoRYA1qVS8DnviDX+OeDeORL7rsG+D3jr5B9ftxg/emAHLIvipgtnuAncxw7zPtKy7kYc+eHnAu3/2NuXQp05E9/4xavc8XzJFwybuZSDIyVQisggz0ai2SZhCe9WM0pwKLz2BAxzMXfsnRfOxqG+cRiWhTsfCc2KYZrGM6WZOEP3CPWxsTz+7B8eQcUwUTYsdDYHd6ArPtdU0lstOPhI/CeKs3FvYQn+67OXQJZVWJQioQXjIvhxLDcPbcOee8IXytMBALpmB25vuepPMb7lEcipLBILzwMBAmKOLElI6facFgoOEcG4AQCEIJ0QWWPYZZX28+4TJ8iDUCEFxnhGbxMmdWXhf1mSm9ohZ5ttt3XUrC5Nzr3oBFW38+TOuwUDd/0dqFGB2tqD9MK1oIqE+d0yhoM6W7X54deaVJ93QstDPYXEzBVITrPnqkTse13vnAwpmYFVsP1ZpRetD8R6ihEjRowYMf7QET/5YpxacN5H3d9tAkIQ4H3r+0lh+L77RAkAfF6X+BcQXIIyuDqcH5+hRKKITPCnDJI/tQMSh714E/4j5T8HyC3BD8x6EcwnYGuDmY6jvhDSMXQIgmSB8LiQzEaQcK5ew4ZcKQUaezxZa+QN66ew6jrbwYphvmC/foIutNLADn4gcB8Lq/bdM6wQQkLyES+vvWyI3O/U02wBKVgVq8SCg+ZPbNcuSei69lM4cteXAWpB654OfcKsqn/hQKO9epz7gmuHe9M2Dlf8tNdKABjf+jSO/fabbpLs8gvRdsEtgWbVV36DzZEkZBedY99bPiKct1pj2h9VXtSu9ggkuqeh552fRX7XRiSnLUJiIr87m3vuiIQtlkQVWb4IhDUhwoQ+lsysef/bf5LTliC77IJAvAfiEtGCPoS1qQo51QQpmXV3iKutEyC5c56Zt+yC6RLNYqEjGEg4pEMIewaG94Mwx0gdYuTYa48HjqntvZC0RJXkFz0nQ9obcZ0oE5tFPGcF9cAjn0UWDqHPUJ94HjkKgXVGcM5XviZwqRRF9jqkvV+4FJKGgnUv4NaLSAEhQCzmwLu/BOSh2tqD7vf/Myr5MciJNP/KJLgXCainRdcQjNlrB8m7L5xrP6Ejg7+4eTnGiyY0YsIM8P9eXAq9dza63/XXOPz9z/JJZNufvbPT3UGhWHHb4N0P9uc9h0erTRGvmiYlGJp0Np+pinsLi3FT5klhvsyKS9G06wj+5B1zcecju7F97xAIAf7fN58XpvejSFWMWxoMyDCojHZVw+Co955RNoICieoSynYjI2OLnAT4SfwSVTFKk9A1BYZlz7NkjRgOAPCNN2fhU+17IBsFDJopPFq0LZ/OXzkJAIGUzCK14jJIkgwiS3h800F8//5dXBmKKnkulajApZLgGIutewaw6uwcOluSbqDvS86YFJGDQFOkgHWTaVrMmki91zOCkDB23lrpCHb6xLnoeu8/wex7E6kZSyEpKgwqft8CELXM2pBku3zfPZ9Zcj5y698FS0mAUJ8oJyvouPYvMPzsL6Gkm5Bbe0ONSmLEiBEjRow/PMSCQ4xTGDV+0PtJ4Cq5RkOIWBGxH1ptGPlV68dy5Fuxj82OKivQN4dAZI7XEDJqoo5djxGZQ8qqt8wTqdtfn2BMOaLNlyayPHjXPzSt/Z9daojFyAmjxniyxGe1fn4aiPrMjE2tPgIgPgsHyyiHXzYf2eUeC01ex5gRx6WJvx4/wem7dvXUWZPLZQgyUaBWWQktJDltMXpv+RIqg4eRmrEsQJL9/wMScCFjB2IkjRmLAFCyVbdaJOy+qnXvRKGOezW0UAG5DgTmod4zA3r3DMAhrmpen5AyhRYOIYSPiPgXpfG1Q25q41wSJSYvDOQhRELbBbcEBIfa1hbOsyQ45kSS0XruTTj222+ByApazn1XeDHsPUoQiGPi9i1wqCoSSqQq7ERYytUhkkSv2fyaR0TXKeC2I7j2NLzU+++5OrNRo4wD3/5zN9i6m59bi8SlSZIEaolkV9E1YD4IHhf2xg4biizh+nNn4scP2TvSbzh/Zkix4naFLhe+NO6mENM3duzOZV9hjnsdfzvCgmwTQkLmQK0rFHzvcaaN2DKNeY4w84m4dVE3YK+/fQA8130OZBWFlhnQCMGu/cPcqc07+3HFWVO5PjifklVxQtS7p4sz8Wx5Ji5ONwu7v7k8GXuM7Ziq9AOyCklPwsqPIDH/bPzFXX04OljAF7/7MgDb+kBXa5PvDn6WX4mf5VcCAFK6jH+esxrAE+75o4PB2B8LprdW447YeKssHHIZDapMYBb5eeLEupAlAjsuN0FCq/1T/YjVjL/quxQfWteEh/boSFADb1szDT1tKRiVoBXJkYF84JgdNNqeE4GA0Kht4XBsuASJEHz2fStx39NvIp3ScfWaSWADprMYtRJQVSngtsq1vHCfayT0BneeD/7SCQC1pRtatsWO+VV1oyXc4OF/lRdNVOd+9j33qGUE3/2YZmpdU9F88R9BlQBoQcu+GDFixIgR4w8dseAQ4xSDa9TOH+FeEuskpaoEg+hFmnMhwZIR/197/x0vR3Hn/7/vnpOPpKOAckACCQQChAhCBBkRZJlkLBAZEwwGDBYOP+w1YBuz7JpkMPgLeAHDhYU1CAMSwSYYMAZEMEkSeYkCIfIKCRRPmvvHnO7pUN3TPfmc83o+HqA509VV1d3VPTP16a7K1WnhmzQ653jdpnHHTQ8F2NmE9d85N8Pm6IyJDGK48s9xp15c7h/ZUU8uhM/FkLQsw53i9l4JC0K4UmXn6/YFKYz7vasjLOSuQFfJxZVvB7VpPf97vuOenecjmF2q1jucQeYJh2CHS1Sns69bKLr+pnqFrZuzX8jb0ROnvLCMa5r6qXbgcLV/+Uk2fcSknrIs1Q0aobpBI2TV1gaWecqJtUsM1yXDtSh03a79mG7zdt5EDR3gXHMlDd73FH1x/zXOsgG7zQkvqhjBN0OT8k+qml1gvraHPUvm/GulvW+FpfXe8uxNYRq6wR2Iir0vvMfXedIlndbgfU/WZ3deonR7mxo3nqTGsVs5HTPZtcOGG3R1lLk/42JeW/puuav6TtpVnR0dXWPn59iGLg0jN1P9sE3U+ul7kqSBM3x3jYYF8wM5mpYlbV9hn+mmzndT+1KwLSZp4+lO85MIrkB1prvONbSiZUk1dWpf+Yn8rJr6/M6xsM84T5KwYRUtpSxLHUrryG9upl0mj1TrhvUaN3KAPPsx5v6LfQz9M6zbY7M7QSPL89HsngzWLqG2/xC1fuidC8LeFfbY84Fqub+7hZwvlv9a57kBQGFfQfy5eBL5971lBa/Rn7Y26c75b+rfvz9N++w8Vm8uyw5NNXXSsGAdu15MGjdQk8YNVGpFsFLz1u4qSWqos4e88Va9VXX6w1f76Ob/b4qsphY1NDWqrb1TVl2jvtH8lm75+1tO2hlTRiqVslRbYyWeTLqhvkb1hrkC/IYNcs0dENKWzjh8G11628uJync7+cBJ2nPqWN1y/yv68gVfwMEwLFVjQ7wgy6p0s/ptOlk/33MjdabTqqlrkDrbPDcl2E+mjR/VP7D+mnVtrjkckj/hYA+LNrh/k47bd6LaVas6tWXmWkhLg2Z9Xyv+fp2T/v5126qlf01gwnP/UE+27FN1IcfHc751Xfn8SUxPOHg+H/0ZdW1b1/CQgWHUOoPHyw5YWa7rYqxTFgCAHoiAA+D0zVnuP1xDJxi6JsI6VpwOZxl/qxifKjDWyZ1RVN0jfly7AwHu8t3ffMNWj9XpkPnyH5yTwrW87Nw/RmKuEdbpZzw+wY5md7Aj1nwYSe/ajsqtwA7YnOubOqIlZecQMaSxkwa2yfd3Ta08t452dijd0S7LNLa2L99gH5O7LhE/Qp00aV+A0Z13SCAiogPCXNGwzkh/spRqGvvK3c1rD23irkdoUM3VsWU56UL2k2udnHM5GAKN/mukLZ3u1JePzQtf372CL9++2+6pzg1rtf6jt9Rv271UN2Cocl+gZFju62i1j5dlSpspOzg8Tsi1IFBsyHmRs5rBfRdY375E5DOHg6mDNqJuzZtM0ajvX6q2Lz9V08aTnLYj93Hu0rjxVlr/QXZc9z6bTw3ptA+rlqkD1tAJHdbR6rq2Dz/8LK1+/WnV9B2gPhN3Ntzpb6pXdF39Txw4509YoDU8p9C+8ewrf5DW3jZfECeGzDw2IdfMsA7tVEqphmZ1rvdORmvV1mU/y7quN1bIORF62MIXRG9Il7EjWrRuba3nCQFvuwyy76BOK636kRO0/r3FzrJUYx/Z31Xc+92e/8fJI8e5FdiVlqX+0w/X2lezw2e5J43N1jYlWWnnmhusd1fmEcfe+VxKpYzfSbL9muZ974kHuU8v3zbXqFMdXR350yYN045bDNGiN7/Q5M0Ga7fJI/0b5uRdm7L0m+/tqI9umi996Z/EPcO+c96ypHEjWjzLOpVSbcsQdabTmfHvu+r9nW9soq033UjvLV+lln5N2mmLjdTa2qnampTaO8wTg/vt2fiqLKU1alB/dbzRqhp1qMNwp/53dh+vulrLmcPA3kz/nA5DBzUlesrCZNSQPrIsS/V1maGe3GqdyZ+z1+84TzjY6uvsoHS665Jrf3Z7bbvZkMC6QwY0O8M3dRoeTewwBCHc/IGDTMHZfBq33ksr335fH73xqp5tHa8vOlu0UW1KjfU1Gje8b2b4KsvSsIGNToDPPj+cc8eyfNfSQIG+08d7/Uk1NIXW3zkXJfXZchetef3pzPsNzWrYeCtJ0rp3F3vWWfPq4xo460SlUimpw7f97muOlccTbAAA9AAEHND7hP0wjkpr/2F5Oyr9Qy6Zyojs1Ap0dqdd//pXs6LvOs7eWufUzptX9rX7DmP777Q/eVj5/temdAUKjMftG96ntAxlxynW/j2UDg4jE9Zp4+kJcHdKBjrFq5WrTXk6sC1Xc7aC29H1o3H0D/6fJEtWTY1SdY3eISr8bcwT7Mh2CFoK62DMVXMr3nENPV8TFRZ88siVX6Dzy/3kQnTvZeHiBp2M9cgcI8swrn66NfvEQ+gTBMp0evXf+UC1tLVmgk2GOwbtXDJFRnRQxxTZXsLyiwggRT/llqsyhvNDUsrwlEsq6qkRT4e56UJuvrjXDRiqmj4DuuaHCN/2gXscoU9u/U+l2zaopk9/9Z+6f7b+/u0psVR9k1qmzJRSqUCgIDZLyerqD+Ab1s0G+aI75oztJUddavsPVfuqzwLvt6/8RPVNm0au6ysoc87WN0nGgEPoisascpUTkUDBobbsb0Dh1xxPYMMT98686LftTK1+4X4nmDJ4/x96uhwzd0mnJSuVmcchnVZne1tmvgVDDe287SdJMkN0ZV7XDhimQbPP0LpXH1fTmC3UtPUM35r+nKL2helUMjxB6wQRUoFrh7sNWSlL6nSFqLs6PC3XVzj/U1Q16tTby79SR0enGhtq9W9HT1Fnp1TXUC/7+69lnzdp73bV1NaovsaSf/CeccP7al1rp1r6NDipt950I206skXvfvSVJOlHh2zj3g2OVCqlTUe2aNzwvrJStXp96Rc69//3vH/nqb4updY28+fGfk2LVW91SF9Lax6TUjpK/lDFntuN0JGzJspSpywrpU7X53GdrxN9j+1GmzvWJTXV12jLcQP14ptfGJfb7Dv4a2uswDBFNerUsIHeTvHGhhpZltRYX6N1G6IDLXWGuVAkOcff3seplKUfzZmk/3fna06SKZsPUU1tjepqU+owzeHgq+sH7Rtp49psgOmzPpsrGxxwtZUury5dqXOfHyFphPNefW1KQwY06fIf7aIO1Wh9a4caan3fuSIuNHYZact7Sc/+67oJw5Kaxm0jq6FZ6Q2ZIaWaNp9m5+TJd+AeR6ujrVWdq1dq4O6HOXMMpRr7Busg/5O25qeWqv7rPAAAJUDAAb2Lu9M++2bXPyGd6VEd/K7Vvfn5H3EISxta0WwecTsETcMrxSrU/QXZ9YvUVIb/daAcb++5+ymAgsRaP2zIhgL5jr/5TnN3l0aMtuNOG1XnkvTdxWhTkZEn+bYrIm1UNpal+kEj1Nm2oesO5bA6hXR0++uR6NgbjpMnT/NQaYHyI/MPWc+wbrqj1ZssbIgZY7ZW8GU5b6WzzOd3Z9v6rsuJr36xfnWXqP7Or/4EP/1zdHhkk5nS5GjTEQEM4x3XVo0nVUhFDK+9aaOfxPJ39khKSw3DNtGoEy7Who/fVdPYrVTT3BJYx5uvqwcoYti10PcM1Ym8FuTKwH1+51zF3p44ZZj3cbFttM9J+mzBpZ5AniTP9c+SAh3B2apZXcHBzAFN1TcGOl6t2jpZpn7bONcTU3DAeEd+cL1Mp6C5ky56v/q6+lKWUo3NGnXCxVrzxtOqHzFBjWO2VGdbW2DNmqZ+Gnro2aqpb1B7e5vqfHMKWZ68u74XOd9n5Hx0N226nfpsMll1zX3Vun6db+4G33dA3z6yshsZemn0BBEU+vCst7aBJum75cFOkPL+BEx1HQPnyR4nKG93VruvDamuY5a9RhgmutAFp0xVQ3MftbV1ODW0LEvnnbKLXnztEw0Z2KTNRreoo6PDabvO0Dm+HfLs68GAmyR9b78t9Kd7X1enYSieGt8QRVZNrfwNPzPck51ATptMpyylfE84dHSmQ+d12HOH0dph88H632Wr1NrWoTbDpNSSZI8GlZmnwduxv2PDe+qbekTS7k51amtSuu0/95OVkk45/xGtcE187Vdfn4rx9S2TZrdthqk9ndIr767QbpNHaJORLVq3boNOnr2N7rn7n4H1/E9j3Ll2qk7p+w81p1r1cutofd13rFNndyu1utp+g2Hy6/q6Gm+DtXzrRYVAM1/Vul53tdccn+1Wbb367zdXa566XanmFvWfcZSrrtn1a1s20pA5Z0qta1Xb0KgN6zP7vP+uB+nrxQ87+Q3Y42jPtprraeVMAwBAT0XAAb2b88PG32nZ9QNT2T9z36IS1gHkKiuyTN/rwN/ZH385v1S7O1nSvu0wBAY867jLjuossOynN9zb48qvnJ2eRZLrbtlE25QgrVNurn1ebqbAnKScgRInvbvDxorYvrBy4tYz+yJ4jEI6KcOGRPMNm2EuLk4d/edueFCm35SZal+9MjOPRWeHauy76GIH7MI6PQ2drcZ0/rUyHcfuf7ML4x2fzg3rclTXdK1LcG0Mrbm/Yy/iehnsZ4hThPnhAee16XPCv/9N+foqkwp+PbNqI55CMFY0Rpn+jwJXW/G387qBw1XbMrjraQj5ttPuZvLvf3fmobXMUU/zG+FzHLleWsGhkkIz9rQ3f5aGTlDjpcUyb64pSNq1E91jk4dpHreNxpzyB31wxSmR6fyVs0I6R03zxFhOJ7Krzv62bb/sGv4kU1KMp0yszFwNgSo6j6m526tpQL7MPrI854khL2U6C1u2/5asuobs506gOl35paxsXjk+fy2nvvZ2d9XM1A7spa56ubcqeuhF3/a5rpeWU09Xnew/PedwOrgXPe3XMj7hIEk1hvljvNkHr3GZXWeMVmXXdDWnhvpa7bzNCKWsTKCjo6Pr6m04Be33w+Zv3mb8IF182i6675llevSFZUqnpVk7jdGQvjWqWeytaE1treR7ojBqiCTThMY1pgnBJbW1d2rSuIG68dez1NbaqqPOfciYrrMzc/d/TSqldsPTUI1W1wTHlu/JGqW1vi36CYd65wmH7PnvPYVTnte7Txmp3bcbrXrXUwV7Tx2jycNmaP2t93ry9g+ptLR9qP5j1UHqa63XZ50t2rchei6eRkPAoa42lQ1mue/5Mp5TEZ8Xlr+N+c5LV/4N47ZVn7FbZ56Q8wzdZv5q4s6/bsAwbfSdn2j1i39X7dBx6rvdLAVXsf9Kuy4tcT+3AQDoWQg4oBdy/WjO+UPT2KPg6hALSePvdHF3zofVquvHRfjkxb46SK5OftddjmnDt2ZPel+97V/E9g9p492ori/QntdS4Iu/sdO3EK7tKwdjB4X/vahjng4Oo+XpCJAhf1OeXdtd5B8qztBHRcjXPs7+sa1zrGRuY+5OJ+eNGEEB582Y7PL9xyQ0ebZTLe99Fri+ePPpP3V/KZ3OzGFRU6t0Z0fMsiLSlOL3rRX6R0C61RxwyDm3R2CFqG20r6lp8zU82zuRbfdRRSYOcERwt6+QOQCy42sHy7JME5xaOcYOdwWo/JMJh84BorhNxQpeGxOddv7yrcB+yFW8L0Pz6/g1SpDOdO6GrG9ZSjUFh90Iz93VrZpjO2r6DgypX7xy3J3cqeZ+nuUt0w6MlY8nT1NHftzzOfJmhrwWdSUIduA6HzGplDfu6/48NkcNXEntcyr8c9n0MW8cFslTheznnymYEna9zF7aLHtlT+H2RNyGDcmW63uKqm9qg/pZ6wJ38Ac6bkO+k5om5P1qTauGNPfJBkokT7sxfxXyH5foC/fc3y/UsIFNuuLne+m4/Sepra1NA/s16OsXH9YqX9r6upTW+R4QaKjPDFlkpe2nbVJOff37orMzHZjXwbb9FkOyuyfiXP58ZeazsSYVHFJJkkZtPCp0/Q2tuQIONc4+cwJDzn6PCLy7gnmWZall8GC5n6XqSJs/P9amG7Q2nXk6yAkoOKeIt/WannCocwdILENr952DnlPPMqQN3Azga8uu63nU9dYOcqWtVCCPPlvuqj7jt1ebVaeaGjlPUPkvPc48FE4kAwCA3id6oFmgV4n6Rmj4Zmv8gervkAnrnPGv7/0G7dzxmKszP+ybt79OUX8H6pckrxj5FkXuvIt+B1Gx8gvJJ7S+VXAnVPaO25h1MbWxrvXzmV/BnJf//ZCyk2Xue+26Q96YbZ77w5NDsvqa78jOHhvz9SBJAXHXMZQTsW6nPfRLgmaUX71KL/fwWnnWNaJ3xTI94eDqIAy/3gU7K41p4+xfy84l4rpv5+8OHEbWz5BBvqyYeSVsS8bjHdkEvNueamj2LO+z1fQYZYZm7v3TN9xa3YBhhnoEvzf43/fPWVA/dGxk7XJdZwITIucRSMzvMzy7Tspy390dv/15v5oZ9qF/21yd/P6PCs+Utv48Qj5Tor42mi675jqZLieW6wmX4PTBpmHbOl3p7Lu/s0PCmD7ns3/Wj9nKs/iutTvojKueMWyH4WkXuxzLW0vvPgys4li1JjMsYXNDrVr6NGQSG9LXGwK5Da5Jmf1tcKOWRm0xdqC2HDdQW28ySCMG9zEOqbTr1sMyEzHHaMIrv87UNZWy1JYO1mfEtG/alfG8396eduZ/CFNbm4r8bPAHAfyBBmfddFqNW+ymhs131tupTfVy25hAbmOGegOrDfU1maCer5M+lcrka3qSpL6uRqYzKHDYnToatsmVJs5Z7w0AGPI1tk1XHezviq7rQNS6AAD0ZjzhgN4r6q4T351B7vettHuYkZAvmmHBBGPnf+4vqNk7yROMq2/84hv1xdi/zP474XjnPUBeneT2j+W0JeOTIFHruUrOOWZHBRmfaPAmyJWDIiZ18JTh5Bdn4lHLsG7UOpbMz+zHEfNutfiT2fqvCfY/IePsm/ZJzuCV/9+wdKba5drezMKmTbbVuveWOO/2mbSbPMfbuabE2CulvI74r5PFyjP0/XyuJZIMw5r4h0CJrpLd/gzlGw9D0nrmPpfNeYbUJ7Ic+2WsE89zbnuGq3Oq7LtehH1Wxjh/THkEgkUdvruSsz1fiYfQG7zfqfrivquVbm/VgN2PUKrBnmDWVQ/7ScmQa4VlWUrVe4Mi9hNJRTn1XB1zabujLud1PPtv9qkyV4UCX53Cty92NY3vpLs6LoPfj1JWSh1p3/cDY9rQarveNA/FlF3f8qR135Wdtr+fRm6R+9rr+3ra1WFuCjj45xTw1imbc3ZiYNfAe6ngEE2ZSY6D30uz9cmsbz+B5u4Qd8qz6xuxw2pNEzkbnnapqwu+1+B/r+uybcnS9G1HaOZOY7V2Q7tSnZ2qa2rU20s/D+brGpIo06Ft3o/1dSntu0smuLfD5oP1P/dtpPXpWmcYpaatZqhh5GbeunT5ak343A02UyAnwNA2/a+t2jq1zDpJVqpWd135pJatzk4wv/Gwvtp317F68Y0vtOyz1c77tV1PK4RdLd2BHZtxkmunIz/sKepM7t5PIFep/gieP2t3HpayY3XF2HfeCbF9RbsLcX3HsOxzuIf8PgIAIAkCDuhdAp0DVvDHcK6/5euEi/sl0t0hYdnDe+Qexzd6MlLDEEz2D/wc+Ub/ba6DPeRT5rV5vWI/aWAaHqTk/B1Cvo7DwN2clhU1Yo7hDcNPPVMwyjcvRiUVdFwjgypybaL/HAkZ5ixJXZLW21C9vBkDjyHpDNta2NMh4etacY5H2rwozKC9j9VHN/9a6Q1rVTtgqPpN3jO6boZOgcB5FKx4dCVMwaCoYECuwIM/+JXHtT76b0N+doex4QkH/xAoofmHVyz3uonOl4T7w5S3f18YA47e66/pehFb0s9t/4qedhCSh+84pTvbw9N2vR24azpwXDL/9Jm4k5on7KDO9WtUO2CIOs3z0marYnd8+s7lbKCiq46hc65EfzdIkjw7X0L4eZfzqIR05Nr5huZovMZaXYvCOiejNsb0MW9lcw18Z8jWMbQcQ3nuIf2cwJk7ACHzVitlyZ4vORufcJVvWcagZru888TYAQBPrcM+F3z5pZS5I/+TFWs1uKXBfGzsyoUen2ygJaptpFKG5Yb92WB4wqGlqS5bnIkvelRjGFLpscWfqH/fJg0f1Khv7To+tJ7n/2Bn9e2TGXpqQN8G/ccPZ0ifb6z2Nx9RR/NgDf/mUZ7iMrsm8x184ZKPQvMNVtn1pIr7u5SVXR51HbNSKaU7peff+EzLPlvjWfbTwydr4xEDdM2CVz3vtxqGe7JS2RsvjJNG19Z4g2iWr3F52qKcoZrSMW4aMC4xXcbdiy3/nD+RuXk/2gLDtaWdYwAAQG9FwAG9W9wOJXtZ4o5vK7iuoRPS/Uizf5m3GoHeNOf9bADD1GHj+ju0qhGdQmF1cq9TwqBAZSZcc37dJNg2Uz3z6RQr7f7sKiTn8vj7PaQjMWwb3D8iPR0pEfnbi4tyd7qvrXf9oPfeMeu7HljBH6N5F2061zynvuXqbfC9r4i/cxVd8Hnku7a4/q4furFGnfg7tX2+TE0bT1KqsY/SHXZHq2sC0xg/8kPLNC2NDFKE9iCZMvL+G5rc8i1KUIZrnbS7489UZKpGQ77z40w+He1Sba1qWwaH5umsF9amDHVwreT913ltnwPmMevDcvZVKBiwt8+1OBXNVZqvHQbKLlRI56q5zXZ9Htd4v1qn/U84FFihmqa+Thmxzmk7iJWNtsjyDfvUGTLnSlgZ3vYTVduwz8SIu5ft70xJjl/odyzX9yvfNSjb3xq/HHvy3kDZuarm/BGWJuUs9Mxx4O7yTKXCNjGTMrSHNXgfuLfM7PIOpbo6uA0fSZYlyxfwcc+DZvnmmKmxMhGP8657Rv/vjBmGetmXF+/xcZ6MsV+bNsvHmVfBcu0/ww4xPeEwsKXBU6vMR3RmXdOeMz5NIemehe9p7PC+oQGHERs1a+zwFjsOpNq6lMYO7ydr+GTVTt5abe2dStXWydTh/tmKtbr5/jeM+Xr42nR2V5gvZKabZ9y+WLlefmHb395pB4fM98qYhrOqrbWy11LX76DQZ+c8E3ZbvtPeda5EXpNcL4zXtsz/XE0zIrOY146K/IYBAKDymMMBvUyCTo7Q4IMCX+pjrR+SNnLisoh1QsfljgqahBfkS2OFlxG6Tk9SwDa5fjQl7eANpC/Vvi3ZMTPU33Wu5NofiTvE3edhzHWjh1FJWnDCtLnKtJz/5UiXY3tzdCLEqk9EHmGd7LV9B6pp3NZK2ePDx9jHceYiqOh5EHadjV1Enm1FUp8tdlbfrXZTny13Vt8tdw10ZBe1fP/xjmyHrvZn+LdsAeK453zS67mpIyrBdgWGvupsT1a+pyrR566p4znnupJS9d4nHDo32EMqxdnGGOdEub4WhH1mxjltrcAL2V/wgrvVyqbs+p9xV0WeNlYgUeSd+yHXecu93PWG/651y7+SIb8B3zpZnVat2tMpzVu7m04/ZBtfXd3V8XboOoEc++mcmuCQSpI9zE82iOE+vQJ1DGFJ2nHSsNDlNf6JrlPmfWfq9HYCDob2kAoEmA1PI7kM6NugsBhojbGjPvjd273M3scDWxpVbwiWBHOLak/KdNhb7lK60ocEFU3bWmfYh1LmOJvzyszZYcprt8kjvSW627J9Lhp2qOVry87LlP+IeROEPTHuveSb96C9Z1OpzNwo/iOW63OmXJdEAACqCQEH9FK+H5ieL/uhv0p974X8ovV8CQ7/ipmrQzrOD//caazgj0zT3+5/86hHz5U9zsmOR1gniBXouCp6J3w5JOzwLqAgY97miZQT5BpZP//1IP9yXAXmsSz6R68xnzz2e+Qd4k41Cj3W0deYWHnH3odFSGde2bzvk2Tj6mWzFHW8SnPOB65PIZ1MCTJMvDwsUB64C73rr9AyovZd6HYlbYeW55/A+yHav1rh+Xvdey+5eqX851tx2kB4UNH/IrN/2r5Y5kn21Qv3h2Vsfh1VZmC/5f68cHcQm4PCVvCtuEK2wTNPguH7WLzDHv5Zbrl6MYN5W4Hz0fhN05WHsR5WMPDhHk4nu4L7HMuu2zxpukacfp1ajvu9TvjpyfrGtiN8uyveOWxZUuMm26n/rJP059W76ubV07WodVx2GzwvfNvhD7q407s2YdK4gdp+4pBA2ZKUShnmZhi9hefvmpYhxjkDNuoKOFiufWn82Ov6u8ZQlq1/33on6bSthnuWzdlzfNcyc1uLOpvqa1M6aMYE573D956gLcYODFkhXte35WtHwRTm4EpdXY0sS/rmTt6JpPfc0fu3ZVmuidwz+jV7J70fOtAb+AzWIDfzVxjDNcd3PTHN05KM9/wKXw4AQO/FkErotTKP7UZ0ZpjGk7UUMU+m/0etoVPB+CumOLzjtMYsw3AnUtSg7eY78F3bVu65FkrB1UGQaO6IiIBN2jQBeXRmKt5EAiElFDGQYepMCduG7H6N30aNx6JY9c/V4V5I1vJfE6JTe/80XYOSFF7INtnHznAMc3X0Bi6bXdth7Nww5BWx3bE7w/MJbDhDXLs6EEtVXsz9Gmg/znwyIemM1/O49XH9m0fby3++kRjrBYJpER2weZWXZ91Nn4eWpVRjszl9ZFYJrw9J6mUoK9ZnW369+xF/uueDyuRv+cYs8Xaw+86TGPXxTJprf3ZkSs5MbWzs2U4ulYq4YSTPz5TIp+9MHarGRMHvgO69aPm+g1qWVNPQpEGDpPaaeqnTNfyX65pjpdOu9V1JUtljVTdsE9UPH6dn53knNzYPyeX/2zXMnLuunsNp6cxjdtD/94cn9OHn3nkF7CGV3G27tv9QNW2zl9a9/A9ZdY0aNOsEbXgoOPFyU0OtNribWcoKfpRZcsbX8T9N4TawX6NTaf/QQx0dad+xyAwdZbnLzhYWuAwf/s3NteNmA5WqSWn4kH566R1vYNMvE6/JBPPSrs+34Ee06+rtnH9d22AKOHRt10F7TNDSj7/Wx1+s1re/samGDWqWOtqdfWfaSw11Nfpabc7frW3ZSWicobwMXyOylfNKWZY6jfNfhbE8y83t0PV3xMeOXWffG96h3Yr4PRsAgO6IgAN6FTvIELwTPfszIHfHve9LZMwvlIk7sPNiBX6geYs0/Xh3dy5m14tVVuAtvlwXzN3pULL2kvs45ROQiG7jIb/c7A6Q2B1u7s7rYNstmrC74+Lsl9Drg7cHJWcnra9zKFZ5peS+/TPvphnVSWd6O2IMb/9+jhuUSnQMA71e3t47/yDPEYEBbzr3i5BraaHnf1Qd4pxu/s/JXPkZqxvRWErcbiPPL2Onblj6ZPVs3nyqalo2UsdX/ydJ2mjWCYnWD5Ur8CL/9xffuq7j0LLDPlr90qPO3/2m7J2gGv5zMo/jGBlYzCPPGG3Jck2k7C0vew7a+8+zqz119ZZjuTqiw6qTmXuh03y9iKi/6Q5s5yPB7iS27Hqbs83WPXr/BIZLUticENn02Y/E6M+0lJUdojPtqn+24l3fvQNPuaRdr7vySlmattUwrXj6A63dkB2qrCbl/u6bvXu934zvqnnH/dXQ1Ee1fVp0xDdX6Zxrn3bWGzm4OZPed37YVfPMD9AVJIsKONhPOEhWdl6JLu3OJN6GzzT/Z6vzOePNY8ywvupMe/e/N5uQ99V1bCzvMTK1umxeKW8Ar0tdbUqyLA0d2KwLfjhd7evXqaaha1LwlPu4ul52bXB9nXc4pg1t/vltDK0+7ue4lQkXeC+Rvs8w+6X/JiBX0MVKpQLnu/tJD//xCwanLG/QIaS6AAD0BgyphF7PfVdZ6OPjsQIMluc/c75lHN869Admko6WmCX10G/SzrBXSbcvZxvJla7ahXSSuheb/0hWSrn3T9exdgakKKTDOmm5mRe58w5d7Oo0i9NB7Ck+R2dXRDAgQQUTKdmxD9uWHPs+cx1wpc2v8BxVC9ufOc63vOWZZ45O72IJndMmIvhnHrIpccmuf5Jvn5Wq0ajjL9LA3Y/Q0IN/ppYd90tcGc9wP3HSxa1bV8daw4jx6jt5T0lS7cDh6r/T/onyCdYr0E2YawXf0hxtythxaFjHUjBdaKFJrmvR6wTqEVWLkHZtPv1zX2O91y5vB6f7VLFc7xurZayt6RxLeTtSPSEbQw5Wdp+aO4OD6Z187c9ky/5eLR2+92b695OnedZxD3Pkz7Wm30ZKNfWTpMDd8JknErLlufZS5v+uX8n2NjTW1+qHh0zWd7+1WaDuA7vmcLCs4JwNHR12xCGYp6RsZ71rQ7yXIEvuSbv3cg1hlLKkXbYZEaiPJ7tAMCLinDOcbk5ZKUNQwNhOXK+7tq3BH3Bo7QgmdmVgtx3TkHtRZ1qS62LO73mma48Mc1JEtedu+10fAIDC8IQDYBT4pq/s0wKmu/Kq48tk8IkG37IcnQCRw0wZ8gvm1QOGVJLy/HHg+nGSz53JPewHiactxugYDO/EzdXjlrMikctM4ykXX5HOjaLXM05+RSgz33PCE0CJWY+wTupAsvBrZSJWRN0iOsaj87Nf512r3GWEPcWX9/kUo43HCQYk2uaIXjFT0mJcm0NuTPB3wNb0HaD+076tVH2mQ9PzuZpPu0jE15Fo2G7LsjT4Wydq0N7HyKptUCrWhOTxvxuknfRpOU9dxA0GWWGLou+6t8s2rBVZXPzj4P6MV+C1fTe41fV+ZK4Rfb25V/C+a/qY9XRyhnXmyu7EDzsJLXnG+wnklX0ywP2EwA4TB+uF//3CSTVj+9HhW2NZmSJCO8INa1qWOju89aqpMR13p5bOX5MnDNG8f5+lL75u08pVq1VXVyvLfTzlbkOevessr62r0cypY/T112v1Pw++5SlzQL/sfBD+JyE6OtOZ4Y3snO3PjlR2IB93YMizLb79Y1mWZmw3WrtPHq50qsYZvql9w3p5fr90Hee0azs8Q3dZTjIj/1MatTWuJwEs89OH3psVvMvq66OfcHAPq+TfcMt9ONxvGiuRY4aGVCp4XbRMA3vl4ATEfCX48s7/JgUAALo3Ag7ohdxfYC3n37yGO/J1NmXHJ/Y8cJtPJQvm3jbDQjmdeV0db8XpdOVLdeEsRQe3ugN3x0xwWIR87kL2nqPR+cerYn7pk02q68/CynQy5OiwTN4RWeTzzt6vCTpoI4c+yqQIlBF6R39Un3asBVEZ5BgT3tQRWOy25eokcdpErE7z6HSBthm33s7pFDLUh2k4GVPbcHdyOsnCg9hON1s5g62Gu1VzBRCMf4cXkFe1KpO3pVRtfabzzbg0j/L817EConrup4ripHWCHIZjHJVP5pIcci1y/o0Y/inGx1mSfRl69XJ9BmarG97R7vzlOSThvcuWZXW1BW+abOd4PEfO3EyvLf1S6zZ0aHD/Rn1r53Hey6ovb2d70tn95E5hKrnd167szn3Lzsy1nXY7socUqqtLafCAJg3pV6PaulrP4XXqYdlBQvPnxZ/uflUPP/dBoF4D+tU7r1vbveN3BTrXFdKqnJ71kBR5XC8tufOMSNfVUW91DVFVW+MNEEydNNyV1j69439g/+/7X3r+fuXd/9NW44dkj7tlKRDkis3Kfq8wBFjtoKdn+KyU62kdQ0Qj+2SFYu53b2OynGBcHt9RAQDoARhSCXBEdUDk7gzM2aEa8X6xRZUTp56I24HpShFjX7ofDY9XjdIcn2Ic98KGnfAkjp2+9OeV5fkn/1ySdfSae6y6Opc9nTPR53XcYVgKZz5msba7qG063r4JWydWak99c18Xch2jksgrW9+1KNdnXrHrbuoUzr1SsrwTVjnpUGTRVQg5R/INJuaVV5zPsSIc17z2V3jbszzj8RdT/OMbNpxXsKs8Vz7utMnbr6kapuu8U0K+n5G+4E70d8iuFym7Iza7fipladyIfvrd6bvpzGO31yU/3Fl9m+rMxQWuAf52kPKGH1x16gg84ZAyf7c1njfOKydvy5fUc5x9ARJL0pr1bZ4Jj20D+jU66y5cvNyz7OmXPgrWJeSd7LXI1emdPciBtZy1I7bbXp7darl2rfma6R82qLPTG3y27Dwt/13+5jNlv13HefLbdbJ3GKhCT/vA+ZIr0hhWbmA/JvlctyL+AgCgdyHgAIQKdg7b48i6v4Tm6kQud6d+dHmGH2QRaYpXbndS8E8ew1thHXbdc5+VJmgSs6OkFJ2fsZSozMQd91Z2H+QI+hT0GH8xNjfBsTKPAZ2wEgWfY+4OiKLsgByLc3cYe8cwL3L5YevELSvveuUo31eG51//63zyVNy2ZdoXMdaLXb3SXFOCgST/95hEmSUv07OeZV6eTZj5v+EpC9Mq3nQhn7X+lS3D8nzEae+52mnE9T7Wkzam9+ynEtxJut5zH4Xkp43ljL0fWOTU2Vy9IQP6aMpmQ9TsDjb4gjbueRn8uUe+Y1nq6DQ/4eCvh72yf/4CX3zFVR9zyf6zqL09eBf+yMF9PMGVM47e3rP8uP0nKXtNd5eZ+SNl+Y9jcN+EXY68cXH3nAfZdP6JwL2vzcfZNCyUqSL+8rP/eK8DB+6+qSaMalFDfY0O2XO8Rg/t59kOv+BTe+Y/8/kcyvULyAq8Y0ppbh/d9bs9AADFxpBK6HWK1kHSrTvY/T8WirMtPSfo4FWe7Qoek7yG+aoqljIDE5Rg/8W48yx6uRVY7Az/klb4+Z1Pp43lny8gLO/89lO+7TPvTmzLMg8cbvrbv6wMbTrXHYfmlUxpwzq/c5UXmnkwL2Pacp73ceoeYyxq07H1d0ol2g+55bqb15c6ae7ugnKun2vS8Yp/Xwg7PlHnY2Q7j3NOJTzfu64roWdG4DqqkP2a6xrkXTf8qQB3ubm3w+6cLzTuGbx8hXfAu3exsdjQY2A5HfH+tYM32sg1Ak2OgI+vjFzXxmzelnOaBU4396mYsrRi5Tq99t4KTz41EfveP4dH2rOt/rRdxQX2geW5LLd3BJ9uOOWgbTzbu9NWI3TsfltqyZufa7fJwzVx7MBAvdIy7O/gJgTeNT0FZP4o7goahbZf7zFyt4a0rMDcGJ1hc7F4yg1fPnRgsy46bRe1pVOqrzUerPB8I8u0z1NXAMC0P9L+MEGw7uZhrgwFGusRcq3omT+NAADIiYAD0CXJMETddXz9nhoQqCbGTpGE67v+Krg+FZFPO6uqtpmjLjnvcjXkk+P6Emval0rsolIcl4gOXHOnot0RELFOrGL9k9e6OqzzmsLHfK7nE2Az3iFu2BHRQ5nlyDNyRUP5hk63tGcdw4Eyd3uGFxs5BrirLp7Vck3tWeD5G1dk0Kj4PPs/zjbE3M6cwZKKCHaEJ84h5LO0qAFwy5LSpo7T3J3xMQsIL1eubfTcrh6Vnfcc9V/67I5ap8M2Kh7lyivYZZ2tm/9SYncO+3ILFGYpM6SP/6b6xxcv1+2PeCdrtidN9tYiUz/nKeTABNjh2yZJVipQJWe9f736ieetHx2yjbYYN8izQk3K0pw9N9M+O49V38aU1rUF87E320pZkm+YqLBYUc6WFZYg5e2ID5uVxA5UyJL8D450dLgjgv5jZsVuhqlUKltOIHWyM7SQS5f7bAgLQkQVYvrukQ06ZII96U4GkwAA9F58CgIh4ty12hM78HviNiVV8D4w/pg2303V6yXeB6bb1kqvNMcqpKPXXma8XTNOPSzPPwWLeyd5YLFr+0qw/6y4vRth6xSnEr6/Q94vRt7FFJZ3SDDI3AVUQGdwMYIyMUopRvKSPKHlZF764EiyJ0HKw5njwtDezE8IlL/u/jCXuxpxq+MecjPyOBiDI8GWZzoHwtIk2mWhQQBjUqccd6exezujy/YFJWLv1Mxyf3Bh+uQR+smR25mvUJZhH3oTuN40hU28ScOq6Dxl0pXIU26ujwjXcmfOBNfKgWSuvK2UPXdF175PeVPnik/nuqZ+/MVaz99L3vo8U8+QYJJTN0ve4+ra2Kihw8zD2IZtQGHfqf1BsBypY+cLAACyCDgAvRAd3eEqtW961jEx/3gsRRk5U0Xcmeb+oR4/38IDJHkNweNZas/PEJWu0H2fuw7VJjjec7KgcGgAI1nPRCC/JPPllGWuBlcZeZWX73kdZ7XQTs+QYxOr3DyDG4b9FGt/xSyvrPvecOd5vPwKOc8jemgjS4m3r4NPBSnknM92fBblqpVj/9mfLaZtMmbnu43c/0RtdFVc+8oftLC8/+UTiPDP1RDWie5dx/3CMi1x/nL2VaCD3R/MyC6tr/X+dG1sqFVLn/rwioW1C9M2+IMldh3CPg9cb6VCdqxnVWfop2Abz+4C97LgZ1po3V05BddMybK8w2EZg1euGIEsKzBpdFSZ5reTnXGe1DHWDRxSfxuPaqch3w/88a9cQ65lz69gPQAA6O0IOAB54MskECbkB39JiipRxsZARKIMXPn43ossM2a+FZTX/Ahx0hZr02Ld9Wjq0DQcswSBgkRyHeuQ9leyz52IwEzS932pcqctQYAl9A7aUn5uBzpXo9JW/jyOJ496FnSsY/QShq6TPH0xwg+Zw55PvROX4nsrIoARI2m2M9vXsW+YCyFyuNHQJdnlxjkhcq3kSZ/yvN3St8GzfOXqDYEMwi/dlud4eY9fSAUCWQSX19SYf067AymGpeY/IzrBswnDA2ve2EjK+74pb0PNbHtsP8oT4Jmz54Rsfrk+xqIXR6wY8hkc8b3MuHdDPgfCPgoy82IU8nlX+DoAAPQ0zOEA5Ikvkwjn/4Gd3wTQtLGgqLH8i5Z/zkNVpPLDjm+iO5AT5JtkfXuugwIneXaOV9o/1nNEJ1bEHcqlUYTgUiHZFW27ipNP9hwwBWG86XwrJrxmWZLSue/iNgVf8t3WvM6nsLcrd31OWnbkdS2vmE/xtt30+ZjoiZJ4hbheRywLeb/QuZkCwedCtsuyMkPn+zYpXvW8ZbvX84dNUpalTlOuXeVnO7CzHfzhu9kKed9dkXj7ZFBLo+fvFavWy7gH3NcjK/cesixvn7/lzzMyMCs7upJ9uiAirX197boCZtu7DMcjeJk1ps/JEBgzDWHk/q+5oVb/ftI0zf/nuxo+uFmHzdw822FvmINezhRJOY63JFkpQ/lxN8a1Ttx07uuMcyyLOBefMVCUkmV1qGjfGQEA6GYIOABAGfSm4EFgyI5Szq9e7v1a5PKi7iAvdPLx4gVmgh0V0emydQgmKWXQoJQNTTHrbtcjR9rEnfMVVuyqJswveE2JcazDb3OOfj/hHc+FKXEbyPXEQY79WPph8eKes3GCjnECBwmKNGaV6bSMdSVItOsMncBht2KHlOPpLLcShqRyBf1inBOeLmZ30NqVR9SNF5aVrYezDa6Jnu31B/X3Bhz+76v1rnWCTTobSMju12Anvq+NmGsYWvew4ZQCecs98bs875vKSqVMneTedPZuS6czf4XNFRK4jyGyjWTz2GrTjbTpqAGqq6tRTSql1rZ2bzrLsJ5dhikooag9GbGyoY5hQYOk163Q71uBnQYAAJJgSCUAQNGUZ1xye4zsIvwAjOjwL4b4QxDFKM/9a90qXh0jywpb1i1+exdSSSvkdRXK4/wpsMCirZfrHC7mnAl5SZB38YeOKnD9cnWQhQQW85/ANdekw5E55btijKxNgVdfh2RXmqgO6GC2Mc4Bf+exsV6Gdbv+H/9q5l3qH8M/0FFvzMK0nyIy8acM21bLkpVKaWC/Rk+SVatb1dbe6Suia+gcZ9stb16SrFS8n8D+sf4tWerft96TZpORLTnPf9N8Deb+7Ij9Y3m3I6w/3D1dg5WyPH8bcs3xXnaJeRMTnq+RaRO01Fhlmr5/WUoZdoh/PpTcT97Z/9pBnyr/ngAAQAUQcACAIuOHh1/pOg0rMaxJzuNr6lChTeTYB8UIHsXLp7Bj4e+AyyevXOvk+fRDMdpYyN2x+WUV57wvuJgESlmYoecv8RMWcUop4w4retsuQnAp7jpxrjW5LuOxyo+RyJ0wYSAmJLNE19LIfRS6KFcQIxgkCisikNawvrkEc951NanA3eh/uG1RZFbhp6I3JGHvEifIY+6p148O204NdTWSpAOmb6KNup66cAfLcnVCWyGfV7nfyXH8PU9reDvw3dtoSB6jLpk3TU/QWF2ZFXM4tED4Ia/zvXD+id3jlhc2MTUAAL0FQyoBACqnGn+MVapOmXEOfL/yDT90SzxyUKbMtPNvovkWYuy7avsBblmW0rHqFLczPkFeUemL2Pkfmndkkuw9nLHySzoGmGkd493ypnWTFZVsxZiZFyW+U7kAQmZ4l7RxWdkYg7NRTamweqZSKaU7On3vVtf1yC1e0MPfQZ+kAMspxdnv7utO5Dlt7hpPK7OfOzv9+zmyIsZzIe6mLP98dXadQE94nsc3Yvt32mq4rj97L321rkMjBvfRhg2tkR/b3jwl97B7madhuv52l+krPxAkcE8vYecQGYfyn//RSe3ghJM4NKgUDHA4T8bYgQnjVCDmc7/cvJ9zBU3UElVIAfkCANB9EXAAAJRQ8X9oZccoVhk634srUQdjoT9Si3nHu2LUPTQY4j5QedSpwj/WEx+zdO6JkLuvArbLd7dt8cRpl96x4EPT+f627E7ASgmta6yV4xYSM11xRY/p7+9Ejxn0KqJCsrNS/g706M7Iksx34xoyKBtDSJpnoPc+m6E9RYMvZuV03xYQHA90rluWq1M9uA0rv94gKTDNs7NeKpVSujPt5G25lss/J0fMpzVa+tSroSFiG3z73FK2zdv7zXtpslzps+u4t8eSb/v9+9jVeR4IL/p3quHSFkjj+TNbp5Ak4Qx1jtrncbINm6siVlVing9xByPrsR/3AAAUiCGVAABVIN+7EItbi9zllabAZB1Bni6TCqjCoEGx8i/lUwVVIvqplLDtThB4SlabIuZVvQofCqkE+6loWZraRvA8ivOkVKFtq6TzaLg7hJ07uVMlDKQFy3b+siKW+dIleQqtKPHwGPG/wBvudhK1bsj7q1ZvCE3rHg7H0+9tp7EMC6PKNaS3rJR5X8eVq9kGd1rgpffM8+7PGJsYq4qBYEfXv5b73xx5KPAUp295zGtBPkHI+MfHv56hu8QfYQutQu/4jAMAwISAAwCgtBLdRZY48wLWLXGeef/QDOmki5tfKTvtsqkKKqM0fBNzFvkJkWKNx5wrn0RDGZVMqQIMFZLvscvnDt5SHrdiZ13xY1uiAK4/X+N1tNLbnou5fqY7u/M/R33tNkbHf6Cf1VC2P5eQLQlm7As+GPO2pL2njvG8t9+um0RXNtd7hloFOthjsNcJndNC3v1nCiOU6pQMb/Vx3om/NJC6KFGs6P1StF0Ws66JvrNW/BoLAEDlEHAAAFRQtQUbyigwjECcTtHybXNek7jm/aRK8rsVq1biIR4qsV0lKDOvDv2IO3cj18mvszVO2nzvgg0uzdFhXGLBcrrJ+ROhFJOph094XPlgdrK+Wn8gIkZbl93HnzvQEFmmvWa+QZCcT115hwT67j5bql9znSSpX3Od9p++iTmfJGVGv23eHkuupySCAWrPeoHT0R9cCWQdrJeTp++4hQRmzH+4AxvB9931dz/NE7pfIt8tLLhXzo/GHhFYBwCgyjCHAwCgZDJjFpe0gDKv39t+lBa+vdFjtSOXwofjSVBWrvMhchyVBMc40XlXaBArYd2ctUz3IOcp72wKKz/6eHbtFyuP7Sxh55yhH7QbMnc+u94ITRu6jm9Zrmuqv/PYXHb4OqaksePCafcf/uUJAoQh9dqof6Mu+8nu+t8PvtSkTQZr8IAmtbV3etN2dZj7n3pzmns6m9qy7E74OJ9Vedyr59kp3i2KiBlktsEz/0zmuhR+RbMCcQxZVmaiiCQByIi0xhhJjqCEKSdv2swW5XPa5/cNLuZantMm+nwEAABBBBwAABVXTT/Yij0UTwEZmf+u8FMQlmXl1X1rr1vdqqd+ZbsT3jSkRj49jSijMhyPuMOLRHYqR+dR3uuBoU0nipG5Ol09uWavy6XannyfFjAemyLUJZ12l1Nwjr7gSPhhcW9OS9967bjlcDU31gWWuVcIf9LIjgOELze9Nqa1n8DI+TSNZKVd8ZiIMr35R+UZ/F4QOtdCV2QlYdMPrZM5hBJZ2WD9DDmX9LKQMwgbrF/0ecRnIwAAYRhSCQBQWfn+Xqv6TtD86lcVd9JV/b4th26yDyJvke1eSte+88w3j/qUYhuqY16PbiRw93RP3G8hnaOxV/feCu8fYif/ZmwFL0mGjmZz/sH7z7N5RQdS/OtF1S8qdeLz136SIqQe2V0QDHhl38q9bZ50lnc/xalxMLiRK4oSEmBL8oSKKW2eDauqblaooqoAAFDNCDgAACqm8j8ik5Vf3Pr6OyCKmLVU2g7ofG7FLLzQHIsr3ZZyq3x7z6jI+PSmpybyqke+6xVZNdQhAWOHZ0/qOesGxyP6aZB46eNKpXKvaw8llLSr3jxUkpxhieLn6Msg5K3AXBGB90KyLFZQQZKslKdOCVdOvkZEsMbOM3gnvvu14SkHX+LAJdh47KLrHiemEG/unBjrhQWakuzebnCdAACgpyDgAABAUZXmB20lOqurpYO85GIN9eBNm3hZvrrBIShpO8k77wTHNHz1/AXuss8jizKdf9V2nldXbXIwVTbn/izlFubqII6+fnn6qeOUVqS2E31jf/LAgeX8LzyP7KTIhmF8kpZpxQ/fZQM9rvfcAYSIoI0V8r5Th8DTFsHAYtwAgDu9MbjhqmdmV8bPN+yJlbJci2KX0a2uRAAAVB0CDgCA0ipl50uVdZS5FfrDOd/1K9Z5WKI7dWMd4ypuB1Wvoruuio5bREdi1SlLnSJ7gOPlkNed5CUO2BYh/6jhgLqrYH97jsBFgqU5vwG4OqGdiZ2N6fxd6YYyPJ3XwcLd7SDX0yWRQ5m5O/ejbsS3sv9kAxzuNMk6/wN1iJlL0idPwsMn+bfzUpwh+QSqwpPHT+9/AiVeYgAAehcCDgAAVEDpAwOF5N/LfyDn6IyqTpWtbzXvr/yfcoi5XpInZFAG3jumo+5K765C5zGIvUkhd/xHF5pocbInJMz1SXKEgtXLYxtDMszVsZ9PS4o3LJS5PjHeTl6fsPyK2KkflZ1VlutoMfLOrw0BANDbEHAAAHRjlXmKAOXHsQrTXfZLd6mnrYj1LVqPYHfbh3nKazsrtW9K9URGoevHuXO+645711348Sph6Mg3rZNryCN/XULW8TwwkGi/hHTQm97LGUzJ/0kE08TeVirlThArP/8uCo0L+IYoCpvPw/AASB6ij1dgWdi2FlKDJE8mFFAOAABIhoADAKDbqsZO6PzrVH3bklyJ7ySvCoXdBVx9KlDfAvZR3POrGq8NVYdd1OMU2nEe//wqbHlU4sR3/ufBO29AsUuIH2gJq0TovAYhcZm4D/HEfmgrZU+SHRYMcQ3nZJXrelvmCxafIQAAFISAAwCgxHpaB22FucaBri6VrFPB3U9FqUVp9kEVHOtSj6tfFcJ65npoADHHnAbVdWyKKXq788410fjvldu35RheKmknd66JrEPX8T36EJgw2bNqIUHO8PrYne25nm7IudtNEz/ErFuc9pRK5f7Jn70kRARMquyyEL3tpats/HO4OM+RAADQHRFwAACgByqkU6u6OhsrUZdq2v54ynLMqqVNVVX7lLpjeylEKdtad9iT1XV9zI9pG3IGBpLmacX/mVmKfZrJszhPKpjmhwgMaVSSbQjWJzSIkef8FEVX4rkmChE2vFQi1bAhAAB0AwQcAAAAuqme0PlZvZLfeY3C5bNnvcPOlPHYmMoqQvn5ntdJJ0Xu6ro2L05SRtL6JnoiJHedjOtF5Bc1Z0QpZWIgwUCIqRamuR9ilZEjH09lYs1NER5AyGf3GdfhegoAQI9DwAEA0D31tB+o3Wl7yjAsR2JVs/+qpR4xVWS/VcmduL1a79yvBOjMku6VyGF3EucYPpxQqY5WrgGM4k5unOtJEcvwqhDO0w2m94118Q8BFa8eiY5emZ6s4NwFAKB7IeAAACgpfiQWW08cE7iw7SlnG4se37qnHRdbdWxXSYfyCRl4vuceU6lnXkvi6q3bnb/EDzE465VmqCHPxMqm0t3LC+nojrG8mNsYNmF0vsGDfMrteqOo+ZebORiU52MZ4YUkrgMAAL0FAQcAAFCwUv2wLscP9lJ35BSkmupS1E7aQvKqpn1SpCFmIKnMu61khVXm2Bd63Sp2p3wlrqMFzWsQmW/xPiNyPV0RUQlzYCWfvPJso3HWSjQUVy/QW7cbAAACDgAAAICHlaOjqILDQlVwjoByqOKq9TCWa04Bw9J8j0OceQESZJX3fBaxliR7Yi3OVCGe3C3/v+Ynp8p40302nQoPIBSr2pzyAAD0PAQcAABAD1AdXRbV3JFbLZLtI/ang7aFEqlkTCxq2KB8M06UT485r4o5rFOxE3pWymOdCugx7QIAgMog4AAA6KZ6549By8p153VZalHc3Cq+PYjSG49Pr9jm3rCNIXrF8S2yWPvMEzwoYWVkGZ8kCKlKgUXlGqe/tPkbk3bb9lsN9a6WoQEBAOjZCDgAACqr2/5wBlAJ3a+zuHrrW437snx1KkI5YRPT9gBJtqNUm5z3cEoRq3kfbkg480ERhncyZhF7GKrwoZj8wzZF5pXvAYsxsXQ+WfeQUwYAALgQcAAAAOitEnUqFnPIju7dw1TZ+nfvfRelKPu1m7etcinV03Kx5gWosgBFgaX6KxG+LLCqeU6Hcog91wOnkxc7BACAWAg4AAAAoCp198BEcbEvUBrFPs9K1VJzPingWV7otNHVJ/rJjeRbEjrxdfxJHBKX2X305G0DAKD0CDgAALqlHtcR2Y22J3TfV3Abelx7ANANcN0J01OuybnCFrE2swwBne6yvytdTZ7UAwCgPAg4AAAqjB9sQOVw/pUanVLVieNSPNl9aZWlQzk4lUCCoeGKXJeo/M2TWfvfjFcjywqmLO283HHnlfCvFhGiCVnGuQgAQM9DwAEAAKBa0RHTPXHcKoh9n0gPbKtOB3YZtq04JVRiDofkwyb1vJaSD/YCAABxEHAAAAAAuomeejdwT92ualeSvZ7jWOZ7rL2r5V9zdz6VbHaJiy720Eycc6HYNwAAFIaAAwAAAFDl6ACrPhyTni/nIfYl8AQzfP/2NHlNVF2CegAAgOpDwAEAgKrQE36G94Rt6F3oMAVQTOW4phRUQpHqV6zNzGf+CSvwohRizOHAxwcAAAhBwAEAUFF0eAJABfTQay+fKckUc39V376v8uBHN1Atw09Vi+pr4wAAVCcCDgAAAFWLzo3uieOG3qscrb+Qfl93p3FZnsgIKSI7tzXXCwAA0LMQcAAAAAAAVLVidMwnzSFXmf7FVgkfCSAuAQAAugsCDgAAVIGecIdjT9gGAACqWbV81uZVjyqpOwAAKC0CDgAAAECvQ8cfpFSqG/4cjNlp3a075uPkW5Jcu/Kukn0HAAC6p274DRMAAAAA0FtVujvcLj+qY77SdSwG9zYQhAAAAHERcAAAAKhSdPB0Txw39GbV3PyrqW5JrxNcVwAAQHdRW+kKoHr8+c9/1i233BKZZsOGDWWqDQAAAAAAAACgOyHgAMeKFSv09ttvV7oaAAAAANCtFfOBBNPTDd3xgYduWGUAAJAHAg5wDBo0SBMmTIhMs2HDBi1btqxMNQIAAEApMDwLuitLUpr2Wx7sZwAAkAcCDnAcffTROvrooyPTvPXWWzrggAPKVCMAAAAA3Uk1B7OKVrcq3kYAAIBKY9JoAAAAAAAAAABQMAIOAAAAAAAUUamf9KjmJ0kAAEDvRsABAAAAANAtWBaTD5dLsfczMRIAAHoHAg4AAAAAAAAAAKBgBBwAAAAAAIiJG/UBAADCEXAAAAAAAAAAAAAFI+AAAAAAAOg2mAugPNjPAAAgHwQcAAAAAADdgkUveLfFsQMAoHcg4AAAAAAAAAAAAApGwAEAAAAAAAAAABSMgAMAAAAAoNuo9NA8vWVkoErvZwAA0D0RcAAAAAAAAAAAAAUj4AAAAAAAAAAAAApGwAEAAAAAAAAAABSMgAMAAAAAAAAAACgYAQcAAAAAAGJiMmUAAIBwBBwAAAAAAAAAAEDBCDgAAAAAAAAAAICCEXAAAAAAAAAAAAAFI+AAAAAAAAAAAAAKRsABAAAAAAAAAAAUjIADAAAAAAAAAAAoGAEHAAAAAAAAAABQMAIOAAAAAAAAAACgYAQcAAAAAAAAAABAwQg4AAAAAAAAAACAghFwAAAAAAAAAAAABSPgAAAAAAAAAAAACkbAAQAAAAAAAAAAFIyAAwAAAAAAAAAAKBgBBwAAAAAAAAAAUDACDgAAAAAAAAAAoGAEHAAAAAAAAAAAQMEIOAAAAAAAAAAAgIIRcAAAAAAAAAAAAAUj4AAAAAAAAAAAAApGwAEAAAAAAAAAABSMgAMAAAAAAAAAACgYAQcAAAAAAAAAAFAwAg4AAAAAAAAAAKBgBBwAAAAAAAAAAEDBCDgAAAAAAAAAAICCEXAAAAAAAAAAAAAFI+AAAAAAAAAAAAAKRsABAAAAAAAAAAAUjIADAAAAAAAAAAAoGAEHAAAAAAAAAABQMAIOAAAAAAAAAACgYAQcAAAAAAAAAABAwQg4AAAAAAAAAACAghFwAAAAAAAAAAAABautdAXQvbS2tnr+fv/99ytUEwAAAAAAUGz+3/n+fgAAAKIQcEAiH3/8sefvH/7whxWqCQAAAAAAKLWPP/5YW221VaWrAQDoJhhSCQAAAAAAAAAAFIyAAwAAAAAAAAAAKJiVTqfTla4Euo+vvvpKzz77rPP3iBEjVF9fH0h36qmnatmyZRozZoz+67/+q6h1KDTvfNaPu06cdLnShC03vf/+++97hrW66qqrNHbs2FjbVC60hfzaQtJl1d4Wqrkd5JNHkvT5nvNxltMWipt3NV8TopZ3x3Yg0Rb4fMio5naQTx58PuSvmttCNV8TopZ3x3Yg0Raq4fOhtbXVM5zyTjvtpJaWlljbCwAAczggkZaWFs2cOTNnuoaGBuffzTbbrKh1KDTvfNaPu06cdLnShC2Pk/fYsWOLvr8LRVvIry3ku8xWbW2hmttBPnkkSZ/vOR9nOW2huHlX8zUhanl3bAcSbYHPh4xqbgf55MHnQ/6quS1U8zUhanl3bAcSbaFaPh+YswEAkC+GVAIAAAAAAAAAAAUj4AAAAAAAAAAAAApGwAEAAAAAAAAAABSMgAMAAAAAAAAAACgYAQcAAAAAAAAAAFAwAg4AAAAAAAAAAKBgBBwAAAAAAAAAAEDBaitdAfRMRx11lFasWKFBgwZVXd75rB93nTjpcqUJW17KfVpKtIX82kK+y6pVNbeDfPJIkj7fcz7OctpCcfOu5mtC1PLu2A4k2gKfDxnV3A7yyYPPh/xVc1uo5mtC1PLu2A4k2gKfDwCA7s5Kp9PpSlcCQH7eeustHXDAAc7ff/3rX7XZZptVsEaoFNoCbLQFSLQDZNEWYKMtQKIdIIu2AAAoFYZUAgAAAAAAAAAABSPgAAAAAAAAAAAACkbAAQAAAAAAAAAAFIxJo4FubNCgQZo7d67nb/ROtAXYaAuQaAfIoi3ARluARDtAFm0BAFAqTBoNAAAAAAAAAAAKxpBKAAAAAAAAAACgYAQcAAAAAAAAAABAwQg4AAAAAAAAAACAghFwAAAAAAAAAAAABSPgAAAAAAAAAAAACkbAAQAAAAAAAAAAFIyAAwCjRx55RN/97ne1/fbba9q0aTrmmGP05JNPVrpaKKOLLrpIEydONP73ne98p9LVQwUtWLBAEydO1B//+MdKVwVltmLFCv3nf/6nZs2apcmTJ2vWrFm67LLLtHbt2kpXDWX0xRdf6Nxzz9Wee+6prbfeWtOmTdPcuXP1+uuvV7pqqLDOzk4ddthhOv744ytdFZRAOp3WbbfdpgMPPFBTpkzR9OnTdc4552jFihWVrhqqAOc/AMBWW+kKAKg+f/rTn3TJJZdo6NChmjNnjtavX6+//e1vOuGEE3TFFVdo1qxZla4iyuCNN95QfX29Tj755MCywYMHV6BGqAaffvqpzj///EpXAxWwatUqHXnkkVq6dKl23313zZw5U6+++qquvvpqPfbYY7rlllvU3Nxc6WqixD799FMddthh+uSTTzRt2jTts88++uijj/TQQw/pscce03XXXadp06ZVupqokHPPPVdLlizRLrvsUumqoAQuuugi3XDDDZo0aZK++93v6r333tNtt92mp59+WrfffrsGDBhQ6Sqigjj/AQA2Ag4APN544w1ddtll2nLLLXXjjTc6PxxOPPFEzZ49W7/97W8JOPQSb7zxhiZMmKDTTz+90lVBFfn1r3+tr776qtLVQAVcccUVWrp0qX7xi1/ohBNOcN6/+OKLdf311+uWW27R97///QrWEOVw+eWX65NPPtHPf/5zz/F+5pln9L3vfU/nnHOOHnzwwQrWEJWwevVqnX322Rz7HuyVV17RDTfcoN12201/+tOfVFNTI0n685//rPPOO09XXXWVfvnLX1a4lqgEzn8AgB9DKgHwuPnmm9XR0aHzzjvPc5fSuHHjdPrpp2uPPfbQqlWrKldBlMVnn32mFStWaOLEiZWuCqrI7bffrscee0x77rlnpauCCvjoo480bNgwffe73/W8f+CBB0qSXnzxxUpUC2WUTqf10EMPadCgQZ6gkyTtvPPOmjZtmpYuXap33323QjVEJdx3333aZ5999OCDD2r33XevdHVQIjfffLMkae7cuU6wQZKOOuoobbzxxlqwYIFaW1srVT1UCOc/AMCEJxwAeDz22GMaNWqUJk+eHFh24oknVqBGqIQ33nhDkgg4wPHxxx/rwgsv1L777qvdd99djz76aKWrhDILm7PD7lxmqLWer62tTXPnzlVdXZ1SqeB9S/X19ZLEnB69zK233irLsnTppZdqypQp2nvvvStdJZTAs88+q6amJm277bae9y3L0s4776y//OUveu211zRlypTKVBAVwfkPADAh4ADAsWLFCn3++efac889tXz5cl1++eV64okntG7dOm2zzTb60Y9+pJ122qnS1UQZ2AGHL7/8UieeeKJeeeUVtbe3a/vtt9fpp59uDEihZ/vVr36luro6nXPOOfrnP/9Z6eqgCqxYsUKPP/64LrjgAvXp00fHHXdcpauEEquvrw+dDHTFihV6/vnnVVdXp3HjxpW1Xqis0047Tdttt50aGxv14YcfVro6KIHW1lZ99NFHmjBhgufpBtuYMWMkZQLQBBx6F85/AIAJQyoBcHz22WeSpJUrV2rOnDl6+eWXdcABB2jmzJlavHixvve97+nhhx+ucC1RDnbA4brrrlNjY6PmzJmjqVOnauHChTrqqKO4u72XmTdvnhYuXKhzzjlHgwYNqnR1UAWuvfZa7bLLLvrFL36h1tZWXXvttRo/fnylq4UKOv/887VmzRp9+9vfVt++fStdHZTRLrvsosbGxkpXAyW0cuVKSVJLS4txuX3OM8dT78P5DwAw4QkHoIfba6+9tHz58sg0W2yxhe6++26tWbNGkrRo0SLtscceuuKKK5zhEY4++mgdc8wx+uUvf6ldd91Vzc3NJa87iidJO5Ckuro6jRo1ShdeeKHnqZaFCxfqpJNO0llnnaVHHnlEffr0KWm9UXxJ28Ly5ct18cUX65vf/Kb222+/clQRZZK0LbgNHTpUJ5xwgpYvX66HH35YJ510kq688krttttupaouSqSQdmD7/e9/r3vvvVfDhw/Xz3/+82JXEWVUjPaAnqe9vV1Sdtg0P/t95nAAAAASAQegxxszZkzojwPb6NGjJckzHvOvfvUrz3rbb7+9DjjgAN1111166qmnNHPmzNJUGCWRpB1I0kUXXWRMM336dO2///6699579eSTT2rWrFlFrSdKL0lbSKfTOvvss1VXV6ff/OY35ageyijpdcFt9uzZzutnnnlGJ5xwgv7t3/5NjzzyCHc6djOFtIPOzk6df/75uvnmmzVgwABde+21PAXVzRXSHtBz2df1trY243I70MANSQAAQCLgAPR4//3f/x07bb9+/SRJAwYMcMZidZs0aZLuuusuffDBB0WrH8ojSTvIZZttttG9996rZcuWFS1PlE+StnDLLbfomWee0cUXX6whQ4aUsFaohGJdF3beeWfNnDlTDz74oJYsWaJp06YVJV+UR77tYP369frZz36mhx56SEOGDNH111+viRMnFrl2KLdifl9Az9G3b1+lUil9/fXXxuWrV6920gEAADCHAwDHxhtvrNra2tC7l+z3uXu1Z2ttbdVLL72kl156ybh8/fr1kmgHvcEDDzwgSfq3f/s3TZw40fnvrLPOkiT94Q9/0MSJEzV//vxKVhNl0NraqieffFJPPvmkcfmoUaMkZSaaR8+3atUqHX/88XrooYe0ySabaN68eQQbgB6svr5eY8aM0YcffqjOzs7AcvtmpAkTJpS7agAAoArxhAMAR319vSZPnqwXX3xRixcv1pQpUzzLX375ZUmZsXvRc61Zs0aHHXaYBgwYoKeeesoz1JYkPffcc5IyTzqgZzvooIM8c3jYXn/9dT3yyCOaNm2apk6dqi233LICtUM5tbe366STTlL//v21cOFC1dTUeJa//vrrkqSxY8dWonooo/Xr1+vkk092vidcffXVGjhwYKWrBaDEdthhB82fP1+vvPKKJk+e7LyfTqf1r3/9S3369OE3AgAAkMQTDgB8jjzySEnShRdeqHXr1jnvP/300/r73/+uCRMmaLvttqtU9VAGAwcO1C677KIvv/xS11xzjWfZ3XffrSeeeEJTpkzx/NhEz3TwwQfr9NNPD/xnz+Gy88476/TTTyfg0As0Nzdrr7320ooVK3T99dd7lt111116+umntdVWW9HZ1AtcdNFFWrx4sSZPnqwbbriBYAPQS8yZM0eSdOmll3qehr7lllv0wQcf6LDDDlNtLfczAgAAnnAAKmbFihXad999tXLlSr300ktqaGiITL9+/XrddNNNeuCBB/Tee+9JykzaN2vWLB177LHq379/Uep14IEH6oknntA999yj/fffX9/85jf1xRdf6MEHH1RjY6POP/98WZZVlLJQve3gnHPO0ZFHHqnLL79czzzzjCZNmqS33npLTzzxhIYMGaKLL764KOUgq1rbAsqvWtvC2WefrSVLlujSSy/VM888o4kTJzrXhcGDB+uSSy7h86GIqrEdfPjhh7rtttskZYZO8QefbHPmzNHIkSMLLg9Z1dgeUB3K1TZ23HFHHXLIIbrjjjt00EEHaY899tDSpUv10EMPadNNN9Wpp55a9G1DfrheAAAqjYADUAGdnZ36zW9+o5UrV8ZK/+mnn+qEE07Q22+/7Xn/zTff1Jtvvqk777xTV199ddHuLL3ooos0depUzZs3T7feeqtzZ+vcuXO1+eabF6UMVHc72GSTTbRgwQJdccUVevzxx/XCCy9o0KBBOvzww3X66aczgXCRVXNbQHlVc1sYOXKk5s+fryuuuEKPPvqonn32WW200UY64ogjdNppp2nYsGEFl4GMam0HL7zwgjo6OiQpcu6WXXbZhYBDEVVre0DllbttnHfeeRo/frxuv/12/fd//7cGDx6sI444Qj/60Y/olK4SXC8AANXASqfT6UpXAuhtfvOb32jevHnO31F3nrS3t+vwww/XK6+8IsuydNhhh2nfffdVTU2NHn74Yf3P//yPOjo6NGrUKC1YsIAv+90I7QA22gJstAVItAN40R4QhrYBP9oEAKAa8IQDUEbr1q3TWWedpfvvvz/2On/5y1/0yiuvSJLOPPNMHX/88c6ynXbaSdttt51++tOfavny5bruuut0xhlnFLvaKDLaAWy0BdhoC5BoB/CiPSAMbQN+tAkAQDVh0migTF544QUddthhzpfAVCre6XfzzTdLksaNG6djjz02sHzffffVXnvtJSkzaVtra2uRaoxSoB3ARluAjbYAiXYAL9oDwtA24EebAABUGwIOQBn87ne/01FHHaU333xTknTwwQdrv/32y7neO++8o3fffVeStP/++4d+eTzooIMkSatXr9bTTz9dpFqj2GgHsNEWYKMtQKIdwIv2gDC0DfjRJgAA1YiAA1AGL730kiRp0KBB+v3vf68LLrhAdXV1OddbtGiR83rq1Kmh6XbYYQfn9b/+9a8CaopSoh3ARluAjbYAiXYAL9oDwtA24EebAABUI+ZwAMqgpaVFp5xyik4++WT17ds39nrvvPOO83rs2LGh6QYNGqQ+ffpozZo1nnVQXWgHsNEWYKMtQKIdwIv2gDC0DfjRJgAA1YiAA1AGV1xxReyxNN0+++wzSZlxOIcNGxaZdujQoXrvvfecdVB9aAew0RZgoy1Aoh3Ai/aAMLQN+NEmAADViCGVgDLI50ugJH311VeSpMbGRtXU1ESmbW5u9qyD6kM7gI22ABttARLtAF60B4ShbcCPNgEAqEYEHIAq1traKkmqr6/PmbahocGzDnoO2gFstAXYaAuQaAfwoj0gDG0DfrQJAEApEXAAqph9x4plWTnTptNpzzroOWgHsNEWYKMtQKIdwIv2gDC0DfjRJgAApcQnBlDF7MdXN2zYkDNtkrtU0L3QDmCjLcBGW4BEO4AX7QFhaBvwo00AAEqJgANQxfr06SMp80Wws7MzMu3atWslSS0tLSWvF8qLdgAbbQE22gIk2gG8aA8IQ9uAH20CAFBKBByAKjZy5EhJUkdHh7744ovItJ999pkkaejQoSWvF8qLdgAbbQE22gIk2gG8aA8IQ9uAH20CAFBKBByAKjZ+/Hjn9QcffBCabsWKFVqzZo0kacKECSWvF8qLdgAbbQE22gIk2gG8aA8IQ9uAH20CAFBKBByAKrbttts6r1988cXQdC+88ILzervttitpnVB+tAPYaAuw0RYg0Q7gRXtAGNoG/GgTAIBSIuAAVLGNN95YEydOlCTdc889SqfTxnQLFiyQlBmLc5dddilb/VAetAPYaAuw0RYg0Q7gRXtAGNoG/GgTAIBSIuAAVLmjjjpKkvTWW2/pmmuuCSx/4IEH9I9//EOSdOihh6qpqams9UN50A5goy3ARluARDuAF+0BYWgb8KNNAABKpbbSFQAQ7bDDDtNf/vIXvfrqq7rsssv0zjvv6KCDDlJdXZ0eeeQR3XTTTUqn0xo+fLhOPfXUSlcXJUI7gI22ABttARLtAF60B4ShbcCPNgEAKBUCDkCVS6VSuvrqq/W9731Pb7/9tu655x7dc889njRDhgzRtddeqwEDBlSmkig52gFstAXYaAuQaAfwoj0gDG0DfrQJAECpEHAAuoGhQ4dq/vz5uvnmm3Xfffdp6dKlamtr0+jRo7X33nvrhBNO0KBBgypdTZQY7QA22gJstAVItAN40R4QhrYBP9oEAKAUrHTY7EAAAAAAAAAAAAAxMWk0AAAAAAAAAAAoGAEHAAAAAAAAAABQMAIOAAAAAAAAAACgYAQcAAAAAAAAAABAwQg4AAAAAAAAAACAghFwAAAAAAAAAAAABSPgAAAAAAAAAAAACkbAAQAAAAAAAAAAFIyAAwAAAAAAAAAAKBgBBwAAAAAAAAAAUDACDgAAAAAAAAAAoGAEHAAAAAAAAAAAQMEIOAAAAAAAAAAAgIIRcAAAAAAAAAAAAAUj4AAAAAAAAAAAAApGwAEAAAAAAAAAABSMgAMAAAAAAAAAAChYbaUrAAAAeqf58+frrLPOynv95557Ti0tLUWsEXqzr776SrNnz9Znn32mu+++W+PHj3eW7bXXXlq+fLkk6ZFHHtHo0aNj51vIurm0trbq29/+tj788EP9+c9/1pQpU4qWNwAAAADkgyccAAAA0Oudc845Wr58uY455hhPsKGa1dfX6+yzz1Z7e7vOOOMMrV69utJVAgAAANDL8YQDAACouGnTpunYY49NtE5TU1OJaoPe5sEHH9T999+vQYMG6bTTTqt0dRKZMWOGvvGNb+iJJ57QJZdconPPPbfSVQIAAADQixFwAAAAFTdy5EjNnDmz0tVAL7R69Wr99re/lSSdeuqp6tevX4VrlNzPfvYzLVy4ULfddpsOOuggbbvttpWuEgAAAIBeiiGVAAAA0GvdeOON+vTTT7XRRhvpiCOOqHR18rLFFlto5syZ6uzs1MUXX1zp6gAAAADoxQg4AAAAoFdatWqVbrjhBknSnDlzVF9fX+Ea5c8Oljz//PNauHBhhWsDAAAAoLdiSCUAANDtzZ8/X2eddZYk6bbbblNjY6MuuOACLVmyRLW1tRo9erSOP/54zZ4927Pe22+/rXnz5umZZ57Rxx9/rLa2Ng0ePFjbb7+9Zs+erenTp+cse/Xq1brzzjt133336f3339eGDRs0btw4HXDAATrmmGO0atUqJ5+5c+fq9NNPd9b917/+5cxdcdBBB+nCCy8MLefMM8/UggULJEk33XSTpk2bZkz35Zdf6pZbbtHjjz+u999/X6tXr9aAAQO05ZZbatasWZo9e7bq6uoiy6ivr9fLL7+s9evX69Zbb9UDDzygpUuXat26dRo6dKh23XVXHXvssZowYULO/bN48WLdcccdWrJkiZYvX67Ozk4NGzZMU6dO1dFHH60tt9zSSfvWW2/pgAMOkCSNGzdODz74YGTe77zzjvbbbz9J0gEHHKBLL700Z33cbr/9dmei5UMPPTTRusUwceLExOuEtZPddttNo0aN0vLly3XDDTfEarsAAAAAUGwEHAAAQI/yxhtv6KKLLtLatWud915//XW1tLQ4f3d2durSSy/VDTfcoI6ODs/6y5cv1/Lly3Xvvfdqzz331CWXXKK+ffsay3rttdf0gx/8QJ9++mng/ddee01/+9vfdNFFFxVx66Ldd999Ouecc/T111973v/888/1+eef6/HHH9f111+vq666SuPHj4/M64MPPtBJJ52kpUuXet5ftmyZbrvtNt1xxx36zW9+o8MPP9y4/tq1a/XrX/9af/3rXwPLli5dqqVLl+rOO+/Uaaed5gRhNttsM2277bZasmSJli5dqsWLF2vKlCmhdbzrrruc1wcffHDk9pjMmzdPkrT55ptr4403Trx+JViWFfr+XnvtpZtvvllPPvmkPvjgg26zTQAAAAB6DgIOAACgRzn//PO1YcMGzZ49W7vuuqu++OILPf7445oxY4aT5pe//KXmz58vSerbt6++853vaPLkyaqtrdU777yju+66Sx999JEeffRRHXfccbr11lsDw+0sXbpURx99tBPY2HLLLTV79mwNGTJEb731lm677Ta9+uqr+tnPflaW7V6wYIHOOusspdNp1dTUaObMmdptt93Ur18/ffLJJ3rggQe0ZMkSvffeezryyCN1xx13hHZId3Z2OsGGiRMn6sADD9TIkSP16aef6s4779Rbb72ljo4OnXfeedpxxx0DwYvOzk6deOKJevHFFyVJffr00cEHH6ytt95a7e3teu6553TPPfeos7NTV155pfr37+886TFnzhwtWbJEknT33XeHBhw6Ozt1zz33SJJGjBihXXbZJdH+evnll7Vs2TJJ8rSNcrrqqqtypnnppZd0zTXXSJKam5ud/WSy++676+abb1Y6ndYDDzygk08+uWh1BQAAAIA4CDgAAIAeZcOGDYGhi0488UTn9T333OMEG7baaiv913/9l4YNG+bJ4wc/+IF+8Ytf6P7779crr7yiP/zhD/r5z3/uSfPv//7vTrDhkEMO0Xnnnaeamhpn+THHHKMTTjhBb7zxRtG30W/p0qU699xzlU6nNWDAAF199dXabrvtPGlOOOEE3Xjjjbrgggu0atUqnXHGGbr99tuN+bW3t2vp0qU6/vjj9Ytf/EKpVHbar6OOOkrf//739eyzz6q9vV1/+ctfnOGsbDfddJMTbNhss810/fXXe/bxIYccov3220+nnnqqOjo6dPnll+uggw5Sv379tP/+++uCCy7QunXrdN999+mss84yzq3w1FNP6ZNPPpEkzZ4921PHOB577DHn9bbbbht7vSeffFIbbbRR7PTr1q0LXTZz5szIdT/66COde+65kjJPMFx44YWeIaj83MGZxx9/nIADAAAAgLIj4AAAACpuwYIFzvwEuZx11lk6/vjjQ5c3NTV5AgxunZ2dzl3lzc3NxmCDJDU0NOj888/XokWL9Mknn+iWW27RySefrP79+0vKDNH01FNPSZLGjx+vc8891xNskKSNNtpIV155pb797W9HdjoXw3XXXaf169dLkn77298Ggg22448/Xs8995wefvhhvfTSS3ryySe12267GdNuttlmOvPMMwND+DQ0NOjUU0/Vs88+K0nO0wi2dDqt6667TpJUV1enK664wriPZ8yYoUMPPVTz5s3TmjVr9NBDD+nggw9W3759tc8++2jBggVauXKlHn/8cWPHvLu95DOc0qJFi5zXSeZSOOeccxKXlY/Vq1frBz/4gT7//HNJmfk/vvWtb0Wu09LSohEjRujjjz/Wyy+/rLa2ttD5OgAAAACgFJLdCgYAAFDltt56azU3NxuXvfrqq86cBDNmzDB2hNuam5t14IEHSsrMR/D00087yx555BHn9ZFHHhnaqTtmzBhnEuRS6ezs1P333y9JGjx4sPbee+/I9EcccYTz2r0dft/61rdC5wvYYostnNdffvmlZ9mSJUucTvIZM2Zok002CS3j2GOP1U9+8hNdfvnlniGR5syZ47x2z9NgW716tR5++GFJ0tSpU/Oaq+DNN9+UJDU2NmrMmDGJ1y+ljo4OnXHGGfrf//1fSdK+++6ruXPnxlp38803lyStX7/eGTIKAAAAAMqFJxwAAEDFTZs2LXJseje7QzXMpptuGrrMHuZHynTq2p3WYdrb253XS5Ys0T777CMpM/6/bccdd4zMY/r06aFDFxXDm2++qdWrV0vKzEcRFUSQpK+++sp57X86wW2zzTYLXeaegNu9jyRp8eLFzuupU6dG1mX8+PE69dRTA+9PnTpV48aN09KlS/XPf/5TK1eu1IABA5zl999/v/NERz5PN7S2tjpBkUGDBoUGVkweeeQRjR49Onb6vfbaS8uXL09UvwsuuED//Oc/JWWG/brwwgtjr+se7mn58uWR5wMAAAAAFBsBBwAAUHEjR47MOZ59XPawRyb2mP+S9Pe//11///vfY+e7YsUK57XdWS1l6h7FP6FysX388cfO66VLl+qHP/xh7HXd2+TnDir41dZmv0Km02nPsv/7v/9zXo8aNSp2XfzmzJmjSy+9VG1tbbrvvvt01FFHOcvsOTiam5tzDjNk8vXXXzv17tu3b951LIVbbrlFN998syRpyJAh+uMf/6jGxsbY6/fr18957Q4uAQAAAEA5MKQSAADoUUwTDNu+/vrrvPO1nyLwv25qaopcz90BXArF2iY//5wUca1cudJ5naSj3G/27NlOHe655x7n/Q8++MB5UmWfffZRnz59Eufd2trqvM5n/VJZuHChfvvb30rKzJVx1VVXafjw4YnycA8ntmHDhqLWDwAAAABy4QkHAADQa7g7wC+77DLtt99+eeXjvit+7dq1kUEOd+d2IcI6j90BjyOPPFLnnntuUcrLl3sf28Me5WPo0KHafffd9eijj2rRokVatmyZxowZo7vvvttJ457rIYmGhgbndbGOT6Hefvtt/fjHP3aGqPqP//gPbbvttonzcbeTQgI+AAAAAJAPnnAAAAC9xpAhQ5zX7777bt75uIdRyjUxr3sYJ79UKvtVzD8Xgl/Y8DiDBw92XheyTcXirs9HH32UM/3ChQv17rvvGgMq7oCCPfyV/e/YsWNzzp8Rpl+/fs68DYU8IVIsK1as0CmnnOI8cXLyySfrO9/5Tl55uZ9aiRoWCwAAAABKgYADAADoNdx3jD/66KM5099zzz0644wz9Pvf/14vvPCC8/7222/vvH7mmWci83juuedCl7mfjIga3kiS3nrrLeP7kyZNcvJZtGiRZ0gjkzfffFNz587VBRdcoHvvvTcybT4mT57svHbvM5Ply5frxBNP1L777qu5c+cGlu+xxx7OJMgPPfSQli1bpjfffFNSfpNF2+rq6pyg0SeffBKYh6KcWltbddppp+nDDz+UJO2555766U9/mnd+7iDP2LFjC64fAAAAACRBwAEAAPQa22+/vfOUwyuvvKJ//OMfoWk3bNigSy+9VH/96191zTXXeJ4w2HfffZ2Jk2+99VatXbvWmMfatWt1xx13hJYxaNAg5/Ubb7yhzs5OY7rnnntOn376qXFZQ0ODZsyYISnTeX311VeHlidJV155pR566CHdeOONeu211yLT5mP77bfXgAEDJGWCOsuXLw9Ne9dddzmvp0+fHlheV1fn3Om/ePFi3XLLLZIyT4bMnj27oHpOmDBBUmaf2Z39lXDWWWdp0aJFkqTNN99cl1xyiefJl6SWLl0qKTPUViGTdgMAAABAPgg4AACAXqO+vl4nnnii8/eZZ56p559/PpCura1NP/3pT53hkDbffHOnU1+SRowY4Qz3s3z5cp1xxhmBIYFaW1t15plnRg4rNHr0aCcA8vHHHzsd6m7vvfeezj777MjtOvnkk51O6htvvFF//vOfjemuu+46Pfjgg5IygYrjjjsuMt98NDQ06JhjjpGU2Qc/+clPjE9dPP/887ruuuskZYb+CQsgHHLIIZKkdDqtm266SZK06667Jp5M2c89HNMrr7xSUF75uuKKK/TXv/5VUma4r2uuucYzP0hSK1eudIInO+ywQ0GBCwAAAADIB5NGAwCAXuW4447T008/rccee0yrVq3SMccco29+85uaPn26mpub9f777+vOO+907sxvamrS7373u0Dn7ZlnnqlFixbpzTff1D/+8Q/tv//+OvTQQzVmzBh9/PHHuv322/Xee+8plUqFPrlgWZYOPfRQ/fGPf5Qk/fa3v9ULL7yg3XbbTalUSosWLdK9996rdevWacqUKVq8eLExn8mTJ+unP/2pLr30UqXTaZ133nm69957tc8++2jo0KH67LPP9OCDD+rFF1901vnVr35VcKd9mFNOOUVPPPGEFi9erJdeekn77LOPDj30UE2cOFFff/21nn/+ef3tb39TOp2WZVk655xz1L9/f2Ne48eP13bbbadFixY581zkO1m02/Tp03XppZdKyjxBsu+++xacZxL33XefrrzySkmZJzZ+/OMf66OPPnLms4ga5mm33XbzTBZue/bZZ53X3/jGN4pfaQAAAADIgYADAADoVVKplK688kqdd955uuOOO9TZ2akHH3zQufPfbcSIEbr88su1xRZbBJY1Nzfrpptu0umnn67nnntOy5Yt0+9//3tPmlGjRmn27Nm66qqrQutz6qmn6vXXX9ejjz6qzs5O3Xfffbrvvvuc5ZZl6aSTTtLWW2+tH//4x6H5nHzyyerTp48uvvhirV+/XosWLXKG6nFramrS2WefrcMOOyw0r0LV1dXp+uuv189+9jM9+uij+vLLL3XttdcG0jU2NurXv/61vv3tb0fmN2fOHGdb+vfvr5kzZxZcx0mTJmnChAl6++239fjjjxecX1LuMjs7O/WrX/0q9rqPPPKIRo8eHXj/iSeekCTV1NRov/32K7ySAAAAAJAQAQcAANDr1NfX6z//8z919NFH6/bbb9ezzz6rTz75ROvWrVNLS4s233xzzZw5UwcffLD69OkTms/AgQN18803669//avuuusuvfrqq1q7dq1Gjx6t/fbbT9/73veMgQx/Xa6++mo9+OCDmj9/vl5++WV99dVXGjx4sHbYYQcdddRR2mGHHfTAAw/k3K6jjz5as2bN0rx58/Tkk09q6dKl+vrrr9XU1KSxY8dq+vTpOvzww50Jk0upb9++uvrqq7Vw4UItWLBAixYt0hdffKFUKqXRo0dr+vTp+u53v2vsOPfbbrvtnNf777+/Z7LtQhx22GE6//zztWzZMr388svaZpttipJvJbS1temhhx6SlJl4eujQoRWuEQAAAIDeyEpHPa8NAACAgsyfP19nnXWWJGnu3Lk6/fTTK1yj7ufKK6/UFVdcISmzP7faaqui5Lt+/XrNnDlTn3/+uY455phETxlUm4cfflg//OEPJRV3HwEAAABAEswkBwAAgKrV3t6u+fPnS5K22mqronakNzY26vvf/76kTCf96tWri5Z3udkTau+9994EGwAAAABUDAEHAAAAVKWOjg6de+65zgTexx13XNHLOOKIIzRkyBCtWbNG8+bNK3r+5fDKK6/oX//6lyzL0ty5cytdHQAAAAC9GAEHAAAAVI33339fM2bM0OGHH67dd99dt99+uyRp88031/7771/08hobG3XOOedIkv70pz91y6ccLrvsMkmZOTwmTZpU4doAAAAA6M0IOAAAAKBqDB8+XJ9++qkWL16sL774QpLUr18//e53v1NtbW1Jypw1a5YOOuggrVy5Utdcc01JyiiVp556SgsXLtQmm2yin//855WuDgAAAIBejoADAAAAqkZDQ4OmT5+u5uZmDRgwQHvvvbduvfVWbbHFFiUt91e/+pVGjRqlG264Qe+8805JyyqW1tZWnXfeeaqtrdXFF1+sxsbGSlcJAAAAQC9npdPpdKUrAQAAAAAAAAAAujeecAAAAAAAAAAAAAUj4AAAAAAAAAAAAApGwAEAAAAAAAAAABSMgAMAAAAAAAAAACgYAQcAAAAAAAAAAFAwAg4AAAAAAAAAAKBgBBwAAAAAAAAAAEDBCDgAAAAAAAAAAICCEXAAAAAAAAAAAAAFI+AAAAAAAAAAAAAKRsABAAAAAAAAAAAUjIADAAAAAAAAAAAoGAEHAAAAAAAAAABQMAIOAAAAAAAAAACgYAQcAAAAAAAAAABAwQg4AAAAAAAAAACAghFwAAAAAAAAAAAABfv/A4VdK2Oqvw3XAAAAAElFTkSuQmCC", "text/plain": [ "" ] }, "execution_count": 26, "metadata": { "image/png": { "height": 378.25, "width": 664.6999999999999 } }, "output_type": "execute_result" } ], "source": [ "a=so.Plot(data=turning_psd_df.dropna(), x=\"Freq\", y=\"turning_psd\", color=\"Shuffled\", linestyle=\"recording_length\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=\"Power\")\n", "a" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
recording_lengthFreqShuffledBatchFlyTrialspeed_psd
023.858025e-07Non-shuffled Data1114.177945e-02
123.858025e-07Non-shuffled Data1213.941180e-02
223.858025e-07Non-shuffled Data1314.859670e-03
323.858025e-07Non-shuffled Data1412.332019e-02
423.858025e-07Non-shuffled Data1511.508095e-02
........................
1007995245.000000e+00Shuffled63811.991003e-08
1007996245.000000e+00Shuffled63913.298466e-07
1007997245.000000e+00Shuffled64016.887603e-07
1007998245.000000e+00Shuffled64113.962093e-07
1007999245.000000e+00Shuffled64211.130525e-06
\n", "

1002000 rows × 7 columns

\n", "
" ], "text/plain": [ " recording_length Freq Shuffled Batch Fly Trial \\\n", "0 2 3.858025e-07 Non-shuffled Data 1 1 1 \n", "1 2 3.858025e-07 Non-shuffled Data 1 2 1 \n", "2 2 3.858025e-07 Non-shuffled Data 1 3 1 \n", "3 2 3.858025e-07 Non-shuffled Data 1 4 1 \n", "4 2 3.858025e-07 Non-shuffled Data 1 5 1 \n", "... ... ... ... ... ... ... \n", "1007995 24 5.000000e+00 Shuffled 6 38 1 \n", "1007996 24 5.000000e+00 Shuffled 6 39 1 \n", "1007997 24 5.000000e+00 Shuffled 6 40 1 \n", "1007998 24 5.000000e+00 Shuffled 6 41 1 \n", "1007999 24 5.000000e+00 Shuffled 6 42 1 \n", "\n", " speed_psd \n", "0 4.177945e-02 \n", "1 3.941180e-02 \n", "2 4.859670e-03 \n", "3 2.332019e-02 \n", "4 1.508095e-02 \n", "... ... \n", "1007995 1.991003e-08 \n", "1007996 3.298466e-07 \n", "1007997 6.887603e-07 \n", "1007998 3.962093e-07 \n", "1007999 1.130525e-06 \n", "\n", "[1002000 rows x 7 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# repeat for all other PSDs\n", "speed_psd=summary_both[\"speed_psd\"]\n", "speed_psd_df=speed_psd.to_dataframe()\n", "#drop batch and fly columns\n", "speed_psd_df=speed_psd_df.drop(columns=[\"Batch\", \"Fly\"]).reset_index().dropna()\n", "speed_psd_df[\"recording_length\"]=speed_psd_df[\"recording_length\"].astype(str)\n", "# speed_psd_df.drop\n", "speed_psd_df" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 41, "metadata": { "image/png": { "height": 378.25, "width": 664.6999999999999 } }, "output_type": "execute_result" } ], "source": [ "\n", "speed_psd_df[\"case\"]=speed_psd_df[\"recording_length\"].astype(str)+\" hr recordings \"+speed_psd_df[\"Shuffled\"]\n", "a=so.Plot(data=speed_psd_df.dropna(), x=\"Freq\", y=\"speed_psd\", color=\"recording_length\", linestyle=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=\"Power\")\n", "a" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 44, "metadata": { "image/png": { "height": 378.25, "width": 678.725 } }, "output_type": "execute_result" } ], "source": [ "\n", "speed_psd_df[\"case\"]=speed_psd_df[\"recording_length\"].astype(str)+speed_psd_df[\"Shuffled\"]\n", "a=so.Plot(data=speed_psd_df.dropna(), x=\"Freq\", y=\"speed_psd\", color=\"case\", linestyle=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=\"Power\")\n", "#add title\n", "a.label(title=\"Speed PSD\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABj0AAAN6CAYAAADVYovjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAB2HAAAdhwGP5fFlAAEAAElEQVR4nOzdd5icd3ku/vut08vOFu2uVlp1yd2ybMsF3LDBxgIDBmNbgQABklBOfiHl5OQkh5xcOYSc5CQkIQQICWCQABtjYzDFDfduWZJttV3V7W12+szbf3/M7uzMTtmqLdL9uS4udt4239mm9XvP8zyC4zgOiIiIiIiIiIiIiIiIljlxsRdAREREREREREREREQ0Hxh6EBERERERERERERHRGYGhBxERERERERERERERnREYehARERERERERERER0RmBoQcREREREREREREREZ0RGHoQEREREREREREREdEZgaEHERERERERERERERGdERh6EBERERERERERERHRGYGhBxERERERERERERERnREYehARERERERERERER0RmBoQcREREREREREREREZ0RGHoQEREREREREREREdEZgaEHERERERERERERERGdERh6EBERERERERERERHRGYGhBxERERERERERERERnRHkxV4ALS+JRAIvv/xy4XFLSwtUVV3EFRERERERERHRfNF1HX19fYXHl19+OYLB4CKuiIiIaGYYetCMvPzyy/jsZz+72MsgIiIiIiIiogXwb//2b7jxxhsXexlERETTxvZWRERERERERERERER0RmDoQUREREREREREREREZwS2t6IZaWlpKXn8b//2b2hvb1+k1RARERERERHRfDp58mRJW+vJ9wGIiIiWOoYeNCOTh5a3t7dj48aNi7QaIiIiIiIiIjqdJt8HICIiWurY3oqIiIiIiIiIiIiIiM4IrPSggl27dmH37t01j9E0bYFWQ0REREREREREREQ0Mww9qCAajaKzs3Oxl0FERERERERERERENCsMPaggEolgw4YNNY/RNA1dXV0LtCIiIiIiIiIiIiIioulj6EEFO3fuxM6dO2se09HRgR07dizQioiIiIiIiIiIiIiIpo+DzImIiIiIiIiIiIiI6IzA0IOIiIiIiIiIiIiIiM4IDD2IiIiIiIiIiIiIiOiMwNCDiIiIiIiIiIiIiIjOCAw9iIiIiIiIiIiIiIjojMDQg4iIiIiIiIiIiIiIzggMPYiIiIiIiIiIiIiI6IzA0IOIiIiIiIiIiIiIiM4IDD2IiIiIiIiIiIiIiOiMwNCDiIiIiIiIiIiIiIjOCAw9iIiIiIiIiIiIiIjojMDQg4iIiIiIiIiIiIiIzggMPYiIiIiIiIiIiIiI6IwgL/YCaOnYtWsXdu/eXfMYTdMWaDVERERERERERERERDPD0IMKotEoOjs7F3sZRERERERERERERESzwtCDCiKRCDZs2FDzGE3T0NXVtUArIiIiIiIiIiIiIiKaPoYeVLBz507s3Lmz5jEdHR3YsWPHAq2IiIiIiIiIiIiIiGj6OMiciIiIiIiIiIiIiIjOCAw9iIiIiIiIiIiIiIjojMDQg4iIiIiIiIiIiIiIzggMPYiIiIiIiIiIiIiI6IzA0IOIiIiIiIiIiIiIiM4IDD2IiIiIiIiIiIiIiOiMwNCDiIiIiIiIiIiIiIjOCAw9iIiIiIiIiIiIiIjojMDQg85qOc2EZdlIZXQMRDOwbCe/XTdhmDZs20EircOy7EVeKRERERERERERERFNRV7sBRAtpMHRDGzbgUuVkMmZyOZMCALg5LMO5DQTtuPAsvIbJEmAZTkQRQEelwyPS4bfq8AwbWRyBizLge04WBHxQhAE5DQTbhd/rIiIiIiIiIiIiIgWA+/O0lnFNG1kciZSGaOwbTzwAADDLK3oGA8/bNtBOmsgnTUwHMtWuHI+TMloJlZEvLBtByG/63S8BCIiIiIiIiIiIiKqgqEH0TwoDlEGRjIQBMClSrBtB5btIOBVF3F1RERERERERERERGcHhh5Ep4HjAN0DqcJjVn4QERERERERERERnX4MPYgWwFAsi2RGh8+toC7oXuzlEBEREREREREREZ2RGHoQLQQHyGlW/n+6hYBXgZ8tr4iIiIiIiIiIiIjmlbjYCyA626SzBvpHMjBMa7GXQkRERERERERERHRGYehBtEiGYzkYpo1YUlvspdBZRjMs9AylkMkZi70UIiIiIiIiIiKiecX2VnTW6B1O4ak93YindGR1E5puIauZyOkmslr+sWk5sG0blu3Ath3YDqDIYuF/blVG0Kci6FMRCbrRXO/FiogPAa8CQRBmtJ501kA6m7/pLAjgoHNaMNmciWzOhCQK8LqVxV4OERERERERERHRvGHoQWeFPYcH8cVvvnDarh/0qVjbGsK6lSFsXl2HpjrPjEKQodEsRpMaIkE3gj7O+qDTyzDtkv8nIiIiIiIiIiI6UzD0oLPCU3u6T+v1E2kd+zqGsK9jCADQEPbgvLURbNuyAqtWBKZ1DdO0EU3kZlU1QjQT4/NkLNtZ5JUQERERERERERHNL4YedFbY0l6HJ17tWrDnG45l8dTrPXjq9R6sbPTjygtacNk5K6AqUs3zTNPG8d4E6gIu1AXdC7RaOtuMV3iYlg3HcRiyERERERERERHRGYOhBxXs2rULu3fvrnmMpi3Pods3X7kGXreC1w4NAAA8LhluVYbbJcOtSvCoMlwuCYokQhIFiKIASRQBIR9E6KYFw7SRzZmIp3Uk0hoGR7MYiGbQP5Ku2SaoZyiFHz/RgUdeOol3bW/H9vNbIInVbzLbtoNoIgfbcRD0qVDk2kEJ0Uw4jgPDGvt+dQDTcqDIDD2IiIiIiIiIiOjMwNCDCqLRKDo7Oxd7GaeFIAi49pI2bFwVRiZnzuu1LctG92AKx3rjOHxyFJ3dsYptgxJpHfc90YGXD/Tj7ndtQVOdt+o1HQcYTWhIpHWE/C6E/S6INYISoukyLRtwSh8rsrh4CyIiIiIiIiIiIppHDD2oIBKJYMOGDTWP0TQNXV0L1yZqOZAkEe0tQbS3BHH9tlXIaSYOnozilQMDOHQiisnxx8n+JP5h12v44PUbcfl5zTWvbVkOovEc0lkDQZ+KkN91+l4InRUmVyWZFoeZExERERERERHRmYOhBxXs3LkTO3furHlMR0cHduzYsUArWp7cLhlbNzVh66YmjCZyeGZfD57d2zvRUgj5G88/ePQwTvYn8P5rN0Ce4p32mm5hSM9CN2w01nlO6/qTGR2yJMLj4q+HM5FtO0imdRzpGoWqSPB5mhHwqou9LCIiIiIiIiIionnBu5pEp1Fd0I33vn09rtnahvuf6MCbx0ZK9j//Rh+6B1P4yC3noCE8dZgRT2lQZBHhwPxXfKSyBrI5E8mMDsdxsGpFYMrB67T8DEQz+H+7X0M8rQMAHniyE3/xie3YtLpukVdGREREREREREQ0d2zkTrQAwn4XPvGe83DnTZvL5iecGkji73e9ipfe6p/WtYZjWYzEs0ikdYzEs4XtpmVXnCUyLqdXn2XiOA5GYlnEUxps24HjAL3DacRTy3NwPVXmOA6++eAbhcADAEaTGh5/5dQiroqIiIiIiIiIiGj+sNKDaIEIgoDt5zWjrcmPb//8LYzEc4V9umHjh48eRu9wCu+7Zj0EofbQ8tHERBiR0y2IgoB01oBLlaDIIgRBgABAkgRYtoOgV0XvcBqrmwPQdAtul4zhWBYBrwKvW8FwLFc+68G0MTSahSQK8LP90RmhoyuGA8ejZdv7RtKwbAeSWPv7joiIiIiIiIiIaKlj6EG0wFY2+vGFuy7B7kcO461J7a6efr0HjgN84LraA+WLZXMTFRyabkHTrbJjEqn8O/u7B1MwTRsuVYKmW0imdYiiALtGhchANANRFOB1K9NeEy1Nh0+OVtyeSOnIaib8Hn6NiYiIiIiIiIhoeWPoQWcln0eBZlhQZRH1YQ8c28FoUkMmZwAAVjb5kdMs2I4DAcgHA44DSRShyCKGRjPQDbv2k9TgdSv4nfechxff7MeDT3VCL6qyeGZvDwJeBTdetnrKio+ZMseepzgYqRV4AMi3uhpKI+R3IRJyFz4ftLzYtoOT/YmK++JpHTmGHkREREREREREdAZg6EFnFZ9HQX3YA5ciIZbUIEkCXGPDultc+R8H07IhSyLcavUfj+Z6H2zHgSKJiKd1mJYNTbdg205Zm6hqBEHAlRe0YPWKAL7x4H4kM0Zh3y+eP4G3jo3gjndsQmujfw6veP7EUxriKQ11QRfqQ1MPXaelpW8kjRO9lUOPRGo88OPXlYiIiIiIiIiIljcOMqezSsjvKoQcQZ8KX4WWTbI09Y+FqkhwqzIkSUQk6EZTnRerVgTQ3hLEquYA3C5p2mta2eTHp993QVn1xMn+JP7lvr042hOb9rUWQiKtw3FqV4dMJaeZ6B1KIZnJt90aTeYwOJpBKqMXqm1o/uR0E5msUbXSQzdtJNL6lFU/RERERERERERESx1DDzpriaJwWto0uRQJzfU+BP0q2lsC8LqnLqhqawrgxstWl23XdAvfeOANPLevF5Y1+3Za88myHPSNpOcUfAzFssjkTAyMZNAzlMJILIdESkf/SAYD0Qxvvs8zTbcwmtCQqzDvZVw8pUMzSvdbtjPngGsm+HUnIiIiIiIiIqK5YuhBdBrIkoimOi8UWcKKiBd+rwJZrv3j9q4r2vGRm8/Blva6ku2GaePHv+nA//3+qzjeGz+dy0Y8reHFN/vwm9e60DWQrHpcJmsilZ1dRcbQaLZkpkjxIHYgH6qMJnOzujZVltVMRBO1P6eJtAZzLFizbAdZzYSmm8hqZs3z5lMyw2oTIiIiIiIiIiKaG870IDrNJElEc70PANA7nEImW/kmsigIuGRLEy7Z0oTHXj6Fh58/XrJ/cDSLr963Fx++aTMuP7d5XtfoOA6e29+Lnz17rGRA+2XnrMAH37ERqlzeriuR1hHwqjN6ntFkDvGUNvVxCQ2yJCLkd83o+lRZTreQmqJt2PP7+3DFBS0AAN2wkNNMOMiHUN4KbeBOh1TWgKpI8Lj4TxMREREREREREc0OKz2IFlBTnRc+z9Q3kG+8fDU+dMNGKJOqQ2wH+MEjh/Gdhw8gp8/PO/Adx8GPn+jA/b/pLAk8AOCVgwP4jwffKGt7BOQrNNLTqPYYb480Es9iJD79Co6h0Sx6h1PoH0mjezAJvcIaaGqWZcM0baQztb9WezuGcLQ7X0mk6RayupmfBbJAM1bsQnUJv85ERERERERERDR7DD2IFpAsiWhp8GFVcwCYYpzIVRe24s8/djm2n1de1bGvYwj/42vP4RsP7Mfz+3uRSOcHgluWPaN2RJmcgV+9eALPv9FX9ZjO7jh2//oQ7AqzHQaiGRhm9VkjumGhayCJ4VgWowkNmGHnokzWRCpjIKdZGIhmZnYyAUAhsEpPCi/CgfIqmuf39wLIf900Pf8/w7QXpOWUadmAg3kL84iIiIiIiIiI6OzEHiJEi8ClSKgPuZFI6TVDg7DfhTtv2ozN7XX4/i8PYvK950MnR3Ho5Ch+/EQH6kNuRJMabNuB1y3D71Hg8yiwLAcjiRwc24EkCWis86KpzoPjvYlpBwn7O4fx1J5uXL9tVcl223bQP5JGW5MfglCe4gzHs9ANG7oxdUurqWh6vuWSm62PZmS8emfyDJarL2zFS2/1YziWLWx769gIgHxQYlkT32yGZcMllrc4m0/W2Dd3paoiIiIiIiIiIiKi6WKlB9EiqQu4sWpFAF731Dfxt25qwmc+eBHqKrw7H8gXUAzHc4V35GdyJgZHszjem8CpgSTSWQMZzUQyY+BYTxwvvtlfMfBQZBF/fPc2/M3vXYWWBl/JvoefO46+kXTZOZpuYWg0W7bdMO2q80tmK5kxCu2yaHqymokn93Thmb09JdtXNvrwt599W8m23uE0hmOZslZiC9FabDz0MAwblu3M+3PWChdPF8ua/nPy+5qIiIiIiIiIaH4w9CBaRKIooCnihShO0esKwPqVYfy3D29F66QwYt7WIgB33bQZK5v88LkVfPK958NbVFVh2Q7++YevI54ur9pIpHXEUxrMsfZaJ/sSGBytXkXiOLO7qZ3M6Djem8DJ/sS05okQcM8vDuCnTx8r274i4kPIp2JNS7Bk+6sHBzH5/vuChB5FAYGmm4gl514dVMww5/YaZhNKWLYz7eDDWoAWYkREREREREREZwP2iSFaZLIkorXBh1hKQ2qKYdNhvwtfuOsSxFIakhkDHV2j2N85jO7B1KyeWxSAgFeFz6Pg1qvX4ty19YV9kaAbH7xhI+755cHCNs2w8H/veRWfuf0irGzyl1xraDRbUvFR7Z31Lx/ox69eOIHRpIaGsAc3XrYam1aHcawnDgECNq4KI+BTK547Xsky3larvSUIWWJ2W81wLIvn9vVW3BcOuCCJAjavrsOJvkRh+57Dg9jQFi45diGqJGLJHJ7e24OAV0HAtxKpjIGGsGdageBUbNuBac0tVNAMC251Zv9kjgcZkpQPTSq1gCs+Vj69HcSIiIiIiIiIiM4KDD2IlgC3S0azS8aAkEFybCh5NZIkoj7kQX3IgzUtQdx0eTviKQ2jiRy8HgUNYQ/iKQ3ZnImhWBYuRcKKiBeqKiGrmejsimE4lkXQ78KF6xsqDrQed/GmRjy7vxfHeuKFbRnNxNd+si8ffDT6q55byZN7uvHTp48WHg/Hsvjho4dLjvG4ZHz6fReUVSBM5jj585vrT0/ly3JmWTZiKQ2vHuyvOjs+6FPzoUd7HX790snC9jc6h3H1Ba0YjmfRM5SCqki47pKVp/XzbFo2/t/uPTjemw9fhmNZ3HR5O3TDmpcZLpbtzHkYu6ZbcClSzeBisuLnNC0Hilwj9LBsQGHqQUREREREREQ0Vww9iJYQr1ueMvSoJOR3IeSfCC/qAm7UBYDWSaGEz62gIeSZ9nUFQcCHbtiIf7t/X0kVSiZn4hsPvIEv3H0Jwv7qoUmxI6dG8VBR4FFNVjPxjQf24092XopIyF3z2FTGQEzVagY3Z5tEWsdwLAvbdtDZHa96XNCnQhQFrG4Owq1KyOn59k+prIF/2P1aybH9I2n8r9+5Yl6qLip57eBAIfAAgOf29+Kmy9vz1RXzEnrYsOy5VatohgXbdiBJMwg9HAfO2NOalg1Frl6VNNdQhoiIiIiIiIiI8tgXhmgJ8bpkSJIAl7p03vHdXO/DX3xsO7af11yyPZnR8Z2fvwVzGq2PNMPCDx45XLXqYLKcbuG+J47AnsYchVhK4xDoItHExED74iBhMo9LhiAIUGQRa1tDNa/52qFBxFPzO2NjnGHaeHrSkPV4Skc6Z8xbWy3bdjDHzAOabk3r+7GYZU3M9Kg128NxHM70ICIiIiIiIiKaJww9iJYQSRKxstGPFREvcHreVD8rLlXCh2/chLdfvLJk+8n+JH76zNTVG4++dBKxopvmAoDbr9+A7ec1Vw14Dp0cxXd+/hb6R9I42hOrWgFjmjaSGQO27UAzLJiWXTJb5GyS081CCOU4DroGklWPHW/TJIkCQv7KM1SKHTgxMj+LnCSR1vDaocGy7X3DaZjTHAI+Fdt25lzpYZj2jIMJ25loq2XUDD0w40CFiIiIiIiIiIgqY3sroiVGHevr3xDyYDi2dG7eC4KA912zHsOxLA6eiBa2P7evF1df2Fp15sMbR4fx+KtdJduuvaQNb7soH6Dc8Y5NiKc0BHwqvnrfXpzsTxadO4I3juZvtgsAbrtmPa69pK3sOQajGYxIAkRBgMctI5HSIUsC6oK122OdKUbiWdQF3EhnJ1qQpXMmsppZ8fjLzl1R+FgUBfjcypTPcejEKK6+cOWUx81Uz1CqZN3j+obTuGBDw5yvH03k8O/378NIPIeP3HIOtm5umvE1LMseqxZxSrZJUu33DViWg/ERIFaNQeqO44CZBxERERERERHR/GClxzIyPDyMv/qrv8L111+P888/H9u3b8fnPvc5HDx4cLGXRqdBOOCCxz2WSy5k1YcAREJuyBXmD4iigN+6eQvqi2ZtOAB+8fyJsmMHRzP40ndexn/97K2S7QGvindtby+5Zl3QDVkScedNm+FzV85iHQA/ffooOrpGK+63LAeGaSORyleEjCRy6BtOI5PL31DP5IxCG6wzqZWQZTuIJTUMRDMYTUxU04xUCMwiQTfWrQzh4zvOK2yTJAF+79ShR2d3bF7WW8y0bMSSldtm/eL54+gerF6pMl3fffgAXnyzHx1dMXz5nleQ0ysHQbWMV2kUf9tM1Xrruf29+IN/ehJ/9M9P443O4apVK8d74/jcP/wGv/93j+Ph547PeG1ERERERERERFSKoccyMTAwgNtvvx0/+MEPsGrVKnzkIx/BFVdcgSeeeAJ33HEHXnrppcVeIp0GTXVerGoOYP3KEBTl9P64ulQJQZ+K5nofIkE3Wht8FQdXe90K3vv2dSXb3jg6jDeODhceRxM5/Ou9ezE06ca7KAr46LvPqTqcurnehz+6extaqlSNOAAefGrqdlrjB6ezBnqH0jjZl0DvUBr9Ixl0DSTRN5ya3jWWAM2wau5PpDQ4Y6+12HC89HN//rp6/OUntuOLn7wCq1YECtslUUTAN/Ug+FP9cw8gJktnjarVKDndwpe+8wreLPq+mo0niqqMMjkTrx8emtH5lu3gwLERfPW+vfi7e15B79j3jl4j9LAsG1+/fz+SaR3RRA7/8dM3qoYku399CN2DKWRyJr710zeRylRu40ZERERERERERNPD0GOZ+MpXvoL+/n78yZ/8Ce655x789//+3/HP//zP+K//+i+Ypon/9b/+12IvkU4DRRbhUiQIgoDmet9pDT78HgVNES/8nvy7/lVFqjpb5IL1DVhddOMcAL77iwOFKoxfvnACqQoti265cg02tIVrrqMu6MbnPnQRtp/XjAqZC3qH0zg6w6qD8RvO6awBTbeQ0ywYZj5MmCpUWEyGaVes2BhnWjZGq1RKjMRzJY/Hq3OkSZ9Utyqhpd475VoSaR2JKnNVZiudM5DNVa+8sG2nYhXRXGgzqPSwLBupjI5//tFeHO2JY++RIfz7j/cDQOH7p5KRRK5khs3x3gRePtBfsUrpxTf7Cx+blo1XDw5Me31ERERERERERFSOoccy4DgOHn30UUQiEXziE58o2XfFFVdg+/btOHHiBI4dO7ZIK6SF4FIktDcHsao5UAgiZFmEUqEN1Wz4POUtjnweBY1hT9l2QRBw+/UbSvIQy3Jw7+MdeP6N3oo3bi/e2Ijrt62a1lq8bgV33rQZf/N7V+OvPnkFNq4Kl+z/6o/34RfPH5/ToOuBaL7qo6s/iVP9iUIbrKUkldWR0UxYFV5nPKXhRF+iZM7EuN7hFH75womSbSsi+WBjcugR8rvQECr/Gldysi8xzZVPj6ZbyFSp9Bj3zN6eWV+/0udmJjTDQmdXDNHERIC0tyNfKVKrvZWmlwci3/n5AfzRPz+N5/b11nxODjQnIiIiIiIiIpobDjJfBgzDwOc+9zkoigJRLL/BraoqACCTySz00mgRuBQJLfU+uF0yJFGAZTsYjGagGRY8Y22jcroJw8jflG2s88ClStB0C+mcgUw2f5N5fMCy4+RneIwPUJ8s6FMRS2plN3lXNwex423r8LNnJ8K24VgW9z3eUXKcKAD/42OXoz7ohiDMbDiJxyXD45Jx4YYGdHTFSvY9+vIpHDgexWduvxDeaQziniynTdyY1g0bvUNpSJKASNCNkH/qdk8LIauZgAMkMjr8HgWZnImQ3wXLsvOVHJPuj5uWjcdeOYUnX+suu9a56+ohikLFlmWR0PQGvp/sT8zLcHEgv1bLcmpWeoybztDwSioFKtkKgUQ1mm6hbyRdtt228/NjHMep+D2drhKgOQ7wtfv34eqLWqs+pz37HI+IiIiIiIiIiMDQY1lQVRUf+9jHKu6LRqN49dVXoSgK1qxZs6DrosVTXJUhiQJaGspnYGiGhZxmFm7gu1UZIb8LqawBw7AQDrigGRZ0w0bQp1Z9LkEQEAm5MTBSHqrdcOkq9I2ka7bkedcVa6ZdSVDN+esb8OBTR8sGkPcMpfDgU0dx97u2zOn64yzLwdBoFrIkVqx8WWjjwVUsqUE38q2WvG4ZqaxRVsVgWTa+/pP9ONoTr3itlY1+GGbl8KBSyLNuZQgb2kJ45KVThW1dA/M310M3LGQ1Ez99euoZLQPRDFob/TN+jkrzMWYyM0MzLPQOl4ceWc2EbTuwbQeSVCH0qNDabdxULcIcVnoQEREREREREc0J21stc1/60peQTqfxnve8B37/zG8K0pnLpUgVb2b7PQrqxqou3KpcM/AYF/Cq8HsrhwC3XLEGripVIisb/bh2a1vFfaIoVJwXUknY78IHrt8AtUIrr9cODZQNTJ+rwdFMzfZFC2G8mgDIhzHJtA7Hyc/qqHTj/Nn9vVUDj/PW1ednwsgixAqVCW61/OsX9ruwIlIapnUPzt8AeM2w8KPHDk8uVqlotmFLskLAUSuQmEwzLPQOlb/mTM6EZTtlIdxsnmOyOXbkIiIiIiIiIiI667HSY5HccMMN6Omp3at+y5Yt+OlPf1p1/z/+4z/iZz/7GZqbm/Enf/In871EohLN9T5k/WY+EDAmAoFIyI1Pvvd83PPLA0hm8jd7G8Ie3HLlGly8qbHsJrsgAOGAC2G/C5btoG8kDcOw4XZJMMx8y6NKrrqgFVdd0Ip0zsC//Oh1DI7mgw7bAR57+STueuf8VHsA+ZChdyiFtib/rNoqzQe9yqDsVKbyDfWDx6Nl285ZE8Elm5twy1XtAABFFstmegCo2KLJ51HQHCkdcD6flR7JjIF9HcPTOvbUQBLbz2+Z8XNU+lylZhBImKaN/goVTqmsjuHRLCJBV8W2cMlpDnyvNKtlLnNqiIiIiIiIiIiIoceiWbVqVWEWRzVtbZXfIW/bNr70pS/he9/7HsLhML75zW8iEomcjmUSlfC4ZLQ2+NA9mCoJJzasCuN/fmw7OrpH4feoWLUiUPHmOpBvpVQ/1u5KkvLVID2DKbTU+2DZDk4NJMtmVRTzuRW8a/safO9XBwvbXj4wgGsvaUNrg7/qnIWZMkwb/dEMVs6irdJ80I3p3fweimXx1rFhHD41WrL983dcjHWtIQCA35P/XeNS5YozPSppbfChKeItzI0BgNGkhv6RNJrry9upTUfx1+bopBkt4za31+HwydLXMhCd3byiuYQelmXDth0MjZY/9x//y9PQDRv1ITf+5veuQltToLBPNyyMJrVpPYdeoZpI06eecUJERERERERERNUx9Fgk3/3ud2d1Xi6Xwx//8R/j0UcfRWNjI/7zP/8TmzdvnufVEVWnyBLamvxlwYdLlXD+uimGXAtAXaC05ZYsiWgbC0kkKd9WKTbFTeOLNzXi1y+dxGDRDem///5rCPlUpHIGPC4ZF6xvwLuvWgv/HGZzZLV8G6NqAU4tlu0gkzOQzZlwqRJs24GqSHC7ZGRzBhzk24bVOncqo4kc/ukHe/IDz4u4FAlrWoIA8pU149UqngptrMa979r1ePCp/HwNj0vG9vObIUsimut96Clq8fTG0eFZhx5Do1k01nkgCAKO9sQqHnPLlWuw/bxm3POLiVBrqjkY1UQT5W3P0lUqZSYzLBsZzawYTIwHUiPxHB586ig+96GLC/v6R9KIJnPTeg7dKK/myc1g0DoREREREREREZVj6LGMxONx/O7v/i5ef/11rF27Ft/61reqVoMQnU6KLCHoUzGamN472sd53XLFdlHFoULQp04ZeoiigJuvaMc9vzxYsj0+dnM8lTHwwht9eOGNPqxpCUIShbEZJvnAZkNbGOtWhqauCHGAnGaWDTWfTjXJSCw7cbO+eBb22GmyJMLrVioGKkOjGaQyBhJpHf0jaaxbGYJc4fP25J7ussADANqa/IW2YnLRHJRarbp23rwFumGjbziFGy9fjZDPBdOysWqFvyT0ONGbgGnZSGeNijNjqrFtB4mMDpcqIeBVcbw3UfG4kN9V0j4NwJTfD5VYtoP+ChUiqez0AhTDtPHC/r4pj/v1iycLoYfjONBNe9rBSqXZHww9iIiIiIiIiIjmhqHHMpHL5fDpT38ae/fuxcUXX4yvf/3rqKurW+xl0Vks6HPl2/jMYPDyeJulWlRFQkPYA1EU8pUcVa5/8aZG7Oscxr6OoZrXO9FXenP9zWMjAE7i3LURfOSWc+BWa/8azE4KPdJZA9FEDisb/VVbRVm2U3GINoDC6zFNGwMjabQ05Nt6jYcatu0gnTVwoi+Bf733ddhOvgXYH965tSS0yOkmXj7QX/Ep2pomWnIp05xJ4lZlfPimTYjGcwj4VCiKCFEUsLY1hBffnHienqEUhmNZpLIGvG4FSoXh8kA+NCjel9XMfIikW5AkAx1V2luF/GpZkJNIzzz0iMZzc2pvde9jR/Dw88dn9JyW7QAOKgZRlaQqfI+wvRURERERERER0dww9Fgm/u7v/g579+7FhRdeiG9/+9vwer1Tn0R0GimyiJDPhXhqejekPW552q2mwmMtsDwuCaaVDwSGYhlkshM3hAVBwJ03bYIkCthzeHDG6z9wPIpv//wA3vu2dUikNciyiPaWIFS5tAVULKkhkdYR9KlQZBHDsSwcB0hm9KqVDtmcAWcaYVAmZ2I4lkNON+FxyagLupHK6HAc4PFXTmFslAZ6hlLYf3QYWzc1Fc59+vWeqlUBWzdPHFepQqSa8aoTVRahyhJsycGGVeGSYwZHs4UwIZXVURdwl+zP3/jPhz51AVehIiY3djNfNyy8dWy4ajAQ9rvKKiViKX1Gs1oM00I8rVV8jmqD4Is5jlNo9TUT5lgrrGmHHtny41jpQUREREREREQ0Nww9JolGo7jlllsQi8Wwf/9+uFy127fkcjncc889+NWvfoXjx/PvCm5ra8M73/lOfPSjH0UoFJrzmrq7u/GjH/0IALBhwwb853/+Z8Xjbr/9drS2ts75+YimKxJyI6sZNYduy7IISRTQ2uCb8YBxRZagjP2Wao74cKw3XlL54VZlfOSWc3D3OzfjxTf7sf/oENJZs6QdUy1HTo3iH3a/Vnjsdcm4/LxmvOuK9pIKENt2ylosZXJm1dBDqzCroZrx0EjTrcJzOI4zVpEy4Z5fHMTR7jiu2boSo0kNv3zhRNm1zl0bwZUXtKC9OVjYJlepxKhkvHJFUSSoigjbQWE2yLih0UwhgMhpFhAovYZp2cjmTKSzBgJeBcpYiGSMBQKaYeHZvb1V1xDwqfC6FQiY+FKnswbiaR3habbTSqR1wEHFuSjpnFFWhTJZ30i66r5azLGUaqrQw7RsSKKAdIX1aQw9iIiIiIiIiIjmhKFHEdu28cUvfhGxWGxaxw8MDOATn/gEOjs7S7YfOXIER44cwf3334+vf/3r2LJly5zW9dprr8Gy8jfCfvKTn1Q97sorr2ToQQtKEgW0NvrRM5gq3NQu5vcqWBHxwnYw48BjMlEU4FKkijeFJUnE1Re14uqL8t//tuPg4PEo4ikNmmFBlkSMxLM4cCKKodHy4dbjMpqJJ/d0o7M7hv92x9aaN8azmlm1+qBWCDQd8VTl1ljP7e/Fc/vLAwOfW8b//Ph2eFzlv9JnUukxPgdEkcXCeQ0hDxRZLHx9c7qFTC7f8itXoRWTadqIpzQYpg3dsAuhR2FotwN0Vhlifts16+FSJEiSAJ9XKanKGB7NTjv0sMbCh0qtrBwHSGQ01Ac9Vc9//XDtlmlVn9car/SoHVzohgVFlip+/iptIyIiIiIiIiKi6WPoUeR//+//jUceeWRax5qmic985jPo7OyEIAi44447cMstt0CSJDz22GP4/ve/j76+PnzmM5/BAw88MKeKj9tuuw233XbbrM8nOp1kSURrow8n+5Ol8zcEoCHsgSAIkOaWdxS4VXla74QXBQHnrasv2/6+a4GhWBb/eu/rSNZoc9Q9mMLPnz2G91+3oeoxtu0gmsghEnSXBR+6Obd363cNJmd0/C1Xrq0YeACAPINPfqHSQxILr0kQgMY6D3qHJqofhmNZ+DwKLMuBblhQlYmWYKZlFwKS4iBsIjQxMZqYqJoRBOB33nM+wgEXrtm6EoIgQFUkhP2uktBjNJmruXbbdgrrt8dCj5F45XMSydqhx8ETI1X31Xpu07IxNJrJz6KpwTBtiIKAXIWKELa3IiIiIiIiIiKaG4YeALLZLP7H//gf+OUvfzntc+699168+eabAIA/+7M/w8c+9rHCvssvvxxbt27FH/7hH6Knpwff+ta38Ed/9Efzvew5GRkZQTQanfF5J0+ePA2roeVOkSX43ArSRe+sD3rVGVUZTIfHJSGZEQo3tWejMezBF+7ahvueOIKT/Un43DLcqoyuwWTJHI6n9/Zgw6owLljfUPVaowkNjpMPd8bZtlOx6mUmugamH3pcuKEBV13YUrbd71Xg9yglgcRUJFGAJAllA9pXRHyloUc8i/axtleZnFkWeowrDj/GP7eD0dJAoCHkwXnr6hEJTYRHblVG2O9C9+BEm7LYFLNjNMMqBD+W7UAzrHybqwpiVSppxp3sm1noND7s3rQc/ODRw1Merxv59laVvk84yJyIiIiIiIiIaG7O+tDjtddew1/91V/hyJEjAABRFGHbU9+w/N73vgcAWLNmDT760Y+W7b/lllvws5/9DI8//jh2796Nz3/+81BVdX4XPwe7d+/GV7/61cVeBp1BwgEX0lkDoiigqc4Dv3f+v989bgVrWhT0DKXmNPsgHHDhU7ddULItmdHxj7v3lNxcv+cXB/B7778Q69vCVa8VS2rwumV43fkh7bpplVa8zMJ0Qw9RFPBbN59TVmni9yporvfN+HlFUagYVLXUe/F60eOh2ESLsGRGLwyeB0qrO3K6idFkrtDiCgAGJoUeKyLe/HMXvYaAV0EoUNrKqlrLr3GaXhR6WDZGYtXbmMXHwhDLdgrD24tNVVUyWTprwOuW8dSeLhzvTUx5vGFakOXKoQcrPYiIiIiIiIiI5mZ+34a9zPz93/897r777kLg8YEPfADvfve7pzzv6NGjOHbsGADg1ltvhShW/jS+//3vBwCkUim88MIL87RqWgiGZSBn5GBa+XddO46DjJEtBGLO2NvWDcsoCclsx4Zh5asdTMtEQksVPradub37f6nzuGS4XRKa672nJfAA8pUIoiigud5XdYj4bAW8Kn773eeg+B64aTnY/cjhKdtVDY5mC9Un2VzlOQ0D0fS0KkAcx0HX4PQGsX/+gxeVzR0J+tVZBR5APnioFHq0NvpLHheHMppuTczrQGmlh6ZbGInlMDCSxr6OIfzosSN4ck93ybVaGnxQFLGkusTtkhHylX4Pxaeo9DBMuzBTw7IdDMerhx6JdP5a2ZxR+FkeP8+y7KoVItWkcwYeeekkvvfLQ9M6XjdtDI1m8cbR8jZaHGRORERERERERDQ3Z3Wlx/79+wEAkUgEf/EXf4Fbb70Vf/Znfzblea+/PvGe58suu6zqcdu2bSt8/NJLL+Haa6+dw2ppPgxnotDM/A1NwzIhSxJMy0LQ7Ycsyohl41AkBVkjh/G360uiBEmQoFs6ZFFGgzeCpJ6CYZkw7PwNblVSYNkWTNsC4ECVVFiOBcu2EMvGYdomVEmF3+WDAEC3DHgVD2K5BFb4GyGL029BtJS11PsgzXNLq0oUWURD2I2MZsCY49DwYmtaQ/jAdRvx4990FLZFEzk8vacHN16+uup5pmljOJ5FU523MDw7p5s4cmoUrxwYwJvH8je3JUlAY9iDLe0RKLKIay5eWRYQxZJaSZswUQBuuHQ1hmJZ7OuYGLDtc8tY1RwsW0vQN/vASRQFqEr512/LmkjJ4wPHozh0MoqNq+ogiQKyWr7FVU43YVrlZS5vHRvBdx4+UPE521b4ochiWUutyUPLk2kdlmVX/f6ybBvG2H7TsvHAU0ervs7kWKihmzYU04ZLkQrD1wUBJW3OpiOVNfDoy6fKtt/1zs14/cgQDp0obSV46EQU33rozYoBBweZExERERERERHNzVkdegSDQfzu7/4uPv3pT8Pv9099wpijRyduprW3t1c9LhKJwOfzIZ1Ol5yzFNx99924+eabZ3zeyZMn8dnPfvY0rGhhGJYJzZx4x/j4O/hj2Xhhm2mX3nS0bAsWrMK+/tRg2XWLrwkAujXxTvHx6+mWjmhmYntyrAqkO94LURAhiRK8igcBlx+6qUOWZKiSMqvXuVgWIvAYJwgCIkE3BkZqD42eqasvasXAaAbP7O0pbHv10ABuvHw1TNPG/qPDGI5lcfHGRjSNtWYCgERKh2070HQLe48M4b4njiAzqerDshz0j2TQP7bmVw8O4A/vugSBouDjZH9pa6s1rSHcevVaAMBz+3vxwFOdEAUBH7huY3lrJgFwzWCGRyWVZoBsWhXGioi3pDXVNx54A411Hnzhrkug6RYM00L3YAqaZqF7MIkX3+pH0Kvindvb8dgr5YHAuPbmIBRJKnstkVDpoPF4WodpO5DGlmdaNgRMfM+Zlg3TcmDZDp7b14tYsnplSDprwrYdmJYNTbfgUiQk0joUWZxydkjl6xkVh5dvP68ZN16+Cr/zN4+VbN/1q0NVKzpY6UFERERERERENDdndejxr//6r1VbU9UyOJi/6S2KIlasWFHz2KamJhw/frxwzlJRX1+P+vr6xV4GId8Sy3ZsmHY+kBnNxgAAoiAi4gkj4PKXzWygPL9HQVQW5zw4fLJ3bW/Hc/t6MD4vfSCawb6OIfzs2WMYiefnPTy5pxtfuOuSkiHmqYyBE30JfO+XBzCdWeujSQ3f/vlb+MwHLoIsi3AcB8/u6yk5ZlXTRCB79YWt2La5CYIgwKWWhxMuRZrz90ql0EMQBFx9USt+8pvOku1Do1k8u68Xt169FlnNwnP7evHjJzpKjjneF68552J1cxCKVF7p0RB2lzyOJTWYY1UZQL6dlSgKhRDEshyYpo0cDDzyUvWQBQB+9uwx3LR9NVyqBE23YLnz4YcgAMOTZoH4PAo+ddv5+M7PD1QNREYTOcQnhSy3XbMejXVeWLaNTavDOHIqNvFaagQrumlXnTVCRERERERERERTO6tneswm8ACARCJ/A8/tdkOSar+r2uv1lpxDNF22Y2M4E0VPoh9pfX6rGc4UgiCgLuie+sAZ8nkUrJ7UOuo7Dx8oBB4AkNVM3PdER8lMCAD49YsnphV4jDvem8CffPUZ/MOu1/Cdhw/gaE+8ZP/G1XUlj90uuSzwGG9pVSkImSlVrvx78fJzKwe8j750EpphIZXV8fBzx8v21wo83KqEhpA7395qUljTMKnSo7M7hv2dw4XHumEVZngA45UeNp54pQvJzNQzOb563z6Ypo1EWitUsFiWUzYA/YL19ThnTQTN9d5KlwEAnOhLlHzNvS4Z113SBlEUqs5JqUVjiysiIiIiIiIiolk7q0OP2dL1/A01VZ26d77L5So5h2imdEvHQGoIsWwcI5nRsbkhNC7gVeBSJQSKZln4vfm2YIIAQMjPqsAM3zi/ub1uymOOnBotGeo9EM3g0MnRkmPWtgaxbUsTPnTDRmw/rxnrVoYqXqtnKFVyUx8ANq4K49xJ8zQqCQVc8LrlsjkYs1GtUmRNS/n8ECBfmZBM6+gbSiOrzexmfWOdF4IgVJzpUR/2lB3/t999GYOjGdi2A8O0C/NDLNuB4wBZzcCDTx8rOeecNRH83vsvKLvWkVOjyOkmHAeFNmSmZZcEWwAQ9LkgigKaG6oPh5/ckiwcyH8dRCHffmvysPmp5NjiioiIiIiIiIho1s7q9lazNV4hMp02MuPvAp9tVclC2rVrF3bv3l3zGE2beb97mh/RsbZXaT2DZn8jVHn2A6vPJIIgoK1pogWYbTtorvehH2mIggC/Vxm76SwgkzPgUiXEU3phmHU1F21oxCMvnsRURRtvHY8WqkJO9pVWNaxpCeLzH7q4sLarxrancwb+cfceRBOlN9hLXxfwoRs2QlEkmDXad0mSAJciobneVxYczCdVkXD+uvrCUPZih06OoqmuPKSYynj1hCKLZb9PfW4ZblUqCQAcB3h+fy+u2doG3bQKLaBM08JgNIMTfYmS2RqSKOCOGzeVDIYvNhzLoaluooIjp1tlA9DDfhWSKKKlvnro0TuUKnkcGgufJEkELBuKPLMKnMFoBpGxCibdsCq2HCMiIiIiIiIiosoYeszCeMuq6QQAM6kKWWzRaBSdnZ1TH0iLyrRN9KUGsTLYAlnkzVBgIoBsqvMU5nuEA6787IuiG8bjN6PdERmCkB8+Xk1Lgw8fvmkTfvKbTug1QoeX3+rHzVe0QxAEDEwaZr2hLVwxHPW5Fdx0+Wr86LEjVa978cZGNNZ54XMriFebASGgcHP8dAYeACBLIq7ZurJi6HG8Jw73LFprjYcWlT5HgiDA71HKqh5O9iWR2KRDAKDKDhzHwd98+2XsPTJUdo1tW1Yg7HehWj4dTZSGHk++1lV2TGOdF5IkFKqHKhmdNM9j/PtMFARAEmfc3qp7MIUtYxU+8ZSOSMjNGR9ERERERERERNPE0GMWfL78O341TYNt2zWrODKZ/E3QYLBya5ilJBKJYMOGDTWP0TQNXV3lNwaXA90ycGK0CxkjC9ux4TgObDhwHBu24xS2OXBgO0XbMXas44ztLz3eHjtHFERIgghJFCEKUuFjWZThlt3wyC64ZRfcigtexQNRmH31j2Vb6E30o8nfALc895ZGZwpBEArvinertX+9NYY9MC0bumEj7HfBdhzkdBOZ7ESLpu3ntWD7eS3oGUrhuf29GBrNoL05iMdfnfgZiKU03P+bTnzwho0YjJaGHk2R6nMgLt2yAk/u6S7MkyhWF3Bhx9vWQRQFeNzyROghAMWlJysiXgS8CxOoCoKAc9bW4653bsaPH++AUTRP41hvHCsb/TXOrqy1ofY5w/HKlTDjlS+mZeOtYyMVAw8AWDk2BD7gqfw5mlxp0zWYKjtmy5o6SKKALe1TtxkbF/Lnn08UBYgQZtzeqqeockQzzELbML+nevBCRERERERERER5DD1mobW1FQBgWRaGh4fR1NRU9djBwUEAqHnMUrFz507s3Lmz5jEdHR3YsWPHAq1o/pyK9eB/PfH/kDGyUx+8AERBQNAVQMgdRNgdRJOvHi3+JrQGmhD2VJ75MJlpm+hN9MMluxBQfZBECT61+k12KiUIAlob/LBtp1AlYdsOTg0ky9pJrWz04453bCo87uiK4VTRLI/n9vfigvX1ZZUeK2qEHrIs4vc+cCFeerMPPo+CizY04tCpUQQ8Cta3haHIIlRFLAwWVxQRXle+6sOlSmhp8M24gmCuZEnA5ec244L1Dfif//5cIX8ZiGbQH03P+HrvvKK95v71bSEc7S4d7D6SmPgZtmwHT+3prnq+350PCQL+ymHBfY934I3OYXz4ps3IaSbemlTFsvNdWxDyuyCJAjauCmPDqjA6u2I11wwAYb8LzQ2+QnXGTAfMl4QeuoWReBaKJDL0ICIiIiIiIiKaBoYes7B+/frCx6dOnaoaaESjUaTT+RuBU1VQ0On18JEnlkzgAQC24yCWSyCWS+DkpH0RTwgb69fh0tYLsCrUOuW1NFODZmoQBQmKKHPWxwwVt4USRQFhvwvDsdrfK+++ag3+46E3YVkTZRff/9UhpCbNjphqzkXY78K7rlhTeHzZOStK9quylK8SEPLv8g941ULosdCBBwAosoScZsHjktHS4EPv8ETQ8ey+3mmcL+LK8/OVM2+7eOWU1SG3XLkGX71vX8m2WFErKdOyYdrVp674PPl/4uoC7qrHHDo5in/+4etIZstbnV2yuQmKLEEU8iHZX3/6Cjz+cheSGQP3Pl69NVkk5C4JKHzumf1T2z1WcaIbFhwHMIzq7dWIiIiIiIiIiKgUQ49ZuOiiiwof79mzB5deemnF41577bXCx1u3bj3t66LqGn3Tb02z2KLZOF7qfh0vdb+OVaFW3LrpBqwJt015nu1Y6E70o9EXQcA181ZDlBf0qfC6ZfQNpwvzQSbb3B7Bp267AN/4yf5CtcPkwCPkV6dssTUVRckP+Pa584GHqkjwuGV4XIvzq1uWJgKiLWsiJaHHdLhVGe+/Lh8A15qRMe6GS1fhyde6S+aIJDMGRhM5/OrFk3DgQDesquf7PAokSZjy6xCrMjMlHHBBlcXC19itKjh/fQOGpgjFJlf4eGdYodE3nIZu2tCKXlutYfZERERERERERDSBoccsrF69Gps3b8bhw4fx0EMP4VOf+lTFQbwPPPAAgPwMkCuvvHKhl0lF3rflXTAsE/v6D8BybIgQIAgCREGEKAgQBLGwrbAdY9vLtk08FjD2/4IA27Fh2Vb+/4s+1i0DOVMb+18OGSOHnFllMPUkXfFefOOV7+Pdm27AFW2XQJGm+pF1MJQeQVrPoMnfMKe5IWcrURSgihLCAReGRqvf3N68ug5vv3glnt7bU3H/VPMqpmN8CHtLg6+wLehTZzU0fD4o8sTzXndJG57d1wN9BlUIxb8mp1OposgSfvcDF+Lz//CbwrZkWsf3f30Ix3riNc7M83mUwvPc9c7N+MEjh6e9VgCoC7ogSyLssWoSWRIAYerKjeKvFwB4ZxhSmZaNeDJXUoXkOPnti1HhQ0RERERERES0nDD0mKW7774bX/ziF9HR0YFvfOMb+L3f+72S/b/61a/wxBNPAAA+9KEPweOp3eZmKdi1axd2795d8xhNm97N+qVGlmTcdeFtuG7tlcgugTZXumUgnksilksgmh1FX3IIfckBdCf6YTml71x3kG/P9fCRJ9AeWom3t1+O85o2VQzaxmWMLKKZGBqWUYXLUhP0qRhNajXfYX/bNesxHM/iwPFo2b5tW0rb3vm9CkRRQCJV3kapIqHyMHa/R6n5tT+diitMAl4V569rwJ7Dg9M+/5LNE5+T6d68D/oUSKIAayx40AxrWoEHAPjcSmGI+N3v2oLVKwL4u++9Oq1zVUUqhDzSWIWLIAhQJBG+GpUbiiyiqc47aVv1kKq1wYc737kZ3334AEaKBrfHUhp87tLnMU2GHkREREREREREU2HoMUt33HEH7r33Xrz11lv4p3/6Jxw9ehTvf//7oSgKHn/8cdxzzz1wHAfNzc34/d///cVe7rREo1F0dnYu9jLOCqqkoNEXGWu7taawXbcMdI6cwEvdr+PwyLGy807Ge3By/wPYVL8Wd5y/A37VV3bMuISWH7Rd761btJvky5kgCKibotpDFAW8/9oNOHTiZRSPlhgfTF6sPuSBLAnIaSYs24EgCDUDFZcilbzTv3hdi0WRRbhUCZqeD+ZaG3zYU6F4wqVI+IM7t+LZvT14/o0+APlWUTdevhqSJMCynJJWWbWfU0LAp5bM8pgOVRbHgouJkGDLmgjCflfVdlbFqlVzKLIIURRKPg/FIkF3WTChytWDituv34gLNzTA71FKQo94SocsiRgczSAScEOWRZhW5e8X23YgCIv7vUFEREREREREtFQw9JglURTx9a9/HR//+MfR2dmJhx56CA899FDJMY2NjfjmN7+JcDi8OIucoUgkMuXAdU3T0NXVtUArOvuokoJzmzbi3KaNeLV3P3781i8qHndk5Di++tJ38altd6HeW1f1egktCd020OxvZKurWfB71fz8hkmzskVRKLQ8agh78L5rN+CnzxwtDDa/5Yo1kMdudPu9CjTDKtx8b6735ec16CZGE9Vvvi/W3I6p+DxK4Wb/5NkV49599RqsbQ2ipd6H22/YiGg8h0jIjQ1tYcRSGqLxXOHzMxVJFBD0zjz08HmUsVZgE59HURRQF5xe6FFt5kg+0LDhcysVQ4+GcHlVn6pUr/SoC7ggCAK8k6o6RpM53PvYERw8EYVblfD/3XkJ6sOVB7JbtgNRFDDNHImIiIiIiIiI6Iy2NO+qLRNNTU34yU9+gu9973v4xS9+gRMnTsAwDLS1teEd73gHPvGJTyASWT7thXbu3ImdO3fWPKajowM7duxYoBWd3S5tvRApLYNfdz4FZ/JddwCxXAL/8doP8NnLP1pzcHnOyKE3MQCXrKLRV386l3zGkUQBHpeMbM4sbIuE3PC4ZPQMpgrb3n7xSmzb0oST/UkEvSpWNvnRFPFCkUW4VQmJ9ERLK1WR8v+Txeqhh5AfhL4U+TwKomMVCU0VQo8LNjTglivXwu2SkNMsiIKAhrAH4YArPy9lLOxQa7R8KiZJIoK+mX8ugj61bH3jaznem5jyfL+n8nMqsghFybe4iiZyZfvrg+XBRGtj5YosQQCCfhcEAWUts/YeHsLBE/m2aTndwi+eP44Nq8IVr2M7DmDnv1+JiIiIiIiIiM52DD0m+fKXv4wvf/nL0z7e5XLhk5/8JD75yU+exlXR2eq6tVfg7e2Xw4GNkcwoHjj4a5yIdRf2x3IJ3LP3fnz4/PfUnN+hWzp0S4csyrAcCw3ehQ/jbNvGSHYUpm2h0VcPWVycYdwz5XUXhR4CEPKpkCQRHndpGOJ1KzhnTf7zqsgiAt6J2Rshv6vsuqoiYUW9FwMjmbJ9dQFXzTkQi8mlSJBlEXUBV2HORrFNq8JwuyR4VLmkIma85ZMy1nKqUuuuSmRRQF2FIGEqwQqfc1EU0BCa3nylap2i8i2+ZHirtb9SyitYNq6qw3Xb2vDka90l2yNBNyRRgCgIZfM7Xj04UPJ4f+cwcpqJSizLBjjrg4iIiIiIiIgIAMC7JERLnCSKkEUZK/yN+NS2u7C15byS/V2JPvzD89/Ev730XRwY7Kh5rdFsDIlcEvFcAoZlnM5lAwAcx4Fm6rBtGz3JfiS1FLJGFsPpkdP+3POlLuDG6uYAJClf9SGN3Vx2q9VDCbdLntZ8hYBXhUuVoCoiAuPVDELlkGQpaQx7EPK7EPApaG0orWI4d20EHpcMlyoVBoADKMzwGJ+1MV2SJKI+NPPQIxIoP2e8vdV0mFZ5oAPkwy1FFmFUmcdiVwiCAODT77ugbFvx58HrKQ1RMhUCjq/dv6/iXA/bdqo+bzWOM/NziIiIiIiIiIiWA4YedFYqnm8hizJksXrR0+R9wjRmYwiCCLc8/zeuJVHCB8+9FRsja8r2dSX6cM+++/Gd1+9DPFe7fc9IZhT9qSFkjCz00xh+DKaH0ZscQF9qsCRkyRhZ9CUHYdvVB3kvJaoiYdWKQMm8huJZEZMp05xXAeTbGtUF3VgR8WJlkx8rIt6yQdhLzXgrJo9LxjVbVxa2b15dh/aWINxqPvQpbrc0Hhbl51fMrMiwqa7y7JBaVjcHyraJAuB1lc/q+D+/d1XZNsepHAiMV6iks5V/bq67ZFXF7QFvebuswWgGgpC/5uRKj0oOHI/iyT3lM5WsWYQetlP9NRIRERERERERLWdsb0VnlYgnDMPlgyIqGM5E4VM8CLmDEAQBWSMHx3EwmB5B2B2AKqkQBQGqrCKRSyJjZOFRPPCrXjgAFFGG4zjQbQOJXBJA/oZuxBOG7diQRRkZIwtZlGE7NgzLgG4ZEEURhmUgrefbGsmiDNOu3LamEkkUcdeFt+Hbr9+Hrnhv2f5Dw0fxry99F7998QexKtRS9TqGZaA/OQhVUtEaWAFRnP2NdsdxyiobUnq68Bo1s3x2RdbIoj81iJbAimlVRSw2WRJR3HGqVqXHTEKLgFctVEEs1eHl1bhVGdvPa0FbYwDJjI6Nq+tKXnv+Y2vs44mvsd8z9Q3+Ys31lWdiTNba4EP/SBobV9fhXVe0l+2XJBEXbWqAJAqF1lxb2uvKhogDU1fbpCqEHls3NeKiTY1Vz7n6whY8t7+v8PgD12+ALIkzCoLe6BjBjZeVvjbbdmCLMww9xl7/0myiRkREREREREQ0e8vrDhudVrt27cLu3btrHqNpVQYvLxMuWYUL+Xdcrww2l+zzKPl2OG1yS9m8ibAnhLAnVHY9QRDgFl1wT7pBKo3dSvSpE+9QH7/+uFg2Drfsgltxj7V9yiGlpyEKEhw4cJzqVRBexYPfvfRuPHfqVbw5cBhdib6S/Sk9jW++ugs7L3wftjRuqHodID/v41S8Fyv8DWVrrMWyLTgAZFFCNBtDxsiiwRuBaZsQBAFD6eiU18iZGuJaEmF3cNrPu1RIkohwwIVMzoBulH6tim/wT2UmVSFLzXh7ppVN/sK24tCjeG6HVBSqSTOsZGmvULUx2eXnNeOumzYDABrrPBVDC1HIz/S4/YYN+PETnQh4Fdx85RoAwPuv24AHnuwsHPvBGzbWfL62Rj8OnxotPFZlEX/8W9tqDhN/33Ub8PJbAzAsG4os4tqtbZAkAQLKB5lXMzBaPgPGsh2IM670YJUHEREREREREZ2ZGHpQQTQaRWdn59QHnuEWasB2cYgScPnhV31wK24EVB/SRgaDqeGa58uijGvXXIFr11yBvuQgHjz0CE4WDTk3bBPf2/cAfnvrB7Gpfm3Na9mOhf7UENqCzVCkqW++WraFvlS+PZVX9SCRSwFw0JccmPLcyUazcXhlN1S5vP3PUtcQ9iCaEBCN50q2L9Uh5PNNEgUoigijKPQpvukvF1paYdqDyyuZzkyPizY0TKyhSqgiigJkScT7r92A7ee2QBBQqDLafl4zBqJpdHbFcf22Npy7NlLz+e5852b872+9WHj80XefO2WFz4o6L/7so5fhSNcozl/fgPaWIDI5A6IoVKw2qWSoSughzTDEsG0HS7++ioiIiIiIiIho5hh6UEEkEsGGDbWrAjRNQ1dXeU95mjtBEBB05d8x71d98IY9iGkJxLLxKc9tCTThdy/diV93PomnTrxU2G45Fu7Zez8+vvVDWB8pb/dTzHFsDGWiCLkC8Mjuiu2udFOHbhuIZmKFllzjrb1my3FsjGRjaAk0zek6i8XrlhEt/hIJM6v0WO78HgWjxkQFWGl1hwBJEiDOsX2ZJIlY3xbC0e7qPwvnr68HIMC2narVFqIAyLIIZ2ydxYFNJOjGZ26/CEOjWaxvC03Zcu2SzU347Xefg+ff6MM5ayI4Z21kytBDEAREQm5cEWpBOJCvRBkPO3zTbG81Es/Btp3C5zmnmfjF88ehSCI++I6NNWfNFLNtB8ugqxwRERERERER0Ywx9KCCnTt3YufOnTWP6ejowI4dOxZoRWc3URQR8YTzw8ZNfcrZH6Ig4JaN16POHcKDhx4pbDdtE9/d+2N8attdWBVqrfmcOSOHnJGDKIio99bBp3ohCiKimRjcigv9ycF5e30lz2tqsB27ZMD8cuFWZTTWeTAcy8JxAGVsRsPZIhJ0I5U1CuFB8Y1/SRLRUu/DaHLubfFue/s6/NMPXoeDfLurdNbA8FiFzdrWIBrCXowmSgOByQRBgCwJhQHeHlWGYej5j10yJEksqf6oRRQFvO+6Dbh400RYN3XoMfGxNCkYm26lh2U7GI5l0BTxwXEc/P33X8PLB/oBAKcGkvjzj11eODanm1VDENtxILDWg4iIiIiIiIjOQAw9iJa4sCuIQXMEzf5GDGdHkTM0ANVb2Vyx6hLYjoOHDj9a2KZbBu7Z+xN8/oqPFapJarEdG0PpEUSzMdR5wojl4kBuytNmzXFs5AwNXtVz+p7kNAr5XTBMG7GkBvcyG0Y+V4IgwF0UHhRXWfjcMgRBQGge5kdce8kqSKKI0aSGze11GI5l8fPnjkOWBOy4el2hqsQwUXOuBjAWTgj5eSqyLMI0baiKCFEQZjRvpLiCRRSFKVt4FYcpyqTnqTXTo3jwOgD0DKVRH/YiGs8WAg8AeOGNvpLQZzShobleqhji5I+ruVwiIiIiIiIiomXp7Lo7R7QM+V0+eBUPRFFEs68RNhx0x3th1xh0ftXqbTBtE7/o+E1hW1JP4RuvfB93nP8etASaoE5zdsdwemRO67dsG3v63sDrfW8hmo3BLbuwsX4trmm/HIGiACZtZJZt6AEAdYF88DGd+RNnGnVsGHt9yF0SGozfbJ9uFUMtoihgZZMfrY3575nVzQH8/u0XFipMRFEYG5ZuTRl6CIIARcoHHspY6DE+h2Umo0dEURhrZyZOq1VU8dB6edIAe2+N9lbnro3gjaMTP4enBpLYsCqM/pHy+R453YTXrcBxHGRyBjTdqhjEdQ8mIQgCzl1bP/XCiYiIiIiIiIiWEYYeRMvA+HwNURQhAmjwRjCYrj3o/Jo125HSM3j65MSMj5FsDP/+yvfgkl24etU2XL/2KijS6fs1kNYz+P7+B3B8tHQOTH9qCK/27MfOC9+HDfVrAAApPYOIJwxpgQbJzzdJEtHS4FvsZSwKRRYhigLqgqc38JGkfEAB5IMG23FgjO8bq/SYbnsqRRbzg9glEbmxqg/bdmZU6TH+vF63DMOsHkKO87hkuF0ScppVVunhr1Lp8e6r1iCTK21r1zOYQiypIZHWy47PavnQQzMsOA6Qzhllocf9T3TgOw8fAADsvHkL7rxp85RrJyIiIiIiIiJaLtjcgmgZ8rt8aPBGppyBcfPGa9HojZRt10wNTxx/Hv/y0rfRFe87LWvMGDl867UflgUe47JmDt/dez+6x57fcWwktNRpWQudXooiwe06/WFVcQWHJIljlR0T+/LBx/T+WVNkCcpYpYeqjFV5iMKUczkmE0UBfo8y7fPCfle+KmXS8R6XjDUtwZJtd960GbffsBEN4dIwaTiWhaZbyOYMTDYekGi6BQBIZozCDBMAcBynEHgAwK5fHSppnUVEREREREREtNwx9CBapoLuAJp8DTWPEQUR79xwTdX9Q+kRfOPV7+P1vrfmdW2O4+C+t36OvlTtweeGbeCHb/4MhpW/UZvS0/O6DloYiiTCU2Vg9nwqHv4tS0JJCDLe3mqq1lbjFFmELInwuGUEvGphuyrPMPQYm2ky3fM8LrmkzdU4QRDwpx+5FBdvasRFGxvx5x+7HNvPa4bHJaOloXQOz1AsCwDIaGbZdTJjQch46GGaNtJFlSKVKlI0vfw6RERERERERETLFUMPomXMq3oQcPkhCCJksfJN5wtWbMH7z7kZ6+pWY3WoFW7ZVbLftC386M2f4fFjz816HSk9g3guAcu2kNBSeOToMzg41FlyzNrwKnzmso/ghrVXlWwfzkTx1IkXAQCGZUA3y1v20NImikLNQdzzpbiaQhLFQggiikJ+Toc8/dDDrUqFIezhwMTPhKLMrGJlvLWXd5qvX5LEqp+rVSsC+OwHL8LHbj0XjeH8fBtZEtHWVCX0yFUKPcYqPQyraNtERUi2QlCS062ybUREREREREREyxVnelDBrl27sHv37prHaJq2QKuh6Wr01aPOE4YsShhIDSGtlw833t52Mba3XQwAyBo5/KLjN3ilZ1/JMY8efQaSIOK6tVdO+Zz9qSG82PU6+lODOBHrnvL4tmALPn7JHVAlBavDK5HS03i56Pmf73oV16zZDlVS8rM9ZLXG1WgpUmcYFsxGySBwSYCDfMAxHnT4PApy06xacKmV1+tSZvZeANfY63bN4PUXV5ZMJk4KbRRZRFtTAJIkwLLybajSWQOZnFGx0iOVzbez0otCD60o1KgcerDSg4iIiIiIiIjOHAw9qCAajaKzs3PqA2nJkceGfzd4I3AcBxkjW/VYj+LG7efegnV1q3H/gV/AtCduiP6q8yms8DfinMYNFc81bRO/6ngKz516BdOdAuCSVNx5wXuhShPvbr9l4/XYP3AIOTMfomWMHPb0vokrVm1FSk8j4g1P8+p0Nhkf/u33Kvl2Vk7+sdc98b0VDkxvmHq1Yecznekxm7CnUnurcZIoAAIAB4CQX0/Qp2JFxIveoYn2b0OxbMWZHomUBt20UTTGY2youQNBECpWh2is9CAiIiIiIiKiMwhDDyqIRCLYsKHyze5xmqahq6vyYGpafJIooTnQhLSewWB6BI5T3r9/3NaW8xDxhPBfr98HzZyo4Pnu3h/jE5d8GOvrVkMSJ27oaqaG7+69H8dGT017PbIo4SMXfwAN3rqS7R7Fje1tWwttrQDg2VOv4PK2i2HaJnJGDm5lejev6ewhyyIURSwMA3ccGy5VQmOdp3DMdNtbVVMtDKlmvitcREGAS5FgmHbJa2lt8JeEHoOj2YqVHomMjmRah+046B9OoynihSyJ0HQLbpdcudJDY+hBRERERERERGcOhh5UsHPnTuzcubPmMR0dHdixY8cCrYhmy6d60SyI6EsO1DyuPdyGj170Afznnh/CLnpr+H/t+RECqg83b7wOmxvWoy85gF92PIneKa43ToCADZF23LLperQGVlQ85qpV2/DMyZdhjwUzw5koDg8fxTmNG5Bh6EEVKLIEtyrD7ZLhOA5s24HXvbj/jNWq2pgNURQgSyIEIT+3ZNyqFX68enDi5697MIlkunz+TSypYWg0i3/64R70DacR8Cr4452XIqub1UMPtrciIiIiIiIiojMIQw+iM5RHccPv8iOlpWoetz7SjuvXXlU2yDypp3HfWw9XPS/sDmJNeBXiWgJ+xYubN14PVVLgUVxVh6oXC7kDuKj5HLze91Zh2zMnX86HHmYWEYSnvAadXSRRKIQcgiDkB4i7T/8A9YUkigIkSYAsyVCL5otsXl1aLfX06z0Vz89qJvYcHkTfcL4qJJkx8OjLJ7Hz5i2oC7iRrdDeqniQuWXZkGbY4ouIiIiIiIiIaClh6EF0Bmvw1MEtqRjJxmq2urpuzZV4a/AI+lND07puW7AZH9/6YfhUz9QH1/C21ZeVhB7HRk+hJ9GPlcFmWLZV0l6LCCid3yFLIpQz7F8xURCgSOJYoDPx4jZOCj2qyekWXnyzr2Tbs/t6cfv1G2FaNlIV5oBoRZUeumnDw9CDiIiIiIiIiJYx3tkgOoOJooigO1A2U2MyRZLx6Ut3Ysemd+Dcxo2QhOphwwp/Iz629Y45Bx4AsDLYjHV1q0u2vdq7HwAQzcbmfH068xTPuRBFYcYzOJY6URQgyyJ8HgWKPPFz2Bj2IOhTpzy/2lDy0UQOWc1EOlseehRXehhm9XCUiIiIiIiIiGg5OMPeI0tElXiUqQMKr+LG29ovw9vaL8NIJoZfdfwGh4aPwXZsrPA3oNFXjw2RdlzcfB4Uaea/OtyKG17ZDQdALJcoVJ5ctWpbyXD0NweO4D2bb0JSS8EtuxBw+Wf8XACQ0bNIGxkYloEV/kZWjdCyIIoCJCc/16OYIAhY1xrC3o7a1Vg53UQ8pZVtP3RyFOtXhacRenCoOREREREREREtbww9iM4CsihBFmWY9vQGFtd7w9h50fvhOM68vJPeo3jQEmgqPPYqHvQk+gE42NywDqqkQrfyQ5mTegqn4j1YE25DLJcoBDbypNBCM3WMZEchQoBbcUMRZfhULwAgrWcwmB4pBCtpPYOgOzDn10F0uomCALHKcPQrL2iZMvRIZgzEkuWhxysH+3HT9tXIVJjpUdzeipUeRERERERERLTcsb0V0VnCo7ghizJaAysgCNP70Z+PwEOVVDR6IyXbXLKKoDtfwaFICs5pWF+y/9DQUQCAYRk4FetGd7wXuqkX9meNHPqSA8gZOWSMLKKZUQykhnAq1oPuRB8GUkMlM0wSegq2Y8N2bAyno3AcZ86vi+h0EMXyKo9xb7t4Jdqba4d33QNJVPruPt6bQNdAEukKMz3Y3oqIiIiIiIiIziSs9CA6S9R76gBPfs7HymAzUlq60P7p9BHQ7G+EXKEdVsQdRs7QoFs6tjRuwL6Bg4V9x0ZPlhxrOzYGMyNwSy7otoGckav4bKZtAhXu2eqmjq54LwDAsi3YcKCbOhw4CLj8CLuDc3iNRPOneGbJZC5VwkdvORfffvgtDEQzqAu4sWl1GM/u6y0cUyvO23tkCNmKoQcrPYiIiIiIiIjozMHQgwp27dqF3bt31zxG08rbptDyIIoT7x5XJQURbxgRhDGUHkFSS52W5wy4fBUDj/H1NPkb0B3vxfpJw8y7E33QTA0u2VXYppt6SbXHTFn2xLvZU0WvN5oZhW4Z8CteuBUXxGlWwRAtNFkSEQm58Ud3byu0nsvkjJLQo5aT/UlYVnmoMT783LYd2LYzb23tiIiIiIiIiIgWA0MPKohGo+js7FzsZdACi3jCSOsZ2M5c3uEtYPJ7zEVBQp0nXPMsVVIgizKC7gAavREMZaIAANtxcGy0C+c0bpjDmqYvpaWQ0lKIeOvgV335uQqCiJypwbZtJPQUmv2NC7IWomokUYAoCrDtiVDC61Zw7toIDhyPTnl+10AS4YCrbHtWy1d6pHMGjnbHUBdwoT7smd/FExEREREREREtEIYeVBCJRLBhQ+2bzJqmoaura4FWRAtBEiUE3QHEsvEZnScKEhp8dYhmYvCr+YqOnJGDT/UiriVR5w6VDR+vxKt4kNCSWB9pL4QeALB/4OCChR7j4rkEMnoGLtmFoMuPgdRQoUJEM3W4ZHVB10M0mSwJ0O3SgPFjt56LP/3qs2XHCgJQPL4mmsghmihvDZfTLWRyBr7wlafQP5KB36Pgy597G9qb2faNiIiIiIiIiJYfhh5UsHPnTuzcubPmMR0dHdixY8cCrYgWSsgVgOM4iOeSqD0VIE8QRDT7G+BW3LAdB17ZDVmSEXTlh5P7VO+0n9ur5kOP81dsxovdrxe2vzXYAd3SoUoLFzRYtgXLtpAzNcRziZJ9ST0FlxypcibRwpBlEbpRWpXVXO/DxlVhdHTFSrZvaY8gkdbRM1S7fV1OM/GrF06gfyQDAEhlDdz76BH8yUcunde1ExEREREREREtBDavJyJIooR6bx28invKY/2qDy3+RrjHjg26/FXndkyHV/FAkRSsq1tdCE0AQLd0HB4+Nuvrzre0nlnsJRBBkcqrp0RRwMZV4bLt27Y0YU3L1NUaOd3CIy+dKtn29N6eWa+RiIiIiIiIiGgxMfQgooKQOwi3XH2YtyIpaBqr8JjX53UFIAoiLlixpWR7x8jxeX2eubBsC1mjvDUQ0UJyqZVDj3PWllYhqYqIS7Y04by1U1cn5XSz4oBzIiIiIiIiIqLliKEHERV4FDdag81oD7ehwVdf2C4KIlRJLanEmE9+lw+iIGJz/bqS7YeGj8Jxpm63tVAYetBi87gmQg+PO19hJQoCLljXgBWRibZyt1y5FnUBNzauroO7QlBSTNMtGAw9iIiIiIiIiOgMwZkeRFRGEIR8wOE4sBwbIXegavXHfBAFEX7Vh7V1qyCLMkzbBAAktBR+c/wF3LDuqmlfazgzil92/AZJLYWQK4DW4ApctvJi+GcwZ6SanKXN+RpEc6HIEiRJAAB4XTKyOROiKEBVJXz29ouw58ggIkE3zl9XD7dLhkuVsLm9Dvs6hqtecyiWRdC3cLNziIiIiIiIiIhOJ4YeRFRV0B1YsOfyKh4kJAXrI+04PHy0sP2Ro09jY/1arAq1FLYNpoaxf+AQQu4AVEnBQGoYPtWLZn8jfvjGQ0jq6cKxbwwexlMnXsJHL74d6+pWz2mNuqnP6Xyi+eBSJTjORKsrURQg2AICPhXXbm0rHCdLAjwuGQ0hz5TXTKT5vU1EREREREREZwaGHkS0JLgVFwAB71z/dhyNnixUewDAv738XXzovFuxrfUC7O17C/e99TAsZ/rteHKmhm++uhu/c8mdqPeGoZs66jxhuOSZvbvddmwYlgFFUmZ0HtF88ntUaLoFlzIWeggCRKH8OFkS4XHJiARnN4PHth2IlS5MRERERERERLSEMfQgoiVBFES4ZRUrg8244/wd2L3/wZL99731MN4cOIyDw52zfo7/3PPDsm1BVwBbGtbjurVXIOIJT3kNzdIZetCi8nkUCAIgSSLqgi5IogBrcjgh5EMPr1tGJFQeemxaXYcjp0ZrPk8yoyPkd83n0omIiIiIiIiITjsOMieiJcMt52/OXtC0GRsi7WX7ZxJ41HvqcF7TpimPS2hJvNyzF//y4rdxZOR4yb5T8V787PBjePzYcxhMjwAANLa4okUmiQL8nnzwVh/yQBQFiEJp6CFL+X/eFVlCU135PJtNq8NTPk8spcFxnLkvmIiIiIiIiIhoATH0IKIlQ5XzN3IFQcCdF9xWMfiYrMnXgC0N6xF2BwvbLm29EF+46lO4+4LbsCbcVuPsCTlTw3df/zFe6NqDhJbCk8dfxL+/fA+eO/UqHj36DL7ywrdwbPQUciaHmdPiEyaFHKIooHiTLE08WNnkKzt/PCypJZnRkdXMmscQERERERERES01bG9FBbt27cLu3btrHqNpvOFLp49Lmpix4Ve9+OS2u/DgwUfwYveekuO8igd3X3AbNtSvKWxzHAdJPQ1JEOFTJ97Z/qltd+HF7tfxy44nS+aEVGI5Fn566BH89NAjZftsx8EvjjyBteHVcByn7KYz0WKSJRFrWkM43hMHkK/wGBfyl7e3chwHHlVCOlf9Z0LTLBjm9GfnEBEREREREREtBQw9qCAajaKzc/bzEojmSpEUiIIIu2hI+Xu33ARVkvHcqVcBCNjedjFuXP82eBVPybmCICDo8pddUxIlXL36UmyuX4c3Bg+h2d+ELQ3rcSreg57EAHqS/Xit941pra870Y+O6HGsDDXDLXPWAS0tkihAlkWYpo2QbyJA9LhkNIQ9GI5lC9vOXRvBwy65ZuiR0y2YVv5nkUPNiYiIiIiIiGi5YOhBBZFIBBs2bKh5jKZp6OrqWqAV0dlIlVXkjFzhsSgIePemG3DtmivhklXIolTj7OoafBFcv/aqwuP2cBvax1pftQZW4OeHH8N0phfs7z+IrS3nwe1vnNU6iE4nVRYhSwLcrol/3iVRwAdv2IBvPvgmbNvBxlVhbFwVhsdV+08ATTdhWfmfCt2wSq5JRERERERERLRU8Q4GFezcuRM7d+6seUxHRwd27NixQCuis5FX8ZSEHuN8qqfC0fPj6tWXYlWwBc+eehUD6WEMpYdhOw5EQcB5jZvwxuDhwrHHRk8irWeQNXLwKOVtg4gWk6pIUOTycV1XXdCKSNCDRErDmpUhSKI4dehhWDBtG5Zlw7Q50JyIiIiIiIiIlgeGHkS0pIRcASS1FAzLWNDnXR1eibvDKwEAWSOH3uQAgi4/Qu4gDvymE5ZjAQCi2Tii2RjqPGGGHrTkqIoEr7v8n3afR0Fj2IPGsAchvwuSNHXokdMtWJYDw8oHH0REREREREREy0H520GJiBaRIAjwKd6pDzyNPIob6yPtaPTVQ5UUrA63luw/Gj0JzdQWaXVE1fk8CmSp/J92VZGgKvnt4UB+Hk2lcKSYpluwbAem5cB2WOlBRERERERERMsDQw8iWnJUWVnsJZRYX9de8rgr3gvN0hdpNUTVSTWGjbvUfMghS/ljpp7pkR9kbpgWbLa3IiIiIiIiIqJlgqEHES05bsm12EsosSpUWunRleiDZVswbWuRVkQ0c7IkQJIECEI+9PC6a4eLP/5NB555vQeabsEu6m7lsOqDiIiIiIiIiJYwhh5EtOTIkgxRkGZ9vihICLj88KpeqLI65/W0BZtLHg+khmBYBrJGds7XJloosiSWtL6aqr0VAPzkyU7sPTIEy7bhOA4s24Fhcr4HERERERERES1dDD2IaEkKuv0zOLq0pY9P9aDBG8EKXwOa/U1zXotP9SLiCRUe246D3uQg0npmztcmWiiTQ4+p2luN+87P34LtODAte6zdlQ2L7a6IiIiIiIiIaIli6EFES1LEE0bQFai63y3nW2D5VC9WBpshCBO/znyqF4KQb+MjixJc8tzbZbUFW0oedyf6kDFysNjiipYJWRYhSRMB4XRDj3TOxNBoJh92jAUflsVqDyIiIiIiIiJamhh6ENGSVe+tQ4OvHopUOnvAq3jQElgBv8uPRl89XLKKFn8jZFGGLMrwKp6y42Vx4gZvg68ePtU7o7VUCj0AB6PZ+MxeFNEimW2lBwAc703AtJxClQcrPYiIiIiIiIhoqZr+HQ8iogUmCAKCLj9kQUJ/ahBu2YUGXz3UsRCkyVdfONatuNHkq0fW1MquE3D5EXIF0J3og0tWEXT5EVB9SMhJ+FQfRrMxJLVU2XmSKEEURLgktTz0iPcBABJaErZjo84TKgtniJYSSRSgKhOzcoo/nkoircMcq+5gpQcRERERERERLWUMPYhoyfOqHkS8dfAqnkLgUYlbcVdsZSWL+Zu7EU+4UAUiCAJC7iAAjFWLuDCcjgKYeAd70BVAnScEx3HQFmqBULR3KBNFzsjBrbiR0tPIGDmsDrVCFGsX0Nm2PeUxAOA4DgRBmPI4oplwqxNBhzmD4OK+xzsgCgLeeUU7LIuVHkRERERERES0dLG9FREtC2F3sGbgMa5WUOB3+aoGDkGXH6tDrWgONKHZ3wRRkOArCkgavBE0+hpKzjkZ7yl8bDsW4lqy5tocx0F/egiOk79hrFsGNFMvOWYwNYyRzCi6E30wOS+E5llxeyu3OrP3Pdz3RAdiyVxhoDkRERERERER0VLE0IOIaIws5eeBeFUPmgONUGW1sM+tuLA61Fpy/Cs9+0sex3P5VlfVaKaGnJFDWs+MHZ9Af2qwEG4ktRRSehrxXAKGZWA4E52vl0ZU5pItTfC5J4KP67e11Tzeth0cOjHKmR5EREREREREtKQx9CAiqsA9qU2WW3LhktbzS7YdGDpSMgvEdiykxgKNSnJj80YSWhKaqSOlZ2DZFvpTg7BtG7FcouT4jJ5BzsjN9aUQVeRSJPzVp6/ExRsb8baLWnHXO7dgZaO/5jm244zN9GDoQURERERERERLE0MPIqJpkCUZGyJrsKKoxZXtODg+2lVyXCwbr9qWanzIes7U0JscgDNWFaKbOnqS/TAso+yc2BQts4jmYkt7BJ/90EW4/fqNaKrz4LdvPbfm8R2nYvjmA2/gGw/sx9BodoFWSUREREREREQ0fRxkTgW7du3C7t27ax6jadoCrYZo6XErbmxqWIeB9HBhW09yABc2n1N4bNomBlNDaA02l5xr2RayRVUbzqQ2WJUCDwDI6FkYlgFlGvNMiGZDEAS4VAmSJGL7ec3Y+a4teOK1LvQNp8uOfXrvxBybbzywH3/xie0LuVQiIiIiIiIioikx9KCCaDSKzs7OxV4G0ZLlklSsDKwo2dab6C87LmdqMC0TsjTxKzY/x2M2LYEc9CYH0BJYMa1B7kQzJQoCVEXKfywKeMdlq3HO2gj+4uvP1zzvpbcmvvczOQNuVYYoCqd1rUREREREREREU2HoQQWRSAQbNmyoeYymaejq6qp5DNGZSpWUsgqOnuQAHMeBIJTe7E0bGYSkIABAM3WM5uKzfl7LthDNxtDsb5z1NYiqEUVAhFDy2OOa3p8HpmVDlkSkMgZyuoVI0H26lklERERERERENC0MPahg586d2LlzZ81jOjo6sGPHjgVaEdHSokoKGrx1UCUF+lg7qoyRRTQbR703XHJsUk8j5A4irWcwmB4pa2c1Uxk9A90yWO1B804UBYhFoZ0kiiWPa0lmdNQF3DAtG2CRBxEREREREREtARxkTkQ0TYqkQBZlrAyUVnscHOooO1Y3daT1DIbmIfAYl9RS83IdomKiIECRJ/4ckOXp/2mQSOsAAMO0Yduzad9GRERERERERDS/GHoQEc2AIik4p7G0Ddybg4crHjuQGoY9T4EHACS1NBxndjeWLdsamytCVEoUBcjSxJ8DykxCj9RY6GHZsBh6EBEREREREdESwNCDiGgGXJKK81dsLtl2ItaN3xx/HpZtTTp6djeBe5MDeP7UaxhKj5Rstx2r0FZrpjRLx0BquMIa6WwnCqWhhyyJEATgk7edP+W50UQ239rKASxr/gI+IiIiIiIiIqLZYuhBRDQDqqQg4gmXtbj6defT+MEbD826EmPciVg3/u2l7+Khw4/iX1/6LvqSgyX7NVOb1XUNywDgIG1k57Q+OvPIsghRLJ7pIcClSnjv29fhA9dtqHEm0DWQgm7kg7TxSo/hWBY5zTx9CyYiIiIiIiIiqoGhBxHRDKiyCgC4YtXWsn1vDh7Gnr43q54bzcbw1ImX8KuOJ/HsyVfwxLHn8ejRZ/DGwCHYjo2UnsHXX/k+rLGWWLql44GDv4ZdFKTkZhl6jFeIpPT0rM6nM5daoZ2VxyVDEAS884r2mucm0jpG4jkAKMz0SOcMpLKzq0giIiIiIiIiIporebEXQES0nKiSAgC4uPlc/KrjKaSN0jkZPz30KFYFW9DkbyjZfmioE9/b9wAsp3J7qXpPHUZzsbLtp+I9ODjUgfOaNgGYfehhjIUeOSOHjJ6FV/XM6jp05hEEoWybx5X/88Drqv1nQiprQNPz39OOk6/2ME0bmZwBgN9jRERERERERLTwWOlBRDQDoiBCkRQokoI7zt9Rtl+3dPzs8GNl2x89+kzVwAMARrKjJRUdxV7u3lv42LRNmNbMWwcVzwKJaYkZn09nF7eaDzskqfafCelJFR26YcFxAN2wOeODiIiIiIiIiBYFQw8iohlyyy4AwOaGdfjSjX+K92y+sWR/Z/QE4rlk4XE8l0RPcmDWz3dk5Bhi2XjhccbMzeh807ZgOxM3oHVTn/Va6OwwPuNDFAVs3dRY9bjx0GO82iOnTwRyusnQg4iIiIiIiIgWHkMPIqIZCrkChY9FQcRVq7ahJdBU2OYA2Nd/oPD40HDnjK5/5apL4FUmWgM5APYPHCo8zs5wGLlpl1aG2I49q2oROvtIooAP3rAR129rq7g/ntbwzQffwJ997Vn83++9ir1Hhgr7nCqVS0REREREREREpxNDDyKiGVJlFW7FXXgsCAIuaTm/5JgXuvbAtPPvfj88fKziddZH2uFXvSXbzm3ciNu2vBM3rLuqZPu+/oOFj7NGrnDt6agUcGgWqz1oaqIoIOBTcfe7tuCf/r9r8T8/fnnJ/v6RDA6eiAIA+kbS+Ocfvo6ewRSAicHmREREREREREQLiaEHEdEsjLe4GndR87mQhIlfqaO5OF7p2QvbcXB8tKvk2Fs33YDfvviD+PjWD+GT2+7C1pbz0BpYgYubz8OHx+aEXNC0BcXjpXuS/fj54cdhWCZsx0Y0MzrttRp2eehRPOODqBpRECCJAiQx/71dH3TD51GqHm/ZDg6cGCl8TERERERERES00OTFXgAR0XLkktSSx0GXH5e3XYwXuvYUtj1+7Hk0+5uQLZrB4ZHduHr1ZRCFfKTR7G/Eh89/T9n1Q+4A1oRX4XhsIjB59tQr6E8N4ncuuRMpPYMGx4YoTJ1dVw49WOlBU5NEAbIkYizzgCAIWNnow5FTsarnJDP5QG28u1VOM+F28c8NIiIiIiIiIloYrPQgIpoF16RKDwC4Ye1VUMSJm7spPY1vvLqr5Jg1dW2FwGMq79pwTVmo0Rk9icH0CAAHOVNDRs/CcRwktBTSeqbidUzLwOHhY9i9/6d46NCjSOuZikEI0WSiKECWRUiiCEEAJEnAqhWBmuc8s7cHR7tjsB0HjuOgbyTNVldEREREREREtGAYehARzYIsSpBEqWRbwOXH1asvrXneurrV036ONXWr8KHz3l22vTN6AgCQMzQMZ6KIZmMYTkcxmo1XvM7DR57At1+/F/sHDuL5rtfw8JEnYFrTnwlCZzdVlvLhhyRClsQpQw8A+MYDb+BUfwKW7cCyHGRybKdGRERERERERAuDoQcR0SxVqvZ4W/vlFY7Mk0UZF6zYMqPn2NpyPt62+rKSbZ0jJwAACS0J0zYRzyUAONAtHcakWR0pLY1Hjz5Tsu3NwSMwbQO2bc9oLXR2UhUx3+ZKzoce61pDU55jWDa++eCbhQqPVJahBxEREREREREtDIYeRESzNHmYOQD4VS/Oa9pUtv3CFVvw+5f9FsLu4IyfZ1vrBSWPj42egmXbsJ3y0CJtZEseH42eLDtGt3T0p4bY4oqmRVXylR7j8z3WrQxhbevU38ddA0n0DKUAAOmswcHmRERERERERLQgGHoQEc1SpdADAK5ata3k8c0brsPdF74PK4PNs3qeFf5G+BRv4bFm6RhID1U8VjdLB5QfGy0PPQDgwGAHTIYeNA2ylK/0UGQRspQPPu68afO0zn3z6AiA/FDzRFo7ncskIiIiIiIiIgLA0IOIaNZckgqgfCj5+kg7PnTerdjSsB7v3ng9rl2zfU7PIwoCVodbS7adivVUPFazSkOP46NdFY977Niz6Bg5Pqd10dkjX+khjg01F3DOmsi0zuvoGi18nEjpNY4kIiIiIiIiIpofDD2IiGZJEAQEXD7IogxRKP11uq31Anxs64dwzZrtEITyYGQqHsVT8rg9tLLk8al4b8XzDKt0VsfJKuEIAPyq46kZr4vOTsUzPSRJhCgK+Oi7z5nyvI6uWOFjw7Q50JyIiIiIiIiITjuGHkREcxDxhNHsb0RomrM6REGEINT+1atIChq8dSXbVoVKKz1qhRnj1R6mZaI/VbkNFgCcile/BlExQRCgjrW3kkQBkiTi4o2NuGHbKgS8StXzegZTyGoTbdRiqYkWV5ZVPpOGiIiIiIiIiGiuGHoQEc2BJEpQZRUhdwCiIE15fFuoFSFXAKIgot5bh9WhlYXzJFGCW3Gj0VcPRVKgSmrhvFWhlpJqkpHsKEYysYrPoZla4RgHE8OjpUnrG8lEy2aAEFWjjFV6jA81FwQB73n7Ovz1p6+qeo4DYGg0U3ic06zCx5phVTiDiIiIiIiIiGhu5MVeAC0du3btwu7du2seo2kcREtUiSiICLsDiGZjVY9xyS7IooSgyw9FkhFw+QEAPtWDpJZCvbcOftVXOL7Z34jhTBQZIwtVUrEm3IZjo6cK+w8Nd+Lq1ZeWPU/W1BAGMJwZLdm+MrgCKT2NaDYOIH9DuivRh/WR9lm/bjp7FLdpk6Xpt2zL6RPhhm070AwLLkWCbtjwuud1iUREREREM+I4DtLpNBKJBHRdL2kVTERES5MoilBVFcFgED6fr2JbeYYeVBCNRtHZ2bnYyyBatoKuAGK5BGyn8h/KvrE5HbIkIyD5C9sDLj80S4dP8ZYcL0syvKoXGSMLANjSsL4k9Dg4VDn0yJkaHMfBUHqkZHvYHYRX8RRCDwA4Getm6EEzpsgSBAFwxgqJGsIeDMeyFY/95oNv4E9+axtWRPKBXk4zx0IPVnoQERER0eLJZDLo7u6GZfHvUiKi5SabzSIej0OSJLS1tcHrnXRPbZHWRUtQJBLBhg0bah6jaRq6uroWaEVEy4soigi6A4gVhQrFJg8nH+eWXWgLtlTdN+6cxg34RcdvCo+PRk9gODNaNv/DcWzkTA19ycGS7WF3EIIg4tDw0cK2U7HKA9GJpqIqErSxKo6737kZ/3Lv3orHWbaDf/nRXvzhXZegIewpnMP2VkRERES0WDKZDE6dOgXHKWoHLEmQJKniO4aJiGhpcBwHlmUVAmvLsnDq1CmsXr26JPhg6EEFO3fuxM6dO2se09HRgR07dizQioiWn6DqrxJ6CFCl6gOfq1ElBaIgwXYsNPrqsTLQjJ5kP4B8e6oXul7DezbfWHbeSHYUA6nhkm0hd7AkRAGA/lRpMEI0XcWhxxUXtEDTLezvHMbRnjgGi+Z4AEBGM7Hr14fwBx/eWgg7TMuGZdmQJBG27UAU+R+XRERERHT6OY6D7u7uQuARCoUQiUTgcrkYeBARLQOO40DTNESjUcTj8cLv9Y0bNxZ+j3OQORHRPJIlGbJYniersjLrP6DzlRz5c69ava1k3+t9byJj5HBs9FShDRYA6KaO4Ux5e6uwO1iyLZZLzGpNRKoy8SeEIom4dlsb7rhxEy7cUF/x+BN9CcRSWqGtlWU7sOz8f2jmdPP0L5iIiIiICEA6nS68QzgUCqGlpQVut5uBBxHRMiEIAtxuN1paWhAKhQDkKz7S6XThGIYeRETzbHI1BQC4pfJt0+V3+RDx5H+JX9R8DrzKxPTnjJHDXz/5FXzz1d34f899E13xvsK+yYFG2B1E0OUv2cbQg2bLpUiFjyVJhDj2H4kBn1r1nI5To3CcsZDDyVd7AEBWY+hBRERERAsjkZj4b6BIJMKwg4homRIEAXV1Ey3fi3+/M/QgIppn7qJQAgAUSYG3yjyP6Qq5g5DFfBXJRc3nVjwmbWTx7dfvxeDYAPNYLlmyvznQVBZ6xHPJkj62RNPlccmQpPx/IMqSAGmsPVVdwF31nMOnRgFMhByW7cC0bBimfZpXS0RERESUp+s6gPwMD5dr9m9OIyKixed2uyFJ+Tdljv9+Bxh6EBHNu6DLj/qx4eJexYO2YAu86txCD0EQ4FPzA5kuW3lR1eMyRhb/tedHSOkZ6NbEL3tJELG+rh1uxQOXNPFOfMuxWO1BsyIIAoI+FZIkQBCEwkyOgLd6pcf+jmFE47mJ0MNyoBtWoeKDiIiIiOh0s+38354cWk5EtPwJglAIPcZ/vwMMPYiITouAyw9BEFHnCc/bH9K+sWqR1sAKvHvj9VClyjeXY7kEnjv1yqT1BCCKIlRJKav2GEqXzv4gmq6gzwVZyv8pMR56+DxK1eMNy8YDT3Uip+V7KGc0A4Zpw7Imqo0cxymEILbNKiQiIiIiOj0YeBARnRkq/T4vn7ZLRERzJgoi6jwhuOTq73qfKbfihihIsB0L16zZjqtXX4qhTBTRTAzPnHoZx0e7Cse+OXik5NywOwAAcEkqAi4/hjLRwr7hTBSbsG5Ga0nkkvAobihS9RvcdOZTZBHBsRkekihAEAB/jdADAN48NoLOrhh8HhnhgBuabpWEG5qRfyxLIgzLhiqI/A9SIiIiIiIiIpo2hh5ERKdJ2B2c92u6ZBVZIwsAkEQJzf5GNPsboUoKvjX6w8Jxk6s3QmNrUWUV9Z4wjo2eKuwbyYzOaA2O4yCajSPs2AiPDVins9d46CEIAmRZhMc99Z8W/3rfXgBAXcCFz33oYkSCbpiWjVTGGLtW/jjLsmEAUIuGphMRERERERER1cL2VkREy4hbrjxory3UglrvhY94wgAAv+LFikBTyb6Zhh45U4PtWDBta0bn0ZmpuApDlSV4K4Qe61dWDsdGkxqe2dsDAEhmdMRTGnK6WRhsbjvgvA8iIiIiIiIimhGGHkREy0i1dllu2YUmX0PV88arTkRRRP1YADJueIahR9bIAQBMh6EHlVIVEV5XeXurCzc2wq1WrtZ4ck83ACCR0mGYNlJZA7qZ/96ybacQgBARERERERERTQdDDyKiZcQtVa70AIBVodaq+4pbbTVPqvQYzkRhWMa016BZOgDAtM1pn0NnB1kSK1Z6+D0KVjfXbvdWCDeciY9t26lZ6eE4HHRORERERERERKUYehARLSOiKEKVKld71Ao9QkWhR0uF0COeS0KvEXyk9DRyYxUemjkWelis9KBSqiJBkcv/tFAVEWtbqoce9z5+BEd7YoXHE+2tald6sPUVEREREREREU3G0IOIaJnxKO6K21eFWqqeU1zp0eRrgChM/PpPaCkMpYeR1FJl51m2hdFsHIOpEfSnhqFbBuyxtla2Y8F2eNOZJqiyWDLjY5wkCGivEXq88EYfvvbjfRhN5oM1jM3ysG0Hmm4hna0cyLH1FRERERERERFNxtCDiGiZ8ciVQ48VvkYoYvk8BQAIuwOFj2VRQqM3UrJ/ODOKjJEt2WbaFnqTAxjNxgA4sB2rbOi5xWHmVESSKv9ZUR/2oLHOU/Nc28mHH+PGQw/DtNE3nMZANFN2DkMPIiIiIiIiIpqMoQcR0TLjVlxQpPJwQxJFtIWay7aLgogGX33JtmpzPUwrP6fDcRz0JvrLZn1kKwQjRJN9fMd5hY8v2dyElY1+1AXcEMXyKpBivcPpwseW5cAumtmR0/Lfmzl9YpYM21sRERERERER0WTl00aJiGhJEwURjb569Cb6y/atDrXi+GhXybbWwAqok0KS1kAT9vUfKDweSkcB5IeUy5IM0zanNajcsE3Ufv8+nY0+cP0GNNV5MBLP4ta3rcNwLAvDtOFzy0hmqs+OGY5l8Y8/2ANNt/CRd2/BplV1hX2Gma/8iCZyWBHxQRIFVnoQERERERERURmGHkREy5BbdkEUpMJ8jXGrguXDzNvDK8u2tQZWlDwezuRDj/HKDmMagQeAQmUI0WTnra9HJmdClkS4VRmpjAGXIiGJ6qFHcQur/7drD9yqBL9XxW/dvAXtzUHopgVNt5DK6Ah4Vdi2U/VaRERERERERHR2YuhBRLRMuRUXMnrpnINVofLQo9K21mBpG6zx0EMfCzumCjMs24IkStOqBqGzkygIEMeGmrtVCQCgKtKMrpHTLeT0LB565hg+/6GLkc2ZsKz8nA/bcWAx9CAiIiIiomXCcRy88MILuP/++3HgwAH09fVBEAS0tbXhiiuuwG/91m+hvb294rmxWAw/+clP8OKLL+LIkSOIx+MwTRPBYBBr167FVVddhbvuugt1dXUVzweAvr4+/OAHP8Bzzz2HEydOQNM0BINBtLe34+qrr8aHP/xhNDY21nwNhmHgwQcfxCOPPIKDBw8iFovB5/NhzZo1uOaaa7Bz506Ew+G5fJqI5gVDDyKiZcotT4QePtULwzIRKhpYPm5NuK1s28pAaegxlBmF4zhTVnpopo7v7v0xTsS6cE7jRnzs4g9Na622bUMUOUbqbCKJQmGGh9slY+3KEEJ+V8ncjuk61hOH4zhIZnQAE0POWelBRERERETLQTQaxRe+8AW88MILZfuOHDmCI0eO4Ic//CH+8i//EnfccUfJ/vvvvx9/8zd/g0wmU3bu8PAwhoeH8corr+Db3/42vva1r+Gyyy4rO+6xxx7DH//xHyObLZ3TOTIygpGREezZswf/8R//gf/zf/4PduzYUfE1HD58GJ///Odx8uTJku2xWAx79+7F3r178e1vfxt/+7d/i5tuumnKzwnR6cTQg4homfIrXsAL2I6NiCeMjJFFf3IQN6y9Ck8cfx4AUO8J4/wVW8rOrfOEoEoK9LGQQzM1pPQMJDH/z0K10OPF7j04NnoKAPDW4BE8evQZfPLSu6Zc63B2FH7VC6/CCSBnC1GcqPQA8iHIe96+DgdPRGd1vURGR0hwAQAs28H+jiG8eWwE73n7OtSH+H1FRERERERLUyaTwZ133lkIC+rr6/GBD3wAmzdvRiqVwjPPPIPHH38cuq7jL//yLxGJRHDjjTcCAH7961/jz//8zwEAiqLg1ltvxbZt2xAKhZDJZPDWW2/hoYceQjweRyKRwB/+4R/iscceg9vtLjz/sWPH8IUvfAGapkFRFNx2223YunUrfD4fhoeH8cQTT+D5559HLpfDn/7pn2Lz5s3YuHFjyWs4cuQI7r77bqRSKQDA1q1b8a53vQsrVqxAIpHAc889h0cffRTJZBKf//zn8ZWvfAU333zzQnx6iSpi6EFEtEzJkoywFCw89shuiIKE69deBb/qQzQbw9vaLysbYg4AgiCg0VePnqJh6MOZKAIuH0zbqtre6sBgR8njR44+jTsveC/8Ll/NteaMHOA4DD3OIqIgQJKEkm0XrK/HdZe04cU3+5DTrSpnVhaN5xDy5UOPVw704z8fegsA8OjLp/Cf//MmuF38k4aIiIiIiJaef/zHfywEHpdffjm+9rWvIRCY6NJw11134d5778Vf/uVfAgC+9KUv4frrr4cgCPjyl78MABBFEf/xH/+BK6+8suTa73//+/GpT30Kt912G0ZHRzE0NITnnnsO73jHOwrH/PCHP4SmaYVrv/e97y25xkc+8hF85Stfwb//+7/Dsix873vfw1//9V8X9pumiT/4gz9AKpWCIAj44he/iLvuKn3z45133omXXnoJv//7v490Oo0///M/x6WXXoqGhoa5fvqIZoW9RoiIzhCCIMCneqBIMq5avQ07Nr8DzYGmqsc3+Ur/+BhK59+B3x3vLVSAFHMcB91FIcm4V3vfqLku0zJh/v/s3XeUFGX28PFvhQ6TIww5IyKSDAQVXSOoyJoT5pzDmuPqrq6uu/vbV1FXXRUFwQi4iqgoqCAKSg6SERiGOHmmp1OF94+erummewI53c85nKmueqrq6Zkeprtu3Xstg4ARbMrTEAeJ7TM9AFK9Lv54Ymeeu+0E/nX3iZx2bLsmH6+kIuAsj/16ubNc6Qvx5c9rd3m+QgghhBBCCLG7VVdX88EHHwCQnZ3Niy++GBfwiLr44osZNGgQAEVFRcyePZt58+axceNGAAYPHpwQ8IgqKCjgwgsvdB6vWbMmbnvs41NOOSXpMW6++WZat25Nnz59yMzMjNs2adIk5xhXXXVVQsAjqn///tx9990A+Hw+Ro8enXScEHuD3BZ5ACktLeXVV19l2rRpbN68mRYtWnDmmWdy8803k5qauq+nJ4TYD6ToXqqC1c5jTak/tl2wXdAj2szcsq2k48sDlZh24t35m6u2NDinaLDDsAynAbo4+KkxPT2iXLoKCmBHMkHOPr4j3/66vknHm/zLOlrmpdG6eTr+YHwm0tK1pZy3uyYuhBBCCCGEELvJDz/8QDgcuanwj3/8I7m5ufWOve222+jfvz8dO3aka9euZGdn891337FhwwYKCgoaPE/btm2d5UAgELct9pxvvPEG99xzT0LPzZSUFKZOnZr02F988YWzfMkllzQ4j4suuoi///3vmKbJlClTuPfeexscL8SeIkGPA0RFRQWXXXYZa9eu5cQTT+S0005jyZIlvPbaa/zwww+MHTtWAh9CCLwub9xjTak/wFCQ0SzucTToUZ8NlZuSrt/WyH5BM1S3bIRIdadg2RZqAwEZceBTlcSgh6IouHWVUNhC11UMI3mALZltZX7+9f4cbjq3Z8I2aWguhBBCCCGE2B/Nnz/fWU7WYDzWUUcdxVFHHRW3rlWrVrRq1Srp+EAgwOrVq1m4cCGfffaZs940429WPPvss/nf//4HwOuvv86kSZM45ZRTOP744zn22GMbvZ44d+5cZ3nFihX8/vvvDY7Pz89ny5YtrFq1Cp/PR1paw+WwhdgTJOhxgBgxYgRr167loYce4rrrrnPWv/DCC7z11luMHTuWG264YR/OUAixP9BVDV3VMWobkW9/90asVunxpa/qC3qU+yuYt/k3Zm2Yl3R7SU05lm0RMkIJQReID3ps8RWj1ajkpGSR4Ulv9PmIA5eqKujbBT0A3C6NUNjC49IwDItBfVozfX5Rk45p2/D6hMRyahL0EEIIIYQQQuyPSkpKnOXWrVvv9HEKCwv5/vvvWb58OevWrWPDhg1s3rwZy0q8kcy24z8fnXTSSVx33XW8/fbbzrHeffdd3n33XVwuF0cddRR/+MMfGDx4cMIcfT4flZWVzuN77rmnyXO2bZvS0lIJeoh9QoIeB4iNGzdSUFDAFVdcEbd+2LBhvPXWW3FRVyHEoc3r8lJdW+KqoWyKVhnx6bElNWWYloUWEygp9pXy6q+jqQn76z1OWaCcqmA1Zf5K2ma1TChfFTLq+oPYtoVhW0l7hoiDj64nvv7cLg0I43Vr+PxhTj2mLZuKfWwu9XFSnzbMXLIprn9HU5hJ3ugLIYQQQgghxL5WXl7uLHu9iTcJNqayspKnn36aL774IiGYAeByuejZsydZWVl899139R7noYce4oQTTuCdd97h559/dkpuhcNhZs2axaxZs3jhhRcYNmwYjz/+uNPXo7q6ut5jNoXP59ul/YXYWRL0OEC8+uqrSddHGwnl5+cn3S6EOPS4NZez3FBPjyxvBunuVKpDNQCYtsXk1dM4s+sfAAibYUYtGN9gwAOgIlBFmb8Cy7aoDvnI8tY1PTNMAytJH5CwBD0OCbpWX9ADPO7I16x0D7df2NvZvuT3kp0IetS9+a8JhHG7tKTnFkIIIYQQQoi9KTbQsX2vjcYEg0GuvPJKli1bBoDH4+HYY4+lR48edOrUiU6dOtGtWzc8Hg8ff/xxg0EPgOOPP57jjz+eqqoqZsyYwc8//8zMmTNZu3YtEMnM+N///kdRURHvvfceiqLEzb9z585MmjRph56DEPuKBD0OUKWlpUybNo3nnnuOtLQ0rr766n09JSHEfsKtxgY96u/p4dJc5KfmOkEPgB/WzqRvyx5g2/x3zvv4Ggl4ANSE/QSMIG7NRVXQh6KopOgeXJqr3owOyfQ4dLlrsz9cuoaqKgmlqTLT3Dt8zNhEj+JyP6leF/nZKUAkIKIlKbMlhBBCCCGEEHta7E3KGzdu5Igjjqh3bDAYZM6cObRp04aWLVvy7rvvOgGPHj168Nprr9G8efOk+8aWoGpMRkYGQ4YMYciQIUCk3NW4ceN4/fXXsSyL2bNnM2PGDE444QQyMzPxeDwEg0E2bNhAKBTC7d7xz2xC7G1yG+QB6I033mDgwIE89NBDhEIh3njjDTp37ryvpyWE2E/EZno01NNDUzVaZ7ZIWD9v02JGzv+kSQGPqMpAFQAhM0Sxr4TN1dsij63kwQ3DMpOm5oqDn0tXURTQNQVNiwQjlJiYxM4EPYLhSDZRIGQQCluUVwcpqYi8foMhY9cnLYQQQgghhBA7oWfPns7ynDlzGhy7YMECrr32Wk4//XSeffZZpk6d6mx78MEH6w14APz2229J1/v9fj7//HNeeukl3nnnnaRj2rZtyz333BN3Q3X0eIqi0KtXLyASlPnpp58afA6hUIi7776bp556iv/+978YhnweE/uGBD32kVNOOYVu3bo1+O+Pf/xj0n2bN2/Oddddx+DBgwkGg9x4443MmDFjLz8DIcT+Std0p5dHQz09AE7ueFzCuh/WzqIiEH+XiEvVaZ6WB0C2N5MsT0bc9vJg/PiwGcayLQyzvjc4NlUhH5Zdd4t+7LI4eCmKQopHR1EUpwRVircu8TRZ0KOxRI1A0CBsmASCtaXUbKioDgEQDCWWVxNCCCGEEEKIveHEE0/E5YrcmPjZZ5812CPj008/dZZPOOEEysrKnMcNNQP//fffmTJlivM42q8DQNM0HnvsMV555RVeffVVQqFQvcfJyclJer4zzjjDWX711VcbDGR8/PHHfPXVV7z//vt8+eWX6LoUGRL7hgQ99pG2bdvSsWPHBv+1adMm6b7nnnsuDz30EC+99BJvv/02wWCQBx98cIdrAwohDl7RbA9drb+8FUDLjOY8duIdjR7v5mOGc8/A63ng+Fu477ibaJ/dOm57RW2mR6ywaTiZHsmyOop9Jawv34hhmc4xLGlIfUiIBjl0TQUFUtx1b4Sz0j0J472eht8oB0IGlb4QgZisDsuyCRsW/gYyPWzbJmxIUEQIIYQQQgixZxQUFHDOOecAUFxczP3335/0+t2XX37JhAkTAGjXrh0nn3wybdu2dba///77SY+/YsUKbrnlFvz+ukoNsYENt9vNaaedBkBFRQV//vOfk37uLikp4cMPPwQiN6r169fP2XbhhRfSrFkzIJKN8sgjjyQNnvzyyy+88MILzuObb7456ZyF2Bsk3LaPvPvuu7vlOAMGDOC0007j66+/ZsGCBfTv33+3HFcIcWBzaS4My2w008OlucjwpNMmsyUbKjclHfPIoNvJ8kYyO/JSswHimpUDVAaTBT3ChIwQHyz6nMVbl9Mltz2X9/ojbq3uTn7LNgmZITTFS2WwCo/mJtWdsiNPVRyAUr21QTlNwa2r6Hrd6zQvy5swXmugTBvA1jI/FdVBFCU+JSQYMpJmeliWjaoqmLWBEZfecHBQCCGEEEIIIXbWo48+yq+//kphYSHfffcdZ511Fueffz6dOnWiuLiYn376yWlC7nK5ePbZZ9E0jYsuuojp06cDMG7cONavX88ZZ5xBfn4+JSUlzJw5k++//z4h86KqKv7z+d13383UqVPx+/2MHz+e+fPnc84559CmTRtCoRCrV69m3LhxVFRUAHDeeefRtWtXZ//U1FT+/e9/c+211xIOh/nss8+YM2cO5513Hp07d6aiooJff/2VL7/80gmoDB06lMGDB++x76kQjZGgxwEgFArx66+/AnD88ccnbG/dOnLHdWzamxDi0ObWXIStxmtnRpueN0vLTRr0aJaa6wQ8YiWUt0qS6RE0Q8zbtIT5m5cAsKx4NV+t/IFhh58eNy5khFBRMC0TvxGQoMchwOOKBBkyUt2EDSuu0Xh+VuLPf8CRLfjml/UNHnPs18u59PRuceuq/WFMKzHLqKwqQF5WCoZpJd0O0gBdCCGEEEIIsXtkZGQwduxY7rzzTubPn09RUREjRoxIGJeVlcULL7zgZFkMHjyYa665xunF8euvvzrXB2O1bt2aZ555hhtuuAHTNFm6dGnc9vbt2/Pqq69yzz33UFFRwZo1a3jxxReTzvWcc87h6aefTlh/7LHH8s477/CnP/2JLVu2UFRUxMsvv5z0GJdccglPPvlkg98TIfY0CXocAAzD4MYbbyQrK4sff/wRTYu/IzX6n1n79u33xfSEEPshl+bCXW8/jTq6FvkzkOPNSro9LzUn6frtAyHb9wABqAn7WVW6Nm7dT4VzOLFDf7JjMkVCZhiLyIXngBFsdM7i4OF2aeRmeokNO+RmJpa3GtS7NdPmFTkNy5OZtWQzZ/RrT25MpojPHwa7LrMDIsGM8qog2RleTNPGMJKXVAsbJppb3iYJIYQQQgghdl3z5s354IMP+Oqrr/jiiy9YuHAhpaWluFwuOnbsyEknncQVV1xBXl5e3H6PPPIIJ5xwAh988AELFiygvLwcTdPIzc2la9eunH766QwdOpSUlBSOPvpofvnlF5YvX86yZcs4/PDDneMcd9xxfPXVV3z00UdMnz6dNWvWUFVVhdvtpnnz5vTr149hw4ZxzDHH1PscjjnmGL755hvGjRvHd999x7JlyygrK0PXdVq0aMExxxzDxRdf7DQ+F2Jfkk/zB4DU1FROOeUUvvnmG9566y1uuukmZ9unn37Kzz//TI8ePeL+MxNCHNrcmpuQFm50nEvVAYWclB0LeuR4s+MeJ+/pEWZrdXHC+rkbF3NKp7oG6iEz7GSlBI0Qlm01WpZLHDzcLg3TjAQeWjdPR1MVCnJT2VJaA0DXttlkpLl54Iqj+fW3LXw9a129x9pYUh0X9Ii2kjFjgh6hsIltR5qfG6aFYSbP9AgbFt7EnupCCCGEEEIIsVMUReHMM8/kzDPP3KH9Bg0axKBBgxodN3r06Aa35+bmcsstt3DLLbfs0PljeTweLr/8ci6//PKdPoYQe4MEPbZTWlrKmWeeSXl5OQsXLsTjSbzjNFYgEGDUqFF89dVX/P777wC0adOGM844g6uuuoqsrOQXEnfUo48+yoIFC/jXv/7FzJkz6datGytXrmT69Onk5+fzz3/+M6GWuRDi0KWrGh6t8Su2iqKgq1r9QY+U5EGPTrnt4h5XJOnpAVDiTyy7t6JkTULQo45NyAjhdSX2dRAHL02L9PVIqW1Yfu9lR/HGp4vwujWuOvMIAPKyUjijf/sGgx41geTZTZZdF9gI1WaLhMImNmAmaeIHkaCHEEIIIYQQQgghDjwS9IhhWRZ//vOfKS8vb9L4LVu2cN1117Fq1aq49StWrGDFihWMGzeO1157bbdkYLRq1Yrx48czYsQIvvvuO3755Rfy8vK49NJLue222ygoKNjlcwghDi5eveGgbZRLcyVkbkTVl+lRkJ6PqqhYduTCcE3YT8gM49ZczhhfqIbqUE3CvusrNhIwgjHzi7/TPmAEJehxCEr11L0l6dEpj7su7kN2hgePW2dzsQ9FwcnWqE9FdfLyaJZlU1UTIj3FRSgcec0GwyaqqtQb3Ag1UEpLCCGEEEIIIYQQ+y8JesR4+umnmTx5cpPGGobBbbfdxqpVq1AUhYsvvpgzzzwTTdP49ttvee+999i0aRO33XYbEyZM2C0ZH82aNeMvf/nLLh9HCHFoaGr2l0vVkzYrh+RBD13VcWkusrwZlPkrnPWVgSry03Kdx1t8iaWtACzbYuHmZfRr0zvp9upwDaqqkelJb9L8xcEhNcUV91jTVFRFiTQ9VyAjzU1ldajBY1RUhyirDOB2aaSluCgu97Ng1Tb6HtaclvlplFYGnCBHKGzi0rUGG5nH9gIRQgghhBBCCCHEgUGCHoDf7+eRRx7hyy+/bPI+H330EYsXLwbg4Ycf5pprrnG29evXj759+3LvvfdSVFTEm2++yX333be7p71LSkpKKC0t3eH91q2rv6yIEOLA5NJ0dFUjy5sZ15Dco3vITVL2yqNHymblpWTHBT0+WvIFPZp3pV/rPqS4vEn7eUR9vep7ejQ/jDR3SsK2kBGizCqXoMchJjbTA0BTFVRVwaWrZKV5yEhzNRr0mLFwIzMWbkxYP/HH37np3CPp3qGuKWCoNvhhmBa2bScECS3LjusFIoQQQgghhBBCiAPDIR/0mDNnDk899RQrVqwAQFVVrHrqe8eKNgfq0KEDV111VcL2M888k88//5wpU6YwduxY7rzzTtzu/acj6tixY3n55Zf39TSEEPsBV21JqiOadeHnwrlAJOBxbZ8LkzYUj5alapvVmlWldYHQ9RVFrK8oYkXJ79x49GX1ZnoA+MJ+vlz5HRf2OCvpdtMyE8pliYPb9sEFTVOcdfnZXupJyGiyr35eFxf0wKau1FXIxLtd0MWybEzTwqUn/g4IIYQQQgghhBBi/3VIf5L/xz/+weWXX+4EPM4//3zOOiv5BbhYq1evZs2aNQCcffbZqGryb+N5550HQHV1NT///PNumrUQQuxebjUSWDj7sFM4s+vJnNi+H3f1v5YOOW1jRinO13R3GgBdcjskPd7q0nVs85WyZbtMjzaZLeMez964kJKaxEbnUYFwYIeehzi4aKrqZF8oioKmKigKHNYue6eOt35LVdzjbeV+3vpsMa+NX8iS30sSxlu2XW/pKyGEEEIIIYQQQuy/Dumgx8KFCwHIzc3l//7v/3juuedwuRq/q3jevHnO8rHHHlvvuKOPPtpZnjVr1i7MVAgh9hxd01EUFV3VOalDf8467BTyUrPjxuSkZJHuTiPDk4amagAclt+p3mP+66c3WFO2Pm7dhT3OonlaXty67cfEKvGXs9UXfzG6MliNX4IhhwRdU9i+spSuqVxzdg8n+yI9ZeczgT6esoLFa0pYvr6Mlz9egLVdgMO0JOghhBBCCCGEEEIciA7p8laZmZncfPPN3HTTTaSnN712/OrVq53l9u3b1zsuNzeXtLQ0fD5f3D77g8svv5whQ4bs8H7r1q3j9ttv3wMzEkLsS9EyUkEjmHR7ujvVKYMV1SazBW7NRcgMN3p8TdFolppHr4LufLvmR2f9hopNHNs6eUNz27YSsj3K/OW4NTcpLm+j5xQHNlVVkpa86t4xl7/dehxL15ZxQu9W3PHP76gJGE06pm3brCmqYHNJDSsLy531xeV+tpTW0DI/ksW0dmMFU35ZT+/DmjHgyJb1HE0IIYQQQgghhBD7o0M66DFixIh6S1M1ZOvWrUCk/0dBQUGDY5s3b87vv//u7LO/yMvLIy8vr/GBQohDQrPUXGrC/qRBD6/LmxDwANBUjd4tjuDXogWNHz8tF01VE0pcbajc1OB+hmVgWiaaqmFaJqZl4rf8GJaJXptxEitshpPOVRx4dE1F2y7o4dI1dE2lZX46Xo+LrHQPXdvmsGDltiYdc96KbYz+cmnSbcUVfgpyUyku93Pfi9MIGRYTZ/zOn2/ozzHdW+zy8xFCCCGEEEIIIcTecUiXt9qZgAdAZWUlAF6vF01LvOgWKzU1NW4fIYTYH7l1N27dHbdOVVTS3Wk0T60/QHpOt9MZ2PYojmzejTRXSr3jCtKbAdAmKz7oUVS1hdWl6wibYX7bupLCio3YdnxJoUBtICYck1ESMkJJz1MT9tc7B3Fg0VQFVYkPenjdkb+5mqaiKJFskKEndMTjrvtbnJbionuH3KTHmz6/qN7zbS2todof5tNpqwkZlrP+zf8t2dWnIoQQQgghhBBCiL3okM702FmhUORim9vtbmQkeDyeuH32Z2PGjGHs2LENjgkGk5e+EUIc+Dxa/P9pmZ4Mcrfr7bG9LG8Gfzz8DCBSGmv+5t+YsPTrhHHRXh7p7lSyvZmUB+oCwf+d837c2D8efgYD2x7lPPaFakhzp8aV0QqZIXRTRwEns8O2bfzhAFnezMafrNjvRQIb8UGPFE/kbYuqgKpEgiL5WSlcf04PPvhmBaZlceHJXfl16ZaE45mWzdpN9d+AsKW0hmDIZMGK+KyRom3Vu+HZCCGEEEIIIYQQYm+RoMdOiGaIbH8xJpnoHcs7m1WyN5WWlrJq1ap9PQ0hxD6iqZpTRgog09N4ryMtpsSUR/fQv01fFm1ZzqrStXHjCtLzneVueZ2YVTS/3mN+9/vPDGjT1/k/tjpUQ55lErJigx5hlLCfoBl2AiphM0zYalpvB7H/05P09HC76jI9nJ4fChzWLocnruvvjNtU4mPhquIdOl9xuZ+QYaLr+//fayGEEEIIIYQQQtRPgh47IVqyqilZDzuSFbKv5ebm0qVLlwbHBINBCgsL99KMhBB7m1tz47f8qIqGrjX+J0JXEkv85afmJAQ9WqY3d5YHtju6waBHZbCKUn85eak5tWtsNlVvdYIxEAl6WLaF3whip+aiKAp+I4BlW8kPKg44mlZ/8EFTFaL3Hbg0FV1X8cc0Mx/QoyWTZ63DMO16jpCouMJPKGwm9BGByA0MTbnRQQghhBBCCCGEEPueBD12QlpaGhAJAFiW1WAWR01NDQCZmft/uZXhw4czfPjwBsesXLmSoUOH7qUZCSH2tmhzcL2RfkXbj4+VkqS3R+fc9pQHqgCbFunN6NH8MJZsXVHvcdeUrY8JeiT28AjVZnXYtkXYDOPW3fjCfiy76Re5xYErtt9Hildn+x97Rpqb2y/ozYsfzW/yMUsqApimjStJpkcwbGJZNqle165MWwghhBBCCCGEEHuB1HDYCa1atQLANE2Kixsun7F161YAmjdv3uA4IYTYH7jUSCw8WQZHMlqSoEez1MQm0pneTNLdqc7ji3s0HDxdXbq+kTPb2LVZHQEzhGVZBMJBbNuSbI9DgKIoaLU3HKR5XehaYhZGh1ZZSRua16e8KpK9mSxuVrilirCx668r05KgnBBCCCGEEEIIsadJ0GMndO7c2Vlev77+C3OlpaX4fD6ARstGCSHE/kCPBj3UpiUCJhvXo3lXvLrHeXx650HoqkZaTNDDo7u5ps9F9R53/uYlrCsvatIcQkaIimAkiwSQbI9DRDQjI8WjJ/T+iIo2Pm+KSl8IXyBMIJTYF2ZraU1cwMLaieCFadmYpgTkhBBCCCGEEEKIPU2CHjuhd+/ezvLcuXPrHTdnzhxnuW/fvnt0TkIIsTtE+3gkK1uVTLJMD4/u4aZjLqdf696c2fUPnNn1ZABSdC9Qd3G6ZUazBo/9n19Hs6pkbaNz8IVrKPNXOI+tmN4ftgRADlrRhuOqqqDX0/8jFDYT1p11XId6j/l7UQWBUOI+lb4QZm1/EJ8/jD+YGBhpjGXZkukhhBBCCCGEEELsBdLTYye0a9eObt26sXz5cj777DNuvPHGpA1OJ0yYAER6gAwcOHBvT3OHjRkzhrFjxzY4pinN24UQB64dz/TQiAQy4i/mtsoo4PwjzgQgtbbHh6qqeF0eAuEAAJmeDFqkN2Nz9bZ6j/9T4Ry65HVocA6xDc4BzJjyVr5QDemetCY9F3FgiS1pFW0+rqoKmqoQNiwUBSp8oYT9Tju2HZN+Wpv0mKuLKggkCWhU+cKYVuR1VVEdxO3SSEvZsf4eli1BDyGEEEIIIYQQYm+QTI+ddPnllwORxt6vv/56wvavvvqKqVOnAnDRRReRkpLY2Hd/U1payqpVqxr8V1hYuK+nKYTYg3RVQ1HUpBkcDe3TkGjvBYB0V12JK0VRuPjIoXTL78wRzbpydZ8LE/ZdV160w9kasT09ygIVku1xkHLFZHdEMz3cLpUW+Wk0z02lTUEGA49sGbfPsd0LUBSFY7oXJD3m6noyPVYUljklrQIhc6cyPUzTkvJWQgghhBBCCCHEXiCZHjvp4osv5qOPPmLJkiX8+9//ZvXq1Zx33nm4XC6mTJnCqFGjsG2bFi1acOutt+7r6TZJbm5uo71HgsGgBD6EOMhluNOaXN4KIiWuDKv+i8CxAZR0dxol/nKnCXmrjAKu7VvX2+OK3ufx3oIJzmNfuIYpa2ZwcsfjmLNxIeWBSrrkdaBjdtukGXYQyfQwTANVVQmbYfxGwMk2EQcPLSboEV32uHQ8Lg2PS8O2bXp1zef7eRvYWlpDWoqLIQM7AHDeSZ3JSHWxuaSGpWtLneMUbqlKeq5ff9vM2cd3pCAvDcuyCYZNLMuut5dIMpa9c71AhBBCCCGEOFgVFhYyevRofv75Z4qKigiHw+Tl5dG3b18uueQSBgwYkHS/8ePH88gjjwDQp08f3n//fVS18fu6R4wYwcsvvwzApEmT4nr2HgoefvhhpyrNwoUL8Xg8jeyxZ4RCIV5//XUmTpzI5s2b8Xg8NGvWjDfffJOWLVuyevVqRowYwezZsykvLycnJ4fjjz+e8847j6uuugqAp556issuu2yvzz32NTRlyhTatGnT5H1jX7fJqKqK1+slNzeXww47jCFDhjB48GC8Xu8uz7spli1bxuGHH75XzrU3SNBjJ6mqymuvvca1117LqlWr+Oyzz/jss8/ixjRr1ow33niD7OzsfTPJHTR8+HCGDx/e4JiVK1cydOjQvTQjIcS+kJ+Wu0Pj3ZqLoBHEq3sImQaWHX+nvK7UBT1UVSXDk0ZlIPnF5SObd6NrbgdWlq511n275kfWlW9w1k39/ScGtj2KPx5+RtJjGKZBabicbG8mAAEjKEGPg5ymKihKJNMjSlEUsjM8/OPOQcxZuoUWeWlOY/NUr4thgzqDAo+/9hM+f7jB41s2zF2+le4da3837Ei/EG/t8WzbRlEUTNNC01RMy3ZKbkUDJNLTQwghhBDiwLWqsHxfT2Gv6NI2e6+d6+OPP+Yvf/kLoVB8SdpNmzaxadMmJk2axIUXXsjTTz+Nrtd/+XL+/PmMGTOGK6+8ck9PWewm9913H5MnT3YeBwIBAoEAzZs3Z9OmTVx66aVUVlY627du3drga+BgYVkWNTU11NTUsGHDBqZOncpLL73E3//+d4455pg9dt6SkhJeeOEFfv31V6dq0cHg4H/F7EHNmzdn/PjxjB49mkmTJrF27VrC4TBt2rTh1FNP5brrriM3d8cuHgohxIHGo3uoCvpokd6cLb5i/GF/3PbtS2XlpeQQMIKEjMR+CwDts9vEBT2AhMc/F87l+LbHxAVowqZBYeVGWqQ1I8ObTpoVKaUVMKQX0aEgLcWFxx3/WnPrGlnpHjq2yoofXNuGRlMVCnJTWVNU0ejxSyoCGGZd0CIYE/TwBQzSU1xU+ELkZnqprgmRlR65a6qsMkBaigvTsiToIYQQQgghBDB16lSeeOIJbNsmIyODq6++mn79+uHxeFi6dCkjR45k3bp1fPLJJ6Snpzd4dzzA//3f/3HaaafRsmXLBseJfW/VqlVOwKN169Y8+OCDtGzZkmAwiKZpjB492gl4nH322VxyySVomkZeXh5bt27dl1Pfre68805OPfXUuHXhcJiqqirWrl3L119/zaxZs9iwYQPXXnstI0eO3GOBj/vuu4+ff/6Z1q1b75Hj7ysS9NjO888/z/PPP9/k8R6PhxtuuIEbbrhhD85KCCH2Xx7NTYrLi6qqeHR3o0EPRVEiWSH1BD2OanUk3675sdHzLitZzQm1QY+gEeKlmW9T4i8n1eXl5mOGk+5Oc7ZF78QXB6/mOals/yP2erRIxkVtkCMqzevC5w+jqQot8poW9CivChAO12UxhcJ1/TlqAmHcukpVTYiMVBfV/rAT9KgJGHhcWm0jc+npIYQQQgghDm2mafLss89i2zaZmZl88MEHcWWm+vTpwznnnMNVV13FkiVLGDVqFBdddFGD5dhramp4+umnee211/bGUxC7YPXq1c7yrbfeypAhQ+K2r1mzBgCXy8Wzzz4b1yP5YAp6tGrViu7duyfddvzxxzN8+HDGjRvH448/TigU4vbbb2fChAm0atVqt8/FNBP7Wh4MpJG5EEKIXeLWXKS7I1kVHs0NQE5KNgCqoqIqiX9q3Kqr3uPlpmRz0zGXN3reFcVrnOWfCudQ4i8HoCYcYM7GRVSHfADYtoUvXNOk5yIOXKqqJAS2Ur2R15kW03tD0xTSU2vXayot89KadPyyqiDBmKBHMGxQ6QthWjahsIkvECYctiirChKqHRc2IqWtwqaFZYFpSqaHEEIIIYQ4tM2ePZsNGzYAkYveyfpqpKen8+STTwKRkj8TJ06s93guV+S9/XfffcekSZP2wIzF7uT3190kmSyzoKYm8tk9Nzc3LuBxKLrgggu4++67ASgvL5eg3g6SoIcQQohdoiiKk1Xh0dzkp+U5/TR0NXlCoUurP+gB0CmnHXm1gZP6rCj5nU+WTMIX8vPN6ulx26at+wXTqrtAXewrY6uvJG6dOPh5XJEsIzUmGOJ2aXhry2BpqkLL/KYFPcqrggRC8ZkexeV+agJhQmGLyupI5lKVL4Rp2pim5Yw3Tbu2vJVkegghhBBCiEPbnDlznOWTTz653nF9+vQhNTVyc93KlSvrHXfjjTc6Nz8988wzlJeX756Jij3CivlMpGlawnbbjtwodij08GiKG2+8kfbt2wORRuhbtmzZxzM6cMgrSAghxC6LvsnUNZ1MLR0AVdESSltFNRb0AGib1crJ3qjP7I0L2Vy9FcuOv5isb3deyzapDlaT5cmod07i4KXGZHp4XBouXUNVFVRVoU3zjCYdo9ofZvnaUjq3yQbAqu3PUV4VdBqVA9S+RydkWARrgx7zV2zlnS9+wzAs7ry4D384uu1uemZCCCGEEEIcWPr27ctNN93Eli1bGuzBYdu2cwE8GKy/T2Pfvn257LLLGDt2LCUlJfz973/nueee2+n5WZbF5MmT+d///seiRYsoLy8nLS2NLl26cPrpp3PppZfi9XoT9ps1axZXXXUVAJMnTyY/P5933nmHyZMnU1hYCECnTp0YOnQol19+OW63e6fnCPD9998zYcIE5s+fT0lJCV6vlxYtWtC/f38uvfRSunbt2ugxVq5cyZtvvsnMmTMpKSkhJyeHPn36cMUVV9C/f/+E8ePHj3f6q/z3v//lxBNPTHrce++918m6Wb58OQAPP/wwEyZMiBsX/X4lU1RURLdu3QDo168fo0ePbvT5RP3888988sknzJkzh5KSElJSUujYsSOnnnoql19+Oenp6Q3u/+OPPzJ69GiWL19OWVkZrVq14uyzz94nrQ00TePiiy/mH//4B+FwmOnTp3PhhRcmjCsrK+Pjjz9mxowZrF69mvLyclwuFzk5OfTq1YvzzjuPk046KW6f7X8msd/z5557jvPPPz9u/IwZM/jiiy+YN28excXF+P1+0tPTadeuHSeccAJXXHHFftXbWoIewjFmzBjGjh3b4JiG/tAIIUQsXdUSgg+x21RFTQhWxOpV0J35m39r9DwbKjcnrMtLyUk61rAMPOzam0tx4IkLetRmeXjckX4fHVtl0qZ5Ohu2Vjd6nJc/WcAztxxHmrcuaBcMJc8eKqnwO30/xn23ipqAAcCb/1vMoD6t0TRJthVCCCGEEIeegQMHMnDgwEbHLV682CmF1Fgfg/vuu4+pU6eyefNmxo8fz7Bhw5p0ju1t27aNe++9l19//TVufXl5ObNnz2b27Nm8++67vPLKKxxxxBH1Hmfjxo3ccMMNrF+/Pm79okWLWLRoEf/73/8YNWoUGRlNuwFre48++ijjxo2LWxdtgr1y5UrGjh3L/fffz/XXX1/vMT755BP+9re/YRiGs27r1q1MnjyZb775hgceeKDB/fdHoVCIxx9/nP/9738J6+fPn8/8+fMZNWoUr7zyCr17907Y3zAMHn/88YTgzJo1axgxYgRff/01/fr126PPIZnjjjvOWZ41a1ZC0OPbb7/lgQcecEqDRYXDYWpqaigqKuLLL7/k4osv5q9//esOn9/n83HPPfcwbdq0hG1lZWWUlZWxYMECxo4dyzvvvMPhhx++w+fYEyToIRylpaWsWrVqX09DCHGQ0FStwYwOj+4hYARRULBsE13VMay6N1zdm3WhZ8HhLNqybIfPXV/nhNjjh8ww7iZknIgDn6ooZGd4qPSFnICFW9fQVBW3S+PGP/bklyWbyUhzsXh1CYvXlNC6eTrFZf64Ph4Aj7/2E326NuP0fu1o1az+O4QCwch+lm2zpbTuzWeFL0R5dZC8rEO7Pq0QQgghhBANefPNN53l2Iu+yUR7gNx2220APPnkk3z++edJMzLq4/P5uPHGG1m6dCkARx11FJdddhnt27entLSUiRMn8sUXX7Bx40auvPJKxo0bR4cOHZIe64EHHmDbtm2cddZZDBs2jJycHJYtW8Zrr73Gpk2b+O2333jllVd4+OGHmzy/qE8//dQJeAwYMIBLLrmENm3a4PP5WLhwIW+//Tbl5eW88MIL9OvXj549eyY9zl/+8hfS0tK4/vrrGThwIIZh8P333zNmzBhs2+b//u//OPHEE5uUMdIUd911F1dffTVTpkxhxIgRQKQc2ZFHHglELtC7XC4ee+wxlixZQrNmzfjvf/8L4JQ5a8wDDzzAV199BUReMxdccAHt2rWjurqaGTNmMHbsWLZt28a1117LJ598QqdOneL2/9vf/uYEPA477DCuv/56OnbsSFFREaNHj2bu3Ln75Lpp586dURQF27ZZtiz++siKFSu4++67MQyDnJwcrrjiCnr16kVWVhabN29mxowZjBs3DsMw+Oijjzj99NOdDJ3ozyTZ9zw2C+vxxx93Ah6DBg3i3HPPpWXLloRCIdasWcOoUaNYu3YtZWVlPPnkk3z00Ud76TvTMAl6CEdubi5dunRpcEwwGHTS8oQQoiGaqjXYsDzNnYqmqBiWQcAwSXWnUBPyO4EJRVE4peNxOxX0CBiBpOtjeyqUByrx6h4yPQ2ntooDn6YqZKV7cOmqk/XhdkWWdU0lO8PDaf3aAdC/R0v8QYMOLTP419i5zFm2NeF481duY/2WKh69+thGMzaCwcRMEJ8/LEEPIYQQQogDwMJV2/jPuIVNygo+GLRpns6tF/SiV5dm+3QeX3/9tXPxunXr1px66qmN7nPqqacyePBgvv76a9avX8+IESN44IEHmnzON9980wl4XHrppTz11FNOGWeI9B8ZNGgQDz30ENXV1Tz66KP1VkvZtm0bjz/+OFdeeaWzrk+fPpx00kmcddZZ1NTUMHHixJ0KekQDHl27duXNN990GrlDXRbNxRdfjG3bjBs3rt6gR0ZGBmPHjuWwww5z1g0aNIiWLVvyj3/8A8MwmDhxIvfee+8OzzGZVq1a0apVK+d7DNCuXTu6d+8eNy4tLdJz0e12J2xryKRJk5zXzN133+0EwKKOO+44zj33XC655BJ8Ph9PPfUUo0aNcrYvX76cDz74AIBjjjmGt956ywma9e7dmyFDhnD//ffzxRdf7MCz3j08Hg+ZmZlUVFQk9Kx5+eWXMQwDl8vF22+/HZeB1Lt3bwYPHswxxxzj/C58/fXXTtAj+jNp6Hu+cuVKp1TZkCFDePHFF+O2Dxw4kIsuuojzzjuPVatWsWDBAjZv3kyLFi126/dgZ0htBeEYPnw4X3zxRYP//vOf/+zraQohDhC6qqFr9cfW01wppLvTcGuRclNe3YNHjy891SJ9595s+8PJS/FFAyqWbVETqqHcX7FTxxcHFpdLxaWrZKV7nHXR8lYAaSnxwbkUj47LpXFM94J6j1laGWDdlqpGz+0PGgnrisvjg3LRfiBCCCGEEGL/8srHCw6ZgAfAhq3VvPLxgn06h4ULF8YFAx577LG4C/sNeeKJJ8jMzATgnXfe4bffGi+XDJGyRu+99x4AHTp04PHHH48LeESde+65DBs2DIg0ZF+4cGHS43Xu3Dku4BHVsmVLTjjhBCASGCkpKWnS/GIVFxc7x0r2fenVqxc333wzt99+e4ON4q+//vq4gEfUpZde6jz3FStW7PD89pWRI0cC0L1794SAR1TXrl25+eabgUiZqNisjXHjxmGakRvW/vrXvyZkCamqyl/+8heysrL2xPQblZISuWkuNuhh2zZlZWVkZ2dzyimn1Fty7cwzz3Saxu9oI/QVK1bQvn17XC5Xvd9Xt9vN6aef7jzeX5qtS9BDCCHEHqGrOi61/qCHpmqkulNw1QZGvLoXj+6JG6MoChcecZbzOM2VSpY3s9Fzh60wMwvn8unSyawvL3LWR4MeITOMZVsYlkHQCO3Q8xIHnlRv4ocBd20zc4CM1MTtmqpwTPcCurVL3h8GYMW6skbP7Q8lBj1KKwOEjbqso7KqAGWVybOThBBCCCGEOFT89ttv3HjjjU5vgmuuuaZJWR5RzZo148EHHwTq+jNEL2Q3ZNGiRVRWVgKRwEZDQZZLL73UWf7xxx+TjmmoHFebNm2cZZ/P1+jcttexY0cApk+fzksvvURpaWnCmHvvvZe77roroXF1rPqakKenp5OXlwfgfE/2d+Xl5SxatAig0V4ugwYNcpZnzZrlLE+fPh2IBE22L3sVlZ6ezmmnnbar090poVDkuoWq1l3KVxSF0aNHM2vWLP7f//t/9e6r67oTrIkep6nOPvtsJk+ezMKFC50m58k0a1Z3w+qOnmNPkfJWQggh9giv7kl6d8z2XJoLj+5BVzW82wU9AI5u1ROv7mFz9TaOanUklYEq/rf8GzZVJZYdivXpsskAzN+8hHsGXEd2ShaGFXnDG9vbozrkS8gwEQcXj0tLWKeqirPe6058O6QqkdJX/Y9swfL1yYMby9eXccaA9mwpqSEjzU16bcaIZduoioIvEGbC94k1Xyuqg4QNE5ceecMaNiws26b+8IoQQgghhNgXbr+oN6+NX0jhlkMj26NtQTq3nN9rn5x77ty53Hzzzc6F9iFDhvDQQw/t8HEuvPBCPvvsM3755ReWLFnCO++802hD7tg7/nv1avj59+zZE1VVsSyr3v4ODTVej96xD8Q1EW+qa6+9lu+//x7TNHnllVf4z3/+Q+/evTn++OM5/vjj6dOnT9yF8fo0VH7I4/Hs9Pz2haVLl2Lbkez5t99+m7fffrtJ+0XL91uWxbp16wAabcJ95JFHJjSR3xuqqiJVBjIyMpJuj/7M/X4/GzZsYN26daxZs4alS5fy66+/OsGx6PdpR0WPb9s2W7dupbCwkLVr17Jy5UoWLFjgBJ0g8v3cH0jQQwghxB7R1CbhbtVFjjdy10GkxJWHoFFXnkpRFI4s6MaRBZG7CnJTsrl7wHUUVW5mxKx3Gj1+wAgy9fefOP+IMzEsE9u2neBHdLs4NEWDcqqqoGkKpln3BlBVFXIyPDTPqb9p3vrNlbw2fiErC8vxuDSuH9aD6fOLWL6+jFb56ZRXBSmvTnx9VfhChMIWqbUZ04ZpYZhS4koIIYQQYn/Tq0szXn3wVFYVlu/rqewVXdpm75Pzfvvtt9x3330EApHs58GDB/PPf/6zSRfvt6coCn/9618ZNmwYwWCQESNGcMYZZ9C2bdt69ykrq7vJKZrlUB+3201GRgYVFRVUVCQvlxwb2Eg2v6jYC9CxvS6253K5nB68xx57LC+99BJPPfUU27Ztw7Is5s2bx7x583j55ZfJycnhjDPO4Lrrrqu30TrU9c5oyM5eIN/btu9z0VTRAFt5ebmTEdRY+ar8/PydOteuKCsrIxwOA9C8efOE7aWlpYwcOZLJkyezbt26pD+3aCP0nfXdd98xduxYZs+e7WRixdqZ39U9TYIeQggh9ild0+N6f+SlZLOtppSwGW5wv1YZBRye35llxatRULCp/w/4L0ULOK/7EBQFwpaBGRP0CNWex7btJmWmiIOTS1edN7qKEnlTqGkKzXLq/8Bi2bCy9gNwMGzy6ri6mr5rN9WfCl7pCxIy6l6DhmljGBamZTt9RoQQQgghhDgUjBkzhmeeeca5O/zcc8/lb3/7m9ODYGd06NCB2267jX//+9/4/X7+/Oc/N/nu/6ao+9yw+967n3vuufVua926NVOnTnUen3baaZx44ol89913fPPNN/z4449O4KasrIwPP/yQ8ePH869//YvBgwcnPeae/Oy7t+/0jy1hdv/99zt9UxqzM/05dH3vX0pfvHixs9yjR4+4bQsXLuTGG2+MC/xkZGTQqVMnunbtSu/evTnuuOO45JJLnH4wO8K2bR5++GE+/fRTZ52qqrRt25ZOnTrRvXt3jjnmGFavXs2zzz67w8ffkyToIYQQYr/idXlpkd6MwoqNDY5TFIWr+lzI+ooiMtzpjF/6JatL19U7fquvhIL0fAzTiCtvZdsWITNMdchHbkr27noa4gDj0jVMyyYctpxeHwC5md4G9to5NX4jrqeHYUaWQ2GTFI9eW/pq5z/kCSGEEEIIcSB4+eWXGTFihPP4mmuu4eGHH94tF+RvuOEGJk2axPLly5kxY0bcRdvtxV78TtYjI5bf73d6ceyrptYQyTgZPHgwgwcPxrZtli1bxowZM/jqq69YtGgR4XCYxx57jOOPP5709PTdcs76slS2V129d8vBxf4cFEWhe/fuO7R/Tk4Ouq5jGEajP/990ecktvfI0Ucf7SwHAgHuvPNOysvLnUbjQ4cOpV27dgnHSJad0RTvvfee87vTq1cv7rrrLo455piEbKZly5bt1PH3JAl6CMeYMWMYO3Zsg2OCQSkDI4TY86J9PoKNlJ5SFYUO2ZFGcMn6gcRaVbqWgvR8QlY4LtMDwBeqodxfSbYnc79MyxR7nktXcelugiEzLgsjWZPzXeULhAmGIucIGxbRJKVA0EBTI71AcjIk6CGEEEIIIQ5eb7zxhhPwUBSFBx54oNHeGztC13WeeeYZLrnkEizL4rnnnuOss85KOvawww5zlhcuXNhgI/JFixY5F/yjTcV3h+XLlzdpXGVlJWvWrKFVq1ZOqaPohf7u3btzww038MgjjzB+/HiqqqqYM2dOgw3Nd0Rs9k1D1wc3b968W87XVF27dnWWf/nlF2644YZ6xxYWFjJx4kTatGlDnz59aNu2LYqi0KlTJ1asWBGXVZHM3r64Hw6HnaCD1+uNa6T+3XffOd/rW265hdtuuy3pMSoqKnY66PHBBx8AkJmZyciRI+sNoG3atGmnjr8nyZUd4SgtLWXVqlUN/os2+RFCiD0t05P4x1RXdSD5XT8pesN35EezQMJmOK6nB0BFoBKwqTH8CftZtrXfNOISe45LV0nx6GSlu1Fj7mDS90DGRU3AwLJswobpZHkAlFYGKK0MxGWBCCGEEEIIcbCZOnUq//rXv4BIqZy//vWvuzXgEdWrVy+uuOIKINK34eOPP046rkePHk62wIQJE5z+Ccl8+OGHznJDwZE9Yfny5Rx77LFccsklzsXoZAYNGuQsh0Kh3Xb+zMxMZ3nDhg1Jx2zatInVq1fvtnM2RYsWLejUqRMAP/74I2vXrq137FtvvcX/+3//j/vvvz8u0HTqqacCsGbNGhYsWJB031AoxFdffbX7Jt4Er7/+Otu2bQPgoosuigs6xF6jPfLII+s9xhdffOEsJ2tO31Bm1fr16wFo165dvQGPQCAQV34tttzYviRBD+HIzc2lS5cuDf5rqPGTEELsThmedNLc8U2kW2Y0J8WVPLhh2g3/YV1Tth7LtpIGPSw7cpE5EK67WyVgBKkJ+/GHA5QH934Kq9i7XLqK162R6nXhcdUFOvQ90GOjJhD5EBUMxQc9bBuqa8IS9BBCCCGEEAet8vJyHn/8cefxgw8+yEUXXbTHznfvvffSunVrgHqDGW63m8suuwyAtWvX8txzzyUt3/Tpp58yceJEAHr37s1RRx21h2adXNeuXZ3nMmbMmHoDD5MmTQIiAaUdLfXUkNiMmPHjxydke4RCIZ5++ul90gD96quvBiIX3P/0pz8lLUP1008/OYGv1q1bx2XAXHjhhU7Jpsceeyxpc/QXXnhhr2Y0TJgwgf/85z8A5OXlcfvtt8dtz87OdpanTZuW9BizZ8/mn//8p/M42e+A2+0GkpfAip5j1apVbNyYWII8GAzy6KOPUlRU1OA59gUpbyUcw4cPZ/jw4Q2OWblyJUOHDt1LMxJCHOrS3Kn4QnV/eDVVw6O58YcTMzKapeY1eKyAEeTLld9z9mGnQj1NzwO15bQM02BrdTFu3Y2qqPhCNeR4s+LugLAsS0phHUS87rq3RFkZdaXSNE3lnEGd+Hz6msi2dDdtm2eweE3JTp+rJhC5uyYYNp3lWBL0EEIIIYQQB6tRo0ZRUhJ5L929e3cGDBjA0qVLG9wnNTWV9u3b79T5UlNT+fOf/8xNN93U4Lhbb72VqVOnsmLFCsaMGcOyZcu47LLLaN++PaWlpXzxxRd8/vnnzjH/8Y9/7NR8doWqqtx+++08+uijlJeXc+GFF3LVVVfRs2dP0tPT2bhxI5988gk//fQTAMOGDaNNmza77fytWrXi2GOP5ddff2XlypVcffXVXHvttTRv3pw1a9YwevRoli5dStu2bfd6pZiLL76YL7/8kpkzZ7JkyRKGDRvGtddeS8+ePfH5fMyYMYOxY8diGAaKovDUU0/hctWVMm7Tpg133XUXf//731m5ciXnnXceN910E0cccQTFxcV8+OGH/PDDD6SkpOD3J16P2FEbN25MeN0Hg0EqKipYsWIF33zzjZNxkpKSwksvvUROTk7c+D/84Q94PB6CwSBjx44lEAgwePBgsrKy2Lx5M9988w1ffvllXOZFsn4rzZo1A6CsrIyRI0dy9NFH07x5c1q0aMHgwYMZPXo0gUCAK6+8khtvvJGuXbsSCoVYsmQJH330EevWxfdW3ds9XeojQQ8hhBD7LY/mdpZVRUNVVNx68h4LRzTvyuTVye9uiJq+7heCRpDzjzgz6faQGcIwDTZWbcGwDIyQiaooTrNzj143n5AVxqs23EdEHJhiMz0ATu/XjoxUN6UVfvr3aEl6qou3PlvMsnVlceMUJZKt0Zjy6iBfzPid0/u1w+1KLJ9lGBbBsIlpWqR641/vwbCJripomgTchBBCCCHEgeeTTz5xlpcuXcq5557b6D79+vVj9OjRO33Ok046iaFDhzpZGsl4vV5GjhzJHXfcwbx585gzZw5z5sxJGNehQwdefPHFnQ7C7KoLLriAVatW8fbbb1NWVsaLL76YdNxJJ53E008/vdvP//TTT3P11Vezbds25s2bx7x58+K2Dx06lIEDB/LYY4/t9nM3RFVVXnnlFe6//36+++47Nm3axN/+9reEcV6vl6effpoTTzwxYdt1111HIBDgxRdfZOPGjTz11FNx21u2bMm1116b9Lg7asSIEU5Pm4Z06dKF559/np49eyZsa968OU888QRPPvkklmUxbtw4xo0blzBu8ODB6LrOF198QVFREcFgEI+n7lrGySefzPjx4wF4/vnnAbj55pv505/+xN13382vv/7KsmXL2LBhA3/+858Tjp+dnc2f/vQnnnzySYC9Xt6sPhL0EEIIsd9yaS5URcWyLfTapmleLXmgoUV6M+497ga+Xf0ji7bU31xs9sZFDO12Gm4tefBks28bhhW9+97Gqr2KHTAC8UEPM9xo83RxcNA1lX5HFMQFNG78Y0/e+eI3Fq0uRlMVrjqrO726NGPCD6uYNq+o/oPV+vbX9fiDBhee0hXLtqnyhchIdaPWltPaWlqDS1cTgh6BoEEgZFKQGyn9Zlk2obCJ1yNv6YQQQggh9pQubbP39RQOCqWlpWzZsmWfnPuxxx7jxx9/TFq2KCo/P5+xY8cyadIkJk6cyOLFiykvLycnJ4eOHTsybNgwzj77bKcM0r7y0EMPceqpp/LRRx8xf/58tmzZgmVZ5Ofn06tXL84555y4hte7U+fOnZk4cSJvv/023377LRs2bCAlJYXDDz+cSy65hLPOOsu5gL63paen89prr/HDDz/w6aefMn/+fIqLi1FVldatW3P88cdz1VVXNVi6/7bbbuPEE09k5MiRzJ8/n61bt9KsWTNOPfVUbrvtNmbNmrXH5q9pGmlpabRs2ZIjjjiC0047jZNPPjmugfz2LrroIjp37sw777zD3LlzKSsrw+Vy0axZM3r06MGFF17ICSecwKRJk/jiiy8IhUJMmTKFs846yznGGWecwdNPP83o0aMpLCzE6/U6pa4yMjL44IMPePfdd/nqq69Yt24doVCI9PR0OnbsyIknnsill15Kbm4u77zzDmvWrOGrr77izjvvbLBXyN6g2Pui0Jo4YG1f3mrixIl07dp1H85ICHGw21i1hUA4QKo7lRbpkbTLtWUbsJL08GiWlsc2XwmGZVBSU06ztDxemvk2m6u3xY27//ibyU/NSdi/IWnuVApqzw9QUlNG3g4eQxyYKqqDBEImVb4QqSk6gaCJZdmYls2m4mrSvC5yMiO9ZraV+3n54/lU+prWMPChK4/h3Um/sbmkhjbN07ntgt6k1AYwFAU6tspyAiEAJRV+AiGT1s0iTeSKy/0oCuRl7dsPXkIIIYQ4eBzsn/vXrFnj3OkcbX4shBDiwJXs/3WpjSCEEGK/lu3NxKW50NW6uxvcugu35kZV6tYpioq7thyWruoUpOejKgoXH5nYh6gyWLXD8wga8Rexw+b+0ZxL7HlZ6R50LRJ4KMhJxeuJvO40VaFN8wwn4AHQLDuFJ67tz58uO4oHrziGLm2yGjz2//tgHptLInfRbNhazZxldXe+2TbUBON7fhimHdf8PGSYTg8Q06z7aln2PmkgKIQQQgghhBBC7GsS9BBCCLFfS3WlkJeSg67Wle9xay7cupsUlydunZokfbJVRgE9mh8Wt+6L5VN4+rv/x4iZI9nmK23SPAwrPrMkZIblovIhRFNVUCKNzWObniej6yptCzJomZ/GeSd1oV1BRr1jg+H419W471bx/uRlrNtUCYDPHx9cM2oDGs5jw3KCHiUVAeeYlm0TMuLHCiGEEEIIIYQQhwIJegghhNjvpbpT4vpneDQ3Hs1Fil53h32K7kVRkv9Zy/Skxz0uqtqC3whQVLWFd+d/QshsSikiG8Osu+vesq3IP8tqYB9xsNA1BVdt8/DtG503pFWzdO697ChO69euyfv88tsWXvlkATWBMDWBMBuLq/HXZnwYpoVp1mVxmFYk8yNsmFTWhDBMi0DIxLRsTNMiZCSWgdteNGgihBBCCCGEEEIcDCToIYQQ4oAQG/Rwa248mhuvKxL0UBSVLG9G0kwPgExP/XfaF9eUMnnV9CbNIdrg3LZtLNvCtEwCZrCpT0EcwDRNRastceV1Nz3oEZWTsWNN78OmxbK1ZZimTY3foLjcD0QyOyAS7LAsG9OM/AuETLChtCJAIGhgWTaW3bSARjBkNDpGCCGEEEIIIYQ4UDRcn0EcUsaMGcPYsWMbHBMMysU9IcS+59Zc2NioioqmaqS6UtCcnh8KEF/SZ/tMj+39VDiH/m360Cwtr8Fx0RJXZvSrbREwgqS6UrBtG6WeoIs48Omagl6b6aFpKrquOgEIl0slHE4eXPC4NQzTIjem70dTVfjq/uYGQyY1gTDRimqmZWPFvM6jZbAqa0IoQFZtUCRcm+lhWXZcQ/SoSDaIZHoIIYQQQgghhDh4SNBDOEpLS1m1atW+noYQQjRKURQUIhdwU10ppLlTnW2qomLZkQu9uqpjWEajQQ/Ltpi1YT5Du53a4LhwbaaHaUcuEgeNoBMAMSwDl+bauSck9nuqqjpBD4iUuIoGPVI8OuFw8hJpzXNTKSn373CmB0QCHbGiPTugrml5VE2gNlvDjoT8LNvGtOr6fYTCJl5P4ts+w7Kd5yGEEEIIIYQQQhwMJOghHLm5uXTp0qXBMcFgkMLCwr00IyGEaFyaOzWut4eqKER7N6e5U6kIVDZY3ipqc/W2RscYTtAjcjHabwSdklqGZUrQ4yCmqQouPSbo4dac7Aq3rqFpCqZpO9uiAQuXpqJpKlnpOx70qPDFB1JigyCmZTtZH0BCw3LTjGZ6RAIagVA9QQ/DwjAl6CGEEEIIIYQQ4uAhQQ/hGD58OMOHD29wzMqVKxk6dOhempEQQjQu1ZUS91iNaWZeF/RoONMDIr09GhPN9Ig2NA8YQafXSDQgIg5esUGD2GbmLl3FpasoSqR5eFa6h62lNWiagqoq6JpCy/w02rfMZN2myiafr9IXX1IyEDJYWVhOKGzSt1vzBktmWbaNZeEENCIN0C00Lb6dm2FahCXoIYQQQgghhBDiICJBDyGEEAeVaOaFoqh4dQ9q7dcU3YvfCNS7X3mgko+XfMFRLY+kfXZr5m5cgi9cwzGtepJRGzQJhAOU+supDFQBYNsWVm2pq2i/D3HwcsdkenjdmtM+RtdV3LpGToYLw7Tw1DY6j5bDivbSePzafnz4zQq+/XV9k85XGZPpYZgWL304n00lPgA++GYFt13Qi46tspLua1mR8lZmbQaIWZv1sX3Qw5TyVkIIIYQQQgghDjIS9BBCCHFQiWZ66LWNzT26B3/YT7O0PNZXFDW475yNi5i7cTHd8juxrHg1ADM3zOO2Y68kyxspkVXur4jbx6oNfJjbBT1My4xpri4OBrGN6jVNpUVuKptLatA1FY9bI9WroyiKU2oqWg5L11QsyyYn08vZx3ekaFs1S9fGZxad2Kc1J/ZtzTMjf3HWbdhaza9Lt3Bs9wJ+31jhBDygNgjy0XyO6JhLQW4qJ/VtE1dCy7RsLDvS4MM0rUh/D9PCG7O/rqkYpoVdO2b7gEis6HghhBBCCCGEEGJ/J59ehRBCHFSiQY9owCHFFbnM2ywtN2GsribG/m1sJ+ABUBGo5LnprzD+ty8Jm4klrCJBD9vJ+IiqCvmwLLmD/mCWnuomPdWFpiqkp7qdoIiqKuh6XeNzTVXQVMXJQkpWlqpnl3wy0xL7foz9ehnL1pVSWpk8S+m330v5bs4G/vHebGoCkR4j1f4wldVBisv9VNWEMC0b06zr7wE4x4uWvwoZVkJfkFhOo3QhhBBCCCGEEGI/J5keQgghDipOpocSCXqkulIopYzmaXkJY9tnt2Z16bomHfeXogXYwHndh/DN6mks2rKcDtlt+GP3wU62R6zqoI8U3YtHde/aExL7tWbZkZ4ymqrErXfX9vkAajMoLGdMm+aJPWY6tc5ygiLb+2Dycvr1aNHgPHwBg3+NncuxRxTw7a/rnabqLk3ljot707l1NqFwJBspGDbx+cOYWbbTHD26ze3SsCwLlx6fpeQPGmSmyWtZCCGEEEIIIcT+TzI9hBBCHFRURSUnJdvJ4nBrLlyai7yUnISxRzTrukPHnr/pN1aVruW733+muKaU2RsXMnfjQizLdIIetm1jWiYhMyTNzQ8B9ZWEystOIS3FBYBem+WhqgqKAj065uF21e139vEdnb4fBbmpCceq8IX45pfG+4CUVgb4euY6J+ABEDYt3v58CZZtO4GNmkAY07QpLvc7Y4Mhk0DIwDQtgmEL2647hmlazr5CCCGEEEIIIcT+ToIeQgghDiqZnnRyUrJIddWVEMryZpKXmhj0OLZ1b1pmNG/yscNWmPcWTIhbN2Hp1xiWGemfAASMIOHaYIfhfJULxocaj0urK2+lqWhaJKihayp52V5u+mNPjj68OUNP6Mgpx7QlvTZAckTHxDJsu6qiOkQwaBIyIsEMfzDyuly7sYJXxy3guXd/ZcbCjQRDJoZpYRh1DdBt2yZkWE4ZLCGEEEIIIYQQYn8n5a2EEEIcVHQt8qfNGxP0yHCn0S67NS0zmrOpaisQCXi4NRd39r+GUn8F7877mG01pUmPGStkhhLWGZbhNDMPGkEnyyQa7AgaQXR34h384tARDYDouoqmKhzWPofObbKBSMPzjFQ31TVhzujXHk1V2bC1imXrynbb+beW19AiL41g2CQQjLwuv5jxOysLywEY/eVSDmuXQ1qKC9OKBDk0VaFoWzWaqmKaNrZtxzVzF0IIIYQQQggh9kcS9BBCCHHQUxSFlunNua7vpfxUOJsU3cPAtkcDkXJY+ak5dMnr0KSgRzLh2qCHYZmELAOLyF3y0UyPoBEi1ZUiF4wPYdGfvaYquHQVw1QxzUjwIdXrQq/t/+H16Fx82mH4/GFmL93C+5OX0UB/8Sb7x3tzaJmfxj2X9sVd269j3optzvawYbF4dTGtmqVhmJHG5z4jXBsgiczTtGz02oyVUNjE7dISziOEEEIIIYQQQuxrUt5KCCHEIUHXdHJTsxjc5URO7NAflxYf9z+pwwCn1NXRrXryp+NupE+LHk5j9IZEy1sZlkHYDGOY8WWtDMvAlBJXgkjGh66pcY3C3S4VV0wmSLTfxzHdC7jp3J677dybin1M/PF3AKd8VSyfP4xl2ZhmJNMjuF0fj9gSV+XVwd02LyGEEEIIIYQQYneSTA8hhBCHDF3VCZvhpNuyvZnc1f/auGyMS3uew+mdB/GPGa81eNyiys3kpmQRNsOEzTB2baZH9FyGZWBYBqqioqpyv8GhTNMUdE3FrdcFHVy6iqoqaJqCx6U5Tc0BurXPpVV+GhuLfbvl/NPnF3H+H7pQmSRoUVEdimR5WJF/2wc9YhukV/lC5GWloKmSvSSEEEIIIYQQYv8iQQ/hGDNmDGPHjm1wTDAod3YKIQ5cLlXH38D2ZOWnsrzpjR73hR//wx39ryHNlYplW4SMSLDDsi1CZhjDMiP/bD/p7rSdnb44COiaiq6r2LYdty761e1SncdRbQsydlvQI6qsKvHveUml38nyMEyLUDi+eXk008M0LWwbgiGDVK9rt85LCCGEEEIIIYTYVRL0EI7S0lJWrVq1r6chhBB7jK7WlRRSFBXbthoYHd1HJ9WVQk24oXAJvDzrHY5qeSQX9Tib2NhJwAjWBj0MQpZBujsNwzSchuvi0BItbxXLVdvPw+PWSE91Y5rxr8uTjmrDrCWbd9scDMOirCqQsL64PIBhWpimTdiwMIx6gh61pbGCIbPJQQ9pgi6EEEIIIYQQYm+RKy7CkZubS5cuXRocEwwGKSws3EszEkKI3Ss20NAsNRdd1dhYtaXR/TI96Y0GPQDmblrMwLZH0zarpbOuJlQD2PiNoBNkqQr5yEnJ2vEnIA54bl2tLQkVCXRomuIEA5rnpAKwfQ5Gy7w0Tju2LVNnFyZtau7WVTq1zuK4Xq1Yt6mSKbMb/jtdWhVInulR4ScYNlFQ8AcNZ71p2VRUBfF6NOcxQCBkNimYETYsgiGD9FR3g+OEEEIIIcShp7CwkNGjR/Pzzz9TVFREOBwmLy+Pvn37cskllzBgwIAG9/f5fEycOJHvvvuOlStXUlJSgqqq5Obm0rVrV/7whz9w9tlnk56ePIN/1qxZXHXVVQA89dRTXHbZZbv9OTbVDz/8wJtvvsmKFSsIBALk5+dz3XXXMXz4cEKhEK+//joTJ05k8+bNeDwemjVrxptvvsnw4cMpKiqid+/efPTRR3t93hs2bODUU08F4I477uDOO+/c63MQYnsS9BCO4cOHM3z48AbHrFy5kqFDh+6lGQkhxO6lq5E/ey7NRbonUmYqzZ2KL1TT4H6ZnnQ2V29r0jnmbFwUH/QIR+6o94cDuLXIXfFBM0TQCOHR5SLwoUarzfLQVAVVVZwsj1h6kj4ZZx/fidP7tWf896sSsj6euK6/E1DYUtrwaxngrc8Ws7UsMYgXCJn8a8xc8rO9tG6Wzh+OakvYMHnpo/lsKa2hTfN0/nHnIGd8MGxSUR0iLUV3GrOHDSvhOZVXBdF1yfIQQgghxIEruGn1vp7CXuFp2Xmvnu/jjz/mL3/5C6FQKG79pk2b2LRpE5MmTeLCCy/k6aefRtcTL2F+//33PPnkk2zZkngjm8/no7CwkKlTp/Liiy/y8MMPM2zYsD32XHbVlClTuOOOO7CsumzrDRs2kJGRAcB9993H5MmTnW2BQIBAIEDz5s33+lyFOBBI0EMIIcQhwxUNeqh1f/6aEvTI8DTe1yNKTbjr3Xa+WrWZHpZlEjACEvQ4xOma4gQLYmmaCgp1L51abpdGl7bZcUGPdgUZcRkUKZ7G39olC3hEFW2rpmhbNQtWFlNVEyYj1eUEUjZsreaHuRs4oU9roK5Mlq6loCoKmqZSXRMiJ9Mbd8ywaaKqic9TCCGEEEIcuqZOncoTTzyBbdtkZGRw9dVX069fPzweD0uXLmXkyJGsW7eOTz75hPT0dB555JG4/WfOnMkdd9xBOBwmKyuLCy+8kKOPPpr8/Hxs22bTpk3MmDGDTz/9lJKSEh588EEsy+Lcc8/dN0+4Ef/5z3+cgMc999zDgAEDCAQCHH744axatcoJeLRu3ZoHH3yQli1bEgwG0TR5ny1EMhL0EEIIccjQVA1FUdFiLsDqauN/CjN3IOihKAqWbbG2fAO6otEuu7WzzQl62DZhy6jvEOIQoesqXnfyDymaqmCaibWsBhzZkkkzfqesKojHrXHdsB5x21Pcu++t3fT5RQnrJs74nYG9WjmPTdMmGDYxLRtVVaj2hxOCHpZlYyR5LkIIIYQQ4tBkmibPPvsstm2TmZnJBx98QOfOdVkmffr04ZxzzuGqq65iyZIljBo1iosuusgpyW5ZFk8++SThcJg2bdowduxYCgoK4s7Rp08fzjzzTC677DKuueYaKisrefrppznppJPIycnZq8+3KVavjmQTHXXUUdx6661x23755Rdn+dZbb2XIkCF7dW5CHIgSayoIIYQQBzFd1fZo0ENTVMb/9hVvzB7Lq7+OZsqaGc62uqCHhWGZznp/OLGptDj4uTQNTz1BjzSvK2npq4xUFy8/cDI3DDuSh688lm7tc+OOEe27sSd9N7uQL39eS2ll5HUbDJv4/GG2lfmdZuexLMvGtBLXCyGEEEKIQ9Ps2bPZsGEDELmIHxvwiEpPT+fJJ58EIkGOiRMnOttmzpzJunXrgEjZp+0DHrF69OjBvffeC0BNTQ3jxo3bbc9jd/L7I9nYrVu3rndbfduFEIkk6CGEEOKQoqs6uhIb9NCI1BKq346Ut1pTtp7ZGxc6j39aPztuu2VZkaCHGcn0CBkhtlRviwuCiEOD26XicSUPUjTPTaV9y0wy0+NLoGmqSprXRY9OeWRneNBUJa6kVVPKW+2KDVureeeL35g8ax2vfLKAkGESDJn4g0ZtcCMxo8O07KRZK0IIIYQQ4tA0Z84cZ/nkk0+ud1yfPn1ITU0FIj1mo2KX27dv3+j5zjnnHJTaMsQrVqzY4fnuDbYdeb+crHdJbJ8PKWclRNNIeSshhBCHlO0zPaLrDMvAo3sIGsHatQpuzUXIDJGbkt3k42+ojG8y7Qv7CRpBPLoHiGR5RDI9DGe7ZVtUB6vJTsna6eclDjzpKS7nw1d98rJS8AcNwuHIBx1dizRAj1JVBT3aHF1T8O7hoEes0soAc5dtZcCRLetW2pHMjtg5WpaNqUjQQwghhBBCRPTt25ebbrqJLVu20LJly3rH2bbtBAOCwaCzPvY99Ny5c+nRo0fCvrEyMjL4xz/+QUpKSqNBEr/fzzvvvMNXX33F+vXr0XWd9u3bM3ToUC6//HLc7sS+jN26dQPgrLPO4t///nfS406bNo0bb7wRgOeee47zzz+fWbNmcdVVV8WNmzBhAhMmTADgvPPOc5ajYsdPmTKFNm3aNPh8orZu3cqoUaOYNm0aRUVFGIZBQUEB/fv354orrnCeQ302btzIyJEj+emnnygqKiI9PZ3+/ftzyy23kJKS0qQ5CLE3SdBDCCHEIUVX9cSgh6Zj2TZZ3gy2VkfeTHt1N3pt0KN9Vhs65rTl97JCAHJTsri4x1Bemz2mSed8a+5HdG/WhUHt+znBDsu2MC3TaaJeFfJJ0OMQo2mNJ9xqqkKb5hls3FZNMGSiaSqKEgl8WJaNpirotWWw2jTPoKI62MgRd6+Fq4rjgx5EetaotdlTlmVj22AmKXslhBBCCCEOTQMHDmTgwIGNjlu8eLFT2qlVq7q+ct27d3eW//3vf1NQUMDpp5/e4A1F55xzTqPnKywsZNiwYaxfvz5u/aJFi1i0aBGTJk3i3XffPeAu8n/55Zc8+uij1NTUxK1ft26d0yz+9ttv5/bbb0/6PZw6dSr33nsvgUBdWWa/38/EiROZPHky999//x5/DkLsKAl6CCGEOKToqhZX3iqyTsfl1vHqdQ2YvboXVVXRVZ02WS256ejhrCpdi0dz0Tqz/ruRkllfUcT6iiJs2+bSnsOc9VXBakJmCICwGWZ9eRF5qTmkuVN34RmKg42mKrTIS2Xdpip0LfIhJDbo4dJV1NqvzbKb/gFMAY7uXkCr/DQ+m75mp+a2YWtVwjrLsqH2V8yqvTPPTpIBIoQQQgghREPefPNNZ/m4445zlo899liOOOIIfvvtN3w+H3feeSdt2rThjDPO4LjjjuPoo492ymLtiLfeeguIlNw6//zzycvLY/ny5bzyyisUFxezYMECXn31Ve67775df3LAkUceyaeffgrAueee65z77rvvBiArK4urr76aKVOmMGLECACeeeYZjjzySACaN2/e6DmiAQvbtikoKODKK6+kb9++aJrGihUrGD16NCtXrmTEiBHous4tt9wSt//8+fO54447ME2T1NRUrrvuOgYOHEg4HGbKlCm8//77PP/887vl+yHE7iRBDyGEEIeUZJkeKbqHVFcKmqqhqzqGZeB1RcpRpbi8KIqC1+Wha16HuOPsqMmrp3Fhj7Ocx2WByrjthmWwpbqYDtltUFVpuyXquHQNl0t1SlmpCihKJLVf11Tcrsj6rAwvZ/Rvz+RZ65IeRwE6t8nihj/2xDAtstI9ZKS62FJaw6wlm5Pu05CqmjBllQFyMusChlZMX4/SigCvfDKfzaU1nH1cR644szthw8SlR34HbdtutMSXEEIIIcS+4l+7iOKv/ku4pGhfT2WvcOW1Jn/IjaR06Lmvp8LXX3/NV199BUSad5966qlx21966SUuu+wytm3bBsCGDRt4++23efvtt9F1nR49ejBw4EBOOeUUevXq1eT3nHfddRe333678/joo4/mpJNO4qyzziIQCPDpp5/utqBHWlpaXNYKQHZ2dty6Vq1asXTpUudxu3btEvapT01NDY8++ii2bXPkkUcycuRIMjMzne19+/blvPPO47bbbmP69Om89NJLnH322bRt29YZ8+yzz2KaJh6Ph1GjRtGzZ91rY+DAgZxwwgnceuutO/zchdjT5IqKEEKIQ4pHcye84c3wpDuBkFRXCp7aIIhbc5PiilzMdWuuuH1c2z1uqtiG5badrOSPjd8IJFkvDnWxPUA0TXXKY2mqgtcdCcKluDVu+GMPHrzyaE7v3y7hGM/fcQK3X9gHj0sjzetC1xRcukbPzvn1nldrJDujaFt13ONodgfAmK+XsWpDBdU1YT78dgXrN1dSEzCc7YGQ6dRpFkIIIYTY3xR/+fohE/AACJcUUfzl6/t6GixcuJCHH37YefzYY4/hcsV//mrbti3/+9//GDJkSMLnO8MwWLBgAa+99hoXX3wxQ4YM4dtvv230vB06dEh6Ab9169aceOKJQKQ3RkVFxc48rb3u008/paysDIgEL2IDHlFut5tnnnkGVVUxTZP333/f2bZs2TIWLlwIwNVXXx0X8Ij6wx/+wHnnnbeHnoEQO0+CHkIIIQ4pjWVQ5Kfl0iK9GRAphZXqipQL2j7I4dJ2LlmyuKaMTVVb44If26sJ+3fq2OLglpFa1zRRVZS4YESqN/J61DQVr1una9scLjntMHIyPc6Yi07tilvfvp+Niq4ppHnrfz0f3iG3wXn9tGhT3ONopodpWkydXRi3bdHqEgKhutd+2LAIGdLvQwghhBBCRPz222/ceOONTv+Ja665JiHLIyovL48XX3yRb775hvvuu49jjjkmITgCsHbtWm6//XYeeeSRBm+4GTRoUL2fF2MboFdWViYds7+ZPn06EMkeOfzww+sd16JFC7p27QrArFmznPXTpk1zls8+++x69z///PN3dapC7HZS3koIIYTYTmz5K1WJvOl1q9sFPXaivBXAk1P/CUCztDxu73cVXt2TMCZZ0CNkhhOyTcShxe2KeV2qCppdF/RI8dS9HqONzl2ayj/uHMRn09bQriCDzm2yE46payouXSM/p/6axx1aZLJkTUm925euLeXzH9cw9PiO+IMG/qBBeqqbpWtLE8aGDYtAqC7TwzQtQmHwuOKDMaZpNanRuxBCCCHEnpR/5s0Uf/0m4eIN+3oqe4Urvw35g2/YZ+efO3cuN998sxNUGDJkCA899FCj+7Vt25abbrqJm266iZqaGubMmcPPP//M9OnTWbFihTNu/PjxtGzZkrvuuivpcQoKCuo9h8dT97ktHA439SntU9GyWOXl5XTr1q1J+2zYUPda//333wHQdZ0uXbrUu0+PHj1QFEUyuMV+RYIeQgghRBPo22V27Gx5q6htvhJ+LpzLyR0HJmwzLTMuyGHZFhWBSpql5e3SOcXBQ1MV7Jigx/Yp/ZoaCXzkZ6Vw8tFtyc3yUlEdxDTjP4hEMz3SU1wM7NmSn7fL2shKc5OdkRiY297U2YUsXl3C1rIacjO9PH5df+Ys25owzhcIEQ5bGKaFrqkYpo1pmWRsF3MJhEzSUiToIYQQQoh9K6VDT9re/CLBTav39VT2Ck/Lzvvs3N9++y333XcfgUCk1O/gwYP55z//ucO9DlNTUxk0aBCDBg3iwQcfZMmSJTz//PP88ssvALzzzjtce+21ZGRkJOyblpbWpHMcKBf3y8vLd3if2CyWkpLIjU9paWnoev2XkFNSUkhNTcXn8+3w+YTYUyToIYQQQjSBrsTfie5SdfJSsinxl+/0Mb9e9UPSoAeAP+x3gh41YT9BM7TT5xEHH1VVUO36e22oiuJkfEAkCKJrkTq9sXQtMk7XVS46pSsDerTg9QmLqAkaKAqcf3IXUj1NC/BtLYuUICitDDBu6kqS9YqsroncFVdVEyInw4tpWXGNz6MCIYO0FMlsEkIIIYQ4FIwZM4ZnnnkGy4qUPT333HP529/+hqZpCWMty6KkpISSkhLy8vJo1qxZg8fu0aMHb7/9NldeeSXz5s3D5/OxaNEijjvuuISxTW12vjP2RaDEMCIZ1kcddRRPPvnkHj1XsrJiQuxLEvQQjjFjxjB27NgGxwSDwb00GyGE2L+oqoqiqE7zcU3ROLf7YN6e+yF74u2rL+wn3Z2GpmoEjRCGmbwHiG3be/TNudg/pXj0Bj84RQMe0a+qquDSVYKh7YMeteXbdBXDsGjXIpOnbxrIysJycjI8tMhLY1v5jveYWbe5kvzslIT1VdGghy8S9DBMi2BtM/Po6zhsWBjbZaQEggZej7xtFUIIIYQ42Lz88suMGDHCeXzNNdfw8MMP1/sZZ9GiRVx88cUA3HLLLdx7772NnsPlcjF8+HDmzZsHRJqR7wkNvT+vrq7eI+dsSHZ2Ntu2baOyspLu3bvv8P75+flAZO6hUAi32510nGma++T5CdEQ+fQoHKWlpaxatWpfT0MIIfZbmqJi2BagoKoq3Zt15c4B1/HSzLcTxioo2LsQDgmEA2yu3kbrzBaELQPLNrEsKy69O2SEMCyTVHfixWVxcEtpJACgqgrRPueR5UjQY3vRdS5dw6VHyk6leHW6xzQvz0pP/uGmIeVVQbzuxDlW10QylkJhi7BhYlo2tg3+oEGqN3J3WGR9XXNz27bZUlpDuxYZEuATQgghhDiIvPHGG07AQ1EUHnjgAa6//voG92nXrp2z/OOPPzYp6AGREkxRDfXu2Bm6rmMYhlOaK5lNmzbVu21P6dKlC9u2bWP16tVOZkx9Ro4cSWpqKh07dqRfv37O/hAJaixdupTevXsn3XfVqlVOVokQ+wspliwcubm5dOnSpcF/bdu23dfTFEKIfUavbXCu1QYeIoGPxIZufzz8dPq36dukYxpW8gwOgLAZjv9qGZgx42uMADXGjt+FLw5+0UAH1PX3cJqFK+Bxa5Fm6NFMD5eKx62ha2pkXExsIc3rSlpqqu9h9ZcS0DSFqprEkmzR8lYANQHD6THiDxq120MEQ2Zc7xHTsgkbFr5AZEyyclhCCCGEEOLAMnXqVP71r38Bkc9Vf/3rXxsNeADk5ORw1FFHAbB48WK++eabJp1v2rRpQKTnR48ePXZy1slF+4MUFRXVO2bGjBm79ZxNES3hZds27733Xr3jFi5cyPPPP8+TTz7Ju+++66w/7bTTnOXx48fXu//nn3++G2YrxO4lQQ/hGD58OF988UWD//7zn//s62kKIcQ+o0WDHrX9PXRVx6W5OK7t0c4Yl+qiT4seHJ7ftCaAZQ30BLFsC8M0nMBIVbCazdXbnO3+cAB/uP67icShS1WI6+ehqopTHiovy0tmmjsu88Ol1wY9dBWXrqKpdVGP5rmp1ATCbK9Xl/qDHv6AQU0g8W6van9dIMQXCDsBjGjQo6omTEllACumNIBhRrI+olki/qCBadZlggghhBBCiANLeXk5jz/+uPP4wQcf5KKLLmry/nfddZeTAfzAAw80etF9/PjxfPLJJwBcfPHFZGZm7sSs69etWzcAVqxYwezZsxO2f/755/z000+79ZxNcdFFF5GamgrAf//7X37++eeEMdXV1Tz22GPO4+HDhzvL7dq148QTTwTgo48+4ocffkjYf968eXGBEiH2F1LeSgghhGiiaNAjege9V3OjKgqndR6Ehc02Xwkntu9PisvLYfkdaZnenE3VDdeL3VZTik2kMXq2NzOhfE9N2O/0EakMVgGRYIdX9xAwgti2hWVbqIrcxyDqRPt4RJejjcxdukpGqhvDtNADsUEPDUVRCIUtdE11mp5nprlJ8ej0OawZ85bXBdwUBXp1zadz6yxWF1UknD9kWBhmkkwPf10gJDYoEgpbtV9NsOOzOaJZHzUBA9u2CRkmNpCeIq95IYQQQogD0ahRoygpKQGge/fuDBgwgKVLlza4T2pqKu3btwdg4MCBPPzwwzz33HP4/X7uv/9+Ro4cyZAhQ+jWrRs5OTn4fD5WrFjBl19+6fTy6N27d5PLYe2IYcOGMXPmTABuv/12brnlFnr37k1VVRVffvkln376KW3btqWwsHC3n7shOTk5PPHEEzzyyCOEw2FuuOEGLrroIk477TRSUlJYsWIFb7/9NuvXrwfg7LPPTmjw/uc//5lhw4bh8/m47bbbuOyyyzj99NPRNI3p06fzzjvvAKBpGmY9fSiF2Bck6CGEEEI0kV6b4aHWBj88ugfbtkl1ebmqzwVUBqqcsaqicsuxw1m0ZTlel4f3FkxIesxR88c5yyd1GMCZXf8Qt90XTixf5Q8H0GKaqhuWia4igQ/hUJW68laxpa6yMzzoWiSTw+2Kz/SIBEYiJa80VUFRIDfLC0SyOmKDHn++fgCaqnD1WUfw8+JNeN0an/24Jq4sVbIqVBXVQZ4f9SvnDOrE1tIavvx5LVnpHq456whaNUsjbERe06aVmOlhWTYhwyJsWJimTXqSkltCCCGEEGL/F826AFi6dCnnnntuo/v069eP0aNHO4+vueYa8vPzef7559m2bRtLlixhyZIl9e7/xz/+kcceewyv17tLc0/m3HPP5YcffuDrr7+mvLyc559/Pm5769ateeONNzjzzDN3+7kbc/755xMKhXjmmWcIh8O8//77vP/++wnjBg8ezHPPPZewvk2bNowaNYqbb76Z4uJiRo8eHfdzUFWVZ599lr/+9a/U1NTs0ecixI6QoIcQQgjRRNFgR6or0gTPo7sJGZG72aMBkagUVwqgcEzrXti2jQKNtjX/Ye1MWmcU0KtFd2ddsvJVASOArtX9CTcsA8M0cOtup++IOLRpMZkescuZaZGm5IqikOaNDxqotdkg0cBHeqobvbbnx9DjO7KmqIKibdUMOLIFR3cvoHBLFRlpbs7oH7njbtr8IkoqGi+3tqW0hjf/t9h5XFzu54uffueITjGNFe1I4ENTlbgASDBkYhhWXPkrIYQQQog9zdOyaaVrReNKS0vZsmXLbjnW0KFD+cMf/sDUqVP54YcfWL58OaWlpVRWVpKRkUFBQQEDBgzg7LPPpmfPnrvlnMlomsaLL77IxIkT+eSTT1i6dCnBYJDWrVtzxhlncP311zt9P/aFSy+9lBNPPJHRo0fz008/UVRURCAQIDs7m969e3PBBRdwyimn1Lv/kUceyRdffMF7773HN998w/r163G73fTu3ZubbrqJY445hr/+9a978RkJ0TjFtuVTo2i6lStXMnToUOfxxIkT6dq16z6ckRBC7D01IT/lgQpaZbZw1oWMEBurtpCbkk1xTamzPj8tjzJ/udN4/Ikp/yJsJfZFSGboYadyQvtj692uKCpp7lSqg9XOuWzbQlM00j1pO/PUxEGs0hdygh2N8fnDeNwaFdVBdE0lK93jbFu7qRLDsHDpKu1bZrKltIYqX10Jq3+NncOGrdU7Pc8X/3QSlhXJ7PAFwhzZKQ+PW2dbmZ+K6iAAmelup8xVx1ZZO30uIYQQQtTvYP/cv2bNGoLBIB6Ph06dOu3r6QghhNhFyf5flzoYQgghRBN5XZ64gAdESkopipJQWirdlYqu7lxC5eTV0zCsxCbQUbZt4QvVpQ4blkHYMvAbkbvso4GWqEBMtohlSwPoQ01sw/KmjI2Uv1LxuOKzhnStrlxWsuOmeHYtgbiiOkRZVYC/j57NU/+dyZ//O5OwYWJada/ZYMjEMCPlrWIzQOQeHiGEEEIIIYQQURL0EEIIIZooWc+MaMAjui2yrKGqakzj8x37cxsywxRWbGpwjB0TvDAsk7Bp4A8HsG2bkpoyKgKVzvbKkK92H5ugkdhcWhzcdiTo4a4NdOi6ise9fdAjchytNujh3s1Bj62lNXw3ZwPF5ZE+NkvWlPDNrPUsXVvK4jXFmJZNMGQ6deLCRl1wL7YpetTBHgixkjVNEUIIIYQQQgghPT2EEEKIXRENeChK9EKw28mmiPb5cGkuajc32erSdXTMaQvA5qqtrCpbR9fcDhSkN0sYG+3pYVgGVSEfvrCfkBUmy5uJaZnUhPyQBmEzTNgySNmF5ysOPNFgxY5I8ejOa3r74ziZHttlguxq0GNLaQ3T5xfFrfvP+IXO8pGd8rh+2JHOY8OwwB25+O/zh0mLaWweNixMy8Lr1p3HOxL8ORBYto3KDv7HIoQQQgghhBCHgIPr058QQgixlymKgqZqtYEPFbfqcoId0UwPt+Zq6BAclteJC444M27dytK1AGys2sKLM0cycfkURsx6h22+koT9IwGPyF3vJTVl2LZFyAhjWRYhM4xlmximQcgMJ5S+EiKZaDZHrLpMj8hXt67icqlEr7unenc96NGQxWtK2FTicx6HjUhwsSZoYFjxZdt8/nBcJkSkTNbBlRlxsGeyCCGEEEIIIcTOkqCHEEIIsYt0VUNVFDRFRVc1J9gR/aqrOvVdn9QUlbO6/oHOue3j1q8r38DGqi18vfIH7Np6PoZl8mvRwoRjRPp/RMbUlb2yCRhBwmakeXpN2E/IDDfYK0SIhuh6NNMj8lhRFNq3yMRbWwYrmlWxs1YUljU6ZlVhubMcMixKKwNsLa3BNOt+war9YapqQnFBDsO048phHQwOshiOEEIIIYQQQuw2Ut5KCCGE2EW6oqHEBDyi1yL1aPBDqbsbPtZheR3p17oPLTKaA9A+uw3ryjc426et/YXlJWvi9pm7aRFnHXZyk+ZVFqhw7gYvC1REymxJORyxk6KNzKOZHlGpXhehsEVoF4MKm0sazvQAmLFwI8vXl5GR6uKs4zqSkeoG4vtbbC2twbLsuECIaVoYtdkrgZCxywGa/YEtUQ8hhBBCCCGESOrA/8QnhBBC7GO6qqMqCqqqRbI6outry1ypavLK+9cddUnc45M69GfU/Lqgx+KtyxL2SXenAbDVV8LE5VMIW2HO7PIH2mW3ThgbNILOsmmZmJaJS3Nh2RYKSkLPBiEa4oqWt9LiXzepHh23rnLsES2Y8mvhHp3DltIapwxW2LC4Ykh3ACerIxQ2nQCIGVPyyjBtUEzAhT9wcAQ9LClvJYQQQgghhBBJSXkrIYQQYhfptT09dEVDVzUnw0NXIxdWo/0+GnN4fhdyvFnOYyNJ/w2tNpAy/rcvWVGyht/LChm76H9O8/TGGJZJddAnvT3EDtM0FUVJ7Pfh9eikp7rpe1gz8rK8e20+c5ZtdS78W5aNbdsEQnWv69jyVqZlOT1A/EEjLjPkQHUQPAUhhBBCCCGE2CMk6CGEEELsomhwQ9d0dFWPy/BQFQ1N0bjsyGFx+5x7+OCE46iKQp+WPRo8V2WwGtMyWRtTBqs8UElpTXmT5mrbFuWBSsIxvT2ifT9iJQu4COH16KR4kmdJpHpd/Pvek+jYKjNufbOclD02n+Iyv7NsWjaBYN3r2ooLetgYtUGPkGERNpsWJNyv2bY0MxdCCCGEEEKIJCToIYQQQuyiaMNyl6qjKAouzeVsc2k6mqJydKue9CqIlOI5LK8TfVp0T3qsoxoJelSFqpm9cVHC+hJ/eZPna1hGXENzX6gmIVMktjSWEFE5GZ4Gy6LlZHi55fxetG+RAUC7ggxuu6D3HpvP+i1VzrJp2fhDRtzjKMO0CITMSPDDrMv6OJDZgMQ8hBBCCCGEECLRgV/QWAghhNjHoheBo8GO2IvCLlVHVVWyUjK5//ib2Fi1hdyUbErrCVI0S8ujd4sjWLD5t3rPN2HpVwnrimtK6UanJs85NpMjaIYIGEFSXXV35AeMIGnu1CYfTxwaUr2uRsd43Tp3X9KXmoBBqlcnPbXxfXbW+i1VGKbFsnVlDOzZgsPa5ji/f2ZMNodp2liWTXlVAGwIG5H+HrFM00LTDpz7gSzLRmIeQgghhBBCCJFIgh5CCCHEbuJSE/+sujQXqqLi1T0EjRAAbs0NKFDPJctTOx3fYNAjmZKash0aH5vpETLDBI1QXNBDMj3EzlJVBctSSEtxoaoKGWmePXau6fOLnOUFK7dx+wW96dI2G6jL9LBtmy2lNXhcGtF4ZDAUX77Ntm38IZP0lAMj6DFn2Rbe/nwJGalu7rioN22aZ+zrKQkhhBBCCCHEfuPA+GQnhBBCHACSlf3x6G5nWa3drqqq0+w8mWapuSjUX0IomZ8K5zBq/jjK/BVNGh/t6WHbNmHToCpY7WR/GJYZ1/OjPtJPQCQT2+dcVRVSk/QAyUiN/F40UClrp0ybvwHbtlm2tpRZSzYTNkxeHbeAv73zC399eybzV2wDwOcPx5W/ChtWbfbH/s8wLf41Zi7rN1exZE0Jb322ZF9PSQghhBBCCCH2K5LpIYQQQuxBHq0u6KEokXsNVBTcmisu2yKWoii0yiigqGrzDp3rt20rAbiqzwWNjjXMyLlDZhiwMSyDkppSCtKbYVgGpmVi23aD/RvCZhh3TFBHCCBSIiocKS2lKpHAx/YuPKULeVkp5GZ6+Ovbv+APNh5ka4pFq0v4fx/Mc3p9/DB3Axu2VgNgmDZf/ryW3l2bYdtQXhUgO8OLpiqEwiamWZcZYlo2+n5a6mrl+nKqakLO49lLt+zD2QghhBBCCCHE/mf//DQnhBBCHCS0mIwOJ9NDUdG1uvsOUlwpePT4EkCndjp+p87327aVmDH9OooqN/Nr0QKqgr64cYZlYlomvnCNsy4SAMHZP/Y4yQTMUIPbxaFJjQmUJQt4AKSluGjdLJ2C3DS6tMlq9JguXaVrbdmqxsQ2N48GPKK2lNbw+oSFfDHjd7aW+tlSGvm9CIZNJ/PDsmxC4f0368OwDvwm7EIIIYQQQgixJ0mmhxBCCLGXqLWZHoqioMf0/2iWlkdlsCquj0b3Zl3o37oPczctpk1WS45t1Zsf1s5ki6+40fP8Y8br9Gvdh7ZZLXl77kdE2x33aH4YpmVySsfjaJfdmqpgNTUhv7Nf2DSwbdvJQDEsIy44s72wGcayLed5CQHxgY7o8rkndebTH1YDUJCbSsdWWc72YSd2ZtHqkgaPefFph3HUYc1Zuq6UkZ8viStNtaOWrStj2boy0lNcnHRUG2oCYfxBwwnWmJZNMGw2qWn7vmDtwnMXQgghhBBCiEOBBD2EEEKIvUhR1EimR20GiEf3oKsaKbqXcipiximcd8QQhh1+BpoaCSp0zevAs9NebvQc5YFKJq+elrB+ydYVABRWbOSRE2+nPFCFZcfe0W4TNsNOP49ifxnNlbx6S1iZlolhmbgbKANkWGaD/UvEwScu06N2+eqzj8Dj0iitDDB8yOFUVIewLBtVVTisXQ4XndqVucu20qFlJmkpLj6bvgYAl6byyDXH0q5FJlW+ED065nHsES2YuXjTLs/z02mrOemoNmzcFsn2cLsir2PLsgmH67IpTNOKlOzaT0jQQwghhBBCCCEaJkEP4RgzZgxjx45tcEwwGGxwuxBCiIZpihqX6ZHmSgHAq3vQVT2hz0c04AGQ4UnfLXPwhf2sKy+ic277hG0BI0h1bSmskBGi2F9Gq4yCpMcxbQvDMnBriXfEm5aJTW22iAQ9DimqCileHX/AcDI9dE1l8IAOBEIGeVkpBEIm/oCBpiqoqsJxPVtxXM9WQKSnRlqKi5WF5fQ/ogXNclJx63W/B5lpe6aPTDR7JJrpEeUPmaR6lHpLde1tli1BDyGEEEIIIYRoiAQ9hKO0tJRVq1bt62kIIcRBLdrjIxr0SK0NeiiKQtusVhRWbKy3wTlA35Y9mLdpyS7PI2AkD2KX+iuw7Lq73APhACEj5GR7xGZumHYk02N7lm3hNwK41Gizdk/CGHHwUlWF/OwUNmypQosrdYXz2OPSnKCIS4/PovB6dM4+viPbyiKl1zRVweWqC5y1yk/bI/M2TRvbtrHsSE+PiuogWekeLMsmEDL2m3JXEvMQQgghhBBCiIZJ0EM4cnNz6dKlS4NjgsEghYWFe2lGQghx8KkLemhoqhZXOkpRFNI9aZT7K+rbnSFdTiJkhin3V5DiSmFV6dqdmoc/HEi6Pr7cVUTYMnATmWfACJCie9FUrba8VSRAY9s2Sm0po5ARwjANFJRGm6GLg4/HpeFxaaR6XQlNzaPZEl535PdAVRRcel1AQ9MUPG4tLliiaUpcpkf3DrlkpLqoqgnv8lxDYRN3TEDFtOza4AeUVATqgh6mtd8EPZKVt4r9/RNCCCGEEEKIQ50EPYRj+PDhDB8+vMExK1euZOjQoXtpRkIIcfDRlboLrOnuxDvWMz0ZVASqsGOyLQAnyJDlzeTK3ucDsKJ4zU4HPTZUbuaY1r2aNDY28yRohNAVrXY+ViQTxBWiKuQjLzUnMsYMEbYMVEXFtCLPwzAbboouDh7R4EBBbmpcSShNVVGUyOvBEw16qAqaqqBpCqZpk53hITvdgz9oxO3n0lVQABvcLo0/XX40c5ZtoVVeGqO+XEogtHPBtfKqIPnZKfy6dDMlFQHOO6kzaamRAJ9l1WV+xM5nXwubVsI6y7LRNAl6CCGEEHvKmtJ1+3oKe0WnJOVv96TCwkJGjx7Nzz//TFFREeFwmLy8PPr27csll1zCgAEDdviY5eXlnH322RQXF3PHHXdw5513Jh3XrVs3AFwuF5988gmHH354o8fesGEDp556KgA33ngj999//w7P70A2a9YsrrrqKgCeeuopLrvssn02lx9++IE333yTFStWEAgEyM/P57rrrmP48OGEQiFef/11Jk6cyObNm/F4PDRr1ow333yT4cOHU1RURO/evfnoo4/2+rxjX0MNvT7rE33dJqMoCi6Xi6ysLNq0acMJJ5zAH//4R9q2bbtLc26q0tJSDMOgefPme+V8jdmrXRl9Pt/ePJ0QQgix39HUhoMeuqqRk5KVsN6jJ5aIyvJm7PQ8Zm6Yy7S1s6gJ+/llw3xWN/BBLhq4gLqAhmVZgE3ACLKhchO+UE3dGCOEUdvkPBow8RvJM0vEwWv7HhiqqjiZHy5dcwIeEOn5Ef2qKEpc43BdU1AUhbTaYIrbpZKd7uHUY9rRvWPeLmVglFUH+W5OIR98s4JvflnPI/+ZQSBYF0CxLBvLigQ9jCTBhn0hbCTOw5Tm5kIIIYQ4wHz88cecddZZvPvuu6xYsQKfz0coFGLTpk1MmjSJq6++msceewzD2LGbT5577jmKi4ubPD4cDvPEE0/Ufr4RB4IpU6Zwyy238Msvv1BeXk4gEGDDhg1kZEQ+H9933328/PLLrF27lkAgQEVFBYWFhfvNxfg9xbZtQqEQ27ZtY968eYwYMYIzzzyTl19+GdPccxUYLMvigw8+YMiQIfz+++977Dw7aq/ecvnggw+yefNmLr/8ci644IK9eWohhBBivxAb9PDoyRsyZ3rSKfNXxGV7eDU3NdTEjctPzSXDnUZVKHJTQfO0PE7peDzfrJ5Oib+s0blMXj2dnwrnUB6oBODiHkM5qtWRCeNiMz1CRoiw5sHUzYQxpmWiKAo1YT9abfkuo7ZcVtAMkWqZcc9fHFpUJdLXI8rtUp3AiEtXCYZMJwiyfVksgKx0Nz5/GK9HR1FMgrXZHbvSX7y8KsjEGXVvzCuqQyxaXUzn1pHAo2VHgh7Y4POHyUrf9/1pDCPxA4thWnFluoQQQggh9mdTp07liSeewLZtMjIyuPrqq+nXrx8ej4elS5cycuRI1q1bxyeffEJ6ejqPPPJIk477448/8umnn+7wfBYuXMioUaO45pprdnhfsff95z//cYJU99xzDwMGDCAQCHD44YezatUqJk+eDEDr1q158MEHadmyJcFgEE07eN4v9+jRg2effTZunWVZ+P1+Nm/ezOzZs5kwYQKBQIARI0ZQXFzMU089tUfm8vnnn/PnP/95jxx7V+zVoMfixYvZunUrc+fOlaCHEEKIQ1Jseav6qIpKiu6hJux31rmTBEg0VePiI8/hy5Xf4dZcnHv4GbTIaI4vXMPny79t9DyGZTgBD4BJK7/jqFZHEjCCVAQqyU/NQ1NVJ+gRMIJYtkXYChM2E++4ChhBTMvEsi1sCzTLwKgdFzYjQREJehy6NE2Ny/7wuHQnuBG5YB92Mj5ie3pE16V6XZGeHy4NVVGcoEdZVXCn57R8XWnCulUbysjL8pKd7sG0IuWtINLjw7JtcjK8O32+3SFZpkeyPh9CCCGEEPsj0zR59tlnsW2bzMxMPvjgAzp37uxs79OnD+eccw5XXXUVS5YsYdSoUVx00UWN9qD1+Xw8+eSTOz2vF198kdNPP53WrVvv9DHE3rF69WoAjjrqKG699da4bb/88ouzfOuttzJkyJC9Ore9JS0tje7du9e7fejQoVx33XXceOONrF27lvfff5+uXbs22tZgZ+zJLJJdsVfLW5WWRj5Y9uvXb2+eVgghhNhvNPWif6o71VlWFQ2Xmvw+hW75nbhrwLXccuwVtMiIpOse27oXHbN3vG5ndcjHNl8J/5rxX/7981v859dRGJaBUduMvDoYySgJGiG2+koS9t9SvY3imsjfetu2CBkhTNuKpNmarAXKawABAABJREFUISfrQxyaVEWJy+DwerS4TA+oC3CoqgIK5GR64gIgqV4XbpeG11P3+7ArpZ3mrdiWsO6rn9fxl7dm8v3cwkh5q9qgh2XZlFcFse1Ir4/ouQOhvdvvQ8pbCSGEEOJANnv2bDZs2ABELkrHBjyi0tPTnQCGZVlMnDix0eP+3//9H0VFReTk5OzQfFyuSKnUmpqa/fJudZHI74/cHJgsQBXdVt/2Q0m7du14/fXXSUlJAeCll146pFpP7NWgR35+PhBpKiSEEEIcipoa9EhzpRDp3Ay6pqElyRBRFTVpc3C35uamYy7nrgHXck2fC+lZ0HhTvqhJK7+jKlQNRJqdL96y3OnPUV1bRsuwDKwmBDAs28K2LTZVbYlkgGxXJzd64RggEJaeHwc7LaaHB4DXrccsRwIgsZkgLl0lLysFT8y4NK+O26XhcdW9hR1wZMu48xzfq1XS8597UmeuOqv+u6Fi2Tb8b9oaJny/iodf/pFXPllAWVUA07SpqA5RUhF5vVbXhDBqgxDmXur5kSzosb/0GxFCCCGEaMycOXOc5ZNPPrnecX369CE1NXIj2MqVKxs85ty5cxk7diyqqvLQQw/t0HxOPvlkDjvsMACmT5/OZ599tkP7i70v+jlS1xM/C8d+5jyYylntrA4dOnDDDTcAkevxH3zwwT6e0d6zV8tbXXDBBbz88suMGjWKc845h9zc3L15eiGEEGKf05Sm3W+gqRopLi/+sB9d1VFVFUVR4/p8aKqGruqECCXsrygKrTIKaJVRQNus1rg1F3M2Lmr0vEu3rYp7PG3dL/Rp2YPNVVux7J27sBowIuWHts/0qA75yPCkY1gmvrAfr6uubFDICCUt6SUOXMmCGnXLGm5X/O9Gqidy111soCQtxRVpdK5qkZigDQN7tmT20i0YpkWKR2fIgPbMXrqFYDj+9daldTatm6fj1jXe+nwxdhOSIz74ZgUQKaH19cx1XHp6N4rL/bh0lfzsFKpqQqSnuDFNi8qaEDkZXsKGiUvfcx+wwkkCHJLpIYQQQogDRd++fbnpppvYsmULLVu2rHdcbHZtMFh/OdNQKMTjjz+OZVlceeWV9OrVa4fmo+s6zzzzDJdeeimWZfHcc88xaNCgHc4YibV69Wree+89Zs6cyaZNm1AUhYKCAo477jiuuOIKOnXqlHS/U045haKiIm655Rbuvfdevv32Wz788EN+++03KisradasGSeccAI33HAD7dq12+n5ARQWFjJ27FhmzJhBYWEhpmmSk5NDr169GDJkCGeeeSaq2vBnV7/fzzvvvMNXX33F+vXr0XWd9u3bM3ToUC6//HLc7sTPc926dQPgrLPO4t///nfS406bNo0bb7wRiDSmP//885k1axZXXXVV3LgJEyYwYcIEAM477zxnOSp2/JQpU2jTpk0j35WIrVu3MmrUKKZNm0ZRURGGYVBQUED//v254oornOdQn40bNzJy5Eh++uknioqKSE9Pp3///txyyy1O1sXedOmll/Lyyy9j2zZTp07l+uuvTxhj2zaTJ09m8uTJLFy4kJKSEkKhEJmZmXTq1ImTTz6ZSy+9lLS0NGefZD+T6ON+/foxevTouG2bNm3iww8/ZObMmRQWFlJRUYHb7SY/P5+jjjqKSy65hL59++62571Xgx633XYb69ev57PPPmPYsGFceuml9OvXj86dO5OZmemklAkhhBAHK0VpetflNHcq/rDfKW2lKSrumF4fkaBH4xdX09wpXNTjbAorNrHVV7xD841+0AiZ8YGVaWtnMXPDXArSmnFBjzNJd6cl2z2OadVdhLZtm4pgFRmedALhAGEzHDd2U/VW2mS2lB4gB5lo+apk0lLi3wemeBPfpsb+/rh0lXDYol1BBs/cMpBFq0ro1i6HjDQ3R3TKZd7yutJVaV6dFvmR12ivrvlcfsbhjPl62Q7NfdaSzVx6euQDTtiwqKgOEgiapHgswqZFpS8S9AiGrT0b9EhW3sqUoIcQQgghDgwDBw5k4MCBjY5bvHixU6qoVavkmbwAr7zyCqtXr6Zly5bce++9bN68eYfn1Lt3b4YPH87o0aMpLS3lueee44UXXtjh4wC8+uqrvPzyywl9Dn7//Xd+//13PvjgA+655x5uuummBo/zxBNP8NFHH8WtKyoq4sMPP2TChAm89tprHH/88Ts1x+nTp3PHHXcQCMRn22/evJnNmzczefJkxowZwxtvvEF6enrSYxQWFjJs2DDWr18ft37RokUsWrSISZMm8e677+6Ti/y74ssvv+TRRx+lpqYmbv26detYt24dn3zyCbfffju333570s/2U6dO5d5774373vr9fiZOnMjkyZO5//779/hz2F5+fj6HHXYYy5cvZ8GCBfj9/rifS3FxMTfffDOLFy9O2LekpISSkhJ+/fVX3n//fUaPHt1gsLI+Y8eO5W9/+xvhcPzn/nA4jM/nY926dUyYMIF77rknoU/LztqrQY+bb74ZAI/HQ3FxMa+88gqvvPJKk/dXFIXffvttT01PCCGE2K+k6pHMB6/uAUDXdHJSsp2gh65EMj2aanCXExm9YPwOzcEm8WLqNl8Jk1Z+B0Cpv4Lp637hzK7/n737jpOivh8//pqy7Xqh9yoICKJIUVQUFVFUbLGgBnvDGE2MGts3GhO/+SXfGGtiNEZQ7F0BCwgIgqiAFOlIOxCucHVvy5TfH3s7t3u3u3cHdwfI+5mHj9ud+czMZ/f2wuy85/1+J09Nj4oNeoTMMCEjhGVZVBsBwpYRN860TMKWIUGPn5lUQY+MukEPT+rPtlvXCIctUKBnp2w8rsj49nlp3HzBEP778WoWr/oJXVO49Ix+uF0qpmnjcWkMO7I9328oZNXm+r1pUgmGTDzuyGeyqDTyd2iYNqZpEw5bmKZFOGxie/UmBTibInFPDylvJYQQQoifl+eff955fPzxxyccs3btWl544QUAHnroobi70JvqjjvuYPbs2ezcuZP333+fc889l9GjRzdpH88++yz/+Mc/AMjJyeHqq69m2LBhQKSXyYsvvkhpaSl/+9vfUBTFyWao6+2336awsJCePXtyzTXX0K9fP0pKSnj11VeZN28eoVCIe+65h9mzZyfMpkilvLyc3/72twQCAfLz87nhhhsYNGgQuq6zbds2Xn75Zb7//nu+++47/v73v/PAAw8k3E/0fT/llFO44IILyM/PZ926dTz99NMUFRXx/fff88wzz/Cb3/ymSfNLZtCgQbz33nsATJw40Tn27bffDkB2dja//OUvmT17Nk8++SQAf/zjHxk0aBAA7dq1a/AY0YCFbdu0b9+eK6+8kqFDh6JpGuvXr2fatGls2LCBJ598El3Xuemmm+K2X758OVOmTME0TdLS0rjmmmsYNWoU4XCY2bNn8+qrr/LYY481y/vRVL1792bdunWEw2E2b97MwIEDgcjNiFOmTHECHmeffTbjxo2jXbt2VFZWsmHDBv773/+ye/dutm/fzl/+8hcnQyf6O0n0nkdL00Ekc+cPf/gDEAlgXnnllfTr14+0tDR27drFp59+yqxZs7BtmyeeeILTTjuNvn377vdrbtWgx5dfful8AVQUJa6WtxBCCCHi6ZqOrupO0CPd5cOju3FpLsJmuNGZHlED2x3BtcdcwgtLX2/0NlaCf6uXFHwf93zelq8bF/SIKY9VbQScn5Uhv5O+riiKM86wDMDT6LmKg19seau66mZHaCnGRsar5GR6qKwOo9UEUzRNISPNTbrPxbXnDOL8k/tgmBYd8tOpDhqYZiRo4Q8YXHRqX0rKA+wsanwzv5+Kq+jeMQvAKY9lmpbTUyNkRLI+LMtG01LPP/p5b6qwUb+fjmR6CCGEEC1j1e51vPDdaxRUND174FDUObMD1x57KYPapy7f09I++eQTZs2aFZlT586MHTu23hjTNLnvvvsIh8OcddZZKfuDNEZ6ejoPPfSQc8P2Qw89xEcffdToTIUtW7Y4F367du3Kyy+/TIcOHZz1w4YN49xzz+WKK66goKCAv//975x22mn07Nmz3r4KCws59thj+c9//oPXW1sCeMyYMdxyyy3MmTOHPXv2sHjxYk466aQmvc7Zs2c7vZaffvrpuHJCRx99NGeeeSaXXnopq1ev5t133+X3v/990t4Yv/rVr7j11lud58ceeywnn3wyZ511FoFAgPfee6/Zgh7p6ekceWR8f76cnJy4ZZ06dWLNmjXO827dutXbJhm/38/vf/97bNtm0KBBvPjii2RlZTnrhw4dyvnnn88tt9zCl19+yRNPPMHZZ59N165dnTGPPvpo5PuGx8PUqVM56qijnHWjRo1i9OjRzZbF0FSxQZ/YXtvz5s1j2bJlAFxzzTX1euKceOKJXHjhhYwfP57i4mJmz56NYRjouu78Thp6z2MDga+88kpc5tbQoUM566yzePbZZ3n88cexLIvPPvusWYIerdrIvFOnTnTs2NH5r1OnTk36b1/SZ4QQQohDWZY308l2SHdF7pbwRDM/1NpMD7fWuDt82qbnN+n4/rC/3rKQUb+HSGPElrCKNi4v8pfU9CmxnWwPqyYjxDCNevsQIsrr0cnL8kaaoNcED6JZGIqioGkKuqbidevOYwCPKzImO8PDbycdy++uHNboYyYKkBixQY+wiWFaCYOFsX03LMsmlCBjozESZ3pI0EMIIYRoCf/+dvphE/AAKKj4iX9/O/2AzmHFihXcc889zvP77rsvYTn8F198kVWrVpGdnc19993XLMceM2YMZ599NgA7duxwLtY2xtSpU52SVo8++mhcwCOqU6dOPProo0AkaPPSSy8l3d/dd98dF/CAyDnuxRdf7Dxft25do+cXVVhYWwI2UV8Qt9vNr371KyZPnsydd96ZtJ9Kjx49El7A79y5sxOI2bNnD2VlZU2e44Hw3nvvsXfvXiDy+4sNeES53W7++Mc/oqoqpmny6quvOuvWrl3LihUrAPjlL38ZF/CIGjNmDOeff34LvYLUYoN3sUGPLVu20LlzZ3w+X9KSa9nZ2U4ptWAwGLd9QyorK7Esi8zMTM4///ykpeqif3cAu3fvbvT+U2nVTI85c+a05uGEEEKIQ16Wp7aGqq5F/tl2enzEBD1cmo5lWzXZEan3l+byOSWyGlIZ8mNYZlxGSaqSU6Zl8U3BckoDFRyR35NMT7oTaDEsA8u2UBXVaW4eV/LKCOHWXE7Dc8Oqf0c74OxDHN6i5bCyMzxOVkg0oAHxWSUuTcXQIoEBl0tDUSKZGoqi0DE/nex0N2VVDQfzCgor6y0zLdsJRIQNC8OwMC2bul/Nq6rDZKVHgpN1m6w3hZS3EkIIIcTP1Q8//MD111/v9FOYPHlywiyPrVu3OlkVd911F23atGm2Odx3330sXLiQ0tJSpk6dyoQJE5wSSaksXLgQgO7duzNixIik40aNGkX37t3ZunWrs01dGRkZSRuyxzbjrqpqfMZyVGwT9dtvv517773XKXUUNWbMGMaMGZNyPyeeeGLSRufdu3d3HpeXl5Odnd3keba2L7/8EohkI/Tv3z/puA4dOtC3b1/WrVvH119/7SyfP3++8zj2An5dF1xwAW+//XYzzLhpQqHa7zqx2eaTJ09m8uTJWJaVsnF97N9Y7L4akpGR4TSYt1J8Z9nX/afSqkEPIYQQQjRNoov7rprgR6Snh4aiqGiqhlt3Y4RSBz00VWd456OZu2VRo+dQWl1Gm/S8lGOCRhCP7mHOjwuZvTly8h49xpgeoziz78kAhGuyNyy7/glPwAiS4Ul3ToYSBXACRpCS6lI6ZbZv9PzFz5vT+0MBj7v21FaLOWnXNBVdi3yudE1F01SMmOBBTqanUUGP7zcWMvHk3nG9SUzTjilvZWKYNlZM5kWFP0Rmmjs+6BEycen7FrgzpJG5EEII0WquH3Y5Lyx9jYLywyPbo3NWB6495tIDcuylS5dy4403Ul5eDsCZZ55Zr9QOREqE3n///QQCAYYPH85FF13UrPPIz8/nd7/7Hb///e8xTZP777+ft956C11Pfgk1HA6zZcsWgIR3+Nd11FFHsXXrVrZt20YwGMTjiS/p26lTp6RlUGPv2K/bLL0xTj75ZPr06cPGjRv55ptvuOCCC+jUqROjR4/mhBNO4Pjjj0+Y5VBX+/bJv4/Fvp66jasPVtESTaWlpfTr17jybjt27HAe//jjjwDouk6fPn2SbjNw4MAD0vKhsrL25q1Ev99owMOyLHbt2sW2bdvYunUr69evZ/ny5XE9tlMFL1KJHqOyspLt27ezdetWNm3axA8//MC3337rjGuu90aCHkIIIcQhxq1G7iF3sjxUHV3RUDWV+sWoIoGTaJDBrbk4odswvtr+LaGaclM9croQNEPsqtiT8Hh//eo5/jj2LkzL4JudK/hq+3f1xpQHq2ire5yAR6y5WxZxSs9ReHQ3ITOUNIMj2ucjmukRThD0qAhWEggHIlkhetOa9omfN01VcMcEEmIzPXRNQddVFCUyTlMVYj9dbXJ8bP2posFjVPrDLFtfyHFHxn/JC4ZM56dlxQc9SsoCeFxaXHZHMGw22PMjmbAp5a2EEEKI1jKofT/+Pv4hNpdsPdBTaRW98ro3PKgFfP755/zmN78hEIh8Hxg3bhx//etfE955/vrrr7NkyRI8Hg+PPPLIPvVIa8iFF17Ihx9+yKJFi1izZg3/+c9/kpb+AeJKOOXnN1xOOPau9rKysnpNtlP1EYl9vbEXh3fu3JmylFS3bt1IT0/H5XLxwgsvcO+99/LVV185277xxhu88cYb6LrOiBEjuOSSSxg3blzS/TW2afyh0s+5KSWboqIBOoDi4mIg8r6kCpD5fD7S0tL2KUtnf+zZU/tdv23btnHrLMviww8/5K233uL7779PWNJMVdX9+l3u2LGD//znP3zxxRfs3Lkz4f6b2wENelRUVLBo0SK+//57SkpKqKqq4oknngBg2bJllJeXc/LJJx/IKQohhBAHHV2LNDePlruKNjxPdsKf7k6jIliJqmhoqkamJ50rhlzAZxvnk+bycW7/M/hs05dJgx4AS3eu4rtdK9lauiPh+opgJTnezKTblwbKaJ/RlpAZpiqUKDQT6flhWqZz50jYNOo1ew7W9BMpD1bSRs+rV3or1r42ihaHJk1V4rInYhuha6qKS7OdDI26AYc2OY1rUAnwwZebOLJHHhk+F6ZpURWIhE9UBTLSIoG4aBAiEDIIGxYV/hCGYdX08jDxB8J43cnLxKWSqLyVkSAQIoQQQghxKHjllVf44x//6HwHmDhxIn/6058SNs8uLCzkr3/9KwBnnXUW1dXVcU2UAQoKCuLGR9dHL/o31sMPP8w555xDIBDg6aefZty4cUkbesdeDG7M94/YDI3mutj7xBNPOGWEEpk6dapTdqtDhw68+OKL/PDDD8ycOZO5c+eyfv16AAzDYOHChSxcuJDx48fzt7/9LeHrbsnvWQciUGIYkXP6Y445hgcffLBFj5WoR01LW7VqFQBer5fevXs7ywOBALfeeisLFixwlum6Tvfu3enduzcDBgzguOOOY86cObzwwgv7dOwvvviCX//6105QEyA3N5devXrRr18/hgwZwrBhwxKWstsfByToEQ6HeeKJJ3jllVeoro7UFK97YWLu3Lk899xz9OvXj7/85S8cccQRB2KqQgghxEFHVVRyfLV1UV2qHilvpblQFJV8Xw42NsX+SCO2TE8GFcFKdFVDqymXdUR+T47I7+nso1NmO5b/tDrpMd9ZMzPlnBbvWIZF8guvC7d9x5AOR9I3vxeWnTwNO2AEMZ31NmEzjFt3R74EKTjZKZWhKnwuL1UhP+0yau+UMi3T6TkS3VYcHrzu+MBfNOihaQqqquD16E5wQ6vz5bJtE4Ielf4wD/zrK8aP6sGcb7fHZXCcM7oXpw7r6jQyj2aAlNeUzgqEDH4q9tfLBmmKsFH/70cyPYQQQghxKHrqqaec3hwQ6S9wzz33JL2gvnnzZioqItm57777bsqL/BDJCnn99deB+Iv+jdGtWzemTJnCX//6VwKBAA8++KDThLyu2J4V0Tv+U4mOURSlUaWkWsqAAQMYMGAAv/nNbygsLGTx4sXMnTuXTz/9lFAoxMyZMzn55JNbpPl2qsBGbCmm1pKTk0NhYSHl5eUceeSRTd4+mr1TWVlJKBTC7U78PdQ0zVZ/fZs2bXIa2B999NFxmSiPP/64E/A48cQTuemmmxg8eHC9+c+YMWOfjr17924niys9PZ077riD008/nQ4dOsSNKykp2af9p9LqQY/KykquvvpqVq1alfIDvmPHDmzbZu3atVxyySW89NJLSZv4CCGEEIebNFftRVqX5kJTI1kc2Z5MMjzphIza/gQezY1bc0fGKInvTjq640AWbvuWsmDDJX4SWbF7DSt2r0m6fknBcpYULOfsI07lxO7Dk46rDgfim5vXBC4qw/5I52ki5w6WbbGnqhjbtsmPCXSEzDC+msdBs+ESWNFgijRGP/R56mROqAmam6fXND/XawIh0cBDUzI9omYu2lJv2YcLNrNs/R56dsrixvMHEwpHAoHRnht79lY7x7T28Q62RJke+xpAEUIIIYQ4UJ577jkn4KEoCnfddRfXXnvtAZ5VvKuvvpqPP/6YNWvWsHjx4qQNqN1uNz169GDLli2sXLmywf2uWLECgM6dOye9ON5Ujz32GI899liD48LhMNu2baO6ujquQXvbtm0555xzOOecc1iyZAlXXnklAPPmzWvWoIeu6xiGEXfXf127du1qtuM1Vp8+fSgsLGTTpk0UFxenLFP24osvkpaWRs+ePRk+fLizPUSCGmvWrGHIkCEJt924caOTVdJa3nzzTefxhAkTnMemafLGG28A0KNHD/75z38mLc21r7+TDz/80Cnl9eCDDzJx4sSE4376qfn7JrX6N/zf/va3rFy5Etu26dChAzfccANXX311vXFjxoxxoj7V1dX8+te/drJChBBCCFHLpepOiadcXzaqoqJrkYu7mqqhKApe3YOmaknTp7M8Gdwx6lquPeYSrj/2Mh4+9Tf0yu3W7HP9bNOXKddXGwHMmCbn0cwOyzIpri6NG2vbFmA7YyCS3RHtXxK7PBnTNvcpfTpRI3ZxYDkNzWtEMz3qBkOiY7u2z3QCI13aJy/N1lQ79lTy5fKd/OfD1YTrNJeMbUK+r9kZUt5KCCGEEIe6OXPm8Le//Q2IlHd65JFHGhXwGDFiBOvWrUv5X+wd6VOmTHGWNyXLI0rXdf74xz865Z3+/e9/Jx17wgknALBlyxa++eabpOMWLlzoNMA+/vjjmzyn/XXJJZdw1lln8atf/SrpmOOOOw6v1wuQsL/D/sjMjJx3x5Yhq2vhwvp9Ilta9Hdh2zYvv/xy0nErVqzgscce48EHH+Sll15ylp922mnO43feeSfp9h9++GEzzLbxNm/ezKuvvgpEAltnn322sy7aagKgX79+SQMexcXFfP31185zs853nFQl2rZt2+Y8HjhwYNJxH3/8sfO4uYJCrRr0WLhwIXPnzkVRFM466yxmzpzJnXfeybHHHltv7DnnnMOnn37KqaeeCkQiSqk+NEIIIcThyqO5nUyFaCq4rmooiuo0O/fobnSltrxVvMg2XpeXvvk96Z3XHbfmIjtFj459FTLDKYMMYTNM2DRixkcyVkzbqglyJNgmpuG5YZnOHfRhy4jLGknEsu0mBzAsy3JqDouDh9sVH9xQFAVNU/C665+8p3lduHSV7IzInXVtsr0N7r9bEwMjc77dnjBAERWbnREbAKmqTh2sS7RPKW8lhBBCiENFaWkp999/v/P8d7/7HRdffPEBnFFqgwYN4pe//CUQyZJIZtKkSU5w5L777mP37t31xuzatYsHHngAiPR1mDRpUgvMOLUxY8YAkaDDtGnTEo6ZPXu2k4kRmw3SHPr16wfA+vXr+fbbb+ut//DDD50G663p4osvJi0tDYgEtxYtWlRvTGVlJffdd5/zPPb3161bN0466SQA3njjDebNm1dv+2XLlsUFSlratm3buPXWW53f5W9/+1vnNUIkABUNdCxbtixh2a2ysjLuuOOOuMbrdf8OYrOV6iYs5ObmOo/nz5+fcJ6zZs3ixRdfTLr/fdWq5a3ee+89ADp27Mhjjz3WYAqX2+3miSeeYNy4cezcuZPPP//8gPwfghBCCHEwS3ZnhUurzQDxaG4s20pY3sqtu+LKYUVleZo/6AFQbQRJc9VeZK4MVVEWqKBDRruaXgu1F3Cj2RpmisCEERf0MGqCGBqmZcb1+IBIzxBNUXHVZMKYtolC0xpKWzQ9UCIOjHa5aSkbhmdneCivCpGV7mbcyO58snhr0rHnndSbJ99c3qTjG0mCHoFg5HPaIT/STLPSHyIjzU110KC4rNopw5VIwp4ekukhhBBCiEPE1KlTnZ4WRx55JCNHjqzXjLyutLQ0unfv3hrTS+hXv/oVn376qZOhkUjv3r2ZMmUK//jHP9i6dSsTJ07k6quv5phjjgFg6dKlvPDCC5SWljr77N+/f2tMP84VV1zBK6+8QmlpKX/6059YtmwZ48aNo3379pSVlbF48WInMyAnJ4fLLrusWY9/7rnnsnjxYgBuvfVWbrrpJoYMGUJFRQUzZ87kvffeo2vXrmzfvr1Zj9uQ3NxcHnjgAe69917C4TDXXXcdF198Maeddho+n4/169fzn//8x8lcOPvss+tl6jz00EOce+65VFVVccstt3DZZZdx+umno2kaX375Jf/9738B0DStXrZEU1VVVdX7u4n2CykoKODrr79mxowZTgDhyiuvrFdayuv1ctJJJzFnzhz27NnDpEmTuPrqq+nRoweVlZUsW7aMN954gz179sRtVzc4Eu1nApG/79zcXHRdZ+DAgZxxxhn861//wrZt/v73v1NYWMjo0aNJT09n+/btzJgxgy+++CLl/vdVqwY9vvvuOxRF4fzzz290zTpd17nwwgt54oknWL9+fQvPUAghhPj5iJS9ivxT79JcGLZZkxESbQwYCS54NU+SoEdGi8yrIljhBD22lRbw/NLXCZkheuZ05fphl6PGNC6MBhdSBRmMmGyOsBP0iDQ1N2yT2DOOYv9eMtxpZNcEPSzbwiJxo8RkLNtKGYQRB49UwQMAXVPp3DYDl65x9YSB5GV5MUyL0oogny3ZFje2Y346D98wigefq3/XVzKmZcd9ngHW/FjMSzPXEAyZXDy2L1eceSTVQQOXrlIdMAgbFrZtJ23gKZkeQgghhDiUvfXWW87jNWvWJK3xH2v48OFJsxJag8/n4+GHH+aaa65JOe6WW27BsiyefvppSkpKnBJesVwuF7/97W+ZPHlyC802tby8PJ5++mluueUWysrK+Pjjj+NKC0W1bduWp59+mry8vGY9/sSJE5k3bx6ffPIJpaWl9fqQdO7cmeeee47x48c363Eb44ILLiAUCvHHP/6RcDjMq6++6gSAYo0bN44///nP9ZZ36dKFqVOncuONN1JUVMS0adPiPreqqvLoo4/yyCOP4Pf792uuq1evbtTfTlpaGlOmTElaPu6BBx5gzZo17Nq1i7Vr13L33XfXG9OpUyeuuuoq53e1adMmjj76aGf9UUcdRdu2bSksLGThwoUsXLiQtm3bsmDBAgYOHMitt97KU089RTgc5sUXX4zL6oi6/PLL2bhxI0uWLGHz5s2NexMa0KpBj2gkt3fv3k3aLhrNLSsra/Y5CSGEED9X6a40JwtEURR8uhfDMnDrLizLcjIkdE1HVTQsO/5uk+wWyvQoC1YSNEKsL/6Rr7Z/65Sw+rF0Oz/u3Uav3G5sLStAVzS6ZHeMBCZSlKkyYnp3GLFBD9uqV96qbvksy7KwtKZV+4wEPfbvzhxx8IiWxXK7VIYP6IDPqzNvaf27+NxuDZ+q07tzNpsKGndOGgyZuHWVz5ZsY/POMo7u25Z5y3YQDEU+P29/sZETj+4C2HjdOoGQAXYksFG3XFdUwqCHZHoIIYQQLapX3oHLMvg5KSkpSVj26VBwwgkncN555/H++++nHDdlyhTGjRvHyy+/zOLFi9m9eze6rtO5c2dOPPFELrnkErp27dpKs05s2LBhzJgxg1deeYUvv/ySrVu34vf7yczMpEePHpx66qlcfvnlZGQ0/01wmqbxj3/8g48++oi33nqLNWvWEAwG6dy5M2eccQbXXnut0/fjQLj00ks56aSTmDZtGl999RUFBQUEAgFycnIYMmQIF154odOKIZFBgwbx8ccf8/LLL/PZZ5+xbds23G43Q4YM4YYbbmDYsGE88sgjLTJ3RVHw+Xzk5ubSt29fRo0axYQJE+IyMerq1KkT7777Li+88AJffPEF27dvx7IssrKy6N27N6eddhoXXnghbrebZ555hvLycmbOnMmFF17o7MPn8/HCCy/wl7/8he+//55QKITX68Xv95OWlsZtt93G4MGDeeWVV1ixYgXl5eV4PB46dOjA0UcfzaWXXsqQIUN47rnnWLJkCTt37uT7779P2gy+0e+HvS/dO/fRsGHDqKqq4rHHHuO8885zln/++edMmTIFRVESprS9+eabPPDAA2RlZbFkyZLWmu5B79133+Wee+7h9ttv55ZbbmmVY27YsIEJEyY4zz/66CP69u3bKscWQgix/yzLoiRQSsgMEwhHanu2S29DabC8TraHwrayAp5ZMjXl/o5q35+jOwxg2vfv1GwFo7sPZ0nB9wSNxE3vTug2jMXblybMljip+wiqwn6+27kSgHF9TuKqoy9id2WRExypS1d1uuV0pjocYFfFbtpltCFN97GldDt5vhxyfNlAJCNkW+kOvC4vnTLbA1BaXYau6WS4I2WGbNumMlRFZoosl+pwgLAZJqsFep6IA8e2bTbtKCMjzcWilTv5z4c/xK3/+69PBuDr1T/x2mfrGrXPB64ewYYde3nts+TZyr++dCjdO2SRkeaisjoMNnRsk540S+Wy+2dExsWYcvEQxo3s0ag5CSGEEA35uX/v37x5M8FgEI/HQ69evQ70dIQQQuynRP+/3qqNzDt27AjAypUrm7TdggULAGjfvn2zz+lQtXv3bv70pz8d6GkIIYQ4xKiqSpruc8peQaTpeexziPT5yG6gvFWWJ4OTu49gQNu+TBo8kdHdjuPGYZM4+4hTefDk2+mV2y3hdgu3fZu0PNSyXaudgAfAl1uXYJhGyswKwzKpDFaxqyJy15hl2xg1442Y7cI1GSFGTKZHpEF6TBPpsJ+yQEXK123ZlvT0+BmKNj5XVQW3K3ky9HED2nPFmf05pl+7Bvc5b9mOlAEPANOMfP6iAQ+AUIK+HVFS3koIIYQQQgghUmvV8lYjR45kw4YNvP/++9x0000p02uivvvuOz777DMURWHEiBGtMMtDwwMPPEB5efmBnoYQQohDkNflIRiTNaGpmtPwPMqtuclw1w96HNW+P8d0HISmqHTP6YJHdzvLj2pf24hPU1UmDT6f/y5/k+1lOxs9t4pQfNMyfzjA3mAZppUqyGBT6C9xnlkxZa1is1ecoEdNCSxVUesFMAzLJBzTGD0R6enx86VrKpqqoKnJ+7yoisKx/dsz9Ih2LF23J+k4gPnLCxo8ZiBU83mLiVuEwyl62CQoZZVomRBCCCGEEEIcrlo10+PSSy9F0zQqKyu5+eabKSoqSjl+7ty5ThMgRVG46KKLWmmmB7c333yTefPmccoppxzoqQghhDgEqYoaF+TQVA2XqpPmTnOWeTQXmqqS482K23Zkl6Ec2bYPR7Tp5QQ8kkl3+7hp2BUM6TBgv+a7q3wP0SvCK3av5Z0fZrK2cGPcGDsmCGHZlhO4CBhBp9F5bKBnb3UZpmU6mR6hmICIbVtxzdHrsm1bgh4/U26XhqoqHNEtN265z6NTt6+4miIw0hSBYP3PmlEnyFdSHsA0LYIhM2FWhyWZHkIIIYQQQgjhaNWgR+/evZk8eTK2bbNq1SrOOOMM7rzzTj744ANnzKxZs3jmmWe4+OKLufnmmykrK3MCHv3790+x98PDrl27eOyxxxg/fjxnnHHGgZ6OEEKIQ5SrppyVqqioikqmO4OcmMblbi0S0BjV9Rhn2cB2R8SVrFKVxI2WY2mqyqguQ/drrjtrylatK9rM9BXvsaTge15a/jYbi7ckHG/ZlpPVAVAVqsIwDSqCVc6yskA5/nB1TaaHjWEaGJbpBDtim6Mn2n+qxuri0OXWVTRVpVfnbI7tHylfpShwy0VDyEr31Bt/9gk99/uYTqZHjGjJK4gENErKA+worKSozJ9wH4YpQQ8hhBBCCCGEiGrV8lYAv/3tbykpKeHdd9/F7/czc+ZMIFJHGeCOO+5wxkZrbJ900kk8+OCDrT3Vg9L999+Py+XiwQcfZO7cuQd6OkIIIQ5R0R4eWk3Gh6qq6EQaJ6tKbbmrk3uMpGt2J0JGmN553Zx/rwFcmk4wRe+BqPy0vP2a6+6qIgZyBEsKljvLbGw+WPc5d4y6Nm5OEGnWbsXUCir276VULSeufhCRhuSWZWGpFoZtophhjJoMkbBl4K0ZFwgH8Lq8znaWbUtPj58pl0sj+ml64NqRfPb1Fnp0zKJ/j3wM06K8KkhMCxhOO64b3TtkUh002LO3mo8X/tjkYwZCCTI9YspVBcMm2JGSVys2FifcR+ryb0IIIYQQQghxeGn1oIeiKPz5z39m1KhRPPPMM2zZsiXp2DZt2nDdddfxy1/+st4FjcPRa6+9xoIFC/j73/9OXt7+XUASQghxeNM1nXR3Gj6Xr3aZqqHUlL7SYspfJWtI7tJcBI1gg8fKcKeR5vLiDwf2aa57KosxLZPVe+IbQu+pKmJTyVb65PcAIhkY64o2k+3NpHtOl7ixZoLMjGojMh8LO7K+JuMDcMpjBY0QZcGK+KAHkZ4e1eEAvpjl4tDn1lWnVJSmKvTvkU+73MjfiK6pZGd4KK2I/8z37VpbCqvSH2Lesob7eMSKzfQIhU2qqsNkpbsjZdcMK279Nz/sTrgPUzI9hBBCCCGEEMLR6kGPqHPPPZdzzjmH1atXs3TpUnbt2kVlZSVer5e2bdsyZMgQhg4ditudul74oerUU0+loCD1l+L+/fvz/vvvA1BQUMBf/vIXTj/9dM4666zWmKIQQoifubZp+ahqfKVLXdVQVdUpe5Uqo8GtNu40QlEU2qW3YUvpjn2a5/c//cDuqsKE6zaU/OgEPV5d8T4r96wDYGyv0fTI6UJ+Wg55vpyE20YDIdHG52EM5/UGaoI5ZcEK53G094dl25iWRbF/Lx0y2qJrB+x0SjQzl67GlYpSFeJuvMnN9FBWGf08RJa5XSqhsIWuq+RmNT0ItnFHKVNn/MCy9bWf8dxMD3+86Xg0TSUUrg3Yrd2yN+E+EvX5EEIIIYQQQojD1QH9lq4oCoMGDWLQoEEHchoHRNeuXRsM6HTpErlL1bZtfv/73+NyuXjooYdaY3pCCCEOA3UDHhDp9aEqkeWaqmGZyYMeqqolDYwoioptW6iKhmWbtE8Q9Dir7yl0ye7IpxvnpwyI2NjsqtiTcN28LV8zostQVEV1Ah4AszcvACJlvK4eejG987on3L48UEHINMjxZREyahudB8IBQkaIqpA/0tjcNAhbkf8ifUBMQqZJyAoTMsN4XR7nfROHLkVRcOm1QQ5NU+MalmuaSqe2GVQHDUrKArhcKm6XhmnZZKa5SPMkPrU+/qiOjDmmCyUVQbbuKmfmoi3Ouh93ltcbv7ciyHvzNnHOib2cLI5g2KQ6WL//B4CZ4u9UCCGEEEIIIQ43rRr0OOeccxg7diynnnoqgwcPbs1DH3ReeumlRo+dPn06ixcv5i9/+Qtt27ZtwVkJIYQ43Omq7tzZrqlaXEPwurSk2SAKPpeXkBFCVVRCpkm2N6ve9sd0OooMdxrDOg/e5ywQgL8s+CedMzskXGdYBp9unM/Nw6+st27htm/5aN1sAM4fcCbDOw+JW7+7qgi75rVVhKqwsQkaIeZsXkjIDDOq61BCZpiQEcKwTbI8Gfv8GsTBSVMV1DoVVn0e3QkyeFwamqqiqQqZaW583sSn1scd2Z62uWm0zU1LGrioa8P20riyVVX+5H+LkukhhBBCCCGEELVa9ZbEDRs28K9//YtLLrmE0aNH88ADD/DFF18QCoUa3vgwNmvWLAB+97vf0a9fP+e/e++9F4B//OMf9OvXj3feeedATlMIIcTPQKYng7SaPh/t0tvQNj0/6Vitpv9HXR7djVt1OaWyAHrnxfcF6Zvfkwx3GgCD2x+JV/fs17wLKn5Kum5rWf1ykpZt8+G6z7Fr/vfuDzOx7fgLx7EBn73VZVQEK3lx6eu8v/ZTZm74gldWvE/IDOMPByjxlxLYx54l4uClKgpqgr5yLlfkc+9162iagq5FMj4yfImzeGPLXnnd9f9mEtmxpzIumFEZkKCHEEIIIYQQQjRGq2Z6tGvXjj17IuUpioqKeOutt3jrrbfwer2MGjWKsWPHcsopp0iT7jrOP/98hg8fXm/5mjVrmD17NiNGjOC4447jyCOPPACzE0II8XPi0Wsv2uqqRqYng7JghVP6KVquKvJYRU0S9NA1Hd12OY0PumV3ZnD7I1mxew0+3cs5R4x1xrs1FwPa9mXprlUt+dLiVNcJUNiAaZvoSrJTo0iT8+U//eAsWVe0ibLqMqfp+86KPXTIbOsEjQAMy0wYGBKHBk1T4np6RLl1FRRI8+pUBw2nBFb3DpkJ95OZXvt35XU37vTbMC2KSv20z0sHoMqf/CYhQ8pbCSGEEEIIIYSjVYMe8+fPZ+3atcybN4958+bx/fffY5om1dXVfPHFF3zxxRcoisLgwYM59dRTGTt2LL17927NKR6ULrjggoTL33nnHWbPns3IkSO55ZZbWnlWQgghDhe6ohG93Op1eQiEA1i2hapqaDV9LLy6x2n47dU8qGokCyRa+kpRVC4ffB5nVY8h05PhBAqi+uT1aHTQQwF65nZj895tjX4NpmWhKgqmbaGrGv6wv96YoBFGT3FBOmzVL0tUFfKT5Y1e6LYpD1TEBT32VBbSJi0PXYv0SrFtO+FF9EQs28K27XrvlWg9qqrE9fSIUhSFDJ8Lt0sjbFjoWuTvIDPdTXaGm7LK+ABFbLZIYzM9AAoKq9izt5qdhZUpAxuWZHoIIYQQQgghhKPVG5n379+f/v37c+ONN1JeXs6CBQuYO3cuCxYsoKSkBNu2Wb58Od9//z1///vf6dq1K6eeeiqnnnoqw4YNS9h0tTmVlJQwfvx4SktLWbFiBR5P6nIbgUCAqVOnMmvWLH788Ucg0oD8jDPO4KqrriI7O7tF5yuEEEK0tNiL7m7NhWEZNf06FDQlsi7Dk0HACKIoKmkuH6ZtYqomhhXJCvHqbgJGkBxf4n8X++T3aPR8zu1/BqO6HsPMDXOZt2Vxo7YpKN/FO2tm8VNlIXm+bI7Ir39TRcgMkY4vwdYRQaP+nfZ+I0AWtXf3RwM/AKZlEjCC7CjfRZesjrhr3oNqI0CeL6fBOQeNEJqqoRF5jxMFTJoSRBFN59KSn3fmZ0c+K5qmoGmR34FLVxk9pDMfL/zRGdc+Ly1uu8ZmegB8vPBHSsobLpsW2/tDCCGEEEIIIQ53rR70iJWVlcVZZ53FWWedhW3brFy5kvnz5zN37lxWr16Nbdts27aNl156iZdeeons7GwWL27cxY19YVkWDz30EKWlpY0av3v3bq655ho2btwYt3z9+vWsX7+et99+m3/+85/079+/BWYrhBBCtI66QY+wGSasGKiKiqaqgEKGK40iSkh3p6GqKoqt4FJNp0+GR/cQMsMJmp5HZHky6JbdiW1lO1POJc3lY3jnowHISdAcPZmP1s/mp8pCAEqqy1i8Y2m9MSEzdY+xYIL1/nB13HPLtggZIdy6O64niFnzuoNGkPJApRP0CJlh3Jor4fECRhCfq7YXRMgMx5UfS7ZMNB89RdDDpUfWaarqjHO7NC4Y04cvvt2Ov6Zh+UlHdwbA59WpDhh4PYkzPVQF6iZsNCbgAWBYkc9XhT9EZpp8HoQQQgghhBCHt1ZtZJ5KtKzVlClTeOutt5g1axZnnnmms962bcrKylp0Dn/4wx/49NNPGzXWMAxuueUWNm7ciKIoXHLJJfz3v/9l2rRp/PKXv0TTNHbt2sUtt9zSYvO+4IILWLdunZS2EkII0aJ0JTbo4UZXdadcj0dz49J0VFXFrbvI8USyHhRFwaN7nICJS3Ohq6nvtbhgwHh65nalc2YHfnn0RUnGnFkTaIFsb+L+CYk0FEwBCJphVuxey4vL3uTTjfMJm/HlrIIxWRxRdXuDAJQGyikPVhKKDXrUZLwEzBCWbWJZFpZlUeQvSTqfgBGMCxIZloFpmXHLEgViRPPR9YZPlXVNQVNrMz3a5vr41SVHc/xRHbn41L6MGNgBgKyavh5uV/2gxxVn9ufhG4/nunMH7dM8LdPGtm2KSqspq6z/ORVCCCGEEEKIw8kBzfSIVV1dzXfffceSJUv4+uuvWb16NaZptkrJhurqau69915mzpzZ6G3eeOMNVq2K1B6/5557mDx5srNu+PDhDB06lDvuuIOCggKef/55fvOb3zT3tPdLcXExJSXJL7Qks3Xr1haYjRBCiINZNHChKhouVcelRvpTAHhdXjI9GQC0ScvDHZN1oCiK0/PDreroqkbITHwMRVHpkNGWG4dNii7Bper1+mh4tdqyk03J9GiMtYUb+eLHr7CJNClPd6dxQrdhzvrGZHoAVIaq8IerSXPXljUya5q/RwMnIStMyAwTCAexbZuwGY5778JmmIARJNNOd5ZFx1m2TZrbV7u/mvdfNL9UmR5RiqLgqQlkeFwaiqLQt1uu04A8O8ODPxh2ylqpCc5tO7fNIN3rIiczdVnVZMKmRXXQwDRtgjV/ZKGwiWXbTSqnJYQQQgghhBA/BwfsW1AgEGDp0qV8/fXXfP3116xatQrTjHxJi5bCANB1nUGDBjFy5EhGjRrV7PP47rvv+J//+R/Wr18PgKqqWFbyRpFR06ZNA6BHjx5cddVV9daPHz+eDz/8kNmzZzN9+nRuu+023O6Dp9zA9OnTeeqppw70NIQQQhwCokGPvLQcFEVB13Q8Wu2/adk12R1evf4FWzUm0yNVQ26v7qE6JoCQ4UknzZ1GWaC83rja4zZv0GPOj1/FPV9f/GNc0COUoKdHokwPiJS5qgxWOs9N28K0TCfjwzCNmgCITdgy2OMvJs+b4wQziv17sW0LK+acyMImVNNPxQl6SKbHQUGrCY5Eb9aJZn5kpLlom+ujtELFpatOY/STj+nMvKUFAHTvkOn0/chO37dzxUDIoHBv5O8n2vC8sLSaDJ9Lgh5CCCGEEEKIw06rfgtavHixE+RYuXIlhlF792Y00KEoCv3793eCHMOGDSM9PT3ZLvfL//t//4/nn3/eeX7BBRcQCoX46KOPUm63adMmNm/eDMDZZ5+dtLn6+eefz+zZs6msrGTRokWcfPLJzTd5IYQQopVoioqu6mTVZBR4NQ++9NpeE6myMjVFxevyoqla0vJWiqLi0d1xQY9MdzrpLl/KoEeay5swG6S5rCvaFPc8caZH43ouWJYVN8+wZTiN0QNGkJARotoIOMGMQM262FJWlm1hWEZcdolpRZrF6ykCSqL1RbM5on0/ohkcLl0l3efitl8MpU22j/KqECcO6ez8DaX5XGiqglm3uUcDAkGTsBH5rER/hsImhls+F0IIIYQQQojDT6sGPSZPnux8qYsNcvTq1Ythw4YxatQoRowYQW5ubqvMZ8WKFQDk5eVx//33c/bZZ3PPPfc0uN2yZcucx8cdd1zScccee6zz+Ouvv5aghxBCiEOSpmrOxXggabA/2bYdM9oB1Lswn+5Ooyrkx6XqcX1DQMHn8pLhrn/Tgycm6KEoCp0y27O1rKDR82kKTVGxbMsp5RVMmOlRv7xVIqZtYsT0CAmaIadxenmwAsBpfG7ZFlZNOazYoIdt21SF/PHZH7aNYYYl6HGQUWsyPeqWx+rYJh1NVVAUhXNP6k1JWXzQTFUUstLd7K1oWl8OKyZIEjYtTNPCNG0nACKEEEIIIYQQh5MDlu+uKAonnHACN954Y8rAQUvKysrixhtv5IYbbiAjo/H1sDdtqr3zs3v37knH5eXlkZ6eTlVVVdw2B4PLL788rlF8Y23dupVbb721BWYkhBDiYKUqKpnufe8bEb3hITbTw6W5yPPlRIIemh5X+ioaZMiI6YkRVbeE1oR+p/HG6o8oqiqmaffGN8y0LT7ZOI+KYBV983vyzppZ9cYk6umRcF+WGZfp4Q/VbhctmxVtfB4bHKmb6REtj2XbNjY2tm0Rsgxq824i2+ualDQ6kKLlrbQ6QY/YIEii3h6qqpCVkTjo0b1DJtv3VGLbNoN6tWHlpiJnXWwgDBv8wchnKFrqSgghhBDiYLV9+3amTZvGokWLKCgoIBwOk5+fz9ChQ7nkkksYOXJkk/ZXWlrK2WefTVFREVOmTOG2225rcJtFixbxySef8O2331JYWEhVVRW5ubl06tSJ448/nvHjx3PEEUck3f7UU0+loKCAIUOG8MYbbzRpvs1p9+7dPPHEEyxYsIDi4mKys7M54ogjePHFFwGYN28ezz//POvXrycQCNCmTRuuueYaSkpKnBL4M2bMoHfv3q0+9yuvvJIlS5bQuXNn5syZ0+rHFz8/rfqNuEOHDvz000/O84ULF7Jw4UJyc3MZPnw4I0aMYMSIEfTq1atV5vPkk0826W7VqD179gCRO13bt2+fcmy7du348ccfnW0OFvn5+eTn5x/oaQghhDhEePT970sVm42Q6U7HpbnQVb1evw+t5t9mT4IeIa46F/O7ZnfkN8dfD8Di7Ut5b+2n+z3PWPO2fA3A0l2rEq5fuWcd28t20TW7Y8r9mLZVpwxX/RCNYRk1Jaxqu73HXsyO7XlmxgRD/CE/WZ4MLMsiZIUpC5TTPqNtyvmIluVkeqjJS78lqgqXmeYmWWu5iSf3oWObdAzTQlUVVj5TG/SoWw6rqjoaQGvuUKAQQghxeKrceHDdyNpSMvq07sXuN998k4cffphQKD6jeteuXezatYsZM2Zw0UUX8Yc//AFdb9wlzD//+c8UFRU1PBAoLCzk3nvv5csvv6y3bs+ePezZs4fly5fzz3/+kwsuuID77ruPtLT6N2YdDKqqqrjssssoKKjNgi8qKnKCNbNnz2bKlClxfYx37NhBZmYmJSUlrT5fIVpaqwY95s6dy5YtW/jqq69YtGgRS5YsoaysjJKSEj755BM++eQTANq2bcuIESMYOXIkI0aMoEuXLi0yn30JeACUl0fqi3u9XjQtdTmJ6P8ZRrcRQgghDlcuzYWiqNi2hdcVyU3I9mbi1b1oSu2/ydHHdQMcznpVczIeYjUmMNM+vQ0Au6sa90WoMZ5e8hLHdjqKs/qeSnpMGbBYpmVh1GRypGKYBmGrdlzdTA/ncczr94cDVAQrCRhBdFWnKuSnNFCOqqhOH5aoQDiAW3c72TSiZUQzOupmesTSYgIiuVke9lYE8Xk0/IH6n5Pxo3rQo2MWAFnpbir98WOsOkGP6mimh2Fh23bKvjtCCCGEEAfCnDlzeOCBB7Btm8zMTH75y18yfPhwPB4Pa9as4cUXX2Tr1q289dZbZGRkcO+99za4zwULFvDee+816vhVVVVcf/31rFmzBoCxY8dy2mmn0a1bNzweD2VlZaxcuZI333yTgoIC3nrrLXbv3s2zzz6Ly+Xan5feIt577z0n4DFq1Ciuv/56fD6fc13y2WefdQIev/71rxk5ciSBQID+/fuzdevWAzZvIVpKq9c+6NGjBz169ODyyy/Htm1WrlzJokWL+Oqrr1i2bBmhUIg9e/bw0UcfOQ3FO3XqxMiRIxk5ciTnnHNOa0+5nmgE2u1u+OKKx+OJ20YIIYQ4nLlUHcMy8GiRf0OzvZELubEX9NWarA+Xkvg0pW16Pnsqi+K2AfBo9TNDsjyZTs8MgBO6DWN4l6P5cN3nLNz27f69mBjf7VzJnqpibjj2Mlxa/S9Blm0SbkSloYAZqpPpERPoID7To/Yytk1hVQmKojjZNCX+vQBkuNLibvKoDPvRjCC5vuzGvzjRZG6Xhqoq6FryYEM0GyQjzUV+to9Q2MLr0Zl4ch9e+KA2s2jMMV04Y0RtOVVNU9Dq7LdupocZk+FhmBYuXXPGxQZbLMt25iGEEEII0VpM0+TRRx/Ftm2ysrJ47bXX4koqHX300ZxzzjlcddVVrF69mqlTp3LxxRfTp0+fpPusqqriwQcfbPQcXnzxRSfg8de//jXh9cbRo0czefJkbrvtNr788ku+/PJL3n77bS699NImvNrWsXnzZufxH/7wh3rl+KNl94855hhuvvnmVp2bEAfCAS34rCgKgwcPZvDgwdx4440Eg0G+++47Fi9ezDfffMPKlSsxDIOCggLefvtt3n333YMi6BG9eNCYu+aipSj2NaukNb3yyitMnz495ZhgsGmNNYUQQohYLk1H1/R6/4aqiopa0zg8mumRqC+Fqqj49EiT89hgBoA7QbDh1uFX8fH62awv/pEj2/bh2E6DATj7iLGUBSpYtWddc700tpft5NNNX3L2EacmXJ8oO6WuEn8p7pjXXbeRubMv20Qh9j20sW2bcJ0eDiErjFetDQZZlgUH/ynJz0Kat/7nPFZ0XX52JOspJ9ODrqmcPLQz783bSHFZgKx0N6cc2zVuO01V6wUqjBQNy7fvriTd56JNtpfyqhAZaW5ceuRDUOEPkZ1RP1gohBBCCNGSvv32W3bs2AHAzTffnLCHREZGBg8++CCXXHIJlmXx0Ucf8etf/zrpPv/v//6PgoICcnNz2bt3b4NziPbeOPHEE1Nea/T5fPzlL39h7Nix+P1+pk2bdlAGPfx+v/O4c+fO9dZXV1cnXSfEz9FB1eXS4/Fw/PHHM2rUKFauXMns2bN55ZVXqKqqijTstA+OusTR1LDGBACakhVyoJWUlLBx48YDPQ0hhBA/Yy7VlbQMlaZqWKaFpkTuSu+W3aneGK/uQVEUfC5vvaCHnaBPRrY3k8sHT6y3XFUUhnQ4slmDHgALt33L8M5H0zY9b5+2t2yTgBGT6WElK29lNermC8M0IKY3imlbIL2tW0W6L3XZA1VVcOmqk4Xh80ROy3OzvDx68wms2VxMxzYZeNzxpVQ1VXECJVE/lVTx6qdrWbdtL2keF8cP7sjoIZEvtJZlU1EVwrZtgmETl0tzgh6V1WEJegghhBCi1X333XfO41NOOSXpuKOPPpq0tDT8fj8bNmxIOm7p0qVMnz4dVVW5++67ueeee1Iev7Kykt27dwORijQNycvLY/To0Xz66af8+OOPhMPhg67EVew100T9T6LrG9sbRYhD3UHzSd++fTsLFy7kq6++4uuvv3Z6YMT+0dZNzTpQ0tPTgUjQw7KslFkc0UhrVlZWq8xtf+Tl5aVMFYTIa96+fXsrzUgIIcTPjdflwad7E66Llq2K/rt6cs9RTF/xHlXhyF1J5/Y73ekF4k3Q5Lxrdidcqu40DB/Qtm/KuURLbDUny7b44sev+MWgCfjD1VSF/LRJy2tyTwXDMln+02pMy6J9RlsWbvuWN1Z9SLY3k4sHnk2uL6dOpkdiIStM2Aw7JbdMu+FsE9E80rwNBD0UcLkSn0Nmpbvp0SlxCTJNU8jN9NKpTTo7i6oAsG1Y8kPki3tZZYi3v9jIyk3FZKW7GT24E907Zjl9QIIhgzSPjqJAIGgQCptYlo3X03xfC+qW0RJCCCGEiDV06FBuuOEGdu/eTceOHZOOi70BOtmNx6FQiPvvvx/LsrjyyisZPHhwk+aydOnSRvVAu+666xg/fjx5eQ3f3DR37lymT5/OihUr8Pv9tGvXjtGjR3P11VcnvLZ5zz338O677wKwYsUKp1R+XSeccAJFRUUMHz6cadOmAXDqqafGNS8H6NevHxDJ6qi77t1333WONWXKFG677bYGXw9ESpK99957zJgxgzVr1lBeXk52djYDBgxgwoQJnHPOOSmvj4ZCId5//33efvtttmzZQigU4ogjjuDSSy9l4sSJjZqDEE1xwIIeZWVlLF682Al0xP4RxkYfhw4dyqmnnsqYMWPo2bPngZpunE6dIneemqZJUVER7dq1Szp2z549ACnHHCwmTZrEpEmTUo7ZsGEDEyZMaKUZCSGE+LlJcyVu9A2RQIZX9zhNtt2ai9+OvpGFW7+lS3ZH+rfp4wRMNFXDrbkJmSGnLJZX93DZ4Il8sPZTMt3pTDxyXMq5uBNknLRJy2Nsr+N5fdVH+/wav/9pDQPa9uXNH2YQNIL0yOnC5KEXEzLDfL1jGV7dw8guxyRt1A7wwdrPWFKwvGZ/P7C1dAembVFSXcqnG7/kF4Oy+M/SN9hUspWj2vfjkkHnoiX4khE2wxT599I+o03kfYopbyUNrltWQxf9VUXBrWsJ19Xd1u1SCdU0hYn+ngf2yneCHoms3xYp67B6czEPXjPCCWoEQia7iqtI8+jYNgTDJqUVQbq2z2zcC2sE27axLKRfiBBCCCESGjVqFKNGjWpw3KpVq5yyTNFrcXU9/fTTbNq0iY4dO3LHHXfw008/NbjfjIwMunTpwo4dO1i9ejV/+MMfuPPOO1PesDxkyBCGDBmScr+WZXH//ffz5ptvxi3fvn07r776Kh988AHPPvssI0aMaHCOB5Pdu3dz8803s3r16rjlRUVFzJ8/n/nz5/Pqq6/y1FNP0aZNm3rbFxcXc9111/HDDz/ELV+2bBnLli3jyy+/jMtwF6I5tGrQY8mSJU6Q44cffnA+0LHZHNnZ2Zx44omccsopnHTSSWRmNt8XsOYSW2tw27ZtSQMaJSUlVFVFvow2lEEhhBBCiEigI9rTA6BDRjtO6z2abG9WpAF6TKAiLy2HQDhIyAzhr8kGGd1tGIPb98ewDHK82ZQGylIeq67rjrmEnJom32sKN1EWrGBr6Y6E23fN6sj28l0oEFdYy7RNXl7xrvN8S+kO/ueLv8dtu7NiN78YGMkG8eoeNLX24nfQCDoBD4DNe7fFbbv8p9W4NJ2NJVsAWLF7LUe1789R7fvXm2N1OIhlmwSNED6XN668VWmgXBqaH0CqquB2JQ56RMtPRWWmuSkuCwC1AZFBvdvw2ZJt9batqzposHFHKYN6R76AVgeMuJ+lFUGCIZOwYdU77r6ybRsLUBuRjSSEEEIIkczzzz/vPD7++OPrrV+7di0vvPACAA899JBTmaUxrrrqKv70pz8B8Oqrr/Lee+8xZswYTj75ZEaMGJE0yJLKypUrWblyJZ07d+bqq69mwIABFBcX8/rrr7NgwQKqqqq45557+OSTT5qtDP5zzz1HOBzmH//4B1988QUA7733HhDpIRe95hrNpjjllFO4/fbbARIGKOqqrKzkiiuuYNu2bWiaxgUXXMDYsWPJz89nz549zJw5k48//phly5Zx7bXX8vrrr+P11mb2m6bJNddcw9q1a4FIZsrFF19MXl4ea9as4bnnnuOjjz46JHohi0NLqwY9rrrqKueOwthAR69evRgzZgynnnoqxxxzzEH/QY+N7C5dupRhw4YlHBdbo3Do0KEtPi8hhBDiUOfW3XEXSqNBDlVRyfXGX6BPc/lIc/ko8pdApHIPmqo55xqJghpxx0pQ3irdHenbNbTjIIZ2HIRt26zcvZZdlYV88eNXcWMvPeo8TMsg05PBtztX8PH6OY1+nct2rWZr6Q5Kqstok5bHNcf8gjxfDgAbS7Y2uP03Bd/HPf9g7WcJgx5WTTmrgBHEo7uxbctp6VEerMDn8uLVPRRVleBzefFo7rgG8oZpJGwob1gmupr4gr1oHEVR6vXriIrN2lEUSPO5KC4P4HVraFrk8z20X1t0TcUwG74rrqQ8kHRdMBT5jPgDzdffw7JBTdBjRwghhDjUlK5YyeZ//ZvqHQUND/4Z8HXpTK8brydn8FEHeip88sknzJo1C4iUaRo7dmzcetM0ue+++wiHw5x11lkpe4MkctVVV7F8+XJmzJgBRBp9z5w5k5kzZzrHHDFiBCeddBInnXRSowMqAwcO5L///W9c1sjpp5/OTTfdxNy5c9m5cyfffPMNJ5xwQpPmm0z0JuucnBxn2ZFHHpl0fE5OTsr1df31r39l27ZtuN1unnvuuXoZOqeddhonnngid999N2vXruX5559nypQpzvrXXnvNCXhcc8013H333c66o48+mjPPPJNJkyaxadOmRs9JiMZo9eiCbdtomsbIkSO59957+eyzz5gxYwa/+93vGDZs2EEf8ADo1q2bUx/vgw8+SNpgPVojLz09vVFpe0IIIcThzqO5UWMuprvVSOBCUZSE5agAdLX2orymak55LF3VUJXkF+YTXbR31QmUKIrC4A5HMq7PSfTN6+Esz/ZmkevLol1GG3wuLwPaHtHwi6ujpDqShVLkL+EvC/7JK9+/i2lZrCtq+gl/ZSh5mSOAaiPgZNhatoVlW5iWSVFVCaXVZZQHKygNlLPHXxx3XhMywwn3FzQS11QWTeNOklmhqooT3HDpKh6XRla6m/Z56ehaZJvcTC+nD+/WqONU+EMNjikqrSYYbp6eL7ZtY1oS9BBCCHHo2/Tsvw6bgAdA9Y4CNj37rwM9DVasWBHXjPy+++6r1zj8xRdfZNWqVWRnZ3Pfffc1+RiKovB///d//P73v09Y1qqgoIB33nmHX//614wePZq//OUvTt/eVO677756+1MUhUsuucR5nqop+8GkrKzMubZ52WWXJb22OXHiREaPHg3A9OnT475PREt9derUiTvvvLPetrm5uTz88MPNPXUhWjfT47zzzmPMmDGceOKJZGRktOahm93ll1/OQw89xIYNG/jXv/7FTTfdFLd+1qxZzJkTuePz4osvxudLXsP8YPHKK68wffr0lGOSNY4SQgghmoOmaqh27YXgaABEVZLfFOGKCXroSm2mh6ZqaKqKZdZeyM1wp2PaFtXharI9mbRLz2dPVTEA/dv0JpXz+p/Bh+tnEzbDnNf/9Lg55afl0CYtL5J1so9W7llHt+3fsqaw6UGPhi4vB40QRkwT85ARuQgeMkOUVIdqxkT+jTdtC70mWBSywqThi+v/YVmW0yxe7J9UPVU0VcG2wVXT9yM/21ev18cFp/Tmq5U7KatMHdTYW9Hw+Zttg786jCdJya2mSHI/kBBCCCFEg3744Qeuv/56J8AwefLkelkeW7du5cknnwTgrrvualSZpkQUReGXv/wlv/jFL5gzZw6ff/45ixYtYu/evXHj/H4/L7zwArNmzWLq1Kl06dIl4f4yMzOTVnqJbWBeVpa8BO/BZMmSJQQCkYzhhm7mPvHEE1mwYAHFxcVs3LiRvn37UlhY6GR5nH766fUCV1HDhg2jW7dubNvWcOlWIRqrVYMe//u//9uah2tRv/jFL3jjjTdYvXo1f//739m0aRPnn38+LpeL2bNnM3XqVGzbpkOHDtx8880HerqNUlJSwsaNGw/0NIQQQhzmYi8EqzWPU/UGcNXN9CA26KERjslWyPFmURGqojpcjaIo3HzcVby/9hM0VWNc75NSzqtNeh5XD70Yr+7BrbkpD1bEre/XphdF2/Y96AE0qURWU9i25fQ9AQiayS+Sm5aJpqgoiuJkegTNEF49UvrIsAwMCXq0OF1XyctyO9kXiRqjZ6V7uPOyY1m2fg/llSHmfLc94b5KGxH0AAgZzdNA0rZtCXwIIYT4Weh9841s/tfzVO9I3OPt58bXpQu9brzugB1/6dKl3HjjjZSXlwNw5plnxpVDgsh5xv33308gEGD48OFcdNFF+31cn8/H2Wefzdlnn41t26xbt47FixezYMECvv76a0KhyLlzQUEB1157LR9++GHCnhzt2rVLWsHG46ktI2oYh8a5dGzj8bo3e6eyfft2+vbty48//uhkffTvX78Ub6yBAwdK0EM0q1YNetS1du1aPvnkE5YvX05RURF+v5+0tDTat2/PgAEDGDt2bFz/jIOJqqr885//5Oqrr2bjxo188MEHfPDBB3Fj2rZty3PPPRdXV+9glpeX12DD9WAwyPbtib9QCyGEEM0tkk2hpMz0qO05oTjlraL/xTZFVxQVl+aKC5J0zu7ANcdcEhcQoF5r8niKoib8MnNy9xEs3/UDVeGG095bQsgMp+xjUhWqnVcwSdkqiAQ9qm2TNJcPwwxj2zbV4UBM0MPEtEznmLqiHRLlSQ81PrdORpobdwMlp7LS3Zw8NHK34cpNRRSWVtcbU1oZH/SwbZs3Zm9gyQ8/0a19JldPGEhWuptQs5W3Imn5VyGEEOJQkjP4KI55+h9Ubjw8+g1k9Emd+dySPv/8c37zm984mQXjxo3jr3/9a73zzNdff50lS5bg8Xh45JFHUmbO7gtFUejfvz/9+/dn8uTJlJeX88ILL/Dvf/8b0zTZsmULH3zwQcJgS2P7fhwq50mlpaX7tF00aFVcXOwsy87OTjYcaFxTdSGa4oAEPcrKyrj//vv5/PPPE67fuHEjCxcu5N///jcnn3wyf/7zn8nNzW3lWTasXbt2vPPOO0ybNo0ZM2awZcsWwuEwXbp0YezYsVxzzTXk5eUd6Gk22qRJk5g0aVLKMRs2bGDChAmtNCMhhBCCmgBG8i8zqqKiq7pzwV9RIsEPwPkJ4NFcKIoS3wOkJjgSG+jI8mZQHojP4oilKEpcMCUqy5vJ3SfezOzNC1m0/buk/TBays6K3awv2kzQDHFS9+Fke+NrCcdmvIRSZHoYtolhGvh0byTAUVMOLNeXjW3bNZkekYvjVSE/6e403K3fJu5nLys9cvegO0W5qbpf8k8a2pm3v6iftVtcFuD1z9dTXFZNVrqbI7rmsnjVLgC27Crny+UFnH1CT8LNmOkhPT2EEEII0VivvPIKf/zjH50edBMnTuRPf/oTmhZ/HlRYWMhf//pXAM466yyqq6tZs2ZN3JiCgoK48dH13bp1c4ISFRUVFBYWEg6HnZ69yWRlZXHHHXfQrl07p/fE4sWLEwY9mjsAU1f0/WktZkyZ4P/+97+Nvqm7Y8eOTT6Wrh/Q+/LFz1Crf6KKi4u59NJL2bFjR6Mim/PmzePCCy/kzTffJD8/v8Xn99hjj/HYY481erzH4+G6667juusOXPqfEEII8XOmqSpKikwPgCxPRlz/DyfoEdPI3FOTqVCbGRIJiiiKgltzETJDaKpGliczZdAjkkGS+EK0W3Mxvu8YTuo+gtJAGdvLdvHe2k8a90L30wvfveb02li+6wdO6z2abG8mR7bp43wBMyyDypCfLE9m0kCSYRmEzTCGZUSanlsmITOMYRqUBytBwcn0qAxVRTJAUmSYiH2jaQ0Hkur+BkcO7MhPxX427yxjV1F8c/tokAPgu7V74tZ9/s02zj6hJ5ZlY5iW0yw9tpdLU1i29PUQQgghROM89dRTTn8OiPTwuOeeexKeg2zevJmKish5+rvvvus02U7m9ddf5/XXXwdg6tSpjBgxAoiUrN+8eTPt27dn/vz5jZrnpZdeyv/+7/8SDAbZs2dPwxs0QexrTXWttLKyslmP25DY7Iz09HSOPPLIJm3ftm1b53FJSeoywIdKnxNx6Gj1oMevf/1rpzxS+/btueKKKzjhhBPo1q0bPp+PqqoqtmzZwsKFC3n11VfZs2cPO3fu5K677uI///lPa09XCCGEEAdYQ5keAFmeTOyaTI3YbI7YTA+f7gVAj1mm1TQ+11QN1dLw6d648lcJ54PSYDmndLePdLePshTBk+YW21y8Kuzn/bWfAjC21wmM7TWaeVsWMXfL1wSNIF2yOnL9sZc6gaBYpmUSNg3CZiToEa4JfpSHKikPVuBzRTJAqsMBwmbYCYCIA6DOn4Wuq1x0al8A/t/L37KzTuAjlWDIxOPWCIVNdE0lbJjYdupMk2Rs28aSqIcQQgghGvDcc885AQ9FUbjrrru49tprW/y43bt3Z/PmzezevZsNGzbQt2/fBrfRNA2v10swGKR9+/bNOp/YjJZAIIDX6603pqSkxOkt0lpiS+AvWbKEwYMHJx27aNEi1qxZQ9euXRk5ciSZmZn06tULVVWxLItVq1Zx8cUXJ91+3bp1zTp3IVq1FsGcOXP45ptvUBSFUaNG8fHHH3P99dczYMAAMjIy0DSNrKwsBg8ezM0338zHH3/MyJEjgcgfz+LFi1tzukIIIYQ4CKhKw5keqlqb3REpdxV5nO7yRXpwKBo+l9dZX5sJoqIS6f2hqxq6pseVx0o2n2SZHnVleBpX17clfbtzJRuLf+STjfMJGpHeDjvKd7FyT+IvFqZlErYMp9l59Gd5oALLtqgKVQM2JdWlAA02NY8GRSy7ddPxDwdaiuDbcQM6NGlfj037hrKqIOVVkd+3P2BgmE3/nVX4Q7zx+Xrem7uR6uCh0aRTCCGEEK1vzpw5/O1vfwMi5/KPPPJIgwGPESNGsG7dupT/zZgxwxk/ZcoUZ3k0ywNg7NixzuPHH3+8UZVoli9f7mQjHHfccU16rQ3JzMx0Hu/YsSPhmAULFjTrMRtj5MiRTkDmjTfeSBp0sW2bRx99lP/93//l9ttvdxq15+XlMXToUAA++eQTqqoS35CzadMmVq9e3QKvQBzOWjXo8fHHHwOQn5/Pk08+SUZGRsrxmZmZPPXUU04zm3feeafF5yiEEEKIg4vWiEyPWCrxmR7Z3kyyPBlxaePt09vQIaNdTePzSOaGrmpOlkeybA81mhnSQBAmKsOd1uh5t5TyQDmb926rt7zYX5pwfNAMYduWEyAJGZEvN7VBi8iXwuh6w06d6RGoGVdYVZxynGg6l66iaYn/Nk4e2pkbzhtEny6pm0ZGlVYE+XTxViqrw4QNi0DIxLRswoYVeZ4ggJHoAsGjLy7hgy838+68TfzjtWVNe0FCCCGEOCyUlpZy//33O89/97vfpcwCaG7nnXcePXr0ACIN1O+9916nbFYi27dv53e/+x0QKdl03nnnNet8YvuKTJ8+vd763bt38/jjjzfrMRujffv2nHnmmQBs3bqVhx9+OOH531NPPcWGDRuASK+V2L7MV1xxBQB79+7loYceqteXxO/3c99997XUSxCHsVYtb7Vs2TIUReHCCy9sMOARlZGRwYUXXsi//vUvifq1sFdeeSXh/7nGCgaDrTQbIYQQIkJVo83GGzleUePKT+X5cuqN8bpqU8YVRUEl0gw9GuyIBE2CgEKay4s/XI2iqHh1t5MJoqt6g1kOme76mR7dsjuxrWwnLlWPK0nVUmxgZ0X9usPV4UDC8dHMjLqZHsk0VN6q2gjgUnWqQn5CRgi37na+LLV0s8fDgcet4a+OfI5yMj0EwybVAQNFUTiyZz79uufxv9O+Yc/e6gb39dXKXYw/vic79kS+9LtdkTJXgZBJddCgXa4Pl16b5VRSHiA/2+c8r6oOs3pzbXBr4YqdzfUyhRBCCPEzMnXqVIqLI+cMRx55JCNHjqzXkLyutLQ0unfv3izHd7vd/Otf/+Kyyy6jpKSEd999l7lz5zJ+/HhGjBhBu3btsG2bXbt2sXDhQj766CNCoRAej4cnn3wSj6d+idj9ccopp5CZmUlFRQVvv/02hmFw7rnn4vV6Wbp0KVOnTqWoqIjOnTvHNWpvDffeey9LliyhsLCQN998k40bN3L55ZfTvXt3CgsLef/99/n000hZ3dzcXO6666647c866yw++OADvvjiCz788EMKCgq46qqr6Ny5M5s2beL5559n48aN+Hw+qqsbPl8VorFaNegR/T+02AhmY0TH79wpX5xaUklJCRs3bjzQ0xBCCCHi6I0sJRUV29OjceNVNEBVFfSahtzR7TPcaXh1D/5wNS5Vr8kMUVEUhY6Z7dhetpM0l48cbxY7K3bX27dLc5HtyaQsWFGzX43rj72c6nA1Ht2DrupUhirxaG4eW/CskxXRVF2yOrKjPNKoelTXY1hTuJHSQLmzfvPerfW2qQ6n/lIRDWY0FNQwGlgfNEJUq5EAi2GbuGv2GTLDpLl9KbcVDfO4aoMeaV4dl65SHYg8V9VIUOnacwYxY9EWvt9Q2OD+Fq3cyenDIxcUTNMmrFiUVQYxTRt/wCA7I/L3GDZM9lYEycn0otUcJ1E5q9jG6EIIIYQQAG+99ZbzeM2aNUycOLHBbYYPH860adOabQ49evTg1Vdf5eGHH2bhwoXs3buX6dOnJ70ZuGfPnvzpT39yyjU1p+zsbB555BHuuusuwuEw77//Pu+//76zXtM07r//flatWtVg8/bm1rZtW6ZOncrNN9/Mli1bWLZsGcuW1c/m7dChA88++2zCfiePP/44d9xxB3PmzGHp0qUsXbo0bv3o0aPp3Lmz03ReiObQqkGPaB24pjbeiY5vqGmo2D95eXlxTYoSCQaDTiN6IYQQojU0JYABNT09mhAoiZTO0lBUxekF4tKiQY90JxtB13R0VUet6R7t0ly4dTdp7jS8Li+aqiUMEJze50Te/WEWlm1zRu+TcGk6Lq22bm+2NwuACweMZ/qK97GxcWtuQg1kWEQd3WEAlx51Lv5wNQEjSJ4vB3+oOi7okSgw4TfqZ3oYlolWE9SpCvnZWLKFDhntaJ/RJunxGwqKWLZFebAy8rgmnd20LcqDFRL0aAZedzQrKRIAiZaCU5RIEKTSH6ZdXhqTzx5AdcDg9/9cmHJ/Xy7fySnHdEXXVQzTQlEiwQ+AqkCYzDQ3qqpQWR0GG/w1y0zLpsJf/zNbHTTITHM374sWQgghWllGn94Hego/GyUlJezeXf9moQOhR48e/Oc//+Hbb7/liy++cDIa9u7di6qqtG3blv79+zNu3DhOP/103O6WO6cZP348ffv25YUXXmDRokUUFRWRk5PD8OHDmTx5MoMHD+aee+5pseOn0qtXLz788EPeffddPvnkE9atW0dZWRkej4fevXtz2mmncfnllyet6uP1ennmmWf4/PPPmT59Ohs2bKCyspKuXbtywQUXcNVVV/Hoo4+28qsSP3eK3ZhuPc3k7LPPZvPmzVx00UU88sgjjd7u/vvv56233qJ3795OXxBxYGzYsIEJEyY4zz/66CP69u17AGckhBDi5y4QDsSVo2pI2AzjqsnYaAx/qBoU8Ghup4G5YRpsKyugR05XLNtiW1kBOd7sSOBD0ZyL9Xury8j0ZKCrGnuqiqmsubhfV1XIj2VbZHpSl/fcWbGbYv9eeuR04dH5TzVq/qO6HsN5/c+IWzZzw1zmbVmccrs2abn85vgbUBQFwzL4att3fLZpAS5N44Ijx/P+2k+pCFWhKSrXH3sZPXK7JtmTQq+8bkmPs620wCkDlpeWS443i+pwgN2VhSn2KRrLtm227IoEuHp2ysa0bH4sKMPn1fG4NEor4rOHyiqD/PWV7yJBiySuO28QA3vm4/PqaKpCpb92rM+r07ltBj8VV1HpD5OR5qJDfjrVQYMfC8q4++n4JpvP33c67fMOfG8bIYQQjfdz/96/efNmgsEgHo+HXr16HejpCCGE2E+J/n+9VTM9jjvuODZt2sT777/P1Vdf3ah/XDZt2sQHH3yAoigMHz68FWYphBBCiINJUzM9mjpeURSnT4ezD00nw50e6SeCiqKoNZkeGgq1fSiiAQ+ANJc3adAjvZENzTtltqdTZnss28arexpV7sqn1w8I5dZkj6RS5N/Lg3P+D9M2Y5qUQ9gK8/KK2rR507aYuWEuNw+/MsmebCzLSpqRG7vv2kyPyDEt22pSvxZRn6IoZKa5CRmRjBtNVdA0Ba9bc8pbxcrO8HDLhUNY8H0BX63clXCfe0r8DOyZj2la2Hb8PqINzYOhyPGiJa1CYTNheatEy4QQQgghhBCiJbXqt8xLL70UgHA4zHXXXceqVatSjl+5ciXXX3+9U97q4osvbvE5CiGEEOLgEhuMaIymNsdWFAUtwYX3XF+289ijufBobnRFi9u/HjO3NJcPpZku4KuKwik9j2/U2H5t6t9EktOIoAdEAhyxQYlktpYVYNkWxf69/PObl3nsy2dYsuN7Z71hJy9xZcUkFUfHOcGPBkpjicbJz/bSIS/dee7z6GSmuZP20ujYJp2Lxx7Bozcdz43nH8Wpw+IzborLIqXPTMsmbMR/Pmw7EviILjdNm0DIiDRQTxDg8AeSZ5QIIYQQQgghREto1UyP/v3784tf/II33niDXbt28Ytf/IJRo0Zx/PHH0717d3w+H9XV1WzdupWvvvqKRYsWYds2iqJw8cUXM2DAgNacrhBCCCEOAk0NYjSVqqhoCXqAxJbI6pjZHkVRIhfpkwQJVEUl3eWjMlSV4lgaNjZ2IwINY3qO4osfv6qX7dE1uxPby3aS4U7jF4POoXtOl3rbdshsh4KCTfNVMX3666lkeNLZUroDgPfXfsKRbXuT6cmIvC8JSopFAiq1c7CizdHt2qBH9H1OlS0iUotkK9U+b5ebhqoqGGbqz1ma10X/7nmEwvHjisuqsWwb27AT/v2VV8X37thVVIWiKEmCHpLpIYQQQgghhGhdrRr0gEh/jqKiIubMmYNt23z11Vd89dVXCcdG242cfPLJPPDAA605TSGEEEIcJlRFbfBie/TCr6ZqkOI6cpYnIy7o0S6jDf5QtbPMq7vRVZ3yYEXkuctLIFy/oTiAz+VlSIcBfL1jWdzye0+6lc3FW8n1ZZOblkN5oKLetjneLMb0HMkXPy5K+bqaoqDiJ4g5lGlbPDr/KQa07cukwefTt01PINIMPZoBU7d1XDTTw6z5GZshEjCDpKnJG5tHb4QRDYuWtUqW6RGlaQqmaZOfHV8ibe3WvfzPvxcRDJtceEpfhg/oELe+bsPySKPzSMZHXVLeSgghhBBCCNHaWv12OrfbzVNPPcXvf/972rRpg23bSf9r27Yt99xzD//85z9xuRrfkFQIIYQQorESlbZKOT5FuS2vy0uaOw235kZRVNJdabRJy3P6Vrg1d1wz86yax4kar6e7fLRLz6+33Kd7aZOeh6ZquDV30rmM63My9500pdGva1/9ULiB5759BcOMXNwurS5z1tUtnRUta5WovFXIiL+QHsu2bcKWXDxvKq0m6OFyJf6Me92R+5/qBj0AKvxhQmGLd+duJBAy2LSjlOKyaiBS4iqRsFF/hWR6CCGEEEIIIVpbq2d6AKiqylVXXcWkSZNYvnw5y5cvp6ioiMrKStLS0mjbti1HH300Q4YMkWBHK3rllVeYPn16yjHBYMMNVYUQQohDSXNnD3TIaEtlqIqyQIXTJN2luQgaQVw1zdAh0nDdW9OEPMuTQbF/b+ysSHP5GNC2Lx+u+9xZ2jmrA56YQIc7QbAkVmyAJZmHxvyalbvX8c6amU14lfG2lhWwN1CGS9UpD1aS58tBVdX6mR71ylvVBkWCZvKgh2lbGJbR4OsV8TRVIc2r07FNOlt2lQNgWbYTtPC6Naqqw3jdOhk+F5XV9ftvBEIm9z6zEAAFmDxhIIP7tEl4vLBRv0eLZHoIIYQQQgghWtsBCXpEaZrGsccey7HHHnsgpyFqlJSUsHHjxgM9DSGEEOKQl6b7COq1F/F1VSNIJNMjkimi4NU9NQEQBa/uxa27nWwHr8uDpmp0zenMUe36sXLPOgDO6H1SXCkuXdVRFTVlM3KP7iFoJL5pwaO58bm8DO8yhDWFG1hTtO/nAcXVpbjVyKllyAzhVb2YlknIDOEPBygLVNA5qwOWZTnzjZa3sm2bkJm84bVlmU7ARDRNbpYXRVHwunUUJRLkq6jpyeFx12Yt5Wd7EwY9YtnAnG+3MbhPG7bvqeDHneX075ZLu7w0gIQ9RKqlkbkQQgghhBCilbV40GP+/Pm89dZbrFy5kuLiYrKysjjqqKOYOHEi48aNa+nDiybIy8ujT58+KccEg0G2b9/eSjMSQgghDk2qqjqlqyASnAAFlxY59dJU1SmTpasaLlUnTfc5QY8MV+QisqaoXDZ4IqP2bqdTVnv6teldUypLAWxURUFXdUJxWRKRdVFn9D4xLlskVrq7todGji9rv17zXn8p7TMiGQBBM0Rl2M+j856koPwnZ8wR+T357Qk3OmWtoj/DluFkfSTq3WHZllM+qzHCZjhhybDDkc8T+cx53BqKEnlumBbVAQNdU9G0yHvdo2MWW3+q3x+mrq0/VfD9hkKmzlyDZdm4dZWbLxgMwNJ1e+qN90umhxBCCCGEEKKVtVjQIxQK8dvf/pbPPvvMWWbbNsXFxcydO5e5c+dy3HHH8fjjj5OXl9dS0xBNMGnSJCZNmpRyzIYNG5gwYUIrzUgIIYQ4dMVedNdUDZemO709NEVzgh4uzYWqqnj02rJVPpe3ZpyKqij0yutGm/R8Z5todoeqqOiqRsisPU6GO52yQDlpLh/+cDXHdR7C1zuWs6eqqN4cx/Q43nmc5kreRLwxSgPlMUGPMLM3LYgLeACsL/6Rr7Z/x4C2fYFIRkgkyyPkZH8EzRBe3UPACOLVPUBteavGChohCXrU4XFFgh5et06H/HR+LChD11R0TSU3y8vpI7uzeNVPBMMNZ9T89+MfnMchw+IfbyxPOrbCH8K0bFSl+UvJCSGEEEIIIUQiLdbI/O677+bTTz8FcBqTxz62bZtvvvmGW265BcOQO8CEEEII8fOlq1pcLw5N1ZzeHtEL+56an6qiOhfs1Zgm6yq1F4xVRXHWxV7c9+oecrxZZHuzaJfeBlBway5+NfJqbh1+FTcfdyWZ7nQAOma2Y2jHgc626TXZJfuqMlTlPA6EA7y7ZlbCcTPXf8Gawg1sLtlG2DSoCFZSWl0O2Fi25ZTiKqkudbZpctAjRX+Qw5XXreFxRzONFLweDVVV0DUVn1ujR4csLjylD80dl6j0h9lZWMnWnyoINSKgIoQQQgghhBD7q0UyPZYvX87MmTNRFAVVVZk4cSITJkygQ4cOVFRUMH/+fF566SUqKir4/vvvefvtt7nkkktaYipCCCGEEAecrupxTbg1RUVXIkGPaIaHrmroqo6u1Z6exfbviL1LXlVVqCkH5dO9lFFOtE+Ipmrkp+VGjqOqmJaJrmp0ze6Erur8zyl3srVsB+3T26LF7D/dvX9Bj6qQ33mcKkCxu6qIF5e9CcCpPY/njD4nEy3HZdk2QSOS/REIB6kOB/C5vE3q6WHZFuEU/UEOV5oWf69Thi/yufO6NTRNJd3n4rgBHWiT4+OJFJkbTVUVCBOsSUUqrQzSLjcNy7JRVcn6EEIIIYQQQrSMFsn0mDlzJhD5cv6Pf/yDRx99lFGjRtGzZ08GDx7MlClTeO2118jIiNS6fuutt1piGkIIIYQQBwVd1XHr8Zke0VJVXs3jLPe6vPEZIUpto+m4oAeKExDxujy4NBddsjvWK1EVPUZUpieDLG8GnTLbxwU8ImMbPi2cfPTF3Dr8l+T7cuutqwpXxz2PfR3JzPnxK/wx21m2RdAM1QRNbCfrw7JtDMt0MoeBpD0+LNsm3ISskMNVRlokCJfui/zUNRWPW6Nnp2wmjevfbMfxB2p/F6YZ+f01poSWEEIIIYQQQuyrFgl6LF26FEVROO200zjttNMSjunduzeTJ0/Gtm3WrFlDKCRlCIQQQgjx85S4vFUkoyM2myPflxMXuNCSlrdSnfJWqqLSLr0Nbs1Vr4+FrsQHPbSY7erKdGckXB6rf9vedM3uyF2jb+T8I8+MWxdb3gogx9u4xuir96xzHkeyNAynPFX0p2mbgI0ZE/jw1wmyOPuwTMJNaHp+uNJrMj/crtrPiMcdeZyZ1nz9UAIxjcwNs6ZvS0iCHkIIIYQQQoiW0yJBj4KCAgCOP/74lONOOeUUAEzTZPPmzS0xFSGEEEKIg0Js1oVbdSVs6qypmtPEHEBVYzM9YgIgdYIXsU3Qkx0zsj81LnskVtfsTuT7cpLO/7z+p8c9T3fHZ5XElrcCsLBpjBW71zqPI9kbkdJWEGlIDpGeHhApm1VtBCLlr4wgVk2Jr58qC53Mj0hDdDtpJohILhoIyUir/3nKzmg4cyeRQEyAw7TsmmXyuxFCCCGEEEK0nBYJelRWVgKQk5OTclz37t2dx+Xl5S0xFSGEEEKIg447SZCiLjUmMBJX3ipFxkasukEPTdHi9ln3WDcOm8SpPY9nSIcBDG4fKXHk1T0MbHcEx3QcFDc+o07j87rlrUKN7Kuxq2J37TZWZJtqIwBEghymZdYEMsCwTMJmmJAZJmSFCVsG/lA1/pCfippMk2iAJLov0XjRoEdmgqDHlIuP3qd9VgVqfw+GaWFZNmHD2qd9RcWWORNCCCGEEEKIulqkkXk4HPly43KlTo2P9vQA8Pv9KUYKIYQQQvx86GribIu6VEUlw5NBZbCyXnkraPjCsa5oZHoyqAhGbkjRFBWSBD0AcnzZnNHnJOf55SiQJGMj3Z0e97wq5Me2bYqrS8l0pxMyGle6tCpUTXmwkg3FP9I3vydZnoy4RuRlwQono8OwDAzbRDFDhE2DsBl2ympFt4kGSEJmuF6PE5GarkU+G9E+H7Fy0j14XFqT+3EEQyahsBkpo2VH+nlEy1xB5LnH1bi/hyjDtHDpTdtGCCGEEEIIcfhokaCHbdsJSzakYppS21cIIYQQoq48Xw6Vwco6mR4KbtWTYqsITdXI1DMImiFCRiiuXFYiXt0T1yvDpelxAYhYGe74TI+yQDn//u5VNu/dRqY73cnWaIiNzZ/mPxWZr6Jxw7DL6J7TxVlfWl3uvHbDNjFMo6apuUXACOIPR44TbV4ezfRINm+RXDTTQ1MVju3fju/W7gHgmH7t0HWVCaN78vYXG53xJwzuRJpXZ8eeStZsKUm63wp/iPzsSAAqGDIxTRvbtqkOGuwsrMLr0ejUJgNVbdz3h7DR+kEPy7IbPT8hhBBCCCHEgdUiQQ9xaHrllVeYPn16yjHBYLCVZiOEEEIIiGSFaKoWF/TI8mTGNUBPxqO50TWdNN1HyAihKSp2gswNl+YibBq4dTfVRhC7JnDgUpMHPby6B4/uIWhEzg1M22Lz3m0ATqmppjJtk3lbvuaqo7vELLWdckaGZRK2DKdReXmwkmgmSjiup0fjy2uJWppW+5m67Iz+9OuWiw0c268dAMcd2YF5ywooKq0m3atz0tGdaZcXCX49+uLXFJUlDnRV+MNO0CPaz8MwbUrKI+MDQZNAyCDNG59hkuxGqlDYIs1bb3GLMiXoIYQQQgghxCFDgh7CUVJSwsaNGxseKIQQQohW5dHccT08GhPwANC1yKme1+VBCaooioKCgqKoeHQ3gXAAXdXpmt2JnyoL8WhuNEXFqAkc6JoOCWIHqqJiYZHnzWZX5Z79f4ExfijckHSdYUYDHtHATW0Ax7JNLMtygh6S6dF0mqqgKGDb4NJVRgzqiFXTfBwFPG6N310xjB17KmiXmxZXBisv25s06LHg+wI8Lo2ObdKd8lj+QJhAsDbTOxgy6wU9qgIGGQlKbe1vT5Cmsixb+ogIIYQQQghxCJGgh3Dk5eXRp0+flGOCwSDbt29vpRkJIYQQAsCjN1zKKuX2NcGMKFVRaJuWR9AIYdqRC8+Z7nTcmisuuOJS9ZrxGpYdvUCt4HN5qQr5yfU1f9Cjrq2lBbyzZhamZXBOv9Pp16ZX0rF7A2VMW/4OhVXFnNxjBF2yOjqBH9E4uq4SDlvkZnqo8IewarIbvG6NSn8Yl67Ss1M2gBMgAcjLSp568d3aPXy3dg9uXeXK8UcyqHcb9lbEZw9XhwxyifTriJbZCgQNPC4Nlx4f5Au3cllc27axJOghhBBCCCHEIUO+BQrHpEmTmDRpUsoxGzZsYMKECa00IyGEEEIAeHT3fm2vqRrumH1oqoZLc+HSau+iT3P5UBQFVVVRrUiQI7re6/LgD/mBSLmt6PI8X27jjq+oTq+NhijElxB6b+0n7K4sBOCt1TO458Rb0JJkukxf8R6Ltn8HwLaynQztNIh26W0adVwR4dJUDMMiO8ODP2igWDZpXp00r4tKf3z2TJscH4V7Iz1gUgU9okKGxQsfrub04d0YO6wbHndtX45gKBLIqA4auF0aHpeGYVpUBw1cMZ9d07IxzdYNQNjUBneEEEII8fMVCoX47LPPmD9/PitXrqS4uJiqqipycnLIy8tj6NChnHjiiZxyyiloWuL+Yvfccw/vvvsuAFOnTmXEiBGt+RISCoVCvPDCC8yYMYOCggJCoRDZ2dlcdtllTJkyBYAPP/yQ1157jY0bN1JZWUl6ejpHH300Dz74IGPHjgVg+PDhTJs27UC+FJ588kmeeirSD/BgeX/FwalFgx4zZsxgzZo1zTo2+scohBBCCHG48Gj7F/QA8MVki3gTZI5EeyeoiopXd+MPV6MrGoqi4tNrgx5u3Y1ekwHSNj2vUcfO8WZTXL23UWNtbEJmCLfmxrItdlXUZpJUhCop8hfTPqNtwm2/3LrEeRwyQ3y9fRnn9D+9UccVEZnpblRVQVUVp9xVVrobl66haYoTcHC5VLIzPJRWBAkbVqOCHlGfLdnG0nV7+O2kY/G6I58l07QJGyahsIlhWHhcGpZlU1YZJN2rO/1GbNvGMOsH0EzLRmuhnhu2LeWthBBCiJ+7+fPn88gjj7Bt27Z66woLCyksLGTdunW89tpr9OnTh/vuu4/jjz/+AMy0aWzb5qabbmLhwoVxy4uKikhPTwfg2Wef5fHHH49bX1ZW1uiSukIcjFo86NGQ6BfsxowFCXoIIYQQ4vCjqYnvJGsKn8vnPE4U9IhSFRVd1ag2gqiqhqooeHUvoAA2btXllL1qn9G4LIoMTzqlgXKnlFZDHpzzf/Rr05tjOg6qt253ZVHSoEddRf4S53F5sJIsTwamZTbL+/lzleFzOeWlNFVFtW1ceuT90jUVs6a0VHpN/400r4uyyiC5TQh6ABSXBfhuzR5OGNLJWVYdNAmFLSzbdkpdhcIWhaXVdMiPfCm37Uimh23bkX4jSiQ4EzZMNHfLfLWxbMn0EEIIIX7O5s6dy6233ophGEAko2HUqFF07twZr9dLZWUlGzdu5LPPPmP79u1s3LiR66+/nieeeMLJgjhYLVq0yAl4ZGVlMXnyZHr27ElVVRUjRozA7/fz9NNPA5FrtJdddhlDhw7Fsiy6dOlyIKcuxH5psaBHS9wNFQ2QCCGEEEKIpnHHlLKKBDES0xQVTY0EPlRFQVO0mpJWOtneLFQU9JqgQceMdo06tkdzke3NpKS6tNHzXVe0iXVFm+ot/6mykMEcScAI4lL1lAGM6nCQymAVfiNAZbCSDFca1eEAGZ70hOMlIBI53/Z5Il8RNE2Ju9jv0lWnDFW0z4bbFfnZMT/xe5rKolW74oIegZBBKGxi1jRPj/6srA5jmhaapsatMy0bVbHRVA3DsGD/E6ISkkwPIYQQ4uersrKSu+66C8MwSEtL4/HHH+fkk09OOPa3v/0tTz31FM888wyGYXDnnXfy8ccfH9TBgXXr1jmPb731ViZPnhy3fvXq1YTDkRKmp5xyCg899FDc+h07drT4HIVoCS0S9JBsDCGEEEKIg5ee4sJ+JNNDx1XT1FxVVTRVI8uTQZYnA6i9uaVnblc6ZbZnZ8VuINIMvSJUVW+fLs3F0R0GMufHhfXWNdVPlYW8seojlu5aRZ4vm8lDf0G79HwMy6g3tjpczZ6qIud5yAzhN5IHPcJm+LAPesTSVAXbrr3pKJoBArVBj+jPDm3Smrz/uvczVQcMwkakdFXYMGt7d9iws6gK27Z58aMf2FVUxSWnHcGIQR2wFQWXDoYZyfxQW6DElW1Hsj2EEEII8fPz1ltvUV5eDsDtt9+eNOABoGkat99+O1u3buXjjz8mEAgwbdo07r333taabpNVVdWem/ft27feer/f7zw+4ogjWmVOQrQGCXoIIYQQQghHJNNDczJD3GrkZ5Yn0xmjKArt0tvgdXm5dfgvmb15AZmedPrm9eSvXz1Xb58ezc35R45rlqDHD4UbnMcl1WXM3/I1Fw08i+pwoN7YsmBF3POAGSKQYByAZVuELYOmFWn6edM0Na6uk6YpeNwawZDpBEDcrkiQKMPn5sxR3Zm1aGvcPjxujQvH9GH6p+uoS1EUVmwsojoYZugR8VlD/kB8ECsYMnlv3ka+XRMJsD355nL6dT+F7IxIqTbTsiKZH6pCKGw682oOkukhhBBC/HytXLnSeTxq1KhGbTN58mQ+/vhjAJYsWdLA6AMr9hxG1+tfBrYsK+V6IQ5V8mkWQgghhBAOVVHRFc1pnh4NftQtMxrNlshPy+WMPic5y3O8WZQGyuPGenQP7TPbcv6AM3n3h1kowHGdh7B57zaK/I1rcJ7MtztXcNHAs/A3IuhRGarCsAxs2673eizLwrQa13PkcBHJ9Kh97nFppOe52PZThRP00DUVj1vD59GZNK4/PTpmk5nmYtSgjny/sQiPruF2qQmDHtt3V/DiR6sBmLt0B7deOISMtMjnrqo6jD8QxuPSnCbm85YVONuGDYuFK3YybmQPACwrEvhwoVIdNJot6LF6czGPv7aUUNjilgsHM2JQx2bZrxBCCCEODhUVteeL27dvp1+/fg1uM3DgQMaNG0dmZibdunVLOTYUCjF9+nRmzZrF5s2bCQaDtG/fnuOPP54rrriCPn36JNwuOo/OnTszZ86cpPt/5513nEyTKVOmcNttt8VtH+uqq65yHg8fPrxewOapp57iqaeectZPmzYt5Wur68svv+T9999n6dKlFBcXo2kaHTt2ZNSoUVx22WX07t27wX1s376dl156iQULFlBQUIDX66VPnz5ceOGFXHjhhU2ajzi8SdBDCCGEEEI4ouWsPDUNElwxvUCSjY/VM7cry3atjluW5orkT4zpMYr++b2wgU6Z7SkPVrKxeAuvrHiXcILyVI313c6VfLdzZb3lZYH4oEfICAFg2ha6En9R3LQtjJpG64ZloinqYd9PTtfiy1ul1TQvd7vUuDJS+dmR369L1+jXLZe2uT5cLo2cTA80MkHip2I/0z9dxw0Tj8KybJ58YzmrNhfTLtfHjecPJi9Bo/SyylCkmTmRgEf0cXXQcDJA9te/31/JT8WRsg/PvL2CYQM6oLVACS0hhBBCHBixQYu//e1vDB06lPz8/JTbaJrGE0880eC+t23bxoMPPsiWLVvilm/dupWtW7fy9ttv88gjjzBx4sR9mfpBo7y8nDvvvJMvv/yy3rqNGzeyceNGpk+fzg033MDtt9+e9Bx7xowZ3H333YRCIWdZKBRi6dKlLF26lBkzZnDkkUe22OsQPy8S9BBCCCGEEA6XqqMoCroWOU1sKOihKfFBj755PesFPdSaMaqikO3Nwq25yfFm4Q9X079tbzpmtmdbWQH76s3VHydc7g9XEzbD9V6DZZmg1g16mE56v2EZ2Ira4Gv/uasb0IpK98W/L9FgiKYpNdtFfmqqUtuXoxHWbClhb0WA3SV+Vm0uBmDP3mrmLd3B+WPq3wVZHTQwa35nlm3HBT2ay6YdZc7jkvIApRUB8rN9zbZ/IYQQQhxYEydOdDIaNm/ezLhx45g4cSLjxo1j6NCh+1Xy6X/+538wDIMePXpw/vnn06VLFwoKCnjnnXfYsmULoVCIBx54gMGDB9OrV6/mekkAPP300wB8/PHHzJgxA4j0LIn27cjJyaG0tJT169fzj3/8A4CzzjqLs88+21nfGH6/n0mTJrF+/XogkpkyceJEevfuTTgc5vvvv+e9997D7/fz7LPPUllZyf33319vP7NmzeLOO+90ynGdcsopjB07Fp/Px6pVq3jjjTdYuHAh33777b6/KeKwIkEPIYQQQgjhqHuhP1XTcwCtTsbE4A79eWP1R3HL2qW3iRurKgrumvJZAGErvM/zbUhJdRntM9rELTNtq944y4rJ9DANLFWCHskyGuoGPaKiJa+id+/pmoppNq1k2L/fX8Wuoqq4ZfOXFyQMegRCBmZNoMOybEzLxjAtTDPyeH8zMhL18bDqf3SEEEIIcQgbNGgQV199NS+++CIQKXc1bdo0pk2bRlpaGkOHDuWYY47huOOOY+jQobjd7gb2WMswDC688EIefvjhuODJVVddxXXXXce3335LKBTizTff5O67727W13XaaacBsGbNGmfZsccey4gRI+LGZWbW9u3r1auXs11j/elPf3ICHhdccAF/+MMf4t6jiRMncsMNN3DdddexceNGpk2bxoknnhjXML6qqopHH33UKUH72GOPxWW/TJgwgSuuuILJkyezffv2Js1PHL4k6CEcr7zyCtOnT085JhgMttJshBBCCHEoqJsNoKs6lwyawOuragMfx3Y6Km6sqmo1jxXAJmy2XNBjZ8VP9YMeMb07ol+uTNt0lhuWgWqrcHjHPJLyuhN/hdA1FRSIxhr2JehQN+ARlSgAEQgaMeWtIpkeoXDkd2iaFloDAbuGhIz6EY6QIX1fhBBCiJ+bu+++m+zsbJ5++mnC4drzUr/fz8KFC1m4cCEAPp+PE044gfPOO4/TTjstaVZsVM+ePfnDH/5QL1vE5/Nxyy23cM011wCwevXqRJsf9AoKCnj33XeBSJ+TRx99NOF70rFjR/7f//t/XHjhhViWxbPPPhsX9HjvvffYs2cPAL/4xS8Slvvq0qULjz/+OBdddFHC80Ih6pKgh3CUlJSwcePGAz0NIYQQQhxC6pa3AhjacRAAm0q2cXqfE+mS3TFubLTcla5qGJZByGy+ckR1FZTvduYTZdZkdPjD1RimQZY3E9O2nFJJhmWiKHJL/76IZnsATgNygLa5Pgr3Vu/zfovL6zeq37Krgp1FVazeXMzLs9bQJtvH9RMHoSoqhmntdzPzcLh+gCMYSh30sCw7rt+JEEIIIQ5+iqJw8803M378eF5++WVmzZpFYWFhvXHV1dV8/vnnfP755/Tr148///nPDBw4MOl+x48fj8uV+C6ao446ynlcVFS0/y/iAJg1axaGETmPv+iii1IGgQYMGMDgwYNZvnw5y5Yto6SkhLy8PAC++OILZ9wVV1yRdB+DBg1i5MiRLFq0qJlegfg5k6CHcOTl5dGnT/3SAbGCwaCkkgkhhBDCUbe8laqoWLbF0I6DGNpxEL3yuseti2wTzfhQwQKfy0t5ML7p+LGdjkrYnLypCsp/qrfMtCx+qtiDP1xNjjcbiJS3smwT27YxLAMSNFgMmWHch3nJq4a4dBXVKW8V+ZmV7ubcE3vxwgf172Ls0TGLrT+V09ANe4++uKTesj17/Tz0XO2X3qLSAK9/tp7LzuiP0YReIskkzPRIEAiJZVoW6n5mmAghhBDiwOjRowf3338/9913Hz/88AOLFy/mm2++YenSpZSVlcWNXbduHZMmTeLf//43xx13XML9RftnJBJbVupQraqydOlS53FRURGff/55yvEZGRnO4xUrVjBmzBgAli1bBkBWVlbK9wxgxIgREvQQjSJBD+GYNGkSkyZNSjlmw4YNTJgwoZVmJIQQQoiDnaqqqIqGVZM94XN5qQr5AVDqZIHU9vRQ456f0ftEpn3/jjPuwgHjGdZpMFv2bqe4unS/5rezYjeWbTsX4gHKghXYNX09olkf0Z9BM0TYMlBQKA2Uk+PNcrbzh6sl6NEAl6aiqLUNzXVdpW2uj6P6tOHisX15c/YGZ6ymKvxi7BH89+Mf2LPX3yzHX/LDbi47oz+BoIGiQGZa/brbhmmhqYrTeySZRAGOhjI9DNPGJd+whBBCiEOaoigMHDiQgQMHcu2112JZFmvXrmXevHm8/fbbzs3A1dXV3HPPPXzyyScJG55nZWXVWxZ7jKhDtVzTrl27nMfRxumNVVxcDEAgEKCyshKIlMFqSPfu3RscIwRA6uJzQgghhBBCNMCl1X7JS3P5nMdqnYvK0ZR3rU7GR/82vRnTYxTZ3iyO7XQUR3cYgKIonNPvdDy6Z7/mFjRDlPj3xi2zYxqZR/t4RJubF1YVY1gmITNEaXXtHX2WbVEdri2xZElH64QimR6Rx7qmkpXuRlEUcjI8nD+mD/87ZTRnjOjGkT3yuHL8kXRsk07HNunNOoclq39i3da9VPrje8XYdqTnh2FYCTNBDDP+d5ow6NFQpocpnwshhBDi50ZVVQYMGMDNN9/MrFmzuOOOO5ygxY4dO5JmOCQKhPycRIMV+7NtbAaNz+dLNtwRmy0iRCo/778+IYQQQgjR4lyaC0VRCBohfDFBD4X4oEfdnh7RRtOaqjF56MWcWXly3Pj+bXvz+xNvxcbm7R9msnL3WgDG9jqBtYWbKKj4iXSXj6pw6l4RBRU/kZ+Wi039QEw02GHU9BWJbapu2RaWbaEqKiEz7ARILMuiMuwnyyNfuupyuTTnIoCmKnjTIpkxeVleAiETt65x7om9CceUjurUJp3vN9Svm72vXv1sHZqqcMdlQ+MCKmHDImxY2ICNhUuPv/8rEDLJ8NUuC4XrBzAak+khhBBCiEPDihUr+PbbbykuLuaMM85gyJAhDW6j6zo33XQTW7du5Z13IpnKa9as4cwzz2zp6SZ0IEtjeb1e5/FXX31Ffn5+k/eRk5PjPPb7G878DYVCTT6GODxJ0EMIIYQQQuwXt6rj0Vx4NQ+6qjnlrtQ65a3U2F4exAdBdC3xaalHj5Qnuuyoczm24yDcupteud04pefxFFUVk+PN4n/mPp5yfq+u/IB31nxCjieTywefR/uMts460zKpDFbxzg8zqQxVcXKPkXTO6hC3XtVUgkYQoyboURX2O0ESEc+lq06oy+fRnQCIoihoNSkgHrcWF/TomN+8mR4ApmXz0YIfOfHoLk5j8bBhYZhWpH+IVr+0VTBkkOGrLV8WMvYh00MygIQQQohDxvz583nyySeByLlKY4IeUWPGjHGCHnv37m1gdNMpihLpNWekPuesqKhIub4ltW3blnXr1gGwefPmfQp6eDwesrOzKSsro6CgANu2U5Yg3b179z7PVxxepLyVEEIIIYTYL7qm49O95Piyap5HMjjqfmFxMjuivT1qnuuaXi9AUpeqaPRv24deud0i26gaHTLb4XV5ObrDgLixbdLy6m0fNILsriri74te4Llvp1MRrAIimR4vLX+Lr7Z/x4rda3lx2RtOcANqy1+FzDBWTVZIdTiAYcdf/I7d5nDm0tS4QEcsTYv8jj2u+EbfHds2f9ADYO3WvU7gIhAyCBkmhmlhWhZh08IfCMeVsKqbxdHU8la2bWNKpocQQghxyBgwoPYccsaMGQ0GGGJVVVU5jzt37tys8wJwuyM3/jRUQioadDgQYoNEc+bMaXD83/72N+69916eeuopCgoKnOXHHnssEHlPV6xYkXIf33zzzT7OVhxuJOghhBBCCCH2i1fz4NbdTuBCVxIHPSAS+IiWmIpmeqS5fKikaiqtkOZOXuP3uM61X7jyfNlce+ylKee7ee82vtr+LRDp7zFvy2JnXWXIz+aSrc7zaPmrkBkGbEzLjCt1FVUeqDhkm1A2J1VVnMyKuiLNw8FdJ+jRJtvHEd1yku5zwuie+zyfaImq8qqQU94qbFiYpk0gZFJUWlsaLWxacX09QkbTyltZlo0lnwEhhBDikDF69Gjy8iI3yxQUFDS6GbdlWbzxxhvO87Fjxzb73KJZE1VVVWzZsiXhmLKyMhYsWNDsx26sM844w3n8xhtv8NNPPyUdu27dOv7973/zzjvv8M9//pP09NqbXmJLgz3//PNJ97Fjxw5mz569n7MWhwsJegghhBBCiP1StzSVrkaeJwpk5HqznUwPXdXJ8maS6812Sl5FRbNCIo9V0l3Jgx6987pzx6jruHjg2dwy/Jf0zutOjjcr5Zy/+HFR0nX+mIbl0eBGtNeHaVuELcNZblgmpmVSHpSgR2N43BpagtJS1547iEtPPyLhNrElp5oqUs7KptIfyeoIGxamZWOYFsGQiT9gEAhF7uq0LBsjJtDR1EwPy5aghxBCCHEocbvd3H333c7zZ555hgceeICSkpKk2+zdu5ff/OY3LFu2DIDzzjuPPn36NPvcYrMo/v73v9c7z6yqquKuu+6itLS02Y/dWP379+eUU04BIhkpN910U8LyU3v27OG2225zXsMll1wS18tj/Pjx9O3bF4BPP/2UZ599tt4+9u7dy69+9Svp6SEaTXp6CCGEEEKIZhUNWCQqWZXlzXQee3Q3Hr22FJWqqDUXjW3yfDkUVhU7+0tLEfQAaJ/RhvYZbdBVHZeqc+GA8bz1w0zKAuUpt0vUg8GMKV1l2CaGaTilrYJGENu2MCwT27b5qWIPuqZHmp5jyx1FDfC4dHSt9l3KSHORle5mZ1EVIwZ2JCvdw3PvrXTWH9u/Hen7EfQIhAwM041l2QSCJpqmoFoKtqY4TcfLKkN4cjVM0yZkWFSHAuRmehM2Mk8UCIFID5GPF/7Ixh1lXHhKH3p2yt7nOQshhBCi9UycOJFdu3bx+OOPA5GMhQ8//JBRo0YxbNgw2rRpg67rFBcX8/333zN37lyn5NQxxxzDww8/3CLzuuSSS5g5cyYAs2bN4tJLL+Xcc88lKyuLjRs38vbbb1NYWMjRRx/N8uXLW2QOjfHoo49y4YUXsmvXLtasWcP48eM5//zzGTx4MJZlsWbNGt566y2nHFivXr2444474vbhdrv505/+xJVXXkkgEODxxx9n/vz5TJgwgdzcXNavX8/rr79OSUkJ+fn5FBcXH4iXKg4xEvQQQgghhBDNKtp8PFUTwkQURcGruwmEA6S70ygPVhI0gmiKVlMWqzYooqs6hlW/7rKqqmiqRt/8ntw9+mZ+2LOel1e8m/SY1Uag3rI3V3/MhuItjOtzEpmeDAJm0FkXMCKPLdskaAQJmSFCZuSOM9u2AK3e/kQtr0dzGpprmkLb3DQ0VcHn0akOGPTrnkvfrjls2F6Kz6Nz4tGd2Z/kiZLySAAj6ts1u3lz9gZ0TeGyM/pzZI88qoMGphU5SHFZNaZpk+Z1EW5CI/MPv9zMCx+sBmDxql1MfWgcad59D9YIIYQQovXcfPPN9O7dm//93/9lx44dVFdXM2fOnKR9KnRd58orr+S2227D6/UmHLO/Ro0axe23384TTzyBbdssX768XnDjhBNO4OGHH26R8lqNlZ+fz+uvv87tt9/OsmXLqKqq4uWXX0449thjj+WJJ54gIyOj3rrBgwczdepUbr75ZoqLi1m6dClLly6NGzNw4ECuuuqquOwcIZKRoIcQQgghhGhWaS4fuqo3OeihKippupewGUZVVHy6tyboUdMrpGafQSOI1+WlMli/saOqqE5PEVVRGNS+HyO7DGXxjmX1xgFUh6vr7QNg+U+rqQhVctvIq6n2lzrLo0EPiC+DBUh5q0bwuDQURUHTFPKzfU4AxOvWqA4YqIrCTRcMZnexn/b5aaiKEtd3o6l2F/vp0SFS6sy0bN6Zu5HqYCRY9t68TRzZIw/DsJwMjmgjcn8gnDDAkaynxwsfrIob8+nXW5l4cvOXuhBCCCFEyzjjjDMYM2YMn3/+OV9++SVr165l586dVFVVoaoqbdq0oXPnzpx00kmceeaZdO3atcXndMstt3DSSSfxyiuv8PXXX1NYWEhGRgb9+/fn/PPP55xzzmHv3r0tPo+GtG/fntdee43Zs2czY8YMli9fTnFxMaZpkp+fz1FHHcWECRM444wzUn4/GDJkCDNmzODll1/ms88+Y8uWLaiqSvfu3ZkwYQJXXXUV8+fPb8VXJg5lEvQQQgghhBDNLsuT0eT+Bqry/9m77zgp63P//6+7Tdu+7NJBOqioqAiCHT3GllijRowlGmOKyTeJMfEXjZrkRE05Keo50ahJRIm994KFYLCCAgosvbO9z8xdf3/MzL0zO7MNlt1FrmcePJiZu8xni2Tmfs91XSphI0xrsvoiVTGSmvehqYmKD8d1COnBDkOP9HkgAGV5pVn7uZ7LB1s/4b0tSztcz9rajTTHW9DTzpea7QHQYrVmnhMJPbqSGmKuayoFkbZKiHBQp454Yt6HA8PK8hhUFKKmMbZbMz3+9PAS/vqzOQA0t5o0t7b9/CrrWrEdF11TaY1lVg1ZlouVo71VZ4PM0+2sae16JyGEEEIMKIFAgNNOO43TTjttl89x2223cdttt3Vr31WrVnW5z9SpU7n11ls73F5aWtrpea655hquueaaDrfPnDmz0+NHjhzZrXVCYqD77ladFBcX873vfY/vfe97ObefdNJJ3V6P2LdJ22EhhBBCCNHrCoL5WcPJu6IoCoaqE9YTbQKCehDAr9zQVA1d1TA0g4BmoCptYURq5oeqqCiKkjFP5JAh+/vD09M98dlLbGnc3uma6qINHW5LD0BAKj16oig/mPFJv1BAByXxd8BIhlyaiqGpBAO71zJs4dJt1DfFuTttVkhKczTxM/xkTRXPLVzH5xsSg0tN28k5v8PM0fJKCCGEEEIIMbBI6CGEEEIIIXqdpmrkB/J6dIyuaKiqSl4ywNBVDV3VUZOVFqn7RvJP6n5BMJ+QkQhK0lthpRQE87l02rm79HXURrvfMiA17Hxf153vQ3qVB4CqKgR0lYChkh9JVPhoqoJhqIRD2cXppYXd75+9cXsjj79ZwfbqlqxtTa0m26qb+fO/lrDgo83c8/Qy1mypx7RcTHvXKz3oWWc3IYQQQgghRC+S0EMIIYQQQuwRutqzT+gbWuJCeCDZ1gogbITaggwlUekR0oN+q6uScBHleYPS5nioWecAmFQ2jomlY3r8NdRG6wFYV7uJF1YvYFX1ug73lUqPpG58G3L1cw4YGoauEUmGHJqqkBcyGDYoOzwbNSR7AGZHtle3sGJdTc5tzS0mb320JWPJz7yzFtf1/Nkf6dLnfFjJUKSj4eZCCCGEEEKI/iEzPYQQQgghxIBgaNkvTSNG2A89tGRlh6alqjkSAUhqGySGlwOEtADtJ37k9bDyBOCZla+hqzpPfvYyHh4LN77PRQefRVGwgOEFQzLWLDM9Ely8XfpkVdDQCOgqhp5sZ6YpFAWDOfedOKqETyuqs77jI8rzGT20gP8sa2tbtnZrxy3KmlotVm3KrObZUpn4zalvjmftHzftjNuGHqA1amXt19mQTiGEEEIIIcSeJaGH8D300EPMnz+/033i8ew3f0IIIYQQvSGgZg+sDhshXDfxiXo92dIqVc0R1AJ+dUgq9EjN7mhf6QH4AUlPPfHZSxn353/6NAAjCobwnRmX+M/tddDWyXEdNFXDdCwC2q4P5d5reB6e5/X4wn8woGEYGpqqoGlKxkyYH110GP8z/2MAyopCzDxgKBWb6/ikohqA6fsP4aIvTUZBQVHh/c924Dhdh1BPvr2GSDD3W6JcraziacPNY6ZDfqRtLkg6qfoRQgghhBCi/0joIXy1tbWsWbOmv5chhBBCiH2UnqPSQ1VU1GRlh6HpGQPKI4FI27Ht21tpBonBCm0Xn4M5gpDdsbVpJ2tqNzK5bBzQ8YXumB0nLxAhZsUIaIa/n4eX8fX0RCpIGYi85P+UHg62CAV0VDVxjKGraGrb8SccPgpNVdlc2cgZR42jpiHGt889hPeWb8fz4LjDR9LcamHbLpGgQXlxmB01rV0+Z9x0coYbruth55rpkdbKKnU7V+jR7dkfQgghhBBCiF4noYfwlZaWMmHChE73icfjbN68uY9WJIQQQoh9SVcBQPvt6TNDVFVFUVS/kkJN3jYd099nXMko3t6wuBdXDOvqNjFp0FjW1W2iKlTLzJGHZu0TtWPkBSJE7RgFXj5N8ebEOhVll6tPWqwoET2UMygaEDx6PMxbTQs5gkb21zV1/CAmjCqiKD9IfXOcUEBj1kHDaYlaGJpK6vBgQGNwSaRboUdH/veJTwgGskOl9PZWtu1iOy6tsezQIyahhxBCCCGEEP1mgL5LEv1h7ty5zJ07t9N9KioqOOOMM/poRUIIIYQQ3RfUjIwQoH3oMXHQOA4ffhCf7PicgKbTasV2+zk1ReWpz1/m/a2fAHDW/l/ipPHHYDsWmqpRnjeIuJ1YQ8yOE7fjNJsthIxQop3XLoYeUSuKqijkD8DQwyNR7bE7QsHswEFTFX9mS9DQ0FQVTU08j6oqiVkvlkvA0BhUFNqt5+9oDkjMdIhbDkFDw3JcTCt3pUgsLRzZlVZfQgghhBBCiF038N4lCSGEEEIIsQvy0tpdAVnzM1RF4asHns65B5xGfayB3/77r7v9nAvWv5tx/7U1CznSr/ZQKAjGcVwH13VxXIdms5WYHcd2HQqC+bv8vFErjq7q5O/CcPY9zvPwdvMafyhHlYWmKn54EDQ0dE3BdhR/m5bWGqukYPdCj46YpkNzq4meHwQv2R7LyhF6xB0cxwVFwbIdQoHuv+2qaYgyqCjcm8sWQgghhBBin7JrTYSFEEIIIYQYYPKMdqFHBzM8VEUhomdfFA9qAa47+mp+PPsqxhaP2qU1tFitOG5qFoRHQ6wJx3NxvMSF8SazBQDbtbGcRFukWA8rTmzXwfUc4nZ8l9a4p3kAuznI29CzQw9VVVDSWlipaUGHqrRVgeiqQuluVnp0xANqGmNYTuJnHLcczA4qPUzbxXFcbMfDsjP3icXtrGMgMUukrjGO48ogdCGEEEIIIXaVhB5CCCGEEOILof18i4DWFnq0nwcSyNFWqihUQGm4mPK80t2qwrDcthkPUSuK57lYbuIit+e5afslHms0Wzocgp6L6yYuoNvuwJwbkRpk3tvUdu2tdE3154BomoKmpW6rlO2h0AOgpi7KztrEvJAOKz1MG9NysJ1E8GHZLp7nUdMQBaCuKU40LfiwkkPT3eTvgZXjnEIIIYQQQojukdBDCCGEEEJ8IemqxuD8MkChKFQIKCjJ8EPNMWMhlFb9URDc9bZRqQqOdGaOxyzHxnEdWs1WnLQwpCupfd3drKbYY/bQujRV9Ss9NE3F0NtCj1SlRyr4KCvuvD2UAnzvvEN6OmsdgJaYTWVtK+8s2cLfn1/B355ZnrVPzHQwrUSVh+24mJZLY4tJY0tivotlOzS1Jm6blkNLNPH74SYrPEy7+78PQgghhBBCiEwy00MIIYQQQnxh5QfyiAZj5AUitJitGJpOi9mac9+IkRZ6BHa90sN0slsX5Qo9XM9hY/0WABzXQVezWzrlkmqVtSeqKXrLnliZpip+yAGgKGntrZLbUvdLC7MrPWYcMJQxwwpRVDhy6lDwFEYOKWDzzqYeraO6PsrdT31KLEdbq5RY3Ma0neTcEQ/XdbFsBycVgtgukPg9aYlabZUeydCjfTssIYQQQgghRPdJpYcQQgghhPhCKw0XE9AMAnoga+5HurDRW5UeZtZjZo7H0jk9aFXlJmeGeJ7bo7ZYfSFqxXhu1eu8uGpBzqBndyRCjcy3L6qiJGd9KGiaiqYltueq9FAUmHXQME44fBThYGLI/ZePHpuxz9Rxg7pcxyuLN3QaeECq0sPBSra3ipk2rfG2kAOPZCVIogIk1SLLb28llR5CCCGEEELsMqn0EEIIIYQQX2hasoIirAcJpc3yCOlBYmnDwCeWtl0ALwoV7PLzmW6OSg+78wDA9jq+iF4XbSBshPy1p7fCcj0XTelehUhf+P2iu1m2cyUAmxq28v1Z3+jV8+taZkMqVVX8xyJBHT1Z6aHkaF+WekxLqxaZMLKYk44YxYcrKxkztJAvHz2O5etqOl1DdUPXg+ctO9HSKhRoa2+VkmplBbCzthXLdv0KltT8clNmegghhBBCCLHLpNJDCCGEEELsE/IDeahplQKnT5rj3w7pQU4YNxtIDD0fUzya4lChv91QjW4/T66ZHl01fEpVekSt7AvqDbEmmuLNbfumBSRuD2aB7GmtZtQPPAD+vemDXn8OQ898+6JpKoNLE9U7qqoQCrZ9puvkmaMz9j1y6lA0LTn7I/l7oCgKpx81jpuuOJLLzjiA0l4cgO4PMnczf/ataQPMo7HEbdf1sGzXb29lOwOrgkcIIYQQQoi9iVR6CCGEEEKIfYKiKCjJYeae5zJ9+MFEjDDbmnYye/QR5AfyaIg1YmgGhmbwvZmX8smOzymLlDCmeBSPLH+ONbUb8DwPK1nNMaxgMAWBPFbXrPefJ3fo0TnHdbAdmx3NVYwqHIauJV6m246N6zm0mFHKIh6KovjtrYAB1d7KdHu3nVUuutYu9FAVtEDutzTnnDCRJauqqKqPMuOAoYwdXoihq37w0V4oqBON2UzZr4SVG+v8x/cfU8rnG2p7vFY/9GgfYHTwI7OdttDDdT08z8tZsSKEEEIIIYTonIQeQgghhBBin6IpKrbnoigKR446jKZ4M/nBfDQlcUFdVzU0RSM/kMdRo6f7x10y7Vz/9o6mSmqjDUwcNJZHlz+Xcf72syxidpznVr3O1sYdHDHikIxzptieQ22sAc9zaTJbKAkX0Wy2oJBqe+RgORYBPZDRCmsgVXr0hZ6EACPK8/nV1bNoaDIJBXUMXUXX1ESlh5Zd8B5Jhh5zpo+iYnM9jusxYWQxJxw+cpdCj7jlYNkua7fUEwkZjCjP73R/x8mc0eK4XlY7LyGEEEIIIUTXJPQQQgghhBD7FFVVwYWAFkBXEy+HEwOxExfCDdXA0HSId3yOoQWDGVowOLG/ltn6Kr3iwfM83tnwHh9tWwbAc6teZ2zJKIYXDMk4Jm7HcZIVHC1mKyXhIqpb6ggbbTNIbNchABmVHu4AqvTIVXXium5GS7G+FjR0AoZDKKihoCRCD1XJmOvh7xvQUBSYOKqEX1xxJLUNUUYNKaCmsesZHrnETIcHXvyMTyqqAThvzkSOOnh4h/tv3tnMQ698TkOzycRRxVx82v6UFvReuy0hhBBCCCH2FRJ6CCGEEEKIfYqmaAT0ACEt6Lc5UhUVVVFRFBVd1QjpQb8NVlfahx6Wk2h9ZTomf//4MdbXb87Y/pfFf+fw4QfhuC5zxs5icH6ZP9MjdVyz2ZJsa9XqP56q8Ph05+e8uHoBYSPMt6bPZXzpfgOiDZKTYxi74zmo/ThGMBVuhIM6tu2ha2qiJZamkB8xMC3HHzJu6CqGrmLaLoV5AQrzAgD+3z31x399nHH/iTcrcoYelu1i2g7/eH4Fa7Y0ALBheyONrSY/uXg6QUPDcVw0TZWWV0IIIYQQQnSDhB5CCCGEEGKfoikqxaFCgnrADxXUZGuroGagazqGZjCsYDDbGnf4x+mqju3aWecLZIUeFpUtNdz13j+JO2bONaQqP7Y17eT/zboia8ZEbWt91jGO69BqRXl42XPE7ET1wV8/eJD/PvEnBNMqQvpLenCT/lj7UKgvaZqKokA4oBPDQdcUVFUhoGsMKY3guh5bq5oxLRdNVYmEDApUhZqGtuqOgl0MPdrzPFi/rYHykgiO45IXMthW08K9zyynqTX79+T9FTtoajEJFoeJmg75YZW45RDKMcPEcVxUVZFARAghBqg1m+v7ewl9ZsKo4j3+HF//+td5//33Abjpppu46KKLunXcnDlz2Lp1K4cccgiPPvronlzigDR58mQATjvtNP74xz/22zrWrl3LHXfcwYcffkh9fT0lJSUcddRR3HbbbQA888wzzJs3j/Xr1+O6LuXl5Vx33XW8/vrrPPXUUwB8+umnBIN9//o39Ts0Y8YM5s2b16Nj039vczEMg7y8PIYOHcphhx3GmWeeybRp03Zzxd1jWRabNm1i/PjxffJ8fUFCDyGEEEIIsU/RVI2gHvCrO6At9CgI5vstrwJq28V6XdUpDhdR3VKTdT5DzXxJ/WLFm7xY8Wa31lLZUs2/Pn2akUXDOWr0dHRVA8gZrjiuw/Kdq/zAA2BTw1Y+q6rg0OFTu/V8e5KToyom12N9KVHVoRIwNGzXQ1cVFDURfABomkJ5SYTt1S2oqsKgohCW7WaEHmVFYQaXhKmsi+72ev7y6NK2tWlK9pDzNJ4HO2paKcoPEovb5IcN4mZb6NHUalIQSQQyMdMhYKgYurbbaxRCCCH2Jn/4wx848cQTGTJkSNc7i363fft2LrzwQhobG/3HKisr0fXE65sHH3yQX/3qVxnHbNy4kZKSkj5dZ3+wLIv6+nrq6+tZuXIl8+fP59RTT+Xmm2+muLh4jz3v4sWL+eUvf8mpp57KNddcs8eep69J6CGEEEIIIfYpYSOUFXakKi3yAhF/eLiqqn6Lq7xAxA8kILPqo32lR08tq1zFsspV1McaOHPKyR3uZ3sOyytXZT3+1obFGaFHf83RSJ81kpKr+qMvaVpifoeqKoSDOq6bHTKEAhqGnvh+KYpCwNDQdRXbTnw9uq7y47mHc8Nf38WyOw5x5p4yhYdeXtnttXUWeKRsq25m3IhCYmayZZrV9vx1jbG00MNGVQ0MeXcnhBBiH9Pc3Mwtt9zC//7v//b3UkQ3zJs3zw88Tj/9dC644AI0TWPQoEEA/s8xEAjw05/+lKlTp9Lc3MzUqVN57LHH+m3dve3pp5/OuO95HvF4nJqaGlasWMETTzzBzp07eemll9i8eTMPPvgg4XC419exfft2Lr300l4/70AgL4uFEEIIIcQ+Jay3DYfWssKPzLBAU1Rsz0VXNTQlEXroqk5eIEJDLPGGrbfaN32w9ZNOQw/Hdfi8ak3W4xvazQyJOXEiau+/KepK7pke/V3poaJpyaoOVUFVEhUU6RRFIS+c+TMMBTSakwGHqsABYwfxlx8fz4MvrWTRp9tyPteQ0t7/nu+oacGyXWJm4nsbtxLhh+N6mJZLLG4TCurELYeAIVUeQggh9k1vvPEGL7/8Mqecckp/L0V0Yd26dUCildN///d/Z1zIr6uro6YmUVX9pS99iYsvvrhf1tgX9t9//w63nXTSSXzzm9/kRz/6EW+++SbLly/npz/9KX/5y196fR2O078fUNqT+m+qoBBCCCGEEP0gfe5BKuRIBRrtaWpb0JEKSPKDef7jsPuVHim26+C1vyKfJm7H2VS/NevxmBVvt1/uOSJ7WkczPfqTriloaVUvitLW2ipdQSTzZ5gKEEoKg/7xmqpy3pyJ/PKqWZx5bGa/42OnjaCsONLby6eytpXWmA1eYm5HPBl+2E4ikIlbyTDEdPzKFCGEEGJfoeu6/7ru17/+dUbLJDEwtbYm5umVlpZmVS5Eo22tREeMGNGn6xpoIpEIf/7zn/05LK+88gpLlizp51XtXST0EEIIIYQQ+6xU6JHeuipdKtwwVN2/HdKC6GkhSX4gr9fWYzpWh9vqY014ZIciMTue0Voqbsez9ukLuWd69G/ooSgKAb3rtzztZ2EEdBUUKC0Mtc3/SP5dEAlw5IFDGTooEXIU5wc57tCRDCoMkSNP2S1V9VFaY4nfCct28ZLhRyrgsGwXy3ZwHA8rGYS0xqxO23AJIYQQXxTFxcWcf/75AFRVVfHb3/62n1ckupL6gE9qhke69Nezubbva4LBIDfccIN//69//Ws/rmbvI79BQgghhBBin6WqiWHmWgehh57W0kpRFFRFRdd0v4KhOFREaaQ467jZow5nQ/0WtjXtzNoW1kNE04aRp2uxWgnqAf++53k4noOu6jSbLTmPiTsmjuugqiqu63YanOxJzgCc6QFg7ELbJ11XCehqZlVQWqIRCurc8I0ZbNjWxNBBETQ1MUS8tChMdf3uDzxPqa6P+u24UlUdtuv5lR6m7WRVf1i2S3PUYnBJ71eeCCGEEAPNT37yE958800qKyt5/PHH+cpXvsKMGTN2+Xx1dXXMnz+ft956i/Xr1xOPxxk0aBCHHXYY5557LkcddVTO4372s5/x1FNPcdhhh/Gvf/2LlStX8o9//IPFixdTXV1NUVERhx56KBdffDFHHnnkLq8PEnNMHnnkEd544w0qKiqIRqMUFhYyfvx4TjjhBC644ALy8jr/UI7neTz77LM8/vjjrFq1CtM0GTp0KHPmzOHyyy+nvLw865ivf/3rvP/++5SVlbFo0aKc543H4xx88MEAnH322dx2220AfsVCytatW/3HRowYwdatmdXMd955J3feeScAt956K+ecc043vjOJ53/44Yd59dVXWbt2Lc3NzZSUlDBt2jTOOeccTjjhhE6Pb2lp4ZFHHuGFF15g48aNKIrCAQccwGWXXdblsXvCjBkzmDBhAmvWrGHRokXEYjFCoVDWfp9++ilPPfUUH374IZWVlTQ3N5OXl8fw4cOZNWsWF198cVb1TPufSfr3fNWqzDmCra2tPPHEEyxcuJBVq1ZRX18PQFFREQceeCBnnHEGp556ar/MFeyIhB5CCCGEEGKf1ll7Kk3VUBXVfwGvqRqGmnoJrVASLiLPyL64XJ43iC9PPonrX789a9vPjvkON7/5x5xVGy1mK6XhYgDqog38c+njVLXUMnPkNCYNGpdzja7nEnPiGLqB7drYbuoiuI2u9d3L/YE40wMgaPT8zVdA1wgGMr93WrsyjsJIkBHlHgV5AZpaTFQVZh80jGcXrst5TlVVcg5S70xlXaLSIxIy/CHmjuP6lRym1TbvI1X9YTsuza0WZUVezlZeQgghxBdJQUEBN954I9dccw2e53HjjTfy7LPPEgwGe3yuN998k5/97Gf+Bd2U7du388ILL/DCCy9w6qmnctttt+W88JzyxBNPcNNNN2FZbR9Eqa6u5rXXXuO1117jmmuu4Xvf+16P1weJsOCSSy5hy5YtGY/X1NRQU1PD+++/z/3338/f//53Jk6cmPMcsViMq6++mrfeeivj8fXr13Pffffx7LPPdnr8QFVRUcHVV1+d9b2prKzk1Vdf5dVXX2XOnDn8/ve/zxkKbdiwgW984xtZAczixYtZvHgxV1111R5df0dmzZrFmjVrsCyLJUuWMGvWLH+bbdvceOONPPnkk1nHNTQ00NDQwOeff86//vUv7rrrrg5Du84sWbKE7373u/68lXSxWIydO3eyYMECnn76af7v//5vwFTpDIxViAHhoYceYv78+Z3uE4/3T7sEIYQQQog9JaAFOtymqRq62vaSOagHURQFXdUI6AaKomRUZqQUhQoyqgRShuaXE9QDHDJ0f5bu+Cxr+5bG7YwqGg7Am+vfZUdzFQDvbv6IFqvjCoJWM0pBMB/bcwCPmB3HdCwKtfwOj+ltA3GmB2S3ruoOVVXIC+lZj6ULBpLtzgIaTS2gKgqXf/lAAobGklWVrN3akLH/b64+isXLt/Phyp1MGFnMIRPL+dszyxIzOzpxy72L+dbZB3Pg+EEANEettuoO2yUaTxxvO57/t+t6tMYs8iOJ303XlQBECCHEF9fJJ5/Mf/3Xf/Haa6+xYcMG7rrrLn70ox/16ByLFy/mmmuuwbIsDMPgq1/9KnPmzKGgoICKigr+8Y9/sGbNGl566SWi0Sh//etfc77WW79+Pb/4xS/Iy8vj0ksvZebMmTiOw+uvv86DDz6I67rceeednHTSSUyZMqXHX+v111/Pli1b0HWdyy67jKOOOor8/Hyqq6t54YUXeP7556mqquLHP/4xTz/9dM5P3i9YsACASZMmcckllzB+/Hi2b9/O/fffz/Lly6mqquKGG27gkUce6fH6OvL0008D8POf/5wVK1ZQXl7O3/72NwD/e15ZWekHCxdccAFf+9rXABg2bFiX59++fTsXX3wx9fX1hEIhvva1r3H00UdTWFjIli1bePrpp3n77bdZsGABP/jBD7jnnnsyvjfNzc18/etfp7KyEkVROOusszjjjDOIRCIsWbKEe+65J+uYvjJ+fNssuZUrV2aEHn/605/8wGPatGlccMEFjBo1CoCNGzfyr3/9i+XLlxONRrn++utZsGCBH0o8/fTTHX7PU6qrq/nmN79JU1MTkUiECy+8kJkzZ1JSUkJVVRUfffQR8+fPJxaL8c477/DYY49lnaO/SOghfLW1taxZs6a/lyGEEEII0acCOUKLFF3VCBltnxQM6YnbqqL6t4M5QpPiUGHO87nJyoevHng6EwaN4fEVL2Zsf2bla2xu2M5XpvwX72/9JGPbJzlCkpTWZCCSqvJojDf7FSmmY/XasPXOuDlnevR/pceuioQyv2ftKz1CyUqQYLJ9lqIo6JrKcYeN5ODxZfzib//J2D8Y0DjusJEcd9hI/7EbL5/J7Q9+SH1Txx8sMm2XOx5bysjB+Zw2eyzjRhT5zwnQ1Gry3vIdqIrC0EFhv81VXVMcTVMJB3Vipp319QghhNjzPl1Txf898SlbKpv7eyl9ZuTgfL597sEcPCG7PdKedOONN7J48WKampq47777OO2007odKti2zQ033IBlWQQCAe69915mzpzpb582bRpnnnkm3/ve93j77bd56623eOKJJzjvvPOyzlVXV0dJSQmPPfaYf/EZYObMmQwfPpzbbrsNz/N4/vnnexx6bN26lffeew+A73//+3zrW9/K2D5nzhyKi4t58MEHWbVqFcuXL/dbTbV33HHHceeddxIItL2OPfnkkznvvPNYuXIlS5cuZcOGDYwZM6ZHa+zI/vvvD+BXWAQCAf+xlIKCAv92eXl51vbO/OIXv6C+vp6ioiIeeOCBjO/twQcfzGmnncadd97JHXfcwcKFC3n22Wc566yz/H3+93//l8rKSiDxuzR37lx/22GHHcbJJ5/M1772Naqqqrr/RfeSwYMH+7fTq5AaGhr4xz/+AcChhx7KvHnzMIy213tHHHEE5557LldccQWLFi1i586dLF26lOnTpwOJn0lX3/P77ruPpqYmAP74xz9y/PHHZ2w/6aSTOPHEE7n44ovxPI9XXnllwIQeA6fRluh3paWlTJgwodM/6f9gCyGEEEJ8EeQKLVIiRpiySKl/PxV0AET0MJC7PVYq9DigPLMtwGHDpgKJCpLpww/m6NFHZB378fbl3LH47z34CvCrQGw38an/FrMVx3VwXAerj2Z85BxkPgAqPXZV+8qI9Pu6rqKqCpqmYBgaqqqQ+rCnokBBXoBJo0v8/WdNzf0JxVBQZ8LI4m6tZ0tlM/c8vYzfP/ghLdG2n+m8Fz/nybfW8PibFfzp4aV+m6u46dDYYvq3hRBC9L27Hvtknwo8IPH/V3c99knXO/ayIUOGcO211wJtLX/cHPPGcnn99dfZvHkzAFdddVVG4JESCAT47W9/S35+oor2/vvv7/B83/jGN3JePzvvvPP86pD2MxO6o7q62r/d0fW5yy67jAsvvJDrrruOkpKSnPsoisItt9ySEXgAGIaREeRUVFT0eI39Ye3atbzzzjsAfPe73+0wTPrOd77jV008+OCD/uOu6/LEE08AifAgPfBIGTVqlP/71dcikbZWunV1df7t1atXM2rUKILBIFdddVVG4JGiKAqnnnqqfz8V7HRXZWUlZWVlHHjggVmBR8r06dMZMmQIADt3Zs8z7C9S6SF8c+fOzfkfdrqKigrOOOOMPlqREEIIIcSeZ/Rg7kV6wJGqAMk1BD2sJ/o8nzT+aCpqNmC5FvmBPI4YMS1jv7xA7mHTNdH6bq8JoDa5f9xOXOT2PBfbtbFc2w9C9rSc7a1yzPnYW6mqQiioEYs7GHris2O6pqKpCrqm+NUXavJixhVfPpD3VuzA0FWOOGBoh+ctjHQcuuVS3RBj8fLtnHjEaKIxm+Xr2vor/2fZds6dMxEtuYbUcPmoaZP7socQQgjxxXHBBRfw3HPP8eGHH/Lpp5/ywAMPcNlll3V53LvvvuvfPv/88zvcr7i4mFNPPZXHHnuMtWvXsmPHDoYOzf7/+I7mJhQUFFBUVER9fT0tLS1df0HtjBo1Cl3XsW2b3/72t4TDYY455piMGQqjRo3illtu6fQ8EydO7LBl1H777effbmhoyLnPQLNw4UL/dnrrp/ZUVWX27NmsXbuWFStW0NzcTH5+PsuWLfMrKNIDgvZOO+00br75ZqLRjlvO7gmmafq309trHXHEEbz00kt4Xucz49KH0qefqzv+8Ic/AHQZIJaVlbFjx44en39PktBDCCGEEELs01Rl14qfU8eV5w2iLFJKdWstAFPKxvuf4hteMIQfzr6C7U2VjCkeSV4gnHGOXEPQd8Wd7/3DnwWSYnsOttM22Ly9mBUjZHQ8hLOncg4y34srPdrTVJXykgjbq1sIJed5BJJBRyRkEAom3lrpmkoch4ChccyhI9A11a++yKUwr2ehB8Dzi9bz/mc70LXs393GpjglhYmfq5Oc82FZLo7jouXYXwghxJ7z3a8ewl+f/JTNO/edao9RQ/K5+pzcLZX2NEVR+OUvf8lZZ52FaZr8+c9/5r/+678YMWJEp8elKhoGDx7sf2K9IwcffDCPPfaYf1yu0GP48OFZj6VEIhHq6+txnJ6/RiotLeWcc87h0UcfZfv27Vx99dUUFhYya9YsjjrqKI455phOnzsl15pT0gfA23bffHBmd332WVsL2C9/+cvdOsZ1XbZu3crkyZNZt26d/3hnLccCgQATJ07k008/3fXF7oJUeymAwsLsFrrps2VqamrYtGkTGzduZM2aNXz66acsWbLE397d6qf2UmGLaZps2bKFTZs2sX79elauXMmHH37oD4/vKoDpSxJ6CCGEEEIIsRtUReW6o6/mgaVPoKsap0w8PmN7abiY0nBxzmNzDUHfVS+tXpDx3I7r+NUeudRG6yn2ioi0C2J2lZPjTdTePNOjvVR1RyigEU4GHIHkY8UFwaz9dF3F8zwCuorrJoaL57IroQdAZV3uTxnWNbeFHqnndD0P03YJtws9YnHbD2tMy/FDHCGEEL3j4Anl/O91J7Jmc31/L6XPTBhV3K/PP378eK6++mr+8pe/0Nrayk033cS9997b6TGpT/kPGjSoy/On79NRJUR6O6L2Uheo0y8O19fXs3379g6PKSsr8z+tf+ONN2IYBg8//DCO49DY2Mgrr7zCK6+8AiQu2p911llcdNFFGQFGutRcja4MpAvYnUmfc9ETqTChpqataraoqKjTY7rzO9Lb0ltSpVdtpHz44Yc88MADLF68OOfv5O4OX29paWHevHm88MILrFmzJmdwoqrqLgcqe4qEHkIIIYQQQuymMSWjuHrG12mOd/xJTlVRs4Z9FwXze20Nb21YzHFjjqQ2Ws+Q/DIAzGR7K9M2abGilIQTb+Rs1yFmx2mxWnsx9PhiV3qkhAK6P8Q8FRKkV1y0tb5S8DwFTVMxdK/DuRq7Gnp0pCFtKLqTDD0cx8O0HD+sSTzmsrWqmbHDi1BVheaoRamEHkIIIb4ArrrqKl5++WVWr17tD63+yle+0uH+qYv76Z+Y70j6hd3dvZicsmDBAq6//voOt3/ve9/jmmuuARLVBr/4xS+46qqreOmll1iwYAFLlizBshLzvlauXMltt93GY489xrx583JepO/O17mr+uPCd6pqJhAI8Oijj3b7uNGjR/f4uXLNzdjTli9f7t8+8MADM7b96U9/4v/+7/8yHhs+fDjjxo1j//3359BDD8VxHP/3p6c2btzIN77xDb+SAyAcDjNu3DgmTJjAQQcdxOzZs7n++uv55JO+n+XTGQk9hBBCCCGE6AUqnb+BDOlBWq3MT+ePKhpBUaiQhlhjr6zhD+/+jWazhaH55Xx3xiXE7TiO69JkttAYb/ZDj1azFcBvfWU5FkaOgew9kXOQ+Reo0iOlIC/gDzXPVRmRCj0MXcNx3WT4oWLZLiWFQWrqYwBomoLjeBQW5P4U5q564KXPqW+Oc9yhI4FEuAFgWm0/C8dxaY3beB7YjktA1YjGbWmBJYQQ4gvBMAx+9atf8bWvfQ3Xdbn11ls55phjOty/uLgYyPzEf0fSh4l3VRWwJw0dOpTLL7+cyy+/nNbWVj744AMWLlzICy+8QG1tLWvXruX3v/89t956a68/d2cVIM3Nfd/KLfVzME2TIUOGUFpa2qPjy8rK/Nvpg8Jz6es5J47j8MEHHwCJCqIDDjjA37ZgwQI/8BgzZgw/+tGPmD17NgUFBRnnePnll3f5+X/4wx/6gcdll13Gueeey4QJE7ICv9bW1l1+jj1FXtEKIYQQQgjRG7r40FxqfoamamiqhqpoaKrKd464mJPGHc25B5xKUM++AD5t6IFZj3Wk2UwMxdzRXMWnO1diORau59AUb8HzXP9Naip8SVVimI7V7efoiPsFn+mRoqltP+hUwJHO0FPVHwq6qqKqCoauUpQfoDg/iKoqDCoKMXpIAbquUlrQe3NVUp5duI7HFqwGwErOE7HsxM+irjFGTUOMlmjiZ277oYiD2cnsESGEEGJvMm3aNC666CIAamtrO734P2nSJAB27tzJzp07Oz1v+jyHsWPH9sJK4ZxzzmHVqlUd/kl9St/zPLZu3crixYszjo9EIhx33HHccMMNPP/88/5F/LfffrtX1peSGpgei8U63KezNl17yoQJE/zb7733Xqf7vvLKKzzwwAMsWLDAH7qdfvyyZcs6PNbzPFavXr2bq+2Zt99+229vddJJJxEKtb1ufPjhhwHQNI17772XL33pS1mBB8COHTt26bk//fRTVqxYASR+R6+//nomTZqUFXi4rpvRgmugkNBDCCGEEEKIXqC2e2mtJAeda2riInhYD6EqGsPyBzMoXIKhJd44FoeLOWn80Rwx4hDsHOFDSTh7YGF3/HvTB/7tVCDhei6u5xK1Ey2QUpUeccfcpedIl3umxxcv9OiKoasoSqLlla6rib81lUjIQFEU8sMGRflBNE2lIGKQF94zxfeLl++gYnOdH2SYdiL0qmuK09hq0hpLzHqxHQ/LdnGSfwshhBBfFD/84Q8ZNmwYAM8880yHF2Znz57t304NKc+lrq6OV199FUh8sr47Q8N70+23386cOXO49NJL2bx5c859Bg0axP777w9APB7Puc+uSl1Qb2lp6bAiYtGiRb36nN1x1FFH+bcfeuihDveLxWL84he/4L//+7+5/vrr0bTEa/SpU6f6vyfPPvtsh0PmFy5c2GUlSG+Kx+P84Q9/ABItyS655JKM7Zs2bQISw81HjRqV8xye5/HSSy/599t/bZ21aEv/HZs6dWqH+y1cuNCvgLHt3LME+4OEHkIIIYQQQvSC9P7IIT3IqMJh6KpOUSgRWhiaTkEwj4AeID+Yh6EmLnZHjLZPbB06LPMNxeRB4ygI7Nrcj/YhDCTaTZm2iZdsO+V6Dq7n+pUeuzOwMlfAkSsI2ReEgzqGrqKpCpqqEAxohAKJN9aDisN+e6xQQEfXVI44YMgeWcdbH2/h8w01vPnRZrZVNdMasxPDzb22IeeO4xI3E29QU9UgQgghxBdBfn4+N910k38/NfeivZNOOokRI0YAcM899/Dhhx9m7WOaJj/96U/99k2XXXZZ7y+4C8cff7x/+3e/+13OfbZt28bSpUuBzi9U74rJkyf7t+fPn5+1fc2aNdx33329+pzdccghhzBt2jQAPvjgA+66666c+91yyy3+0PMLL7zQDz0A5s6dC0BFRQV//vOfs46tqanh17/+de8uvBMtLS386Ec/Ys2aNQCce+65HHTQQRn7pNqy1dXVZVQgpbiuy+233+7/PgB+dUtKINA2W659i6rU+QHeeeednOusqKjgxhtv9O939N9Yf5CZHkIIIYQQQvQCRVEwNAND1SmLlKJrOsMLhmA6FoqioioqRcG2knM9WelREMinJTljY/bow/lo26ekooeTxh9NfY55HwoKHp0HFFWttbieh5oWxriei90unDBtEzdZ8VEXa6A0XNzTLx3ooNLjC9jeqjsGFYdRFQXPS1R8pM/+SG+PFQpoxEyVq885GMdZimm5XHTKFH5133tE47v/SbnP1tfy2fr3AXjx3fX88luzCQcy3wJajusPPG+N2RTmuTnbdnmet0cHnwohhBB7wgknnMCpp56a8Wn39nRd59Zbb+Xyyy8nHo9z2WWXccEFFzBnzhzy8/OpqKjgn//8p9/a6JhjjuHCCy/sqy/Bd+SRRzJjxgzef/99XnnlFS644AIuvPBCRo8ejWmarFy5kr///e80NTWhKApXX311rz7/6aefzl133YXjONx55500NDQwZ84cABYvXsy8efOwLIvBgwf3ebujX//613z1q18lGo3yl7/8haVLl3LeeecxdOhQtm7dyr/+9S/efz/xmmjMmDFcddVVGcdffvnlvPjii3z22WfcfffdrF69mvPPP59BgwaxYsUK7r77bnbs2EE4HCYajeZaQo98/vnnGfc9z6OlpYXq6mqWLl3KM88841eVHHLIIRnBQsopp5zCkiVLALj66qv55je/ydSpU/E8j4qKCh577LGs52lpacm4X1xcjK7r2LbNSy+9xLHHHksgEGDatGlMnz6dsrIyqqureeutt/jud7/L2WefTXl5OdXV1bz99ts888wzGe3O+mOmS0ck9BBCCCGEEKIXqCgYqs7QgsH+Y7qm43guWrLVVSroADBUHUVRCRlBVEXD9RyGFwzhW0dczOrqdUwYNIZRRcNxcwwDD+lBonbH/ZQBTMdk3tInuGTauf7Fatdzs4KImB33B443xZuJGGFCOWaLdCVnpccXcJB5dwSTIYfjdj4YXNNUQgGdvLDBlWceRNDQGFwa4WeXTueBFz6nMC/AktVVvbIm2/F4+q01fO3kKRmPO46HmWpzZjpsr25m9NBCahqilBaG/N8d03b9r0sIIYTYm9xwww28++67nQ6hnjlzJnfddRfXXnstzc3NPPjggzz44INZ+51zzjn84he/6LcPAvzhD3/giiuuYPXq1SxdujTjU/wpgUCAG264gVmzZvXqc48ZM4brrruO2267Ddd1+ec//8k///lPf3s4HOb3v/898+bN6/PQY+LEifz973/nmmuuoaqqinfeeSdndcKECRO45557yMvLy3hc13Xuu+8+vv3tb7N06VLefPNN3nzzzYx9zj33XGpqanjrrbd2e71nnXVWl/soisJZZ53FTTfdlDHLI+Wiiy7inXfeYdGiRdTU1HDbbbdl7RMKhbj++uu57bbbiEajrF27NmO7ruscc8wxvPnmm2zfvt2vYHr55ZcZO3Yst956K9/97ncxTZPXX3+d119/Pes5ZsyYwaRJk3jwwQcxTZPNmzd32G6rL0noIYQQQgghRC9QFMWf35FOU7Wc/XJ1VSdihFAVFVVRSH7YnjHFIxlTPNLfryCY3d7qvyYcy7MrX+1yTZ9Xr2Fb005GFA4FEmXu7YOImGPiJoecO65DqxndtdAjR1XHvlrpkWJoakZlRy6RUOItmaYq/gWUwyYPoTg/hKLAzn9+wLbqls5O0W0ffLYzK/SwHRfTavs5mZZLXVOMusY4uqZSlJ/4XYjFbQk9hBBiF00YVdzfS9inlZWVcd111/Hzn/+80/1OOOEEXnvtNR588EHeeustNm3ahGVZDB06lGnTpnH++edz+OGH99Gqcxs8eDBPPPEEjz/+OK+++iqrV6+msbGRYDDI8OHDOeqoo7jooosYPXr0Hnn+yy67jEMOOYR//vOffPjhh9TX11NeXs7s2bO54oorGDduHPPmzdsjz92VQw89lFdeeYVHHnmEBQsWsGbNGpqamsjLy2Py5MmccsopfPWrX81o6ZSutLSUBx98kOeee47HH3+ctWvXYpomEyZM4MILL+Tcc8/lW9/61h5bfzAYpLCwkLFjx3LYYYdxxhlnMHHixA73DwQC3HPPPTz88MM8//zzrF69mlgsRl5eHqNGjWLWrFlcdNFFjBgxgtdff52FCxfy1ltvEY1GCYfD/nluv/12brvtNt555x0aGhooLS2lsrKSsWPHcuyxx/Lkk09y33338d5771FVVYWqqgwaNIgpU6Zw5plncvLJJ7Ns2TI/JHzppZeyKmn6g+LtTuNesc+pqKjgjDPO8O8///zznf4HKIQQQgixr2g2WzBti9JIcda2HU2VGRUgALZjE3dM8gIRtjbuIG7nHjZpOTY3Lvh9xmM3nfBDXly9gA+2ftKttf2/I7/B0ILBlEVKiTsmTfG20nNN1XBcl9FFw9nUsJWAFmBk0bBunTfdo8uf5/EVL2Q8dtmhX+W0SXN6fK590c7aVnRNYVBR4k3oxu2NGLrKR5/v5J5nlmfsqygwbWI5rgetMYvKulYamrs3jP6P/++4bq+puCBIWXEYx/Woa4xRVhzu+iAhxF7vi/6+f926dcTjcYLBIOPGjevv5QghhNhNuf5dl0oPIYQQQggheoGKipajogPA0Iysx3RN9ytDUu2vch+b/ZK9JFzEtUd9i28+81Ma401dru0fSx/nJ0ddnWhv1a7SI1WNYbqJwYOmY2I7dkYrrq54nofbwSBzmQXRPZqq+APOATQtMQB98n6lnHTEKJatrWF4WR6zDx7OsEF55IXbfqdefW8jL/1nQ7eeJzXnJRa3eXfZdgxdxdBV/v3JNgaXhDnnhInkJ89t2cmB966L7eybrcqEEEIIIcTeR0IPIYQQQgghekFH7a0AAjlCj9QxAGoHx0GiDdb04Qfz4bZPARhVOIxh+YNRFIXRRcNZXrmqy7XVxxrZ2rid0kixP7S8PdOxMm53N/SwXQfTNnMPMvccLMcioOduIyDaaJqSMXReU1V0TUXXVc46fgKnHzUOVVVw3exC/ZKC7rcjq22IEQxo3PHYUqrqMgdxbq1qpiAvwNnHTQDAtBO/K47j+QGIEEIIIYQQA52EHkIIIYQQQvQCRVHQ6Sj06Pyiv6ooBPQApt2+RZFCUaiAM6ecTHneIOJ2nFMnneCHJWEje6hhRxriTbiei92t0MMkQu5WRo7roKkalmNhaAZN8WZURelwpkfcMSX06AZNVUkviNE0BV1T0TSFUEDHskyCAY1ozG7bSQE8OHDcoG4/z5ot9bz+wSZqGmI5t7+zZKsfetjJoMN2Pan0EEIIIYQQe42O6+iFEEIIIYQQ3abS80qPFE3RyDMiRIwwiSvZyeN0g7AewtB0jhszk5MnHEtxqNDf3pPQoy7agOu5uJ5LVUstC9a9y+rqdf520zHTblu5TgEkwxPXpclMDNeO2jGcHAPSARzPxXJt3BzbRCZdUzLagGmqgqYpaKpCKJD4vUofJD6oOEQkmPgM26ihBUzffwiQmPfxpSP36/B5Hnl9dYeBR3uel2hx5TgujuPh5KgyEUIIIYQQYqCRSg8hhBBCCCF6QWftrbqaaaEpKpqqURDMJ2pFqWqpASCkBwnoAVRF9YMDTWl7jkRI0j1vrFvECWNns75uM/d+9C8sN1ExcMx+MxhVOIyxJftREIwAnYcecduk2q1LXBEnUc1hKzZOzpkeTmIehOsQ0OTzVp1RVQXSQgVdU/0/oWS4EQy0/ezzQobfcipoaHz3vINZuqqKSMggL2LwyuKNu7yW1phFJJSa6+H4LbVq6qMMLo3s8nmFEEIIIYToCxJ6CCGEEEII0Qs0VUPtZCB5Z1RVJagF0FUNQ217iR7RE6FGxAjTnKysUNOGpYd7EHrEHZMHPnmCjfVb/MADYOHG9wHIMyL8cPaV5AcimI6F53mYjoWiKAQ0g4ZYI0WhQizHImpF/ed2XBdbTYQb7Tmei+M52K7dZbXLvk7XVOy0ihgtGXhoqoqhqahpFR9AYgB5MkgKGBqW7TJmeBFAzrkfPVHTGPNDj5jZFno0tiRabBXld3+GiBBCCCGEEH1NPm4lhBBCCCFEL9jVwAMSw8pTg8P1ZOgRNsJEAolgoSRcRKrtVXqlR1mkJOtchcGCDp9n2c6VNMabc25rsVr5eNuy5L1E4GE5Fi1mK7ZjUxttwHIs7GRg4rgOnufheg62Y2N3VOnheR3OERFtNFVJVHskBY1UoJEIPAw9EYKgJOZ9KErisdTsDz2tkib9PLsivf3V5p2N/OGhj/jdgx+ybE01VfVRtlY1U9MQzToubsnPWQghhBBC9D8JPYQQQgghhOhnwbRB54kWWQoFwTz/MUMzCCaHgWtp4cpBg6dQHCrKONcB5RN3eR2f7Pjcvx13TEzHotlsYXtzJZ7n0hRv8bc7nuMPL3c8t4NKDydR6eHYWdtEJkVRMoILQ0/O8Qi0/Z1ooda2n66phALJsExPPGYYavpYmF3y0rsbePi1VSxfV80TC9awfF0N26pbeOiVlcRiNtGYTV1TPOu41liiLZrTbui558ksECGEEEII0Xck9BBCCCGEEKKfpc/8UBQFXdUIpAUh0BZ2pLe3CukBvjPzEj800RSVY8fM3OV1bG3awcb6LQDE7Timm6j2sJIzPprMtiqR9OHlnuf6FSDpLMdOzPTooApEZMpVoZEaXp76W9dUjFTAoat+y6tUEBI0NAojgazzpJs4qpg5h4/qcHtlXSvvrdjBfc+uYMnqKv/xuOWwcmNd4o6XmPcBUNeUqAyJxmw8z6M52jYTxrJdbEdCDyGEEEII0XdkpocQQgghhBADjKEZWTMwUjND0ttoKYrClLLx/GDWN1hXu4kxJaMYXTxit577vo8f5SdHXUVAC/jD01MygwrPD0MAms3WrHPZro3ruVnnSWxzOhz8LtqkArFUxYemKX7ooWkq4eSQ81R7LENXu5y5MXl0iX9cT6WqOQBMyyVuOjS3WpQUhLBsl8YWEzut0sOyHTQZYi+EEEIIIfqQvPoUQgghhBBigAkboazHNFXz532kqIpKQDMoDRczfcTBlEVKCGgGM0ZM8/cJ6T0bOm06Jv/e9CGmY3U5iyPumP7tXJUefuiR4zy59hcdS1V65IWMjDZYobTwQtcUNDVzvkeu88w8cCgFeZ1Xg3QkfW6HZbs0Ry0/5LAdl/rmOE5aZUfccrLaXQkhhBBCCLEnSaXHXuT222/n/vvvz7ltypQpPPPMM328IiGEEEIIsSfkGeGsxzRFRW9XGWGoul/9kaqmCKgGp0w8DgVoiDdx/Jgj+dfy52iINXb7+T+rquDUicd3uZ+ZVumRq5rDSs7ycHPMdOhO6OF5Xkbrr31Z6vvQWRWHrqv+bI9cZh00jCOnDiM/EvDbYvVUfXPbLA/TcojGbRzHw3E9PA8syyWge2n7uP58EiGEEEIIIfqChB57kZUrVxIIBLjqqquytpWVlfXDioQQQgghxJ5gtGttBYlKj/btoFJzP1Khh6ok9okYYc4+4BR/v3wj3KPQo6qlBtfzULsIHMy0So9cwUbUjrG+bjNleaWMLBqWsa2rKhJIzBUJ5ah6EbkZmoaupsKRAA3NbT+f4oIg5584yb8/dFAeCpD6qRXlBTh08mDe+nhLp89RnzbAvDlq4bqJM8TNthArvb2VaTkdBiyO66HlmGMihBBCCCHE7pDQYy+ycuVKJkyYwDXXXNPfSxFCCCGEEH1MV3K0t0oONU/N+dBVzR94rigqXrL6Ii8QyTqfpmg4OQaMp3y64zOmDTsw57aoFWNncxXDCgYTTLbPcnOca9nOlSzbuZKAZnDj8T9gctl4f1t3Kj2iEnr0iK4rfqXHt889hNv++YG/7ezjJmTsWxAJMPuQ4Sz6ZBuaqvCVY8YzeUwJn62vpbIuez5LSkNapUcq8ACIm07W4w3NceKWkzMQA4jFbfLC2QGfEEIIIYQQu0NmeuwlKisrqa2tZfLkyf29FCGEEEII0Q/UHJUe/rZkRYaman4QEkyrFokY2aHH8WOPpCCQ1+HzvVTxFo6b3bKqNlrP7xbdzV8/fIg/L/47LWYUACdHe6sU07GYt/SJjMe6U+kRsxMX2L0OLpqLTIau+fM8Zh44lDOPHcfwsjzOOm48h0zKrgy/+uyD+Okl0/n55TM4bMpg8kIGP/36dK48c2qHz5He3ipdLC30cJKhR01DDLzMcCTzGJnrIoQQQgghep+EHnuJlStXAkjoIYQQQgixj0pUenQUeiRe1muqhqqoaKrmt74ChbxA9oyQofnlXHf01R0+X0O8ie1NO7Mef33tv2m1EkFHbbSePy++j0eWP8em+m2drn91zXr/tuu5OF2EHo7r+O2zWqzsygM3RyCzrwuntZHSNZVLTjuAn1w8nUtOO4BwsH2VkEJRfpAR5fmUFIRIdTJTVYXJo0voqLNZY4tJQ0t28LGztpVla6v9llcx0/bDDqeD0CMal9BDCCGEEEL0PmlvtZdIhR51dXVcccUVLF++HNu2Oeyww7jmmms4+OCD+3mFQgghhBBiT1JVFcPL/fLdDz2Sf4f0YFvFhx4gP0d7q8Jgfs7ZIem2Ne3MmMVRG63n4+3LM/ZpjDezZPuK7n8hJOZ/5Bp8ns50LL/SpCneQn5aVYrneZiOSUiV1lfpNC3zM22p4eeqkpj3AW0hQySkoygKqqrgOB55YYPm1sRgel1TKcoPZszvSPE8uPlvixk5OJ/y4jBnHjse03L448NLiMZtIiGd674+ndJY4mfjuB6Ok/tnbdkujuNmrVsIIYQQQojdIaHHXiIVetx7772ccMIJnHvuuaxbt463336b//znP9xxxx2ccMIJ/bxKIYQQQgixJ3UUUqTP9IBE6OElHw9oRs72VgXB/C6fb1tapUdjrInf/fvuXVh1m8Sw9cSsEddz8TzPvzCfYjoWAc1IVnl4OK5D3DaJ2XFC/vwQF8u1kcijc2pySLiqKgSMxO9ISWGQuOn4szQ0VcEC8sMGLVGLVCex0sJQztAjZUtlM1sqm2mJ2VTWtvpVG60xm5v/tpjTZo9hRHk+819ZiWm7XH3OwZx4xGh/cLnrejiOhyWhhxBCCCGE6GUSevSTOXPmsHXr1k73mTJlCs888wwAhmEwYsQIbrvtNmbMmOHv8+9//5tvfvObXH/99bzxxhvk5XXcl1kIIYQQQnwxpUKPVCgS0kOYjomqqBiqTiTHMPCCQNehx8fbl3PsmJmsr9vMYyte2O11bmncweii4f6MDsdz0ZVEUGM5FoZmYDkWqqIStxOtrWzXwfUcdjZXMSSvjJARwvHcrJkgjut0OPNkX6WpCoqSqPgwdBUUKCkItdtHBRyCAR1DVzGtRFXGiPJ81m1t6PI5Vm+qy/n4i+9uyLj/9+dWcOjkcoKGTmFeADtZ/WE7Mq9FCCGEEEL0Lgk9+smoUaMIBAKd7jNy5Ej/9u23355zn6OPPprTTz+d5557jkWLFnHyySf36jqFEEIIIcTAlxpkHlAToUdQD+C4DrqqoWu5X/IbHTyeznQsfvvvv/baOrc2bKcsUuKHNKk12q7DlsYdjC0ZlWhdlazsAIgn/3Zch9poPcONoX6lR3qlSNwxiajZs0t2194epuh6MhDTNQK66ld/pGiqgqYlQpGAofmhx1EHD2fh0swPaeWFdFpiuzaHozlqsbWymfxIAENX/eDLsh2g8zZrQgghRGfWrl3rXxfbvn079fX1FBQUUFZWxvTp0znppJM46qijch77s5/9jKeeegqATz/9lGAw2JdL95mmyd13383zzz/Pjh07CAaDlJeXc++99zJs2DDWrl3LHXfcwYcffkh9fT0lJSUcddRRnH322VxyySUA3HzzzXzta1/r87Xfcccd3HnnnQC88cYbGdczhegvEnr0k3/+85+9dq6DDjqI5557js2bN/faOYUQQgghxN5DVVQURc0IOFQ1MdDcUA2GFwzJ2D/VJgpgZOFQtjTu6JN11kUbMG2TYPL5a6J1DC8YQtyO4yXbXbmeS8yOY7uJi+ux5DBzgJgdx7RNXNfFdixarFbyjAiKomDaJhGj+6FH3DYJ6p1/CAn2/tDD0FKhh0oomP32T9MUgobm75MypDTCjAOG8P5niRZnQUPjhstnsrW6mTsf+2SX1tIatckPB2hojvtttNwOhpwLIcQXyZrN9f29hD4zYVRxnz2Xbdv89re/5cEHH8RxMitAa2trqa2tZfXq1cyfP58ZM2bw61//mv3226/P1tcTP/7xj3n11Vf9+7FYjFgsxuDBg9m+fTsXXnghjY2N/vbKykp0XS7rCtER+a9jL2Capj/TI9fA8lgsBkAoJF2NhRBCCCH2RSE9SKDdvA9NUdEUDUPVGV08goOH7M+nOz9HVRS+dtCZlOUNoqa1jlMmHs/9Hz+K67kUBPIpjRSzsX7LHllnY7yJqpZa/rn0cbY37eTYMTP56oFnEE8GG57n4eLRbLb4x6QqPVJM1wIPLNemOd5CSAuiazq26/QooGi1ol2GHq7n4nQxcH2gS5+XkR/OrqhQVQVDT3zPggEdaPt+nzdnEuGgTk1DjFNmjSEU1Bk/opixwwtZv60x61xdiZmJICs1MB0Sg84747peVnWKEEIIAXDTTTfx+OOPA3DggQdy1llnMXHiRAoKCohGo1RUVPDss8+yZMkS3n//fS677DIefvhhhgwZ0sWZ+9aaNWv8wGPEiBFcd911DBs2jHg8jqZpzJs3zw88Tj/9dC644AI0TWPQoEFUVlb259KFGLAk9NgLtLS0cP7551NcXMy7776LqmYO+vvggw+ARMWHEEIIIYTY9wT0AIXtBpOrSqLSQ1VVyvMG8Y3DzmdV9ToiRpipQyaTF0gMN/e8Mfxw9pVsadjOpEHjWLjxvT0YejTz0pq3WF2zDoAXVy9g5ohDKQoXAomQIfUnxXKsjHPYroNCogKj1Y1hu3Yi9PB6FnrEHbPL/d1k5UlnUsPZBypdawsMwjkqPVRFIWAk9skPG+RHDJpbLcpLwpiWy1nHTUBRoLggSF1jIhApLQztUugRNds+hRuN22zc0cjEUcUMLol0eIzluAT34kobIYQQe8YHH3zgBx4XXnghN910U9b1siOOOIKLLrqIv/zlL9x1111s27aN22+/nf/5n//pjyV3aO3atf7tb3/725xyyikZ29etS7xuMgyD//7v/yYcbqtsldBDiNwG7qtz4SspKWHWrFnU1dVx9913Z2x75plnWLhwIdOmTctZBSKEEEIIIfYNBe1CD03V/Av6qqISMcLsVzyC8rxSv71VqjpkbMloDh12IHmBcFYrrN7UGG/m3xvf9+97wIIN7/rVHO0Dj1xs106rvvCwvcSFdNd1soabd8a0E6FHZ7q1HmfXZlz0FT2t0iM1/ySdpqkZba3KisNomkJBJOAHJpqmZpyntHDXKsxj8cT3qiVmcfu8D7j7qWXcePd/2LyzqcNjbNv1538IIYQQKY888ggAhYWF/H//3/+XFXik+/73v88hhxwCwMsvv0xVVVWfrLG7otGof3vEiBFZ21tbWwEoLS3NCDyEEB2TSo92amtrOfXUU6mvr+/WAKNYLMYDDzzAyy+/zPr164HEAPKTTz6ZSy65hKKiol5Z1y9+8Qu+9rWv8ac//YnFixdzwAEHUFFRwcKFCykvL+e3v/1trzyPEEIIIYT44giobS/3U0GHrup+GJIYfK4Q1AyaAVA6DT0UFAbnDWJnS/UurafVimY9FrPifrDgem0XuG3XZntTJWWRUsJG20V2x3UyPrqVCi5s1/EDkK7Yjo3t2tieQ64GV6nqjfTKEwUlZ2hguTaBnGcZGNIDjVw0VSGQto+uqQwdlIeqKv4QdFXJbJPV1Tk7EktWenzw2Q4amhMtzaJxm6ffXss150/DcT20dq2sHNfDcb2MihUhhBBizZo1AAwdOrRbw8e//OUv88knn+A4DmvWrKG8vHxPL7HbXLftAxaall3dmHptJDM8hOg++a8ljeu63HTTTdTX13dr/507d/KNb3zD/4c2ZfXq1axevZonnniCv/71r0yZMmW31zZ27Fieeuop7rjjDt555x0++ugjSktLueCCC7jmmmsG1D/WQgghhBBiYDDS5nwEtEDWY6qqEtIDfggSMoKURkooChXSEGtrX3T8mFmcMHZW8jwG179++y6tJ1foYaYNKnfTBpnfsfgf1ETrKAjkc/URcxkUKQFS7a3aLoCnqjscz/WHn6eL2fGMwe2u67KlcXvimA4qPWJWnEggnAw8vMTFCEVBV7IvROR6zoEkPazIxdDVrH1SbbBSAYSmqehpYcS4Ebv2wa5UpcdbH2e2T3v1vY1ceeZU4qZDcUHmhSvHdZOhxy49pRBCiC+o1AcRNmzYQG1tLaWlpZ3uf+KJJ1JYWEhpaWmn1+kqKiq49957Wbx4MTU1NZSUlDBt2jQuvvhiZs6cmbX/k08+yfXXXw/A3/72N4499tic5/3hD3/Iiy++CMCqVasA+NnPfsZTTz2Vsd8ll1zS4dq2bt3K5MmTAZgxYwbz5s3r5CvO9J///IfHH3+cjz76iJqaGsLhMGPHjuXEE0/koosuIj8/v9Pj//3vfzNv3jxWrVpFXV0dw4cP5/TTT+fKK6/s9hqE6EsSeqS55ZZb/MFBXbFtm+985zusWbMGRVE4//zzOfXUU9E0jddff50HH3yQ7du3853vfIennnqqVyo+hg0bxm9+85vdPo8QQgghhNg3pM+rSLR9ULJmWESMsD+TIs8IE7NinD3lSzy98hUAzpryJaaUj++V9eQKPay00MAlUenx7qaPqInWAdBkNvPRtmWcPCFxEcF2bbS0GRqO6yQrRNycIcbO5iqG5Q8mkBxabnuOX1mSPqTcdCwCmoHtOsScOBHCfuWJ47koHpBjtoQ1wEMPvYvQo7PtqW2qomQEI2OHFTG8LI9t1S0dHZpTNDnIPFfFTENzHDXH467r4XYx7FwIIcS+Z8qUKXz22WeYpsn3vvc9brvtNkaPHt3h/sOHD+fMM8/s9JyPP/44v/nNb7Dttv9vr6ys5NVXX+W1117jJz/5CVdccUWvfQ19wTRNbrjhBp555pmsx5cuXcrSpUt54IEHuOuuu/wWYOls2+aGG27ICmfWrVvHHXfcwSuvvMKMGTP26NcgxK6Q0INE77zrr7+el156qdvHPProoyxfvhxIJLOXXXaZv23GjBkceuih/PCHP2Tr1q3ce++9/PjHP+7tZe+Wmpoaamtre3zcxo0b98BqhBBCCCFEX9BVLSMwAIgEIn7bhIgRpoY6ppSP57qyb6OQ+wL1ropasazHzLRB5alKj1fXvpOxz4L179JqRfnKlJPBdaiK1xK1YpRFShIzPpJhR4vZSnGoENOxcD2XsB7CcR1idtwPPdKDkfTbta11DC0YjGmbfpuJ1HqcZNusXE2sejJHpD+0bxfVo2OTQYemKhntpVRV4QcXHMrnG2r5xwufdft8ryzeyJTRJeRakWk5OX/XHCfR3koIIYRId/HFF/PMM8/gOA4fffQRX/rSlzjiiCOYM2cORx55JJMnT+7xa5hf/vKX5OXlccUVVzBr1ixs2+att97ioYcewvM8/ud//odjjz2WiRMn9srX8P3vf59LL72UN954gzvuuAOAX//610ydOhUAy7IwDIOf//znrFixgvLycv72t78BEIlEuvUcP/nJT3j55ZcBmD17Nueeey6jR4+mubmZRYsWMX/+fKqqqrj88st5/PHHGTduXMbxv/nNb/zAY9KkSVxxxRWMHTuWrVu3Mm/ePD7++OOsDjhCDAT7fOjx0UcfcfPNN7N69Wog8Qm49F56HUmVkI0ZMyZn6dmpp57Kc889xxtvvMH8+fO55pprCAQGTq/f+fPnc+edd/b3MoQQQgghRB9SVTUr9AhoBpZjoSgqhmagqzq2a+f81H3KUaOns2jTh/79uQefxcsVb1ETre/0+XNWemSEHi4uuS9wL96yhMll42k2W3jq85dxPY8Txs7m1IknUN1a5x+/uWE74KGrOkok8TWYbttz5Ao9LMei1YpiORYxJ+6HHP5Mj07eH6TaW3me16sB0UCgqQqKkgg5FEVB0xQcJ/HzCRgah0wsJxjQiJvdD37+/OjSnI/XN8fJD2e/X3JcD8fp+v2ZEEIMZD/809u7dfwf/99xe815n/tD59UUveXAAw/k5z//Ob/61a/wkq0o33vvPd577z0AiouLmT59OkcffTRz5sxhyJCOZ5alFBQUMH/+fCZNmuQ/dswxxzBs2DB+97vfYds2zz//PD/84Q975WsYPnw4w4cP5/PPP/cfGz16NPvvv3/Gfnl5eQAEAoGsbZ158cUX/cDjBz/4Ad/5zncyts+ePZuzzjqLCy64gJaWFm6++WYeeOABf/uqVat4+OGHAZg+fTr33XcfoVBiztohhxzCKaecwrXXXssLL7zQg69aiL6xaxPoviB+97vfcdFFF/mBxznnnMNpp53W5XFr165l3bp1AJx++unJVgHZzj77bACam5v5z3/+00urFkIIIYQQYtfoipbV3gpAVdrCkPT5Fx2ZNfIwikOFAIwtGcUB5RM554BTKYuUUB4p7XAYeq5WUGa70KOutaHD532x4k1eqngLN1mZ8ub6d2k1W4hmhCltg9Ab483+c7iui+VYGcPOU7ejdhyAumgDDbEmPwxxXdev9Mg1JN1xHT8QGehtrnaVrquoyWqRwrzsUOL4w0b2yvNU1UZxXQ/Lzgw4bMeV9lZCCCFymjt3Lvfffz/77bdf1rb6+npef/11br75Zo4//niuueYatmzZkuMsba644oqMwCPlwgsv9D/YkLqGuDf4+9//DsD++++fFXikTJw4kW9961sAvPfeexlVG0888QSOk3j986tf/coPPFJUVeWXv/xlr7T0F6K37dOhx6effgpAaWkp//M//8Ott96KYRhdHAVLlizxbx9xxBEd7nf44Yf7t1NJsxBCCCGEEP1FVTW0HMO4VUX1P8gTNkJZ29sryyvl2qO+xXVHX803D78ITdUYX7of1x71Lf5wyo1ceuh53V5T1Ir6Mzb+s+kjfvX2nzvct6qlJqtapLPqkliynZZpW7RYrZiOlVHpYTuJoCJVrdFstiRmg3ip9lbJ0MPNXe3hJAedQ+ZA9i8SXVP9FlnFBSHywga63vY28uhDRlCcnwjK8sMG43dxyHlNY+JnZdmJn09rzMJJBh6p77EQQgjR3uzZs3n55Zf5xz/+wUUXXZRzrofrurz66qucccYZvPXWWx2eq6Mh5Pn5+QwaNAiAxsbGXln3nlZfX8+yZcsAmDVrVqf7HnPMMf7t9OuXCxcuBBKhSfu2Vyn5+fmcdNJJu7tcIXrdPt3eqrCwkG9961tcddVV5Ofnd/u4tWvX+rdzpckppaWl5OXl0dLSknHMQHDRRRdxyimn9Pi4jRs38t3vfncPrEgIIYQQQuxpHVV6KIqCoSbeGoSNcLfOVRQqQPfPpZCqsEBRmDxoPJMHjWNVzbouz1MTrefXb/+F1hzzPrrjsRUvcNX0uUQ6CWtcz6Ep3kzECGcML0/dbj+Xw3HTQw8Px3NyNt1y3bah6KZt5R76sZeLBHW/0kNTFYaVJVps1DXGqGmIkR82uO7r09la2cz4kUVU10e5//kVVNVFGTu8kDVbOq7cSVebDD3sZPusbdUtlBeHk+2tJPQQQgjRMVVVmTVrln9xf9u2bbz33nssWrSIhQsXUl9fDyRm+v7gBz/giSeeYMKECVnnGTp0aIfPEQwmAv70IecD2eeff+7PbLv//vu5//77u3Xc5s2bgURQlJrrO2XKlE6PmTp1Kk888cRurFaI3rdPhx533HFHh62pOlNZWQkk/lHtqifg4MGDWb9+vX/MQDFo0CA/pRZCCCGEEPuGXDM9UgwtUfGsqxpBPUg82fIpF0VRCelBWsxWdFUnoBl+BYaCgqooXHroV9netJN/LXvGn7nRkV0NPAB2NFfxy7f+xNjiUZTnDeLUicfnrFaJ2XF0zcBxHT7c+imvrV1IYTCf/zf7SnQ1822R6zmJ/uCpSg/PhXbVBqZtJkMTD8/zvrCVHoX5QUwru7VXYV6AmoYYKDC8PI9wUGfIoAiqqvCrb82mqcVke00Lv3vwo249T2ss0ebMcVxsxwUPWqIWLa1Wp/NlhBBib9DR7Ix97bx9Zfjw4Zx99tmcffbZ2LbN008/ze9//3vq6uqIxWLcc889/Pa3v806LjU7ozPeXlJ9mAp6eipVyVJfX++3tuqqfVVZWdkuPZcQe9I+HXrsSuABbf8AhEIhNC37k3LpIpFIxjFCCCGEEEL0F13ROnwNbKRd+C8I5HUaegQ1w2+TVRwqJJ52wV8BPCURfIwoHMrwgqFdhh69YX39ZtbXbyZshDh14vE597Eci6Z4C0+vfAXbdWiIN/Hwsme58KCvZO37WVUFf3r3XlqtKGft/yVmjW5rXet6LtubK/2qGMd1OpzpYTs2urb3vu3SVIVwMHv9mqaiKJAXNoiEDOoa4+iaSklhyB/qHg50/+veUtnMinU1TD9gCKHk8z399lpe/s8GggGN6y+dwWFTBvfa1yWEEGLvFYvFqKqqoqamhmnTpnW6r67rnHfeeUydOpVzzz0X27ZZvHhxzn2VPRiy52qTuSelAguAa6+9lqOPPrpbx+3KfA5d33tf54gvLvmt3AWmmXhTFwh0Xb+eKn9LHTOQPfTQQ8yfP7/TfeLxjt/8CiGEEEKIgU3XdNQuKj0A8gKRZFCR+9OMYSPsnyegGZntoRQFxUvft+vB6L3p7Q2LOw09lu5YkbHeD7Z+wlcPPD1r3399+gwN8SYAnln5KtOGHojjOlS11hKzYrieS3NyULrt2hmzQkzHIpD8fppuooJhbw4+OqJrKgFD82d+aKpCcX6QptbEe59QjrAkoKuYdvaFn4rN9VRsruflxRu47btH0xqzeOW9jXhAzHSY/+pKCT2EEEIAcOutt/Lwww8D8Oqrr3baej5lypQpzJw5k0WLFlFVVeUH9Lsr/RydVYE0Nzfv9nP1RHp4oSgK+++/f4+OLykpQdd1bNumtra2033lg95iIPrivfLuA6lPx3XnH8fUP3i7WlXSl2pra1mzZk1/L0MIIYQQQuwhAdXocFt6pYemagQ0I6NlU8QI02pFiRhhSsJF/pBwVc2sHmn/CrmypaZ3Ft8LXM+lIZb9xtz1si/Cr06bR2I6FhsbtqC1e03vuC5RO0bcMf1WWLZjUxutZ2jB4OQ+DnHP+2KGHrqKoatoWuL7oqkKqqr4IUgwoFGYF6CxJRmCBDRuuvJIPt9QS3V9lBff3ZB1zi2VzSxdXUlji4Xrtl08WrVxz1cLCSGE2DuMGTPGv/3vf/+7W6EHQDicqNAcPHhwr1V1pHeA6eyDwjt27OiV5+uuiRMn+rfff/99rrzyyg733bx5M88//zwjR45k2rRpjBo1CkVRGDduHKtXr2b58uWdPtfKlSt7bd1C9JaBfyV+AEq1rOpO1UNPqkL6W2lpKRMmTOj0z6hRo/p7mUIIIYQQYhd19kGc9gPOA2mVH2EjzJD8chRFJT+YlzxXYn9NUdGVtmMVP/ZI/H1Aedub7r5iOVaH21rM1qzH2g8yz/lJzXYPRa0Y//fBPH799l/4zTt3ErVi2K5DqxWl1Yr653RcB7OT9ezNdE3F0FQ0VUFR8MOP1OBzQ1f5+qn7o6kKhqZyzvETCAV0Dp00mHEjOm6f8dn6Wn+YfDrH6dvWIEIIIQamE044wQ8t7rvvPhoaGro8pqWlhQ8//BCA6dOn99paCgsL/dtbtmzJuc/27dtZu3Ztrz1ndwwdOpRx48YBiWBow4YNHe5733338ac//Ylrr72WVatW+Y+feOKJAKxbt45PPvkk57GmafLyyy/33sKF6CUSeuyC1GCjeDzeZU++1tbEm6r0fwQHqrlz5/LCCy90+uf//u//+nuZQgghhBCiDxiagaEZBPUgxaHCxIwGPUhET3xKMtHeSkFTc8wJURTyA4kPCh058rC0IKRv1McasRyL19YuZN4nT7KqOnGhoaqllsVblmTtnxrC3mrF+NeyZ7n939mvedvP7Pho2zK2NG4HYH3dZj7d+TkxK0azlXj9nwpebM/JmHmSy7amnTmDmoE+LFXXFAw98bNP/Z14PHG7tDDEKbPG8Jcfn8Avr5rFEQcM9ffpbN5HQ7NJSzR7Rsq26pbeWroQQoi92JgxYzjzzDMB2Lp1K1deeWWnF/VbWlq49tprqa+vR1VVrrjiil5by6RJk/zbTz75ZNYHpE3T5JZbbumX/0+/9NJLgcR8jx/96Ec521C9++67PPbYYwCMGDGC445rG2J/3nnn+dUxP//5z3MOR//tb3/L9u3b98Dqhdg9X7wa6z4wfPhwIPGPRnV1NYMHd9xbtrKyEqDTfYQQQgghhBhoDE0nYoQpDhX6VSBFoUI/4FAVxW/3pKVVeiRmengUhgpoNlsZWzKKH86+krve+2eXF/97S120gc+qKnhj3SIAVlWv5ZqZlzN/2TM5939j3SKqWmrY1LA1q+oj5e9LHuWHs65kSH4ZAM+vfiNj+zMrX2XmyEP9+5ZrEyY55LyTSg/P84hZcaJWLGOuCkDMjhM2Qv5913UHVNvcoKH51R0Bo+13INXeKj+SqHbPC+uYVub3Nde8j5Rt1c2UFGTPgtmwvZFBRSEioY7btAkhhNg33HLLLWzcuJElS5bw6aef8uUvf5k5c+Zw/PHHM3z4cEKhENXV1Xz00Uc8/fTT1NQk2m3+9Kc/5YADDui1dQwfPpwjjjiCDz74gIqKCi699FIuv/xyBg8ezLp165g3bx6ff/45o0aNYvPmzb32vN1x/vnn89JLL7F48WJWrFjBV77yFS6//HIOOuggWlpaWLRoEfPnz8e2bRRF4eabb8Yw2v4/duTIkXz/+9/n9ttvp6KigrPPPpurrrqKAw44gOrqah555BHefvttwuEw0Wi0T782IboioccuGD9+vH9706ZNHQYatbW1tLQkPo00YcKEPlmbEEIIIYQQvSGgBVAVNaPtVfoF+PRtmqKiKhpBPYACaKqOoSjoqoaqqEwdPJkLpn6ZBz55otvPP7JwmF9JATA4b1C354PUxRpZtrOtPYPtOjzx2UvsbK7Kuf97Oao/cnlsxQt8b+alObdlBD+0VXo4rpMRpLiei6qoNMQaKQoVJitIPGJ2nEIKss6Rmq8CiRAkEgh3a619IZwWXGSEHpqKYah++KHm6JveWaXHztpWRg8pyHq8pdWiodnsMvToreG0QgghBq5QKMS9997LH/7wBx5++GG/zVJHrZYKCwu57rrr+OpXv9rra7nlllu49NJLqaqqYsmSJSxZkvm64owzzmDWrFn8/Oc/7/Xn7oyqqtx1111ce+21vPnmm2zfvp3f/OY3WfuFQiFuueUWjj322Kxt3/jGN4jFYvz5z39m27Zt3HzzzRnbhw0bxuWXX57zvEL0Jwk9dsEhhxzi3/7444877AX40Ucf+bcPPfTQnPsIIYQQQggxEBmqjt5uzkd7qcHomqoR0gOE9CAKin/BOdX6SlVU8pLtrtKNLRmFpqisqd2Yte2wYVMZXjCY1TXrmVI2nqH5g3l65SvdWvtTn2df8NjUsLVbx3ZmS+N2LMfGyDGU3HJtHNf1q19S7bBs18HzXGzXQVc16qIN5AfyqI02UBDM94fFx+y4/3dIT1Q52J5D3I77oUfUjg2o0CNV5QGZoQdAJNgWTKRmfKQLBjr+3WqN2WyvyZ690hyzsOzclTjpTNslaHT+uyuEEH1pwqji/l7CF1J+fj433XQTl1xyCa+99hqLFi1i69at1NXVYZomZWVljB49mpNOOonTTz+d0tLSPbKO8ePH8/zzz3P//ffz+uuvs2XLFsLhMFOmTOGCCy7gtNNO48knn9wjz92V/Px8/vrXv/L222/z9NNPs3TpUqqrq1FVlREjRnDUUUdxySWXdDrD9zvf+Q7HHnssf//731m6dCmVlZWUl5dz4okn8p3vfIf33nuvD78iIbpHQo9dMHr0aCZPnsyqVat49tln+eY3v5nzk0RPPfUUkJgBMmvWrL5eZo899NBDzJ8/v9N9ujO8XQghhBBC7P0URelyFoeevPivKAoRI0xAD5B+iKHqqIqKmjbjI92owuGcNukE7vlwPuvqNmVsixhhzjngVP9+Rc363fhqek+T2UxpuDjntv/7YB7fPuJiNFVrq/TwEjMAbcdCVzVazFZazFY8z6XFbPWrQGzXxnQsWsxWAqqBqqq4rkvMjVMQzAcSocdAFdAz227lhdveaqrJQecAqZbmqqYQDmpE47lDjA3bsofStrRaWHbumYq24/qzRBzHxVGVjFBGCCHEF9fYsWO56qqruOqqq3p87G233cZtt93W5X4LFizodHtxcTE/+tGP+NGPfpRz+znnnMM555zT420A8+bN63DbzJkzM4aPd+S4447LmNfRU1OnTuUPf/hDzm2nnHJKt9YgRF+SV4G76KKLLgKgoqKCu+++O2v7yy+/7P+D+NWvftUf/DOQ1dbWsmbNmk7/9HX/QSGEEEIIMXClKj0AIoEIQS2QEZSkZlSoqpaz0iNVMRHUA9nnbjffoihU2Ctr3l2N8eYOt21p3M6KytUAWI5NQ6wRLxl6WK6N7djYbuIPQE1rfUaQUdVSQ4vZiuMlgxDP8StGXNfFtK0BO9zcaBd6hNLaV6mKQsDQKCtue09kaGqn80nMHOFGS8zC8xIBRzrP82hqaZsX47oejjswv09CCCGEEGLPk0qPXXT++efz6KOPsmLFCv74xz+ydu1azj77bAzD4I033uCBBx7A8zyGDh3Kt7/97f5ebreUlpZ2OXskHo9L8CGEEEIIIYC2Sg8gZyusVHChKipBLTvYSF3AD2rZQ6sDukHYCBO1EoMxi0PZMx76Q2O8GdfLXW0A8N7WpRw8dH/Ao6a1zn/cdh1a21VquJ5DLG3AdzzZ4spNfl9c1/ErRVKzPxzX8b/vnufRYrWSH8jrjS9tt7SvfE9vaaUoiVCkKD+IoihU1rZi6Cpx0+7RczRHE9Uzlt1W1ZG63xy1KClMzJxxJPQQQgghhNinSeixi1RV5a9//SuXX345a9as4dlnn+XZZ5/N2Ke8vJx77rmH4uLi/llkD82dO5e5c+d2uk9FRQVnnHFGH61ICCGEEEIMZIba+duJVOihKWrOdrBmsgVU7kqPAPmBiB96BHKEJunyjDBRO+YHBntKY7yJVqvjNlOumzsQsRyLpk6qRNL5lR5poUeqOsRybT/0MB2L+mjjgAg9OqOqij/zozAvQE1DlGBAw3Z69rNqSQs9wmk5mWm7xE0Hx/XQVCVR6eF0HEwJIYQQQogvNgk9dsPgwYN58sknmTdvHi+++CIbNmzAsixGjhzJiSeeyDe+8Y09NiRJCCGEEEKI/qZ1Meg81d5KURQUJbuVUWqIdyhH6FEQzM8KVaaUjWdl9drkMUG+N/My3t+ylLxAmNmjDqc22sDK6jVsb6pi6Y4Vu/Q1daUp3kyL2dLhdpfcF/KbzVboYFvWOZJBh+O5eJ6L67ppg9HbqiPidhzTMbEdO6PqZqDRVCVj5kc4qFOY13mIlUtzNPH70n6YuZWslombNpGQgeN6qFLpIYQQQgixzxq4r4z7SXcHGKUEg0GuvPJKrrzyyj24KiGEEEIIIfZuao5KDz0ZauSq4sgPRFCTQUlAC2A6JqdPmkPcNmmxonx58kmURUo4bdIJ/jFD8ssYkl+G53mMKxnN2toNfLLz8179OlZUVvhzO3LZWL+FeZ88yfFjjmRU0fC0Ld2/CO+6Lm4y8IDEbI9UVUzqb4B4MjQyXWtAhx6KoqCnhR6lhSEMvfPALJft1a24nkdL1GJQURjX9ahuiPrD0eOWI6GHEEIIIYSQQeZCCCGEEEKIPU9VVE4ad3TGY7NHTwcgrIey9o8YEdRkJUnYSGwvzxvEt46Yy3VHX82hw6f6oUl7iqIwY+QhfO3gMwnp2fNCOnLYsKld7lPdWkt12qyOXFZUruYfSx6j1YrRbLZiu06n+7fnJqs7UmzX9qtiUnM/EreTlQ9pQchApCoKgbSQI9XqKhzsPKgJBTKDkaZWk/dX7MC0XCzbob45TmOzSXNr8ntjJr7PjutmDTsXQgghhBD7joH7cSAhhBBCCCHEF4amqMwePZ3q1lq2N1dx6sTjGVcympgd90ONdHmBMFqy0iNshGiINRIxwrRaUVRFRVc0DM1IBASdDBY/bNhU3t38UbfWeNTo6ayr20R9rHHXvsg0LVaUW9+5E8dz0FWdS6ady4TSMd061vHa2lkBOK6D5STupyo9bNfxg5D0fQei9KHm6b519kH86eElHR43dXwZAB9+vtN/7JHXV1OcHyQY0PyQI1XpYSbbXLmuh6sk5npomnzOTwghhBBiXyOvAIUQQgghhBB7nKqoDMkv48KDvsJ1R1/NaZPmMChSgq5qOasxwnrInwUS1AJEAhF/P1VRUFUVXdX8FlhqjpkhAP81/hgOHjIl6/H8QCTrscJgfq8OBbdcG9fzMB2Lpz9/Ba/dkPUWM8pjK17g7g8f4rOqCv9x13MzqjeidtxvdeV6LqZj+QPeAT8Qac/u4PGB4thDR3L28eMZM6yQE48YlbW9OD/IYVMGZz3+zMK1ROPZX5tpuyxbU83vH/qIvzyyhOXranCkzZUQQgghxD5HKj2E76GHHmL+/Pmd7hOPxzvdLoQQQgghRC6qqlESKqLViqIk53soioKu6jkHoqeGoGuKipY8NjXEO1Xp4akemqphuzYhI0Sr2Zp1nrAR4sKDzmRV9Tp/BkZQDzK8YAira9Zn7JsXyGNofjlbGrd3+fUo9GRKB1S31lHZUsOQ/DL/sQXrF/HRtmUAbKjbwndmXMKoomE4yWAjJT3gAGiINeKkVbd01N4q5sTJH8CzPgxd5eunHsCx0xopyg/wxgebM7YXFwQ54oAhPP3WGirr2r4HO2paWbGuhoPGl2Xs7zgev533IfXNifcsjruSG684kvywsee/GCGEEEIIMWAM3FfAos/V1tayZs2a/l6GEEIIIYT4AgpoBrqmEzHCGRfp06s10unJICSQDD+CegDFSYQlqqImKj3Q/BZYQS1ATInlbHWlKgqnT5rD0ytfxfNcThp3FJsbtuXc77/GH01NtI7NDdv8WRxBLcDoouFYrk1IDzJ9+MFMHTKZn712W4++B59XVTAkv4w1tRtYW7uJRZs+9Ld5ePxz6eP8/Njv4XouK6rW8taG/zAoXMLxY4/k9bX/Zm3tRqaUT+BLE47N+J7Zrk3MjmdVzMRts1crV/aEVOurvBzBRCigEQ7o/PzyGVz754VYaXM6Pl9fmxV67Khu8QMPgM/W19IatTJCD8/z/NBNCCGEEEJ8MUnoIXylpaVMmDCh033i8TibN2/udB8hhBBCCCHaiySHlScqM9oGe+uqTkEwv8PjAlrAv22oOqD4lR6KqvjDzjVVQ9d0zORw7/ZmjJzG/uWJ17oFwXweXf5Czv2KQoV8a/pc/35QD2YMD08X1kNE7ViHa29vdc16xpSM4r6PHs5ZJdJstrClcQfDC4Zw94cP+ZUtn+z4jIZ4EwA7W6rZr2gEBwyemHFsZXM1o4tH+Pc9z/MrWwYyLRl66Dlmb5QVh9E0lUjI4JLT9ue+51b422oaE5UftQ0xnnirgoZmkyn7lWSdozWWWQUTNx1CXQxQF0LsG9q3HBRCCLF3yvXvubzaE765c+cyd+7cTvepqKjgjDPO6KMVCSGEEEKIL4qAnggvVEXN+KS9rmocWD6JolAhDckB4ieMnd12nNb2KX1FUTA0PVnpoaF4il/poasaAdXApOML/enhyoyRh/Dx9mX+/VGFw9L2TDSvUt+fNvgAAQAASURBVBTVP38uX5nyXzyy/LnOv/A0Na11vLnu3U7bYv1n80eMLhrhBx6AH3ikvLr2nazQw3ZtXM9FVVRcz8VxHRw3NdjbRVUTX8dArHRQVQVNUzn/pEk8+vpqAEaU5zN6SAG6qqCpKqVFmcPuaxsSYdPzi9bx2fpaALZWNWedu64pzn5pX3PUtDNCD8t2MXQZdSnEviT176HjOAPy30QhhBDd53kejpN4zZv69x0k9BBCCCGEEEL0IU1RSb+8pKs6iqLy82OvYf6nT1EaKeGSQ871txtaZtsjQ9VRlWTYkRZK6Eqi0qM9RVH9IeDJRygKFXD0fjN4dPnz1Ebrk22tjvH3COgGpm0mB6ZnzxtJOXjI/jSbraypWc/g/DI+q1xNTbS+w/0b4k1ZAUZ7H29fzsfbl3e6T2VLTc7HHddB1VTqo43YnuO3+mq2WikM5lPTWkfECBM2QjmP7y+6pqCpCnO/NIVIUKeuKc7MA4aiKAqalvgzqDCccUxdUxzX9ViyuqrTcze1mli2S8DQcFwP02r7XTAtB1NCDyH2OYFAgGg0iuM4xONxQqGB9W+iEEKI7ovFYn7oEQi0VYhL6CGEEEIIIYToM5qioaRVT2jJio0xJSO5ZNp5FAbziQTaLnAb7YIMQzMyqkVSoYSu6hlVISml4SJqWuv8+7qqMSiSaIP006O/zdIdnzEkbxCjikf4s0aCWiAZeqionXwCWFNVTp80h8ZkkHH6pDl8unMl8z99uiffkh7rqCWL7ToYmkGL1YrlWP73udVsJaQHaYg1EdQDOY/tTwEj8TNUVYXZBw+nJdrWkkpTVTRVIRjQyAsb/jbH9dhe3dLluVuiFmYy9LBsB8dNhB6tMQvH9bAdF5BB50LsSwoLC2loaAASs02HDRsm1R5CCLEX8jyPurq21/mFhYX+bQk9hBBCCCGEEH1GVVXSaz0URUFTNP92+wtP7YecJyo90kITRUVJDjaP6OF2lR0KhcECms1Wfy5Hetl7XiDCIUP3BxRKQkVUtlQDidCjKfncuYasZ6ynXShTGi7u6lvQCzyqW+t4duVrRK0oJ084jomDxuC4DpZj+eGN57l4noftOlS11AAe7gDsYZ8KPaBtxgckQhBVVfzHSgtDGYHIyo21XZ67udXCtBwIG1i2i+Mkvv6GZhNDVxmA3w4hxB6Wl5eHpmk4juOHHyUlJYRCIQk/hBBiL+B5HrFYjLq6Ov/fcU3TyMvL8/eR0EMIIYQQQgjRZ7R2Mz3StZ/3kYuhGRmVDlpaiytVVckzwjSbiQqAgGagKAoBzfBDD11pu8CeCkDCRoiQ0TZoXVd1fz2aoqKpGp4Hrtc2gD19PanjHNehJFTU9TdhN3nAo8ufY1PDNgD+ufQxrpl5OaXhYjw78yp+KvRIrd11s7+G/pYRemiJn7+hqxTmJ+fApIUem3e2tQd7ftH6Ls/dHLVoiVqUFASJxm1c18PzPFpiFoamYhjS2kqIfY2iKIwcOZJNmzbheR4NDQ00NDSgaRqapknwIYQQA1hqhkeqpRW0/bueMTewPxYnhBBCCCGE2Dcl2lvlvqCkKAoqXVRWqHrGkG9N1SiNFPv3yyKlFAbzsV2HViuaOCat7VX6jI5UFUdAM9BVLRFyqJofhqiKgqqo6KqOh4dpZwYGiqL6IUpACxB1o0SMECWhIupiDV19K9rOA/xg1hX8/eNHu5z5kZIKPCDR1uqpz19mYtm3sWw7Yz8nbbYHMCArPYJpwYOqKqDA6KEF/u+JriW2Dyrsed/9J99aw2GTB6OqCtGYjaoqOK4HXmKIOXJtU4h9UiQSYfTo0WzZssW/cNb+IpoQQoiBT9M0Ro4cSSQSyXhcQg/he+ihh5g/f36n+8Tj8T5ajRBCCCGE+CJq394qY1s3Kj10LRFApBia0S7UUAmpyYvjyVMF1Lbt6ZUeqQqR1N+GZqClBRmaoiVDDw0PMDEz1qKltb8KaAZRK4qiKJy1/5d44rOXAI+ySCnr6jZlHHf2/qcwLL+c19f9m8qWGo4aPZ2h+eUUBvO7HXq0t6F+S2Jmh5Y5s8NybUj7fjk5qlX6m6Gnt7dSMbTM34OAoaEoMGFUMQs+2tzj87/y3kbOOX4CAK7rYdttIVD6bSHEviUSiTBx4kRaWlpobGzENE1cV/5NEEKIgU5VVQKBAIWFheTl5eV8/yChh/DV1tayZs2a/l6GEEIIIYT4gtPSqi3SqWTP9MjFyDGwPJf8QF5y/7a3PekzPVKBRWo9hmagoqRVerS1t1JyBDWp6hAgY4j65LJx/H/HfheANTUbMkKPgBbgiBGHoCoK3zjsgozzhY0wu2NHUyXDCgZnPGY7mZUfqaoP23XwPNf/XrZaUWJ2vI9mknRMUxV0PbvaJxjQmLxfCVPHDWL5upoenfO95dv90AMgbrUFP54HpuVktNgSQuw7FEUhPz+f/Pz8/l6KEEKIXiShh/CVlpYyYcKETveJx+Ns3tzzT1cJIYQQQgiRkpqZ0Z6qdFwFsrvPl2pRlVnpoWX8XRjII2bH/QHm6e2tcq2qrRWW0mEQM650NEPyy9nZXIUCXDD1DNQOgp2I0fP2TemqWmv90MNxXapaawjqwYx9Uu2t4nYcz/P8dcdtk1YzSmm4GNOxMkKcvqRpit/OKl3Q0InFHS46eQp/e3YZ67c1dvucpu0Si9vUNsXYtKOJ6fsPyXiOaNyW0EMIIYQQ4gtEQg/hmzt3LnPnzu10n4qKCs4444w+WpEQQgghhPgi6qjSQ1G6V+nRU4qiMLRgMPXRhozn9is6kn+HjBCBZHuotuAj0d4qfdZIQA9g2hZGsm2WoemZYUpyqHnqPN+bcSkrq9dQFinNqsRIF97N0KMuWg+A6Zjc9f48djZXURou5srDL/QrOFLtreK2iYtLPolqGMux/FkpMSvWf6GHqqJr2b8D4ZCOrinUEOO75x7Cu8u2s3lnE4dOKidgaNz5+Cednvc/y7fz3L/X4Xnw7MJ1XH/pERREEj/raNymKD/Y6fFCCCGEEGLvIaGHEEIIIYQQok/pnYQeqXZRvS2gGYSMkD+/AxJD0SFzzkcqANE1PRF6qCq6p2dUoOQH8lCDKvmBiH+etjBFoSRURHVrbdvzaDoHDZnS5Roju9neqjaaGJ6+ePMSdjZXJR+r5+Ntyzlp/NEAfr/6uGNmzPcwXQvXc3Fch5gdp5CC3VrLrtLU3JUe+WEDJ6hT0xBD01SOmTbC32bbLoqSaFXVkWcXrvNvR+M2r72/ifqmOK0xi9OOGsuQ0sgeCdyEEEIIIUTf2zPvKIQQQgghhBCiAx0FG90ZZL47Qnowo9JDUzVURcuY85ES0UN+pYeh6okh58ljQ1qAwmC+/3Xomp4MbDR0VSM/mIeyC+HN7ld6JEKPFyvezHj89XX/9m+nZnrE7TimbfkhiJWc/WE5FjE7vlvr2B2qqmDkmOkBiUDEMLK36brKmGGFPXqehUu3smxtNWu3NvD351ZQVRfdpfUKIYQQQoiBR0IPIYQQQgghxICgsucqPSBR7dG+tVZAN3I+Z34gz5+9oakaiqJQFCoEFAJ6IGPfVMWIpqr+cPOOqllySa0poueu9FCAYQWDmVw2vtPzpFeXpAtqbet1PZeYFUuGHx4xO47lWHjJMCRqx7FdJ+d5+kow0HFDgmAHszdOOGzULj9fc9RixfoatlQ2ZQw5B6htjO3yeYUQQgghRP+Q9lZCCCGEEEKIASFXxcWeFtJzz3LQNT0rICkKFuQMZlKzPfS0NleaqmE5FkE9iOu5WI7V4RqKQoXUttblHGR+1pSTOXTYgQT1IBvqt7Kqem2H56mN1rOudlPW43HHZNGmD1EVlbpoAydNOJpAcs1RO0bcSRvqbUUBD8d1/K/Fdmx0re/eOmpqx9U+wYBGc2v293Lq+EFMHFVMxeZ6AKaMKWHlhrpuP2djs0ks7rCtqplRQwrQNRXP86hrjBEKaERC/TPjRAghhBBC9JxUegghhBBCCCEGhPTZGn2lo9ADyGq1pSgKhaHsWRepQCCkB/0KDz1Z/WFoBoXB/E5WoPitssI5ZnoMLxxKMLnGSWVjmT3qcH9bWA9RGi7K2P+ej+bnfJbnVr3OMytf5Z2N7/G7hX/1B623WlEaYo3+fjHbBPC3A1jJAecDQUDvYB6MqnDFl6dy9nHjuehLU/jOuYdQEOl+UFHXlKjocBzPb3UVjdt4HrTGBs7XL4QQQgghuiaVHkIIIYQQQogBQe1BS6jeEtI6Dj26K9XeKmKEiTuJ0CAV4ARUPRlm5K46SLXDKgoVEmrXNgtgWH65fzugBThj8kmURoqpbqljxshDaDZbuf/jR3q03iazhS2NO9iveESOCpTENHDbc0itxnJtUnGM7dgoipJVBdNXOpr3UVoYorYhxrGHjqS4IIhlu4woz2flxu5Ve9Q3tc0xaY1ZeJ5HSzQRdphW/7b7EkIIIYQQPSOhhxBCCCGEEGJA0PbgPI+O9EZLrVRFSFAP+Lc1tW3Iea75HiE9SMyO+9uKQgVMKZtAWA8RtRNVB+NL9yOoh3C9xEX3gGagKgpHjz7CP4/neeiq1uM5HC1Wa6fbTcdCVVSCWgA7rdLD8Vw8zxtQoYdhqJQUBGlojuM4HqqqoKkKw8u6H3rUpYUengcx06EllgiETNvtncULIYQQQog+IaGH8D300EPMn5+7HD4lHo93ul0IIYQQQohd1b6d1N4ooCVaKqVCgYCaGJSuKpofXkBijkesucpvg6UqKiWRYi499Ks8tvw5QnqQrx10JoamE7edjHOnUxSFomAhNdHuz68AiFqdD+iuba2nJFyEoeq4bttFf8dzEvc7aQu2JymKgq6r2GlBRNBIDJo3dBXHcVAVBU1T2G9YYbfPm2pvlX4/9Ry27eK4XqezRoQQQgghxMAhoYfw1dbWsmbNmv5ehhBCCCGEEHu9VHur1LwPXdMwk+GFpmpEjDCKovqhR8qxY2YypngEAIMiJcTsOKmPHQW07PZXAIWh/B6HHq1WNOsxz/NYtnMlK6vXMnHQGI7d70gcz8VJC2sc1/FnfJiOlTOI2dOMHKEHgK6pgIOqKngoHDi2lAPGlvLZ+touz5ne3gqgNZo5x8OyHLSgvH0WQgghhNgbyKs24SstLWXChAmd7hOPx9m8eXMfrUgIIYQQQoi9k6ZqhPQgarJll65omEBBcqi5oigE9UBW66vEfQXw0BQNQ02ECqqiZWxr21+nKJg9XL0rL6xeQIsZ5ej9jiDPCPP+1k94duVrfsDx8fblDIqUcuTIQ3G9tudzPBfbSQQCMSvWP6GHpuJHNgoEA4nvYar1laoqgIqmqVz5lal8sqaaik11vLtse4fnbGiOd1rNYdouof4pbhFCCCGEED0koYfwzZ07l7lz53a6T0VFBWeccUYfrUgIIYQQQoi9k6ZqFIeL/Pu6qqOrOmWRUv+xIXllHRyr4rgOqqqiJ9+ypcKR1DZItMQqjRRTuAuhB8BbG/7D5sZtFIcK+WjbsqztL6xewIwR07IqPUw3OevCsXBcp8/ne5QUhhhUFCJmOoQCGpqWmp+S+FtTFRQgEtJpjdlMm1jOtInlnYYergfbqpsZNTj399J1vZyPCyGEEEKIgafvJwUKIYQQQgghxBecqqhEjLB/X1c1ikIFKIqSNuxcyxkYaMnWWKqiEkjN/FBV/5iUgGaQH8ijONT92RXtra3dmDPwANhYv6Vthgdguw6O52I5Np7nYbl2xgD1ZrMFz9vz4YChJ6o48sKGH3ikHgf8QeblJWF6MiZm3ZaGjPuu67GztgXX9fxql5hp5zpUCCGEEEIMIFLpIYQQQgghhBB7mKEZhPVQt/ZVU6GIoiZngij+jJBUIAIQ0BMzPkrCxb261rZ1qLiei+O5mLZJ3DFxXQfPc4k7JrZrY7s2QRLraIw14XoehckWXn3N0FNhkYIRTA6SNzTiZiKYGTu8kPXbGv39h5RG2Fnb6t//eFUl0bhNaWGIgyaU8cd/fUxVfZTy4jA3f/NISgtD1DfFGTpI3kYLIYQQQgxkUukhhBBCCCGEEHtYXiDiV2t0RVXbAg5FUQjpAX/geWalRyJsKMoRMmjK7r/VM1QdOxlyxOw4cdvETra6itlxLCcReqSY7e73NV1TUJTUTI+E1LwPgFNnjfFndpQWhrjgpEkZx2/a2cQr723kX6+t4sZ73qWqPjE5pKo+ykvvbgCgNWbTErX28FcihBBCCCF2h4QeQgghhBBCCDGApAKLVEiSF4hQFErMmgglgw7AHyI+tmQ0htpWfTCxdEyvrMPQdOqiDVS31NJithJz4n6rq2azBUi0uIJE6ys3rRVWf1AUhbLicMYw8qDRFnpMHFXCr789m8vPOIDrLj6c/YYWkh/OPYjdcTLbdD2/aD2e5+G6HturW6TNlRBCCCHEACahhxBCCCGEEEIMIJqioaa1sSoKFfoVHqk5IYqiEkwGIAWhfM7a/0sUhQoZUTCE0yefiOPtfvjQbLbyy7f+xO/fvYf7ljyCaVtUtdRSH2vEtE0A4raJ67lYTqL6wU4bet4fivKDGffTQw9dVxkztJCDJ5RTUhQiGNQ4/aix3T73lqpm/3ZrzPbnl/TFHBMhhBBCCNF90oxUCCGEEEIIIQYQVVH8uR7t6ZpOQAugpg1E11WNw4cfxOHDD/L3mzb0AJbu+My/f9DgySyrXNXjtcTsOACf7vgcz3VZVrkKVVH4yuSTOXLUocTtONsad2JoibeWrpsdejiug6qo/nr7UiAt9AgYqt/6Ki9kEI3bzJk+ik8qqli5sa7Lc330+U6mjisDoCVq4Xkeg4rCxC2HUEDeWgshhBBCDBRS6SGEEEIIIYQQA4iqahha7rZLAIWhAkJG21D09NZWKXPGzvYf11Wdkyccu9vrSoUmrufxzMpX/AoH0zFpMRMDwXNVmDiug9sLlSe7QlUVDD3xtjdoaGhaIviIhHQChkbA0Ljiy1O5/IwDOPPYcZ2e67P1tf7tuOlQ1xTHcVxMy8V1pdpDCCGEEGKgkI+jCCGEEEIIIcQAoikqQT3Q4fbCYD620zZTQld1FEXFSwsWhhUO4ZY5P+bDrZ8wtmQ05XmlDAqXUBPtuqKhOzwSVSDhtPAFEgEHgO3Y6MnqD9t1QFHQ0Nqfpk8EDA3LcSmIBNBUBU1TMHSNUMAlbjnousrBE8oBWLGuljVb6nOeZ+WGWjzPa6tY8cB2PRzXxXZcAmr/fH1CCCGEECKTVHoIIYQQQgghxACiKSohPdjpPqlAARIDvCPtwoc8I8LIwqHMGDmN4YVDAJg6ZHKvrrMh1pj1mOu5uJ5L1I75j9mu3W+VHgDBgEZxfpBAstLD0BJvgyMhA03NfEs8aXRxh+epa4pT0xjLeMxxElUettN/X58QQgghhMgkoYcQQgghhBBCDCCqqhHSOg892ssLRDLu5wciqMnKgzwjse2UCSdw/oFncMqE4/l/s65ASxuWPjS/nBuO+36PnvPuD+fz2tqF/hDzlJgdT1R3JNmeg+v2XyhQUhCkrDjs3w8G0oaba5lzRg6dNBhN7Xj2yOadTRn3XdfDcTwsW0IPIYQQQoiBQtpbCd9DDz3E/PnzO90nHo/30WqEEEIIIYTYMzzPRVEG7ue/DFXv8dDviN52UV9XdUJGyA8ewkaIhngT5XklHD1mBq3J+RunTjqeV9e8Q0gPcvb+p5AfiDBjxDTe37q0W88ZtWO8sW4RnudlzAyJWXG/ssN2HWzXwVX7LxRo/70Mpg0317TM34Oy4jBXn30wH63aycjBBWze2cR7K3b421tjdsb+tuNhuy6a0/dD2oUQQgghRG4SeghfbW0ta9as6e9lCCGEEH1qoF/8FEL0Ps+2UIyeVVL0pY4Cj87+vVJVFVVRcT3Xr/pQk+fRVI3ySCn5wTyidtuHmI4efQQzR0xDU3V/36H55T1e74L172aEHq12FF3VcT2X5ngztmvjeE4nZ+hbgfTQI0dVx4RRxUwYVQzAswvXZmx7fEEFpuVwwuGjAHBcF8fxsFUZZC6EEEIIMVBI6CF8paWlTJgwodN94vE4mzdv7qMVCSGEEHveQL/4KYTofZ5twV74373n2CidDDjXVR3TMf3h4qqiAgqaopIfzAMS80LSGZqRcf/w4VN5dtVrPV6b4zpoyXZapm2i6AqWY9NiRZNzPgZOKNBV6JEuFMh+y/zswnWMKM9n0ugSHNfDdT1aYxZ1TTFKCkI5ziKEEEIIIfqShB7CN3fuXObOndvpPhUVFZxxxhl9tCIh9h1OtBk1lNfjVhZC9CfPtlB0o+sdBzjPsfE0HUXVut5ZCPGF4Dn23lnlZVvQaeihYTpKxhB0TVX9MAJSQUhuqqIS1INcOu08/rn08R4trbq1jiH5Zf59x3WwXIu4HQeUfh1k3hlFUTB0tcOZHKFA7v9vuOfpZdz+3aNxHM8PPmoaYhTmBVGVjqt1RBvP8/aq79Petl4hhBBiXyahhxCiV1n1lWiRQlBV1E7elIs2nudiN1ajKwpaKK/tcdfp84uw/fGcYu/lmjG0ARR6eK4DitLzi5iODZ4LyO++EPsM1wHPg73s+qXn2J1uT8zyCGYEG4aa+ZZP7fCirUJppITKhh2Uhot7vLY//udeQnqQ0nAxJ4ydxUFD9sfy1+vheC6mY6GrGv8/e/8ZbcdxXgnDu6rTyeFm5BxIgiQIEAxgEMVMikESlWzZsiRLHsnZM+MwnvkmesbzTh7N59dxbFm2ZEm2ssQgMYk5Z4IkCJAgMm6+J3eqen9Ud3U8N4BgkNV7LS7intOnu7q6uqp77+fZD+Mc6rvoeSNvqLAdK/W7Yj59nXMZx19+/0V88LKNUBQKQ1OQN1Q02ibsnomR4epb2eSfeHC/0D1594wDH/2eh1m3Kd5zMmTIkCFDhgzvemSiR4YMGd40mC28oVm3BdZtgnWbAACaL0Ep1kB/Au0j3k6wXgdgLlinERE93NYs1Mrg29YOpzULcA61XH/bjpkhwE9i9CB3THBeOuXtPmnxjTOAA1CWJnpw1wFn7JTwLtx1QJTs8SrDPx78YxPD/bmWuzbAGPAWnRvnDOD8lPRdOCOFu/a826pUiWR1AEk7K8Wb7CihIITA9cjngpZDXjXArC4GCyf3LNBzTBxtnsCXn/s2fuvCIRS0wOqJcYae3QMDh+mYGI3VDrFdO2G11Q9vZlymZfjkcyoa7XTRo17pb1e15/Vp7Hn9MQAiI+TTN54BoA5qzmWix0JgXmZN7DqeTAbWqZ6nuG2CGIXIZ8yx4HYameiRIUOGDBky/ITgJyyfO0OGDO8GcM7AbFNGaLntOdiTh+G2ZyPbsW4LzuwJcMde8CX93QL+DvhNs15L/N/qBZ/ZJlxPPHo728Gsztt6zLcDrE/k5lsJznkQwbgIuO25U36PnIqxnHYO4c+464gsiVMI7toLRjIDALO6KW1j4Gzp9imc+Zkebx7ciV7HxZzLW4m3Y/y/E/PmqcZizuFk79F++/5J6Tdu9ZbU1ndqvV/scbm31nLmCmHirWqP1Ttl9394XknbZ/jcVaoir0aDTRQ3Opf7WSAqVWEoIiuXMweaooESAm71MJh/8wEQr069jl6oaDpjLkzXwnRnFl1bXAfHdeT4spYwdrhtLrzREn6bNwKxuloykM8Ff9fLiwve6VkufvToQYBzsF4XPcuBab97ire/25B2D7JeG87s+JL3xbqtU7becc5lQFfkc9sKhJoMGTJkyJAhw7semeiRIUOGBcGZC2Z2wDmDPXMc1ok3YE8ehnXiAJzGlCTtU3/r2LAmDsFtN97GFi8dnHPYM8fhzBx7W4kozjmY6ZG3npgEBNkfcQL1LW2L64Bb5pLI+nc73F4b3EyS4285mLNossuZm4DTmBJWK6cSb5Js48yF20kKb9wOSAXuOgmi8c0SntyxAc7mvQ8553BbM2lfnFQ/ctdZkMjwRZaF5gceI13SiJO3E2/L+Hedd0RcPJXgIdG57zZ95uOF5kzWSa5/nLM3Rdqeasx33zLHEtmI87TX/z1nLrizuLnnVN8bzOoteC04Z0GAgWdr91aJNMzsnrJ5PbyepI1DtzUr70FN0WDERA819Btmm6DUEz0UFbpnRcpdF5qigTgiA8Yvev5m0LLaYJxJYptxDtO1AHAwzuDYPcyaDUx3ZwEsTfToN374AmMViK5jPlSFopjXUCsbGK7nMTYQRPmXi4u3a3318CzAHMC1MT7dQbNP9kgGeBZz0bVXZF6K+2Ypz6OcsUTw1UmDs/RnKOb2bdNPioidIUOGDBky/DQhEz0y/NSBu3Yk84D12nBasyIym3M4jSm47TlYU0fhNGfky6XbbaUSgD9JCD+QM3NxEf1upwFr/KAQBGbHwXrtyAuK254VROP8R4bbbYI51jse9ZwGzhns6WNgvTaY2U0lqBbcR+wlyGlMiXPu8+Lt93+8P30SzM+4WOx1erPgnHnkDIczN3GS++BwT6Lv3kqwbvMdiTrmrrto0cHttQHwUx49GCfflwq30wR3UiIdHUsSWEL0iJ5nGpm0FHCPcANzIvdPhPSzemBmLxmlzdnJiXauO2+GCLO6YL2OWCcWIMfD400QwO8s4cXshYngNwvOHHCzK0jnPpHz4Wvp9trvmLiaNh9w5qZmDiW26yd6LDDmmdlOzOVinL97BOb5BHbuWGIs21biukmh3vSyJ1xHZE4tAqd6feOus/D8w1xw22+rqOmx2OCCpZKazOz0J0iX+CwUyfQIRcdzx/bW3iac6WPgrgND1cG9QBUf1Ps9d2w4MydAvEImKlWCTA/OoYKA2xY0kEQdkJNBy+qAc3HcuV4LM905WKFzsc0uLNeGw0S2h30KMj24bYrAkXnQ79lsqJaTVlaKQpHPqVAUgnJhaTXqXKsHcBeW5cJyxBhIK5K+0PPJm8lECv/27SbkFytocsYSzz6cBc9PixGjQz88ZSI/Z26ftYL1DcqwJw5FMrYzZMiQIUOGDO88MtPpDD9VcJrTQXQwIVAKFbjdlohg7LXEg2zoZcy1unDbM6B6TrzQUwpu98BsE2p1GG5nTjysE/GSCEJAVE3uw/d1VwpVScbRXHFBz1nOXIAxEK9AMLN6IJq+9OK88F50PF9pa/IwaK4AQijc9hyUYi21ZgTnTNgyODacxqT8nPXaSz5+8GMX9sQhgCrQh1a+qzzvndlx8BDh5bZnoRQX78PMmQt7+hjU8iCokRfWVF60GVF16MOrItuzXhv2zHGo1ZFElgwzO6C5Irhlyr+X0paTRZiAYb22iARdYi0W7tpw5iZAjTyIooGZXTFu30EveG5biShCQJDYnLFIDZW++3BsgABkkT7jQPSlfd7tpNiEeUl37thyPkjdR4pv/JsVGLmdbs3CHWHvwClJzUZiji0jKjjngOv0bXvQVlv2L3dtgFCAMbBeR45DpzUNomhQS3UwUwhF3OpFPLc5n1886ucTzt357a1YtyUEc9sU52/k++4zLHK8VcT2UuqGcFuIzfHxcTL3eN9jMAZmtsE7DRDdgFYd8T4PPNa5bQLe8dzmFEht9B2ZG7htJe5l0UcptmRUidSrSROwOOeJMREHsy0QqwfqjVXuE+2LJDTfino/Phkvr49jA6HhwDkHd21QVZeWLty734keXDfW64AQGvQfc4WYsNDxOffW3VNYQ8p1wEn6tRAZlRygCphtonf8dTQe+x6UyhAqO66W16Yf/CwKoqaT3/F7UgTY9LfCYbYJZQnPQdwNjT3mrRsKBbN7oLQAgMsIeaKocDsNUOZCKVTAmQuNicAe1wsEIB4pHba3AmNC9HAdaFQBwZsnyo80joMzGw8eegq37r8PhAA3bbkaF6w6BwBgmx3YCgejClzO4PDo2BGiDkudq3zRPT4XMssUa0SsPpm/rSC009dHTY3OSaW8hgbjKBhLe2ZtzsygZgBgDmxbgcs4Wl0L9XK0Ngjrzf+Mx3ptKPly4vO0tSwy3zo2mNWDUih7f1sgb2N9Pe5Y4IoKQhU4zWmo5YE+G7Jk3BRncg5htgm6iOe04HfOvM9LiwZj6WPEH5/MBcL3uyeScNsE9P71XzJkyJAhQ4YMby+yTI8MP1Xwo/vEHxxuey4gHG0zPdpP2g8J2xS30wC3TTgzx8E6TUEQd1viAd82xb9t04s0awuCe/qoyJSYm4A9dQT2zHEZDRSOAmRmV9THmD4Ga+qIR8y6cGbH4cwclw/gi4lKdFqzcJozsCcOwTpxANb4Qa9YdlOcNwS5b42/IeoJOLYQhbpN2JNHYE8fiwgepwzMlcd/N8BtzyXEHO46S7LdsKeOgtumEMEQFYe4Y0Wj1TmT2znNqcDaygPrteG2ZgGPbGBW9y31HJeIR+ufRLSaPy6DiF873YIoBOZYooD6Yva/xEhFQdo5qVYr3LHhNqe97RawNTI78pwWjZQMiNQ2hrMX/LkoZBMj22DPc3zXTY0ufLMZBn6WQrzfuWt7Yo03H8WiuqPkXHq0JODNUd74CEflcscTqpgriHTv+KzXgduchj1zXM4hCXKV9c/0sGeOw20mx6MYt7yv6MQ5h9trg9lijk9mtkTnivB4C9t0nEos1hZJkutem5ljBX0XGjNLjjqPnxNzwcyuGC+he8X1syhjGS/ccaRNyMnULnozfcpS1k+Wcl1Zr50Yu+HfynW4D4Hqj1sRxCAyCuR+/GeGRZ7Hmwo4CLcpNB6Z2YU9fSz0XUz0cSyZRcAdW55HQuS0OmJ+kvOXuyihz8+6TQNzrJOyS5tv7XZa09LGjrsOxr/xX9F64T7MPfRNTN/9NwvbYlnmvEJ2fA7257R+91Z8jV3w+I6wkJu49U9w/B/+M6bu/GuRxeW6UWHFu8bM7IqgHq8NlBCRPecFWlBv7tSoClVRRUFzcKgQ18ZQNHDm4uLVu/q26azRrfi3l/0mfu/iz/fd5lhrHF95/ru47bX7wcHBOMeP9t8v7w/T7sJxXTiuC5e5YLF5mDlW/+wdlm5Nxh3xDM5jEfn+HCTGQWwe7/N8UaQWSnktUt/Dx6YVpb7nPdfo+icA22HomQ5MywXnPFLjQ1jH9qv5w4Ks4Ngc5QfGRLYP9ZNYs4LnS1886oc3YzUnn1m8jHmxQxFE5j/T9s14YsH49ecD7mUdiwy8BTIrw3Oyb6F2KjLIOIuu5f587r+Hxc7HX5PThfGsBkiGDBkyZMjwTiETPTJkOEmcbBS1/xJgTx2BNXkY1vgbcFozsKaOwp4+CqcxKR6emQt76his8TfAXRvM7MKaOAh7dlzUyPCIIrfThNOYgtOchtOYEvucPAy3OQW3NR16IezzUuU6cBqTsCYOwm3NiKyHt9iOxe3MvSvqRvh9l4bFkv7MsWR/sV5HkC6xFy4/64MzF9aJNwKhw3uxS7Qr7EnMOdx2Q5CGb2GfJQi/RVi9JPbh94Nf+8B1JVnX76Xebc3AbU4t7L/NuexXzjns2RMLN8h1IKNf48S9ExTLdltz854vM9up37udRl/BZtEWL+F+97LBrIlDgrAKWYXNR3KLWiwpRb2XaOvFbBOOJwSJtnlEbegl3+00pP1Umggbz3AJR9TyWN0HbnXlfRa+Z7jjyPHObRP2xCFhueaTtWFRMX5PhDJn5Gl4Qq+fwZR23vK3EORlJPPJFPV14FkgJcjhGCEnxptv/2UvKup9IfhjWAqL/r22wFwt+leQR257TthvSBLN7/tu5LovqqB3nLANnaMUxeALhp2IDZr4jku7JGd2fN71VJJNHqHmZ9YltouLUaEaSRG4dnI+sLoJQpuZ7STZGhINnJYnmjI39V6XBKt/jSK2kDPzZgFEz4NHMgL7FdiNI1UIDROMnhVUYJMUG8dWT/SJN4/6IlViXnEdYYUnM9YWOfd5czB3bZgnDghS17/Wtgl76iisqSOLmscEISz2x7pN2ClFkEU2j+uNn6NwQmtIe8+DsXveW8MidTC6sXEeJT/jc7Ar1770+z9+DeMBEAm4NtovP4Lm0z+C25hC44nb0Nn7BOI1Bvx5E+AhIjYISAjEOgeEUKiehZWuaNCIKvrIdaBTDWAuLlt3IVZVl0OnKt67+nz8h4t/FT9z5s342Jk34WNn3oyclkMtX8XPnHlz36a/MPEqWGj8t+2OV9cDaJttIcZwF5Zjwo1netimtFnknMOePir7jseI6fBvxP8tMLMj12lmB6JHWOTmjp36zMfMDli3gXolB0NLZqWVcgrO25AufDywt+llHorzaXYsWLaLrulgrmmi2REBBcwKCYaOLQMBZLu8NrvtqHCQ9kwSDo5gZjt6Tr742gfO3MSCAkNqYW/ORUCAVxvHD5Tws8LcXgsA7z++Q1Ztcj2TwWjWgu8jkTb5ARLzBIlw1xaC0AJCsi+8yLnAE6fkuImLc77NX0p7F7y3M2TIkCFDhgxvGTLRI0OGdxDcNgWp3ZxOJS0BLh/ixZ8crNsEwOHMjou6I3PjcNuzgjxuz8osk3c1OAfr9i9+/lbCf/lgjuXVrkgn+BYbKRaNthPZQ3GijHVbcJozIoPjJCK+3OaUqKsyffJF1kUU9jye7QnRY2mZDSyUKeWPZc5sSXT3y+7xXzz97JfUtnl1FLgkLGywbmthwld+z1Osa2wpMnDXmjfanFm9yHhgZte79yZk9lj8pdZ/4Y9H+KWJL8GOPYGAczitmUifzXc9hNCWJnokhbL5xoDbnBbjV0aoizHhNKck4e40p6UVFHNduJ05uOF7hbFoxgQLbCr8DDn5lU9UxorOcr+wKQuEg761YuLnl1LTg1kdmbXGU8gQSU5zX1RpRUS1MOHMep1EHyaLrgZEF3cDe6tIFOwSwXptcKsrxyn3iHu3NSPvg7R9+/cVd4OaFQFJK/rCbc969l2+OLUYkjl27yWISkuOGdZrRTNe5HUNBDHWa/cXoP36C54wIbK3kve+G1pTBKGfnr3pZyz49xRzLE+YcWUf2nPjHkEs5obI9fUjmLvt4G83uMbhPgCCey4Qb/wI6PR6D/H6ItIyxQcL6lawXrvvmIoIif78HhZjmScKW6Z3rWJrgBdZHpD7zMv0iIt8rhAw/fvedVKFPnGNJ0O/8+aXxpS4HmYH3OoGkf3MBbd6Yt1L6afwnMgdS6xxkjyOtdG3EmOOPFYYSqkejMuQyGTPjQfZEpYpBU2/hgYQXGcWytzx71fxBwv6M5x54Ak+4b4GkLpWcs+2Z/LWP4l8PnXnF8V5hWs3ePY+4o+AiAai6yx3bOS1HDTFFz105BRNiFaODV1RwRnDgFHG58/5KP7V2R/DjWsvAGEuzh47DecuPwtGyOrr7LHT8PFNV+Ks0dMS7U9D26tbZjqm7LdutwE3tH4wqyezZZ3mDKzjr4mAAF8Y6Jfp4QbjgFs977k5NC5cR87LTnNG1l9joXpVfl/C21eavRwlQCWf/ir9xGttPH2gA3TngO4c2l0blsPQ6TlodW1MzfVgdTvyOQQAnMYE3OZMZBz7NlWs246sd9wO+k2KQOF11BGZVPK7mNCT7DNn/uAKziJCYfA7O+hP///efebfl/G2RX4fEmJ9UZnLe6bniSL9181IBqF/D4fnhpRnIGfmeCTjPhXevuzZE17mVCcybqRYHJtz0rKS+NtUmy9DhgwZMmTIkEQmemTI8BOMdKHkJwNue/aksglOFpy5MpvGmjoCe+oo+gkegCcSpEQfxwvwxklUkaWRQj62pqMZHEsFZ4F9Wh9S0u00U1/imNWT5x2HPTcuomzjUW99LFvSwKwu3OZUYHHgE5y+J3OnmXruQnjwIvO6rcSLLbOEUGNPHBLRj76dkhRXkpY00f2Hvkshqv19if9ihJ//Auvb0oSIeUGkRCOB3W6MlGeCLHRmjgcvxSmkQdzeSrbL6sqXfc5Z1LYiJhhGSK7IF9ECofNZCYlsMo+A8QtfS1spUYQ5XJ+CuS4mv/sFHP/qf8SJr/8hzBMHgv6KRB074Cw0LjybHF8M4I5Hqnrn7tcg4vPYVCXOMfK3iMR0e0F2ToSE4DwhlknSxCdarJ6saQTEBa3+Apr4tytIdP86hgo/s25r0cXt4/AzJljXjx4WBC53bCF2h4TkcAS6tNhhdmCp6BNLHunq2zfKuSMlMyp6vsE1ZR7xlkYsiX71ImVD5KEkFUNZA44v2qeR2945uL1WQJDFBCSR7RQQS25rJpKxE42EZwGJBYBJQY3L+5Z1RHCBsC1sikwGOTZ9wUT0GfdJPghyK5hbrOix5RhxQseLZSV1W7BOHIA9dSyYT33xQQppTojEbkTGY3je4iGyM2xzJAlhX3CSxGJMoPWvrb9/7koxV/zOJ119O5uwMJSydnYaURu7kOAFCKJSCB9m5Jy4Y8OZPQHu2nBas/JcIhlfXkS/7M9wRLxHZst+YSyx3unDq6QYxO1eYGPmuoFNkmMJId/sgnWbYN1GNOuGBTXhItH68v5vCos+7zvOQoKSl90LwBOW49k0gXAc79MwSSz3G5rjmG0FfRwjxcdKw6BeXQhNUYXo4dkLKoQCrgPWmJIip2KawVjvtkBiWX5bSmP42LYbsBi0LH9uCsj4XqcBgIN596gzewKu2QW3zIhVZmANx5MZXqExyW0zIij5Int4/RfPZnPCEsvsyr729zVf1hIlQDnXvy7RA3ubgGsDnVlPeAMabQuMcTgOgznriYDMhWv1grnYv18960V75rg41/A85lvOISD5uZctFV6H7amjwRztfeaPG7kv7xkn0k+uWDPM46+LJnbbERFFQq7p/n+ODLhwu81oAIB/LA/dgy/i2Ff+PY7+zf8P3QPPB9cmdl5g0cCOyPwQFtRDcyx3bCFsxASHSN3G1kykH8Nw/edW5sJtzXrvBKFt/fs6nhXGk7VAmNXLLK4yZMiQIUOGdwiZ6JEhQ4ZTBrfXRvfAC2jvfRztVx4V1hT9vIpdp28E51IQtsTou41jwZ48HBDVITuB/uAJol6Q2CciUedvxge575E5Q+OpH2Lyjr9AZ//Tke/czhzs2fHA6smzJxM1Y8ZTI+L9KMc4scU5k9GDqT7E4RfLefqYdVvihdCKRRn6L9he7Zwk2RwidvxiwKFjue05WBOHxIum1UWCTLR7fYWe8HYAEj7+UjjxfeVj4oMz52UGxG2OgCjB5Fn5xCOufV97ZnYl0cS67YTFkm/b4e04SYpLYoxLotaP1nW9bBdBQKeTP+GocW5bffsqXLOEmb0kie1F3Powj+xFZ9+TYvtOA3OPftf7cYwAZ8E5hQm+IFOAR0kDPxI6Jp70Q1hQ8j6AXzfInjkho+ojv4nX4PDJYb9Qsx0QyixERgY/CKK8/fPijIFZPRz/6h/g2Jf+FY5/+d/C6TQ9CyMhXrnt2UD48caK05wRtZ5S7HjC58isriDefYHIFy38jAGr69nvOKH7Q1jcABB1NkJkjRTr2nPBNpIkd73rEESo+1kYkpTyrqU9fcwTF+KihxWy0XE9wcLPMPAjZINId0kgxW2zPFs7cY91vHN3/I6R2wnbsYB0d716Wz5BK0SLQGyBd72EZ37M9z5CGJuib0PkHSL3lBkhC12zHcwtfj0cFhqbQGKuCYNJ4YYHtVf88diaFVknjgNp5eTNL3KubUzJMSzJdrMbyr4L7qtAYPWFm6go4x8jIhyFRBDWbcXEpJAo4wvePtnuz4X+fGn1Etl9zOqKsWz3EusRM7uwxg964ronlpgh0cO1o4JKqF1ua0ZGWssstZj4S/OVoM1WLyD2vftJrm2uC2ZbcBpeO5gTWC8xvwZSVKRGqC9EVu6smFe8mlOi9kYzmPs4iwjbzOxGRJRox3iZdWFC1Y2utazX7rO+R8VNQ9GRVzQxHrxsQ6c5DcpcoNcGIQQKIdJ+kHRbIKE+l2vsIizbAJHpIc43INx73vh3uSvFPtsV4ilCwSR+G8R5RJ9TImu2n/nsz2fMtzc7gu4bL8Ce8+Zd75oxO7b2+QJrylq0Th3HzTNfwkUH/xzn6K8nvl+pTGFj43Hcfs/zwtrLzwpl3nm0JuGYXZi2aFNrLnh2c3u+6BG1ewpnI4WzUIJnEzd4ppGdwrx1KBCXWK8VzSiKZfm53ZaYH72+Y7Yp5yb/WP7vmRcMEQjpbpBVEp4f/GOEbEonb/1TOLMn4MyewMStfwLmZY0EmR7es5PVk4El4pqGsjtCQQfh+8CeG/fqMsXWprBIbHZgTRyENX4w0medfU/iyF/9Do5/7T9h7tHvCVHM71e5fvnCjG/nGgtg8a+F/wxyCmwuM2TIkCFDhgxLR7IqW4YMGTIsEa0XH0BrzwOyMHQY2tAq1C76IPTBFckfcg63NQu1MnhSx2WOBXvqCEAolEIVSrECQqNRd9x14EwfW3TWQhj+Sx3V8yB6Dk5zCgAPbGF67bfESqy95yE0n7kTAGAefRVqdRj60EoAATHptudANEMIR5E6CUnS0A1HxLoOiKqLaPReR74kMrOL1gv3wWnNoHTabugjq8WLmp6Xx1NLtdT2Bhk7YVLCDvU5D9oWssNIkNFWD9AMuK1ZKKWajKYOt138P4g0Bu8BnIHqudh5s8h5I0yMMFeeN7fE77kbKjIctlmJix6lejSK2bPeAufgdg9Ez4trEn6xtnqgRiFCFlLNgNOIFrLnLBkhGC4KzF1HRGhapvApnz0BpTQQERWIZ1Xik0mcCesZqucFoWMLQosQIogr734Jiy/M6ib6k1lduW8A6Ox9LPK9Hw3KZRQ+AyE0GlUbtnIJ2/dYASHZPfwSJr79v8EdC7VLPozCurMxH3xyxG3NQS3Xpa84/Ej45gwQy7wK97Hba6P9ymNgvRaYI6JbK2dfAaJqIhutjze4MzcOauRAFE0KNO1XHkPv4IuiP47uQ+u5e5Bfc4Y4505DEKIe6Qrmgug5Yf3k1+fIl0CNgtffPXkNnLkJQQz695FPPttewXcOWZNDHMMnb0PiWqIOiTePRATcXmj/giQkmiEi8L3v3E4TRPF9/z2yK0VEDospQDAvRe4910Xi2lg9IFcM7cjPZmkEglq4UC5nIIomyC3/PvEL09smuDe+Wa8FmiuAqHoQ9ezaIqvAsURmUKcBmq+AqKFHY7nPQDDkLCguzG0T0Az4GUSs1wFVDcDIy2vt26bFC+GG9y/PPzQfsF4bqAyFSMZZKMUqQBTRZpmNYsG1TdB82asF1BT3ty8OWN1Q20PEXOh7JSKsdkGNvEdGh4UpJq+ZiALvgoTnCcY8mywL4CI6nfU60EfXROZyblvC5ioupHN/vySReRfdLih6zBwLVNU9EjRqBerPb9x1AkHEu/bx4AAlX45c68AakEt7paDPepEx7Fu5wcsgIarm9SVD98Bz4K6DSq4YEQv99rBe2/s3B1fUQPzrtsAMMV6ZZ8/kxrMxRQvAuk3MPf4DsPYsiqdfBH14dXTd6mslyuHMngBRdVA9D0wcxNT9XwfVczBPHIAzcxxKZRDVqz4NJZdDy+5BIRScOXB7begAqG2B5/JivHlz+mKtMVtWB+DB2sYcGzPdWcw6TdSVHMqKDpczYXeVCNEL6pX4feq25wCqRJ4BI5ktHpluHt2P41/7Q3DHwuzD38boB/6pWJ+9QA2aC2p0hG3t4s+WHyg8jjKbAxjw4cKjeM5aDRdim1E6i39WuRWUcLD2M3j+hRzOPK8e/Ji5gNmCqxlwmQvNtNFqdaHpDAqlYs61ehEhL9Ie15vjmFe03stGAuci8yKWVcAsUz5L+HVE/Awfv2+A4JmMdZuiT1ggqkhr2G4LvFCB05wCzRXEPMpcECYyvDg4CKPJuc2/Tt0maK4oahSGso+dmeNCcNNygXgRykyJZDuF5w7vuhIleB4AQs/C8Xak2aJyBmvqCLT6KKiex/R9X5Njp/Xi/ShtuwRKsYbGs3d7z+YExa0XoH7JR4TgHq8l5jrghIB1mqH5wAGBljx2hgwZMmTIkOEtRSZ6ZMiQ4U2htefBIMo7BfbkIUz+4I8xfPOvQ6uOJL4X0c821PKg93KweLjNafHyw124rWmAOVCrw/J7zlzYU0cWLXgIcokIwke2b84j8Ah8UsV/wXRD/uQnA7fXhtuchjawLEIozz3+g2AjztHZ+7gUPcJttTxLoTD86FxQCrc1J/YbfhlzbEDVhf2LR4a4nSYmb/tTOF7UY+/QHox+8J9BKdbEPpkLtzUDpVhN+FqnecED3otlLOLTaUyCmR0QRYNaGUxG89omFIhsFkJTXpojpGZAaDBwAIPe+TuwZ46DaoYg0V97Bk5zCsXTL5ZjI+wrH1gfeAXPQ/UjuGUGL99A4NEfj872bXLMHkAV9A69jNlHvgNwhuoFNyPnCUdBFGUTSrGS9G5P8ycPiQZwHUke2l5tDGa2JQEQaZcUdbpB8VY/utjqgRh5QdBpBqhmRGo0CHsrQaa47Tmo5QGvCHBwnUlMFMmvPRMRKxDmAgqFb+ESjsBl3WZE8AoTsjN3/63005998BvIrz4jcm8k4EXwuu1ZKKWqjMz1keqP7/WxPTsObvUw9/j3I8RgaesFUNSa13/9CTxmW6BUlQTp5B1/Hvl++q6/xopP/xcA8ARTj3TmwjaGUyVyD7jtOVCjANZrw549AaVUl5Y/0QN7Fj1h4cg2AUIT19FpTKH90kOgxSpKp+2WfSnFnEgNCjOwOfEtOjQDEUujbhM0X45kxPSO7cPco98DVTRUzr3OE7H7ZPiFM4FCkcfy+7g449WdcENFw4NoZwZu9gQxbHak2BYeW4JMtUT/uI4QXf3aFK6wX7EmDmHyjr8At7owlm3E8Ad+M7XtfnYA86znRD+aULw+tScORc5B2q7ITI+o6MFMUdNDrQ6DqDrAo1Hq3BXkcjzzhKgauOME9Voc07MwEuS525yCv175mTLwsivguiJmPkzScQ7Xz5rgDMxsS+GHuy6c1gk0n/ohiG6gfOZlAUHsWDJq2+21xVxRG5HnKbM8/Bod/jksECywUOFihGogcNsEV9T0NZ4xwBM9pODiCnI2nKkl2t+C05oR65InJgbZgE5EmI1kt3iZMD4JzcyuuJYA5h77Htp7HgQAdF9/FkNX/2LQNK+/IzZboXuDOxbsySPQBsbArC6m7/kyuq8/m9odc49+F+2XHwEAWBMHMXzTb8TW6XmyNHttAG04zjiOffUPksXYG1Ow9z8FZdvFUAgRlleelR5VcyLzwzaFgOvP41YXZS2Ppj2/fek3X7oN6yvLUYEQJY/MHMJf7b0DFnPw/Xwd/3n358Xc3qcQfDgzTWTSzYEWyv3P18uSm7nva4HQ3GmgvfcxlM+8TPwunmEYyu4CgB1bR/DUy+MAONaoU3KzIrUwqszhqDsAALg2/xwoEe2gBCgcfhw495xoWwA4rnh66fUsmN0uLEKQN4QY4VtaRRB7BpKWasyVooBrtkFodM3kjm9DyILnAH9fnMl127e04o4NRgIxm3UaEVFQ1KfjsKeOiQMoSiAUcg5OoveJ33bfclFkqKUV/O6A6Xm0XrwfndeehT6yBtWd1wbnwVwwuwfiCTbiuSIkpDMXU7f9GfLrz0Zh83lecEeQqUao0l9Q9eYFouVgHdsf+cqeFuc5c8+X5TWZe/hbUMsDyK3aGmQ2MybmZ+aCuFTM0dxve2ZvlSFDhgwZMrwTyESPDBJf/vKX8ZWvfGXebUzzXV4gO8PbgtlHvgvrxGvipcd7GZgP3LEwc9/XMXzDL8uXlTBYrw3L6kEbWAaqGYtqQ9zOAhDkPc2VRESVqovo6kUIHpxztF74MRpP/RBgLqq73ofStkvjW4Ua7Ioi1ieRPRKGeeQVzPz4qwBVoA2uQGHDDiiFMuJkf+/QSwA+sOj9MrMDUAVueyZBGPsEdLjvms/8SAoegCB6Wy/cj/qlywF4xLFvvaRqMopffBkQSgBkhoEgZJORvIHdSbIOBTM7ooA0c9PtPHz7hLg9kRfZSAiF05gUPvy2ieZTd6D57N0AgPbLj2DNr/2ZsCyKFTwO/9uZmwjZ05gQ5KG/AYuStqF2AwCzOiCKipkH/0EW7J69/+sY+dDvynb65yHum1iUO3MT9lb+S748ToioBHyy22uj68h+6B19Fa1n7oaxfCNyK7d623rZD+0ZUCMPt9uCwrkgtsNEI+cwj7+O8W/8VzCzg9yq0zBw5SdBWEhMiF0fquVgTRyS2Ql+5KUvQoiMBr9fY8KOfy6ODWv8jeBjqwenNQMtJGQC4jrNPvwtWCfeQGHzLgxc/nNgvRam7/mKGKeEQMmXkV97ZqKPg34SxJ2wzIgKeczsSsFP/qTbgtOYhD68Wghyfn+qujhGTGxJIFy82MuyiWeRMFPUsHGaU4KITsmcE+cf2DNFvxAZCP4c6vbawjLEI8dZt4XqruvFplbKWu5F6fvZGOHxKu1LnKB2gi/+Tt3+53I8uL0Whq//XPS8bBONJ2+HM3MClV3Xo7B+u3ceTqLPUjOdwv3HQhZ9Xpuc2ROh/o1a9QX1OcKEtV+HQYzL5rN3S7LXPLYPvQMvwhhbl+geGekcI6rTrPNkRotvPwQExJzroHfoJUzf82Vwx0Lp7CswdOUvpHq+u7Hi7mLu8wR4GYXt2asgPC/5tmbBnOFnw4SzQHyYh19F7+helE7bDarngnuVuZi6/c9hTx8FADhzkxi84hPimJ6gYk0cxOTtfwFu92As34TBq39R3iOiPwILKCBdiFwKwnZO3La8bJ5A+JXZa8wBuBJZTzlnsGdOoLv/qcg+2y/ej87exzD6kX8BxSh6+w5lv/kZOF5mVXBudvRe8WzJuOtIwQMAzMOvwO21oXhZTMLKqy2ySP3sPBatwSTGZxu9g3v6Ch7F0y9CZ+9joEYR2tBKqNUhcKsL4mWMLRa9I6/0rRHXfOYu1M58DyihMjtAAQElBCpVwKwecqoBmwkCnzsW3jN6Jr5/+LHU/YXxF49+Eb94cBKD5WH8cEUdlkdQn+jO4IGDT+CCka1wOQPjLJKZAIQzSoSQ79ehiM/ncntfpH3j+cjn3f1Pe6KHt7fweJGZFeL/H79mK/a+MSMKkMdQIiaqpIMhpYEdxoHId2vcA3jjeAtrNg75jQEAuIyBMaDT6YHZFixbRd5Q4TKGeKUQxriwGkOwhoYtygIhpwWiR5+juWV6wR48eF7wsyjC9yPnQc2ZWJZMGNJ6zxN4CQBpxck5OCGwJg5ByRVFdppshwjacBoTIJRCKQ9E1jnW62DupYfQePxWAIA9cRBUM1DefiUIpaK2j2MDfoCU//wpMz5nYR7bB/PYPtB8GfnVp3uiGBNCXb4073O7XwMmDn1sA8xj+xB/nujsfwq5lVtgHtuP8W//L7itGZTOuBgDl39C9HMsIyVDhgwZMmTI8PYjEz0ySExPT2Pfvn3vdDPeUtiz43Bmx4Vfba8lLAP8f9sm/KLGYI5Ik1c0EFWDUqxBLdeRW3ka9JHV8x7Dr5PA7C6oqoMaxSVnMLzboQ0uR3vPA6nfKaUB6KNr4TanIgSmPXEQ5tF9yK3YnL5T5sKeOgq1OgwlX0rfBh5pMX3cKyAeJxm5JGfEC8fC58I5R+PJ29F67h752dxj34dSGUJ+9el9f9ffMmLxsLzIYDAX9sRBzE0cTN3OWLYh1V6hH5zmtCA+OU9aSLmOIMLC3scpwlX75UdE1HZ1OPKCS1QNbmMKSnnAi5rzifRZzD76PfQOvQSi6qhf8hEo+TKU8oAkesJI7T/mBp/HowT9c2tNp5LL3LaEnYBHSFqTh6XgAYhIxdbLj0AfXJ66XyAgneXftgWixOzSUryZw4XVHTYjBQ9AZJWkWWX1KzyeRvoysyP8qV0HNC3rIRRBzjpNOJ05HPubfw0wF81n78LgtZ+FNrwysJcyu7CmjoosAkpBc8VEf8/c+3ciK0fVJeGvlgfQO7gHzDYjhB4A0HwR3Oph5pHvwJ46ivI5V6JyzlWSNBJZWfNHOvrZEGHMPvgNDLz348J+xkPz+R+js/dxAEDj8R8gv+5scMfE3MPfktuolSFP9EjpLln3gmPuse8n/P3jYoI1/gYmbv0TgLnQR9Zg6H2fF9Zdjgm3JwikfrWJpu76EtTyAJTyIArrz47YVyX7Q8xfC4mpflR52j3CrS64IqLbrYmDUvAAgNbz96Ky8xo4jSmopXrquuR7v8tsiJDIJ8l138bG7IB1mhEBzDr+GphtikwrxuDMTaD7+rNyvEx87/9g+af+H0GSpd3HYeuxbis920tmNnkRyhGS0olkwIAzUS8BYdEjROhxLi3JfHRfexrG2DqxH0JANT+rSYbsBm3szGH2ri/B7TRQPvtyQbL5kdD+tiwp3DSevEPOna3n7kH1vBtS58lE5pdvp7dIuKGxzXotSZCG5197+hjGv/O/Ac7QeOI2jNz8m8h5bXEak8GaCqD3xgsY//b/Qm7tNlS2Xwnz2H5MfP+PxJeKKutLhefZeKbHohbm+cCCwt3cMcE8Erf5/L1oPn0nlEIFA1d9CmptFIRGx8/sA3+PzqtPpO6W2yam7/xrDL/v8+Iwi7Cu5L4lnBM9Pytk2yOb3WtB8WyuJu/4C1jHX4NaHcHQtZ8RIitjiXnE7bbQeuG+vsfXBlfI51bzyCswj7yCyo5rku10HVHnaPoYZh74eyj5MkpnXITi1gu99qesRxIEChWCh0IoVKpAJQoU72/W60BjBEUth5aX3XHu0Cb0dA13vvbgPPsFZuHi/6ys4hePjuP1TnTee2xyL3bQEjqP3YoeASq7bkB+dG1wTqExJe9xx4EzN4mZ+78OcI7Kzmuh1Ua87/pcz3ggzjyZHuurDv7H587BwRdfAGLD6KrVbWye/VHqIb7QuAaHvv8G/vUvjqFe1AJLLpfDcRmsThfgDLYjxnWn56Bc0CP7sBwXihRh/KxTB9zLjAzquIhgpLlHvwe30/Du031ovXAfiKJh4IpPILdyS2CDaHYj55zILExFMmAjbGU5dev/Re/A84CiivU7V4bqZS0BwXijmhGRau2ZY1Lw8NF85k70juzF0LWfDX0q7nlpO8cZnM5cJPu698YL4hneC7AJagD1n3+4Y4msljCoAqJqqf3iz/Nzj3zHy7ADWi/ch8Kmc5GLvT+82fqFGTJkyJAhQ4aTQyZ6ZJAYGBjAxo0b593GNE0cOnTobWrRqcfkbX8KZ56CsQuCqqmiR+/IXtiTR9A7/DKsyUPRiB5CoNbGYCxbj8LGHdAGVyYsgvpBvNjzRZPdC8FpzcA8th/O9DEwx4KSL0EfWQtj+cZkLQxH2Pwo5YFEewsbd6D98iOwYyR99fwbUTrjEvF7zjH1o7+Cefhl+X339Wf7ix4AwBmc2RNwm1Mgqg61PiaP7RdVdLvNxdXRWKC4uX+O0z/+O/TeeCHx3eyD34CxbMOCmSfW5GHMPfZ9gBAUN57rRetyFDbtSr3O1tRRzD7w96IwYwrJG0ftoltQ3HL+gttFwNy+L67cteG0YhYWaRZAjoXO3sdFFGooEh+GiCCHokEt1eRL7vSPvwrr+GveMRxM3/lFsSNCMXTdP0mNnD4ZsE662MRtEyxEarRffjjyfW7NtkTGQGLf3ejLLncsWBMnBJlOKKrnXge1NjLPyyuHM3088olSrInI6RixEIY1cRBOc1qII2YX+fXboZaFTYbbbeDEN/47nJljUMqDGL7+c5HIycjRPf/92fv/PnKsuYe/g7J3X8ptJXHdjZCaAOA0pmAe3QulWIM+vBpKsQqlUAU4x/Tdf5N6bKVYReulhyRBZx7bh9yabYki5vOBqMl7zTr+Gmbu/7q0huGco/nUHZFtms/cmbhHnPZsUJsgBr8AOBCQFmEExaQF5h77gexPa/wNmEdeFaSRV6zb7bVBtBzU6jDsycOR34bnltyq0wKrnT4C0GL6ye21YB5/DWplUFyXxAkIO6o0O8Hxb/9PuQaWd1yD4uZdUAqV4Pi2JcQE7kXTh6Lq4/3CbTMxz6i1EWGXZvUw8d0vRKzkxI842i8Ij/RU+JkczIXbnBaCXPz7cOZEzDKEdVvJ9UEShUGWWDhSOgFFQ2vPQ5h79LsgVEHtkg8DEPWVqFEEzRVhLN8EfXA55p64XV7jmXu/AuNj/xJUz8Ntz3pNDFkxcQ6nMYUT3/zv0XuOM1jjB2As25DaJcJKzATR896asnjRIFLc23VgTx0FFBXawDL5eePJ24PxyFw0n7sb+pCowZVm4WhPH4U2uBzmiQOYvPWPgy+8mkOJ9jtmUrx5E5BjE54wzQGnNYvGE+I8nMYkZh/6JvLyfgugLrAGWCeCgtS8Tz2fMCTpGrPkso4nC1v716J3cI9cK525cbRfeQyVHVcn6wIAAGd96woBIpMwCgKixcjy8YOY+P7/P/KZY3Ux+9C3oFaGxfPfPIE5SnkACqGghIASCp1q3r8JNKoCnEFTFOhQpehBCcF7V+1aUPQAAItS/PHKgZRvONoPfhNsdhzMO9fcLb8deq4K1eHys69cG1M/+ivZv/bkYYze8tuAomLmgX9IF7ziz76pmR4OmNmB22mg6LoYyiXH88ZlBfA5CpIyt59wxTz9l997ET9z6Qoo3MVInsN+7QmQV+4BMcrg57wfjjoGQIgeOd5D4+FvwZmbgL5sA8zJo2gQhqFrPwulWA/OO5bpAQBzj3xXirlTP/rL4HxcG7MPfxtjH/5dEWTBucg+fLP3px+wwVzYsyeE4AEAroPpO/8aAECMAkZv+e2IuEtiz9fTP/qr1N3bEwfRPfA8ipvO9c7Ds8z053rGYB7bh+kfB24F9swJb1tX1mRazPoaznoGAGqIeTftmdoPmIiPq5kH/gHLPvYvYxuLdfkfWxBchgwZMmTI8G5HJnpkkPj4xz+Oj3/84/Nu8+qrr+KGG254m1p06qHkK29K9DBG16R+PvvgNxJ2LxKcw5k5BmfmGNp7HoRSHoRaHYYxsgaFTeemEpjNZ+5CZ9+TojgtZ6CFKnIrtyC/5gwYKzYvSQThzMXsw9+GeXRfX5Kd5ssobt6F3KrTAUrRfvkRdPY/BbUyhOHrPw8SIw4Joaidf1PkRVop1VE8bXdoG4LStktgHtmL3MotKGzcidyq0xbXZs8n2xp/A1ptFEQ34MyNLxCNuHTMPXFbquABQFgszZyYN7OHOzam7/prKRqEfYA7+59GffcHozVGOMfMj/9O2LEsEvrI2kVvuxj4vu+yTYylElUA0H7lURS3XhBs6/qZUG4kwpW7TirJ4x0AMw/8PcY+9DuJr5zmNBqP3wru2iIiM0TERXbBOXqHXoIzN478uu2pBdWZbUZsZ/wXXh+5VaclXjbdbgvd/U8ht/ZMqKV6SpaFg+m7vijnjOnOHJatPn3ejIV4X1IvQyGtCG1n/9Og+RJmH/xm5N5U62NS9Gi9+ACcGZGJ4zan0H71CVS2X5F+7F4HcB2YR6MZe05rOvDsj4MzcCd6Ph3P+sVtz6LbnoU2tApVTY9G0MegFKpohGvRAJj58VehD68Cdx0UNpwzf20OAFRRkFt7ZkCYeDAPvyKsuPKlSOFTH71DL6Gw4Zzoh64D89g+me1hjR9E9+AeEEqRX3eWJPp9cjqMcHF5pzEFa/xA5Pvu68+CqDq0+hiaz9yJ1ov3gxrFUCRp6slBKVQC26OTBHdsjH/rf8hsovL2K1E8bXckO84neaiRB9FyEfI2vP41n7oDrRfuQ/3SjwKuA2PlVqDXQvulh6CWB5Fbuw00HFVvdsGZi9aL98OZnUBx6wWJ6GnqWQN1Xn08KXh4cEPiIjM7MvslaL8oPM1dO0E02TMn0Hj6RyCqhtpFH0ZcV/ZrNXDOYR3bD7fXRn7NGV7xdSeU1SPmwLQ2GqNrMXPf1zwij6Hx+K3IrTpNZhcBQPG03XCGV6G778mg3Y6F3uFXpH0XIDKRQAjU6gjU2giaz9wVIdZlP3TSxXynNYOpO/4CztwEcqtOQ2nbpZi576tgtonq+TdJErA/grm+8eTtMvutdNZ7QfU87Jnjnn1igO6B58FsC539T6HxRDTq2gc1CujufzoZXMAY1OpQcHTORa2jfq3jDL1DL4N15pBfd3ZiLKQibAPFXHCrJwIwQveVdWy/qNFRHoz8VBvon+mXaNsinjfsycPovPYM1PIgaK4IfWQNlHwp/Rp780PjqR9GPm8+cycqO66OWqGF27GEzB6iGSILjXP41z7NNs3H7KPfw8j7f3Pec+WOhd4rj8GdOAh764UwakPgnINCZHoYigadJud2bvZfLxYD13XBQvMVa0zCbc9CLdXRfO4emCcOQMkVQY0C8ht2QB9cDqc1KwUPQMzvrZcfhj60Cq1Q5me0oRyNp38EZnZQOv0iaH4NLr8wuLcff60glICk1Csh3QZIoQa0o7aEk24JllfA+vB4C//1H14BAFy2nuD9c7eDcBewOsAr98DZ9VEAotbH3NN3wvSEC5+IdwGMf+cLGP3w7yay5cL2kfHstUi/ht8DmCOy42xLZCKV6osOyorDF13syeQaDQDc7KDx5O2oX3SL/CwuesyHxpN3hOY7Yf3G7B6sqSPgnEMbXBHJYHNmTwi7T3hZQSmZz2mIB0JQXRR077z6ZHJbs5N6f9kTByNZdoBY6zh4ajBChgwZMmTIkOGtQyZ6ZPipgihyeJIgFNrQqtSv/MK7i4HbnILbnIJ5+GU0nr0LlR3XoHT6RRFCkOaK0WLLnTl09j4mvJtzJeQ3bEdxywUybX/+Ziswj+2fN6uAdZtoPnt3xA4IAJyZ45i664sYuvozEcKYcw4Ojvzas9A9+CKokUf90o8lxBhjbAPGPvav5rWrmhfMFd6/inbKBQ9mW+jsTfecLm27FOWzr0iNEg+js/+pvv7k1rH9mPjB/4uxj/y+7Dtn5viSBA+i6gtGpi4dUZJKkI75qE+9B3vqCOzZcTnO3G5LCgO+tQSzTU/w6x997DYmRdZQKKLc7TQxeesfy/4zTxzAyE2/7hVBjqKz70kZ0dp6/scYuOIXwLpNaAPLoFYEwSZedvvXnAhHUZvH9qP14v3oHXrZi6Q1UTnnqsRxHc8OT/49cxz25OF5x3Nc9PDPOe6Xzpkr6rmk9Bs3u7CmjqDz6hMJG6nmU3dERI/GUz9E79BLcNuzYL02Bq/6FIwVmyLRipWd1ybq38yHuDBc2LQTnf1PY/bBb/T9zcwDf5/4rL3nAfijqnvgeZS3XQqaK0bELWZ2MfvgN2BNHkJh0y5QPf2e6x3ag+Lm8xKZFICYv9Is2vwxbY6/gckf/LEkRVsv3o/RD/0OqFFIz3IKXavOa08nvu+8+gQ6rz6B2u4PoPXi/d55zCN4AMJOitK+2T6LRffgnoh9WvOZO9F+6SEM3/wbUEsi+tevvQEIMdoXzdLAra7MytJH14FQCtMTb8tnX4HBqz4ZyZRovXAfGk/cJtry+jOonn9TZH9Uz6F3ZC/mHv1e/5Pw1onGM3eh/fIjGHn/b0atnVxX9qf0qbcttF9+OCKsWcdfw/ANvwKAYOa+r6F3ZC9yq7aiftGH0NrzoCTsO8s2Yui6XwrqlYQQF0eNFZuhlGqxAvOzcOYmItu1X3oI7ahWINrpjbnuwRfRfPaeRDZkP7jt2dTsxNaLD8hj9w69FBEo5h75Lgrrzl5U5DB3ncj6HrZzjINQRVg+Pn9v323smeMwj+xNOxKm7/oS3PYc3E4D1V3XJ7KwOHPR2fsYlNIAuq8/h86rQkyaffR7QjAiBCAUbmsGankA5XOujs65nMWIRp5aA+rI//3nWP7JPwQhFM3n70Xv0Esi02UBzD1+K8wjr8BYuRWl03eD5suJWmRut4XO3sdElkwIRM9j+MZf9fz/AyiluhR09JHViWhy/zw4Sz7nLMZmS+7B7uHY3/0HsF4bwzf+KmiuiMkf/L99t3dmjsGeOjJv8XjWbaLx8LcBAJP7noI6uhbGjitBh9cCAEpqHhpVwTkHYRycEpS1PAgDzq6twbOzb/Td97znktKm1vgBlAoVmONvwAzdC/rwamBwOezJZCZ6+8UHYNZH+x7Hnjwkf2ce3YfRD/0umGOB9KkNQglA7E5i5aa5IlAcAI+JHiXawxW5F/CctRoTLHgGUo8+B1II5iNyYi+YY8NszIA5BObeR1KP7zYmwdoNUf8NwtbKmT4GtToCauRhTczf38ayIKOfOzbsqaOYvOPPwbotGMs2YuDyn0999nVaM+gdehn6yGrogyuSO/aKo/erPwMAnVceRW33B6WwstgafmL/UXHB7TQw9aMvivo8VMHwjb8KouekSOjbDKrlARmw0TuyV9hnUQXFLecht2prIlsyHtxBVA1Td34xdR3lZhduOz1oyDxxAMZwEDTFXQfkTTr7ZciQIUOGDBmWjkz0yPBTBW1guWeZUQLNl6Dky6C5EpR8SVhHqBqIqgsff8a8SNOuJBTTHtCZbSUIeaLnQI2iSKmORftE4JFIpVCGBADk1p4JPPzt1Ihg1muh/eIDaO95EMbyzVAKZRA9j9zyjTBWbk2N0jKWbUCnT9TtQrCOv47pe7+CgSs+IfdNCIExsgbG5WskiZSWfUIo7UsQ947shTVxqG/Uug9ZZ+UUo/fG84mX6uEbfw3UKKQS72lov/TQvN/n1myLEFKd/UkC1YexbGOCKFHrYyBURG26zWlRXyYkHCwWzLFEfZqUsaHkS1j2s/9G2pmNf/cLEdK3e+A5aNuv9HbkBoXIXVvUwbHN9KLjMRz/6h8gv2EH6hd/CERR0XjytgjhzC1Bfg9d90tBu+0eqJaLWHiwXjtC4lR2Xovy2Zcjkr3i2NH7jpBIRpUzN47ewT3yb/PYPiBF9EiL1u0degnFzbv6nmf8fu93vcS5p78BO40JzD3+g0WJqW5rJpL94LRmEzUpqJ6HXwSe9dqp2WWd155FbvVpoKqeiFBUK0OYfeDv5yXGzMOvzNtO8/DL0uouuGZA66UH0T3wHACg+fQPoQ2uTP397AP/gPYrj8EYW4/6ZR/HzL1fju4/NbK6g8Yzd6IZi6zmXk2S/Mad6XNsKEK5d6BPJhiA1osLW7f4UMoDMI/tB2eusJOJ+8gvEmnEi7FyK9zmNLhjQa2OgDAmi2urpdq8okcY8fHefPYuDFzx8xGhwBc8AHGftWKiXJyYT4M9cRDtvY9Jm7LJW/8EIx/4p4GVoW3KQuvWxEE4jSl0X3smsV976igaT/0QSqkuibbu/qeRW7klkqFgHtsn6ph48zrnTNTvMTsJ8lmtjaQKaGYs26cfmNWD221h+p4vL6lwbfieM08cQHf/U9AGlqPtiWpp4HYP1uQh6MOrF8yiWkpgBndstJ6/F0qx2lfUTxc8kt+l/d5tzWL2oW8lPofrJGxiTIj6UvroWgxc/vNQ8uWUgt/pGTvgHK1n70F5+xVw5ib7ZyPG4Is99vQxtJ67B9rQStQv/SjUyjAIpWC2hcnb/iQ1a5hbXUze+seRZxZaqIrIfEJFjaeY8KZ4WX2+/Rh33Ug2Y1qmR37dWei+/lxq+/01aPruv+mfgRyCPXN8Sc9YzokDcH/8deQ+8E8BVUdZy4NzjtkHvwF135PgA2MoXPlJWCrFVZV1MI/sxZxKcdlMB7cOljCnLS5T2U1Zb9pP3I7esrVwOnMRSYLmhKBkpYjibreZmtGXem6zJ7x5kIPGbMJ8EAIQuxtZvfMbd8Ie2wT0ZhG/63PEwU2Fp3BN/jn8wez70eCirSuUacRBfvjfMMkckNJQ4rtIO3ttKIUymNXD+He/ALcxCaJqGHn/b8E89tq8v6W5ghCoCAGzTbRevF9mSJjH9mH6nr+V9TOc5jSIqgOEYOK7X5DPf0PXfx7G2DqY429AyZWglGogVMH03X+z4PzvTB+DNrgczed/3HcMpyE+x1knXheCBwAwF3OPfR9abSySmWlPHYFaHgC3TXDXEVly3rnOekLX4FWfimShs1iGUlp2aQCe+pwIiIyXsOiRIUOGDBkyZHhnkIkeGX6qUD33WpTPfM8p3Sd3benVrg2tQnHLedKaBhDEm3lsP9qvPi6IwVg0Z/nsyxORmkquCGPlZpiHXvYKcqeQo5zDPBIQje0X74c2tArD138usb/c8k3ovPIoQBXow6thjK0HzZdhTx1B9/Vn+5KZNF9CbsUWFLac3zflfan1Ruypo5h74lZBjBCKwsYdMkL5rYY9NwHz8Csgei5hxVM68z3Qh9Mzefqh/p6fgT1zHDP3fiX1ez30wtN66aG+kbPG8k0YvPrTmLrj/0aEDz8ijxCC3qGXMPfod4N9j6xBbfcH+1pC+ei+/hxmHvwHL4ui/4s0UTWolUFUz70Osw99M/T751HxRY8YfEJlMcQKAHT3PwVj2XoUN5+X+mJsHtsHt9MAzRUxfdeX0Dv00oJ2JM1n7kJp26WRF+I4waEUKpFxqo9FvfPDdgaccxH5OTuO7htJiwiq5xKfcc82gWp60t7KK1wbf2HvRyYCSGRc9UP71SfQ2Re1XHBbM8msEteGNX4Qkz/8C3Crh+LWC1Db/UH5vTV1FDP3fgX6yGoMXvWpRJF5Qui87V0qGk/eDrU+hvzq0xOChD2VJK3kdxMH4cyNY9nP/lt0Vm0V86OHtPbFo7DD6B16uX/mntd/TnM6Ue8kjPRo7XSYR/ZKMriw+TzUL/7Qon/bO7IXc499H0TVU9ev7v6nAvIHgFobRWXnNciv2SYJ1ZMFd9309cdDOOtksTCP7otYsDmzJzD74D+gfrGonSFq63B0Xnum79zqo3dwTyILo/lsMovBmZuAUqjg+N//Z88HnwGKCiNmH6hVR9B87t7kgRYpYLjtWUHcL0HwAAKi2jz+GiZv+7NFW6BN3vonoEYB9fd+HPrwKljHX4daXwalWAW3unI+spdynbxjn4p7Ps060Wklyd6FYJ04gNbzPxbPLZOHkFt3NpR8GfrIajE/NdIzWc3xA8i3Zk4qWMCHPXkY49/874vePj5/5tduAyEU9uwJTN76pwkBym1O48hf/35kzJTOvAzVXdcDALThlZG5DkCqDVYci1qXqSLuhyVmoPFOA72XHgJdvhna8Cr0Du1BZ+9jIADI5BHMfPU/wth+OarFMj55LBhHA7aLOwZL0BnH/oIGk/YXf7tpdoqtGXQdE1qMmJ687c8wdP3nYU1EMz0qO69F++WHlzSWJ773f1C76Bbk128XQVCxZ19KCEisvpEzNwFn35+l7u/B3macpR9EmfZwrvEa7u5tA8CxTp1IbEuYGAOkNX+QUnO2AWNgDJ1XH5d1d7hj48Q//JfEttrAsoiQ2339ObH+DS7H0Ps+n5g/zaOvwpo8jO6B50VGGFWglgcj2cCTt/4xlPIA3KZ3LxMiMvgWMe/1jr4KbXB58NslgDMXnX1PivocsfFhHduP/PrtQGhZnr77b6ANrULtgptAFC1xbwIisysqeiw+KxYAZn78d6mf21NHhJA5ewKs24LbbYLqedQuuCl1+wwZMmTIkCHDW4NM9MiQ4U1CyRUxeNWn+n5PjQLya89Efu2ZYHZPRByeeB2Np38Eqhkobj4v9Xf1iz8CoqogVIV5/DV0X38O3QPPzevvbE8eQvO5e1A+56rIi5og1X8R+ui6RPRa9YIb0d3/DLqvPwe32wB3HCilGvJrz0Rxy/kLRpAuBZxzTIdrWnCG1gv3oXbBzafsGPNh7qFvJbIpBAgKC3ijc87QfPZuWOMHUdiwHYUNO6DVx6DVx6DkS4KoikGrj6G15yF0X3sa1njS5qR05mVQijUUt5wnCuZedAsmb/sTuO05EKOA0mkXAhCEVTjCGhAFlafv+VsUNu6EsWxjau0R7jqYeeib4FbPK3wcFT2c5jSUYjUiCOTWbAMe/pYkOp0ZUfSeqsmoR/8l2Fnky2tp26XIrdiSqL8Rhnn0VcATeQDMSzqLc7ThNCah1ceC84qRPX7RT7+tJHYubqchvJ8JRe+NF/oW6x563y/DGF0rjss5WLcJa/wgpu/+EgAgv+EcIGaJ0XzmTjSfvxdDV/+itNjizO1fS2YJSHuBd1szCXuGOc+axEf75UdQPG237DORscRhjb+BiVv/JEHm9w5HCbdTgblHvyeu9RJhjG0AoRRaZRgmgnYt1kZIbj99tO/x/UjPcDbQqURn72PQBpahuPWCBUVjpzmN6bv+WkZi9xubkd/MnsD0XV/ysvNOLqPEB7dNuN2mqK+0hHoIS0WYlPPPtfNKuv1gGLk12xJWTWkWgm63icYzd0Yzsby6L2GYR/elFu9eLNzWzElZmLmdJtxuC1N3/vWSa74ws4Op2/98ycd8O+ATm6K2RwduY2rRom4crRfuk//2I8SNFVuQX3dWn3VdZKCd+PofntTxThXya87A7MPfnj8zNEYWt56/F/l1Z0EfWonB9/48jn4pWhT5VKwfIx/851Arg9LObKloP/VDtJ/6IcpnXwHzRDK7wHzmbihj6yKfrTIdfOboLADgr5ZV8Uqxv71Rx0o+I7BBbw5KEUTM2ROJrDalPAClWFuygDf74DeEnaOighpFFLecJy0wCSGJ48+XSXVRLsh82qSewN3Yhvfm9qCuLI1c91HacQ0auQG4jMFcRPaSUh5MZK9xx4J14gBmfvw1OLEaFgBgHnkVLV/8ZW6qwB8RLbyC6IuB/7uligtut4m5x76fsPwMo/vaM4nP7MlDmLjtTzH24X+Bys7r0Hgy+iztzJ6ANXlYWtDNV7tsKbCnj8FtzWDiO/9bfqaUBzLRI0OGDBkyZHibkYkeGTK8jaBaDvrQSuhDK5Fftx2Ol5KehrAtVG7FZuRWbEbtgpvRfeN5NJ64vW+6fvOZO2HPHMfA5T8f+OYaeeRWbunbpuLWCyIFq98qiOLml2I25P/ffvkRlE7bfdK1KzhnYL0OiKot6A9cu+iDqZFwpbMug1br7/nMmYvmM3eh+cydAIRVD4iCwvqzAXhe+Ho+EXmm1kY9oSTmsUwV1C/9aKTgLQColUGMvP+3YE8dhTa0AlTPg9kWpu/5W3A3aT/hzE2g8eTt0JdtwNBVn46MJbfXhnnoZXDvxdI89hqMZRtlFpLTmsHEd7+AwpbzUT33Ovk7JV8SJEFIOGg+eQd6h1+BWhtBbfcHoOSjtXEWm+nReuE+FE/bDZYikhS3XghjxSb0Dr8ispKWgMnb/hSjt/y29EyPt8caP4CJH/wxnOY0WCeF/GAu5h7+DkrbLkXz+R/3OQqB05hE89m7Rc2dmWMJf/huP/sy18Hsw99Gadul6B1+OVGoe0nwitSax/alFhl227OL8oDvHX4ZWn0MzO5FiIK0yP0w2Xiq4Dan5iUv0kCNQkA8LVBvZzHoR0L23ngBzOoliNT8urPn9StfCuYe+Q6cuQnULnx/5HPOGTqvPgFndhyFjTsx9+RtJ23vN33Xl950O/17Z16bxlMAfWQNOOdw5iZgTx1BYcM5fYlsH7m1ZyK36rR561P4cNuzCwqoamXoTQt8bnsWierqMeRWnwFnblxGWOdWnwF9dA1m7v96Yg35SYd5bB/aex+HWhnE5K1/cur3f+SVVEI2bT1OgmC+WlSnAtrQKnQPvLCgFWYams/ejcErPgEoav+M35NE7ZKPyFpd1sShkxaiAAhR//rPYfL7f5T4js5DyrsL3CcdJSnY8sFlQhRMCf7pvvp4QtzQB1dgJv78tRS4DlhnTtYy80FVFdwoeAI5Tw3i4FSVmRs+NmgnUKctvC/f3+p0XugFmLNTgPsyzMIZiygETubdJpwhGEb75YfxVt0b7ZcfhlKoLH0tdZ0lPzOEfzv7yLdRv/SjYHYvsWZMfPcLAMT9qg+lW2yGMXjNZzB1x1/Mu409eQjN5++NfMY6TWktliFDhgwZMmR4e5CJHhkyvENQCmVZiHCxIKqGwoYdyK87G9bkYbjNaTCrh2Y4gpUQ5FZsftc+VBc2nIPm0z8KRBvmYu6J28TL/RLAOUPjyTvQeeVRGTGmDS5HZcc1kVT1MNTKEKrn3xSxiVLKg6lFrP1jtPc8FNnex9yj30Fu1RZQLQdCFRTWb/deFAWIngfVdFR2XI3ekVdkFJxSHsTAe38Wesxah+aKom6MUYCxPCg0yXotKMVaqn+4D+vYfkzd9SUMXvkLIIqK3pG9mL7rSxHbsu7+p0A1A7XdHwCzTUx8/4/AzA5az90DfWQN8qtPD/qkFBU9/GLNztw4msWajFTrHngBnf1PLTrqVB9Zg9aeB9B+8YHI58ayjajt/gDMY/uXLHgAIuNk+t6vYOiaz8BpzaQSOP18l320X344cv3ioIUy3MakrEuxVDizJyJi30mDM7Seu6dvZK7TmllUdL9v6WWNH5y3Vse7BYUt56Oy/UpZj6RfwfM0aEOrAEJEsdpFEIfcsXDsb/914vPi1vNPmegBCNGlet4NkWy61gv3iUKreGvEJmPF5kjNBaLnYYyt65vVMvXDv1yS4BG3Ulks2nseRO+NF711gfQV6cOgRmHe2hJhuO3Zhc+D0Dd9LyjFemoGVgSUYvSW3458ZB5/TV73f2yYfeAfUNv9gSX9RilWQTRj3nXPR5yQVkp1jH3kX+DEt/7nvPVs4uvcW4Hyme8R9V1OAr03XsDMg99A9dzrQFQdfAkFzedDft1ZKGzcAUAEdPSz51k0mAvrxAEQLQdu989EjmNNz8a+QnrdDADoKhTfGi7DJsDlMx0MugAbWQ3t9r8GSZnHnZi1FVF1z97vzYtb1Ch4tWRctPY8CGPFZpR2XA6ar4BZXUzd/meReS9/yc+hXV0H3pkDvfsL8nODOPhE8X5oZOFsLq4XQULZLpyqUM68BvaT3wIFMPNs8rk0DqVYPalxs9gaKIuBWh9LBFM0vHpObyd6B57HVK+Foes+B6VQwdwj30lsEy5qPx8Wa83V2ft45G/u2uBmByRXXFyjM2TIkCFDhgxvGm/O9yBDhgzvCAhVYIysQWHDOSiddiFG3v+bKGw5H/l1Z2Pofb/8tmRtnCyIoqISyiwAxMt968X7F+VT7aP1wv1oPXdPJEXenjqKqbu+BKePv7doQFQMqp57XaqFl9OawdTtf5EqeADCWqj7WkCAls66LGKbVNi0E4AgAZd97F9h4MpfwND1n8PoLf88IXgAAM2VkhF5VIFaHsDQNZ/B6Ef+BTAPmW0eeQVtzw6m+fSdqeSdNrQSzDZx/Gv/CSzktT5z31cjhWDza89C6YxLUD3/pkSb2nsCwaJ35JWI4KENrujbPhCKyrnXw04pNOrXHWguImK7H8wje9F8/l6Mf/O/vyVEllKswe1jybVYxG21ThbzWZGwThPuIooW+wSN24zeK6oX/ftuQu2iW1C/6JZIAXa6xEyPkRt/FeVzrj7pNhBFgz66HvQUkxXHvvxvYHmFUrnroNU30+jNQSkPIL/uLJTPviL6eaE8732bWiB6HuTXbU9+tn47ahfdAmPl1nl/GxBtHObRfRj72X8z7/adVx6V2XcLobP38URmVhxpGQP1y352UfsHRP2Awas/Dac5z/oDUZ/C7bXBQ8Rt++VHEtuVt1+B/IYdiz7+O4XStksX2IKj9dJDImMhBfXLPi6z9AAhxI3e8jsY/eA/R/H0ixc8PtEMGKuCsVU6w/sNn99mLDyfvBWguSLMY/vxZgj3ziuP4tiX/60krvWxddDH1i9ox9kPy37+32PgvT8XEcaNFZtPun2ynXsfX7C+WBznzXVBFxChH63m8VQlj/+2ZhCHr/lZ8GXrgEUS8jRfBqGKLHL+ZtB4/Ac4+sV/AWviIBpP3IrWvV/C8a/9J0x893+DUCUh9BZHVsLQFCBfBR+LznvrtVgNIpasE9Y552PgV/wmah/5Nyid9V6QVWeB7/4kHH1ptWmUyuC81lvzQa0v7Xr2Q/nsy0/Jfk4FrOOvw546guLm8/rW9FoM4haqgJiHVnz6v2DZx//d/L89hfXRMmTIkCFDhgwLI8v0yJDhHwGUfBn1i255p5sBQLzoK/kymNkR1lPdNuIv/fn129Ha82DEh3/u0e+h+/rzGLjiExFrrzi6B/eg9dw9ScsoH16hw8qOdIKzuPUCcNeBefRV5NdsQ37dWanbdfY+vqC9SvuVx1Dccj4AQC3VMXDFL6C95wEopXqk+Dc1CsivPmPefVEjD+7YcENReUqxKiPK1FId1fPeh7lHv5f6+8q516N42gXgjMEaP5C6jbF8I7r7n0rYfhBFg1KqAyAAISidfpH8rvH0D9EPcRKxsuNqGMs3RYuKd5swj7wKbWgFtNooars/iPFv/Y/I79TyAJhtegRREkqxuihP7rcyUlot1ZbsQR2HUqwmioaeeizOW9s68To6rz2D2Ye+Ffk8HlmtlOpveTT0QsiFspB8LCXTw2lMglndJWfWhaEvF7VEilsvXDTRvhhwx8bk7X+OsY/8PnqH9vStdfNmEC6KPBerDcRtc1F2Hj6oUZj3PsitOR3M6qL1/L3ys+LWC2GMrQNR9b6ZUsayjZH51jz+GvLrzoI2sHxBW6q3CgNXfAL5NdsWLKbugxYq4LaJ6vk3Yfb+r/fdjnWbOP6Vf4fKrutRPvMyMKuLbszybvCazyK3YhMAoDW0sq/4fiogah7MnvTv8+vOXjAryZk5DporgblRAnbg8k8gv3YbaK6A2fv/Htx1UL3gZmnVmF9/dkRoT4PbmMTIjb8G88TrIlPSr7u0QIT7UuaQOLTBFWBWLyEaAyIrzZ44BJorzps9uFSUzrgEUDRQTYdSGUJ+wzkwD70sMzEXA6rFCHZC0d67cO2cheDMjaO4fPeCGZVhVF2Gzx2ewdPlHB6uLSxM/NGrd+DaFTvxHkUDFpGR5T9H0ny577xKjAKM0XXoHXxxES3mmLztTyOf2NPHElmBRNWhV+ooWS4suwsU6ugHk6vY74xgux48C3+1fQFqk8O4dAiwuYrJFe/Bf3/yIDovO/jkxgksRaKy+jxTLQapWVKEYvh9v4yJ7/8RFivmafWxk7ZnU8qDyK/dJmoOnsxzCKGJGknW+BuiVs7Vn8bsg99YMFO5ftnH0dn7GMyjr0IbWgW1Oiyt4SLwjuOGAorS4LZngMG3rj5WhgwZMmTIkCGKLNMjQ4YMpwaEgOg5qLUR0FzRezEYTY2mJISgev6NiawLa/wAxr/9P9B8/sepxQRbLz2M6Tu/2F/w8NB85k4c+cvfwcwD/5A8NlVQPvM9GLrmM/NmxJTPvhz6yNp5j2NPHpIFtwEgt2ITBq/6FGoXvj8SuboQiKqLiMRQ9DpRtURUeemMSzD64d9L/L607VKUz7oM1ok3MPvwtxLfA55nvmtjLkUYKG27BIQqUKvDkevVePpH4Cne2czqev77UYJcG1ieyJpR8mUUNu6AVhsFdx1YKQWnlfIAiKL2jV4dvvHXox8Q2jdqOA6yhOswH6hRhHU8Wax1KVBDxdYlCMHIzb+ZsvVbb0+3GDKX5kogb4IcfLNQKoOJGjIAFt2mwWs+g2U/+69B9XzqfgBxXw1e9Wk59omigRai81ZuhbBbKp9zFZRire/xaGFpkbgAUDr9IhBK0Xiqv8AIoG9kaj5WGyiOsN1f3MucmV1oSxA9FhL+qFFEddf1GL7hV1DefgWGrvslGF4x4/kKxOY3nBP5u/3SQ5j+8d9BH1276Hs9jPqlH13S9vro2sj8oxRrMJYLenG+qPrRD/8eht73yxi4/OdhLNsAqudSM/nSoHhjxZo4HCl+rpQHYCzfIP8ubr0A1fNuSP6+VD+pvolj9EO/A2149Un/frHZYWkR57nVYmzmlm/C6Ef+BcY+9i9lrSwAMEbWoP6en0Fu9RmonncDRmK2YD4mb/8zgPNA8GDuguTjfPcxAIx+6HdR3Hph5LP8hh0onXkZBq/+dOq9QFQd9Ytuwcj7fxODV38aRInWbCud+Z55jzl/g1W0nrsbjSdvx8w9f4vOvqfAlmDHljZ/EEJAjWT2WvW8GyKZpYVN52L0I7+fvmOqIL9+O4pbzkduzbbkMUq1xGfce/ZbbTq4eXLxmQh3HHlq0WOe5jzRY57sPG520Dv0EmoXfzjxu5OFNrAMhBAUDBX1ehk83z+jaNytoEaj42jSLeO2Z+fwn757FJMNC1/68XFMNB20TYaXX1/Y7i2MpTyHpiH+/F455yroI6uhVAYX34ZcCbTP2jsfiKJh7MO/i+qu9wnhZAmoXXQLimdcjPqlH0l854voSq6Igff+HMp9AqR8qNUhDF7zGaz49H/ByE2/hqFrfhH68k2J7bhjg5kduN2FRI8s0yNDhgwZMmR4O5FlemTIkOGUQK2OpGZo0HwpNYrUGFmD+qUfw+xD34xEZLJuC43Hf4DW8/eiuusG5DfuCOqTcA6i5xJEfOms96L13L2IR5519j4GZnVRu+BmSTAtFkRRMfS+z2Py1j+GdeJA3+2mfvRXGL7p15cULZ04lqZ7/zfg+09TPQ+q6gBVIoSYWh7A4LWfRevZe8A5g7F8E8oekdJ46nZYKYVDCxt3wu00MP6N/5b4Th9bLyJIvXZQPScj6oxlG9B++ZGEF77bmgXNFSLXjai6IHxTIuvkNoqKNDJfLQ+CUAW13R9MrelBc8VopCBnKJ/5XjSfuUtuow0sB2cunNkT8jOlWENt9wcw9aO/Sm3PUpAasTvPuWpDq1DZdT2mQpGhaQXMh675LNQUeyFtaOWivKXfajCri8rOazD38Lff1H4qO6+DNrQSU3f8ufyM5kqCCGfpNjT1yz7eN+trIXur4tYLUb3w5oiNS1zIAEQUcPX8G6FWhpBbdybsicOguQKIqmHm3r9D7/ArMFZsRnHzeQAESVg8/SI0Hv9B6nFzK7ag+8bzqWJhPxQ27IB5/LV5/fBL2y4FM7vJMaGoKJ/1XjCzC/PIK4nfEUWDPrKm/8EJgZIvi2joU1Cs3L8u+siaxHH79QlRdegp90B3/9MACOrv+Sj0oVWYffjbMI++uqh2dA/uAdGMxfnZE4rqeTdAG14NpVCB222idPrFoN68XNlxNbpvvJjIkKtf+jGo5QGonj2fD2uR961PNsct/4yx9ZFxSxQVpW2Xorj1QrjdJtxOA6zTEORjsQanOQ2aK4BqOViThzDxgz9eVLYXIOZWoqhLtouL7COlUHJu1WlQSvUFC3gTqgT/JgQgSmKbwoZzUAiJYst/4T9h6s4vRuq52FNH0Hr+x8ivERmVbqcRiSwniobClvNl1ohSrMFYsalvJoZfD6Ky81oQVYPbmkXxjIsjokpaofQwYU6oguLpu6VlXfH0i1DefiU6+58B64SIT0Vd8HoRo5B4hqGaMW9mmFIU2Ym+1WW/7FclV4i0x3+e0ZdtQO/QS8gt3yTv5fzGnejue1JuW9l1PQqbdkHxhIWBy38eR7/0LyPnU37f5zH3nS+AhNuq54GQaHRdbjlu6y2c0cXBYakajEXc1tRbO8JjLA59bD2KW84XY2zTTgAEhBAc/9p/XBQ5bazcAvNwdN7VR9fJfxcqVUzPI3o4XMG6mN3VBBPXudl1cfuzM9hzuIMROocr8y/gfGNpmRv5dWefdLZR9cL3o7j1QrBOA53Xn4VaHpT3lzawDO4irQ+pkYdSqIpaR7Hnpdyabajuuh4nvvU/AdeOfBcOWKnsvBb29LFFZ6QVNu4EUVSYKYEqnb2Po7LzWij5MgilqGy/EvnVZ2D82/8zsa0+tl4E84QDtCiFPZE+xzutmYh1bBr8emoZMmTIkCFDhrcHmeiRIYMHpTwoHlYpFYUxmSuIXD0nXqBPMj37pwG0UO5PTmoGiKqBO3biu8KGc2CMrcPMfV9PWEmxXhsz938N7VcfR/2Sj0AtD6B0+m7k152FxlOigDnNl1Hcch7K51wFtzmF7uvPJY7RO/A8OgPLUd5+ReK7ZGMVYfXRmhYCCyGoXfxhTHz3/whCkiqo7LwGjSfvEEQtoSid+Z4+gsfii2f6tR4IISCaKFhKPBsKqhmJiNLc8k3IpUSaGaPrE6JHfuNO1C/9aCphqBSrGLrun8gXOqKoIFQRRVMdC8bYeozc/Js4/tX/EPmd05oGjZEtam1EZItUhlK98X0UN++CM3tC2qGo9TFoQyvk+Re3Xhh5SaeFCgiloEYxEilc3HIBCpt2wW3NQB9eLe1QAMCaPAynMYXcik1Qq6Opokdu9RnzWlr4fTAfqufd0Nd2Rq0OwRhbn/pdZdf7YB7bh/yabTCWb4RaH01so6eJHooKbWA5qFGAWqrDac2cdGH1fsitOi2SvVTZcTUK67fDGFsPbpuwZ8cXLMi+7Of/PYhqRIkCAJxz6KPrhAUKoRi86pMgqgFr4iCY2Y7Yk+XXb49EfAOIzCNqeRCVXe/rKz7k152VKOieZm+llr2IVUUFVTRooWsxeNWnwDlL7EetDvc9d5orYPkn/iO6B56HWh1G59UnEtkVYehj66FWBtF45s5IAezCpl2oXnATWK8N7tjQ6qOYefAbid+P3vLbUEt1lLdfAWvyEHhsrlAqg6CaLos9x+8vX/DUaqMw36Tokd+4M4VgDOZBliZAeIKDEhMOgu8JWK8NtTo8b5aJUhoQ87YHZ3YchQ07FkX4DV37WejDa6AUq6jsvDa572INQ9d+Ft3XnhEWfFRBbsVm5DdsT91fcdO56B14PnIfpYHqeXReewaNJ6OWY/0yeoiqpYos4b/1oVUYvuFXMPGd/534fWXX+2BNHIR14gCGr/9cxNpOH1oJuG5/S0dFRfW8G7zaKEcibQIApTIUIUHTLOniqF74gZN6riKKivzaMxNF7HUvo4hzBnvySOQ7tT6G6rnXQcmX4PbaqJ573bzBDEp5UKzHRj6WZSPGc7+xGBePapd+FPk1Z4A7DvSxdSBUwegHfgu9wy9Dqy8TkfiE4PhX/yDYR66I4Zt+HZO3/xlcrz5Z6YyLwWKiIdVzifoxhU3novvGi6Cagfp7PgaaL6P3+nPQhlaiuOX8VNI4ntnATCHm5FedlhAjq+fdAKVQAXcslE7bnZgLCSFQCtWo7Ve3BfuqnwPptlFjQpBqjKwAac+BTB0FnTyKc5afiddePIhX8gu/ls6csQtjjy1sMejq4hmqn+gxeO1nI89R/jzPOV+U4EFUHcXN5ydED2PZBvjBHXqxDLVcR79qdaPKbOKzORaQ/ffuEQR6jthLFjyoUUD57PcuWvRQqyPy2Y0oGvJrzxLXs1hFOVa3p7Tt0tQgjjQQqmDo2s+AaAYaT9wW1K0iFJWd10IdXIGxD/8uzOOvY+beL8u2KOXAFkwbWIbRD/0Ougeex8yP/26BE1fEf0BfSyynMRXJ/FRrozKgiqg6iKqjev6NKJ1xSSJDjRAF03f9dWKf5XOugloZhHlkfmH+VBaJz5AhQ4YMGTIsjEz0yPBTBaUyBJUqcDtNsG4DanUE4BxurwmlWIWSL4IomqhF0WnKSDEQCqVUgzN7Qr4QpoHoOVA9D7fTANUMcMeSZNO7D4sn5QHPTsZ1ACJeEMBc8eJPVagp9gVh0Fyp78uHUqxh8OpPY+7xH6D9yqOJiEfr+GuYvPVPMPL+3xIRY/kS6hfdgvK2S6FUhiSxWj3/JjCrlyBClPLAIgqtipc8tTYMqufBXQusIwhArTqMkZt/HebRV6GProdWH0Vh/TlwWjPCDipXlC9YkYyM+ijc1syiIo3DBa6pnoNrm0H2h6pHIiLnQ2HLeWg+e1fkMz8yNc3ax1i5NURME0kOUCMP1yP8lUIZhU270Hn1cfm76TuTL3xqdQREUaAUynAaE0I0UtTU8V897wYYyzfBbc8iv/7sCKlc2naJGAdeRKBfG4VoOhRtUBAKhIAzN5UABASB5wtRSqmaiGIvnfVe1HZ/ENN3/y06e5OZJUp5MFngu74s4XFd3HI+iKLAPPYa8mvPhD62Dk5jCm5jCkp1GEqukLqv8pnvkdk5AEA1HaWzLkfrubsBeIShkkLUuA6cuQmM3PwbUMsD4JzDOnEArT0PLEhADFz5C9CHVklyrZ+oU73gZiFC9NpQyoMobt0NbnWkvUScDCKKBh6K0Kxe+IGkb7y/LSEYuv6fwBo/CK02Iq03tPooWnuiEeFpFiNKoQpmdsDMDohmzGt5QVKiz2muhGUf/3ewp4/AaU7DaUxDKVa881CD+zjS5lDEvSe6qNWhvsdlvQ606jDgkb6VnddAq43Cnj2RED9ovoT6JcJ+o3LOVTAPvywI/voyVHZdD6oZqVH0YaglQQwZo2sx9pHfQ/vlRyNCkFoeFBkPrguAo7TtUnQPPAfWawvR+DRh36PWRhasYeRDX7YBuRVb4LZnUdh0LqzxgyCqiuruD8CdGY+MB2rk5JoZnwtL2y5F8fSL5fpBc8Vk5DpnKGzcCWB+a63i6bvReOz74g9FRf09H4NWH4M2tBK9g3v6Cpz1y34WxrINIKqasOaLnLM3pxA9nxrhH8/IK227dEHRgxgFkBSh6c1kDQKAPrgC2uCKiDgBQuS40MfWQa0OY/CaX5SkeWXHNQAIjvxl0j6K5oqo7b4F+bXbUNx6AY5+8ffl/Fw970YAgpT3s8GUYg3FLefB7bZBHv2enGdya8+EUqyhd+A5kT215XxRI2aRdWzsmROYe+IH4JYZPTcPQmQmmHvku4kME6VUA1E1lM++HETVoBTr6KaMCbU6DKc5nbqugFCxpjlW3zbH20VVA7nVZ0S2p0YBhQ075PoYDwhhVg9KsYaRm38L5pFXRKH25Zsw+9A3E+eEWMR59fybUL/kowA4aL4E1m1B8wI+qJ6D2w7a4N9PcQsk34aO5ooJizAlV0T13OtSz122Ydf1AAiUYgUkXwbPFYHuNJArI1eogXGOdq8BZhTABsaAM3YjZ/Xw80Nn4uArD+HPVvavgQEAc/VhjIyuAT3xBrhmALYFkvIsa2peIETKvF6/5KMobNyZGpXfr25aGNrActQuvgU0l/JctWITiCKId6KoqAyPYDZlH8f5IGZdHVtp9LmCp2TDtvn860AcpW2XorDpXCjlQS8Tur/w7qP+no+i8fhtcDsNlM+5ct7aesbIGox++PdgTRxcnE2mZ0dZ3n4lWK8Ne+YESqfvFs8BigpSG4FSqMhABxFIVRH3kze3EkVFft1ZmLnva30zbMWxcvK5Ni2rQq2NQo/Z+RFKUb/4I1BKNWFP5gX/UD2XtOWjFPXLPi4FGgCeQHIpaKGMbqxGSLwu2uyD30D1gptldlSGDBkyZMiQ4a1FJnpk+KkC9chltVQDL1YkoSWj8zz/ZUJoxMtW9fxr1foycNsEs3rei29LRClSBYQqnpcvlS/MzDbhduagFKoAY2C9FtxOE0sSGzzSa1E2HYvaIYFaHQYhFPbM8YU3V0WRayVfFpYOriOJuMX6BdNcEW5rBkqxBqKocGJp8URRUbvgZpTPvgLtlx5E8/kfB+KHZ+1B9OhLXzzCUClUMHTNZ2DPTcA68boQP6iCyo5rIlkAaVCrwxHrCKVQlaIHAKiVIaiVgOhUitXo+CjVAUplEWiiGVByRSi5ItxuM1EcWpxzQBbTkOghMjzmZJsXanvkPEp15NedFWS8EArDi2RMqzWQW7U1+IOGyF2vDXK/i/Bv1qrDAFW9NnvZKnoOcJ1UW5vcyi3ecaOCkVoZwuBVn0Jn35PQhlaisHmXt/1WtF96CJ3mFIwVmxdtV0YUFcXTdqP51B2incOrvTGhYujqT8G54Cb0jryC6bu+JH9T2/0BtJ67V5LARM/BGF2TED2IqqG49cKI77uSLwOja6VgWj778khmhOHVhoiAqhh+3+egDa4AN9sobNyZWly2fslHBWHpzS+EEBhj68BdZ17Ro7LzWuRWnQZCKHJrtqH3xgupggfRDKjlAYx+6Hdgz44jt/I0qKUa7OmAbCZ6VNAwVm6GPrQanVcfh75sA4qbd81vcUaoFOJ8uL025h75drRL8qXAU97bF9EMKHoOzOxAyZdTSfDSWZeBO3Z6HRAvcttYthHGsth3VAGhNPEb+b2qe+LtNNTyIGihEiHMiKqBMwZmtiOe81QzUD77ciGSVobgNqfhtKbhNqcxdP3npNColgcw9pHfhz03Dq02mkrAxzNn4mBmN5H5IurlaCCqCu7Y0EdWY/kn/xDd15+HPrRCzuHzZa/EUdlxTeQa6kMrQY0CFC0HUh+F05iSwgDNlcDMHqzJQwkiWhtYDrVUk2KHUh5MkMn5DTtAjYKo0TBPIdvChnOgVoZgHX8NudVnyAj14uZd0EfXJESP4tYLYazYhLxXg4D4VoILgKoa3DRbo1hGXpxUS4NSKEFJsZXSBpYlN46JKguhtO1SGRFN9BxGbvoNdPY9KSL0fTtDqkCrBvU4iKqhvONqNP3aMoqKsY/8PpR8SRDoZhdqcRBDN/wKOi8/DG1gOQqbd4FoBsrbr4RaG4M9/gaKW87zrLsIahd/CM1n7oJSqqF63g1irTz/xuC0jBShqw+4a8M8lJ7ZRlQd+sgaYfOVJpgWqwCIFDeJqkVq3QCCDB394D8D5wzcTpkfVW1B0SMOqmpgbjLL1W8zdx0xX4SvL3MB1wHVdOTXngkAcDtziUxWpTSAoWs/K0hyx4RSHoRSrMKePgZwLu6HXls8o3o11wBvnq+Pwp44JJ7nYuQrN9sgXobuycBvsw/GOVSqol6oQeUELhiqegEuZ5iz2t5zdw9sw9lY2WnCYIdhzjPVPTb1KlZedDMmmxMYHFgJ3bFQsh20uINlXAHvtWF1GpipD4FxDq02mihWXdi0E1TLgSEqetgzJ6QFWuq5rTsblV3vg1KsivnYEzfC96Y2vAru3KQg36mCYrmSFD1GNiK/7SN49sXj2Hzii6BeLsj9vZTnAwAdrqd+3g/V824A8UTz6rnXQVt9Jma+/4V5f6MNrsTQdb+06GP4QSfziR5+DRtfLKaaIYV+CUpBOI+8FRGiCOEI0bmVUAW51afPW3ycaDkpKDa8574wBi7/+dS1Pr82Vo+G0r6BENVd16P98sOwjr8GbWgVCpvPA1EU2JNHYMfq1unDq9CNr12sv2iTIUOGDBkyZDi1yESPDD+1iFuWLO43xMvmEC+P3KmDc9Y3GpdqBmiIVKBGHjRfBrdNcNeORE3LqHhCQPMlUL0A1mtBKdWFp3RzRlid9HmBTrQ1FoENQqDWRkE1XRbX1EfWwO22wDpzMhI4egIKtMEVkpgjnrizVFDNEOnqnr0Mc8yIqOBDyZdQ2XENCht3Yub+r4PblozYXSy06jC06rD04F8IRNVTvbL7ZSmkgRbKIITCmZsEYuNByZfhtmYjJDNRNWgDy2FNHgY4i5AL1MgLktIbn0slHmq7Pwju2LDnJlDedqkkyKmejL43loWsHcJEbWxbpbyw6CHsrajcF7dNUZxdL8CZp76BVh8F63UiKf+5lVsCUcRDefsV4I4FpzmN4mm7543KDiCyVwYu+1koxSq4baGwYTsIpSBUE/eC2hBFci+4GeaRvcitPh25FZuhFKuYffAbYGYX1QtuFMWGlwD/PilsOAftlx/xrKoISqfvjm5IFellX952sZwT8uvOxtxj35dkSu2iWzzP8STSMg+Wf+r/8cbeTGQM1S66Ba3KELhtIr9xB6Z++JeSoPYLNlM9D2NkDZRCGUSNFaYv1lDYcj4IoSB6HrlVW1Hccj7KZ783OHdNX1RNC1FzwUq9lkquBKKIaEtBMArrN0IoaK4IpVRLRCFXdl2P6q73gSjqEouFemRoSj0B2Z5CxRMzCKhRQP2iWzD76Hel/QxnDPrIGuRWnxE5H0GyUmHd5gl4fVuhaqm1LXwYK7ei/fIj8m9tYFkkYyeNyFHLg17EschSoUYRRFGQW7EpQtYtVIy6sPk8gHPkVm4RgkfclogGBJVWHYblET9E1cR4SBPZDFE7Ra2Nwjr+eqqo498vYn3qD2oUkV99OvIptkpqZRja0CppF1c+5ypUzrkq2hZFkyRb+DPOnGhtiJBALfuAUHGPheISFir4DgBKoQaoKvSx9bA87/nCpl2p90PC5nABEaSw4RwQzYA9fRSF9edArQym1nQIZ64QzUB523sA5sKZnUDxtAtltDdRDRDbAqEK8qu2whgOLLiUYg3cdVDaej6cZesFue71ZWH9dhTWb09vJCGgRg4A8axl+mfRAulrGM2XwV0H1fNvFCSzqqcKeMayDVCKFXDG5JprjKxFZdf1aDx+K4ieR/WCm71m0YS4C4h7WRYm7/P8WD47aqNJNB3oE7BCNAMwO2Lcx64ls3pQQvN24+k7E/2jlsTaruQK4I4GzbuHCaViTPpWlbYpxjdV4Nfw8edv3m0mRA/XFOT0YkTAxYASgrJRQiVXBjO7IJygTPMgIGCco+Ofp6IC2y/Dmv33YG8jmcnj48XZg3hxVswvxcN5fHbT1cgVBgHXAi0OQSUKiEKhqgROz0bx9N1oPn+v7OPq+Td592zsPiME3def6XtcbWgVqhe+PxKhrxhFlM++HM2nfwQAqJx7PaiqgymqDFhKu5/VoVWolzRctWsFcOKj4HvvxdPjOu7onpV67B7X0WEaCjT6/P+AdhEuth+MfGZ4wSzUKIpz7DSg1JZBu/afQXvtfij5cqQ/gtOnWDAgK8WOTh9bF7FVJXoO+vBqFDbuQHHrBeC2BaVY9Z4Dk/snRAEnngjgz2uUekE0nlgXmvtqF9yE8YmDfWtnUCMvhLSU5/fKeTcIUZmzBW31CFWia4JsmwKq5zB8/efBzDaInvcEMA1qJZkhVti4E+aJN7y6OQTDN/1aqtVmhgwZMmTIkOGtQSZ6ZMjwJkBULSURfX5QPQfoOXDOgmKbhEIbWglmdkBzJUn+hNPL1cogeKkmrEQUJbVGhg+lWINaGYQ9c1y8pBMKmi9GMgoA8TKmlmpAqQa30/T8fImXyVDxCNmli0OpbQo95KvlQVidFvq9YKmVIQxd+0uyjW8l+mUMUKOQIFXTNwz6iOqGsHJRokIFzZfgNgO/eaLnvL6vg8dfPKkCGopSJ+rSIvyoUcDgVZ9KfC4ijwNLs9yq02ShXv+48t+KGrGaya3aumChY310nbxWos1tgCgeqYXUl2XAi96n6oI+x0q+jMGrPx3UPViMFzwNMrmKHqEfPkf42TSEoHT6RSidfpH8XquNYvh9vyyOXaqDKDriZ09zRWFPkkZqUQXg4jhD130W1vHXRcZQ3AM9PL5D95qSL2Ho+s+h88pj0OqjKJ99RdJmwW/r8EpoA8thT4tCsKVtl8r6MHHRTMkVPfsRgcGrPoX2ngdACxVpJRY0R5HEgxg3BNrAMgzf8Ctwm1MgXo2ROMFLVR1uWPToc63U8gCYZQKhWgzBThQxb+l5sF5bCh6A579NSGLMKPmyOFYf8UIpVkEULZlp5pFfUrRLsf4iXkQ+0XRQPY/cqtMwtuo0cM4xfdeX0Dv4IuypI1B3XuuRi162i6Kesjk0t3IrtOHVIpJUUVHZ9T6ZfQIEhbHDUMt1Qdx4pDw1coL4BTzrDnGP64OrUNi4E2ptBI0nbkvsJ7diM/LrAkKO6rmI1WNk/lA1ef6EqqB6HlqKmEONgiCSiRCdjGUbYI2/gdBOZcYE1XQYK7f2rWGTnqVDRFscC0PX/CLarz4BJVdE6awrwDqzEWGbaDoIja41RDcAh0bu7/D9qpTqcJvTqdZobAECX5y/GFMDl30c7b2PgWpGX2EzQvwRIsboAsdIFYGoIu5FzjyLuFFZ24JqBpiqeVZXsZ9pBrjVEYJxbE0mqhZZL0RtqIXFeiE0aaCFEtRSHdbEITlPEFUXa6NXO0u0NypE0FwRYx/9l+I7f+1RNBS3XoDGk7eJNderw5VbdbqXddQBZ44gNI08hq7+RZS2vQdKrhixI02rQybaK46jj66FsWJzxE5TrQyhuPWC0PaqfP5KPf95rOvaLz8cEanSgh+UyqB3DDX6HBEKTpE1wvy1TlFBcyK7i2oGWLeZmDfcxpSIlic0mMe8/S4l2yiMSq4q7xEl1B8VLY8upSBUhU4ITNfGcK46r+gRRtvu4n/t+Q7OG9qM61eei6leAzW9COhlEMLgUgo9X8bQtb+E9iuPQh9eheLWC7wxGhKnVQ3MZWg+c1fiGMUzLkZl57WJ52fxQ4qBKz+J3MotIIoGbWilEJQUVdo8EkVF4fRL0Nlzv/iNoiK36Ty4mgJmcrB1F4CPbMSX/uaNvpIDB8HT1jpclAvGm8Mpvj+zEdd/eDvmbvsj712EoHKOGDcigEbxfs9hlpZh+NKfAWE2WnseTDx7KoXygs+8VM8n1vvKzuswfc/fgpkdVM+7AaXThFCtFGsAOFzbEoJkP2GTKgAXbfHnNUKoty4ooFoO4ZYqxRqGb/hVYQdKFRjLNkZqbFAtJzIbLUTEbkDUwyOqDqKo8z7Pih1RGTAAiDmYdVvBWkNpJNudKGrk2V22tzSA0Q/8FroHnoc+ug6l0y5MbJMhQ4YMGTJkeOuQiR4ZMrxD8COWuWOD6nkQqqRaskR+QxVo9VEQzRDkUBqJrOqBHZdHDi4GIqpb86IDT87WYLEgVAE1ki9PkW3eYrEDgIjYepOiRySqW8sBZjfRf1SPvrRRTZBdYYusMPw6A4BPPMfJhrR6LPPXaFGKVVR2XY/ms3dDLddjxVmRqCGh1kYEGcY5qGZg5P2/idaLD8CeOZZqMaKWapLUkAQLpeKFkSqguUIyu4dQQayodOHMmtDLJyCylOzJw/P+RhJhnrd2eFu/aDsgrjXnvC+JSDUDhc27oNwX1OcYvOpT0OpjcDsNOHMTyWP75CLES3jcSiXeRu9HAKFQijW4rWnklm+CMbJG7CNf6it6UKph9KO/j8aj3wPNl2TGBlEWvo+N0bXSqkgQYqVATFAUSUhz1xZ95ll2+G0VDYhFyEfIIZIgyMXHIoKT9LN54EzME7kCSC8f8dj357TBqz6F0hkXw545AdZtQh9ZK/abQoBTIw+1MiSInsYUwvdKPHpbkKNtSXYTRZWEl4ieDfpV1Cn5JVgTh6HkitJah1AK7jJB/p6iiGlCKcY+/HvovvYM9JE1HtlcENcrRPxGzrtQFeSewkKR6p6tni8oqTr02ijql34UbnsuVfRgdjhzh4BoeSB8TWPErsz2URQhbBZrybbpOWkFRlQNubVnovns3fL7gct/LrL94DWfQfvF+9B68YGIiJzeWWLcCvLaEkV9vdpOVNPAQpHufl8gNm6I6kVqUwpmelHKiuqNMRVKsSoyqRQlKZioOopnXAzWaYK7NnoH9ySbqBmA60AplFHZfkXi+/C5UFX31hBxbKIoC5tkppDURFEBzsAdFlnvuePIOlI+1Oow3E5DkOaaLgTQFMsXKVz5839IHEicSjizxNtGLQ96zwQFISK7DohuQFENYb9k5GFPH0tk1LJeG83n7on0nciWWo7ln/xDtF96GPrgCrnOElUDcdTEc5NaqoNousgO845HcyUxr4cEW388AeK+H77xV2GNHxT91J6V9XWCc+1TSNsXcVQN/dbteKZJmt0WNQogBN46GjzniWcGby5TDTA05RwnhEbvHLzrHe/XzquPSztRMT5EMIdSqAiLuZMQP1RN9zJQiHe64pxVqoJQglquAuo6MJmLkVz6c9F8eGxyLx6b3IvrVpyLG1efB6pqADPBFAowAmNsPZSR1TDKg3DbszIDzwc1iuCdZHZgYdMu1C/+KLjdR2CkCqiiSnE2HPjhjxVaqGDw0o/C7HZBOjPA+vOQK9fgMAbLdsE0AyAK8jpFx+pve/SD7naco78usz32OaPoWhx721UcXPZRbMsdQ2H5elkTyBefiaKBcQcgFC3TRVlDqv2kUqwJUdB1ABDvWkW3o7li8NzujWNjdC2Wf+IPRBFw7/y563gZjlRmGVE9l2oNSKgCMP/ZUYgV/rURNmspmTKlGmq7PwgA6B2N1qISQUVibJd3XC0EEddBfu1ZXnakCiVfAbd78waPEaJ4zyBFcO6CqoZY00KZ72HhSIgeycADpVCGUqqLGkYLvONlyJAhQ4YMGU49MtEjQ4Z3EGp1ZNGihA8/skgpDYB1GyJKj1C47RlBUIezBJa67xRLh7cKkZendwhqqd63j4iRjxAe/RCxptIMuEgKNiKiU5AbRNGCGjL99hkjSahmiDoFxSqcxpQQEMyOzBICZ1Dro3BmTiBV+KAK9OFVqChqpIB29JixNhMRfelbpCn5sixgOv3jv0N3/9Ny2/L2K0SGUqimh39cAF4B+kpC9IhGh+t9BQw/o0Ju70ey9rEwSNt/YlvFj4b1LOuoCqeP6EH0HBTOMXzjr6K7/yloQyuljUk/uzdCFfDE2EnauEREMy+KWi3XwR0TNFeU9WCoNk/GD1Wgluoox4hT384kYUPUh7BSKkNexLMpbEj8yNxSXRQ8VkTUvhCMaMj2LkQWExqpaQFKpRjoW3lx1/GKjVJ5/8QLL+fXnCGFLn1weWp71VIdGNsgLJz8wxOaIK/9OkZ+nySuQUik8/+mpATXFz304H5VCuXEnEC1nKhpE+4L357O6ytAWPGwXjuVcBJtWtgSjOYKMJZvhFoZgtOaEfvXDBEdq+eQW3U6eocEwa6UBoQ1oKKAEB2KLxz5182b72muKCxuqALz2P7U47JQ24TtWGy+iPU51Qy4tiWusaaDKEqkoK5SrMEYWyczAnxrr4ErP4neGy9G6m3IOa5QQXn7lShsPh/HvvSv4M91ec8+KZxtoBQqYs7wx2IsYt0n4Vi3KSN/49tRzQDjHESj4IxJyz4pxhEv0pfQhDWWWh7A4BW/IGpZlQcweduforP3cfm9Uh6ct4h32J6SKGogDuk5ADyYr+e5v8OZPMF1UsA5BWAH87WWA3hA5sldecWu/bnXF+Ajcx4J5gFJVCpqoj/EcQyopTrs6ajoIcXnXEmIHlYHVDWkWCGJxRQhxY/WFhkVwb2sFmuRLBdf5CZUAaex+88X3/171pvjAJHx5zSnwB07am8FMf9wr+g4IQS0UJZrHNHzsngzwnMCZ1LUlsQ7c1HcdinaL9znba+gsGFH9DxTnpX8oAJCFXASnYMJ8UlkPdLXkedD334pJchFZjUoCmi+AmZb8pmNqJqIeFfUdFvUFEihxSfFPbKZEgJV1ZBjBC5noEYBo/n5C5nPh9uOPIGdgxuxYmAZYJlwqQolVwTRdLQnDiIXynjx13+1Pir6gCrIrToNvUMvAQBKZ1yCgfd+HDRfgj2V8mzgC32x5xgAEcswJVcEUVRUd38QlBC0ezY0lUIFQcfSYCsaQCluOreOrz401ffc2jyHP25ehRsLT8HmCr7VEXaJf/jtwwAMEKzFr4yuwBhjUBQVIBQHjzcxlsuBsw5AKDqmg7LmiY9xy0EvqIF3WyIjkANwHS9rXDxbkpD1K/Xqa/kiJw/3BWNifsyXgrmrTxAGoRTcew4nVI3MMUKMTnnGCs17bjtaL4MaefksXtp6IfT6KNxeR9ZKIooGqudEFsjUkf71Er1nHJovgTuWuLa9dpC5mRB/hS1tYfN56Hg12fSx9aCGqDPknqq6jBkyZMiQIUOGJeHUeC5kyJDhpLBUUSIMtVSDPrwaamUQarnuZYmQiCXWuxk0V5Qkw9sFpVSHWhuFUhoAQGSx6TQQQtNFoNg1i1gkGHlBOMRIBJHVU4A2sAza8MolZ7GotRGo9VEoxSpooSQEBu/lWilUoNZGoOSKUKtDqefkF9SkhZQISkKglAbSX0hTiCsA0r4AECRK6azLxTGUWKaH97dSrCWiiOP7T7PxIrroT6VYi0RmSgJnoYyk8P4Tfv2q/LwfSSc2oDJTRK0MonTGJShsPDe4d/tdS88POiKK5UvQ6qOx7aKZHn471MpQYJ0gbab6NTFpOSN+R6MEJVX6zg9E1aRXuV+EWIoehbKwylBVIdjNk+khot5p6G9V3kdUM6B69XlkFL3X7vp7fkb2VWHLedBH1vYVlOLnGN5PuN1yk3wpMr7j40Z+FxI9lFDEZngekNHesX6VfSH7xI9U1eVnanUonQzW89KPfyEQ1dsfpYKsp4okhqhRRP3Sj6Cw5Xzk1mzDwBWfkOIU1fMyk9AneKlmiP/nhaViODo5jogg49kS+X0VPl/ZTs2Q50pVHUTRMXDZz6B05mUobD4Pyz7+byWBJvpV7Ce/+nTUL/lwpA6Eb8dDNENkO1aHUL9MjBd1YBnK26+UxJL4geLNkYa8b5R8Sc7dhAb9B0IjIrS8PiF7OL8gsDxPqkjC2M/STCPm/N8QQhOZdVptRNq3xEFUPVLMXBK03jXz5yPAy5KQbVNEUXnPSlKS7oQEwl3Yp14Si8IOJnG/eeRjcI0C4QCAtGmU7QwJfX5GTBhKvhxsT5XEnE9zBSj5krBWCmdT+WLLPM9LRDegVoYjwni4P+X+QiJkqOHeuXrtp1GRSdo/eeelD68WNkZen8r1KGS/pdVH5Vrs71ctD4AWylCKFUmQ+r+tXXAz8ht2QK2Pof6en4nO01RJr+VGvDFMo0JveDxK6z4/6yCU1SFI6fK8NltEEdlk1LsHlEJFRtEr5YFERoo8TiyiXYq/NCocgRDoag66loeiaFD0PMbytb7tWQzuPf4cmGf2xBQFlibuYZPZ3typSAtSoukiQ0/VQVUNg9d/DrWLP4z6ZR9HZdf13rhIX3uVfFnMr974CZ9//F4iqo6coUJRCFSFglICRVGg1pf5HYTdmytYPZLMFAjjoDuEP2pejT9rXYEJFs1S5gB+8PQMbC7uWdN2YTsMTBHiLQiF4+l9A5d+NPLb2sUf8uYjPyOoKOYaIydEcSOw6pLX2Hs+kRlj3loTCKTib/+5ou9zb2j99PcTCJCBzVp4ngvPHZ29T0R2Zyzf7GWmKVAKJSjFGvTB5XL+kGuXHwwh30Piz/be/KgZYp73xXE5T0Sff/zzG77xV1HZcQ3K26/E4BWfAIDI80SGDBkyZMiQ4e1FlumRIcM/EtB82SNNfzJua0IVaQ/ythxP1UVtBkJEBL5nnzPvb/RC1MIFoiiuqH3ibRMjfZRSLdXDfylWY4l2hIgmtVgHKAW3unBtU1jHeASJqE9CgroX/u+9l0UlX5T2TD6U0oCwpko7rqIGMZyhCGJ9ZA1GPvBPYY2/AWPFFlE3QM8Ffuj+OAy9NMr9hS2mwsRUCvGi5EtArigiCi0jINvCtUPmycYhodoOEeKChmrV+KRe3J7Hi7wPk5JKrgTH6kXb3c/GRRGZHkqhCqc1AzA3IAQ0A9w2odbHEpkePkkbIfFVzSt2TlOzBNCHdJVkgRc1LoQLPfo7xgDwyOeC1E+z+uHRiHj/ehAaXAca80qnAVkhCBPdOycvAtnr/+LWC0RkebeF4um7RWTzIkRRSbQaefBOU7SbUBF13RVZFVSNjS1//GiG6NsQEeu3zRcBuGOlFjXWBpbBnjwi+sSPIHdZQIh4GS9UM8AcKxhzVAUoE/3ueYKrlUGvH7S+5KZaG4HbmAyIZ6/eCQAohRK4awkRoDKEwSs/GfiVk6QIJM7dIy6L1cC6S9rtJNF85k5ZY8CvI0AUDdrgClgTh5JCk1GIeKbTXAEgBNVd14tCt4Mr4HabQbaBkRc1MlLWA2oUwbotbx7oAERB/aJbUNiww/OhbwqRQ5LPhidUcVkAXViWdUX/UiU4B1WL+rJrOXDXhTa0QohFmiGIctcGuk0vAy40pvUcELJ18fYCPyoaIAClYiyGz8knAmOEKs0VRXYOIOcJ+G31RSLO5D1PCxWxHtiWWHuoIqxbzG7QH7kSqJ6DY3Vjc5c3P+fLMeFOzDWiOK8K4mdaeNtIwT1fjtYukQKkBigK1PIg3G5TRlLTXEHOGWplMGHnKe4Z6t2Xsfpjqh4pIB0H1XJR8So0X4pC60HWTDyjUJDWNNR+Ra5f0panq8h5AeFaboRImyr/eicEJL/GQ6EC6h3PbyuhCkiuCLU6jJGbfwPO7Ako5YGIfRs1Ciif9V6Yh1+Rn9V2f0CIqIQKi7rwXEkD8VxkwJC+4rxWG0Fu7ZnBWEOQOSWOnQ8i9iHGiojuV6Hky+CuG7EsUqsjYN4aGb7/iSqyQqT4YfpdoyCvGtC4A24UoMDBSK6W2tbFouvaYBDrpKuoaDEbOVKAw1zYYKByHYpmZEDVoBYqKJ2+W2aiiIzStL4j4t7z1mOiauCW27cGGyHC5hFmF6oiRg/V81CpAZgmQCnKxSJ+42M7Mdc28R/+8tEFS5al4fVjLbSZjoKiwLTEfctUA4wDIBSME7iMobD1AhT2P43u68/AWL4Jhc3nyfMQ/VIAs0wQ7wmQ5stgtgXf7gy2FazhUoj3nkMIxPN1/JkqJQM6yJ4LCQihbE357KhqItN5dlz0IyHgtgnOgfplP4PG47ei+8YLgOugvO0Sbx4swa8bJO5/b16OZWdr9VHYs+NQS/Vova+U57ywLWqQjeJl1PlBPrkiKjuvCdUH0oP7MEOGDBkyZMjwtuMngx3NkCHDgqB6Lj2a/l0MpVj1/OhP4u3OQ0BOq6JAb5p1jKpDC0d6ESLrnszbvnxJkHD+i61mCJKtPRO8FMde5NK86/1jngrISEL/hTN2zcN/+3YvNBQhF8d8mUFhQi5ek0OrjwXknKJHhBm/fcloRz+60hBETUT0CJHu/stwyApMyZckgRmIHhqUQlVYsMStGhC9NuF/R6JdFY+kj1g0EajlAdhTR6O/yxWABpJRtYRArY7Abc+C2ya0geXiJZe5IPkSSK8lCBHFjzovw3GdJImXJlqGsjjEsVRP9OpJOxW/6GfCusr7jGoG3PasiFYMnTvNFcAdO+LFLfpES5LYmgHOQoJVrHaGb+dFFCXal1SVhD58739VjRIPRgGEEGi1EbBiVWyTZlOVBkoBEFC9EPSFokAt1uFwLm1Yot3sRclXBuGT0qKpoh6Sf6/SXAluZy61gC2VNh+2jGqFG4gwoDQofk6DjB9CKaDqAHNlnZGgH/JwO968EiIhaa4oLFKknQr1BBM/+laDWhoAjxE6oIq03Uq03yeuQ3UIiKqni2ox+HYmNFeQpHDC6oMqkWhvqhkRUVTsJxDIqKqDlgfAeu3oveztXxTlVT2f9SAqGEQRxeWNHHzBRhKael7YclFhaQZFAbzr5YttVDMi9mVqeQDMKMj+kSSgn9Hhnb8UijQDTAroglRTK4OyBo7va+/G6vEEWSnxDJlAYKNGQQjbSqgN3jpHFMWziCNCPMiH92GI+zw038iIf0WVtntScCIkmIu8DEffUkpkPfhFhoX4yhUV8MRYJZ6REcp09O2pHNsMiD/vGP3IYXGeuRQRLd+3/pY455RMQU+I9Gvf+J8l5ljP3kqK5FJU9O4vPScI1JQ1XNY38qPcVTVRcJ34mUeh38soeUWVYpVf84Lq+VgNMAP5DTtQ2HI+Oq88Cm1oJSrnXh+12SHRuTixFs8TDKMPLsfgNZ/B9N1/A6VQwcB7g1o6fjulSOeJPMHnBRlIQVRhG5So4UQ8QYibEdLYb3tJL0LROAyUobQnUc4l6x58cvuHMN2dw22v3gub9a/DAACcAK43j7nchWVZYLkqXHA4AAxFhaUQ5EPnBXjzmk7gdpvw6x6Bpq+t/twh7xMvQGS+enhE1UCsHgxdkX2rMCLiBQiFni9AtSnq5RwGq3lMzvapI7IAOo6Cjg30vFHEQMGJIgMCXOZCyZcwfNOvwZk5LjPoZB94/1ENkqhX8mVwx5HtZrYpSX6iagDn0XFHWWJuC+o36eCuDW1wuScWBFlhcm6OCSZCuPXWUy0XsvAzkFu+E2qpDmZ24faa0srSny+IJo5HdCPxrOOfjz68Svzh1fuKBEOEkAgaAYLaWOHtqSL7RN7r82TrZsiQIUOGDBneOmQrcIYM/4iQlmHwbgahiiCtbbNvEemFoFaH4TanRSR0aw5uK1ngVq0OLZjVkdo+RYVWHwNnjogw82wuaK4URCTHCdW3KZqLeNFucUI2eOkWwo7NWBCV7FsSceZZk/QvOAsgQmQqRjHwK/ci4JPHDLcjKXrQXAk0VwShCuzpYwhbNlFVh29hotVHYY0fTBDxMu1E1g7RoHiF0sMZLEqxKuwZwrVTwvsKkZx+JKeIDBbZCsS3M/BtWoJGAkgWyvYJYJl9EytgK6JAe3JftFCBklZXI25HBZ+kCyyE1PIgQAjc1jS06ghszoMIZUojhTWFhY8qyNP2rMxs8MlZqhfAqQ3Xivn5p9h9EU0HnNDYDmV6iGMpUPPlUO0VL6rS7wMtID3jNkqSeFIUEDcUbb6Ie9YXR6hXeBdhi45cSYge8XvEJ2s0IzFn+kSt+HclPfPC34+qBpkDlIrhGSLkJbHtZduIY6sAYUAaOarngE5DZFAMLIPbaUQscALyxLNYCtnESOJJ0YT4CyEU0j7R8VI0DRdA9uyi5oNSrEmxxRc1iJZLXXvk+csPYtZfoWwwuf9CJRJt65PUwpIQXjRxSFiiFEoubF9GIpk5RPHrbxCPmCQeOe1nutSifaCoqUIwUdTgvg5lhgAIMm7KdRCqQimUA2HBG5/MjGd6BCR8vA6H3MYowG3NBPZwhhCXiFerhMbaIdtKCJRCNRCHND04Z6rI6O00+0IxboPaKDKzBNExQ1Q1df2I16uiuSLQmIqMBT/Lph/S1ur4dYr9It2iyRe5YkR7QnCJZ3qEyEz/737BDNICLHRPJmwxKU3YTvqCKC1UomIQFRk98IRSblsgmg41V8ToLb8N6/jrILkCVL8+j7f/SFZcbG726yf1A6EKKmdfDmNsPbTaSDTzKZzN53+maPJ6UlWHWh2B05iEWgkJrEr4+CJbhHsCfvRaaFB9cQ6ApmhQ8jq2jWzGC+N7AQA51cCGgTXYqmjYvXonGmYLRa2AvUdfxF+/9IPE+XAAjiew2t787TAHjAgRhOp5mI6FvJYHVXUhAnjPHvCsmvxHjXBUv1hbvcLz4UAEr08AEpmTk/3snacvsucKUE2OUkFHs61A0Q2ojMBiHPWykSp6/P7HTsfhI+P40v2Tie8kVAMTjTaQF89ots3w3KEeHIVi44gGl9NgzMIXNCx5PYgvpsee69SyEMipkYfii/0Q6yh3rNA9RADuRsckIMUjpVSTa1vEchGAbx+YWBfyRW/Oy4MaeRkQIO9lT6TMrdwifyOFay+Tl2o5MNdNnV/CzykicGVZ6rWMBMz4v9Fz8IVT+Z2/Zuv5YD5YTBBHhgwZMmTIkOGUIxM9Mkh8+ctfxle+8pV5tzHNrBBbhlMLP+PCnhtPFLruC0JFcWurKyKEvYwDmi8mRA+lVA+8zU8C1MiDcwaASKKKGnm4rRkvGq6/H/ZbCZGWnyQoiU+wEvEiHi8C7Ue/KvmyZ4fVHxHRwY/mZm6SmEyJsk2rhxI+XhoZS6UQoyUFByBKlgLypVTJBbZdRFGhlAcTBFkkYyNsg6IZQQYQVaORn0YefqFlAIEHfzwK0ShIO6M073mqGmBohggkEiWt5P5pUkRTlND5qiC6R9R7GQKC3PSjK1UADgTtQ6QFEpGR2b5tkwbuCLsrrihwWwEhI88zJVoZWpRYi7zke1ZNUnTxCvQGdTKC+g3UKESJL0kuqkFBXkpB+CKESo/4Jp74hUg0tVd/JDGOFBnNPR8IVebNCBPjrxsZE36tEqVUj1zvcLYV8a5FgiDWfDGhJI7tZWHweCacjEyPjXFCBJnqRVnHffUj26ZFxqsatOow8ht3orvvych3g1d/WtxbxWpwHWVdi1wqoZNsnyDEgkyPNLE0Xm/FJ4/8rJZQNLFH7kZrcqjRuYeqULzI8XBGl3/+80Vmx6H4RG9arSdAXi8gdO4eIZ20twpIcH1IWISJdoUysfScV1PEO3fNs+xSVICq865rYt3ikpQXnxXEWPWGUz/bM6IZgC1quIjMEBY9J68tacJFvE1EUUHzJdCQp304snyx8I9d2nYpWn7BbwCFLeeLNqfcy4K8ToqniXHpzQXxeiAkNq5S2+XXh1FEFoyo25Os6RUXZfz2Rp4f/H0RAn1kNQDAbc1GAgiIpoMmMtdidZRimR6Lff7xM00W3M6fZzwohTJovhhbD0LHl1ZuHqEdyfKLPgfoVIhGN2+9Bo7roO30cNX6S6CF+rRiiHunklajDAADh+mYcLkL3as/4jAXLghszkFzRXQ706h52zue6BFqfPBvKWqoXnBDCc7ceDJ7UFG9cThP3ZlYJgBRNCjURiGnwtQ16EYOqsNg2QwrR0p49dBs5PdnrB/EcD0Pp73AnEUooBfkff5n334ODz53DABw5Tlj+LnLvILefvCIZkQEPyUl0yYO+Szni6nM9bLwvIAHnlI7B/BqLxVlpnTwRSj7zw9CCZ+Sd/21AfE8y1iQfSba7QV8pPU/DeqrEX3+53VqFOG6Tl/xKlJryBcn00Rc7/mF5goRK88MGTJkyJAhw9uPTPTIIDE9PY19+/a9083I8FMKtVSH1WkhCOdPh/CRF1FYLJYdQlU9koVAVD2VXF4q/ELk0l9YE5FdfsHYdwpx0tQH0eaxqfGjXxch1oQLk/sR15y5IkvEt98hNNX+J60OQrzt3I5aUlE9L3/nWx5F9um9UCcIB1WDn8GiVIbSbUgk4Z6PRuuF+0FRoZTr0m6Nain9GyOUgBCp0yeCOV7YvS/SvNdDEf1SXEEQxRgpsk0pSK4A7jqSVKBGQZDkfl0QQLz4+1ZJst5G0s4q2bwQ6Rkvnqto4hgICrly5oaIlSC7Iy54BaJTyLqIUHC6CNu7cG0RJVqbhRAqSPoUAWw+e53o7vtfM7kPGhSaVgrl1N9Jkt3bzreGiu5PiH1KbF5JjGcanHMcVNXh2iYQFlrma3v4M893fOTmX0d3/zNw2nPoHXwRxth6VHZeC6c5lT6+Q8LhgohHpae1IfJ3kuCV1ztUOFfuPtavYYJW1hMAFjX/JdpmhLIzFv0jIcrJOhzSNirsF68FWQWxflRD85n4v8ju86PS5z10bAxQI6ipIe//+G88MUOKvYoCwlPEjX5ZJinzvloe6Cs6LxW1Cz+A7hsvwm1OoXz25TCWbeg/r4ZEhHkRLuweEq3T1rXkMXxLLz+qfPEiWhxh4cWf5xM1wggFSOx8SUysiM0PZLH9TZOC/v/H3nuHyW2dV+MHbfps79zGXkSqkKIK1Xu1ZFm2bKu5yO2Texw7duwkjvNLPudzEte49yLLkqxuq1u9S5TE3tv2On0G9eL3xwUwwABTdrkkRQnnefhwZwblArj3AnjPe847k7Y7m2QQ0Lpe7CsGAW2qOqETK5HERMBQyrRGm/DB1dcAoOoPXdehEg3258NI0Nua843JXdj8+LfBALhi2QVYO+84KESFzrJQCFVJSapE7RgZBlqJ6tIxNs2/OapKZngBSLOe81QpgeM6R/YkElNpxrEICAzyoQD4UAh8gZKNZ6/pxstbxpAtFMmBC9b2AgyHxkgNr+5GuyVFw3MbR6yvH31tFNecM986JtouzmmLOoPxadq+6ea8zHLUZFD37kvUppF1J9xY9YTc59Z7x0VbxNL1S0HJbnrPZ6tY6XKRGHS1tuQ+s/aQ3b7Svk8dJYSjT3r48OHDhw8fRwQ+6eHDQlNTExYtWlRxGUmSMDAwcJha5OPtBIYTwMUaDB/2ArRsspgtbgMbihaDwB4ZjFy8CSSfAspZXswSXEkWr9Ayr7I11BEEGwh71rgAjGA0w9QU9DVtZUxbA4bjjVobNMuYFlIvU7izygseDbCW+jZHi+REWVsevmyWOs1SLLMeS72VeUMVVFzP/rJvkApG/IgJBAGtJCDCldT/QLEfliU9rILGlftLOdsF04+/3DlxtA0M2HAcmqHEsWd529urM8XgmqWSsG+rCmmF0poeJctTNYlsLcPafcPL9Q27rQXjthHzbIZNsWEndkx4qZkYlgM7B/WPTJLFLJpsWrd5LmuSS1XOAVfnzhR3bYt1H6fj9xoCR5XaybA8gvOWICwEEe47hgatWVog23t/tVv6lZJlLhh92Apulo4Zu0LHY1uV1C1guaJqYhbB3dlYFzIGyRPqWQY8d6e5IcRWnOZcLhAumxnt2uYM1CkOqy+juDvAUJs8D7CBoGN8MIxRg8bVLu/AqCdZWmqBdRDKy0BbL7o/8l+Qp4YQ7lkOLZ9BuUQJhvGo3+G1nGVn5a1eqbiuofCYyTUpCw8y000muOs+mdZtxTY5M+Vr7etetRQOBqayylIqMqxF1HFWjRDn/kxFh8AJEFUJLMOCZ3kQnUCH7iAoypEeALWzAoD7tj+K4zqWQ1IlgBMgazIIISA6gUJUBDjBWtbW8OLfxvXgglGrb3PRBg+lhwA2XKXvmOuwHFjjPAR4ev+IxmJgGBY8T/fd01aHj75zFf7w4FZMpkScdcI8HL+0FYmpNEIBFqEAB1H2sMgEMJHIo7WRzht5UXGVzNs5LqFznnHuGfpcMltbVruKp5i0wFj1LErB2GrZOL43khdM67xqsOwirWQClloXeia8cEVip8px0veQxorLFNtcrIXkqYwtXd6ySPPhw4cPHz58HE68OSN2Po4IrrvuOlx33XUVl9m5cycuv/zyw9QiH283mKoM07eXiHla6NyGasFYLhSlhVgVuXJwbYYoDebUlAV6hMAGI9DKZLQxLFdzZjbDCRbhYX62PMzNrP2DIJZcme5l6m7YQb3dPYKDJQWJvcA3dnhaAlnbDjtJBZYPQGdLSA/e20rFbL+nZQ9brCtQDa6XZZvSo2qGOUNf7tlACFqpxZaD9AgVbaRQJIwqtsPdUEdwqDSQyTe0QU2MFb3Pawk+cjYCg+Wg6zUEYmyBQoZ1Kj3M7Xju6yACrxZ4oVj/geUqzglWn6lyHsqRdo5tVTuXHoHRWsEaVidmsN4kdACUtXWa2Q4qB1at2huaCr6xHaURO8amFLEXNXe0v9y2Oa6qpd+cwxgnkUVr0Hb13yO/7UXEV18IoaR2ChsMVx9zs4B9XJpKEcBZu8bRXJMUspMl5Qp4zxIHmyzA8II1TthguHwg0UOV57k9GxFZLVvfta5Vp+jgSQ+HxVb5hTyVLY5752yvTY32VjWD48AGnKSdpVQMxz3PmWAcR1gIISNlwTIsOJYDqzMu0iNQgyJA1hTsmtqPle3LwPACNKJB0mhCiKqpEFjeKnputdGcU5hiEN2ecMDHGy3LN2sdXkC1M2fWMHLU2zG2H4nSccpz9PtYREBvexxfumEtoOuIhAUIPGc90zbEAhid9q6D983fvoyvffhkNMZDkDyIke37kzjzhB6j3eUtuUwlTCVY84tBeNB7MMr2o7LbY1lrTqpF6cEwDITmecVlmQpkOseDYXlPmznPpszgmdas1eOu48O5+wPL0lonPnz48OHDh4/DCr+qlg8fPt6UYANhzyzXWgJvjOHzfqTqbRxpMLzgso2wwHJW7YAZb9emZCgGew7NOS73clwuk5sRQuWPuco2rd/LEBaOz5XILrZ8gd7ZBltncq1oRqNhRVZavNtR3FlwFGM2CxzPBGZmZ3Gb7sxNvrFjZsFAu9IDtWUoOyxheKHmoN1cBPAZTgBn1FZhWK7GuWn2hIS1jWqZsB52G7Vv2yzuzlhWVLUobmoFa/OPL98GHly0Dlwo6i4qbg9kV7D58tyuEJwb4mYGYGx2SdGlp6D+5CsQ7l3hWo4VggfdLzz3P8N7oN1q7k0LG+HKcHzZ+zwXjs34+LlIefWAZ1N45/3wYFF13HpZIAJVVYQ17Zvhap4/awErhMBFve97bCDk2cdY47pG+DAABhzLgWc4cCwHjilVwdTW1u2Tu0F0zRpfeYWSBQpRQHSq+nA2wqj/UAuJPQMwDEuTckIR13VmWXoswQA9xlCAA8+z4FgGHEf/57kiAd0QK9+viQ488eogAHiqQbbuK9a9K6fUBQBV00FIDRaTxrHBVMGytSmsHOuzxRpWta7rmMs9alzZt32orKXsxdwd33PufTIsX1Yt6cOHDx8+fPg4dPCVHj58+HjTwiyeTf2SIwC8fch9uFEu65yZxQuptc1gBDoxitqadSqqFIaca5QnQ2KztmiY0f4rBAkYhimrRCrngX8w+3PvxKbAKS0oXEHhwgbDMz93tSg3bJnltcCsdzFT2Im4mVi5HCwcFjK8UBvRcBBWIta+qllXMeysg6CMYBBgZqH6KjU4Zr79UHXykQ+CizV4/+YolszPKPg4l9Y9Ne/TnoHPMHOqPqxp/4dhTjzcYNjaiDhqz1ibVU1xnZmRFxb5P0eEVbU5pJwaZE6u80HU9PACF2+avW0SyyLIBwylBwtWZ0BqqfHkge1Te6ASFfuTQ2iONIA35kbNIDx0DzUZG4xaWfxzCTYcBysEy6qTggKHgEAD+EGBg6rS5y2WZajSg2HBsiwa6yqTt0+9PgRR1vDK1lHXbzsHktg5kMDinkawFZJWNEIAsBYhUw3UXtJUydS0ypyBYVmHetX52+yTAGrev6vullvp4VXHy4cPHz58+PBx6OGTHj58+HjTgmEYCE2d9EX/EL+0vG3A8dXrNZQBwwuO+h6MWZT0TYDDFdyrdry1FsiuFTN5SXYocWZwjWfTHw5JVjrLVS/27rUebws8HqEgb60Kgrnop9XtrWYf5GGt2iNmEegqNThmuv1ACLqmVlyGizXUZktUg/f7kYbb+//N3+Y3PZjaibhDfV+Y6+tZlXSp0bJrdvvm5vR8Hey2eJYDAwYcw0FndPBVilCXQ1JM49vP/RxThSQElsdNq9+L/sYeaEQD0XVvpUcgdEiUwuZ9otI1jEXoM0RAYJEzHKw4ljWUHgyCdfXobuMBuAkNO17aUv73u5/YjS/ecGLFpApCdEAnEPhax9oRNI8w65OUweFWfdPry3h858OHDx8+fPg43PDtrXz48PGmBisEfcJjDlHOVmJW26pUNPhtiiOZWW1/sZ/JS/5srToOBWZDGlmBQo8i829HzElhZYaxCt3OZbCG4fiq6qVa9zdXlkKHFD7pMfeYYyLuzYRq/WOuSfWZ7Ptwg2d5sCyt6UH/0b9NCDMY/1OFJABAISoe3PUkABikBwGBh9LjMNvg2RGPGMXebX2c42h9kWCAQ7ihBV2tM6s9U4rdQykAlfuTqunQarS3OuKw1Z7y/Nmjbx/KY2NYriY1rA8fPnz48OHj0OOt+dbgw4cPHz48MZcBTM4nPXzMMWYVrDfVLWag3sfBK7BMK6Y5treim56b7b3ZgrRecCs9jgKi5k0OhmFoweS3IQ5lAsibbTzxLGcVMjfretQF42AYFiE+iLZoC+bFO2a83X1JWu/CtLcqVXqYdbGOFASe7pu3KSw4w2KqLhpAOBzE/K76g9rHdFoEUHwelBW33ZZGyFFDejCzqD2laaT6QgeBt6K1oA8fPnz48HE0wo8O+PDhw4ePWcFX4PiYa8xOdeIHF+YaDGMWMT/4GiRvZ7iCp77FyZzg7WoVc0hJszcZkcSzPDiGtQgPnuEQEcIQOB7RQAQBTsBlS88DO4v56ccv/x6P7n4aGtEcNT1cVldHEDxXPC6T9IiFBQQFDgu7G3DuiT2z3nZBUpEXFetztqC4liFEN+p6HAVg2RnXTVMPMenhw4cPHz58+HhzwCc9fPjw4cOHDx8+fBTBUn//N1v299EO/3zODd6u5/FQEpBvNnKTZ3mwRuFujuUgcAKCfABBLoAwHwLDMFjZvhRfOeNTOHne8TPa9r7kIP6y42/YOL7NIjo0okGtUm/ocILniq/oZjFxjisWFv/olSvx/z51Buqis1P1JTKS9Xc2Lzt+UzUCVdNpXY+jAAzDgg3NzPKLkKPn+Hz48OHDhw8fs4dPevjw4cOHDx8+fPiwYBaUf7tm1B8qvF2D9XMO/zy+5cGzHDimOP+Y9TzCQggBw76vKdyA+U29uGrFxbhk8dkz3sd3n/8lvv63/8G92x6BSjRoNqWHPsvC6XMFnmOtWthexcQFgYPAs7jk1P5Zbd+0uNJ1HbJCoKjU4kojOvKiSu2ttKODFGBmUauO6AA5wtfYhw8fPnz48HHo4ZMePnz48OHDhw8fPoowrW784PKcwic95gb+eXzrg2M58B6B7KhQtDEKcAI4wxKxPdY2q/1k5Bx+/8adGM2OQyPF2haSKlVY6/CA51gIPGvV+bDDtLw6fkkrVi1sBgD0tMcRCtQW/J9KFqCoxLJ5yhWoykVRNYiyCk3ToR4l9lazIed1/Sgq1O7Dhw8fPnz4mDX8twYfPnz48OHDhw8fFkyrGz+4PLfwz+fcwFcgvT0geNQwKbXhMkmPtmjzQe3ryX0v4IplF1qfJU1GgA+AnUWdqbkCzzEICN593VSChAI8vv7RU7F3OA2WAfYOp/GLezchL1W26hqcyGI8kUdjPAgASGRExCMCVJVAkjVMJAsQOBZdLbE5P643A3Qdvr2VDx8+fPjw8TaA//blw4cPHz58+PDhwwU/uOzDh48jhVrqjFBSgkFrtOmg9qVoqkPpoRAViqYiyM+uZsZcoCEeAs+WPwccy0DTdAg8ayk/Fsyrx9c/eiqeem0Q9z+7t+y6UykRBUlFLEyJJU3TkS0oIETHX5/bi/uf2QuWYfCJq4+dtYXWmxm+0sOHDx8+fPh4e8C3t/Lhw4cPHz58+PDhw4cPH0cVWJYDx7JoDje6futv6J7BlnQH6aESDQpR5qCFs0csLCAULJ+fyLEMOI4BwzBWgXOA1gDpaK5c2DuRFgEdSOeKRcxzooKCpOL+ZyhZQnQdP71rw0EexZsTRNehaUeHfZcPHz58+PDhY/bwSQ8fPnz48OHDhw8fPnz48HFUgWUYcAwHnuPRGimqPXiWR3ustebt6ABUvUh6aESDqlW2iDrS4LmiwoMtUYSYCo5ySGUp2SHJxWMuiKpV4NyEqukQ5Tf3eZgNdL+QuQ8fPnz48PG2gE96+PDhw4cPHz58+PDhw4ePowosw4JnObAMi0sWn4MAFwAD4KJFZyHMB2veDiEExFa4WyUqFPLmDvZzHAuOo6/yLANwXJH4iEUqkx4mkVGQVAxPZKERHboOTKdE17JpgyB5q9TAuPfp3fjMfz+Of/np8xiayB7p5vjw4cOHDx8+DiH8mh4+fPjw4cPHLKHrek2+4z58+HjrQCcEDPvWzBvy5zQfRxt4jgfHsFjRthj/eOYnwYBBW6wFj+1+puZtKESBathb0XoPGmSN2ltlpCziwRhSYhr1obpDcgyzAc8x0HU6VjmORTzKYypJSYtYuHItkoKkYmw6j/+94w1k8jK622L47DUnIFtwW3olMhLamiLI5GXUx4LQNGKRLUcbkhkJv7h3MwjRkckruPXh7fjCdWuOdLN8+PDhw4cPH4cIR+cTiw8fPnz48GGDJklHZL9Elqsv5MOHj7cUiHJkvf4PJXT1zZ3d7sNHKQSWNwqaAyE+iFgwigAn4NiO5QjzoZq2IakyiK6BEALVUHhIqgxd15GRskhLWSQKKehzbImka1r1hcqAZRlEDRsrlmFQFw1aao+AwKIxHnQsa4coqXhu4zAyefoMMziexfod48h5kB6DYxkAQCpLn7My+aN3/nth04hDsfLE+sEj2BofPnz48OHDx6GGT3r48OHDh48ZQde0qi/qOiFQs4fPNkCamDgiBASpQLboxC+S6cPHWxFHiuyc6ZwymwDtwQRhffg4EuAZjhbzZkyrJxYBLoAAJ+D/rL0e63qqZ/LvSRzAvsQACqoISTXHtw5JkyFpCiZz0yA6QUGhSgoyR+TgwWwnKHCIhijpEQxw4FjGofD4yJUrEQpwCAgsPvve4x3rFmQNT7025Pjuwef3eSo9xhJ5pHMyZIVA1QjSuSOTZDIXKCV/fPjw4cOHDx9vbfikhw8fPnwcZdAk6YhmGhdGRiGOj1dcRisUII6NH5bAvyaKIKIEJZM55PsCnKoSO+lRGmCshRzycXjxVs7Q93H4QOS560e19klNkqCJbr/9StBn0d911Z+zfBxd4I0u6yQ9BMCwubpi2QX4wrqPWb97YTw3hR+/8gf85rXbIarF+3pWykHXCWipc0DWjPoWczQHmM8IsyEoIyHBCuKHg9SxOm7U8mCJgjNP6MZ/feZM/OgfzsO5J/ZC4IvH71WfI1dQkM65Cd1cQcF4Ig+A2mKZ5MfRiAA/89DHXKt7fPjw4cOHDx+HDz7p4cOHDx9HCXRCQFQVWi4PJZk8Im2gBIMILV9wZSgq6bT1N5FkQNcrKiGAuXmZVA2yo9K+zMxsnZAZ71PXNEewUcvni3/b9qkVnAFJXVWP+iD7XGWzvllwtF+PNxPMcfR2DAjpirfSgygKnWNqJHt1Tat5WSWZAmZIIs/G9k8nbtLj7XiN3+44mqwbWSOAz7Im6cGAYRgE+aLqoTPehk+d/AHMb+ipuK0n9r2ApFh8lsnIOcfvsmF9VW4OmDEIga5pVZ+VrMWNOaYUoQBH/w/yEAQWAZ2OY4FnwRnEiKkKKQdZJXj05QOu77N5xeR8LDssST465wkW7jbKSnmilyjKjOddLxwN58aHDx8+fPh4K8InPXz48PGmgJrPe75kK+kM1FwOai4HaWLybZ05r4kilFQKaj4PJZWGmstVX2mOoaSMYICuQ00XlRVEliGNT0DN0jaZwbZKmcm6roPMMHPZaxvmPvUymYc6Ida50lV1xtnPmiShMDxsBRo0UYSu6zRgqRgBEE0DkUpID40c1UF2XdehpFLO745yy665zNB/u0PL52m/95i33+rzdLl+RGTZIEmlms6BTkjZecu1bUmE7pGdXbmdMw/Meik9ZlLn461+7UthD2ba/54taaDmCwfdprmAVnhztKMmGH2uJdIElmEd9T0CBvER4APoirfj8qXnVd3cdD5h/U1kJxmhaIqVgDIX0IkOnRBoYm2kh9ecKyeTYJiibVN9NGCRHgx0Sw0SDfOzamO2UNxfXqTHPTadRzbvbEdOVCsSCHOBg32mkmX3dSu19LLPYURR5+S552giEX348OHDh4+3EnzSw4cPH0cMRFEgjo0jf+AAxOER5A8MID8wSO2bVBWFoWFI4+OQJiYgjU9ASaWQHxyCnExV3/hbEFq+ADWdsTICpYnJw7p/O3kA0BdtNZ+HnExZ5IZsKFDMNmoVAjhUCXFwgQOtIBbtIUqCbeaLKlEUqz1E1Wb80kxECSC6dSxEVmjbVdXaJw12FvdH969RksVsxxyrJqplcR9scEAriK7AV62WXTPZ90wtew4Gc5adW+v+PIIluqYdVVmfXtdb1zRo+QKdqyUPsjqTgTydcH1/KHAkguw68R4HRKFzg66qNaksdEKAEmWF5/nW9RkH33RNm1VRcl1zr6Mb2ehV1yXEofib8b5nMS40UTyiZKw9mGkn1O3Xv9b2EVmGNDEx6/lBJ2TOxl3p3F/rnK5kModNIVi8x9P9mSSHnfRoCjUAAIIcJT/iwVjV7T60+yl844nv4vsv/BrjUyOO3xSNjkM7OSgqxXuYahs/9jFTbvyYY0tXFMc5LhckN0lVE1qhALVkzNWFeQQ5es0YooE1CJFIFaVHOThIAaNrEqIjJzqvcyYnQ/RQgNSKav2eqKrn80KluclMUDHhRcpkSsgb1abm1VVvZc1MMRurQR8+fPjw4cPHwcMnPY4yPPbYY7j++uuxevVqnHzyybjhhhvw7LPPHulm+fBREzRJQn5w0MoeF8fGoGYyjqxZIkkoDAyiMDBovXTrajHApCsK5Kmpo9p2R9d1yNMJiOO117zQdR1aPkfPg2krU+YF8KDbp2megSstX3DK/AmBODIKNZ2yrJ2IKEIcG7OCbVrBbYNlra4cvP2TPTDjCDDoOuREkv6tKFaQQNdmvk9NpPtQczn6Aq0oNACp2lQesgzdCIZYVloaJViUVApEliFPT8/6OF1tkqSqmYNKOnNQ51crFEAk2dFHa7XjqdWqAyjakx1KWMGxw6z0UGxqKFORpOZyLgWNFyxCzUauzRVmsj2vOYYoCogsg5Tph7qiQs17K9HMcQHM3vLDbL9WKFStL3QoYAYqS8cXkWSLbCC1ZG57WGHZA27WYjK1C4TuXLbSfdCr39jPtyZJnuefeCk9aiTqiCxb/Xym0HV9xiSNrusQR0ePqKLOTvrZ537dRujrNY5hrVCAriieAdJKx2hXVsrT0wf9fKQTAq2EzFQz2ZrW1fIFKInDQ3iaiQi6Wjw3PMuDZVjouo6wEEIkEAbLsBCM76NCpOp2N45tQ14pYCgziscGXnRcD6Jr0BSFEp/GmEhJGRBjHMvEm7goe7/WiWGJpzmSQMo92+kacdxf1WyOPo/YxychCDB0OVbXLBVINXurcsjmvfueKBXbS0kQxdP2qlaoWhXSQ5Qc48pEJYJZmphwPCd6kR6lx6fZknuIos6JvVXpePLhw4cPHz58HB74pMdRhJ/97Ge4+eabsX//flx99dW48MILsXnzZnz4wx/Gww8/fKSb58OHC5okWf7mheFhFIaGQEQJ0sQksnv2VgwKVQwQ6DqUVBpKOnPYilebKBcomgnyBwYgT09DTWcgjpUP2GmShPyBA8jt24/CwKBnwNYMxFfDTIJCaj4PaXLSFYzTJI+XcF0HkRWouWJApDQ4Ui6obWZEHwwc2aim5ZSuQ8vlrRdXK/ta02jAYIY2AyZhouayFnFhV6nomkb7hao5glu6SgNfajZHr3cmaxBKmYMOYmuFQtVt6KrqCLqXgihKxW0QWTKury2TuQbSg5JA5ftbaX9VMtk5Vz6U9nfzGlbKnp0LOKxuCLHIDaKqUDKUSFRzOSgGIVcJRdJXnXOSdybWeF5qLaIohspDApGplVNhqGgBp2sqJQI9rqucTFrXp9JYrCnIK8kHnUFLCcnag7S6rlPllyi5ziNRZBBVg64VlR6V+rauuRUUXufbPMbSsUfE8sXNzfmu2DbFcb7VbM6VIW6u5wIhnoG/0mOziDCvoH2VeXc2yhStIFrz7JGCM7Bt9GtVdShmdE2raQxbY95LRVQmsGve3+zr10LuVuqXpk2bHWq2tmctXVVrssaq5ZmEBvTLL6fmc4bVVLGtPMOBZRgQWQZj2MFxLGf7N7NX3w3J3dBLAtayQvudqEoghCAnF6AZdlJ2pYeDxCgT9NaJbiko7UrEcudQ11RHX9AKec/7NDHUZgwhMNytEA3PjvQoVUKYUFQCzZhjZEUDdEA07KO0GVrxAahaHF2TRM/+UFpPzQ4iSo6xJ3uQMtm8s79rok1BPEOlR7lxNVeKER8+fPjw4cPHzOCTHkcJtm3bhm9/+9tYvnw57rvvPnz1q1/Fv/3bv+HOO+9EOBzGv//7vx/pJvp4m0CTJEiTUzQQPzIKJZ2BJkkQx8dddg6FoWGII6NQ0mlDJeDMRDsYKIkEtb4aG4d8mLIK5ekECgODkA4is1iTJKcFRi5XNuAqjY0XrZTKBIyURBL5/QcqB9Z0HeLoWM3Eh5bPA8SddVsxc7nCC66cSLiCg2q+4AjW1Ar7MeiEuAJBOiGQJ6cgjo9ZgRtzHU2SjWBsMfhXzT9dk6RiXyW6FazWVbVYQF2lVj9moMG0vdCJZgWG1WzOCkwoyQSkyYOzJtMKBU/vfTt0TS1r56QTAqKoFbMPrXolqj14RzwDtXaUBvwc7ZYkiKOjxWWNIqGmYqASSVMNuq5bCiU158yWJ/Y6LB5zjz3IcTBwBECNjG0iyzQQmC9Ank5YhFU1+yMzkFNrlnit0DXN2nYtFjauZVQ6hkAIHceyDGlyClqhUAy6qipAdM95S81ki0H8MkV51VwOarZ8ZrllL6eqnsqEmUBOJCAnErUTw0Z7lVTKFWwnskLnGLUYwPSqW0RsJIaLyJBlV4DcCmKXzLNEkV193YRdIQkYBIHDPkdyJA1YgW4vcqNMcfZSxVLR1tBmD2OrhVQJpSQNYIyhknubQ61iEdvOgK/X3zNFreS4veaD2Y7SfqlrpCZyzrzuXu3WJDeJqJvBbuN7S/lQg42fODpa9vyY49vcH1UwKjaysfzcZc4P1c59tf6g5nKQJifKWmBS20pDiWjrmzzLgWVYEEm2riHHcuAYFjzLzyrwXFpvQ1UVasGqiBBVCYAOzVBhqUQD0Z1Wl4CbtLLOj9F+XS0qPbyebYrrFeuJUCKzOJfalwGhyR8MIWAMT6pIaHY1PZIZqWytjoJBIogyrWsmKRoI0V31PhzHUOYaeJEe9vsPEZ2EqjWPKm6SrrgvzXFuvO2tFMczqq4VlU7lCsd77kvXyxIwtVqD+vDhw4cPHz7mFj7pcZTgd7/7HTRNwze+8Q00NDRY3/f39+PTn/40zj77bKRqsMvwcfjwVsroIQp9wSOKgsLgEJRkkgbxcjlI4+MoDAxCTWdQGBqCNDmF/IEDlIggBFqhAHlq7mx9vKCk0ofcI19Jp60sfzWT9bQgqQVeL0Tl7ExqDbroquqZsWttP0PrgHh573u1xQyilQbeavGo9wShpIuSStHC4/k8lGTCUEu4A0FyIlE24FkYGkJ29x6LTEBpEEhVaeDJCA7KSZv1lizTwIIsIz84BGIjLoBiEMJRxLLMOVOz2WKAr5AvKklsAX+vAKKaz1NlTJUM1mogklSWWCgeD/EMGFmFllXVVXzdsQ+1mLVsrUvoMVUiCfQyChJz/tDyBWu8mudAHBtHYXgESnJ2BKYZUJCnpo16E06Sza6Msf4nxULzXoHm2cAe4Db7jnmuoet0DjH6ZqXsVHuxWjuRNhuotvNN2yNafcceMCeqCmly0hqngBFUNeZ/gJ4zcXi4GOg1gnXmODKD3SZR5gr0GcST2S/LWXfJiaQrAO7YjmobayX1JmZ6HYlEg8bSxIR7P171NUyrtJIxqNuCl3YlmDm/26+BODpWVEKW7IMospNo1HXLKsyLINEK5UgPlY5X6zidWdJEpPcEkyw076OedWjKkh5px33KDBTa72lmn6gaCCfENadp+TykkoQKc4xphYKlMLQflz2YXu4+Yvf5Lw2+W7aEtntDpQC9nZS0AtCaVtI3iuR7xXnfqv9UnKOK7XIrI5Vkkt7XLPs+2fF/2TbrtEZVueOy23Q5/jdslNR0xln3wE40lahOysHe7tLnN3k6AXFk1CDuypAeRhJDqUUcZ5EexQA5z/KW0kOfRaH4jOTsR4qqQFMViJpkkB4AMawtNV2zrK40OyFmTwrSNGus6LqRSEC0EtLM+7jN54pSYsSu7jHHPZFlsDqxyOXZKj10AE+sH8TvHtiKJ18bBLGRr6JE+0RBUqBNTEDXCPKSilzBTVgCdEyWJjeYy2ma7tg2AEiTU8XfJclhZWbOP6Ti+SKOMad43EuzBcWaK3RCQFXDtqSPGdTkKSUcLWKmSrKIDx8+fPjw4ePQwCc9jhI8+eSTmDdvHo499ljXbzfddBP+9V//FfX19UegZUcXNFFEbv8BZPfsQW7vPuQHh6yaA2adBfuLHFEU5AeHaDarR4BWk6QyGXnObGbACBqYD+6FgtsLvKQ+g5k1LidTrhdYrVBw2HHMOhhdAbqm0eB0KoX8gQEUBgdpdnolcoHoxku44rRvOcSEhK6qUBLJQ+brrRUKrqLhlUiGSiCi+4XbKzgwU190aWq67PGbtS3K9RPzBZSoKu23ZiDC9hJpZuTPGroOaWIS+f0HIE9OQSuIRtDQsKOSJPpPFCFPJ1z1Lyz7B5XWNCGi6KlSKLWvUhIJK8uP2vCoVmYlzUzPW4FX6zwV7IHrykEhoGjfZQbLrICVR9BWSaWt81EuQ7sazPNQk9LDIxBgvpjbC7x77cMMzuuOjGXNKuJcrr/Zz4Frm7pOAwrWNbFl/BsZwjMt5Gv2G7OPSFNTTnsKQhyKHSsoJ4qO4HytFjnWdj3aqDlID8n63ysgoxnWLF7jkpjkHYxAo1bM7LW3wYQ5hot9sLg/NZMu6dOy1Tft9ni6okDL5SFNTFptN8ka65zlCzTju0zfVXN5ozaDZm3fDnN7xQzdYh+1Z5GTKsobcwzYLeaKx1f7/VC3tUXL0/ogzroXHnOMbR4kJWPDPEZdM45TVa15zn7PpoSjU1mhSRJVKpWo7OTphKWyc5MeCkqtjIqBaqq2KSotpKL9kklMGkoBNZOlgVJjfNJjcyq8Sud/s/iy/V5lD3ib+7VqIVWYM8x9EJUqoMSxMasNaiZLFZaTU8Zx0L4sjk9Y/cxZF0y29ueZUGDONSqtwVRqMSlNThoB9yLRZ9lOGaS9Y3uGoob2JTM7XHXPm9a5sT3nlZ5Tc8415rL8gYHi+NScNR+IqkJOJCiJbAb+zWtX0ieUTMapxDXHYRlyxCQ17X2afqbj1bR6NH9XUunivGMLUNv3XwoHWVRCCMg28ru8GkW2Asn288izPBjD3so8DzzLgQUDnuFAMlms6zrec5vlkCg4k8o0VYFGNIiyiIJqkHA6ta7UNBWqQTrIxvODo46R8Wxv2f8ZRKNdDUSft3TP+4ZJaJRappXaqZlgiAYYSojZFjIHgAee34f128dx95O78YXvPYUHn98HousQZZXWchMVgGjQxQJyBQWirHnW6JATSYc6SiNFooPoOiTbs7U5f2r5vPUMqqvF661ms/Q+rniT59YcZxs3nkqPTMFKUihaNBLrXmZP0qiU0EZE0bEvy/ZRL1qY+fDhw4cPHz4OL2anc/VxWDE9PY2JiQmcc845GBoawne+8x08/fTTKBQKWLVqFT7zmc/gpJNOOtLNPCogT08XbTVAH0ALwyOAroPheevFjuF5AMWAnzg8DIbjwAYC4OMxMLwAPhKGPDkFnRBwkQgCDfUQx8bABoPQcnkQWYY4OgqhsRFcMEgDeaqGUEc7DSopCrhQCHxdHGo2Z2VDBpqbIMTjkBNJqNkMlVnnQmB4DkRRwAoBmpGk6wDLQFeoV7wQj4MLR8DHolBSKciJBIItrSCqCi4cAhcMep4T3cjaJrKCQEM9dF2HND7hqsOgqxo0dXZB2sMBeXoacjKBQGMj2GAIgA5WEMAKs3/Js7adSLiIGzWXh65pYDiu5u2UC8KUZnzrhEBJz1C5RQgKg0NgBAF8JIJAUyNtZzbnCPq72kQIpMkJALpla2Vt0vbypiSTM2tPGeiqCnMPlsVUOg1NFGlGvBWIUxznV83lwAYC1nZK7SasNqtKWTKAyLLj+NRs1lJ+EFM5Ahqw42NRGkyvkI1fPCZnwNVe18DdCGfmbjkomQyEeNzzNytDtAalh1nnxDyPZuDMzLLVRBFEVcHyzscBh42YfT9mcI3oIJDBhULu/aoqQGzEACFgOM4ZLJZlsIGAp+WLkkqDr6srO2eVQs3mwPLFcaiaBICxX8tCq4SM0vIFq/2aJIMN1WA/oyjQ8nmwxnqlbbTqv+QLltWWJong4D5PWr6A3N59YINBRLrnGW2j10rXiGVPRFTVylBRM1nwdXEoqRSCzc3WtpRkAgzLQBwfB8OwCDQ3g62L0yBtLg+G48FHwvQYDG9x3SCfiCyDCwad/vMFem6sjG+TxChTnNyEqQC0tpPLQ5Mk6zxZJIU5J6kqdI3QOkITk4j29VpBdLtKwbUfrWhvZW6HDQRo4FmSgWjUOPf0epTrS3ZrIID2Ha0gIjyvCyzPU3JQFcDyvHUcdjLTy0rJYXGnFO0J1XQaXCgILhIpBkFtWepaLm+RvWZfJYrinHtLC5kbqjaz39DlUwi2tjiy9C2CkzHXs13rfB6aKIILhxyKDiJKYGO8q53Werb5Emh0qCdAqJKAj0UtlZWpdiiOOclxXaiKTCsWQ28vId5lyZHd7lDDGMFIhmWNe4BBNpXcCzRD8cjwHEw7Ld0I1jMsC4ZlLetAc860WwhpBRHi8AhCXZ3gI5GiusecY4kZwFaLha513ZFtron0vDAsCzWbhVBXZ7u+JsGhFskZY3xS4kSBklYh1MXpeSc6tEIBbCjkuD6aJFrnA6BEEBElcK3OcVie9DDvMea9rDh36paaSbPmWDVPyU4+GnFtgyaNTICPRMBwHDRRBCsI9D6kqGAFAZooWf1CyWQd92mTjKbPv8XnOaIoRQK1ROmhEx2qJAHG8Qc4AUo6A4ZRoMsq3jn/HGwe34FUjc+0CSmN8dwU9icHsbipH0EtBI4Q6KoKyRhTmk6g5fNQAcgkg1BDELJUKKqndJ0+uxtFxYkkQdEUaJoKluhWcF63kcy6qgI8DzWbA8Ox9N6gFse17iDBnHZqJhhCwGiUFMiL7nvcwo4ojuurw2RWxVObqdqtPsxhdW8Mj28v/xz60Iv70d0Ww8q+eqR1Dqphc6Xn88jko4BO7aoYqQAllYLQ0EDvM7Z5CICliuEAEKJDmppGKB6j85lBcKrZHPiobR1FAcOyhm1kvpgQUwJiP48GJA/SI50VoWthBwltTxoxzyeRJGiihGALvfcSWQbDcdZcQRPh7KS4ao1hM1nEhw8fPnz48HF44ZMeRwHGjfoByWQSV199NRoaGnD55ZcjkUjgoYcewoc+9CF897vfxfnnn3+EW3qUwnzJtL9Ae2VXaVrRs5xhwEUiRf9ySYKaSVNiwCadV7M5qPk8WJ63XprzAwPWw7ndA92EPDkF2choNFHqaWtf1oSSovYUfDxGM8gJcahN+HgMgeZmmD7NajYLLhSCOFJchmFZEEksW3j6TQ+iu6y0Ak1NDgKAj0W91iy/SVn2LCxrWtUEW1tr3pacTMGr9oWuKNYLPK1PwszKzsbMJFMB65hlW8BMK9C6BgxTfOM0iQ5p3MPaxXhZkyYnvc9BDe0Byzr25wVpatqRYWxvr3m91EwWvI0EKKfAqGSpQWQZDFMUOJrZ6kRWLAWGvdZCYWjI83qZSG/ZivS27ahbtgR1K1YY7ZIBhqHFTffuB8NxCHd1eq5flpxRVUgTE2ADASvYZSfXirZHlTPh7RnbHMdRG5tUGrpO/dnNLEZxZBSRnu6Sttnnw5LALstagVYvaKJUDNoaQWWO45zZ+LaCv57tV1WglFCQJKiZrBVwsL4vFIBwCGxJP9MJoXOwaYFjBvKIaWtTABsQrOC/l9KjNNCmFUQaFDXOrSNoa2S+67pO51Ar61ouT47qzqx+SrjFLKJJyWQoUWgG1sQCGIGHmsmAj8Vo/zD89sWxcbo90HuVUBenASaDRCWKgnBnR1E1ZVwPaXwC4XldTougQgFobLTGveUhX4PlnpwsyYrO5a3zVCQpbAofTYWuFC1YiC07uhRmsIuoGhhbprg9SGi3l1PTaXDhiKsvAQYh7EEc6ooCIopgYzEalNU0gOdBzCC9nbh0ZFoXiY5ieyk5Q/el0YCvQd5SmxamSMLZ5i4zC14cHXPMi87AtmTNT0RVweg6CsMjxe2bAWtCLJWJVafHdtxKKkUDrqqGyaefQWrjJnRedimCba2WwpWSISX1RCxljlgM1NoJpFwWYFmLvCsN0sqTUwjP63KcP6JqgFwMZjsIZVm2VGTm/dK+bmFwEJHeXktVoRVE1/XVcnnoOgGr87QPccX+o4oiHU9WULnYp0wFh5mcoiSSlPSwkwFaSTDaqAHFhUMOlYalbuI4F+lRJMyL9na6ogAG2aYVCiCSVCQ9rBVL5mMb6WS1ydFvjfnXUy1pCyCbNkl2pUcJ0aezrJU0YD/f5rbVbM4gZ+j9XBNFgzixKdjEAoB6aIUClJIabeYy0sQEwl3F/lK0JiwqKBiOA89yRoJDUfUZ5ALQxAyQz4IDgw6+Dp/rfye+ued2SKQ62X3H8NPA8NMAgIgQwudXvA8doNefEeirtJLPgZdlaByBkleBhhYohsLCPPfS5BSEhgYwrKFE0TVoqgoGxjk1xpFdkcUKAlU+MQwCjQ22Oa9U6eFdy4bRCXQjiYRj3c9iH1/XAiYQhCoEEWMUpEUNpy2IYzRbXfn42CsDWNEZwZQsATy9TxJRBGcMTU0jYLJZqhCUZOu51K5EUvIi2FAQTDYHcWgEYZZeNy4ctvq4msuBsSU26IoC3SC0LPtAc7zannGt82DMJ6wgQFHcSo1srmglCdsYLJK4xRotajZjPYMoqRTAcgg2NxnHLlmkln3/xJirfdLDhw8fPnz4OPzwSY8jhHPPPRdDQ0MVl1m2bBnuuece5Iysyddeew1nn302vv/97yNgvNRed911uOGGG/DVr34V69atQyQSqbRJH3MFXXdkswLlA5i0mKtSfbk5gprx9rBWM9nibwwD6DqUkhcgaXISB2Vh9CaEnEhAqK+jRTHHJxBsbyubQe8Fy46o3G8Mi0BTo5VRWQ6aJEFJJcv/XihAzeXK2sbMBKbaQMsXnEV0DZsO3jZPVCsWrGQyMyY8dE3D0D33Yeq55xFobMT8mz6IUEdHhQZ79zlNpEESk3BkA7YsT9uYSm3ajMlnnkWwtRVdV77DpVoorkQDwqVQUimbxY4MXVUcAUXXZozaFHt/8SsAwNSzz2HRp25GdH4/fallGOz50U8w9vCjAIDOyy9F2zlnu7ZTTqlRDBIVwAWDUNJpGiixZYjSPwiUTAZcMOhQwQBOMsEMmtGC6hkwQsBR+Ne02bCTU+WUHrpGwHBuS5Hi71oxE9vIumfMecah9JCKbfNAaTCZ4TgoqRTUbBaB5iYaLDTGsa4q0FUBOuMcgzS7WrbmPdNr22FfYQTf6DnzLuLtJD0KVtAODAM0NIAoCqTJSQjxuuI5sB+XcS3LQTfsFRmGgZrPG6QHbaNlicQw1AbOChhqVv+w9mW3ZbIR8mYbHKoN+3FLEtRMxuGVrhVo/QcroFQSYKyE0sLd5nlVc7nidSU6lHTGUnrYa6tYKi4POyWaJcwU/7aSForKCPP6FoZo3R6G56GTsGuOlpOJsiQODdDGaH0Nzbbt0n5vU1J5kjSSSI/VSCSw17UgigKG4626Hs5aFBnD6spOctCi5ZHubquNVjMMazJdUaDbVF0AIA6POAPyJTYr5rmbfullHPjdHwAAk888h2O/9U1w4XCxNggpkjNcOFw8d0ZAmyl5nlBzOcvCTzcVPdkMuHAIDM/TAL6h+KLn1lBImDXbS2zadFWj1lU2hY/jXBvn1rQ9oxZ37ppKDMcBupnJbduXKDru98Vi0cUgtGnXppnEnJ0MIIYyxLDbA2gfC/ItVjFlYvsH2V27paj00IrqEkkGH6P7UDNGgN+4v1vr2q5P8fxni6QHKVEF2NQTpXO/V60Oa3ypGtgSEpzQxtN1bbUaTAsx8xnDvJ9TMsrZD82aDPJ0wjWOTOtX08bLHMcWcWOb101y2WyjrijQRBFCKARFlsFrAM/xVM3FB7Eg3IGtuQHMBHlFxLMjr+OqtlOKAXJFQWF8CuFwPTRRhCIbpKuuWooi85zoigJwRWJG1mSwhCsqlWS52M8KBTAcbyWFqNmczd6qZBwQ+3Upfs/ohN4jNQ1nre7Gnx/fZf12zrIGo10ymOkprGNGIaxYCIbnwQeqq5j3jaShKzI0hgHDclY7rHu2RsAUiiS2NGUkahFjzmcYSMkkgm1tULMZOmbDAjSJkh6WBSghUGxkOpEVS8VTasNJRBFcOGzt0zonqgoIAmSPd7BMoWglaapQ7OSfbiM9TLKJ5Xnj/+LyRJaL7bK1ybLHO8Tvfz58+PDhw4cPN3zS4wihp6fHIi7Kodt4sWVtD1Bf+9rXHOutXr0al19+Oe6++24899xzvtrDR20wg2OlQd23GOEBAKalgBl0ksbGoasqAo2NlVczglteXtR2KMkkuFAQfCxWsttiIIGoKlXUVFANKOm0Q8VTCVqhAIbny1t36TrEsTFPb3stl7NID13TKtaVIKoKPecMyow/+hhSm7ci1NGOtrPPQqij3bXe4B13YvqllwFQ27HRBx9G/wdvrOnYHG0VnVnmXnVOpIkJ7P/dH6CrKrI7dwEMg+53vdOxjJLJgoiFsqoce+DdtDYpDdxax/bnuzD13POu70f++gD6rr8WfF0diCRbhAcAjNz/V2/So8wLsBUkyudpUN3wtOajUVpo3kZUSRMTCLa2ukgP+8u+NDVN+4wQoMECtSRAZigdGNs27MHg0roFukYz7b0Cj6XZ6kSSrAxNvSSAS7ddpkhtidUSw3GWKknNZiGNT4DheJpFbRa1Ls1iNcgBeog6Jp58GpkdO9F82jq0nXWGK/DmpfSgiok6axu0aL0GTROpokdVIU9NURsnW3BHLyVQKox9um+auVq0lTGjscWsUyWZpOSFWKwVYq7r2p6qWnVy7N/Zt+3Iti2d53Qd4ti4tW3TBm029whzv0oyZWVFA4A8NQWzeLUZuFRzOdsxm0WdVTAMQ1UvikptHTUNmi2wZwWhFcURSDWTDrQ8VchwwSA0SaIBqwrzrWN7NjuZ6VdexdjDj0KanISazqDhhOOx4OMfcSmZrO2Y48EKjsrW9jLbduDALbeCyDL6brgOsYULbOfMua3c3n3Y+4tfQSsU0Hb+eVj86Zsd9aFoYNjclzNQ5xVE9pp7Bm651bZ/lc7bH7geSiaL8UcfAxeJYN5VV0KeTiDUGXTW0RBFBylNv9QBFIP45j1VHB2DYNx/lVQKXDRKCQtCg8OEFAm20jlGzeVgWr55gdZ5oP3JVJnZlXJElsHyHIhxjKZdmGVBU0JWmuotEEK9+c3fjXoLRQVEUekhJ5OOZyyrdgsMW0yDtKH15OzqDLsSw1bUWpEtS0HrPGSzJQFv4iZ4jPo6jEkSGstLU1PFa2fWVzLsBzVRhGSrcaIVCpATCUdf0h3B9WIdES/IU1PWsZtEEJHE4rUxx5ZhM+lVWF1XtSJxbRADum1uT2/dhvzefWg7/zxE+3oNctimiE4kEWpvA1HoPBIWQobqgcXZzaswJicxrdSubg6LBKE7n8Qg2YTwRWcicvpJ0EUJum4cZzYHjQ9Bk2QoqmG9ZTtnRFXA6ARKMond//MdSjiLEvhoFMu/+mXHtdXyeaell71weck9mNgC9PbzyOo6YIy/+V2NuGZdFx55fQxdrXGcuZA+t+o7tyF55+2AroNr70TdjTehuyGI5V1RbB2ubGn48KvDuHB1Z5F80HVAM5RMkgLB0U9t9qmSDIZjqZ0ZL0AQ8yBGfQ+tUIAWDjmOT1U18Jxh16bILhLb7N9awZv0IIoKLgxP0qMgmiSgbNgbm3NWsa4PULyXmdZ/uqJAN57zLRLaZndnqUpN0uOt+I7lw4cPHz58vMnhkx5HCL/5zW9qXjZuZLM2NDSgp6fH9fuKFStw991348CBA3PWPh8+3kqQkylHMENOJMDH4+UVAcY6pTYL5aBmsy7SQ56cRKCJSt7F4RHPALEd1QiP/OAgBu+4E4WBQQAAIwhoOe1UdFxysedxlC00nM0h0Nxs+YlXCljoigL7r6kNGzH64MMAgMLAAJKvv4GFH/8oQp0dmHjyaai5HILNTRbhYSK7e48rm7QWEIkGe6w6PB5Bxcnnnnec26lnn0PnZZeCC9IAfnrLFuz7ze+hqyqaTlqLnve+p+I+tYJhX+KhmMoPDHoSHgCQ27MXW77x76hftRKd77jc9bvX8VvBIEfNA8UiXMzADVFUGhwMhWhAyn7NjKAaSsRLDnWZQn3gWeOceAWuiaw4iBMlncbQn+9CYWgYzaetQ7S/zwg2adAJZ1nIuOzSbNnntFaQDBY0aOOwVzGzncsEAXRVMTLhjToFU9NWkEuenAJ0w8veCKITVQXDlSg9jIA6AKQ2bsJ+I4t9+sWXEFs43wpO2FUCjnOiqg77Fy2XLwaLjcxbZyDUDGx7B5UrgQZsGBsx4VxfVxSXdZ+ljiijllGzWad9TYllkIPYEiWghDSyE3/EKC4+G5hWTVqhAEYtBvCK5EQxq91urahrWtGKMRgskn06XP1Gyxes7HhTOWFZPykKtEIejCpQu76xcXeAvgREkmn9B4ddFoE0OYnEK69ay8nT05YVW2nQGXArY4itKP3Q3fdQ4gfAvt/8Dsf8y9c8bdCIomDfb35rBc7GH30M3Ve/03n9bAoZ8/jHH38ChaFhNJ20FrEF84vn1dYGO0r7XGFgALqqYtf3foDMtu0AAHFkFJ2XXmxYTdnGRqEAnQHyg0NQUinEly5x3JeI4rSNkafpcSvpDK2nIwjFWhgmXyAWXPcnrZCn/bhcTSejuLWWyzlUEwzHWaQdUXQwhtLDvMF5FYQ31zWVSqVqLbMeDl1fsoKlhYFB7P/DH6EkEmg7/3x0XHS+1TdUo84PkWWqqDAtb4xAvmO/xtilihXntZETSWdbzLoRjpNBilnvttojSspps0mz5jXIk5MutVtpooFLJURIaZkZ5/q2+yiRJau2HVBiIUY0hyVg6T5Ni0LdsJoz58nM9h3Y89OfAwBGH34Ua37yv8Z1sSUy5HKW9R8AhHmjHhM4dIda8E9LrsWW1F78bPDh8gdiw6kbslh6QIKMYci/vQOh44+h84VpZ6ipIIZFk6YbtaTsRJExXzM8D2mwqPjXWNaY74rnnBI+3oSMqd6QE0lkd+9GbMF86PP7XbXZ7NsCgHetbceJHQLYhkYQo2B8/sXnrPOjjY1A2bUD7Opj8alzu/C9v41g+3B5RfDDGyawdF4cop7H7U/S59P3nB3EiqVdkNIZhI3liK47LCiJLIOwLAghkKamIIQDIDpdTsvlUCgZbxrRYTpcUcLEOVdaBL1RYwgoJT3o74rq7rAFuWhDxTKmkshGrlpKD1MRTFVLRFUBQ+FiJ5oKwyMId8+zznlu335Mv/gyov196H7P1VWV4T58+PDhw4ePuYNPehwF6O3tBc/zUMoENszvQx4FZX34ONqhaxrSW7YiuWEjcrv3gI9F0X7Rhag/ZkXt2ygdO0SHODwMsCy4cNjyxjezotlAAGqmvK1VKdR83uH9r2salHSGZtwVxKqER9X2E4L9v/ld0erGOKaJJ56CPDWNvg/cUDOhYLYt0FBvWSjUiumXX3FuS1Gw/3d/QLh7HtKbt5RdT8vnIU9OupQW+cFBaPkCYosWer8EGhmb5YK6uqYh8cp61/epjRvRdOIaAMDYo3+zzv/0Sy+j9awzEGxtLVtjwQw0lQZ48wOD2Pmd75U9xuK+NyG2ZLHju1Bnp+f1MQNIWkG0SA9pctLWGFK0QpEI8gODRVWCUdCXC4ddih6qCigh7CoECgGa4aiJHK17E49h7KFHMPX8i/TYB29H88knIdLbU7SEstQRJQoRe8HQsXFqAeVhbwXQTOT0lq3I7t6D+lUrEe3vs7VHhZrLGcW0FccYtoLlNnsYWti6JAhCNCv7dfie+xy/HfjDrei++ipLaWCdI9s4ph71RoZ2NusKNJrHXwqtTL2ZStANqw9d1YyC0NUzQq2s0zIWTWom6wqW2gMz9qA1/aKyGqWS1V8lODLiPc6XWXcEcCsciCxTsoRhin7pXkStrkOanIKazRp2Vk6CTdU0sIJiK+BcxaZL1y2C2a70KC2IrmYy1vxUzq6udLsmOWFuH6CB2XI1oqaee95lW5ndsxdhm2UgDXQWFTIjDz6M4bvvBQAkXl2PFf/0VQh1lBmlhb6rk3LUjiZhER4AMP7Y39B56cWWBZ+1f1HE1AsvYP9vKbEYX7oE8z96U1HtWNpHzXV1HUSk98jE62+A5AtoWH08LRxf8BhHxnpeigDACHjqTktRomrQdVuBYVPdYF8v750koGSyZe0dqe2VqVDKQ0lnkdm+HZPPPGtd2+F770Pj6uOtelTm8komU8yM1zSQAlXKyNPTUHN5WgfKZt1mv/cDxXGkE4LpF1+Cmsuj/cILXG1U83lqUWaSLh4BcTNrvpZ6PdRSqaR2RIXECeeOdNccYiY2UAVfmecRuyJEI1DzectedvDPdxW3JYoYvvtetJx+mqOuAgCHHW1myzbI09NQgjwUaNASGXS0N+IMphfPaPvdisESrNpl63uKisJLryOwdCEIIdAMBojoGhQxD0CHqilWjQvdKFbOgrEKt1ttLBSw47+/g/kf/qBV+wIoP1dpogQ5mcTO73yPEsIsi+P++z/BR73r1lkKHY3Q02MbA9LgoGNZedcOcGuOA8sD161pxP9mJYyly8+ZL++cxvp9acgGoXDH0/vwtcUdkBJJoJ7OmQVJRTRUJJvVgggZLDTDKXLbSA63vziKlngAN5zehVhJGSZVIwgKhmJLVcCQknuFYdVGiU9DaeFRq1H2qOlhkh6aKIENBK3zVVSKOpUepkUbZWlsdTvMtkiS1Q41n8eu7/8Quqpi6rnnwcdi6LzskrLn0ocPHz58+PAxt/BJj6MAgUAAxx57LNavX4/XX38dxx9/vOP3jRs3AqA1QHz4eCsgPzhIg5QMA3k64VBcKKkU9v3y1+h65xVoPeP0We/DKhQpSrQAfDRCiyUyLBiOnVmmNtFRGBpCpKcHWkGkgWtdL1tfZabI7NjpCnqYSG3chKnnX0DLulNr3p6SSICPRryDSmWgFQrI7tjp3lYqRTNHqyB/YMAR0Jt85jkM3XU3AKD+2FXo/8AN3m1NJspei/SWrZ7Bqsz2HahftQrS2Cjy+50KuO3f+h+woRB633cN6letdG/UI4AjTyes+h21YMgWiAEAcWQEb3zhSxAaGxBfvBhdV77DCniYdicADSCVKnTUnC2YaSxXGB7Gnp/8HGo+j7Zzz0bX5Zc518nnZ9Z/QceDmskYVkA5jD3ymO1HgrFHH0PPe6+BVXjbzHw0FCKm4kOanMT+3/4e0sQkWs88A61nnl4snlxCeiRefsU6rxNPPoWlX/w7hNrbrWPVcnka7C4NzhvQFdmWiam7gnZ2yxklmXT8ZvnCa8RhMUNkuUh6mDUcdB1yIulpF+d5Lmu0qbNDyWSt4ImuKBVrgJjQVc0gv8qcH6+6Bw71RvUgpzg6iuTrGxDp7UHdiuXF9qYzUJJJhHu6XYRedtcuTDz1LNRsBrFFi9Bx0QUVz10lUlgriCCiBGKqBioEWK2MaFu9EsAIzGoatDLkaTUUrcY0KyBmQhwdLSosNA35wSEwLEuD1mWgFQrUAqYE5Syb0lu2ur6TJicRam9Hfv8B8NEIAi0txeug69hrZL/TDRNMvfACOoyguFk8vhp0QjzJf5qN7rZDG33gIetjZvsOFAYGEeml6uRqAfWB2+7A1LPPGce7Bf0fvNFT2WcdUoVaLK5lJdFR1NsL5fZVSe1JFMUaQ+LICHZ+/4duNRQhSL72BlpOX1fyvZMwIoqK6Vdewc7v/gAgBEJDA3rf/17EFi0EgLLZ/mOPPGpZKU6/9BKWfflLjgQCu+Ue4G0RSRSlYtKErutIbdwEcWQEjatPANdrC9aTYo0bnRAM3XUPUhs3IrZ4MXre+56KaloAVnF6Y0cVlzX3R1TVIk9MpZSJ1KbNlPSosK3EK68itWGj6/vVAHobOPzh0ubq7bA3KZUBFAUagNsHnsTjk6+jOVCHTwepqlTN5aBk88ju2QOhuwvhcNRSMTKBAHRbXxZHRjD+xJOY984roKsqGEEoe210RcHYQ48UFXCEYOyhR9FxyYXeyxuEuiZJUA/sA9PSBjYcKeWH6KYyabAsA10HQgKLm8/swG2vTmLziPd96YVdScfnRFbB1FgCzUQFEATRdRREJ+khFUQoDA9CdKgawf/7634UZAKMFBAUOHz4zC7HNjXNqXDVS0gP2AgPoqqQx8cdz1NmP5MrKD10VUVq82bk9u5D3fLlmHjiKSiptPEMwqDrHZeBFQQQUSzatZVTikrUam/q+Rcd96M9P/25T3r48OHDhw8fhxE+6XGU4P3vfz/Wr1+Pb37zm/jVr36FsOFX+vzzz+Phhx/GokWLcMIJJxzhVvrwURlEVZHftw/ZXbshjk+g77r3uzLuiapi4NbbIY6MVNzW8L33I9Lbg2hfX8XlamsYKXpGQ6sYbCkHXdUgjk+ASGJtWbSEYO8vf42Oiy5EpKfbc5nCyAik8QlMvfBSxW2N/+0JNJ9ycs2SeV3TUBgeqRpksFsXKekM+FisIsHBx+NQMxmwwYAroJrbtx+Na1Zbn0f++oD1d2rDRsjTCUd2o4lKRdRLbbRM5Pftw5Zv/H9l7XiIKGLonntRt/KYqgoZrVDAnp//smzQaSZQEklMv/QyAk2NaL/gfGP7xZdyzSMw7Aougma3moGO8cceR/MppyDc020FmIisoDAygtSGTYjO70e8RHniBbvVmTQx6fo9t2cf1GwG4tgYGJa1CCyrSHUmC6EujtEHHrLIseF77kXdMSsQbG0B4A4sjj32t+IHXcf440+i933XWMtqoghG4D1rbQCGVZD9Nw97FzO4G1u0ENldu62fWs46w9qPPQDsKOBuy/AvR7x4YTZKD3tfpfYzpCZLOEsJVGvb7MWvqyjQlHQGO779PWu5/g/eiPpVK5Heuo2SVbqOumOOQf+HbgSRJCTf2ABd0zB01z3WtcjvP4BAYwPaL3TXG9M1DanNW8CFw4gvXuR9fGKhau0hr+3OZg4vB7tXvqmWMKFmc9j9ox/jmH/5Jwz++W6MP0rJwsY1qzHv6qtcyhCAnneGYRDp70d+3z7re7NQszg6CqGujha013U6V5e2SZQw8KfbkTDUd93XvBvNJ59U9hik8Qnb8aggKrU54mOxsqq3+JLFxTohJe3XSzPpRdE1bwj1dcV1qvRRk/AAKJFvFksvC6NWx+hDjyDx6npA19H9nqtRt9yd/KMkU0bm99xaymiSiIHb7kDi5ZcrKoc0SURyw0awgoD4sqWuMa1msmADAgb+dIc1bpRkEvt/93us+GdqeaaJIrI7dyHY1moRwwActaPkqWnk9u5z1IYxg8BWWwrucURkJ+mhZrNIbd4CLZ+Hli8gvWUrxNFRAMDk089i+df+EVzIzIYnlnoqvWWrZf+YXP8ags1N6Lj4oipnsbwVpxdojRXTYsg9J+tV1GoAKipc05Eqxbu97LeMdkzJaTw8Qa3vJvLT2PzLn6FtfwpJQbAKvAc6O7Ds85+FQsu/gwmHHKQHQMdCfv9+FAaHUHfMCvR/4AYwHIfsnr3Y+4tfgY/FwMdiiPb3uZ6Bxh57DO0XnGc0VUdm23YQWUb9ymMsi8Gd3/4uxNExQBAQv+Y6hHs9nqFzGYv0AIBwgMW1J7Xg58+MY+9UbaT+f/xpExa2BvHpC3pRF+KgmSotQsCxLBRJxkS2gL+8Nob1+7OU8DDwyOZpi/Qw74NaDdfWTILRCgVouTzk6WkcuOVWyMkUOi6+CL3ve08Z0oPeb1MbNmL/b38PAJhofpKqO23PFe0XnGclRVjPwpZNpvN+SpWoGqTxcfjw4cOHDx8+jhx80qME09PTuOSSS5BMJrFhwwYEPV5Y7RBFEb/97W/x4IMPYu/evQBoAfILL7wQN954I+rr6+ekXVdccQWefvpp3HvvvbjssstwwQUXYHJyEg899BBCoRD+4z/+Y8Z++T58HC5okozJp57C+BNPOYJ704sXofnUUxzLjj3yaFXCAwBACEbu+wsWfermuW5uWWR27ERu3z7EFixAdOEC15jTZmAXJScSyGzdhtzuPZj/0ZscnusAMP3SKxi4/Q53INcDRJZp5m9bm+fvuq4DhDiCW9WCUIXhEQz++U7Mu/IKRHp7EGpvw9J/+CLGH30M43973HMdNZNB82nrMO+qKzH17POWkgMAsjuLKhFiFLi2Qxwd9SQ9ykHN55G22a7YUU4VY4eSSELN5iDEYxWXswpozyESr71RJD3yBTA2O6VSlKoGlHQG+X37i1/oOsSREZBjVlikhzQ+hh3/813adxgG82/6IOqWL0dF2II5dnLAhDg6iqG77rGCbF1XXI7Ws860Ckcr6TSEurgjeAlQQqvtnLNobYTYYzxGAADF5ElEQVSSvpzZus3x2UVw6bpVq6Fcm70UEWo2i+mXX0WgtRn1K1Zg6rkXXMdkFTq1WXUBzgxyq+C3JFW1fnLAY1kllQJRVQSamqreq9Nbt2Pfr38DOZFEx0UXov38c8suq+XztdvLwJgrJiaQ3b0HsUWLEGwpn9U8+cwzjkDOyAMPoX7VSow++JC1z/Tmzcjt2Yvhe+9DweZPb0dm+w60eKjy9v7qN1Yf6Lj0ErSfd477+Iz7hb0dqU2bMfrQIxDiMcy7+l0INjd5HGdtRJCaz2Pgj39C/sABNK5di87LLnHX3lFN+yqCXT/4oWsbqdc3ILVpMwKNDeDr4lDTGSReXY/Eq+sRXTAfrWeegVBnp+tc8xFnUF/L57HnZ79AdsdOsMEgFnz0JgSamzznH2nCWVtk+O570XTimrIEBsPzUFIp6BpBkOcx8KfbMf3Ci2CDQSy8+eOIdHdDaGyAYrNwqz/uOKsGhRd0XYeSSEJobIA4Mur4LdjaCsH2/Dvy1wcw9dwLCHV2oO+G6yDU2QgRj/GtZrOVSQ9QdYj9HAz++U4s/+pXytZOmglxVgvSm7e45jsvjP71Qevv1nPORtfllzp+1woFSigPDzu+V7M5yMkUhPo67Pj2d2ktI5bFgo/ehPiSxZ5qIVdAnxAnme5V66Gkdld29x4M3naH57FohQImnnqqqBqyEYyZEiXo2COPofWcc6z6WuVgKRVqAFXmqVCzOez4n+94LVF1G5We0xYMyzjttSyePSGGE+sWQdMJ3sjsRXuwARe1rMYf9j3iWkfOpPH81AY8Ob2puJ1BCa3b0tBBbZysZUdGKQG2fCGkPftAylgGmnNpevMWpLdsRf2qlbRGkyhCFkXIk5MoDLnnWy4Uxo5vfxdcKOS459WtPAbzP/QBDN11DyU8AKpO2fwGmP5e13ZIJgMGcCTSCByLj5/ZjoJMsHkkjzvWT7vWK8XuCQl3vDKO609ug2pYrBUkFbFwAKpG8MsnB7FjrLKqUVYJeBbQZBmA+51cmprG9IsvIdDcjJYzTqOHlkxC1zTs/eWvrblp4I+3om7FMgj5NAIEkBnBskEjOqBoOhKvvlbc75T7+IgoAoZVnf15xRxjRFUBXadqEFmiahT/3dyHDx8+fPg4ovBJDxsIIfiXf/kXJEtsMMphbGwMH/7wh7Fr1y7H9zt27MCOHTvw5z//GT/+8Y/nzHbqP//zP7F27Vrceuut+OMf/4hIJIJzzz0Xn/rUp7BkyZI52YcPH7NFaVayaYmQ2boN6S1bPV9sRx9+BI1rVlsFlLVCAdMvvOjeOMOgcc1qcOEQJp9+1vo6t3cf3vjCl1C38hh0v/vqqgHs2ULXdQzfex8mn3oGADAGasnUe937q9o3lIOZFUtkGXt//gss/D+fsBQfOiGYevFFT8Ij1NGOJX//dxi49TbIiQSaTz0F9atWlm1HessWDPzpdoBh0HXFO9C4urIiTJNkjD3yKCaefAogBMP33mcRS1wwgM7LLkHjiWuw83s/8FRSxJcuAcMwaDxxNYbuudc6BmlikhZ37OqEPO1+mazJImtwEEqSFsnN79/vOj+Na08EACv7uRqk8bGqfYYNBNByxukYtSlTDhbS2Jg1XnSt+FJci31Seqvb6kbN56ErMmCUDJ1+6ZXiudF17P35r3Dst75pFCInSL72OqTJSRBZBh+NoemUk8BHItb2cnZSxUCgqYnWdGFZo1/cD6KoaDvnLOiEeuR7Zt7a629oGkYffgSTzzznWbtg3lVXuNevokYo9fbXNQ27f/QTK7BTqvAwwUfCgG74dpcjPYwM/5nYwHkhsf41DNx6G3RNQ8sZp2HeO6+suPzUc89BSVK1x+iDD6FxzWoEGhs8l51pXZ7CyIhFiLHBIJZ84fMO0kATRaQ2bAIbDCC9xUlKSWNjUHM5F7mx+4c/rrzPoSHXdZSmphyk1+hfH/AkPUpVc5ok4cAtt4JIEkQAe3/2Cyz+3Kdd/vhErj6WdF3HwJ9ut+yjEq+8gqaTTnSRx7qq0aCWrkNOes9T0y+8iLHHHnfNibk9e5HbsxcMz2PhzR93KBNLg/rpLVstlRSRJIw//iSa1zmTAkzkDww4yC4iy8ju2o340iWeRGDi5VeoIoIQ1B+7yrL3IZKEwTvuwpLPfdqlzmMDAVf/ii9fBlYQoEkydv3ghxCHhxHqaEdDyX0lPK9oTVMYGsb4Y49b52P88Scx78p3WL97jS81l4cmDWL6hRcR6uhA82nrHM8XhaFhSniwLOZddSXC8+Yh2NpSmVCcATlYC8Yf/Vv1hUow8fgTCDQ2INjW5lA3pbft8Fxey+eQ37+fEh4AnXvvuQ+LP/dpDN15t2v5/b/5HbR3vwsNJxxvjYlSMtmyqxodRbC11fH8oOu6leleDmMPPQIlkUTrWWci0tdrjW0vK7DUGxvQdNKJru91QpDbsxdCfR0CLS30fsWy1vXTCUF2124kX3sdRFXRfsF5CLW1WRn140886fnMUEvyV9naIQZWJAJoaTsZx8X7IbA8Lm9biwDLQyYqwrK7D23bvwmP9zc4vjtzfXkiJ/n6G4gsnof83n1V2woAY48+RkmPErWpV+KKmsl4qlLTmzZDyWQdyiAAyL/+GhouvNi1PBFF6PmsVYvGBMswiAY5nNQfx4bBPHaMV783PrMjhfeuaQEhumVzFQsHIMtaVcIDABRVgzIxiqHv/xBTHR2IdM9DbNFCNK5ZDaIo2PW9H1jvF1o+T59JVA1Tz7/gImM3/9PXcY3x93igEb/spfNQg5zG2LMvIL15c8W2eKlxAZqcktu3H/t+/Vuo2SzaLzgPnZddAl0jfkKiDx8+fPjwcYThkx42/Ou//isefvjhmpZVVRU333wzdu3aBYZhcM011+CSSy4Bx3F49NFH8fvf/x4jIyO4+eabcdddd82J4oNlWVxzzTW45pprqi/sw8dhhJLJYPePfoLe970Xkd4e5AcHMfrAQ44CqF5Q0xmkNm6ybI+4cBjL/vHLyO3eDTWXB8NxYAUBkf4+CMbLlzQx6dpuetNm7B4bw5IvfN6Sns8lJp962iI8TKQ2bMRIQ4MjeFMrNElyFLElkoyhu+7B4s98EgDNruv/4I3Y9b0fuFQL7RddCIZh0HPNu8tm9Vr7EUXs//0frWD6gVtuBRsIoH7lMWXXye/bh4nHn7A+5/bugyZJDpuWUHsb2s45G6MPPOhYV2hosAI5XCiE2IL5joDzjv/+NpZ+6QueGXSSx3d2JNa/hgN/+CMAINw9D9ESZUzzulPRfv652PKNf6+4HTt2//AnWP7Vr1RVmDQcu3JOSQ8ADpWJWTdCq6EWhNeYUtO0FgdRVbA8j8LQsHu97TtQt3wZhu+9H5NPO/tyYXgYfddfa32WS4JXfTdej/pjV2HDl77iIJpGH3gQajaLee+8wtiOW6Fl+m4n39iA7d/8lvV9viSYygYDEBoayh12eZQEMtNbtxUzWeGtWgFo/1QMyxGGKWazElGyipmbSo9a6mtUwtijj1nEyuTTz4KPxdF61hkoDI+gMDTktqZjWASam6HmctByOeQHBsqSHrXUD9ENxQ/DMDRQatZjkSSkN21C61lnQk4mMfrgw1UJw83//K81HbMd8nQCaj7vINasIG5JOxmWdc03duT27nOQg9LEBDZ99Z/Rdu45aD37TAzcehsKQ8NoPvUUtJ1/bsWAU3bHTqQ3bYbQ2ICOCy+0CmjboWSyYHkOWi5P1UplFHK6piHQ2FhWpairKsYeegQLPvYR6zumZF+lVjXpzZsRnd/v+E5obEDf9deBYRns3OEMlCc3bEBsyWIUht3jH4B13UvrGRQGBiCOj7sUJWom4yIVzUD69IsvWsoEcXTMoWYAgJCtpkni1Vcdv00+9bST9PCw0Nr1/f91tiWXR8dFxULdY488ah3T2MOPoOX00xDtc2asE1lGYWQE4a4uStSIIsYffwKpNzZCmphAsLUF8656J+JLa08akqamMPn0M2CDoZoUhV4wyYrea99nPftkd+7yXHb3D3+CUKezPow4Oorxvz1R1t5x8I47Mfbo39D97nch0NQItmQsTT71NIbvvd/6HFuyGAs+9hFqIVSjqnH6pZcx/dLLmP+xmxBfQhMdVI91s7t2uUgPXdex5yc/R7YkYSzS34f5H/4gxLFx7P/Nbx3kRH7/ASz78hcNOy3F8YziAMNASaUw8sBDCHd2INTRgVBXp/X8qBPi2U47YjkVvfWLQHIF6AJBwEjKCbA8WlT3vBQreCSnyOUVukSSIOdykDySP7xgXpOZKGK8IE9OonHNCRYBaWLsRz/wXF4cHEK4swN6Jg8m7n6H7W0KWqTHvMI4Lht/Fryu4ZX65Xi5YTl0233VtJQS8yKm7r0HyeQUsGoNgLhruzxRoTIcdF2HODqKqS07wWXTACEQh4chDg9DKxTQuGY1Uhs2Os7LyP1/Qds5ZwEAUpu3VDwfbXICXeIECmwQNx24F9MHalBVl7nfamIBI399wCKcxh55DM2nngKhrs5TyaaJoouo9+HDhw8fPnwcGvikB4BCoYCvfOUreOCB2oNat912GzZtolLmL3/5y/jgBz9o/XbSSSfhhBNOwOc//3kMDQ3h5z//Ob7whS/MdbMPClNTU5iu8YHbjv373RnAPt66UPN55PbshTgyCnF0FN3XvMfTqmDy6WchjY1j5/f/F+F5XY6Afim4cBh1x6xAtL8P4e5uVz0LLhhE3YoVZddvP/88V+A3umA+5l31zkNCeEgTExj5i/fcMPn0M2hZd4pn1nolcMEgUBKLy+/fDzmZRMAI/PLRKITGRkdgpf7YVVbx7XKEh5xIQs1kEOntQfL1N5zqAV3H8H1/8SQ9xLFx7PvN76BmMmB43pGZLU9PI1wSeGk75yzomorMth3QiYZwVxdazz7TUu0AQHzZMlfQee8vfoW4hzLNLEiaHxxCagMNSkV6utF61plgOA6TNhuRwuCQK9s80teLxGuve56TShi6627Mv+lDyOzchezOnbRv9vWBqCotNMowCLa2onHtiTUrSGqBNGZTmeg65OnpqqoGwJtYUDIZEEWGkkyB4TnPIr/ZXbtRt3yZi/AAgORrr6P76quszHOlxHJDaKgHwzDgoxGr9o2JyaefsUiP/AH3/UHN5kBU1dMWyI5gW7sVoJ5+5VUQWUbLulMrruM4htffAB+P11zfIrN7L/b86CfQCgV0XHIR2s8rWkgp6TSCzc3FAqXGGJKTSQzfcx+UVBrtF5znWT8AMCyzCAHL89A1DdKY09N79IEHHYRhbNFCp7pAJw4f8JG//JUG3kUJ2Z27EGhqROs5Z9PtE4LEq+uRHxhE4wnHu4LkE089g7GHHwFfV4f+D1yP3J69jt8DTVTlMfCn2y2VwaFAYWgY+X37EO7pQXzJYk+FiprLgcgKtv/ntxBbvAj1q1ai7pgVVsASKF/Amo/HMfHU05ZqY/TBhxBfvhSRbu96SUAxKKYkkhj4022YfuUVLPw/H7f64ehDj2DskUfBCjz6P/gBh3rBDQaBpvKkB0CJx7FH/wY+FrVsa6ohs82ptmk980xE+6laZP5HPoS9P/+V9Vt25y7s/uGPXde4dD73gldR+x3//W3Xdxbp8fKrrt/sMAu5K+kMJmzqTC9UIhXrVixHYWQUYw8/gkhvt3VPSW0sWgmpmSyCbW1QUikUhobB18XB8jz2/OyXUJJJ8HV1WPqFz2H0wYcx9fwL1nrSxCQG/nQ7ln/tKxbpWBgZQXbHTsQWLUJ4Xhe0QgGjDz8CJZVG6xmnY/DPd7qyx2eL4fv/YpEe5YgqIstU1ViCsYfdNkt2KMkk9v78l2g771xMv0wLd0cXzMe8d16BxPrXHMtmd+yEODqGcGfHjImcvT/9BdrOPQedl13isEYzkVj/GtR8Hkoqhfbzz0PDcccit3efi/AAgPy+/Zh+8WW0nL4OXDjsID3kqSkoyRS4SKSiIlLXNBSGhqiyyfgu0t+PBR/7CDLbtlFivYplKMlkkfvbsyg87aylxkTCWNUfcS0fy7u3V0lTlN25C/ivPTVZlwKUNE689rpnvwu2tnjW4PLcTiKBxhPXuEgPzUMZEunvg5bLYfdPfgamqQXRd7qT7fqag4ipeZw/8TKW5Yp99NypV9GkpPFgW/H+XZBUBHgWo395ANIGo/8NDqK+551ICUW17bmTr2BNchuSQgxjL0kY//OfPUmD8Lwu41qXIXjhfo7xwo2DD0CH63G8LApDQwi2tblrO+XyyO3eU/xC15HffwDhefOows6jbT7p4cOHDx8+fBwevO1Jj1dffRVf//rXscPImGNZFqSGB9Hf/e53AID+/n7ceOONrt8vueQS3HfffXjsscdwyy234NOf/rSVLfRmwC233IIf/MA7u+etDFqQkRbitaT0DFPz51BHuyNb1YSazdEsVY6lgVLzX42FpctB13Xq6zw1Rf8lElbRSC4cogUNo1Hw8RiE+gbw0chB7xOgQe7RBx9G8vU3HC8cgeYmdF56iWNZTRSLAWlCyhIeDM+j9cwz0Hbu2VW9uishOr8f7Reeb8n0Qx0d6LjoQivIMtcYf+LJijUF0lu2ovWs6qRHqf1X+/nnIb1lmyOgkd68BS2nrQNQVHsM3XUP5KlpNJxwPFrWnVJVKj/y1weQfP0NdL/7aky/6C6ALk9OQpqaRrC5CURVMXTX3cju3GWpL9rOOxf5AwccmafylJv0YDgOHRddiI6LLizblsYT12D8b487skflqWlH4Mn6fnoaqY2bsO/Xv7W+S23YSO2lTj/NWcfCA9G+Xow/8aTr+97rr8WB399Sdr30tu1Ibd5C90sIxv/2BJZ+8e8w+sBDSG3cRG1Ili1F09o1IJLkypKeLfIHBhBbtND6bA8Y6YRAmpgEFw45vO8BYMHHbsLI/X91tEPNZEBkGUSSoevE095Cnpoqa80AUB93kwwr9fEX6miWJxeJukgPgM5/fCzqOfannn0O6S1boVWxFAm106C/JskY+ctfoaYzCLa0OIqw67oOLZ8HFw475jlNFLH/d3+ouP1S7LQFdMceegQt60615iUtl4Pe2IjCwCAy23cgtngRIj3dGL7nPuu8H7jlViz/x3+w1klv2w41k0GgsQFqvoD9v/kdQp0dYIPVAxvb//O/0PPea9C4dg0YhkGgucXxuzw5hb2/+LXju7HHHseiT34C0vgEBm69DQAw9fwLWPiJj1mFjAsjIxi+5156TIUC9vzk5577z+7Ze0gJD4CqCSaefBpaoYBgayv1Pi8Bw7BIvPoqdE1DZtt2ZLZtR3T9a1h08yesZUprH5kINDUi8eqrYHgeQkMDAo0NkMbGMXj7n6GJEuZd+Q7UrXDWtSm12Ws68UTLck5OpjD26GOAroPICobv/yv6b7zOc98dF1+E9gvOowXcq6BUHVcNpaSxvUh1pKfH8Zs8Ne2poqtGeMz/6E2AXlsAljUCdWrWPcfYEWhuNsjO//UM7qq5HPhoFEBl+zg7MWQSPGzAmdwQ7ulG/aqVSK5/DQduudW9r3Qa0y+/4nnfUVIpKMkk0lu3ueyiut/9LhSGR6zi3Kk3NpRt52ygpjMYuvuespZkc4GpF160alhQ4iAJyUNllT8wgHBnB/IHDsx4H9MvvYyOiy902U21nH4apl962bKx2/+7P0Coq7OuuxdG/vJXtJ17Nnre/z7s+p7zHUVJpRBoaUJ+f/k2aqKIQgk5EGprxY7/+Q7kydrIAQAuwgMA9HwBXfvcY2nTohBVHBrPZk1JFUKZR0YAYAShak21UpR7huGiMTQtXIDpF9ztLYU8PY3Y4sVVlwt1daH/Azdgz09/QRV5k9OIqKpLmdbTGMBZU685CI+nmo7HuukNOD69E080nwCRC6G7MIb003tQ1zMP2RdtY5AQzM8P4fX6pQCA3vwoTkpugcgKyHFhjN12W9k2jj74MEYffBgxjwQaXdPAcJznc5AXZmI+NXzv/Ri+937Ely9Dz3uvsRJXShMbADrH7fn5L13ft51/bsUx4MOHDx8+fPiYW7ytSY9vfetb+PnPi0GAd73rXZBlGffff3+FtYDdu3djzx6a0XHZZZeBLRNkvuqqq/DYY48hm83i+eefx1lnnTV3jfcxK4w//gTSmypLnith/k0f9FQhDNx2O9JeUmqGcZIg1j8WDMdbJEnXFZcjtnChY1Vd17Hl6/82I0k7w3EQGuoh1NN/rWed6VJSVIKu65h+6RUM332PZ0btxBNPoWntiQ5lQ2b7Ds/aDib4eBwtZ5yGphPXOAqbHgzaL7wAQl0dkhs2IrZwASL9fa5lSkmG2UDN5pB4xZmlFerqhGjLts9s34HWs86suB2iqhh94CHEFtIC6KZ1S/3KFU7SY8tWi/QAAD4SQd9176+5vfkDA0gaWZyDt91edrnsrt0INjdh9MGHHS/LgaYmtJ9/Lobuvtex/L5f/QaLPnWzK4u8GoR4DEv//vPY//tbaPYxy9IaCx4B1sLgkGfgOvHaG1Y2bDlw4TACLS2ugF/zqaeg8YTjqW/97X/2XpkQ7Pvlr4ufdR17fvoLKEZtJ2l8HNL4OIKtLej/wA3IDwxg53e+X7E9pZj/kQ8jt2+fwwN+5IEH0Xbu2a5ldV3HwK23IfHqejAch74br0fdMSusvhxsbkbzKSc7SA8lnXbUPmg+bR0YQXDUTJCMTNlyyO7ejfqVxxhFYm1zDsNYWY18JAwv2qQwPIz4ksUINDcj0NTkCiZ7eb2XwpxTJp96CqphOzX457uw7MtfhDw1BTYYxIFbbkV2x04E29qw8OaPWwoApUKx5VqgaxqkiQlw0SiksXEE29uhiRJ2fPu7NJjFslj86U86zrmWz1P1zDErwLAspp5z+4HPJCN84E+3geE5NK4+oWJxcavNioKd3/k+GJu6reG4Y61+C8BlyeflgW8nGQ8lEq+utwK70sSE6/fGtSeCyBImnnza8X39ypXW3+lt2zF0512e209t2Gipv+TJSciTk5QwMOzPBm6/A8u/+hWHfVVpZnqoow1qNoct//bvLqKAWqq473NCY4NVPLeaTd7BItTZ4bCN4qLRmlQc1SCOjFStc2DtMxSCJorWGC2H6RdfxoQHCW1Cmpy0An8zVWiWFqnvMCwflQptGv/bE2V/S23ajOF77nN9L08nLMJjtqh2fSafftZRp2yuUVq0u5yNVmFwEPpJJ2Ly2Zkdb6ijA3UrV2DHt7/n+J6Px9F15TswbSs0D13H/t/fgqVf/Lvy2+uiaqpSqzLAmOeJjvxgmcQaQQAfjbrmXXk6MSPCoxLUaAjPLg8iLOsISQQTjTy2LCwm8YRFgvc+XPl+N1PCoxKURAJ1K2qrGznx1DPgQpUTjhZ/9tPgohHs/uGPbQoSHVpiGnxrG1iWASE6eJ5FBMCqjJOUPXP69eK2coPozw/jmOw+YAjIerixNSrFMXt8egfGAo0YCbWAJ7XNadkd7lo4SjoNoa6uZqu22SCzdRv2/e73qFu6BNH58z2fcaaefwHS2Jjr+3lXvRN8zCc9fPjw4cOHj8OFtzXpsWEDzdpqamrC1772NVx22WX48pe/XHW9114rSsPXrl1bdrk1a9ZYf7/44os+6fFmADnIQpaMN8FVSQmgq+qsrCUYhgEXjcyI9NA1zZHt2XSSu3/m9u/H5DPPgQ0EQGQZ4ugolETSKjRcbfsTTz2D7quvsr6rP3YVuq64HKMPPeKwHWBDIcy76ko0rj5hTtQndjAMg+ZTT0Hzqd5FXomqYuM//CMAKoPvuPgiV5ZvLRh//AnHtRMaG9B77fux47/+x/pOSaUsL/pymHz6WUw88SQmnngSDMchOr8f8z/yYcSXL3dYZ2W2bace5J0zV63ouo7h+yoTtiZyu3cjtmgBJp9yBhhDXZ048Mc/eaoZdv3gh4j09aHumOUOK6BqEOrrseDjH8XIfX9B08knIdTZgW3f/JYjCBGd34/c3n2e/S9/4ICjRkMpuq64HEJjIxiGsSyyTLScTgmkYCvNnGeDAc+xVgp74NiEaW3DBgKILVkCaXzcczkvBBobEGg8ntpKmDUoCMHuH/0ESiaL+lUr0XnJRQCo1ZRph6BrGvb96jdgOA6Rvl703XA9hLq4q7hoqfqi4dhViM6fjy1f/4b1nTg6VrG9qTc2ovOSi122Q3wsalmpcRHvF3VxZATxJYsRX7wI/R/+AHb8l9sWpxqoQiKPsUces76TJyex56e/cAU2pPFxjD/2OBqOOxZTL7xICxofJHZ+t5hV3HXlFcjt3eu4VgN/cmedShMTtM7JDAoks6EQuq++yqpPY0dq4ybEly6p2aILcAbQkq+9juZTT6ZNluVZ2b052hoMVrSSab/gPMf1qoRKYxgwCm172McFW5uRP3AA4e5uDPzpdpcKyVq/xEKE4TiwwWDRDz+dgTQ2bo1jNZ+HOOoMjgqNjeBjUcQWLfSsnbP/D+5s66Vf/HsQWcb43x5ArsbCxLVixT9/FXIiib2//DW0XA6Na9Y4iHyGYSA0NBxUQFdobAQfjWLk/r/WtPzk00+DFaq/OlQiPABKCGr5AlKbNoELhtD9nqvLE9NVYJLxiVfK2w9WCoB6ER4AMP63xz2/n0vwdfGqBNLhQH5gAPn9BzyDtJUgJ6aR37ffZesWaGyANDHpSohRksmK11kcHsbAn27HvKuvQvOppzjUOSZp69nfGQar/v0bYDgO223PZwAl2OYKbCiIDUu9n/XCIsElz6QQUA/yPWMGUDKZmlUDWi6HobvurrhMfnAQLetOhdDY6LDNIokpkHAY0eYG5CQN4QCPVLZ8EgUAXDjxIgS98jvFycktOC69E8OhVizIU6uqdnl2tXJM7PrfH6HpxBOrL3iQyO/eg7zd0qoEpRasJkqtsXz48OHDhw8fhxZva9Kjrq4OH//4x/Gxj30MsVis+goGdu8uZrb09bkzzE00NTUhGo0il8s51nkz4Nprr8XFF1884/X279+PT37yk4egRYcHeo0euuVQLrBdjSyout0y9RmCzc2esula4aWs0BXFUgPUgkBLM+JLliDc1YlQV5dLOcIwDFrPOhPNp56C3N59kBMJsMEg6pYvO2KetaMPPmz9XRgaxt5f/Apt55+L6Pz50FUV4a6uqlm5ciKJyWecGZgtp5+GUEc7mk5ai3BPN+JLllTNytZ1HVMvFF/cdU1DoKUZrCAg1NbqygTd96vfYNmXvzRjoohhGDSeuMbl525H/bGrEFu0ELHFizD59DOufpvetJlmaF5xuaPQqYn8/v0QGmau1mF5Hl3vvAIw1Dfd73on9vzsF4CuI75sKRZ89CaaxX3X3e4Cx4R42laZYDgeDceuQv7AAZcfuVmvINLXhxVf/2fwsSik8Qns+9Wva/bBpgfAIjxvHgBqn+GVXVgJXCQCoa4O/R+4AYn1r0FOJFAYGLSsa8bHxtC09kTwsainRY6uacjt2YsDt/wRCz72EbeftIeNAx+LOkkeQrDnp972RgANKI0/8aSr3opQVwedEGR370FmuzsQDDjrjPAzuJc69tNQDz4SQbCt1ZGpW+5cpzZtRmbHzhkH6WqBND7uIv5Kg/ZsMFC21k8ltJx2KuJLl1iksx2pDRsP2j4ttWkLYgsXojAyOuuMYi4cxop/+RpYQcDgHXd62gL1f/BG1K9aicz2HcgfGAAAhLvnIdjSAiWTgZpOQ5qYRHz5MqiZTNkAUDXs/fmvEOnrRc817ylLeHgh1NEONhRy+Kzv+J/voP9DH0B0wXxs+fq/OZZneB58LIbJ555H/bGrPEmP0rmpftVKcMEAxNERl0/+XMBUbC7+zCcxdNc9aF7nJvkDjQdHegQaG2aUdaxmc5gqsU0MdXZWrGXihZkQHJH+fuT37fP8jQ2FwIVCmH7plarE2lwgtmRJTfM/H4+j/fxzkXjtjbJtB/CmIDwAQBweQWrT5uoLlmD+TR+GrmkuGzahoaGsDVXy9TcqbjOzcxeUVMr1/JrduQutZ56BQEsLogvmO591dB1qNgsuEoFY8sxca3JCLRDyMgDbc61JeDMMrnk4gYbswb0LzBgH+U5TCnMMBVtbHYrc7N13AAAysRhi770e4aY+TE+6FXt2KAyP33dfjLMm16NNTSOmeKvJQkSxCI+5gJJIYuyRR+dse3MKhvGtrXz48OHDh4/DjLc16fH973+/rDVVJYwbBUZZlkV7e3vFZdva2rB3715rnTcLmpub0dxc3ULjrYa6Y5ZbQTzoesX/daJTn2vbZy7iLQ3nwmHwsRh0TXP8qxXlSI9AczPYgEBtY5qbEWxuop7aOqAV8lAzWai5HJR0Bkoq5cqq8wpQB2q87mxAQOc7LkfzKSfXFIBnAwHEl7r9dQ83cvv2e2aZ2q2FAKBh9QnofvfVnoXZdUJw4JY/OsgIvq4OLetOBcMw6Hnve2puT2FgwBEsYzgOnZdcYv0dbGt12GXJU9OYfOZZtJ55Rs37MNF88klIvva6y8Ii2NqKJV/4nGUjQhTFZdsVaGpC87pT0HDcseXVRQyD1rNm3i66KmN5XseXLsExX/9nKOkUQoaqpW7ZUsS//CUoiSStS2LLUk97BGPYgIBwdze4KK2xYye6isvQa8vyPFjDeznU3oZlX/4SBv50O6Zf8vBb8MDiz37assWJ9Pai9ZyzkXjlVfDxONRspmrgijPqANWvWon6VSuR3rYde3/2C8cyO7/3A9Qfs6JiRnJ25y4k39iAhuOOdXyvFQogiuKwiWEYBkJ9g6MgdjUk17+OUEeH47vC0DA2/MM/VgyuiDbigY9E6HWuoH5oOvkkZLZtd9gtCfUNAIBQe3tNtlC1WGbNFmIN56wWxZAXAk1N4KNRzL/pQxh98KE5VwfUr6J2UOVqK9WCzssvtfqS0NDguUyghaqn+m64zhp77Rde4CCBTYvB3WVqidSK/P4D2P6t/57ROsH2drA87ywuC2Do7nvQcto61/050NgAhmGQPzCA1BuVg7ImrPoWHnVuDhZ2ZWKwpQXz3nUVBv74J2iSbAXd2849x0E4lkP3u9+FwTvu9PyNj8XAzTAIV5qIcaiDeO3nn4u9Ht74AL1ume07PJVYhwJaIQ+wbNn5sP/DH0T9MUUL1FIy4M0KXdMw8eRTM1onvnSJVWOm6aS1jvtpbOFCBJoa0HD8cVVJjlLUr1oJLV+AUO+sZ5XeshV7fvYLzL/pQ2g7+yxs/6//cdwrlHQGme075pwIsIPNS7CTHu1TKq58MokDHYHDT3gYsFtbHixM9VuozbtOnZbNIvWLH2Pe//3/oJWQHsKSZVB2brfu/REiYUqox75IFxZMzYwUtcDzwCzt+0JdnVj8mU9h8z//q6dd7+HGkr//PGRdK/u+58OHDx8+fPg4NHhbkx6zITwAIG1kG4ZCIXBVHl4iRrArfZCe4z7mBu3nnwctP/cFI/s/cIPrO13XAUIoYaKq0DUCXTP+Jxp0VbP+D5Z5wei87BJ0XfmOmmtTaKIIJZmixTnTaat2hB1CfT0YjqtIyoS756H3uvcj1NZW037fTFCSSbCCUPUlJ7n+NRBZRv8HbnCTOgzjepHsuOgCK4heDZoo0ixHlnXV1ogvX+bIrA11dDhIDwAYfeAhNJ9ycs37s6Pnmvdg329+a2VWR/r70Hvt+x0B8fSWrY7CqVw0iqX/8PdWYJ8NuPsNALRfcD6iFdRtMwEfi7oyjBmGQaCpEXUrljtIj1LEFi3Cgo/d5Hh51MTyNjxe6HnvexBbvMjTZsiOcE83It3zHG3suvxSdF1+qfWdks5gy7/+m9fqCHV1OuoIAN5qCC2Xq4mESW/ajMYTjodQX+8gDaZffBktp69DfmAAbDCIUFsbQu1tFUmP9gvOt4o1A9SuSUkmEWhqorZt5hxRJYgkTUxaAW6G48BHIxVrBLDBgKsOhxngEhqr10WI9PWh+93vwo7/nrmNVi2ohXSZTXY7UCTAYosWYtGnbsamf/r6rLzHW88+E6H2dgz8qTi/xBYvRrS/D7t/9FNkd5V497MsQh3trrnGC0HbvG8WmHctY5AbgaYm9F77Ps9lzPvWoVDjVEOoox3w4N2URNKbIDUIjPC8eZ42W17gwnSdUoXZQYFlEWioR8fFFzm+1lUFqY2bHN/VYr/UftEFqFt5DFasWI4t3/h31+98PH7QpIV5HkzUH7sKub37ai4kbEfHpRcjvWUr8vuKta4mn32u7PJCQwNiixdVfaaZCZb8/eex89vfc20vtmQJ5t/0QWz7v/+vrHqgNGFAmcU5OFiU3htqxgzJAnFsHNv/63/Ax+sg1MUR7p4HLhRCpK8XTaecBJbnwdfVzZj0mHzqaTStPdFbqaxp1vMaH68DbHP1+ONPzHmx+VIwBQks0UFYOrf1jcgISzqW7p/Z88dMwdfXoX7Fckw9/6Lrt6G77kZs+TLE+vow+uBDB7Ufu9KjErZ85WvuNra2QxsZBskU7+1xNY+XG5ZjSe4AusXKyhATE4EGHAi3o2vNsZj3+t+QTBXw657L0CKnMD8/jFOTm6pvBFS9xAoCGo4/DtLkZEUV9FyjbsVypLdstT6He3oQ7uyEHji4OoM+fPjw4cOHj5njbU16zBayEUwN1BCQDBpBZ/lNkGXi4/CCYRjAKFyOGRbqNDHTAp9cKASuI0QDPuXaxbLove79lnd/qK0NwbY2aPkcMtt3gI/FUH/csa5A7dGChuOPQ7inB9MvvFg1KJTetBl7fvZL1K1YjlajGC1Ar13HJRdhz09+BoC+ADatre4RrOs61HQau374Y7dNk4GmE9c4Pgc9lDe9176vMuHBMGBY1jPIE2hqxJLPfxayUacl2NbqIs1MOxoTjauPd1xvPhZF3cpjLIUFGwig7wM3oG7Z0vJtKgEbCiHQ1FhTkLUUpRZqpWg6ea0rWy7S1+soCl9L9nLDCcdDHBnF9CuvlFVreF0fO7gwVX+1nnM2Jh5/wvretC3zKsIuxKtbQAWam8FFwq6M/eTrbyC2eBHqjllhFdkNtLSg6eS1UPN57P7hTxBbvAjzP/xBhLvnuQKldoTnddFAuC1wNHzPfQh3z8Piz38Guqa5bIC8QEQRWi5vkVjd73k3WEFA4tX1rloLgFGI2KYE4aJRa66rZjtHi7tf52k/NFeoRkIEW1vQffVV2PWDH1bdVqS3xxpvtJ7P/JJttTr6bS1Y8ImPIb54kaueRqi9jc4LxD0v9F1/LYbuvrfqthlBQLizqPaJLVqIxZ/9NIiqQkkkIE1MQs3lar436ZrmCvyaBEOpMnE24GMxT2Xa6F8fLN8mj8xh83jsBGc1TD71DKZfeNFVWLvxxDUQ6upmVBOC4TjUHbMCfTdc56msZD0SGKqBDQbQceEF1me+rs5lESbEYwdNerAlNpaBpka0nLYOw/f/xVNxFOrsRPO6UzD0Z4+i9DoQ7upykB6ZrdvK7luIx8GwLCL9fS5VTzkwPA+G58v2v1BbG7ho1HWuGo5bBZbnKalgIz0YjkP9ccdCiMcQsqm/CyMjjuOohtiihYgvX4aR+/5S8zqe21m8CGo2e0jnSAAgkkTPg3H/aDj+OPTdcJ1jmVBbG2JLFjuskmpBsK3VcxyIo2MYfehhQ4Fsq2/D8zMmPPh4DB2XXAwiiqhftRJb//2bNa23eL+E0RYe9VkNp24sT+7PFmwkAraxDurQKJhgEHxPJ+Lz56Pr3PMQWLoI43feC63keaX54vMRb+3AxFNPO+5f8665GkO31W4lp+VyUDJZB/FdKwrPuhXWcTWPRKAO97WfjvcNPYJGNYuRYDNeaDwGV416K4vu7DgbiUAdPrZ/A8j0FA7EFyDPh3GADyNEvMklvrkZ6pT3M3fs0ivQHhYw8OvfILt1q+cyc4lQVycaTjjeQXoEDNU90Q+dCsmHDx8+fPjw4Y2jM6p5hGEqRGrJvteNwM5sVSWHE3/4wx9wyy3uQp12SBWKmvo4elBqjwPQ4nql1jZHK4LNTei87BK0X3Aedn73B5Zkn4/FoImiI+iV3bEDSirpID0AGjiILpiPwvAIeq99X0VJuiaKSLzyKsYffxJqJlM247Ru5TGoW3mM47tIb4/jMxeNupYxwQYEgKVEWqijHYWBAVfAzUSgsaFse0sL+EZ6e13L9F1/LZKvvQE2GED9ymNmLMlnBQF8JOKqWVILAs3NYEMhz6AUF4mgzmYfYqLz8ksw+fQzVjC9+93vqrofhmHQedkl6LzsErzxhS95H4dHsJHhOUsJxAYC0AoFqgTiecjT02g543SEOjuQ3bkLkb4e1/q1EDLd734XDvzxVs/fBm//MxZ96mYEW5qhFUQ0n3oyWEFAeus2EFlGevMWZPfsRePqE5DauAmFwSFPL3o+FkOkt9elbCgMDmHsoUcw76orywaVSzH60MMItraAj0bBRaOI9PZAKxS8SY+S6xqw2fAFqig92s47F5ntOyrWBJh1pnONaDrlZET6+1C/amVFUgkAuq95Nw78/hYoqTQ6Lr7IRXgFW1tmRHpE+voQW7QQgNuGK2ioMupWrHBltYba2yteRy4cBlEUdF52qUXkmd8X56j53itXgJJKuTLIl3/1yygMDmHozrshTdSW/VsOS7/0BWz+5389qG0ANGALAKGurqr2bCa86rIAQLirE41rT8Tks89ZheDL1UkyoWsahIaGslaSXqrNaiCSDKKqFqEdaGx0BfL5WIySFhUsm6qhNEGCC4UQW7QQSz73GQzdcy8mn3rG+i26YD4WfOwjYHgek08/61KiZbZtQ6TPfT/quORijD7gQWQZ5yvY3Fwz6RHt70PD8cdh6J77POveMByHQFOT61yFDUKs1Hap573v8SS3Z1pTJ7trN1rPPnNG67Sde46LXOPCITSuWV2R9Fj8uU8DDIvC4KDnXNp52aUYe/QxEEkCGwqh7/prXRZjdrUoQIuye6H32vdhx399u6b7iAlKLtW5vlczGYw9/ChiixcjtmihdYzlnjH4WNSlOpx/0wcBlkOku7uoNmUZNJ+2DlMVVEUmLn7+0Cj38/UhdF37HvBtrQB0aNNJsI31YBgG9aE6sIEAgovnI7zmWGQfd9abQ10UiZdfcRAebDiEumNXQWcYDP/pjprbkVy/Hq1nnYn40iXULuwgEFfpuU8Jcfys70rUqXkk+RjWpLyJzIFQGxKBOvTlh9E0/Do0hsULDSut3ycC7ueD9k/cDFLfjIn/dCdo6JqGvKiA4xjIs1BUzgahjg6XEkxoqIeu6yBVCrv78OHDhw8fPuYePukxC5iWVbUQADNRhRxpTE9PY1epJYYPH28icNEIfdEm1QNSAA1KLbz540ht2oyAYYOhJFPY/l//7fDkjy1Y4FqXYRha8yMcglDnfvkGKKk5fO/9mH7xxaoe/00nrUX3u9/lIkvjS5cg2NpiFdbuvvqqsoQqF42B5SnpQa2gmmZVvLU0yB3qdJNdrCCg6SRvdUuwrQ1EkaEkko7vA81N0EQJmi0TnI9GoKRmFiRgWBbheV2eQawln/+sZwAw2t+PxZ/9NKZffgVRIyDtPCCmYr8pR7J4kUdcOGz5+LNGTRhWENBx8YWO5RpOoIFUUmK9VU1FJTQ2ItLXV7FWSKS/D9H5/dZnoihIb95ifR668y40rl5Ns/QVFWxAwMAf/2SREHwsSq27xsYwXVKYGAAyO3dC13UIDQ01BatM1YmJRZ/5pGc9CC4SASkJBtqXq6T0iC5cgPYLzkNuT+XgptDY4CA9gm1t0MTCnBQN5sJhNJ20FgzDoO+G65DdvQdcJILUho0Yf+xvruXDnZ1Y+sUvlN1etYzauhXLwcfjUFIp8LEYOi+/tGgbVRIwNu0I645ZjpH7i9niDM/TelRlgtrheV1Y/PnPQlfVGasLq0GamnZ8jvT1go9EEF+yGMu+/EWIo6OYePpZMByHxKvrZ6T+CPd0g49GUX/sqpoLwM//6E0INDQg0NyErf/xn1DTaUT6+9F4Ip3ruGCAbs/IGg91dULL5T1JtLoVyz2te/hYDHwkgsWf+SSmX34VoY52NJ64piLpEWxtddSCKEU55V/zaeuQ3rLFNReb0AoFsHEajK5ftdJFsPGxGBiGoZZ0s6xNki8pUs/aSDOhzmlRFO7qsvpY15XvcNU2yu3dZxEZdrScdiqyu3a56lVZ95m4d8DdC0JjI5pPPQUNxx8HoqrY+u/ftAiK1nPOBkBrGtgLkMeWLEGkmyoQS2ullSNYI/394MJhBzkQX7YUgeZmdFx0Abb93//nIg4iHvaRC2/+BBiOg5rNQicEub37kNm2HfWrVqJx9QkepEcY4a6uiudAnk6g4bhjkdvrtvxpXHsiWs85Cy1nno7Uho2I9PUh2NyE5lNPwdTzL5TdZrnnJCEeR8/734sDf/ijFZQPtpW3XjQTLNhQCIGWZk/VrFYooGntWiTf2FC2fhEfj2Pp338e+37zO+T27AXD85j/4Q9atecYQbCuO8vz6P/ADYgvWYyCJiE/OITsU89DaGiAnEzWVFciG2bx5JoYLntmdqTIY+9ajA+1tcD05eOaGqzfiEHCqroGwrvHhyawSG5wKl3i604CAgK4GpSldsQWLwYA9Lz/vdj9o584aviEOtoR6uio2bIsrhaJBsJwSAp0nDYq7nvxpvgCPN10PABgNNiCOzrPwUiwGTk+Yi2TFNzHoscbIJep8SUlkiAkAEXRoBScz2ESwyOoz65eiBf4WAz9H/4gWIHHZIkNGROPQ2M8HRd9+PDhw4cPH4cYPukxC0SNLF1JkkAIqajiyBsP+HVlXgbeTGhqasKiRYsqLiNJEgYGBiou48PHTMDH41AzGQiNjWAYBppYgJYvgOHpi6+uEbACDzAsQm1tkKcTM8ri5qNRNJ98kvU50NSIzssuxdCdd1vfeWWWAuX97E0wDAOW56sSHh0XX4i288/zJDMYjsPiz34aqY2bEWxvrVgzg49FacDc2A5XQ9FoO4gsI/Hqetf5q+bf7GwwDZAxXBxEVqAZNmlgGQQaG0EUBfl8HozAW22sSHqUyTCO9HS7SI+e97/XMyjOcBy4YBD1x64sa43Fx2IVg97zrrzCXQyXZdF44hqwAQFcOGKdNwfpYQtGlmZ+0+ANA8B42a7xWgVbW6Hlcgi2tkKennZ7yy9ebBWGH7j1NuT276ekme08iiOj0HUChmXBGcRM1xWXgw0EoGazaDvvHLA8j/iSxa5r0LzuVMx75xVgOA7xZUtQGJx5UWw+EnEFarlIBCv/7etIb9sOJZ2GPDUNLZ+36lwA3kqPSF8v2GAI8658BxiWRaCpvOUYH4shtnCBw1am4bhVaL/gfCTf2FC1hks1zLvqSlqsHfT6xpfQAFGkex7kqSlHMKjcuOKiERBZga4oqF91DMYefqRspnLXlVc4ioOb0AlxBQ1NpUeorc2hQmk+5WSouQpZrgxD67HMMeEBwF0cuWQODHV0oOc9VwMAWs86E5lt2xCd348d//2ditvl43F0XX4ZAKDt3LOR3rylppoOB35/C3qvvxahjnYc8y9fo6RAKOSYm3vfdw2m+vsBnaDplFOw7T/ctjfhefMQ6esrS3pQNV4Hut5xWcX2NK5ZjfaLL0Swqanicl5KO4bjMO/Kd9C+/fobCLY0Y/ieey0CHQC0XB6CQQi0nXMWkq+/btV7AopkwWwJj8YT1yDUTlWHAACWRcvp66zfQx3tiC1ZAqG+DkJ9PWILimqhumVLsfhzn8bO73zfsc2oh1UVFw5j4Sc+hgO33uaoudJ00loAQP2xK12kY98HbkD9qpXY9s1vQZ4snpO65cusbXIAFnzkQ5h4+lkEW5rRfv65AICW009DatNmaPk8Iv196LvhWmv9UiJn5C8PoHndqeBKbL74SBid77gcQ3feBZ0QdF99FZpPOdn63UslxEciCHV1QRwepvtqbEB0fr9DAdRw7CrgyncAADRJAl8Xd9zbqNouAjYguJSgwbY2hDo7rPtoYWjY8Xuoqws917ybJlU0N6Nx9QnWb2qVTHmT9LCTCSbqli3Fin/+KvL794PhBQSam7Hl69/w3E73u+l8wDAMet7zbgzecadLEbbvl79G343XY/FnPoXh++53qIlM9N1wLfhYDAv/z8dRGBqCUFfnqBPCBgLQjHYygmCpWgOKiPjyZZh/8aVgOA6bvv4NaDWMj+k6Drt6Q3hiDcHZrxaX37IghE0LwwjKBCdsz6N31FsBlCTlz6/pGqBqGrh6N8EnyzIlDG2IrTsZRCfgPGqI2dG4ZjXk6WmIo2NoPedshLs6AVCyasnnP4vUxk1gAwHEFi8GFwyAqGrNpMfZ069hPNiIPVHnc9lgqBUBoqBRyaBByeDR1rXYHuu3fpe4AHZF3SpZnWGxvXcNlh54FQAQX3sSNJZzFVU3kdm5G1iwHLJKQEQR9rtPOtaC1kwxAejx5tVoP2EVTunkkf7dL9wbA1wkph1EURA13iXkaSfZz9bHUGBUAO53AB8+fPjw4cPHoYVPeswCXUYGlaZpmJycRFuFTM1xIyhRaZk3C6677jpcd911FZfZuXMnLr/88sPUIh9vdTA8j2BrC4gkIdBQbwR2GmnwimWhZrLQxIKjoLrQUA8lk65Z7eGFltPWgeE4jD74ENRMFvL0NIiizCrLueOSi6Bms64i1IGmJjSccDzqli9zZOQDAFiGBrSnpqCrmpE9XrlmCBsIuBQODMuCC4XKvoSVQpNkDN5xp3vbHsqDcrZUXChkBeD4WNQiPczi56wRPDDPJRcKVQz2c8GgZ/ub1q7FxFPPWMH4cE8PDfZ4IGDU3eCCQXiFExhBsEgPhuOg68TVfxrXnAAlnUJ68xbkDwwg1NWF1rPOQKCxEVw0Su1OslnomkYtrxgGDMcWg5EMAzYUBJFlK+jDsCwYloMZiuVjsZqK+4baWhFoasSyL38ROiFIvLoeA7feZu2n46ILqEUWIRDHxhyZmI7zUqK04GMxl+1XoKkJve+7BhNPPgUuGkPTSSei4fjjwDAMtUk54XiMP+pWMDhPsPv6crEYuGDQUWBYy+ehFQoId3ZAGhujATmWRaetKDwbCCDS32eRFqGuTiz69CcdQenSTGsTkb4+dF56EQoldWS0ggiG4xBsaal8HDXAy1rNRMuZZziCQfOuutJzOS4cARtQoSSSCLW10WDuH/8E2cOT3IvwAAAlmXIGNBkGvC241fO+a6jaiWVRv2olJp58umy7+SpBsZqtj+xqKqNPuOoxVJi3g81NCJ62zvO36Px+LPrUzQBoANDeHyI9PVj0qZuR2bnTVcsj0tsDoaEB4sgIterq70O0v0gs2628rMMIBNB65unQdR1Dd93jqXTq/9CNZccyH4+DDQWh5QuOcVFqcbXsy19EqLMTrCBUn8NZd7AstmQxuHAIjMBb9oy0zouN9JCcyplScoOfYRa4o0mhEFpOPw2hzg7omgZxdBStZ5+JUFsbpHEahKxbvswiGbwQ6XEHNpvWnojEK0VyvvPyInHUdu7ZyO3dB3lyEi2nn2YFaCPd3Zbih+F59H/getStoGO1++qrsOenPwd0HYGWFtcYji1ahFhJwk94XheWfeVLUFJpq1aOCYbzqLliELzWfZNhwAgBNJ+8Fg3HrgQYxkWKeJJ0LINFn/wEdn3/h9A1FfOuemdZyzOA3vPiixc7bAQDTU1gOA5CY6Pr3iCNj0Mr5BEw7gPzrroSzSefhMLQMApDQ2g65WRrf6aK0UTPe65G5yUXQc3nMfnUM67At0mgCfG4K+gL0GcD+3kWGhpcNkCNa1ajYfXx1ufYooVY9uUvYvDOu13WU/L0NBiWReell0CeTiK9iZK8wfY2LPncZ4rXhGU9+xkXDBafX3geLE+fWQSOB8OwYBh6HgJ19SjUQHpITTQhbvPCMI7bUUBjRkM+yOCV5REk6ukz1v7OAJbtE9E/LGPJAafyIKXmQHQdrEdyjG5TegRWLgNz76PQDdKs/rSToYyMOeboQGsr+I4WkIwIocw9pPOyS6gqqLvbShYJd89zkKKsIDiIL/NclRbqroRl2f0u0mNbfD62xW12iTUm7gDAGz0n4cTTVwFEQ6CvH7oO6AVvwmj4jjvQ+IV/hApAlyQH5TDYswrBhuPw130akkIcaT6Kk7k4+E534kXD6uMRX7wYfDyOobvuhjzl7t9EkkAUBamNm5AtsZZj6uLIEwkhLuRaz4cPHz58+PBxaOGTHrPAwoULrb8PHDhQltCYnp5Gznigrqag8OHjLQ+Wpf7K6Qz1amZZqwhpsK3VkclqBdXjMWrLYt+MIECoq3e9LM8UzaecjKaT1kITRStzu/ZjKQb3GJZF9zXvRv2xq5B45VWwgQDqVx2D+PLlZW2q+FiMZt8S4sjKdYFhaGAgkSgbnOIiYbCBQEX1i1m4VQDQfOrJmLJJ7xtOON5zeT4e87RMsWdK8pGIqWFwBHS4cMQiPRiOAxsMuCye6H44MGWsnkId7Vjyd59DdscOBNtaEV+yxDPbmQ0EIBhe4q7fjcArVWqEAZah50InrgxYhuPQfv55aD//PNc++EiEkgCRCNRsFmwgQC3GeK4YIBJ4MBxv/a2ZpAfP0YxbVYPQUG8FSptOWusiykzYFQIMy6Jp7YlgAwFktm1H3TErEJ3fj4Bh4RTq7ER+/4Gy57AWNK5Z7elJzwVDnv3DhdKABctCqK+HLssItDQ7Am/i+ASifb1Y8vdfoFn9CxZQm5U9e63t9F33fow++DB0QtBx0QWuceQVBGxceyJ633cNAGrdYodmWCZx4YMLOPDxuCtwaUe0rxd9H7gB6S1bEV+8CDFDBeLaTiQMXdet8RWd349lX/4i9vz05w77no5LLy67r72/+rXzixIigAuFHNc0vWlz2W21X3B+2d8Aet60UqWIQYpp+YI1zoR4HbRCAUSWIdTFwXA84suXOYiPxrVrKu7LROn4aDnjdNuubf2BpcHkSG8PIr09LtIj2N5u9YuZgmEYpDa6bbMW3vwJNK4+Adm9ez2z2gONDWCFAAgnO4jj5nWnQslkURgaRtPaNQi20mLNXkH0Upiksh2R7nlghAA4QbCuT/6Acy6YfukVh3qw97r3Q0mnoWazUDNZSwUyU/TdeD2iC+Zb67dfQOdNobGx4hjxQuvZZ2HiCVoEOTq/H8HWViz+7Kcw/dLLNHnAFnQNtbVh2T/8PXRCwPI8GJ4DH6+Dkkig78brIY6MgI9GHfep+JLFWPr3f4fCyAjqli+rai9ogo9EPJ8NXNZRLOsgCjRVBcOxYA21qhex5gVGoIrC2IIFWP5P/1iT1RvDcdAKJTWSWprBBgQESkiPrndegUBTE2IL5ltt4oJBROf3uxMz4LZUE+rrwIXDCALQ1uRcpIep9GCDAU+VSSl6r3s/dv/wx9a8P++qK9Fy+mmey3pdB1MVyAoCFn3y40i+9gakyUnUr1pV1g6u3PExgmCpU3lOcNzT2i86H/t++Zuq2zum71iE25vw57Hn8IdLm9A6rSIZ5yCGiuNbZxlsXRDGtv4Q2qemUJ+jRMXOniA0nSCrFVBn2DkNipMYFqexItaDsBCmRbB1AjYWRd0H34P848+Bq69D/PILkT2wH7EliyGOjEDNZBGa1wXCsdB0Ao6nz6iDtxXretQdfxyEU1cjIoQR5G1q1WCwpmvXftEFyGzfUZO67vW6JVWXKVUAVoKoEAjdlMTSGRbQCQIrVkF+Yz2UYafVXt0pp9LECwDQnEk8+e7FCC1uwr7x4pyZl+nxNF9wAaYeeQQAwLe3o+8DN1oWZ0rqHCSzEph5vcjd+juQbJH8VjMZz1o0iEchszqG0oOIFDrQGPZO3PDhw4cPHz58zD180mMWOO6446y/169fjxNP9M7QfvXVV62/TzjhBM9lfPh4u0Cor0OwuRl6a6sriFkuSMIwjGdQXKivO2jSA6DB05kSHnw8DqG+zpENxzBM1YxWO8xAEV9XB4bjXYXFrX3FYgg0NoDIUnm/7IYGQNehpNNls+XYAFU6SKKI9gsvRGb7DhoYZhg0rzvVvXwwAD4adZEeXDhcLPwJk9AI0mKntqxQPhZ1EBBcOOxNerDlSQ8ACHd2IOxRb6S0rfb22MFHI1CzObBCgFqRCQHDNo0DqrzUFzfK0EK/oLYhRBKNfsmB4Xhrn4xgECHGP9o4ziCQ4tRKJRg0/Op1tJxxOtJbt1qZ1/bgcLDNbYvUcNyxaDju2GKzeEqyhDs7yzY9NK/2osxeYAMC+GgUDccfZwW4hMZGdF1xOfhoFIWhIYw+8KArQMJHwuDDYSiyTLO+bYE3aXwc0b5eK6ufi0aNayNYdi+Bpib0Xvu+GbU11F4keEqDjFqhAIbnwYZqCz6asKtUAO/rUoqGY1eVVSQBNDueDQSglygnGJZF52WXYNf//hi6oiDU1YWWM88oux1SUlOMqzKPtV90gat+QrC1FY1rTihr72dtOxx2kR5sMAAuFAaRFTAcByJJ4OMxMAJPPfhZDlw0gvbzzkV2x05aqLu+3pU1XK5/Np92KlKbN0PL5RFdMN9do8cAH42Bj8co+eKBWggFqpKJelo8BRqbXLZ4sYULKLEXiyG+dImDUGo7/1xwkYhFNJtZ/9B1sIKALpuqibaPc9WwYHgOuuoMJnLBoGMcgmXRdPJJVFkn8MXrU3Iuk+tfs+zDrLZ7oLTGQqSvF2omCzWXRf0qSujbUb9qJRiWpeo229zOhYI0mOylDiqjGOq87BKEOjtAZJkqbFTaV8qRcYyNZODjcQSbjaLjmla2lgWtRVAbCVwN0QXzEWwvzmv288sGAtByeXpv42z3Npal10bXrfPQceklGP3rA9YiuqJg/Sc+ib4br/e8L3udPz4WtUhdE0J9HVgh4LYKJKRi3RjX7kqUr1w0CiIn6d+RqGt5s5A5w/P0nl/lHhtbMB/d73k3Eq+8gkhvr2VVVmxA8XhLk18Aav1ljhU2EER86RKrXkctsD87sDxvHS8r8CCKao2l5nWnIvHK+qp1g0ItLTi+rg9D0jReSG7DaGt55bDOMrj/zAas3ZyDJDB47jia1JJQ86jjI9iVG8FvhqnC8vHpjfjKovdA0+m5YDgWwaULIXTShDs1FgLDc8ju2Ek3zjBoPvcsyCyoBTPHoPnkk5BKTyH31AsINDSi6R0XIavkUVBFtMdawTEsTQphGMOmk147UZUQ4r0I124s/6evIrtjBw7ccmvZ4xwLNGI4dPAKSzsktTgG2GgMJEsVvLHrPgR1YD+y9/4Zej4PtqER4XWnQYvFQCQJGieAVelxJfkYotEAwgHn3JuX6bZb3/0exOZ1Ij08CrLmBLAcB1mSwTBA40kn4dcP7MPrT2Vxkx6AfZSp2aznHJQXWPxo058wKSYR3fUX/Ms5n0d/o1t95MOHDx8+fPiYe/ikxyzQ29uLpUuXYvv27bj33nvx0Y9+1DOj+6677gJAa4CceqrHC4wPH0cxhIYG8PEY1CxVMynJZNngKsNzltVOOfXDTMAKghVsCba2QNeIp53CXIMNBqkqhWHARYvWTjPbCGsF0Rkj2FYaODLBG0qYSoFts7aDlXXttUuBkhgSJiDUxbHo059CdtcuhLvnOazD7MtzoZAryOIVeAi20boT9kBzqQ1XoKEBRJadgVNTBcG71RszgSNb064W4nlqJ5XNgQ0UgxlmnQ0rpFjFuocVBKvP8rGoRfowHO9UswQCht0VtbSi37M0q9hmTcYGBAQaGiDE41j2lX/Atv/4JtRszpENX62WDIxaMgzHehaht7bT2gYlk/bsW7XAPJ+9173P6u9tF5xH+yPDILZwAZpPPQUbv/zVkvV4qyZPKVEgjU9AqK+3lEnm9eMiEU+P+3KYd/VVGPozvccyPI9mo44AGxAQnT8fS7/4d9AJARcKgYtEwAaDVZUejqAy3PYzNdW+KQ3i21VhHIdgKw0AURWQ00Iu0tOD5V/9MqSxcYR7ehCorys7pjsuutARbKpWOyK+ZDGaTlqLxPrXEO3vw/yPfLisnR8XCdPsceM4vEhpLkQVZlyI9mtdU4tWdgBYno6J6EJ6LQojo4gvXgQuFAIfi4IoqkWWevXPSE8Pjv3mfyC3fz/+//b+O0qO6lofv586FTtOHqVRQhJKIEQUQUYEGRMEFjnIBIPBxhYOPxwA25jLvSbZGO4L2IDhC4aLyAIEBmTAGBDBJEkggiWBRjmPZjSpc71/VFd1VafpGU3S6PmsxaLVXV19uvp0mLNr720MGpSb3ZM+zlbwr/BniGfhuQA1HIbs9+UPelRW5DT/thcHlWAIw88+Eyu3bkW8aSdqjz4KQ2efAjMRh1AVa/E0grzP0QmoCZEnWBtAfGezZx4JXcfwc89Gy9dfI9nahkHfOg5aRYX1OK7XJ7vxc84Z/K75KQcCVlmtVAp1Z5yKr/58j7WJqmLk+XOcRfNkJILGJUudueruMaGVVyCyZYvzGWoHsYWm5WQqFCplKAmByoMOhDB0q0RhsR5QWZzsAk0ruczjrpKEwLgfz0XTJ59Cq6xA2X77ZcokqemSSulMQJtQFJimCTMed45D5SEH5e3n075hQ95stpxjKgRkvx9jr7gcybZ2RDZvhlBUBEaORCoWzykDGMvX7L5A0NEK6sue290nQuQLstq/ASRZtuZkCa9j1bSDUTXt4JzrJTWdqbJtGwAz7+NpFRVOlk++kyes68ycAKJ1oxVotz+fJVVNZ12lT2Yw4WRwKT4fRl18EZb+9Mqiz0WutvryfGvQQfioaQXiZjprQA1he57G3dsqFLw03fsaNabaMBLAC1szWW4tyQg+2bkaRxnp5XXh/bwwVQF9/31QN+dctK1YibIp+8IYPgxRISElmTAlCUkzhfp9alF2wLnYt2oC2st9wJqvYZopNEaaUOWryGQraTogtSElJGzf2YChocF5f7eroSD8+0zMub5svylo/eprNEkGnqs4olNZHKXYvjOKR97fimPHl2FYrQ9oawWSSUiSBHXEKJT/4CdIbt0CuboGcUWFEggBkkD9pCMxaskrSEoCr1cfgH0NBX7deyxbo9ZrlhIC1UcchvjaDWiQrfd8PJFCezSBD9e24sM11vtdjng/cyI7dyI0cQK0vUYi9rX1vRHYZxI+a12HbZFG6zHi7Xh7zYcMehAREfUSBj266LzzzsPvfvc7rFixAvfccw9+8IMfeG5/+eWX8c9/WmfpnHnmmfCVmN7elx555BHMmzev6DbRaNcWz2hgkGQ5/cenCa2q0lr8txe4UynEm5tzFpDVinKrPFCRhamuUEMhxOIJKOEwkEohtmNHl89oL4USCkKvrs4sgAf8XQp6yD4j5w9IxR9ALHvhT0idKsejBAKFgx6a6izM26Vncs64tqXP9LPv516sE3qexU9dLxqUAax5Y9TWonX1GiCVgiTLUIJBmKlUSYuSnsfz+zzP01PGwj77NxyyFi/sMzfTc1RSVatOt5AgB/xItrdDDbuyhvIsAGXXNXeuV+TM62M3CxfCyu6wsz+EHQxxLX6pVqktkV7cGzr721Zz7fTjlu03xVOaJR97gUeSFfiGDsnJSADSfQU01TobP29pMcV5voWaaNuPo1VUoO602db4NQ1mKgmhakhGI3kXzs30awzkBgoimzdDr6l2PivsoJdWWYFkpL3kAI3dEDiycROqDjsUemUFEq2t8I8YAUBCcOxeiGza7Gwv6zqSipLTbN4hBGq/eawn6CE0DXv//P9DbNs2RLdugzG41np/FFioB3IXuH3DhiGyaTPMeBz+EcO9c0FTkcw69moo5GSCCd0KZNr3cb/GZftNQcXyFWj+8j8Ijd/bUwIoH0kIDD/7TAw/+0xAkqBVVuStSw7YAai4My+EqnqyD4SuO9lcImHATKYg0vNXOHNTTp81rEOvqfHMA62qCrEdO6ygh6rCTCRz5qBQFajl5fAVCIQJXUcqYs0/9zGtOPggT6Nrd+PoQoSuF8w2rDp0GhoXL3H+bWU4pMsWBfwIjBqJCb/8uXO7ms46sTI90n2NdCNnvsg+A4mW1nSQ1PrcsktlCU3zZD7Z24cnjMfU/99tiG7anAm4KoqnXNOwb5+MVfc/6Px75PmZPm3WZ6XpnMWt+H0QioJ4UxNCEyZgzI8uR1t9PWqOmmGNKf35IBsGhp99JtY+9gSUYBBDTnL14NE1p2wlkHn9ZT036CEM3cpKKHRyhCTSwXDFyoRKpawsowL3kQOBzNn5WqYvSqF+VM7jZJckK7DwLwyjYIkp2TCczARPbwjd7u0he+alJFulEJPxuHUc2tuhhkKY+Our8Pl//Y9337rhZAC5A0Vyes5ntrOyatSycqhl5ZANA77hdZB1HYmWFuc9J6kq1FDI6X1lU0IhmKlkbuk6ZIKFkhDW5066J4mkyDBTKQRGjfQcZ2PoEOcEDKEogJ6bHZD7IIWzENWyMJRQEEJTEd2yJW8QSKusgOwzEN+Rvy+ZbFi9t7KDmXYwxH6eZirueT9JigwBZJqcyzLkDspllU3ZF0p5OUwziSothLkjT8bnLasxvGI4RiKEr9o24sH13t5Yo3y1GKpX4p3GzAkPjYk2RJLxnCDJf9o34BvmvunxCE8gQUqfmFFx2CGoSn8PxCQgJSRA1yBJCu5auQBLGq3SieckW3FE9Yz0gVYRiUewHTtQHrSCNkJL92WrKAN2rLeaoUu5v99N00QUuQGlqkOnYeT5c7Byczsanvm66HHrqqXr2rCmIYZfTxprzVVJcuZiSshQhriyLYQAZBlrq/bC03udB9lMISlkfCNkwJ+V6RFJZ3rEU9Z7OJ5MIiKZiCRjSCRTaG6P4YE3Mz3DdqoBhJOZ90/TpvWoliSop5wI5dNPEEsCZd+Yju1bP/Y8TiLVcVkwIiIi6h4MenTRWWedhSeeeAKfffYZbrvtNnz11Vc49dRToaoqXnvtNTz00EMwTRODBw/G5Zdf3tfDLUlDQwNWrlzZ8Ya0x5AUBWYyCSUYtBa6Av6CZwfrNdXQKiuchW3AOvNPz/pDu7uoZWUQRjqAkA7GdCnzogSyzwe9ttZbM7+zfUBc+8q5LuAHsjJVZJ+vU1kxSjhsZd0IKWcRQ0qffWo32i5EGAb0qkoneCBU7wKuXCAAUApJlqFVlCO2vSF95r2GVDzu1NGWFAWSENb4JMk6SzpPeQy9pgZta9c5c8yT6SFJ1gKFzwc1FHIW350sC1UFIFlndAasP+LVcAjxnU1Aysxky7iyPwrVBrdLeFjPTUDoOsxk0lOuRsp3Fncw6JTAkv0+VOw/Ff4RI9C+bh1knw/BsWNyHiubsI+ZLCD7fKg95qh0A+MMY8hga0FUy32/2n1W7DPls8v3uJ+jtb3iuk4GEunnrGo5ZZYAa3HeXhjWKso9t+387HMAmT4RmQCRgL+uDvGdzYht395hrXBJCFSnS8DIAb+VeZMerxK2yom5F9VEOiiVN6NECAw7bTb8I0d6MrhSsRhS8ZinbIrQtLyL2DZrYdK6TRgGZF2HbOhI2HPD/bCqiiSyApX2mIVwXjv7dXAfE6EoGHHu2XkOjJRZqLTHFAgg2dbmydzILBbnBoFknw+JllaYiUSmZJuiWEEPIZxAm71tKpq5vzX/pczt2QvHQliZepoOoBmSokJSErlBD00r2sBZaKo1z7IyPWqPOhKtX69CbPt2VB02zWl0XYy9QJ3vWIQmTUR48iTs/OxzCF1H9TemZ4IUkuTMc/exs0tZ2e9TO+jqXkS3+gO1pssgpY+VriEJaxHT/VktuUvvpI+fcxzsoE/6jPXQxImoO+M0NK9YifL9pyI0aSLaVtU7Y0sl4k5pP2EYEIaBeFMT9JpqCE1FcMxe8A+vQ9uatdbxSL+/Bx33TVRNPwLJlpbM65Iei2z4nM8Qd7m/nONsz6N4gbJH6c9L6zmJdJaeBklRkGxvywTd0j0H3LXz3Y8ndD0nmOhsp8hQ/D4k2qy3WioWz/x2cH9eGFZgL5Yn6CEMHWo47DRrdwLjrs/77NKNdv+qZGv6OyX9WEogt0yU0DUnS1AfVIu2+tXO8/aMMR0ck3UNKTtAaTfvVlWU7TMZgdGj0LqqHmYqlVP6SasoR6KlNW/Qw/0dY32v2VkA6RMnyssw+PhvYePfX4TQddSddab1/IWUGYfrezRf7xt3dmp2oMr+/SMbRjpzMPfPVSWQ+S7NLhFnHUcdkqzkBD2UgN/pgyIUBSnTzLynFbs3lyuoIMvO7xTP8Zu8N8oOOhBmLIah+x+ELe07kNQUSJKEWr0MleokyFWVSG5vgD9PA+uw4ke56n39X938ET5QchuEr27fgkgyind2fAGRCOKgoftaf8CnfzdZT0wGotYxNmUrGy0Z0LGtpckJeADAY2tfx7TJVulEEfQjtaMJkXgEW9p3wJ+MQ9Y0yD4/4ooVhEyYScjIDXrEknGrz0g2WUCSJIwd5MNxk8rx7tfNqPQraIslsbXFeo0PGB7AtNFBrNnRgr9/mvndvleNiqDPwCdr8v8mcdvRlsCmhjYMSp/gkow349EPtmHZhjaMrTFw/rQaaIqAKQQSEFa/DklCMh3AKSsPIOD3Pq+GtgTuW7QZh7f7cPIBtYgnk0goAlvbG6DEBNZs937vv1exD87Y+LrzvJWpE7G2ZQtiikDZ9EMQa4khripoSXi/58N6/h59RERE1P0Y9OgiIQTuvvtufPe738XKlSuxYMECLFiwwLNNTU0N7r33XpSny/r0d5WVlR02XI9Go1i7dm0vjagHpOvzpyIR64zlZNI5i819xlnOmdMdlMAp9niSLHLS67XqKsSbdhb+478EwjCgV1chumVrp8rClEotC0Ovqcks5JZAkmXo1dWINzZCDvgL9qHoLu4ySlplBdp7KOihhEM5AQhR7KzxQiQp/yJHvj/oOxlUkSQJvmHW2W3R7ds9/TjsYIV7ccpNrSi3SpmYZs6Z6O7Lu5qto5aVIdHSapXdME2YqczjCV2HVlmByKbN0KurICkK2teu8+4gvchmz3tkLQACrpJSsBbGFVejXvvMa0kWkNPloYSmQSgqUvE4lKB1lrb77NpCQQ8lEHA1MU+fmZ3et9NHIM9CjPv1F5qOZFu71d+iqjLruWbKImVzghHp/w8+/luoOOgg1D/wICKbNkPoGmqPPspZZMy5vyxDKCoAE7LPn3dO2Gfq28fLfb39uglJyh/0SKWc0i7+UaM9t/nqhgHI9InInlNqOAQlGEDbmjXO52a+Hgdudq8WOzijhsNO3X/TTAEp01oAEwLhieMRbdiRXtjxYfDx34SkavANGQLTTCEwcgR2fp5ZdGpftx6+wZkSYlbPgvScy/O9YGVDWWVd1FAwPX41bxk3SdWs76NY1Hmt7TP87UV46/6KtUBrH+t8GUmGYfUsSKUgqSra165zPpuUgB8wU84CozAMp0SK/Z6zn49v6BDrDPJ0aSanXJGiIAWrt5CnX4+uW4/pmuvCFYiQDR3ubzmn1JydTaYqMBPWY2U/n2L9OISiwtSSVsk6SXJeC2PoUOx70+8R3bqt5CCtPVZj6BBEt271LALLho7Rl16MyPoNUMJlUPw+7/s667IkrECkXb4MyJw9rlWUI7LR6t/kBEyFqxyeqkKY1vGTdR0JNHu2zXm89H2s52CdsS5JEmqOmoGqww61ejukyyyZiaS1n3ZY52answiAdCDPMKwShO0R6zHsjKb0nJOEgJAFUu7XOf1a2iXOvN8drmB0+vElIVv9R+zfPVmfcfaxkFTFCbxZpTGrANNE+7r1SMViELqBVCzuDf64vqvcmRfZ7xWRzo6Qk0nr6lg8vQhuZV5IsoxYY6PVf6xA4FX2+Z1ykfY+7efvBD3Sn5/2b0nruafnfPq7x0zk/21lpt9Pkqo47yX7d5gky5kMLPt73eeD2dJizT8pE3SQVAVjfvgDxLZvh3/0aEgwnXKk1uuiQBi5GRmSqkKrTvdhsIMBduZHOmNF1nUMP+dMVB1+KAAJwTGjEW9q8pyoIBvW9xskCXpVVU7vMiUYynwmZQWq3K+tJAuE9h7n+b1VecjBmd8kQuT9rLBf68yO0plLgYAzTiuQ5LpP+r3qyaRIB+JyjhMkVEyehFT6tVGEQMpvQMQUCCGAFKAZPrQDCCh5gh5qAJVq7m/kpkRuEGpHdCd+/tl91j+2AfUtG3FO+SGQVAWLNy7DJ5u/xD7hvXBMaCISqTjk9KJ6StewrSH3+z1ppqzjpqqAqgLxOEzJRHs8gpAehBLwoz0VhaQqSKaSyI55JFNJxJIxJM0kgmPHoGXlV84xD08YDzOegCRJOGVqFWZOsDJXTdNEykQ6S9aPVGsLqsJxTKiT8N5XMcA0cezkABat9gMlBD0A4P3PN2Lt+kaEAxoMJLB0nXXs/rM5gvdWtaA9YeLdv69HJJ5EKuv3VDjsQ7As93VdviWC5S99CZ+cwqP/3IxoEjjlsHJMrdawrcUbTF0ZGI4XR87EeaPiaBhWC6W6EklJQjz9eyUWTyKiCrSlvL+TyowQiIiIqHcw6LELamtrMX/+fDz88MN48cUXUV9fj3g8jrq6Ohx77LG4+OKLUVlZ2fGO+ok5c+Zgzpw5RbdZsWIFZs2a1Usj6n5KIAAlFEKyPWKdZdbejviORisI0diIRHMLJFWFv24YEq1tiDVsByQBY1At2tdvsP54tssbpftZ2Cn0ybZ268y9UMg561sNBSE0DZEtW6zFhFQKss9wavqnEgnrLDTThNA1RLdu8wZf0mdxyj4D8cYm6NVVSEaiEJoKtbzcWugeXod4UxPiOxqtkhD2H/m7UOpJaKpTCqGzC91qOAQ13Ps/6GVdh1pRgfiOHd27Y0kqGIDoKHMim1penv+PZ3sxI+vs7K5SgkHEG5ucmvuZs7Jz//CWVBVaZWXerBJ3zWvZ17XMFs9jCQH/8DoA1lnrZiJhBSjSZdJkXbdKAEkSTPu9lp7H2YvasYYd+edm1hm27rOBhapY77X0QogdgBCaai0ApIMiTvmRivKCZW/ciyl2r5NMTfBMT4+ix6NIPxO1rLzgXBZO8CZzf726yinFJPv8UMJBa6Eya74JTYWkKunFUhNywJ83eJddmsW5LGRIsgkIGcLOAMlaWFQCmXJ2ajiIoafOxoZnn4Mky052grOgn+c1tMatIZlwLYilrM9Pez7aZ3vbzwmSlFlkthdmJQmy4UcqGnEWsyZc9Uu0b9yEZGsrhKFDkoTV6FxVIEFCcOwYJKNR+EeMQGDkCJTtuy/sUijWWAyI9KKsXuvNOrJut8/6lqAErYUnoSpI5Su/4jOghoJItLU5TZGFrqeDHpngnVBkQLIbNweh19YiFY2ifd16Z19aZaUneOrOaJF9PpgpV9DD1UtH9vmcknGyz3DmuxPssMv1pF9rOc8iaXZjdnf2hcgqc2Mv/Mq6K6CTdWwkRYEaDndQokiBMDVXUEZGKpaygpiadfZ7dim8gvuyx6ooUMvKYCat0kqpWAxC0yFicRiDBzu9l7xBQJGzH2d/dnaGIkNSMmXtUvG4k6EhycJZWBbp8nuSqkK2P4+F5M1okLxBh0xgUnZOopD9fqSiUU9/DajW51Qq3cjX/bmgVVdlAg5C8vQ2cB7X1Tw88/ha5v9ZWW3uIIRaXoHYtm2Z93U6u0mvrnayJazHsB5bpI+V/bzssklqWRjRrdusOdXc7Jk3nkVye/4JYZXBdGVRCs1aCDdNE2YyhWRrq5P5JDQValkZ1PIyqxSVq0eI+2QZxe/LPFeRLueUXni3Ax3uzIhke3s6iOkqoSRkmHlKAwFwAhxO8F5V0v1frACW0FQk29o9PaUgeV8fu7RcKhWBXlMDxe+33qPpoIc7eJFN8fs9ZeqsrDn7NZJhl3BUQyHnt5FsGDBTKauhvH3MdCvoITSrBJonm0OSoAT8iDol1DKBKru/hvNcZAWyYWDMD3+ADQueh1pWhsEnHA9J1ZxjnS8rTNZ1z28rJRhAornFCorZATu/H3B9RthZX+7vEPvkAWPIEEQ2ZkobBfabDF3zIR5NZ29JCiRNgyKpUOQUYjtj0A0/2gH41dwM37A/jFG+QRCQkELnfq9/tnUFWoL7Yme0DY//5wUAwBdbV0IdFsNI3yA88vV8bIs2YUTZUFTrueUyY8mE89zkshCSO5oAIRBJxhCEiaQikIgmAUVBKpl7wleLnERLSzM0RcfQU7+N+gf+hnhLC/b63sXQa2oQ2WAdJ9k+jrICKZmALAFyeYUVdG9tAWDCr0s4ZpKO9kgSalBHdVXpv33fXLwhfSk3UPTCp8X/FigvD8AIKlBlCfFk7vH/ywvLncsL3tmBvb4ZRls8d54t04ZCHDocqdbtSMkykiaQSADbd0aQSgERSUKr6f19xUwPIiKi3sOgR5abbroJN910U8nb67qO733ve/je977Xg6Oi7qKErDP2lfQipeL3O3+0iZoamImktQAgy1DDISswEolANgz4R47InAkvpVPHy6LWGXWShFQiYTWpzFO+yKln7k6jl2XIrtI7gHVGcKK5GUrADzkQ8CxEC1WFEgpBLfMuTkuSBK28HJIsIxWLZRbaFAWpeNwKpAB5z8iWZBnG0CHpRuDbkYpEoYRD0GtquqXheG/TqyrTZTGscgZdbeDsJvuMgoEf99m4HRIStPLcPz5tzsIGrEX3fNkfpZJ1HcagQVbZLPeCrKsBvPWgUtHXWlJV6NU1iO/cCSXUvX+kWSWWrLkv+32ZRXB7ES/daDRztnrQKeEBWIux+RY6srMbskvBmHkytqweBemz8dMBmDisXhbFSuzY7M8Qpwmqq7xVR8cg/w3WAl+iudkKDOm65/3rlAST0w1j0wu0Uvr1tG5zZYPYTZ9VFbI/AJipdAAo/flRXY3Ihg3eISgFgh6KDMlMpRflrOvrzjwD65540tlm1EUXerJghp50PCqmToFaUY7AyJHW9a5FtbyHwF0aRlifk8loFL4hg9G+fgP0QYPQvn69FQBRVSsenRVEsoM+9jgzi4TW2GTdyJSFSS9c1sw4EjUzjnT2oVVXehpDyz4jnUEUhEjXyo83NTmvkX1mtt3zwn4u+TJu7M9+93eAbOhINDc7i3XOfE4vdtvBbqHrnkyT7KwG2e9DvLHRWiRNjxOQrMVnOzCXXvTSq6vRtm6dJ8DnlArzWwtQ7jJ0Oa+VK9BnH2f3wr87oOss/KYXU4Vd9sZ11r9aZmXqoEjAXaTL0DifXUIGEIfs+lzQqqoQM7cjGY0WzdR0j91e8I01NKSDHlqmIblmlftzl7QqFBy0n7sdDLCzP+wAFGB9zrvPUpcU1cpIkiQr60PXoZaVeQOv7kwL17wRiowUALsXVHxH5jPAN3Soazt74d37nIFMYMPOUrHfN5KSLh8kZfUecX22Zi+eC0WxzqiPRqGGQ4g1bLcWp10L9Wo4jPjO5kz5MztwkM5wsOens0/NyuhyggxZv4/s4K0kW+NXggEnyOYep9B167yQZMLJqBKq4pTIcpc6ssel+P1IpFJW7wp3Noec+azPZENkAkDWSTbt1rh13fkskBQZiOUGBQFYgQHX/uwykyL93GW/1b9Lcgd68pRStLN37Ewsz2e6u/dOVmkp95gkIaCEg06GiPs5u18zwJpHns+y9Peo/d0uGzoS6TPlrbJVmcwVz1zOyq60x109/XDnpAlrOzX9OyH3t4C7p4odeLJ7FVm/Paz3lOzzWUFO13Gxjrvd8yOT6TL02yfj63vvA1IpKOEQyo8+EmpchpQu+yerCiRNhSzpUP0qsLMRmmZY8yfPZ5lm+FGuBnHupFl45PPnc27vyCMb38S6yDbPdS9sfh9jA0OwpsXKqvly21d57xtNRKwxyQJCD1iZlMkkYokY2uMRxJJxxFMJSIqMZDxPcM5vAE1AKpVEaNxYTPj9f6GpaRsGjd/X8xtLCAmSpkGuHYzotnWQoyYknx+QJGxNNOL5xn8hLlKYEdwHlaiACPoxZVwVnv7nCuSJtXSrsrAPSZGArgD5nqJbe8zEpqYoIsnck39SJtDQav1OTQjhnCMRj1sXoqaESE7Qg5keREREvYVBD9qjFFvIl4RwygPZrLrc9tmahRetnNsLLFQ7j9tBIEHx+5yATLaOSkU5PQxcf/zJsgz/8DqYdmmIuFX+Qh9Ua5XwcJ3pJ+tDkGhtdQJDuysniyYet86ELtIssxTFmkqLAlkA+di9HAruK10+Rquq7HK/EO/jpc+Wyy4fFAohmg56qGVlBecbACf7Quha3jNCd5X93lJD4bwNw4WeyUBQQkFPbW6hannfT3YQspB8gQh7IdmuXW6fKVxKwANA1sKc6lkUK+V+2Vk+WmWFVT5NtRpdK6EgEqZpLegpipOhIkmSVZe9ublgpoZ95rHQrTJeQtOsRSZVtbJpkGn46i3xp+Tsy74spVKwS4MBQMX++6H5P/9By/IVKJuyL6oOPSSzqK6q1iJRMOgEZAFr4dTd+yGbt7xJpgeK7PNBLS+HrOtQAtaZu5KiQDJNmNmfv+kFOdmXzuixFzXTi7fC0GG2Z2rO5xuLewFOaJnvA/u5qOVlVqPgcAhta9a4xpp5D1uBl/y9kJxjkT6b3VkUTi+sC0N3+t3Ifp/zPrSD91YJICVn7LJhWAvgduZGOmPQfTzdmQB6dY1Tx98Zk5CcLCZ3o9+O5GR3GDqSrW1WcNN1m1pe5iwCG7W1SLS0pLMn05+rrt4RTtAi3Tcgu4F3JrPE8PQV8A0bivYNG6zG7Mmk08fGXuR1l3Fzxqtp6QXwVqvPRrtdkivdh8R9FrrnjPT880eSZavEGKysHyfo5vN7PmeEqnjmvV5TnZNp5l5w9ixOy4pVyi8dkARyg4CAK3gl8twmZwIbsq5ZQaV0MMFuniwpMozBg9C+dp03oGzoOZk5xuBB1n4lydpX+jjEtkvOXFQCfsQiEWeOS7LVRNo5Vu4Atqale8BkAq5uSiiI2PaGTDPq9LF3s4Plss8KCKjpnkNSujeG53jY7w813dg5oWfNOeENHsiZoJB98osSDCC6zer9Yn9e25/J2Vk8NrvslfvxncdQVStDQgjvb1IhITuzUPYZEJqG6JYtsEpB2u931ftezwl6uPujGFbPk/RvhuysCneAxx5j5r52+TQ7g9JwMk3s31VO0MOVVZrdh8p+3nYQ0cr4c2XCpTM13bKDKMl2q2wl0lnIzmehEJnfSsgEvzNlszKZZKFxYzHuJ1cgsmEjqo8+EpGQD1JTxPosDvjhrylDe7wZqtCh6gLQVGiyChT4LREOlENOyjh4yH4YVzkS1y26M2ebYrIDHgCwM9GGj5vyBzrc2hMRhIXk/D6UNBVm1EQsGUdrrA2JVAJJM2X1oTGtoKQSDiEVtfpcpXwaAAlJMwWhajDjEZhONpBwXichAFFWjhhiUAYFgU0xZ4681PQOVses0opPRd/EWeUzUR3wIaRo+PHxe+GTVQ34ZGMUWxs7ztbripBfw/bWRuiqhJZox38jROMm2gokHza1JxHwAQmhQqSSME0TDcmdkBQFlZKcU94qzPJWREREvYZBD6IBpNAirSRJnrJCkixnyh3Y26TPvBwI7CwaY/AgSLKMyKbNzqKypKrps6lbOtiL9Ud6vh4czu3pvgCpeKxozwGg46CVlM7k0SoqOhzXrvAuxpb2FdATAQ/P/gP+/OW1dB1IL2oLXYdwZTtYzWDz7KsLY3WXiBF2yYwSj03OmNOlOYSmdhg0cRZqDcMp7WEMGewpu5Vsb4dQVCjhEGLbtlsBGtd+lUAgvSDl7WfjXhSUfQb02lqYiQTMVMrqPaKqkOzSYekFMXc99oKZHk7QQ3YW64WmYdQF3wGAdEPkTOkqoamZ3hw55VcK91yQVFeGSrpRvL0ArFVa7xEniCHL6e2yyiSly+U4C//2a2ovMmua04Tb6UPh7jOQdfa0u8RbZqFadTK4lIDdw0P2LFZLilJ0PtlZTc5zQeb4O6VmhMgJwNp9D/KdLW5nFQgjE9TMzq5xH//skoRC02AMGuR5npCkkjLQcoIemhX00KurPY9pz3M7C0LoOhKtbZkeCXb5FZ/hnGkuGzpkvz/d58R1pr8iI4l0CadYzJMJYDc0TkUjSMXi0CorEd26tejnn9BUJ4DpzhCyGlK7y70JZ87kK2dnL35n5mAmuCb7DG/2aNaxzVdaz/2cvVmiMmTdb5UZsr/r8gVhimRY2eOwnr/VE8IqFZbOPhFWhpGczjJyZxrIuo5kVs1891jtRWShKFCCQWe+yT4ry8/J3HOV0bIDIe59ZDIE8vTBCqaDHnKmMb0TGJckQHJlOKQ/f5wsBD03o9MJGKuK0wfEezBlV9Aj81mhVVZm3sd2jxf7/Zz+HSDJVvBaUlSEJ0/Gzs8+c3Zbc9SRnswMd3aUEgh4ApiesWZ93yiBAFLpkmeSLJzAglpR4XzuAa4sIQAQUtZ71O8s6ttjyA765QugAXCyvOysCitomP7dZO9PkYEonCCmWl6e8/khyXKmHJ4QMFMpb78xVXNKoLmbvDu365rzvNwZaO7n4Fx2Bd98w4Y6x8m+n79uGPx1w6BXVsKUkpDkdOaK3w/VUIB4M9RgCLIZhdA1qLLqfEYcMmw/vL9+qbUfxcC4qtGQm2KQZAW67sfYylFY2VCf91h2t/Z4BGElc4KIpGsw4wkAJlpirlJRigLTUCEpivPbNLJ5s5XMoCow4/F0lrKElOv3WAIpSGYKiq5D+PyQ5TZAliDKM79v10U3O5fjZhL/bP0Ye8tTkEISQ2uDGFZp4HAthLvnf9LtgY8xdWWAlER7KgZDLe0kr7a4ibYCKSGNbXEEwgpMACkh8E77MiyJrAAAHKo1IZL0Nq0qY6YHERFRr2HQg2gP41nALPFM9t2VfTa27PdBkhXEm5qgV1Vai4WtrQUbRUOSoIZDTl+TQuzm4dGtWxFv2ll4O1Ut2BvCJlQNcrj0zJGuEqqaaSybp79IXyiUmaEEAoht256udS95AzZa/rHnyxjpDLsESb7eKyXd384ECHX8R6290C40DclWq2mvO8jmlFtRFYj02ev5mjPnOxburAqtosIpmWOmUjCTqcwikfuxXEEPd1kYZzE0vUAr2U11JSk3WOL6fNEqrIyMJDLNkN1kvfB8t8q4+JFsb7POxvXnBhzssjvOwnz2GcI5fQbsIIm7JI3wZJwIJVNSTWQtutoLdYUo6YVckbWAKklSx+9/XbMaxLvKH1qPGXCyE6TsxUC7MXKB4JESCGRlA2RKWhW7H2A9d89Z7apacpA0e78dZYm4AytqOORtim7oTtaFVSLIgFpWlvuZIazyQUJRnMC+TdZ1mGoKMFNIJRJQggHEm5qgVZQX/Ny2FmYzPVkA+2x+Jbf8jhAwU8m8C/HZ5S6toKicGVcyCUkIBEaNLC3D0hUQ8JzNnv6OyW4ony1TNq1AacD054zdL0GomtOzA5L37Hr3/q1eNPGcfWbGl8lGMgbVeu6nBIPpHk7Ck3ljfVdlBYL86eyYPJkedokr6zNV92SzKcEAUtGYNxDjDiAVyHi0ApZq3u8DSZY9/ZXsz7PswLud0eN+TEkWUAJhQAgMPv44tK1di8TOnag4+CCEJ020ek+5MrGcz4P050hOn7E85a3scQGZ95gSCFoZcp4ePKoVjJMkq7dRVtkw9+Mi3dPDuW+Bx7VZ/UTswJ/VT0UfVOs50cB+ze0sxpzgk2uuS0LAhPe7wwkKCQEzmYQc8JbZEroONWQHmzrIvnQFU+1MV+d+QrJKF6XL1BqygqRifbdZr4d1TBVdhxyNQzJ0KOnAmAkTx487GkISaG7biSOHHgBVVqCqEqCokJICdeEhuxz0kCDBLKE/SCQRhaS6fmvIMiTn+9PbVzBVrkMJ1TpzQausRCKyzcoOicdhpsuCuoMecSQRi7UhWD4ISAFCtvpxJVUBVZYQSyQQTXmzUzfGtiKajMFQUmiXUwiGKlEpy/jldw7C2m2NuPvpzxCLd0/Nq7lnTkUkYb12Pl0ABfrruLVFgfZY/sdviiQwTDWQSKWwrnmdE/AAgPc2vefZVhUKDKVnTyQiIiKiDAY9yPHII49g3rx5RbeJ5ukLQdTfKYEgZJ8BM5W0Fk2EgBoKI97UBADpM02tWvoQEnxDhuTtzVKIHAjkLJ6p5eVQy8usev8lnB2tBAO9FoQSuoFkonWX+ob0BnsRyymPkdWfo9B9dukxdS3dM2HXAlClZE05ZTuc2vm5TccBa/HSdGUn5Iw5e5FZUz1BD/ftVuZDgWbUiQRkw0C8qSn3TGJZZMquCFdJHk1z6qRbzXYzc9jOTHDmWXYJkjzjcB5PVaGGgjDjsYILVe6+A/mOg1Vbw3W76yxv+1hIWWViJFUF0kEPd114q1dC8c8Ee4HMXS7FeewO5qXs81mLss4ipR2EKZKNkF6ML5TdpAQCucfctficL4BWiCRJJZfzyykXpVrlhkr5fNMqKz3/ln1+ayFeCOsscFXNGxywenClm1+7+gwA6cwT04SZSEJJ3y77/VCCQadsTzaR7gkBuLIDZLuRdHbQw2pKnW+eZr+PshfySy2Fl3ks63n56oZ5A3Lp7zVnmyKL0fnKoVmDywQQZd0KdLizMKw+K3ZDe59nH/ZndcFxK/lfN8nuX9RqBTc9C+7pDBM32WekF8jzv5+UsLdvm5QuyaWGM9/3ecdXYG66S45lv4ft/iPWZSXntXbG5PflBnzT/S/MZBK+oUMw5Q83Irp5C8qm7JtTDtVqBB7MuX/O+PM8BzvLwAl6hELpkk6uUoOKFchTw6Gir6HznF3vrUIBNOe5uz4LJSHgHzE8a3+Zz1jZnxvwsO/nBPjSz8NdFsw5eSM9R/WaGk+pRlnXIdeUtshctDSmbAWCk+3t1piEjFS6DJkky1AhQ5Ot46cIBcLwQUgCsqIgmUrCrxqYPfF4mIk4pKQJWchQdBWQrfHvP2Qy3lz9HlLpsdeFh2BwsAYfbvikpLEDgC4URFKFg4+29ngEkpGVGZnn+0BSZJgANkcaEEj5UeWvgGToQMSEpOsw29qRUq2AkOkOekgpROIRVBp6usd4CrKQ0Bxvg88nIRqJ5TwWAGxv245QqAxtcgIGUlAhQ1EEBtVoOOuEYfi/BWtLPhaFHD61DKOHhtDQ3ggA8BtWP6h8JL0VECmY7SG0xUy0FQp6tCcBXUMyaeJfq98u+vhhffcuIUxERLS76d8rXtSrGhoasHLlyr4eBlG3s//w1qurnT/+9Zpqa8EiZUJN19mOps+E7kzAA0iXZ0kv+kqyDLW8zCkFoHeQLWLrzawb2dDTmQX9/ytAq64C8tSxKnWRsLOcHjcFFrBKVer4rHIt3trtntvs/iLpAEnekjVZ9+soYJNvrsk+n9VAWpaRjEZzF/gUJRP0cJVysc/Ct86e31nS2cbu51eIUBRIgQCknTs9gYucbfKcXZ95nnLOAp11x0w2hbt2PmAdS/ucT6e8jiRZi+8lLlR0pcSaGg5nejLlqc+fj12uplB2U965knUGdGd09nPReRxNK/mzJnvMSsCPVDwBodnN2As8huts/JwMH3vRWos772unJFmeAJU9DtldCie9cCyU3LP+JVUB0g20c/aTZ87kCzqWSrIDEVnHM/u9VSzIZs3rPO/TdO8NwJVp5DNcx1Vy+pbl6ztVKFsCKB5gs0tIZbfeylu2zXUWfj72HHEv3suGAWEYnvKIpdJrajxlQHPG7RwPX8HP/HyBBHcGgV22yqyq8maiuF5Tu6Sfs8/s11cq3B/J7qcCIBMMcr8/FAWmphUt5ZnZV1bwsoNMj473p2RKfxV6LyoKZOczW1ivpTuQ7wpiZ2cgdSdJtvpKJSMRT7DSnWEY1K3noMkqFFeWTDLSBkCCTzXQDhN+w4AsK1AhAGGNd1CwGt8/aA4+27ICw8KDsU/tePxzVfEF9GyKpKDQAr7bc1++guXVe6EmWI3xVXuhNlid8xmSMlNYvm0VDFXHqPI6NEV2IqBagRwAEIYGU9MQS8SwcM072LZzG84bOgLlvjIkYCKRSkLRNIhIEknJyi6NpFqhqD60JlrzDQubW7dhWGA4UkggkoxAlVWoqkA8HsOUCeW4PCjjL/PqO3VMslWX62iPRxBNWoEXw8g/f+WaNVBHfQ5JAhKbRqItti/aY/kzQna0JQBFQSQWxYaWjUUfvzFaOCuciIiIul//X/GiXlNZWYmxY8cW3SYajWLt2l0/04aoL2T/UaeVl3v/nbWwUPJ+JQlywI9UJAJj6NB+n0Eh+/2Qmpp2i/JmOWU8elimxnvvlB+QDcM5WzhnMTV9drut0KKUfSZsKr2o15VglruhvVFbk3O70DSrLJYQnlr7sq4jnj5zPt60M++ccoILnViYBeCUPCkUALAbFhfZQf7xuM+KFiIr6OE+C19xtleKLOh2l0xpK6XDclg2u4xPqbJLInVGV9+LVumfrr2fMmfZ5zalLnyn/H0GZNcZ99nlgjrcpd1MPk/gR6uoQHtrW9EAnGccJT5mwfuX8Dp0VLqs4Hsqq4SZZ45IEiTFbsKep99Ike+TDjMIFAXI6plRbM50JtAn+wNW9kdWtkQp3JkIeW+zF7a7GBAE0kFBIZzSYoUeq+g4i/bWKD4vJVWBXEJJJHscUnZ5qwKPWwqhyJDsHlaFyt9JrobbRR7P3b+pJ9gBwOw+M+5SkEEt3adIyNAU63otEEQsFoWhaJDTYw/o1pzUZBkp1/5GltdhZHmd829dzj/PT9r7GPx9+T9zrm9PlRbYa4614P10BslL0r8wd9qFGBoa5NweSUTx4OInUd+4DgBwwrijMGPUodgR2YmydBNuSdMgGToe+eRZvL7qHQDA8tfX4caZv0IcKQAmkgIQwnS+bg1dAUQKrYn8PfW2tG5DLBWH31ARiUYQQgiaKhBNRhGUDAT8Cr5xUCXe+rChpOeZz6g6H9ri7YglrOCQz1cga2zIKqcCqDJ4NT7+eAyQyP961G9LYNWmGIzq/MEct31rx3dt4ERERNQl/XtljnrVnDlzMGfOnKLbrFixArNmzeqlERH1rl0JAijBEKRQqN8HPABrMamUszqp58k+w6k7n6/ZtbvBdKEzYa39+JygR6m9FwrJX0JLh5lMpfefWSRQggHoZnXBbA6bpKp5y690OBYlf3aL/VjuBaec2+UCZyFLwrP4LVylciTX/pzSWe7yKr1AKXLWeM62nVzItZsJd8WunNHtbgLflcct1MMn7/Z5moIDu/b5bve4yLfYLxsG1IqKkrJzgF3PUiuWUWErdrwkVSkYGBUFykZZdxSZUnydLM/SUf8ooaroTLX+zgTs7OPV3d/N7r43u1KuRuh6OqNsF+ZnVkk3z/6LfEYC6UyPEsdv9/1w/t1BeauOdPbzS8oqWeimlpf1aNDDDvpmZw+632uKa2y+dN8GWdUg/D4YiuH02/ApBoQkIIRAqkAmEQDoBXo/TBk0EUs2fob1zZs91yfNzve8SJkpvLX6fZy9z8nOdc99+Q8n4AEA7679GDNGHYr2eLsnECNCQbz+4TvOvzc0b8bHG5ehVrKOVVyYkBUTqXRAz6fLSKQSiKbyNybf0roNQgL8hoKWtnakzBRMKQZJwAkKTptaidHDA2hvN7FpWztWrWvFuk2RvPvLts9oHyorjHSzduu1KC/PM2ekFIThHaPwNSPVnD9zuy0GPP3KZsi1q6GNKj6G/YfsU9JYiYiIqHv0/9N8iYh2A4rft0tne/Y2taJrWS3UvZwSKZKUv1muu25/kQUd922F6t3vCqFpRUvKZJeOyhlfui9AZ0lqgf4D9riK9QUR+ftISLIr6JGV6SH7DFcPh0ymx672iumMziwEFguE5dOZPh7daVfKxRXKsCimu18vuyRVobmoV1V2mM3QfWPp+Fh2mOlRMDhZrFxc/mBSKUoJCvTUe6ynTkTorkCoSPdPKZbpUYpCr01HASdr4b70uevpgbSL5a06TWQCb9nyNUHvTnYZU/d3nNXoPv+x86nWZ1aZHsKI6hEIGyGnNJQsZAi7L5YQkAoEvAwlT58NWBkllb7yXXg2Xp9vWYF40sp8iCfjWLzxM8/tjZGdSKUDKk3RZgCAaZpoS+X25miKNFsZmLKMOJKQlUwWkaYqSJlJqHr+ElwbmjdBliVo6de4PdmGtmQrBCQI12dIbZWOkXUGpk2twBknDENFuPgcP2ByGc4+pRYzjghDVmS4m7UfsE9lzvaSmpsxo0/8AJLelnf/ItgAffI70EZ9UXQcqfYAZu41veg2RERE1L0Y9CAi2gPtDhkpexJ37fyu3t9e8NnVTI+8+9e1giVlAPsMXKng4qakdi3oITSt6IJp0RI4BY6Du7yVFfRwlWuRJKjpfg/29UIrnG3SEzqzwN/Z93GvLlB24+N2doG5u98DHfXJAXbtbP/OKOV9VLSpeIHeQEDx+SQVCZbsiborSCPret7yf50eT6HsnU5kSZXCM3d6OehhBVn67rdLdrkzSZIKBpK1dINyXdGgyioUIUOWBIQk53xWKAWyV/KVtwpoAchCYMaoQ7v6NHJEkzG8tOJfSJkpNMfyl2hqjVkL/qaZQkN7I25e9Bf89xv/m7Nd0kwCsoAIBtAWa0dbstm5zY7r2YGTbG3xCFY1WmWlhABaEs1IIQlJSMiOCUaTUXy85SN82bgMc2YPxTnHj8ZVFxyE3112ACbslTlxQJKA6tHb8ffNj+D/Lf8b3l33gWc/5WEN3/n2CAjXA0ha/swRdfSynOtEeBu0iR9ABDru1RFfMwGPv7rSye4lIiKinsdVLyIioj5mDBmyS4umkixDq65CdPOWHmlQL8ly0TPMrcWgwo8rtE70ZMi6X0fjKnhboeMgZRqFWzXZsxrI+7zNfnuyZAqVprvLKXVWX2XIdFWxBfmiAZFiWVUMlPcIoWlWs/RdKG8FFH7tejJLLTvzoadJQhTNRurxx8/T46Uz32tCiLwBDlnITqaFm5Yn0yOUbpZeVzYE3xo7AwtXvlHy4xfzztqPsK1tB44efVje23dGWxDSrWDCs18sRGMk/yJ/IpWApKoQFTpiyVje6dEWz1/eCgA+2PAJJtSMhSwLyEKCEJLTW0MSgJkCIAHPr1qAzW1Wea/tVQ04cfSJkKUUdsZ24sSjBmFIrYEt26OYMr4cr+14zArGAHh99VsYVlaNfQZlemvsP7ECflXHi4vWYd2mdiBPpgcAyOEGQI4Dycx7Sq5ZB0kqLYhhRn14/JXlGFIVwLEHjyjpPkRERLRreMoWERFRH+uOs8TVUAhKKNhjZ5x3tLhTbFG0q4vGuxQIKlTuJasZbvaioNA0K2slfX/BoMdup7sX6PsqQ6Yn9FZGCpXO+kzqmT/JejLoIRSld+eT1MvltPLYleNp9/HIpoj8n1d+JTfrL6RlshiOHn0Y6sKDuzyebMu3f42XVrye97bmqJUB8vaaD7F8+6qC+2iPRzucy+3xwj04Pt+yHBubt0ARAoosICRATmdhCEkCJKAt2ewEPABg2fZPEUlEsDNmBWKEkHDgPuU4YcYgVFWbaI57M0teWvEvmKaJnZFmvLv2Y6xt3oCKCoGjDrV6dhTK9AAA4fcGe4ReOICTzYxZvyXWbMqf6UJERETdj0EPIiKiAUKvru6zxy7aD6APFqoKltrqoCSLJEmQDcO5f2/1aiCiPZdUoFdFf9bbn+uSLPo846ijxvDFyJIMRco9ZvmyP4QkozqQ228irHt7PhVqdl6KMyef5JThsq1p2pB3253RFqxsqMfz/3m16D7bE7kBA9M0kUwlnbJOkUT+TArA6rbxRv2/IWSr8TkAKOl5JkmAIiTEUrn3jyWt/iK65j2Wpprbh2N7+w58vnUFbn3nr3juy3/gng8extdNX6MibL22RYMewSYAJmorjbzbJpvLkWysRmJLXe6dU9bcrastvW8XERER7RrmqpPjkUcewbx584puE40W/qFKRER9qy/Pgt2dggMdLTDKfn9mW54ZT0Q9rCfKEg40/eE7Zld6pMiSgJwnwJEvEBLSA0imcrMMy31hz7+NXQh67Dd4EurCg/Hn9x9GNJnblNytOdaCbW0NHe4zkhX0aIm14rFPF2Blw2qMqRyJC6eekRMYGV89Bv/Z9pXz7693rIE2VoGhKTBNE/9pWI6tbVtR5x+NQf4aJMzcsbYmWlFmhBH0q4jGks71jbHGvON8eOl8z7+/2PE5Ro4cCVURQJGghzp8OYLD18PwG/C1bc+5PfblIYApIBmtUGrXOden2jKBjsl7VRXcPxEREXUv/sImR0NDA1auXNnXwyAiot2QUHqujEp36+hs4c40Eyci2lV9XbZpd9CTpbpKtSv9goQkIOfp3aLIClRZhSxkRNKln3yqAZFn2wqjzPPvfM3OS6UIGYOCNfjm2G/ghf+8VnTb5mhLh4ERwFu6KplK4f99/AQ2NFulqL5qWI0P1i9FJO49gW7KoAmeoMfOaDPuW/I3XHLAOVjZsApPfv4CAMCnLMElU87Pm+nRlmjFIK0Squw9Zqub1uVsm8+29m0AgJFD/VhToKeHLYpWbG3LbfhuxjTAtB7fjASQ2D4YStUmmCkJiQ1jnO2GVAdKGhMRERHtOgY9yFFZWYmxY8cW3SYajWLt2rW9NCIiItpd9GWD2c7q6KxqWe/62bNERJ3V12WbqDS7kvlnNTLPfZ39qg/+Mh/iyTjWpstL6bKWN+gR1kOef29r29Hl8dgOGjoFr6x8q2hQ4711izG6YniH+9oR2Ym313yIoBaABDgBD9vXO9bkZHqUG2EYiu4pe7WpZSte/eot1Detd65rT7Tj022fOT0+3J5e+TSuOOhSCDmTHbO6pR4fbfikwzEDQFOsCbFkDEdNq8H/LS+c6VGMGfdm5gyNHIo1n20G4irMmJU9OmP/OmaPEhER9SL+wibHnDlzMGfOnKLbrFixArNmzeqlERER0e6iP5yFWyqWkiGi/oSZHnsGVS78PanKqhMUyVcGCwACmt/z772rRmONKzBQqgOH7utcNhS9pGyPVTs6PultxfZVWFGk0fnapo052S4+1UC5Ecamlq2e6/+9fknO/d9Y83bBfS9a9x7OrjgJkgDMFLBky+IOx+vWEGnAPiNHQlkTQ9Ls1F0BZBqVA8BB+5bjqGnVSKWG4e//2oz/fN2CqjID5xy3d+d3TERERF3GRuZERES0yySx+/yk4FnVRETU21S5+HePTzWK9ukYUTYUYSMETdFgKDr2HTS+U48vS1ZJq6NHHea5fvqIg3HY8AM6ta+u2Bltxo5Ik/NvCRIqjDKUG+Ei9yrN4k2fArCanUsSsCPa2Kn7v7J2IdY2r0PSzPQE0WQNmiithJg76OHTrd9DQkiYdfQg/Og7o/E/PzgCdbWhQncnIiKiHsC/+omIiIiIiIh6UL6SVW5VvgrPovs5+56Cxz5dAACYWD0WI8qHObclkglEElFcd/TP8Nbq9/Ha14WzIADg7H1mYergyQXLKx05chreXftxqU+lWxw6/AAEND8qfGUdb1yCf616D8s2r8C4yr3QGs/tu1HMzlgz/t/ixz3XhbQgYok4YqmO+5m4gx4BwypPljJTkCQJAZ9qNUknIiKiXsWgBxEREREREVEfEkJAuAoxnDrxeNT4K7GldTuOGX2EZ1tFViAkgXFVo2EoeodBj6AWLNpPotwIo0wPoSnaDMDqNXLEiIPwyldv7cIzKu6EcUdDlVUEVH/HG5fg5ZX/AgCsa/aW/PKrPqhCcZ5bqcJ6EM1mG5rjHd/PjPqcy1WhEHyKD63xVkiQOgx2ERERUc/gNzARERERERFRPyJJEvYZNAH7D5kMQ80te+VTDciS1f+jwiieLRHSAh0+1skTZsJQdChCxrn7noIjRx7SbQGJfEaUDYUmq0V7nXSHCiOMUSU0Ys9Wpoegy4XLjbmlWjMlulShwqdYQZCg4St5H0RERNS9GPQgIiIiIiIi6mdEOjsjX3Nzn+qDSPfTOm3S8U6T8HzlokJ60LVP2Wma7rZP7Xj8ZsYV+O9jfoFvjTsKumL0WK8PQ9Hh13xQZRWTa3u2wXfYCGFYaFCn71fmC8NQjI43BGBGM8GhyjIDWjqQE9ANhDT28iAiIuoLDHoQERERERER9TNSOpAh5ymR5FetbAK7zNXPDvseLj3wXNxy3K9ztg3pVqZHQPNjVEUdagNVeR9PEYrTcF0WoluCHqrIzeSwM1M0WUW1vwLfGjsDfjV/gOGY0Yfj/zv8Uhw/dkaXHr9MD2NC9RjPdbWBaiegVEiFEe5Eloa1r2G1PtTVBBH06VBlBX5Ng5InYEVEREQ9j0EPIiIiIiIion7GWpiX8mZ62Ivpds+I6kAlJtaMQ9iV1WELadZ1dikpQzUgCxlCyt2vnT0iSwIBzY8DhuyT93FLdeDQfXOus7NRNFkDABw9+jBce9RPcc03fuQpqTWibChmjJqG2kAVDqnbv1OPayszQqgNVjvPQwJw7F6HI2WaRe9XHaiEr4RMj5Hh4TjvpFE4YUYtrjjXahZv6DIMVYWuqVBkgQ7iK0RERNQD2MicHI888gjmzZtXdJtoNNpLoyEiIiIiItpzCQjIovh5iu4G5ZIkQZIkHDlyGt5c/W8AwOEjDoKuaEAUUFxBjtpANZKpJLa0bvPsz+4TYgdaThh3FFY3rsP29kb4VR++MfIQLFz5Rknjr/ZX4PixR+K9dR97ri8zrJJPipBhKDoiCetvzLARwpVHXIa1TesR0oMYEqx1np9fNbBP7Xgs2/Kfkh7b5lN0qLKKMyefhCNGHISA6kO5rwyPfrqg6P2Gh4fgK2N90W0A4Kx9TkG5OQzbY9tQEfSjrRXwaQoMTYOhKojJSWgKsz2IiIh6G4Me5GhoaMDKlSv7ehhERERERER7PEmSnCBEIcJV+soOEFx+yPkYUzkSLbFWnDrpBMQSMQDw9PLwqQaSqWTO/mSnpJb1uCE9iCumXYQNLVswNDQIm1u25dznhHFHIZaMQxEK1jZtwOdbVyCsB3HelNkw8pStco+5zAgj0rLV+bdfNTA+qxyV7Zx9T8GSTZ9BEQoMWceDS5703K7JGnRZQ3OsxbluRHkdagPVWL9zI4aFBzvXDy8birVNG/I+zoFDpqCubAjKfcX7cZwy9iRMrBmDTVujqPVXIaAbaG+LQtdk+DUdPlVDmxyFprLABhERUW9j0IMclZWVGDt2bNFtotEo1q5d20sjIiIiIiIi2jMJSTjlpoptk31ZFjIOG3EgmiI7oQgZiXQwJLs0lZyVaeHeh/txDdXAXhUjAEjp/3tV+ys9DclbY20wFD1vWS4AKDcyzdYDmh9BLYCWWGvR52mP/6ChU6ApGtpjEQwJ1mJjyxYAwH6DJ+HEcUchkojiLx/8HyKJKKYOnoxRFXXQFQ1CkpEyM0GeY0cfkRM0sV184FlQZRVBvXh5qymDJ8BQVQBRaIoKv64hHLCCT0HDgKooUOQ4NJWZHkRERL2NQQ9yzJkzB3PmzCm6zYoVKzBr1qxeGhEREREREdGeSZIkT0mqfIQT0FAgkCl1JZDJErF7eeQLQgwJDcL2th3YGW227icKN0+v8JXl7RkiZTWtCGh+z79njDoUb9S/Z20LCUeMOChnv6UEPWyyJENTVPzwkAuwunEdqvwVTp+QMgC/nfFjNMdaUW6EoaazWxRZRiyRCXrsXT0Gs/Y+Fi8sf82z76AWgCxkaEKFpuY2YXcL+XUosgJZliALAUUWKA9ZfUrK/VbzeE2VocjM9CAiIupt/PYlIiIiIiIi6mcEpJIyPRShIKQHPcEHIQknyCELGXqBzAtJklAdqISh6Na2WeWt3BQh591HLBEvOD5JEpgxahr2HzIZw0KDcfrkEzAoWJ21386diykkq7m7KisYWzXKCXjYZCGj3Ah79u1+DCEJDA5VY/rIg3P2bSi6FUASAsNCg3Nut00ZNME5VqoiIITVT0VN9++wAx1+g+eZEhER9QV+AxMRERERERH1M0ISJfX0KDNCEJJAykw511v9QDIBk5AWKLqfcqMMm1q2eEpkZbOv22/wJCzd9Ln1OJAwpjK35BUAaIqGsBaEaaZw9j4ne8bsJkkSFKEgkUoUHaMzDklGSjJL2tbO9FBdQY+QHkRA9UPKk82iy5pTBmxS7TgMCw3G+uZNWY8vcMSIg5ztVEV2Mm6yMcuDiIiobzDoQURERERERNTPSOmMhmIqfGUQkkB7PIK4K2iQfd9gB0EPv+aDIpRMSSyh5ARS7NtOn3QiVjeuQ1NkJ2ZP/BZCeUpeAUBA9UNTtJzrs4MeAKDI+YMehmogEo/k3F8Wck6fjlySK9MjcywCmh+SJEFXNEyoHoMvt33l3DZ1yCSnpJiQBP7r2Cvx3BcLUeErQ114CP6z7SuMLK9DbaDKOb6aIlBaCIaIiIh6C4MeRERERERERP1MaT09rACCImRvU3N4Mz06KpMFWAEGeztFVjA4WIMNzZud2+1F/rGVI/HL6T+AX/Wh0lfu2cbN7o1RaMxu+UpcSZJAuR7GpuyghxCQUyYMVUdbrK3g8xkUrIaW7mfiLnNll/IyFB1HjToUK7bXI2kmEdZDOGTYVE+wKKwHccSIg+FTDbTH23HwsP3gU31oj7c7+1QVgUSSYQ8iIqL+hEEPIiIiIiIion5IkUv7k11JZ2bYhCQgOsgSyeZTdM8+DNWALGQkU1Y2hR1EEUK4si28AQz39oqQ09tmMjKsHiS541KFgnKjDI2RJuc6XVZhqDoACXDlUsiSAIS1/2JBD7/qc/qc2AEKO+ABAJpQMapiOH562MXY1LIVYytH5e19IiQBVSiISAKmmYImq2iPtzvbKYqAiRSIiIio/2CBSSIiIiIiIqJ+KF+AIB9Jkjx9K0rJEsnmUwxPdghgBQ4Aa+HfDiBYl63/3EEPXdExLDzENXZrPJqswApcAFX+irwlu1RZQbkR9pTDMhQDQhLpwIfVI0RIcrrXiYChGM5+swlJ9jR2t46N5Nm/fWxrAlXYd9AE+FQj7/EWkuQEPqzno7r2CWiKDFnkHwcRERH1DQY9iIiIiIiIiPqhfKWgClHlTCkpSRIllbRyU2QlJyARUP0Achuby5KAgORkfQASNFn1lNmyAwjlRhnC6b4fuivTIvtxhBCe3iN+1Uj/3wdJEhgcrIWmqJAlAUUoUGXFE6TwpQM01nPJytYQAoaiQZfdQY/cLJp8ARkhrGPpNC6XVef5WrdLUJXOBZiIiIioZzHoQURERERERLSbcwcARBcyPfLxaz5oipaTASILK5NCSAIVvjJoiuoEXVRZtcprpe/j13yZUlAFxmRnZZTpIWiKBk3WYNhBD8VAQPVBETJ0WYOw/58V2Kkwws7lfI/jV32eoEemEXnudW72c7GDJLIkoMmqJ8ikKlxaISIi6k/Y04OIiIiIiIhoABHpvhfdodJXjuZoi+c6O+gBAGE9hHgqkSn7JKswTTNne6QzQ4qRJAlDQ4OQSPcFAaygRHk6oKHZAZX0fuR0cEMRCgzVgCIUJFKJvFkcQT3oeXxJkiALGX7Fh1gilt5PoaCH5PRXEZKALx2QISIiov6JQQ8iIiIiIiKiAaYzpbGK8as+iKzeGYqQneskSYIh69CEFfTQhIKkSHq2lws0MM9HSAKa7B27nY2hy96sEzndW8TuPVIbqEIkEYU35JIZczZN1qC7+3zkyRDJZHqkH1PIDHoQERH1cwx6kOORRx7BvHnzim4TjUZ7aTRERERERETUHxhZi/yKUDxhEMPVBNxQDRhZvTtkIXe6x0g+7lJU9n4rjDDKfWWZccgq2uPtJe3Prxqeklb5y1tZZbwUKdOvxKcw6EFERNSfMehBjoaGBqxcubKvh0FERERERET9mCJkTwkrdxZFdsADSGd6dEOPkXz7dTcwt8diN2DviF/1QUqHb4J6sGBPD1kSVuAmHfSwS3sRERFR/8SgBzkqKysxduzYottEo1GsXbu2l0ZERERERERE/Y3dO6NUVqZH9wc9VFn1lKeylZpV4u4/UuUrL9rIXEgi72MRERFR/8OgBznmzJmDOXPmFN1mxYoVmDVrVi+NiIiIiIiIiPobRShIpVIlby8kATVPc/Fd5c/K8ugKSZKgyVregAeQCXoAgJ4ni4WIiIj6n+7pbEZEREREREREewRFyBCdLPGkyWq3j6O7ykwVa0yuSLLzOPlKdxEREVH/w6AHEREREREREXWK2skgRk8EPbpL0aCHKwNEl1neioiIaHfAoAcRERERERERdUqhclCFdDZI0puKZXAorrJcbGBORES0e2DQg4iIiIiIiIj2WHbPjry3ldgUnYiIiPoPfnsTEREREREREREREdGAwKAHERERERERERERERENCAx6EBERERERERERERHRgMCgBxERERERERERERERDQgMehARERERERERERER0YDAoAcREREREREREREREQ0IDHoQEREREREREREREdGAwKAHERERERERERERERENCAx6EBERERERERERERHRgMCgBxERERERERERERERDQhKXw+A+o9HHnkE8+bNK7pNNBrtpdEQEREREREREREREXUOgx7kaGhowMqVK/t6GEREREREREREREREXcKgBzkqKysxduzYottEo1GsXbu2l0ZERERERERERERERFQ6Bj3IMWfOHMyZM6foNitWrMCsWbN6aURERERERERERERERKVjI3MiIiIiIiIiIiIiIhoQGPQgIiIiIiIiIiIiIqIBgUEPIiIiIiIiIiIiIiIaEBj0ICIiIiIiIiIiIiKiAYFBDyIiIiIiIiIiIiIiGhAY9CAiIiIiIiIiIiIiogGBQQ8iIiIiIiIiIiIiIhoQGPQgIiIiIiIiIiIiIqIBgUEPIiIiIiIiIiIiIiIaEBj0ICIiIiIiIiIiIiKiAYFBDyIiIiIiIiIiIiIiGhAY9CAiIiIiIiIiIiIiogGBQQ8iIiIiIiIiIiIiIhoQGPQgIiIiIiIiIiIiIqIBgUEPIiIiIiIiIiIiIiIaEBj0ICIiIiIiIiIiIiKiAYFBDyIiIiIiIiIiIiIiGhCUvh4A7V5isZjn36tXr+6jkRARERERERFRd8v+Oz97HYCIiKi/Y9CDOmXjxo2ef//oRz/qo5EQERERERERUU/buHEjJk+e3NfDICIiKhnLWxERERERERERERER0YDAoAcREREREREREREREQ0IkmmaZl8PgnYfO3fuxPvvv+/8e8iQIdA0LWe7yy+/HGvXrsXw4cPxl7/8pVvHsKv77sr9S71PKdt1tE2h2/Ndv3r1ak+JsbvuugsjR44s6Tn1Fs6Frs2Fzt7W3+dCf54HXdlHZ7bv6nu+lNs5F7p33/35M6HY7bvjPAA4F/j9YOnP86Ar++D3Q9f157nQnz8Tit2+O84DgHOhP3w/xGIxT2nrQw45BOFwuKTnS0RE1B+wpwd1SjgcxsyZMzvcTtd15//jxo3r1jHs6r67cv9S71PKdh1tU+j2UvY9cuTIbj/eu4pzoWtzoau32frbXOjP86Ar++jM9l19z5dyO+dC9+67P38mFLt9d5wHAOcCvx8s/XkedGUf/H7ouv48F/rzZ0Kx23fHeQBwLvSX7wf28CAiot0Zy1sREREREREREREREdGAwKAHERERERERERERERENCAx6EBERERERERERERHRgMCgBxERERERERERERERDQgMehARERERERERERER0YDAoAcREREREREREREREQ0IDHoQEREREREREREREdGAoPT1AGhgOu+889DQ0IDKysp+t++u3L/U+5SyXUfbFLq9J49pT+Jc6Npc6Opt/VV/ngdd2Udntu/qe76U2zkXunff/fkzodjtu+M8ADgX+P1g6c/zoCv74PdD1/XnudCfPxOK3b47zgOAc4HfD0RERLtOMk3T7OtBEFHXrFixArNmzXL+/cILL2DcuHF9OCLqK5wLZONcIIDzgDI4F8jGuUAA5wFlcC4QEdFAxvJWREREREREREREREQ0IDDoQUREREREREREREREAwKDHkRERERERERERERENCCwkTnRbqyyshJz5871/Jv2TJwLZONcIIDzgDI4F8jGuUAA5wFlcC4QEdFAxkbmREREREREREREREQ0ILC8FRERERERERERERERDQgMehARERERERERERER0YDAoAcREREREREREREREQ0IDHoQEREREREREREREdGAwKAHERERERERERERERENCAx6EBERERERERERERHRgMCgBxHl9dprr+E73/kODjjgAEybNg3nn38+3n777b4eFvWim2++GePHj8/737e//e2+Hh71oWeeeQbjx4/Hn//8574eCvWyhoYG/M///A+OO+44TJkyBccddxxuu+02tLW19fXQqBdt27YN1113HY4++mjss88+mDZtGubOnYsvvviir4dGfSyVSuGss87CRRdd1NdDoR5gmiYef/xxnHLKKZg6dSqmT5+Oa6+9Fg0NDX09NOoH+P4nIqL+ROnrARBR//PXv/4Vf/zjH1FbW4vTTz8dkUgEf//733HxxRfjjjvuwHHHHdfXQ6Re8OWXX0LTNFx22WU5t1VXV/fBiKg/2Lx5M2644Ya+Hgb1gaamJpx77rmor6/HkUceiZkzZ+Kzzz7D3XffjTfeeAPz5s2D3+/v62FSD9u8eTPOOussbNq0CdOmTcPxxx+PDRs24JVXXsEbb7yB++67D9OmTevrYVIfue6667B06VIcdthhfT0U6gE333wzHnjgAUyaNAnf+c53sGrVKjz++ON499138eSTT6K8vLyvh0h9iO9/IiLqTxj0ICKPL7/8ErfddhsmTpyIBx980Pnj5ZJLLsHs2bPx+9//nkGPPcSXX36JsWPH4oorrujroVA/8tvf/hY7d+7s62FQH7jjjjtQX1+PX/3qV7j44oud62+55Rbcf//9mDdvHr73ve/14QipN9x+++3YtGkTfvGLX3he7/feew/f/e53ce2112LhwoV9OELqCy0tLbjmmmv42g9gy5YtwwMPPIAjjjgCf/3rXyHLMgDgkUcewfXXX4+77roLv/71r/t4lNQX+P4nIqL+iOWtiMjj4YcfRjKZxPXXX+85W2vUqFG44oorcNRRR6GpqanvBki9YsuWLWhoaMD48eP7eijUjzz55JN44403cPTRR/f1UKgPbNiwAYMGDcJ3vvMdz/WnnHIKAODjjz/ui2FRLzJNE6+88goqKys9gS8AOPTQQzFt2jTU19fj66+/7qMRUl948cUXcfzxx2PhwoU48sgj+3o41EMefvhhAMDcuXOdgAcAnHfeeRgxYgSeeeYZxGKxvhoe9RG+/4mIqL9ipgcRebzxxhsYNmwYpkyZknPbJZdc0gcjor7w5ZdfAgCDHuTYuHEjbrrpJpxwwgk48sgj8frrr/f1kKiXFerhYi9ws+zdwBePxzF37lyoqgohcs+d0jQNANjjZQ/z6KOPQpIk3HrrrZg6dSqOPfbYvh4S9YD3338fPp8P++23n+d6SZJw6KGH4oknnsDnn3+OqVOn9s0AqU/w/U9ERP0Vgx5E5GhoaMDWrVtx9NFHY/369bj99tvx1ltvob29Hfvuuy9+/OMf45BDDunrYVIvsIMeO3bswCWXXIJly5YhkUjggAMOwBVXXJE3KEYD229+8xuoqoprr70W//rXv/p6ONQPNDQ04M0338SNN96IQCCACy+8sK+HRD1M07SCDWobGhrw4YcfQlVVjBo1qlfHRX3rhz/8Ifbff38YhoF169b19XCoB8RiMWzYsAFjx471ZHnYhg8fDsAKgjPosWfh+5+IiPorlrciIseWLVsAAI2NjTj99NPx6aefYtasWZg5cyaWLFmC7373u3j11Vf7eJTUG+ygx3333QfDMHD66afj4IMPxqJFi3DeeefxLP89zGOPPYZFixbh2muvRWVlZV8Ph/qBe++9F4cddhh+9atfIRaL4d5778WYMWP6eljUh2644Qa0trbi5JNPRjAY7OvhUC867LDDYBhGXw+DelBjYyMAIBwO573dfs+z59eeh+9/IiLqr5jpQTTAHXPMMVi/fn3RbSZMmIDnnnsOra2tAIDFixfjqKOOwh133OGUqpgzZw7OP/98/PrXv8bhhx8Ov9/f42On7tOZeQAAqqpi2LBhuOmmmzzZPYsWLcKll16Kq6++Gq+99hoCgUCPjpu6X2fnwvr163HLLbfgm9/8Jk488cTeGCL1ks7OBbfa2lpcfPHFWL9+PV599VVceumluPPOO3HEEUf01HCph+zKPLD96U9/wvPPP4/BgwfjF7/4RXcPkXpRd8wHGngSiQSATAm7bPb17OlBRERE/QWDHkQD3PDhwwv+gWKrq6sDAE997t/85jee+x1wwAGYNWsWnn32WbzzzjuYOXNmzwyYekRn5gEA3HzzzXm3mT59Ok466SQ8//zzePvtt3Hcccd16zip53VmLpimiWuuuQaqquJ3v/tdbwyPelFnPxfcZs+e7Vx+7733cPHFF+OXv/wlXnvtNZ7xuZvZlXmQSqVwww034OGHH0Z5eTnuvfdeZoPt5nZlPtDAZX+ux+PxvLfbwQ6eFEVERET9BYMeRAPc3/72t5K3DYVCAIDy8nKnNq/bpEmT8Oyzz2LNmjXdNj7qHZ2ZBx3Zd9998fzzz2Pt2rXdtk/qPZ2ZC/PmzcN7772HW265BTU1NT04KuoL3fW5cOihh2LmzJlYuHAhli5dimnTpnXLfql3dHUeRCIR/PznP8crr7yCmpoa3H///Rg/fnw3j456W3f+XqCBIxgMQgiB5ubmvLe3tLQ42xERERH1B+zpQUSOESNGQFGUgmdx2dfzLN6BLRaL4ZNPPsEnn3yS9/ZIJAKA82BP8PLLLwMAfvnLX2L8+PHOf1dffTUA4H//938xfvx4zJ8/vy+HSb0gFovh7bffxttvv5339mHDhgEAduzY0ZvDoj7S1NSEiy66CK+88gpGjx6Nxx57jAEPogFM0zQMHz4c69atQyqVyrndPiFq7NixvT00IiIioryY6UFEDk3TMGXKFHz88cdYsmQJpk6d6rn9008/BWDVcqaBq7W1FWeddRbKy8vxzjvveMqeAcAHH3wAwMr4oIHt1FNP9fR0sX3xxRd47bXXMG3aNBx88MGYOHFiH4yOelMikcCll16KsrIyLFq0CLIse27/4osvAAAjR47si+FRL4pEIrjsssuc3wl33303Kioq+npYRNTDDjzwQMyfPx/Lli3DlClTnOtN08S///1vBAIB/o1ARERE/QYzPYjI49xzzwUA3HTTTWhvb3euf/fdd/GPf/wDY8eOxf77799Xw6NeUFFRgcMOOww7duzAPffc47ntueeew1tvvYWpU6d6/uClgem0007DFVdckfOf3dPn0EMPxRVXXMGgxx7A7/fjmGOOQUNDA+6//37Pbc8++yzeffddTJ48mQtee4Cbb74ZS5YswZQpU/DAAw8w4EG0hzj99NMBALfeeqsnK3zevHlYs2YNzjrrLCgKz6kkIiKi/oG/Soj6SENDA0444QQ0Njbik08+ga7rRbePRCJ46KGH8PLLL2PVqlUArEaSxx13HC644AKUlZV1y7hOOeUUvPXWW1iwYAFOOukkfPOb38S2bduwcOFCGIaBG264AZIkdctjUf+dB9deey3OPfdc3H777XjvvfcwadIkrFixAm+99RZqampwyy23dMvjUEZ/nQvU+/rrXLjmmmuwdOlS3HrrrXjvvfcwfvx453Ohuroaf/zjH/n90I364zxYt24dHn/8cQBWGZvsAJjt9NNPx9ChQ3f58SijP84H6h96a24cdNBBOOOMM/DUU0/h1FNPxVFHHYX6+nq88sor2GuvvXD55Zd3+3OjruHnBREREYMeRH0ilUrhd7/7HRobG0vafvPmzbj44ouxcuVKz/XLly/H8uXL8fTTT+Puu+/utjNsb775Zhx88MF47LHH8Oijjzpn+M6dOxd77713tzwG9e95MHr0aDzzzDO444478Oabb+Kjjz5CZWUlzj77bFxxxRVsat3N+vNcoN7Vn+fC0KFDMX/+fNxxxx14/fXX8f7776OqqgrnnHMOfvjDH2LQoEG7/Bhk6a/z4KOPPkIymQSAor18DjvsMAY9ulF/nQ/U93p7blx//fUYM2YMnnzySfztb39DdXU1zjnnHPz4xz/mwng/wc8LIiIii2SaptnXgyDa0/zud7/DY4895vy72Bk4iUQCZ599NpYtWwZJknDWWWfhhBNOgCzLePXVV/F///d/SCaTGDZsGJ555hn+wbEb4TwgG+cC2TgXCOA8IC/OByqEc4OycU4QERFZmOlB1Iva29tx9dVX46WXXir5Pk888QSWLVsGALjqqqtw0UUXObcdcsgh2H///fGzn/0M69evx3333Ycrr7yyu4dN3YzzgGycC2TjXCCA84C8OB+oEM4NysY5QURE5MVG5kS95KOPPsJZZ53l/BAVorS338MPPwwAGDVqFC644IKc20844QQcc8wxAKxGgrFYrJtGTD2B84BsnAtk41wggPOAvDgfqBDODcrGOUFERJSLQQ+iXvCHP/wB5513HpYvXw4AOO2003DiiSd2eL+vvvoKX3/9NQDgpJNOKvgD9tRTTwUAtLS04N133+2mUVN34zwgG+cC2TgXCOA8IC/OByqEc4OycU4QERHlx6AHUS/45JNPAACVlZX405/+hBtvvBGqqnZ4v8WLFzuXDz744ILbHXjggc7lf//737swUupJnAdk41wgG+cCAZwH5MX5QIVwblA2zgkiIqL82NODqBeEw2F8//vfx2WXXYZgMFjy/b766ivn8siRIwtuV1lZiUAggNbWVs99qH/hPCAb5wLZOBcI4DwgL84HKoRzg7JxThAREeXHoAdRL7jjjjtKrq3qtmXLFgBWXdZBgwYV3ba2tharVq1y7kP9D+cB2TgXyMa5QADnAXlxPlAhnBuUjXOCiIgoP5a3IuoFXfkhCgA7d+4EABiGAVmWi27r9/s996H+h/OAbJwLZONcIIDzgLw4H6gQzg3KxjlBRESUH4MeRP1YLBYDAGia1uG2uq577kMDB+cB2TgXyMa5QADnAXlxPlAhnBuUjXOCiIgGOgY9iPox+8wdSZI63NY0Tc99aODgPCAb5wLZOBcI4DwgL84HKoRzg7JxThAR0UDHby2ifsxOJY5Gox1u25mzdWj3wnlANs4FsnEuEMB5QF6cD1QI5wZl45wgIqKBjkEPon4sEAgAsH6MplKpotu2tbUBAMLhcI+Pi3oX5wHZOBfIxrlAAOcBeXE+UCGcG5SNc4KIiAY6Bj2I+rGhQ4cCAJLJJLZt21Z02y1btgAAamtre3xc1Ls4D8jGuUA2zgUCOA/Ii/OBCuHcoGycE0RENNAx6EHUj40ZM8a5vGbNmoLbNTQ0oLW1FQAwduzYHh8X9S7OA7JxLpCNc4EAzgPy4nygQjg3KBvnBBERDXQMehD1Y/vtt59z+eOPPy643UcffeRc3n///Xt0TNT7OA/IxrlANs4FAjgPyIvzgQrh3KBsnBNERDTQMehB1I+NGDEC48ePBwAsWLAApmnm3e6ZZ54BYNVmPeyww3ptfNQ7OA/IxrlANs4FAjgPyIvzgQrh3KBsnBNERDTQMehB1M+dd955AIAVK1bgnnvuybn95Zdfxj//+U8AwJlnngmfz9er46PewXlANs4FsnEuEMB5QF6cD1QI5wZl45wgIqKBTOnrARBRcWeddRaeeOIJfPbZZ7jtttvw1Vdf4dRTT4Wqqnjttdfw0EMPwTRNDB48GJdffnlfD5d6COcB2TgXyMa5QADnAXlxPlAhnBuUjXOCiIgGMgY9iPo5IQTuvvtufPe738XKlSuxYMECLFiwwLNNTU0N7r33XpSXl/fNIKnHcR6QjXOBbJwLBHAekBfnAxXCuUHZOCeIiGggY9CDaDdQW1uL+fPn4+GHH8aLL76I+vp6xONx1NXV4dhjj8XFF1+MysrKvh4m9TDOA7JxLpCNc4EAzgPy4nygQjg3KBvnBBERDVSSWahjFRERERERERERERER0W6EjcyJiIiIiIiIiIiIiGhAYNCDiIiIiIiIiIiIiIgGBAY9iIiIiIiIiIiIiIhoQGDQg4iIiIiIiIiIiIiIBgQGPYiIiIiIiIiIiIiIaEBg0IOIiIiIiIiIiIiIiAYEBj2IiIiIiIiIiIiIiGhAYNCDiIiIiIiIiIiIiIgGBAY9iIiIiIiIiIiIiIhoQGDQg4iIiIiIiIiIiIiIBgQGPYiIiIiIiIiIiIiIaEBg0IOIiIiIiIiIiIiIiAYEBj2IiIiIiIiIiIiIiGhAYNCDiIiIiIiIiIiIiIgGBAY9iIiIiIiIiIiIiIhoQGDQg4iIiIiIiIiIiIiIBgQGPYiIiIiIiIiIiIiIaEBQ+noAREREtGeaP38+rr766i7f/4MPPkA4HO7GEdGebOfOnZg9eza2bNmC5557DmPGjHFuO+aYY7B+/XoAwGuvvYa6urqS97sr9+1ILBbDySefjHXr1uGRRx7B1KlTu23fREREREREuytmehARERHRHu/aa6/F+vXrcf7553sCHv2Zpmm45pprkEgkcOWVV6KlpaWvh0RERERERNTnmOlBREREfW7atGm44IILOnUfn8/XQ6OhPc3ChQvx0ksvobKyEj/84Q/7ejidMmPGDHzjG9/AW2+9hT/+8Y+47rrr+npIREREREREfYpBDyIiIupzQ4cOxcyZM/t6GLQHamlpwe9//3sAwOWXX45QKNTHI+q8n//851i0aBEef/xxnHrqqdhvv/36ekhERERERER9huWtiIiIiGiP9eCDD2Lz5s2oqqrCOeec09fD6ZIJEyZg5syZSKVSuOWWW/p6OERERERERH2KQQ8iIiIi2iM1NTXhgQceAACcfvrp0DStj0fUdXbA5sMPP8SiRYv6eDRERERERER9h+WtiIiIaLc3f/58XH311QCAxx9/HIZh4MYbb8TSpUuhKArq6upw0UUXYfbs2Z77rVy5Eo899hjee+89bNy4EfF4HNXV1TjggAMwe/ZsTJ8+vcPHbmlpwdNPP40XX3wRq1evRjQaxahRozBr1iycf/75aGpqcvYzd+5cXHHFFc59//3vfzu9TE499VTcdNNNBR/nqquuwjPPPAMAeOihhzBt2rS82+3YsQPz5s3Dm2++idWrV6OlpQXl5eWYOHEijjvuOMyePRuqqhZ9DE3T8OmnnyISieDRRx/Fyy+/jPr6erS3t6O2thaHH344LrjgAowdO7bD47NkyRI89dRTWLp0KdavX49UKoVBgwbh4IMPxpw5czBx4kRn2xUrVmDWrFkAgFGjRmHhwoVF9/3VV1/hxBNPBADMmjULt956a4fjcXvyySed5t9nnnlmp+7bHcaPH9/p+xSaJ0cccQSGDRuG9evX44EHHihp7hIREREREQ1EDHoQERHRgPLll1/i5ptvRltbm3PdF198gXA47Pw7lUrh1ltvxQMPPIBkMum5//r167F+/Xo8//zzOProo/HHP/4RwWAw72N9/vnn+MEPfoDNmzfnXP/555/j73//O26++eZufHbFvfjii7j22mvR3NzsuX7r1q3YunUr3nzzTdx///246667MGbMmKL7WrNmDS699FLU19d7rl+7di0ef/xxPPXUU/jd736Hs88+O+/929ra8Nvf/hYvvPBCzm319fWor6/H008/jR/+8IdOIGjcuHHYb7/9sHTpUtTX12PJkiWYOnVqwTE+++yzzuXTTjut6PPJ57HHHgMA7L333hgxYkSn798XJEkqeP0xxxyDhx9+GG+//TbWrFmz2zwnIiIiIiKi7sSgBxEREQ0oN9xwA6LRKGbPno3DDz8c27Ztw5tvvokZM2Y42/z617/G/PnzAQDBYBDf/va3MWXKFCiKgq+++grPPvssNmzYgNdffx0XXnghHn300ZzSR/X19ZgzZ44TXJk4cSJmz56NmpoarFixAo8//jg+++wz/PznP++V5/3MM8/g6quvhmmakGUZM2fOxBFHHIFQKIRNmzbh5ZdfxtKlS7Fq1Sqce+65eOqppwouiqdSKSfgMX78eJxyyikYOnQoNm/ejKeffhorVqxAMpnE9ddfj4MOOigngJJKpXDJJZfg448/BgAEAgGcdtpp2GeffZBIJPDBBx9gwYIFSKVSuPPOO1FWVuZkvJx++ulYunQpAOC5554rGPRIpVJYsGABAGDIkCE47LDDOnW8Pv30U6xduxYAPHOjN911110dbvPJJ5/gnnvuAQD4/X7nOOVz5JFH4uGHH4Zpmnj55Zdx2WWXddtYiYiIiIiIdhcMehAREdGAEo1Gc8pIXXLJJc7lBQsWOAGPyZMn4y9/+QsGDRrk2ccPfvAD/OpXv8JLL72EZcuW4X//93/xi1/8wrPNf/3XfzkBjzPOOAPXX389ZFl2bj///PNx8cUX48svv+z255itvr4e1113HUzTRHl5Oe6++27sv//+nm0uvvhiPPjgg7jxxhvR1NSEK6+8Ek8++WTe/SUSCdTX1+Oiiy7Cr371KwiRaQN33nnn4Xvf+x7ef/99JBIJPPHEE05pMdtDDz3kBDzGjRuH+++/33OMzzjjDJx44om4/PLLkUwmcfvtt+PUU09FKBTCSSedhBtvvBHt7e148cUXcfXVV+fttfHOO+9g06ZNAIDZs2d7xliKN954w7m83377lXy/t99+G1VVVSVv397eXvC2mTNnFr3vhg0bcN111wGwMjluuukmTzmwbO4A0ZtvvsmgBxERERER7ZEY9CAiIqI+98wzzzj9Kjpy9dVX46KLLip4u8/n8wQ53FKplHN2vd/vzxvwAABd13HDDTdg8eLF2LRpE+bNm4fLLrsMZWVlAKxyWe+88w4AYMyYMbjuuus8AQ8AqKqqwp133omTTz656MJ3d7jvvvsQiUQAAL///e9zAh62iy66CB988AFeffVVfPLJJ3j77bdxxBFH5N123LhxuOqqq3LKKem6jssvvxzvv/8+ADhZGTbTNHHfffcBAFRVxR133JH3GM+YMQNnnnkmHnvsMbS2tuKVV17BaaedhmAwiOOPPx7PPPMMGhsb8eabb+YNDrjnS1dKWy1evNi53JneGtdee22nH6srWlpa8IMf/ABbt24FYPWD+da3vlX0PuFwGEOGDMHGjRvx6aefIh6PF+zfQkRERERENFB17pQ4IiIion5un332gd/vz3vbZ5995vSomDFjRt7FeJvf78cpp5wCwOpP8e677zq3vfbaa87lc889t+DC8vDhw53G3D0llUrhpZdeAgBUV1fj2GOPLbr9Oeec41x2P49s3/rWtwr2j5gwYYJzeceOHZ7bli5d6izUz5gxA6NHjy74GBdccAF++tOf4vbbb/eUpzr99NOdy+6+HbaWlha8+uqrAICDDz64S70rli9fDgAwDAPDhw/v9P17UjKZxJVXXon//Oc/AIATTjgBc+fOLem+e++9NwAgEok45buIiIiIiIj2JMz0ICIioj43bdq0or0K3OxF3UL22muvgrfZJZcAa2HZXjgvJJFIOJeXLl2K448/HoDVD8J20EEHFd3H9OnTC5aR6g7Lly9HS0sLAKs/SbFABgDs3LnTuZydpeE2bty4gre5m8K7jxEALFmyxLl88MEHFx3LmDFjcPnll+dcf/DBB2PUqFGor6/Hv/71LzQ2NqK8vNy5/aWXXnIyW7qS5RGLxZzATGVlZcHgTj6vvfYa6urqSt7+mGOOwfr16zs1vhtvvBH/+te/AFgl2G666aaS7+suvbV+/fqi7wciIiIiIqKBiEEPIiIi6nNDhw7tsL9BqewSVPnYPSAA4B//+Af+8Y9/lLzfhoYG57K9YA5YYy8mu8l3d9u4caNzub6+Hj/60Y9Kvq/7OWVzBzayKUrmJ6Rpmp7btm/f7lweNmxYyWPJdvrpp+PWW29FPB7Hiy++iPPOO8+5ze7J4vf7Oyz5lE9zc7Mz7mAw2OUx9oR58+bh4YcfBgDU1NTgz3/+MwzDKPn+oVDIuewOcBEREREREe0pWN6KiIiIBpR8Ta9tzc3NXd6vnU2Rfdnn8xW9n3sRuid013PKlt2jpFSNjY3O5c4s1mebPXu2M4YFCxY4169Zs8bJ2Dn++OMRCAQ6ve9YLOZc7sr9e8qiRYvw+9//HoDVO+Wuu+7C4MGDO7UPd2m3aDTareMjIiIiIiLaHTDTg4iIiPYY7kX42267DSeeeGKX9uPODmhraysaaHEvsO+KQgvY7qDLueeei+uuu65bHq+r3MfYLkHVFbW1tTjyyCPx+uuvY/HixVi7di2GDx+O5557ztnG3fujM3Rddy531+uzq1auXImf/OQnTrmw//7v/8Z+++3X6f2458muBJ2IiIiIiIh2V8z0ICIioj1GTU2Nc/nrr7/u8n7cJa06ahbtLqmVTYjMT7Hs3hjZCpUqqq6udi7vynPqLu7xbNiwocPtFy1ahK+//jpvUMcd1LBLkdn/HzlyZIf9VAoJhUJOH49dyZTpLg0NDfj+97/vZN5cdtll+Pa3v92lfbmzd4qVKCMiIiIiIhqoGPQgIiKiPYb7zPnXX3+9w+0XLFiAK6+8En/605/w0UcfOdcfcMABzuX33nuv6D4++OCDgre5M0SKlZoCgBUrVuS9ftKkSc5+Fi9e7Ckvlc/y5csxd+5c3HjjjXj++eeLbtsVU6ZMcS67j1k+69evxyWXXIITTjgBc+fOzbn9qKOOchpzv/LKK1i7di2WL18OoGsNzG2qqjqBq02bNuX0JelNsVgMP/zhD7Fu3ToAwNFHH42f/exnXd6fO9A0cuTIXR4fERERERHR7oZBDyIiItpjHHDAAU62x7Jly/DPf/6z4LbRaBS33norXnjhBdxzzz2eTIsTTjjBaeb96KOPoq2tLe8+2tra8NRTTxV8jMrKSufyl19+iVQqlXe7Dz74AJs3b857m67rmDFjBgBrAf3uu+8u+HgAcOedd+KVV17Bgw8+iM8//7zotl1xwAEHoLy8HIAVWFq/fn3BbZ999lnn8vTp03NuV1XVyXhYsmQJ5s2bB8DKkJk9e/YujXPs2LEArGNmBxz6wtVXX43FixcDAPbee2/88Y9/9GQAdVZ9fT0Aq+zZrjSSJyIiIiIi2l0x6EFERER7DE3TcMkllzj/vuqqq/Dhhx/mbBePx/Gzn/3MKU219957O4EFABgyZIhTemn9+vW48sorc8ozxWIxXHXVVUVLPNXV1TlBmI0bNzqL+m6rVq3CNddcU/R5XXbZZc5C+YMPPohHHnkk73b33XcfFi5cCMAKllx44YVF99sVuq7j/PPPB2Adg5/+9Kd5s08+/PBD3HfffQCsMkyFghhnnHEGAMA0TTz00EMAgMMPP7zTDb6zuUtjLVu2bJf21VV33HEHXnjhBQBW6bV77rnH0y+msxobG50AzoEHHrhLwRMiIiIiIqLdFRuZExER0R7lwgsvxLvvvos33ngDTU1NOP/88/HNb34T06dPh9/vx+rVq/H00087GQo+nw9/+MMfchaQr7rqKixevBjLly/HP//5T5x00kk488wzMXz4cGzcuBFPPvkkVq1aBSFEwQwOSZJw5pln4s9//jMA4Pe//z0++ugjHHHEERBCYPHixXj++efR3t6OqVOnYsmSJXn3M2XKFPzsZz/DrbfeCtM0cf311+P555/H8ccfj9raWmzZsgULFy7Exx9/7NznN7/5zS4HDgr5/ve/j7feegtLlizBJ598guOPPx5nnnkmxo8fj+bmZnz44Yf4+9//DtM0IUkSrr32WpSVleXd15gxY7D//vtj8eLFTt+TrjYwd5s+fTpuvfVWAFYmzQknnLDL++yMF198EXfeeScAK3PlJz/5CTZs2OD0NylWcuuII47wNLC3vf/++87lb3zjG90/aCIiIiIiot0Agx5ERES0RxFC4M4778T111+Pp556CqlUCgsXLnQyINyGDBmC22+/HRMmTMi5ze/346GHHsIVV1yBDz74AGvXrsWf/vQnzzbDhg3D7NmzcddddxUcz+WXX44vvvgCr7/+OlKpFF588UW8+OKLzu2SJOHSSy/FPvvsg5/85CcF93PZZZchEAjglltuQSQSweLFi52ySW4+nw/XXHMNzjrrrIL72lWqquL+++/Hz3/+c7z++uvYsWMH7r333pztDMPAb3/7W5x88slF93f66ac7z6WsrAwzZ87c5TFOmjQJY8eOxcqVK/Hmm2/u8v46y/2YqVQKv/nNb0q+72uvvYa6urqc69966y0AgCzLOPHEE3d9kERERERERLshBj2IiIhoj6NpGv7nf/4Hc+bMwZNPPon3338fmzZtQnt7O8LhMPbee2/MnDkTp512GgKBQMH9VFRU4OGHH8YLL7yAZ599Fp999hna2tpQV1eHE088Ed/97nfzBlOyx3L33Xdj4cKFmD9/Pj799FPs3LkT1dXVOPDAA3HeeefhwAMPxMsvv9zh85ozZw6OO+44PPbYY3j77bdRX1+P5uZm+Hw+jBw5EtOnT8fZZ5/tNPHuScFgEHfffTcWLVqEZ555BosXL8a2bdsghEBdXR2mT5+O73znO3kX77Ptv//+zuWTTjrJ0wB+V5x11lm44YYbsHbtWnz66afYd999u2W/fSEej+OVV14BYDVDr62t7eMRERERERER9Q3JLJY7T0RERES7ZP78+bj66qsBAHPnzsUVV1zRxyPa/dx555244447AFjHc/Lkyd2y30gkgpkzZ2Lr1q04//zzO5Vt0d+8+uqr+NGPfgSge48RERERERHR7obdDYmIiIio30okEpg/fz4AYPLkyd26mG8YBr73ve8BsAIFLS0t3bbv3mY3eT/22GMZ8CAiIiIioj0agx5ERERE1C8lk0lcd911TlP5Cy+8sNsf45xzzkFNTQ1aW1vx2GOPdfv+e8OyZcvw73//G5IkYe7cuX09HCIiIiIioj7FoAcRERER9RurV6/GjBkzcPbZZ+PII4/Ek08+CQDYe++9cdJJJ3X74xmGgWuvvRYA8Ne//nW3zPa47bbbAFg9XSZNmtTHoyEiIiIiIupbDHoQERERUb8xePBgbN68GUuWLMG2bdsAAKFQCH/4wx+gKEqPPOZxxx2HU089FY2Njbjnnnt65DF6yjvvvINFixZh9OjR+MUvftHXwyEiIiIiIupzDHoQERERUb+h6zqmT58Ov9+P8vJyHHvssXj00UcxYcKEHn3c3/zmNxg2bBgeeOABfPXVVz36WN0lFovh+uuvh6IouOWWW2AYRl8PiYiIiIiIqM9JpmmafT0IIiIiIiIiIiIiIiKiXcVMDyIiIiIiIiIiIiIiGhAY9CAiIiIiIiIiIiIiogGBQQ8iIiIiIiIiIiIiIhoQGPQgIiIiIiIiIiIiIqIBgUEPIiIiIiIiIiIiIiIaEBj0ICIiIiIiIiIiIiKiAYFBDyIiIiIiIiIiIiIiGhAY9CAiIiIiIiIiIiIiogGBQQ8iIiIiIiIiIiIiIhoQGPQgIiIiIiIiIiIiIqIBgUEPIiIiIiIiIiIiIiIaEBj0ICIiIiIiIiIiIiKiAYFBDyIiIiIiIiIiIiIiGhAY9CAiIiIiIiIiIiIiogGBQQ8iIiIiIiIiIiIiIhoQGPQgIiIiIiIiIiIiIqIBgUEPIiIiIiIiIiIiIiIaEP7/UcX/fVc2cBUAAAAASUVORK5CYII=", "text/plain": [ "" ] }, "execution_count": 45, "metadata": { "image/png": { "height": 378.25, "width": 678.725 } }, "output_type": "execute_result" } ], "source": [ "a.save(\"Speed PSD.pdf\")" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 46, "metadata": { "image/png": { "height": 378.25, "width": 678.725 } }, "output_type": "execute_result" } ], "source": [ "# repeat for turning\n", "turning_psd=summary_both[\"turning_psd\"]\n", "turning_psd_df=turning_psd.to_dataframe()\n", "turning_psd_df=turning_psd_df.drop(columns=[\"Batch\", \"Fly\"]).reset_index().dropna()\n", "turning_psd_df[\"recording_length\"]=turning_psd_df[\"recording_length\"].astype(str)\n", "turning_psd_df[\"case\"]=turning_psd_df[\"recording_length\"].astype(str)+turning_psd_df[\"Shuffled\"]\n", "turning_psd_plot=so.Plot(data=turning_psd_df.dropna(), x=\"Freq\", y=\"turning_psd\", color=\"case\", linestyle=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=\"Power\")\n", "turning_psd_plot.label(title=\"Turning PSD\")\n", "turning_psd_plot.save(\"Turning PSD.pdf\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 47, "metadata": { "image/png": { "height": 378.25, "width": 678.725 } }, "output_type": "execute_result" } ], "source": [ "#repeat for r\n", "r_psd=summary_both[\"r_psd\"]\n", "r_psd_df=r_psd.to_dataframe()\n", "r_psd_df=r_psd_df.drop(columns=[\"Batch\", \"Fly\"]).reset_index().dropna()\n", "r_psd_df[\"recording_length\"]=r_psd_df[\"recording_length\"].astype(str)\n", "r_psd_df[\"case\"]=r_psd_df[\"recording_length\"].astype(str)+r_psd_df[\"Shuffled\"]\n", "r_psd_plot=so.Plot(data=r_psd_df.dropna(), x=\"Freq\", y=\"r_psd\", color=\"case\", linestyle=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=\"Power\")\n", "r_psd_plot.label(title=\"R PSD\")\n", "r_psd_plot.save(\"R PSD.pdf\")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 48, "metadata": { "image/png": { "height": 378.25, "width": 678.725 } }, "output_type": "execute_result" } ], "source": [ "#repeat for angle\n", "angle_psd=summary_both[\"angle_psd\"]\n", "angle_psd_df=angle_psd.to_dataframe()\n", "angle_psd_df=angle_psd_df.drop(columns=[\"Batch\", \"Fly\"]).reset_index().dropna()\n", "angle_psd_df[\"recording_length\"]=angle_psd_df[\"recording_length\"].astype(str)\n", "angle_psd_df[\"case\"]=angle_psd_df[\"recording_length\"].astype(str)+angle_psd_df[\"Shuffled\"]\n", "angle_psd_plot=so.Plot(data=angle_psd_df.dropna(), x=\"Freq\", y=\"angle_psd\", color=\"case\", linestyle=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=\"Power\")\n", "angle_psd_plot.label(title=\"Angle PSD\")\n", "angle_psd_plot.save(\"Angle PSD.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot individual means for each fly" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary_both" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# t2s=test2.sortby(\"Fly\").expand_dims(\"Batch\").stack(flyid=[\"Batch\", \"Fly\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# t1s=test1.sortby(\"Fly\").expand_dims(\"Batch\").stack(flyid=[\"Batch\", \"Fly\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# t1cd=t1c.to_dask_dataframe()\n", "# t1cd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# t1cd.map_blocks(sparse.COO)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# t1cs=t1c.map_blocks(sparse.COO)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# t1c=xr.concat([t1s, t2s], dim=\"flyid\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# t1c.groupby(\"flyid\").mean(...)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# xr.concat()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# testkey=test1.stack(flyid=[\"Batch\", \"Fly\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch=[]\n", "batch=1\n", "# nestedlist_batch.append(glob.glob(\"CirclingData_2h_B\"+str(batch)+\"_F*.nc\"))\n", "test1=xr.open_mfdataset(nestedlist_batch[:3], combine=\"nested\", concat_dim=[\"Batch\"])\n", "\n", "for batch in np.arange(2,7):\n", " nestedlist_batch=[]\n", " nestedlist_batch.append(glob.glob(\"CirclingData_2h_B\"+str(batch)+\"_F*.nc\"))\n", " test2=xr.open_mfdataset(nestedlist_batch, combine=\"nested\", concat_dim=[\"Batch\"])\n", " test1=xr.concat([test1,test2], dim=\"Batch\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# test1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# import dask" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# dask.config.set({\"array.slicing.split_large_chunks\": False})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# DS_2h_stacked=test1.stack(flykey=[\"Fly\", \"Batch\"])\n", "DS_2h_stacked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate the by fly mean for all parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DS_2h_stacked_mean=DS_2h_stacked.groupby(\"flykey\").mean(...)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ditto std\n", "DS_2h_stacked_std=DS_2h_stacked.groupby(\"flykey\").std(...)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate histograms of global averages" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "speedplot=np.log(test1[\"speed\"]).plot.hist(bins=np.arange(-7,7,.1))\n", "speedplot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "angleplot=test1[\"angle\"].plot.hist(bins=np.arange(-3.5,3.5,.1))\n", "angleplot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "turnplot=test1[\"turning\"].plot.hist(bins=np.arange(-7,7,.1))\n", "turnplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate PSDs and lowpass filter each fly data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test1[\"turning\"].groupby([\"Fly, Batch\"]).mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tc=testcombined.stack(flyindex=[\"Batch\", \"Fly\"])\n", "tc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tcm=tc.groupby(\"flyindex\").mean(...)\n", "tcm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tcm['speed'].values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.log(testcombined['speed']).plot.hist(bins=np.arange(-7,7,.1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch=[]\n", "for batch in np.arange(1,4):\n", " # nestedlist_fly=[]\n", " # for fly in np.arange(1,43):\n", " nestedlist_batch.append(glob.glob(\"CirclingData_2h_B\"+str(batch)+\"_F[0-5].nc\"))\n", " # nestedlist_batch.append(nestedlist_fly)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Too many dims here\n", "\n", "# nestedlist_batch=[]\n", "# for batch in np.arange(1,9):\n", "# # nestedlist_fly=[]\n", "# for fly in np.arange(1,43):\n", "# nestedlist_fly.append(glob.glob(\"CirclingData_2h_B\"+str(batch)+\"_F\"+str(fly)+\".nc\"))\n", "# nestedlist_batch.append(nestedlist_fly)\n", "\n", "# # nestedlist_batch\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch[0:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nestedlist_batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test0=xr.open_mfdataset(nestedlist_batch[:3], combine=\"nested\", concat_dim=[\"Batch\", \"Fly\"])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test1=xr.open_mfdataset(nestedlist_batch[0:2], combine=\"nested\", concat_dim=[\"Batch\", \"Fly\"])\n", "test1[\"speed\"].plot." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.log(test1[\"speed\"]).plot.hist(bins=np.arange(-7,7,.1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n=xr.open_mfdataset(nestedlist_batch, combine=\"nested\",concat_dim=[\"Batch\", \"Fly\"])\n", "n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=xr.open_mfdataset(\"CirclingData_2h_B1_F*.nc\", combine=\"nested\",concat_dim=\"Fly\")\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s=xr.open_mfdataset(\"CirclingData_2h_B1_F[1-3].nc\", combine=\"nested\",concat_dim=\"Fly\")\n", "s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alog=np.log(a[\"speed\"])\n", "alog" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alog.plot.hist(bins=np.arange(-10,10,.1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.log(a[\"speed\"]).plot.hist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.log(a[\"speed\"]).plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xr.open_mfdataset()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.log(a[\"speed\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[\"angle\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "p=lcm.calculatepowerall(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p=_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "px.imshow(p.squeeze(), x=\"Freq\", y=\"Fly\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "px.line(data_frame=p.squeeze(), x=\"Freq\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.histplot(a[\"angle\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[\"Fly\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "lcm.calculatepowerall(a[\"angle\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "lcm.calculatepowerforonefly(.2, a, 0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[\"angle\"][0,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load summary data from all flies" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pwd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filelist=glob.glob(\"Circling_Summary*\")\n", "# filelist" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xrall=xr.open_mfdataset(filelist[:], coords='minimal', compat='override')\n", "xrall" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bins=xr.open_dataset(filelist[0])[\"turning_bins\"]\n", "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xrall_s=xrall.stack({\"flyid\":[\"Fly\", \"Batch\"]})\n", "xrall_s" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'xrall_s' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#plot the turning_psd as a function of frequency using seaborn.objects\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#get xrall_s into a pandas dataframe\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m turning_psd\u001b[38;5;241m=\u001b[39m\u001b[43mxrall_s\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mturning_psd\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 4\u001b[0m turning_psd\n", "\u001b[0;31mNameError\u001b[0m: name 'xrall_s' is not defined" ] } ], "source": [ "#plot the turning_psd as a function of frequency using seaborn.objects\n", "#get xrall_s into a pandas dataframe\n", "turning_psd=xrall_s[\"turning_psd\"]\n", "turning_psd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "turning_psd_df=turning_psd.to_dataframe()\n", "turning_psd_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PSD_plot_24h=so.Plot(data=turning_psd_df, x=\"Freq\", y=\"turning_psd\", color=\"Shuffled\").add(so.Line(), so.Agg()).scale(x=\"log\", y=\"log\").add(so.Band(), so.Est()).theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"}).label(x=\"Frequency (Hz)\", y=\"Power\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PSD_plot_24h.save(\"F1C_PSD_24h.pdf\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testturning=xr.open_mfdataset(\"CirclingData_2h_B1_F11.nc*\")\n", "a=testturning[\"turning\"].plot.hist()\n", "a[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xrall_s[\"turning_bins\"].values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.heatmap(xrall_s[\"turning_bins\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lcm.makehistogram(xrall, \"direction\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lcm.makehistogram(xrall_s, \"turning\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "df=lcm.makehistogram(xrall_s, \"turning\")\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xrall=xr.open_mfdataset(filelist[0:5], concat_dim=[\"Batch\"], combine='nested')\n", "xrall" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xrall[\"direction_bins\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Combine data from multiple flies to get global histograms\n", "This is to process all the data at once" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "asdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Brief interlude: estimating mean via meboot\n", "The goal of this is to do bootstrapping sensitive to dependencies in data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "glob.glob('meboot.py')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from meboot import meboot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Get 1 time series" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rawdata=glob.glob(\"CirclingData_24h*\")\n", "rawdata[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testdata=xr.open_dataset(rawdata[0])\n", "testdata['speed'].squeeze().to_series()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_series=testdata['speed'].squeeze().to_series().dropna()\n", "test_series" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "replicates=meboot(test_series.iloc[0:10000], num_replicates=9999)\n", "ping(h)\n", "replicates" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "replicates" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rmelt=replicates.iloc[:,:].melt(var_name=\"Replicate\", ignore_index=False)\n", "rmelt.reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.lineplot(rmelt, x=\"timestamps\", y=\"value\", errorbar=\"pi\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "replicates.iloc[0:100,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm pretty sure this method is, to first approximation, useless. As I understand it, it is a way of applying noise that matches the distribution of values, but the time series aspect of it is completely dumb and completely apes the original order" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testseries.mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# BOUTS BOUTS BOUT\n", "The best method I can think of to handle dependencies in time is to break thinks into bouts rather than frames, which can at least eliminate the mechanical dependencies of a behavior into on epart" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rawdata=glob.glob(\"CirclingData_24h*\")\n", "rawdata[0]\n", "testdata=xr.open_dataset(rawdata[0])\n", "testdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "testdata['speed'].squeeze().to_series()\n", "test_series=testdata['speed'].squeeze().to_series().dropna()\n", "test_series\n", "test_angle_series=testdata['angle'].squeeze().to_series().dropna()\n", "test_r_series=testdata['r'].squeeze().to_series().dropna()\n", "test_turning_series=testdata['turning'].squeeze().to_series().dropna()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_turning_series" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_series_trunc=test_series.iloc[700:900]\n", "test_series_trunc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "#calculate bouts\n", "bouts=(((np.log(test_series_trunc)>.5).rolling(10, center=True).sum())>1)\n", "boutnumber=((np.round(bouts).diff())>0).cumsum()\n", "groups=boutnumber*bouts\n", "plt.plot((np.round(bouts).diff())>0)\n", "plt.plot(boutnumber*bouts)\n", "plt.plot(test_series_trunc)\n", "\n", "testseries_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_series_trunc.groupby(groups).mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bouts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "# groups=(np.log(test_series_trunc)>.5).diff().cumsum()\n", "\n", "\n", "groups=(((np.log(test_series)>.5).rolling(10, center=True).sum())>1).diff().cumsum()\n", "# groups.plot()\n", "plt.plot(groups+.5)\n", "plt.plot(bouts)\n", "\n", "plt.plot((np.log(test_series_trunc)>.5)-.5)\n", "# plt.plot(np.log(test_series_trunc))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# groups=((np.log(test_series)>.5).rolling(10).sum()>8).diff().cumsum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# groups=(((np.log(test_series)>.5).diff())>0).cumsum()\n", "groups=(((np.log(test_series)>.5).rolling(10, center=True).sum())>1).diff().cumsum()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chunkmeans=test_angle_series.groupby(groups).mean()\n", "plt.hist(chunkmeans,np.linspace(-1,1,101))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_turning_series.reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_turning_series" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_series.reset_index().groupby(np.array(groups), axis=0).mean(numeric_only=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_turning_series.reset_index()['timestamps'].groupby(groups, axis=0).mean(numeric_only=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chunkmeans=test_turning_series.groupby(groups).mean()\n", "plt.hist(chunkmeans,np.linspace(-7,7,101))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# test_turning_series.groupby(groups).length()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_turning_series.groupby(groups).count()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(test_turning_series.groupby(groups).count())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chunkmeans" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bouts.values.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_turning_series.values.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.log(test_series)>.5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bouts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "allDB=pd.DataFrame({\"Frame Turning\":test_turning_series[np.log(test_series)>.5]})\n", "allDB.melt()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(test_turning_series[(np.log(test_series)>.5)].resample('1H').mean()).dropna()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# rollingaverage_hour=(test_turning_series[bouts].resample('1H').mean()).dropna()\n", "# hourDB=pd.DataFrame({\"Hour Turning\":rollingaverage_hour})\n", "# rollingaverage_hour=(test_turning_series[bouts].resample('1D').mean()).dropna()\n", "# dayDB=pd.DataFrame({\"Day Turning\":rollingaverage_hour})\n", "# # dayDB\n", "# rollingaverage_min=(test_turning_series[bouts].resample('60s').mean()).dropna()\n", "# minDB=pd.DataFrame({\"Minute Turning\":rollingaverage_min})\n", "# # minDB" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chunkDB=pd.DataFrame({\"Average Bout Turning\":chunkmeans})\n", "turning_all=pd.concat([chunkDB.melt(),allDB.melt(), dayDB.melt(), hourDB.melt(), minDB.melt()], axis=0)\n", "# chunkDB.melt(value_name=\"Turning\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chunkDB" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "turning_all" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hourDB.reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=hourDB.reset_index(), x=\"timestamps\", y=\"Hour Turning\").add(so.Dots())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "a=lcm.calculate_bouts(testdata)\n", "# a.reset_index()\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=a, x=\"value\", color=\"variable\").add(so.Bars(), so.Hist(common_norm=False, stat=\"density\", bins=100))#.limit(x=(0, 3), y=(0,1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a.reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pd.Categorical(int(testdata[\"recording_length\"]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pd.Categorical(testdata[\"Fly\"].values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a[\"Recording Length\"]=int(testdata[\"recording_length\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Here we compare the estimated average speed by measure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While this works reasonably well for small samples, seaborn struggles with huge datasets, so skip down to the next section to use the boostrapintervals method from lcm to get data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=a, y=\"value\", x=\"variable\").add(so.Bar(), so.Agg()).add(so.Range(), so.Est())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "a1=lcm.calculate_bouts(testdata, target_var=\"direction\")\n", "a1\n", "# a.reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=a1, y=\"value\", x=\"variable\").add(so.Bar(), so.Agg()).add(so.Range(), so.Est())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rawdata=glob.glob(\"CirclingData_24h*\")\n", "rawdata[0]\n", "testdata2=xr.open_dataset(rawdata[1])\n", "testdata2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "a=lcm.calculate_bouts(testdata2, target_var=\"direction\")\n", "# a.columns=[\"Chunk Direction\"]\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=a, y=\"value\", x=\"variable\").add(so.Bar(), so.Agg()).add(so.Range(), so.Est())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.arange(14)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a1.concat(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "null=[]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all=pd.concat([a, a1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all[\"Fly\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all[\"FlyID\"]=\"B\"+all[\"Batch\"].astype(str)+\"-F\"+all[\"Fly\"].astype(str)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=all, y=\"direction\", x=\"variable\", color=\"FlyID\").add(so.Bar(), so.Agg(), so.Dodge()).add(so.Range(), so.Est())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=all, y=\"direction\", x=\"FlyID\", color=\"variable\").add(so.Bar(), so.Agg(), so.Dodge()).add(so.Range(), so.Est(), so.Dodge())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate confidence intervals on each fly individually (not using seaborn) to avoid problems" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rawdata=glob.glob(\"CirclingData_24h*\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "all=lcm.compareflies(rawdata, np.arange(3,5), variables=[\"speed\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "individualityplot=so.Plot(data=all, y=\"value\", x=\"FlyID\", color=\"variable\").add(so.Bar(), so.Agg(), so.Dodge())\n", "individualityplot\n", "# .add(so.Range(), so.Est(), so.Dodge())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.arange(3,8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sci.stats.bootstra" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all[all[\"variable\"]==\"Chunk speed\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "test=lcm.bootstrapintervals(testdata, target_var=\"direction\")\n", "ping(h)\n", "test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test[\"FlyID\"]=pd.Categorical(\"B\"+test[\"Batch\"].astype(str)+\"-F\"+test[\"Fly\"].astype(str))\n", "test" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=test, x=\"FlyID\", y=\"Mean\", color=\"Method\", ymin=\"CI_low\", ymax=\"CI_high\").add(so.Bar(), so.Dodge()).add(so.Range(), so.Dodge())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "test" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ping(h)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "lcm.bootstrap_helper(test, \"asd\", \"adf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "test=lcm.bootstrapintervals(testdata, target_var=\"direction\")\n", "np.mean(test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "lcm.bootstrap_helper(test, \"Hour\", \"direction\", fly=xarray[\"Fly\"], batch=xarray[\"Batch\"], recording_length=xarray[\"recording_length\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "so.Plot(data=test, x=\"Fly\", y=\"Mean\", )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(test.values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data=(test.values, )\n", "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bs=sci.stats.bootstrap(data, np.mean, method='basic')\n", "bs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bs.confidence_interval[0][0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "rng = np.random.default_rng()\n", "from scipy.stats import norm\n", "dist = norm(loc=2, scale=4) # our \"unknown\" distribution\n", "data = dist.rvs(size=100, random_state=rng)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "variables=['direction', 'speed', 'turning', 'angle', 'r']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pd.MultiIndex.from_product([rawdata, variables])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "variables=['direction', 'speed', 'turning', 'angle', 'r']\n", "pall2=lcm.compareflies(rawdata[0:8], variables=variables)\n", "ping(h)\n", "pall2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp.reload(lcm)\n", "variables=['direction', 'speed', 'turning', 'angle', 'r']\n", "pall3=lcm.compareflies(rawdata, variables=variables)\n", "\n", "ping(h)\n", "pall3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pall3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pall=pd.concat(a, ignore_index=True)\n", "pall" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pall2_plot=so.Plot(data=pall2, x=\"FlyID\", y=\"Mean\", color=\"Block Method\", ymin=\"CI_low\", ymax=\"CI_high\").layout(size=(3, 10)).add(so.Bar(), so.Dodge()).add(so.Range(), so.Dodge()).facet(row=\"Variable\").share(y=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pall2_plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pall2_plot.save('pall2.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pall" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure 1C" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal for this figure is to show some measure of drift with statistical confidence: current goal, Day 1 vs Day 2 vs Day 8 or vs all days\n", "Other thoughts:\n", "* Average Daily Std vs Global Std (should expect higher global Std if shift in bias by day)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "testdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "seriesofinterest=testdata[\"direction\"].to_dataframe()\n", "seriesofinterest.reset_index()[\"timestamps\"]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(seriesofinterest.reset_index()[\"timestamps\"].dt.days)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "daygroups=seriesofinterest.groupby(seriesofinterest.reset_index()[\"timestamps\"].dt.days)\n", "daygroups" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "daygroups" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "daygroups" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "python_data_analysis", "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": "3.10.10" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }